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Technical Support Document
EPA's Air Toxics Screening Assessment
2017 AirToxScreen TSD
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EP A-452/B-22-001
March 2022
Technical Support Document
EPA's Air Toxics Screening Assessment
2017 AirToxScreen TSD
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Health and Environmental Impacts Division
Air Quality Assessment Division
Research Triangle Park, NC
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Contents
Contents iv
Tables viii
Figures ix
Common Acronyms and Abbreviations xi
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 7
1.6. How AirToxScreen Results Should Not Be Used 8
1.7. The Risk Assessment Framework AirToxScreen Uses 9
1.8. The Scope of AirToxScreen 10
1.8.1. Sources of Air Toxic Emissions That AirToxScreen Addresses 11
1.8.2. Stressors that AirToxScreen Evaluates 12
1.8.3. Exposure Pathways, Routes and Time Frames for AirToxScreen 12
1.8.4. Receptors that AirToxScreen Characterizes 13
1.8.5. Endpoints and Measures: Results of AirToxScreen 13
1.9. Model Design 14
1.9.1. The Strengths and Limitations of the Model Design 16
2. Emissions 18
2.1. Sources of Emissions 18
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 29
2.2. Preparation of Emissions Inputs for CMAQ 30
2.2.1. Sectors in the CMAQ AirToxScreen Platform 31
2.2.2. Fires and Biogenics 33
2.2.3. Speciation 33
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 57
3.1. Modeling Overview 57
3.1.1. Photochemical Model Selection 57
3.1.2. Dispersion Model Selection 59
3.2. Meteorological Data 59
3.2.1. Input WRF Data 59
3.2.2. MCIP Processing for CMAQ 59
3.2.3. MMIF Processing for AERMOD 61
3.2.4. Meteorological Data Outside the Contiguous States 61
3.3. CMAQ Setup 61
3.3.1. Sources Modeled in CMAQ 61
3.3.2. Boundary and Initial Conditions 62
3.4. AERMOD Setup 65
3.4.1. Sources Modeled in AERMOD 65
3.4.2. Receptor Placement 65
3.4.3. Model Options 68
3.4.4. AERMOD Simulations 68
3.4.5. Post-processing of AERMOD Results 70
3.5. Hybrid Modeling 71
3.5.1. Overview 71
3.5.2. Treatment of Species 72
3.6. Other Source Characterizations 74
3.6.1. Background Concentrations Used for non-CMAQ Areas and Pollutants 74
3.6.2. Fires, Biogenics and Secondary Concentrations Used for Non-CMAQ Situations 74
3.6.3. Non-CONUS Fires 75
3.6.4. Secondary Concentrations 76
3.6.5. Biogenics 78
3.7. Model Evaluation 78
3.7.1. Ambient Monitoring Data Preparation 79
3.7.2. Model Performance Statistics 80
3.7.3. Hybrid Evaluation 81
3.7.4. Non-hybrid Evaluation 95
4. Estimating Exposures for Populations 98
4.1. Estimating Exposure Concentrations 98
4.2. About HAPEM 98
4.3. HAPEM Inputs and Application 99
4.3.1. Data on Ambient Air Concentrations 100
4.3.2. Population Demographic Data 100
4.3.3. Data on Population Activity 100
4.3.4. Microenvironmental Data 101
4.4. Exposure Factors 103
4.5. Evaluating Exposure Modeling 104
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4.6. Summary 104
5. Characterizing Effects of Air Toxics 105
5.1. Toxicity Values and Their Use in AirToxScreen 105
5.2. Types of Toxicity Values 106
5.2.1. Cancer URE 106
5.2.2. Noncancer Chronic RfC 109
5.3. Data Sources for Toxicity Values 109
5.3.1. U.S. EPA Integrated Risk Information System 110
5.3.2. U.S. Department of Health and Human Services, Agency for Toxic Substances and
Disease Registry 110
5.3.3. California Environmental Protection Agency Office of Environmental Health Hazard
Assessment 110
5.3.4. U.S. EPA Health Effects Assessment SummaryTables 110
5.3.5. World Health Organization International Agency for Research on Cancer Ill
5.4. Additional Toxicity Decisions for Some Chemicals Ill
5.4.1. Polycyclic Organic Matter Ill
5.4.2. Glycol Ethers 112
5.4.3. Acrolein 112
5.4.4. Metals 112
5.4.5. Adjustment of Mutagen UREs to Account for Exposure During Childhood 113
5.4.6. Diesel Particulate Matter 114
5.5. Summary 115
6. Characterizing Risks and Hazards in AirToxScreen 116
6.1. The Risk-characterization Questions AirToxScreen Addresses 116
6.2. How Cancer Risk Is Estimated 117
6.2.1. Individual Pollutant Risk 117
6.2.2. Multiple-pollutant Risks 117
6.3. How Noncancer Hazard is Estimated 118
6.3.1. Individual Pollutant Hazard 118
6.3.2. Multiple-pollutant Hazard 119
6.4. How Risk Estimates and Hazard Quotients Are Calculated for AirToxScreen at Tract,
County and State Levels 120
6.4.1. Model Results for Point Sources: Aggregation to Tract-level Results 120
6.4.2. Background Concentrations and Secondary Pollutants: Interpolation to Tract-level
Results 120
6.4.3. Aggregation of Tract-level Results to Larger Spatial Units 120
6.5. The Risk Characterization Results That AirToxScreen Reports 121
6.6. Summary 123
7. Variability and Uncertainty Associated with AirToxScreen 124
7.1. Introduction 124
7.2. How AirToxScreen Addresses Variability 124
7.2.1. Components of Variability 125
7.2.2. Quantifying Variability 126
7.2.3. How Variability Affects Interpretation of AirToxScreen Results 128
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7.3. How AirToxScreen Addresses Uncertainty 129
7.3.1. Components of Uncertainty 129
7.3.2. Components of Uncertainty Included in AirToxScreen 131
7.4. Summary of Limitations in AirToxScreen 135
8. References 138
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
1. Introduction to HAPEM and its Use in AirToxScreen E-2
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Tables
Table 1-1. Summary of previous screening assessments 6
Table 2-1. Summary of emissions sources in the 2017 NEI 19
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 CMAQand AERMOD 30
Table 2-8. Platform sectors for the 2017 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 2008 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 57
Table 3-2. Vertical layer structure for WRF and CMAQ (heights are layer top) 60
Table 3-3. Geographic information for the 12-km CMAQ modeling domain 62
Table 3-4. Boundary conditions from 2017 remote concentration estimates 63
Table 3-5. CMAQ HAP boundary conditions applied as zero value 63
Table 3-6. Background Concentrations used for non-CMAQ areas and pollutants 74
Table 3-7. Fire concentrations (pig/m3) for selected HAPs in Alaska, Hawaii and Puerto Rico 75
Table 3-8. Concentrations (pig/m3) used to derive secondary concentrations for formaldehyde,
acetaldehyde and acrolein; acrolein is primary concentrations only 76
Table 3-9. List of hybrid HAPs evaluated 82
Table 3-10. 2017 annual air toxics performance statistics for the Hybrid, CMAQand AERMOD models 85
Table 3-11. List of non-hybrid HAPs evaluated 96
Table 4-1. Key differences between recent versions of HAPEM 99
Table 4-2. Microenvironments used in HAPEM modeling for AirToxScreen 103
Table 6-1. Criteria establishing AirToxScreen drivers and contributors of health effects for risk
characterization 122
Table B-l. Pollutants excluded from AirToxScreen B-2
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Table E-l. HAPs Assessed in AirToxScreen, with their HAPEM7 HAP Phases and Surrogate Chemical
Assignments E-10
Table E-2. Boiling-point Definitions Used to Classify HAPs for HAPEM7 Modeling for the 2014
AirToxScreen E-16
Table E-3. HAPs Modeled in HAPEM7 for AirToxScreen E-17
Table E-4. Truncations Applied to Exposure Factors for AirToxScreen E-17
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 10
Figure 1-4. Conceptual model for AirToxScreen 11
Figure 1-5. The AirToxScreen risk assessment process and corresponding sections of this TSD 15
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 3-1. Map of the CMAQ modeling domain; the purple box denotes the 12-km national
modeling domain 62
Figure 3-2. CBSAs exceeding 1 million people 66
Figure 3-3. Dense (left) and coarse (right) receptor grid layout in CMAQ Lambert Projection 67
Figure 3-4. Example grid cell with subgrid cells and census blocks 67
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 71
Figure 3-6. Acetaldehyde total concentrations for a) Alaska, b) Hawaii and c) Puerto Rico/Virgin
Islands 77
Figure 3-7. Formaldehyde total concentrations for a) Alaska, b) Hawaii and c) Puerto Rico/Virgin
Islands 78
Figure 3-8. 2017 monitoring locations for the hybrid HAPs evaluation 84
Figure 3-9. Acetaldehyde boxplots of (a) distribution (pig/m3) and (b) bias difference (pig/m3) for
CMAQ, AERMOD and Hybrid models compared to ambient observations 85
Figure 3-10. Mean bias (%) for acetaldehyde at 2017 monitoring sites in the Hybrid modeling domain 86
Figure 3-11. Mean error (%) for acetaldehyde at 2017 monitoring sites in the Hybrid modeling
domain 86
Figure 3-12. Mean bias (%) for acetaldehyde at 2017 monitoring sites in the CMAQ modeling domain 87
Figure 3-13. Mean error (%) for acetaldehyde at 2017 monitoring sites in the CMAQ modeling
domain 87
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Figure 3-14. Mean bias (%) for acetaldehyde at 2017 monitoring sites in the AERMOD modeling
domain 88
Figure 3-15. Mean error (%) for acetaldehyde at 2017 monitoring sites in the AERMOD modeling
domain 88
Figure 3-16. Formaldehyde boxplots of (a) distribution (pig/m3) and (b) bias difference (pig/m3) for
CMAQ, AERMOD and Hybrid models compared to ambient observations 89
Figure 3-17. Mean bias (%) for formaldehyde at 2017 monitoring sites in the Hybrid modeling domain 89
Figure 3-18. Mean error (%) for formaldehyde at 2017 monitoring sites in the Hybrid modeling
domain 90
Figure 3-19. Mean bias (%) for formaldehyde at 2017 monitoring sites in the CMAQ modeling domain 90
Figure 3-20. Mean error (%) for formaldehyde at 2017 monitoring sites in the CMAQ modeling
domain 91
Figure 3-21. Mean bias (%) for formaldehyde at 2017 monitoring sites in the AERMOD modeling
domain 91
Figure 3-22. Mean error (%) for formaldehyde at 2017 monitoring sites in the AERMOD modeling
domain 92
Figure 3-23. Benzene boxplots of (a) distribution (pig/m3) and (b) bias difference (pig/m3) for CMAQ,
AERMOD and Hybrid models compared to ambient observations (BENZENE_24_HOURS) 92
Figure 3-24. Mean bias (%) for benzene at 2017 monitoring sites (BENZENE_24_HOURS) in the Hybrid
modeling domain 93
Figure 3-25. Mean error (%) for benzene at 2017 monitoring sites (BENZENE_24_HOURS) in the
Hybrid modeling domain 93
Figure 3-26. Mean bias (%) for benzene at 2017 monitoring sites (BENZENE_24_HOURS) in the CMAQ
modeling domain 94
Figure 3-27. Mean error (%) for benzene at 2017 monitoring sites (BENZENE_24_HOURS) in the
CMAQ modeling domain 94
Figure 3-28. Mean bias (%) for benzene at 2017 monitoring sites (BENZENE_24_HOURS) in the
AERMOD modeling domain 95
Figure 3-29. Mean error (%) for benzene at 2017 monitoring sites (BENZENE_24_HOURS) in the
AERMOD modeling domain 95
Figure 3-30. 2017 monitoring locations for the non-hybrid HAPs evaluation 97
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Common Acronyms and Abbreviations
|jg/m3
AERMOD
ASPEN
ATSDR
CAA
CHAD
CAP
CMAQ
CONUS
EC
EPA
EGU
HAP
HAPEM
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 Data Update 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. A key part of
the Air Toxics Data Update is the Air Toxics Screening Assessment, or AirToxScreen. 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. Ambient and exposure concentrations and estimates of risk and
hazard for air toxics in each state are typically generated at the census tract level.
This AirToxScreen Technical Support Document (TSD) describes the data and approaches EPA used to
conduct AirToxScreen, including descriptions of how we:
ii compiled emissions data and prepared them for use as model inputs (Section 2);
«i estimated ambient concentrations of air toxics (Section 3);
ii estimated exposures to air toxics for populations (Section 4);
ii selected toxicity values (Section 5);
ii characterized human-health risks and hazards (Section 6); and
ii 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:
ii Appendix A - a glossary of the key terms and their definitions
ii Appendix B - a list of air toxics included in AirToxScreen
ii Appendix C - procedures used to estimate AirToxScreen background concentrations
ii Appendix D - additional model evaluation summaries
ii Appendix E - documentation on HAPEM7 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.
Compile National
Emissions Inventory
Estimate ambient
concentrations of air
toxics across U.S.
Estimate population
exposures
Characterize
potential public
health risks from
inhalation
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
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 5.2. 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
detailed source attribution. CMAQ also provides the biogenic and fire concentrations, as these sources
are not run in AERMOD. Only AERMOD is run in areas outside the lower 48, or contiguous, U.S. states
(referred to as CONUS) that are included in AirToxScreen: Alaska, Hawaii, Puerto Rico and the Virgin
Islands. Special steps are taken to estimate secondary HAPs, fires and biogenics in these areas. Section 3
of this document details the steps EPA used to model for AirToxScreen.
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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 tract-level concentrations of all modeled air
toxics. Then, using the HAPEM7 exposure model, we account for human activity patterns and develop
exposure concentrations, or ECs, for each census tract. Finally, we estimate census tract-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 2017 AirToxScreen, we preliminarily modeled using AERMOD the 2017 NEI, 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 reviewed the June 2020 version of the 2017 point source inventory (without airports and
railyards) along with a first-pass modeling and risk results. We reviewed and incorporated comments
into the AirToxScreen modeling process and results. We then previewed these final results with S/L/T
agencies before releasing the 2017 AirToxScreen in March 2022.
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 tract 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 lifetime)
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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:
ii Which air toxics pose the greatest potential risk of cancer or adverse noncancer effects across the
entire United States?
ii Which air toxics pose the greatest potential risk of cancer or adverse noncancer effects in certain
areas of the United States?
ii Which air toxics pose less, but still significant, potential risk of cancer or adverse noncancer effects
across the entire United States?
ii 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)?
ii 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 assessments have been completed - representative of air
toxic emissions in 1999, 2002, 2005, 2011 and 2014, respectively - based on significant triennial updates
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of the national emission inventories. In general, the scope of NATA progressively expanded with
subsequent versions, and some methods were refined and improved. The current AirToxScreen
assessment uses the same basic methods as used in the 2014 NATA. Table 1-1 summarizes the six
screening assessments EPA conducted prior to the 2017 AirToxScreen.
Table 1-1. Summary of previous screening assessments
Inventory
Year
Year
Completed/
Published
Air Toxics Modeleda b
Key Attributes
1996
2002
33 - Includes 32 HAPs,
focusing on those of
concern in urban areas,
plus diesel PM
ASPEN used to model ambient concentrations
HAPEM4 used to model inhalation exposures
1999
2006
177- Includes 176 HAPs,
including all those with
chronic-health toxicity
values at the time, plus
diesel PM
ASPEN used to model ambient concentrations
HAPEM5 used to model inhalation exposures
Doubled the number of emission sources covered compared
to 1996 NATA
2002
2009
181- Includes 180 HAPs,
including four4 with
additional health
information, plus diesel
PM
ASPEN and HEM (with ISCST3) used to model ambient
concentrations
HAPEM5 used to model inhalation exposures
2005
2010
179- Includesl78 HAPs for
which emissions data and
chronic-health toxicity
values were available, plus
diesel PM
Emissions inventory updated to include recent information on
industrial sources, residual-risk assessments, lead
emissions from airports and other sources
ASPEN and HEM-3 (with AERMOD, a more refined dispersion
model) used to model ambient concentrations; HEM used
for more source types than in 2002
Exposure factors derived from 2002 NATA used to estimate
inhalation exposures
CMAQ model (EPA 2015f) used to estimate secondary
formation of acetaldehyde, acrolein and formaldehyde and
decay of 1,3-butadiene to acrolein
2011
2015
180- Includes 179 HAPs
for which emissions data
and chronic-health toxicity
values were available, plus
diesel PM
CMAQ and HEM-3 more fully integrated as a hybrid modeling
system for about 40 HAPS and diesel PMto improve mass
conservation.
HEM-3 used with background for remaining HAPs not covered
by the hybrid approach (also for areas outside the
contiguous U.S. CMAQ modeling domain)
HAPEM7 modeled inhalation exposures for a subset of air
toxics and used to provide exposure factorsfor the
remaining air toxics
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Inventory
Year
Year
Completed/
Published
Air Toxics Modeleda b
Key Attributes
2014
2018
181- Includes 180 HAPs
for which emissions data
and chronic-health toxicity
values were available, plus
diesel PM
AERMOD used as main dispersion model.
CMAQ and AERMOD fully integrated as a hybrid modeling
system for 51 HAPS and diesel PM.
AERMOD used with background for remaining HAPs not
covered by the hybrid approach (also for areas outside the
contiguous U.S. CMAQ modeling domain)
HAPEM7 modeled inhalation exposures for a subset of air
toxics and used to provide exposure factorsfor the
remaining air toxics
a Note that "air toxics" and "HAPs" are sometimes used interchangeably. In this document, however, "air toxics" refers to HAPs
plus diesel PM. HAPs are those air toxics which we are required to control under Section 112 of the 1990 CAA Amendments
(EPA 2015h). Diesel PM is not a HAP but is likely carcinogenic to humans, although we have not yet developed a unit risk
estimate for it. Given these concerns, the adverse noncancer effects of diesel PM are estimated in NATA (using an Integrated
Risk Information System reference concentration) but its cancer risks are not estimated.
bThe number of air toxics included in a NATA emission inventory can be slightly larger than the number of air toxics actually
modeled. Some air toxics are not modeled because of uncertainty in the emissions numbers or in the ability to model air
concentrations or health risk accurately. For example, asbestos is included in the inventory but not modeled and not included in
the counts presented in this table.
Notes: HAPs = hazardous air pollutants; diesel PM = diesel particulate matter; ASPEN = Assessment System for Population
Exposure Nationwide; HAPEM4, HAPEM5, HAPEM7 = Hazardous Air Pollutant Exposure Model, version 4, 5 and 7; HEM =
Human Exposure Model; CMAQ = Community Multiscale Air Quality model. ISC and AERMOD are Gaussian dispersion models.
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 air toxics;
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
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ii provide a multiple-pollutant modeling framework linking air toxics to the Criteria Pollutant Program
(EPA 2015c).
Similarly, S/L/T air agencies use AirToxScreen to:
ii prioritize pollutants and emission source types;
ii identify places of interest for further study;
ii get a starting point for local assessments;
ii focus community efforts; and
ii 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:
ii as a definitive means to pinpoint specific risk values within a censustract;
ii to characterize or compare risks at local levels (such as between neighborhoods);
ii to characterize or compare risks between states;
ii as the sole basis for developing risk reduction plans orregulations;
ii as the sole basis for determining appropriate controls on specific sources or air toxics; or
ii 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 at the census tract level in AirToxScreen, average risk
estimates are far more uncertain at this level of spatial resolution than at the county or state level. To
analyze air toxics in smaller areas, such as census blocks or in 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
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evaluations of National Ambient Air Quality Standards), AirToxScreen fundamentally differs from such
assessments in that it is not a regulatory program.
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 2015d) 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
Exposure Assessment
Toxicity Assessment
Hazard Identification
Dose-response Assessment
Risk Characterization
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Figure 1-3. The general air toxics risk assessment process
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. Point, nonpoint and mobile
sources are included from Alaska, Hawaii, Puerto Rico and the U.S. Virgin Island (with impacts from
biogenics and fires estimated). 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
microenvironments
Water
Food
Soil
Inhalation
H
Ingestion
Dermal
=1
General
Population
Receptors/Subpopulations
Male
Female
Age
0-1
Age
2-4
Age
5-15
I
Age
16-17
~r~
Age
18-64
Age
65+
X
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
=C
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. 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 inclue 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:
ii upper-bound estimated lifetime individual cancer risk; and
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ii 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 tract level. This approach is used only to determine
geographic patterns of risks within counties, and not to pinpoint specific risk values for each census
tract. 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 tract 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 tract-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:
1. compiling the nationwide inventory of emissions from outdoor sources;
2. estimating nationwide ambient outdoor concentrations of the emitted air toxics;
3. estimating population exposures to these air toxics via inhalation; and
4. characterizing potential health risks associated with these inhalationexposures.
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
Emission Inventory
(Section 2)
1
Identify Toxicity
Values
(Section 5)
National
Emissions
Inventory
Conduct Air
Air Quality Modeling
(Section 3)
1
Ambient
Concentrations
Cancer Risks,
Chronic Noncancer
Figure 1-5. The AirToxScreen risk assessment process and corresponding sections of this TSD
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ii 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.
ii Section 5 discusses the dose-response values used for AirToxScreen, the sources from which these
values are obtained and assumptions made specific to AirToxScreen.
ii Section 6 provides the calculations used to estimate cancer risk and potential noncancer hazard.
ii 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:
ii 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 tracts,
counties and states. All questions asked, therefore, must focus on the variations among these
geographic areas (census tracts, counties, etc.). Moreover, as previously mentioned, results are far
more uncertain at the census tract 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
tracts.)
ii 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.
ii 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.
ii AirToxScreen does not include results for individuals. Within a census tract, 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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ii 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.
ii 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).
ii 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.
ii 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.
ii The assessment does not fully reflect variation in background ambient air concentrations.
Background ambient air concentrations are average values over broad geographic regions.
ii 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.
ii Short-term (acute) exposures and risks are not included in AirToxScreen.
ii Atmospheric transformation and losses from the air by deposition are not accounted for in
AirToxScreen air toxics that are not modeled in CMAQ.
ii 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 3.3.2. 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 3.6.1). 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 2015h) 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 Invento , 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 the 2017 NEI documentation.
Table 2-1. Summary of emissions sources in the 2017 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 data included: TRI data for 2017,
augmentation of HAPs using emission-factor ratios (of HAP to CAP) applied to S/L/T-reported CAP
emissions and rule-based emission factors (e.g., emissions factors using the 2010 test program
conducted in support of Mercury and Air Toxics rule), and methane emissions reported by landfill
operators in compliance with Subpart HH of the Greenhouse Gas Reporting Program (GHGRP) as a
"surrogate" activity indicator.
Point airports
EPA estimates used the Federal Aviation Administration (FAA) Emission Dispersion Modeling
System using landing and takeoff (LTO) information from FAA databases and updated where
S/L/T-provided LTO. For smaller airports (general aviation) without detailed aircraft-specific
activity data, straight emission factors were used. Lead emissions were estimated based on per-
LTO emissions factors, assumptions about lead content in the fuel and lead-retention rates in the
piston engines and oil. For some airports, estimates were provided by S/L/T. NEI has
approximately 20,000 airports (including heliports and seaplanes); all are inventoried as point
sources. In addition to LTOs, EPA's emissions estimates for airports included emissions of ground
support equipment.
Point rail
yards
The 2017 NEI includes non-zero emissions estimates for nearly 1000 rail yards. EPA emission
estimates are associated with the operation of switcher engines at each Class 1 rail yard. EPA
estimates were developed by the Eastern Regional Technical Advisory Committee's (ERTAC) rail
group using a "top-down" approach that apportions 2017 national fuel use data to rail yards and
used national fleet-wide information to create weighted average emission factors. S/L/T
submitted point rail yard emissions were given priority over the ERTAC estimates when present.
HAP emissions were estimated by applying fractions to the VOC or PM estimates. There are also
railyard emissions in the nonpoint inventory (see locomotives).
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 by S/L/T. Where S/L/T submitted CAPs but not HAPs, missing HAP emissions were
augmented.
Biogenics
Based on Biogenic Emission Inventory System (BEIS3.61) using 2017 meteorology from the
Weather Research Forecasting Model (WRF). Gridded emissions were used in AirToxScreen and
summed to annual county-level estimates for the NEI. Includes VOC, NOx and three HAPs:
formaldehyde, acetaldehyde and methanol.
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Source
Description
Locomotives
Emissions at county-level resolution for Class 1 line haul, Class ll/lll line haul, passenger, commuter
and rail yards. ERTAC's rail group developed EPA estimates for Class 1 line haul and Class ll/lll line
haul. S/L/T also submitted data for locomotives. All passenger, commuter and county-level rail
yards estimates were from S/L/T. EPA rail yard emissions were included as point-source rail yards
as described above. HAPs were estimated by applying toxic fractions to the VOC or PM estimates.
CMVs
Emissions from category 1 and category 2 (C1/C2) and category 3 (C3) marine vessels at ports or
underway. C1/C2 includes fishing boats, ferries and tugboats and is assumed to use diesel fuel; C3
includes oceangoing vessels and large ships and are assumed to use residual fuel. Emission
estimates were developed using 1) activity data (kilowatt hours or kW), 2) engine operating load
factors and 3) emission factors and HAP speciation profiles. This "bottom up" approach was used
for the first time for the 2014 NEI. EPA's CMV estimates use satellite-based automatic
identification system (AIS) activity data from the US Coast Guard.
On-road
Except for California, on-road emissions were generated using the SMOKE-MOVES emissions
modeling framework, which leverages emission factors generated by the latest released version
of the Motor Vehicle Emissions Simulator (MOVES) (MOVES2014b, code version: 20180726,
database version: movesdb 20181022); county and SCC-specific activity data and hourly
meteorological data. These models used state- or EPA-provided input details specific to each
county. California's emissions were developed via their EMFAC on-road model, but VOC HAPs
were speciated from California-reported VOCs consistent with the MOVES2014b speciation. 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.
Nonroad,
excluding
airports,
locomotives
and CMVs
Except for California, EPA estimated these emissions using the MOVES2014b model. Several S/L
provided inputs to the model. MOVES2014b also replaces toxic emission estimates for nonroad
previously generated using the National Mobile Inventory Model (NMIM), which was used for
2014 and earlier NEIs. MOVES2014b was used for all states other than California, which uses their
own model. EPA added DIESEL-PM10 and DIESEL-PM25 for all diesel fuel SCCs, and they were set
equal to the PM10 and PM2.5 emissions from exhaust emissions in these diesel SCCs.
Fires
For purposes of AirToxScreen, fires include agricultural burning, prescribed burning and wildfires.
EPA estimated agricultural burning (included in stationary nonpoint) using remote-sensing data,
crop-usage maps and emission factors as daily point estimates. Many states submitted their own
data as county estimates; these were used ahead of EPA estimates. EPA estimates were modeled
as daily point estimates, and the state-submitted data were converted to point estimates for
modeling.
EPA developed day- and location-specific prescribed burning and wildfire emissions via the
SMARTFIRE2 system (which includes the BlueSky modeling framework) with inputs from state
agencies where available. Georgia and Washington state submitted emission estimates (day and
location specific).
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.
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
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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 cresylic acid (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
PAH_114E1
57976
7,12-
Dimethylbenz[a] Anthracene
0.114
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PAH Group
NEI Pollutant Code
NEI Pollutant Description
URE l/(|ig/m3)
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
PAH_880E5
91576
2-Methylnaphthalene
4.8E-05
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PAH Group
NEI Pollutant Code
NEI Pollutant Description
URE l/(|ig/m3)
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; no emissions in 2017 NEI
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 Factor4
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 NEl, it was generated separately for AirToxScreen modeling
from the NEI and was not included as a separate NEI pollutant. However, starting with the 2014 NEl,
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, RfCand 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 ofNEI data categories to AirToxScreen categories
NEI Data Category
AirToxScreen Category (Reflecting AirToxScreen Summary
Resu Its)
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.
Excludes airports and railyards, which are nonroad mobile.
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.
Agricultural, CMVs and locomotive emissions are
included. Biogenic emissions that come from
vegetation are also included.
Same as NEI nonpoint except excludes locomotive, CMV,
biogenic emissions and agricultural fires.
On-road
On-road
Emissions estimates for mobile sources, such as
cars, trucks and buses. EPA's MOVES2014b model
currently generates these estimates (except in
California, which uses different models).
Same as NEI on-road.
Nonroad
Nonroad
Emissions estimates for nonroad equipment such
as lawn and garden equipment, agricultural,
construction, industrial and commercial equipment
and recreational equipment. EPA's MOVES2014b
model generates these estimates (except in
California, which uses different models).
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
Resu Its)
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 HAPs.
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 2017 NEI is the main basis of the emissions fed into the air quality models for AirToxScreen,
there were several differences between the 2017 NEI and emissions data used for the AirToxScreen
modeling.
State, local, and tribal agencies reviewed emissions information along with preliminary AERMOD risk
information and submitted comments and changes. EPA reviewed the comments and changes and
incorprated the accepted changes into the modeling platform. Information about the S/L/T changes that
were applied is included in the supplemental data.
Ethylene oxide emissions from commercial sterilizers in the NEI were replaced with EPA estimated
values using the same methodology developed to model emissions for a forthcoming proposal to amend
the National Emissions Standard for Hazardous Air Pollutants (NESHAP) for Commercial Sterilizers. We
adjusted to year 2017 estimates based on control equipment operating in year 2017 and ethylene oxide
throughput in year 2017. Much of the information used in this methodology was sourced from the
responses to the December 2019 information collection request for this industry. The year 2017
estimates of ethylene oxide were developed prior to: 1) the collection of additional information through
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a September 2021 information collection request to the industry, and 2) the development of the refined
industry average fugitive emission rates based on responses to both information collection requests. A
table of facilties for which ethylene oxide emissions were based on this methodology is listed in the
supplemental data.
Additionally, these minor differences were applied to the input files for AERMOD modeling:
FIPS code corrections were made for the Kusilvak Census Area in Alaska. FIPS code 02158 replaced
the old FIPS code (02270) for this area. For 2017 NEI nonroad emissions, we updated the FIPS code
for input data using the old FIPS code. For onroad emissions, we edited the county cross-freference
file so that FIPS code 02270 serves as the representative county for FIPS code 02158.
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.
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
use minimum value or
Less than 1 ft (0.3048 m) or greater than
SMOKE range
maximum value in feet
1300 ft (396 m)
stkdiam
Outside
use minimum value or
Less than 0.001 ft (0.0003048 m) or
SMOKE range
maximum value in ft
greater than 300 ft (91.4 m)
stkvel
Outside
use minimum value or
Less than 0.001 ft/s (0.0003048 m/s) or
SMOKE range
maximum value in ft/s
greater than 1000 ft/s (304.8 m/s)
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Field
Existing Value
New Value
Conditions/Notes1
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
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, there were differences in the HAP VOCs in California. These were due to
changes in the post-processing approach to adjust California-submitted pollutants consistent with
the MOVES2014ba speciation. Emissions for other HAPs, including metals and PAHs, were used as
provided by California.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 (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.
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ii 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.
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).
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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 grid cells
Airports
Point
Point - runways & 10 m2 areas consistent
with NEI geographic coordinates
12-km grid cells
Locomotives
Point (railyards)
and
County/Shape
Nonpoint - 12-km grid cells in the CONUS
domain, tract (non-CONUS)
Point - point fugitives
12-km grid cells
CMVs, ports and
underway
County/Shape
Shapes from the NEI; separate shapes
used for CMV at ports versus underway
12-km 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 grid cells
Agricultural burning and
biogenic emissions
County
Not modeled
12-km grid cells
Fires (prescribed and
wild)
Point
Not modeled
12-km 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 2017 information, please see in particular:
https://www.epa.gov/air-emissions-modeling/2017-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.
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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.
Table 2-8. Platform sectors for the 2017 emissions modeling platform
Platform Sector:
abbreviation
NEI Data
Category
Description and Resolution of the Data Input to SMOKE
EGU units:
ptegu
Point
2017 NEI point source EGUs, replaced with hourly 2017 Continuous Emissions
Monitoring System (CEMS) values for NOX and S02 where the units are
matched to the NEI. Emissions for all sources not matched to CEMS data come
from 2017 NEI point inventory. Annual resolution for sources not matched to
CEMS data, hourly for CEMS sources.
Point source oil and
gas:
pt_oilgas
Point
2017 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. Annual resolution.
Aircraft and ground
support equipment:
airports
Point
2017 NEI point source emissions from airports, including aircraft and airport
ground support emissions. Annual resolution. The January 2021 version of
2017 NEI corrected the aircraft emissions in the April 2020 release of the 2017
NEI.
Remaining non-EGU
point:
ptnonipm
Point
All 2017 NEI point source records not matched to the airports, ptegu, or
pt_oilgas sectors. Includes 2017-specific rail yard emissions. Annual resolution.
Agricultural:
ag
Nonpoint
2017 NEI nonpoint livestock and fertilizer application emissions.
Livestock includes ammonia and other pollutants (except PM2.5).
Fertilizer includes only ammonia. County and annual resolution.
Agricultural fires with
point resolution:
ptagfire
Nonpoint
Agricultural fire sources for year 2017 that were developed by EPA as
point and day-specific emissions.1 Agricultural fires are in the nonpoint
data category of the NEI, but in the modeling platform, they are
treated as day-specific point sources.
Area fugitive dust:
afdust
Nonpoint
PMio and PM2.5 fugitive dust sources from the 2017 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 2017 gridded hourly meteorology (precipitation and snow/ice
cover). Emissions are county and annual resolution.
1 Only EPA-developed agricultural fire data were included in this study; data submitted by states to the NEI were
excluded.
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Platform Sector:
abbreviation
NEI Data
Category
Description and Resolution of the Data Input to SMOKE
Biogenic:
be is
Nonpoint
Year 2017 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.
Category 1, 2 CMV:
cmv_clc2
Nonpoint
2017 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
Within state and federal waters, 2017 NEI Category 3 commercial
marine vessel (CMV) emissions based on AIS data. Outside of state and
federal waters, emissions are based on AIS data in selected areas, and
are gapfilled with emissions from the Emissions Control Area (ECA)
inventory. Point and hourly resolution.
locomotives:
rail
Nonpoint
Line haul rail locomotives emissions for year 2017. County and annual
resolution.
Nonpoint source oil
and gas:
np_oilgas
Nonpoint
Nonpoint 2017 NEI sources from oil and gas-related processes. County
and annual resolution.
Residential Wood
Combustion:
Rwc
Nonpoint
2017 NEI nonpoint sources with residential wood combustion (RWC)
processes. County and annual resolution.
Remaining nonpoint:
Nonpt
Nonpoint
2017 NEI nonpoint sources not included in other platform sectors,
including solvents. County and annual resolution.
Nonroad:
Nonroad
Nonroad
2017 nonroad equipment emissions developed with MOVES2014b.
MOVES was used for all states except California, which submitted their
own emissions for the 2017 NEI. County and monthly resolution.
On-road:
Onroad
On-road
2017 onroad mobile source gasoline and diesel vehicles from parking
lots and moving vehicles. Includes the following emission processes:
exhaust, extended idle, auxiliary power units, evaporative, permeation,
refueling, and brake and tire wear. For all states except California,
developed using winter and summer MOVES emission factors tables
produced by MOVES2014b.
On-road California:
onroad_ca_adj
On-road
California-provided 2017 CAP and metal HAP onroad mobile source
gasoline and diesel vehicles from parking lots and moving vehicles
based on Emission Factor (EMFAC) 2017, gridded and temporalized
based on outputs from MOVES2014b. Volatile organic compound
(VOC) HAP emissions derived from California-provided VOC emissions
and MOVES-based speciation.
Point source fires:
ptfire
Events
Point source day-specific wildfires and prescribed fires for 2017
computed using SMARTFIRE 2 and BlueSky.
Non-US. fires:
ptfire_othna
N/A
Point source day-specific wildfires and agricultural fires outside of the
U.S. for 2017 from vl.5 of the Fire INventory (FINN) from National
Center for Atmospheric Research (NCAR, 2017 and Wiedinmyer, C.,
2011) for Canada, Mexico, Caribbean, Central American, and other
international fires.
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Platform Sector:
abbreviation
NEI Data
Category
Description and Resolution of the Data Input to SMOKE
Other dust sources
not from the NEI:
othafdust
N/A
Area fugitive dust sources from Canada interpolated to 2017 between
2015 and 2023, with transport fraction and snow/ice adjustments
based on 2017 meteorological data. Annual and province resolution.
Other point sources
not from the NEI:
othpt
N/A
2017 Canada point source emissions interpolated between 2015 and
2023, and Mexico point source emissions for 2016 (provided by
SEMARNAT). Annual and monthly resolution.
Other non-NEI
nonpoint and
nonroad: othar
N/A
Year 2017 Canada interpolated between 2015 and 2023 (province
resolution) and projected year 2016 Mexico (municipio resolution,
provided by SEMARNAT) nonpoint and nonroad mobile inventories,
annual resolution.
Other non-NEI on-
road sources:
onroad_can
N/A
Monthly onroad mobile inventory for Canada interpolated to 2017
between 2015 and 2023 (province resolution).
Other non-NEI on-
road sources:
onroad_mex
N/A
Monthly onroad mobile inventory from MOVES-Mexico (municipio
resolution) for 2017.
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.
2. The biogenic zero-out run used all fire and anthropogenic emissions but with the option to generate
biogenic emissions turned off.
3. 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.
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.
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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 2017 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. 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 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. Ancillary files providing the surrogate assignments and describe the data used for the
surrogates are available in the Supplemental Data provided with this TSD.
2.3. Preparation of Emissions Inputs for AERMOI)
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
^pollutant i = q * EmiSSionSpoiiutant i
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 tract 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. Note that the run groups developed for the
county-level emissions were treated differently for CONUS versus non-CONUS domains. For the CONUS,
we allocated to grid cells using the spatial resolution in the table. For the non-CONUS areas (AK, HI, PR,
VI), we used these same run groups but allocated to tracts rather than grid cells. Design documents for
the generation of the helper files for on-road, nonroad and nonpoint sources in CONUS and non-CONUS
areas are provided in the Supplemental Data files.
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 (crz; 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 (crz; 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 crz = RH/2.15. C3
uses RH = 20 and az = 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 (crz; 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 (crz; 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 (crz; meters) and Temporal
Approach
Description of Sources
NPL012
NPL09AK
NPL03L0
NPL03PR
NEI: nonpoint
Platform: some
of nonpt
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, cjz = 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 (crz; 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
NR-CMV_ClC2_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
NPL012
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- AgriculturalLivestock
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;2 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.
2Offshore 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
and the file is posted on the emissions modeling platform ftp site.
The SMOKE FF10 was split into the CMAQ platform sectors (see Table 2-8) ptnonipm, ptegu and
Pt_°ilgas, 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.3 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.
3AERMOD 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_YDIM4 - 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 (crz) 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)
4These 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.5
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
5AERMOD 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 FACILITY_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. The starting-point airport facilities and final runway airports resulting from the matching effort,
and detailed set of process steps taken to do the matching, are available in the Supplemental Data
folder. 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.
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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
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 NEI-
specific lead6 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
6We 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 emissons 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; also, CMAQ does not cover non-CONUS areas. As in CMAQ, the spatial surrogates
are assigned according to SCC codes. The SCC-to-surrogate cross reference and surrogate
documentation workbook is provided in the Supplemental Data folder.
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 have been updated since the 2014 NATA.
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,
the same as used for the 2014 NATA. 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.
We used the same temporal allocation approach as used in the 2014 NATA. For more information,
please see the Emission Modeling Platform documentation.
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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 nonwood 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/chimneas 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 2017 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 hours,county(B&nZ&n&)
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 2017 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;
Scalar|= -—— x
# of days. v 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
crnv
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 all 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.
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 AH Emission Sources
The urban/rural determination for all emissions sources was based on the urban/rural classification of
nearby census blocks. We classified each populated census block in the United States as urban if its
centroid falls within an "urbanized area." For the 2010 Census, an urbanized area comprises a densely
settled core of census tracts or census blocks, or both, that meet minimum population density
requirements (50,000 or more people), along with adjacent territory. The adjacent territory generally is
also densely settled, but it may also contain some lower-density areas as well as nonresidential urban
land uses. About 500 such areas are included in the 2010 Census.
For point sources, each facility was designated as urban or rural based on the urban/rural classification
of the nearest census-block centroid receptor to the average facility latitude/longitude (averaged across
all release points emitting HAPs). Each gridded source was designated based on the urban/rural
classification of the nearest block receptor to the center of the 12-km grid cell (so all 4-km sources
within the same 12-km grid cell are treated the same, based on the center of the 12-km grid cell). There
were 353 grid cells on the borders of the United States that were inadvertently excluded from the
approach; these were assigned a classification of rural, which would be expected based on their
locations.
For CMV ports, each individual port shape was designated based on the urban/rural classification of the
census block nearest to the center of the port shape. All CMV underway sources were designated as
rural.
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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 CMAQv5.3.1
(https://doi.org/10.5281/zenodo.3585898; https://www.epa.gov/cmaq/cmaq-models-Q;
http://www.cmascenter.org.) with the Carbon-Bond 6r3 (CB6r3-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 CB6r3-CMAQ chemical
mechanism treats formaldehyde, acetaldehyde, benzene, methanol, and naphthalene explicitly within
the chemistry. 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 CMAQv5.3.1 used for AirToxScreen. Benzo-
A-Pyrene (one of many species of Polycyclic Organic Matter) was a new HAP added in CMAQ for the
2017 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
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Air Toxic
CMAQ Species Name(s)
Acrylic acid
ACRYACID
Acrylonitrile
ACRY_NITRILE
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
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Air Toxic
CMAQ Species Name(s)
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
Tetrachloroethylene (Perchloroethylene)
CL4_ETHE
Toluene
TOLU
Trichloroethylene
CL3_ETHE
Triethylamine
TRIETHYLAMIN
Vinyl chloride
CL_ETHE
3.1.2. Dispersion Model Selection
For AlrToxScreen air dispersion modeling, we used AERMOD (Cimorelli et al. 2005; EPA 2015e), 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 19191 was used for all AirToxScreen rungroups.
3.2. Meteorological Data
3.2.1. Input WRF Data
For use in all AirToxScreen modeling, we derived gridded meteorological data or the contiguous United
States from version 3.8 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 (https://www.mmm.ucar.edu/weather-research-and-forecasting-model). The WRF
simulation used the same 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 396 by 246 grid cells and 35 vertical layers up to 50 millibars.
3.2.2. MCIP Processing for CMAQ
The 2017 WRF meteorological outputs were processed using the Meteorology-Chemistry Interface
Processor (MCIP) package (Otte and Pleim 2010), version 4.4, 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.
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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
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
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3.2.3. MMIF Processing for AERMOD
WRF output was processed through the Mesoscale Model Interface (MMIF) program to create AERMET-
ready meteorological input data and processed in AERMET (version 19191) 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 2016). 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 was run with the adjusted u* option to better represent concentrations in AERMOD under low-
wind stable conditions.
3.2.4. Meteorological Data Outside the Contiguous States
For meteorological data covering areas outside the contiguous states (Alaska, Hawaii, Puerto Rico and
the U.S. Virgin Islands), we used WRF data processed through the AERMET program (version 19191)
specific to each of the areas. The WRF runs were based on WRF v3.9.1.1; there were three domains:
• Alaska with 9 km horizontal grid spacing (325 cells in the x-direction, 265 cells in the y-direction)
with 35 layers centered on 62.785° N and 149.114° W.
• Hawai'i with 3 km horizontal grid spacing (244 cells in the x-direction, 220 cells in the y-
direction) with 35 layers centered on 20.594° N and 157.519° W.
• Puerto Rico and Virgin Islands with 3 km horizontal grid spacing (169 cells in the x-direction, 169
cells in the y-direction) with 35 layers centered on 18.202° N and 66.469° W.
For MMIF processing to AERMET, all three domains passed through MMIF calculated mixing heights
using the MMIF guidance heights (25, 50, 75, 100, 125, 150, 175, 200, 250, 300, 350, 400, 450, 500, 600,
700, 800, 900, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, and 5000) using the 'TOP' interpolation
technique. AER_MIXHT was set to 'AERMET,' allowing AERMET to calculate mixing heights. The
minimum wind speed threshold was set to 0.0 m/s (AER_MIN_SPEED = '0.0').
3.3. CM \Q 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).
The annual simulations included a "ramp-up" period of 10 days to mitigate the effects of initial
concentrations. All 365 model days were used in the annual average levels of air toxics modeled.
The CMAQ model runs were performed for a domain covering the contiguous United States (CONUS), as
shown in Figure 3-1. This single domain covers the entire CONUS and large portions of Canada and
Mexico using 12-km by 12-km horizontal grid spacing. The model extends vertically from the surface to
50 millibars (approximately 17,600 meters) using a sigma-pressure coordinate system. Air quality
conditions at the outer boundary of the 12-km domain were taken from a global model. Table 3-3
provides some basic geographic information regarding the 12-km CMAQ domain.
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Figure 3-1. Map of the CMAQ modeling domain; the purple box denotes the 12-km national
modeling domain
Table 3-3. Geographic information for the 12-km CMAQ modeling domain
12-km CMAQ Modeling Configuration
Map Projection
Lambert Conformal Projection
Grid Resolution
12 km
Coordinate Center
97 W, 40 N
True Latitudes
33 and 45 N
Dimensions
396 x 246 x 35
Vertical Extent
35 Layers: Surface to 50 mb level (see Table 3-2)
3.3.2. Boundary and Initial Conditions
The lateral boundary and initial species concentrations were provided by a northern hemispheric
application of a CMAQ modeling platform to the year 2017. The hemispheric-scale platform uses a polar
stereographic projection at 108-km resolution to completely and continuously cover the northern
hemisphere for 2017 with meteorology, emissions, and atmospheric processing of pollutants.
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Meteorology is provided by Weather Research and Forecasting model (WRF v3.8) using 44 non-
hydrostatic sigma-pressure layers between the surface and 50 hPa (~20-km asl). Emissions were
provided by the Near-Real-Time modeling project from the Office of Research and Development. The
emission platform was consistent and combines EDGAR-HTAP (Janssens-Maenhout et al. 2012), real-
time fires from the FINN system, and GEIA climatological natural emissions. The CMAQ model also
included the on-line windblown dust emission sources (excluding agricultural land), which are not
always included in the regional platform but are important for large-scale transport of dust. The Near-
Real-Time system used a combination of model version and stratospheric treatment depending on the
date. The atmospheric processing (transformation and fate) was simulated by CMAQ (<=2017-09-23:
v5.2; >=2017-09-24: v5.3) using the Carbon Bond (cb5e51, cb6r3) with linearized halogen chemistry and
the aerosol model with semi-volatile primary organic carbon (AE6svPOA). The stratosphere was
simulated by the Potential Vorticity scaling method, which was only operational after May. As a result,
the hemispheric scale simulation is expected to underpredict stratospheric contributions in spring.
Because Hemispheric CMAQ (H-CMAQ) 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, Colo. (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.
Table 3-4. Boundary conditions from 2017 remote concentration estimates
Pollutant
RCEat 298Kand 1
atm (|ig/m3)
2017
RCE (pptv)
2017
Remote
Network
Location(s)
CMAQ HAP
Chloroform
0.082
16.9
AGAGE
Trinidad Head
X
Methyl chloride
(chloromethane)
1.151
558
NOAA GMD
KUM, MLO, NWR, BRW,
ALT
x
Carbon tetrachloride
0.509
81.0
NOAA GMD
KUM, MLO, NWR, BRW,
ALT
X
Methyl bromide
(bromomethane)
0.027
7.0
NOAA GMD
KUM, MLO, NWR, BRW,
ALT
Methyl chloroform (1,1,1-
trichloroethane)
0.012
2.2
NOAA GMD
KUM, MLO, NWR, BRW,
ALT
Dichloromethane
(methylene chloride)
0.220
63.4
NOAA GMD
KUM, MLO, NWR, BRW,
ALT
X
Tetrachloroethene
(perchloroethylene,
tetrachloroethylene)
0.013
1.8
NOAA GMD
KUM, MLO, NWR, BRW,
ALT
X
Table 3-5. CMAQ HAP boundary conditions applied as zero value
Air Toxic
2017 CMAQ Species Name(s)
1,1,2,2-Tetrachloroethane
CL4_ETHANE
1,3-Dichloropropene
DICL_PROPENE
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Air Toxic
2017 CMAQ Species Name(s)
l,4-Dichlorobenzene(p)
DICL_BENZENE
2,4-Toluene diisocyanate
TOL_DIIS
Acetonitrile
ACET_NITRILE
Acrylic acid
ACRYACID
Acrylonitrile
ACRY_NITRILE
Arsenic
AASI, A AS J, 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
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
(isomers and mixture)
XYLENE
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
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Air Toxic
2017 CMAQ Species Name(s)
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 (1 km in highly populated areas, 4 km otherwise)
2. Populated census-block centroid receptors (discussed in Section 3.4.2.2)
3. Monitoring site receptors (discussed in Section 3.4.2.3)
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 interpolating to block receptors and
monitor receptors. Receptors at the monitoring locations were used in the model evaluation.
For non-CONUS areas (AK, HI, PR, VI), where we do not run CMAQ, we used:
1. Census-block centroid receptors (both non-populated and populated)
2. Monitoring site receptors
3.4.2.1. Gridded receptors
We used gridded receptors throughout the CONUS area. 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. We based spacing of the gridded receptors on the 2013 populations
of Core Base Statistical Areas (CBSA). For CMAQ grid cells that intersected a CBSA with a population of 1
million people or more (Figure 3-2), the receptors were placed 1 km apart, resulting in 144 receptors per
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12-km CMAQgrid cell. Otherwise, the receptors were placed 4 km apart, resulting in nine receptors per
12-km CMAQgrid cell. This resulted in 1.4 million receptors nationwide, with 1-km spacing used in 6,935
12-km cells and 4-km spacing used in 49,490 12-km cells. Correct receptor placement was verified by
overlaying receptors with CBSA's in ArcGIS.
Legend
USA Core Based Statistical Areas
Type
| Metropolitan CBSA
| Micropolitan CBSA
Figure 3-2. CBSAs exceeding 1 million people
Each gridded receptor, with either 1- or 4-km spacing, represented the center of a subgrid cell within the
12-km CMAQgrid cells (Figure 3-3). These gridded receptors, plus populated block and monitor
receptors when available within a subgrid cell, were averaged (Figure 3-4). These subgrid-cell averages
were then used to calculate the overall AERMOD average of the 12-km 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 11103).
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Figure 3-3. Dense (left) and coarse (right) receptor grid layout in CMAQ Lambert Projection
+
• •
• •
•
• • •
• • •
• •
•
• •
• +
•
•
•
*
• *»•••
• • •_•••• *
• . ••'••••
• • *
• • «*•••••
• •
• •
• •
• • •
• •
•
• •
• •
• •
• • •
+'
+
+
+
.
• •
• +
•
•
•
• Census block
^ Gridded receptor
Figure 3-4. 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
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was explicitly modeled. For CONUS gridded sources, any gridded receptor within 50 km of the center of
the 12-km or 4-km 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. Gridded receptors were not modeled for the non-CONUS grid sources.
3.4.2.2. Census-block centroid receptors
The locations of census-block centroids were based on the 2010 U.S. Census (2010). When performing
the dispersion modeling for point sources in the CONUS area, populated block receptors within 10 km of
any emission point at the facility were explicitly modeled in AERMOD. For non-CONUS point sources, the
distance was 50 km. For airports in the CONUS area, any populated block receptor within 10 km of any
point along a runway or 10 km from any point of the 100-by-100 m area sources was modeled. For
airports in the non-CONUS areas, the distance was 50 km. For gridded CONUS sources, census blocks
were not modeled; these were later interpolated from gridded receptors during model post-processing.
For ports and underway sources in the CONUS area, any block receptor within 10 km of a side of the
polygon or within 10 km of the center of the source was explicitly modeled. For ports and underway
emissions in the non-CONUS areas, the distance was 50 km. For the non-CONUS gridded emissions, we
used the same methods as used for CONUS gridded emissions, except the distance was 50 km instead of
10 km. For all non-CONUS sources, point, airports, etc., non-populated blocks were modeled as
receptors as well using the same methodology and distance criteria as used for populated blocks. Non-
populated blocks were not modeled in the CONUS area.
3.4.2.3. AERMOD receptors at monitoring sites
The Ambient Monitoring Archive for HAPs monitoring data sites was used for the model evaluation
(Section 3.7.1). Therefore, we obtained a unique set of geographic coordinates for all monitors to
include as receptors in the AERMOD modeling. AERMAP (version 11103) 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.
3.4.3. Model Options
For all AERMOD runs, the FASTALL option was used to decrease model runtimes, especially for gridded,
airport, CMV shapes and tract sources. For all AERMOD runs excluding point and airport sources, the
FLAT option was used (terrain ignored).
For sources determined to be urban, the AERMOD urban option was used.
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 CONUS
gridded sources. For all non-CONUS sources, the closest meteorological station to the source was used.
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
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a total group (group ALL). Similarly, for the CMV runs and 12 gridded sources, each CMV shape or 12-km
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 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 365 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.
7215.9957 is the conversion factor from tons/hr to g/s.
24
E;=
10000 xEFj
x 251.9957
365
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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 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 10 to 50 km from the source in . For the gridded
source types, census blocks and monitors were interpolated 0 to 50 km from the sources. Interpolation
was done for each individual facility (point source, airport, CMV shape) or gridded source.
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 rungroups 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. Interpolate the results from step 1 to unmodeled census/blocks and monitors within 50 km for each
facility or gridded source.
3. 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.
4. 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 8,760 hours. The number may be less
if calms and missing data are present in the meteorological data.
5. 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.
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.
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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.
3.5.1. Overview
For 52 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 2015e) 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 12-km horizontal CMAQ grid cell in the CONUS (see
Figure 3-1 and Figure 3-5), resulting in at least nine, and sometimes more than 10,000, receptors per ceil
and 6.5 million receptors nationwide.
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
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.
/ CMAQpnfb )
C - ALRMODrec x , . ) + cmaqsec + CMAQpfires + CMAQpbiogenics + (¦MAQbackground
Where:
C = concentration at a receptor
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CMAQpnfb
concentration in CMAQgrid cell, contributed by primary emissions,
excluding fires and biogenics
AERMODREc
concentration at AERMOD receptor
AERMODgridavg
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
CMAQsec
contribution from atmospheric reactions in CMAQgrid cell
CMAQpfires
contribution from primary emissions of fires in CMAQ grid cell
CM A Qpbiogenics
contribution from primary emissions of biogenics in CMAQgrid cell
CM A Qbackground
contribution from background in CMAQ grid cell for carbon
tetrachloride
This hybrid approach, which builds on earlier area-specific applications to Philadelphia, Pa. (Isakov et al.
2007) and Detroit, Mich. (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 52 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:
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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
CMAQgrid cell (see Section 3.4.2.1 for details on gridded receptors and example subgrid cells). This
results in either 9 or 144 subgrid-cell averages in each CMAQ cell.
3. Average the 9 or 144 averages in each grid cell to calculate an overall average for the grid cell,
AERMOD GRIDAVG-
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.
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 CMAQgrid 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.
For CMAQ HAPs in non-CONUS areas, the hybrid program calculated the background, fire, secondary
and biogenic concentrations as discussed in Section 3.6 and added them to the total AERMOD
concentrations. For non-CMAQ HAPs in all areas, relevant background was added to the total AERMOD
concentrations. The hybrid program then output hybrid concentrations for each run group for each HAP.
Separate files were created for gridded receptors, block receptors and monitors. The block and monitor
files contained all areas, including non-CONUS areas comprising the AERMOD concentrations with
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relevant background, fire, secondary and biogenic concentrations. 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. However, they were also summarized at the tract level. Tract-average concentrations for the
entire United States, Puerto Rico and the Virgin Islands were calculated by population-weighting the
populated block hybrid concentrations in each tract.
3.6. Other Source Characterizations
3.6.1. Background Concentrations Used for non-CMAQ Areas and Pollutants
For non-CMAQ HAPs and non-CMAQ areas, background concentrations were included with the hybrid
results. Table 3-6 lists the pollutants for which 2017 background was included in non-CONUS areas. For
methyl bromide and methyl chloroform, the background was also included in the CONUS area, as these
two HAPs were not CMAQ HAPs.
For background, we used the remote concentration estimates from five NOAA GMD sites: Cape
Kumukahi, Hawaii (KUM); Mauna Loa, Hawaii (MLO); Niwot Ridge, Colo. (NWR); Barrow, Alaska (BRW);
Alert, Canada (ALT); and the Trinidad Head Site (AGAGE). These are the same values used for CMAQ
boundary conditions for pollutants not estimated by GEOS-Chem. Appendix D details how these were
developed.
Table 3-6. Background Concentrations used for non-CMAQ areas and pollutants
Pollutant
RCEat 298Kand
1 atm (|ig/m3)
2017
RCE (pptv)
2017
Remote
Network
Location(s)
Chloroform
0.082
16.9
AGAGE
Trinidad Head
Methyl chloride (chloromethane)
1.151
558
NOAA GMD
KUM, MLO, NWR, BRW, ALT
Benzene
0.108
34.0
NOAA GMD
KUM, MLO, NWR, BRW, ALT
Carbon tetrachloride
0.509
81.0
NOAA GMD
KUM, MLO, NWR, BRW, ALT
Methyl bromide
(bromomethane)
0.027
7.0
NOAA GMD
KUM, MLO, NWR, BRW, ALT
Methyl chloroform (1,1,1-
trichloroethane)
0.012
2.2
NOAA GMD
KUM, MLO, NWR, BRW, ALT
Dichloromethane (methylene
chloride)
0.220
63.4
NOAA GMD
KUM, MLO, NWR, BRW, ALT
Tetrachloroethene
(perchloroethylene,
tetrachloroethylene)
0.013
1.8
NOAA GMD
KUM, MLO, NWR, BRW, ALT
3.6.2. Fires, Biogenics and Secondary Concentrations Used for Non-CMAQ
Situations
In the CONUS, fires, biogenics and secondary concentrations are determined by CMAQ. For non-CONUS
areas, we developed an approach to estimate these components based on available information,
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including hemispheric CMAQ concentrations for formaldehyde and acetaldehyde for secondary
concentrations.
3.6.3. Non-CONUS Fires
For the HAPs that include fire contributions, an approach for the non-CONUS areas was developed using
average CMAQ fire concentrations of states with similar fire emission densities in the CONUS area. For
example, North Dakota had a similar emissions density for formaldehyde and acrolein as Alaska. North
Dakota's formaldehyde density was 75 and Alaska's 89. For acrolein, North Dakota's formaldehyde
density was 11 and Alaska's was 13. For Hawaii (formaldehyde density = 685, acrolein density = 118), the
state with the closest densities was Louisiana (formaldehyde density = 557, acrolein density = 99). For
Puerto Rico (formaldehyde density = 74, acrolein density = 13), Maryland was chosen as the
representative state (formaldehyde density = 79, acrolein density = 13). For the Virgin Islands, fire
emissions were assumed zero.
To calculate the fire concentrations for fire HAPs in Alaska, Hawaii and Puerto Rico, the average primary
fire concentration was calculated for each of the representative states based on the CMAQgridded
results that overlapped the states. The resulting averages were then applied to the appropriate non-
CONUS areas. Table 3-7 lists the fire HAPs with the Alaska, Hawaii and Puerto Rico fire concentrations.
Table 3-7. Fire concentrations (^g/m3) for selected HAPs in Alaska, Hawaii and Puerto Rico
HAP
Alaska
Hawaii
Puerto Rico
Acetaldehyde (primary)
2.7xl0"2
6.2xl0"3
1.3xl0"2
Acetonitrile
4.9xl0"3
2.8xl0"3
3.8xl0"3
Acrolein (primary)
1.4xl0"2
2.9xl0"3
2.7xl0"3
Acrylic acid
9.5xl0"4
1.7xl0"4
2.5xl0"4
Arsenic
0.0x10°
0.0x10°
1.6x10 10
PAHJDOEO
5.1xl0"4
2.2xl0"4
1.5xl0"4
PAH_176E4
5.3xl0"4
2.3xl0"4
1.5xl0"4
Benzene
2.7xl0-2
l.lxlO"2
9.7xl0"3
Benzo-a-pyrene
2.2xl0"5
5.5xl0"6
6.6x10 s
PAH_880E5
2.0xl0"3
9xl0"4
6.0xl0"4
PAH_176E3
4.8xl0"4
2.1xl0"4
1.4xl0"4
1,3-butadiene
7.3X103
9.5xl0"4
8.8xl0"4
Cadmium
1.3x1011
0.0x10°
1.6x10 11
Carbonyl sulfide
1.5xl0"6
2.5xl0"7
4.5xl0"7
Chlorine
8.2xl0"7
2.7xl0"7
1.3xl0"9
Ethyl Benzene
1.8xl0"3
9.5xl0"6
3.7xl0"4
PAH_176E5
4.1xl0"4
1.8xl0"4
1.2xl0"4
Formaldehyde (primary)
5.2xl0"2
l.OxlO"2
1.4xl0"2
Hexane
4.6xl0"3
3.5xl0"4
6.4xl0"4
Lead
0.0x10°
3.7x1011
9.1x10 11
Manganese
0.0x10°
3.7x1010
0.0x10°
Mercury
l.lxlO"7
9.4xl0"8
6.5xl0"8
Methanol
6.4xl0"2
2.6xl0"2
4.2xl0"2
Methyl chloride
7.2xl0"3
3.3xl0"3
2.2xl0"3
Naphthalene
l.OxlO"2
2.2xl0"3
2.4xl0"3
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HAP
Alaska
Hawaii
Puerto Rico
Nickel
6.0xl0"9
0.0x10°
1.3x10 10
Styrene
2.6xl0"7
3.6x1010
1.9xl0"9
Toluene
1.5xl0"2
4.4xl0"3
3.5xl0-3
Xylene
4.7xl0"3
7.2xl0"4
1.3xl0"3
3.6.4. Secondary Concentrations
For formaldehyde and acetaldehyde in non-CONUS areas, secondary concentrations were based on
results from a 2017 hemispheric CMAQ simulation. This simulation included limited HAPs, e.g.,
formaldehyde and acetaldehyde, which are explicit model species in the CB6r3 chemical mechanism.
The simulation used a 108-km resolution hemispheric grid that included these areas. Emissions were
provided by the Near-Real-Time modeling project from the Office of Research and Development. The
emission platform was consistent and combines EDGAR-HTAP (Janssens-Maenhout et al. 2012), real-
time fires from the FINN system, and GEIA climatological natural emissions. The CMAQ model also
included the on-line windblown dust emission sources (excluding agricultural land), which are not
always included in the regional platform but are important for large-scale transport of dust. Hemispheric
CMAQ results were used to determine a representative secondary concentration for formaldehyde and
acetaldehyde for non-CONUS areas. For each area, the secondary concentration was assumed to be 75
percent of the total concentration from the hemispheric CMAQ simulations. The total concentration
used for the calculations was based on visual inspections of the modeled concentrations over each area.
Acrolein was processed differently than formaldehyde and acetaldehyde. The secondary concentration
for acrolein, the statewide primary acrolein concentrations averages for Alaska and Hawaii (territory
averages for Puerto Rico and Virgin Islands) were calculated based on the AERMOD results. Averages
were based on modeled census blocks and monitor receptors. The acrolein secondary concentration
was assumed to be 25 percent of the primary concentration, as 25 percent is the average ratio of
secondary to primary concentrations in the CONUS.
Table 3-8 lists the secondary concentrations for acetaldehyde, acrolein and formaldehyde, and
Figure 3-6 and Figure 3-7 show the spatial distribution of the total formaldehyde and acetaldehyde
hemispheric CMAQ concentrations. For Alaska and Puerto Rico, the chosen values were based on an
average of the range of concentrations across each area. For Alaska, this was the mainland area. The
value chosen for Hawaii was based on the areas with populated areas, and the value for the Virgin
Islands was the CMAQ grid cell value.
Table 3-8. Concentrations (ng/m3) used to derive secondary concentrations for formaldehyde,
acetaldehyde and acrolein; acrolein is primary concentrations only
HAP
Alaska
Hawaii
Puerto Rico
Virgin Islands
Acetaldehyde (hemispheric average)
0.2
0.3
0.6
0.4
Formaldehyde (hemispheric average)
0.1
0.5
0.80
0.50
Acrolein (non-CONUS average)
0.01
0.007
9.1xl0"4
7.4xl0"4
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„ 0.00000
Range 0.412 to 0.813
Hg/m3
0.416 pg/m3
0.354 (ig/m3
0.321 |Jg/m3
.299pig/m3
0.312 ng/m,3
o.oooso
0.00045
0.00040
0.00025
i
0.00010
0.00005
0.00000
¦V-
Figure 3-6. Acetaldehyde total concentrations for a) Alaska, b) Hawaii and c) Puerto
Rico/Virgin Islands
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Island:
O.lf
Inte
0.174
0.00050
0.00045
0.00040
| ] 0.00035
0.00030
-. 0.00000
>Dmv
¦ 0.0010
0.526 |ig/m3
0.487 ng/m3
Range 0. 523 to 1.06
|ig/m3
0.0008
' 0.0007
10.0006
10.0005
0.0004
0.0003
0.0002
0.0001
j 0.0000
ppmv
0.559 |ag/m3
0. 549 ng/m3
0.548 [ig/m3
0.378 Kg/m3 a353 [ig/m3
0.00050
0.00045
0.00040
0.00035
0.00030
0.00025
0.00020
0.00015
0.00010
0.00005
"J 0.00000
Figure 3-7. Formaldehyde total concentrations for a) Alaska, b) Hawaii and c) Puerto
Rico/Virgin Islands
3.6.5. Biogenics
To calculate formaldehyde and acetaldehyde biogenic concentrations for non-CONUS areas, we used the
total hemispheric CMAQ concentrations shown in Table 3-6. Biogenic concentrations were assumed to
be 20 percent of the total concentrations. For methanol, biogenic concentrations in the non-CONUS
areas were assumed to be zero.
3.7. Model Evaluation
An operational model performance evaluation of the HAPs simulated for the 2017 AirToxScreen was
conducted using the Ambient Monitoring Archive for HAPs for the year 2017
(https://www.epa.gov/amtic/amtic-air-toxics-data-ambient-monitoring-archive); more details found in
Section 3.7.1 below). 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
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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.
3.7.1. Ambient Monitoring Data Preparation
EPA has created annual average concentrations for year 2017 using data in the Ambient Monitoring
Archive for HAPs. These data primarily come from AQS; however, they also come from special studies
and various other networks that may not have been included in AQS. All annual averages are in units of
micrograms per cubic meter (ng/m3) using local meteorological data where available or standard
conditions otherwise. An annual average is created for each unique pollutant/monitoring site/sampling
duration used the following procedures:
3. 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.
4. Hourly monitoring data are averaged to daily (and subhourly data are averaged to hourly) using the
following criteria translating to the ceiling of 75% completenes:
Sampling Duration Averaging To Minimum Count
5 MINUTES
HOURLY
9
10 MINUTES
HOURLY
5
15 MINUTES
HOURLY
3
30 MINUTES
HOURLY
2
150 MINUTES
DAILY
8
90 MINUTES
DAILY
12
1 HOUR
DAILY
18
2 HOUR
DAILY
9
3 HOURS
DAILY
6
4 HOUR
DAILY
5
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5 HOUR
DAILY
4
6 HOUR
DAILY
3
8 HOUR
DAILY
3
12 HOUR
DAILY
2
24 HOURS
DAILY
1
5. The median Regression on Order Statistic (ROS) is the annual average used, requiring at least 80% of
daily averages to be above the method detection limit (MDL).
6. Some pollutants were summed to better reflect the AirToxScreen modeled pollutant. This occurred
for PAH groups (summed individual PAHs belonging to the PAH groups), xylenes (summed m/p with
o-xylene), and 1,3-dichloropropylene (summed cis and trans).
The data used in the model evaluation are provided in the Supplemental Data folder.
3.7.2. 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)
1 "
Mean Bias = ~ V {M - 0)
n i
1 "
Mean Error = — Y \M - C\
n j
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Root Mean Square Error ¦
t
Z(M-0):
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)
I(M-O)
Normalized Mean Bias = —
n
i(o)
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.
t\M-0\
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 =
(n- l)
z
o-o
-------
CMAQ and AERMOD modeling systems to replicate the 2017 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. Table 3-9 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, Section 3.7.2). Figure 3-8 shows the 2017 HAP monitoring
locations.
Table 3-9. List of hybrid HAPs evaluated
Model Air Toxic
Measured Air Toxic
No. of Sites
Acetonitrile
ACET_NITRILE_24_HOURS
56
Acrolein
AC RO LE1 N_24_H 0 U RS
28
Acrylonitrile
ACRY_NITRILE_24_HOURS
6
Acetaldehyde
ALD2_24_HOURS
119
Arsenic
ARSENIC_PM10_24_HOURS
39
ARSENIC_PM25_24_HOURS
271
ARSENIC_TSP_24_HOURS
51
Benzene
BENZENE_l_HOUR
27
BENZENE_24_HOURS
245
BENZENE_5_MINUTES
7
Beryllium
BERYLUUM_PM10_24_HOURS
33
BE RYLLIU M_TSP_24_HOU RS
35
Ethylene dibromide
BR2_C2_12_24_HOURS
10
1,3-Butadiene
BUTADIENE13_l_HOUR
25
BUTADIENE13_24_HOURS
144
Benzo-a-pyrene
Ba P_PM 10_24_H OU RS
24
Cadmium
CADMIUM_PM10_24_HOURS
39
CADMIUM_PM25_24_HOURS
136
CADMIUM_TSP_24_HOURS
49
Carbon tetrachloride
CARBONTET_24_HOURS
227
CARBONTET_l_HOUR
3
Carbonyl sulfide
CARBSULFIDE_5_MINUTES
7
Chloroform
C H C L3_24_H 0 U RS
209
Vinyl chloride
CL_ETHE_24_HOURS
89
Chlorine
C L2_P M 10_24_H 0 U RS
2
CL2_PM2_5_24_HOURS
291
Ethylene dichloride
CL2_C2_12_24_HOU RS
140
Methylene chloride
CL2_ME_24_HOURS
227
CL2_ME_5_MINUTES
7
Trichloroethylene
CL3_ETHE_24_HOURS
89
1,1,2,2-Tetrachloroethane
CL4_ETHANE_24_HOURS
36
Tetrachloroethylene
CL4_ETHE_24_HOURS
157
CL4_ETHE_5_MINUTES
7
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Model Air Toxic
Measured Air Toxic
No. of Sites
Chromium Compounds (only
hexavalent chromium was modeled)
C R_V l_P M 10_24_H 0 U RS
19
l,4-Dichlorobenzene(p)
DICL_BENZENE_24_HOURS
102
1,3-Dichloropropene
DICL_PROPENE_24_HOURS
2
Ethyl benzene
ETHYLBENZENE_l_HOUR
27
ETHYLBENZENE_24_HOURS
222
Formaldehyde
FORM_24_HOU RS
119
Hexane
HEXANE_l_HOUR
26
HEXANE_24_HOURS
125
Lead Compounds
LEAD_PM10_24_HOURS
43
LEAD_PM25_24_HOURS
286
LEAD_TSP_24_HOURS
176
Manganese Compounds
MANGANESE_PM10_24_HOURS
43
MANGANESE_PM25_24_HOURS
289
MANGANESE_TSP_24_HOURS
75
Methyl chloride
METHCHLORIDE_24_HOURS
196
METHCHLORIDE_5_MINUTES
7
Naphthalene
NAPHTHALENE_24_HOURS
35
Nickel Compounds
NICKEL_PM10_24_HOURS
40
NICKEL_PM25_24_HOURS
285
NICKEL_TSP_24_HOURS
62
Polycyclic Organic Matter
PAH_OOOEO_24_HOU RS
32
Polycyclic Organic Matter
PAH_176E3_24_HOURS
21
Polycyclic Organic Matter
PAH_176E4_24_HOURS
32
Polycyclic Organic Matter
PAH_176E5_24_HOURS
32
Polycyclic Organic Matter
PAH_880E5_24_HOU RS
34
Propylene dichloride
PROPYL_DICL_24_HOURS
61
Styrene
STYRENE_l_HOUR
27
STYRENE_24_HOURS
161
Toluene
TOLUENE_l_HOUR
27
TO LU E N E_24_H 0 U RS
241
Xylene
XYLENE_24_HOURS
222
XYLENE_l_HOUR
27
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In this section, we present paired annual model-to-monitor site comparisons for the hybrid model, along
with CMAQand AERMOD, for three key HAPs: acetaldehyde, formaldehyde and benzene. The annual
model performance statistical results for these three HAPs are presented below in Table 3-10. Boxplots
showing model distribution (units of jig/m3) and bias differences (units of |ig/m3) as compared to
ambient observations are presented in this statistical analysis. These boxplots display boxed
interquartile ranges of 25th to 75th, along with whiskers from the 5th to 95th, quartiles. Also plotted on
these boxplots are summary statistics of correlation (r), RMSE, NMB, NME, MB and ME. Regional spatial
maps that show the mean bias and error calculated at individual monitoring sites are also provided for
acetaldehyde, formaldehyde and benzene. Appendix D presents more details of the hybrid evaluation.
Both CMAQ and hybrid model predictions of annual formaldehyde, acetaldehyde and benzene showed
relatively small to moderate bias and error percentages when compared to observations. AERMOD
showed larger biases and errors; these underestimates are expected for secondarily formed HAPs (e.g.,
-86.1% for acetaldehyde and -87.3% for formaldehyde) given the exclusion of atmospheric chemistry in
AERMOD. Differences in bias and error statistics between the hybrid and CMAQ models were negligible
for formaldehyde and acetaldehyde. Technical issues in the HAPs data consist of (1) uncertainties in
monitoring methods; (2) limited measurements in time/space to characterize ambient concentrations
("local in nature"); (3) commensurability issues between measurements and model predictions; (4)
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emissions and science uncertainty issues may also affect model performance and (5) limited data for
estimating intercontinental transport that effects the estimation of boundary conditions (i.e., boundary
estimates for some species are much higher than predicted values inside the domain).
Table 3-10. 2017 annual air toxics performance statistics for the Hybrid, CMAQ and AERMOD
models
Air Toxic Species
Model
MB (|ig/m3)
ME (|ig/m3)
NMB (%)
NME (%)
Acetaldehyde
(ALD2_24_HOURS)
Hybrid
-0.4
0.4
-26.5
32.9
CMAQ
-0.4
0.4
-27.7
33.9
AERMOD
-1.1
1.1
-86.1
86.3
Formaldehyde
(FORM_24_HOURS)
Hybrid
-1.2
1.2
-47.3
48.7
CMAQ
-1.2
1.2
-48.1
49.2
AERMOD
-2.2
2.2
-87.3
87.3
Benzene
(BENZENE_24_HOURS)
Hybrid
-0.1
0.3
-19.3
46.9
CMAQ
-0.2
0.3
-31.4
45.4
AERMOD
-0.3
0.4
-43.8
61.3
(a)
(b)
I.HOURS, Toxics, 20170101 to 20171231
ALD2_UMG3_24_HOURS, Toxics, 20170101 to 20171231
Figure 3-9. Acetaldehyde boxplots of (a) distribution (jj.g/m3) and (b) bias difference (jj.g/m3)
for CMAQ, AERMOD and Hybrid models compared to ambient observations
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• Toxics
Figure 3-10. Mean bias (%) for acetaldehyde at 2017 monitoring sites in the Hybrid modeling
domain
Figure 3-11. Mean error (%) for acetaldehyde at 2017 monitoring sites in the Hybrid modeling
domain
• Toxics
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• Toxics
Figure 3-12. Mean bias (%) for acetaldehyde at 2017 monitoring sites in the CMAQ modeling
domain
Figure 3-13. Mean error (%) for acetaldehyde at 2017 monitoring sites in the CMAQ modeling
domain
• Toxics
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• Toxics
Figure 3-14. Mean bias (%) for acetaldehyde at 2017 monitoring sites in the AERMOD
modeling domain
Figure 3-15. Mean error (%) for acetaldehyde at 2017 monitoring sites in the AERMOD
modeling domain
• Toxics
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(b)
FORM_UMG3_24_HOURS, Toxics, 20170101 to 20171231
I Toxics
I 2017_AirToxScreen_CMAQ
I 2017_AirToxScreen_AERMOD
I 2017 AirToxScrean HYBRID
FORM_UMG3_24_HOURS, Toxics, 20170101 to 20171231
Figure 3-16. Formaldehyde boxplots of (a) distribution (ixg/m3) and (b) bias difference (jjg/m3)
for CMAQ, AERMOD and Hybrid models compared to ambient observations
• Toxics
Figure 3-17. Mean bias (%) for formaldehyde at 2017 monitoring sites in the Hybrid modeling
domain
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FORM_UMG3_24_HOURS ME (ug/m3) for run
for 20170101 to 20171231
• Toxics
Figure 3-18. Mean error (%>) for formaldehyde at 2017 monitoring sites in the Hybrid modeling
domain
• Toxics
Figure 3-19. Mean bias (%) for formaldehyde at 2017 monitoring sites in the CM AO. modeling
domain
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FORM_UMG3_24_HOURS ME (ug/m3) for run 2017_AirToxScreen_CMAQ for 20170101 to 20171231
Figure 3-20. Mean error (%>) for formaldehyde at 2017 monitoring sites in the CMAQ modeling
domain
FORM_UMG3_24_HOURS MB (ug/m3) for run 2017_AirToxScreen_AERMOD for 20170101 to 20171231
coverage limit - 75%
Figure 3-21. Mean bias (%) for formaldehyde at 2017 monitoring sites in the AERMOD
modeling domain
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FORM_UMG3_24_HOURS ME (ug/m3) for run 2017_AirToxScreen_AERMOD for 20170101 to 20171231
- U9'tn3
rage limit - 75%
Figure 3-22. Mean error (%) for formaldehyde at 2017 monitoring sites in the AERMOD
modeling domain
(a)
(b)
ALD2_UMG3_24_HOURS, Toxics, 20170101 to 20171231
BENZENE_UMG3_24_HOURS, Toxics, 20170101 to 20171231
E3 2017_AirToxScreen CMAQ
¦ 2017_AirToxScreen_AERMOD
¦ 2017_AirToxScreen_HYBRID
RMSE
NMB
NME
-43.8
61.3
-0.25
0.35
0.09
0.49
-19.3
46.9
-0.11
0.27
Figure 3-23. Benzene boxplots of (a) distribution (ixg/m3) and (b) bias difference (ixg/m3) for
CMAQ, AERMOD and Hybrid models compared to ambient observations
(BENZENE_ 24_ HO URS)
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HYBRID for 20170101 to 20171231
coverage limit = 75%
I
BB 1.2
1
0.8
0.6
0.4
• Toxics
Figure 3-24. Mean bias (%>) for benzene at 2017 monitoring sites (BENZENE_24_HOURS) in the
Hybrid modeling domain
coverage limit = 75%
• Toxics
Figure 3-25. Mean error (%) for benzene at 2017 monitoring sites (BENZENE_24_HOURS) in
the Hybrid modeling domain
BENZENE_UMG3_24_HOURS ME (ug/m3) for run 2017
for 20170101 to 20171231
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for 20170101 to 20171231
coverage limit = 75%
-0.6
-0.8
-1
-1.2
-1.4
-1.6
• Toxics
Figure 3-26. Mean bias (%) for benzene at 2017 monitoring sites (BENZENE_24_HOURS) in the
CMAQ modeling domain
BENZENE_UMG3_24_HOURS ME (ug/m3) for run 2017_AlrToxScreen_CMAQ for 20170101 to 20171231
coverage limit = 75%
• Toxics
Figure 3-27. Mean error (%) for benzene at 2017 monitoring sites (BENZENE_24_HOURS) in
the CMAQ modeling domain
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for 20170101 to 20171231
coverage limit = 75%
-0.6
-0.8
-1.2
-1.4
-1.6
• Toxics
Figure 3-28. Mean bias (%) for benzene at 2017 monitoring sites (BENZENE_24_HOURS) in the
AERMOD modeling domain
coverage limit = 75%
>2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
• Toxics
Figure 3-29. Mean error (%) for benzene at 2017 monitoring sites (BENZENE_24_HOURS) in
the AERMOD modeling domain
3.7.4. 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 2017 HAP observed
ambient concentrations. Statistical assessments of modeled results versus observed pairs were paired in
BENZENE_UMG3_24_HOURS ME (ug/m3) for run 2017 AlrToxScregn AERMOD far 20170101 to 20171231
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time and space and aggregated on an annual basis. Table 3-11 provides a list of HAPs evaluated in the
non-hybrid model performance evaluation and the number of pairs (based on completeness criteria of
observations, Section 3.7.1) used in the annual median. Figure 3-30 shows the 2017 non-hybrid HAP
monitoring locations. Results from the non-hybrid evaluation are presented in Appendix E.
Table 3-11. List of non-hybrid HAPs evaluated
Model Air Toxic
Measured Air Toxic
No. of Sites
Antimony
ANTIMONY_PM25_24_HOURS
139
ANTIMONY_PM10_24_HOURS
23
ANTIMONY_TSP_24_HOURS
22
Cobalt
COBALT_PM25_24_HOURS
137
COBALT_PM10_24_HOURS
29
COBALT_TSP_24_HOU RS
24
Selenium
SELENIUM_PM25_24_HOURS
286
SELENIUM_PM10_24_HOURS
26
SELENIUM_TSP_24_HOURS
10
Methyl Bromide (Bromomethane)
METHYLBROM_5_MINUTES
7
METHYLBROM_24_HOURS
111
Methyl Chloroform (1,1,1-Trichloroethane)
MTHYLCHLRF_5_MINUTES
7
MTHYLCHLRF_24_HOURS
120
MTHYLCHLRF_l_HOUR
2
Carbon disulfide
CARBNDISULF_24_HOURS
57
Methyl Isobutyl Ketone (4-Methyl-2-
pentanone)
MIBK_24_HOURS
61
Propanal (Propionaldehyde)
PROPIONAL_24_HOURS
95
Cumene (Isopropylbenzene)
CUMENE_24_HOURS
29
CUMENE_l_HOUR
27
CUMENE_3_HOURS
1
2,2,4-Trimethylpentane
TRMEPN224_24_HOURS
84
TRMEPN224_l_HOUR
27
TRMEPN224_3_HOURS
1
Bromoform (Tribromomethane)
BROMOFORM_24_HOURS
17
Chlorobenzene
CHLROBZNE_24_HOURS
75
1,2,4-Trichlorobenzene
TRICBZ124_24_HOU RS
47
Benzyl Chloride (alpha-Chlorotoluene)
BENZYLCHLO_24_HOURS
22
Hexachloro-l,3-butadiene
HEXCHLRBT_24_HOURS
20
Methyl tert-butyl ether
MTBE_24_HOU RS
20
p-Dioxane (1,4-Dioxane)
P_DIOXANE_24_HOURS
10
Vinyl Acetate
VINYLACET_24_HOURS
18
Ethyl Chloride (Chloroethane)
ETHYLCHLRD_24_HOURS
56
Ethylidene Dichloride (1,1-Dichloroethane)
ETHIDDICHLD_24_HOURS
43
Methyl Methacrylate
MM ETACRYLAT_24_HOU RS
11
Vinylidene Chloride (1,1-Dichloroethylene)
VINYUDCLOR_24_HOURS
30
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Model Air Toxic
Measured Air Toxic
No. of Sites
Methyl Iodide
MTHYLIODIDE_24_HOURS
2
1,1,2-Trichloroetharie
TRICLA112_24_HOURS
31
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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 tract-level ambient concentrations
estimated with air quality models, as described in Section 3, and yielded census tract-level exposure
concentration estimates that we used to determine potential health risks for AirToxScreen.
EPA used version 7 of the EPA Hazardous Air Pollutant Exposure Model (HAPEM7) 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 HAPEM7 for a selected group of surrogate pollutants and source categories.
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 tract, we multiplied the
ambient concentration of the pollutant by the surrogate's exposure factor, 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, HAPEM7 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.
Following other improvements, HAPEM version 4 and later (including HAPEM7) 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 2015b). These changes make HAPEM7 suitable for regional and
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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 both used HAPEM7
(EPA did not use HAPEM6 for NATA). 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 HAPEM7 (EPA
2015b).
Table 4-1. Key differences between recent versions of HAPEM
Characteristic
HAPEM4
HAPEM5
HAPEM7
Data source for population
demographics
1990 U.S. Census
2000 U.S. Census
2010 U.S. Census
Characterization of
microenvironmental factors
Point estimates
Probability distributions
Same as HAPEM5
Method for creation of
annual average activity
patterns from daily activity-
pattern data
Resampling of daily diaries
for each of 365 days
without accounting for
autocorrelation
Sampling a limited number
of daily diaries to
represent an individual's
range of activities,
accounting for
Same as HAPEM5, except
now includes commuter-
status criterion
Interpretation of exposure-
concentration range for a
given cohort/tract
combination
Uncertainty for the
average annual EC for the
cohort/tract combination
Variability of annual ECs
across cohort/tract
members
Same as HAPEM5, except
now includes adjustments
based on proximity to
roadway
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 HAPEM7 (EPA 2015b).
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
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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 ensure that exposure concentrations were
completed in time for AirToxScreen's release, the ambient data used in the EC calculations came from a
preliminary assessment based on NEI version 1 emissions data; this concession does not significantly
affect the ECs calculated. 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 2015a). To develop the version
of CHAD (version June 2014) used in AirToxScreen, data from 21 individual U.S. studies of human
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
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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. Microenvlronmental 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:
ii 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;
«i a proximity factor that is an estimate of the ratio of the outdoor concentration in theimmediate
vicinity of the microenvironment to the outdoor concentration represented by the ambient air
concentration input to the model; and
ii 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.
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The relationship between the estimated ECs, the input ambient concentrations and these three factors
is demonstrated by the equation below.
C(i,k,t) = CONCm) x PENk x PROXk + ADDk
Where:
C(i,k, t)
EC predicted within census tract i and microenvironment k for time step t, in
units of ng/m3
CONC(,,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
A way-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 HAPEM7 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, including HAPEM7.
4.6. Summary
ii 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.
ii We estimated inhalation ECs for each pollutant/source group/census tract for AirToxScreen using
the HAPEM7 model.
ii These ECs can be used to determine census tract-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.
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.
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).
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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 for each individual. 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 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
estimate individual cancer risks, given certain assumptions regarding the exposure conditions.
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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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 10-6 ng/m3, no more than
three excess tumors 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
"Carcinogenic to Humans." Adequate evidence consistent with this descriptor covers a broad spectrum.
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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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 (listedbelow).
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 RELfor 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 consider the primary National Ambient Air Quality Standard (NAAQS) for lead, which
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). The lead NAAQS, a rolling 3-month average level of lead in total
suspended particles, was used as a long-term value in AirToxScreen.
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 RfC for 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 2014).
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 jug 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
ii 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.
ii 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.
ii 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).
ii 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.
ii 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 AirT oxScreen 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:
ii Which air toxics pose the greatest potential risk of cancer or adverse noncancer effects acrossthe
entire United States?
ii Which air toxics pose the greatest potential risk of cancer or adverse noncancer effects in specific
areas of the United States?
ii Which air toxics pose less, but still significant, potential risk of cancer or adverse noncancer effects
across the entire United States?
ii 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)?
ii 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/(ng/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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Riski = 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 tract 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 2016). 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 assumed to
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
AirToxScreen at Tract, 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. Model Results for Point Sources: Aggregation to Tract-level Results
The AirToxScreen modeling step generates ambient concentrations at the block level. For risk and
exposure calculations, we aggregated these results from the block level to the tract level by taking a
population-weighted average of all block-level concentrations within a given tract, as follows:
I ConcbtockjxPopblockj
Conctract- ——
^ "blockj
Where:
Conetraai = ambient concentration for census tract i
Conebi0ckj = ambient concentration for census block j (contained within tract /'), estimated by
AERMOD
Popbiockj = population of blocks contained in tract i
6.4.2. Background Concentrations and Secondary Pollutants: Interpolation
to Tract-level Results
Background concentrations, as well as estimated concentrations of secondary pollutants generated by
the CMAQ model, were estimated for levels other than census tract and thus required interpolation
"down" to the tract level. Background concentrations were estimated at the county level. To obtain
tract-level concentrations, the county-level estimate was assigned to all census tracts within that
county. For secondary pollutants, concentrations were estimated using CMAQ. The results for each grid
were then applied evenly to all tracts located within the grid.
6.4.3. Aggregation of Tract-level Results to Larger Spatial Units
Tract-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 tract-level
concentrations by the population of each tract, summing these population-weighted concentrations,
and dividing by the total county population encompassing all tracts to obtain a final population-
weighted, county-level concentration. The process for aggregating from the tract to the county level can
be expressed using the following equation:
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I ConctractxPoptractj
C°nCcountyk p
^ county k
Where:
ConCcountyk = population-weighted concentration for countyk
Conctracti = ambient concentration in tract i (contained withincounty k)
Poptracti = population in tract i (contained within countyk)
Popcoumyk = population in countyk
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 tract, 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. As was done with the ambient-level concentrations, the block-level ECs were
used to estimate cancer and noncancer effects and to aggregate these concentrations up to larger
spatial scales. To aggregate tract-level concentrations up to the county-, state- or national-level
concentrations, the tract-level concentrations were population-weighted.
6.5. The Risk Characterization Results Th\t Mi 1 ovScreen 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 2017. 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
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.
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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).
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 tract, 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
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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
ii 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.
ii 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.
ii 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.
ii 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.
ii Air toxics data for AirToxScreen are presented at the national, state, county and census tract 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 AirT oxScreen 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 tract 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 tract. 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 tract. The
ambient concentration estimated for the tract is only an approximation of conditions at all locations in
the tract. Different locations within that tract 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 (i.e., census block centroid or census tract centroid; see discussion below)
based on the emission sources and meteorological conditions affecting those specific tracts. 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 tract. Although
results are reported at the census tract level, average risk estimates are far more uncertain at this level
of spatial resolution than at the county or state level. Census tracts are small, relatively permanent
statistical subdivisions of a county, typically having between 1,200 and 8,000 residents, with most
having close to 4,000. Census tracts do not cross county boundaries. Their areas vary widely depending
on the density of settlement. Census tracts tend to be small in densely populated areas but can be very
large in sparsely populated areas.
Within census tracts are census blocks, 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.
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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.
For a given source type and modeling approach, variation in ambient air concentrations within a grid cell
or census block is not explicitly modeled. For estimates at the block level, a representative ambient air
concentration is estimated for a single location near the center of the block (i.e., the centroid, which is
typically, but not always, the geographic center of the block chosen by the U.S. Census Bureau as a
reference point). EPA then averages ambient concentrations estimated at the block level for the
encompassing census tract, with concentration and risk results reported at the tract level. Assessment
results do not reflect variations in the susceptibility of people within a census tract because the focus is
to compare typical exposures and risks in different tracts. As a result, individual exposures or risks might
differ by as much as a factor of 10 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 tracts. 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 tract
have the same exposure and risk. This assumption allows AirToxScreen users to examine variation in
individual exposure among census tracts, but not the variation within a census tract. 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 tracts, producing differences in the typical pollutant
concentrations, exposures and risks in different tracts. Differences in susceptibility, however, can
produce differences in risk between two individuals in the same census tract, 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 tract, 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
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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 tracts. 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).
7.2.3. How Variability Affects Interpretation of AlrToxScreen 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 tracts. 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 tracts 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 tracts 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 tracts would be expected to have a
risk above 1-in-l million. Although a person may live in a census tract 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
tract, 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 tract.
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 tracts 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
tracts. 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
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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.
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
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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
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.
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7.3.2. Components of Uncertainty Included In AlrToxScreen
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
AirToxScreen Components that Include
Uncertainty
quality models. Uncertainty in exposure is due to
• Ambient concentrations
• Exposure estimates
• Risk estimates
uncertainty in activity patterns, locations of individuals
within a census tract, 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.
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 tract), 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.
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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
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 census-tract 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
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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
Section 3.7 of this document.
Measured concentrations were taken from EPA's Ambient Monitoring Archive for HAPs, which includes
National Air Toxics Trends Stations, state and local monitors reported to the Air Quality System, and
other monitoring data collected from sources outside of AQS. For AirToxScreen, the exact locations of
the monitors were used for the model-to-monitor comparison, 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:
ii emission characterization (e.g., specification of source location, emission rates and release
characterization);
«i meteorological characterization (e.g., representativeness);
ii model formulation and methodology (e.g., characterization of dispersion, plume rise, deposition,
chemical reactivity);
ii monitoring; and
ii boundary conditions/background concentrations.
Underestimates for some pollutants could be a result of the following:
ii The NEI may be missing specific emission sources.
ii 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.
ii 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
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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 Section4.3.3 HAPEM algorithms consider the variability in
activity patterns among individuals within a cohort-tract combination. They do this largely by addressing
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 (2010). HAPEM uses this information, reflecting 2010 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
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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
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:
ii apply to geographic areas, not specific locations;
«i do not include comprehensive impacts from sources in Canada or Mexico;
ii are restricted to the year to which the assessment pertains (because the assessment uses emissions
data only from that year);
ii do not reflect exposures and risk from all compounds;
ii do not reflect all pathways of exposure;
ii reflect only compounds released into the outdoor air;
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ii do not fully capture variations in background ambient airconcentrations;
«i may underestimate or overestimate ambient air concentrations for some compounds due to spatial
uncertainties;
ii are based on default, or simplifying, assumptions where data are missing or of poor quality; and
ii 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
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 tract, 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 tract 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 tract.
The results for the current AirToxScreen are restricted to 2017 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 2017.
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 RfC for 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
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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 utilize CMAQ in all areas (i.e., not in Alaska, Hawaii, Puerto Rico and the U.S.
Virgin Islands) and therefore does not estimate fires, biogenics and secondary formation based on
location-specific data in these areas. It also 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 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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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 would contract
cancer if exposed continuously (24 hours per day) to the specific concentration over 70 years (an assumed lifetime).
This 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:
• assess the extent of pollution;
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• 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 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:
Away 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).
Emission Inventory System (EIS):
An EPA information system for collecting emission inventory data and generating emission inventories.
Emissions:
Pollutants released into the air.
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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).
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.
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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.
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.
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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.
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.
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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:
Away 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.
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.
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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
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
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• 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, "AirToxScreen_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:
ii the names used in the National Emissions Inventory (NEI);and
ii 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 2017
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 2017
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
1-bromopropane
106945
This pollutant was added to EPA's list of HAPs
in February 2022, after the 2017 assessment
was completed.
n
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
The memo in this appendix contains the methods we used to estimate background concentrations for
AirToxScreen.
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STi
Sonoma Technology, Inc.
Environmental Science and Innovative Solutions
Technical Memorandum
December 19, 2018 STI-915414-6931-TM
To: Madeleine Strum, U.S. Environmental Protection Agency
From: Michael McCarthy
Re: NATA Remote Background Estimates Updated for 2017 - revised
Background
EPA contracted with STI to update the National Air Toxics Assessment (NATA) remote background
estimates developed for the 2011-2017 values. In previous work, STI developed methods for
estimating remote background concentrations for 2011 and 2014 (McCarthy et al., 2015; McCarthy,
2016). For this project, we updated only the Remote Northern Hemisphere background estimates
with 2017 values.
Remote Northern Hemisphere (NH) background is the annual mean concentration at remote
receptor areas not impacted by local-scale (50 km) or regional-scale emissions of the pollutant.
Examples of remote locations include the summit of Mauna Loa, the Aleutian Islands in Alaska,
portions of the Pacific Coast between Oregon and California, and some parts of the Mountain West.
These estimates represent the lowest annual mean concentrations that would be observed in the
United States.
Methods
STI used concentrations from networks with monitoring sites in remote locations, where little
influence from regional emissions is expected, to estimate remote background concentrations for
some air toxics. Gases are measured at remote National Oceanic and Atmospheric Administration
(NOAA) Global Monitoring Division (GMD) sites1 and the Trinidad Head Advanced Global
Atmospheric Gases Experiment (AGAGE) site;2 metals are measured at remote IMPROVE sites.3
Annual mean concentrations for 2017 were generated at five NOAA GMD sites: Cape Kumukahi, HI
(KUM); Mauna Loa, HI (MLO); Niwot Ridge, CO (NWR); Barrow, AK (BRW); and Alert, Canada (ALT).
www.esrl.noaa.gov/gmd.
http://agage.mit.edu.
http://vista.cira.colostate.edu/improve.
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Other remote sites are available but were not selected, to be consistent with previous NATA
background estimates, The annual mean concentrations were cosine-weighted by latitude to
generate annual mean background estimates for carbon tetrachloride, benzene, dichloromethane,
tetrachloroethene, and bromomethane. Measurement methods from this network have been
reported in peer-reviewed articles such as Montzka et al, (2011: 1999). Cosine-weighted latitude
averaging accounts for differences in the amount of NH air (i.e., there is more air at the equator than
at the poles). The data obtained from the NOAA 6MD ftp site were the monthly average estimates
for carbon tetrachloride, bromomethane, chloromethane, methyl chloroform, dichloromethane, and
tetrachloroethene. Raw benzene data were obtained through personal communication with Dr.
Stephen Montzka at NOAA GMD, as those data are not publicly available on the ftp site. STI
generated monthly average and annual mean concentrations from the raw benzene data.
Annual mean concentrations of chloroform were measured at AGAGE network sites. While multiple
AGAGE sites are available in remote locations such as Mace Head, Ireland, and Cape Grim, Tasmania,
the only measurements representative of the eastern Pacific Ocean are made at Trinidad Head,
California; monthly average concentration data from this site were used to generate "remote"
concentration estimates, although this single site is not as remote as the NOAA GMD or IMPROVE
network sites used in this analysis. AGAGE data can be accessed at
(DOI: 10.3334/CDIAC/atg.dbl001) and the original
reference for the network is available from Prinn et al. (2000).
Metals are measured at the IMPROVE sites across the United States. Some of these sites are
representative of clean air coming off the Pacific Ocean and were used to generate remote
background concentration estimates for lead, manganese, and nickel. Annual mean concentrations
from 2016 were generated for data from the Denali, Alaska (DENA): Kaltrnopsis, Oregon (KALM);
Point Reyes, California ^PORE); Redwoods, California (REDW); Trapper Creek. Alaska t'TRCR!; Tuxedni,
Alaska iTUXEi; and Haleakala, Hawaii (HACR) sites; only four months of monitoring data were
available for 2017 as of April 9, 2018. Less than 21% of the metals measurements were above method
detection limits (ME>L) for all sites in 2016, so a simple average MPL is reported as an upper limit for
the remote concentrations of these metals. The IMPROVE network data were used as available from
the AQS AiiE'ata API taccessed in April 2018), Recent work on IMPROVE detection limits was
described by Hyslop and White (2011)
Results
Estimates of NH concentrations from the remote network are presented in Table I. We present
concentrations for the gas-phase species in units of pptv, pg/m1 at standard temperature and
pressure, and pg/irr at local conditions. Average concentrations of metals measured at IMPROVE
sites were almost all below MDL when using MDL/2 substitution for non-cletect values i 1 )
Thus, the remote concentration estimates should be considered upper-limit thresholds for
concentrations.
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December 19, 2018
3
For chromium VI, the total chromium estimate was multiplied by the average ratio of chromium
VI:chromium TSP (0.0125) seen in air toxics archive measurements for previous years. This is still an
upper-limit threshold, as measurements at IMPROVE sites were below the average MDL at those sites
in 2017.
Input files and R code used to generate these estimates are provided as separate electronic file
attachments in the delivery email.
Table 1. Remote concentration estimates (RCE) for 2017 or 20163
Pollutant
RCE at 298K
and 1 atm
(|ig/m3)
2017
RCE LCb
(Hg/m3)
2017
RCE
(pptv)
2017
Remote
Network
Location(s)
Chloroform
0.082
0.085
16.9
AGAGE
Trinidad Head
Methyl chloride
(chloromethane)
1.151
0.976
558
NOAAGMD
KUM, MLO, NWR, BRW, ALT
Benzene
0.108
0.098
34.0
NOAAGMD
KUM, MLO, NWR, BRW, ALT
Carbon tetrachloride
0.509
0.433
81.0
NOAA GMD
KUM, MLO, NWR, BRW, ALT
Methyl bromide
(bromomethane)
0.027
0.023
7.0
NOAA GMD
KUM, MLO, NWR, BRW, ALT
Methyl chloroform
(1,1,1-trichloroethane)
0.012
0.010
2.2
NOAA GMD
KUM, MLO, NWR, BRW, ALT
Dichloromethane
(methylene chloride)
0.220
0.188
63.4
NOAA GMD
KUM, MLO, NWR, BRW, ALT
Tetrachloroethene
(perchloroethylene,
0.013
0.011
1.8
NOAA GMD
KUM, MLO, NWR, BRW, ALT
tetrachloroethylene)
Chromium VI3
<1.7E-6
IMPROVE Cr
and NATTS Cr
VI:Cr ratio
DENA, KALM, PORE, REDW,
TRCR, TUXE, HACR
Arsenic3
<2.3E-4
IMPROVE
DENA, KALM, PORE, REDW,
TRCR, TUXE, HACR
Chromium3
<1.3E-4
IMPROVE
DENA, KALM, PORE, REDW,
TRCR, TUXE, HACR
Lead3
<6.7E-4
IMPROVE
DENA, KALM, PORE, REDW,
TRCR, TUXE, HACR
Manganese3
<3.3E-4
IMPROVE
DENA, KALM, PORE, REDW,
TRCR, TUXE, HACR
Nickel
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4
Table 2. IMPROVE metal MDLs (|jg/rn3) and average percentage of samples below MDL at
seven remote sites in 2016.
Parameter
Year
Average % Below
MDL
Average MDL
(Hg/m3) LC
Arsenic
2016
99.3
2.3E-4
Chromium 2016 99.5 1.3E-4
Lead
2016
79.8
6.7E-4
Manganese 2016 84.2 3.3E-4
Nickel
2016
100.0
1.1E-4
Trends in remote air toxics concentrations (ppt) of the NOAA GMD pollutants by site are shown in
Figures 1 through 6. Note that not all axes start at zero ppt in order to better display the relative
trends in concentrations over time.
2000
2005
2010
2015
Year
Figure 1. Trends in annual mean benzene concentrations (ppt) at remote NOAA GMD sites.
site
— ALT
— BRW
KUM
MLO
NWR
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5
110
Q.
Q.
d
03
0
E
TO
3
C
c
CD
2;
O
O
100
90
80
site
— ALT
—- BRW
¦— KUM
-t- MLO
NWR
1990
2000
2010
Year
Figure 2. Trends in annual mean carbon tetrachloride concentrations (ppt) at remote NOAA
GMD sites.
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6
Q-
Q_
C
CD
CD
E
ro
3
c
c
CD
(D
c
CD
1995
2000
2005
2015
Year
Figure 3. Trends in annual mean dichloromethane concentrations (ppt) at remote NOAA GMD
sites.
40-
site
— ALT
— BRW
— KUM
-i- MLO
— NWR
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site
— ALT
— BRW
— KUM
-t- MLO
NWR
2005
2010
2015
Year
Figure 4. Trends in annual mean bromomethane concentrations (ppt) at remote NOAA GMD
sites.
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2000 2005 2010 2015
Year
Figure 5. Trends in annual mean chforomethane concentrations (ppt) at remote NOAA GMD
sites.
Q_
Q_
C
cu
CD
E
03
13
C
c
CO
CD
c
co
£
0
E
o
s—
o
iz
O
600-
575-
550-
525-
site
—- ALT
— BRW
— KUM
-i- MLO
NWR
2000
2005
Year
2010
2015
Figure 6. Trends in annual mean tetrachloroethyiene concentrations (ppt) at remote NOAA
GMD sites.
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8
Table 3 shows the comparison of remote concentration estimates generated for NATA 2011, 2014,
and 2017. Note the 2011 and 2014 values are directly transcribed from the previous technical
memoranda and may not be 100% comparable to those generated using the methods in this
technical memorandum, since previous averaging methods did not generate monthly averages for
temperature corrections prior to generating annual means. Additionally, values shown in Table 3 are
in ng/m3 based on a cosine-weighted latitude average to account for the amount of air at different
latitude bands. Figures 1-6 show site-specific concentrations in units of ppt for the gases; these are
not affected by temperature or pressure calculations.
Table 3. Remote concentrations in 2011, 2014, 2016, and 2017 based on all available data as
of April 2018. Estimates for 2016 were used for the metals due to less than 8 months of
available data in 2017.
Parameter Name
Concentrations in |ig/m3 at 1 atm and 25°C
2011
2014
2016
2017
Arsenic PM25
<2.16E-04
<2.05E-04
<2.29E-04
<2.19E-04
Benzene
1.1SE-01
1.06E-01
1.03E-01
1.08E-01
Bromomethane
2.99E-02
2.76E-02
2.85E-02
2.73E-02
Carbon tetrachloride
5.51E-01
5.29E-01
5.15E-01
5.09E-01
Chloroform
5.99E-02
7.22E-02
7.71E-02
8.22E-02
Chloromethane
1.15
1.16
1.19
1.15
Chromium PM25
<1.21E-04
<1.13E-04
<1.30E-04
<1.12E-04
Di chloromethane
1.47E-01
2.01E-01
2.08E-01
2.20E-01
Lead PM2.5
<6.71E-04
<6.94E-04
<6.67E-04
<6.56E-04
Manganese PM2.5
<3.14E-04
<3.08E-04
<3.34E-04
<3.30E-04
Methyl chloroform
3.47E-02
2.00E-02
1.43E-02
1.21E-02
Nickel PM2.5
<1.00E-04
<1.03E-04
<1.13E-04
<1.10E-04
Tetrachloroethylene
1.31E-02
1.21E-02
1.26E-02
1.25E-02
References
Hyslop N.P. and White W.H. (2011) Identifying sources of uncertainty from the inter-species covariance of
measurement errors. Environ. Sci. TechnoL, 45(9), 4030-4037, doi: 10.1021/esl02605x. Available at
http://pubs.acs.org/doi/abs/10.1021/esl02605x.
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9
McCarthy M O'Brien T Du Y and Russell A {2015^ Methods for estimating background concentrations
foi the National Ail Tomcs Assessment iMATA/ 2011 Final report prepared for the U.S.
Environmental Protection Aqencv, Reseatch Tnanqie Patk, NC, by Sonoma Technology, Inc.,
Petaluma CA STI-91511Q-6315, Auqust 13
McCarthy M. (2016) NATA remote backg ound estimates updated for 2014 Techntca, memorandum
prepared for the U.S. Enviionmental Protection Agency, STI-915217-6569-TM, August.
Montzka S.A, Butler J.H, Eildns J.W. Thompson T.M., Clarke A.D., and Lock LT (19991 Present and future
trends in the atmosphei ic butden of ozone-depleting halogens. Nature, 398, 690-694.
Montzka S.A., Dlugokencky EJ., and Butlei J.H, (2011) Nort-CO_ titt-enhouse gases and climate change.
Nature, 476(7358), 43-50, tloi In 1038/naturel0322, August 3.
Prinn R.G., Weiss R.F., Fraser Pt Sirrtmonds P.G., Cunnold D.M., Htyea FN, O'Dohertv S Salameh R, Miller
F R HLiang I Wang RH I Haitle, D.E., Harth C., Steele l.R, Stunock G, Midqle\ PM and
McCulloch A COuiM h Inst. n\ of rnemically and radiatively irnpottant gases in ait deduced from
ALE GmGE AGAGE ' Geuphv, Rts 105. 17,751-717,792.
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Appendix D. Model Evaluation Summaries
In addition to the evaluations show in Section 3.7, 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: Ted Palma, U.S. EPA Office of Air Quality Planning and Standards
From: John Hader and Chris Holder, ICF
Date: June 25, 2018
Re: HAPEM7 Modeling for AirToxScreen
AirToxScreen is a nationwide EPA modeling assessment of the air concentrations, exposure
concentrations,1 and potential human health cancer and noncancer risks and hazards
associated with inhalation exposure to Hazardous Air Pollutants (HAPs) emitted by manmade
and natural sources of outdoor origin. As part of AirToxScreen, the Hazardous Air Pollutant
Exposure Model (HAPEM), which is a U.S. Environmental Protection Agency (EPA) model, is
used to perform screening-level assessments of long-term inhalation exposures to HAPs.
For AirToxScreen, ICF ("we") used Version 7 of HAPEM (HAPEM7), with modeled air-
concentration data provided by EPA, to model exposure concentrations for seven selected
HAPs, stratified by census tract and emission source type. These HAPs also are used as
surrogates for the many other HAPs included in AirToxScreen, as described in detail below. We
used the annual-average exposure concentrations by age group, provided by the HAPEM
modeling, to estimate lifetime-average exposure concentrations. We then divided the lifetime
exposure concentrations by the corresponding annual-average air concentrations
creating "exposure factors". EPA can then estimate exposure to each AirToxScreen HAP
in each census tract by multiplying the air concentration for the HAP by the exposure
factor derived for the chemical's surrogate HAP.
In this memorandum, we discuss HAPEM7, how we assigned the gas or particulate phase of
the AirToxScreen HAPs, how EPA selected the seven HAPs modeled in HAPEM, how we set
up the HAPEM runs, and how we developed the exposure factors.
1 Exposure concentrations are time-averaged air concentrations to which a simulated individual is exposed. The
time averaging considers the air concentrations in each place where the individual is simulated to spend time, and
how long he or she spends there.
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l. 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 HAPs. We updated HAPEM and its default, ancillary files in 2015, as
discussed in the 2011 NATA documentation.2 This latest version of HAPEM (HAPEM7)3
estimates exposure concentrations using
• demographic and behavior data from the 2010 U.S. Census (covering all 50 U.S states,
the District of Columbia, Puerto Rico, and the U.S. Virgin Islands),4
• compiled activity data from a recent version of EPA's Consolidated Human Activity
Database (CHAD),5 and
• updated data on population proximities to major roadways.
• HAPEM7 stratifies exposure concentrations by
• location (U.S. Census tract),
• time of day,
• age group, and
• the individual emission source types and HAPs being modeled.
HAPEM is a probabilistic model that selects some model inputs from distributions of data to
characterize exposure. These elements include commuting patterns, activity patterns,
employment work location, microenvironmental factors, and (if desired) air concentrations.
Including more simulated individuals (termed "replicates" in HAPEM) can increase the range of
values selected for these probabilistic elements, across the simulated population.
2. Air Quality Inputs to HAPEM7
2.1. HAPs Assessed for AirToxScreen
For AirToxScreen, EPA compiled an inventory of the annual mass of HAPs emitted from four
source types, namely
• point,
• non-point,
• on-road mobile, and
2 Appendix G of the Technical Support Document of EPA's 2011 National Air Toxics Assessment. December 2015.
EPA Office of Air Quality Planning and Standards. Available at https://www.epa.gov/sites/production/files/2015-
12/documents/2011-nata-tsd.pdf.
3 As of October 23, 2017, HAPEM7 is available for download from https://www.epa.gov/fera/human-exposure-
modeling-hazardous-air-pollutant-exposure-model-hapem. The HAPEM7 User's Guide also is available from that
link, and it contains detailed discussions of the model's functionality and input files.
4 For additional information on the census data parameterized in HAPEM, see Appendix B of The HAPEM User's
Guide, Version 7 (July, 2015), available at https://www.epa.gov/sites/production/files/2015-
12/documents/hapem7usersguide.pdf.
5 For additional information on CHAD and how its data were parameterized in HAPEM, see
https://www.epa.gov/healthresearch/consolidated-human-activitv-database-chad-use-human-exposure-and-
health-studies-and and Appendix B of The HAPEM User's Guide, Version 7 (July, 2015), available at
https://www.epa.gov/sites/production/files/2015-12/documents/hapem7usersguide.pdf.
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• non-road mobile.
These emissions were nationwide, typically at spatial resolutions of 12-km grids down to specific
points. The assessed HAPs are shown in the far left column of Table (under the "Chem"
heading). In some cases, emissions of several chemicals were aggregated and modeled as a
group, as indicated on the right side of Table (under the "AirToxScreen" heading; the columns
under the "AirToxScreen" heading provide the chemicals and chemical groups as they were
modeled for AirToxScreen). Using these estimated emissions, temporal emission profiles based
on source type, and hourly meteorology data selected by proximity, EPA used AERMOD6 and
CMAQ7 to estimate ambient air concentrations. The EPA air-modeling results provided for
HAPEM exposure modeling were annual-average air concentrations at the level of
census tracts, stratified by HAP, source type, and at three-hour increments across the
day.
For all source types, HAPEM7 models concentrations for three chemical phases. These
phases are
• gaseous ("G"),
• particulate ("P"), and
• variable for typical atmospheric conditions ("G/P").
HAPEM estimates concentrations for user-specified microenvironments (ME). An ME is a
generic location, such as indoors at home or outdoors near roadway, where a person spends
time and breathes air, leading to chemical exposure. In HAPEM, MEs are treated as having
well-mixed air concentrations at a given time, and the model estimates those ME concentrations
relative to the outdoor air concentrations provided as input to the model. HAPEM7 is
parameterized for 18 MEs, including eight indoor MEs (including residential, school, office,
bar/restaurant, and similar), two in-vehicle MEs (personal vehicle and public transit), six outdoor
MEs near roadways, and two outdoor MEs not near roadways.
Starting with outdoor ambient chemical concentrations, the model uses phase-specific
penetration and proximity factors to estimate the ME chemical concentrations. A penetration
factor describes how much of the chemical in the outdoor air penetrates the indoor or in-vehicle
air. The penetration factor is a ratio in the form of [chemical air concentration in an indoor/in-
vehicle ME] to [chemical air concentration directly outdoors of the ME], A proximity factor
parameterizes the heterogeneity of air concentrations across a Census tract. It describes how
much higher or lower we expect the outdoor air concentration to be in the immediate vicinity of
the ME relative to the Census tract outdoor concentration supplied in the input file, and it is a
ratio in the form of [outdoor chemical air concentration in the immediate vicinity of the ME] to
[tract-level outdoor chemical air concentration input to the model], HAPEM7 has one set of
penetration and proximity factors for mobile on-road sources, and a second set for all other
source types. For mobile on-road sources, there are four categories of factors: three are for
6 AERMOD: the atmospheric dispersion model developed by the American Meteorological Society and the EPA
Regulatory Model Improvement Committee. See httpsi//www. epa .gov/scra m/a i r-q u a I ity-d ispersion-modeli ng-
preferred-and-recommended-models#aermod
7 CMAQ: EPA's Community Multiscale Air Quality model. See https://www.epa.gov/cmaq
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specific HAPs (benzene, 1,3-butadiene, and diesel particulate matter [DPM]8) and a fourth
category representing all other HAPs.
To prepare for AirToxScreen exposure modeling, we categorized each of the AirToxScreen
HAPs as G, P, or G/P based on available boiling-point data, as defined in Table . We provide
each HAP's boiling point and assigned HAPEM7 phase in Table (left side under the "Chem"
heading). If chemicals were modeled as a group, we assigned the group a G, P or G/P
designation, as indicated in Table (right side under the "AirToxScreen" heading) and as
discussed later in this section.
We obtained the vast majority of boiling-point values from
• the Centers for Disease Control (CDC),9
• the National Institutes of Health (NIH),10 or
• the Royal Society of Chemistry using their ChemSpider web site.11
These were judged to be the most reputable, comprehensive, user-friendly, and readily
available sources of chemical boiling-point data. Each source allows the user to search by
chemical name or Chemical Abstract Service (CAS) Registry Number. For HAPs whose boiling
points we could not identify using these three sources, we searched a variety of additional data
sources. We provide the source of each chemical's boiling-point value in the "Source" column of
Table .
We could not identify empirical boiling-point data for some of the HAPs. For 25 HAPs (9 percent
of all evaluated HAPs), we used predictive boiling points in order to categorize their chemical
phases. These values came from ChemSpider, which generates estimated boiling points using
three software modules: EPA's EPIsuite, the ACD/Labs Percepta Platform - PhysChem Module,
and ChemAxon's predictive software platform. We typically selected the ACD/Labs values when
available because these values were presented with confidence intervals and the conditions
under which the boiling-point values were predicted (typically standard temperature and
pressure); the other two platforms did not provide such information. If an ACD prediction was
not available, we used the EPA EPIsuite value. Those boiling points that are predictive rather
than empirical are flagged with a "P" in Table .
Note that the boiling-point ranges in Table have imprecise endpoints (e.g., the high end of boiling
points for G HAPs covers a range of 240 to 260°C). Forty of AirToxScreen HAPs have boiling
points within these imprecise endpoints, meaning there was some uncertainty associated with
assigning the phases for these HAPs. In order to make appropriate phase designations, we
conducted a literature review for each of these HAPs to identify relevant information regarding
its typical physical state. For example, 1-nitropyrene has a boiling point of 445°C, within the
overlap of G/P and P boiling points. A review of the literature yielded several studies and reports
identifying 1-nitropyrene as a particulate at typical atmospheric conditions, leading us to assign
a designation of "P" to this HAP with a high degree of confidence. Where literature searches
were uninformative, we assigned HAP phase based on the categorizations used for HAPEM5 to
support the 1999 NATA. The combination of the additional literature review and consultation of
8 DPM is not a HAP as defined under Section 112(b) of the Clean Air Act; however, for ease of discussion we refer
to it as a HAP in this memorandum.
9 CDC: httpi//www.cdc.gov/niosh/npg.
10 NIH: http://pubchem.ncbi.nlm.nih.gov/.
11 CS: h 11 p: //wvyyy. c h e m s p i d e r. c o m.
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the HAPEM5 designations allowed us to make phase designations for these HAPs. Seventeen
HAPs have boiling points within the 240-260°C range; based on the literature review and
HAPEM5 designations, we categorized 16 as G and 1 as G/P. Twenty HAPs have boiling points
within the 400-480°C range; we categorized 16 as P and 4 as G/P. Three HAPs have wide-
ranging boiling-point values; we categorized the two with higher ranges of boiling points
(extending into the 400-480°C range) as G/P, and we categorized the one with a lower range of
boiling points (extending only into the 240-260°C range) as G.
In addition to the above 40 HAPs, boiling-point values were specified by the CDC or EPA as
"variable" for three HAPs (see the "V" designations in the boiling-point-value column in Table).
We categorized two of these HAPs (coke oven emissions and cyanide) as G/P. We categorized
coke oven emissions based on an EPA characterization of this pollutant as consisting of a
mixture of particulates, volatiles, and semi-volatiles.12 We characterized cyanide based on the
fact that cyanide is not typically found in isolation in nature, but rather in a variety of compounds,
some of which are typically solid (e.g., calcium cyanide, sodium cyanide) and some of which are
typically gaseous (e.g., hydrogen cyanide).13 The third HAP with "variable" boiling points was
DPM and we assumed that it is typically a particulate.
For five HAPs, boiling-point data were either unavailable or were ill-defined (see the "NA", "D",
and "S" designations in the boiling-point-value column in Table); one was Fine Mineral Fibers,
which we categorized as P, and we left the remaining four (Extractable Organic Matter (EOM),
propoxur, quinone, and toxaphene) uncategorized. For these four HAPs, EPA was health
protective and set exposure concentrations equal to ambient outdoor concentrations per census
tract, as indicated by "EF=1" designations in the "Matching Chemical Modeled in HAPEM"
columns in Table . A setting of EF=1 is health-protective because it assumes that people always
breathe outdoor ambient air, receiving no protection that may otherwise be afforded by the
barriers and filtration systems of a building or vehicle (that is, a penetration factor of 1 rather
than below 1). Wthout indoor sources of emissions (which are not included in AirToxScreen),
outdoor air concentrations necessarily are the same or higher than those of indoor or in-vehicle
MEs.
Where EPA modeled several HAPs as one group, we assigned the group's chemical phase
based on the phase most frequently assigned to the group's component chemicals. Two
AirToxScreen chemical groups did not easily accommodate this criterion, as discussed in the
bullets below.
• For AirToxScreen chemical group CHROMHEX, its two components are Chromium (VI)
and Chromium Trioxide, which are P and G/P phases, respectively. We assumed that
Chromium (VI) was the primary chemical in the group (the one emitted in higher
amounts) and assigned the group to P accordingly.
• For AirToxScreen chemical group CYANIDE, its two components are cyanide and
hydrogen cyanide (HCN). Cyanide has a variable boiling point, while HCN's boiling point
is 26°C (making it a G chemical). Because HCN has the higher level of emissions, we
assigned the group to G accordingly.
12 https://www.epa.gov/sites/production/files/2016-01/documents/coke rra.pdf
13 http://www.atsdr.cdc.gov/
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2.3. i t i
As noted previously, except for air-concentration data, HAPEM7 does not use any settings or
inputs that are specific to each individual AirToxScreen HAP. Penetration and proximity factors
vary only by the phase of the HAP, not by each HAP individually (except for mobile on-road
sources, where benzene, 1,3-butadiene, and DPM have specific penetration and proximity
factors). Therefore, except for those three HAPs for mobile on-road sources, the model inputs
for HAPs of a given phase vary only by air concentration, and so their exposure concentrations
output by HAPEM (i.e., the time-averaged air concentrations across the MEs where individuals
are simulated to spend time) vary only by their air concentrations. This means that we can
estimate the exposure concentration of one HAP of a given phase in HAPEM, and then in post-
processing we can calculate the relationship between its air and exposure concentrations and
apply this relationship to other HAPs of the same phase, significantly reducing the level of effort
needed to conduct the exposure modeling.
EPA identified a small subset of AirToxScreen HAPs for which to conduct HAPEM exposure
modeling. AirToxScreen uses the modeling results of this subset as-is, and uses the results as
surrogate values for the remaining AirToxScreen HAPs not modeled in HAPEM. EPA used the
following decision criteria in identifying this subset of HAPs to model.
1. The subset must include at least one HAP per phase (at least one G HAP, one P HAP,
and one G/P HAP).
2. Each emission source type must be represented for each phase (for example, G HAPs
must collectively cover the point, non-point, on-road mobile, and non-road mobile source
types, and so on for P and G/P HAPs).
3. It is preferred that the selected HAPs have the potential to pose higher risks to human
health nationwide, relative to other HAPs.
4. It is preferred that the selected HAPs be emitted in many spatially-diverse locations
across the United States.
Using these criteria, EPA selected seven HAPs for exposure modeling. We list these HAPs
below and in Table . The right four columns of Table indicate how these modeled HAPs were
mapped to the other AirToxScreen HAPs not modeled in HAPEM.
• Benzene and 1,3-butadiene are emitted by many processes (and all four modeled
source types) in nearly all U.S. locations. They each also have specific penetration and
proximity factors for emissions from mobile on-road sources. Benzene was selected to
be the surrogate for all other G HAPs.
• Unspeciated, generic PAHs ("PAH, total") are emitted by all four source types and from
a wide variety of processes, so it was selected to be the surrogate for all G/P HAPs
except coke oven emissions, which is a special case that was modeled by itself for
point sources but not used as a surrogate for any other HAPs.
• Chromium (VI) is a highly toxic HAP that was selected as the surrogate for all P
HAPs emitted by point and non-point sources, except for DPM, which was modeled
as itself for non-point and mobile sources. DPM has specific penetration and proximity
factors for emissions from mobile on-road sources. Note that the AirToxScreen air-
concentration modeling included chromium (VI) emissions from all four source types, but
its use as an exposure surrogate included only point and non-point sources because
those are its major emitters.
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• Nickel was selected as the surrogate for P HAPs emitted by mobile sources,
except for DPM, which was modeled as itself for non-point and mobile sources. Nickel is
emitted by a variety of processes across the United States. Note that AirToxScreen air-
concentration modeling included nickel emissions from all four source types, but its use
as an exposure surrogate included only mobile sources because chromium (VI) was
designated as the more appropriate surrogate for point and non-point sources.
We used the air-concentration modeling outputs provided by EPA for these seven HAPs,
stratified by source type, three-hour increment of the day, and census tract, as the air-quality
inputs for seven HAPEM7 runs.
3. HAPEM7 Runs
3.1. Design
For each of the seven HAPEM7 runs (one run for each modeled chemical), we used the
HAPEM7 default census- and CHAD-based files. Each run assessed the 18 HAPEM7 MEs
and all populated census tracts in the United States, Puerto Rico, and the U.S. Virgin
Islands. The air-quality inputs were in three-hour periods for each HAP, tract, and source type
(that is, for each day, average air concentrations for the first three hours of the day, the second
three hours, etc., totaling eight values per day for each HAP, tract, and source type). We used
the six default HAPEM7 age groups (discussed below in Section 3.3) and three day-types
(summer weekdays, non-summer weekdays, and weekends). We linked each HAP to its
appropriate HAPEM7 penetration- and proximity-factors files, and we used 30 replicates
(simulated individuals) per age group evaluated per tract. ICF has previously shown that the
tract-mean exposure concentrations of an age group are reasonably stable when using
30 replicates, striking a good balance between model runtime and the stability of model outputs
(that is, at a 95-percent probability level, 30 replicates are needed to estimate the tract-mean
exposures to within 20 percent).14
3.2. Quality Assurance unci Quality Control
We reviewed the HAPEM7 control files ("parameters" files) for accuracy, and then we reviewed
the log, "counter," and "mistract" HAPEM7 output files to identify any potential errors in the
modeling. One tract identifier was modified by the U.S. Census after 2010 (after the HAPEM7
population files were developed using the 2010 census)—the AirToxScreen air-concentration
data used the tract's latest identifier of 51019050100, which we renamed to its old identifier
(51515050100) for HAPEM7 modeling (its geography did not change). We switched it back to
the current identifier in the results delivered to EPA. We detected no other errors in the inputs or
outputs. We present below additional information gleaned from the QA/QC activities.
• EPA modeled all U.S. census tracts to develop air concentration estimates. However,
584 tracts (less than 1 percent of U.S. tracts) were not modeled in HAPEM because the
2010 Census indicated zero population.
14 Source: "Benzene Case Study for P03-NTA006-ICF", a technical memorandum from Arlene Rosenbaum, Michael
Huang, and Jonathan Cohen (ICF) to Ted Palma (U.S. EPA), September 30, 2002.
AirToxScreen 2017 Documentation
E-7
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• A total of 73,450 tracts were modeled for exposure concentrations for the AirToxScreen.
We utilized the HAPEM7 outputs for the seven modeled HAPs to estimate exposure factors that
EPA then applies to all HAPs assessed in AirToxScreen, based on HAP phase and source type.
For each tract and source type in a HAP-specific HAPEM7 run, we calculated the estimated 70-
year lifetime-average exposure concentration for each modeled replicate. We calculated
these average concentrations as the time-weighted average of exposures for the six HAPEM7
age groups, as shown below.
Lifetime
average exposi
ure conc.
=
[ages
00-01
exposure
cone.
X
(2/70)
+
[ages
02-04
exposure
conc.
X
(3/70)
+
[ages
05-15
exposure
conc.
X
(11/70
+
[ages
16-17
exposure
conc.
X
(2/70)
+
[ages
18-64
exposure
conc.
X
(47/70
+
[ages
65 +
exposure
conc.
X
(5/70)
We then calculated the population-median lifetime-average exposure concentration in
each tract (median calculated from the 30 replicates in a tract), stratified by HAP and source
type. For a given HAP and tract, summing these median concentrations across source types
yields an estimated "typical" lifetime-average HAP exposure concentration from all contributing
sources.
For each assessed HAP in a tract, we calculated exposure factors by dividing these median
lifetime-exposure concentrations by the corresponding annual-average outdoor air
concentrations (stratified by source type as well as the all-source total). EPA can then
multiply these exposure factors by the air concentrations of any HAP of the same phase-
type, resulting in estimated lifetime-exposure concentrations for that HAP. For example,
for a given tract, to estimate the exposure concentrations of fluorene (a G/P HAP) emitted by
non-point sources, EPA will multiply its non-point air concentrations by the non-point exposure
factors for "PAH, total" (the surrogate for G/P HAPs). The exposure factors typically were
between roughly 0.4 and 1.0 (larger factors generally for on-road mobile sources and gases;
smaller factors typically for the other source types and particulates).
The primary limitation of this exposure-factor method is related to commuting. HAPEM7
simulates the movement of replicates to and from work. Some replicates will work in the same
tract where they live, so that the outdoor air concentrations of their "home tract" equal those of
their "work tract". For these replicates, the exposure-factor method is accurate, because the
exposure factors account only for home-tract exposure to the modeled HAP, and the air-
concentration denominator in the factor calculation also corresponds only to home-tract air
concentrations of the unmodeled HAP. For replicates who commute outside their home tract,
they work in a tract with different air concentrations than in their home tract, affecting their
exposure during work hours (usually less than half the day). When the work air tract
concentrations are similar to the home tract air concentrations, then the exposure factor method
provides a good approximation of exposure for unmodeled HAPs. However, when work tract
and home tract air concentrations are highly dissimilar, the exposure factors method estimates
are less accurate because the exposure factors account for some home-tract exposures and
some very different work-tract exposures to the modeled HAP, while the air-concentration
AirToxScreen 2017 Documentation
E-8
-------
denominator in the factor calculation corresponds only to home-tract air concentrations of the
unmodeled HAP.
Instances of highly dissimilar work tract and home tract concentrations are most easily detected
when exposure factors are above 1, indicating the work-tract air concentrations are higher than
those of the home tract. For a relatively small number of tracts, exposure factors were larger
than 10 and, for seven tracts, exposure factors were larger than 100. In an effort to improve the
representativeness of the modeled exposure factors and their application to unmodeled HAPs,
we limited exposure factors to be no larger than the values shown in Table (exposure
factors that were above these values were set to the maximum values). To define these upper
limits on the exposure factors, we calculated the median exposure factor and standard deviation
of exposure factors across all tracts for each combination of HAP and source type; the upper
limit was then defined as median+standard deviation. The calculations for medians and
standard deviations did not include exposure factors of 100 or larger, which we considered to be
outliers. They also did not consider instances where air concentrations or exposure
concentrations were zero, which occurred at 584 tracts as discussed in Section 3.2. In these
instances, we set the exposure factors equal to 1.0, indicating that the exposure concentration
equals the air concentration. All applications of the exposure factors for the AirToxScreen use
these truncated values, including exposure factors initially set equal to 1.0 and including the
HAPs explicitly modeled in HAPEM7.
4. OlltpiltS
We performed quality assurance checks of the post-processing by thoroughly reviewing and
testing the post-processing R code in order to ensure calculations were being performed
correctly, and we also performed other broad checks to ensure all records were properly read in
and that flagged records were properly processed.
AirToxScreen 2017 Documentation
E-9
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Table E-l. HAPs Assessed in AirToxScreen, with their HAPEM7 HAP Phases and Surrogate Chemical
Assignments
Boiling Point
AirToxScreen
Matching Chemical
Modeled in HAPEMd
Source13
(°C)a
Phase0
Phase0
P
NP
M-OR
M-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
Benzotrichloride
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
httD://www.cdc.aov/niosh/
G
BT-PRPLACTN
G
Benz
Benz
Benz
Benz
docs/81 -
123/pdfs/0528.pdf
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 2017 Documentation
E-10
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Chem.
AirToxScree
n
Boiling Point
Matching Chemical
Modeled in HAPEMd
Name
Value
Source13
Name
(°C)a
Phase0
Phase0
P
NP
M-OR
M-NR
Chromium III
2,672
httD://books.aooale.com/b
P
CHROMTRI
P
Cr6
Cr6
Ni
Ni
ooks?id=SFD30BvPBhoC
&Da=PA123&toa=PA123
&da=chromium+lll+meltin
a+Doint&source=bl&ots=u
oHIiDrKMv&sicpdlSMKFL
5z0sVI0z8Z4NhlsFHaaE
&hl=en&sa=X&ei=4nklVP
LvJ4LS8AGbiYD4DA&ve
d=0CFkQ6AEwCQ#v=on
eDaae&a=chromium%20ll
l%20meltina%20Doint&f=f
alse
Ethylene dichloride
83
CDC
G
CL2 C2 12
G
Benz
Benz
Benz
Benz
Trichloroethylene
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.eca.aov/reaion
P
DIESEL PM10
P
DPM
DPM
DPM
DPM
1/eco/airtox/diesel html
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
AirToxScreen 2017 Documentation
E-ll
-------
Boiling Point
AirToxScreen
Matching Chemical
Modeled in HAPEMd
Source13
(°C)a
Phase0
Phase0
P
NP
M-OR
M-NR
Ethylene glycol
197
CDC
G
ETHGLYCOL
G
Benz
Benz
Benz
Benz
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
httD://msdssearch.dow.co
G
m/PublishedLiteratureDO
WCOM/dh 012d/0901b8
038012d976 odf?fi leoath
=oxvsolvents/odfs/norea/
110-
00977.odf&fromPaae=Ge
tDoc
Methoxytriglycol
249
httD://msdssearch.dow.co
G
m/PublishedLiteratureDO
WCOM/dh 012d/0901b8
038012d976 odf?fi leoath
=oxvsolvents/odfs/norea/
110-
00977.odf&fromPaae=Ge
tDoc
N-Hexyl carbitol
260 E
CS
G
Phenyl cellosolve
245 E
CS
G
Propyl cellosolve
150
httcV/msdssearch.dow.co
G
m/PublishedLiteratureDO
WCOM/dh 012d/0901b8
038012d976 odf?fi leoath
=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
AirToxScreen 2017 Documentation
E-12
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Chem.
AirToxScree
n
Boiling Point
Matching Chemical
Modeled in HAPEMd
Name
Value
Source13
Name
(°C)a
Phase0
Phase0
P
NP
M-OR
M-NR
Hexamethylene Diisocyanate
212
NIH
G
HEXAMTHLE
G
Benz
Benz
Benz
Benz
Hexane
69
CDC
G
HEXANE
G
Benz
Benz
Benz
Benz
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
http://www. usa.com/conte
P
MINFIB
P
Cr6
Cr6
Ni
Ni
nt/dam/USG Marketina
Communications/united s
tates/croduct cromotiona
materials/finished asset
s/usa-mineral-wool-300a-
msds-en-75850002.Ddf
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
httcV/www.cdc.aov/niosh/
P
docs/81 -
123/cdfs/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
AirToxScreen 2017 Documentation
E-13
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Chem.
AirToxScreen
Boiling Point
Matching Chemical
Modeled in HAPEMd
Name
Value
Source13
Name
(°C)a
Phase0
Phase0
P
NP
M-OR
M-NR
Pyrene
404
NIH
P
3-Methylcholanthrene
178
nttD://www,SDeclab.com/c
G
PAH 101E2
G
Benz
Benz
Benz
Benz
omDound/c50328.htm
7,12-Dimethylbenz[a]anthracene
122
ittDV/www.siamaaldrich.c
G
PAH 114E1
G
Benz
Benz
Benz
Benz
om/cataloa/Droduct/sucel
co/442425?lana=en&reai
on=US
Dibenzo[a,h]pyrene
308 E
CS
G/P
PAH 176E2
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
PAH 176E3
P
Cr6
Cr6
Ni
Ni
7H-Dibenzo[c,g]carbazole
544 P
cs
P
Benzo[a]pyrene
360
nttD://www,SDeclab.com/c
G/P
ompound/c50328.htm
Coal tar
>250
nttD://www.inchem.ora/do
G/P
cuments/icsc/icsc/eicsl 41
5.htm
Dibenzo[a,e]pyrene
552 P
CS
P
Methylchrysene
449 P
CS
P
1 -Nitropyrene
445 P
cs
P
PAH 176E4
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
nttD://www,SDeclab.com/c
P
omDound/c193395.htm
Benzo[k]fluoranthene
480
ittD://www.sDeclab.com/c
P
PAH 176E5
P
Cr6
Cr6
Ni
Ni
omDound/c207089.htm
Carbazole
355
ittDV/www.siamaaldrich.c
G/P
om/cataloa/croduct/siama
fc5132?lana=en&reaion=
US
Chrysene
448
ittDV/www.sDeclab.com/c
P
omDound/c218019.htm
Dibenzo[a,h]anthracene
262
ittDV/www.siamaaldrich.c
G/P
PAH 192E3
G/P
PAH
PAH
PAH
PAH
om/cataloa/Droduct/sucel
co/48574?lana=en&reaio
n=US
12-Methylbenz(a)anthracene
410 P
CS
P
PAH 880E5
G/P
PAH
PAH
PAH
PAH
1 -Methylnaphthalene
240
NIH
G
1-Methylphenanthrene
359
ittoV/www. nature, nos.aov
G/P
/hazardssafetv/toxic/chen
1met.pdf
1-Methylpyrene
372
ittDV/www.chemicalbook.
G/P
com/ChemicalProductPro
Dertv EN CB7421679.ht
m
2-Chloronaphthalene
256
ittDV/www.chemicalbook.
G
com/ChemicalProductPro
Dertv EN CB8854627.ht
m
2-Methylnaphthalene
241
httD://www,SDeclab.com/c
G
omDound/c91576.htm
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
AirToxScreen 2017 Documentation
E-14
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Chem.
AirToxScree
n
Boiling Point
Matching Chemical
Modeled in HAPEMd
Name
Value
Source13
Name
(°C)a
Phase0
Phase0
P
NP
M-OR
M-NR
Benzo(c)phenanthrene
430 P
CS
P
Benzo(g,h,i)fluoranthene
406 P
cs
P
Benzo[e]pyrene
465 P
CS
P
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
ittD://www.eDa.aov/rea3h
G/P
wmd/bf-
r/rea ional/analvtical/sem i-
\rolatile.htm
Perylene
276
ittD://www.siamaaldrich.c
G/P
om/cataloq/product/aldric
n/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
OO
CO
CM
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
ittp://www.siqmaaldrich.c
G
STYROXIDE
G
Benz
Benz
Benz
Benz
om/cataloa/croduct/aldric
h/s5006?lana=en&reaion
=US
Titanium tetrachloride
136
httD://www.siamaaldrich.c
G
TITATETRA
G
Benz
Benz
Benz
Benz
om/cataloa/croduct/aldric
h/697079?lana=en&reaio
n=US
2,4-Toluene diisocyanate
251
NIH
G
TOL_DIIS
G
Benz
Benz
Benz
Benz
AirToxScreen 2017 Documentation
E-15
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Boiling Point
AirToxScreen
Matching Chemical
Modeled in HAPEMd
Source13
pc r
Phase0
Phase0
P
NP
M-OR
M-NR
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
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
omcound/d 582098.htm
G
TRIFLURALIN
G
Benz
Benz
Benz
Benz
2,2,4-Trimethylpentane
99
NIH
G
TRMEPN224
G
Benz
Benz
Benz
Benz
1,1,2,2-Tetrachloroethane
147
CDC
G
TTCLE1122
G
Benz
Benz
Benz
Benz
Vinyl chloride
-14
CDC
G
VINYCHLRI
G
Benz
Benz
Benz
Benz
Vinyl acetate
72
CDC
G
VINYLACET
G
Benz
Benz
Benz
Benz
Vinyl bromide
16
CDC
G
VINYLBROM
G
Benz
Benz
Benz
Benz
Vinylidene chloride
32
CDC
G
VINYLIDCLOR
G
Benz
Benz
Benz
Benz
m-Xylene
139
CDC
G
XYLENES
G
Benz
Benz
Benz
Benz
o-Xylene
144
CDC
G
p-Xylene
138
CDC
G
Xylenes (Mixed Isomers)
139
NIH
G
Note: Information under the "Chem" header indicates chemicals for which EPA developed emissions data; we identified boiling points and
HAPEM phase designations for these chemicals. When EPA ultimately conducted air-concentration modeling, for use then in exposure
and risk modeling, they grouped some chemicals together as indicated in the information under the "AirToxScreen" header (note the
occasional many-to-one relationship between the "Name" column under "Chem" and the "Name" column under "AirToxScreen").
Assigning phase designations to groups of chemicals required professional judgment, as discussed in Section 2.2.
a D=decomposes; E=experimental; NA=not available; P=predicted; S=sublimes; V=varies depending on compound.
b CDC=http://www.cdc.qov/niosh/npq; CS=http://www.chemspider.com; NIH=http://pubchem.ncbi.nlm.nih.gov/.
c G=gaseous; G/P=gaseous or particulate depending on conditions; P=particulate; NA=unknown.
d Source Types: P =point; NP=non-point; M-OR=mobile on-road; M-NR=mobile non-road.
Matching Chemicals: Benz=benzene; Buta=1,3-butadiene; Coke=Coke Oven Emissions; PAH=PAH, total; Ni=Nickel; Cr6=Chromium (VI);
DPM = diesel particulate matter; "EF=1" means no surrogate assignment, so the exposure factor is set to 1 (before any truncations are then
applied), meaning all exposure concentrations are set equal to ambient outdoor concentrations.
Table E-2. Boiling-point Definitions Used to Classify HAPs for HAPEM7
Modeling for the 2014 AirToxScreen
HAPEM7 HAP Phase
Boiling-point
Range (°C)
G (Gaseous)
< 240-260
G/P (Either gaseous or particulate depending on conditions)
240-260 to 400-480
P (Particulate)
> 400-480
Source: Adapted from the "Classification of Inorganic Pollutants" table at EPA's
Volatile Organic Compound web page (available as of 23 October 2017 at
httDs://www.eDa.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 2017 Documentation
E-16
-------
Table E-3. HAPs Modeled in HAPEM7 for AirToxScreen
AirToxScreen HAP
HAPEM7
HAP Phase3
P
Modeled in HAPEM7b
NP M-OR M-NR
Benzene
G
¦/
¦/
¦/
¦/
1,3-butadiene
G
¦/
¦/
¦/
¦/
Coke Oven Emissions
G/P
¦/
PAH, Total
(polycyclic aromatic hydrocarbons; aggregate mass
of unspecified congeners)
G/P
¦/
¦/
¦/
¦/
Chromium (VI)
(compounds of hexavalent chromium)
P
¦/
¦/
DPM
(diesel particulate matter)
P
¦/
¦/
¦/
Nickel
P
¦/
¦/
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 2.3.
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
HAP
Point
Non-point
Source Type
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 2017 Documentation
E-17
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United States Office of Air Quality Planning and Standards Publication No. EPA-452/B-22-001
Environmental Protection Health and Environmental Impacts Division March 2022
Agency Air Quality Assessment Division
Research Triangle Park, NC
------- |