Technical Support Document
EPA's 2011 National-scale Air Toxics Assessment
2011 NATATSD
December 2015
Office of Air Quality, Planning, and Standards
Research Triangle Park
North Carolina 27711
-------
EPA's National-scale Air Toxics Assessment
This page intentionally left blank.
-------
EPA's National-scale Air Toxics Assessment
This page intentionally left blank.
-------
EPA's National-scale Air Toxics Assessment
This page intentionally left blank.
i v
-------
EPA's National-scale Air Toxics Assessment
Contents
List of Exhibits viii
Common Acronyms and Abbreviations x
1 Background and Introduction 1
1.1 The Purpose of this Document 1
1.2 What NATA Is 2
1.3 The History of NATA 3
1.4 How States and EPA Use NATA Results 4
1.5 How NATA Results Should Not Be Used 5
1.6 The Risk Assessment Framework NATA Uses 6
1.7 The Scope of NATA 7
1.7.1 Sources of Air Toxic Emissions that NATA Addresses 8
1.7.2 Stressors that NATA Evaluates 8
1.7.3 Exposure Pathways, Routes, and Time Frames for NATA 10
1.7.4 Receptors that NATA Characterizes 11
1.7.5 Endpoints and Measures: Results of NATA 11
1.8 Model Design 12
1.8.1 The Strengths and Weaknesses of the Model Design 14
2 Emissions 17
2.1 Sources of Emissions Data 17
2.1.1 Developing NATA Emissions from the 2011 NEI 19
2.1.2 Categorization of the NATA Emissions in the NATA Output Data 23
2.1.3 Modifications to NEI Emissions Data 25
2.1.4 2011 NATA Emissions: CMAQ versus HEM-3 27
2.2 Emissions Preparation for CMAQ 29
2.2.1 Emission Inventories and Approaches: CMAQ 29
2.2.2 Emissions Processing Steps and Ancillary Data 38
2.3 Emissions Preparation for HEM-3 52
2.3.1 Overview of Differences in Emissions Processing Between CMAQ and HEM-
3 52
2.3.2 HEM Run Groups 55
2.3.3 Point Excluding Airports 58
2.3.4 Point: Airports 64
2.3.5 Nonpoint HEM Run Groups: NPlOm and NPOtherLow 69
2.3.6 Nonpoint HEM Run Groups: CMVs 70
2.3.7 Nonpoint HEM Run Groups: RWC 70
2.3.8 Nonroad HEM Run Group 72
2.3.9 Onroad HEM Run Groups: Light Duty and Heavy Duty 73
2.4 Source Groups 75
2.5 Uncertainties in Emissions/Emissions Processing 77
2.6 Summary 78
3 Air Quality Modeling & Characterization 79
3.1 Hybrid Model Description 79
3.1.1 Overview 79
3.1.2 Treatment of Species 81
-------
EPA's National-scale Air Toxics Assessment
3.1.3 Meteorological Processing 81
3.1.4 Emissions Processing Overview 82
3.1.5 Initial and Boundary Conditions 82
3.1.6 Source Attribution 82
3.2 Treatment of Non-hybrid Air Toxics and Areas Outside the CONUS 83
3.2.1 Background Concentrations 83
3.3 Model Evaluation 86
3.3.1 Overview 87
3.3.2 Observations 87
3.3.3 Model Performance Statistics 88
3.3.4 Hybrid Evaluation 89
3.3.5 Non-hybrid Evaluation 103
3.4 Summary 105
4 Estimating Exposures for Populations 107
4.1 Estimating Exposure Concentrations 107
4.2 About HAPEM 107
4.3 HAPEM Inputs and Application 108
4.3.1 Data on Ambient Air Concentrations 109
4.3.2 Population Demographic Data 109
4.3.3 Data on Population Activity 109
4.3.4 Microenvironmental Data 110
4.4 Exposure Factors 112
4.5 Quality Assurance in Exposure Modeling 113
4.6 Summary 113
5 Characterizing Effects of Air Toxics 115
5.1 Toxicity Values and Their Use in NATA 115
5.2 Types of Toxicity Values 116
5.2.1 Cancer URE 116
5.2.2 Noncancer Chronic RfC 118
5.3 Data Sources for Toxicity Values 119
5.3.1 U.S. EPA Integrated Risk Information System 119
5.3.2 U.S. Department of Health and Human Services, Agency for Toxic Substances
and Disease Registry 119
5.3.3 California Environmental Protection Agency Office of Environmental Health
Hazard Assessment 120
5.3.4 U.S. EPA Health Effects Assessment Summary Tables 120
5.3.5 World Health Organization International Agency for Research on Cancer 120
5.4 Additional Toxicity Decisions for Some Chemicals 121
5.4.1 Poly cyclic Organic Matter 121
5.4.2 Glycol Ethers 121
5.4.3 Metals 122
5.4.4 Adjustment of Mutagen UREs to Account for Exposure During Childhood 122
5.4.5 Diesel Particulate Matter 123
5.5 Summary 123
6 Characterizing Risks and Hazards in NATA 125
6.1 The Risk-characterization Questions NATA Addresses 125
6.2 How Cancer Risk is Estimated 125
6.2.1 Individual Pollutant Risk 126
vi
-------
EPA's National-scale Air Toxics Assessment
6.2.2 Multiple-pollutant Risk 126
6.3 How Noncancer Hazard is Estimated 127
6.3.1 Individual Pollutant Hazard 127
6.3.2 Multiple -pollutant Hazard 128
6.4 How Risk Estimates and Hazard Quotients are Calculated for NATA at Tract,
County, and State Levels 128
6.4.1 Model Results for Point Sources: Aggregation to Tract-level Results 129
6.4.2 Background Concentrations and Secondary Pollutants: Interpolation to Tract-
level Results 129
6.4.3 Aggregation of Tract-level Results to Larger Spatial Units 129
6.5 The Risk Characterization Results that NATA Reports 130
6.6 Summary 132
7 Variability and Uncertainty Associated with NATA 133
7.1 Introduction 133
7.2 How NATA Addresses Variability 133
7.2.1 Components of Variability 134
7.2.2 Quantifying Variability 135
7.2.3 How Variability Affects Interpretation of NATA Results 137
7.3 How NATA Addresses Uncertainty 137
7.3.1 Components of Uncertainty 138
7.3.2 Components of Uncertainty Included in NATA 139
7.4 Summary of Limitations in NATA 143
8 References 147
Appendix A: Glossary A-l
Appendix B: Air Toxics Included in Modeling for the 2011 NATA and Source Classification
Codes that Define Diesel Particulate Matter B-l
Appendix C: Crosswalk for Air Toxics Names in NEI, the NATA Toxicity Table, NATA
Results, and the Clean Air Act; and, the NATA Toxicity Table and Metal
Speciation Factors C-l
Appendix D: Additional Information Used to Process the 2011 NATA Inventory: Inventory
Sectors and Model Run Groups; SCC Groupings; Speciations for Mercury,
Xylenes, and Other Metals D-l
Appendix E: Estimation of Background Concentrations for the 2011 NATA E-l
Appendix F: Model Evaluation Summaries F-l
Appendix G: Exposure Factors for the 2011 NATA G-1
Appendix H: Toxicity Values Used in the 2011 NATA H-l
Appendix I: Adjustments from the 2011 Emissions/Modeling Approach 1-1
vii
-------
EPA's National-scale Air Toxics Assessment
List of Exhibits
Exhibit 1. Summary of the Five Completed NATAs 3
Exhibit 2. The General Air Toxics Risk Assessment Process 7
Exhibit 3. Conceptual Model for NATA 9
Exhibit 4. The NATA Risk Assessment Process and Corresponding Sections of this TSD 13
Exhibit 5. NEI Data Sources for HAP Emissions 18
Exhibit 6. 2011 NEI v2 PAHs Grouped for CMAQ and HEM-3 Modeling based on URE 20
Exhibit 7. 2011 NEI Compounds or Compound Groups for which Emissions are Adjusted for CMAQ and
HEM-3 Modeling 23
Exhibit 8. Map of NEI Data Categories to NATA Categories 24
Exhibit 9. Key Emission Differences between CMAQ and HEM-3 for 2011 NATA Modeling 28
Exhibit 10. Sectors Used in Emissions Modeling for the 2011 NATA CMAQ Platform 30
Exhibit 11. Preparation of HAP Inventory for each Sector for the 2011 NATA CMAQ Platform 32
Exhibit 12. SCCs for RWC 34
Exhibit 13. SCCs for CMVs and Locomotive (clc2rail and c3marine) 35
Exhibit 14. SCCs for Agricultural-Field Burning (agfire) 35
Exhibit 15. Summary of Spatial and Temporal Allocation of Emissions for the 2011 NATA Platform.... 38
Exhibit 16. U.S. Surrogates Available for the 2011 Modeling Platform 40
Exhibit 17. Total and Toxicity-weighted Emissions of CMAQ HAPs Based on the CMAQ Surrogate
Assignments 42
Exhibit 18. Gaseous Species Produced by SMOKE for the 2011 NATA Platform 47
Exhibit 19. Particulate Species Produced by SMOKE for the 2011 NATA Platform 50
Exhibit 20. Approach for Spatial Allocation—HEM-3 versus CMAQ 52
Exhibit 21. Temporal-allocation Approach—HEM-3 versus CMAQ 54
Exhibit 22. HEM Run Groups Based on the Nonpoint and Nonroad NEI Data Categories 56
Exhibit 23. Fields in the HEM-3 Facility List Options File 60
Exhibit 24. HEM-3 Assignments of Emission Release Point Type 62
Exhibit 25. Monthly Temporal Profile for Alaska Seaplanes (Counts and Percentages) 65
Exhibit 26. Diurnal Temporal Profile for General Aviation (Counts, Zero-outs, and Final Percentages).. 66
Exhibit 27. Lead Emissions (kg/yr) at SMO in 2008, by Aircraft Operation Mode 66
Exhibit 28. Hourly Pattern of Activity for SMOKE Profile 26 69
Exhibit 29. Example of RWC Temporal-scaling Factors, January(l)-April(4) (top) and May(5)-
August(8) (bottom), for King County, Washington 72
Exhibit 30. Example of Temporal Scalars by Hour-of-day for Onroad HEM Run Groups 74
Exhibit 31. Source Groups for NATA 75
Exhibit 32. Air Toxics Utilizing the Hybrid Modeling in NATA 79
Exhibit 33. CMAQ Domain with Expanded Cell Showing Hybrid Receptors 80
Exhibit 34. Background Concentrations Added to the HEM-3 Concentrations for Non-CMAQ Air Toxics,
All Areas 84
Exhibit 35. Background Concentrations Added to the HEM-3 Concentrations for Non-CONUS Areas
Only 85
Exhibit 36. Hybrid Air Toxics Evaluated 89
Exhibit 37. 2011 Monitoring Locations for the Evaluation of Hybrid Air Toxics 90
Exhibit 38. 2011 Annual Air Toxics Performance Statistics for the Hybrid, CMAQ, and HEM-3 Models90
Exhibit 39. Acetaldehyde: 2011 Boxplots of Observed and Modeled Concentrations (top) and Modeled-
Observed Bias Difference in Concentrations (bottom) for the Hybrid, CMAQ, and HEM-
3 Models 91
Exhibit 40. Acetaldehyde: 2011 Mean Bias (%) at Monitoring Sites in the Hybrid Modeling Domain .... 92
Exhibit 41. Acetaldehyde: 2011 Mean Error (%) at Monitoring Sites in the Hybrid Modeling Domain... 93
viii
-------
EPA's National-scale Air Toxics Assessment
Exhibit 42.
Exhibit 43.
Exhibit 44.
Exhibit 45.
Exhibit 46.
Exhibit 47.
Exhibit 48.
Exhibit 49.
Exhibit 50.
Exhibit 51.
Exhibit 52.
Exhibit 53.
Exhibit 54.
Exhibit 55.
Exhibit 56.
Exhibit 57.
Exhibit 58.
Exhibit 59.
Exhibit 60.
Exhibit 61.
Exhibit 62.
Exhibit 63.
Exhibit 64.
Acetaldehyde: 2011 Mean Bias (%) at Monitoring Sites in the CMAQ Modeling Domain.... 93
Acetaldehyde: 2011 Mean Error (%) at Monitoring Sites in the CMAQ Modeling Domain .. 94
Acetaldehyde: 2011 Mean Bias (%) at Monitoring Sites in the HEM-3 Modeling Domain ... 94
Acetaldehyde: 2011 Mean Error (%) at Monitoring Sites in the HEM-3 Modeling Domain.. 95
Formaldehyde: 2011 Boxplots of Observed and Modeled Concentrations (top) and Modeled-
Observed Bias Difference in Concentrations (bottom) for the Hybrid, CMAQ, and HEM-
3 Models 95
Formaldehyde: 2011 Mean Bias (%) at Monitoring Sites in the Hybrid Modeling Domain ... 96
Formaldehyde: 2011 Mean Error (%) at Monitoring Sites in the Hybrid Modeling Domain.. 97
Formaldehyde: 2011 Mean Bias (%) at Monitoring Sites in the CMAQ Modeling Domain .. 97
Formaldehyde: 2011 Mean Error (%) at Monitoring Sites in the CMAQ Modeling Domain . 98
Formaldehyde: 2011 Mean Bias (%) at Monitoring Sites in the HEM-3 Modeling Domain .. 98
Formaldehyde: 2011 Mean Error (%) at Monitoring Sites in the HEM-3 Modeling Domain. 99
Benzene: 2011 Boxplots of Observed and Modeled Concentrations (top) and Modeled-
Observed Bias Difference in Concentrations (bottom) for the Hybrid, CMAQ, and HEM-
3 Models 100
Benzene: 2011 Mean Bias (%) at Monitoring Sites in the Hybrid Modeling Domain 101
Benzene: 2011 Mean Error (%) at Monitoring Sites in the Hybrid Modeling Domain 101
Benzene: 2011 Mean Bias (%) at Monitoring Sites in the CMAQ Modeling Domain 102
Benzene: 2011 Mean Error (%) at Monitoring Sites in the CMAQ Modeling Domain 102
Benzene: 2011 Mean Bias (%) at Monitoring Sites in the HEM-3 Modeling Domain 103
Benzene: 2011 Mean Error (%) at Monitoring Sites in the HEM-3 Modeling Domain 103
Non-hybrid Air Toxics Evaluated 104
2011 Monitoring Locations for the Evaluation of Non-hybrid Air Toxics 104
Key Differences between Recent Versions of HAPEM 108
Microenvironments Used in the HAPEM Modeling for the 2011 NATA 112
NATA Drivers and Contributors of Health Effects for Risk Characterization 131
ix
-------
EPA's National-scale Air Toxics Assessment
Common Acronyms and Abbreviations
|jg/m3
microgram/cubic meter
AERMOD
atmospheric dispersion model developed by the American Meteorological Society and the
U.S. Environmental Protection Agency's Regulatory Model Improvement Committee
ASPEN
Assessment System for Population Exposure Nationwide
ATSDR
Agency for Toxic Substances and Disease Registry
CHAD
Consolidated Human Activity Database
CAP
Criteria air pollutant
CMAQ
Community Multiscale Air Quality model
CONUS
Continental United States (modeling domain for CMAQ)
EC
exposure concentration
EPA
Environmental Protection Agency
EGU
electricity generating unit
HAP
hazardous air pollutant
HAP EM
Hazardous Air Pollutant Exposure Model
HEM
Human Exposure Model
HI
hazard index
HQ
hazard quotient
IRIS
Integrated Risk Information System
ISC
Industrial Source Complex
MOVES
Motor Vehicle Emissions Simulator
NATA
National-scale Air Toxics Assessment
NEI
National Emissions Inventory
NMIM
National Mobile Inventory Model
OAQPS
Office of Air Quality Planning and Standards
PAH
polycyclic aromatic hydrocarbon
PM
particulate matter
POM
polycyclic organic matter
RfC
reference concentration
RTR
Risk and Technology Review
see
Source Classification Code
S/L/T
State, local, or tribal agency
URE
unit risk estimate
WRF
Weather Research Forecasting model
x
-------
EPA's National-scale Air Toxics Assessment
1 BACKGROUND AND INTRODUCTION
1.1 The Purpose of this Document
This document describes the data and approaches used to conduct the U.S. Environmental Protection
Agency's (EPA; referred to throughout this document as "we") National-scale Air Toxics Assessment
(NATA), an ongoing comprehensive evaluation of air toxics in the United States. It presents the
approaches EPA used to conduct NATA, including descriptions of how we
• compiled emissions data and prepared them for use as model inputs,
• estimated ambient concentrations of air toxics,
• estimated exposures to air toxics for populations,
• selected toxicity values,
• characterized human-health risks and hazards, and
• addressed variability and uncertainty.
This technical support document (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 the 2011 NATA. 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.
Appendices to this document include:
• Appendix A—a glossary of the key terms and their definitions;
• Appendix B—a list of air toxics included in NATA and a list of source classification codes
(SCCs) for diesel particulate matter (diesel PM);
• Appendix C—a crosswalk of pollutant names across inventories, assessments, and regulations,
with metal speciation factors;
• Appendix D—a crosswalk table for NEI sectors to the source groups and Human Exposure Model
(HEM-3) run groups used to present the NATA results, and additional speciation information
including for xylenes, mercury, and other metals;
• Appendix E—procedures for estimating NATA background concentrations;
• Appendix F—additional model evaluation summaries;
• Appendix G—a table of average ratios of exposure concentration to ambient concentrations
applied in NATA;
• Appendix H—a table of toxicity values applied in NATA; and
• Appendix I—adjustments to the approach.
1
-------
EPA's National-scale Air Toxics Assessment
We also provide a "SupplementalData" folder with this document that contains the Microsoft® Access™
and Microsoft® Excel™ files referenced throughout this TSD.
This document does not provide quantitative results for NATA and thus presents no exposure or risk
estimates. Results and other specific information for NATA, including for the 2011 NATA and previous
assessments, are found on the NATA website.
1.2 What NATA Is
NATA is a screening tool intended to evaluate the human-health risks posed by air toxics across the
United States. We developed this tool so that state, local, and tribal agencies could prioritize air toxics,
emission sources, and locations of interest for further study.
NATA assembles information on air toxics, characterizes emissions, and prioritizes air toxics and
locations that merit more refined analysis and investigation. This information is used to plan, and assist
with the implementation of, national, regional, and local efforts to reduce toxic air pollution. Using
general information about sources to develop estimates of risks, NATA provides screening-level
estimates of the risk of cancer and other potentially serious health effects as a result of inhaling air toxics.
The resulting risk estimates are purposefully more likely to be overestimates of health impacts than
underestimates, and thus they are health protective.
NATA uses emissions data compiled for a single year as inputs for modeling ambient air concentrations
and estimating health risks. Results include estimates of ambient concentrations and exposure
concentrations (ECs) of air toxics and estimates of cancer risks and potential noncancer health effects
associated with chronic inhalation exposure to air toxics. The estimates are generated within each state,
at both county and census-tract levels.
NATA provides a "snapshot" of outdoor air quality and the risks to human health that might result if air
toxic emission levels were to remain at the same levels as those estimated for the assessment year. The
estimates reflect only risks associated with chronic (relatively long-term) exposures to the inhalation of
air toxics at the population level. The assumptions and methods used to complete the national-scale
assessments limit the types of questions that NATA can answer reliably. These limitations, described
throughout later sections of this document and summarized in Section 7, must be considered when
interpreting the NATA results or when using them to address questions posed outside of NATA.
NATA results are useful for prioritizing air toxics and emission sources, identifying locations of interest
that require additional investigation, providing a starting point for local-scale assessments, focusing
community efforts to reduce local emissions of air toxics, and informing the design of new monitoring
programs or the re-design of existing ones. NATA results also can provide general answers to questions
about emissions, ambient air concentrations, and exposures and risks across broad geographic areas (such
as counties, states, the nation) at a moment in time.
NATA was designed to answer questions such as the following:
• Which air toxics pose the greatest potential risk of cancer or adverse noncancer effects across the
entire United States?
• Which air toxics pose the greatest potential risk of cancer or adverse noncancer effects in specific
areas of the United States?
2
-------
EPA's National-scale Air Toxics Assessment
• Which air toxics pose less, but still significant, potential risk of cancer or adverse noncancer
effects across the entire United States?
• When risks from inhalation exposures to all outdoor air toxics are considered in combination,
how many people could experience a lifetime cancer risk greater than levels of concern (e.g., 1-
in-1 million)?
• When potential adverse noncancer effects from long-term exposures to all outdoor air toxics are
considered in combination for a given target organ or system, how many people could experience
exposures that exceed the reference levels intended to protect against those effects (i.e., a hazard
quotient greater than 1)?
1.3 The History of NATA
As discussed on the NATA website. EPA's first national-scale air toxics study was the Cumulative
Exposure Project (Caldwell et al. 1998), which was developed based on estimates of air toxics emissions
present before the Clean Air Act (CAA) was amended in 1990. The Cumulative Exposure Project
provided estimates of outdoor air toxics concentrations in each of the more than 60,000 continental U.S.
census tracts.
For the first NATA, the Cumulative Exposure Project framework was enhanced 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. This assessment
was submitted for a technical peer review in January 2001 to a panel of EPA's Science Advisory Board
(EPA 2001 b). 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 the assessment and published the
results of that assessment in 2002. Since then, four assessments have been completed, based on national
emission inventories that are updated significantly on a tri-annual basis, representative of air toxic
emissions in 1999, 2002, 2005, and 2011, respectively. In general, the scope of NATA has progressively
expanded with subsequent versions, and some methods have been refined and improved. Exhibit 1
summarizes the five NATAs EPA has conducted to date.
Exhibit 1. Summary of the Five Completed NATAs
Inventory
Year
Year
Completed/
Published
Air Toxics Modeled a b
Key Attributes
1996
2002
33—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—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—180 HAPs, including 4
with additional health
information; plus diesel PM
• ASPEN and HEM (with ISC) used to model ambient
concentrations
• HAPEM5 used to model inhalation exposures
3
-------
EPA's National-scale Air Toxics Assessment
Inventory
Year
Year
Completed/
Published
Air Toxics Modeled a b
Key Attributes
2005
2010
179—178 HAPs, for which
emissions data and chronic-
health toxicity values are
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 2015a) used to estimate
secondary formation of acetaldehyde, acrolein,
formaldehyde, and decay of 1,3-butadiene to acrolein
2011
2015
180—179 HAPs for which
emissions data are; available
plus diesel PM
• CMAQ and HEM-3 more fully integrated as a hybrid
modeling system for about 40 HAPS and diesel PM to
improve mass conservation.
• HEM-3 with background for remaining HAPs (also for
areas outside the U.S. continental CMAQ modeling
domain) not covered by the hybrid approach
• HAPEM7 to model inhalation exposures for a subset
of air toxics and used to provide exposure factors for
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 2015n). Diesel PM is not a HAP but is 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.
b The 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
they are 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.
1.4 How States and EPA Use NATA Results
NATA was designed as a screening assessment and functions as a tool to inform both national and more
localized efforts to collect air toxics information, to characterize emissions, and to help prioritize air
toxics and geographic areas of interest for more refined data collection and analyses.
Ultimately, NATA results are intended to focus resources on air toxics, locations, or populations that are
associated with the greatest potential health risks. Thus, the goal of NATA is to identify those air toxics
of greatest potential concern with regard to their contribution to population risk. The results are used to
set priorities for the collection of additional air toxics information, including emissions and monitoring
data. NATA was designed 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.
4
-------
EPA's National-scale Air Toxics Assessment
We use NATA to identify those air toxics and source sectors (e.g., stationary sources, mobile sources)
having the highest exposures and health risks. The assessment results also help to identify geographic
patterns and ranges of risks across the country. Specifically, we use NATA 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
• provide a multiple-pollutant modeling framework linking air toxics to the Criteria Pollutant
Program (EPA 2015d).
1.5 How NATA Results Should Not Be Used
As described in Section 1.2, NATA is a screening-level assessment that was designed to answer specific
types of questions. The underlying assumptions of NATA and the methods limit the range of questions
that can be answered reliably. NATA results should not be used independently to characterize or compare
risk at local levels (e.g., between neighborhoods), nor should they be used to estimate exposure or health
risks for individuals or groups within small geographic areas such as census blocks or to design control
measures for specific emissions sources or pollutants.
NATA evaluations use emissions data for a single year as inputs to models that yield concentration and
risk estimates. These estimates reflect chronic exposures. Given these characteristics, NATA results
should not be used for the following:
• as a definitive means to pinpoint specific risk values within a census tract,
• to characterize or compare risks at local levels such as among neighborhoods,
• to characterize or compare risk among states,
• to examine trends from one NATA year to another,
• as the sole basis for developing risk reduction plans or regulations,
• as the sole basis for determining appropriate controls on specific sources or air toxics, or
• as the sole basis to quantify benefits of reduced air toxic emissions.
The limitations of the assessment methods prevent NATA from serving as a stand-alone tool.
Furthermore, 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. For analysis of 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.
5
-------
EPA's National-scale Air Toxics Assessment
These caveats are integral to the proper interpretation of NATA results. NATA results should be used
to address only those questions for which the assessment methods are suited. Moreover, as noted above,
NATA results from different assessment years generally should not be compared to each other. From one
assessment to the next, EPA has improved its methodology and incorporated additional data that enhance
the utility of the results, but compromise the ability to compare across periods. Specifically, each
subsequent assessment has offered the following relative to the previous NATA:
• a better and more complete inventory of emission sources,
• an overall increase in the number of air toxics evaluated1, and
• updated health data for use in risk characterization.
Successive improvements in methodology and improved data make comparing earlier assessments with
later assessments inappropriate. Differences in emissions, ambient concentrations, or risks observed in the
results of two assessments might be due either to improvement in the assessment methodology or to
actual changes in emissions, populations, or other "real-life" characteristics.
NATA is not used solely as the source of information leading to regulations or guiding the enforcement of
existing rules. Thus, even though some of the methods used to conduct NATA are similar to those used in
air-related risk assessments conducted under the CAA mandate (such as residual risk assessments of HAP
emissions from point sources, or assessments of exposures to criteria air pollutants (CAPs) for evaluations
of National Ambient Air Quality Standards), NATA fundamentally differs from such assessments in that
it is not a regulatory program.
1.6 The Risk Assessment Framework NATA Uses
The methods applied in conducting NATA are consistent with the general risk assessment framework
used throughout EPA. This section provides background information on EPA's risk assessment
framework and summarizes the NATA process. The analytical components of this process are then
described in detail in subsequent sections.
EPA has published a series of guidelines (EPA 2015e) that establishes and explains the recommended
methods for assessing human-health risks from environmental pollution. Included in this series are
recommendations for carcinogen risk assessment, exposure assessment, chemical mixtures risk
assessment, and other major EPA-wide risk assessment guidelines. In addition, EPA 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 provides information on 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 (Exhibit 2), 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.
1 Since the 1999 NATA, the number of pollutants has depended largely on the emissions inventory.
6
-------
EPA's National-scale Air Toxics Assessment
• 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.
Exhibit 2. The General Air Toxics Risk Assessment Process
Source: Adapted from EPA (2004a)
An air toxics risk assessment starts with problem formulation. This initial step begins with the systematic
planning and scoping that should be conducted before any analyses are begun to 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 for a risk assessment of air toxics 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 and emitted
air toxics, potentially exposed populations, chemical fate and transport processes, expected routes of
exposure, and potential health effects.
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 NATA). The analysis
phase is the stage at which the risk assessment processes are used to evaluate the problem at hand. The
planning and scoping activities and problem formulation we conduct before carrying out the analyses,
however, are critical in that they set the course for the assessment and inform EPA's decisions regarding
specific methods, models, and data sources to use. The conceptual model developed for NATA—which is
the product of the first phase—is described in the following section. An overview of the analytical steps
then follows in Section 1.8. Detailed descriptions of each step are presented in the other sections of this
document.
1.7 The Scope of NATA
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
7
-------
EPA's National-scale Air Toxics Assessment
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 sometimes can 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 sequential 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 five NATAs
completed to date. Moreover, the conceptual model described in this document is very similar to the one
presented in the documentation for the 1996 NATA.
NATA 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, NATA is intended to provide EPA
with the best possible national-scale population-level estimates of exposure to and risks associated with
air toxics, taking into account data availability, technical capabilities, and other potentially limiting
factors. The conceptual model for the 2011 NATA is presented in Exhibit 3. Each component included in
the model is described briefly in the sections that follow.
1.7.1 Sources of Air Toxic Emissions that NATA Addresses
Sources of primary air toxic emissions included in NATA (i.e., the NATA categories) are point,
nonpoint, mobile onroad and nonroad, biogenics, and fires in the continental United States, and all
these except biogenics and fires in Alaska, Hawaii, Puerto Rico and the U.S. Virgin Islands. Examples of
point sources are large waste incinerators and factories. Nonpoint sources include residential wood
combustion (RWC), commercial cooking, and consumer and commercial solvents. Mobile sources
include vehicles found on roads and highways, such as cars and trucks, and nonroad equipment such as
lawn mowers and construction equipment. Nonroad sources also include marine vessels, trains, and
aircraft. Background sources, also included in NATA, can include natural sources and anthropogenic air
toxics emitted in prior years that persist in the environment, or air toxics emitted from distant sources,
including (for those HAPs modeled in HEM-3 but not the Community Multiscale Air Quality [CMAQ])
air toxics transported farther than 50 kilometers. Certain HAPs (i.e., formaldehyde, acetaldehyde, and
acrolein) are formed in the atmosphere through photochemical reactions, and these "secondary"
contributions are included in NATA through the photochemical air quality modeling platform. For the
2011 NATA, results are presented by broad categories and the more detailed NATA source groups
through source attribution included in the air quality characterization. Details on the emission sources are
presented in Section 2; details on air quality modeling and characterization are presented in Section 3.
1.7.2 Stressors that NATA Evaluates
The stressors evaluated through NATA can include any of the 187 HAPs defined in the 1990 CAA (190
HAPs were included originally but 3 have since been removed from the list). The set of air toxics
included in NATA 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 NATA.
8
-------
EPA's National-scale Air Toxics Assessment
Exhibit 3. Conceptual Model for NATA
Sources
Stressors
Pathways/
Media
Routes
Major
stationary
Nonpoint
Mobile
(onroad &
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
Inhalation
Soil
Ingestion
Dermal
IT"
General
Population
Subpopulations
Male
Female
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
Age
0-1
~TI
Age
2-4
Age
5-15
Age
16-17
Age
18-64
Age
65+
Hispanic
I
White
African
Asian
American
American
Cancers
(leukemia, lung, others)
Respiratory
Neurological
Blood (including
marrow & spleen)
1
Liver &
kidney
Cardio-
vascular
I
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
Distribution of
estimated
values
(HQ or HI)
Respiratory System Hazard Index
Estimated percent of
population within specified
ranges of quotient or
index values
Blue boxes indicate elements included in the 2Q11 NATA; 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 NATA is presented in Section 2. DPM refers to diesel PM. PBTs refers to chemicals
that are persistent, bioaccumulative, and toxic. HQ and HI refer to hazard quotient and hazard index, respectively.
9
-------
EPA's National-scale Air Toxics Assessment
The 2011 NATA assesses the pollutants shown in Exhibit B-l of Appendix B. Exhibit B-2 lists the CAA
pollutants that are not included in the 2011 NATA and the reason. A spreadsheet file with more detailed
information on the NEI and NATA pollutants is provided in the SupplementalData folder accompanying
this TSD.
This exposure and risk assessment does not include the classes of compounds known as dioxins,
asbestos, and radionuclides. We did not evaluate exposure and risk related to dioxins and radionuclides
in the 2011 NATA because we did not evaluate the completeness or accuracy of the State, Local, and
Tribal (S/L/T) agency data for these groups. Also, the most significant exposure route for dioxin is
ingestion, not inhalation, so dioxin's relative contribution to NATA's inhalation risk estimates likely
would not be large. Although the 2011 NATA emissions inventory includes asbestos, it also was not
modeled for NATA because, like radionuclides, their ambient concentrations and inhalation exposures
used in risk assessments typically are not expressed using mass-based concentrations, given methods used
to develop the toxicity values that match each material's specific toxicological characteristics. Health
risks of radionuclides are estimated using specific activity (a measure of radioactivity, which occurs as
energy is emitted in the form of radiation from unstable atoms), and air concentrations of asbestos often
are measured in terms of numbers of fibers per unit volume. 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.
1.7.3 Exposure Pathways, Routes, and Time Frames for NATA
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 particular 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 NATA includes evaluating the transport of emitted particles and gases
through the air to receptors within 50 kilometers of sources. Transformation of substances in the
atmosphere (also referred to as secondary formation) and losses of substances from the air by deposition
are included in the modeling, 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,
background concentrations are estimated. Taking into account fate and transport of emissions and the
presence of some background concentrations, NATA estimates outdoor ambient concentrations across the
nation.
NATA 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 NATA accounts for the
difference between ambient outdoor concentrations and the ECs (i.e., long-term-average concentrations to
which people are actually exposed after taking into account human activities).
To date, NATA 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, NATA has not estimated
10
-------
EPA's National-scale Air Toxics Assessment
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.
NATA estimates average annual outdoor concentrations that are used to develop long-term inhalation
exposures for each of the air toxics. For cancer and chronic (long-term) health effects, the exposure
duration is assumed as lifetime (i.e., 70 years for the purposes of this analysis). Subchronic and acute
(lasting less than 24 hours) exposures are not estimated in NATA 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 NATA.
1.7.4 Receptors that NATA Characterizes
NATA characterizes average risks to people belonging to distinct human subpopulations. The population
as a whole 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 who are 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, for a "typical" individual
residing in that tract. Risk results for individual cohorts are not included in the outputs of NATA.
To date, NATA evaluations have not included non-human receptors (e.g., wildlife and native plants).
The complexity of the varied ecosystems across the vast geographic area that is the scope of NATA
precludes considering potential adverse ecological impacts at this time. Local- and urban-scale
assessments could 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 NATA.
1.7.5 Endpoints and Measures: Results of NATA
NATA reports estimated cancer risks and noncancer hazards attributed to modeled sources. Key measures
of cancer risk developed for the 2011 NATA include:
• upper-bound estimated lifetime individual cancer risk, and
• 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 10-in-l million).
For noncancer effects, the key measures presented in the 2011 NATA are hazard indices summed
across all air toxics modeled for the respiratory system. Individual pollutant hazard quotients are
provided for other target organs and systems.
NATA characterizes cancer risk and potential noncancer effects based on estimates of inhalation ECs
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
11
-------
EPA's National-scale Air Toxics Assessment
differences in overall average population risks within the county. EPA is 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 NATA, 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 geographic areas of interest for more
refined data collection such as 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.
1.8 Model Design
Consistent with the general approach for air toxics risk assessment illustrated in Exhibit 2, the analysis
phase of NATA 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 NATA. This set of
activities is referred to here as the "NATA risk assessment process."
The NATA process can be characterized by four sequential components:
1. compiling the nationwide inventory of emissions from outdoor sources;
2. estimating ambient outdoor concentrations of the emitted air toxics across the nation;
3. estimating population exposures to these air toxics via inhalation; and
4. characterizing potential health risks associated with these inhalation exposures.
The fourth component (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 NATA, it does require that we make
important scientific and policy decisions regarding the appropriate values to be used in NATA. Because
these decisions are critical to the risk results, the identification of appropriate dose-response values is also
described in this TSD as a fifth assessment component.
Collectively, these five components make up the NATA risk assessment process illustrated in Exhibit 4.
The development of the emission inventory, air quality modeling, inhalation exposure modeling, and risk
characterization must be conducted sequentially—the completion of 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.
12
-------
EPA's National-scale Air Toxics Assessment
Exhibit 4. The NATA Risk Assessment Process and Corresponding Sections of this TSD
Compile Nationwide Identify Toxicity
Emission Inventory Values
(Section 2) (Section 5)
T
Conduct Air
National Air Quality Modeling
Emissions (Section 3)
Inventory
1
Model Inhalation
Ambient Exposures
Concentrations (Section 4)
T
1
Conduct Risk
Exposures Characterization
(Section 6)
Cancer Risks, Chronic
Noncancer Hazards
• Section 2 contains an explanation of the source types and air toxics included in the NATA
emissions inventory. It also describes the processes we carried out to prepare the emissions for
the air quality models.
• Section 3 contains a discussion of 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 NATA.
• Section 4 contains explanations of the processes used to estimate population-level exposure to
outdoor ambient levels of air toxics, taking into account information on activities and other
characteristics that can affect inhalation exposures.
• Section 5 contains descriptions of the dose-response values used for NATA, the sources from
which these values are obtained, and assumptions made specific to NATA.
• Section 6 contains an overview of the calculations used to estimate cancer risk and potential
noncancer hazard.
• Section 7 contains explanations of the uncertainties and limitations associated with the NATA
process that must be considered when interpreting NATA 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—the 2011 NATA. Accordingly, although the following
sections contain information on the NATA process that are generally applicable to all previous NATAs,
13
-------
EPA's National-scale Air Toxics Assessment
references to specific technical processes and supporting details typically emphasize what was done for
the 2011 NATA.
1.8.1 The Strengths and Weaknesses of the Model Design
EPA developed NATA to inform both national and more localized efforts to collect information and to
characterize air toxics emissions (e.g., prioritize air toxics or geographic areas of interest for monitoring
and community assessments). Because of this targeted objective, tools other than NATA might be more
appropriate for assessing health risks outside the specific purpose of NATA (e.g., for evaluating risks
from either a broader or more specific perspective). To further define and clarify what NATA should not
be used for, this section contains descriptions of some of the important data and results that are not
included in NATA.
• NATA 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.) Additionally, NATA does not include data appropriate for addressing
epidemiological questions such as the relationship between asthma or cancer risk and proximity
of residences to point sources, roadways, and other sources of air toxics emissions.
• The results do not include impacts from sources in Canada or Mexico other than from limited
pollutants and source groups. Thus, the results for states bordering these countries do not
comprehensively reflect sources of transported emissions that could be significant.
• NATA 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.
• The results do not include exposures and risk from all compounds. For example, of the 180 air
toxics included in the 2011 NATA (some of which encompass multiple substances), only 138 air
toxics have been assigned dose-response values. The remaining air toxics do not have adequate
data in EPA's judgment to assess their impacts on health quantitatively, and, therefore, do not
contribute to the aggregate cancer risk or target-organ-specific hazard indices. Of particular
significance is that 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. NATA, however, does
model noncancer effects of diesel PM.
• Other than lead, which is both a CAP and a HAP, the results do not include the air pollutants,
known as CAPs (particulate matter, ground-level ozone, carbon monoxide, sulfur oxides, nitrogen
oxides), for which the CAA requires EPA to set National Ambient Air Quality Standards (other
than CAP impacts on secondary formation of formaldehyde, acetaldehyde, and acrolein).
• The results do not reflect all pathways of potential exposure. The assessment includes risks only
from direct inhalation of the emitted air toxics compounds. It does not consider air toxics
compounds that might then deposit onto soil, water, and food and subsequently enter the body
through ingestion or skin contact.
14
-------
EPA's National-scale Air Toxics Assessment
• The results do not include multipathway exposures because sufficiently refined tools and data
required to model multipathway concentrations and human exposures for many air toxics on the
national scale are not readily available for use.
• The assessment results reflect exposure at outdoor, indoor, and in-vehicle locations, but only to
compounds released into the outdoor air, which could subsequently penetrate into buildings and
vehicles. The assessment does not include exposure to air toxics emitted indoors, such as those
from stoves, those that out-gas from building materials, or those from evaporative benzene
emissions from cars in attached garages. The assessment also does not consider toxics released
directly to water and soil.
• The assessment does not fully reflect variation in background ambient air concentrations.
Background ambient air concentrations are average values over broad geographic regions.
• The assessment might not accurately capture sources that have episodic emissions (e.g., facilities
with short-term deviations in emissions resulting from startups, shutdowns, malfunctions, and
upsets). The models assume emission rates are uniform throughout the year.
• Short-term (acute) exposures and risks are not included in NATA.
• Atmospheric transformation and losses from the air by deposition are not accounted for in NATA
air toxics that are not modeled in CMAQ.
• The evaluations to date have not assessed ecological effects, given the complexity of the varied
ecosystems across the vast geographic area that NATA targets.
15
-------
EPA's National-scale Air Toxics Assessment
This page intentionally left blank.
16
-------
EPA's National-scale Air Toxics Assessment
2 EMISSIONS
The systematic compilation of a detailed, nationwide inventory of air toxics emissions is the first major
step in the NATA risk assessment process. This section contains descriptions of the emissions used for
the 2011 NATA. Section 2.1 contains summaries of the sources of emissions data included in NATA.
Section 2.2 contains summaries of the processing of emissions for input into CMAO (EPA 2015g), and
Section 2.3 contains summaries of the processing for input into HEM-3 (see also the HEM-3 User's
Guides. EPA 2014e).
For simplicity and consistency throughout this TSD, all aspects or details of the HEM-3 model are
referred to overall as "HEM-3," although most often the AERMOD component of HEM-3 is pertinent to
the discussion. EPA designed and maintains AERMOD separate and apart from HEM-3; HEM-3 merely
incorporates AERMOD.
2.1 Sources of Emissions Data
NATA is intended to address outdoor emissions of all HAPs and diesel PM (together called "air toxics" in
this document). To model air
toxics, emissions of both air
toxics and CAPs are used so
that the chemical interactions
that occur across all
pollutants are addressed.
The 2011 NATA combines
modeling from CMAQ and
HEM-3 for the continental
United States. CMAQ
multipollutant modeling
addresses all sources in the NEI for CAPs and about 40 HAPs. Emissions from outside the United States
are represented by CMAQ boundary conditions for benzene, formaldehyde, and acetaldehyde. 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 HEM-3 is used. For these pollutants and geographic
regions, spatially uniform background concentrations based on remote concentrations are added to the
HEM-3-modeled data to represent influences from transport and emissions outside the modeling domain.
Similar to previous NATAs, HEM-3 modeling addresses the 180 NATA HAPs and diesel PM included in
NATA and all anthropogenic sources except prescribed and agricultural burning.
The main source of the emissions data for the CAPs and HAPs modeled for NATA is the 2011 NEI v2.
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. The NEI is
prepared every three years by the EPA based primarily upon emission estimates and emission model
inputs provided by S/L/T air agencies for sources in their jurisdictions, and supplemented by data
developed by the EPA. These data are submitted electronically to the Emissions Inventory System (EIS).
CAPs must be submitted under the 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. For the
2011 NEI, facilities with potential to emit greater than 5 tons per year (TPY) were required to report lead.
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 2015n) plus diesel
PM. The 1990 Clean Air Act Amendments required EPA to control 190 HAPs
(EPA 2008b) and provided for revisions to be made to that list. Currently, the
list includes 187 HAPs. Diesel PM is not a HAP, and EPA currently does not
have sufficient evidence to develop a unit risk estimate for it. Some evidence
does indicate that localized high lifetime cancer risks are, however,
associated with exposure to diesel PM. Given such concern, the potential
adverse noncancer effects associated with diesel PM are estimated in NATA
(using an Integrated Risk Information System reference concentration) but its
cancer risks are not.
17
-------
EPA's National-scale Air Toxics Assessment
To build as complete an NEI as possible, and to ensure use of up-to-date emission factors from test
programs resulting from regulatory development, we gap-filled emissions using various sources of
information including the Mercury and Air Toxics Rule (MATS) test data, the Toxics Release Inventory
(TRI; EPA 2015j), and the application of HAP-to-CAP emission-factor ratios to CAP emissions reported
by S/L/T.
Exhibit 5 contains a summary of the sources of emissions data in the NEI. More information on these data
sources can be found in the 2011 NEI documentation.
Exhibit 5. NEI Data Sources for HAP Emissions
Stationary Point
Most stationary point source HAP data were submitted voluntarily by S/L/T. For electric
generating units (EGUs), we estimated emissions using test-based or average emission
factors from the 2010 test program conducted in support of MATS for metal HAPs,
hydrochloric acid, and hydrogen fluoride (except where S/L/T data were from testing or the
configuration of the units changed such that the MATS test results would no longer be
applicable for 2011 emissions).
For some point sources, EPA gap-filled HAPs. Sources of data included: rule-based
emission factors (e.g., mercury for electric arc furnaces), TRI data for 2011, augmentation of
HAPs using emission-factor ratios (of HAP to CAP) applied to S/L/T-reported CAP
emissions, and 2008 emissions data for selected categories.
The data sources for point-source data are provided in the NATA facility-level data emission
summaries provided on the NATA website.
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 with
S/L/T inputs. For some 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 over
19,800 airports (including heliports and seaplanes).
Point Rail yards
EPA estimates were grown from the 2008 emissions that were developed by the Eastern
Regional Technical Advisory Committee (ERTAC) to 2011 estimates. Locations were
identified using a database from the Federal Railroad Administration. CAP emissions were
estimated by applying emission factors to the total amount of distillate fuel used by
locomotives. Each railroad company provided fleet mix information that allowed ERTAC to
calculate railroad-specific emission factors. The company-specific, system wide fleet mix was
used to calculate weighted average emissions factors for switchers operated by each Class I
railroad. EPA emission factors were used for PM2.5, SO2, and NH3. HAP emissions were
estimated by applying toxic fractions to the VOC or PM estimates. For some rail yards,
estimates were provided by S/L/T. The NEI has about 750 rail yards.
Stationary Nonpoint
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 (BEIS3.60), using 2011 meteorology from the
Weather Research Forecasting Model (WRF). Gridded emissions were used in NATA and
summed to annual county-level estimates for the NEI. Includes VOC, NOx, and 3 HAPs:
formaldehyde, acetaldehyde, and methanol.
Locomotives
EPA estimates developed by applying growth factors to the 2008 NEI values based on
railroad-freight-traffic data from the 2008 and 2011 R-1 reports submitted by all Class I rail
lines to the Surface Transportation Board and employment statistics from the American Short
Lines and Regional Railroad Association for Classes II and III. The emissions were allocated
to line-haul-shape IDs and yard locations based on 2008 allocations. HAP emissions were
estimated by applying toxic fractions to the VOC or PM estimates. For some areas, estimates
were provided by S/L/T.
18
-------
EPA's National-scale Air Toxics Assessment
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, tugboats, and vessels on the Great
Lakes; C3 includes ocean going vessels and large ships. For C1/C2 marine diesel engines,
the emission estimates were consistent with the 2011 Locomotive and Marine federal rule
making. We derived HAP estimates by applying toxic fractions to VOC or PM estimates.
These national emissions estimates were geographically allocated based on the available
port and underway activity (e.g., Army Corps of Engineers Waterborne Commerce, National
Marine Fisheries Service, etc.)
C3 commercial marine inventories were developed for a base year of 2002 from gridded
Emissions Control Area model data and then projected to 2011 by applying regional
adjustment factors to account for growth.
For some states, estimates developed by Lake Michigan Air Directors Consortium estimates
replaced EPA's (these data include HAP emissions).
Onroad
We generated emissions using the October 2014 version of MOVES2014 (database version
movesdb20141021: MotorVehicle Emissions Simulator: EPA2015k). then usina SMOKE-
MOVES to generate data for a detailed set of new source classification codes (SCCs),
designed around emissions modes (SMOKE = Sparse Matrix Operator Kernel Emissions
modeling system; Houyoux et al. 2000). These models use state- or EPA-provided input
details, specific to each county. California's emissions were developed via their EMFAC
onroad model, but VOC HAPs were speciated from California-reported VOC consistent with
the MOVES2014 speciation, and SCCs were modified to match the rest of the country.
Nonroad
Except for California and Texas, data are from the National Mobile Inventory Model (NMIM:
EPA 2015p), which uses the NONROAD model with 2011 state-submitted and/or default
inputs. All metals are modeled in NMIM using emission factors, and all the other HAPs are
estimated from VOC or PM by applying toxic fractions. California data are from the California
Air Resources Board, which uses its own model, and Texas data are from the Texas
Commission on Environmental Quality, which runs NONROAD (exception: Hg and arsenic
are from NMIM).
Fires
We estimated agricultural burning (included in stationary nonpoint) using remote-sensing
data, crop-usage maps, and emission factors. State data received from numerous states
were used ahead of EPA estimates. Inventoried at the county level.
Day- and location-specific prescribed burning and wildfires estimated via the SMARTFIRE2
system (which includes the BlueSky modeling framework) with inputs from State agencies
where available. Georgia and North Carolina submitted estimates, Florida estimates were
scaled to conform to Florida's summaries, and Delaware fires were modified (some fires
zeroed out) per state comments.
2.1.1 Developing NATA Emissions from the 2011 NEI
Two modeling platforms were developed for the two air quality models run for NATA—CMAQ and
HEM-3. The starting-point emission files for both were based primarily on "flat file" formats of the 2011
NEI produced by the EIS for the SMOKE modeling system. Onroad emissions were generated by
SMOKE-MOVES (Motor Vehicle Emissions Simulator; EPA 2015k), and an FF10 summary of the data
was developed for input into HEM-3.
The grouping/speciation of NEI pollutants for the purposes of NATA produced diesel PM from PM and
produced various grouped pollutants from individual compounds reported in the NEI, such as grouping
individual glycol ethers into the single NATA HAP "glycol ethers." Appendices B, C, D, and H, along
with the spreadsheet file "NATA_Pollutants_AppendixB_AppendixC.xlsx" within the
"SupplementalData" folder accompanying this TSD, show additional information on the NATA
pollutants. All pollutants that were grouped for purposes of NATA are included in emission summaries as
both the ungrouped NEI pollutant and the group sum. The group sum is called "Sum of' concatenated
with the group name. All groups are listed below:
19
-------
EPA's National-scale Air Toxics Assessment
• Sum of Chromium VI (hexavalent) Compounds
• Sum of Cyanides
• Sum of Cresol/Cresylic Acid (Mixed Isomers)
• Sum of Glycol Ethers
• Sum of Nickel Compounds
• Sum of Polychlorinated Biphenyls (Aroclors)
• Sum of POMP AH
• Sum of Xylenes
The below subsections contain additional descriptions of these pollutants groupings.
2.1.1.1 Diesel PM
Diesel PM is neither a CAP nor HAP as defined by Section 112 of the CAA, and it is not a separate
pollutant in the 2011 NEI. For NATA, we generated emissions of diesel PM using PMio in the NEI from
mobile-source engine-exhaust emissions for engines burning diesel or residual-oil fuels. Diesel PM
emissions were set equal to PMio emissions for these onroad and nonroad engines. Although stationary
engines also can burn diesel fuel, only mobile-related diesel-engine SCCs were used. A list of the SCCs
for which PMio emissions were assigned to diesel PM and the corresponding NEI data category is
provided in Exhibit B-3 of Appendix B.
2.1.1.2 PAH/POM
The PAH/POM results are presented as a group. The 2011 NEI v2, however, contains dozens of specific
PAH/POM compounds with different unit risk estimates (UREs; i.e., the concentration that yields 1-in-l
million lifetime risk of cancer). For NATA, we grouped the individual PAH/POM compounds for
modeling in CMAQ and HEM-3 based on the URE. These groups are shown in Exhibit 6 along with the
specific NEI compounds (note: CAS = Chemical Abstracts Service). In NATA, the risk results are
provided in the most aggregated form: PAHPOM. A spreadsheet version of this crosswalk is available in
the SupplementalData folder ("NATA Pollutants AppendixB AppendixC.xlsx").
Exhibit 6. 2011 NEI v2 PAHs Grouped for CMAQ and HEM-3 Modeling based on URE
NEI Category
CMAQ/
HEM-3 PAH
Group
NEI Pollutant Code
(CAS No.)
NEI Pollutant Description
Event
Nonpoint
Nonroad
Onroad
Point
URE 1/(|jg/m3)
PAH_000E0
120127
Anthracene
~
~
~
~
~
0
PAH_000E0
129000
Pyrene
~
~
~
~
~
0
PAH_000E0
85018
Phenanthrene
~
~
~
~
~
0
PAH_176E5
86748
Carbazole
~
~
1.76E-05
PAH_176E5
218019
Chrysene
~
~
~
~
~
1.76E-05
PAH_880E5
130498292
PAH, total
~
~
~
~
0.000088
20
-------
EPA's National-scale Air Toxics Assessment
CM AO/
HEM-3 PAH
Group
NEI Pollutant Code
(CAS No.)
NEI Pollutant Description
NEI Category
URE 1/(|jg/m3)
Event
Nonpoint
Nonroad
Onroad
Point
PAH_880E5
191242
Benzo[g,h,i,]Perylene
~
~
~
~
~
0.000088
PAH_880E5
192972
Benzo[e]Pyrene
~
~
~
0.000088
PAH_880E5
195197
Benzo(c)phenanthrene
~
0.000088
PAH_880E5
198550
Perylene
~
~
~
0.000088
PAH_880E5
206440
Fluoranthene
~
~
~
~
~
0.000088
PAH_880E5
208968
Acenaphthylene
~
~
~
~
0.000088
PAH_880E5
2381217
1-Methylpyrene
~
0.000088
PAH_880E5
2422799
12-Methylbenz(a)Anthracene
~
0.000088
PAH_880E5
250
PAH/POM - Unspecified
~
~
0.000088
PAH_880E5
26914181
Methylanthracene
~
~
0.000088
PAH_880E5
65357699
Methylbenzopyrene
~
0.000088
PAH_880E5
8007452
Coal Tar
~
0.000088
PAH_880E5
832699
1-Methylphenanthrene
~
0.000088
PAH_880E5
83329
Acenaphthene
~
~
~
~
0.000088
PAH_880E5
86737
Fluorene
~
~
~
~
0.000088
PAH_880E5
90120
1-Methylnaphthalene
~
0.000088
PAH_880E5
91576
2-Methylnaphthalene
~
~
~
0.000088
PAH_880E5
91587
2-Chloronaphthalene
~
~
0.000088
PAH_176E4
193395
lndeno[1,2,3-c,d]Pyrene
~
~
~
~
~
0.000176
PAH_176E4
203123
Benzo(g,h,i)Fluoranthene
~
~
0.000176
PAH_176E4
203338
Benzo(a)Fluoranthene
~
~
~
0.000176
PAH_880E5
284
Extractable Organic Matter (EOM)
~
0.000088
PAH_176E4
205823
Benzo[j]fluoranthene
~
0.000176
PAH_176E4
205992
Benzo[b]Fluoranthene
~
~
~
~
0.000176
PAH_176E4
207089
Benzo[k]Fluoranthene
~
~
~
~
~
0.000176
PAH_176E4
224420
Dibenzo[a,j]Acridine
~
0.000176
PAH_176E4
226368
Dibenz[a,h]Acridine
~
0.000176
PAH_176E4
5522430
1-Nitropyrene
~
0.000176
PAH_176E4
56553
Benz[a]Anthracene
~
~
~
~
~
0.000176
PAH_176E4
56832736
Benzofluoranthenes
~
~
~
0.000176
PAH_176E3
192654
Dibenzo[a,e]Pyrene
~
0.00176
21
-------
EPA's National-scale Air Toxics Assessment
CMAQ/
HEM-3 PAH
Group
NEI Pollutant Code
(CAS No.)
NEI Pollutant Description
NEI Category
URE 1/(|jg/m3)
Event
Nonpoint
Nonroad
Onroad
Point
PAH_176E3
194592
7H-Dibenzo[c,g]carbazole
~
0.00176
PAH_176E3
3697243
5-Methylchrysene
~
~
0.00176
PAH_176E3
41637905
Methylchrysene
~
0.00176
PAH_176E3
50328
Benzo[a]Pyrene
~
~
~
~
~
0.00176
PAH_192E3
53703
Dibenzo[a,h]Anthracene
~
~
~
~
0.00192
PAH_101E2
56495
3-Methylcholanthrene
~
~
0.01008
PAH_176E2
189559
Dibenzo[a,i]Pyrene
~
0.0176
PAH_176E2
189640
Dibenzo[a,h]Pyrene
~
0.0176
PAH_176E2
191300
Dibenzo[a,l]Pyrene
~
0.0176
PAH_114E1
57976
7,12-Dimethylbenz[a]Anthracene
~
~
0.1136
PAH_880E5
779022
9-Methyl Anthracene
~
0.000088
2.1.1.3 Metals
Metal emissions in the 2011 NEI
represent only the mass of the metal or
cyanide with a few exceptions for
specific compounds of hexavalent
chromium (chromium VI) and nickel
of known composition (Exhibit 7). This
is different from previous NATAs
whereby the NEI included hundreds of specific metal compound (e.g., arsenic oxide). In such previous
NATAs, after grouping or renaming the air toxics, emissions reported in NEI for each metal compound of
known composition are adjusted so that the emissions rate used for NATA modeling corresponds to the
mass of the elemental metal (or cyanide) only, and not the entire mass of the metal compound (see the
adjacent text box for an example calculation). Thus, for the 2011 NEI these types of adjustments were
made by S/L/T before submitting the emissions.
The three nickel compounds and three chromium VI compounds in the 2011 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. Although the different
nickel compounds have different UREs, they are combined in CMAQ and hence a single URE needs to be
used. We chose the URE for nickel subsulfide. The vast majority of nickel in the NEI is ""nickel"
(pollutant code 7440020).
While hydrogen cyanide and cyanide may both be reported to the NEI, neither is adjusted in this way.
Calcium cyanamide is adjusted to cyanide to allow the cyanide URE to be applied to only the cyanide
portion of calcium cyanamide.
Example: Adjusting Emissions for Chromium VI Compounds
Chromic Acid (VI) (hhCrCM) 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 NATA modeling.
22
-------
EPA's National-scale Air Toxics Assessment
Exhibit 7. 2011 NEI Compounds or Compound Groups for which Emissions are Adjusted for
CMAQ and HEM-3 Modeling
NEI Category
NEI
Pollutant
Code
(CAS No.)
NEI Pollutant
Description
Event
Nonpoint
Nonroad
Onroad
Point
NATA Website Pollutant Name
Metal/CN
Speciation
Factor
18540299
Chromium (VI)
~
~
~
¦/
CHROMIUM VI (HEXAVALENT)
1
7738945
Chromic Acid (VI)
¦/
CHROMIUM VI (HEXAVALENT)
0.4406
1333820
Chromium Trioxide
¦/
CHROMIUM VI (HEXAVALENT)
0.52
7440020
Nickel
~
~
~
¦/
NICKEL COMPOUNDS
1
1313991
Nickel Oxide
¦/
NICKEL COMPOUNDS
0.7412
604
Nickel Refinery Dust
¦/
NICKEL COMPOUNDS
1
156627
Calcium cyanamide
CALCIUM CYANAMIDE
0.4406
2.1.1.4 Other HAP Pollutant Groups: Xylenes, Cresols, and Glycol Ethers
Other HAPs may be reported to the NEI as compounds but are grouped together for purposes of NATA.
These are glycol ethers, xylenes, cyanides, polychlorinated biphenyls, and cresols. The pre-grouped PAHs
are further summed into the group PAHPOM. No multiplication is used to convert any individual
compound in one of these groups; the mass is simply summed. Appendix C and a spreadsheet file
("NATAPollutantsAppendixBAppendixC.xlsx") provided in the SupplementalData folder shows the
specific NEI pollutants that constitute the groups.
2.1.2 Categorization of the NATA Emissions in the NATA Output Data
As explained on the NEI website, the 2011 NEI includes five data categories: point, nonpoint (formerly
called "stationary area"), nonroad mobile, onroad 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 category
"Fuel Comb - Comm/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.
NATA summaries are provided by NATA broad summary categories and by more detailed source groups.
The broad NATA summary categories are point, nonpoint, onroad, 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 NATA nonpoint category is not the same as the NEI nonpoint category
because the NEI nonpoint category includes CMVs and locomotives, while the NATA nonpoint category
does not. As another example, the NEI nonroad category is not the same as the NATA nonroad category
because the NATA nonroad category includes airports, CMVs, and locomotives, while the NEI Nonroad
category does not. Exhibit 8 contains comparisons between the NEI data categories and the NATA
categories. Secondary is not included in Exhibit 8 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.
23
-------
EPA's National-scale Air Toxics Assessment
Exhibit 8. Map of NEI Data Categories to NATA Categories
NEI Data Category
NATA Category (Reflecting NATA Summary Results)
Point
Pointa
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, which 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:
1. Excludes portable sources, which are not modeled in
either CMAQ or HEM-3 because no geographic
information other than the state code is included.
2. Excludes airports and railyards, which are nonroad
mobile.
The 2011 NEI v2 contains over 96,000 facilities
(excluding about 1500 portable facilities that are not
used in modeling due to a lack of geographic
coordinates or county location); About 19,800 of these
are airports.
Nonpoint
Nonpoint
Sources which individually are too small in magnitude or
too numerous to inventory as individual point sources,
and which 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, which come from
vegetation are also included but are estimated solely
through EPA models.
Same as NEI nonpoint except excludes locomotives,
CMVs, biogenic emissions, and agricultural fires
On road
On road
Estimates for mobile sources such as cars, trucks, and
buses which are estimated via EPA models (other than
in California which uses different models)—currently
MOVES2014.
Same as NEI onroad.
Nonroad
Nonroad
Estimates for nonroad equipment such as lawn and
garden equipment, and construction and recreational
equipment which are typically estimated via EPA models
(other than California which uses different models)—
currently the NONROAD model which is run through
NMIM.
Same as NEI nonroad but also including CMVs,
locomotives and aircraft engine emissions occurring
during LTOs, and the ground support equipment and
auxiliary power units associated with the aircraft
Note that emissions data for aircraft, locomotives, and
CMVs are not included in NEI nonroad starting with the
2008 NEI.
24
-------
EPA's National-scale Air Toxics Assessment
NEI Data Category
NATA Category (Reflecting NATA Summary Results)
Event
Fires
Prescribed and wildfire emissions computed as day- and
location-specific events
Wildfires, prescribed burning, and agricultural burning.
These are modeled in CMAQ but not HEM-3.
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 is inventoried at the county level and
allocated to 12-km grid cells for input into CMAQ.
Biogenic Emissions
Emissions of formaldehyde, acetaldehyde, and methanol
from vegetation (trees, plants, and soils) computed from
the Biogenic Emission Inventory System within CMAQ.
They are gridded to 12-km cells for CMAQ and are not
modeled in HEM-3.
a In results presented online for assessments for the 2002 and earlier NATA inventories, point sources were divided into
major sources and area sources and 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 the 2011 NATA, and stationary-source emissions are
referred to only as point-source or nonpoint-source emissions. Point sources in the NATA 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 Modifications to NEI Emissions Data
Although the 2011 NEI v2 is the main basis of the emissions fed into the air quality models, there were
differences between the 2011 NEI v2 and emissions used for the NATA modeling. There were also
differences between the emissions used for CMAQ and the emissions used for HEM-3 (see Section
2.1.4). Because CMAQ could not be rerun before the final NATA, all emissions changes resulting from
the 2011 NEI v2 review were corrected via adjustments to HEM-3 and their impact on risk or
adjustments to modeled concentrations and/or risks based on scaling. The resultant NATA emissions
summarized with the results or provided with the maps reflects the adjustments made to HEM-3 modeling
(except biogenics and fire emissions in the Continental United States, which were only run in CMAQ2).
The review of the 2011 NEI v2 data for NATA resulted in changes to emissions values and, for point
sources, changes to geographic coordinates and release-point parameters. Nonpoint and onroad changes
resulting from comments occurring after CMAQ modeling are provided in Exhibit 9, which also
documents changes between the HEM-3 and CMAQ emissions. Other differences that result from
differences in emissions processing (which reflect the specific role and function of the resulting
inventory within the context of the NATA risk assessment process) are more accurately described as post-
processing procedures rather than substantive changes. Examples of these types of changes include:
• For onroad emissions (CMAQ and HEM-3 used the same emissions although temporal allocation
differed somewhat), changes were due to post-processing. Differences are provided in the
spreadsheet file "201 leg NATA onroad versus NEIv2.xlsx" in the SupplementalData folder. The
most significant is that manganese was higher (23.4 TPY in NATA versus 5 TPY in the NEI)
2 Biogenic and fire emission summaries on the NATA website reflect only the emissions input into CMAQ.
25
-------
EPA's National-scale Air Toxics Assessment
because NATA includes manganese from brake and tire wear whereas the NEI does not.
Manganese from brake and tire wear was computed from speciation of PM2.5. There were also
small differences in the HAP VOCs and PAHs in California due to changes in the post-processing
approach to adjust California-submitted pollutants consistent with the MOVES2014 speciation.
While both the NEI and NATA applied these adjustments, NATA used updated temporal profiles
for California (which are included in the approach), resulting in slightly different emissions.
Finally, the NATA inventory includes a more refined set of SCCs that includes road type to
support spatial allocation of county-level emissions to finer scales.
• Emissions from the 2011 NEI for wildfires, prescribed burning, and agricultural-field burning are
not used in NATA for Alaska, Hawaii and Puerto Rico (the U.S. Virgin Islands have none in the
NEI) because these areas are not part of the CMAQ domain and these sources were only modeled
in CMAQ.
• FIPS ending in 777 (inflight lead) were removed from the nonpoint inventory.
• Nonpoint tribal data (FIPS beginning in 88) were not used in the modeling because no surrogates
are available and possible double counting would introduce uncertainty.
• For the residential wood-combustion sector, we removed emissions of 7,12-
dibenzo(a,h)anthracene due to inconsistencies in estimates of this HAP between controlled and
uncontrolled stoves (other than in the state of Minnesota in which 7,12- dibenzo(a,h)anthracene
was retained in HEM-3 but not in CMAQ).
• For the residential wood-combustion sector, HAP-outlier issues were discovered with the data
Clark County, Nevada submitted. The corrections made for the NATA emissions dropped some
key pollutants. As a result, we replaced the NEI data with a complete set of CAPs and HAPs
based on the EPA residential wood-combustion data set for Clark County.
• To minimize overstatement of fire emissions in single grid cells, we spatially allocated fires (from
the event category, modeled only in CMAQ) that were larger than 20,000 acres on a single day.
Those fires were projected as a circular area over the overlapping CMAQ grid cells. Each fire
was then allocated to the grid cells it overlapped based on the area of overlap with that grid cell.
In the emission inventories, these grid-cell ""subfires" have names that end in _a, _b, etc. These
are the identifiers of the 2011 fires that were split across grid cells:
State-County FIPS
Fire ID
56013
SF11C1791126
46065
SF11C1503125
48003
SF11C1718109
48243
SF11C1738273
48081
SF11C1742329
48415
SF11C1742358
48243
SF11C1747162
48125
SF11C1749358
48371
SF11C1750272
48353
SF11C1759082
35027
SF11C1760072
35027
SF11C1760460
32007
SF11C1774898
32013
SF11C1774993
32007
SF11C1775252
26
-------
EPA's National-scale Air Toxics Assessment
• Air toxic name conversions, placing individual air toxics into groups, and similar transcription
and phraseology conversions (e.g., for the purpose of crosswalking the identity of an emitted air
toxic to a substance with a quantitative dose-response value);
• Adjustments to emission rates of metal compounds based on the toxic-metal proportion of the
compound's molecular weight (e.g., only the mass fraction of chromic acid that consists of
chromium VI). In the emission summaries, pollutant groups entitled "Sum of " includes the
adjustments made, but emissions provided for the specific pollutants are presented pre-adjusted.
In addition, background concentrations for some air toxics are estimated for NATA based on
monitoring and other data (no national inventory for emissions from background sources exists), and the
secondary formation of a few air toxics is addressed in NATA but is not included in NEI.
2.1.4 2011 NATA Emissions: CMAQ versus HEM-3
Differences in the emissions inputs to CMAQ and HEM-3 were due to: (1) design differences in how the
models were run and (2) differences due to timing of the model runs that did not allow for NATA review
comments from the S/L/T agency reviews to be incorporated into the CMAQ modeling.
By design, there were differences in the sources of emissions used by the two models. The emissions
input into HEM-3 excluded NATA categories more appropriately addressed by CMAQ: biogenics and
three types of fires—wildfires, prescribed burning, and agricultural-field burning. Biogenic emissions
were generated by running the Biogenic Emission Inventory System (BEIS) model in SMOKE using
hourly meteorological inputs to generate hourly gridded (12 km xl2 km) emissions of several
photochemical-model species including three HAPs: formaldehyde, acetaldehyde, and methanol. This
category 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 were also run in CMAQ but not HEM-3 because CMAQ provides in-line plume rise
to higher vertical layers based on the acres burned. Agricultural burning does not use these algorithms.
We chose, however, to include agricultural burning with the other two types of fires as the spatial
resolution for these estimates is more appropriate for CMAQ than the local-scale resolution provided by
HEM-3. Also, grouping agricultural burning with the other fires enabled us to retain source attribution
from the CMAQ zero-out runs (although not between the different fire types).
Also, CMAQ and HEM-3 treated emissions from outside the domain differently. CMAQ used boundary
conditions for formaldehyde, benzene, and acetaldehyde, while other CMAQ pollutants used zero
boundary conditions. For HEM-3, a spatially uniform remote concentration was added for non-CMAQ
HAPs to all census-block centroids to account for transport and emissions coming from outside the
domain. The secondary contribution (resulting from emissions from all categories that participate in
photochemical reactions) was generated only in CMAQ, as HEM-3 does not account for secondary
transformation.
In addition to differences in the sources of emissions used for in the two models, there were also
differences in the way the emissions are processed. The processing of emissions for CMAQ and HEM-3
are described in Sections 2.2 and 2.3, respectively. Here we point out two differences in the resolutions of
spatial and temporal allocation. For CMAQ, gridded (12 km x 12 km) and vertically allocated emissions
were used for each modeled species for each hour of the year. Different temporal schemes were used for
different categories to generate the hourly emissions, including continuous-emissions-monitoring data and
hour- and pollutant-specific emissions for mobile sources, based on meteorologically dependent emission
factors. For HEM-3, spatial and temporal approaches varied for different categories. Spatially, emissions
were provided as point or HEM-3 "area" sources, where an area source could be (for example) a fugitive
27
-------
EPA's National-scale Air Toxics Assessment
area within a facility, a runway at an airport, a port or underway water shape, or a census tract. This
approach supports better resolution of air-concentration for sources with known locations (i.e., the sources
in the NEI point category). Temporally, allocation schemes varied for different source categories. A
summary of the spatial differences is provided in Exhibit 20 in Section 2.3.1.1.
For most categories, the resulting hourly emissions for HEM-3 and CMAQ used the same level of
specificity, but some differences occurred in the nonpoint, onroad, and nonroad categories for which
temporal profiles were aggregated from individual categories to a broader "HEM run group" (described in
2.3.2) and cannot vary by specific pollutant. Exhibit 21 in Section 2.3.1.2 contains descriptions of the
temporal approach for each HEM run group.
In addition to design differences, there were also emission differences resulting from the timing of the
runs. The emissions input to CMAQ did not include adjustments resulting from the review/comment
periods. Below is a list of key differences in the emissions between CMAQ and HEM-3 due to comments
during the NATA review/preview periods
Exhibit 9. Key Emission Differences between CMAQ and HEM-3 for 2011 NATA Modeling
Emissions category
CMAQ
HEM-3
Point
2011 NEI v2 with very small
updates.
Post-NATA review and NATA-preview
comments resulted in emission changes for
about 200 facilities.
Diesel PM
Diesel PM was included in Federal
Waters outside the U.S.
boundaries (i.e., Gulf of Mexico)
for C1/C2 marine but not for C3
vessels, Canada, or Mexico.
Diesel PM was included only in the United
States.
Two very small diesel PM sources in California
were dropped because they had SCCs for
railyards but not the facility type of the railyards
(facility type was used for point-source attribution
of airports and railyards).
Non-U.S. sources
Benzene, formaldehyde,
acetaldehyde, and methanol are
included from Canada, Mexico,
and offshore sources.
Not included.
Onroad: Extended-idle
emissions—California and
Maricopa County
Used emissions consistent with
2011 NEI v2/v6.2 platform.
For California—extended-idle emissions were
changed to match the California Air Resources
Board (CARB)-provided extended-idle emissions
(RPH) by county/pollutant for CAPs, and split
into extended-idle and APU emissions from v2.
For HAPs, which CARB did not provide for RPH,
we used CAP ratios to calculate HAP emissions.
For example, if RPH VOC = 5% of total 220280
(heavy-duty diesel) VOC in a given county, then
RPH benzene is set to 5% of total 220280
benzene in that county. All VOC HAPs (e.g.,
benzene, toluene) used VOC ratios. All PAHs
(e.g., fluorene, benzo[a]pyrene) used PM2.5
ratios. Pollutants with zero RPH emissions in
SMOKE-MOVES, including all metals and
dioxins/furans, were kept at zero.
Maricopa extended-idle and APU emissions
were cut in half consistent with the revised
hoteling hours computed based on revised 2011
VMT by the Maricopa Association of
Governments
28
-------
EPA's National-scale Air Toxics Assessment
Emissions category
CMAQ
HEM-3
Ethylene oxide (EO) from
EO sterilizers
Used emissions consistent with
2011 NEI v2 (this pollutant is not in
the v6.2 platform).
Removed EO sterilizer emissions submitted by
the state of Maryland, which were based on
1999 methodology (prior to EO sterilization
regulations).
Note that this change was also made in Puerto
Rico (we had carried forward older emissions in
the 2011 NEI v2, and so we removed these for
NATA).
Changes to trichloroethylene
emissions in Clark County,
Nevada
Used emissions consistent with
2011 NEI v2 (this pollutant is not in
the v6.2 platform).
Replaced Clark County-submitted emissions
with EPA estimates due to use of old EPA
methodology.
Benzene from Utah
counties—oil and gas
Used emissions consistent with
2011 NEI v2/v6.2 platform.
Replaced benzene based on data provided by
Utah (lower benzene to VOC).
Portable-fuel-container
speciation impacting
benzene and other HAPs in
NY
Used emissions consistent with
2011 NEI v2/v6.2 platform.
Replaced New York state emissions (all
counties) with the EPA estimates that account
for the benzene fuel limits from the MSAT rule.
7,12-dibenzo(a,h)anthracene
emissions in MN from
residential wood combustion
Removed from 2011 NEI v2 for
purposes of v6.2 platform
Inadvertently did not remove this pollutant from
the state of MN.
2.2 Emissions Preparation for CMAQ
EPA routinely prepares emissions for photochemical grid models through the development of 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,
and the approaches used to transform inventories for use in air quality modeling. Several platforms have
been developed for 2011 NEI emissions. For NATA, EPA developed a multipollutant emissions modeling
platform ("201 leg_nata_v6_l lg 2011\ 2"). referred to here as the "NATA CMAQ platform," to
generate the emission inputs for the version of CMAQ used for NATA (version MPv5.0.2). This version
of CMAQ includes more air toxics than any other version—approximately 50 HAPs and diesel PM.
The NATA CMAQ platform is largely the same as was prepared for the 2011 NEI v2-based modeling
platform (201 lv6.2) with respect to the modeling domain (i.e., lower 48 states, parts of Canada and
Mexico), grid resolution (12 km), and temporalization approaches. The platform differs in terms of
speciation—it adds numerous air toxics model species required by CMAQ version MPv5.0.2.
2.2.1 Emission Inventories and Approaches: CMAQ
For the purposes of preparing the air quality model-ready emissions, the 201 1NEIv2 was 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 of the SMOKE programs except the final merge (Mrggrid)
independently from the other sectors. The sectors used for the NATA CMAQ platform were the same as
used for the 2011 NEI v2 platform except that the point-source fires (prescribed and wild) were included
as a single sector (ptfire). Exhibit 10 contains descriptions of the sectors used for the SMOKE/CMAQ
modeling for NATA, and Exhibit 11 contains information on whether the sector contains HAP emissions.
29
-------
EPA's National-scale Air Toxics Assessment
Exhibit 10. Sectors Used in Emissions Modeling for the 2011 NATA CMAQ Platform
Platform Sector
and Abbreviation
2011 NEI Data
Category
Description and Resolution of the Data Input to SMOKE
EGU sector: ptegu
Point
2011 NEI v2 point-source EGUs. The 2011 NEI v2 emissions were replaced
with hourly 2011 CEMS values for NOx and SO2, where the units were
matched to the NEI. Other pollutants were scaled from 2011 NEI v2 using
CEMS heat input. Emissions for all sources not matched to CEMS data came
from 2011 NEI v2. Non-CEMS sources used daily resolution created using
average fuel/region temporal files.
Point source oil
and gas: pt_oilgas
Point
2011 NEI v2 point sources related to emissions from processes of oil and gas
production based on the following North American Industry Classification
System (NAICS) codes, though some individual facilities were moved to
ptnonipm due to conflicting facility source types.
NAICS
NAICS Description
2111
Oil and Gas Extraction
2212
Natural Gas Distribution
4862
Pipeline Transportation of Natural Gas
21111
Oil and Gas Extraction
22121
Natural Gas Distribution
48611
Pipeline Transportation of Crude Oil
48621
Pipeline Transportation of Natural Gas
211111
Crude Petroleum and Natural Gas Extraction
211112
Natural Gas Liquid Extraction
213111
Drilling Oil and Gas Wells
213112
Support Activities for Oil and Gas Operations
221210
Natural Gas Distribution
486110
Pipeline Transportation of Crude Oil
486210
Pipeline Transportation of Natural Gas
Annual resolution.
Remaining Non-
EGU point:
ptnonipm
Point
All 2011 NEI v2 point-source records not matched to the ptegu or pt_oilgas
sectors, annual resolution. Includes all emissions from aircraft and ground-
support equipment, which were inventoried at airports (point sources in the
NEI), and some railyard emissions.
Annual resolution.
Agricultural: ag
Nonpoint
NH3 emissions from NEI nonpoint livestock and fertilizer application.
County and annual resolution.
Area fugitive dust:
afdust
Nonpoint
PM10 and PM2.5 from fugitive-dust sources from the 2011 NEI v2 nonpoint
inventory, including building construction, road construction, agricultural dust,
and road dust. However, emissions from unpaved and paved road dust differ
from the NEI in that the NEI data do not have a precipitation adjustment.
Instead, the emissions modeling adjustment applies a transport fraction and a
meteorology-based (precipitation and snow/ice cover) zero-out.
County and annual resolution.
Nonpoint source
oil and gas:
np_oilgas
Nonpoint
2011 NEI v2 nonpoint sources from oil and gas-related processes.
County and annual resolution
Residential wood
combustion: rwc
Nonpoint
2011 NEI v2 NEI nonpoint sources with RWC processes.
County and annual resolution.
Agricultural fires:
agfire
Nonpoint
Agricultural burning from the 2011 NEI v2 nonpoint inventory.
County and monthly resolution.
30
-------
EPA's National-scale Air Toxics Assessment
Platform Sector
and Abbreviation
2011 NEI Data
Category
Description and Resolution of the Data Input to SMOKE
Class 1 & 2 CMV
and locomotives:
c1c2rail
Nonpoint
Locomotives and primarily category 1 (C1) and category 2 (C2) sources of
CMV emissions from the 2011 NEI v2 nonpoint inventory.
County and annual resolution.
Commercial
marine: c3marine
Nonpoint
Category 3 (C3) sources of CMV emissions from the 2011 NEI v2 nonpoint
inventory.
County and annual resolution.
Remaining
nonpoint: nonpt
Nonpoint
2011 NEI v2 nonpoint sources not otherwise included in other platform
sectors.
County and annual resolution.
Nonroad: nonroad
Nonroad
2011 NEI v2 nonroad equipment emissions developed with NMIM using
NONROAD2008 version NR08a. NMIM was used for all states except
California and Texas, which submitted their own emissions to the 2011 NEI
v2.
County and monthly resolution.
On road: onroad
Onroad
2011 emissions of gasoline and diesel vehicles from parking lots and
roadways. Includes the following modes: exhaust, extended idle, auxiliary-
power units, evaporative, permeation, refueling, and brake and tire wear. For
all states except California, based on monthly MOVES emissions tables from
MOVES2014. California emissions are based on EMFAC. MOVES-based
emissions computed for each hour and model grid cell using monthly and
annual activity data (e.g., VMT, vehicle population).
County and hourly resolution.
Onroad California:
onroad_ca_adj
Onroad
2011 emissions of gasoline and diesel vehicles from parking lots and
roadways, plus refueling, generated from SMOKE-MOVES, California only.
Non-refueling California onroad emissions were adjusted so that they match
emissions values provided by CARB, by county, by fuel type (non-diesel and
diesel), and by the following vehicle types: motorcycles, passenger cars, and
all other vehicles.
County and annual resolution.
Point source fires:
ptfire
Fires
Point-source day-specific wild and prescribed fires for 2011 computed using
SMARTFIRE2 using State inputs where available, except for Georgia- and
North Carolina-submitted emissions and corrections (scaling and zero-outs for
certain fires) for Florida and Delaware. Consistent with 2011 NEI v2.
Point and daily resolution.
Other point
sources not from
the NEI: othpt
N/A
Point sources from Canada's 2010 inventory and Mexico's 2008 INEM
inventory. Also includes annual U.S. offshore oil 2011 NEI v2 NEI point-source
emissions, and non-United States, non-Canada C3 CMV emissions.
Annual resolution.
Other nonpoint
and nonroad not
from the NEI: othar
N/A
Annual year 2010 Canada (province resolution) and year 2008 Mexico INEM
(municipio resolution) nonpoint and nonroad mobile inventories. Does not
include Canadian afdust emissions.
Annual resolution.
Other onroad
sources not from
the NEI: othon
N/A
Year 2010 Canada (province resolution) and year 2008 Mexico INEM
(municipio resolution) onroad mobile inventories.
Annual and province or municipio resolution.
Other area fugitive
dust not from the
NEI: othafdust
N/A
PM10 and PM2.5 from fugitive dust sources from Canada's 2010 inventory.
Emissions adjustments include a transport fraction and a meteorology-based
(precipitation and snow/ice cover) zero-out.
Annual and province or muncipio resolution.
31
-------
EPA's National-scale Air Toxics Assessment
Platform Sector
and Abbreviation
2011 NEI Data
Category
Description and Resolution of the Data Input to SMOKE
Biogenic: beis
N/A
Year 2011, hour-specific, grid cell-specific emissions generated from the
BEIS3.6 model; includes emissions in Canada and Mexico.
Hourly and grid-cell resolution.
Exhibit 11. Preparation of HAP Inventory for each Sector for the 2011 NATA CMAQ Platform
Platform Sector
and Abbreviation
2011 NEI Data
Category
Approach/Adjustments for HAPs
EGU sector: ptegu
Point
Used explicit HAPs from inventory. Did not speciate VOC to get primary HAP
emissions.
Point source oil
and gas: pt_oilgas
Point
Used explicit HAPs from inventory. Did not speciate VOC to get primary HAP
emissions.
Remaining Non-
EGU point:
ptnonipm
Point
Used explicit HAPs from ptegu inventory. Did not speciate VOC to get primary
HAP emissions. Generated diesel PM from PM10 emissions from diesel
ground-support equipment and rail-yard emissions.
Agricultural: ag
Nonpoint
No HAPs in this sector.
Area fugitive dust:
afdust
Nonpoint
No HAPs in this sector.
Nonpoint source
oil and gas:
np_oilgas
Nonpoint
Used explicit HAPs from inventory. Did not speciate VOC to get primary HAP
emissions.
Benzene overestimated in Utah in the 2011 NEI. Did not change CMAQ
results but adjusted HEM-3 (and therefore hybrid).
Residential wood
combustion: rwc
Nonpoint
Used explicit HAPs from inventory. Removed 7,12-dibenzo(a,h)anthracene
due to inconsistency in different types of wood stoves.
Agricultural fire:
agHre
Nonpoint
Used explicit HAPs from inventory.
Class 1 & 2 CMV
and locomotives:
c1c2rail
Nonpoint
Used explicit HAPs from inventory. Generated diesel PM from PM emissions
of residual oil and diesel-fueled CMVs and locomotives.
Commercial
marine: c3marine
Nonpoint
Used explicit HAPs from inventory. Generated diesel PM from PM emissions
of residual oil and diesel-fueled CMVs.
Remaining
nonpoint: nonpt
Nonpoint
Used explicit HAPs from inventory.
Nonroad: nonroad
Nonroad
Used explicit HAPs from inventory. Generated diesel PM from PM emissions
of diesel-fueled engine-exhaust processes. Certain SCCs have no HAPs:
national inventory does not have HAPs for liquefied petroleum gas and
compressed natural gas (2267* and 2268*) and some records in the California
inventory have VOCs but no HAPs. We did not add HAPs via speciation for
situations in which there were no HAPs in the NEI.
32
-------
EPA's National-scale Air Toxics Assessment
Platform Sector
and Abbreviation
2011 NEI Data
Category
Approach/Adjustments for HAPs
On road: onroad
Onroad
Used explicit HAPs from inventory.
MOVES integrates emissions of the following onroad-emitted species in
development of the chemical-mechanism species such that the emissions of
the explicit HAPs and chemical-mechanism species are consistent.
Pollutant ID
5
Pollutant Name
CMAQ HAP?
n
20
Benzene
Y
21
Ethanol
n
22
MTBE
n
24
1,3-Butadiene
Y
25
Formaldehyde
Y
26
Acetaldehvde
Y
27
Acrolein
Y
40
2.2.4-TrimethvlDentane
n
41
Ethvl Benzene
n
42
Hexane
n
43
Propionaldehvde
n
44
Stvrene
n
45
Toluene
Y
46
Xvlene
Y
185
Naphthalene qas
Y
Metal-HAP emissions were also included in this sector. Manganese, the only
HAP included in PM2.5 speciation, was estimated using a grams/mile emission
factor for exhaust and from speciation of PM2.5 for brake and tire wear.
Generated diesel PM from PM emissions of diesel-fueled engine-exhaust
processes.
Onroad California:
onroad ca
Onroad
HAPs were not exactly what California submitted to NEI. NEI used California's
HAPs and total PAH. In the platform, we used California's VOCs as a starting
point and speciated based on MOVES. We also speciated total PAH using
specific-PAH-to-total-PAH ratios based on MOVES.
Point source fires:
ptfire
Fires
Used explicit HAPs from the inventory. Corrected CMAQ results for two PAH
groups—PAH_176E3 was missing emissions (in some states) of
methylchrysene due to use of incorrect pollutant code 248, and PAH_880E5
was missing emissions of methylbenzopyrene due us of incorrect pollutant
code of 247.
Other point
sources not from
the NEI: othpt
N/A
One of the Canada point inventories had pre-speciated VOCs, which included
ALD2 (treated as acetaldehyde), FORM (formaldehyde), MEOH (methanol),
but not BENZENE. This ALD2/FORM was mapped to
ALD2_PRIMARY/FORM_PRIMARY in addition to itself, thus providing explicit
HAP emissions for formaldehyde and acetaldehyde. In the other Canada point
inventories and the Mexico point inventory, ALD2, FORM, ALD2_PRIMARY,
FORM_PRIMARY, BENZENE, and MEOH were all created via speciation.
Other nonpoint
and nonroad not
from the NEI: othar
N/A
No explicit HAPs. ALD2, FORM, ALD2_PRIMARY, FORM_PRIMARY, BENZENE,
and MEOH were all created via VOC speciation.
Other onroad
sources not from
the NEI: othon
N/A
No explicit HAPs. ALD2, FORM, ALD2_PRIMARY, FORM_PRIMARY, BENZENE,
and MEOH were all created via VOC speciation.
33
-------
EPA's National-scale Air Toxics Assessment
Platform Sector
and Abbreviation
2011 NEI Data
Category
Approach/Adjustments for HAPs
Other area fugitive
dust not from the
NEI: othafdust
No HAPs in this sector.
Biogenic: biog
N/A
HAPs generated by BEIS included acetaldehyde (ALD2 and
ALD2_PRIMARY), formaldehyde (FORM and FORM_PRIMARY), and
methanol (MEOH).
2.2.1.1 Point Sources
The point-source components of the platform were derived from the SMOKE-formatted FF10 files
exported from the emissions inventory system from September 2014. These data were supplemented with
ethanol-plant emissions provided by EPA's Office of Transportation and Air Quality (OTAQ); these
ethanol-plant emissions were included in 2011 NEI v2 but some had different names or EIS identifiers
that were corrected in the HEM-3 data and NATA geoplatform. Point sources were separated into three
sectors: ptegu, ptnonipm and pt_oilgas. The ptegu were separated due to the use of CEMs NOx, SO2, and
heat input data for temporalization of NOx, SO2, and other pollutants to hourly. Other pollutants,
including PM, VOCs, and HAPs, used hourly heat input. Airports were included in the ptnonipm sector.
2.2.1.2 Nonpoint Sources
The nonpoint-source components of the platform were derived from the SMOKE-formatted FF10 files
exported from EIS. Biogenic emissions were removed since that sector is estimated from the BEIS model
within SMOKE (see Section 2.2.1.6). The file was then split into rwc, clc2rail, c3marine, np_oilgas,
nonpt, afdust, ag, and agfire based on SCCs. Sectors afdust and ag had no HAPs.
The rwc sector included the SCCs shown in Exhibit 12; these are the same for the rwc modeling sector in
the NATA CMAQ platform and the "RWC" HEM run group.
Exhibit 12. SCCs for RWC
scca
SCC Level 3
SCC Level 4
2104008100
Wood
Fireplace: general
2104008210
Wood
Woodstove: fireplace inserts; non-EPA certified
2104008220
Wood
Woodstove: fireplace inserts; EPA certified; non-catalytic
2104008230
Wood
Woodstove: fireplace inserts; EPA certified; catalytic
2104008310
Wood
Woodstove: freestanding, non-EPA certified
2104008320
Wood
Woodstove: freestanding, EPA certified, non-catalytic
2104008330
Wood
Woodstove: freestanding, EPA certified, catalytic
2104008400
Wood
Woodstove: pellet-fired, general (freestanding or FP insert)
2104008420
Wood
Woodstove: pellet-fired, EPA certified (freestanding or FP insert)
2104008510
Wood
Furnace: Indoor, cordwood-fired, non-EPA certified
2104008610
Wood
Hydronic heater: outdoor
34
-------
EPA's National-scale Air Toxics Assessment
scca
SCC Level 3
SCC Level 4
2104008700
Wood
Outdoor wood burning device, NEC (fire-pits, chimenas, etc.)
2104009000
Firelog
Total: All Combustor Types
a SCC levels 1 and 2 are Stationary Source Fuel Combustion and Residential.
The rwc temporalization was based on daily temperature (for day-specific emissions); different diurnal
profiles were applied based on the SCC. More details are provided in the Technical Support document for
the 2011 NEI v2-based Platform.
The clc2rail and c3marine SCCs are shown in Exhibit 13, and the ag fire SCCs are shown in Exhibit 14.
Exhibit 13. SCCs for CMVs and Locomotive (c1c2rail and c3marine)
Sector
SCC a
SCC Level 2
SCC Level 3
SCC Level 4
c1c2rail
2280002100
Marine Vessels, Commercial
Diesel
Port emissions
c1c2rail
2280002200
Marine Vessels, Commercial
Diesel
Underway emissions
c1c2rail
2285002006
Railroad Equipment
Diesel
Line Haul Locomotives: Class I
Operations
c1c2rail
2285002007
Railroad Equipment
Diesel
Line Haul Locomotives: Class II / III
Operations
c1c2rail
2285002009
Railroad Equipment
Diesel
Line Haul Locomotives: Commuter Lines
c1c2rail
2285002008
Railroad Equipment
Diesel
Line Haul Locomotives: Passenger
Trains (Amtrak)
c1c2rail
2285002010
Railroad Equipment
Diesel
Yard Locomotives
c3marine
2280003100
Marine Vessels, Commercial
Residual
Port emissions
c3marine
2280003200
Marine Vessels, Commercial
Residual
Underway emissions
c3marine
2280004000 b
Marine Vessels, Commercial
Gasoline
Total, All Vessel Types
a SCC level 1 for all of these is Mobile Sources.
b This SCC does not have any HAPs associated with it. It was reported by only Washington State (county level, not shape
level) in the NEI.
Exhibit 14. SCCs for Agricultural-Field Burning (agfire)
SCC
Description
2801500000 a
* - whole field set on fire; Unspecified crop type and Burn Method
2801500600
* - whole field set on fire; Forest Residues Unspecified (see also 28-10-015-000)
2801500100
* - whole field set on fire; Field Crops Unspecified
2801500141
* - whole field set on fire; Field Crop is Bean (red): Headfire Burning
2801500170
* - whole field set on fire; Field Crop is Grasses: Burning Techniques Not Important
2801500181
* - whole field set on fire; Field Crop is Hay (wild): Headfire Burning
35
-------
EPA's National-scale Air Toxics Assessment
SCC
Description
2801500261
* - whole field set on fire; Field Crop is Wheat: Headfire Burning
2801500320
* - whole field set on fire; Orchard Crop is Apple
2801500330
* - whole field set on fire; Orchard Crop is Apricot
2801500350
* - whole field set on fire; Orchard Crop is Cherry
2801500390
* - whole field set on fire; Orchard Crop is Nectarine
2801500410
* - whole field set on fire; Orchard Crop is Peach
2801500420
* - whole field set on fire; Orchard Crop is Pear
2801500430
* - whole field set on fire; Orchard Crop is Prune
2801500500
* - whole field set on fire; Vine Crop Unspecified
2801500150
* - whole field set on fire; Field Crop is Corn: Burning Techniques Not Important
2801500220
* - whole field set on fire; Field Crop is Rice: Burning Techniques Not Significant
2801500250
* - whole field set on fire; Field Crop is Sugar Cane: Burning Techniques Not Significant
2801500262
* - whole field set on fire; Field Crop is Wheat: Backfire Burning
2801500300
* - whole field set on fire; Orchard Crop Unspecified
2801500440
* - whole field set on fire; Orchard Crop is Walnut
2801500450
* - whole field set on fire; Orchard Crop is Filbert (Hazelnut)
*=Miscellaneous Area Sources; Agriculture Production - Crops - as nonpoint; Agricultural Field Burning.
a This is the only SCC used for the EPA estimates; all other SCCs were used by state data submitters.
2.2.1.3 Onroad Sources
Highway-vehicle emissions data for NATA were largely based directly on the 2011 NEI and were
generated using MOVES2Q14 (EPA 2015k) using Carbon Bond 05 (CB05) speciation.
SMOKE-MOVES uses the emission factors along with activity and meteorology data to produce
hourly gridded emissions. This is explained in more detail in the Technical Support document for the
2011 NEI v2-based Platform.
For NATA, MOVES2014 was used everywhere except California. In California, we use the CARB-
submitted data mapped to the MOVES2014 SCCs (new set of SCCs for onroad), except the VOC HAPs
for which we used CARB VOCs speciated with MOVES2014-based profiles. These HAP VOCs are
benzene, 1,3-butadiene, formaldehyde, acetaldehyde, naphthalene, acrolein, ethyl benzene, 2,2,4-
trimethylpentane, hexane, propionaldehyde, styrene, toluene, xylene, and methyl-tert butyl ether. We
chose this approach to achieve consistency in speciation and to use the current MOVES2014-based
speciation data.
36
-------
EPA's National-scale Air Toxics Assessment
2.2.1.4 Nonroad Sources
The inventory estimates for nonroad engines were developed using the National Mobile Inventory Model
(NMIM; EPA 2015p) for all sources except CMVs, locomotives, and aircraft, which are in different data
categories of the NEI and are generated using different tools from NMIM. NMIM produces, in a
consistent and automated way, county-level mobile-source emissions inventories nationwide for the NEI
and for EPA rulemaking. NMIM is a consolidation of two EPA models: the Mobile Source Emission
Factor model (MOBILE; EPA 2015o) and the NONROAD (EPA 2015q) model; but only the NONROAD
portion was used for the 2011 NEI. NMIM estimates toxic emissions for nonroad sources using toxic-to-
VOC ratios for gaseous air toxics, toxic-to-PM ratios for PAHs, and mass-per-mile emission factors for
metals (EPA 2005b). MOVES2014a estimates toxic emissions from nonroad engines using updated data
(EPA 2015b); however, this version of MOVES was not available at the time modeling for NATA 2011
was done.
EPA did not use NMIM for two states. California and Texas submitted emissions data that were used in
place of EPA's NMIM run. Monthly emissions for these states were computed from the NMIM data.
These states' data were further supplemented by EPA—for Texas, we added mercury and arsenic
emissions from EPA's run of NMIM since they were missing from the Texas inventory; and, for
California, missing VOC emissions from some SCCs were added as the sum of benzene, acetaldehyde,
and formaldehyde.
The same data were used in 2011 NEI v2 as the 2011 NEI vl, other than for Delaware, California, and
Texas. Delaware submitted updated inputs used for the 2011 NEI v2 and NATA.
2.2.1.5 Fires Sources: agfire and ptfire
Agricultural burning was pulled from the nonpoint FF10 file based on SCC codes. Prescribed fires and
wildfires were already in their own sector. Corrections to ptfire emissions for two PAH groups were made
after the CMAQ model was run, and so adjustments were applied to the CMAQ model concentrations.
The ptfire corrections were needed because we dropped pollutant codes 247 (methylbenzo(a)pyrene;
assigned to PAH_880E5) and 248 (methylchrysene; assigned to PAH_176E3) from ptfire because these
were incorrect codes in the ptfire modeling file for some states and we did not map that erroneous code to
the PAH groups. The correct codes for these to HAPs are 65357699 (methylbenzo(a)pyrene) and
41637905 (methylchrysene). Delaware, Georgia, and North Carolina had correct codes and as did some
fires in Nevada, New Mexico, South Dakota, Texas, and Wyoming. Correct codes were used in the EIS.
Because other PAHs in ptfire are mapped to PAH 880E5 and PAH176E3, we corrected concentrations
for these PAH groups by multiplying the concentration by the ratio of the corrected PAH group's
emissions to the erroneous PAH group's emissions. The ratios were computed at an annual state-specific
resolution and applied to the grid cells based on an area-weighted average of state factors (for grid cells
that were in more than one state).
A zero-out run of CMAQ provided source attribution from fires. In this zero-out run, emissions from both
the agfires and ptfire sectors were set to zero.
2.2.1.6 Biogenics
Biogenic emissions were computed by running the BEIS3.60 model in SMOKE. BEIS creates gridded,
hourly, model-species emissions from vegetation and soils. It estimates CO, speciated VOC, and NO
emissions for the contiguous United States and for portions of Mexico and Canada. The speciated VOCs
included isoprene, terpene, sesquiterpene, and three HAPs: formaldehyde, acetaldehyde, and methanol.
37
-------
EPA's National-scale Air Toxics Assessment
One of the updates we made was to be able to distinguish primary formaldehyde from secondary
formaldehyde by assigning the formaldehyde to both FORM PRIMARY and FORM species. This, along
with a CMAQ zero-out run, provided a way to determine the source attribution of primary-emitted
biogenic emissions. In this zero-out run, emissions from biogenic sources were set to zero.
In the United States, primary biogenic formaldehyde constitutes 71 percent of the total emissions of
primary formaldehyde, primary biogenic acetaldehyde constitutes 86 percent of the total, and biogenic
methanol constitutes 95 percent of the total.
2.2.2 Emissions Processing Steps and Ancillary Data
The processing steps and ancillary data associated with the spatial and temporal allocation were the same
for the NATA platform as for the 2011 NEI v2 platform (used for other non-NATA modeling) and are
documented at the 2011-based Modeling Platform at EPA's Clearinghouse for Inventories & Emissions
Factors website. All sources were horizontally allocated to 12-km grid cells through either spatial
surrogates or spatial coordinates. Sources were also vertically allocated to layer 1 or distributed vertically
based on plume rise. A summary of these allocation methods is shown in Exhibit 15, and the Sections
2.2.2.1 and 2.2.2.2 expand further on this information.
Exhibit 15. Summary of Spatial and Temporal Allocation of Emissions for the 2011 NATA Platform
Platform Sector
Spatial-allocation Method
Speciation?
Temporal Resolution
Plume Rise
afdust
Surrogates
Yes
Annual
ag
Surrogates
Yes
Annual
agfire
Surrogates
Yes
monthly
beis
Pre-gridded land use
in BEIS 3.60
computed hourly
c1c2rail
Surrogates
Yes
annual
c3marine
Surrogates
Yes
annual
nonpt
Surrogates & area-to-point
Yes
annual
nonroad
Surrogates & area-to-point
Yes
monthly
np_oilgas
Surrogates
Yes
annual
onroad
Surrogates
Yes
monthly activity,
computed hourly
onroad_ca_adj
Surrogates
Yes
monthly activity,
computed hourly
rwc
Surrogates
Yes
annual
oth afdust
Surrogates
Yes
annual
othar
Surrogates
Yes
annual
oth on
Surrogates
Yes
annual
othpt
Point
Yes
annual
in-line
pt_oilgas
Point
Yes
annual
in-line
ptegu
Point
Yes
daily & hourly
in-line
ptprescfire
Point
Yes
Daily
in-line
38
-------
EPA's National-scale Air Toxics Assessment
Platform Sector
Spatial-allocation Method
Speciation?
Temporal Resolution
Plume Rise
ptwildfire
Point
Yes
Daily
in-line
ptnonipm
Point
Yes
annual
in-line
2.2.2.1 Spatial Allocation
Sectors with county-level resolution were allocated to 12-km grid cells using spatial surrogates. Where
data for a particular county were not available, a secondary or tertiary surrogate was used. Exhibit 16
contains the spatial surrogates available for the Continental United States (CONUS) domain used by
CMAQ. These surrogates were assigned to sources based on SCC. Some county-specific SCC-to-
surrogate assignments were made, as it was determined that at the census-tract level, the assignments of
certain surrogates (particularly for RWC) were improperly concentrating emissions in some urban-county
tracts. County-specific-surrogate assignments can be discerned from the first column of the cross-
reference file noted below. The following files are available in the SupplementalData folder and provide
more information on the surrogate assignments and underlying surrogate data:
• Spatial-surrogate cross-references by SCC ("SCC spatialsurrogatesxref.xlsx")
• Spatial-surrogate definitions/sources of data ("US_SpatialSurrogate_Workbook_v072115.xlsx")
• "Technical Memorandum oil and gas surrogates and modeling data 2014-2-18.pdf' which
describes the development of the oil and gas surrogates, surrogate codes 681-698
The 12-km spatial surrogate data are posted under the data for the 2011v6.2 Platform. Generally, the same
spatial surrogate data (i.e., shapefiles of surrogate activity weights) were used for HEM-3 but they were
computed separately from the underlying shapefiles or underlying raw data, as in the case of the updated
oil and gas surrogates. As discussed in Section 2.3.1.1, the HEM-3 surrogates were allocated from
counties to tracts instead of 12-km grid cells. Also, because the HEM-3 domain includes Alaska, Hawaii,
Puerto Rico, and the U.S. Virgin Islands, some of the CONUS surrogates were not available and
secondary surrogates had to be used. Furthermore, a few surrogate changes were made to HEM-3 after
CMAQ was run—in particular, assignments from SCC to surrogate were made for specific counties, and
three new county-specific surrogates were developed and used. These changes resulted from comments
received during the NATA review, which revealed that, for some situations, that the surrogates were
unreasonably concentrating emissions arbitrarily in one or two tracts in the county. Any county-specific
surrogate can be identified using the first column of the surrogates-assignment file in the
SupplementalData folder.
39
-------
EPA's National-scale Air Toxics Assessment
Exhibit 16. U.S. Surrogates Available for the 2011 Modeling Platform
Code
Surrogate Description
100
Population
110
Housing
120
Urban Population
130
Rural Population
137
Housing Change
140
Housing Change and Population
150
Residential Heating - Natural Gas
160
Residential Heating - Wood
165
0.5 Residential Heating - Wood plus 0.5
Low Intensity Residential
170
Residential Heating - Distillate Oil
180
Residential Heating - Coal
190
Residential Heating - LP Gas
200
Urban Primary Road Miles
210
Rural Primary Road Miles
220
Urban Secondary Road Miles
230
Rural Secondary Road Miles
240
Total Road Miles
250
Urban Primary plus Rural Primary
255
0.75 Total Roadway Miles plus 0.25
Population
260
Total Railroad Miles
270
Class 1 Railroad Miles
261
NTAD Total Railroad Density
271
NTAD Class 1, 2, 3 Railroad Density
280
Class 2 and 3 Railroad Miles
300
Low Intensity Residential
310
Total Agriculture
312
Orchards/Vineyards
320
Forest Land
330
Strip Mines/Quarries
340
Land
Code
Surrogate Description
520
Commercial plus Industrial plus Institutional
525
Golf Courses + Institutional +lndustrial +
Commercial
527
Single Family Residential
530
Residential - High Density
540
Retail Trade
545
Personal Repair
550
Retail Trade plus Personal Repair
555
Professional/Technical plus General
Government
560
Hospital
565
Medical Office/Clinic
570
Heavy and High Tech Industrial
575
Light and High Tech Industrial
580
Food, Drug, Chemical Industrial
585
Metals and Minerals Industrial
590
Heavy Industrial
595
Light Industrial
596
Industrial plus Institutional plus Hospitals
600
Gas Stations
650
Refineries and Tank Farms
675
Refineries and Tank Farms and Gas Stations
680
Oil & Gas Wells, IHS Energy, Inc. and USGS
681-
698
New set of Oil and Gas Surrogate Codes
consistent with Oil and Gas Toola
710
Airport Points
711
Airport Areas
720
Military Airports
800
Marine Ports
801
NEI Ports
802
NEI Shipping Lanes
807
Navigable Waterway Miles
808
Gulf Tug Zone Area
40
-------
EPA's National-scale Air Toxics Assessment
Code
Surrogate Description
Code
Surrogate Description
350
Water
810
Navigable Waterway Activity
400
Rural Land Area
812
Midwest Shipping Lanes
500
Commercial Land
850
Golf Courses
505
Industrial Land
860
Mines
510
Commercial plus Industrial
870
Wastewater Treatment Facilities
515
Commercial plus Institutional Land
880
Drycleaners
535
Residential + Commercial + Industrial +
Institutional + Government
890
Commercial Timber
a See additional information in Exhibit 17 and "Technical Memorandum oil and gas surrogates and modeling data 2014-2-
18.pdf" in the SupplementalData Folder.
Some of the surrogates listed in Exhibit 16 were not used in CMAQ and/or HEM-3. For example, the
"Airport Areas" surrogate was used in HEM-3 but not CMAQ; most airport-related emissions were in the
point inventory and did not need to be spatially allocated.3 To look at the relative importance of the
surrogates within the platform sectors, we computed the toxicity-weighted emissions for the CMAQ
HAPs for the surrogates used in CMAQ (i.e., based on the SCC-to-surrogate assignments for CMAQ);
these are shown in Exhibit 17.
3 SCCs for airport aviation-gas distribution (2501080050, 2501080100, 25080201), which were in the nonpoint NEI
category, uses code 711 ("airport areas") for HEM-3 but not CMAQ. For CMAQ, SMOKE used the area-to-point
algorithm, which assigned these emissions to the geographic coordinates of airports using the ARTOPNT ancillary
file, which included the geographic coordinates of major airports and allocation fractions to assign county-level
emissions to multiple airports in the county.
41
-------
EPA's National-scale Air Toxics Assessment
Exhibit 17. Total and Toxicity-weighted Emissions of CMAQ HAPs Based on the CMAQ Surrogate Assignments
Surrogate
Code
Surrogate Description
Total CMAQ Emissions (HAP and Diesel PM):
Fraction of Sector and Total
Cancer-weighted CMAQ Emissions:
Fraction of Sector and Total
Respiratory-weighted CMAQ Emissions:
Fraction of Sector and Total
agfire
c1c2rail
c3marine
nonpt
nonroad
np_oilgas
onroad
i
o
Total
(TPY)
agfire
c1c2rail
c3marine
nonpt
nonroad
np_oilgas
onroad
i
o
Total
(TPY)
agfire
c1c2rail
c3marine
nonpt
nonroad
np_oilgas
onroad
i
o
Total
(TPY)
100
Population
0.99
0.01
216,700
0.92
0.08
18,074
0.57
0.43
22,562
140
Housing Change and
Population
0.11
0.89
60,448
0.07
0.93
16,629
0.00
1.00
170,238
150
Residential Heating -
Natural Gas
1.00
591
1.00
2,960
1.00
1,543
165
0.5 Residential Heating
- Wood plus 0.5 Low
Intensity Residential
1.00
58,529
1.00
263,784
1.00
107,990
170
Residential Heating -
Distillate Oil
1.00
99
1.00
5,490
1.00
315
180
Residential Heating -
Coal
1.00
148
1.00
17
1.00
533
190
Residential Heating -
LP Gas
1.00
49
1.00
248
1.00
173
200
Urban Primary Road
Miles
1.00
59,697
1.00
63,561
1.00
123,133
205
Extended Idle
Locations
1.00
21,888
1.00
59,215
1.00
70,100
210
Rural Primary Road
Miles
1.00
37,604
1.00
34,492
1.00
93,523
221
Urban Unrestricted
Roads
1.00
133,013
1.00
138,931
1.00
211,287
231
Rural Unrestricted
Roads
1.00
81,752
1.00
83,453
1.00
153,654
240
Total Road Miles
1.00
2,557
1.00
261
1.00
133
250
Urban Primary plus
Rural Primary
1.00
2,306
1.00
942
1.00
1
256
Off-Network Short-
Haul Trucks
1.00
3,651
1.00
5,057
1.00
2,841
257
Off-Network Long-
Haul Trucks
1.00
317
1.00
817
1.00
686
258
Intercity Bus Terminals
1.00
7
1.00
22
1.00
20
259
Transit Bus Terminals
1.00
24
1.00
70
1.00
63
260
Total Railroad Miles
1.00
58
1.00
24
0.00
0
261
NTAD Total Railroad
Density
0.59
0.41
916
0.82
0.18
556
0.60
0.40
3,083
42
-------
EPA's National-scale Air Toxics Assessment
Surrogate
Code
Surrogate Description
Total CMAQ Emissions (HAP and Diesel PM):
Fraction of Sector and Total
Cancer-weighted CMAQ Emissions:
Fraction of Sector and Total
Respiratory-weighted CMAQ Emissions:
Fraction of Sector and Total
agfire
c1c2rail
c3marine
nonpt
nonroad
np_oilgas
onroad
rwc
Total
(TPY)
agfire
c1c2rail
c3marine
nonpt
nonroad
np_oilgas
onroad
rwc
Total
(TPY)
agfire
c1c2rail
c3marine
nonpt
nonroad
np_oilgas
onroad
rwc
Total
(TPY)
271
NTAD Class 1 2 3
Railroad Density
1.00
28,137
1.00
15,144
1.00
98,881
280
Class 2 and 3 Railroad
Miles
1.00
1,155
1.00
541
1.00
4,074
300
Low Intensity
Residential
0.31
0.65
0.03
36,870
0.53
0.39
0.08
71,144
0.74
0.13
0.12
21,011
310
Total Agriculture
0.53
0.02
0.46
101,453
0.93
0.01
0.06
226,969
0.48
0.02
0.50
305,616
312
OrchardsA/ineyards
0.97
0.03
539
0.61
0.39
3,502
1.00
0.00
1,424
320
Forest Land
0.99
0.01
97
1.00
0.00
391
0.99
0.01
264
330
Strip Mines/Quarries
1.00
9
1.00
302
1.00
6
340
Land
1.00
643
1.00
641
1.00
759
350
Water
1.00
87,373
1.00
53,031
1.00
8,205
400
Rural Land Area
0
1.00
0
119,240
0.00
1.00
0.00
43,368
0.00
1.00
0.00
7,442
500
Commercial Land
1.00
4,292
1.00
14,162
1.00
7,080
505
Industrial Land
0.74
0.26
21,496
0.84
0.16
15,594
0.35
0.65
25,782
506
Education
1.00
229
1.00
424
1.00
301
507
Heavy Light
Construction Industrial
Land
1.00
21
1.00
40
1.00
30
510
Commercial plus
Industrial
0.11
0.31
0.59
82,646
0.02
0.30
0.67
86,727
0.17
0.49
0.34
61,363
515
Commercial plus
Institutional Land
1.00
1,144
1.00
9,169
1.00
2,249
520
Commercial plus
Industrial plus
Institutional
0.04
0.96
44,805
0.00
1.00
42,735
0.00
1.00
13,253
526
Residential - Non-
Institutional
1.00
473
1.00
352
1.00
128
527
Single Family
Residential
1.00
9,681
1.00
2,103
1.00
1
535
Residential +
Commercial +
Industrial + Institutional
+ Government
0.13
0.87
345,828
0.00
1.00
319,891
0.05
0.95
112,380
540
Retail Trade (COM1)
1.00
48
1.00
15
1.00
29
545
Personal Repair
(COM3)
1.00
11,879
1.00
1,019
1.00
780
43
-------
EPA's National-scale Air Toxics Assessment
Surrogate
Code
Surrogate Description
Total CMAQ Emissions (HAP and Diesel PM):
Fraction of Sector and Total
Cancer-weighted CMAQ Emissions:
Fraction of Sector and Total
Respiratory-weighted CMAQ Emissions:
Fraction of Sector and Total
agfire
c1c2rail
c3marine
nonpt
nonroad
npoilgas
onroad
i
o
Total
(TPY)
agfire
c1c2rail
c3marine
nonpt
nonroad
np_oilgas
onroad
i
o
Total
(TPY)
agfire
c1c2rail
c3marine
nonpt
nonroad
np_oilgas
onroad
i
o
Total
(TPY)
555
Professional/Technical
(COM4) plus General
Government (GOV1)
1.00
90
1.00
67
1.00
63
560
Hospital (COM6)
1.00
10
1.00
337
0.00
0
575
Light and High Tech
Industrial (IND2 +
IND5)
1.00
239
1.00
28
1.00
50
580
Food, Drug, Chemical
Industrial (IND3)
1.00
554
1.00
1,777
1.00
490
585
Metals and Minerals
Industrial (IND4)
1.00
10
1.00
4
1.00
1
590
Heavy Industrial (IND1)
1.00
25,345
1.00
22,024
1.00
291
595
Light Industrial (IND2)
1.00
21,569
1.00
885
1.00
987
596
Industrial plus
Institutional plus
Hospitals
1.00
0
0.00
0
0
0
600
Gas Stations
0.23
0.77
51,562
0.84
0.16
10,963
1.00
0.00
5
650
Refineries and Tank
Farms
1.00
3,087
1.00
1,660
1.00
1
675
Refineries and Tank
Farms and Gas
Stations
1.00
16
1.00
9
0
0
680
Oil and Gas Wells
1.00
0
1.00
1
1.00
0
681
Spud count - Oil Wells
1.00
14
1.00
24
0
0
682
Spud count -
Horizontally-drilled
wells
1.00
70
1.00
202
1.00
201
683
Produced Water at all
wells
1.00
107
1.00
202
0.00
0
684
Completions at Gas
and CBM Wells
1.00
212
1.00
597
1.00
256
685
Completions at Oil
Wells
1.00
309
1.00
835
1.00
328
686
Completions at all wells
1.00
1,053
1.00
2,434
1.00
1,321
687
Feet drilled at all wells
1.00
627
1.00
1,935
1.00
1,747
688
Spud count - Gas and
CBM Wells
1.00
35
1.00
42
0.00
0
44
-------
EPA's National-scale Air Toxics Assessment
Surrogate
Code
Surrogate Description
Total CMAQ Emissions (HAP and Diesel PM):
Fraction of Sector and Total
Cancer-weighted CMAQ Emissions:
Fraction of Sector and Total
Respiratory-weighted CMAQ Emissions:
Fraction of Sector and Total
agfire
c1c2rail
c3marine
nonpt
nonroad
np_oilgas
onroad
i
o
Total
(TPY)
agfire
c1c2rail
c3marine
nonpt
nonroad
np_oilgas
onroad
i
o
Total
(TPY)
agfire
c1c2rail
c3marine
nonpt
nonroad
np_oilgas
onroad
i
o
Total
(TPY)
689
Gas production at all
wells
1.00
36,433
1.00
31,775
1.00
1,131
692
Spud count - all wells
1.00
254
1.00
709
1.00
707
693
Well count - all wells
1.00
835
1.00
1,895
1.00
663
694
Oil production at oil
wells
1.00
8,720
1.00
15,982
1.00
1,648
695
Well count - oil wells
1.00
3,518
1.00
8,951
1.00
4,431
697
Oil production at gas
and CBM wells
1.00
4,666
1.00
5,980
1.00
1,042
698
Well count - gas and
CBM wells
1.00
13,023
1.00
40,438
1.00
28,683
700
Airport area
1.00
54
1.00
72
1.00
3
801
Port Areas
1.00
44
1.00
31
1.00
3
806
Offshore Shipping
NEI2011 NOx
0.76
0.24
25,173
0.56
0.44
22,225
0.76
0.24
89,752
820
Ports NEI2011 NOx
0.34
0.66
6,620
0.08
0.92
17,240
0.34
0.66
23,643
850
Golf Courses
1.00
1,135
1.00
1,674
1.00
200
860
Mines
1.00
414
1.00
104
1.00
1,388
870
Wastewater Treatment
Facilities
1.00
692
1.00
357
1.00
257
880
Drycleaners
1.00
8,300
1.00
830
1.00
0
890
Commercial Timber
1.00
2,263
1.00
1,313
1.00
3,233
Notes: Point sources including fires do not need to be spatially allocated using surrogates so are excluded from this table. Shading in the "Total" columns corresponds to the emission
value, with warmer colors corresponding to higher emissions.
45
-------
EPA's National-scale Air Toxics Assessment
2.2.2.2 Speciation
Speciation converts the inventory species to the species needed by the model (model species). To generate
the appropriate model species from the inventory species, inventory species need to be speciated and/or
aggregated. These are done as a part of Smkinven and Spcmat SMOKE modules.
During the reading of the inventory, Smkinven uses the inventory table (ancillary file input to SMOKE)
to subset the pollutants to only those that will be used in CMAQ. In addition, the inventory table provides
the metal conversion factor for inventory compounds that contain additional non-metal components, to
adjust the mass emissions to just the metal portion. Most metal-compound emissions in the NEI
correspond to only the metal portion, so only a limited number of compounds require the metal
conversion factor. Starting with the 2008 NEI and continuing for the 2011 NEI, most metal compounds
(i.e., selenium oxide, lead nitrate) were retired (i.e., no longer allowed to be reported) and S/L/Ts were
provided a set of factors to convert to just the metal portion. The few compounds reported for the 2011
NEI for which the SMOKE inventory table applies a metal conversion factor are nickel oxide (factor =
0.74 1 24), chromium trioxide (factor = 0.52), chromic acid (VI) (factor = 0.4406).
Also during the reading of the inventory, SMOKE computes NONHAPVOC by subtracting benzene,
acetaldehyde, formaldehyde, and methanol (BAFM) from VOC for sources within sectors in which
integration of CAP and HAP will occur (other than onroad since speciation is done within the
MOVES2014 model). Integration allows the emissions of BAFM in the inventory to be used directly for
speciation in place of the speciation fractions provided in the speciation profiles. This in turn provides
consistency between the inventory and the CMAQ-model species for these explicit CMAQ HAPs. To
implement HAP integration, the speciation profiles were revised to remove BAFM and were
renormalized. The resultant non-BAFM profiles were renamed NONHAPTOG profiles. (TOG is total
organic gases, and is VOC plus species of nonreactive organic gases such as methane.)
The remainder of the conversion of inventory species to model species occurs within Spcmat. Spcmat
converts VOC and NONHAPVOC into model species using TOG and NONHAPTOG speciation profiles
based on the inventory SCC. Spmat converts PM2 5 into model species using PM speciation profiles also
based on the SCC. Most inventory HAPs are mapped to themselves, except metals are speciated into
coarse and fine particulates, and xylenes (mixed isomers) are speciated into m-, o- and p-xylenes. In
addition, mercury is speciated into elemental, divalent, and particulate forms. We used unit-specific (i.e.,
specific to particular boiler unit at a power plant) profiles to speciate mercury from electricity generating
units (EGUs) based on EGU speciation data. For new units since 2005, profiles were mapped to units
based on the fuel, configuration, and technology. Profiles for other mercury-emitting categories are based
on SCC, and were the same as those used in the 2005 platform (for mercury). Speciation profiles for
mercury and other HAPs are in Appendix D.
Speciation with HAP integration is further described in detail in the 201 lv6.2 platform documentation.
Sources in nonpoint and nonroad data categories have partial integration. Sources need to have at least
one BAFM compound to be integrated. For example, nonroad sources fueled with liquefied petroleum gas
or compressed natural gas were not integrated because NMIM does not estimate any BAFM.
The one key difference between the 201 lv6.2 platform and the NATA platform is the speciation of the
non-integrated U.S. sources (i.e., point, fires, and any sources within nonpoint or nonroad that are not
4 This factor should have been 0.7858 because the molecular weight for nickel is 58.6934 and for nickel oxide is
74.69. This occurred due possibly to an incorrect CAS for this compound.
46
-------
EPA's National-scale Air Toxics Assessment
integrated). For NATA, for non-integrated sources, we did not use explicit BAFM from the profiles, but
rather we used these HAPs from the inventory. In this case, we created un-normalized NONHAPTOG
profiles that remove BAFM, and we used inventory BAFM for the model species. In other platforms, the
BAFM are dropped from non-integrated sources because these species are created from the inventory
voc.
The chemical mechanism used for the 2011 NATA platform was the CB05 mechanism (Yarwood 2005).
The assignment of compounds to model species was based on a version 1 mapping of CB05 that assigned
numerous compounds to "FORM" and "ALD2" (which were meant, in CB05, to be explicit formaldehyde
and acetaldehyde). It was believed that these compounds reacted quickly in the atmosphere to form
formaldehyde and acetaldehyde. However, subsequent to the NATA platform, these assignments were
redone so that only formaldehyde and paraformaldehyde are assigned to FORM and only acetaldehyde is
assigned to ALD2.
Exhibit 18 and Exhibit 19 contain the model species provided by SMOKE. The last column indicates
whether the model species was added specifically for the NATA CMAQ Platform case. Note that some of
the model species came from directly from the inventory and others were generated via speciation of
inventory pollutants. As mentioned earlier, FORM, which is formaldehyde, was made up of inventory
formaldehyde and some NONHAPVOC species (from speciation) that were mapped to FORM but were
not explicit formaldehyde. Some inventory HAPs were mapped to multiple model species or were
duplicated. For example, inventory formaldehyde was mapped to both FORM and FORMPRIMARY,
which allowed the secondary formaldehyde to be estimated (FORM - FORM PRIMARY). Note
FORM PRIMARY was generated solely from inventory formaldehyde, and ALD2 PRIMARY was
generated solely from inventory acetaldehyde. Inventory benzene was mapped to BENZENE, PAR, and
UNR. Other than in Canada and Mexico, we did not create HAPs by speciating VOCs.
Exhibit 18. Gaseous Species Produced by SMOKE for the 2011 NATA Platform
Inventory Pollutant
Model Species
Description of Model
Species
NATA
Air
Toxic?
Added
for
CMAQ
NATA?
Cl2
CL2
Atomic gas-phase chlorine
Y
N
HCI
HCL
Hydrogen Chloride
(hydrochloric acid) gas
Y
N
CO
CO
Carbon monoxide
N
N
NOx
NO
Nitrogen oxide
N
N
N02
Nitrogen dioxide
N
N
HONO
Nitrous acid
N
N
SO2
S02
Sulfur dioxide
N
N
SULF
Sulfuric acid vapor
N
N
NHs
NH3
Ammonia
N
N
NH3_FERT
Fertilizer ammonia
N
N
Benzene
BENZENE
Benzene
Y
N
Acetaldehyde
ALD2
Acetaldehyde
Y
N
47
-------
EPA's National-scale Air Toxics Assessment
Inventory Pollutant
Model Species
Description of Model
Species
NATA
Air
Toxic?
Added
for
CMAQ
NATA?
Acetaldehyde
ALD2_PRIMARY
Acetaldehyde-primary
emissions
Y
Y
Acrolein
ACROLEIN
Acrolein
Y
N
Acrylonitrile
ACRYLONITRILE
Acrylonitrile
Y
Y
1,3-Butadiene
BUTADIENE13
1,3-Butadiene
Y
Y
Carbon tetrachloride15
CARBONTETb
Carbon tetrachloride15
Y
Y
Chloroform
CHCL3
Chloroform
Y
Y
1,4-Dichlorobenzene(p)
DICHLOROBENZENE
1,4-Dichlorobenzene(p)
Y
Y
1,3-Dichloropropene
DICHLORPROPENE
1,3-Dichloropropene
Y
Y
Ethylene dibromide (Dibromoethane)
BR2_C2_12
Ethylene dibromide
Y
Y
Ethylene dichloride (1,2-Dichloroethane)
CL2_C2_12
Ethylene dichloride
Y
Y
Ethylene oxide
ETOX
Ethylene oxide
Y
Y
Formaldehyde
FORM
Formaldehyde
Y
N
Formaldehyde
FORM_PRIMARY
Formaldehyde-primary
emissions
Y
Y
Hexamethylene-1,6-diisocyanate
HEXAMETH_DIIS
Hexamethylene-
1,6diisocyanate
Y
Y
Hydrazine
HYDRAZINE
Hydrazine
Y
Y
Maleic Anyhydride
MAL_ANYHYDRIDE
Maleic Anyhydride
Y
Y
Methanol
MEOH
Methanol
Y
Y
Methylene chloride (Dichloromethane)
CL2_ME
Methylene chloride
(Dichloromethane)
Y
Y
Naphthalene
NAPHTHALENE
Naphthalene
Y
Y
Specific PAHs assigned with URE = 0
PAH_000E0
Specific PAHs assigned with
URE = 0
Y
Y
Specific PAHs assigned with URE =
1.76E-5
PAH_176E5
Specific PAHs assigned with
URE = 1.76E-5
Y
Y
Specific PAHs assigned with URE =
8.80E-5
PAH_880E5
Specific PAHs assigned with
URE = 8.80E-5
Y
Y
Specific PAHs assigned with URE =
1.76E-4
PAH_176E4
Specific PAHs assigned with
URE = 1.76E-4
Y
Y
Specific PAHs assigned with URE =
1.76E-3
PAH_176E3
Specific PAHs assigned with
URE = 1.76E-3
Y
Y
Specific PAHs assigned with URE =
1.76E-2
PAH_176E2
Specific PAHs assigned with
URE = 1.76E-2
Y
Y
48
-------
EPA's National-scale Air Toxics Assessment
Inventory Pollutant
Model Species
Description of Model
Species
NATA
Air
Toxic?
Added
for
CMAQ
NATA?
Specific PAHs assigned with URE =
1.01E-2
PAH_101E2
Specific PAHs assigned with
URE = 1.01E-2
Y
Y
Specific PAHs assigned with URE =
1.14E-1
PAH_114E1
Specific PAHs assigned with
URE = 1.14E-1
Y
Y
Specific PAHs assigned with URE =
1.92E-3
PAH_192E3
Specific PAHs assigned with
URE = 1.92E-3
Y
Y
Propylene dichloride
(1,2Dichloropropane)
PROPDICHLORIDE
Propylene dichloride
(1,2Dichloropropane)
Y
Y
Quinoline
QUINOLINE
Quinoline
Y
Y
1,1,2,2-Tetrachloroethane
CL4_ETHANE1122
1,1,2,2-Tetrachloroethane
Y
Y
Tetrachloroethylene (Perchloroethylene)
CL4_ETHE
Tetrachloroethylene
(Perchloroethylene)
Y
Y
Toluene
TOLU
Toluene
Y
Y
2,4-Toluene diisocyanate
TOL_DIIS
2,4-Toluene diisocyanate
Y
Y
Trichloroethylene
CL3_ETHE
Trichloroethylene
Y
Y
Triethylamine
TRIETHYLAMINE
Triethylamine
Y
Y
m-xylene, xylene (mixed isomers)
MXYL
m-xylene, xylene (mixed
isomers)
Y
Y
o-xylene, xylene (mixed isomers)
OXYL
o-xylene, xylene (mixed
isomers)
Y
Y
p-xylene, xylene (mixed isomers)
PXYL
p-xylene, xylene (mixed
isomers)
Y
Y
Vinyl chloride
CL_ETHE
Vinyl chloride
Y
Y
Mercury
HGNRVA
Elemental mercury
Y
Y
HGIIGAS
Divalent gaseous mercury
Y
Y
VOC
ALD2
Acetaldehyde
N
ALDX
Propionaldehyde and higher
aldehydes
N
CH4
Methane a
N
ETH
Ethene
N
ETHA
Ethane
N
ETOH
Ethanol
N
FORM
Formaldehyde
N
IOLE
Internal olefin carbon bond
(R-C=C-R)
N
ISOP
Isoprene
N
49
-------
EPA's National-scale Air Toxics Assessment
Inventory Pollutant
Model Species
Description of Model
Species
NATA
Air
Toxic?
Added
for
CMAQ
NATA?
MEOH
Methanol
N
NVOL
Non-volatile
N
OLE
Terminal olefin carbon bond
(R-C=C)
N
PAR
Paraffin carbon bond
N
TERP
Terpenes
N
TOL
Toluene and other monoalkyl
aromatics
N
UNK
Unknown
N
UNR
Un reactive
N
XYL
Xylene and other polyalkyl
aromatics
N
VOC species from the biogenics model
that do not map to model species above
SESQ
Sesquiterpenes
N
NR
Non-reactive
N
a Technically, CH4 is not a VOC but part of TOG.
b Carbon tetrachloride CMAQ concentrations were not used; it was dropped from the hybrid.
Exhibit 19. Particulate Species Produced by SMOKE for the 2011 NATA Platform
Inventory
Pollutant
Model Species
Model species description
NATA
Air
Toxic?
Added
Special
for
NATA
Platform
PM10
PMC
Coarse PM > 2.5 microns and < 10 microns
N
N
DIESEL_PMC
Coarse PM > 2.5 microns and < 10 microns, diesel sources
Y
Y
PM2.5
PEC
Particulate elemental carbon < 2.5 microns
N
N
PN03
Particulate nitrate <2.5 microns
N
N
POC
Particulate organic carbon (carbon only) <2.5 microns
N
N
PS04
Particulate Sulfate < 2.5 microns
N
N
PAL
Particulate aluminums 2.5 microns
N
N
PC A
Particulate calcium < 2.5 microns
N
N
PCL
Particulate chloride <2.5 microns
N
N
PFE
Particulate iron < 2.5 microns
N
N
PH20
Particulate water < 2.5 microns
N
N
PK
Particulate potassium < 2.5 microns
N
N
50
-------
EPA's National-scale Air Toxics Assessment
Inventory
Pollutant
Model Species
Model species description
NATA
Air
Toxic?
Added
Special
for
NATA
Platform
PMG
Particulate magnesium < 2.5 microns
N
N
PMN
Particulate manganese < 2.5 microns
N
N
PMOTHR
Other particulate matter < 2.5 microns
N
N
PNA
Particulate sodium < 2.5 microns
N
N
PNCOM
Particulate non-carbon organic matter < 2.5 microns
N
N
PNH4
Particulate ammonium < 2.5 microns
N
N
PSI
Particulate silica <2.5 microns
N
N
PTI
Particulate titanium < 2.5 microns
N
N
DIESEL_PMEC
Particulate elemental carbon < 2.5 microns, diesel sources
Y
Y
DIESEL_PMFINE
Other particulate matter <2.5 microns, diesel sources
Y
Y
DIESEL_PMN03
Particulate nitrate <2.5 microns, diesel sources
Y
Y
DIESEL_PMOC
Particulate organic carbon (carbon only) < 2.5 microns,
diesel sources
Y
Y
DIESEL_PMS04
Particulate Sulfate < 2.5 microns, diesel sources
Y
Y
Nickel, nickel
oxide, nickel
refinery dust
NICKEL_C
Coarse particulate nickel
Y
Y
NICKEL_F
Fine particulate nickel
Y
Y
Chromium VI,
chromium
(VI) acid,
chromium
trioxide
CHROMHEX_C
Coarse particulate chromium VI
Y
Y
CHROMHEX_F
Fine particulate chromium VI
Y
Y
Chromium III
CHROMTRI_C
Coarse particulate chromium III
Y
Y
CHROMTRI_F
Fine particulate chromium III
Y
Y
Arsenic
ARSENIC_C
Coarse particulate arsenic
Y
Y
ARSENIC_F
Fine particulate arsenic
Y
Y
Beryllium
BERYLLIUM_C
Coarse particulate beryllium
Y
Y
BERYLLIUM_F
Fine particulate beryllium
Y
Y
Cadmium
CADMIUM_C
Coarse particulate cadmium
Y
Y
CADMIUM _F
Fine particulate cadmium
Y
Y
Manganese
MANGANESE_C
Coarse particulate manganese
Y
Y
MANGANESE_F
Fine particulate manganese
Y
Y
Lead
LEAD_C
Coarse particulate lead
Y
Y
LEAD_F
Fine particulate lead
Y
Y
51
-------
EPA's National-scale Air Toxics Assessment
Inventory
Pollutant
Model Species
Model species description
NATA
Air
Toxic?
Added
Special
for
NATA
Platform
Mercury
PHGI
Particulate divalent mercury
Y
Y
2.3 Emissions Preparation for HEM-3
For the 2011 NATA, we used HEM-3 to run AERMOD. HEM-3 does not perform the emission-
processing steps of temporal and spatial allocation, nor does it provide defaulted release characteristics, so
there were a number of processing steps that needed to take place.
The key processing steps for preparing emissions for HEM-3 (i.e., for AERMOD) were source
characterization temporal and spatial allocation. We developed "HEM run groups" to organize the
modeling by these processing steps. Different HEM-3 runs were made for each grouping due to spatial,
temporal, and release-parameter (height/building) differences.
It should be noted that the following emission sources were not modeled in HEM-3 (only modeled in
CMAQ): agricultural burning, prescribed fires and wildfires (which were combined as "fires"), and
biogenic emissions.
2.3.1 Overview of Differences in Emissions Processing Between CMAQ and HEM-3
Sections 2.3.1.1 and 2.3.1.2 below contain discussions on the spatial and temporal allocation used in the
HEM-3 modeling and how they differ from those used in the CMAQ modeling.
2.3.1.1 Spatial Allocation
Exhibit 20 contains the different geometries used for inputting emissions into HEM-3 depending on the
types of sources in the inventory. For CMAQ, all emissions for the 2011 NATA were gridded
horizontally at 12-km resolution, regardless of the category. For HEM-3, different sources used different
treatment. Point sources were modeled using the geographic coordinates provided in the inventory. For
some facilities, multiple release points were given different coordinates to characterize the specific release
points at a facility. The release-point-specific coordinates were used directly by HEM-3. For other
facilities, there may be multiple release points provided, but only a single latitude and longitude. In this
situation, all release points were modeled at that single location. Fugitive-release points at facilities were
also associated with geographic coordinates that were used for modeling. Airports were similar; however,
a separate dataset of runway information was assigned to NEI airports and used (details are provided in
Section 2.3.4).
Exhibit 20. Approach for Spatial Allocation—HEM-3 versus CMAQ
Category
NEI Resolution
Spatial Approach for HEM-3
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,
vertical based on plume
calculations
Airports
Point
Point—runways & 10-m-square
areas consistent with NEI
geographic coordinates
12-km grid cells
52
-------
EPA's National-scale Air Toxics Assessment
Category
NEI Resolution
Spatial Approach for HEM-3
Spatial Approach for CMAQ
Locomotives
Point (railyards)
and County/Shape
Nonpoint—tracts
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
Onroad, nonroad
equipment, and other
nonpoint
County
Census tracts
12-km grid cells
Agricultural burning and
biogenic emissions
County
Not Modeled
12-km grid cells
Fires (prescribed and
wild)
Point
Not Modeled
12-kmgrid cells, vertical based on
plume calculations
Three different approaches for spatial allocation were used to produce emission inputs for HEM-3: point,
shape, and tract, where fugitives, shapes, and tracts were treated as HEM-3 "area" sources and point
sources were run as HEM-3 point sources. Point sources were run at a specific latitude and longitude.
Area sources were run as a flux over a specific polygon. For fugitives, the polygons are generally small
(i.e., smaller than facility dimensions) and well-defined rectangular polygons based on the NEI point-
source inventory and were be modeled at the specific geographic location and dimensions provided by the
release-point parameters in the NEI. Shape-level emissions were new to NATA, introduced to the NEI in
2008. Shapes provide sub-county-resolution emissions data estimated by S/L/T or EPA, accounting for
the fact that the emissions are limited to particular areas of the county. These were available for CMVs at
ports and underway and for locomotives. For NATA, the port and underway shapes were simplified into
polygons that can be input into HEM-3. The locomotive shapes were not used due to the large number of
them; instead, they were allocated to tracts like the other county-level emissions.
For onroad, nonroad, and nonpoint data categories other than locomotives and CMVs, the NEI reported
emissions at the county level. The NEI reported locomotives and CMVs as shapes. In the 2011 NATA,
HEM-3 was run at the tract level for locomotives and sources reported at the county level, and HEM-3
was run at the NEI shapes for CMVs. Census tracts are land areas defined by the U.S. Census Bureau that
vary in size and typically contain about 4,000 residents each. Census tracts are typically smaller than 2
square miles in cities, but are much larger in rural areas. Similar to previous NATAs, we spatially
allocated the county-level emissions reported in NEI to the census tracts within the county. To make the
county-to-tract emissions assignments, we used spatial-allocation factors derived from the distributions of
various "spatial surrogates" that have geographic patterns expected to be similar to the geographic
patterns of the source of the emissions. The spatial-surrogate underlying data were the same as those used
for CMAQ except that (see Section 2.2), for areas outside the CMAQ domain (i.e., Alaska, Hawaii,
Puerto Rico and the U.S. Virgin Islands), some of the surrogates were unavailable. In such cases, a
secondary or tertiary surrogate was used.
The workbook of surrogate documentation provided in the SupplementalData folder includes the
geographic extent of the spatial surrogates. We added three county-specific county-to-tract surrogates
(Maricopa truck stops for extended idling, Maricopa golf courses, and Hawaii commercial lawn) and
made some changes to spatial-allocation assignments as a result of the S/L/T agency comments received
during the preview (which were not utilized for the CMAQ modeling).
The following data related to the census-tract surrogates can be found in the SupplementalData folder:
53
-------
EPA's National-scale Air Toxics Assessment
• Cross-references of spatial surrogates by SCC ("SCC spatialsurrogatcs_xrcf.xlsx")
• Definitions and sources of spatial-surrogate data
("US_SpatialSurrogate_Workbook_v072115. xlsx")
• Database of census-tract spatial-surrogate fractions
(""SpatialSurrogatc_CcnsusTract_FRACTIONS_finalNATA.zip")
• County-specific census-tract fractions for commercial lawn equipment and garden equipment for
Maricopa County, AZ and Honolulu, HI, and for truck stops for Maricopa County
(""countyspccific_ccnsustractsurrogatcs.zip").
Spatial-allocation factors for county-to-tract were generated by the Spatial Surrogate Tool, which was the
same tool used to generate surrogates for county-to-grid-cells. This tool had to be updated to produce
surrogates based on census tracts for NATA.
2.3.1.2 Temporal Allocation
Exhibit 21 contains descriptions of the differences between HEM-3 and CMAQ in the temporal variation
of emissions used for NATA. While CMAQ read in hourly emissions for each pollutant within a 12-km
grid cell, HEM-3 used different temporal keywords along with scalars that provided the variation in
emissions at seasonal, monthly, day-of-week, or hourly time steps. That does not mean the temporal
approach is more refined in CMAQ, as hourly emissions were not available for all sources in the
inventory and temporal profiles were used to produce the hourly data. In particular, the level of detail for
temporalization for point sources was the same in HEM-3 and CMAQ. For other categories, CMAQ had a
more refined approach because HEM-3 was not temporalized by pollutant (other than onroad, which
retained season seasonal variations at the pollutant level) and because all sources within a HEM run group
(see Section 2.3.2) used the same temporal profiles (with the exception of onroad sources, which use
county-specific, but not SCC-specific, temporalization across pollutants and sources).
Exhibit 21. Temporal-allocation Approach—HEM-3 versus CMAQ
Category
Resolution Available for
NEI Modeling a
Temporal Approach for HEM-3
Temporal
Approach for
CMAQ
Point (including
airports)
Annual
Monthly/day-of-week/hourly profiles
applied to sources consistent with
NATA CMAQ platform
Hourly by pollutant
Point—EGU
sources
Hourly emissions for NOx,
SO2 and hourly heat inputb
Hourly (HOUREMIS) by facility and unit
using emissions from CMAQ
Hourly by pollutant
RWC
Annual
Hourly (HOUREMIS) based on CMAQ
hourly emissions (summed across
SCCs) of VOCs and PM2.5
Hourly by pollutant
Nonpoint and
locomotives
Annual
U n ifo rm mo nth ly/d ay-of-wee k/h 0 u rly
profiles based on the most-used profile
across these SCCs from CMAQ
platform (HROFDY)
Hourly by pollutant
CMV
Annual
U n ifo rm mo nth ly/d ay-of-wee k/h 0 u rly
profiles based on monthly pattern from
CMAQ platform (MONTH)
Hourly by pollutant
Nonroad
Monthly
Pollutant-specific seasonal variation.
Uniform seasonal/monthly/day-of-week
profiles. Hourly profile based on the
most-used profile across these SCCs
from the CMAQ platform.
Hourly by pollutant
54
-------
EPA's National-scale Air Toxics Assessment
Category
Resolution Available for
NEI Modeling a
Temporal Approach for HEM-3
Temporal
Approach for
CMAQ
Onroad
Hourly
Pollutant-specific seasonal variation.
Monthly/weekday-Saturday-
Sunday/hourly profiles based on single
pollutant
Hourly by pollutant
Agricultural
burning
County-monthly
Not Modeled
Hourly by pollutant
Biogenic
emissions
Hourly
Not Modeled
Hourly by pollutant
Fires (prescribed
and wild)
Daily
Not Modeled
Hourly by pollutant
a NEI has annual resolution but some of the data that was used to create the NEI (or was available prior to inputting the
data to SMOKE or the process for HEM-3) was sub-annual.
b From continuous emissions-monitoring data reported to EPA's Clean Air Markets Division (EPA 2015h).
2.3.2 HEM Run Groups
Exhibit 22 contains descriptions of the HEM run groups. HEM run groups provided a way to organize the
sources that shared similar approaches to emission characterization, such as the release characteristics,
spatial allocations, and temporal allocations. HEM run groups were further divided into source groups for
purposes of providing risk results. For all but the point HEM run groups, all sources within the same
HEM run group were modeled with the same release characteristics and spatial and temporal profiles. For
the point HEM run groups, sources can have different temporal profiles for different facilities or sources
within the facility.
55
-------
EPA's National-scale Air Toxics Assessment
Exhibit 22. HEM Run Groups Based on the Nonpoint and Nonroad NEI Data Categories
HEM
Run
Group
NEI Category and
NATA CMAQ Platform
Modeling Sector
HEM-3 Modeling Features: Release Height
(RH; meters), Initial Vertical Dispersion (cz;
meters), and Spatial and Temporal Approach
Description of Sources
NP—
10 meters
NEI: nonpoint
Platform: some of
nonpt, np_oilgas
RH=10
CTZ =4.7
Spatial: tract
Temporal: uniform monthly/day-of-week/hourly
profile 26 (smooth curve with midday peak -
see Exhibit 28).
Sources: industrial processes
(e.g., chemical plants, oil and
gas, refineries, mines,
metals); solvents (industrial
surface coating;, graphic
arts); fuel combustion ICI;
bulk gas terminals; waste
disposal
RWC
NEI: nonpoint
Platform: rwc
RH = 6.4
CTZ = 3.2
Spatial: tract
Temporal: hourly by county: sum PM2.5 and
VOC by hour and county based on the 2011
NEIv6.1 platform (the 2011 NEI v1 emissions)
Sources: fireplaces,
woodstoves, hydronic
heaters used for residential
heating
NP—
OtherLow
NEI: nonpoint
Platform: part of
c1c2rail, partofnonpot
RH = 3.9
az = 3.6
Spatial: tract
Temporal: same as NP—10meters
Solvents (consumer,
commercial); nonindustrial
surface coating (architectural
coating); dry cleaning;
solvent degreasing,
commercial cooking; gas
stations (stage 1);
miscellaneous non-industrial
NEC (portable gas cans, auto
repair shops, structure fires,
and nonpoint mercury
categories such as human
cremation, dental amalgam,
residential fuel combustion
except wood, locomotives).
Note locomotive release
height based on web
searches.
Nonroad
NEI: nonroad
Platform: nonroad
RH = 2
CTz = 1
Spatial: tract
Temporal: seasonal, pollutant specific. Monthly
emissions were uniform within each season.
Diurnal use profile 26 (it was determined to
apply to 77% of risk- weighted HAP emissions
and 93% of diesel PM)
Nonroad equipment such as
lawn mowers, turf equipment,
construction equipment,
commercial generators,
power-washing equipment,
pleasure craft
56
-------
EPA's National-scale Air Toxics Assessment
HEM
Run
Group
NEI Category and
NATA CMAQ Platform
Modeling Sector
HEM-3 Modeling Features: Release Height
(RH; meters), Initial Vertical Dispersion (ctz;
meters), and Spatial and Temporal Approach
Description of Sources
CMV with
port and
underway
shapes
NEI: nonpoint
Platform: part of
c1c2rail, c3marine
RH = 8 (based on range of 5-10 m for c1/c2
and was chosen with input from OTAQ)
ctz = 7.4
Spatial: port and underway shapes in the NEI
Temporal: Uniform monthly/day-of-week/hourly
profiles based on monthly pattern from CMAQ
platform (MONTH)
c1/c2 and c3 marine vessels,
excluding: rail (locomotives)
which is in MP-other low, and
emissions in Federal Waters
(FIPS=85)
Onroad
Light
Duty
NEI: onroad
Platform: onroad,
onroad_ca
RH = 1.3
CTz = 1.2
Spatial: tract
Temporal: seasonal temporal variation is
pollutant-specific and county-specific. County-
specific seasonal/monthly/day-of-week/hourly
profiles were the same across all pollutants and
were based on benzene hourly emissions from
SMOKE-MOVES.
Passenger cars, motorcycles,
light-duty trucks, and
refueling of all sources (light-
and heavy-duty vehicles)
Onroad
Heavy
Duty
NEI: onroad
Platform: onroad,
onroad_ca
RH = 3.4
CTZ = 3.2
Spatial: tract
Temporal: same as above but the profiles were
based on hourly emissions of PM2.5
Heavy-duty diesel, heavy-
duty gas, buses
Point,
excluding
airports
NEI: point
Platform: part of
ptnonipm, ,pt_oilgas,
ptegu
RH and ctz based on stack parameters for point
sources. For area sources, RH based on
release height and cz was 0.
Spatial: point = coordinates; area = coordinates
+ dimensions
Temporal:
For ptnonimpm and pt_oilgasj. monthly, day-of-
week, diurnal based on SCC (same approach
was used in 2011 modeling platform)
For ptegu: hourly profiles based on sum of PM,
CO, and VOC emissions
Facilities in the point
inventory—for all facilities
except those with facility
source code = 100,
emissions used at facility-
unit-process-release point
level. Also excluding asphalt
plants with no geographic
coordinates (those that move
around) and facilities with no
HAP emissions.
57
-------
EPA's National-scale Air Toxics Assessment
HEM
Run
Group
NEI Category and
NATA CMAQ Platform
Modeling Sector
HEM-3 Modeling Features: Release Height
(RH; meters), Initial Vertical Dispersion (cz;
meters), and Spatial and Temporal Approach
Description of Sources
Airports
NEI: point
Platform: part of
ptnonipm
RH = 3
az = 3
Spatial:
For runway-area (line) sources: length based on
NTAD or OTAQ 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 non-runway sources. 10-m-square area
centered on NEI coordinates.
Temporal: same as used in CMAQ—
monthly/day-of-week/hourly profiles. Monthly
and hourly profiles different for commercial
versus general-aviation airports; monthly also
different for Alaska seaplanes.
Facilities in the point
inventory—all emissions
where facility source type
code = 100 (airports).
Emissions used at the facility
unit's process release point.
2.3.3 Point Excluding Airports
Point sources in the ptnonipm sector of the NATA platform were modeled differently 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 (including ptoilgas) 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 2.3.3.2. Non-EGU units were modeled
in HEM-3 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 HEM-3 run.5 This ensured that ambient impacts were
calculated for a consistent set of receptor locations for all sources at the facility.
2.3.3.1 Point: Non-EGU
Sources in the ptnonipm sector were modeled as point sources or area sources in HEM-3, with temporal
variations addressed using the variable-emission (EMISFACT) feature of HEM-3. This allows the model
to read emission scalars, which adjust the annual-average emissions based on month, day-of-week, and
hour-of-day. Different options were used, depending on how the emissions vary (hour-of-day only, hour-
of-day and day-of-week, etc.). The actual profiles applied were the same as those applied for the
SMOKE/CMAQ processing (see bullet 9d below for more details).
The following processing steps were used:
1. Temporal profiles to be used for each emission point were read from the SMOKE ptnonipm
temporal-profiles report. Key data fields were the facility ID, unit ID, release point ID, process
ID, monthly temporal profile code, daily temporal profile code, and hourly temporal profile code.
5 AERMOD temporalization is perfonned at the level of source IDs, so using different temporalization schemes at
one facility is possible.
58
-------
EPA's National-scale Air Toxics Assessment
2. The annual-emission file (SMOKE FF, ptnonipm) was read. Key fields were the facility ID, unit
ID, release point ID, process ID, the emission release type code, SCC, pollutant code, annual
emissions, latitude, longitude, stack release point parameters, and fugitive release point emission
parameters.
3. Annual emission file records with a facility source code of "100" (airports) were excluded.
Airport emission records were processed separately.
4. The annual-emission file is matched to the temporal file using a concatenation of facility ID, unit
ID, release point ID, and process ID. The monthly, daily, and hourly temporal profile codes were
then copied into the annual file.
5. The annual file was matched to a pollutant cross-reference file, which links the pollutant codes
(primarily CAS numbers) used in the NEI to the pollutant library used in HEM-3. The cross-
reference file, developed for the 2005 NATA and Risk and Technology Review (RTR; EPA
2015i) modeling efforts, was updated for the 2011 NATA relying on the valid codes in the
pollutant-code table in the EIS. HEM-3 HAP identifiers were copied to the annual-emissions file,
along with factors that will be used to adjust emissions of certain HAP compounds to reflect the
fraction of HAP content. For instance, chromic acid (ftCrO/O emissions were multiplied by a
factor of 0.4406 so that only the chromium portion of the molecule mass will be compared with
the dose-response value. In this step, different NEI HAP species may be linked to the same HEM-
3 pollutant, so that emissions will be combined in the modeling step. For example: glycol ether
species were put in the lumped pollutant category called "glycol ethers," specific polychlorinated
biphenyls were put into the category "Polychlorinated biphenyls," and hydrogen cyanide and
cyanide were lumped into "cyanides."
6. Missing source parameters were defaulted for fugitive sources (emission release type code = 01).
Length and width values less than one meter were also replaced. The following defaults were
applied:
a) If width or length was missing, the parameter was assigned a value of 10 m.
b) If width or length was less than 1 m, the parameter was set to 1 m.
c) If width, length, and height were all missing, height was set to 3.05 m, and length and
width were each set to 10 m.
d) If height was missing but length and width were populated with nonzero values, height
was left at 0.
e) If angle was missing, it was left at 0.
7. The NEI had no missing parameters for vertical stacks (i.e., emission release type code = 02). For
emission release types of 03-06 (i.e., non-fugitive releases), there may be missing parameters. In
those cases, missing vertical stack parameters were defaulted as follows:
a) Exit-gas velocity: computed from flow rate and diameter if those parameters were
available, otherwise used SCC-specific6 or global SMOKE default (4 m/s) value.
b) Exit-gas temperature: used SCC-specific or global SMOKE default 295.4 K) value.
6 The SCC defaults, which are in metric units (i.e., m, m/s, and K) are in the "PSTK" ancillary file
(pstk_20nov2006_v0.txt) which is available from the 201 lv6.2 platform ancillary data (specifically here).
59
-------
EPA's National-scale Air Toxics Assessment
c) Inside release-point diameter: used SCC-specific or global SMOKE default (0.2 m)
value.
d) Release-point height: used SCC-specific or global SMOKE default (3 m) value.
8. Within each facility, an alphanumeric source ID that was no more than eight characters in length
was assigned to each unique combination of unit ID, process ID, and emission release point ID.
The source ID sequence began with S0000001 for each facility. The linkage of source ID to
inventory IDs (i.e., unit ID, release Point ID, process ID, and emission release type code) was
archived. (Source IDs were unique within a particular facility, but not among facilities.)
9. An intermediate file was written with a separate record for each source ID and for each HEM-3
pollutant (consolidating some inventory pollutants such as glycol ethers). This was used to create
four separate files used by HEM-3, as follows:
a) Facility list options file, one record per facility. This file contained the facility ID, along
with model settings to be used in HEM-3, such as the overall radius of the modeling
domain, the cutoff distance for discrete-receptor modeling, and the specifications of the
polar grid to be used for interpolation calculations in the region beyond the discrete
modeling cutoff. Exhibit 23 contains a description of the fields in the facility list options
file.
Exhibit 23. Fields in the HEM-3 Facility List Options File
Field
Default Setting
(if field left blank)
Description
Facility ID
Alphanumeric string identifying the facility being modeled; up to 30
characters long.
Met station
Met station selected by
model as closest to the
facility
The name of the meteorological surface station to be used by HEM-3
when modeling each facility; up to 20 characters long; generally
chosen by model but you have the option of specifying.
Rural/Urban
D for default
Used to set the type of dispersion environment for HEM-3. "R"
indicates rural land use surrounding the facility; "U" indicates urban
land use; and "D" indicates the default setting under which the model
will find the nearest Census block to the facility center and determine
whether that Census block is located in an urbanized area as
designated by the 2010 Census. [Note: The default setting when
using the 2000 Census is always rural.]
Max distance
50,000 m
The outside maximum radius of the modeling domain (< 50 km).
Modeling
distance
3,000 m
The cutoff distance for individual modeling of ambient impacts at
census blocks; beyond this distance, ambient impacts were
interpolated rather than explicitly modeled. [Note: For polygon source
types, set the modeling distance > the largest distance across the
polygon.]
Radials
16
The number of radials in the polar-receptor network emanating from
the facility center.
Circles
13
The number of concentric circles in the polar receptor network,
centered on the facility center.
Overlap distance
30 m
The distance, measured from each emission source at a facility, at
and below which a source and receptor are considered to be
overlapping. Must be < 500 m.
60
-------
EPA's National-scale Air Toxics Assessment
Field
Default Setting
(if field left blank)
Description
Acute
N
Selecting "Y" directs the model to include short-term (acute)
concentration calculations and hazard predictions.
Hours
1 hour
The short-term (acute) averaging period that HEM-3 should use for
ambient concentrations. Four averaging period options are available:
1, 6, 8, and 26 hours.
Elevations
Y
Elevations of receptors were accounted for by default; selecting "N"
excludes elevations from the model run.
Multiplier
10
The acute multiplier applied to the average emission rate and used to
approximate the short-term emission rate (e.g., 10 times the rate
entered in the HAP Emissions input file). Multi-facility HEM-3
assumed that this short-term rate could occur at the same time as the
worst-case meteorological conditions, making the acute results
conservative estimates.
First ring distance
(ring 1)
Calculated by model to
be just outside the
source locations, but not
less than 100 m from
facility center
The distance to the first ring (circle) of the polar network as measured
from the facility center. You can override the default distance
calculated by multi-facility HEM-3 to fit the size and shape of the
facility properties to be modeled.
Deposition
(dep)
N
Deposition was not modeled by default. Selecting "Y" directs the
model to calculate deposition in the model run (particle, vapor, or both
as designated below). Note: if not modeling deposition, ignore the
depletion, phase, and particle and vapor deposition fields (below).
Depletion
(depl)
[depletion modeled
automatically with
deposition]
The current model automatically depletes the calculated deposition
flux from the ambient concentrations if you opt to calculate deposition;
therefore no entry necessary.
Phase
B
The default value "B" directs the model that both particles and vapor
deposition will be modeled; use "P" for particle-only deposition
modeling; use "V" for vapor-only deposition modeling. Value must be
consistent with emissions (e.g., do not use "B" if emissions are 100%
„p„ or..v»)
Particle
Deposition
(pdep)
WD for wet and dry
particle deposition
The default value "WD" directs the model to incorporate both wet and
dry deposition for particles. Use "WO" for wet only; use "DO" for dry
only; use "NO" if not modeling deposition of particles.
Particle Depletion
(pdepl)
[particle depletion
modeled automatically
with particle deposition]
The current model automatically depletes the calculated deposition
flux for particles from the ambient concentrations if you opt to
calculate particle deposition; therefore, you need not enter anything in
this column.
Vapor Deposition
(vdep)
WD
The default value '"WD" directs the model to incorporate both wet and
dry deposition for vapor pollutants; use "WO" for wet only; use "DO"
for dry only; use "NO" if not modeling deposition of vapor pollutants.
Vapor Depletion
(vdepl)
[vapor depletion
modeled automatically
with vapor deposition]
The current model automatically depletes the calculated deposition
flux for vapor pollutants from the ambient concentrations if you opt to
calculate vapor deposition; therefore, you need not enter anything in
this column.
All Receptors
Y
"Y" directs model to calculate results for all receptors by pollutant and
source. Select "N" to receive pollutant and source contributions for the
maximum-populated and maximum off-site receptors only.
User receptors
N
Select "Y" to include user receptors in a separate input file.
61
-------
EPA's National-scale Air Toxics Assessment
Field
Default Setting
(if field left blank)
Description
Building
Down wash
(bldg_dw)
N
Selecting "Y" directs the model to include building-downwash
calculations in the model run. Note: if you are modeling building
downwash, building-dimension information is required in a separate
input file.
Urban Population
None; only needed if "U"
specified in Rural/Urban
field
If you indicate "U" for urban land use (in Rural/Urban field above),
then you must provide model with the urban population size,
otherwise leave blank. Note: if you specify "U" in the Rural/Urban field
but provide no urban population value in this field, the model will re-
set your"U" to default.
FASTALL
N
FASTALL was not used by default. Selecting "Y" directs the model to
use the control option FASTALL, which conserves model run time by
simplifying dispersion algorithms.
b) Stack parameter file, with one record for each facility and source ID (as defined in step
8). This file specifies the source latitude and longitude, the source type (A = area, P =
point), and stack parameters for point sources or fugitive-release parameters for area
sources. Source types were assigned based on the emission release point type code in the
inventory, as shown in Exhibit 24. Note that the stack- and area-source parameters were
converted from English units (used in the NEI) to metric units (used in HEM-3; e.g.,
meters, meters per second, and Kelvin rather than feet, feet per second, and Fahrenheit).
Exhibit 24. HEM-3 Assignments of Emission Release Point Type
Emission Release Point Type Code
Emission Release Point Type Description
HEM-3 Source Type code
01
Fugitive
A
02
Vertical
P
03
Horizontal
P
04
Goose Neck
P
05
Vertical with Rain Cap
P
06
Downward-facing vent
P
99
Unknown
P
c) Pollutant emissions file, with one record per combination of facility ID, source ID, and
pollutant, giving annual emissions in TPY.
d) Temporal allocation file, with records for each facility ID and source ID. These records
give scalar factors that should be applied to the annual-average emission rate to compute
hourly emissions. (The scalar factors are dimensionless.) HEM-3 has a number of options
for the temporal-allocation records, depending on how the emissions vary. We checked
the SMOKE temporal profiles to see which HEM-3 keyword should be used. The
following keyword options were used for ptnonipm depending upon value of profile
codes:
62
-------
EPA's National-scale Air Toxics Assessment
• MHRDOW: emissions vary by month, day type, and hour. Scalar = (monthly
factor/average monthly factor) x (daily factor/average daily factor) x (hourly
factor/average hourly factor)
. HRDOW: emissions vary by day type and hour (monthly profile code 262,
uniform through the year). Scalar = (daily factor/average daily factor) x (hourly
factor/average hourly factor)
• HROFDAY: emissions vary by hour only (monthly profile code 262, daily
profile code 7, both uniform). Scalar = (hourly factor/average hourly factor)
• MONTH: emissions vary by month only (daily profile code 7, hourly profile
code 24, uniform). Scalar = (monthly factor/average monthly factor)
2.3.3.2 Point: EGUs
EGUs were treated similarly to the ptnonipm sector other than for temporalization (see bullet 9d from the
previous section). Ptegu sources were modeled as point sources or area sources (fugitives) in HEM-3,
with temporal variations addressed using the hourly-emission (HOUREMIS) feature of HEM-3. The same
processing steps as for ptnonipm were followed, up until step 9d. For temporalization, the following steps
were applied:
1. Hourly-emissions data for CAPs were read from 365 SMOKE hourly-emissions reports, one per
day. Each daily file contained the hourly-emission values for that day for all CAPs. This covered
a large regional data set, but only the records matching the batch filter (from step 1) were read.
The following fields were read: date, hour, data source ID, region code (FIPS), state, county,
SCC, SIC, facility ID, unit ID (char 1), release point ID (char 2), process ID (char 3), plant name,
CO tons/hour, NOx tons/hour, SO2 tons/hour, PM2 5 tons/hour, and VOC tons/hour.
2. Hours in the SMOKE hourly-emission reports were in Greenwich Mean Time (GMT). The hours
were shifted from GMT to local time using the time zone of the county.
3. A separate facility temporal file was created for each source location and stack height covered in
the hourly-emissions data files, and data from all 365 daily files were copied to this file. Sources
with the same location and stack parameters were lumped together for this algorithm. For each
hour of the year, an hourly emission scalar value was computed, as follows:
r . _ (VOC + CO + PM2.s)houri
ca arhour t ^ h
-------
EPA's National-scale Air Toxics Assessment
of the coke oven stacks modeled in 2002 and earlier NATAs had stacks shorter than 126 ft (heights
overall ranged from 10 ft to 315 ft).
2.3.4 Point: Airports
Airports were inventoried in the NEI as point sources. Their emissions were part of the ptnonipm sector
of the NATA modeling platform and were provided with other sources in that sector. They were modeled
in HEM-3 as line sources where runway endpoint data were available, and as small area sources in all
other cases. Where line sources were modeled, the line-source option in HEM-3 was used, which creates
an area source using line endpoints, source width, and other source parameters. In all cases, temporal
variations were addressed using the variable-emission (EMISFACT) feature of HEM-3. This allowed the
model to read emissions scalars, which adjust the annual-average emissions based on month, day-of-
week, and hour-of-day. Airport-emission records were extracted from the annual ptnonipm emission file
based on the facility source code. In the NEI, a facility source code of "100" indicated an airport. All
sources of emissions contained within an airport facility (i.e., aircraft, ground-support equipment,
auxiliary-power units, and any other source reported by S/L/T agencies at an airport facility) were
summed and modeled the same way.
For airports modeled as runway line sources, the NEI geographic coordinates were not used directly in the
modeling of the airport emissions, but instead they were used to ensure the runway location data were
assigned correctly to EIS facilities. The runway-location data were from two sources. One source was the
public-use-airports dataset from the 2012 National Transportation Atlas Database (NTAD), developed by
the Bureau of Transportation Statistics of the U.S. Department of Transportation.7 The NTAD is a set of
nationwide geographic databases of transportation facilities, transportation networks, and associated
infrastructure. The NTAD shapefile for public-use airports was derived using textual (non-spatial) data
from the Federal Aviation Administration (FAA) National Airspace System Resource Aeronautical Data.
The NTAD airport dataset contained data on approximately 6,600 runways at 4,600 airports. Using GIS
software, EPA's Office of Air Quality Planning and Standards (OAQPS) converted these lines to
endpoints, which were the source location inputs for the line-source option in HEM-3. The EIS IDs were
assigned to these airports by comparing identification data in the EIS to airport-facility data in FAA Form
5010 records (FAA 2015) and other FAA databases, the FAA's Emissions and Dispersion Modeling
System, state-supplied data, and online tools (e.g., Great Circle Search8). Where there was no match, we
used location coordinates to determine if airports were geographically close enough to be the same
facility. The typical runway width in the NTAD data was 50 m, and this value was used as the width for
all runways for the modeling of the NTAD runways.
The second source of runway location data used to derive line-source inputs was a dataset developed by
OTAQ. For airports not included in the NTAD dataset, OTAQ used the same FAA textual data that were
used to derive the NTAD data to create runway endpoints for the smaller airports that constitute the
OTAQ dataset. The OTAQ airport dataset contained data on approximately 9,000 airports, and in almost
all cases, there was one runway per airport. For the smaller airports, the FAA data typically included two
runway endpoints, a single endpoint with a runway length and heading, or a single location (presumed to
be the runway center) with a runway length and heading. These data were used by OTAQ to derive
runway endpoints, which were used as the source-location inputs for the line-source option in HEM-3.
The EIS IDs were assigned to these airports in the same way as the airports in the NTAD dataset. The
widths of the runways in the OTAQ dataset were smaller than those in the NTAD data, and a width of
25 m was used for all runways for the modeling of the OTAQ runways.
7 See this site.
8 See this site.
64
-------
EPA's National-scale Air Toxics Assessment
Airports without runways (e.g., heliports) or without sufficient data to generate runway endpoints were
modeled as small area sources (10-m square) with the NEI location coordinates as the southwest corner.
During the NATA review, it was discovered that the seaplane runways were often over land, so seaplanes
with runway endpoints were all re-modeled as small area sources. While the small area source may
characterize a helipad fairly well, it likely would not for a runway. However, without good data on
runways it was difficult to characterize such sources. Modeling a larger area source (or line source) could
result in the source overlapping nearby population receptors, resulting in overestimates of exposure.
Further, the emissions for the smaller airports are likely much lower. Therefore, the small area source was
used for all airports not included in the NTAD or OTAQ datasets. There were some airports in the NTAD
and OTAQ datasets with runway geographic coordinates significantly different from the coordinates in
the NEI for the same airports. Coordinate differences of 2-to-3 km were expected because airports are
generally large and the runway coordinates may be far from the single coordinate set that represented the
airport in the NEI. There were 112 airports in the NTAD and OTAQ datasets with coordinates more than
2,500 m away from the NEI coordinates, and we modeled these airports the same way we modeled
airports without runways or without sufficient data to generate runway endpoints.
The file "Airport List.xlsx", provided in the SupplementalData folder, includes all the airports modeled
and indicates which airports were modeled using runway endpoints (NTAD and OTAQ) and which were
modeled as area sources (EIS). The list of seaplanes is in "Airport List_seaplaneispoint.xlsx."
For some airports, census block receptors fell on or near the runway or within or near the small area
source. However, people do not live within the airport boundaries or close to runways. Thus, blocks that
fell either within 30 m of the geographic coordinates of the area source or within 50 m of the runway are
assigned the next-highest receptor concentration of a receptor that lies outside these distances. This
approach was the same taken for other point sources and ports.
2.3.4.1 Temporal Profiles Assigned to Airports
Special care was taken to assign realistic operating conditions to airports. For Alaska seaplanes, it was
found (Meredith Pedde, personal communication) that they operate daily other than in winter (November
thru March), when they run two days per week. Thus, we assigned the monthly profile in Exhibit 25 to all
airport facilities in Alaska with a description of "seaplane." A database of EIS facilities with facility
descriptions, "EISAirportwDescription.accdb, is provided in the SupplementalData folder.
Exhibit 25. Monthly Temporal Profile for Alaska Seaplanes (Counts and Percentages)
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sept
Oct
Nov
Dec
Sum
20
3.4%
20
3.4%
20
3.4%
70
11.9%
70
11.9%
70
11.9%
70
11.9%
70
11.9%
70
11.9%
70
11.9%
20
3.4%
20
3.4%
590
100%
We also looked at the diurnal profiles. Meredith suggested that for general aviation we use a diurnal
profile that has no activity between 10 p.m. and 6 a.m. (due no tower operation). We used this profile for
airports that were: 1) not commercial (based on list from Meredith Pedde) and 2) not heliports. A
complete list is in "general aviation airports for 6amtol0pm profile.xlsx" in the SupplementalData folder.
It was developed by taking all airports from the FF10 (facility type = 100), merging in the list of
commercial airports provided by OTAQ (Meredith Pedde), removing them, and removing seaplanes
(using the facility description file, EISAirportwDescription.accdb, discussed above).
65
-------
EPA's National-scale Air Toxics Assessment
This new profile (shown in Exhibit 26) was developed by using the diurnal profile weights (1st row in
below table), setting hours to 0 between 10 p.m. and 6 a.m. (2nd row), and then renormalizing (3rd row)
Exhibit 26. Diurnal Temporal Profile for General Aviation (Counts, Zero-outs, and Final
Percentages)
HrO
1
2
3
4
5
6
7
8
9
10
11
198
186
182
187
210
250
311
388
467
528
571
604
0
0
0
0
0
0
311
388
467
528
571
607
0%
0%
0%
0%
0%
0%
3.741%
4.667%
5.618%
6.351%
6.869%
7.266%
12
13
14
15
16
17
18
19
20
21
22
23
620
631
635
624
594
548
531
509
425
327
257
218
650
631
635
624
594
548
531
509
425
327
0
0
7.458%
7.591%
7.639%
7.506%
7.145%
6.592%
6.388%
6.123%
5.112%
3.934%
0%
0%
2.3.4.2 Lead Adjustment for Piston Aircraft
The NEI estimated that lead emissions from piston aircraft totaled 486 TPY, nationally. Of these
emissions, the NEI provides estimates of lead emitted during several modes of operation at and near
airports, which total 248 TPY. These estimates were used in NATA. The NEI also estimated in-flight lead
emissions at the state level (238 TPY) which was not included in NATA. Lead emissions near airports
included lead emitted during the climb-out and approach modes, which occur at altitudes and are not
included in NATA. To account for this, we adjusted down by 50 percent the NEI-specific emissions
estimates used in the HEM-3 modeling, 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
Exhibit 27).
Exhibit 27. 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 processing of data for each type of airport source for HEM-3 is described below.
Line Source Airports
1. Within each airport facility, an alphanumeric source ID that was no more than eight characters in
length was assigned to each unique combination of category name (an identifier of the source
type used in HEM-3, e.g., APL for airport line) and runway ID. For each facility, the source ID
sequence began with APL00001. Source IDs were unique within a particular facility, but not
among facilities.
66
-------
EPA's National-scale Air Toxics Assessment
2. For each airport facility, airport emission records were linked to the airports in the runway data
files by the facility ID, and the annual emissions were apportioned to each runway-by-runway
area.
3. Temporal profiles to be used for each emissions source were read from the SMOKE ptnonipm
temporal-profiles report. Key data fields were the facility ID, unit ID, release point ID, process
ID, monthly temporal profile code, daily temporal profile code, and hourly temporal profile code.
4. Key fields from the annual airport file were the facility ID, runway ID, unit ID, release point ID,
process ID, lat/long of the beginning of the runway, lat/long of the end of the runway, pollutant
code, and annual emissions.
5. The annual airport emission file was matched to the temporal file using a concatenation of facility
ID, unit ID, release Point ID, and process ID. The monthly, daily, and hourly temporal profile
codes were then copied into the annual file.
6. The annual airport file was matched to a pollutant cross-reference file, which linked the pollutant
codes (primarily CAS numbers) used in the NEI to the pollutant library used in HEM-3. The
cross-reference file was developed for the 2005 NATA and RTR modeling efforts, and updated
for the 2011 NATA relying on the valid codes in the pollutant-code table in the EIS. HEM-3 HAP
identifiers were copied to the annual-emissions file, along with factors, which will be used to
adjust emissions of certain HAP compounds to reflect the fraction of HAP content.
7. Line-source release parameters were assigned to each record as follows: release height = 3 m, cz
= 3 m, and runway width = 50 m for NTAD runways and 25 m for OTAQ runways.
8. An intermediate file as written with a separate record for each facility ID and source ID and for
each HEM-3 pollutant (consolidating some inventory pollutants such as glycol ethers). This was
used to create four separate files used by HEM-3, as follows:
a) Facility list file, with one record per facility. This file contained the facility ID, along
with model settings to be used in HEM-3, such as the overall radius of the modeling
domain, the cutoff distance for discrete-receptor modeling, and the specifications of the
polar grid to be used for interpolation calculations in the region beyond the discrete
modeling cutoff.
b) Stack parameter file, with one record for each facility ID and source ID. This file
specified the source type (N = line), beginning and ending lat/long of the line (runway),
and release parameters for line sources (release height, cz, and width).
c) Pollutant emissions file, with one record per combination of facility ID, source ID, and
pollutant, giving annual emissions in TPY.
d) Temporal allocation file, with one record for each facility ID and source ID. These
records gave dimensionless scalar factors, which should be applied to the annual-average
emission rate to compute hourly emissions. HEM-3 had a number of options for the
temporal allocation records, depending on how the emissions varied. We checked the
SMOKE temporal profiles to see which HEM-3 keyword should be used. The following
keyword options were used for ptnonipm:
• MHRDOW: emissions vary by month, day-type, and hour: Scalar = (monthly
factor/average monthly factor) x (daily factor/average daily factor) x (hourly
factor/average hourly factor)
67
-------
EPA's National-scale Air Toxics Assessment
. HRDOW: emissions vary by day-type and hour (monthly profile code 262,
uniform through the year). Scalar = (daily factor/average daily factor) x (hourly
factor/average hourly factor)
• HROFDAY: emissions vary by hour only (monthly profile code 262, daily
profile code 7, both uniform). Scalar = (hourly factor/average hourly factor)
• MONTH: emissions vary by month only (daily profile code 7, hourly profile
code 24, uniform). Scalar = (monthly factor/average monthly factor)
Small Area Source Airports
1. Within each airport facility, an alphanumeric source ID that was no more than eight characters in
length was assigned to each unique combination of unit ID, process ID, and emission release
point ID. The source ID sequence began with APP00001 for each facility. Source IDs were
unique within a particular facility, but not among facilities.
2. Temporal profiles to be used for each emissions source were read from the SMOKE ptnonipm
temporal-profiles report. Key data fields were the facility ID, unit ID, release point ID, process
ID, monthly temporal profile code, daily temporal profile code, and hourly temporal profile code.
3. Key fields from the annual airport file were the facility ID, unit ID, release point ID, process ID,
pollutant code, annual emissions, latitude, and longitude.
4. The annual-airport-emission file was matched to the temporal file using a concatenation of
facility ID, unit ID, release point ID, and process ID. The monthly, daily, and hourly temporal-
profile codes were then copied into the annual file.
5. The annual airport file was matched to a pollutant cross-reference file, which linked the pollutant
codes (primarily CAS numbers) used in the NEI to the pollutant library used in HEM-3. The
cross-reference file was developed for the 2005 NATA and RTR modeling efforts, and it was
updated for the 2011 NATA relying on the valid codes in the pollutant code table in the EIS.
HEM-3 HAP identifiers were copied to the annual-emissions file, along with factors, which will
be used to adjust emissions of certain HAP compounds to reflect the fraction of HAP content.
6. An intermediate file was written with a separate record for each facility ID and source ID and for
each HEM-3 pollutant (consolidating some inventory pollutants such as glycol ethers). This was
used to create four separate files used by HEM-3, as follows:
a) Facility list file, with one record per facility. This file contained the facility ID, along
with model settings to be used in HEM-3, such as the overall radius of the modeling
domain, the cutoff distance for discrete-receptor modeling, and the specifications of the
polar grid to be used for interpolation calculations in the region beyond the discrete
modeling cutoff.
b) Stack parameter file, with one record for each facility ID and source ID. This file
specified the source type (A = area), lat/long of the southwest corner of the area source,
and release parameters for area sources (length of the sides in the x and y directions,
angle, release height, cz).
c) Pollutant emissions file, with one record per combination of facility ID, source ID, and
pollutant, giving annual emissions in TPY.
68
-------
EPA's National-scale Air Toxics Assessment
d) Temporal allocation file, with one record for each facility ID and source ID. These
records gave dimensionless scalar factors, which should be applied to the annual-average
emission rate to compute hourly emissions. HEM-3 had a number of options for the
temporal-allocation records, depending on how the emissions varied. We checked the
SMOKE temporal profiles to see which HEM-3 keyword should be used. The following
keyword options were used for ptnonipm:
• MHRDOW: emissions vary by month, day-type, and hour. Scalar = (monthly
factor/average monthly factor) x (daily factor/average daily factor) x (hourly
factor/average hourly factor)
. HRDOW: emissions vary by day-type and hour (monthly profile code 262,
uniform through the year). Scalar = (daily factor/average daily factor) x (hourly
factor/average hourly factor)
• HROFDAY: emissions vary by hour only (monthly profile code 262, daily
profile code 7, both uniform). Scalar = (hourly factor/average hourly factor)
• MONTH: emissions vary by month only (daily profile code 7, hourly profile
code 24, uniform). Scalar = (monthly factor/average monthly factor)
2.3.5 Nonpoint HEM Run Groups: NP10m and NPOtherLow
The NPlOm and NPOtherLow HEM run groups constituted most of the anthropogenic sources in the NEI
nonpoint data category. Note that the nonroad source of locomotives was included in NPOtherLow. These
were run as separate run groups due to their different release characteristics. NPlOm used a 10-m release
height whereas NPOtherLow used a 3.9-m release height.
The emissions were allocated to census tracts such that the sources were modeled as tract areas, with
emissions based on the fraction of emissions assigned from the county to the tract based on the spatial
surrogate. These two HEM run groups used the same diurnal temporal profile (SMOKE profile code 26,
which was also used for nonroad sources and is shown in Exhibit 28).
Exhibit 28. Hourly Pattern of Activity for SMOKE Profile 26
7
Profile 26
_ 6
5?
>
t> 4
ro
4—
° 3
c
O
t5 2
ro
1
0
4
4
12 16 20 2
Hourofday
69
-------
EPA's National-scale Air Toxics Assessment
2.3.6 Nonpoint HEM Run Groups: CMVs
CMVs consist of two sources groups, which are also the HEM run groups: ports and underway. Both
groups were modeled with an 8-m release height, and both used a monthly temporal profile derived from
SMOKE profile 19531.
The majority of the emissions from these groups were modeled at the port or underway shapes (GIS
polygons) that they had been assigned to in the NEI. To reduce computational requirements for HEM-3,
GIS was used to smooth and sometimes split the shapes, thereby reducing the number of vertices of each
shape. For shapes that were split into multiple parts, the emissions for the shape were allocated to each
part according to area.
A small amount of county-level CMV emissions was not assigned to port or underway shapes in the NEI;
we assigned them to water-body shapes such as large lakes and rivers within the county. These water
body shapes were modeled in the same way as the port and underway shapes.
To gap-fill missing 2011 CMV emissions in the Puerto Rico municipos San Juan, Ponce, and Guaynabo,
the corresponding 2005 tract-level CMV emissions from the 2005 NATA were used. Guaynabo's 2011
emissions resulted from a change of census-tract boundaries between 2005 and 2011. Emissions from
2005 were divided up into CMV diesel, CMV residual fuel, and diesel pleasure craft, and they were
already allocated to tracts from the 2000 Census. All of the 2005 CMV emissions were assigned to the
ports source group, and the 2000 Census tracts were mapped to 2010 Census tracts by using GIS to
determine the area percentage of the 2000 tracts in the 2010 tracts. Also, 2005 CMV POM air toxics were
assigned to "PAH, total" (NEI pollutant code 130498292). The modeling results ("chai/Q") for Puerto
Rico for the NPlOm HEM run group were used to compute the CMV HEM-3 concentrations for 2011 for
the gap-filled municipos.
The HEM-3 CMV outputs (ports and underway) were checked to see if any receptor overlapped a port or
underway shape. The concentration at any overlapped receptor was assigned the concentration from the
maximum non-overlapped receptor.
2.3.7 Nonpoint HEM Run Groups: RWC
RWC was both a HEM run group and source group, allowing the tract-level risks to be apportioned out to
this group (but not to individual sources within the group). It was consistent with the RWC CMAQ
platform sector, and it included the sources listed in Exhibit 12.
The HEM-3 release height and initial vertical dispersion parameter cz were computed based on a 50/50
split of 1- and 2-story houses in a tract. Release heights were set to 50 percent of the initial plume height
from the chimneys, with plume being based on the sum of the chimney height plus plume rise. The
vertical dispersion, cz, was set equal to the plume height divided by 4.3, consistent with guidance for area
sources in the AERMOD User's Guide. Plume-rise estimates were based on a series of AERMOD
simulations for 1- and 2-story houses with different chimney-to-house orientations (chimney on long edge
of house, chimney on short edge of house, etc.) and chimney temperatures (394 and 588 K). The
dimensions of the houses are:
Number of stories
Length (m)
Width (m)
Roof height (m)
Chimney height (m)
1
15.24
9.14
6.1
7.0
2
12.2
7.3
10.1
11.0
70
-------
EPA's National-scale Air Toxics Assessment
The resulting release height and cz for the RWC emissions were 6.4 m and 3.2 m, respectively.
This HEM run group was spatially allocated from the county to the tract using the spatial surrogates 165
and 300, described below.
Surrogate ID
Surrogate
Description
Source of Data
Vintage
165
0.5 Residential
Heating-
Wood, plus 0.5
Low Intensity
Residential
Combination of 50% Residential
Heating-Wood and 50% Low Intensity
Residential
American Community
Survey (U.S. Census
Bureau) and National
Land Cover Database
2005-2010
300
Low Intensity
Residential
2006 NLCD 30-m data resampled to
250-m resolution for land-use class 22
(Developed, Low Intensity); areas with
a mixture of constructed materials and
vegetation. Impervious surfaces
account for 20% to 49% percent of total
cover. These areas most commonly
include single-family housing units
National Land Cover
Database
2006 Landsat
Imagery
All SCCs were allocated the using the same surrogate (either 300 or 165)9; the national default was 165
but for some counties it was found that surrogate 165 concentrated emissions to small tracts within urban
counties. For these counties, a county-specific surrogate of 300 was used. Additional urban counties were
assigned the 300 surrogate to HEM-3 after CMAQ was already run.
Annual emissions were allocated using an hourly profile that was created for each county based on the
sum of the hourly emissions of PM2.5 and VOCs across all RWC SCCs, based on the approach used for
the 201 lv6.1 platform (processing of the 2011 NEI vl. All SCCs for the HEM-3 runs used the same
profile, a weighted average profile based on the summed PM and VOC emissions.
Hourly emissions were estimated from (1) an annual-to-day profile based on the daily-minimum
temperature and (2) a diurnal profile putting more of the emissions in the evening and morning when
people are typically using these sources. The meteorological-based approach is documented on pages 57-
61 of this document.
This temporalization changed slightly between vl and v2 in CMAQ due to the assignment of some RWC
SCCs to a uniform diurnal profile and due to a fix in the SMOKE code that shifted the day-specific
profile (created by SMOKE based on the daily-minimum temperature) and the hourly profile (based on
the diurnal variation) to a consistent time basis (GMT). This change was not implemented for HEM-3,
however. To avoid rerunning the model, we instead applied the new platform emissions to the ""chai/Q"
values output by HEM-3 based on the vl temporalization. An example of this hourly, day-specific
temporalization for King County, WA is provided in Exhibit 29 for January-April (months 1-4) and
May-August (months 5-8).
9 We inadvertently assigned fireplaces to 165, but they should have used use 300 for all counties.
71
-------
EPA's National-scale Air Toxics Assessment
Exhibit 29. Example of RWC Temporal-scaling Factors, January(1 )-April(4) (top) and May(5)-
August(8) (bottom), for King County, Washington
1
T
n
n
(1 fl
n n
J#
Jt
Jt
Jt
1W
Jil
J Ml
J1#
ii
2
fl
J
J hp
In
JlJjlJ-
iTI
T
jt
Aj
Jt
JMl
in
i
fl
n fi
3
fl \
fl „ n
n
T
Jt
Jt
Jsr
A
Jt
Jsr
Jt
Jt
JW
lW
J
4
> S,
J t
Jt
Jt
Jt
jy
\ST
Jt
s ^
x L
At
JW
j My
u U
il tJ
y IP
400
hour_in_rnonth
\l
MM
lAJ
nJ
i
i
M
"•J
M
\j
^ Wjv v ^ I ^
ifi
k
w
\
M
\_rUk_j^
juj
M
\ Ik.
VJk
\
400
hour_in_rnonth
2.3.8 Nonroad HEM Run Group
Nonroad emissions were ran as a single HEM ran group. The sources in this group were the same as the
sources in the nonroad data sector in the NEI and the nonroad platform sector. For temporalization, we
72
-------
EPA's National-scale Air Toxics Assessment
used diurnal profile 26 (see Exhibit 28) along with monthly factors based on toxicity-weighted emissions
from the FF10 nonroad file.
2.3.9 Onroad HEM Run Groups: Light Duty and Heavy Duty
The onroad emissions included light- and heavy-duty-vehicle emissions for on- and off-network and
refueling, consistent with the onroad emissions used for CMAQ. The emissions data came from a
MOVES2014 and SMOKE-MOVES run done for the NATA platform for CMAQ. As part of that run, an
FF10 dataset of monthly and annual emissions was produced with all CAPs and HAPs, and sufficient
SCC specificity to support spatial allocation using the same surrogates as used for the NATA platform for
CMAQ. Diesel PM was taken as all-PMio from the diesel-exhaust-related SCCs (see Exhibit B-3 of
Appendix B).
For the HEM-3 runs, onroad emissions were temporalized separately for light- and heavy-duty vehicles to
account for monthly, day-of-week, and hourly variations using the HEM-3 MHRDOW (monthly/day-of-
week/hourly) option. Although seasonal variations were specific to each pollutant and profiles also varied
by county, season-to-month, day-of-week, and hourly variations were the same for all pollutants within
the HEM run group.
All refueling (from light or heavy) was put into light-duty vehicles because refueling is a source group
and we did not want to have source groups that were in multiple HEM run groups. In addition, heavy-
duty refueling was very small compared to other onroad sources. Thus, all pollutants received the same
monthly, day-of-week, and hourly temporal variations. However, we split the runs into four seasons (i.e.,
December-February, March-May, June-August, and September-November) to allow pollutant-specific
seasonal variation at the seasonal level.
MHRDOW factors were created from both the FF10 file (for the monthly factor only) and a specially
created file of county/SCC/hour totals (in tons/hour) generated from the SMOKE-MOVES model
(running MOVES2014) based on the NATA platform. The hourly emissions data were created for one
week per month and converted to local time (taking into account daylight saving where appropriate). For
each month, the period was late evening on the 21st through late evening on the 28th, local time. (In
GMT, the timeframe covers the 22nd 0:00 through 28th 23:00.)
Benzene was used for light duty and PM2 5 for heavy duty. Emissions are aggregated to the HEM run
groups via a crosswalk of SCCs to HEM run group (see Appendix D or the spreadsheet file
"NP_NR_OR_SourceGroup_to_SCC_CrossReference.xlsx" in the SupplementalData folder). The
following calculations were made to develop the scalar inputs to HEM-3, using keyword MHRDOW to
provide county-specific, monthly/day-of-week/hourly scalars.
^ monthly factor daily factor hourly factor
average monthly factor average daily factor average hourly factor
= (12 x monthly factor) x (3 x daily factor) x (24 x hourly factor)
Where the factors were computed as follows:
Monthly factors computed as:
monthly emissions
monthly factor =
annual emissions
73
-------
EPA's National-scale Air Toxics Assessment
Daily factors computed as:
weekday factor =
Saturday factor =
Sunday factor =¦
average of weekday emissions for all 12 months
sum of 7-day emissions for all 12 months
average of Saturday emissions for all 12 months
sum of 7-day emissions for all 12 months
average of Sunday emissions for all 12 months
sum of 7-day emissions for all 12 months
Hourly factors computed as:
weekday hour-1 factor =
Saturday hour-1 factor =
Sunday hour-1 factor =
(etc.)
sum of hour-1 for all weekdays
sum of 24-hour emissions for all weekdays
sum of hour-1 for all Saturdays
sum of 24-hour emissions for all Saturdays
sum of hour-1 for all Sundays
sum of 24-hour emissions for all Sundays
Exhibit 30 contains plots of temporal scalars for three counties, for January and August and a weekday
versus a Saturday.
Exhibit 30. Example of Temporal Scalars by Hour-of-day for Onroad HEM Run Groups
Light Duty (Weekday-left, Saturday-right)
JAN
Heavy Duty (Weekday-left, Saturday-right)
JAN JAN
county
Cook County
— HarttortCT
— Waka County
I
\
r\
74
-------
EPA's National-scale Air Toxics Assessment
2.4 Source Groups
Source attribution is a key feature of NATA because it allows users to prioritize sources and pollutants of
interest. The source groupings for each NATA are a little different, in part due to methods that have
increased the number of source groups allowable, and also due to the results of previous NATAs. For the
2011 NATA, we considered the NEI sectors, source groups developed for previous NATAs, and sources
whose impacts were generated solely through CMAQ. Other than secondary formation and background,
source groups originated by aggregating sources in the emission inventory based on attributes such as
SCC and facility source type. The secondary-formation source group was based on the CMAQ estimates
of the concentration of HAPs (formaldehyde, acetaldehyde, and acrolein) formed from the atmosphere
due to photochemical reactions of anthropogenic and biogenic VOCs and NOx. Background is the sum of
two components:
• Impact of the remote-concentration estimates added to the HEM-3 runs for non-CMAQ HAPs
(such as carbon tetrachloride) and non-CMAQ areas (i.e., outside the CONUS CMAQ domain)—
see Section 3.2.1.
• Impact of the CMAQ concentration estimates in areas in which all HEM-3 receptors were zero
but CMAQ were nonzero.
The source groups are provided in Exhibit 31.
Exhibit 31. Source Groups for NATA
Broad
Group
NEI Data
Category
NATA Source Group
Description
HEM Run Group
Based On
Point
Point
Point (PT)
Point sources excluding
airports and railyards
Point—no airports
Point NEI
data category
Onroad
Onroad
OR-LD_Gas
Onroad light-duty gasoline
Light Duty
SCC
OR-HD_Gas
Onroad heavy-duty gasoline
Heavy Duty
OR-LD_Diesel
Onroad light-duty diesel
Light Duty
OR-HD_Diesel
Onroad heavy-duty diesel
Heavy Duty
OR-Refueling
Onroad refueling (including
heavy- and light-duty
gasoline and diesel
refueling)
Light Duty
Nonroad
Point
NR-Airport (PT)
Point—airports
Facility
source type =
100
NR-Railyard (PT)
Point—no airports
Facility
source type =
151
Nonpoint
NR-CMV Underway
CMVs Underway
CMVs
SCC
NR-CMV Ports
CMVs at ports
NR-Locomotives
Nonpoint—10-m
release height
Nonroad
NR-Pleasurecraft
Pleasurecraft (nonroad
equipment)
Nonroad
SCC
NR-Construction
Construction equipment
75
-------
EPA's National-scale Air Toxics Assessment
Broad
Group
NEI Data
Category
NATA Source Group
Description
HEM Run Group
Based On
NR-Gas/Other
Nonroad gasoline equipment
other than pleasurecraft and
construction
NR-Diesel/Other
Nonroad diesel equipment
other than pleasurecraft and
construction
Nonpoint
Nonpoint
N P-B u 1 k_g as_te rm
Nonpoint bulk gasoline
terminals
Nonpoint—10-m
release height
see
NP-Chemical_Mfg
Nonpoint chemical
manufacturing
NP-Mining
Nonpoint mining
NP-lndustrial_NEC
Nonpoint industrial
processes not elsewhere
classified
NP-Nonferrous_metals
Nonpoint nonferrous metals
NP-Oi I/Gas
Nonpoint oil and gas
production
NP-Refineries
Nonpoint refineries
NP-Storage_Transfer
Nonpoint storage and
transfer
NP-ICI_fuel_comb
Nonpoint industrial,
commercial, and institutional
emissions
NP-Landfills
Nonpoint landfills a
NP-
SfcCoating_lndSolvent
Nonpoint surface coating
and industrial solvent use
NP-
WasteDisposal_Other
Nonpoint other waste
disposal
NP-Gas_stations
Nonpoint gas stations b
Nonpoint—low
NP-Comm_cooking
Nonpoint commercial
cooking
release height
NP-Misc_non-ind
Nonpoint miscellaneous
nonindustrial sources
NP-Non-
RWC_ResFuelComb
Nonpoint residential fuel
combustion excluding wood
RWC
Nonpoint RWC
NP-
Consumer comm solve
nt
Nonpoint consumer and
commercial solvents
NP-Solvent_degreasing
Nonpoint solvent degreasing
NP-Dry_cleaning
Nonpoint dry cleaning
NP-Non-ind_sfc_coating
Nonpoint non-industrial
surface coating
Biogenics
Nonpoint
Biogenics
Not run in HEM-3
Zero out of
CMAQ run
76
-------
EPA's National-scale Air Toxics Assessment
Broad
Group
NEI Data
Category
NATA Source Group
Description
HEM Run Group
Based On
Fires
Event
Fires (ag, prescribed and
wild)
Fires - includes agricultural
burning, prescribed burning
and wildfires
Zero out of
CMAQ run
Secondary
N/A
Secondary
Secondary transformation
from anthropogenic and
natural (i.e. all) sources
Zero out of
CMAQ run
Background
N/A
Background
C
a Most landfills were in the point inventory in the 2011 NEI.
b Excluding Stage 2, which was covered in refueling.
c Non-CMAQ pollutants/areas: remote concentrations (e.g., carbon tetrachloride)—See Appendix E. For CMAQ
pollutants/areas, background was only computed as the CMAQ concentration for grid cells with no HEM-3
contribution.
2.5 Uncertainties in Emissions/Emissions Processing
Uncertainties in emissions result from uncertainties in locations and release characteristics of sources,
emission estimation techniques such as emission factors and models, inputs to emission models such as
NMIM or MOVES, speciation factors, and activity data. For nonpoint, onroad, and nonroad categories,
additional uncertainty lies with the spatial resolution of the activity data. For example, MOVES and
NMIM estimated inventories using combined data for specific vehicles, activities, and fuels from states
and/or EPA and other government agencies along with vehicle- and engine-emissions data. Activity data
for vehicles and nonroad equipment were typically available at the levels of national, state, or
metropolitan statistical area, and thus must be allocated to counties using surrogates such as population
and land use. This allocation introduces significant uncertainty to county-level estimates of emissions.
RWC data were also estimated at the county level for all appliance types. For fireplaces and wood stoves,
these allocations and burn rates were primarily based on American Housing Survey (AHS) data where
available. AHS data was available for 47 select metropolitan areas every 6 years and assumptions were
made based on survey-response rate, fraction of households that burn wood versus gas for primary and
secondary heating, urban versus suburban and rural representation of wood-burning device-type use and
burn rates. All of these characteristics have uncertainty and EPA used more local state and regional-level
survey data where available. For other devices such as outdoor wood boilers (OWB) and indoor furnaces,
national sales data were extrapolated from growth estimates to estimate total appliances. Spatial allocation
utilized inverse population density with thresholds to prevent assigning OWBs to highly populated urban
counties. Similar burn-rate zero-outs were applied for high-population-density counties for primary
heating from most types of RWC devices. Each assumption in burn rate and appliance profile contained a
varying level of uncertainty because of the sparse availability of survey data at the county level for each
RWC device type.
Canada and Mexico did not have HAP emissions in the inventories except where VOC was speciated into
benzene, formaldehyde, acetaldehyde, and methanol, which were model species for CB05. For Mexico,
onroad VOC emissions tended to be overestimated, which caused very high benzene estimates at the
border with Texas. This resulted in unreasonably high tract concentrations in Maverick County. To
address this, we used the non-hybrid approach for the onroad HEM run groups for this county.
We did not include mercury emissions from Canada or Mexico (both had been included in the 2005
platform modeling).
77
-------
EPA's National-scale Air Toxics Assessment
2.6 Summary
• The 2011 NEI v2 was the primary source of emissions data for NATA, although some data were
modified based on: comments received during the NATA review period, the requirements of the
air quality models, and the available data on pollutant toxicity.
• Emissions were grouped into various categories of sources to track source contributions and to
conform to the requirements of the air quality modeling methods.
• Emissions were processed separately for CMAQ and HEM-3 which have different temporal,
spatial and speciation requirements for the input emissions.
• Emissions were allocated spatially based on various demographic, activity, and land-use
surrogates.
• Emissions were allocated temporally primarily using established temporal factors.
78
-------
EPA's National-scale Air Toxics Assessment
3 AIR QUALITY MODELING & CHARACTERIZATION
The NATA emission estimates described in Section 2 were 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 data, and other information to simulate the behavior and
movement of air toxics in the atmosphere. The air quality models used for NATA estimated outdoor
concentrations of air toxics at specified locations. The NATA approach included the development and
application of a new hybrid approach blending a chemical transport model (CMAQ: EPA 2015g)
with a dispersion model (HEM-3 running AERMOD: see also the HEM-3 User's Guides. EPA 2014e)
to estimate ambient concentrations of 40 of the more prevalent and higher risk HAPs as described in
Section 3.1.1. The air toxics modeled in the hybrid approach captured approximately 99 percent of the
total risk nationally. The remaining "non-hybrid" air toxics were treated similarly to the NATA 2005
approach, which merged HEM-3 model estimates with observations, as described in Section 3.1.2.
For simplicity and consistency throughout this TSD, all aspects or details of the HEM-3 model are
referred to overall as "HEM-3," although most often the AERMOD component of HEM-3 is pertinent to
the discussion. EPA designed and maintains AERMOD separate and apart from HEM-3; HEM-3 merely
incorporates AERMOD.
3.1 Hybrid Model Description
The subsections below contain discussions on the hybrid air modeling approach developed for the 2011
NATA.
3.1.1 Overview
The air quality modeling structure for 40 of the most prevalent and highest risk air toxics (see Exhibit 32)
utilized a hybrid method combining the fine spatial scale and source attributions of AERMOD in HEM-3
(Cimorelli et al. 2005; EPA 2015f) with the full treatment of chemistry and transport afforded by CMAQ
version 5.02 with CB05 (Byun and Schere 2006; Brown et al. 2011). The CMAQ model 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.
In this application, HEM-3 treated all species as chemically non-reactive. The emissions and
meteorological data sets used in CMAQ were processed further to generate HEM-3 inputs consistent with
CMAQ. HEM-3 receptor locations were based on the centroids of populated census blocks, monitoring -
site positions, and 5 evenly distributed points within each 12-km horizontal CMAQ grid cell in the
CONUS (see Exhibit 33), resulting in at least 5, and sometimes more than 10,000, receptors per cell and
6.5 million receptors nationwide.
Exhibit 32. Air Toxics Utilizing the Hybrid Modeling in NATA
1,1,2,2-Tetrachloroethane
1,3-Butadiene
1,3-Dichloropropene
1,4-Dichlorobenzene(p)
2,4-Toluene diisocyanate
Benzene
Beryllium
Cadmium
Chlorine
Chloroform
Formaldehyde
Hexamethylene
Hydrochloric acid
Lead Compounds
Maleic anhydride
Nickel Compounds
Nickel Compounds
Propylene dichloride
Quinoline
Tetrachloroethylene
79
-------
EPA's National-scale Air Toxics Assessment
Acetaldehyde
Acetonitrile
Acrolein
Acrylonitrile
Arsenic
Chromium (VI)
Diesel PM
Ethylene dibromide
Ethylene dichloride
Ethylene oxide
Manganese Compounds
Mercury Compounds
Methanol
Methylene chloride
Naphthalene
Toluene
Trichloroethylene
Triethylamine
Vinyl chloride
Xylenes
v O • Qo
o
• *
o o
The equation below was used to calculate the 2011 annual-average estimates of air concentrations at
receptor locations, which were constrained to CMAQ-grid-average values, with HEM-3 providing sub-
grid scale spatial texture.
C = HEM-3rec X (¦
Where:
( CMAQpnfb \
HEM-3gridavg)
+ CMAQsec + CMAQPF!RES + CMAQPB!0GENICS
€
CMAOpttm
HEMSma
HEM-Sammm
CMAOs$c
CMAOpbres
concentration at a receptor,
concentration in CMAQ grid cell, contributed by primary emissions,
excluding fires and biogenics,
concentration at HEM-3 receptor,
average of all HEM-3 results within a CMAQ grid, calculated through
surface interpolation of all HEM-3 receptor locations to eliminate
concentration discontinuities,
contribution from atmospheric reactions in CMAQ grid cell,
contribution from primary emissions of fires in CMAQ grid cell, and
Exhibit 33. CMAQ Domain with Expanded Cell Showing Hybrid Receptors
80
-------
EPA's National-scale Air Toxics Assessment
CMAQpbiogenics= contribution from primary emissions of biogenics in CMAQ grid cell.
This hybrid approach, which builds on earlier area-specific applications to Philadelphia, PA (Isakov et al.
2007) and Detroit, MI (Wesson et al. 2010), reflects an evolution of national-scale modeling of HAPs that
attempts to optimize characterization of non-reactive and reactive species across multiple spatial scales.
However, the blending of two different modeling platforms challenges adherence to basic mass-
conservation principles. For example, the 2005 NATA was susceptible to duplicate counting as the
secondarily formed species generated by CMAQ were added directly to HEM-3 estimates driven by
primary emissions, in addition to an added "background" concentration based on ambient observations.
Consequently, diagnosing model behavior based on paired model-to-measurement values was
compromised by the dual use of observations and largely inconsistent model inputs driving HEM-3 and
CMAQ. CMAQ tracks primary and secondary contributions by source type, enabling the HEM-3 estimate
at each receptor location to be normalized to the CMAQ primary contribution. By anchoring
concentration averages to CMAQ, mass conservation was largely retained. The constraint to CMAQ
average grid values imposed by the above equation minimized possible redundancies and was appropriate
when combining results from vastly different model architectures.
3.1.2 Treatment of Species
In this application, the hybrid model was applied to 40 of the highest risk air toxics (shown in Exhibit 32)
among 180 air toxics included in the 2011 NATA. Although this application focuses on air toxics, it
reflects the second major application of the CMAQ multipollutant version incorporating CAPs and HAPs
following a national assessment of increased ethanol use associated with renewable fuels (Cook et al.
2011). The atmospheric chemistry treatments in chemical transport models such as CMAQ are based on
gas-phase reaction processes optimized to characterize ozone, linked with a variety of heterogeneous and
thermodynamic processes to accommodate PM formation. Consequently, the inclusion of explicit
chemical species in current chemical mechanisms is predicated by its relative importance in ozone
chemistry. Formaldehyde and acetaldehyde are high risk HAPs that generate significant amounts of
peroxy radicals leading to enhanced ozone production and secondary PM formation and exemplify
multipollutant linkages driven by atmospheric processes. Chemical species that are not incorporated as
explicit species in chemical mechanisms are added as non-reactive 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 non-reactive tracers and participate in
atmospheric reactions as lumped carbon bond species with the assumption that atmospheric chemistry
minimally influences air concentrations. HEM-3, which treats all pollutants as nonreactive, was applied to
the remaining air toxics not incorporated within CMAQ. Due to relatively spatially invariant
concentration distributions and uncertainty in emission estimates, carbon tetrachloride risk was based on
observations modulated spatially by HEM-3 estimates.
3.1.3 Meteorological Processing
The gridded meteorological data for 2011 at the 12-km CONUS-scale domain (see domain in Exhibit 33)
was derived from version 3.4 of WRF (Skamarock et al. 2008). The WRF meteorological outputs were
processed using the Meteorology-Chemistry Interface Processor package (version 4.1.3) to derive hourly
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 (Otte and Pleim 2010). A
performance evaluation of the meteorological model can be found in EPA (2014a). CMAQ resolved the
vertical atmosphere with 25 layers, preserving greater resolution in the planetary boundary layer. The
meteorological inputs driving CMAQ were processed for HEM-3 through the Mesoscale Model Interface
Program (MMIF; EPA 2014d) to provide representative meteorological inputs for every fourth CMAQ
81
-------
EPA's National-scale Air Toxics Assessment
grid cell and for over 700 National Weather Service station locations. Isakov et al. (2007) demonstrated
successful use of meteorological variables derived from prognostic modeling to drive dispersion models,
motivated primarily to address spatial gaps in meteorological monitoring. Additionally, MMIF outputs
have been shown to compare favorably well against observed meteorological data when used in
AERMOD (EPA 2015m). For the 2011 NATA, we took advantage of prognostic meteorological
modeling to harmonize boundary-layer inputs across both modeling platforms. The MMIF outputs were
based on the first CMAQ application using the 2011 NEI vl. The final CMAQ application, based on the
2011 NEI v2, included a new WRF simulation that was not processed further to update MMIF outputs.
The differences in these WRF simulations were insignificant with respect to affecting the MMIF results.
3.1.4 Emissions Processing Overview
See Section 2 for additional detail. The 2011 NEI provided the root emissions data for CMAQ and HEM-
3 (EPA 2014b). Emissions to HEM-3 were grouped into four broad categories (emissions-input resolution
of over 150 SCCs was retained) with similar spatial and temporal delineation: major point sources,
nonpoint sources (excluding transportation, fires, and biogenics), onroad mobile, and nonroad mobile
(including locomotive, aviation, and CMVs). Fires (combined wild and prescribed) and biogenic
emissions were handled only through CMAQ. NEI data were provided as specific point and aggregated
county-level annual estimates and were processed to hourly values distributed over 12-km horizontal
grids through SMOKE (Houyoux et al. 2000). Hourly temporal allocations were developed for HEM-3,
consistent with CMAQ, which was a departure from previous NATAs that were based on annual-average
inputs. Nonpoint, onroad, and nonroad emissions for HEM-3 typically were allocated spatially to
population census tracts using a variety of surrogates (e.g., land use classifications, population).
3.1.5 Initial and Boundary Conditions
The CMAQ lateral-boundary and initial-species concentrations for benzene, formaldehyde, and
acetaldehyde were generated by a year-201 lGEOS-Chem simulation (Yantosca et al. 2015). The 2011
GEOS-Chem simulation used a grid resolution of 2.0 degrees x 2.5 degrees (latitude x longitude). The
predictions were processed using the GEOS-2-CMAQ tool and used to provide one-way dynamic
boundary conditions at one-hour intervals (Akhtar et al., 2012). A GEOS-Chem evaluation was conducted
for the purpose of validating the 2011 GEOS-Chem simulation for predicting selected measurements
relevant to their use as boundary conditions for CMAQ. This evaluation included using satellite retrievals
paired with GEOS-Chem grid cells (Henderson, et al., 2014). Due to the scarcity of observations suitable
for establishing boundary conditions and the extended calendar year simulation, zero-value initial and
boundary conditions were used for the remaining air toxics.
3.1.6 Source Attribution
Since the HEM-3 concentration estimates were based on separate emissions-group runs, estimates of the
source contributions associated with primary emissions were generated by the following ratio technique
normalized to CMAQ concentrations for sources within the four broad categories of sources:
CMAQpnfb
Crecj = HEM-3recj X VPWFB
HhM-3GRIDAVG
Where:
Crecj = contribution to concentration at a receptor REC from category J, excluding
secondary formation.
82
-------
EPA's National-scale Air Toxics Assessment
This ratio approach provided an estimate of primary-emission contributions only. Primary-emission
contributions from biogenics and fires were processed only through CMAQ, and all contributions from
secondary formation processes were aggregated into CMAQsecnfb.
3.2 Treatment of Non-hybrid Air Toxics and Areas Outside the CONUS
The 138 HAPs not simulated by CMAQ were estimated by adding HEM-3-modeled concentrations to
observed ambient concentrations assumed to reflect background conditions, utilizing the same emissions
and meteorological processing used for HEM-3 in the hybrid modeling. The non-CONUS locations
(Alaska, Hawaii, Puerto Rico, and U.S. Virgin Islands) were modeled with the same non-hybrid approach,
but with all NATA air toxics included.
3.2.1 Background Concentrations
Appendix E contains further details on background concentrations. For the 2011 NATA, we added
background concentrations to the HEM-3-modeled concentrations (i.e., those pollutants that were not
modeled in CMAQ). Background concentrations were added to account for (1) natural sources, (2)
emissions of persistent air toxics that occurred in previous years, and (3) long-range transport from distant
sources. The largest impact of the background concentrations on the NATA risk was carbon
tetrachloride, a ubiquitous, globally persistent HAP for which the background concentration of 0.55
|ig/m3 was 3.2 times higher than its URE of 0.17 |ig/m\ thus providing a nationally consistent background
cancer risk of 3-in-l million. Although no large sources of this pollutant exist because the Montreal
Protocol established a timetable for its phase-out, observations in national and remote networks show
fairly uniform concentrations, possibly partly due to its long residence time (30-50 years) or potentially
resulting from unknown global sources.
Various approaches have been used in previous NATAs, including use of ambient concentration data,
remote concentration data, and emissions. The approach for the 2011 NATA was simplified from
previous approaches because the available data did not support the generation of regionally varying
background concentrations. For all air toxics, we estimated a spatially uniform, remote Northern
Hemisphere air toxics concentration for the background. The remote concentrations were estimated from
measurements made at remote monitoring sites, from lower-level concentrations from the national
monitoring network, from the scientific literature, or from application of national-emissions and
residence-time ratios to a measured remote concentration.
The generalized methods hierarchy was as follows:
1. Remote network. Measurements made at networks/sites in remote Northern Hemisphere
locations with citations in peer-reviewed literature. Examples include National Oceanic and
Atmospheric Administration Global Monitoring Division sites, the Trinidad Head Advanced
Global Atmospheric Gases Experiment site, and remote Interagency Monitoring of Protected
Visual Environments (IMPROVE) sites.
2. Ambient national network. Estimates of background concentrations made at routine monitoring
network sites in the United States where concentrations were measurable and reliable down to the
10th percentile. Air toxics measurements were used from Phase XIII of the air toxics archive.
3. Literature. Measurements of species at remote or regional sites during a single-measurement
study for a specific pollutant. These measurements are different from the ongoing measurements
used in Step 1 because of their "one-off nature.
83
-------
EPA's National-scale Air Toxics Assessment
4. Emissions. Emissions-based estimates of remote background concentrations. An estimate for
pollutant /' was derived from the national total emissions from the 2011 NEI (E), atmospheric
residence times (t), and a comparison to tetrachloroethylene (PCE). The below equation shows
the relationship used to derive these remote estimates.
^ _ Ej x tj x CPCE
EpCE X tpcE
For NATA, we do not use background concentrations for formaldehyde, acetaldehyde, or acrolein
because Alaska, Hawaii, Puerto Rico, and the U.S. Virgin Islands used national-average secondary
concentrations from the CMAQ run, and adding background for these HAPs would be double counting.
Also, we did not use the estimates for PAHs due to uncertainties in the residence times.
Exhibit 34 contains the background concentrations for the non-CMAQ air toxics, used for both the
CONUS and non-CONUS areas, and the method used to generate them. Residence-time data were used
only for the emissions-based approach. In addition to the background concentrations in Alaska, Hawaii,
Puerto Rico, and the U.S. Virgin Islands also used the background concentrations for the pollutants listed
in Exhibit 35. Diesel PM does not have a background concentration.
Exhibit 34. Background Concentrations Added to the HEM-3 Concentrations for Non-CMAQ Air
Toxics, All Areas
Pollutant
Residence
Time
(days)
2011 Remote
Concentration
Estimate (|jg/m3)
Method
Year
Locations
Methyl chloride
(Chloromethane)
365
1.09
Remote
Network
2011
Trinidad Head, CA
Carbon tetrachloride
10950
0.547
Remote
Network
2011
KUM, MLO, NWR,
BRW, ALT
Acetonitrile
365
0.170
Literature
2004-
2008
New Hampshire
n-Hexane
2.6
0.114
Emissions
2011
2,2,4-T rimethylpentane
4
0.093
Emissions
2011
1,1,1 Trichloroethane
(Methyl chloroform)
1825
0.06
Remote
Network
2011
KUM, MLO, NWR,
BRW, ALT
Methyl bromide
365
0.029
Remote
Network
2011
KUM, MLO, NWR,
BRW, ALT
Ethylbenzene
1.7
0.016
Literature
2005
Jungfraujoch,
Switzerland
Bromoform
540
0.01
Literature
1994-
2004
Ocean cruises
Bis(2-ethylhexyl) phthalate
200
5.7E-03
Emissions
2011
Carbon disulfide
7
5.5E-03
Emissions
2011
Methyl isobutyl ketone
1
4.6E-03
Emissions
2011
Styrene
0.25
8.7E-04
Emissions
2011
1,1,2-Trichloroethane
49
3.9E-04
Emissions
2011
84
-------
EPA's National-scale Air Toxics Assessment
Pollutant
Residence
Time
(days)
2011 Remote
Concentration
Estimate (|jg/m3)
Method
Year
Locations
Cumene (Isopropylbenzene)
2.2
3.7E-04
Emissions
2011
Selenium
10
2.0E-04
Air Toxics
Archive
2010-
2012
All national sites
Benzyl chloride
3
1.4E-04
Emissions
2011
Propionaldehyde (Propanal)
0.1
9.8E-05
Emissions
2011
Antimony
10
6.6E-05
Emissions
2011
Cobalt
10
4.1E-05
Air Toxics
Archive
2010-
2012
All national sites
Vinyl acetate
0.25
3.5E-05
Emissions
2011
1,2-Dibromo-3-
chloropropane
36
1.3E-06
Emissions
2011
Benzidine
1
2.5E-08
Emissions
2011
Exhibit 35. Background Concentrations Added to the HEM-3 Concentrations for Non-CONUS
Areas Only
Name
Residence
Time
(days)
Best 2011 RCE
Estimate (|jg/m3)
Method
Year
Locations
Dichloromethane
(Methylene Chloride)
30
0.146
Remote
Network
2011
KUM, MLO, NWR,
BRW, ALT
Benzene
3
0.116
Remote
Network
2011
KUM, MLO, NWR,
BRW, ALT
Chloroform
80
0.058
Remote
Network
2011
Trinidad Head, CA
Toluene
0.5
0.041
Literature
2005
Jungfraujoch,
Switzerland
Xylenes
0.2
0.023
Literature
2002
Trinidad Head, CA
Tetrachloroethylene
6.5
0.013
Remote
Network
2011
KUM, MLO, NWR,
BRW, ALT
1,4-Dichlorobenzene
31
9.3E-3
Emissions
2011
Trichloroethylene
6
4.1E-03
Remote
Network
2011
Trinidad Head, CA
Ethylene dichloride
42
2.0E-03
Emissions
2011
1,3-Butadiene
0.08
2.0E-03
Literature
2011
Jungfraujoch,
Switzerland
1,1,2,2-Tetrachloroethane
91.3
8.4E-04
Emissions
2011
Lead
10
6.6E-04
Remote
Network
2010-
2012
DENA, KALM, PORE,
REDW, TRCR,
TUXE.HACR
1,3-Dichloropropene
1.25
6.2E-04
Emissions
2011
85
-------
EPA's National-scale Air Toxics Assessment
Name
Residence
Time
(days)
Best 2011 RCE
Estimate (|jg/m3)
Method
Year
Locations
Naphthalene
0.25
4.9E-04
Emissions
2011
Ethylene oxide
7
3.4E-04
Emissions
2011
Manganese
10
3.2E-04
Remote
Network
2010-
2012
DENA, KALM, PORE,
REDW, TRCR,
TUXE.HACR
Acrylonitrile
5.6
3.2E-04
Emissions
2011
1,2-Dichloropropane
(propylene dichloride)
30
2.9E-04
Emissions
2011
Ethylene dibromide
50
1.9E-04
Emissions
2011
Arsenic
10
1.4E-04
Air Toxics
Archive
2010-
2012
All national sites
Vinyl chloride
2
1.2E-04
Emissions
2011
Nickel
10
1.0E-04
Remote
Network
2010-
2012
DENA, KALM, PORE,
REDW, TRCR,
TUXE.HACR
Mercury (gaseous)
365
1.0E-04
Literature
2005
U.S. MDN sites
Cadmium
10
3.8E-05
Air Toxics
Archive
2010-
2012
All national sites
Beryllium
10
8.5E-06
Air Toxics
Archive
2010-
2012
All national sites
Chromium VI
3
1.5E-06
Remote
Network and
National
Measurements
2010-
2012
All national sites
Hydrazine
0.25
5.7E-8
Emissions
2011
For the 2011 NATA, two pollutants from the 2002 and 2005 NATA background lists were excluded:
quinoline and total chromium (although chromium (VI) was included in 2011 NATA). Four pollutants (or
grouped pollutants) that had been in the 1999 NATA background list were excluded: polychlorinated
biphenyls, lindane, phosgene, and hexachlorobutadiene. In contrast, some pollutants and groups that had
never previously been estimated were added—these include acetonitrile, hexane, 2,2,4-trimethylpentane,
ethylbenzene, methyl isobutyl ketone, styrene, 1,3-dichloropropene, 1,1,2-trichloroethane,
cumene(isopropylbenzene), selenium, propionaldehyde (propanal), antimony, cobalt, and vinyl acetate.
3.3 Model Evaluation
We describe below and in Appendix F our efforts to evaluate the performance of the NATA models. As
noted at the beginning of Section 3, discussions of "HEM-3" in this document often are specifically
related to the AERMOD dispersion model component of HEM-3, but we use "HEM-3" throughout for
simplicity and consistency. In this section in particular, discussions of HEM-3 model values are
specifically related to the air concentrations predicted by its AERMOD component.
86
-------
EPA's National-scale Air Toxics Assessment
3.3.1 Overview
Using the air toxics archive Phase IX for the year 2011. we conducted an operational model performance
evaluation of the air toxics simulated for the 2011 NATA (more details found in Section 3.3.2 below).
The model evaluation included both the air toxics modeled with the hybrid approach ("hybrid air toxics")
and those modeled without the hybrid approach ("non-hybrid air toxics"). The hybrid evaluation looked at
the air toxics for which there were valid ambient data (i.e., completeness criteria protocol) to compare
against the CMAQ, HEM-3, and hybrid model predictions. Likewise, the air toxics non-hybrid evaluation
used similar observational-completeness criteria constraints to compare against air toxics estimated by
adding HEM-3 to observed ambient concentrations assumed to reflect background conditions.
Spatial-scale differences exist between CMAQ, HEM-3, and the hybrid model predictions. A CMAQ
concentration represents a 12-km grid-cell volume-averaged value. The HEM-3 model concentration
represents a specific point within the modeled domain. The hybrid model concentration combines the
HEM-3 point-concentration gradients with the CMAQ 12-km grid-cell volume average. The ambient
observed measurements were made at specific spatial locations (latitude/longitude). Several annual
graphical presentations and statistics of model performance were calculated and prepared. Graphical
presentations included box and whisker plots (which show the distribution and the bias of the predicted
and observed data) and regional maps (which show the mean bias and error calculated at individual
monitoring sites).
3.3.2 Observations
Observations were extracted from the air toxics archive, Phase IX for the year 2011. While most of the
data in the archive are a snapshot of the Air Quality System (AQS) database (downloaded in July 2014),
additional data (such as from special studies) were in the archive but not reported to AQS. In the air toxics
archive, pollutant concentrations were converted to (ig/m3 in local conditions where temperature and
pressure data were available (i.e., at the vast majority of sites). In addition, any negatives and data flagged
as "non-detect" without a value were given a value of 0. Also, any data determined to have been
substituted with half the method detection limit (i.e., MDL/2) was changed to 0.
For comparing annual averages of modeled and monitored data, data from the archive were aggregated to
2011 annual averages by site and parameter code. Data below MDLs were used as-is. Data were removed
for which there were no MDLs. Naphthalene data from parameter code 45850 (canister method) were
removed because that method may not be as reliable as the method used in the National Ambient Air
Toxics Trends Program. Also, those sites were removed that reported naphthalene as code 17141 in which
it was determined to use the canister test method.
Only site-parameter pairs in which measurements from at least three seasons were 75 percent complete
(i.e., 75 percent of the scheduled days contained non-null values) were retained for developing annual
averages. First, the sub-annual data were allocated to 24-hour averages. Seventy-five-percent
completeness was required to create a daily average from sub-daily data, such that 75 percent of
scheduled sub-annual data were available. For example, hourly data required 18 of 24 hours of data,
three-hour data required six of eight three-hour periods of data, etc. For each quarter, the number of days
to meet 75% completeness depended on the sampling frequency (note that more than one monitor at the
site that measured on the same day was counted once). For example, one-in-six-day sampling required 12
days for the quarter.
For sites with multiple monitors (known as "POCS"), only the daily data with the same measurement
duration (i.e., hourly, 3-hour, 24-hour) were averaged across the POCs. That is, daily data based on
87
-------
EPA's National-scale Air Toxics Assessment
hourly measurements were not averaged with daily data based on 24-hour measurements. Where a site
met the 75% completeness for multiple durations, the 24-hour duration data were chosen.
Annual averages were created by averaging all daily measurements with the same measurement duration
for all sites that met the above completeness criteria. The only sites used were those for which 50 percent
or more of the data were above the method detection limit (MDL). A spreadsheet file
("2011monitored_data_annualmeans_PhaseIXarchive.xlsx") of the ambient annual averages (in (ig/m3) is
provided in the SupplementalData folder.
Uncertainties in the ambient data result from limited sites, data below MDL and measurement
uncertainties.
3.3.3 Model Performance Statistics
The Atmospheric Model Evaluation Tool (AMET) was used to conduct the 2011 NATA air toxics
evaluation (Appel et al. 2011). There are various statistical metrics available and 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); and, three error metrics (mean error and normalized mean error, root mean square
error, and correlation coefficient).
Common variables are:
M = predicted concentration
O = observed concentration
X = predicted or observed concentration
o = standard deviation
Mean Bias (MB), Mean Error (ME), and Root Mean Square Error (RMSE) (all in ^g/m3):
71
MB
rii
i
n
ME = -Y\m-o\
nZ—i
RMSE =
M
Z?(M - Of
n
MB quantifies the tendency of the model to over- or under-estimate values while ME and RMSE measure
the magnitude of the difference between modeled and observe values regardless of whether the modeled
values are higher or lower than observations.
Normalized Mean Bias (NMB) and Normalized Mean Error (NME) (both unitless):
Zi(M - 0)
NMB = — -
88
-------
EPA's National-scale Air Toxics Assessment
NMB is used as a normalization to facilitate a range of concentration magnitudes. This statistic averages
the difference (model - observed) over the sum of observed values. NMB is a useful model performance
indicator because it avoids over-inflating the observed range of values, especially at low concentrations.
Ei|M-0|
NME =
NME is similar to NMB, where the performance statistic is used as a normalization of the ME. NME
indicates the absolute value of the difference (model - observed) over the sum of observed values.
Correlation Coefficient (r; unitless):
r=ly /O-OWM-M
The value of r provides an indication of the strength of linear relationship and is signed positive or
negative based on the slope of the linear regression.
3.3.4 Hybrid Evaluation
We conducted an annual operational model performance evaluation for hybrid air toxics, resulting in
comparisons between CMAQ and HEM-3 predictions as well as an evaluation of the ability of the hybrid
model to replicate the 2011 observed ambient concentrations. Inclusion of all three model results was
intended to demonstrate the merged attributes of the hybrid model used for the 2011 NATA. Statistical
assessments of modeled versus observed concentrations were paired in time and space and aggregated on
an annual basis. Exhibit 36 contains a list of air toxics evaluated in the hybrid model performance
evaluation and the number of paired sites (based on completeness criteria of observations, Section 3.3.2)
used in the annual average. Exhibit 37 is a map of the 2011 monitoring locations for hybrid air toxics.
Acrolein and ethylene dibromide were excluded in the model evaluation given the data uncertainty and
sampling. Annual averages of xylene species (m-, o-, and p-) were summed together to calculate a
"Xylenes, total" air toxic group based on the individual risk for each species being the same.
Exhibit 36. Hybrid Air Toxics Evaluated
Hybrid Air Toxic
Number of Paired Sites
1,3-Butadiene
83
1,3-Dichloropropene
5
1,4-Dichlorobenzene
22
Acetaldehyde
110
Acrylonitrile
18
Arsenic PM10
34
Benzene
214
Cadmium PM10
27
Chlorine
123
Chloroform
92
Ethylene dichloride
40
Hybrid Air Toxic
Number of Paired Sites
Formaldehyde
110
Lead PM10
33
Manganese PM10
40
Methylene chloride
123
Naphthalene
36
Nickel PM10
29
Propylene dichloride
5
Tetrachloroethylene
72
Toluene
211
Trichloroethylene
13
Xylenes
163
89
-------
EPA's National-scale Air Toxics Assessment
Exhibit 37. 2011 Monitoring Locations for the Evaluation of Hybrid Air Toxics
\ ^ aRo9no ONTARIO
^Winnipeg
* rt
\
-»»«" fcoRTH DAKOTA T %
f * . 1 — ~
* * ~ T sou,„ MICHIGAN * " i
£ >D'«HO ' » HAko A O anscONSIN >' I'
f ORFGO* ? ^ , ? T f'f' V O? f o JK ?
r. . -WYOMING f- !. a i \J\
;V' t , t t . ^ ;4'*t
t •.#,v t- "" * h *. % T tvWw/JkW-
*•r ««v. Vt +VfcirT
;t t» c0u,,»»o *-_je /
>"*• « 1"D ,.s T,y ,l * ~ %~i' .*«
AY-
W T H „ , f . .r' J;,, ' ' >% , JK«
1flr?4¥" "4$*' t . , "' » $ *
1 f, *$• tt£ . 4 . V c-#"
¦A-" xp"° . a . r(l.-i t
jfeu^lin m-i ituiSlKVip T' 1 ' T» Jcictaoiwille
t- « v
T' \ nSai
-- «/> v An'
\ 1W- *
Torreon v o nv,ns*llle $>,{¦"
~ ilU
In this section of evaluation of hybri d air toxics, we present annual-average model-to-monitor site
comparisons for all three models for three key air toxics: acetaldehyde, formaldehyde, and benzene.
Presentation of these comparisons includes summary statistics (Exhibit 38), boxplots containing model
distribution and bias differences as compared to ambient observations (Exhibit 39, Exhibit 46, and
Exhibit 53), and regional spatial maps with plots of the mean bias and error calculated at individual
monitoring sites (Exhibit 40-Exhibit 45, Exhibit 47-Exhibit 52, Exhibit 54-Exhibit 59). The boxplots use
boxed interquartile ranges of 25th-to-75th percentile, along with whiskers from the 5th to 95th
percentiles, and they also contain summary statistics of r, RMSE, NMB, NME, MB, and ME. More
details of the hybrid evaluation are in Appendix F, including statistical assessments of all the evaluated
hybrid air toxics. Note that HEM-3 and AERMOD are equivalent terms in these exhibits.
CMAQ and hybrid model predictions of annual formaldehyde, acetaldehyde, and benzene showed
relatively small-to-moderate bias and error percentages when compared to observations. HEM-3 showed
larger biases and errors, with underestimates for secondarily formed air toxics (e.g., -80.4 percent for
acetaldehyde and -78.8 percent for formaldehyde), as expected given HEM-3's exclusion of atmospheric
chemistry. Differences in bias and error statistics between the hybrid and CMAQ models were negligible
for formaldehyde and acetaldehyde. Technical issues in the air toxics data consisted 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)
emissions- and science-uncertainty issues potentially affecting model performance, and (5) limited data
for estimating intercontinental transport that effects the estimation of boundary conditions (i.e., boundary
estimates for some species were much higher than predicted values inside the domain).
Exhibit 38. 2011 Annual Air Toxics Performance Statistics for the Hybrid, CMAQ, and HEM-3
Models
Hybrid Air Toxic
Model
MB (pg/m3)
ME (fjg/m3)
NMB (%)
NME (%)
Acetaldehyde
Hybrid
0.5
0.7
30.9
43.9
CMAQ
0.4
0.7
27.1
41.4
90
-------
EPA's National-scale Air Toxics Assessment
Hybrid Air Toxic
Model
MB (|jg/m3)
ME (|jg/m3)
NMB (%)
NME (%)
HEM-3
-1.3
1.3
-80.4
80.6
Formaldehyde
Hybrid
-0.8
1.0
-30.8
37.3
CMAQ
-0.9
1.0
-34.2
38.8
HEM-3
-2.2
2.2
-78.8
79.3
Benzene
Hybrid
0.0
0.5
1.6
60.7
CMAQ
-0.2
0.5
-22.7
57.8
HEM-3
-0.3
0.5
-33.1
60.2
Exhibit 39. Acetaldehyde: 2011 Boxplots of Observed and Modeled Concentrations (top) and
Modeled-Observed Bias Difference in Concentrations (bottom) for the Hybrid, CMAQ, and HEM-3
Models
2011v2eg_NATA_CMAQ_annual Acetaldehyde for 20110101 to 20110131
E
^3)
Toxics
2011 v2eg_NATA_CMAQ_an nual
2011 v2eg_NATA_AERMOD_annual
2011 v2eg NATA_Hybrid_annual
r
-0.04
0.12
RMSE
1.5
0.91
NMB
-80.4
30.9
NME
80.6
43.9
MB
-1.33
0.49
ME
1.33
0.7
—I 1
Toxics 2011v2eg NATA CMAQ annual
T
2011 v2eg NATA Hybrid annual
91
-------
EPA's National-scale Air Toxics Assessment
2011v2eg_NATA_CMAQ annual Acetaldehyde for 20110101 to 20110131
~ 2011 v2eg_N ATA_CM AQ_an nual
¦ 2011 v2eg_NATA_AERMOD_annual
H 2011 v2eg_NATA_Hybrid_annual
e
ch
03
CO
£
©
o
<
8
r
0.12
-0.04
0.12
RMSE
0.87
1.5
0.91
NMB
27.1
-80.4
30.9
NME
41.4
80.6
43.9
MB
0t43
-1.33
0.49
ME
1.33
0.7
20l1v2eg NATA CMAQ annual
~r
2011v2eg NATA Hybrid annual
Exhibit 40. Acetaldehyde: 2011 Mean Bias (%) at Monitoring Sites in the Hybrid Modeling Domain
Acetaldehyde MB (ug/m3) for ru n2011 eg_NATA_HYBRID_annuaI allHAPs for 20111001 to 20111031
units = ug;'m3
coverage limit =
CIRCLE=Toxics:
92
-------
EPA's National-scale Air Toxics Assessment
Exhibit 41. Acetaldehyde: 2011 Mean Error (%) at Monitoring Sites in the Hybrid Modeling Domain
Acetaldehyde ME (ug,'m3) for run2011eg_NATA_HYBRID_annual_allHAPs for 20111001 to 20111031
CIRCLE=Toxics;
Exhibit 42. Acetaldehyde: 2011 Mean Bias (%) at Monitoring Sites in the CMAQ Modeling Domain
Acetaldehyde MB
-------
EPA's National-scale Air Toxics Assessment
Exhibit 43. Acetaldehyde: 2011 Mean Error (%) at Monitoring Sites in the CMAQ Modeling Domain
Acetaldehyde ME (ug/m3) for run2011 eg_NATA_CMAQ_annual_allHAPs for 20111001 to 20111031
CIRCLE=Toxics;
Exhibit 44. Acetaldehyde: 2011 Mean Bias (%) at Monitoring Sites in the HEM-3 Modeling Domain
Ac*<*klBhj4»MB(u9ni3>to' ruri3011»Q NAT A AEflUOO annual allHAP* tot 30111001 to 30111031
C'flCLE«To«cs;
94
-------
EPA's National-scale Air Toxics Assessment
Exhibit 45. Acetaldehyde: 2011 Mean Error (%) at Monitoring Sites in the HEM-3 Modeling Domain
Awwathyd* Mt (ugmjUo. ni(i30ll«sLWATAXrmOO..»nnmi WiKAPt fcy J011HO1 to 3)111331
CRCl
Exhibit 46. Formaldehyde: 2011 Boxplots of Observed and Modeled Concentrations (top) and
Modeled-Observed Bias Difference in Concentrations (bottom) for the Hybrid, CMAQ, and HEM-3
Models
2011v2eg_NATA_CMAQ_annual Formaldehyde for 20110101 to 20110131
5 _ ¦ Toxics
~ 2011 v2eg_NATA_CM AQ_an nual
¦ 2011 v2eg_NATA_AERMOD_annual
¦ 2011 v2eg_NATA_Hybrid_annual
f
0.41
-0.02
0.37
RMSE
1,21
2.37
1.16
NMB
-34.2
-78.8
-30.8
NME
38.8
79.3
37.3
MB
-0.9
-2.16
-0.81
ME
1.02
2.17
0.98
—I 1 1 1
Toxics 2011v2eg NATA CMAQ annual 2011v2eg NATA Hybrid annual
95
-------
EPA's National-scale Air Toxics Assessment
2011v2eg_NATA_CMAQ_anriual Formaldehyde for 20110101 to 20110131
~ 2011v2eg_NATA_CMAQ_annual
¦ 2011v2eg_NATA_AERMOD annual
¦ 2011v2eg_NATA_Hybrid_annual
o
O
r
-0.02
0.37
RMSE
2.37
1.16
NMB
-78.8
-30.8
NME
79.3
37.3
MB
-2.16
-0.81
ME
2.17
0.98
1 1 1
2011v2eg_ NATA CMAQ annual 2011v2eg NATA Hybrid, annual
Exhibit 47. Formaldehyde: 2011 Mean Bias (%) at Monitoring Sites in the Hybrid Modeling Domain
Formaldehyde MB (ug,'m3) for run2011eg_NATA_HYBRID_annual_allHAPs for 20111001 to 20111031
CIRCLE=Toxics;
96
-------
EPA's National-scale Air Toxics Assessment
Exhibit 48. Formaldehyde: 2011 Mean Error (%) at Monitoring Sites in the Hybrid Modeling Domain
Formaldehyde ME
-------
EPA's National-scale Air Toxics Assessment
Exhibit 50. Formaldehyde: 2011 Mean Error (%) at Monitoring Sites in the CMAQ Modeling Domain
Formaldehyde ME (ug/m3) for run2011eg_NATA_CMAQ_annual_allHAPs for 20111001 to 20111031
units = ug/m3
coverage limit = %
CIRCLE=Toxics;
Exhibit 51. Formaldehyde: 2011 Mean Bias (%) at Monitoring Sites in the HEM-3 Modeling Domain
CIRCLE-Toxics;
98
-------
EPA's National-scale Air Toxics Assessment
Exhibit 52. Formaldehyde: 2011 Mean Error (%) at Monitoring Sites in the HEM-3 Modeling Domain
Formjfc**r>v6* ME [ugm3; tor fur&oiloq RATA AERMOD annual allHAP* to* 10111001 lo 301 UOSi
CiRCLE-To*k»:
99
-------
EPA's National-scale Air Toxics Assessment
Exhibit 53. Benzene: 2011 Boxplots of Observed and Modeled Concentrations (top) and Modeled-
Observed Bias Difference in Concentrations (bottom) for the Hybrid, CMAQ, and HEM-3 Models
2011v2eg_NATA_CMAQ_annual Benzene for 20110101 to 20110131
-
3
¦ AQS_Daily_PM
~ 2011 v2eg_NATA_CM AQ_an nual
¦ 2011 v2eg_NATA_AERMOD_annual
¦ 2011 v2eg_NATA_Hybrid_annual
r
0.18
0.22
FtMSE
0.83
0.8
NMB
-22.7
-33.1
1.6
NME
57.8
60.2
60.7
MB
-0.2
-0.28
0.01
ME
0.52
0.52
1
AOS Daily PM
1 T
2011v2eg NATA AERMOD annual
if)
S
2011v2eg_NATA_CMAQ_annual Benzene for 20110101 to 20110131
E
d)
CD
~ 2011 v2eg_N ATA_C M AQ_an n ual
¦ 2011 v2eg_N ATAAE RMODannual
¦ 2011 v2eg_NATA_Hybrid_annual
O
O
o
O
o
o
o
o
r
0.18
0.22
RMSE
0.83
0.8
NMB
-33.1
1.6
NME
60.2
60.7
MB
-0.28
0.01
ME
0.52
0.52
2011v2eg NATA CMAQ annual
1
2011v2eg NATA Hybrid annual
100
-------
EPA's National-scale Air Toxics Assessment
Exhibit 54. Benzene: 2011 Mean Bias (%) at Monitoring Sites in the Hybrid Modeling Domain
Benzene MB (ug/m3) for rur>201 leg NATAHYBRID annualallHAPs for 20111001 to 20111031
units = ug.'m3
coverage limit =
> 2
18
1,6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
-0.8
-1
-1.2
-1.4
-1.6
-1.8
< -2
CIRCLE=Toxics;
Exhibit 55. Benzene: 2011 Mean Error (%) at Monitoring Sites in the Hybrid Modeling Domain
CIRCLE=Toxics;
101
-------
EPA's National-scale Air Toxics Assessment
Exhibit 56. Benzene: 2011 Mean Bias (%) at Monitoring Sites in the CMAQ Modeling Domain
Benzene MB (ug.'m3) for run2011eg NATA_CMAQ annual allH APs for 20111001 to 20111031
Exhibit 57. Benzene: 2011 Mean Error (%) at Monitoring Sites in the CMAQ Modeling Domain
Benzene ME (ug/m3) for run2011eg_NATA_CMAQ_annual_allHAPs for 20111001 to 20111031
CIRCLE=Toxics:
102
-------
EPA's National-scale Air Toxics Assessment
Exhibit 58. Benzene: 2011 Mean Bias (%) at Monitoring Sites in the HEM-3 Modeling Domain
Benzene MB (ug/m3) far run201 leg NATA AERMOD annual allHAPs for 20111001 to 20111031
Exhibit 59. Benzene: 2011 Mean Error (%) at Monitoring Sites in the HEM-3 Modeling Domain
Benzene ME (ug/m3) for run2011eg_NATA_AERMOD_annual_allHAPs for 20111001 to 20111031
CIRCLE=Toxics;
3.3.5 Non-hybrid Evaluation
To estimate the ability of HEM-3 to replicate the 2011 observed ambient concentrations of air toxics, we
conducted an annual operational model performance evaluation for air toxics used in the non-hybrid
model calculation. Statistical assessments of modeled versus observed concentrations were paired in time
and space and aggregated on an annual basis. Exhibit 60 contains a list of air toxics evaluated m the non-
hybrid model performance evaluation and the number of paired sites (based on completeness criteria of
observations, Section 3.3.2) used in the annual average. Exhibit 61 is a map of the 2011 monitoring
locations for non-hybrid air toxics. Complete results from the non-hybrid evaluation are presented in
Appendix F; paired values of the monitored and modeled data are provided in the spreadsheet
103
-------
EPA's National-scale Air Toxics Assessment
"nonhybridpolls-modelevaluationpaired.xlsx" in the Supplemental Data folder. Data-completeness and
measurement-uncertainty issues exist for: 1,1,1-trichloroethane, 1,1,2-trichloroethane , 1,2,4-
trichlorobenzene, alpha-Chlorotoluene, hexachloro-l,3-butadiene, 1,1-dichloroethane , 1,1-
dichloroethylene, chloroethane, and 3-chloropropene; therefore, these air toxics were not included in the
non-hybrid evaluation. Also excluded were air toxics with no monitoring data available.
Exhibit 60. Non-hybrid Air Toxics Evaluated
Non-hybrid Air Toxic
Number of Paired Sites
4-Methyl-2-pentanone
35
Acetonitrile
36
Antimony (TSP) LC
11
Antimony PM-c LC
18
Bromomethane
26
Carbon disulfide
23
Carbon tetrachloride
163
Chloromethane
155
Cobalt (TSP) LC
12
Cobalt PM10 LC
15
Ethylbenzene
170
Non-hybrid Air Toxic
Number of Paired Sites
Hexane
125
llsopropylbenzene
23
Methyl tert-butyl ether
6
Propanal
68
Selenium (TSP) LC
11
Selenium PM10 LC
14
Selenium PM2.5 LC
58
Styrene
71
Tribromomethane
5
Vinyl Acetate
25
Exhibit 61. 2011 Monitoring Locations for the Evaluation of Non-hybrid Air Toxics
ONTARIO
¦ ° MONONA NOUTHOAKOTA
f
4'
¦Z-
IDAHO
"» "''""a-iscoKsm MICHIGAN O
WYOMING MHv-aukeeT q Roclfeten? new ^ ** in
'"Tltt, ~ NEBRASKA f „ —^Ekydand O
* ~ ""N"?5 <*#***'-
If 1/1 tolORADO ?AKSAS ? C— ** Y
, • T~ -r I < . JO Washington D C.
NFVADA 1 Missouri o SfoUiyille ^ o
W, a UNITED STuATES y ^Untuck, ' ^SS.'n'T
KufcIJnia . a T ^ O
•FrSSo' ' k" . l-j oArtu *V9 ,
f ' -4oio«Jpo I OKI .I&H Y plIteai;
I- PLATEAU a T J ° 1
ARIZONA <*» [ARKANSAS T S CAROLINA
W™ NF.W»E.,Jfo ¦&¥. V'c"4l,
^ * y-
°EI Paso TEXAS a . •. (
' 1 I' V'
&
AnI
<
„ ••••
°"|mS FL^RW
£
*q Timr,.i
Torreon "^.Wonkrrey ¦ ^own5vl,';
Cui,»c«n M p v , r
104
-------
EPA's National-scale Air Toxics Assessment
3.4 Summary
• Forty of the more prevalent and higher-risk air toxics, accounting for 99 percent of the national
human-health risk of all NATA air toxics, were modeled for air transport using a hybrid approach
with CMAQ and HEM-3 (with AERMOD).
• Redundancies and double counting were minimized in the hybrid-modeling approach by
anchoring air concentrations to those estimated by CMAQ.
• All other air toxics were modeled solely in HEM-3 (with AERMOD), except for background
concentrations, which were estimated using monitoring and emissions data.
• Model evaluation generally shows better agreement for air toxics with more measurement sites
and fewer measurement uncertainties, such as benzene.
• Characterization of model performance for NATA is significantly constrained given the multiple
air toxic species assessed, limited air toxics observations and monitoring network, as well as
multiple species across multiple spatial scales of interest. There are no accepted benchmarks for
air toxics evaluation, in contrast to ozone model performance where error and bias for paired
(monitoring site and hourly resolution) comparisons typically are within 35 and 15 percent,
respectively (Russell and Dennis 2000; Seigneur and Dennis 2010). Such criteria are not realistic
for air toxics due to characterization uncertainty in air toxics data.
105
-------
EPA's National-scale Air Toxics Assessment
This page intentionally left blank.
106
-------
EPA's National-scale Air Toxics Assessment
4 ESTIMATING EXPOSURES
FOR POPULATIONS
Estimating inhalation ECs is a critical step in determining potential health risks because ambient
concentrations do not take into account movements of individuals among geographic locations and
microenvironments where pollutant concentrations can differ. Different individuals have different daily
activities, spend different amounts of time engaged in those activities, and engage in those activities in
different locations. Most activities occur in indoor environments (e.g., the home, workplace, school, and
vehicles), where pollutant concentrations can differ from those in the outdoor environment. Therefore, the
average concentration of a pollutant that people breathe can differ significantly from the ambient
concentration at a fixed outdoor location.
Please refer to the version of this document accompanying the 2005 NATA (EPA 2011) for descriptions
of exposure estimations for previous NATAs. This section contains a discussion of how ECs were
estimated for the 2011 NATA. It begins with an overview of the hybrid approach used that included new
exposure modeling for some pollutants and applications of exposure-to-ambient concentration ratios for
the remaining NATA pollutants. This introduction 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 the 2011
NATA can be found in Appendix G.
4.1 Estimating Exposure Concentrations
For the 2011 NATA, EPA used a hybrid of direct modeling and exposure factors to estimate inhalation
ECs for NATA. 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
were used to determine potential health risks for NATA.
Direct exposure modeling used the EPA Hazardous Air Pollutant Exposure Model (HAPEM). HAPEM,
described in detail in Section 4.2, is a screening-level exposure model that estimates inhalation ECs
corresponding to estimated ambient-pollutant concentrations. EPA used version 7 of HAPEM (i.e.,
HAPEM7) for the 2011 NATA for a selected group of pollutants. For each modeled pollutant and NATA
category (i.e., point, nonpoint, onroad mobile, and nonroad mobile), and for each census tract, EPA also
calculated the ratio of EC to ambient concentration (i.e., an exposure factor). For the remaining pollutants
not modeled with HAPEM, EPA selected one of the modeled pollutants as a surrogate, and sometimes the
selected surrogate was specific to a NATA category. Per census tract, EPA multiplied the ambient
concentration of the pollutant by the surrogate's exposure factor, resulting in estimated ECs. Section 4.4
contains additional description of 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 initial version of HAPEM used carbon
monoxide as a tracer for highway mobile-source air toxic emissions. EPA has since updated and
improved HAPEM to enable the prediction of 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. More recent versions of HAPEM incorporate a range of
107
-------
EPA's National-scale Air Toxics Assessment
enhancements, and, as a result, HAPEM version 4 and later versions can be used to predict annual
average human-exposure levels on a nationwide basis at a spatial resolution as fine as the census-tract
level (EPA 2002b. EPA 2005d. EPA 2007. EPA 2015c). The enhancements incorporated into recent
versions of HAPEM facilitate its use for regional and national inhalation risk assessments such as NATA.
Inhalation ECs for the 1996, 1999, and 2011 NATAs were estimated using HAPEM4, HAPEM5, and
HAPEM7, respectively (EPA did not use HAPEM6 for NATA). Exhibit 62 outlines some key differences
between these three versions. A complete history of HAPEM can be found in the User's Guide for
HAPEM7 (EPA 2015c), the latest version of HAPEM available at the time this document was prepared.
Exhibit 62. 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
autocorrelation
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 among geographic locations. As
described in the following section, personal-activity and commuting data specific to a hypothetical
individual's demographic groups are used to determine the census tracts containing residential and work
locations and the microenvironments within each tract. Empirically based factors reflecting the
relationship between ECs within each microenvironment and the outdoor (ambient) air concentrations at
that location are selected by the model through a stochastic sampling process to estimate ECs.
To estimate long-term ECs for a hypothetical individual, 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 person is
a hypothetical individual residing at the centroid of a census tract and engaging in a range of activities
(both indoor and outdoor) representative of those in which individuals of that demographic group in that
census tract might engage. Additional technical information on HAPEM can be found in the User's Guide
for HAPEM7 (EPA 2015c).
4.3 HAPEM Inputs and Application
HAPEM requires four primary 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)
108
-------
EPA's National-scale Air Toxics Assessment
microenvironmental data. The subsections below contain additional discussion on these inputs,
accompanied by descriptions of the data used for NATA 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 is typically applied using annual-average, diurnally distributed ambient air concentrations. Input
concentrations can be monitoring data or concentrations estimated using a dispersion model or other air
quality model.
For the 2011 NATA, EPA estimated annual-average ambient concentrations for each census tract using a
hybrid CMAQ-HEM-3 approach discussed in Section 3. EPA stratified the air quality outputs for a
selected group of pollutants by one or more of the four principal NATA categories (i.e., point, nonpoint,
onroad mobile, and nonroad mobile), and EPA used those results as surrogates for the remaining
pollutants not modeled in CMAQ-HEM-3. Thus, exposure-model results generated for NATA can be
summarized for each principal NATA 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 encompass 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. In HAPEM7 used for the 2011 NATA,
cohorts were defined using residential census tract and age so that the population in each census tract was
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 draws on 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 in coordination
to place a hypothetical individual who commutes to work either in the home tract or the work tract and in
a specific microenvironment at each 3-hour time step (the time step used for NATA). The
microenvironment assignments and locations derived from these data are then used to calculate ECs, as
explained in the next section. EPA discusses these two types of data in more detail in the following
paragraphs.
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 NATA is
EPA's Consolidated Human Activity Database (CHAD; EPA 2015a). To develop the version of CHAD
used in the 2011 NATA (i.e., version June 2014), data from 21 individual U.S. studies of human activities
were combined into one comprehensive data system that contains over 45,000 person-days of activity-
pattern records (previous NATAs used previous versions of CHAD containing of fewer studies and
person-days of data). Because of limitations of the study designs of the surveys from which it is derived,
CHAD might not be representative of all demographic groups, particularly ethnic minorities and low-
income populations. Another limitation of the activity-pattern data in CHAD is that most are for
individuals over a one- or two-day period only. Extrapolation of these short-term records to the annual
109
-------
EPA's National-scale Air Toxics Assessment
activity patterns required for assessments of air toxics exposure introduces some uncertainty into the
analysis.
The algorithms in HAPEM address this extrapolation uncertainty by implementing 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 represent the variability more fully 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. These algorithms were used beginning with HAPEM5 (i.e., beginning
with the 1999 NATA).
Commuting-pattern data, the second type of population activity data used in HAPEM, are derived for
each cohort from a U.S. Census database containing information on tract-to-tract commuting patterns.
These data specify the number of residents in each tract that work in that tract and every other census tract
(i.e., the population associated with each home-tract/work-tract pair) and the distance between the
centroids of the two tracts. An important limitation is that the commuting-pattern data included in
HAPEM do not account for the movement of school-age children who travel (or commute) to a school
located outside of their home tract.
4.3.4 Microenvironmental Data
A microenvironment is a three-dimensional space in which human contact with an environmental
pollutant occurs. In HAPEM, this space is treated as a well-characterized, relatively homogenous location
with respect to pollutant concentrations for a specified period. The inhalation exposure estimate is
determined by the sequence of microenvironments visited by the individual. The concentration in each
microenvironment is estimated by using the three microenvironmental factors listed below to adjust the
ambient-concentration estimate for the census tract where it is located:
• a penetration factor that is an estimate of the ratio of the microenvironmental concentration to
the concurrent outdoor concentration in the immediate vicinity of the microenvironment;
penetration factors are pollutant-specific estimates that are derived from reported measurement
studies;
• a proximity factor that is an estimate of the ratio of the outdoor concentration in the immediate
vicinity of the microenvironment to the outdoor concentration represented by the ambient air
concentration input to the model; and
• 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 NATA.
The relationship between the estimated ECs, the input ambient concentrations, and these three factors is
demonstrated by the equation below.
110
-------
EPA's National-scale Air Toxics Assessment
C(i,k,t) = CONC^i ^ X PENk X PROXk + ADDk
Where:
Caxt) = EC predicted within census tract /' and microenvironment k for time step t. in units
of (.ig/ni3
CONG,./, = ambient concentration for census tract /' for time step t. in units of (ig/m3
PEN/, = penetration factor for microenvironment k
PROX/, = proximity factor for microenvironment k
AD Da = additive factor accounting for sources within microenvironment k, in units of
(ig/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. The 2011 NATA used the 18 microenvironments
shown in Exhibit 63. 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 NATA because indoor-source data are currently
incomplete (recall that NATA 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
with regard to this and other issues for NATA.
111
-------
EPA's National-scale Air Toxics Assessment
Exhibit 63. Microenvironments Used in the HAPEM Modeling for the 2011 NATA
Indoors
Outdoors
In Vehicle
Residence
Near-road
Car/Truck
Residential
Motorcycle/Bicycle
Public Transit
Other Building
Outdoors, Near Roadway
Air Travel
Outdoors, Parking Garage
Bar/Restaurant
Outdoors, Service Station
Hospital
Residential Garage
Office
Waiting Outdoors for Public Transit
Public Access
Away-from-road
School
Ferryboat
Waiting Inside for Public Transit
Outdoors, Other
4.4 Exposure Factors
HAPEM exposure modeling for NATA requires substantial time and resources for data collection and
processing, computing, and model processing. Due to these requirements, we conducted HAPEM
modeling for the 2011 NATA only for selected pollutants, which we present below along with how we
used them to estimate ECs for the remaining NATA pollutants.
• Coke oven emissions (emitted by point sources and present in ambient air as either particulates
or gases) and DPM (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 NATA 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
NATA 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 NATA 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 NATA
categories, and 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.
Exhibit G-l in Appendix G to this document contains the overall average exposure-to-ambient
concentration ratios (i.e., exposure factors) calculated from HAPEM and air quality outputs for each
pollutant. EPA presents this factor for each of the four principal NATA categories (i.e., point, nonpoint,
onroad mobile, and nonroad mobile). EPA used factors specific to each census tract, so these averages are
for summary/informational purposes only. Overall, the HAPEM exposure predictions are lower than the
corresponding predicted air quality values. This reduction likely results from the inability of many
112
-------
EPA's National-scale Air Toxics Assessment
pollutants to penetrate efficiently into an indoor environment. (Recall that indoor sources of air toxics
have not been included in any versions of NATA completed to date).
4.5 Quality Assurance in Exposure Modeling
A model-performance evaluation can provide valuable information regarding model uncertainty when
using computer-simulation models of human exposures to pollutants, and a well-conducted evaluation can
substantially increase confidence in model results for a given application or use. One type of performance
evaluation is the use of measurements and environmental data as a benchmark for comparison of
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 NATA 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 NATA. 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.
4.6 Summary
• Estimating inhalation ECs is a critical step in determining potential health risks because ambient
concentrations do not account for movements of individuals among geographic locations and
microenvironments where pollutant concentrations can differ.
• We estimated inhalation ECs for each census tract for the 2011 NATA using direct exposure
modeling with HAPEM7 for some pollutants and exposure-factor surrogates for the remaining
pollutants.
• These tract-level ECs can be used to determine potential health risks.
113
-------
EPA's National-scale Air Toxics Assessment
This page intentionally left blank.
114
-------
EPA's National-scale Air Toxics Assessment
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 process of identifying and quantifying the
adverse health effects associated with exposure to a chemical is accomplished with EPA risk assessment
methods by way of a toxicity assessment. As indicated in Exhibit 2 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).10 Ultimately, the results of the toxicity assessment, referred to in this
document as "toxicity values," are used in conjunction with exposure estimates to characterize the health
risks for exposed populations as described in Section 6. Although the toxicity assessment is integral and
important to the overall air toxics risk assessment, it is usually accomplished prior to the risk assessment.
We have completed this toxicity assessment for many air toxics and have made available the resulting
toxicity information and dose-response values, which have undergone extensive peer review.
This section contains explanations of how toxicity assessments are used in the NATA risk assessment
process. Specifically, the sections that follow provide an overview of the cancer and noncancer toxicity
values used in NATA and the primary sources of these values. Several adjustments and assumptions to
toxicity values that are specific to the NATA risk assessment process are also described.
5.1 Toxicity Values and Their Use in NATA
The toxicity values used for NATA 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
NATA is focused on long-term exposures, the toxicity values used in NATA 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 period of time (possibly a lifetime, but the time frame also can be
shorter).
The toxicity values that are combined with ECs to conduct the risk characterization in NATA 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 NATA, the results
of cancer dose-response assessments for a given chemical were converted to a URE that incorporates
10 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.
115
-------
EPA's National-scale Air Toxics Assessment
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,
chronic dose-response data were used 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 NATA are consistent with those OAQPS has compiled for chronic inhalation
exposures to air toxics. The full set of toxicity values (and the sources of the values) used for the 2011
NATA are found in Appendix H. Sources of chronic dose-response assessments used for the 2011 NATA
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 NATA is best described as an estimate within a range of possible values
appropriate for screening-level risk assessments. Of importance to note is that the uncertainty in the dose-
response assessments and toxicity values that NATA 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 NATA 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, a
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 as a result of exposure over a 70-year lifetime to a given hazard (e.g.,
exposure to an air toxic).
For an inhalation risk assessment (and for NATA), 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. Specifically, the
URE represents the upper-bound of the excess cancer risk estimated to result from continuous exposure to
a concentration of 1 (ig 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 1.5 x 10~6 (ig/m3, no more than 1.5 excess tumors
would develop per 1,000,000 people if they were exposed daily for a lifetime to a concentration of 1
(ig/m3. To the extent that true dose-response relationships for some air toxics compounds are not strictly
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 (|jg/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.
116
-------
EPA's National-scale Air Toxics Assessment
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 provides an estimate of 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. These data are evaluated 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 and 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. This
descriptor 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.
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 not sufficient 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
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
117
-------
EPA's National-scale Air Toxics Assessment
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 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 positive animal
experiment(s) but the results are judged to be not relevant to humans, the narrative discusses why the
results are not relevant.
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 NATA are classified as "likely" carcinogens. The term likely, as used in this instance, means that data
are not sufficient 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 of the air toxics were developed from animal data using health-
protective methods to extrapolate to humans. Actual human responses might differ from those predicted.
For more information, see EPA's Guidelines for Carcinogen Risk Assessment (EPA 2005a).
5.2.2 Noncancer Chronic RfC
The RfC is an estimate of a continuous
inhalation exposure that is thought to be The RfC is an estimate (with uncertainty spanning
without an appreciable risk of deleterious Pehrh,aPs an order of magnitude) of a continuous
¦ rr j T-i i inhalation exposure to the human population (including
health effects over a lifetime. The population sensitive subgroups) that is likely to be without an
considered in the derivation of RfCs includes appreciable risk of deleterious effects during a lifetime.
sensitive subgroups (i.e., children, asthmatics,
and the elderly). The RfC is derived from the review of a health-effects database for a chemical, and
identification of 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 that 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-
118
-------
EPA's National-scale Air Toxics Assessment
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 typically is 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 NATA in Section 6 of
this document.
5.3 Data Sources for Toxicity Values
Information on dose-response assessments for evaluating chronic exposures for NATA was obtained from
multiple sources and prioritized according to OAQPS risk assessment guidelines and level of peer review.
We have an approach for selecting appropriate toxicity values and, in general, this approach places
greater weight on the EPA-derived toxicity values than those from other agencies (listed below).
Additionally, the approach of favoring EPA values (when they exist) has been endorsed by EPA's
Science Advisory Board, and it ensures the use of values most consistent with well-established and
scientifically based EPA science policy. Appendix H to this document lists the toxicity values, the source
of those values, and supporting information for both cancer and noncancer chronic effects used in the
2011 NATA. Cancer effects are characterized according to the extent to which available data support the
hypothesis that a pollutant causes cancer in humans. Additional information on individual air toxics is
included in the footnotes to the table in Appendix H.
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
NATA. 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 NATA
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
119
-------
EPA's National-scale Air Toxics Assessment
effects (other than cancer) over a specified duration of exposure. For noncancer values in the 2011
NATA, 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 and
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 the 2011 NATA, 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 2014). 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 2008a) 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,
were last updated in 2001. NATA 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 2015) on the carcinogenicity of a wide range of substances. The following "degrees
of evidence" the International Agency for Research on Cancer has published are included in Appendix H
to this document as supporting information when EPA WOE determinations were not available for a
substance or are out of date:
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.
120
-------
EPA's National-scale Air Toxics Assessment
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
NATA. Important changes made for the 2011 NATA are outlined below and are reflected in Appendix H
to this document.
5.4.1 Polycyclic Organic Matter
A substantial proportion of polycyclic organic matter (POM) reported in the 2011 NEI was not speciated
into individual compounds. For example, some emissions of POM were reported in NEI as "7-PAH" or
"16-PAH," representing subsets of certain POM, or simply as "total PAH" or "polycyclic organic matter."
In other cases, individual POM compounds were reported for which no quantitative cancer dose-response
value has been published in the sources used for NATA. As a result, simplifying assumptions that
characterize emissions reported as POM were applied so that cancer risk could be quantitatively evaluated
for these species without substantially under- or overestimating risk (which can occur if all reported
emissions of POM were 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 and on information for the POM speciation of emission sources, such
as wood fires and industrial processes involving combustion.
For the 2011 NATA, unspeciated POM emissions were divided into eight POM groups. The first two
groups included unspeciated POM (including "total PAH") and individual POM species with no URE
assigned. Both groups were assigned a URE equal to 5 percent of that for pure benzo[a]pyrene taking into
account toxicity and the estimated emission profile of POM compounds. Groups 3 through 7 comprised
POM compounds, the emissions for which were reported as individual compounds and for which UREs
have been estimated. Compounds in these groups were categorized based on toxicity, and an appropriate
URE was assigned to each category based on toxicity of the compounds included in the group. Category 8
was composed of unspeciated polynuclear aromatic hydrocarbons reported as 7-PAH and was assigned a
URE equal to 18 percent of that for pure benzo[a]pyrene. We discuss the POM groups used for the 2011
NEI in Section 2.1.1.2.
We concluded that three PAHs—anthracene, phenanthrene and pyrene—are not carcinogenic and
therefore no URE was assigned for the 2011 NATA. 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) 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.
121
-------
EPA's National-scale Air Toxics Assessment
5.4.3 Metals
Several decisions made for the 2011 NATA regarding the toxicity values used for metal compounds are
discussed in this section.
Chromium (VI) compounds. The IRIS RfC for particulate chromium (VI) was used 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 NATA.
Nickel compounds. The cancer inhalation URE for most of the emissions of nickel compounds included
in NATA (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 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 NATA 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 crystalline, insoluble form.
5.4.4 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 2005c). Where available data do
not support a chemical-specific evaluation of differences between adults and children, the Supplemental
Guidance recommends using the following default adjustment factors for early-life exposures: increase
the carcinogenic potency by 10-fold for children up to 2 years old and by 3-fold for children 2 to 15 years
old. These adjustments have the aggregate effects of increasing by about 60 percent the estimated risk (a
1.6-fold increase) for a lifetime of constant inhalation exposure. EPA recommends that these default
adjustments be made only for carcinogens known to be mutagenic for which data to evaluate adult and
juvenile differences in toxicity are not available.
122
-------
EPA's National-scale Air Toxics Assessment
For NATA 2011, the UREs for acrylamide, benzidine, chloroprene, coke oven emissions, ethyl
carbamate, methylene chloride, nitrosodimethylamine, and PAHs were adjusted upward, by multiplying
by a factor of 1.6, to account for the increased risk during childhood exposures. Although
trichloroethylene is carcinogenic by a mutagenic mode of action, the age-dependent adjustment factor for
the URE only applies to the portion of the slope factor reflecting risk of kidney cancer. For full lifetime
exposure to a constant level of trichloroethylene exposure, the URE was adjusted upward by a factor of
1.12 (rather than 1.6 as discussed above). For more information on applying age-dependent adjustment
factors in cases where exposure varies over the lifetime, see Toxicological Review of Trichloroethylene
(EPA 2014c). These air toxics are the only ones that met the criteria described in the previous paragraph
at the time of this assessment. The overall lifetime adjustment was applied because a single, lifetime-
average EC was estimated for NATA rather than age-group-specific exposures. The URE for vinyl
chloride includes exposure from birth, although the IRIS assessment contains UREs for both exposure
from birth and exposure during adulthood. This value already accounts for childhood exposure; thus, no
additional factor was applied.
5.4.5 Diesel Particulate Matter
EPA uses an IRIS RfC for adverse noncancer effects of diesel PM. Recently, several large epidemiology
studies (Attfield. et al. 2012; Garshick. et al. 2012; Silverman, et al. 2012) have been published that
strengthen the WOE that diesel exhaust is carcinogenic to humans. Two of these studies included
quantitative estimates of exposure. Partly on the basis of these studies, the International Agency for
Research on Cancer elevated its classification of diesel exhaust to "carcinogenic to humans" (Group 1)
in 2012 (I ARC 2013). We requested the Health Effects Institute (HEI) evaluate the suitability of the new
epidemiology studies for developing a cancer potency. In November 2015, HEI published its report on
these new studies (HEI 2015) and concluded that they are sufficiently robust to estimate quantitative
cancer risks and estimate uncertainties. At this time, a URE for diesel PM, based on these current studies,
has not yet been derived and a quantitative assessment of the cancer risk has not been included in the
2011 NATA. Evidence exists, however, that the general population is exposed to levels close to or
overlapping with apparent levels that have been linked to increased cancer risk in epidemiological studies.
Based on the Health Assessment Document for Diesel Engine Exhaust (EPA 2002a), we concluded that
national-average lifetime cancer risk from exposure to diesel exhaust (which contains both gases and
particulate matter) could exceed 1 in 100,000 and could be as high as 1 in 1,000, although the lower end
of the risk range includes zero.
5.5 Summary
• To evaluate the potential of a given air toxic to cause cancer and other adverse health effects, we
identified potential adverse effects that a particular substance causes and evaluated the specific
ECs at which these effects might occur.
• The URE represents the upper-bound excess cancer risk estimated to result from continuous
exposure to a concentration of 1 (ig of a substance per m3 of air over a 70-year lifetime.
• 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 deleterious effects. The population considered in the derivation of
RfCs includes sensitive subgroups (i.e., children, asthmatics, and the elderly).
• 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.
123
-------
EPA's National-scale Air Toxics Assessment
• After considering dose-response information, EPA adjusts some chronic-toxicity values to
increase accuracy and to avoid underestimating risk.
124
-------
EPA's National-scale Air Toxics Assessment
6 CHARACTERIZING RISKS AND HAZARDS
IN NATA
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, providing 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 the EC to an RfC associated with
observable adverse health effects (i.e., an HQ).
This section contains information on the risk characterization conducted for NATA. After a brief
overview of the risk-related questions that NATA is intended to address, the methods used to conduct
characterization of cancer risk and noncancer hazards for NATA are described. A discussion of the
quantitative results included in NATA follows this description.
6.1 The Risk-characterization Questions NATA Addresses
The NATA 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 NATA 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 information to improve future
assessments. 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 NATA, which was limited to inhalation risk from outdoor sources, was designed to
answer the following questions:
• Which air toxics pose the greatest potential risk of cancer or adverse noncancer effects across the
entire United States?
• Which air toxics pose the greatest potential risk of cancer or adverse noncancer effects in specific
areas of the United States?
• Which air toxics pose less, but still significant, potential risk of cancer or adverse noncancer
effects across the entire United States?
• When risks from inhalation exposures to all outdoor air toxics are considered in combination,
how many people could experience a lifetime cancer risk greater than levels of concern (e.g., 1-
in-1 million)?
• When potential adverse noncancer effects from long-term exposures to all outdoor air toxics are
considered in combination for a given target organ or system, how many people could experience
exposures that exceed the reference levels intended to protect against those effects (i.e., a hazard
quotient greater than 1)?
6.2 How Cancer Risk is Estimated
To estimate cancer risks in NATA, the results of cancer dose-response assessments for a given chemical
were converted to a URE that is then multiplied by the estimated inhalation EC to obtain an estimate of
125
-------
EPA's National-scale Air Toxics Assessment
individual lifetime cancer risk. The approach used in NATA for characterizing cancer risk is consistent
with EPA's 2005 final Guidelines for Carcinogen Risk Assessment (EPA 2005a). When used in
conjunction with the cancer UREs described in Section 5, the approach is also consistent with EPA's
associated documentation on Supplemental Guidance for Assessing Susceptibility from Early-Life
Exposure to Carcinogens (EPA 2005c).
6.2.1 Individual Pollutant Risk
Individual lifetime cancer risk associated with exposure to a single air pollutant was estimated by
multiplying an average estimated long-term EC by the corresponding URE for that pollutant. Thus, the
below equation estimates the probability of an individual developing cancer over a lifetime due to a given
inhalation exposure.
Risk = EC x URE
Where:
Risk = estimated incremental lifetime cancer risk for an individual as a result of 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 |ig/m3
URE = the corresponding inhalation unit risk estimate for that air toxic, in units of
l/(fig/m3)
Of importance to note is that UREs are typically upper-bound estimates, so actual risks might be lower
than predicted. Also, the true value of the risk is unknown.
6.2.2 Multiple-pollutant Risk
The individual lifetime cancer risk resulting from exposure to multiple air toxics was estimated by
summing the chronic cancer risk for each air toxic that can be quantified. This estimate of risk focused 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 = Ris/q + Risk2 + —I- Riski
Where:
Risktot = total cumulative individual lifetime cancer risk, across /' substances
Risk = individual risk estimate for the/'th substance
For NATA, the estimated ECs were not considered upper bounds. Rather, they represented central-
tendency estimates of ECs for each demographic group at the geographic unit of analysis (e.g., the
census-tract level). Because cancer slope factors were 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.
126
-------
EPA's National-scale Air Toxics Assessment
The NATA approach assumed an additive effect from simultaneous exposures to several carcinogens.
Summing cancer risk estimates is not appropriate when effects from multiple chemicals are synergistic
(greater than additive) or antagonistic (less than additive). Notwithstanding the statistical limitations of
summing traditional risk estimates and the implicit assumption that the toxicities will be additive (i.e., no
interactions such as synergism or antagonism occur), the numerical ease for combining risk in this way
makes this method the most popular for approximating cumulative risks in the short term, at least for a
screening level of assessment. Information on non-additive interactions is not readily available in a form
that can be used for NATA. In the absence of specific information, therefore, 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 NATA. More information on EPA's methods for conducting risk assessment of mixtures can be
found in the Framework for Cumulative Risk Assessment (EPA 2003).
6.3 How Noncancer Hazard is Estimated
To evaluate the potential for noncancer adverse health effects,
chronic dose-response data were used to estimate a threshold that
is the EC at which adverse health effects are assumed to be
unlikely (i.e., the RfC). (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 procedures for
the identification of carcinogens. EPA has published guidelines, however, for assessing several specific
types of chronic noncancer effects (mutagenicity, developmental toxicity, neurotoxicity, and reproductive
toxicity) that can be found at Products and Publications Relating to Risk Assessment Produced by the
Office of the Science Advisor (EPA 20151)._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's Chronic Noncancer
Guidelines
• Mutagenicity (EPA 1986)
• Developmental Toxicity (EPA 1991)
• Neurotoxicity (EPA 1998)
• Reproductive Toxicity (EPA 1996)
Chronic noncancer hazards were estimated for NATA by dividing a chemical's estimated long-term EC
by the RfC for that chemical to yield an 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
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 1.0 indicates that the exposure is not likely to result in adverse
noncancer effects. An HQ value greater than 1.0, however, does not necessarily suggest a likelihood of
adverse health effects and cannot be interpreted to mean that adverse health effects are statistically likely
127
-------
EPA's National-scale Air Toxics Assessment
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
Chronic noncancer hazards for multiple air toxics were estimated by summing chronic noncancer HQs for
individual air toxics that cause similar adverse health effects to yield 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 = HQ1 + HQ2 + - + HQi
Where:
HI = the hazard index for chronic exposure to air toxics 1 through /, unitless
HQi = the hazard quotient for the rth air toxic, where all / 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 1.0 indicates that the exposure is not likely to result in
adverse noncancer effects. An HI value greater than 1.0, 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 of
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 NATA, noncancer
hazards could be combined for six target organs or systems: respiratory, cardiovascular, blood,
liver/kidney, nervous, and immune. Results from the assessment indicated that the primary noncancer
hazards for inhalation exposures to the modeled chemicals were respiratory hazards. As a result, the 2011
assessment presents noncancer results for all target organ endpoints in the form of HQs; His are
reported only for respiratory endpoints.
6.4 How Risk Estimates and Hazard Quotients are Calculated for NATA 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 NATA 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). NATA point, nonpoint, mobile onroad, and mobile nonroad sources were
modeled at the census-block level in HEM-3. However, nonpoint, mobile onroad, and mobile nonroad
(except CMV) emissions were allocated from county to the census-tract level. Secondary formation, fires
(wild, prescribed and agricultural field burning) and primary biogenic concentrations were estimated at
the 12km grid level using CMAQ.
128
-------
EPA's National-scale Air Toxics Assessment
6.4.1 Model Results for Point Sources: Aggregation to Tract-level Results
HEM-3 was used to estimate ambient concentrations for point-source emissions, and model results were
generated at the block level. For risk and exposure calculations, we aggregated concentration results to
the tract level by taking a population-weighted average of all of the block-level concentrations within a
given tract, as follows:
^ P0Pblock j * C°ncblock j
LouCiraci i — v-i n
2j PopbiQc-k j
Where:
Conetracti = ambient concentration for census tract i
Conebiockj = ambient concentration for census block j (contained within tract /'), estimated by
HEM-3
Pop biockj = population of blocks contained in tract i
Unweighted average concentrations also were calculated at the tract level as follows:
2 Concbiock j
Conctract i
n
Where:
Cone tracti = ambient concentration for census tract i
Cone biockj = ambient concentration for census block j (contained within tract /'), estimated by
HEM-3
n = number of census blocks contained in tract /
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, regional, and national level
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,
129
-------
EPA's National-scale Air Toxics Assessment
county-level concentration. The process for aggregating from the tract to the county level can be
expressed using the following equation:
„ _ Yi(C°nctract i * P0Ptract i)
L onccounty k — —-
rutJcounty k
Where:
Conccountyk = population-weighted concentration for county k
Conetracti = ambient concentration in tract /' (contained within county k)
Poptracti = population in tract /' (contained within county k)
P OQcountyk = population in county k
This same method was applied when aggregating up to the state, regional, or national level, using the
appropriate concentration and population values. NATA includes ambient concentrations, ECs, cancer
risks, and noncancer HQs at the tract, county, state, regional, 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 the HAPEM modeling
(i.e., ratios of EC to estimated ambient concentration). (See Section 4 for a more thorough discussion of
NATA 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-, regional-, or national-level concentrations,
the tract-level concentrations were population-weighted.
6.5 The Risk Characterization Results that NATA Reports
NATA 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 2011. Individuals were assumed to spend their entire lifetimes exposed to these air
toxics. Therefore, 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, were not
accounted for. Each NATA represents an update and enhancement to the previous NATA. Because
improvements in methodology are made with each successive assessment, comparing assessment
results from year to year is not meaningful. Any change in emissions, ambient concentrations, or risks
might be due to either improvement of methodology or to real changes in emissions or source
characterization.
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. NATA
reports a summary of these findings. Cancer risks are presented as lifetime risks, meaning the risk of
developing cancer as a result of 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).
130
-------
EPA's National-scale Air Toxics Assessment
Using these quantitative results, NATA classifies certain pollutants as drivers or contributors at the
national or regional scale based on certain criteria. Exhibit 64 contains the criteria for classifying the air
toxics included in NATA 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.
Exhibit 64. NATA 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
1.0
25
Regional noncancer driver
1.0
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.
For example, for a pollutant to be categorized in NATA 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 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 NATA as a regional driver of noncancer health effects, the
chronic hazard index for that pollutant must have been shown to exceed 1.0 and the number of people
exposed to that pollutant must have been shown to be at least 0.01 million.
The NATA results for 2011 indicated that most individuals' estimated risk was between 1-in-l million
and 100-in-l million, although a small number of localized areas showed risks of higher than 100-in-l
million. Although individuals and communities might be concerned about these results, recall that NATA
was 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. Furthermore,
readers are reminded that the risks estimated by the assessment do not consider indoor sources of air
toxics or ingestion exposure to any pollutants. Also, although NATA 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 air toxics.
Analytical results (including modeled ambient concentrations, exposure, and risks) for each NATA are
also provided at the census-tract, county, and state level for those who wish to do their own technical
131
-------
EPA's National-scale Air Toxics Assessment
analyses using the most refined output available. The results from all NATAs can be found here. In
performing such analyses, users must be extremely mindful of the purposes for which NATA was
developed. NATA was developed as a tool to inform both national and more localized efforts to collect
air toxics information and characterize emissions (e.g., prioritize pollutants or geographic areas of interest
for more refined data collection such as monitoring). The results are most meaningful when viewed at the
state or national level. Nevertheless, reported spatial patterns within a county likely represent actual
variations in overall average population risks. Less likely, however, is that the assessment pinpoints the
exact locations where higher risks exist or that the assessment captures the highest risks in a county.
Using these results alone to draw conclusions about local concentrations and risk is inappropriate.
This assessment did not focus on the identification of geographic areas or populations that have
significantly higher risks than others. Rather, it focused 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, however, universal 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 NATA 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. We 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.
6.6 Summary
• The purpose of NATA is to understand cancer risk and noncancer health effects to help EPA and
others identify pollutants and source categories of greatest potential concern and to set priorities
for collecting additional information to improve future assessments.
• Cancer risk was expressed as a statistical probability that an individual will develop cancer.
Cancer risks were assumed to be additive across chemicals for NATA.
• Noncancer hazard was expressed as an HQ, which is the ratio of the EC to an RfC associated with
observable adverse effects.
• NATA estimated most individuals' risk to be between 1-in-l million and 100-in-l million,
although a small number of localized areas showed risk higher than 100-in-l million.
• Air toxics data for NATA are presented at the national, regional, state, county, and census-tract
levels. The results are most meaningful when viewed at the state or national level. Using these
results in the absence of additional information to draw conclusions about local concentrations
and risk is inappropriate.
132
-------
EPA's National-scale Air Toxics Assessment
7 VARIABILITY AND UNCERTAINTY ASSOCIATED WITH NATA
7.1 Introduction
Presented in this section are discussions
on variability and uncertainty associated
with the NATA process. Clearly
understanding these two fundamental
concepts—inherent in all broad-scale
assessments that rely on models and
data—will enable the users of the NATA
results to understand which questions can
be answered appropriately and which
cannot.
As stated in Section 1, NATA results
should not be used for limited-scale or
site-focused applications. NATA 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 the
NATA 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 geographic 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 risk from air toxics vary throughout the United States is essential for understanding NATA.
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, necessarily always involve assumptions. Assumptions are
necessary to simplify the problem at hand, while also making assessment possible given available
information and resources. Assumptions introduce uncertainties into the results because confidence that
the assumptions are entirely correct is not possible. Understanding the magnitude 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 NATA Addresses Variability
The NATA process focuses on the variation in ambient air concentrations, exposures, and risks in
geographic areas of the United States, Puerto Rico, and the U.S. Virgin Islands. Included, for example,
are variations in the locations of various sources and the amounts of pollutants that these sources emit,
variations in meteorological conditions in various parts of the country, and variations in the daily
activities of people. This section presents information on the key components that drive variability in
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.
133
-------
EPA's National-scale Air Toxics Assessment
risks associated with air toxics and the variability components that NATA addresses. A brief explanation
is also provided on how NATA results should be interpreted in light of variability.
7.2.1 Components of Variability
The NATA results show how air concentrations, exposures, and risks vary across broad geographic
regions 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. NATA results also do not fully characterize how
ambient air concentrations might vary temporally and they do not characterize how concentrations vary
spatially within a census tract. The following list contains explanations of some of the components of
variability that determine differences in ambient air concentrations and individual risks. Key components
driving variability in risk associated with air toxics include temporal variation, geographic variation, and
variations in where people live, their levels of activities, and their degrees of susceptibility or sensitivity,
as described below.
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 ambient concentrations at a particular location
depends on the degree of atmospheric dispersion of the emissions as they travel from the source to the
receptor. Dispersion depends on both meteorological conditions, which vary from place to place, and the
travel distance from source to receptor. As a result, the ambient air concentration can vary greatly among
different locations. The NATA 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 might have different average ambient concentrations. Therefore,
exposures and risks also can vary.
Individual activity patterns. Two individuals might 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 does, the
average air concentration to which the two are exposed will differ, even though the ambient air
concentration is the same. Similarly, one person might spend more time in a car than the other person
might 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 actually inhaled by these two individuals would differ. In other
words, the exposure differs for these two individuals.
In addition, buildings and vehicles vary with respect to the amount of outdoor pollution that penetrates
into the indoor and in-vehicle microenvironments due to differences in ventilation and building and
vehicle integrity. Thus, two people who live in the same location and spend the same amount of time
indoors can still be exposed to different pollutant concentrations.
Susceptibility. Two individuals might live at the same location and engage in the same activities, but one
person might be more susceptible than another might be. 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.
134
-------
EPA's National-scale Air Toxics Assessment
An individual who is more susceptible might 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 though the exposures for both individuals are the same. For example, people breathe at different
rates; two individuals placed into exactly the same air might bring different amounts of a pollutant into
their bodies. The amount of a pollutant reaching an organ or tissue also might vary from individual to
individual, even if both bring the same amount into their lungs. The amount of time the pollutant remains
in the body also might differ. Finally, the innate sensitivity to the effect might 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 these two
individuals, even though the individuals are exposed to exactly 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 NATA to understand how ambient air concentration, exposure, and risk vary
geographically and 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 NATA calculations. For example, meteorology 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 takes into account some important
temporal variations, these time-stratified model outputs are averaged prior to the risk characterization step
and are not included in the NATA results reported by EPA.
The NATA concentrations and risks, however, do reflect a degree of geographic variation. The smallest
geographic area for which NATA 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 2,500 and 8,000 residents. 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,
which are areas bounded by visible or virtual features, such as streets, streams, city, or town boundaries.
Census blocks are typically small in area; 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.
Air concentrations are estimated in NATA 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
tract level—these tract-level emissions originate from even broader geographic scales (county and
national level) and are less certain at these finer geographies 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
135
-------
EPA's National-scale Air Toxics Assessment
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. Exposure or risk determined in NATA should be considered as representative of the
geographic area where an individual lives, but not necessarily be considered as that individual's personal
risk.
Thus, the results of the NATA analysis do not allow for a comparison of ambient air concentrations,
exposures, or risks between two individuals. They do, however, enable the user 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 might differ from the typical value for the county or
state if that individual lives in a part of the geographic 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 the examination of the variation in individual
exposure among census tracts, but it does not allow the examination of 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 NATA. EPA took this approach for NATA for two primary reasons:
• An overall purpose of NATA is to examine broad differences driven by geography. NATA
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 NATA.
• 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, estimating variability in the rates at which people breathe
air might be possible, but this variability is only a small component of the overall variation in
susceptibility. EPA therefore has chosen not to incorporate this source of variation between
individuals.
Taking into consideration 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 than the age of residents varies by
location. Variability in susceptibility is not included for the reasons given above. Temporal variation in
inputs is addressed in the development of 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).
136
-------
EPA's National-scale Air Toxics Assessment
7.2.3 How Variability Affects Interpretation of NATA Results
The NATA 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 NATA, in that differences in activity patterns
are taken into account 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 NATA 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 atypical 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 an individual might live in a census tract where the typical or average risk is less
than 1-in-l million, that individual might live nearer the source than the average person in the census
tract, or might have an activity pattern that leads to greater exposure, or might 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 also could have a lower risk by living farther from the source, or 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 NATA 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 for the identification of 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 for the identification of 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 located in
geographic regions characterized by a high risk than in those geographic regions characterized by a low
risk. The same can be said for exposure (i.e., individuals with a high exposure are more likely to be found
in geographic regions characterized by high exposure than in those regions characterized by low
exposure).
7.3 How NATA 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 NATA 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
137
-------
EPA's National-scale Air Toxics Assessment
regarding variability can be made. Of importance to note is 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, considering the process by which a study such as NATA is performed is instructive, as
described in the following sections.
Problem formulation. The problem to be addressed must first be defined. 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 NATA 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), along with information about 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
NATA, 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 also might 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 NATA 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 NATA analysis uses a series of mathematical
models. Models are used in NATA to produce the emissions inventory; to calculate ambient air
concentration; to calculate exposure; and to calculate risk (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 that are inherent in the development of a
model introduce uncertainties.
Typically, more than one model is available for application 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, NATA 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
138
-------
EPA's National-scale Air Toxics Assessment
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 NATA 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 these parameters can be estimated, and the methods used to collect the data and to compile the
databases introduce uncertainties. All of these factors introduce parameter uncertainty.
Although parameter uncertainty has both quantitative and qualitative aspects, common practice is to
characterize this source of uncertainty quantitatively, with some qualitative caveats. For example,
parameter uncertainty might be characterized by a confidence interval, which states that the true value of
the parameter (such as the stack height for a facility) probably lies somewhere between 40 and 60 meters
or that the stack height is "known to be within" a factor of 1.2, or that the stack height is "accurate to
within" 20 percent. Attached to this quantitative characterization of uncertainty will be a qualitative
caveat such as "the estimate of this uncertainty is based on measurements made in 1990 at facilities
similar to the one considered in this study, but a change in the design of stacks might have been made
since 1990." This qualitative statement provides some idea of the confidence with which the quantitative
assessment of uncertainty can be applied.
7.3.2 Components of Uncertainty Included in NATA
For this discussion, the uncertainties in NATA have been
divided into three sources, based on the three steps leading
from the estimate of emissions to the calculations of risk.
Uncertainty in ambient air concentrations is due to uncertainty
in the emissions estimates and in the air quality models.
Uncertainty in exposure is due to uncertainty in the activity
patterns, the locations of individuals within a census tract, and
the 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 concentration. Considering first the predictions of ambient air concentration, the specific
sources of uncertainty derive from the parameters for the following: emissions, the stack, 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, which is a composite of estimates produced by state and local regulatory agencies, industry, and
EPA. Some of these data were further modified during the NATA review. The quality of specific
emissions rates and locations in the NEI and resultant NATA emissions (e.g., industrial emissions from a
specific census tract) has not been fully assessed, although reviews have been conducted. Some of the
parameter values could be out of date, errors might have been introduced in transcribing raw data to a
NATA Components that Include
Uncertainty
• Ambient concentrations
• Exposure estimates
• Risk estimates
139
-------
EPA's National-scale Air Toxics Assessment
computer file, and other data-quality issues might be present. Emission estimates use a variety of methods
such as emission factors, material balances, engineering judgement 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,
county-level air toxic emissions from nonroad equipment are estimated by applying fractions of toxic
total hydrocarbons to estimates of county-level hydrocarbons for gaseous air toxics and fractions of toxic
particulate matter to estimates of county-level particulate matter for PAHs; emission factors based on
milligrams per mile are used 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 NONROAD model. In the NONROAD model, uncertainties are
associated with emission factors, activity, and spatial-allocation surrogates. National-level emissions in
NONROAD are allocated to the county level using surrogates, such as construction costs (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 mobile and nonpoint sources, population is used to allocate vehicle miles
traveled from state or metropolitan statistical area to county, which is a source of considerable
uncertainty.
For mobile and nonpoint sources, the emissions rates are typically allocated from the county level to
census-tract levels through a surrogate such as population or land use. 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 NEI
database does not include information on chemical speciation of the pollutants of interest, but instead
contains the total rate of pollutant emitted in all its forms. Assumptions about chemical speciation are
made based on values estimated to be representative at such sources, taking into account information on
source type, typical feedstock materials, knowledge of the process involved, or other relevant factors. Any
one source, however, might actually 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 the CMAQ model
estimates include consideration of topography, the HEM-3 model estimates as implemented for NATA do
not in all cases. The HEM-3 model estimates for point sources include consideration of 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 census
tracts. Not accounting for terrain introduces uncertainty into predictions of ambient air concentrations,
particularly in areas with hills or mountains.
Another source of uncertainty in the modeling of ambient air concentrations is the values used for the
boundary conditions used in CMAQ and background concentration estimates that are added to
AERMOD concentrations from the non-CMAQ HAPs. These sources might 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.
140
-------
EPA's National-scale Air Toxics Assessment
The representation of meteorological parameters in the CMAQ model is advanced, as the parameters
are derived using WRF. HEM-3 requires less complex representation of meteorological parameters,
primarily the direction and speed of airflow and the stability of the atmosphere (which affects how high
gases rise once they are emitted). For HEM-3, NATA uses meteorological data from the nearest available
monitoring station or grid cell. Uncertainties arise from the fact that the data typically are not measured at
the precise location of a given source and sometimes are not for the same year, and therefore might not
represent the meteorological conditions accurately.
The model equations used in the air quality models represent another source of uncertainty. The version
of HEM-3 used for NATA uses the Gaussian equations implemented in the AERMOD computer model
that has been studied extensively. The CMAQ model is more complex in its treatment of pollutant
dispersion and atmospheric dynamics; 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 HEM-3 models results in improved treatment of
chemistry and transport, there are uncertainties in the implementation. The approach requires consistent
emissions and meteorological inputs to be used in both models. While emissions were as consistent as
possible, some simplifications were necessary. CMAQ was not re-run after all of the emissions changes
made during the NATA review; instead, the CMAQ results were adjusted based on HEM-3 adjustments,
which could have resulted in some uncertainty. Differences also existed in the spatial and temporal
treatment of the emissions. Emissions were allocated from county to tract for HEM-3 and from county to
grid cell for CMAQ. While the same underlying data were used for the allocation, there may have been
differences (introduced by the irregularly shaped census boundaries) in developing the county-to-tract
surrogate fractions and in simplifying the tract boundaries for modeling tract-level emissions. The
temporal allocation used in HEM-3 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 HEM-3. The
HEM-3 meteorology data used the MMIF at every fourth grid cell (as opposed to every grid cell);
additionally, the HEM-3 meteorology data also used the non-gridded National Weather Service station
data, which were not used in CMAQ. In addition to inconsistencies in model inputs, the hybrid approach
uses a HEM-3 grid-cell average for normalizing the individual HEM-3 concentrations within the grid cell.
The HEM-3 surface values are less representative of the true HEM-3 average in grid cells where there are
fewer census-block receptors and where the block receptors are clustered unevenly within the grid cells.
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, EPA compared the predicted annual-average concentrations at the monitor location to the
sampled annual-average concentrations. These comparisons showed reasonably good agreement.
Measured concentrations were taken from EPA's Ambient Monitoring Archive which includes National
Air Toxics Trends Stations and state and local monitors reported to the Air Quality System. For the 2011
NATA, 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:
• emission characterization (e.g., specification of source location, emission rates, and release
characterization);
141
-------
EPA's National-scale Air Toxics Assessment
• meteorological characterization (e.g., representativeness);
• model formulation and methodology (e.g., characterization of dispersion, plume rise, deposition,
chemical reactivity);
• monitoring; and
• boundary conditions/background concentrations.
Underestimates for some pollutants could be a result of the following:
• The NEI might be missing specific emission sources (some of the emissions parameters are
missing for many of the sources in the NEI).
• The emission rates could be underestimated or overestimated due to emission-estimation
techniques and/or spatial allocation of national estimates to county, and county estimates to tracts.
• 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 variation all
can cause the ambient concentrations to be inaccurate or imprecise representations of the true
atmospheric averages.
• Model-to-model spatial comparisons are imprecise. The results suggest that the model estimates
are uncertain on a local scale (i.e., at the census-tract level). EPA believes that the model
estimates are more reliably interpreted as being a value likely to be found within 30 km of the
census-tract location.
Exposure. Sources of uncertainty in the relationship between ambient air concentrations and ECs include
those associated with microenvironmental factors and activity patterns. HAPEM calculates the EC in
various microenvironments (e.g., indoors at home, in a car) based on inputs of predicted ambient air
concentrations and microenvironmental factors. The factors are characterized as probability
distributions to reflect the variability found in air-toxics measurements more fully. For many air toxics,
the measurement studies needed to estimate microenvironmental factors are not available, so the values
used are based on measurement studies of similar compounds in similar situations. This practice
introduces uncertainty into the estimation of ECs for such compounds. In addition, even for air toxics
with measurement studies, the estimated microenvironmental factors have some uncertainty because the
number of such studies is limited. Furthermore, the uniform application of the microenvironmental factors
to all census tracts introduces uncertainty by not accounting for possible geographic differences among
tracts (e.g., different window-opening behavior, different levels of building integrity).
The activity-pattern sequences for individuals used in HAPEM are based on CHAD. As explained in
Section 4.3.3, the algorithms in HAPEM consider the variability in activity patterns among individuals
within a cohort-tract combination, 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 that are not recent and some
for which the data are based on non-random sampling. How well the model algorithms represent actual
daily autocorrelation between types of activity also is uncertain. 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 NATA.
The commuting data used in HAPEM are based on an EPA analysis of information from a special study
by the U.S. Census. HAPEM uses this information, reflecting 2010 data, in coordination with the activity-
pattern data to place an individual either in the home tract or the work tract at each time step. These data
142
-------
EPA's National-scale Air Toxics Assessment
introduce some uncertainty because they simplify commuting patterns to a pair of home and work census
tracts and might not reflect certain details of some commutes (e.g., the additional census tracts
encountered by commuters who travel to non-adjacent tracts; 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. Cancer-risk estimates are based on the
assumption that a compound either is a carcinogen or produces a noncancer effect. This judgment is based
on the results of a hazard-identification stage in which the evidence that an air toxic produces either
cancer or a noncancer effect is assessed. Because the evidence for either judgment is never unequivocal, a
compound labeled as a carcinogen or one deemed to produce noncancer effects, in fact, might produce no
such effect in humans. This possibility introduces uncertainty into the calculation of risk because the risk,
in fact, could be zero. As the evidence for the original conclusion (i.e., that the compound produces the
effect) increases, this uncertainty decreases.
Cancer-risk estimates are based on the assumption 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 the risk than to an underestimate. Other
scientifically valid, biologically based models are available, which produce estimates of cancer risk that
differ from those obtained from the linear model. Uncertainty in risk estimates therefore, is, 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 the true
risk is less than that predicted than in the conclusion that the risk is greater than that 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 HQ for noncancer health risk. The RfC, which (like the URE) is based on limited
information, is uncertain, and 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 NATA
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 geographic areas of interest
for monitoring and community assessments). As described above, many of the elements in the assessment
143
-------
EPA's National-scale Air Toxics Assessment
process for NATA, as in other assessments that derive results from environmental data and modeling of
environmental data, are characterized by uncertainty and variability. 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. In many cases assessments
that are more localized, incorporating appropriately scaled local monitoring and modeling, could be
necessary to better characterize local-level risk.
Recognizing the specific limitations in NATA results is critical to their proper interpretation and utility,
including that the results:
• apply to geographic areas, not specific locations,
• do not include comprehensive impacts from sources in Canada or Mexico,
• are restricted to the year to which the assessment pertains (because the assessment uses emissions
data from that year),
• do not reflect exposures and risk from all compounds,
• do not reflect all pathways of exposure,
• reflect only compounds released into the outdoor air,
• do not fully capture variations in background ambient air concentrations,
• might underestimate or overestimate ambient air concentrations for some compounds due to
spatial uncertainties,
• are based on default, or simplifying, assumptions where data are missing or of poor quality, and
• might not accurately capture sources that have episodic emissions, and contain uncertainty.
The results apply to geographic areas, not 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 different areas. They cannot be used to
identify "hot spots" where the air concentration, exposure, or risk might 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 asthma or cancer risk or 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 NATA 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 air concentration, which is chosen to represent a typical ambient air 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 2011 NATA are restricted to 2011 because the assessment used emissions data from
2011. 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
144
-------
EPA's National-scale Air Toxics Assessment
continue to decrease, however, as (1) mobile-source regulations are phased in overtime, (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 2011.
The results do not reflect exposures and risk from all compounds. Only 138 of the 181 air toxics (i.e., 180
CAA HAPs plus diesel PM) modeled in NATA have dose-response values. The remaining 43 air toxics
do not and therefore are not considered in the aggregate cancer risk or target-organ-specific hazard
indices. Of particular significance is that 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
apparent levels that have been linked to increased cancer risk in epidemiology studies. Currently, a URE
for diesel PM has not yet been derived; therefore, a quantitative estimate of the cancer risks has not been
included in the 2011 NATA. An IRIS RfC for diesel PM has allowed a quantitative estimate of the
noncancer effects.
The results do not reflect all pathways of exposure. The assessment includes only risks from direct
inhalation of the emitted pollutants. It does not consider pollutants that might then deposit onto soil and
into water and food, and therefore enter the body through ingestion or skin contact. Consideration of these
routes of exposure could increase estimates of exposure and risk.
The assessment results reflect only compounds released into the outdoor air. The assessment does not
include exposure to pollutants produced indoors, such as from stoves or out-gassing from building
materials, or evaporative benzene emissions from cars in attached garages. For some compounds such as
formaldehyde, these indoor sources can contribute significantly to the total exposure for an individual,
even if only inhalation exposures are considered. In addition, the assessment does not consider pollutants
released directly to water and soil. It does take into account transformation of one pollutant into another
(i.e., secondary formation) 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 utilize CMAQ for all pollutants and hence may not
appropriately estimate the long-range transport for these non-CMAQ pollutants. For pollutants not
estimated in CMAQ, the assessment uses background ambient air concentrations that are based on remote
concentration estimates, but these would not account for variations due to the regional transport of these
pollutants.
The assessment might 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) are not always available or are 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 concentration,
exposure, and risk.
The assessment might not accurately capture sources that have episodic emissions. Some facilities might
experience short-term (a few days or weeks) deviations from their typical emissions patterns, such
145
-------
EPA's National-scale Air Toxics Assessment
as during startups, shutdowns, malfunctions, and upsets. NATA modeling assumes that emission rates
are uniform throughout the year.
146
-------
EPA's National-scale Air Toxics Assessment
8 REFERENCES
Akhtar, F., Henderson, B., Appel, W., Napelenok, S., Hutzell, B., Pye, H., and Foley, K. 2012. Multiyear
Boundary Conditions for CMAQ 5.0 from GEOS-Chem with Secondary Organic Aerosol Extensions.
11th Annual Community Modeling and Analysis System Conference, Chapel Hill, NC, October 2012.
ATSDR (Agency for Toxic Substances and Disease Registry). 2015. Toxic Substances Portal Minimal
Risk Levels (MRLs) for Hazardous Substances. Available online at
http://www.atsdr.cdc.gov/mrls/index.asp. Last updated 28 October 2015. Last accessed 10 December
2015.
Appel, K.W., Gilliam, R.C., Davis, N., Zubrow, A., and Howard, S.C. 2011. Overview of the
Atmospheric Model Evaluation Tool (AMET) vl.l for Evaluating Meteorological and Air Quality
Models. Environ. Modell. Softw., 26(4): 434-443.
Attfield, M.D., Schleiff, P.L., Lubin, J.H., Blair, A., Stewart, P.A., Vermeulen, R., Coble, J.B., and
Silverman, D.T. 2012. The Diesel Exhaust in Miners Study: A Cohort Mortality Study with Emphasis on
Lung Cancer. Journal of the National Cancer Institute, 104: 1-15. Available online
at http://inci.oxfordiournals.org/content/104/11/855. Last accessed 10 December 2015.
Brown, N., Allen, D., Amar, P., Kallos, G., McNider, R., Russell,, A., and Stockwell, W. 2011. Final
Report: Fourth Peer Review of the CMAQ Model. EPA/ORD/NERL, Research Triangle Park, NC.
Available online at http://cfpub.epa.gov/si/si public file download.cfm?p download id=525232. Last
accessed 11 December 2015.
Byun, D., and Schere, K.L. 2006. Review of the Governing Equations, Computational Algorithms, and
Other Components of the Models-3 Community Multiscale Air Quality (CMAQ) Modeling System.
Applied Mechanics Reviews, 59(2): 51-77.
Caldwell, J.C., Woodruff, T.J., Morello-Frosch, R., and Axelrad, D.A. 1998. Application of Health
Information to Hazardous Air Pollutants Modeled in EPA's Cumulative Exposure Project. Toxicology
and Industrial Health, 14(3): 429-454.
Cimorelli, A.J., Perry, S.G., Venkatram, A., Weil, J.C., Paine, R.J., Wilson, R.B., Lee, R.F., Peters, W.D.,
and Brode, R.W. 2005. AERMOD: A Dispersion Model for Industrial Source Applications. Part I:
General Model Formulation and Boundary Layer Characterization. Journal of Applied Meteorology, 44:
682-693.
Cook, R., Phillips, S., Houyoux, M., Dolwick, P., Mason, R., Yanca, C., Zawacki, M., Davidson, K.,
Michaels, H., Harvey, C., Somers, J., and Luecken, D. 2011. Air Quality Impacts of Increased Use of
Ethanol under the United States' Energy Independence and Security Act. Atmospheric Environment, 45:
7714-7724.
EPA (U.S. Environmental Protection Agency). 1986. Guidelines for Mutagenicity Risk Assessment.
EPA/630/R-98/003. EPA, Washington, DC. Available online at http://www2.epa.gov/risk/guidelines-
mutagenicitv-risk assessment. Last accessed 10 December 2015.
EPA. 1991. Guidelines for Developmental Toxicity Risk Assessment. EPA/600/R-91/001. EPA,
Washington, DC. Available online at http://www2.epa.gov/risk/guidelines-developmental-toxicitv-risk
assessment. Last accessed 26 October 2015.
147
-------
EPA's National-scale Air Toxics Assessment
EPA. 1994. Methods for Derivation of Inhalation Reference Concentrations and Application of Inhalation
Dosimetry. EPA/600/8-90/066F. EPA Office of Research and Development (ORD), Washington, DC.
Available online at http://www2.epa.gov/risk/methods-derivation-inhalation-reference-concentrations-
and-application-inhalation-dosimetrv. Last accessed 27 October 2015.
EPA. 1996. Guidelines for Reproductive Toxicity Risk Assessment. EPA/630/R-96/009. EPA,
Washington, DC. Available online at http://www2.epa.gov/risk/guidelines-reproductive-toxicitv-risk
assessment. Last accessed 10 December 2015.
EPA. 1998. Guidelines for Neurotoxicity Risk Assessment. EPA/630/R-97/0. EPA, Washington, DC.
Available online at http://www2.epa.gov/risk/guidelines-neurotoxicitv-risk assessment. Last accessed 10
December 2015.
EPA. 2000. Supplementary Guidance for Conducting Health Risk Assessment of Chemical Mixtures.
EPA/630/R-00/002. Risk Assessment Forum, Washington, DC. Available online at
http://ofmpub.epa.gov/eims/eimscomm.getfile7p download id=4486. Last accessed 8 December 2015.
EPA. 2001a. NATA—Evaluating the National-scale Air Toxics Assessment 1996 Data - An SAB
Advisory. EPA/SAB/EC/ADV-02/001. Science Advisory Board, Washington, DC. Available online at
http://archive.epa.gov/airtoxics/nata/web/pdf/sabreptl201 .pdf. Last accessed 29 November 2015.
EPA. 2001b. National-scale Air Toxics Assessment for 1996. Draft for EPA Science Advisory Board
Review: January 18, 2001. EPA-453/R-01-003. EPA Office of Air Quality Planning and Standards
(OAQPS), Research Triangle Park, NC. Available online at
http://archive.epa.gov/airtoxics/nata/web/html/sabrev.html. Last accessed 29 November 2015.
EPA. 2002a. Health Assessment Document for Diesel Engine Exhaust. EPA/600/8-90/057F. EPA ORD/
National Center for Environmental Assessment (NCEA), Washington, DC. Available online at
http://cfpub.epa.gov/ncea/cfm/recordisplav.cfm?deid=29060. Last accessed 4 December 2015.
EPA. 2002b. The HAPEM User's Guide Hazardous Air Pollutant Exposure Model, Version 4. EPA
OAQPS, Research Triangle Park, NC. Available online at
http://archive.epa.gov/airtoxics/nata/web/zip/hapem4guide4.zip. Last accessed 29 November 2015.
EPA. 2002c. Comparison of ASPEN Modeling System Results to Monitored Concentrations. EPA.
Available online at http://archive.epa.gov/airtoxics/nata/web/html/mtom pre.html. Last updated 22
October 2015. Last accessed 30 November 2015.
EPA. 2003. Framework for Cumulative Risk Assessment. EPA/630/P-02/001F. EPA ORD/NCEA,
Washington, DC. Available online at http://www2.epa.gov/risk/framework-cumulative-risk assessment.
Last accessed 26 October 2015.
EPA. 2004a. Air Toxics Risk Assessment Reference Library. Volume 1: Technical Resource Manual.
EPA-453/K-04-001A. EPA OAQPS, Research Triangle Park, NC. Available online at
http://www2.epa.gov/sites/production/files/2013-08/documents/volume 1 reflibrarv.pdf. Last accessed 2
December 2015.
EPA. 2004b. Air Toxics Risk Assessment Reference Library. Volume 2: Facility-Specific Assessment.
EPA-453/K-04-001B. EPA OAQPS, Research Triangle Park, NC. Available online
http://www2.epa. gov/sites/production/files/2013 -08/documents/volume 2 facilitvassess .pdf. Last
accessed 2 December 2015.
148
-------
EPA's National-scale Air Toxics Assessment
EPA. 2005a. Guidelines for Carcinogen Risk Assessment. EPA/630/P-03/001F. EPA, Washington, DC.
Available online at http://www2.epa.gov/risk/guidelines-carcinogen-risk assessment. Last accessed 26
October 2015.
EPA, 2005b. EPA's National Mobile Inventory Model (NMIM), a Consolidated Emissions Modeling
System for MOBILE6 and NONROAD. EPA-420-R-05-024. EPA Office of Transportation and Air
Quality (OTAQ) Assessment and Standards Division, Ann Arbor, MI. Available online at
http://www.epa.gov/otaq/models/nmim/420r05024.pdf. Last accessed 10 December 2015.
EPA. 2005c. Supplemental Guidance for Assessing Susceptibility from Early-Life Exposure to
Carcinogens. EPA/630/R-03/003F. EPA, Washington, DC. Available online at
http://www2.epa.gov/osa/memoranda-about-implementation-cancer-guidelines-and-accompanving-
supplemental-guidance-science. Last accessed 4 December 2015.
EPA. 2005d. The HAPEM User's Guide Hazardous Air Pollutant Exposure Model, Version 5. EPA
OAQPS, Research Triangle Park, NC. Available online at
http://www2.epa. gov/sites/production/files/2013 -08/documents/hapem5 guide .pdf. Last accessed 29
November 2015.
EPA. 2006a. Air Toxics Risk Assessment Reference Library. Volume 3: Community-Scale Assessment.
EPA-453/K-06-001C. EPA OAQPS, Research Triangle Park, NC. Available online at
http://www2.epa. gov/sites/production/files/2013 -08/documents/volume 3 communitvassess .pdf. Last
accessed 2 December 2015.
EPA. 2006b. Comparison of 1999 Model-Predicted Concentrations to Monitored Data. EPA OAQPS.
Available online at http://archive .epa. gov/airtoxics/nata 1999/web/html/99compare .html. Last updated 13
September 2015. Last accessed 30 November 2015.
EPA. 2007. The HAPEM User's Guide Hazardous Air Pollutant Exposure Model, Version 6. EPA
OAQPS, Research Triangle Park, NC. Available online at
http ://www2 .epa. gov/sites/production/files/2013 -08/documents/hapem6 guide .pdf. Last accessed 29
November 2015.
EPA. 2008a. Health Effects Assessment Summary Tables (HEAST). EPA NCEA, Washington, DC.
Available online at http://cfpub.epa.gov/ncea/cfm/recordisplav.cfm?deid=2877. Last updated 21 July
2008. Last accessed 4 December 2015.
EPA. 2008b. The Clean Air Act Amendments of 1990 List of Hazardous Air Pollutants. EPA Office of
Air and Radiation (OAR), Washington, DC. Available online at http://www.epa.gov/ttn/atw/orig 189.html.
Last updated 10 September 2015. Last accessed 10 December 2015.
EPA. 2009. Comparison of 2002 Model-Predicted Concentrations to Monitored Data. EPA OAQPS.
Available online at http://archive .epa. gov/nata2002/web/html/compare .html. Last accessed 30 November
2015.
EPA. 2010a. Development of a Relative Potency Factor (RPF) Approach for Polycyclic Aromatic
Hydrocarbon (PAH) Mixtures: In Support of Summary Information of the Integrated Risk Information
System (IRIS) (External Review Draft). EPA/635/R-08/012A. EPA, Washington, DC. Available online at
http://cfpub.epa.gov/ncea/iris drafts/recordisplav.cfm?deid=194584. Last accessed 19 October 2015.
149
-------
EPA's National-scale Air Toxics Assessment
EPA. 2010b. Results of the 2005 NATA Model-to-Monitor Comparison, Final Report. Prepared by
Eastern Research Group for EPA OAQPS. Available online at
http://www3.epa.gov/ttn/atw/nata2005/05pdf/nata20Q5 model2monitor.pdf. Last accessed 30 November
2015.
EPA. 2011. An Overview of Methods for EPA's National-scale Air Toxics Assessment. Prepared by ICF
International for EPA OAQPS. Available online at
http://www3.epa.gov/ttn/atw/nata2005/Q5pdf/nata tmd.pdf. Last accessed 10 December 2015.
EPA. 2014a. Meteorological Model Performance for Annual 2011 WRF v3.4 Simulation. EPA. Available
online at http://www.epa.gov/ttn/scram/reports/MET TSD 2011 final ll-26-14.pdf. Last accessed 10
December 2015.
EPA. 2014b, Profile of the 2011 National Air Emissions Inventory, EPA. Available online at
http://www3.epa.gov/ttn/chief/net/lite finalversion verlQ.pdf. Last accessed 10 December 2015.
EPA. 2014c. Toxicological Review of Trichloroethylene. EPA NCEA, Washington, DC. Available online
at http://cfpub.epa.gov/ncea/iris/search/index.cfm?kevword=trichloroethvlene. Last updated 12
September 2014. Last accessed 10 December 2015.
EPA. 2014d. Draft User's Manual for The Mesoscale Model Interface Program (MMIF) Version 3.1.
Prepared by ENVIRON International Corporation for EPA OAQPS. Available online at
http://www3.epa.gov/scram00l/models/relat/mmif/MMIFv3.1 Users Manual.pdf. Last accessed 14
December 2015.
EPA. 2014e. Human Exposure Model (HEM-3) User's Guides. Prepared by EC/R Incorporated for EPA
OAQPS. Available online at http://www.epa.gov/fera/human-exposure-model-hem-3-users-guides. Last
accessed 24 March 2015.
EPA. 2015a. Consolidated Human Activity Database (CHAD). EPA, Washington, DC. Available online
at http://www2.epa.gov/healthresearch/consolidated-human-activitv-database-chad-use-human-exposure-
and-health-studies-and. Last updated 30 September 2015. Last accessed 17 November 2015.
EPA. 2015b. MOVES 2014a. EPA OTAQ. Available online at
http://www3.epa.gov/otaq/models/moves/index.htm. Last updated 1 December 2015. Last accessed 10
December 2015.
EPA. 2015c. The HAPEM User's Guide Hazardous Air Pollutant Exposure Model, Version 7. EPA
OAQPS, Research Triangle Park, NC. Available online at http://www.epa.gov/fera/hazardous-air-
pollutant-exposure-model-hapem-users-guides. Last accessed 29 November 2015.
EPA. 2015d. What Are the Six Common Air Pollutants? EPA, Washington, DC. Available online at
http://www3.epa.gov/airqualitv/urbanair/. Last updated 18 September 2015. Last accessed 2 December
2015.
EPA. 2015e. Risk Assessment Guidance and Tools. EPA, Washington, DC. Available online at
http://www.epa.gov/risk/guidance.htm. Last updated 1 December 2015. Last accessed 2 December 2015.
EPA. 2015f. User's Guide for the AMS/EPA Regulatory Model - AERMOD. EPA-454/B-03-001.
Addendum June 2015. EPA, Research Triangle Park, NC.
150
-------
EPA's National-scale Air Toxics Assessment
EPA. 2015g. Community Multiscale Air Quality (CMAQ). EPA, Washington, DC. Available online at
http://www.epa.gov/air-research/communitv-multi-scale-air-qualitv-cmaq-modeling-svstem-air-qualitv-
management. Last updated 8 December 2015. Last accessed 10 December 2015.
EPA. 2015h. Clean Air Markets. EPA, Washington, DC. Available online at
http://www.epa. gov/airmarkt/. Last updated 17 November 2015. Last accessed 10 December 2015.
EPA. 2015i. Risk and Technology Review. EPA OAR, Washington, DC. Available online at
http://www.epa.gov/ttn/atw/rrisk/rtrpg.html. Last updated 1 December 2015. Last accessed 10 December
2015.
EPA. 2015j. Toxics Release Inventory Program. EPA, Washington, DC. Available online at
http://www.epa.gov/toxics-release-inventorv-tri-program. Last updated 13 November 2015. Last accessed
10 December 2015.
EPA. 2015k. MOVES 2014. EPA OTAQ, Washington, DC. Available online at
http://www3.epa.gov/otaq/models/moves/moves-docum.htm. Last updated 5 November 2015. Last
accessed 10 December 2015.
EPA. 20151. Products and Publications Relating to Risk Assessment Produced by the Office of the
Science Advisor (OSA). EPA OSA, Washington, DC. Available online at
http://www.epa.gov/osa/products-and-publications-relating-risk-assessment-produced-office-science-
advisor. Last updated 3 March 2015. Last accessed 8 December 2015.
EPA. 2015m. Evaluation of Prognostic Meteorological Data in AERMOD Applications. EPA-454/R-15-
004. EPA OAQPS, Research Triangle Park, NC. Available online at
http://www3.epa.gOv/ttn/scram/l lthmodconf/MMIF Evaluation TSD.pdf. Last accessed 14 December
2015.
EPA. 2015n. Overview by Section of CAA. EPA OAR, Washington, DC. Available online at
http://www.epa.gov/ttn/atw/overview.html. Last updated 10 September 2015. Last accessed 10 December
2015.
EPA. 2015o. MOBILE Model (onroad vehicles). EPA OTAQ, Washington, DC. Available online at
http://www.epa. gov/otaq/mobile .htm. Last updated 5 November 2015. Last accessed 10 December 2015.
EPA. 2015p. National Mobile Inventory Model (NMIM). EPA OTAQ, Washington, DC. Available online
at http://www.epa.gov/otaq/nmim.htm. Last updated 5 November 2015. Last accessed 10 December 2015.
EPA. 2015q. NONROAD Model (nonroad engines, equipment, and vehicles). EPA OTAQ, Washington,
DC. Available online at http://www.epa.gov/otaq/nonrdmdl.htm. Last updated 5 November 2015. Last
accessed 10 December 2015.
FAA (U.S. Federal Aviation Administration). 2015. Airport Data & Contact Information. FSS. Available
online at http://www.faa.gov/airports/airport safetv/airportdata 5010/. Last updated 3 November 2015.
Last accessed 10 December 2015.
Garshick, E., Laden, F., Hard, J.E., Davis, M.E., Eisen, E.A., and Smith, T.J. 2012. Lung Cancer and
Elemental Carbon Exposure in Trucking Industry Workers. Environmental Health Perspectives,
120:1301-1306. Available online at http://dx.doi.org/10.1289/ehp.1204989. Last accessed 10 December
2015.
151
-------
EPA's National-scale Air Toxics Assessment
HEI (Health Effects Institute). 2015. Diesel Emissions and Lung Cancer: An Evaluation of Recent
Epidemiological Evidence for Quantitative Risk Assessment. HEI, Boston, MA. Available online at
http://pubs.hcalthcffccts.org/vicw .php?id=446. Last updated 24 November 2015. Last accessed 10
December 2015.
Henderson, B.H., Akhtar, F., Pye, H.O.T., Napelenok, S.L., and Hutzell, W.T. 2014. A Database and Tool
for Boundary Conditions for Regional Air Quality Modeling: Description and Evaluation. Geosci. Model
Dev., 7:339-360.
Houyoux, M.R., Vukovich, J.M., Coats, C.J. Jr., Wheeler, N.M., and Kasibhatla, P.S. 2000. Emission
Inventory Development and Processing for the Seasonal Model for Regional Air Quality (SMRAQ)
Project. Journal of Geophysical Research, 105(D7):9079-9090.
IARC (International Agency for Research on Cancer). 2013. IARC Monographs on the Evaluation of
Carcinogenic Risks to Humans, Volume 105 (2013). Available online at
http://monographs.iarc.fr/ENG/Monographs/voll05/index.php. Last accessed 10 December 2015.
Isakov, V., Irwin., J., and Ching, J.K. 2007. Using CMAQ for Exposure Modeling and Characterizing the
Sub-grid Variability for Exposure Estimates. Journal of Applied Meteorology and Climatology, 46:1354—
1371.
NRC (National Research Council). 1983. Risk Assessment in the Federal Government: Managing the
Process. Committee on the Institutional Means for Assessments of Risk to Public Health, Commission on
Life Sciences. NRC. National Academy Press, Washington, DC.
NRC. 1994. Science and Judgment in Risk Assessment Committee on Risk Assessment of Hazardous Air
Pollutants, Board on Environmental Sciences and Technology, Commission on Life Sciences. NRC.
National Academy Press, Washington, DC.
NUATRC (Mickey Leland National Urban Air Toxics Research Center). 2011. Available online at
http://www.sph.uth.tmc.edu/mleland/. Last updated 30 December 2011. Last accessed 10 December 2015.
OEHHA (Office of Environmental Health Hazard Assessment, California). 2014. Air Toxicology and
Epidemiology. OEHHA, Sacramento, CA. Available online at http://www.oehha.ca.gov/air/allrels.html.
Values last updated June 2014. Last accessed 10 December 2015.
OEHHA. 2015. Hot Spots Guidelines. OEHHA, Sacramento, CA. Available online at
http://www.oehha.ca.gov/air/hot spots/index.html. Contents last updated 14 August 2015. Last accessed
10 December 2015.
Otte T.L., and Pleim, J.E. 2010. The Meteorology-Chemistry Interface Processor (MCIP) for the CMAQ
Modeling System: Updates through v3.4.1. GeoscientificModel Development, 3:243-256.
Russell, A.G., and Dennis, R. 2000. NARSTO Critical Review of Photochemical Models and Modeling.
Atmospheric Environment, 3:2283-2324.
Seigneur, C., and Dennis, R. 2010. Technical Challenges in Multipollutant Air Quality Management,
edited by: Hidy, G.M., Brook, J.R., Demeijian, K.L., Molina, L.T., Pennell, W.T., and Scheffe, R.D.
Springer, Dordrecht.
152
-------
EPA's National-scale Air Toxics Assessment
Silverman, D.T., Samanic, C.M., Lubin, J.H., Blair, A.E., Stewart, P.A., Vermeulen, R., Coble, J.B.,
Rothman, N., Schleiff, P.L., Travis, W.D., Ziegler, R.G., Wacholder, S., and Attfield, M.D. 2012. The
Diesel Exhaust in Miners Study: A Nested Case-Control Study of Lung Cancer and Diesel
Exhaust. Available online at http://dx.doi.org/10.1093/inci/dis034. Last accessed 10 December 2015.
Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Barker, D.M., Duda, M.G., Huang, X., Wang, W.,
and Powers, J.G. 2008. A Description of the Advanced Research WRF Version 3. Available online at
http://www2.mmm.ucar.edu/wrf/users/pub-doc.html. Last updated 5 December 2014. Last accessed 16
December 2015.
Wesson, K., Fann, N., Morris, M., Fox, T., and Hubbell, B. 2010. A Multi-pollutant, Risk-based
Approach to Air Quality Management: Case Study for Detroit. Air Pollut. Res., 1:296-304.
doi: 10.5094/APR.2010.037.
WHO (World Health Organization). 2015. Complete List of Agents Evaluated and their Classification.
WHO IARC, Lyon, France. Available online at http://monographs.iarc.fr/ENG/Classification/index.php.
Last updated 26 October 2015. Last accessed 10 December 2015.
Yantosca, B., Sulprizio, M., Yannetti, M., Lundgren, L., and Xu, J. 2015. GEOS-Chem vl0-01 Online
User's Guide, Atmospheric Chemistry Modeling Group, School of Engineering and Applied
Sciences, Harvard University, Cambridge, MA. Available online at
http://acmg.seas.harvard.edu/geos/doc/man/. Last accessed 10 December 2015.
Yarwood, G., Rao, S., and Whitten, G. 2005. Updates to the Carbon Bond Mechanism, CB05,
Final Report, RT-04-00675. Prepared by ENVIRON International Corporation for EPA.
Available online at http://www.camx.com/files/cb05 final report 120805.aspx. Last accessed 3
December 2015.
153
-------
EPA's National-scale Air Toxics Assessment
This page intentionally left blank.
154
-------
EPA's National-scale Air Toxics Assessment
Appendix A
Glossary
"N"-in-1 million cancer risk:
A risk level of "N"-in-1 million implies a likelihood 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 those cancer cases that would normally occur in an unexposed
population of one million people. Note that this assessment looks at lifetime cancer risks, which should not be
confused with or compared to annual cancer risk estimates. If you would like to compare an annual cancer risk
estimate with the results in this assessment, you would need to multiply that annual estimate by a factor of 70 or
alternatively divide the lifetime risk by a factor of 70.
Activity-pattern data:
In an inhalation exposure assessment, activity-pattern data depict both the actual physical activity (including an
associated inhalation exertion level); the physical location; and, the time of day the activity takes place (e.g., at
midnight, while sleeping at home, jogging in the park at 8 a.m., or driving in a car at 6 p.m.). The Hazardous Air
Pollution Model (HAPEM) uses activity-pattern data from EPA's Comprehensive Human Activity Database (CHAD).
AMS/EPA Regulatory Model (AERMOD):
EPA's preferred model for near-field (i.e., within 50 km) simulations of dispersion of emissions. In simulating
boundary-layer turbulence, it has the capability to model complex terrain, elevated sources, numerous discrete
receptors, and source types ranging from point to line to volume, at hourly resolution.
Air toxics:
Also known as toxic air pollutants or hazardous air pollutants*; those pollutants known to cause or suspected of
causing cancer or other serious health problems. Health concerns could be associated with both short- and long-
term exposures to these pollutants. Many are known to have respiratory, neurological, immune, or reproductive
effects, particularly for more susceptible or sensitive populations 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 NATA.
*Diesel particulate matter is not a hazardous air pollutant but is included in the NATA air toxics.
Ambient:
Surrounding, as in the surrounding environment. In NATA assessments, ambient air refers to the outdoor air
surrounding a person through which pollutants can be carried. Therefore, the ambient concentrations estimated by
NATA are those concentrations estimated in the outdoor environment. NATA also estimates exposure
concentrations that result from an individual's movement through various microenvironments, including the indoor
environment.
Area and other sources:
Include sources that generally have lower emissions on an individual basis than "major sources" and are often too
small or ubiquitous to be inventoried as individual sources. "Area sources" include facilities that have air toxics
emissions below the major source threshold as defined in the air toxics sections of the Clean Air Act and thus emit
less than 10 tons of a single toxic air pollutant or less than 25 tons of multiple toxic air pollutants 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.
A-1
-------
EPA's National-scale Air Toxics Assessment
Assessment System for Population Exposure Nationwide (ASPEN):
A computer simulation model used to estimate toxic air pollutant concentrations. The ASPEN model takes into
account important determinants of pollutant concentrations, such as: rate of release, location of release, the 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 being released (i.e., reactive decay), settling of
pollutants out of the atmosphere (i.e., deposition), and transformation of one pollutant into another (i.e., secondary
formation or decay). The model estimates toxic air pollutant concentrations for every census tract in the United
States, Puerto Rico, and the Virgin Islands.
Atmospheric transformation (secondary formation):
The process by which chemicals are transformed in the air into other chemicals. When a chemical is transformed,
the original HAP no longer exists; it is replaced by one or more chemicals. Compared to the original chemical, the
newer reaction products can have more, less, or the same toxicity. Transformations and removal processes affect
both the fate of the chemical and its atmospheric persistence. Persistence is important because human exposure
to chemical is influenced by the length of time the chemical remains in the atmosphere. Note that in NATA the
terms atmospheric transformation and secondary formation are used interchangeably.
Background concentrations:
For NATA, the contributions to outdoor air toxics concentrations resulting from natural sources, persistence in the
environment of past years' emissions, and long-range transport from distant sources. Background concentrations
could be levels of pollutants that would be found in a particular year, even if there had been no recent manmade
emissions. Background concentrations are added to the AERMOD concentrations but not to the CMAQ modeled
concentrations which account for long range transport and emissions from outside the domain through boundary
conditions. The vast majority of risk from the NATA background concentrations is from carbon tetrachloride, a
ubiquitous pollutant that has few sources of emissions but is persistent due to its long half-life.
Biogenic emissions:
Emissions from natural sources, such as plants and trees. These sources emit formaldehyde, acetaldehyde, and
methanol, as well as large quantities of other non-HAP volatile organic compounds (VOCs). Formaldehyde and
acetaldehyde are key risk drivers in NATA. Biogenic emissions are typically computed using a model which utilizes
spatial information on vegetation and land use and environmental conditions of temperature and solar radiation. In
addition to being a primary source of HAPs, other VOCs emitted by biogenic sources react with anthropogenic
VOCs and NOxto produce secondary-formed HAPs. The NATA biogenics source group includes only the primary
emissions.
Cancer risk:
The probability of contracting cancer over the course of a lifetime, assuming continuous exposure (assumed to be
70 years for the purposes of NATA risk characterization).
Carcinogen:
A chemical or physical agent that can cause cancer.
Chemical Abstracts Service (CAS) Number:
A unique number assigned to a chemical by the Chemical Abstracts Service, a service of the American Chemical
Society that indexes and compiles abstracts of worldwide chemical literature called "Chemical Abstracts." The
purpose is to make database searches more convenient, as chemicals often have many names.
Census tracts:
Land areas defined by the U.S. Census Bureau. Tracts can vary in size but each typically contains about 4,000
residents. Census tracts are usually smaller than 2 square miles in cities, but are much larger in rural areas.
A-2
-------
EPA's National-scale Air Toxics Assessment
Cohort:
Generally defined as a group of people within a population who are assumed to have identical exposures during a
specified exposure period. The use of cohorts is a necessary simplifying assumption for modeling exposures of a
large population. For the exposure assessment, the population is divided into a set of cohorts such that (1) each
person is assigned to one and only one cohort, and (2) all the cohorts combined encompass the entire population.
Community Multi-scale Air Quality (CMAQ) modeling system:
A multi-pollutant air quality modeling system using a three-dimensional gridded simulation environment with
atmospheric chemistry to model transport of emissions across local to long-range scales.
Consolidated Human Activity Database (CHAD):
The Consolidated Human Activity Database (CHAD) is an EPA comprehensive human-activity database consisting
of data from numerous activity studies since 1982 and supporting assessments of human exposure, intake dose,
and risk.
Diesel particulate matter (diesel PM):
A mixture of particles that is a component of diesel exhaust. EPA lists diesel exhaust as a mobile-source air toxic
due to the cancer and non-cancer health effects associated with exposure to whole diesel exhaust. Diesel PM
(expressed as grams diesel PM/m3) has historically been used as a surrogate measure of exposure for whole
diesel exhaust. Although uncertainty exists as to whether diesel PM 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.
Dispersion model:
A computerized set of mathematical equations that uses emissions and meteorological information to simulate the
behavior and movement of air pollutants in the atmosphere. The results of a dispersion model are estimated
outdoor concentrations of individual air pollutants at specified locations.
Emission Inventory System (EIS):
An EPA information system for storing all current and historical emission inventory data. It is used to receive and
store emissions data and generate emission inventories beginning with the 2008 National Emissions Inventory
(NEI). Partners used the EIS Exchange to submit Facility Inventory, Point, Nonpoint, Onroad and Nonroad data
categories to the EIS Production or Quality Assurance (QA) environments.
Exposure assessment:
Identifying the ways in which chemicals might reach individuals (e.g., by breathing); estimating how much of a
chemical an individual is likely to be exposed to; and, estimating the number of individuals likely to be exposed.
Hazard index (HI):
The sum of hazard quotients for substances that affect the same target organ or organ system. Because different
pollutants (air toxics) can cause similar adverse health effects, combining hazard quotients associated with
different substances is often appropriate. EPA has drafted revisions to the national guidelines on mixtures that
support combining the effects of different substances in specific and limited ways. Ideally, hazard quotients should
be combined for pollutants that cause adverse effects by the same toxic mechanism. Because detailed information
on toxic mechanisms is not available for most of the substances in NATA, however, EPA aggregates the effects
when they affect the same target organ regardless of the mechanism. The hazard index (HI) is only an
approximation of the aggregate effect on the target organ (e.g., the lungs) because some of the substances might
cause irritation by different (i.e., non-additive) mechanisms. As with the hazard quotient, aggregate exposures
below an HI of 1.0 derived using target organ specific hazard quotients likely will not result in adverse non-cancer
health effects over a lifetime of exposure and would ordinarily be considered acceptable. An HI equal to or greater
than 1.0, however, does not necessarily suggest a likelihood of adverse effects. Because of the inherent
conservatism of the reference concentration (RfC) methodology, the acceptability of exceedances must be
A-3
-------
EPA's National-scale Air Toxics Assessment
evaluated on a case-by-case basis, considering such factors as the confidence level of the assessment, the size of
the uncertainty factors used, the slope of the dose-response curve, the magnitude of the exceedance, and the
number or types of people exposed at various levels above the RfC. Furthermore, the HI cannot be translated to a
probability that adverse effects will occur, and it is not likely to be proportional to risk.
Hazard quotient (HQ):
The ratio of the potential exposure to the substance and the level at which no adverse effects are expected. A
hazard quotient less than or equal to one indicates that adverse noncancer effects are not likely to occur, and thus
can be considered to have negligible hazard. HQs greater than one are not statistical probabilities of harm
occurring. Instead, they are a simple statement of whether (and by how much) an exposure concentration exceeds
the reference concentration (RfC). Moreover, the level of concern does not increase linearly or to the same extent
as HQs increase above one for different chemicals because RfCs do not generally have equal accuracy or
precision and are generally not based on the same severity of effect. Thus, we can only say that with exposures
increasingly greater than the RfC, (i.e., HQs increasingly greater than 1), the potential for adverse effects
increases, but we do not know by how much. An HQ of 100 does not mean that the hazard is 10 times greater than
an HQ of 10. Also an HQ of 10 for one substance may not have the same meaning (in terms of hazard) as another
substance resulting in the same HQ.
Hazardous Air Pollutant Exposure Model (HAPEM):
A computer model that has been designed to estimate inhalation exposure for specified population groups and air
toxics. Through a series of calculation routines, the model makes use of 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 individuals.
Human Exposure Model (HEM):
The Human Exposure Model (HEM) is a computer model used primarily for conducting inhalation risk assessments
for sources emitting air toxics to ambient air. HEM-3 contains the AERMOD dispersion model for air-transport
simulations and U.S. Census data for identifying population receptors.
Inhalation:
Breathing. Once inhaled, contaminants can be deposited in the lungs, taken into the blood, or both.
Integrated Risk Information System (IRIS):
The Integrated Risk Information System (IRIS) is 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
NATA risk characterization).
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.
Maximum-likelihood estimate:
The most accurate maximum likelihood estimate is, by definition, the mode of a data set (i.e., the most frequent
observation). When data are too limited to identify a clear mode, the average or the median of the data is usually
substituted. For some air toxics for which adequate human data exist, EPA has based the unit risk estimate on the
maximum-likelihood estimate for response data or for fitted curves.
A-4
-------
EPA's National-scale Air Toxics Assessment
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.
Motor Vehicle Emission Simulator (MOVES):
A state-of-the-science 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.
Microenvironment:
A small space in which human contact with a pollutant takes place. A microenvironment can be treated as a well-
characterized, relatively homogenous location with respect to pollutant concentrations for a specified period. For
NATA, the Hazardous Air Pollutant Exposure Model considers cohort activities in 18 microenvironment locations
that include (1) indoor locations (e.g., residence, office, store, school, restaurant, church, manufacturing facility,
auditorium, healthcare facility, service station, other public building, garage); (2) outdoor locations (e.g., parking
lot/garage, near road, motorcycle, service station, construction site, residential grounds, school, sports arena,
park/golf course); and (3) in-vehicle locations (e.g., car, bus, truck, other, train/subway, airplane).
Microgram:
One-millionth of a gram. One gram is about one twenty-eighth of an ounce.
National-scale Air Toxics Assessment (NATA):
EPA's ongoing comprehensive evaluation of air toxics in the United States. These activities include the expansion
of air toxics monitoring, improvement and periodic updating of emission inventories, improvement of national- and
local-scale modeling, continued research on health effects and exposures to both ambient and indoor air, and
improvement of assessment tools.
National Emissions Inventory (NEI):
EPA prepares a national database of air emissions information with input from numerous 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. The database includes
estimates of annual emissions, by source, of air pollutants in each area of the country, on an annual basis. The
National Emissions Inventory 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 application containing EPA's NONROAD model for estimating county level inventories of nonroad mobile
emissions.
Noncancer risk:
The risk associated with effects other than cancer, based on the reference concentration, which is an estimate,
with uncertainty spanning perhaps an order of magnitude, of an inhalation exposure to the human population
(including sensitive subgroups) that is likely to be without appreciable risks of deleterious effects during a lifetime.
Nonroad mobile sources:
Mobile sources not found on roads and highways (e.g., airplanes, trains, lawn mowers, construction vehicles, farm
machinery).
On-road mobile sources:
Vehicles found on roads and highways (e.g., cars, trucks, buses).
A-5
-------
EPA's National-scale Air Toxics Assessment
Percentile:
Any one of the points dividing a distribution 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. In this assessment, the
distribution of values represented (national, state, or county percentiles) depends on the presentation format of the
results (map, bar chart, or data table).
Polycyclic organic matter (POM):
Defines a broad class of compounds that includes polycyclic aromatic hydrocarbons. Polycyclic organic matter
(POM) compounds are formed primarily from combustion and are present in the atmosphere in particulate form.
Sources of air emissions are diverse and include vehicle exhausts, 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 speciated. As a result, EPA applies some simplifying assumptions
to model and assess the risk from the individual pollutants that comprise polycyclic organic matter.
Reference concentration (RfC):
The reference concentration is an estimate (with uncertainty spanning perhaps an order of magnitude) of a
continuous inhalation exposure to the human population (including sensitive subgroups that include children,
asthmatics, and the elderly) that is likely to be without an appreciable risk of deleterious effects during a lifetime. It
can be derived from various types of human or animal data, with uncertainty factors generally applied to reflect
limitations of the data used.
Risk:
The probability that damage to life, health, or the environment will occur as a result of a given hazard (such as
exposure to a toxic chemical). Some risks can be measured or estimated in numerical terms (e.g., one chance in a
hundred).
Rural:
Consistent with the definition EPA used in the analyses to support the Integrated Urban Air Toxics Strategy, 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 does not designate more than 50 percent of the population as "urban." Note
that this definition does not necessarily apply for any regulatory or implementation purpose.
Sparse Matrix Operator Kernel Emissions (SMOKE):
A modeling system that processes emissions data for use in gridded 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 onroad mobile emissions.
Science Advisory Board (SAB):
A panel of scientists, engineers, and economists who provide EPA with independent scientific and technical advice.
Stationary sources:
Emission sources other than mobile sources such as large industrial sources such as power plants and refineries,
smaller industrial and commercial sources such as dry cleaners and commercial cooking, and residential sources
such as residential wood combustion and consumer products usage. Stationary sources may be characterized as
being emitted from "major" sources or "area" sources based on the 10-ton or 25-ton definitions contained in the
Clean Air Act. For presentation purposes, the NATA results are identified as "point" and "nonpoint" sources rather
than "major" and "area" sources. The point and nonpoint designations reflect the way each source of emissions is
modeled. Some smaller sources that are area sources in the inventory (based on the amount of their emissions)
are modeled as point sources because the location of their emissions was identified with latitude and longitude
coordinates.
A-6
-------
EPA's National-scale Air Toxics Assessment
Susceptibility:
An increased likelihood of an adverse effect, often discussed in terms of relationship to a factor (e.g., life stage,
demographic feature, or genetic characteristic) that can be used to describe a human subpopulation.
Toxicity weighting:
A relative risk evaluation tool that normalizes the emissions rates of each pollutant to a hypothetical substance with
an inhalation unit risk value of 1/|jg/m3 (for carcinogenic effects) or a reference concentration of 1 mg/m3 (for non-
cancer effects). It is entirely emissions-based and toxicity-based, and does not consider dispersion, fate, receptor
locations, and other exposure parameters. It may be calculated based on the emissions data for all pollutants
released from a facility or source being assessed. It is particularly useful if the number of pollutants is large and the
desire is to focus the risk analysis on a smaller subset of pollutants that contribute the most to risk.
Typical:
Describes a hypothetical person living at the census-tract centroid (defined as a reference point that is usually but
not always located at the geographic center of a census tract) and engaging in a range of activities (indoors and
outdoors) that are representative of those in which individuals residing in that tract might engage. To characterize
the risk that this person might experience, NATA divides the population as a whole into cohorts (groups who are
assumed to have identical exposures during a specified exposure period) based on where they live, how old they
are, and what their daily-activity patterns might be. For each combination of residential census tract, age, various
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 for a "typical" individual residing in that tract.
Upper bound:
A plausible upper limit to the true value of a quantity; usually not a true statistical confidence limit.
Upper-bound lifetime cancer risk:
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).
Unit risk estimate (URE):
The upper-bound excess lifetime cancer risk estimated to result from continuous exposure to an agent at a
concentration of 1 |jg/m3 in air. The interpretation of the unit risk estimate (URE) would be as follows: If the URE =
1.5 x 10 s per |jg/m3, 1.5 excess tumors are expected to develop per 1,000,000 people if they were exposed daily
for a lifetime to 1 |jg of the chemical in 1 m3 of air. UREs are considered upper-bound estimates, meaning they
represent a plausible upper limit to the true value. (Note that this is usually not a true statistical confidence limit.)
The true risk is likely to be less, but could be greater.
Urban:
Consistent with the definition EPA used in the analyses to support the Integrated Urban Air Toxics Strategy, 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.
Weight-of-evidence (WOE) for carcinogenicity:
The weight-of-evidence (WOE) narrative for carcinogenicity is a summary that explains what is known about an
agent's human carcinogenic potential and the conditions that characterize its expression. The narrative should be
sufficiently complete to stand alone, highlighting the key issues and decisions that were the basis for the evaluation
of the agent's potential hazard. The WOE characterizes the extent to which the available data support the
hypothesis that an agent causes cancer in humans. Under EPA's 1986 risk assessment guidelines, the weight of
evidence is described by categories "A through E," with Group A for known human carcinogens through Group E
A-7
-------
EPA's National-scale Air Toxics Assessment
for agents with evidence of non-carcinogenicity. The approach outlined in EPA's guidelines for carcinogen risk
assessment (2005) considers all scientific information in determining if and under what conditions an agent can
cause cancer in humans, and provides a narrative approach to characterize carcinogenicity rather than categories.
To provide 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. Five standard hazard descriptors are recommended: (1) carcinogenic to humans, (2) likely to be
carcinogenic to humans, (3) suggestive evidence of carcinogenic potential, (4) inadequate information to assess
carcinogenic potential, and (5) not likely to be carcinogenic to humans.
Carcinogenic to humans: This descriptor indicates strong evidence of human carcinogenicity. It covers different
combinations of evidence. This descriptor is appropriate when the epidemiologic evidence of a causal association
between human exposure and cancer is convincing. An exception is that this descriptor might also be equally
appropriate with a lesser weight of epidemiologic evidence that is strengthened by other lines of evidence. This
descriptor can be used when all of the following conditions are met: (a) 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; (b) there is extensive evidence of carcinogenicity in animals; (c) the mode(s) of
carcinogenic action and associated key precursor events have been identified in animals, (d) 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 the evidence 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. 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, 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 WOE suggests
carcinogenicity; 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 data base that includes negative studies in other species. Depending on the extent of the
data base, 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
robust for deciding that there is no basis for human hazard concern. In some instances, there can be positive
results in experimental animals when the evidence is strong and consistent that each mode of action in
experimental animals does not operate in humans. In other cases, the evidence in both humans and animals that
the agent is not carcinogenic can be convincing. "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.
A-8
-------
EPA's National-scale Air Toxics Assessment
Appendix B
Air Toxics Included in Modeling for the 2011 NATA, and Source
Classification Codes that Define Diesel Particulate Matter
This appendix contains three tables. The first two are related to the air toxics included in the 2011 NATA, and the
third lists the source classification codes (SCC) for which the PM10 emissions were considered to be diesel
particulate matter (PM).
Exhibit B-1 contains the air toxics included in the 2011 NATA and indicates the inventory types(s) reporting them.
The names shown in this table match the terminology used in the 1990 Clean Air Act (CAA) Amendments; for
example, this table lists "chromium compounds" but does not indicate which individual compounds containing
chromium were modeled, and it lists four forms of xylenes (0-, m-, p- and mixed isomers) but these were grouped
and modeled as a single entity. See Appendix C for the names of the actual substances included in the 2011
NATA. Exhibit B-1 also contains indications about whether cancer risks and chronic non-cancer hazard quotients
were estimated for each air toxic. Appendix H provides the toxicity values used in NATA.
Exhibit B-2 contains the air toxics that were not modeled for the 2011 NATA and why. Note that although diesel PM
was modeled for NATA and is included in Exhibit B-1, it is not categorized as a HAP in the CAA. Diesel PM
emissions were computed based on PM10 emissions from onroad and nonroad mobile sources burning diesel or
residual fuels (see Exhibit B-3).
The excel file "NATA_Pollutants_AppendixB_AppendixC.xlsx" in the SupplementalData folder provides the data in
spreadsheet format and includes additional fields such as the CMAQ model species names.
Note that NEI = National Emissions Inventory.
B-1
-------
EPA's National-scale Air Toxics Assessment
Exhibit B-1. Air Toxics Included in NATA
Air Toxic (Clean Air Act
Name)
NEI Pollutant
Code(CAS
Number)a
Data Category in the NEI
Background'
Assessed for
Cancer
Assessed for
Noncancer
CMAQ
Secondary
Eventb
Nonpointc
Nonroad
Onroad
Point d
Ag burning e
Rail yards'
Airports 9
Locomotives h
o
s
<
1,1,2,2-Tetrachloroethane
79345
~
~
~
NC
1,1,2-Trichloroethane
79005
~
~
Y
~
~
1,1-Dimethyl hydrazine
57147
~
1,2,4-Trichlorobenzene
120821
~
~
~
1,2-Dibromo-3-chloropropane
96128
~
Y
~
~
1,2-Diphenylhydrazine k
122667
~
~
1,2-Epoxybutane
106887
~
~
~
1,2-Propylenimine (2-methyl
aziridine)
75558
~
1,3-Butadiene
106990
~
~
~
~
~
~
~
~
~
~
~
NC
~
~
1,3-Dichloropropene
542756
~
~
~
NC
~
~
1,3-Propane sultone
1120714
~
~
1,4-Dichlorobenzene(p)
106467
~
~
~
NC
~
~
1,4-Dioxane
123911
~
~
~
~
2,2,4-Trimethylpentane
540841
~
~
~
~
~
~
~
~
Y
2,4,5-Trichlorophenol
95954
~
2,4,6-Trichlorophenol
88062
~
~
~
2,4-D, salts and esters
94757
~
~
2,4-Dinitrophenol
51285
~
~
2,4-Dinitrotoluene
121142
~
~
~
~
2,4-Toluene diamine
95807
~
~
2,4-Toluene diisocyanate
584849
~
~
~
~
~
2- Acetylaminofluorene
53963
~
~
2-Chloroacetophenone
532274
~
~
~
2-Nitropropane
79469
~
~
~
~
3,3'-Dichlorobenzidine
91941
~
~
3,3'-Dimethoxybenzidine
119904
~
3,3'-Dimethylbenzidine
119937
~
4,4'-Methylene
bis(2-chloroaniline)
101144
~
~
4,4'-Methylenedianiline
101779
~
~
~
4,6-Dinitro-o-cresol, and salts
534521
~
4-Aminobiphenyl
92671
~
4-Nitrobiphenyl
92933
~
4-Nitrophenol
100027
~
~
Acetaldehyde
75070
~
~
~
~
~
~
~
~
~
~
~
~
~
~
Acetamide
60355
~
~
~
Acetonitrile
75058
~
~
Y
~
Acetophenone
98862
~
~
Acrolein
107028
~
~
~
~
~
~
~
~
~
~
~
~
~
Acrylamide
79061
~
~
~
~
Acrylic acid
79107
~
~
~
Acrylonitrile
107131
~
~
~
NC
~
~
B-2
-------
EPA's National-scale Air Toxics Assessment
Air Toxic (Clean Air Act
Name)
NEI Pollutant
Code(CAS
Number)a
Data Category in the NEI
Backgroundj
Assessed for
Cancer
Assessed for
Noncancer
CMAQ
Secondary
Eventb
Nonpointc
Nonroad
Onroad
Pointd
Ag burning e
Rail yards'
Airports 9
Locomotives h
o
s,
<
Allyl chloride
107051
~
~
~
~
Aniline
62533
~
~
~
Antimony Compounds
7440360
~
~
~
y
Y
~
Arsenic Compounds (inorganic
including arsine)
7440382
~
~
~
~
~
~
~
~
y
NC
~
~
Benzene (including benzene
from gasoline)
71432
~
~
~
~
~
~
~
~
~
~
y
NC
~
~
Benzidine
92875
~
Y
~
~
Benzotrichloride
98077
~
Benzyl chloride
100447
~
~
Y
~
Beryllium Compounds
7440417
~
~
~
~
~
y
NC
~
~
Beta-Propiolactone k
57578
~
Biphenyl
92524
~
~
Bis(2-ethylhexyl)phthalate
(DEHP)
117817
~
~
Y
~
~
Bis(chloromethyl)ether
542881
~
~
Bromoform
75252
~
~
Y
~
Cadmium Compounds
7440439
~
~
~
~
~
~
y
NC
~
~
Calcium cyanamide
156627
~
~
Captan
133062
~
~
Carbaryl
63252
~
~
Carbon disulfide
75150
~
~
Y
~
Carbon tetrachloride
56235
~
~
~
Y
~
~
Carbonyl sulfide
463581
~
~
~
~
Catechol
120809
~
Chloramben k
133904
~
Chlordane
57749
~
~
~
Chlorine
7782505
~
~
~
~
~
y
~
Chloroacetic acid
79118
~
Chlorobenzene
108907
~
~
~
Chlorobenzilate
510156
~
~
Chloroform
67663
~
~
~
NC
~
Chloromethyl methyl ether
107302
~
Chloroprene
126998
~
~
~
~
Chromium Compounds 1
multiple
~
~
~
~
~
NC
Cobalt Compounds
7440484
~
~
~
~
y
Y
~
Coke Oven Emissions
140
~
~
Cresols/Cresylic acid (isomers
and mixture) m
1319773
~
~
~
Cumene
98828
~
~
~
~
~
y
Y
~
Cyanide Compounds
multiple
~
~
~
Diazomethane
334883
Dibenzofurans
132649
~
~
Dibutylphthalate
84742
~
~
B-3
-------
EPA's National-scale Air Toxics Assessment
Air Toxic (Clean Air Act
Name)
NEI Pollutant
Code(CAS
Number)a
Data Category in the NEI
Backgroundj
Assessed for
Cancer
Assessed for
Noncancer
CMAQ
Secondary
Eventb
Nonpointc
Nonroad
Onroad
Pointd
Ag burning e
Rail yards'
Airports 9
Locomotives h
o
s,
<
Dichloroethyl ether
(Bis(2-chloroethyl)ether)
111444
~
~
Dichlorvos
62737
~
~
Diethanolamine
111422
~
~
~
Diethyl sulfate
64675
~
Dimethyl aminoazobenzene
60117
~
~
Dimethyl carbamoyl chloride
79447
~
Dimethyl formamide
68122
~
~
~
Dimethyl phthalate
131113
~
~
Dimethyl sulfate
77781
~
~
Epichlorohydrin
(l-Chloro-2,3-epoxypropane)
106898
~
~
~
~
Ethyl acrylate
140885
~
~
Ethyl benzene
100414
~
~
~
~
~
~
~
y
Y
~
~
Ethyl carbamate (Urethane)
51796
~
~
Ethyl chloride (Chloroethane)
75003
~
~
~
Ethylene dibromide
(Dibromoethane)
106934
~
~
~
~
NC
~
~
Ethylene dichloride
(1,2-Dichloroethane)
107062
~
~
~
~
NC
~
~
Ethylene glycol
107211
~
~
~
Ethylene imine (Aziridine)
151564
~
Ethylene oxide
75218
~
~
~
NC
~
~
Ethylene thiourea
96457
~
~
~
Ethylidene dichloride
(1,1-Dichloroethane)
75343
~
~
~
~
Formaldehyde
50000
~
~
~
~
~
~
~
~
~
~
~
y
~
~
Glycol Ethers
N/A
~
~
~
~
Heptachlor
76448
~
~
Hexachlorobenzene
118741
~
~
y
~
~
Hexachlorobutadiene
87683
~
~
~
~
Hexachlorocyclopentadiene
77474
~
~
~
Hexachloroethane
67721
~
~
Hexamethylene-
1,6-diisocyanate
822060
~
~
~
~
Hexamethylphosphoramide k
680319
Hexane
110543
~
~
~
~
~
~
~
~
y
Y
~
Hydrazine
302012
~
~
~
~
Hydrochloric acid
7647010
~
~
~
~
Hydrogen fluoride (Hydrofluoric
acid)
7664393
~
~
~
Hydroquinone
123319
~
~
Isophorone
78591
~
~
~
Lead Compounds
7439921
~
~
~
~
~
~
~
y
NC
~
Lindane (all isomers)
58899
~
~
~
~
B-4
-------
EPA's National-scale Air Toxics Assessment
Air Toxic (Clean Air Act
Name)
NEI Pollutant
Code(CAS
Number)a
Data Category in the NEI
Backgroundj
Assessed for
Cancer
Assessed for
Noncancer
CMAQ
Secondary
Eventb
Nonpointc
Nonroad
Onroad
Pointd
Ag burning e
Rail yards'
Airports 9
Locomotives h
o
s,
<
Maleic anhydride
108316
~
~
~
~
Manganese Compounds
7439965
~
~
~
~
~
~
~
~
y
NC
~
m-Cresol m
108394
~
~
Mercury Compounds
7439976
~
~
~
~
~
~
~
~
~
y
NC
~
Methanol
67561
~
~
~
~
~
~
~
y
~
Methoxychlor
72435
~
Methyl bromide
(Bromomethane)
74839
~
~
Y
~
Methyl chloride
(Chloromethane)
74873
~
~
~
Y
~
Methyl chloroform
(1,1,1 -T richloroethane)
71556
~
~
Y
~
Methyl hydrazine
60344
~
~
Methyl iodide (lodomethane)
74884
~
~
Methyl isobutyl ketone (Hexone)
108101
~
~
~
Y
~
Methyl isocyanate
624839
~
~
Methyl methacrylate
80626
~
~
~
Methyl tert butyl ether
1634044
~
~
~
~
Methylene chloride
(Dichloromethane)
75092
~
~
~
NC
~
~
Methylene diphenyl
diisocyanate (MDI)
101688
~
~
~
m-Xylenes "
108383
~
~
~
~
~
~
~
~
~
y
NC
~
N,N-Dimethylaniline
121697
~
~
Naphthalene
91203
~
~
~
~
~
~
~
~
y
NC
~
~
Nickel Compounds
7440020
~
~
~
~
~
~
~
~
y
NC
~
~
Nitrobenzene
98953
~
~
~
~
N-Nitrosodimethylamine
62759
~
~
N-Nitrosomorpholine
59892
~
~
N-Nitroso-N-Methylurea k
684935
~
o-Anisidine
90040
~
o-Cresol m
95487
~
~
~
o-Toluidine
95534
~
~
~
o-Xylenes "
95476
~
~
~
~
~
~
~
~
~
y
NC
~
Parathion k
56382
~
p-Cresol m
106445
~
~
~
Pentachloronitrobenzene
(Quintobenzene)
82688
~
~
Pentachlorophenol
87865
~
~
~
~
Phenol
108952
~
~
~
~
Phosgene
75445
~
~
Phosphine
7803512
~
~
Phosphorus
7723140
~
~
~
y
Phthalic anhydride
85449
~
~
~
B-5
-------
EPA's National-scale Air Toxics Assessment
Air Toxic (Clean Air Act
Name)
NEI Pollutant
Code(CAS
Number)a
Data Category in the NEI
Backgroundj
Assessed for
Cancer
Assessed for
Noncancer
CMAQ
Secondary
Eventb
Nonpointc
Nonroad
Onroad
Pointd
Ag burning e
Rail yards'
Airports 9
Locomotives h
o
s,
<
Polychlorinated biphenyls
(Aroclors)
1336363
~
~
y
~
Polycyclic Organic Matter °
N/A
~
~
~
~
~
~
~
~
~
~
y
~
p-Phenylenediamine
106503
~
Propionaldehyde
123386
~
~
~
~
~
~
~
y
Y
~
Propoxur (Baygon)
114261
~
Propylene dichloride
(1,2-Dichloropropane)
78875
~
~
~
NC
~
Propylene oxide
75569
~
~
~
~
p-Xylenes "
106423
~
~
~
~
~
~
~
~
y
NC
~
Quinoline
91225
~
~
Quinone
106514
~
Selenium Compounds
7782492
~
~
~
~
y
Y
~
Styrene
100425
~
~
~
~
~
~
~
y
Y
~
Styrene oxide
96093
~
~
~
Tetrachloroethylene
(Perchloroethylene)
127184
~
~
~
NC
~
~
Titanium tetrachloride
7550450
~
~
Toluene
108883
~
~
~
~
~
~
~
~
~
~
y
NC
~
Toxaphene (chlorinated
camphene)
8001352
~
~
Trichloroethylene
79016
~
~
~
NC
~
~
Triethylamine
121448
~
~
~
~
Trifluralin
1582098
~
~
Vinyl acetate
108054
~
~
~
Y
~
Vinyl bromide
593602
~
~
~
Vinyl chloride
75014
~
~
~
~
NC
~
~
Vinylidene chloride
(1,1-Dichloroethylene)
75354
~
~
~
Xylenes (isomers and mixture)"
1330207
~
~
~
~
~
~
~
~
~
~
y
NC
~
Diesel PM p
~
~
~
~
y
~
a In 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.
Note: Actual categories in the NEI are: point, nonpoint, onroad, nonroad, and event. The above categories break out point and nonpoint into additional
groups. See footnotes for more details
b Event category has prescribed and wildfires (day-specific)
c Excluding ag fires, locomotives, and commercial marine vessels
d Excluding airports and rail yards
e Ag burning is agricultural field burning and is a part of the nonpoint data category in the NEI
f Railyards are part of the point data category in the NEI
9 Airports are part of the point data category in the NEI
h CMV = commercial marine vessels, part of the nonpoint category in the NEI
' Locomotives are part of the nonpoint category in the NEI
j Remote concentration estimate added: NC="non-CONUS" (concentration added only to non-continental U.S. areas: Alaska, Hawaii, Puerto
Rico, and U.S. Virgin Islands), Y=non-CMAQ air toxic remote concentration estimate added everywhere
k Not in 2011 NATA because there were no emissions
1 NATA includes only hexavalent chromium
m Modeled as cresols
n Modeled as xylenes
0 About 50 specific compounds are in the NEI. They were modeled as 9 discrete PAH groups representing different URE "bins" since specific
compounds have a wide range of UREs.
p Diesel PM is not a HAP and not on the Clean Air Act list but it is modeled in NATA.
B-6
-------
EPA's National-scale Air Toxics Assessment
Exhibit B-2. Pollutants Excluded from NATA
Pollutant
NEI Pollutant
Code(CAS
Number)a
Reason for Exclusion
In Previous
NATAs?
2,3,7,8-Tetrachlorodibenzo-p-
dioxin
1746016
Dioxins and furans are not in the 2011 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 NATA's inhalation risk
estimates likely would not be large.
n
Other dioxins/furans
multiple
n
Radionuclides
Radionuclides are not in the 2011 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 2011 NEI.
y
Fine mineral fibers (including
rockwool and slag wool and fine
mineral fibers)
Fine mineral
fibers: 383
Rockwool:617
Slagwool:616
Rockwool has 0 emissions and slagwool and fine
mineral fibers are excluded from previous
assessments
n
Asbestos
1332214
Inhalation exposures not typically expressed in
mass units
n
Diazomethane
334883
This pollutant has 0 emissions in the 2011 NEI
y
Hexamethylphosphoramide
680319
This pollutant has 0 emissions in the 2011 NEI
n
a In 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.
B-7
-------
EPA's National-scale Air Toxics Assessment
Exhibit B-3. Source Classification Codes For which PM10 Emissions were assigned to Diesel Particulate Matter
NEI
Category
see
Description
Point
28500201
Internal Combustion Engines;Railroad Equipment;Diesel;Yard Locomotives
2270008005
*M;Off-highway Vehicle Diesel;Airport Ground Support Equipment;Airport Ground Support Equipment
Nonpoint
2280002100
*M;Marine Vessels, Commercial;Diesel;Port emissions
2280002200
*M;Marine Vessels, Commercial;Diesel;Underway emissions
2285002006
*M;Railroad Equipment;Diesel
Line Haul Locomotives: Class I Operations
2285002007
*M;Railroad Equipment;Diesel
Line Haul Locomotives: Class II / III Operations
2285002008
*M;Railroad Equipment;Diesel
Line Haul Locomotives: Passenger Trains (Amtrak)
2285002009
*M;Railroad Equipment;Diesel
Line Haul Locomotives: Commuter Lines
2285002010
*M;Railroad Equipment;Diesel
Yard Locomotives
2280003100
*M;Marine Vessels, Commercial;Residual;Port emissions
2280003200
*M;Marine Vessels, Commercial;Residual;Underway emissions
Nonroad
2270001060
*M; Off-highway Veh
cle Diesel
Recreational Equipment;Specialty Vehicles/Carts
2270002003
*M; Off-highway Veh
cle Diesel
Construction and Mining Equipment
Pavers
2270002006
*M; Off-highway Veh
cle Diesel
Construction and Mining Equipment
Tampers/Rammers
2270002009
*M; Off-highway Veh
cle Diesel
Construction and Mining Equipment
Plate Compactors
2270002015
*M; Off-highway Veh
cle Diesel
Construction and Mining Equipment
Rollers
2270002018
*M; Off-highway Veh
cle Diesel
Construction and Mining Equipment
Scrapers
2270002021
*M; Off-highway Veh
cle Diesel
Construction and Mining Equipment
Paving Equipment
2270002024
*M; Off-highway Veh
cle Diesel
Construction and Mining Equipment
Surfacing Equipment
2270002027
*M; Off-highway Veh
cle Diesel
Construction and Mining Equipment
Signal Boards/Light Plants
2270002030
*M; Off-highway Veh
cle Diesel
Construction and Mining Equipment
Trenchers
2270002033
*M; Off-highway Veh
cle Diesel
Construction and Mining Equipment
Bore/Drill Rigs
2270002036
*M; Off-highway Veh
cle Diesel
Construction and Mining Equipment
Excavators
2270002039
*M; Off-highway Veh
cle Diesel
Construction and Mining Equipment
Concrete/Industrial Saws
2270002042
*M; Off-highway Veh
cle Diesel
Construction and Mining Equipment
Cement and Mortar Mixers
2270002045
*M; Off-highway Veh
cle Diesel
Construction and Mining Equipment
Cranes
2270002048
*M; Off-highway Veh
cle Diesel
Construction and Mining Equipment
Graders
2270002051
*M; Off-highway Veh
cle Diesel
Construction and Mining Equipment
Off-highway T rucks
2270002054
*M; Off-highway Veh
cle Diesel
Construction and Mining Equipment
Crushing/Processing Equipment
2270002057
*M; Off-highway Veh
cle Diesel
Construction and Mining Equipment
Rough Terrain Forklifts
2270002060
*M; Off-highway Veh
cle Diesel
Construction and Mining Equipment
Rubber Tire Loaders
B-8
-------
EPA's National-scale Air Toxics Assessment
NEI
Category
see
Description
2270002066
*M; Off-highway Veh
cle Diesel
Construction and Mining Equipment;Tractors/Loaders/Backhoes
2270002069
*M;Off-highway Veh
cle Diesel
Construction and Mining Equipment;Crawler Tractor/Dozers
2270002072
*M;Off-highway Veh
cle Diesel
Construction and Mining Equipment;Skid Steer Loaders
2270002075
*M;Off-highway Veh
cle Diesel
Construction and Mining Equipment;Off-highway Tractors
2270002078
*M;Off-highway Veh
cle Diesel
Construction and Mining Equipment;Dumpers/Tenders
2270002081
*M;Off-highway Veh
cle Diesel
Construction and Mining Equipment;Other Construction Equipment
2270003010
*M;Off-highway Veh
cle Diesel
Industrial Equipment
Aerial Lifts
2270003020
*M;Off-highway Veh
cle Diesel
Industrial Equipment
Forklifts
2270003030
*M;Off-highway Veh
cle Diesel
Industrial Equipment
Sweepers/Scrubbers
2270003040
*M;Off-highway Veh
cle Diesel
Industrial Equipment
Other General Industrial Equipment
2270003050
*M;Off-highway Veh
cle Diesel
Industrial Equipment
Other Material Handling Equipment
2270003060
*M;Off-highway Veh
cle Diesel
Industrial Equipment
ACRefrigeration
2270003070
*M;Off-highway Veh
cle Diesel
Industrial Equipment
Terminal Tractors
2270004031
*M;Off-highway Veh
cle Diesel
Lawn and Garden Equipment
LeafblowersA/acuums (Commercial)
2270004036
*M;Off-highway Veh
cle Diesel
Lawn and Garden Equipment
Snowblowers (Commercial)
2270004046
*M;Off-highway Veh
cle Diesel
Lawn and Garden Equipment
Front Mowers (Commercial)
2270004056
*M;Off-highway Veh
cle Diesel
Lawn and Garden Equipment
Lawn and Garden Tractors (Commercial)
2270004066
*M;Off-highway Veh
cle Diesel
Lawn and Garden Equipment
Chippers/Stump Grinders (Commercial)
2270004071
*M;Off-highway Veh
cle Diesel
Lawn and Garden Equipment
Turf Equipment (Commercial)
2270004076
*M;Off-highway Veh
cle Diesel
Lawn and Garden Equipment
Other Lawn and Garden Equipment (Commercial)
2270005010
*M; Off-highway Veh
cle Diesel
Agricultural Equipment;2-Wheel Tractors
2270005015
*M;Off-highway Veh
cle Diesel
Agricultural Equipment;Agricultural Tractors
2270005020
*M; Off-highway Veh
cle Diesel
Agricultural Equipment;Combines
2270005025
*M;Off-highway Veh
cle Diesel
Agricultural Equipment;Balers
2270005030
*M; Off-highway Veh
cle Diesel
Agricultural Equipment;Agricultural Mowers
2270005035
*M;Off-highway Veh
cle Diesel
Agricultural Equipment;Sprayers
2270005040
*M; Off-highway Veh
cle Diesel
Agricultural Equipment;Tillers > 6 HP
2270005045
*M;Off-highway Veh
cle Diesel
Agricultural Equipment;Swathers
2270005055
*M; Off-highway Veh
cle Diesel
Agricultural Equipment;Other Agricultural Equipment
2270005060
*M;Off-highway Veh
cle Diesel
Agricultural Equipment;lrrigation Sets
2270006005
*M; Off-highway Veh
cle Diesel
Commercial Equipment;Generator Sets
2270006010
*M;Off-highway Veh
cle Diesel
Commercial Equipment;Pumps
2270006015
*M; Off-highway Veh
cle Diesel
Commercial Equipment;Air Compressors
B-9
-------
EPA's National-scale Air Toxics Assessment
NEI
Category
see
Description
2270006020
*M;Off-highway Vehicle Diesel
Commercial Equipment;Gas Compressors
2270006025
*M;Off-highway Vehicle Diesel
Commercial Equipment;Welders
2270006030
*M;Off-highway Vehicle Diesel
Commercial Equipment;Pressure Washers
2270006035
*M;Off-highway Vehicle Diesel
Commercial Equipment;Hydro-power Units! new SCC in 2002v2
2270007010
*M;Off-highway Vehicle Diesel
Logging Equipment;Shredders > 6 HP
2270007015
*M;Off-highway Vehicle Diesel
Logging Equipment;Forest Eqp - Feller/Bunch/Skidder
2270009010
*M;Off-highway Vehicle Diesel
Underground Mining Equipment;Other Underground Mining Equipment
2270010010
*M;Off-highway Vehicle Diesel
Industrial Equipment;Other Oil Field Equipment
2282020005
*M;Pleasure Craft;Diesel;Inboard/Sterndrive
2282020010
*M;Pleasure Craft;Diesel;Outboard
2285002015
*M;Railroad Equipment;Diesel;Railway Maintenance
Onroad
2202210181
*D;Passenger Cars;Off-network: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202210281
*D;Passenger Cars;Rural Restricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202210381
*D;Passenger Cars;Rural Unrestricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202210481
*D;Passenger Cars;Urban Restricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202210581
*D;Passenger Cars;Urban Unrestricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202310181
*D;Passenger Trucks;Off-network: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202310281
*D;Passenger Trucks;Rural Restricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202310381
*D;Passenger Trucks;Rural Unrestricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202310481
*D;Passenger Trucks;Urban Restricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202310581
*D;Passenger Trucks;Urban Unrestricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202320181
*D;Light Commercial Trucks;Off-network: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202320281
*D;Light Commercial Trucks;Rural Restricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202320381
*D;Light Commercial Trucks;Rural Unrestricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202320481
*D;Light Commercial Trucks;Urban Restricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202320581
*D;Light Commercial Trucks;Urban Unrestricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202410181
*D; Intercity Buses;Off-network: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202410281
*D;Intercity Buses;Rural Restricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202410381
*D; I ntercity Buses;Rural Unrestricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202410481
*D; I ntercity Buses;Urban Restricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202410581
*D; I ntercity Buses;Urban Unrestricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202420181
*D;Transit Buses;Off-network: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202420281
*D;Transit Buses;Rural Restricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
B-10
-------
EPA's National-scale Air Toxics Assessment
NEI
Category
see
Description
2202420381
*D;Transit Buses;Rural Unrestricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202420481
*D;Transit Buses;Urban Restricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202420581
*D;Transit Buses;Urban Unrestricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202430181
*D;School Buses;Off-network: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202430281
*D;School Buses;Rural Restricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202430381
*D;School Buses;Rural Unrestricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202430481
*D;School Buses;Urban Restricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202430581
*D;School Buses;Urban Unrestricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202510181
*D;Refuse Trucks;Off-network: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202510281
*D;Refuse Trucks;Rural Restricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202510381
*D;Refuse Trucks;Rural Unrestricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202510481
*D;Refuse Trucks;Urban Restricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202510581
*D;Refuse Trucks;Urban Unrestricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202520181
*D;Single Unit Short-haul Trucks;Off-network: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202520281
*D;Single Unit Short-haul Trucks;Rural Restricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202520381
*D;Single Unit Short-haul Trucks;Rural Unrestricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202520481
*D;Single Unit Short-haul Trucks;Urban Restricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202520581
*D;Single Unit Short-haul Trucks;Urban Unrestricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and
Hoteling
2202530181
*D;Single Unit Long-haul Trucks;Off-network: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202530281
*D;Single Unit Long-haul Trucks;Rural Restricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202530381
*D;Single Unit Long-haul Trucks;Rural Unrestricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202530481
*D;Single Unit Long-haul Trucks;Urban Restricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202530581
*D;Single Unit Long-haul Trucks;Urban Unrestricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and
Hoteling
2202540181
*D;Motor Homes;Off-network: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202540281
*D;Motor Homes;Rural Restricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202540381
*D;Motor Homes;Rural Unrestricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202540481
*D;Motor Homes;Urban Restricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202540581
*D;Motor Homes;Urban Unrestricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202610181
*D;Combination Short-haul Trucks;Off-network: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202610281
*D;Combination Short-haul Trucks;Rural Restricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
B-11
-------
EPA's National-scale Air Toxics Assessment
NEI
Category
see
Description
2202610381
*D;Combination Short-haul Trucks;Rural Unrestricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and
Hoteling
2202610481
*D;Combination Short-haul Trucks;Urban Restricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and
Hoteling
2202610581
*D;Combination Short-haul Trucks;Urban Unrestricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and
Hoteling
2202620153
*D;Combination Long-haul Trucks;Off-network
Extended Idle Exhaust
2202620181
*D;Combination Long-haul Trucks;Off-network
All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202620191
*D;Combination Long-haul Trucks;Off-network
Auxiliary Power Exhaust
2202620281
*D;Combination Long-haul Trucks;Rural Restricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and Hoteling
2202620381
*D;Combination Long-haul Trucks;Rural Unrestricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and
Hoteling
2202620481
*D;Combination Long-haul Trucks;Urban Restricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and
Hoteling
2202620581
*D;Combination Long-haul Trucks;Urban Unrestricted Access: All Exhaust, Evaporative, Brake, and Tire Except Refueling and
Hoteling
*M= Mobile Sources. *D=Highway Vehicles-Diesel
B-12
-------
EPA's National-scale Air Toxics Assessment
Appendix C
Crosswalk for Air Toxics Names in the NEI and Metal Speciation
Factors
Exhibit C-1 contains the air toxic name crosswalk and metal speciation factors used to conduct the modeling of
emissions for the 2011 NATA. This crosswalk contains a link between lists of air toxic names in two data bases
used for NATA:
• the names used in the National Emissions Inventory (NEI), and
• the names used for NATA.
This table also contains the corresponding names for each air toxic as used in the 1990 Clean Air Act
Amendments. In addition, Exhibit C-1 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 2011 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.
A master pollutant list for NATA in spreadsheet format, "NATA_Pollutants_AppendixB_AppendixC.xlsx", is
provided in the SupplementalData folder. The second sheet in the workbook is an electronic form of the PAHPOM
groupings shown in Section 2.1.1.2 of the TSD.
C-1
-------
EPA's National-scale Air Toxics Assessment
Exhibit C-1. Crosswalk for Air Toxics Names in NEI, NATA Results, and the Clean Air Act, with Metal Speciation Factors
Air Toxic
(Clean Air Act
Name)
NEI Pollutant
Code
(CAS Number)a
Pollutant Category Name
Pollutant Description
NATA Website Pollutant Name
Metal
Speciation
Factor
Glycol Ethers
112072
Glycol Ethers
2-Butoxyethyl Acetate
GLYCOL ETHERS
Glycol Ethers
112152
Glycol Ethers
Carbitol Acetate
GLYCOL ETHERS
Glycol Ethers
112254
Glycol Ethers
2-(Hexyloxy)Ethanol
GLYCOL ETHERS
Glycol Ethers
112276
Glycol Ethers
Triethylene glycol
GLYCOL ETHERS
Glycol Ethers
112345
Glycol Ethers
Diethylene Glycol
Monobutyl Ether
GLYCOL ETHERS
Glycol Ethers
112356
Glycol Ethers
Methoxytriglycol
GLYCOL ETHERS
Glycol Ethers
112367
Glycol Ethers
Diethylene Glycol
Diethyl Ether
GLYCOL ETHERS
Glycol Ethers
112492
Glycol Ethers
Triethylene Glycol
Dimethyl Ether
GLYCOL ETHERS
Glycol Ethers
112594
Glycol Ethers
N-Hexyl Carbitol
GLYCOL ETHERS
Propoxur (Baygon)
114261
Propoxur
Propoxur
PROPOXUR (BAYGON)
Bis(2-
ethylhexyl)phthalate
(DEHP)
117817
Bis(2-Ethylhexyl)Phthalate
Bis(2-
Ethylhexyl)Phthalate
BIS(2-ETHYLHEXYL)PHTHALATE (DEHP)
Hexachlorobenzene
118741
Hexachlorobenzene
Hexachlorobenzene
HEXACHLOROBENZENE
3,3'-
Dimethoxybenzidine
119904
3,3'-Dimethoxybenzidine
3,3'-
Dimethoxybenzidine
3,3'-DIMETHOXYBENZIDINE
3,3'-
Dimethylbenzidine
119937
3,3'-Dimethylbenzidine
3,3'-Dimethylbenzidine
3,3'-DIMETHYLYBENZIDINE
Polycyclic Organic
Matter
120127
Polycyclic Organic Matter
Anthracene
PAHPOM
Catechol
120809
Catechol
Catechol
CATECHOL
1,2,4-
Trichlorobenzene
120821
1,2,4-T richlorobenzene
1,2,4-
Trichlorobenzene
1,2,4-TRICHLOROBENZENE
2,4-Dinitrotoluene
121142
2,4-Dinitrotoluene
2,4-Dinitrotoluene
2,4-DINITROTOLUENE
Triethylamine
121448
Triethylamine
Triethylamine
TRIETHYLAMINE
N,N-Dimethylaniline
121697
N,N-Dimethylaniline
N,N-Dimethylaniline
N.N-DIMETHYLANILINE
1,2-
Diphenylhydrazine
122667
1,2-Diphenylhydrazine
1,2-Diphenylhydrazine
1,2-DIPHENYLHYDRAZINE
Glycol Ethers
122996
Glycol Ethers
Phenyl Cellosolve
GLYCOL ETHERS
4-Nitrophenol
100027
4-Nitrophenol
4-Nitrophenol
4-NITROPHENOL
Ethyl benzene
100414
Ethylbenzene
Ethyl Benzene
ETHYLBENZENE
Styrene
100425
Styrene
Styrene
STYRENE
C-2
-------
EPA's National-scale Air Toxics Assessment
Air Toxic
(Clean Air Act
Name)
NEI Pollutant
Code
(CAS Number)a
Pollutant Category Name
Pollutant Description
NATA Website Pollutant Name
Metal
Speciation
Factor
Benzyl chloride
100447
Benzyl Chloride
Benzyl Chloride
BENZYL CHLORIDE
4,4'-Methylene
bis(2-chloroaniline)
101144
4,4'-Methylenebis(2-
Chloroaniline)
4,4'-Methylenebis(2-
Chloraniline)
4,4'-METHYLENE BIS(2-CHLOROANILINE)
Methylene diphenyl
diisocyanate (MDI)
101688
4,4'-Methylenediphenyl
Diisocyanate
4,4'-Methylenediphenyl
Diisocyanate
4,4'-METHYLENEDIPHENYL DIISOCYANATE (MDI)
4,4'-
Methylenedianiline
101779
4,4'-Methylenedianiline
4,4'-Methylenedianiline
4,4-METHYLENEDI ANILINE
p-Xylenes
106423
Xylenes (Mixed Isomers)
p-Xylene
XYLENES (MIXED ISOMERS)
p-Cresol
106445
Cresol/Cresylic Acid (Mixed
Isomers)
p-Cresol
CRESOL_CRESYLIC ACID (MIXED ISOMERS)
1,4-
Dichlorobenzene(p)
106467
1,4-Dichlorobenzene
1,4-Dichlorobenzene
1,4-DICHLOROBENZENE
p-Phenylenediamine
106503
p-Phenylenediamine
p-Phenylenediamine
P-PHENYLENEDIAMINE
Quinone
106514
Quinone
Quinone
QUINONE (P-BENZOQUINONE)
1,2-Epoxybutane
106887
1,2-Epoxybutane
1,2-Epoxybutane
1,2-EPOXYBUTANE
Epichlorohydrin
(l-Chloro-2,3-epoxypr
opane)
106898
Epichlorohydrin
Epichlorohydrin
EPICHLOROHYDRIN
Ethylene dibromide
(Dibromoethane)
106934
Ethylene Dibromide
Ethylene Dibromide
ETHYLENE DIBROMIDE (DIBROMOETHANE)
1,3-Butadiene
106990
1,3-Butadiene
1,3-Butadiene
1,3-BUTADIENE
Acrolein
107028
Acrolein
Acrolein
ACROLEIN
Allyl chloride
107051
Allyl Chloride
Allyl Chloride
ALLYL CHLORIDE
Ethylene dichloride
(1,2-Dichloroethane)
107062
Ethylene Dichloride
Ethylene Dichloride
ETHYLENE DICHLORIDE (1,2-
DICHLOROETHANE)
Acrylonitrile
107131
Acrylonitrile
Acrylonitrile
ACRYLONITRILE
Ethylene glycol
107211
Ethylene Glycol
Ethylene Glycol
ETHYLENE GLYCOL
Chloromethyl methyl
ether
107302
Chloromethyl Methyl Ether
Chloromethyl Methyl
Ether
CHLOROMETHYL METHYL ETHER
Vinyl acetate
108054
Vinyl Acetate
Vinyl Acetate
VINYL ACETATE
Methyl isobutyl
ketone (Hexone)
108101
Methyl Isobutyl Ketone
Methyl Isobutyl Ketone
METHYL ISOBUTYL KETONE (HEXONE)
Maleic anhydride
108316
Maleic Anhydride
Maleic Anhydride
MALEIC ANHYDRIDE
m-Xylenes
108383
Xylenes (Mixed Isomers)
m-Xylene
XYLENES (MIXED ISOMERS)
m-Cresol
108394
Cresol/Cresylic Acid (Mixed
Isomers)
m-Cresol
CRESOL_CRESYLIC ACID (MIXED ISOMERS)
Toluene
108883
Toluene
Toluene
TOLUENE
C-3
-------
EPA's National-scale Air Toxics Assessment
Air Toxic
(Clean Air Act
Name)
NEI Pollutant
Code
(CAS Number)a
Pollutant Category Name
Pollutant Description
NATA Website Pollutant Name
Metal
Speciation
Factor
Chlorobenzene
108907
Chlorobenzene
Chlorobenzene
CHLOROBENZENE
Phenol
108952
Phenol
Phenol
PHENOL
Glycol Ethers
109864
Glycol Ethers
Ethylene Glycol Methyl
Ether
GLYCOL ETHERS
Glycol Ethers
110496
Glycol Ethers
Ethylene Glycol
Monomethyl Ether
Acetate
GLYCOL ETHERS
Hexane
110543
Hexane
Hexane
HEXANE
Glycol Ethers
110714
Glycol Ethers
1,2-Dimethoxyethane
GLYCOL ETHERS
Glycol Ethers
110805
Glycol Ethers
Cellosolve Solvent
GLYCOL ETHERS
Glycol Ethers
111159
Glycol Ethers
Cellosolve Acetate
GLYCOL ETHERS
Diethanolamine
111422
Diethanolamine
Diethanolamine
DIETHANOLAMINE
Dichloroethyl ether
(Bis(2-chloroethyl)eth
er)
111444
Dichloroethyl Ether
Dichloroethyl Ether
DICHLOROETHYL ETHER (BIS[2-
CHLOROETHYLJETHER)
Glycol Ethers
111773
Glycol Ethers
Diethylene Glycol
Monomethyl Ether
GLYCOL ETHERS
Glycol Ethers
111900
Glycol Ethers
Diethylene Glycol
Monoethyl Ether
GLYCOL ETHERS
Glycol Ethers
111966
Glycol Ethers
Diethylene Glycol
Dimethyl Ether
GLYCOL ETHERS
1,3-Propane sultone
1120714
1,3-Propane Sultone
1,3-Propanesultone
1,3-PROPANE SULTONE
Hydroquinone
123319
Hydroquinone
Hydroquinone
HYDROQUINONE
Propionaldehyde
123386
Propionaldehyde
Propionaldehyde
PROPIONALDEHYDE
1,4-Dioxane
123911
p-Dioxane
p-Dioxane
1,4-DIOXANE
Glycol Ethers
124174
Glycol Ethers
Butyl Carbitol Acetate
GLYCOL ETHERS
Chloroprene
126998
Chloroprene
Chloroprene
CHLOROPRENE
Tetrachloroethylene
(Perchloroethylene)
127184
Tetrachloroethylene
Tetrachloroethylene
TETRACHLOROETHYLENE
Polycyclic Organic
Matter
129000
Polycyclic Organic Matter
Pyrene
PAHPOM
Polycyclic Organic
Matter
85018
Polycyclic Organic Matter
Phenanthrene
PAHPOM
Dimethyl phthalate
131113
Dimethyl Phthalate
Dimethyl Phthalate
DIMETHYL PHTHALATE
Nickel Compounds
1313991
Nickel Compounds
Nickel Oxide
NICKEL COMPOUNDS
0.7412
Cresols/Cresylic acid
(isomers and mixture)
1319773
Cresol/Cresylic Acid (Mixed
Isomers)
Cresol/Cresylic Acid
(Mixed Isomers)
CRESOL_CRESYLIC ACID (MIXED ISOMERS)
1
C-4
-------
EPA's National-scale Air Toxics Assessment
Air Toxic
(Clean Air Act
Name)
NEI Pollutant
Code
(CAS Number)a
Pollutant Category Name
Pollutant Description
NATA Website Pollutant Name
Metal
Speciation
Factor
Dibenzofurans
132649
Dibenzofuran
Dibenzofuran
DIBENZOFURAN
Xylenes (isomers and
mixture)
1330207
Xylenes (Mixed Isomers)
Xylenes (Mixed
Isomers)
XYLENES (MIXED ISOMERS)
Captan
133062
Captan
Captan
CAPTAN
Asbestos
1332214
Asbestos
Asbestos
Not used in NATA
Chromium
Compounds
1333820
Chromium Compounds
Chromium Trioxide
CHROMIUM VI (HEXAVALENT)
0.52
Polychlorinated
biphenyls (Aroclors)
1336363
Polychlorinated Biphenyls
Polychlorinated
Biphenyls
POLYCHLORINATED BIPHENYLS (AROCLORS)
Chloramben
133904
Chloramben
Chloramben
Chloramben
Coke Oven
Emissions
140
Coke Oven Emissions
Coke Oven Emissions
COKE OVEN EMISSIONS
Ethyl acrylate
140885
Ethyl Acrylate
Ethyl Acrylate
ETHYL ACRYLATE
Glycol Ethers
143226
Glycol Ethers
Triglycol Monobutyl
Ether
GLYCOL ETHERS
Ethylene imine
(Aziridine)
151564
Ethyleneimine (Aziridine)
Ethyleneimine
ETHYLENEIMINE (AZIRIDINE)
Calcium cyanamide
156627
Calcium Cyanamide
Calcium Cyanamide
CALCIUM CYANAMIDE
0.5
Trifluralin
1582098
Trifluralin
Trifluralin
TRIFLURALIN
Chromium
Compounds
16065831
Chromium Compounds
Chromium III
Not used in NATA
Methyl tert butyl ether
1634044
Methyl Tert-Butyl Ether
Methyl Tert-Butyl Ether
METHYL TERT-BUTYL ETHER
Glycol Ethers
16672392
Glycol Ethers
Di(Ethylene Glycol
Monobutyl Ether)
Phthalate
GLYCOL ETHERS
Glycol Ethers
171
Glycol Ethers
Glycol Ethers
GLYCOL ETHERS
Chromium
Compounds
18540299
Chromium Compounds
Chromium (VI)
CHROMIUM VI (HEXAVALENT)
Polycyclic Organic
Matter
86748
Polycyclic Organic Matter
Carbazole
PAHPOM
Polycyclic Organic
Matter
218019
Polycyclic Organic Matter
Chrysene
PAHPOM
Polycyclic Organic
Matter
130498292
Polycyclic Organic Matter
PAH, total
PAHPOM
Polycyclic Organic
Matter
191242
Polycyclic Organic Matter
Benzo[g,h,i,]Perylene
PAHPOM
C-5
-------
EPA's National-scale Air Toxics Assessment
Air Toxic
(Clean Air Act
Name)
NEI Pollutant
Code
(CAS Number)a
Pollutant Category Name
Pollutant Description
NATA Website Pollutant Name
Metal
Speciation
Factor
Polycyclic Organic
Matter
192972
Polycyclic Organic Matter
Benzo[e]Pyrene
PAHPOM
Polycyclic Organic
Matter
195197
Polycyclic Organic Matter
Benzo(c)phenanthrene
PAHPOM
Polycyclic Organic
Matter
198550
Polycyclic Organic Matter
Perylene
PAHPOM
Polycyclic Organic
Matter
206440
Polycyclic Organic Matter
Fluoranthene
PAHPOM
Polycyclic Organic
Matter
208968
Polycyclic Organic Matter
Acenaphthylene
PAHPOM
Polycyclic Organic
Matter
2381217
Polycyclic Organic Matter
1-Methylpyrene
PAHPOM
Polycyclic Organic
Matter
2422799
Polycyclic Organic Matter
12-
Methylbenz(a)Anthrac
ene
PAHPOM
Polycyclic Organic
Matter
250
Polycyclic Organic Matter
PAH/POM -
Unspecified
PAHPOM
Polychlorinated
Biphenyls
2050682
Polychlorinated Biphenyls
4,4'-Dichlorobiphenyl
(PCB-15)
POLYCHLORINATED BIPHENYLS (AROCLORS)
Polychlorinated
Biphenyls
2051243
Polychlorinated Biphenyls
Decachlorobiphenyl
(PCB-209)
POLYCHLORINATED BIPHENYLS (AROCLORS)
Polychlorinated
Biphenyls
2051607
Polychlorinated Biphenyls
2-Chlorobiphenyl
(PCB-1)
POLYCHLORINATED BIPHENYLS (AROCLORS)
Polycyclic Organic
Matter
26914181
Polycyclic Organic Matter
Methylanthracene
PAHPOM
Polycyclic Organic
Matter
65357699
Polycyclic Organic Matter
Methylbenzopyrene
PAHPOM
Polycyclic Organic
Matter
8007452
Polycyclic Organic Matter
Coal Tar
PAHPOM
Glycol Ethers
20706256
Glycol Ethers
2-Propoxyethyl
Acetate
GLYCOL ETHERS
Polycyclic Organic
Matter
832699
Polycyclic Organic Matter
1-Methylphenanthrene
PAHPOM
Polycyclic Organic
Matter
83329
Polycyclic Organic Matter
Acenaphthene
PAHPOM
Polycyclic Organic
Matter
86737
Polycyclic Organic Matter
Fluorene
PAHPOM
C-6
-------
EPA's National-scale Air Toxics Assessment
Air Toxic
(Clean Air Act
Name)
NEI Pollutant
Code
(CAS Number)a
Pollutant Category Name
Pollutant Description
NATA Website Pollutant Name
Metal
Speciation
Factor
Polycyclic Organic
Matter
90120
Polycyclic Organic Matter
1-Methylnaphthalene
PAHPOM
Polycyclic Organic
Matter
91576
Polycyclic Organic Matter
2-Methylnaphthalene
PAHPOM
Polycyclic Organic
Matter
91587
Polycyclic Organic Matter
2-Chloronaphthalene
PAHPOM
Polycyclic Organic
Matter
193395
Polycyclic Organic Matter
lndeno[1,2,3-
c,d]Pyrene
PAHPOM
Polycyclic Organic
Matter
203123
Polycyclic Organic Matter
Benzo(g,h,i)Fluoranthe
ne
PAHPOM
Polychlorinated
Biphenyls
25429292
Polychlorinated Biphenyls
Pentachlorobiphenyl
POLYCHLORINATED BIPHENYLS (AROCLORS)
Polychlorinated
Biphenyls
26601649
Polychlorinated Biphenyls
Hexachlorobiphenyl
POLYCHLORINATED BIPHENYLS (AROCLORS)
Polycyclic Organic
Matter
203338
Polycyclic Organic Matter
Benzo(a)Fluoranthene
PAHPOM
Polychlorinated
Biphenyls
26914330
Polychlorinated Biphenyls
Tetrachlorobiphenyl
POLYCHLORINATED BIPHENYLS (AROCLORS)
Glycol Ethers
2807309
Glycol Ethers
Propyl Cellosolve
GLYCOL ETHERS
Polycyclic Organic
Matter
284
POM as non-15 PAH
Extractable Organic
Matter (EOM)
PAHPOM
Polychlorinated
Biphenyls
28655712
Polychlorinated Biphenyls
Heptachlorobiphenyl
POLYCHLORINATED BIPHENYLS (AROCLORS)
Hydrazine
302012
Hydrazine
Hydrazine
HYDRAZINE
Diazomethane
334883
Diazomethane
Diazomethane
No emissions in 2011, so not in 2011 NATA)
Polycyclic Organic
Matter
205823
Polycyclic Organic Matter
Benzo[j]fluoranthene
PAHPOM
Fine Mineral Fibers
383
Fine Mineral Fibers
Fine Mineral Fibers
Not used in NATA
Polycyclic Organic
Matter
205992
Polycyclic Organic Matter
Benzo[b]Fluoranthene
PAHPOM
Carbonyl sulfide
463581
Carbonyl Sulfide
Carbonyl Sulfide
CARBONYL SULFIDE
Formaldehyde
50000
Formaldehyde
Formaldehyde
FORMALDEHYDE
Polycyclic Organic
Matter
207089
Polycyclic Organic Matter
Benzo[k]Fluoranthene
PAHPOM
Chlorobenzilate
510156
Chlorobenzilate
Chlorobenzilate
CHLOROBENZILATE
2,4-Dinitrophenol
51285
2,4-Dinitrophenol
2,4-Dinitrophenol
2,4-DINITROPHENOL
C-7
-------
EPA's National-scale Air Toxics Assessment
Air Toxic
(Clean Air Act
Name)
NEI Pollutant
Code
(CAS Number)a
Pollutant Category Name
Pollutant Description
NATA Website Pollutant Name
Metal
Speciation
Factor
Ethyl carbamate
(Urethane)
51796
Ethyl Carbamate
Ethyl Carbamate
ETHYL CARBAMATE (URETHANE) CHLORIDE
(CHLOROETHANE)
2-
Chloroacetophenone
532274
2-Chloroacetophenone
2-Chloroacetophenone
2-CHLOROACETOPHENONE
4,6-Dinitro-o-cresol,
and salts
534521
4,6-Dinitro-o-Cresol
4,6-Dinitro-o-Cresol
4,6-DINITRO-O-CRESOL (INCLUDING SALTS)
Polycyclic Organic
Matter
224420
Polycyclic Organic Matter
Dibenzo[a,j]Acridine
PAHPOM
2-
Acetylaminofluorene
53963
2-Acetylaminofluorene
2-Acetylaminofluorene
2-ACETYLAMINOFLUORENE
2,2,4-
Trimethylpentane
540841
2,2,4-T rimethylpentane
2,2,4-
Trimethylpentane
2,2,4-TRIMETHYLPENTANE
1,3-Dichloropropene
542756
1,3-Dichloropropene
1,3-Dichloropropene
1,3-DICHLOROPROPENE
Bis(chloromethyl)ethe
r
542881
Bis(Chloromethyl) Ether
Bis(Chloromethyl)Ethe
r
BIS(CHLOROMETHYL) ETHER
Polycyclic Organic
Matter
226368
Polycyclic Organic Matter
Dibenz[a,h]acridine
PAHPOM
Carbon tetrachloride
56235
Carbon Tetrachloride
Carbon Tetrachloride
CARBON TETRACHLORIDE
Parathion
56382
Parathion
Parathion
Parathion
Polycyclic Organic
Matter
5522430
Polycyclic Organic Matter
1-Nitropyrene
PAHPOM
Polycyclic Organic
Matter
56553
Polycyclic Organic Matter
Benz[a]Anthracene
PAHPOM
Polycyclic Organic
Matter
56832736
Polycyclic Organic Matter
Benzofluoranthenes
PAHPOM
Cyanide Compounds
57125
Cyanide Compounds
Cyanide
CYANIDE COMPOUNDS
1,1-Dimethyl
hydrazine
57147
1,1-Dimethylhydrazine
1,1-Dimethyl
Hydrazine
1,1-DIMETHYLHYDRAZINE
Beta-Propiolactone
57578
Beta-Propiolactone
Beta-Propiolactone
Beta-Propiolactone
Chlordane
57749
Chlordane
Chlordane
CHLORDANE
Polycyclic Organic
Matter
192654
Polycyclic Organic Matter
Dibenzo[a,e]Pyrene
PAHPOM
2,4-Toluene
diisocyanate
584849
2,4-Toluene Diisocyanate
2,4-Toluene
Diisocyanate
2,4-TOLUENE DIISOCYANATE
Lindane (all isomers)
58899
Lindane (All isomers)
1,2,3,4,5,6-
Hexachlorocyclohexan
e
1,2,3,4,5,6-HEXACHLOROCYCLYHEXANE
C-8
-------
EPA's National-scale Air Toxics Assessment
Air Toxic
(Clean Air Act
Name)
NEI Pollutant
Code
(CAS Number)a
Pollutant Category Name
Pollutant Description
NATA Website Pollutant Name
Metal
Speciation
Factor
Vinyl bromide
593602
Vinyl Bromide
Vinyl Bromide
VINYL BROMIDE
N-Nitrosomorpholine
59892
N-Nitrosomorpholine
N-Nitrosomorpholine
N-NITROSOMORPHOLINE
4-
Dimethylaminoazobe
nzene
60117
4-Dimethylaminoazobenzene
4-
Dimethylaminoazoben
zene
4-DIMETHYLAMINOAZOBENZENE
Methyl hydrazine
60344
Methylhydrazine
Methylhydrazine
METHYLHYDRAZINE
Acetamide
60355
Acetamide
Acetamide
ACETAMIDE
Nickel Compounds
604
Nickel Compounds
Nickel Refinery Dust
NICKEL COMPOUNDS
Fine Mineral Fibers
616
Fine Mineral Fibers
Slagwool (Man-Made
Fibers)
Not used in NATA
Fine Mineral Fibers
617
Fine Mineral Fibers
Rockwool (Man-Made
Fibers)
Not used in NATA
Methyl isocyanate
624839
Methyl Isocyanate
Methyl Isocyanate
METHYL ISOCYANATE
Aniline
62533
Aniline
Aniline
ANILINE
Dichlorvos
62737
Dichlorvos
Dichlorvos
DICHLORVOS
N-
Nitrosodimethylamine
62759
N-Nitrosodimethylamine
N-
Nitrosodimethylamine
N-NITROSODIMETHYLAMINE
Carbaryl
63252
Carbaryl
Carbaryl
CARBARYL
Diethyl sulfate
64675
Diethyl Sulfate
Diethyl Sulfate
DIETHYL SULFATE
Polycyclic Organic
Matter
194592
Polycyclic Organic Matter
7H-
Dibenzo[c,g]carbazole
PAHPOM
Glycol Ethers
67425
Glycol Ethers
(Ethylenebis(Oxyethyl
enenitrilo)) Tetraacetic
Acid
GLYCOL ETHERS
Methanol
67561
Methanol
Methanol
METHANOL
Chloroform
67663
Chloroform
Chloroform
CHLOROFORM
Hexachloroethane
67721
Hexachloroethane
Hexachloroethane
HEXACHLOROETHANE
Hexamethylphosphor
amide
680319
Hexamethylphosphoramide
Hexamethylphosphora
mide
no emissions in 2011, not in 2011 NATA
Dimethyl formamide
68122
N,N-Dimethylformamide
N,N-
Dimethylformamide
DIMETHYL FORMAMIDE
N-Nitroso-N-
Methylurea
684935
N-Nitroso-N-Methylurea
N-Nitroso-N-
Methylurea
N-Nitroso-N-Methylurea
Polychlorinated
Biphenyls
7012375
Polychlorinated Biphenyls
2,4,4'-
Trichlorobiphenyl
(PCB-28)
POLYCHLORINATED BIPHENYLS (AROCLORS)
C-9
-------
EPA's National-scale Air Toxics Assessment
Air Toxic
(Clean Air Act
Name)
NEI Pollutant
Code
(CAS Number)a
Pollutant Category Name
Pollutant Description
NATA Website Pollutant Name
Metal
Speciation
Factor
Benzene (including
benzene from
gasoline)
71432
Benzene
Benzene
BENZENE
Methyl chloroform
(1,1,1-Trichloroethan
e)
71556
Methyl Chloroform
Methyl Chloroform
1,1,1-TRICHLOROETHANE
Methoxychlor
72435
Methoxychlor
Methoxychlor
METHOXYCHLOR
Manganese
Compounds
7439965
Manganese Compounds
Manganese
MANGANESE COMPOUNDS
Mercury Compounds
7439976
Mercury Compounds
Mercury
MERCURY COMPOUNDS
Nickel Compounds
7440020
Nickel Compounds
Nickel
NICKEL COMPOUNDS
Antimony
7440360
Antimony Compounds
Antimony
ANTIMONY COMPOUNDS
Arsenic
7440382
Arsenic Compounds
Arsenic
ARSENIC COMPOUNDS(INORGANIC INCLUDING
ARSINE)
Beryllium
7440417
Beryllium Compounds
Beryllium
BERYLLIUM COMPOUNDS
Cadmium
7440439
Cadmium Compounds
Cadmium
CADMIUM COMPOUNDS
Cobalt
7440484
Cobalt Compounds
Cobalt
COBALT COMPOUNDS
Methyl bromide
(Bromomethane)
74839
Methyl Bromide
Methyl Bromide
METHYL BROMIDE (BROMOMETHANE)
Methyl chloride
(Chloromethane)
74873
Methyl Chloride
Methyl Chloride
METHYL CHLORIDE (CHLOROMETHANE)
Methyl iodide
(lodomethane)
74884
Methyl Iodide
Methyl Iodide
METHYL IODIDE (IODOMETHANE)
Cyanide Compounds
74908
Cyanide Compounds
Hydrogen Cyanide
CYANIDE COMPOUNDS
Ethyl chloride
(Chloroethane)
75003
Ethyl Chloride
Ethyl Chloride
ETHYL CHLORIDE
Vinyl chloride
75014
Vinyl Chloride
Vinyl Chloride
VINYL CHLORIDE
Acetonitrile
75058
Acetonitrile
Acetonitrile
ACETONITRILE
Acetaldehyde
75070
Acetaldehyde
Acetaldehyde
ACETALDEHYDE
Methylene chloride
(Dichloromethane)
75092
Methylene Chloride
Methylene Chloride
METHYLENE CHLORIDE
Carbon disulfide
75150
Carbon Disulfide
Carbon Disulfide
CARBON DISULFIDE
Ethylene oxide
75218
Ethylene Oxide
Ethylene Oxide
ETHYLENE OXIDE
Bromoform
75252
Bromoform
Bromoform
BROMOFORM
Ethylidene dichloride
(1,1-Dichloroethane)
75343
Ethylidene Dichloride
Ethylidene Dichloride
ETHYLIDENE DICHLORIDE (1,1-
DICHLOROETHANE)
C-10
-------
EPA's National-scale Air Toxics Assessment
Air Toxic
(Clean Air Act
Name)
NEI Pollutant
Code
(CAS Number)a
Pollutant Category Name
Pollutant Description
NATA Website Pollutant Name
Metal
Speciation
Factor
Vinylidene chloride
(1,1-Dichloroethylene
)
75354
Vinylidene Chloride
Vinylidene Chloride
VINYLIDENE CHLORIDE
1
Phosgene
75445
Phosgene
Phosgene
PHOSGENE
Titanium tetrachloride
7550450
Titanium Tetrachloride
Titanium Tetrachloride
TITANIUM TETRACHLORIDE
1,2-Propylenimine (2-
methyl aziridine)
75558
1,2-Propylenimine
1,2-Propylenimine
1,2-PROPYLENEIMINE
Propylene oxide
75569
Propylene Oxide
Propylene Oxide
PROPYLENE OXIDE
Heptachlor
76448
Heptachlor
Heptachlor
HEPTACHLOR
Hydrochloric acid
7647010
Hydrochloric Acid
Hydrochloric Acid
HYDROCHLORIC ACID (HYDROGEN CHLORIDE
[GAS ONLY])
Hydrogen fluoride
7664393
Hydrogen Fluoride
Hydrogen Fluoride
HYDROGEN FLUORIDE (HYDROFLUORIC ACID)
Phosphorus
7723140
Phosphorus
Phosphorus
PHOSPHORUS
Chromium
Compounds
7738945
Chromium Compounds
Chromic Acid (VI)
CHROMIUM VI (HEXAVALENT)
0.4406
Hexachlorocyclopent
adiene
77474
Hexachlorocyclopentadiene
Hexachlorocyclopenta
diene
HEXACHLOROCYCLOPENTADIENE
Dimethyl sulfate
77781
Dimethyl Sulfate
Dimethyl Sulfate
DIMETHYL SULFATE
Selenium
Compounds
7782492
Selenium Compounds
Selenium
SELENIUM COMPOUNDS
Chlorine
7782505
Chlorine
Chlorine
CHLORINE
Glycol Ethers
7795917
Glycol Ethers
Ethylene Glycol Mono-
Sec-Butyl Ether
GLYCOL ETHERS
Phosphine
7803512
Phosphine
Phosphine
PHOSPHINE
Isophorone
78591
Isophorone
Isophorone
ISOPHORONE
Propylene dichloride
(1,2-Dichloropropane)
78875
Propylene Dichloride
Propylene Dichloride
PROPYLENE DICHLORIDE (1,2-
DICHLOROPROPANE)
1,1,2-Trichloroethane
79005
1,1,2-Trichloroethane
1,1,2-Trichloroethane
1,1,2-TRICHLOROETHANE
Trichloroethylene
79016
Trichloroethylene
Trichloroethylene
TRICHLOROETHYLENE
Acrylamide
79061
Acrylamide
Acrylamide
ACRYLAMIDE
Acrylic acid
79107
Acrylic Acid
Acrylic Acid
ACRYLIC ACID
Chloroacetic acid
79118
Chloroacetic Acid
Chloroacetic Acid
CHLOROACETIC ACID
1,1,2,2-
Tetrachloroethane
79345
1,1,2,2-T etrachloroethane
1,1,2,2-
Tetrachloroethane
1,1,2,2-TETRACHLOROETHANE
Dimethyl carbamoyl
chloride
79447
Dimethylcarbamoyl Chloride
Dimethylcarbamoyl
Chloride
DIMETHYLCARBAMOYL CHLORIDE
2-Nitropropane
79469
2-Nitropropane
2-Nitropropane
2-NITROPROPANE
C-11
-------
EPA's National-scale Air Toxics Assessment
Air Toxic
(Clean Air Act
Name)
NEI Pollutant
Code
(CAS Number)a
Pollutant Category Name
Pollutant Description
NATA Website Pollutant Name
Metal
Speciation
Factor
Toxaphene
8001352
Toxaphene
Toxaphene
TOXAPHENE (CHLORINATED CAMPHENE)
Polycyclic Organic
Matter
3697243
Polycyclic Organic Matter
5-Methylchrysene
PAHPOM
Methyl methacrylate
80626
Methyl Methacrylate
Methyl Methacrylate
METHYL METHACRYLATE
Hexamethylene-
1,6-diisocyanate
822060
Hexamethylene Diisocyanate
Hexamethylene
Diisocyanate
HEXAMETHYLENE DIISOCYANATE
Pentachloronitrobenz
ene (Quintobenzene)
82688
Pentachloronitrobenzene
Pentachloronitrobenze
ne
PENTACHLORONITROBENZENE
(QUINTOBENZENE)
Polycyclic Organic
Matter
41637905
Polycyclic Organic Matter
Methylchrysene
PAHPOM
Polycyclic Organic
Matter
50328
Polycyclic Organic Matter
Benzo[a]Pyrene
PAHPOM
Dibutylphthalate
84742
Dibutyl Phthalate
Dibutyl Phthalate
DIBUTYLPHTHALATE
Polycyclic Organic
Matter
53703
Polycyclic Organic Matter
Dibenzo[a,h]Anthracen
e
PAHPOM
Phthalic anhydride
85449
Phthalic Anhydride
Phthalic Anhydride
PHTHALIC ANHYDRIDE
Polycyclic Organic
Matter
56495
Polycyclic Organic Matter
3-Methylcholanthrene
PAHPOM
Polycyclic Organic
Matter
189559
Polycyclic Organic Matter
Dibenzo[a,i]Pyrene
PAHPOM
Hexachlorobutadiene
87683
Hexachlorobutadiene
Hexachlorobutadiene
HEXACHLOROBUTADIENE
Pentachlorophenol
87865
Pentachlorophenol
Pentachlorophenol
PENTACHLOROPHENOL
2,4,6-Trichlorophenol
88062
2,4,6-T richlorophenol
2,4,6-T richlorophenol
2,4,6-TRICHLOROPHENOL
o-Anisidine
90040
o-Anisidine
o-Anisidine
ANISIDINE
Polycyclic Organic
Matter
189640
Polycyclic Organic Matter
Dibenzo[a,h]Pyrene
PAHPOM
Naphthalene
91203
Naphthalene
Naphthalene
NAPHTHALENE
Quinoline
91225
Quinoline
Quinoline
QUINOLINE
Polycyclic Organic
Matter
191300
Polycyclic Organic Matter
Dibenzo[a,l]Pyrene
PAHPOM
Polycyclic Organic
Matter
57976
Polycyclic Organic Matter
7,12-
Dimethylbenz[a]Anthra
cene
PAHPOM
3,3'-
Dichlorobenzidine
91941
3,3'-Dichlorobenzidine
3,3'-Dichlorobenzidine
3,3'-DICHLOROBENZIDINE
Biphenyl
92524
Biphenyl
Biphenyl
BIPHENYL
4-Aminobiphenyl
92671
4-Aminobiphenyl
4-Aminobiphenyl
4-AMINOBIPHENYL
C-12
-------
EPA's National-scale Air Toxics Assessment
Air Toxic
NEI Pollutant
Metal
(Clean Air Act
Name)
Code
(CAS Number)a
Pollutant Category Name
Pollutant Description
NATA Website Pollutant Name
Speciation
Factor
Benzidine
92875
Benzidine
Benzidine
BENZIDINE
4-Nitrobiphenyl
92933
4-Nitrobiphenyl
4-Nitrobiphenyl
4-NITROBIPHENYL
2,4-D, salts and
esters
94757
2,4-Dichlorophenoxy Acetic
Acid
2,4-Dichlorophenoxy
Acetic Acid
2,4-D, SALTS AND ESTERS
o-Xylenes
95476
Xylenes (Mixed Isomers)
o-Xylene
XYLENES (MIXED ISOMERS)
o-Cresol
95487
Cresol/Cresylic Acid (Mixed
Isomers)
o-Cresol
CRESOL_CRESYLIC ACID (MIXED ISOMERS)
o-Toluidine
95534
o-Toluidine
o-Toluidine
O-TOLUIDINE
2,4-Toluene diamine
95807
Toluene-2,4-Diamine
Toluene-2,4-Diamine
2,4-TOLUENE DIAMINE
2,4,5-Trichlorophenol
95954
2,4,5-T richlorophenol
2,4,5-Trichlorophenol
2,4,5-TRICHLOROPHENOL
Styrene oxide
96093
Styrene Oxide
Styrene Oxide
STYRENE OXIDE
1,2-Dibromo-3-
chloropropane
96128
1,2-Dibromo-3-Chloropropane
1,2-Dibromo-3-
Chloropropane
1.2-DIBROMO-3-CHLOROPROPANE
Ethylene thiourea
96457
Ethylene Thiourea
Ethylene Thiourea
ETHYLENE THIOUREA
Benzotrichloride
98077
Benzotrichloride
Benzotrichloride
BENZOTRICHLORIDE
Cumene
98828
Cumene
Cumene
CUMENE
Acetophenone
98862
Acetophenone
Acetophenone
ACETOPHENONE
Nitrobenzene
98953
Nitrobenzene
Nitrobenzene
NITROBENZENE
Lead Compounds
7439921
Lead Compounds
Lead
LEAD COMPOUNDS
Polycyclic Organic
Matter
779022
Polycyclic Organic Matter
9-Methyl Anthracene
PAHPOM
Diesel PM b
Diesel PM
DIESEL PM
a In 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.
b Diesel PM is not a Clean Air Act HAP.
C-13
-------
EPA's National-scale Air Toxics Assessment
This page intentionally left blank.
C-14
-------
EPA's National-scale Air Toxics Assessment
Appendix D
Additional Information Used to Process the 2011 NATA Inventory:
Inventory Sectors and Model Run Groups; SCC Groupings;
Speciations for Mercury, Xylenes, and Other Metals
This appendix contains additional information related to the emissions processing and categorization of the results.
As described in Section 2 of the TSD, emission sources were grouped into Human Exposure Model (HEM) run
groups and then disaggregated (using source classification code [SCC] information) into source groups. Both
source groups and HEM run groups were categories used to group the results for the 2011 NATA.
Exhibit D-1 contains indications of how the source groups related to National Emissions Inventory (NEI) and
Emission Inventory System (EIS) sectors.
Exhibit D-2 contains the SCCs for each source group. A spreadsheet file
("NP_NR_OR_SourceGroup_to_SCC_CrossReference.xlsx") containing a cross-reference file with SCC
descriptions is provided in the SupplementalData folder. The point-source groups (point, airports, rail yards) were
created based on the facility source type (100 for airports, 151 for rail yards).
Mercury and other metals and xylenes from the inventory were speciated for use in modeling. Mercury in the
inventory was reported as pollutant code 7439976 and needs to be speciated into the three forms for CMAQ:
elemental, divalent gaseous, and divalent particulate. Xylenes (mixed isomers) were speciated into m-, o- and p-
forms, and metals (other than mercury) were speciated into coarse and fine particulate, which were needed by
CMAQ. Exhibit D-3 and D-4 contain summaries of the profiles. Most were applied across an entire sector or
multiple sectors (i.e., the nonroad profiles were applied to the nonroad-related sector and the stationary profile was
applied to the stationary-related sectors). The "other mercury" profiles were SCC-specific. The spreadsheet file
"nata_metals_split.xlxs" in the SupplementalData Folder contains information on how the profiles for mercury were
assigned to SCCs "other mercury" categories listed in Exhibit D-4.
Also included in this appendix (not related to the source groups) are mercury speciation factors used for specific
units at electricity generating units (EGUs) and for non-EGU categories (based on SCC). The same data were
used as was used for the "2005 Platform -CAP-BAFM 2005-Based Platform, Version 4.1 (use for Mercury)"
documented here. For EGUs, which are in the ptegu CMAQ NATA platform modeling sector, we used unit-specific
speciation factors based on those developed for the Clean Air Mercury Rule (CAMR) development and
documented for the Utility MACT here. These are provided in the file spreadsheet file "nata_metals_split.xlsx" in
the SupplementalData folder, and the methodology used relied on matching 2011 NEI units to identifiers used in
CAMR in order to use the same speciation data. New units were mapped to mercury speciation bins based on
configuration. The methodology and supporting files are at "Hgunit-specific-speciation bins2011nei.zip" in the
SupplementalData folder.
D-1
-------
EPA's National-scale Air Toxics Assessment
Exhibit D-1. Relationship of NEI Sectors with HEM Run Groups and Source Groups
NEI Data
Category
EIS Sector
HEM Run
Group
HEM
Group
Abbrev.
Source Group
Source Group Abbrev.
Point
[Multiple]
Point, excluding
airports
Point
Point stationary (PT)
Point stationary (PT)
Mobile-aircraft
Airports
Airports
NR-Railyard (PT)
NR-Railyard (PT)
Mobile-locomotives
Point, excluding
airports
Point
NR-Airport (PT)
NR-Airport (PT)
Nonpoint
Fuel Comb - Industrial
Boilers, ICEs - Coal
Nonpoint 10m
ReleaseHeight
NP10m
Industrial
Commercial
Institutional Fuel
Combustion
NP-ICI_fuel_comb
Fuel Comb - Industrial
Boilers, ICEs - Oil
Fuel Comb - Industrial
Boilers, ICEs - Natural Gas
Fuel Comb - Industrial
Boilers, ICEs - Other
Fuel Comb - Industrial
Boilers, ICEs - Biomass
Fuel Comb -
Comm/lnstitutional - Coal
Fuel Comb -
Comm/lnstitutional - Oil
Fuel Comb -
Comm/lnstitutional -
Natural Gas
Fuel Comb -
Comm/lnstitutional - Other
Fuel Comb -
Comm/lnstitutional -
Biomass
Fuel Comb - Residential -
Other
Nonpoint Low
ReleaseHeight
NPlow
Nonpoint Residential
Fuel Combustion -
Except Wood
NP-Non-
RWC_ResFuelComb
Fuel Comb - Residential -
Oil
Fuel Comb - Residential -
Natural Gas
Fuel Comb - Residential -
Wood
Nonpoint
Residential
Wood
Combustion
RWC
Nonpoint Residential
Wood Combustion
RWC
Onroad
Gas Stations
Onroad
Lightduty
(includes
refueling)
OR_LD
Onroad Refueling
OR-Refueling
Mobile - On-Road non-
Diesel Light Duty Vehicles
Onroad Lightduty
Gas
OR-LightDuty_Gas
Mobile - On-Road non-
Diesel Heavy Duty Vehicles
Onroad
Heavyduty
OR_HD
Onroad Heavyduty
Gas
OR-HeavyDuty_Gas
Mobile - On-Road Diesel
Light Duty Vehicles
Onroad
Lightduty
(includes
refueling)
OR_LD
Onroad Lightduty
Diesel
OR-LightDuty_Diesel
Mobile - On-Road Diesel
Heavy Duty Vehicles
Onroad
Heavyduty
OR_HD
Onroad Heavyduty
Diesel
OR-HeavyDuty_Diesel
D-2
-------
EPA's National-scale Air Toxics Assessment
NEI Data
Category
EIS Sector
HEM Run
Group
HEM
Group
Abbrev.
Source Group
Source Group Abbrev.
Nonroad *
(*excludes
airports,
CMV and
locomotives)
Mobile - Non-Road
Equipment - Gasoline
Nonroad *
(*excludes
airports, CMV
and
locomotives)
nonroad
Nonroad Gas Other
NR-Gas/Other
Nonroad
Nonroad
Construction
NR-Construction
Mobile - Non-Road
Equipment - Other
Nonroad
Nonroad Gas Other
NR-Gas/Other
Mobile - Non-Road
Equipment - Diesel
Nonroad
Nonroad Diesel
Other
NR-Diesel/Other
Nonroad
Construction
NR-Construction
Nonpoint
Mobile - Commercial
Marine Vessels
Commercial
Marine Vessels
(CMV)
CMV_P
Nonroad CMV Ports
(shapes)
NR-CMV_ports
CMV_UW
Nonroad CMV
Underway (shapes)
NR-CMV_underway
Nonroad
Mobile - Non-Road
Equipment - Gasoline
Nonroad
nonroad
Nonroad
Pleasurecraft
NR-Pleasurecraft
Mobile - Non-Road
Equipment - Diesel
Nonpoint
Mobile - Locomotives
Nonpoint Low
ReleaseHeight
NPlow
Nonroad
Locomotives
NR-Locomotives
Industrial Processes -
Chemical Manuf
Nonpoint 10m
ReleaseHeight
NP10m
Nonpoint Chemical
Manufacturing
NP-Chemical_Mfg
Industrial Processes - NEC
Nonpoint Industrial
Not Elsewhere
Classified
NP-lndustrial_NEC
Commercial Cooking
Nonpoint Low
ReleaseHeight
NPlow
Nonpoint Commercial
Cooking
NP-Comm_cooking
Industrial Processes - Non-
ferrous Metals
Nonpoint 10m
ReleaseHeight
NP10m
Nonpoint Nonferrous
Metals
NP-Nonferrous_metals
Industrial Processes -
Petroleum Refineries
Nonpoint Refineries
NP-Refineries
Industrial Processes - Oil &
Gas Production
Nonpoint Oil and Gas
NP-Oil/Gas
Industrial Processes -
Mining
Nonpoint Mining
NP-Mining
Solvent - Non-Industrial
Surface Coating
Nonpoint Low
ReleaseHeight
NPlow
Nonpoint
Nonindustrial Surface
Coating
NP-Non-ind_sfc_coating
Solvent - Industrial Surface
Coating & Solvent Use
Nonpoint 10m
ReleaseHeight
NP10m
Nonpoint Surface
Coating and
Industrial Solvent
NP-
SfcCoatingJndSolvent
Solvent - Degreasing
Nonpoint Low
ReleaseHeight
NPlow
Nonpoint Degreasing
NP-Solvent_degreasing
Solvent - Dry Cleaning
NP-Dry_cleaning
Solvent - Graphic Arts
Nonpoint 10m
ReleaseHeight
NP10m
Nonpoint Surface
Coating and
Industrial Solvent
NP-
SfcCoatingJndSolvent
Solvent - Consumer &
Commercial Solvent Use
Nonpoint Low
ReleaseHeight
NPlow
Nonpoint Consumer
Commercial Solvent
NP-
Consumer comm solvent
Industrial Processes -
Storage and Transfer
Nonpoint 10m
ReleaseHeight
NP10m
Nonpoint Storage
and Transfer
NP-Storage_Transfer
D-3
-------
EPA's National-scale Air Toxics Assessment
NEI Data
Category
EIS Sector
HEM Run
Group
HEM
Group
Abbrev.
Source Group
Source Group Abbrev.
Miscellaneous Non-
Industrial NEC
Nonpoint Low
ReleaseHeight
NPlow
Nonpoint
Miscellaneous
Nonindustrial
NP-Misc_non-ind
Bulk Gasoline Terminals
Nonpoint 10m
ReleaseHeight
NP10m
Nonpoint Bulk
Gasoline Terminals
NP-Bulk_gas_term
Gas Stations
Gas Stations
Nonpoint Low
ReleaseHeight
NPlow
Nonpoint
GasStations - Stage I
NP-Gas_stations
NP-Gas_stations
Waste Disposal
Nonpoint 10m
ReleaseHeight
NP10m
Nonpoint
Wastedisposal Other
NP-WasteDisposal_Other
Waste Disposal
Nonpoint Landfills
NP-Landfills
ag burning
agburning-modeled
only in CMAQ
agburning-modeled only
in CMAQ
Biogenics - Vegetation and
Soil
Biogenics-modeled
only in CMAQ
Biogenics-modeled only
in CMAQ
D-4
-------
EPA's National-scale Air Toxics Assessment
Exhibit D-2 Source Group and HEM Run Group Cross-reference to Inventory SCCs
NEI Data
Category
HEM Ru
Group
HEM
Run
Group
Abb rev.
Source
Group
Source Group
Abbrev.
SCCs
Nonpoint
Nonpoint 10m
Re lease Height
NP10m
Industrial
Commercial
Institutional
Fuel
Combustion
NP-ICI fuel comb
2102001000,
2102004002,
2102007000,
2103001000,
2103004002,
2103008000,
2103010000
2102002000, 2102004000,
2102005000, 2102006000,
2102008000, 2102011000,
2103002000, 2103004000,
2103005000, 2103006000,
2103011000, 2801520000,
2102004001,
2102006002,
2102012000,
2103004001,
2103007000,
2801520004,
Nonpoint Low
Re lease Height
NPlow
Nonpoint
Residential
Fuel
Combustion -
Except Wood
NP-Non-
RWC ResFuelComb
2104001000, 2104002000, 2104004000, 2104005000,
2104006000, 2104006010, 2104007000, 2104011000,
Nonpoint
Residential
Wood
Combustion
RWC
Nonpoint
Residential
Wood
Combustion
RWC
2104008100, 2104008210, 2104008220, 2104008230,
2104008310, 2104008320, 2104008330, 2104008400,
2104008420, 2104008510, 2104008610, 2104008700,
2104009000,
On road
On road
Lightduty
(includes
refueling)
OR LD
Onroad
Refueling
OR-Refueling
2201110162
2201110562
2201210462
2201310362
2201320262
2201420162
2201420562
2201430462
2201510362
2201520262
2201530162
2201530562
2201540462
2201610362
2202210262
2202310162
2202310562
2202320462
2202410362
2202420262
2202430162
2202430562
2202510462
202520362.
2202530162
2202530562
2202540162
2202540562
2202610462
2202620362
2205210262
2205310162
2205310562
2205320462
2201110262, 2201110362,
2201210162, 2201210262,
2201210562, 2201310162,
2201310462, 2201310562,
2201320362, 2201320462,
2201420262, 2201420362,
2201430162, 2201430262,
2201430562, 2201510162,
2201510462, 2201510562,
2201520362, 2201520462,
2201530262, 2201530362,
2201540162, 2201540262,
2201540562, 2201610162,
2201610462, 2201610562,
2202210362, 2202210462,
2202310262, 2202310362,
2202320162, 2202320262,
2202320562, 2202410162,
2202410462, 2202410562,
2202420362, 2202420462,
2202430262, 2202430362,
2202510162, 2202510262,
2202510562, 2202520162,
2202520462, 2202520562,
2202530262, 2202530362',
Onroad
Lightduty
(includes
refueling)
OR LD
Onroad
Lightduty Gas
OR-LightDuty_Gas
2201110181
2201110581
2201210481
2201310381
2201320281
2205210181
2205210581
2205310481
2205320381
Onroad
Heavyduty
OR HD
Onroad
Heavyduty
Gas
OR-HeavyDuty_Gas
2201420181
2201420581
2201430481
2201510381
2201520281
2201530181
2201530581
2201540481
2201610381
2203420281
Onroad
Lightduty
(includes
refueling)
OR LD
Onroad
Lightduty
Diesel
OR-LightDuty_Diesel
2202210181
2202210581
2202310481
2202320381
2202540262,
2202610162,
2202610562,
2202620462,
2205210362,
2205310262,
2205320162,
2205320562
2202540362,
2202610262,
2202620162,
2202620562,
2205210462,
2205310362,
2205320262,
2201110462,
2201210362,
2201310262,
2201320162,
2201320562,
2201420462,
2201430362,
2201510262,
2201520162,
2201520562,
2201530462,
2201540362,
2201610262,
2202210162,
2202210562,
2202310462,
2202320362,
2202410262,
2202420162,
2202420562,
2202430462,
2202510362,
2202520262,
2202530462,
2202540462,
2202610362,
2202620262,
2205210162,
2205210562,
2205310462,
2205320362,
2201110281
2201210181
2201210581
2201310481
2201320381
2205210281
2205310181
2205310581
2205320481
2201420281
2201430181
2201430581
2201510481
2201520381
2201530281
2201540181
2201540581
2201610481
2203420381
2202210281
2202310181
2202310581
2202320481
2201110381
2201210281
2201310181
2201310581
2201320481
2205210381
2205310281
2205320181
2205320581
2201110481
2201210381
2201310281
2201320181
2201320581
2205210481
2205310381
2205320281
2201420381
2201430281
2201510181
2201510581
2201520481
2201530381
2201540281
2201610181
2201610581
2203420481
2202210381
2202310281
2202320181
2202320581
2201420481
2201430381
2201510281
2201520181
2201520581
2201530481
2201540381
2201610281
2203420181
2203420581
2202210481,
2202310381,
2202320281,
D-5
-------
EPA's National-scale Air Toxics Assessment
NEI Data
Category
HEM Ru
Group
HEM
Run
Group
Abb rev.
Source
Group
Source Group
Abbrev.
SCCs
On road
Heavyduty
OR HD
Onroad
Heavyduty
Diesel
OR-Heavy Duty_Diesel
2202410181
2202410581
2202420481
2202430381
2202510281
2202520181
2202520581
2202530481
2202540381
2202610281
2202620153
2202620381
2202410281
2202420181
2202420581
2202430481
2202510381
2202520281
2202530181
2202530581
2202540481
2202610381
2202620181
2202620481
2202410381,
2202420281,
2202430181,
2202430581,
2202510481,
2202520381,
2202530281,
2202540181,
2202540581,
2202610481,
2202620191,
2202620581
2202410481
2202420381
2202430281
2202510181
2202510581
2202520481
2202530381
2202540281
2202610181
2202610581
2202620281
Nonroad
Nonroad
(excludes
airports, CMV
and
locomotives)
nonroad
Nonroad Gas
Other
NR-Gas/Other
2260001010
2260001020
22600030302, 260003040
2260004016
2260004021
2260004031
2260005035
2260006035
2265001050
2265003030
2265003070
2265004016
2265004026
2265004036
2265004050
2265004066
2265005010
2265005030
2265005055
2265006015
2265007010
2267002003
2267002030
2267002054
2267002072
2267003030
2267004066
2267006010
2267006035
2268003040
2268005060
2268006020
2285006015
2260004020,
2260004025,
2260004035,
2260006005,
2260007005,
2265001060,
2265003040,
2265004010,
2265004025,
2265004030,
2265004040,
2265004051,
2265004071,
2265005015,
2265005035,
2265005060,
2265006025,
2265007015,
2267002015,
2267002033,
2267002057,
2267002081,
2267003040,
2267005055',
2267006015,
2268002081,
2268003060,
2268006005,
2268006035,
2260001030, 2260001060,
2260004000, 2260004015,
2260004026, 2260004030,
, 2260004036, 2260004071,
2260006010, 2260006015,
2265001010, 2265001030,
2265003010, 2265003020,
2265003050, 2265003060,
2265004011, 2265004015,
2265004031,
2265004041,
2265004055,
2265004075,
2265005020,
2265005040,
2265006005,
2265006030,
2265010010,
2267002021,
2267002039,
2267002060,
2267003010,
2267003050,
2267005060,
2267006025,
2268003020,
2268003070,
2268006010,
2268010010,
2265004035,
2265004046,
2265004056,
2265004076,
2265005025,
2265005045,
2265006010,
2265006035,
2267001060,
2267002024,
2267002045,
2267002066,
2267003020,
2267003070,
2267006005,
2267006030,
2268003030,
2268005055,
2268006015,
2285004015,
Nonroad
(excludes
airports, CMV
and
locomotives)
nonroad
Nonroad
Construction
NR-Construction
2260002000,
2260002027,
2265002006,
2265002024,
2265002039,
2265002057,
2265002078,
2270002009,
2270002024,
2270002036,
2270002048,
2270002060',
2270002075,
2260002006,
2260002039,
2265002009,
2265002027,
2265002042,
2265002060,
2265002081,
2270002015,
2270002027,
2270002039,
2270002051,
2270002066,
2270002078,
2260002009,
2260002054,
2265002015,
2265002030,
2265002045,
2265002066,
2270002003,
2270002018,
2270002030,
2270002042,
2270002054,
2270002069,
2270002081,
2260002021,
2265002003,
2265002021,
2265002033,
2265002054,
2265002072,
2270002006,
2270002021,
2270002033,
2270002045,
2270002057,
2270002072,
2270009010
Nonroad
(excludes
airports, CMV
and
locomotives)
nonroad
Nonroad
Diesel Other
NR-Diesel/Other
2270001060,
2270003040,
2270004031,
2270004066,
2270005015,
2270005035,
2270005060,
2270006020,
2270007010,
2270003010,
2270003050,
2270004036,
2270004071,
2270005020,
2270005040,
2270006005,
2270006025,
2270007015,
2270003020,
2270003060,
2270004046,
2270004076,
2270005025,
2270005045,
2270006010,
2270006030,
2270010010,
2270003030,
2270003070,
2270004056,
2270005010, .
2270005030,
2270005055,
2270006015,
2270006035,
2285002015
Nonpoint
Commercial
Marine Vessels
(CMV)
CMV P
Nonroad CMV
Ports (shapes)
NP-CMV_ports
2280002100, 2280003100
CMV UW
Nonroad CMV
Underway
(shapes)
NP-CMV_underway
2280002200, 2280003200
Nonroad
Nonroad
(excludes
airports, CMV
and
locomotives)
nonroad
Nonroad
Pleasurecraft
NR-Pleasurecraft
2282005010, 2282005015, 2282010005,
2282020005, 2282020010
D-6
-------
EPA's National-scale Air Toxics Assessment
NEI Data
Category
HEM Ru
Group
HEM
Run
Group
Abb rev.
Source
Group
Source Group
Abbrev.
SCCs
Nonpoint
Nonpoint Low
Re lease Height
NPlow
Nonroad
Locomotives
NP-Locomotives
2285002006, 2285002007, 2285002008, 2285002009,
2285002010
Nonpoint 10m
Release Height
NP10m
Nonpoint
Chemical
Manufacturing
NP-Chemical_Mfg
2301000000, 2301020000,
Nonpoint
Industrial Not
Elsewhere
Classified
NP-lndustrial NEC
2302000000, 2302080000, 2305000000, 2305070000,
2305080000, 2307000000, 2308000000, 2309000000,
2309100010, 2309100030, 2309100050, 2312000000,
2399000000
Nonpoint Low
ReleaseHeight
NPlow
Nonpoint
Commercial
Cooking
NP-Comm_cooking
2302002000, 2302002100, 2302002200, 2302003000,
2302003100, 2302003200
Nonpoint 10m
ReleaseHeight
NP10m
Nonpoint
Nonferrous
Metals
NP-Nonferrous metals
2304000000, 2303000000
Nonpoint
Refineries
NP-Refineries
2306000000, 2306010000
Nonpoint Oil
and Gas
NP-Oil/Gas
2310000000
2310000550
2310010200
2310011501
2310020000
2310021100
2310021300
2310021351
2310021502
2310021509
2310111100
2310121401
2310002421
2310011100
2310011506
2310012512
2310012522
2310021101
2310021203
2310021600
2310021605
2310022420
2310022506
2310023102
2310023302
2310023509
2310023515
2310023602
2310030300
2310000220,
2310000660,
2310010300,
2310011502,
2310020600,
2310021202,
2310021302,
2310021400,
2310021503,
2310021603,
, 2310111401,
', 2310121700,
, 2310010700,
2310011450,
2310011600,
2310012515,
2310012526,
2310021102,
2310021301,
2310021601,
2310022010,
2310022501,
2310023010,
2310023202,
2310023310',
2310023511,
2310023516,
2310023603,
2310030401,
2310000230,
2310010000,
2310011000,
2310011503,
2310021010,
2310021209,
2310021309,
2310021500,
2310021505,
2310021700,
2310111700,
2310002401
2310010800,
2310011500,
2310012020,
2310012516,
2310020700,
2310021103,
2310021303,
2310021602,
2310022090,
2310022502,
2310023030,
2310023251,
2310023351
2310023512,
2310023600,
2310023606,
2310112401,
2310000330,
2310010100,
2310011201,
2310011505, ,
2310021030,
2310021251,
2310021310,
2310021501,
2310021506,
2310030000,
2310121100,
2310002411,
2310011020,
2310011504,
2310012511,
2310012521,
2310020800, ,
2310021201,
2310021504,
2310021604,
2310022105,
2310022505,
2310023100,
2310023300,
2310023400,
2310023513,
2310023601,
2310030210,
2310122100
Nonpoint
Mining
NP-Mining
2325030000, 2325060000
Nonpoint Low
ReleaseHeight
NPlow
Nonpoint
Nonindustrial
Surface
Coating
NP-Non-ind_sfc_coating
2401001000, 2401002000, 2401003000
Nonpoint 10m
ReleaseHeight
NP10m
Nonpoint
Surface
Coating and
Industrial
Solvent
NP-
SfcCoatingJnd Sol vent
2401005000,
2401005800,
2401020000,
2401040000,
2401060000,
2401080000,
2401200000,
2425030000,
2402000000
2401005500,
2401008000,
2401025000,
2401045000,
2401065000,
2401085000,
2425000000,
2425040000,
2401005600,
2401010000,
2401030000,
2401050000,
2401070000,
2401090000,
2425010000,
2440000000,
2401005700,
2401015000,
2401035000,
2401055000, ;
2401075000,
2401100000,
2425020000,
2440020000,
Nonpoint Low
ReleaseHeight
NPlow
Nonpoint
Degreasing
NP-Solvent_degreasing
2415000000, 2415005000, 2415010000, 2415020000,
2415025000, 2415030000, 2415035000, 2415040000,
2415045000, 2415050000, 2415055000, 2415060000,
2415065000, 2415100000, 2415130000, 2415230000, ;
2415245000, 2415300000, 2415345000, 2415360000
D-7
-------
EPA's National-scale Air Toxics Assessment
NEI Data
Category
HEM Ru
Group
HEM
Run
Group
Abbrev.
Source
Group
Source Group
Abbrev.
SCCs
Nonpoint
Drycleaning
NP-Dry_cleaning
2420000000, 2420000055, 2420010000, 2420010055,
2420010370, 2420020000
Nonpoint
Consumer
Commercial
Solvent
NP-
Consumer_comm_solvent
2460000000, 2460100000, 2460110000, 2460120000,
2460130000, 2460150000, 2460160000, 2460170000,
2460180000, 2460190000, 2460200000, 2460210000,
2460220000, 2460230000, 2460250000, 2460270000, ,
2460290000, 2460400000, 2460410000, 2460420000,
2460500000, 2460510000, 2460520000, 2460600000,
2460610000, 2460800000, 2460810000, 2460820000,
2460900000, 2461021000, 2461022000, 2461023000,
2461800000, 2461850000, 2461850001, 2461850004,
2461850005, 2461850006, 2461850009, 2461850051,
2461850052, 2461850053, 2461850054, 2461850055,
2461850056', 2461850099, 2465000000, 2465100000,
2465200000, 2465400000, 2465800000
Nonpoint 10m
ReleaseHeight
NP10m
Nonpoint
Storage and
Transfer
NP-Storage_T ransfer
2501000150, 2501995120, 2501995150, 2505000120,
2505010000, 2505020000, 2505020030, 2505020060,
2505020090, 2505020093, 2505020120, 2505020121,
2505020150, 2505020180, 2505030120,
2505030150, , 2505040120, 2510010000, 2520010000
Nonpoint Low
ReleaseHeight
NPlow
Nonpoint
Miscellaneous
Nonindustrial
NP-Misc_non-ind
2501011011, 2501011012, 2501011013, 2501011014,
2501011015, 2501012011, 2501012012, 2501012013,
2501012014, 2501012015, 2810025000, 2810030000,
2810035000, 2810050000, 2810060100, 2810060200, ,
2840000000, 2840010000, 2850000000, 2850000010,
2850001000, 2851001000, 2861000000, 2861000010,
2862000000
Nonpoint 10m
ReleaseHeight
NP10m
Nonpoint Bulk
Gasoline
Terminals
NP-Bulk_gas_term
2501050120, 2501055120
Nonpoint Low
ReleaseHeight
NPlow
Nonpoint
GasStations -
Stage I
NP-Gas_stations
2501060050, 2501060051, 2501060052, 2501060053,
2501060100, 2501060101, 2501060102, 2501060103,
2501060200, 2501060201, 2501070100, 2501080050,
2501080100, 2501080201
Nonpoint 10m
ReleaseHeight
NP10m
Nonpoint
Wastedisposal
Other
NP-WasteDisposal_Other
2601000000, 2610000100, 2610000300, 2610000400,
2610000500, 2610030000, 2630000000, 2630020000,
2630020020, 2635000000, 2640000000, 2660000000,
2680001000, 2650000000, 2650000002
Nonpoint
Landfills
NP-Landfills
2620000000, 2620030000, 2620030001
agburning-
modeled only
in CMAQ
agburning-modeled only
in CMAQ
2801500000, 2801500100, 2801500141, 2801500150,
2801500170,
2801500181, 2801500220, 2801500250, 2801500261,
2801500262, 2801500300, 2801500320, 2801500330,
2801500350, 2801500390, 2801500410, , 2801500420,
2801500430, 2801500440, 2801500450,
2801500500, 2801500600
Biogenics-
modeled only
in CMAQ
Biogenics-modeled only
in CMAQ
2701200000
D-8
-------
EPA's National-scale Air Toxics Assessment
Exhibit D-3. Speciation of Xylenes and Metals
Xylenes
Source Type
Profile
Pollutant
MXYL
OXYL
PXYL
Other
0000
XYLS
0.52
0.16
0.16
Nonroad
0001
XYLS
0.68
0.32
Onroad
0002
XYLS
0.74
0.26
Metals
Source Type
Profile
Metal
Fine
Coarse
Nonroad
NOARS
ARSENIC
0.83
0.17
NONBE
BERYLLIUM
0.39
0.61
NONCD
CADMIUM
0.38
0.62
NONMN
MANGANESE
0.67
0.33
NONNI
NICKEL
0.49
0.51
NONPB
LEAD
0.88
0.12
Stationary
STAN I
NICKEL
0.59
0.41
STACD
CADMIUM
0.76
0.24
STAMN
MANGANESE
0.67
0.33
STAPB
LEAD
0.74
0.26
STABE
BERYLLIUM
0.68
0.32
CRSTA
CHROMHEX
0.71
0.29
STARS
ARSENIC
0.59
0.41
Onroad
CHROMHEX
0.86
0.14
MANGANESE
0.64
0.36
NICKEL
0.83
0.17
ARSENIC
0.95
0.05
D-9
-------
EPA's National-scale Air Toxics Assessment
Exhibit D-4. Speciation of Mercury (Other than EGUs)
Mobile Mercury
Mobile
Profile
Pollutant
HGNRVA
HGIIGAS
PHGI
Mobile Diesel
HGMD
EXH HGSUM
0.56
0.29
0.15
Mobile Gasoline
HGMG
EXH HGSUM
0.91
0.086
0.004
Other Mercury
Profile Code
Description
Elemental
Divalent Gas
Particulate
HBCMB
combustion
0.5
0.3
0.2
HGCEM
cement
0.75
0.13
0.12
HGCHL
chloralkali processes
0.95
0.05
0
HGGLD
Gold mining
1
0
0
HGINC
Incineration
0.22
0.58
0.2
HGMD
Mobile diesel
0.56
0.29
0.15
HGMG
Mobile gas
0.91
0.086
0.004
HGIND
Other Industrial
0.8
0.1
0.1
Note: EGUs use unit-specific profiles provided in the SupplementalData Folder spreadsheet file "nata_metals_split.xlsx".
D-10
-------
EPA's National-scale Air Toxics Assessment
Appendix E
Estimation of Background Concentrations for the 2011 NATA
The report in this appendix contains the methods we used to estimate background concentrations
for NATA.
E-1
-------
Methods for Estimating Background
Concentrations for the National Air
Toxics Assessment (NATA) 2011
Final Report Prepared for
U.S. Environmental Protection Agency
Research Triangle Park, NC * m
Sonoma Technology, Inc.
August 2015
E-2
-------
This document contains blank pages to accommodate two-sided printing.
E-3
-------
Prepared by
Prepared for
Michael McCarthy, PhD Dennis Doll
Theresa O'Brien Madeleine Strum
Yuan Du Ted Palma
Ashley Russell, PhD
U.S. Environmental Protection Agency
Sonoma Technology, Inc. Office of Air Quality Planning and Standards
1455 N. McDowell Blvd., Suite D 109 T.W. Alexander Dr.
Petaluma, CA 94954-6503 Research Triangle Park, NC 27709
Ph 707.665.9900 | F 707.665.9800 919.541.5693
sonomatech.com
Final Report
STI-915110-6315
August 13, 2015
graphic illustrates the normalized emissions gradient for benzene. See Section 4.2 for details.
-------
E-5
-------
Contents
Contents
Figures iv
Tables iv
1. Introduction.................................................................................................................................. 1
1.1 Overview 1
1.2 Background Definitions 1
1.3 NATA Background Approaches 2
2. Technical Approach.....................................................................................................................5
2.1 Methods for Remote Concentration Estimates 5
2.1.1 Remote Network Method 8
2.1.2 Ambient National Network Method 9
2.1.3 Literature-Based Method 11
2.1.4 Emissions-Based Method 11
3. Results......................................................................................................................................... 13
3.1 Remote Network Estimates 13
3.2 Ambient National Network Estimates 15
3.3 Literature-Based Remote Estimates 19
3.4 Emissions-Based Estimates 21
4. Discussion.................................................................................................................................. 27
4.1 Cancer Risk and Noncancer Hazard 27
4.2 Spatial Estimates of Risk 29
4.3 Uncertainties 32
5. Summary.................................................................................................................................... 33
6. References.................................................................................................................................. 35
Appendix A: Emissions-Based Method.................................................................................... A.l
Appendix B: Hexawaient Chromium Ratio Data..................................................................... B.l
E-6
-------
Figures and Tables
Figures
1. Remote Northern Hemisphere estimates of cancer risk based on concentration estimates
from "Fable 7 28
2. Normalized emissions-based gradient map for arsenic emissions 30
3. Scatter plots and linear regressions of emissions-based county gradients and ambient
network concentrations for arsenic, benzene, and carbon tetrachloride 31
Tables
1. Pollutants for which background concentrations were estimated in the NATA 1996 and
1999 modeling efforts 3
2. Hazardous air pollutants (HAPs) and methods applied to determine background
concentrations for NATA 2002 and 2005 4
3. List of pollutants and method used to generate remote concentration estimates 7
4. Remote network concentration estimates. Site locations are defined in Section 2.1.1 14
5. Summary statistics for the ambient network estimates of county background
concentrations 16
6. Literature studies that reported pollutant concentrations for air toxics of interest 20
7. Emissions-based method calculated remote background concentrations, 2011 NEI
emissions, residence times, and best remote concentration estimates 22
E-7
-------
1, Introduction
- : "n
1.1 Overview
This document describes the methods used to estimate "background" concentrations for the U.S.
Environmental Protection Agency's (EPA) 2011 National Air Toxics Assessment (NATA). Background
concentrations were derived for the previous NATA modeling estimates in 2005, 2002, 1999, and
1996. In most previous iterations of NATA, "background" concentrations were defined as that portion
of concentrations reflecting contributions transported from farther than 50 km, emissions originating
from outside the United States, unidentified emissions sources within a 50-km buffer, and natural
emissions sources. These contributions can be significant for some air toxics. In the 2005 NATA, the
chemical transport model CMAQ (Community Multi-scale Air Quality model) was used for a subset of
pollutants; the CMAQ model eliminates the need to account for emissions originating from beyond
50 km and outside the model domain when examining background concentrations.
For the 2011 NATA, the background concentration estimates will be applied pollutant by pollutant,
and only in areas outside the contiguous states where the CMAQ model domain does not extend.
(The final NATA 2011 documentation will describe exactly which background concentrations were
applied for each pollutant in each area of the NATA domain.) In other words, many of the
background estimates described in this report will be overridden in NATA 2011 by the CMAQ
predictions. Thus, we caution that results shown here should not be considered representative of the
background concentrations applied in NATA 2011.
1.2 Background Definitions
For the 2011 NATA, we investigated two types of "background" concentration estimates:
• Remote Northern Hemisphere (NH) background. 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 concentrations that would
be observed in the United States. Generating remote background concentrations is the goal
of this project.
• Regional background. The annual mean concentration of air in locations uninfluenced by
local-scale (50 km) emissions sources of the pollutant. These background concentrations are
important for densely populated sections of the country, such as the industrial Midwest, the
East Coast, and portions of the Southeast. Regional background concentrations are higher
than remote NH background concentrations and capture the regional transport of emissions
1
E-8
-------
1, Introduction
over scales of hundreds of kilometers. Generating reasonable regional background
concentrations was the operational goal of previous NATA iterations.
Ultimately, our investigation did not demonstrate that regional background concentration estimates
were statistically reliable, and thus those concentrations were not applied in the 2011 NATA. See
Section 4.2 for additional details on the regional background issues.
13 NATA Background Approaches
In previous NATA iterations, different approaches have been applied to develop background
concentrations. In the 1996 effort, background concentrations were gathered in a literature search
performed as part of the Cumulative Exposure Project (CEP). The CEP literature review was originally
performed to acquire background concentrations for 1990 (Rosenbaum et al., 1999; Woodruff et al.,
1998). The result of the literature search was a single remote background value representing 12 air
toxics.
For the 1999 NATA, two approaches were used to estimate background concentrations (Bortnick et
al., 2003). The primary approach used measurements from ambient monitors to estimate background
concentrations. Estimates from individual locations were extrapolated to counties without
measurements and were based on a population regression. When ambient measurements were not
available from the ambient monitoring network, background concentrations from the CEP were used.
Table 1 lists the pollutants for which background concentrations were estimated for NATA 1996 and
1999.
For the 2002 and 2005 NATAs, background concentrations were developed using a three-pronged
approach that selected background concentrations depending on the atmospheric residence times of
the pollutants, an ambient network method, and an emissions-based method. In this approach,
background concentrations were assigned for four chemicals with known, globally averaged
concentrations (i.e., long-residence times). Background concentrations were determined for another
13 pollutants by a method that utilized routine ambient measurements. Finally, background
concentrations for another 15 pollutants were determined using a novel, emissions-based method.
Each of these methods is described in McCarthy et al. (2008). Table 2 lists the pollutants and
approach used for the NATA 2002 and 2005 results.
For the 2011 NATA, Sonoma Technology, Inc. (STI) developed spatially uniform estimates of remote
Northern Hemisphere background concentrations for each of the air toxics of interest, following
previous approaches with some modifications. In addition, STI investigated emissions-based and
ambient-based methods for estimating spatially varying background concentrations. However,
because of a lack of agreement and predictive capacity between these two methods, they were not
applied in the final analysis.
E-9
2
-------
1. Introduction
Section 2 of this report describes the technical approach used, Section 3 presents results, and
Section 4 discusses their implications for contributing to modeled cancer risk and hazard. Section 5
briefly summarizes the results, and Section 6 lists references cited in the report
Table 1. Pollutants for which background concentrations were estimated in the NATA 1996 and
1999 modeling efforts. This table is adapted from the NATA 1999 website.1
NATA 1996
NATA 1999
Benzene
Benzene
1,3-Butadiene
Carbon tetrachloride
Carbon tetrachloride
Bis(2-ethylhexyl)phthalate
Chloroform
Chloroform
Bromoform
Dichloromethane
(methylene chloride)
Dichloromethane
Carbon disulfide
Ethylene dibromide
(1,2-dibromoethane)
Ethylene dibromide
Chlordane
Ethylene dichloride
(1,2-dichloroethane)
Ethylene dichloride
Hexachlorobutadiene
Formaldehyde
Formaldehyde
Hexachloroethane
Mercury
Mercury
Lindane
Polychlorinated biphenyls
Polychlorinated biphenyls
Methyl bromide (bromomethane)
Tetrachloroethene
(perchloroethylene,
tetrachloroethylene)
Tetrachloroethene
Methyl chloride (chloromethane)
Trichloroethene
(trichloroethylene)
Trichloroethene
Methyl chloroform
(1,1,1-trichloroethane)
Hexachlorobenzene
Acetaldehyde
Phosgene
1,1,2,2-Tetrachloroethane
Vinyl chloride
1,2-Dichloropropane (propylene
dichloride)
Xylenes
1 http://www.epa.gov/ttn/atw/natal999/99pdfs/backgroundtable.pdf.
E-10
3
-------
1. Introduction
Table 2. Hazardous air pollutants (HAPs) and methods applied to determine background
concentrations for NATA 2002 and 2005. Formaldehyde and acetaldehyde concentrations (in
italics) were determined using the Community Multiscale Air Quality (CMAQ) model for the
NATA 2005 exercise.
Globally-Averaged
Concentrations (Long
Residence Time)
Ambient-Based Method
Emissions-Based Method
Carbon tetrachloride
1,3-Butadiene
Hydrazine
Chloromethane (methyl
chloride)
1,4-Dichlorobenzene
(p-dichlorobenzene)
Chromium (VI)
Bromomethane
(methyl bromide)
Acetaldehyde
Ethylene dichloride
(1,2-dichloroethane)
Methyl chloroform
(1,1,1-trichloroethane)
Arsenic
Naphthalene
Benzene
1,2-Dichloropropane (propylene
dichloride)
Chloroform
Ethylene oxide
Chromium
Acrylonitrile
Dichloromethane
(methylene chloride)
Cadmium
Formaldehyde
Beryllium
Lead
Ethylene dibromide
(1,2-dibromomethane)
Manganese
Benzidine
Nickel
Quinoline
Tetrachloroethene
(perchloroethylene,
tetrachloroethylene)
Bis(2-ethylhexyl)phthalate
Toluene
l,2-Dibromo-3-chloropropane
Trichloroethene
(trichloroethylene)
Vinyl chloride
1,1,2,2-Tetrachloroethane
E-11
4
-------
2 Technical Approach
2. Technical Approach
The project's initial goal was to develop spatially varying background concentrations appropriate to
individual county-level estimates for the air toxics of interest In the initial approach, the remote-
concentration methodology was only a step to provide a lowest-level concentration estimate for air
toxics. The lowest-level concentration would bind the lowest possible county concentrations for
remote areas of the United States uninfluenced by regional emission sources. Regional influences
would be estimated by examining emissions gradients and ambient concentrations measured at sites
across the country. Counties without measurements would be assigned background concentration
estimates according to a regression of ambient data against predicted emissions gradients.
As described in Section 4.2, using the combined emissions-based and ambient-based approach to
predict spatial variability in background concentrations showed no statistically significant relationship
between the predicted ambient and emissions-gradient background concentrations for any of the
primary pollutants of interest. Therefore, for the 2011 NATA background concentration estimates, we
have chosen to use only remote concentration estimates (RCEs).
2,1 Methods for Remote Concentration Estimates
Multiple methods were used to develop estimates of remote Northern Hemisphere background air
toxics concentrations for NATA 2011. These methods include using concentrations reported in the
scientific literature, averages of measurements made at remote monitoring sites, estimates based on
national monitoring network lower-level concentrations, and estimates based on national emissions
and atmospheric residence times. The generalized hierarchy was as follows:
1. Remote network method (Section 2.1.1). Measurements made at networks/sites in remote
Northern Hemisphere locations with citations in peer-reviewed literature. Examples include
National Oceanic and Atmospheric Administration (NOAA) Global Monitoring Division (GMD)
sites,2 the Trinidad Head Advanced Global Atmospheric Gases Experiment (AGAGE) site,3 and
remote Interagency Monitoring of Protected Visual Environments (IMPROVE) sites.4
2. Ambient national network method (Section 2.1.2). Estimates of background concentrations
made at routine monitoring network sites in the United States where concentrations were
measurable and reliable down to the 10th percentile.
3. ature-based method (Section 2.13). Measurements of species at remote or regional
sites during a single measurement study for a specific pollutant. These measurements are
different from the ongoing measurements used in Step 1 because of their "one-off" nature.
. Other AGAGE sites are available, but they are not upwind of the continental U.S.
E-12
-------
2 Technical Approach
4. Emissions-based method (Section 2.1.4). Emissions-based estimates of remote background
concentrations. These estimates are derived from the 2011 National Emission Inventory,
atmospheric residence times, and a comparison to tetrachloroethene.
The first method relies on routine monitoring networks dedicated to measurements at remote
locations that are likely to have minimal influence from local emissions. When available, these
measurements were considered the best and most reliable source of remote concentrations for the
year 2011. However, these networks do not target the entire list of air toxics of concern. In addition,
some of the metals measured at the IMPROVE monitoring sites have method detection limits (MDLs)
that are too high to accurately constrain the remote concentrations for some of their target
pollutants.
The second method relies on using ambient measurements taken from 2010-2012 at routine
monitoring sites in the United States. These sites are often located in urban areas and may be
affected by local and regional emissions. However, the lower concentrations (i.e., 10th percentile)
observed at these sites may be representative of the transported regional background
concentrations. If measured reliably, these measurements were considered a viable means of
estimating background concentrations.
The third method uses literature estimates from single studies that measured a pollutant of interest.
This method was applied when ambient measurements were too high for estimating remote
concentrations.
Finally, when none of the above three measurement-based methods produced a value, an emissions-
based method was applied. Table 3 lists the pollutants for which remote concentration estimates
were developed and shows the method selected to generate the remote concentration value. For
some pollutants, multiple methods were applied; in such cases, the generalized hierarchy was used to
select the most appropriate background concentration estimate. Only two pollutants from the 2002
and 2005 NATA background lists were excluded: quinoline and total chromium (though hexavalent
chromium is included in 2011 NATA). Four pollutants (or grouped pollutants) that had been in the
1999 NATA background list were excluded: PCBs, lindane, phosgene, and hexachlorobutadiene. In
contrast, 17 pollutants and groups that had never previously been estimated were added. These
include acetonitrile, acrolein, hexane, 2,2,4-trimethylpentane, ethylbenzene, methyl isobutyl ketone,
styrene, 1,3-dichloropropene, 1,1,2-trichloroethane, isopropylbenzene, selenium, propanal, antimony,
cobalt, and vinyl acetate. As noted in the introduction, estimates developed here may not be used for
some of the pollutants or in certain locations; see the final NATA documentation for a description of
the relevant background values applied in the NATA 2011.
E-13
6
-------
2. Technical Approach
Table 3. List of pollutants and method used to generate remote concentration estimates.
Pollutant
Method3
Pollutant
Method3
1,1,2,2-Tetrachloroethane
Emissions
1,3-Butadiene
Literature
1,1,2-Trichloroethane
Emissions
Acetaldehyde
Literature
l,2-Dibromo-3-chloropropane
Emissions
Acetonitrile
Literature
1,2-Dichloropropane
(propylene dichloride)
Emissions
Acrolein
Literature
1,3-Dichloropropene
Emissions
Bromoform
Literature
1,4-Dichlorobenzene
(p-dichlorobenzene)
Emissions
Ethylbenzene
Literature
2,2,4-T ri methyl pentane
(iso-octane)
Emissions
Formaldehyde
Literature
Acrylonitrile
Emissions
Toluene
Literature
Antimony
Emissions
Xylenes
Literature
Benzidine
Emissions
Arsenic
Ambient national
Benzyl chloride
Emissions
Beryllium
Ambient national
Bis(2-ethylhexyl) phthalate
Emissions
Cadmium
Ambient national
Carbon disulfide
Emissions
Chromium VI
Ambient national
Ethylene dibromide
(1,2-dibromoethane)
Emissions
Cobalt
Ambient national
Ethylene dichloride
(1,2-dichloroethane)
Emissions
Selenium
Ambient national
Ethylene oxide
Emissions
Benzene
Remote network
Hydrazine
Emissions
Carbon tetrachloride
Remote network
Isopropylbenzene
(cumene)
Emissions
Chloroform
Remote network
Methyl isobutyl ketone
Emissions
Chloromethane
(methyl chloride)
Remote network
Naphthalene
Emissions
Dichloromethane
(methylene chloride)
Remote network
n-Hexane
Emissions
Lead
Remote network
... 7
E-14
-------
2. Technical Approach
Pollutant
Method3
Pollutant
Method3
PAHs (polycyclic organic
matter - POM)b
Emissions
Manganese
Remote network
Propanal
(propionaldehyde)
Emissions
Mercury (gaseous)
Remote network
Styrene
Emissions
Methyl bromide
(bromomethane)
Remote network
Vinyl acetate
Emissions
Methyl chloroform
(1,1,1-
trichloroethane)
Remote network
Vinyl chloride
Emissions
Nickel
Remote network
Tetrachloroethene
(perchloroethylene,
tetrachloroethylene)
Remote network
Trichloroethene
(trichloroethylene)
Remote network
" Remote network: generated by monitoring networks with sites in remote areas.
Ambient national: generated by national networks in the United States.
Literature: based on a single literature study in a remote location.
Emissions: based on emissions-based method.
b Estimates are not used in NATA because of uncertainty.
2.1.1 Remote Network Method
STI used concentrations from networks with monitoring sites in remote locations, where little
influence from regional emissions is expected, to estimate background concentrations for some of
the most important (from a risk perspective) air toxics. Remote NOAA GMD sites5 and the Trinidad
Head AGAGE site5 primarily measure gases, and remote IMPROVE sites7 measure metals.
Annual mean concentrations for 2011 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).
These annual mean concentrations were cosine-weighted by latitude to generate an annual mean
background estimate 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 northern hemisphere air (i.e., there is more air at the equator then at the
poles).
5 www.esrl.noaa.gov/gmd/.
6 http://agage.mit.edu/.
7 http://vista.cira.colostate.edu/improve/.
E-15
-------
2 Technical Approach
Annual mean concentrations for 2011 of chloromethane, trichloroethene, and 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; data from this site were used to generate
remote concentration estimates. AGAGE data can be accessed at
http://cdiac.ornl.gov/ndps/alegage.html (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 U.S. 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 2010 to 2012 were
generated for data from the Denali, Alaska (DENA); Kalmiopsis, Oregon (KALM); Point Reyes,
California (PORE); Redwoods, California (REDW); Trapper Creek, Alaska (TRCR); Tuxedni, Alaska (TUXE);
and the Haleakala, Hawaii (HACR) sites. These were cosine-latitude averaged to generate mean
concentrations for the toxics measurements made at those sites. The IMPROVE network data can be
accessed at http://vista.cira.colostate.edu/improve/default.htm, and recent work was described by
Hyslop and White (2011).
In each case, the remote concentration estimates made from these networks were corroborated
using the next three methods as well. If a reliable lower estimate could be generated using the
ambient or literature methods, it supplanted this primary network as the estimate of choice. For
example, arsenic was estimated using the IMPROVE network data, but slightly lower concentration
estimates were generated using the ambient network method described in Section 2.1.2.
2,1,2 Ambient National Network Method
Ambient air toxics data were acquired for 2010 through 2012 from the EPA's Air Toxics Monitoring
Archive (AMA)8. Data from AMA were acquired in February 2014. Air toxics measurements are
primarily collected as 24-hr duration samples. These samples are most often collected at l-in-3-,
l-in-6-, or l-in-12-day frequencies. Any samples collected with less than 24-hr duration (e.g., 1-hr or
3-hr samples) were aggregated into 24-hr averages if measurements were collected for at least 75%
of the day in the AMA. For example, at least 18 1-hr samples were required for aggregation to a
24-hr average. This criterion ensured reasonable diurnal concentration representation. In addition,
daily data were adjusted to local conditions (LC) if they were reported in units of standard
temperature and pressure (STP) using local pressure and temperature conditions in the AMA.
The following steps were initially developed with the intention of providing spatially varying
background concentrations. However, given the results discussed in Section 4, we converted this
methodology into an independent method for assessing remote background concentrations. The
chromium VI remote concentration estimate (RCE) was based on a hybrid approach in which
g
EPA, 2013. Ambient Monitoring Archive for HAPs, Phase VII. Prepared by Eastern Research Group, Inc. Delivered February 28, 2013.
9
E-16
-------
2 Technical Approach
concentrations of Chromium PM25 from the IMPROVE remote sites and the ratio of Cr VI:Total Cr was
calculated at ambient network sites. This will be discussed in more detail in Section 3.1.
Remote background concentration estimates were then developed using the following
ambient-based method. This method consists of seven general steps:
1. Determine annual completeness for each parameter at each site to screen out those
parameters at sites with incomplete (i.e., unrepresentative) years. For NATTS program
measurements, completeness was based on completeness for a given parameter; for other
programs, completeness was based on the number of daily average samples employing the
same method. In both cases, require 11 daily average samples per calendar quarter and 3
valid calendar quarters. Require at least one valid year from 2010-2012 for inclusion.
2. Isolate data from valid years into year-seasons based on warm (April to September) and cold
seasons (October through March).
3. Calculate 10th percentile concentration for each valid year-season.
4. Is the average MDL greater than the RCE (developed from the remote, literature, or emissions
methods, in preferential order)?
a. If yes, is the 10th percentile greater than the RCE?
- If yes, use the 10th percentile for seasonal averaging.
- If no (which means ambient data should not be used), use RCE for seasonal
averaging.
b. If no, is the 10th percentile greater than the MDL?
- If no (which means ambient data must be less than RCE from other methods and are
at MDL or lower), use the MDL for seasonal averaging and add flag for RCE QC check.
- If yes, use the 10th percentile for seasonal averaging.
• If the 10th percentile is less than or equal to the RCE, add flag for RCE QC check.
• If the 10th percentile is greater than the RCE, no flag is needed.
5. Average the warm and cold season 10th percentiles for 2010-2012 to get the overall 10th
percentile estimate for each site. If there is more than one 10th percentile estimate for a site-
parameter (due to collocated measurements or method changes), then
a. Use the estimate with the lowest average MDL (i.e., most sensitive method).
b. If the MDLs are the same, use the estimate with the most measurements.
c. If the sample counts are the same, then average the estimates.
6. Select the lowest background estimate from each county for each parameter.
7. For those parameters measured in at least 18 counties, compare the concentration estimates
from each county to the best remote concentration estimate method for the alternate
methods (remote network, literature, and emissions-based method). If at least four county
estimates are below the other remote concentration estimate, assess the average percent
difference between the ambient county estimates and the other remote concentration
10
E-17
-------
2 Technical Approach
estimates. Choose the ambient-based approach if the average percent concentration
difference is lower than alternate methods by more than 30%. Average the concentrations at
all sites below RCE to generate an ambient based RCE. The ambient network RCE is used only
when it is lower than other methods; the ambient network RCE is not used as the primary
estimate when it is higher, since it is more likely to be influenced by regional emissions.
2,13 Literature-Based Method
A literature search for remote concentration estimates was performed for air toxics on the target list.
Two citation-based methods were applied. In the first method, a primary publication (e.g.,
Rosenbaum et al., 1999; Woodruff et al., 1998; McCarthy et al., 2006) was investigated for follow-up
citations. Any citations that cited these primary documents were then followed to examine whether
other references in their bibliography provided useful, updated remote concentration estimates for
any hazardous air pollutants of concern. In the second method, keyword searches were performed
for each of the key target air toxics that were expected to have potential contributions to risk or
hazard based on NATA 2005 risk driver classifications.
eel Method
Almost half of the air toxics listed in Table 3 were not measured in remote monitoring networks, had
inadequate ambient data for the ambient-based method, and were not identified in literature
searches for remote background concentration estimates. Remote background estimates based on
the available ambient data for these pollutants would either be represented by too few sites from
which to extrapolate data or represent poor quality measurements (i.e., mostly below MDL).
The minimum ambient concentration represents a geographically remote concentration estimate.
Remote concentration estimates were based on (1) the sum of 2011 v2.0 National Emissions
Inventory (NEI) emissions9, (2) atmospheric residence times gleaned from the literature, and
(3) comparison to a long-lived pollutant with measured remote concentrations and substantial U.S.
emissions. In this and previous studies, that pollutant is tetrachloroethene (C2CI4). Equation 1 shows
the relationship used to derive these remote estimates:
e *t *rr 1
j i i L tetrachloioethene-1
1 E ^ t
tetrach loioeth en e tetrach loioeth en ei
where [C] is the remote concentration, E is the 2011 NEI value in tons per year, t is the residence time
in years, and / is the pollutant of interest. Most residence time estimates were developed for NATA
2005 and were directly used in this work.
9 NEI v2.0 was a pre-released version from September 22, 2014 for all emissions categories other than onroad mobile. Onroad
mobile was not available; vl.O onroad mobile emissions were used in its place.
11
E-18
-------
12
E-19
-------
3. Results
3. Results
Sections 3.1-3.4 present background concentrations for all the pollutants listed in Table 3.
3.1 Remote Network Estimates
Estimates of Northern Hemisphere concentrations from the remote network are presented in Table 4.
For the IMPROVE sites, annual mean concentrations were generated for each parameter for 2010-
2012. Each site was then averaged across all three years. All sites were then averaged using a cosine-
latitude weighting scheme. Arsenic, chromium, and nickel annual mean concentrations at these sites
were all below IMPROVE method detection limits; the estimates of RCE were thus considered upper
limits for these pollutants. 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 (see
Appendix B).
E-20
-------
3. Results
Table 4. Remote network concentration estimates. Site locations are defined in Section 2.1.1.
Pollutant
RCE
(|ig/m3)
Remote Network
Year(s)
Location(s)
Chloroform
0.058
AG AGE
2011
Trinidad Head
Methyl chloride
(chloromethane)
1.09
AG AGE
2011
Trinidad Head
Chromium VI
1.5E-06
IMPROVE Crand
NATTS CrVI:Cr ratio
2010-2012
DENA, KALM, PORE, REDW,
TRCR, TUXE, HACR
Arsenic
<2.0E-4
IMPROVE
2010-2012
DENA, KALM, PORE, REDW,
TRCR, TUXE, HACR
Chromium
<1.2E-4
IMPROVE
2010-2012
DENA, KALM, PORE, REDW,
TRCR, TUXE, HACR
Lead
6.6E-04
IMPROVE
2010-2012
DENA, KALM, PORE, REDW,
TRCR, TUXE, HACR
Manganese
3.2E-04
IMPROVE
2010-2012
DENA, KALM, PORE, REDW,
TRCR, TUXE, HACR
Nickel
<1.0E-4
IMPROVE
2010-2012
DENA, KALM, PORE, REDW,
TRCR, TUXE, HACR
Benzene
0.116
NOAACMDL
2011
KUM, MLO, NWR, BRW, ALT
Carbon tetrachloride
0.547
NOAACMDL
2011
KUM, MLO, NWR, BRW, ALT
Methyl bromide
(bromomethane)
0.0294
NOAACMDL
2011
KUM, MLO, NWR, BRW, ALT
Methyl chloroform (1,1,1-
trichloroethane)
0.06
NOAACMDL
2011
KUM, MLO, NWR, BRW, ALT
Dichloromethane
(methylene chloride)
0.146
NOAACMDL
2011
KUM, MLO, NWR, BRW, ALT
Tetrachloroethene
(perchloroethylene,
tetrachloroethylene)
0.0131
NOAACMDL
2011
KUM, MLO, NWR, BRW, ALT
E-21
-------
3. Results
3.2 Ambient National Network Estimates
The ambient-based method was applied to 36 HAPs. The results of the ambient-based method
provide county-level background concentration estimates for counties in the United States and
associated territories. Table 5 provides summary statistics for the pollutants to which this method
was applied. These summary statistics include the number of counties with ambient measurements
between 2010 and 2012, and the minimum, mean, maximum, and 10th, 25th, and 50th percentile
background concentrations estimated across these counties. The RCE from the ambient method is
the value in the last column of the table; if null, the ambient method was not used. This new RCE is
the mean value of the subset of counties below the initial RCE and is unlikely to match any of the
summary statistics in the other fields of the table.
In addition to the summary statistics for the background estimates, the table lists the number of
counties with "reliable" background estimates that were lower than the RCE from the best of the
remote network, literature, or emissions-based methods. If more than three counties were lower than
the RCE, we characterized whether these background estimates were within measurement
uncertainty (-30%) of the other RCEs from other methods. For carbon tetrachloride, chloromethane,
chromium VI, chloroform, and acetonitrile, the background estimates from the ambient method were
within this range and were therefore considered consistent with the RCE. In contrast, estimates for
the metals of arsenic, cadmium, beryllium, selenium, and cobalt were all lower than the RCEs from
other methods by more than 30%. The RCE from the ambient method was considered a more reliable
estimate for these pollutants. Because we did not override the literature- or emissions-based
estimates if the ambient network estimates were higher, some estimates of remote background
concentrations are lower than ambient network estimates would suggest. However, given that the
ambient network is primarily in urban areas, it is likely that even the lowest concentrations generated
with this method are influenced by local and regional emissions.
E-22
-------
3. Results
Table 5. Summary statistics for the ambient network estimates of county background concentrations. The number of counties with lower
RCE estimates indicates that the ambient method generated lower RCE values than those generated from the emissions, literature, or
remote network method; if these values were outside of a 30% range of the other RCE value, the new RCE was generated based on the
ambient national network method. The "Within 30% of RCE" column was filled only for sites with at least four counties below RCE.
Pollutant
Minimum
(|ig/m3)
Pet 10
(|ig/m3)
Pet 25
(|ig/m3)
Pet 50
(|ig/m3)
Mean
(|ig/m3)
Number of
Counties
No. of
Counties
with
Lower RCE
Within
30% of
RCE
New
RCE
(|ig/m3)
Carbon tetrachloride
7.0E-02
4.4E-01
5.0E-01
5.5E-01
5.1E-01
76
24
Yes
Beryllium (PMi0)
1.8E-06
3.5E-06
8.6E-06
1.2E-05
1.1E-05
32
19
No
8.5E-06
Chloromethane
(methyl chloride)
8.8E-01
9.7E-01
1.0E+00
1.1E+00
1.1E+00
47
19
Yes
Cadmium (PM10)
1.2E-06
2.6E-05
3.5E-05
4.4E-05
4.9E-05
32
17
No
3.8E-05
Arsenic (PMi0)
2.5E-05
1.1E-04
1.7E-04
2.0E-04
2.2E-04
35
15
No
1.4E-04
Chromium VI
1.5E-06
1.5E-06
1.5E-06
1.5E-06
5.1E-06
30
13
Yes
Selenium (PMi0)
1.1E-04
1.2E-04
1.5E-04
2.4E-04
3.2E-04
18
13
No
2.0E-04
Cobalt (PM10)
2.5E-07
2.1E-05
3.8E-05
5.9E-05
1.2E-04
18
11
No
4.1E-05
Chloroform
5.0E-02
5.8E-02
5.8E-02
5.8E-02
6.2E-02
105
7
Yes
Acetonitrile
1.2E-01
1.7E-01
2.1E-01
2.8E-01
1.1E+00
28
6
Yes
Lead (PM10)
2.5E-04
6.6E-04
6.6E-04
1.0E-03
1.3E-03
46
3
NA
Antimony (PMi0)
5.2E-05
6.2E-05
2.5E-04
3.6E-04
6.0E-04
24
2
NA
Formaldehyde
2.9E-01
7.2E-01
9.1E-01
1.2E+00
1.2E+00
79
2
NA
Dichloromethane
(methylene chloride)
1.4E-01
1.5E-01
1.5E-01
1.7E-01
2.0E-01
112
2
NA
Nickel (PM10)
8.2E-06
1.0E-04
1.0E-04
4.7E-04
6.4E-04
37
2
NA
E-23
16
-------
Acetaldehyde
Manganese (PM10)
Trichloroethene
(trichloroethylene)
1.3-Butadiene
2,2,4-Trimethylpentane
(iso-octane)
4-Methyl-2-pentanone
Acrylonitrile
Benzene
Carbon disulfide
Ethylbenzene
Ethylene dichloride
(1,2-dichloroethane)
Hexane
Isopropylbenzene (cumene)
Naphthalene (total tsp &
1.4-Dichlorobenzene
(p-dichlorobenzene)
Propanal (propionaldehyde)
Styrene
1.4E-01 3.9E-01 5.0E-01
7.8E-05 5.7E-04 1.1E-03
4.1E-03 4.1E-03 4.1E-03
2.0E-03 2.0E-03 2.0E-03
9.2E-02 9.2E-02 1.3E-01
4.6E-03 4.6E-03 4.6E-03
3.6E-04 3.6E-04 3.6E-04
1.1E-01 1.8E-01 2.4E-01
5.4E-03 5.4E-03 5.4E-03
1.6E-02 1.6E-02 1.6E-02
2.0E-03 2.0E-03 2.0E-03
1.1E-01 1.1E-01 1.4E-01
3.7E-04 3.7E-04 3.7E-04
1.9E-04 6.6E-03 1.0E-02
9.2E-03 9.2E-03 9.2E-03
9.8E-05 9.8E-05 5.3E-02
8.6E-04 8.6E-04 8.6E-04
3. Results
7.5E-01 7.5E-01 81 1 NA
1.7E-03 2.3E-03 36 1 NA
4.1E-03 4.2E-03 114 1 NA
2.0E-03 6.3E-03 121 0 NA
1.6E-01 1.8E-01 40 0 NA
4.6E-03 1.8E-02 48 0 NA
3.6E-04 1.7E-03 40 0 NA
3.2E-01 3.3E-01 126 0 NA
9.7E-03 4.9E-02 55 0 NA
4.9E-02 6.4E-02 116 0 NA
2.0E-03 1.2E-02 105 0 NA
1.9E-01 2.5E-01 60 0 NA
3.7E-04 2.4E-03 57 0 NA
2.4E-02 2.8E-02 33 0 NA
9.2E-03 1.5E-02 81 0 NA
1.0E-01 1.0E-01 56 0 NA
8.6E-04 8.3E-03 115 0 NA
E-24
17
-------
Pollutant
Minimum
(|ig/m3)
Tetrachloroethene
(perchloroethylene, 1.3E-02 1.3E-02 1.3E-02
tetrachloroethylene)
Toluene 4.1E-02 1.5E-01 2.6E-01
Vinyl acetate 3.5E-05 3.5E-05 3.5E-05
Xylenes 2.3E-02 2.3E-02 6.6E-02
3. Results
1.3E-02 2.5E-02 115 0 NA
4.2E-01 4.4E-01 119 0 NA
2.0E-01 2.9E-01 25 0 NA
1.9E-01 2.2E-01 114 0 NA
E-25
18
-------
3. Results
3.3 Literature-Based Remote Estimates
A literature survey was performed to obtain remote Northern Hemisphere background
concentrations that are most likely to represent annual average concentrations at sites not impacted
by local or regional emissions of that pollutant. Numerous monitoring studies are available that
report short-term or long-term average air toxic pollutant concentrations. We focused on obtaining
remote concentrations for the pollutants likely to be of highest risk based on NATA 2005 modeling
results. Table 6 outlines the literature survey results. When multiple literature sources were available,
the best estimate was selected according to criteria including
• Year of measurement (2011 being the most appropriate)
• Monitoring locations upwind or within the continental U.S. (e.g., Pacific Ocean)
• Satellite-based modeling efforts
Pollutants for which remote concentration estimates were found in the literature are listed in Table 6.
This table also provides the location of the measurements, the year(s) of the measurements, and the
remote concentration measured.
E-26
19
-------
3. Results
Table 6. Literature studies that reported pollutant concentrations for air toxics of interest.
Pollutant
RCE
(|ig/m3)
Year(s)
Location(s)
First Author, Year
of Publication
Citation
1,3-butadiene
0.002
2005
Jungfraujoch, Switzerland
Loov, 2008
DOI: 10.1029/2007JD009751
Acetaldehyde
0.14
2004
Pacific Ocean modeled
Millet, 2010
D01:10.5194/acp-10-3405-2010
Acrolein
0.016
2005
Jungfraujoch, Switzerland
Loov, 2008
DOI: 10.1029/2007JD009751
Bromoform
0.01
1994-2004
Ocean cruises
Butler, 2007
DOI: 10.1029/2006GB002732
Ethylbenzene
0.016
2005
Jungfraujoch, Switzerland
Legreid, 2008
DOI: 10.1029/2007JD009751
Formaldehyde
0.43
2006
Pacific Ocean satellite,
flights, model
Boeke, 2011
DOI: 10.1029/2010J D014870
Toluene
0.041
2005
Jungfraujoch, Switzerland
Loov, 2008
DOI: 10.1029/2007JD009751
Xylenes
0.023
2002
Trinidad Head, California
Millet, 2004
DOI: 10.1029/2003J D004026
Mercury (gaseous)
0.0001
2006
Mercury Deposition
network
Butler, 2007
www.arl.noaa.gov/documents/
reports/M DN_report.pdf
Acetonitrile
0.17
2004-2008
New Hampshire
Jordan, 2009
DOI: 10.5194/acp-9-4677-2009
Gaseous mercury measurements made in the Mercury Deposition Network are not available in the air toxics monitoring archive and are thus considered "Literature" for the purpose of
this report. If data had been in the air toxics monitoring archive, it would have been considered an ambient network pollutant.
E-27
20
-------
3. Results
3.4 Emissions-Based Estimates
Table 7 provides the emissions, residence times, and estimated remote concentrations for the air
toxics included in this study. Measured remote concentration estimates were used for
comparison/validation of the method and are also shown in Table 7.
The emissions-based method is not an appropriate method of estimating background concentrations
for pollutants with very long residence times in the atmosphere (>350 days) and/or secondary
production in the atmosphere (e.g., formaldehyde, acetaldehyde, acrolein), and may be inaccurate for
pollutants that are emitted in Asia at very different rates than in the U.S. Long residence time
pollutants that last multiple years in the atmosphere include carryover from previous year's emissions
and thus build up over time relative to pollutants with residence times of days to months.
E-28
21
-------
3. Results
Table 7. Emissions-based method calculated remote background concentrations, 2011 NEI emissions, residence times, and best remote
concentration estimates. RCE is an acronym for Remote concentration estimates. Best 2011 RCE estimate is the final background estimate.
The table is sorted from highest to lowest Best 2011 RCE.
Name
Residence
Time
(days)
2011 NEI
Emissions
v2 (tons
per year)
Emissions x
Residence
Time (tons)
Fraction
of C2CI4
Measured
Remote
Cone 2011
(|jg/m3)
Estimated
Remote
Cone 2011
(|jg/m3)
Best
2011 RCE
estimate
(|ig/m3)
Chloromethane
(methyl chloride)
365
13,349
4872385
61.07
1.09
0.80
1.09
Carbon tetrachloride
10950
107
1171650
14.68
0.547
0.192
0.55
Formaldehyde
0.13
1371230
178260
2.23
0.43
0.03
0.43
Acetonitrile
365
433
158045
1.98
0.17
0.026
0.170
Acrolein
0.5
52,038
26019
0.33
0.016
0.004
0.0160
Dichloromethane
(methylene chloride)
30
8,727
261810
3.28
0.146
0.043
0.146
Acetaldehyde
1
826915
826915
10.36
0.14
0.14
0.14
Benzene
3
279,718
839154
10.52
0.116
0.138
0.12
n-Hexane
2.6
266,516
692942
8.68
0.114
0.11
2,2,4-T ri methyl pentane
(iso-octane)
4
141,463
565852
7.09
0.093
0.093
Methyl chloroform (1,1,1
trichloroethane)
1825
22,690
41409250
518.99
0.06
6.80
0.06
Chloroform
80
783
62640
0.79
0.058
0.010
0.058
Toluene
0.5
650,831
325416
4.08
0.041
0.053
0.041
E-29
22
-------
3. Results
Name
Residence
Time
(days)
2011 NEI
Emissions
v2 (tons
per year)
Emissions x
Residence
Time (tons)
Fraction
of C2CI4
Measured
Remote
Cone 2011
(|jg/m3)
Estimated
Remote
Cone 2011
(|jg/m3)
Best
2011 RCE
estimate
(|ig/m3)
Methyl bromide
(bromomethane)
365
6,167
2250955
28.21
0.0294
0.37
0.03
Xylenes
0.2
376,110
75222
0.94
0.023
0.012
0.023
Ethylbenzene
1.7
76,774
130516
1.64
0.016
0.021
0.016
Tetrachloroethene
(perchloroethylene,
Tetrachloroethylene)
6.5
12,275
79788
1.00
0.0131
0.013
0.013
PAH_880 E5a
10
6284
62837
0.79
1.0E-02
1.0E-02
Bromoform
540
34
18349
0.23
0.01
0.0030
1.0E-02
1,4-Dichlorobenzene
(p-dichlorobenzene)
31
1,821
56451
0.71
0.009
0.009
Bis(2-ethylhexyl)
phthalate
200
175
35000
0.44
0.0057
5.7E-03
Carbon disulfide
7
4,752
33264
0.42
0.0055
5.5E-03
Methyl isobutyl ketone
1
27,977
27977
0.35
0.0046
4.6E-03
Trichloroethene
(trichloroethylene)
6
3,374
20244
0.25
0.0041
0.0033
4.1E-03
PAHJL76 E4a
10
1680
16803
0.21
2.8E-03
2.8E-03
Ethylene dichloride
(1,2-dichloroethane)
42
295
12390
0.16
0.0020
2.0E-03
1,3-Butadiene
0.08
61,576
4926
0.06
0.002
0.0008
2.0E-03
PAH_176 E3a
10
984
9844
0.12
1.6E-03
1.6E-03
E-30
23
-------
3. Results
Name
Residence
Time
(days)
2011 NEI
Emissions
v2 (tons
per year)
Emissions x
Residence
Time (tons)
Fraction
of C2CI4
Measured
Remote
Cone 2011
(|jg/m3)
Estimated
Remote
Cone 2011
(|jg/m3)
Best
2011 RCE
estimate
(|ig/m3)
PAHJL76 E5a
10
683
6830
0.09
1.1E-03
1.1E-03
Styrene
0.25
21,102
5276
0.07
8.7E-04
8.7E-04
1,1,2,2-
Tetrachloroethane
91.3
56
5113
0.06
8.4E-04
8.4E-04
Lead
10
3,105
31050
0.39
6.60E-04
0.0051
6.6E-04
1,3-Dichloropropene
1.25
3,036
3795
0.05
6.2E-04
6.2E-04
Naphthalene
0.25
11,881
2970
0.04
4.9E-04
4.9E-04
1,1,2-Trichloroethane
49
48
2352
0.03
3.9E-04
3.9E-04
Isopropylbenzene
(cumene)
2.2
1,035
2277
0.03
3.7E-04
3.7E-04
Ethylene oxide
7
298
2086
0.03
3.4E-04
3.4E-04
Manganese
10
999
9990
0.13
0.000323
0.0016
3.2E-04
Acrylonitrile
5.6
351
1966
0.02
3.2E-04
3.2E-04
1,2-Dichloropropane
(propylene dichloride)
30
58
1740
0.02
2.9E-04
2.9E-04
Selenium
10
287
2870
0.04
2.00E-04
4.7E-04
2.0E-04
Ethylene dibromide
(1,2-dibromomethane)
50
23
1150
0.01
1.9E-04
1.9E-04
Benzyl chloride
3
291
873
0.01
1.4E-04
1.4E-04
Arsenic
10
126
1260
0.02
1.40E-04
2.1E-04
1.4E-04
Vinyl chloride
2
354
708
0.01
1.2E-04
1.2E-04
E-31 24
-------
3. Results
Name
Residence
Time
(days)
2011 NEI
Emissions
v2 (tons
per year)
Emissions x
Residence
Time (tons)
Fraction
of C2CI4
Measured
Remote
Cone 2011
(|jg/m3)
Estimated
Remote
Cone 2011
(|jg/m3)
Best
2011 RCE
estimate
(|ig/m3)
Mercury (gaseous)
365
56.0
20440
0.26
1.00E-04
3.4E-03
1.0E-04
Nickel
10
943
9430
0.12
1.00E-04
0.0015
1.0E-04
Propanal
(propionaldehyde)
0.1
5,960
596
0.01
9.8E-05
9.8E-05
Antimony
10
40.0
400
0.01
6.6E-05
6.6E-05
Cobalt
10
57
570
0.01
4.10E-05
9.4E-05
4.1E-05
Cadmium
10
29
290
0.00
3.80E-05
4.8E-05
3.8E-05
Vinyl acetate
0.25
857
214
0.00
3.5E-05
3.5E-05
Beryllium
10
8
79
0.00
8.50E-06
1.3E-05
8.5E-06
PAHJL92 E3a
10
3
32
0.00
5.2E-06
5.2E-06
Chromium VI
3
76
229
0.00
1.50E-06
3.8E-05
1.5E-06
l,2-Dibromo-3-
chloropropane
36
0.22
8
0.00
1.3E-06
1.3E-06
PAH_114 Ela
10
1
7
0.00
1.1E-06
1.1E-06
PAH_101 E2a
10
0
1
0.00
1.3E-07
1.3E-07
PAHJL76 E2a
10
0
1
0.00
9.7E-08
9.7E-08
Hydrazine
0.25
1
0
0.00
5.7E-08
5.7E-08
Benzidine
1
0.15
0
0.00
2.5E-08
2.5E-08
" Background concentrations for PAHs are not used because of the uncertainty - the residence time of 10 days is based on a PM2.5 residence time and is likely an overestimate.
E-32
25
-------
E-33
-------
4. Discussion
4. Discussion
4.1 Cancer Risk and Noncancer Hazard
The best 2011 RCEs displayed in Table 7 were used to estimate Northern Hemisphere cancer risk
levels from background concentrations. Cancer risk levels were obtained from the EPA Office of Air
Quality Planning and Standards (OAQPS) dose-response assessment value and a mutagenicity
adjustment factor for cancer risk applied in the HAPEM model in NATA.10,11 Total remote background
cancer risk is approximately 16-in-a-million for all pollutants examined; those shown in Figure 1
account for about 95% of the total. Of the pollutants listed in Table 7, only four had estimated
background cancer risk values above 1-in-a-million. Of these, formaldehyde, carbon tetrachloride,
and the polycyclic aromatic hydrocarbon (PAH) group PAH_176 E3 dominated the total cancer risk, as
shown in Figure 1. However, it is important to note that acetaldehyde, formaldehyde, and PAH RCEs
generated in this work will not be used in NATA 2011. Acetaldehyde and formaldehyde are covered
by the CMAQ model, while the PAH RCEs were based on unreliable residence time (10 days, which is
the same as PM2.5). Thus the actual NATA 2011 risk from background will likely be lower than those
estimated here.
Formaldehyde and acetaldehyde are both photochemically produced from precursor volatile organic
compounds (VOC) throughout the atmosphere. These pollutants are not transported across the
United States; carbonyls are constantly being created and destroyed through atmospheric photo-
oxidation processes. The RCE values for these pollutants are representative of the concentrations in
areas remote from local and regional VOC emissions. However, since these pollutants are used in
CMAQ, and an average secondary production estimate is being applied to non-CMAQ areas (Alaska,
Hawaii, Puerto Rico, and the Virgin Islands) it was decided not to apply these background estimates
to avoid double counting.
Carbon tetrachloride is a globally distributed pollutant that has been phased out as a result of the
Montreal Protocol to reduce chlorofluorocarbons and their impact on the stratospheric ozone layer.
Its multi-decade-long atmospheric residence time means that concentrations will decline only slowly
over time.
PAH remote concentration estimates are based on emissions from the 2011 NEI and an atmospheric
residence time of ten days. There is significant uncertainty in the residence time estimate, as this
assumes that the PAHs are in the particulate phase in particles of less than 2.5 micrometer
aerodynamic diameter. The emissions-based estimates were applied to 48 individual PAH species;
these were summed to the PAH risk group level for use in NATA 2011. Within the PAH_176 E3 group,
10 v^w,?,eD/iqcv/r€:ia/dc;e--'ej:x.:e;ax:ated--exocr:;r>a.:>:arciur:-.;i;r-::>c.fi—ts.
11 vvwvv/jnvn^iov/fex/dovv'skjnrldi;,;-!!.!'"!- c-xoosi,'e- mode!-' era
E-34
27
-------
4. Discussion
which has a total RCE risk of 2.8-in-a-million, methylchrysene is contributing about 77% of the
background risk, and benzo[a]pyrene is responsible for the other 23%. For the PAH_880 E5 group, 20
PAHs are included, of which fluoranthene, acenaphthylene, benzo[g,h,i]perylene, and
benzo[c]phenanthrene each contribute more than 0.1-in-a-million risk to the total background risk.
Due to the uncertainty in the approach, the emissions-based background was not used; instead, a
value of 0 was used. This may result in an underestimated risk from transported and background
PAHs.
Benzene has a background risk of 0.9-in-a-million. This estimate is based on annual mean
measurements at a remote network and is consistent with previous remote concentration estimates.
Arsenic has a background risk of about 0.6-in-a-million. This estimate is based on ambient
measurements from National Air Toxics Trends Stations (NATTS). Since most of the NATTS locations
are urban, this RCE may be skewed high.
Figure 1. Remote Northern Hemisphere estimates of cancer risk based on concentration
estimates from Table 7. PAH categories are consistent with groupings used in NATA 2011. Note
that these estimates are not directly applied to final NATA 2011 background estimates.
Noncancer hazard quotients (HQs) were also calculated for the RCEs. Acrolein dominates remote
concentration hazards, with a HQ of 0.8. This estimate is based on a literature estimate; emissions-
E-35
28
-------
4. Discussion
based estimates are far lower but are unable to capture secondary formation of acrolein from
1,3-butadiene photo-oxidation.
Noncancer hazard quotients for all other pollutants were below 0.05. Formaldehyde, acetaldehyde,
and chloromethane all have HQs between 0.01 and 0.05.
4.2 Spatial Estimates of Risk
In the initial approach to estimating background concentrations, two methods were used to estimate
the spatial gradients in background concentrations that are expected to result from regional
transport and emissions of pollutants. In the first approach, ambient-based measurements were used
to estimate spatial variability in counties with monitoring stations (see Section 2.1.2). In the second
approach, the 2011 NEI v2.0 was used to generate emissions-based gradients in expected
background concentrations. This approach is described in detail in Appendix A. This emissions-based
approach summed emissions from counties within a predetermined buffer distance (<500 km) to
estimate the relative impact of nearby emissions on that county for every pollutant. An example of
the resulting emissions-based gradient map is shown in Figure 2.
The ambient-based approach covers only a very small fraction of U.S. counties due to the limited
number of ambient measurement sites (<200 out of -3200). To extrapolate the results to other
counties, we attempted to use the ambient-based county measurements in a multipoint regression
with county-based normalized emissions gradients. These two data sets could be used to infer what
background concentrations for the counties without measurements should be.
E-36
29
-------
4. Discussion
Figure 2 Normalized emissions-based gradient map for arsenic emissions. Counties colored
red are expected to have the highest average regional background concentrations; blue
counties have the lowest expected regional impacts.
Figure 3 shows regression examples for benzene, arsenic, and carbon tetrachloride, three of the most
important cancer risk pollutants. The x-axis (normalized emissions) shows the county emissions-
based estimates on a scale of 0 to 1. The y-axis (average concentration background) shows the
ambient national network county estimates in units of fxg/rrr. A regression line is fit to each data set,
anchored so that the y-intercept must cross at the best remote concentration estimate value from
Table 7. In each of these three cases, and in most cases overall, there appears to be no statistically
significant relationship between the emissions-based and ambient-based county estimates. For
benzene, one of the best measured and characterized air toxics in the United States, Figure 3 shows
that low emissions-based method counties have a huge range of background concentrations and the
high emissions-based counties do not have high background concentrations. In other words, there is
no relationship between the two methods. This poor result could be because of the measurement
uncertainty and variability associated with 10th percentile concentrations, or because the emissions-
based method does not account for prevailing winds or unrealistic transport distances. Regardless, it
is clear that the two methods were incompatible and that predicting spatial variability in regional
background concentrations would be based on a method with no statistical backing.
E-37
30
-------
4. Discussion
After reviewing these results, the EPA project team decided that a method that attempted only to
characterize the remote concentration estimates would be more scientifically and statistically justified
than a method that also attempted to predict spatial variability within regional background
concentrations. Thus, the approach used in Section 2 was adopted.
Arsenic (PM10) STF/LC
y = 0.00 04348157x+2e-04
Benzene
y = 1.419779x+0.12
d^o o
o°°J° *o
6b«5b °°o8
°/oc °
Normalized Emissions
Normalized Emissions
Carbon tetrachloride
y=-0.1391613x+0.55
0.04 0.06 0.08
Normalized Emissions
Figure 3. Scatter plots and linear regressions of emissions-based county gradients and
ambient network concentrations for arsenic (top left), benzene (top right), and carbon
tetrachloride (bottom). The y-intercepts were forced through the best available RCE from the
remote network approach.
E-38
31
-------
U ;
4. Discussion
Each of the methods for estimating background concentrations has different levels of
uncertainty. The most certain estimates are those from the remote measurement networks, followed
by the ambient measurement networks, and then by literature values. The emissions estimates are
the most uncertain. Using round numbers, our best estimates for the relative uncertainties are:
• Remote network estimates - highly certain ±25%
• Ambient network estimates - moderate certainty ±50%
• Literature estimates - moderate to low certainty ±50 to 75%
• Emissions estimates - very low certainty ±100%
E-39
-------
5. Summary
5. Summary
Remote concentration estimates were determined for 62 pollutants (with PAHs in groups of
pollutants). Remote concentration estimates were generated using four distinct technical approaches:
remote networks, ambient national network, literature-based, and emissions-based. Each of these
approaches was applied to the target pollutants to the extent possible.
Key pollutants with remote concentration estimates exceeding the 1-in-a-million risk level include
formaldehyde, carbon tetrachloride, and two groups of PAHs. Remote concentration risk levels for
benzene and arsenic were just below 1-in-a-million. Of the 62 pollutants, none had remote
concentrations that resulted in a hazard quotient greater than 1.
Spatially varying regional background estimates were generated but did not appear statistically
justified, as indicated by regressions between the emissions-based and ambient-based background
approaches. Thus, spatially invariant background concentrations were chosen as a more reasonable
approach for NATA 2011.
E-40
33
-------
E-41
-------
6. References
6. References
Bortnick S.M., Coutant B.W., and Biddle B.M. (2003) Estimate background concentrations for the national-
scale air toxics assessment. Final technical report prepared for the U.S. Environmental Protection
Agency, Research Triangle Park, NC, by Battelle, Columbus, OH, Contract No. 68-D-02-061, Work
Assignment 1-03, June.
Hyslop N.P. and White W.H. (2011) Identifying sources of uncertainty from the inter-species covariance of
measurement errors. Environ. Sci. Technoi, 45(9), 4030-4037, doi: 10.1021/esl02605x. Available at
McCarthy M.C., Hafner H.R., and Montzka S.A. (2006) Background concentrations of 18 air toxics for North
America. J. Air and Waste Manag. Assoc., 56, 3-11, (STI-903550-2589), January. Available at
McCarthy M.C., Rubin J.I., Penfold B.M., and Hafner H.R. (2008) Estimation of background concentrations
for NATA 2002. Draft final report prepared for the U.S. Environmental Protection Agency, Research
Triangle Park, NC, by Sonoma Technology, Inc., Petaluma, CA, STI-906206.05-3291-DFR, January.
Montzka S.A., Butler J.H., Elkins J.W., Thompson T.M., Clarke A.D., and Lock L.T. (1999) Present and future
trends in the atmospheric burden of ozone-depleting halogens. Nature, 398, 690-694.
Montzka S.A., Dlugokencky E.J., and Butler J.H. (2011) Non-C02 greenhouse gases and climate change.
Nature, 476(7358), 43-50, doi: 10.1038/naturel0322, August 3.
Prinn R.G., Weiss R.F., Fraser P.J., Simmonds P.G., Cunnold D.M., Alyea F.N., O'Doherty S., Salameh P., Miller
B.R., Huang J., Wang R.H.J., Hartley D.E., Harth C., Steele L.P., Sturrock G., Midgley P.M., and McCulloch
A. (2000) A history of chemically and radiatively important gases in air deduced from
ALE/GAGE/AGAGE. J. Geophys. Res, 105,17,751-717,792.
Rosenbaum A.S., Axelrad D.A., Woodruff T.J., Wei Y.H., Ligocki M.P., and Cohen J.P. (1999) National
estimates of outdoor air toxics concentrations. J. Air Waste Manage., 49,1138-1152, (10), Oct.
Woodruff T.J., Axelrad D.A., Caldwell J., Morello-Frosch R., and Rosenbaum A. (1998) Public health
implications of 1990 air toxics concentrations across the United States. Environ. Heaith Persp., 106,
245-251, (5), May.
E-42
35
-------
E-43
-------
Appendix A
Appendix A: Emissions-Based Method
The emissions-based method was developed to estimate the spatial variability in regional
background concentrations on the basis of spatial differences in county-level emissions. This
approach is best applied to pollutants that are emitted directly by a few large sources and that have
short residence times in the atmosphere. The emissions-based method consists of four general steps:
1. Import emissions inventory data into a geographic information system (GIS) and create
emissions density maps.
2. Apply a spatial weighting scheme for deriving emissions gradients.
3. Normalize the emissions gradients.
4. Convert emissions gradient values to background concentration values.
The emissions-based method uses GIS technology to spatially weight and distribute county-level
emissions estimates for each pollutant based on its residence time and air parcel transport potential.
These county-level emissions gradient values are then post-processed using lower- and upper-
bound anchor points to convert emissions values to background concentrations.
Import Emission Inventory Data into a GIS and Create
The 2011 county-level NEI data were imported into a GIS, and county-level emissions density maps
were generated. Because the NEI data consist of a single emissions value for each county by
pollutant, it is necessary to spatially distribute the emissions values across county boundaries to
account for pollutant transport. To address this, emissions inventory data were spatially weighted and
distributed across county boundaries using a distance-residence time weighting scheme for each
pollutant. To account for differences in pollutant lifetimes or residence times (i.e., some pollutants
remain in the air longer than others), a weighting function was derived and applied within the GIS to
create emissions gradients for each pollutant. As an example, Figure A-l shows the countywide 2011
NEI data for ethylene dibromide. The methodology figures are based on the previous report and
have not been altered for this report.
Development of Spatial Weighting Scheme for Deriving
The dispersion and dilution assumptions in a Gaussian plume dynamics model lead to concentration
dilution of multiple orders of magnitude within a few kilometers. This approach is appropriate for
modeling plume movement away from a discrete point source; however, it is less useful for modeling
E-44 A.l
-------
Appendix A
county-level transport Based on an average wind speed of 3 m/s, air parcel transport is
approximately 250 km per day. Consequently, significant transport can occur over two days for
pollutants with long residence times. While pollution can be transported farther distances, it is likely
that emissions contributions from counties at distances greater than 500 km will be relatively small.
Figure A-l. Countywide ethylene dibromide emissions (tons/year) as reported in the 2011
NEI. Each county is colored according to the magnitude of its total emissions, with tan
indicating no reported emissions.
Residence time is another factor contributing to pollutant concentrations over time. Chemical or
physical removal competes with dilution if the residence time is on the same order of magnitude as
the transport time. If pollutants are removed at rates much slower than they are diluted, they can be
treated as inert on the timescale of a few days. In contrast, if pollutants are removed on the timescale
of a few hours, the removal processes compete with dilution, and the observed gradient in
concentrations is sharper.
For each pollutant of interest, buffer distances based on the residence time and dilution factors were
calculated. For the dilution factor of a completely inert pollutant with no deposition, a maximum
buffer distance of 500 km was assumed to be the range of influence. While pollution can be
transported around the globe, most point source emissions of pollution are fully diluted well within
Normalized Emissions
E-45
A. 2
-------
Appendix A
500 km. This initial 500-km distance was then reduced as a function of the pollutant residence time.
Equation A-l defines the drop-off as a function of distance:
1.5 1
where Bx is buffer distance and t is residence time in days. The exponential equation 1.5A(0.5/t) was
empirically selected to provide buffer distances that reflect our expectations. Table A-l summarizes
the buffer distances computed using Equation A-l. Metals in particulate matter were assigned a 10-
day residence time, based on estimated residence times of PM2.5 in the atmosphere. Because
emissions are not broken out by particle size fractions in the NEI, these estimates likely overestimate
the range of influence of particulate metals. This approach was chosen because it is more
conservative and protective of human health.
Table A-l. Calculated buffer distances for example HAPs of interest.
Pollutants
Buffer Distance (km)
Hydrazine
222
Chromium (VI)
498
Ethylene dichloride (1,2-dichloroethane)
498
Naphthalene
222
1,2-Dichloropropane (propylene dichloride)
496
Ethylene oxide
485
Acrylonitrile
482
Cadmium
490
Beryllium
490
Ethylene dibromide (1,2-dibromomethane)
498
Benzidine
409
Quinoline
499
Bis(2-ethylhexyl)phthalate
500
l,2-Dibromo-3-chloropropane
497
Trichloroethene (trichloroethylene)
483
1,1,2,2-Tetrachloroethane
499
Vinyl chloride
451
Chloroprene
40
Acrolein
333
1,3-Dichloropropene
425
E-46
A. 3
-------
Appendix A
To form a conceptual model of how the buffer distances are applied, consider chloroprene and
cadmium. Chloroprene has a relatively short residence time and a resulting buffer distance of 40 km.
Assume that the emissions point source for chloroprene is located at the county centroid. As the
distance from the county centroid increases, the concentration of chloroprene rapidly decreases due
to dilution and chemical reaction. When the distance from the county centroid equals 40 km, it is
assumed that the concentration of chloroprene equals zero. Therefore, the contribution of
chloroprene from one county to another is likely to be small, because this pollutant has a relatively
short residence time. In contrast, cadmium has a much longer residence time and a buffer distance
of 490 km. The concentration of cadmium does not reach zero until the distance from the county
centroid is 490 km; therefore, the contribution or influence of cadmium from one county to an
adjacent one could be relatively high.
For each pollutant, the buffer distance (Bx) was used in Equation A-2 to estimate the fraction of
emissions contribution from a particular county as the distance from the county centroid increases:
f1 = [(Bx-r)/Bx)]2 (A-2)
where r is the distance between county centroids, Bx is the distance from the county centroid where
the pollutant concentration equals zero, and fa is the fraction of emissions contribution from a
specific county. The resultant value, fh is the fraction of the total emissions of a particular county that
are transported to a nearby county.
Calculation Example
Contribution of chloroprene from County 1, assuming a distance from the centroid of 30 km
(r = 30 km):
fi = [(40 km - 30 km)/40 km )]2 = (0.25)2 = 0.063
Contribution of cadmium from County 1, assuming a distance from the centroid of 30 km
(r = 30 km):
fi = [(490 km - 30 km)/490 km )]2 = (0.94)2 = 0.882
Figure A-2 illustrates the process used to develop and apply the spatial weighting scheme.
E-47
A.4
-------
Appendix A
(1)
(2)
Calculate buffer distances for each pollutant
and create buffers centered on county centroids.
Calculate the distance(s) between all county
centroids within the buffer. Each county centroid
has an associated emissions value representing
total county emissions for a specific pollutant.
(3)
Use the following distance weighting
equation to calculate the fraction of
influence that each adjacent county has on
the center county :
ft = [(Bx - r)/Bx )]2
(4)
Use the following summation equation to
compute the additive influence of each
emission value within the buffer:
(5)
Z f.E»
county
max(ZAO
Spatially distributed emissions values based on the
spatial weighting scheme. Note that red indicates
areas of high emissions influence and blue represents
areas of low influence. Also note that emissions are
higher in the center of the buffer zone because as the
distance from the county centroid increases the pollutant
emissions value decreases as a function of the pollutant
buffer distance (listed in Table 5 by pollutant).
Figure A-2. Illustration of the process used to apply the weighting scheme to spatially
distribute county-level emissions.
Normalize the Emissions Gradients
Circular buffers centered on a county centroid were created within the GIS. The /i values for all
counties were calculated within the GIS, and the combined contribution of each county was summed
E-48
A. 5
-------
Appendix A
for a given buffer region. Equation A-3 was then used to normalize the emissions contributions from
all counties that influence a single county within the buffer zone:
±f,A
Fcounty = 4 (A"3:
max(X./;/-;)
1
where Fcounty is the county of interest, n is the number of counties with emissions that influence that
county, fn is the fraction emissions value calculated using Equation A-2, £,. is the county emissions
value from the 2011 NEI, x is the number of counties that influence the highest emissions county in
the country, and max indicates the county with the highest emissions in the country for a given
pollutant This calculation is repeated for all counties with reported emissions by pollutant The
weighted emissions values for individual counties were summed and normalized using the county
with the maximum emissions contribution (post-calculation). The resulting Fcounty is a unitless value
between 0 and 1 representing the lowest and highest transport values in the country, respectively.
The normalized Fcounty values were mapped to display the resulting emissions gradient by pollutant
The emissions gradient for each pollutant represents a unitless number corresponding to a range of
emissions values. Figure A-3 shows an example of a normalized emissions gradient field for ethylene
dibromide.
Figure A-3. Final normalized emissions-based gradient map for ethylene dibromide using the
2011 NEIvl.5.
E-49
A. 6
-------
Appendix B
Appendix B: Hexavalent Chromium Ratio
Data
The following data were used to determine the ratio of hexavalent chromium to total chromium. The
ratio was taken as the average of the mean and the median of the last column of the table, and
rounded to the nearest 0.25, so that the value of 1.25% was obtained.
Table 8. Data used to generate the hexavalent chromium to total suspended particulate
chromium ratio.
AQS Site
Code
Sample
Date
Chromium
TSP (|ig/m3)
Chromium
VI (ng/m3)
Chromium VI:Chromium
TSP (percentage)
010730023
15-Jul-05
0.00204
4.07E-05
2.00%
010730023
21-Jul-05
0.0032
7.23E-05
2.26%
010730023
27-Jul-05
0.00276
9.97E-05
3.61%
010730023
08-Aug-05
0.00365
7.93E-05
2.17%
010730023
20-Aug-05
0.00257
1.55E-05
0.60%
010730023
13-Sep-05
0.00327
2.99E-05
0.91%
010730023
07-0ct-05
0.00174
1.63E-05
0.94%
010730023
19-Oct-05
0.00576
8.17E-05
1.42%
010730023
12-Nov-05
0.00463
5.12E-05
1.11%
010730023
06-Dec-05
0.00269
1.81E-05
0.67%
010730023
30-Dec-05
0.00436
6.24E-05
1.43%
010730023
ll-Jan-06
0.00489
5.2E-05
1.06%
010730023
23-Jan-06
0.00407
4.855E-05
1.19%
010730023
16-Feb-06
0.00456
3.04E-05
0.67%
010730023
28-Feb-06
0.00528
4.04E-05
0.77%
010730023
24-Mar-06
0.00452
1.59E-05
0.35%
010730023
05-Apr-06
0.0053
0.000192
3.62%
010730023
29-Apr-06
0.00968
0.000166
1.71%
010730023
ll-May-06
0.00456
2.06E-05
0.45%
010730023
23-May-06
0.00485
0.000125
2.58%
E-50
B.l
-------
Appendix B
AQS Site
Code
Sample
Date
Chromium
TSP (|ig/m3)
Chromium
VI (ng/m3)
Chromium VI:Chromium
TSP (percentage)
010730023
04-Jun-06
0.00432
2.5E-05
0.58%
010730023
16-Jun-06
0.00685
9.3E-05
1.36%
010730023
28-Jun-06
0.00369
4.58E-05
1.24%
010730028
15-Jul-05
0.00336
2.96E-05
0.88%
010730028
21-Jul-05
0.00417
8.13E-05
1.95%
010730028
27-Jul-05
0.00502
3.8E-05
0.76%
010730028
08-Aug-05
0.00342
6.18E-05
1.81%
010730028
20-Aug-05
0.00504
3.47E-05
0.69%
010730028
13-Sep-05
0.00426
7.61E-05
1.79%
010730028
07-0ct-05
0.00402
3.48E-05
0.87%
010730028
12-Nov-05
0.00294
6.86E-05
2.33%
010730028
24-Nov-05
0.0027
1.76E-05
0.65%
010730028
06-Dec-05
0.00521
5.57E-05
1.07%
010730028
18-Dec-05
0.00324
4.14E-05
1.28%
010730028
ll-Jan-06
0.00568
6.38E-05
1.12%
010730028
04-Feb-06
0.00454
4.6E-06
0.10%
010730028
16-Feb-06
0.00501
3.75E-05
0.75%
010730028
28-Feb-06
0.00764
6.41E-05
0.84%
010730028
24-Mar-06
0.00586
2.4E-05
0.41%
010730028
05-Apr-06
0.00778
8.71E-05
1.12%
010730028
17-Apr-06
0.00654
5.82E-05
0.89%
010730028
29-Apr-06
0.00569
2.15E-05
0.38%
010730028
ll-May-06
0.00594
2.61E-05
0.44%
010730028
23-May-06
0.00577
8.88E-05
1.54%
010730028
04-Jun-06
0.00552
2.31E-05
0.42%
010730028
16-Jun-06
0.00532
5.96E-05
1.12%
010730028
28-Jun-06
0.00637
0.0002
3.14%
010731009
21-Jul-05
0.00144
2.56E-05
1.78%
010731009
08-Aug-05
0.00125
2.26E-05
1.81%
010731009
20-Aug-05
0.00143
1.92E-05
1.34%
010731009
25-Sep-05
0.00128
2.43E-05
1.90%
E-51
B.2
-------
Appendix B
AQS Site
Code
Sample
Date
Chromium
TSP (|ig/m3)
Chromium
VI (ng/m3)
Chromium VI:Chromium
TSP (percentage)
010731009
12-Nov-05
0.00144
1.02E-05
0.71%
010731009
06-Dec-05
0.00165
3.9E-06
0.24%
010731009
30-Dec-05
0.00182
3.7E-06
0.20%
010731009
23-Jan-06
0.00311
5.5E-06
0.18%
010731009
16-Feb-06
0.00346
2.54E-05
0.73%
010731009
05-Apr-06
0.00337
1.56E-05
0.46%
010731009
17-Apr-06
0.00284
1.05E-05
0.37%
010731009
29-Apr-06
0.00425
2.57E-05
0.60%
010731009
ll-May-06
0.00365
1.26E-05
0.35%
010731009
04-Jun-06
0.00383
8.8E-06
0.23%
010731009
16-Jun-06
0.00315
5.9E-05
1.87%
010731009
28-Jun-06
0.00269
3.18E-05
1.18%
010736004
21-Jul-05
0.00513
4.5E-05
0.88%
010736004
27-Jul-05
0.00432
9.16E-05
2.12%
010736004
08-Aug-05
0.00199
4.37E-05
2.20%
010736004
20-Aug-05
0.00297
3.29E-05
1.11%
010736004
13-Sep-05
0.00356
4.73E-05
1.33%
010736004
25-Sep-05
0.0025
2.86E-05
1.14%
010736004
12-Nov-05
0.00334
4.25E-05
1.27%
010736004
06-Dec-05
0.00302
0.000104
3.44%
010736004
30-Dec-05
0.00434
3.43E-05
0.79%
010736004
23-Jan-06
0.00247
1.7E-06
0.07%
010736004
16-Feb-06
0.00531
2.52E-05
0.47%
010736004
28-Feb-06
0.0074
6.73E-05
0.91%
010736004
24-Mar-06
0.0109
5.79E-05
0.53%
010736004
05-Apr-06
0.00475
0.000125
2.63%
010736004
17-Apr-06
0.00567
5.2E-05
0.92%
010736004
29-Apr-06
0.00573
0.000198
3.46%
010736004
ll-May-06
0.00446
4.185E-05
0.94%
010736004
23-May-06
0.00533
0.000123
2.31%
010736004
04-Jun-06
0.00998
1.58E-05
0.16%
E-52
B.3
-------
Appendix B
AQS Site
Code
Sample
Date
Chromium
TSP (|ig/m3)
Chromium
VI (ng/m3)
Chromium VI:Chromium
TSP (percentage)
010736004
16-Jun-06
0.00551
0.000107
1.94%
010736004
28-Jun-06
0.0133
0.000213
1.60%
060371103
12-Jan-07
0.0015
0.000118
7.87%
060371103
01-Mar-07
0.0042
0.000139
3.31%
060371103
25-Mar-07
0.0038
0.000129
3.39%
060371103
12-May-07
0.0036
0.000137
3.81%
060371103
24-May-07
0.0041
0.000066
1.61%
060371103
05-Jun-07
0.0036
0.000108
3.00%
060371103
17-Jun-07
0.0039
0.000032
0.82%
060371103
ll-Jul-07
0.0039
0.000057
1.46%
060371103
23-Jul-07
0.0049
0.000165
3.37%
060371103
04-Aug-07
0.0036
0.000134
3.72%
060371103
16-Aug-07
0.0057
0.000173
3.04%
060371103
28-Aug-07
0.0057
0.0003235
5.68%
060371103
09-Sep-07
0.0033
0.000043
1.30%
060371103
21-Sep-07
0.0045
0.000194
4.31%
060371103
03-0ct-07
0.0051
0.000103
2.02%
060371103
15-Oct-07
0.0034
0.000198
5.82%
060371103
08-Nov-07
0.0038
0.000074
1.95%
060371103
20-Nov-07
0.0047
0.000029
0.62%
060371103
02-Dec-07
0.0036
0.000087
2.42%
060371103
14-Dec-07
0.0032
0.000196
6.13%
060371103
26-Dec-07
0.0036
0.00013
3.61%
060371103
01-Jan-09
0.0048
0.000106
2.21%
060371103
13-Jan-09
0.0079
0.000297
3.76%
060371103
18-Feb-09
0.0035
0.000097
2.77%
060371103
02-Mar-09
0.0054
0.000188
3.48%
060371103
26-Mar-09
0.0041
0.000063
1.54%
060371103
07-Apr-09
0.0034
0.00008
2.35%
060371103
19-Apr-09
0.0036
0.000094
2.61%
060371103
01-May-09
0.006
0.000057
0.95%
E-53
B.4
-------
Appendix B
AQS Site
Code
Sample
Date
Chromium
TSP (|ig/m3)
Chromium
VI (ng/m3)
Chromium VI:Chromium
TSP (percentage)
060371103
13-May-09
0.0051
0.000059
1.16%
060371103
18-Jun-09
0.0044
0.00004
0.91%
060371103
30-Jun-09
0.0033
0.000139
4.21%
060371103
12-Jul-09
0.0033
0.000089
2.70%
060371103
24-Jul-09
0.0056
0.000096
1.71%
060371103
22-Sep-09
0.0059
0.000125
2.12%
060371103
04-0ct-09
0.0037
0.000074
2.00%
060371103
16-Oct-09
0.0077
0.000122
1.58%
060371103
09-Nov-09
0.0059
0.000108
1.83%
060371103
03-Dec-09
0.0055
0.000157
2.85%
060371103
15-Dec-09
0.0034
0.000194
5.71%
060371103
21-Dec-09
0.0032
0.000154
4.81%
060371103
14-Jan-10
0.0056
0.00018
3.21%
060371103
20-Jan-10
0.0015
0.00009
6.00%
060371103
01-Feb-10
0.0052
0.00003
0.58%
060371103
25-Feb-10
0.0034
0.0001
2.94%
060371103
09-Mar-10
0.0015
0.00011
7.33%
060371103
21-Mar-10
0.0046
0.00007
1.52%
060371103
02-Apr-10
0.0037
0.00007
1.89%
060371103
14-Apr-10
0.0038
0.00007
1.84%
060371103
26-Apr-10
0.0032
0.00005
1.56%
060371103
08-May-10
0.0044
0.00005
1.14%
060371103
20-May-10
0.0038
0.00011
2.89%
060371103
01-Jun-10
0.0045
0.00011
2.44%
060371103
13-Jun-10
0.0041
0.00002
0.49%
060371103
25-Jun-10
0.0049
0.00004
0.82%
060371103
07-Jul-10
0.044
0.0001
0.23%
060371103
19-Jul-10
0.0043
0.00011
2.56%
060658001
01-Mar-07
0.0067
0.000145
2.16%
060658001
25-Mar-07
0.0053
0.000185
3.49%
060658001
18-Apr-07
0.0047
0.000132
2.81%
E-54
B.5
-------
Appendix B
AQS Site
Code
Sample
Date
Chromium
TSP (|ig/m3)
Chromium
VI (ng/m3)
Chromium VI:Chromium
TSP (percentage)
060658001
16-Aug-07
0.0067
0.00017
2.54%
060658001
22-Aug-07
0.0057
0.000241
4.23%
060658001
28-Aug-07
0.0049
0.0004515
9.21%
060658001
09-Sep-07
0.0038
0.000053
1.39%
060658001
03-0ct-07
0.0069
0.001039
15.06%
060658001
15-Oct-07
0.0015
0.000052
3.47%
060658001
26-Nov-07
0.0065
0.001307
20.11%
060658001
02-Dec-07
0.0015
0.000385
25.67%
060658001
14-Dec-07
0.0015
0.000988
65.87%
060658001
26-Dec-07
0.0033
0.000688
20.85%
060658001
01-Jan-09
0.0043
0.000405
9.42%
060658001
13-Jan-09
0.0015
0.000043
2.87%
060658001
25-Jan-09
0.0015
0.000047
3.13%
060658001
18-Feb-09
0.0034
0.000173
5.09%
060658001
02-Mar-09
0.0047
0.00039
8.30%
060658001
08-Mar-09
0.0015
0.000066
4.40%
060658001
14-Mar-09
0.0031
0.000037
1.19%
060658001
26-Mar-09
0.0039
0.000086
2.21%
060658001
07-Apr-09
0.0049
0.00012
2.45%
060658001
19-Apr-09
0.0051
0.00021
4.12%
060658001
01-May-09
0.0059
0.000066
1.12%
060658001
13-May-09
0.0049
0.000037
0.76%
060658001
25-May-09
0.0042
0.000038
0.90%
060658001
06-Jun-09
0.0035
0.000088
2.51%
060658001
18-Jun-09
0.0045
0.000076
1.69%
060658001
30-Jun-09
0.0055
0.000115
2.09%
060658001
12-Jul-09
0.0036
0.000047
1.31%
060658001
24-Jul-09
0.0053
0.0000785
1.48%
060658001
05-Aug-09
0.0015
0.000223
14.87%
060658001
17-Aug-09
0.0015
0.000046
3.07%
060658001
29-Aug-09
0.0015
0.000163
10.87%
E-55
B.6
-------
Appendix B
AQS Site
Code
Sample
Date
Chromium
TSP (|ig/m3)
Chromium
VI (ng/m3)
Chromium VI:Chromium
TSP (percentage)
060658001
10-Sep-09
0.0052
0.00016
3.08%
060658001
22-Sep-09
0.0034
0.000253
7.44%
060658001
04-0ct-09
0.0035
0.000054
1.54%
060658001
16-Oct-09
0.0062
0.000394
6.35%
060658001
28-Oct-09
0.0015
0.000034
2.27%
060658001
09-Nov-09
0.0056
0.000225
4.02%
060658001
21-Nov-09
0.0042
0.000087
2.07%
060658001
03-Dec-09
0.0041
0.000128
3.12%
060658001
15-Dec-09
0.0037
0.000147
3.97%
060658001
27-Dec-09
0.0032
0.000297
9.28%
060658001
08-Jan-10
0.0036
0.00026
7.22%
060658001
20-Jan-10
0.0015
0.00011
7.33%
060658001
01-Feb-10
0.0045
0.0001
2.22%
060658001
13-Feb-10
0.0035
0.0001
2.86%
060658001
25-Feb-10
0.0042
0.00012
2.86%
060658001
09-Mar-10
0.0035
0.00008
2.29%
060658001
21-Mar-10
0.0035
0.00006
1.71%
060658001
02-Apr-10
0.0049
0.00008
1.63%
060658001
14-Apr-10
0.0044
0.00006
1.36%
060658001
26-Apr-10
0.0053
0.00006
1.13%
060658001
08-May-10
0.0057
0.00004
0.70%
060658001
20-May-10
0.0051
0.00009
1.76%
060658001
01-Jun-10
0.0048
0.00004
0.83%
060658001
13-Jun-10
0.0037
0.00006
1.62%
060658001
25-Jun-10
0.0076
0.00015
1.97%
060658001
07-Jul-10
0.0046
0.00007
1.52%
060658001
19-Jul-10
0.0043
0.00007
1.63%
060658001
31-Jul-10
0.0047
0.00006
1.28%
060658001
05-Sep-10
0.0044
0.00004
0.91%
060658001
17-Sep-10
0.0034
0.000035
1.03%
060658001
29-Sep-10
0.0053
0.00005
0.94%
E-56
B.7
-------
Appendix B
AQS Site
Code
Sample
Date
Chromium
TSP (|ig/m3)
Chromium
VI (ng/m3)
Chromium VI:Chromium
TSP (percentage)
060658001
10-Dec-10
0.0037
0.00003
0.81%
060658001
22-Dec-10
0.0015
0.00003
2.00%
170314201
28-Jan-05
0.00348
0.0000282
0.81%
170314201
27-Feb-05
0.00281
0.000112
3.99%
170314201
29-Mar-05
0.00341
0.000029
0.85%
170314201
10-Apr-05
0.00254
0.0000227
0.89%
170314201
16-Apr-05
0.00247
0.0000601
2.43%
170314201
22-Apr-05
0.00171
0.0000504
2.95%
170314201
10-May-05
0.00182
0.0000626
3.44%
170314201
16-May-05
0.00159
0.0000423
2.66%
170314201
22-May-05
0.00024
0.000009
3.75%
170314201
15-Jun-05
0.00219
0.0000558
2.55%
170314201
27-Jun-05
0.0033
0.0000507
1.54%
170314201
03-Jul-05
0.00307
0.0000829
2.70%
170314201
15-Jul-05
0.00126
0.0000346
2.75%
170314201
21-Jul-05
0.00127
0.0000456
3.59%
170314201
27-Jul-05
0.0011
0.0000145
1.32%
170314201
02-Aug-05
0.00186
0.0000315
1.69%
170314201
08-Aug-05
0.00218
0.0000312
1.43%
170314201
14-Aug-05
0.00289
0.0000225
0.78%
170314201
20-Aug-05
0.00263
0.0000651
2.48%
170314201
26-Aug-05
0.0043
0.0000229
0.53%
170314201
07-Sep-05
0.00379
0.0000273
0.72%
170314201
25-Sep-05
0.00367
0.000034
0.93%
170314201
ll-Jan-06
0.003
0.0000402
1.34%
170314201
17-Jan-06
0.004
0.0000216
0.54%
170314201
23-Jan-06
0.003
0.00002325
0.77%
170314201
17-May-06
0.004
0.0000522
1.30%
170314201
07-Nov-06
0.004
0.0000495
1.24%
170314201
19-Dec-06
0.004
0.0000397
0.99%
170314201
25-Mar-07
0.003
0.0000055
0.18%
E-57
B.8
-------
Appendix B
AQS Site
Code
Sample
Date
Chromium
TSP (|ig/m3)
Chromium
VI (ng/m3)
Chromium VI:Chromium
TSP (percentage)
170314201
12-Apr-07
0.003
0.0000046
0.15%
170314201
24-Apr-07
0.003
0.0000088
0.29%
170314201
18-May-07
0.003
0.0000442
1.47%
170314201
24-May-07
0.003
0.0000494
1.65%
170314201
ll-Jun-07
0.003
0.0000153
0.51%
170314201
05-Jul-07
0.007
0.000307
4.39%
170314201
17-Jul-07
0.003
0.0000122
0.41%
170314201
04-Aug-07
0.003
0.0000168
0.56%
170314201
03-Sep-07
0.002
0.0000097
0.49%
170314201
09-Sep-07
0.002
0.0000234
1.17%
170314201
15-Sep-07
0.002
0.00001675
0.84%
170314201
15-Oct-07
0.003
0.0000285
0.95%
170314201
20-Dec-07
0.005
0.0000272
0.54%
170314201
26-Dec-07
0.004
0.0000102
0.26%
170314201
13-Jan-08
0.003
0.0000079
0.26%
170314201
06-Apr-08
0.004
0.0000226
0.57%
170314201
18-Apr-08
0.003
0.0000193
0.64%
170314201
24-Apr-08
0.005
0.0000558
1.12%
170314201
30-Apr-08
0.004
0.0000204
0.51%
170314201
06-May-08
0.005
0.0000476
0.95%
170314201
30-May-08
0.003
0.0000356
1.19%
170314201
05-Jun-08
0.004
0.0000159
0.40%
170314201
29-Jul-08
0.003
0.0000123
0.41%
170314201
28-Aug-08
0.006
0.0000248
0.41%
170314201
21-Sep-08
0.006
0.0000292
0.49%
170314201
27-Sep-08
0.004
0.0000174
0.44%
170314201
08-Dec-08
0.003
0.0000302
1.01%
170314201
14-Mar-09
0.003
0.0000243
0.81%
170314201
18-Jun-09
0.003
0.0000337
1.12%
170314201
24-Jun-09
0.003
0.0000629
2.10%
170314201
30-Jul-09
0.003
0.0000343
1.14%
E-58
B.9
-------
Appendix B
AQS Site
Code
Sample
Date
Chromium
TSP (|ig/m3)
Chromium
VI (ng/m3)
Chromium VI:Chromium
TSP (percentage)
261630033
10-Jan-05
0.007575
0.000036
0.48%
261630033
28-Jan-05
0.006205
0.0000055
0.09%
261630033
03-Feb-05
0.0110876
0.0000309
0.28%
261630033
15-Feb-05
0.0115975
0.0000752
0.65%
261630033
21-Feb-05
0.0066257
0.0000165
0.25%
261630033
ll-Mar-05
0.008995
0.0000334
0.37%
261630033
23-Mar-05
0.03338
0.0000805
0.24%
261630033
29-Mar-05
0.015265
0.0000742
0.49%
261630033
04-Apr-05
0.009915
0.0000587
0.59%
261630033
10-Apr-05
0.00548
0.0000464
0.85%
261630033
16-Apr-05
0.0073
0.000146
2.00%
261630033
22-Apr-05
0.006525
0.000102
1.56%
261630033
04-May-05
0.007195
0.0000714
0.99%
261630033
10-May-05
0.014895
0.000126
0.85%
261630033
22-May-05
0.00567
0.0000854
1.51%
261630033
28-May-05
0.005535
0.0000792
1.43%
261630033
03-Jun-05
0.00683
0.0000659
0.96%
261630033
21-Jun-05
0.008405
0.000136
1.62%
261630033
27-Jun-05
0.0155
0.0000902
0.58%
261630033
03-Jul-05
0.034815001
0.0000891
0.26%
261630033
15-Jul-05
0.005995
0.0000418
0.70%
261630033
21-Jul-05
0.00559
0.0000739
1.32%
261630033
27-Jul-05
0.004675
0.0000265
0.57%
261630033
02-Aug-05
0.00579
0.0000514
0.89%
261630033
08-Aug-05
0.008825
0.0000889
1.01%
261630033
26-Aug-05
0.00842
0.0000665
0.79%
261630033
07-Sep-05
0.009725
0.000105
1.08%
261630033
13-Sep-05
0.01208
0.0000619
0.51%
261630033
19-Sep-05
0.00866
0.0000616
0.71%
261630033
25-Sep-05
0.0069
0.0000776
1.12%
261630033
01-Oct-05
0.008396
0.0000861
1.03%
E-59
B.10
-------
Appendix B
AQS Site
Code
Sample
Date
Chromium
TSP (|ig/m3)
Chromium
VI (ng/m3)
Chromium VI:Chromium
TSP (percentage)
261630033
07-0ct-05
0.005262
0.0000545
1.04%
261630033
13-Oct-05
0.0072485
0.0000787
1.09%
261630033
19-Oct-05
0.0096345
0.000068
0.71%
261630033
31-Oct-05
0.0108525
0.0000604
0.56%
261630033
06-Nov-05
0.008167
0.0000581
0.71%
261630033
12-Nov-05
0.0070885
0.0000765
1.08%
261630033
18-Nov-05
0.012625
0.0000271
0.21%
261630033
24-Nov-05
0.005605
0.00002215
0.40%
261630033
30-Nov-05
0.0062765
0.0000268
0.43%
261630033
06-Dec-05
0.005948
0.0000338
0.57%
261630033
12-Dec-05
0.005207
0.0000387
0.74%
261630033
18-Dec-05
0.004663
0.000058
1.24%
261630033
24-Dec-05
0.005546
0.0000882
1.59%
261630033
05-Jan-06
0.0031204
0.0000604
1.94%
261630033
ll-Jan-06
0.0067323
0.0000415
0.62%
261630033
17-Jan-06
0.0050784
0.00022
4.33%
261630033
23-Jan-06
0.0100253
0.0000197
0.20%
261630033
29-Jan-06
0.0050225
0.0000559
1.11%
261630033
04-Feb-06
0.0031199
0.000064
2.05%
261630033
10-Feb-06
0.0062883
0.0000482
0.77%
261630033
22-Feb-06
0.0109229
0.0000704
0.64%
261630033
28-Feb-06
0.0037052
0.0000242
0.65%
261630033
06-Mar-06
0.0042686
0.0000236
0.55%
261630033
18-Mar-06
0.0057351
0.00003025
0.53%
261630033
24-Mar-06
0.0048841
0.000232
4.75%
261630033
30-Mar-06
0.0086551
0.0000697
0.81%
261630033
05-Apr-06
0.0050755
0.0000344
0.68%
261630033
ll-Apr-06
0.01128215
0.000105
0.93%
261630033
17-Apr-06
0.00471765
0.0000189
0.40%
261630033
23-Apr-06
0.0025276
0.0000578
2.29%
261630033
29-Apr-06
0.0030684
0.0000152
0.50%
E-60
B.ll
-------
Appendix B
AQS Site
Code
Sample
Date
Chromium
TSP (|ig/m3)
Chromium
VI (ng/m3)
Chromium VI:Chromium
TSP (percentage)
261630033
05-May-06
0.0066617
0.0000333
0.50%
261630033
ll-May-06
0.0048568
0.0000288
0.59%
261630033
17-May-06
0.0057822
0.0000336
0.58%
261630033
23-May-06
0.0064621
0.0000777
1.20%
261630033
29-May-06
0.00468725
0.0000388
0.83%
261630033
04-Jun-06
0.0029205
0.0000259
0.89%
261630033
10-Jun-06
0.0033129
0.0000146
0.44%
261630033
16-Jun-06
0.0138529
0.000116
0.84%
261630033
22-Jun-06
0.004676
0.0000687
1.47%
261630033
28-Jun-06
0.0070246
0.0000789
1.12%
261630033
04-Jul-06
0.00638965
0.000496
7.76%
261630033
10-Jul-06
0.0052803
0.0000636
1.20%
261630033
16-Jul-06
0.00700115
0.00005615
0.80%
261630033
22-Jul-06
0.00404065
0.0000236
0.58%
261630033
28-Jul-06
0.00578725
0.0000518
0.90%
261630033
03-Aug-06
0.0042248
0.00012
2.84%
261630033
09-Aug-06
0.00894825
0.00012
1.34%
261630033
21-Aug-06
0.0084533
0.0000761
0.90%
261630033
27-Aug-06
0.00342715
0.0000333
0.97%
261630033
02-Sep-06
0.00376825
0.000103
2.73%
261630033
08-Sep-06
0.0093528
0.0000609
0.65%
261630033
14-Sep-06
0.00366095
0.0000349
0.95%
261630033
20-Sep-06
0.00569815
0.0000396
0.69%
261630033
02-0ct-06
0.0084389
0.000254
3.01%
261630033
08-0ct-06
0.00659315
0.000108
1.64%
261630033
20-0ct-06
0.0059855
0.00006525
1.09%
261630033
26-Oct-06
0.0103236
0.00016
1.55%
261630033
07-Nov-06
0.00494715
0.00011515
2.33%
261630033
13-Nov-06
0.00494715
0.00001565
0.32%
261630033
25-Nov-06
0.00971735
0.0000556
0.57%
261630033
01-Dec-06
0.00413425
0.0000205
0.50%
E-61
B.12
-------
Appendix B
AQS Site
Code
Sample
Date
Chromium
TSP (|ig/m3)
Chromium
VI (ng/m3)
Chromium VI:Chromium
TSP (percentage)
261630033
13-Dec-06
0.0070503
0.00002335
0.33%
261630033
19-Dec-06
0.0056269
0.0000125
0.22%
261630033
25-Dec-06
0.0031296
0.00003685
1.18%
261630033
31-Dec-06
0.00572405
0.0000235
0.41%
261630033
06-Jan-07
0.00262085
0.0000043
0.16%
261630033
12-Jan-07
0.0082493
0.0000532
0.64%
261630033
18-Jan-07
0.00732745
0.0000319
0.44%
261630033
24-Jan-07
0.0045273
0.00001565
0.35%
261630033
30-Jan-07
0.0049467
0.0000105
0.21%
261630033
ll-Feb-07
0.0080771
0.0000173
0.21%
261630033
17-Feb-07
0.0035706
0.0000193
0.54%
261630033
23-Feb-07
0.0039646
0.0000105
0.26%
261630033
01-Mar-07
0.0022323
0.0000151
0.68%
261630033
07-Mar-07
0.00509755
0.0000158
0.31%
261630033
13-Mar-07
0.0122883
0.000133
1.08%
261630033
19-Mar-07
0.00958605
0.00002665
0.28%
261630033
25-Mar-07
0.0036816
0.0000263
0.71%
261630033
31-Mar-07
0.00320865
0.0000182
0.57%
261630033
06-Apr-07
0.0037542
0.0000118
0.31%
261630033
12-Apr-07
0.0068328
0.0000315
0.46%
261630033
18-Apr-07
0.00597985
0.0000196
0.33%
261630033
24-Apr-07
0.0082121
0.0000511
0.62%
261630033
30-Apr-07
0.0050975
0.0000188
0.37%
261630033
06-May-07
0.0032916
0.0000071
0.22%
261630033
12-May-07
0.00392465
0.000012
0.31%
261630033
18-May-07
0.0061486
0.0000356
0.58%
261630033
24-May-07
0.0087943
0.0000929
1.06%
261630033
30-May-07
0.01143405
0.000113
0.99%
261630033
05-Jun-07
0.0031619
0.0000174
0.55%
261630033
ll-Jun-07
0.0075505
0.000037
0.49%
261630033
17-Jun-07
0.0037399
0.0000123
0.33%
E-62
B.13
-------
Appendix B
AQS Site
Code
Sample
Date
Chromium
TSP (|ig/m3)
Chromium
VI (ng/m3)
Chromium VI:Chromium
TSP (percentage)
261630033
23-Jun-07
0.00633665
0.0000506
0.80%
261630033
29-Jun-07
0.0050638
0.000092
1.82%
261630033
05-Jul-07
0.0099178
0.000208
2.10%
261630033
ll-Jul-07
0.0089198
0.00001785
0.20%
261630033
17-Jul-07
0.0053044
0.0000369
0.70%
261630033
23-Jul-07
0.0095315
0.0000733
0.77%
261630033
29-Jul-07
0.0028393
0.0000218
0.77%
261630033
04-Aug-07
0.0036665
0.0000122
0.33%
261630033
10-Aug-07
0.004089
0.0000192
0.47%
261630033
16-Aug-07
0.0047761
0.0000143
0.30%
261630033
03-Sep-07
0.0039197
0.00001
0.26%
261630033
09-Sep-07
0.003146
0.0000459
1.46%
261630033
15-Sep-07
0.0026065
0.0000111
0.43%
261630033
21-Sep-07
0.0091525
0.0000933
1.02%
261630033
27-Sep-07
0.0046416
0.0000289
0.62%
261630033
03-0ct-07
0.005981
0.0000272
0.45%
261630033
06-0ct-07
0.005018
0.0000261
0.52%
261630033
09-0ct-07
0.006514
0.0000452
0.69%
261630033
21-Oct-07
0.0064791
0.0000363
0.56%
261630033
27-Oct-07
0.0036747
0.0000286
0.78%
261630033
02-Nov-07
0.011796
0.000104
0.88%
261630033
08-Nov-07
0.0073626
0.00006955
0.94%
261630033
14-Nov-07
0.00491865
0.0000085
0.17%
261630033
26-Nov-07
0.0087624
0.0000771
0.88%
261630033
02-Dec-07
0.00757165
0.000114
1.51%
261630033
08-Dec-07
0.00290345
0.0000052
0.18%
261630033
14-Dec-07
0.0058518
0.0000094
0.16%
261630033
20-Dec-07
0.0035523
0.0000265
0.75%
261630033
26-Dec-07
0.0074522
0.0000269
0.36%
261630033
01-Jan-08
0.0021103
0.0000207
0.98%
261630033
07-Jan-08
0.00776675
0.0000895
1.15%
E-63
B.14
-------
Appendix B
AQS Site
Code
Sample
Date
Chromium
TSP (|ig/m3)
Chromium
VI (ng/m3)
Chromium VI:Chromium
TSP (percentage)
261630033
13-Jan-08
0.00263115
0.0000218
0.83%
261630033
19-Jan-08
0.0069021
0.0000154
0.22%
261630033
25-Jan-08
0.00689135
0.00002345
0.34%
261630033
31-Jan-08
0.0056087
0.0000118
0.21%
261630033
06-Feb-08
0.0020398
0.0000577
2.83%
261630033
18-Feb-08
0.0071664
0.0000163
0.23%
261630033
24-Feb-08
0.0068614
0.0000405
0.59%
261630033
07-Mar-08
0.00454905
0.0000129
0.28%
261630033
13-Mar-08
0.00700795
0.0000171
0.24%
261630033
19-Mar-08
0.0036055
0.0000262
0.73%
261630033
25-Mar-08
0.01249485
0.0000499
0.40%
261630033
06-Apr-08
0.00341975
0.0000068
0.20%
261630033
12-Apr-08
0.0036005
0.0000106
0.29%
261630033
18-Apr-08
0.01058655
0.000116
1.10%
261630033
24-Apr-08
0.0134431
0.0000972
0.72%
261630033
30-Apr-08
0.0082923
0.0000658
0.79%
261630033
06-May-08
0.00720835
0.0000643
0.89%
261630033
12-May-08
0.0039805
0.0000364
0.91%
261630033
18-May-08
0.0041587
0.0000177
0.43%
261630033
24-May-08
0.00435555
0.0000306
0.70%
261630033
30-May-08
0.01394445
0.000145
1.04%
261630033
05-Jun-08
0.01139835
0.000046
0.40%
261630033
ll-Jun-08
0.00839205
0.0000337
0.40%
261630033
17-Jun-08
0.0060467
0.0000164
0.27%
261630033
23-Jun-08
0.007937
0.000105
1.32%
261630033
05-Jul-08
0.00944635
0.000392
4.15%
261630033
ll-Jul-08
0.00784415
0.0000328
0.42%
261630033
17-Jul-08
0.01200845
0.0000669
0.56%
261630033
29-Jul-08
0.00836735
0.0000683
0.82%
261630033
10-Aug-08
0.00291315
0.0000049
0.17%
261630033
22-Aug-08
0.0073331
0.000075
1.02%
E-64
B.15
-------
Appendix B
AQS Site
Code
Sample
Date
Chromium
TSP (|ig/m3)
Chromium
VI (ng/m3)
Chromium VI:Chromium
TSP (percentage)
261630033
28-Aug-08
0.00407345
0.0000306
0.75%
261630033
03-Sep-08
0.01224205
0.0000838
0.68%
261630033
09-Sep-08
0.0042144
0.000039
0.93%
261630033
15-Sep-08
0.00446255
0.0000181
0.41%
261630033
21-Sep-08
0.0030254
0.0000065
0.21%
261630033
27-Sep-08
0.00515685
0.0000353
0.68%
261630033
03-0ct-08
0.0052431
0.0000406
0.77%
261630033
09-0ct-08
0.0074004
0.0000188
0.25%
261630033
15-Oct-08
0.0133567
0.000132
0.99%
261630033
21-Oct-08
0.00618275
0.0000139
0.22%
261630033
27-Oct-08
0.00618555
0.0000099
0.16%
261630033
02-Nov-08
0.0022856
0.0000207
0.91%
261630033
08-Nov-08
0.0065656
0.0000225
0.34%
261630033
14-Nov-08
0.0060115
0.000111
1.85%
261630033
26-Nov-08
0.0037806
0.0000033
0.09%
261630033
02-Dec-08
0.0063334
0.0000084
0.13%
261630033
08-Dec-08
0.00391805
0.0000312
0.80%
261630033
26-Dec-08
0.00228905
0.0000216
0.94%
261630033
01-Jan-09
0.00302505
0.000372
12.30%
261630033
07-Jan-09
0.00194745
0.0000103
0.53%
261630033
13-Jan-09
0.0025347
0.0000142
0.56%
261630033
25-Jan-09
0.0037742
0.00001915
0.51%
261630033
31-Jan-09
0.00513625
0.0000219
0.43%
261630033
06-Feb-09
0.006904
0.0000278
0.40%
261630033
24-Feb-09
0.0040371
0.0000126
0.31%
261630033
08-Mar-09
0.0023407
0.0000288
1.23%
261630033
14-Mar-09
0.0053647
0.0000262
0.49%
261630033
20-Mar-09
0.0086397
0.0000885
1.02%
261630033
26-Mar-09
0.00506635
0.0000183
0.36%
261630033
01-Apr-09
0.0089369
0.0000405
0.45%
261630033
19-Apr-09
0.0020641
0.0000088
0.43%
E-65
B.16
-------
Appendix B
AQS Site
Code
Sample
Date
Chromium
TSP (|ig/m3)
Chromium
VI (ng/m3)
Chromium VI:Chromium
TSP (percentage)
261630033
25-Apr-09
0.0110231
0.0000758
0.69%
261630033
07-May-09
0.0062342
0.000036
0.58%
261630033
13-May-09
0.0046832
0.000317
6.77%
261630033
19-May-09
0.0071426
0.0000865
1.21%
261630033
31-May-09
0.002204
0.00001925
0.87%
261630033
24-Jun-09
0.00577255
0.0000577
1.00%
261630033
30-Jun-09
0.0020965
0.0000181
0.86%
261630033
06-Jul-09
0.00457115
0.0000211
0.46%
261630033
12-Jul-09
0.0018438
0.0000082
0.44%
261630033
24-Jul-09
0.004156
0.0000392
0.94%
261630033
30-Jul-09
0.00377575
0.000048
1.27%
261630033
05-Aug-09
0.0058714
0.0000224
0.38%
261630033
ll-Aug-09
0.0058233
0.0000173
0.30%
261630033
17-Aug-09
0.01014465
0.0000402
0.40%
261630033
04-Sep-09
0.00984245
0.000026
0.26%
261630033
10-Sep-09
0.0039165
0.0000054
0.14%
261630033
16-Sep-09
0.00372635
0.000024
0.64%
261630033
22-Sep-09
0.00403935
0.0000596
1.48%
261630033
28-Sep-09
0.0037259
0.0000374
1.00%
261630033
22-Oct-09
0.0089921
0.0000705
0.78%
261630033
28-Oct-09
0.0064514
0.0000317
0.49%
261630033
03-Nov-09
0.00428915
0.0000113
0.26%
261630033
09-Nov-09
0.00741965
0.0000516
0.70%
261630033
15-Nov-09
0.0033398
0.0000141
0.42%
261630033
21-Nov-09
0.00323275
0.000126
3.90%
261630033
27-Nov-09
0.00284575
0.0000145
0.51%
261630033
03-Dec-09
0.0023493
0.0000189
0.80%
261630033
09-Dec-09
0.0055543
0.0000711
1.28%
261630033
21-Dec-09
0.0028519
0.0000182
0.64%
261630033
14-Jan-10
0.0134654
0.000081
0.60%
261630033
20-Jan-10
0.0030915
0.0000177
0.57%
E-66
B.17
-------
Appendix B
AQS Site
Code
Sample
Date
Chromium
TSP (|ig/m3)
Chromium
VI (ng/m3)
Chromium VI:Chromium
TSP (percentage)
261630033
26-Jan-10
0.0066768
0.00002165
0.32%
261630033
01-Feb-10
0.0064505
0.00005
0.78%
261630033
19-Feb-10
0.0037339
0.0000145
0.39%
261630033
25-Feb-10
0.0025884
0.0000187
0.72%
261630033
03-Mar-10
0.003534
0.0000192
0.54%
261630033
09-Mar-10
0.0115973
0.000138
1.19%
261630033
15-Mar-10
0.00270175
0.0000262
0.97%
261630033
21-Mar-10
0.0021123
0.0000091
0.43%
261630033
27-Mar-10
0.0024709
0.000015
0.61%
261630033
02-Apr-10
0.0092247
0.000125
1.36%
261630033
08-Apr-10
0.00361325
0.0000307
0.85%
261630033
14-Apr-10
0.00529515
0.0000546
1.03%
261630033
20-Apr-10
0.009515
0.000112
1.18%
261630033
26-Apr-10
0.00283225
0.0000313
1.11%
261630033
02-May-10
0.00503465
0.0000799
1.59%
261630033
14-May-10
0.0042193
0.0000438
1.04%
261630033
20-May-10
0.00542165
0.0000712
1.31%
261630033
26-May-10
0.0084684
0.0000535
0.63%
261630033
07-Jun-10
0.0041909
0.0000261
0.62%
261630033
13-Jun-10
0.00230345
0.0000477
2.07%
261630033
19-Jun-10
0.0042031
0.0000349
0.83%
261630033
25-Jun-10
0.00648495
0.0000512
0.79%
261630033
01-Jul-10
0.00425045
0.0000425
1.00%
261630033
07-Jul-10
0.00618485
0.0000499
0.81%
261630033
13-Jul-10
0.00563975
0.0000647
1.15%
261630033
19-Jul-10
0.0033244
0.000032
0.96%
261630033
25-Jul-10
0.0021857
0.00003195
1.46%
261630033
31-Jul-10
0.0045473
0.0000423
0.93%
261630033
06-Aug-10
0.0074914
0.0000206
0.27%
261630033
12-Aug-10
0.00444175
0.0000367
0.83%
261630033
18-Aug-10
0.0044524
0.0000289
0.65%
E-67
B.18
-------
Appendix B
AQS Site
Code
Sample
Date
Chromium
TSP (|ig/m3)
Chromium
VI (ng/m3)
Chromium VI:Chromium
TSP (percentage)
261630033
24-Aug-10
0.00345765
0.0000323
0.93%
261630033
05-Sep-10
0.0045117
0.0000312
0.69%
261630033
ll-Sep-10
0.003195
0.0000537
1.68%
261630033
17-Sep-10
0.00462125
0.000049
1.06%
261630033
29-Sep-10
0.0086121
0.0000878
1.02%
261630033
05-Oct-10
0.00424295
0.0000245
0.58%
261630033
ll-Oct-10
0.0082004
0.0000446
0.54%
261630033
23-Oct-lO
0.0102699
0.000154
1.50%
261630033
04-Nov-10
0.0058438
0.0000116
0.20%
261630033
10-Nov-lO
0.0029787
0.0000343
1.15%
261630033
16-Nov-10
0.0072548
0.000099
1.36%
261630033
22-Nov-10
0.00786985
0.000124
1.58%
261630033
28-Nov-10
0.0046147
0.0000537
1.16%
261630033
04-Dec-10
0.002488
0.0000146
0.59%
261630033
10-Dec-10
0.00705425
0.000061
0.86%
261630033
16-Dec-10
0.0035897
0.0000415
1.16%
261630033
22-Dec-10
0.0018619
0.0000209
1.12%
261630033
28-Dec-10
0.00493515
0.0000324
0.66%
450250001
19-Jan-08
0.001
0.000018
1.80%
450250001
12-Feb-08
0.0005
0.0000039
0.78%
450250001
13-Mar-08
0.0015
0.0000064
0.43%
450250001
24-Apr-08
0.002
0.0000025
0.13%
450250001
30-Apr-08
0.0015
0.0000079
0.53%
450250001
18-May-08
0.0025
0.0000036
0.14%
450250001
30-May-08
0.003
0.0000051
0.17%
450250001
05-Jul-08
0.008
0.0000133
0.17%
450250001
23-Jul-08
0.0055
0.0000156
0.28%
450250001
29-Jul-08
0.003
0.000003
0.10%
450250001
10-Aug-08
0.001
0.0000046
0.46%
450250001
09-Sep-08
0.003
0.0000127
0.42%
450250001
15-Sep-08
0.002
0.0000098
0.49%
E-68
B.19
-------
Appendix B
AQS Site
Code
Sample
Date
Chromium
TSP (|ig/m3)
Chromium
VI (ng/m3)
Chromium VI:Chromium
TSP (percentage)
510870014
03-0ct-08
0.00221
0.0000046
0.21%
510870014
14-Nov-08
0.00233
0.000008
0.34%
510870014
13-Jan-09
0.00312
0.000013
0.42%
510870014
20-Mar-09
0.00176
0.0000192
1.09%
510870014
06-Jul-09
0.00171
0.0000134
0.78%
510870014
12-Jul-09
0.0015
0.0000144
0.96%
510870014
24-Jul-09
0.00169
0.00001565
0.93%
510870014
16-Sep-09
0.00195
0.00000425
0.22%
510870014
21-Nov-09
0.00209
0.0000111
0.53%
510870014
03-Dec-09
0.00196
0.000008
0.41%
510870014
09-Dec-09
0.00226
0.000015
0.66%
510870014
20-Jan-10
0.00225
0.000026
1.16%
510870014
19-Feb-10
0.00276
0.000017
0.62%
510870014
09-Mar-10
0.00314
0.0000183
0.58%
510870014
21-Mar-10
0.00216
0.00000345
0.16%
510870014
02-Apr-10
0.00223
0.0000136
0.61%
510870014
08-Apr-10
0.00256
0.000019
0.74%
510870014
20-Apr-10
0.00242
0.0000197
0.81%
510870014
26-Apr-10
0.00198
0.0000229
1.16%
510870014
20-May-10
0.00205
0.0000236
1.15%
510870014
26-May-10
0.00208
0.0000267
1.28%
510870014
01-Jun-10
0.00175
0.0000209
1.19%
510870014
13-Jun-10
0.0017
0.0000363
2.14%
510870014
25-Jun-10
0.00156
0.00002
1.28%
510870014
07-Jul-10
0.0019
0.0000201
1.06%
510870014
13-Jul-10
0.00214
0.0000146
0.68%
510870014
19-Jul-10
0.00177
0.0000111
0.63%
510870014
25-Jul-10
0.00195
0.00001165
0.60%
510870014
31-Jul-10
0.00168
0.0000171
1.02%
510870014
06-Aug-10
0.00205
0.0000282
1.38%
510870014
12-Aug-10
0.00154
0.0000399
2.59%
E-69
B.20
-------
Appendix B
AQS Site
Code
Sample
Date
Chromium
TSP (|ig/m3)
Chromium
VI (ng/m3)
Chromium VI:Chromium
TSP (percentage)
510870014
18-Aug-10
0.00142
0.0000266
1.87%
510870014
30-Aug-10
0.00178
0.0000178
1.00%
510870014
ll-Sep-10
0.001626
0.0000114
0.70%
510870014
17-Sep-10
0.001835
0.000021
1.14%
510870014
23-Sep-10
0.002045
0.0000183
0.89%
510870014
ll-Oct-10
0.002208
0.0000147
0.67%
510870014
10-Nov-lO
0.001874
0.0000154
0.82%
510870014
16-Nov-10
0.001565
0.0000117
0.75%
510870014
22-Nov-10
0.002089
0.0000099
0.47%
510870014
04-Dec-10
0.001555
0.0000136
0.87%
510870014
10-Dec-10
0.00269
0.0000146
0.54%
510870014
16-Dec-10
0.001957
0.0000171
0.87%
510870014
22-Dec-10
0.001965
0.000017
0.87%
510870014
28-Dec-10
0.001719
0.0000153
0.89%
550270007
13-Oct-05
0.00155
0.00003645
2.35%
550270007
12-Nov-05
0.00136
0.0000117
0.86%
E-70
B.21
-------
EPA's National-scale Air Toxics Assessment
Appendix F
Model Evaluation Summaries
This appendix provides results of the model evaluation. For the pollutants modeled using the hybrid approach, we
include here the information in Section 3.3 of the TSD and provide additional model performance statistics. The
modeled and monitored values are provided in comma-separated value files in the folder"hybrid-polls-
model_evaluation_paired" within the SupplementalData folder.
We describe here our efforts to evaluate the performance of the NATA models. Discussions of "HEM-3" in this
document often are specifically related to the AERMOD dispersion model component of HEM-3, but we use "HEM-
S'' throughout for simplicity and consistency. In this section in particular, discussions of HEM-3 model values are
specifically related to the air concentrations predicted by its AERMOD component.
F.1 Overview
Using the air toxics archive Phase IX for the year 2011. we conducted an operational model performance
evaluation of the air toxics simulated for the 2011 NATA (more details found in Section F.2 below). The model
evaluation included both the air toxics modeled with the hybrid approach ("hybrid air toxics") and those modeled
without the hybrid approach ("non-hybrid air toxics"). The hybrid evaluation looked at the air toxics for which there
were valid ambient data (i.e., completeness criteria protocol) to compare against the CMAQ, HEM-3, and hybrid
model predictions. Likewise, the air toxics non-hybrid evaluation used similar observational-completeness criteria
constraints to compare against air toxics estimated by adding HEM-3 to observed ambient concentrations assumed
to reflect background conditions.
Spatial-scale differences exist between CMAQ, HEM-3, and the hybrid model predictions. A CMAQ concentration
represents a 12-km grid-cell volume-averaged value. The HEM-3 model concentration represents a specific point
within the modeled domain. The hybrid model concentration combines the HEM-3 point-concentration gradients
with the CMAQ 12-km grid-cell volume average. The ambient observed measurements were made at specific
spatial locations (latitude/longitude). Several annual graphical presentations and statistics of model performance
were calculated and prepared. Graphical presentations included box and whisker plots (which show the distribution
and the bias of the predicted and observed data) and regional maps (which show the mean bias and error
calculated at individual monitoring sites).
F.2 Observations
Observations were extracted from the air toxics archive, Phase IX for the year 2011. While most of the data in the
archive are a snapshot of the Air Quality System (AQS) database (downloaded in July 2014), additional data (such
as from special studies) were in the archive but not reported to AQS. In the air toxics archive, pollutant
concentrations were converted to |jg/m3 in local conditions where temperature and pressure data were available
(i.e., at the vast majority of sites). In addition, any negatives and data flagged as "non-detect" without a value were
given a value of 0. Also, any data determined to have been substituted with half the method detection limit (i.e.,
MDL/2) was changed to 0.
For comparing annual averages of modeled and monitored data, data from the archive were aggregated to 2011
annual averages by site and parameter code. Data below MDLs were used as-is. Data were removed for which
there were no MDLs. Naphthalene data from parameter code 45850 (canister method) were removed because that
method may not be as reliable as the method used in the National Ambient Air Toxics Trends Program. Also, those
sites were removed that reported naphthalene as code 17141 in which it was determined to use the canister test
method.
Only site-parameter pairs in which measurements from at least three seasons were 75 percent complete (i.e., 75
percent of the scheduled days contained non-null values) were retained for developing annual averages. First, the
sub-annual data were allocated to 24-hour averages. Seventy-five-percent completeness was required to create a
daily average from sub-daily data, such that 75 percent of scheduled sub-annual data were available. For example,
hourly data required 18 of 24 hours of data, three-hour data required six of eight three-hour periods of data, etc.
For each quarter, the number of days to meet 75% completeness depended on the sampling frequency (note that
F-1
-------
EPA's National-scale Air Toxics Assessment
more than one monitor at the site that measured on the same day was counted once). For example, one-in-six-day
sampling required 12 days forthe quarter.
For sites with multiple monitors (known as "POCS"), only the daily data with the same measurement duration (i.e.,
hourly, 3-hour, 24-hour) were averaged across the POCs. That is, daily data based on hourly measurements were
not averaged with daily data based on 24-hour measurements. Where a site met the 75% completeness for
multiple durations, the 24-hour duration data were chosen.
Annual averages were created by averaging all daily measurements with the same measurement duration for all
sites that met the above completeness criteria. The only sites used were those for which 50 percent or more of the
data were above the method detection limit (MDL). A spreadsheet file
("2011monitored_data_annualmeans_PhaselXarchive.xlsx") of the ambient annual averages (in |jg/m3) is provided
in the SupplementalData folder.
Uncertainties in the ambient data result from limited sites, data below MDL and measurement uncertainties.
F.3 Model Performance Statistics
The Atmospheric Model Evaluation Tool (AMET) was used to conduct the 2011 NATA air toxics evaluation (Appel
et al. 20111). There are various statistical metrics available and 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); and,
three error metrics (mean error and normalized mean error, root mean square error, and correlation coefficient).
Common variables are:
M = predicted concentration
O = observed concentration
X = predicted or observed concentration
o = standard deviation
Mean Bias (MB), Mean Error (ME), and Root Mean Square Error (RMSE) (all in |jg/m3):
MB =
i
ME =
1
n
SM-01
E?(m - oy
RMSE =
N
MB quantifies the tendency of the model to over- or under-estimate values while ME and RMSE measure the
magnitude of the difference between modeled and observe values regardless of whether the modeled values are
higher or lower than observations.
Normalized Mean Bias (NMB) and Normalized Mean Error (NME) (both unitless):
£?(M - 0)
NMB~ Wo
1 Appel, K.W., Gilliam, R.C., Davis, N., Zubrow, A., and Howard, S.C. 2011. Overview of the Atmospheric Model
Evaluation Tool (AMET) vl.l for Evaluating Meteorological and Air Quality Models. Environ. Modell. Softw.,
26(4): 434-443.
F-2
-------
EPA's National-scale Air Toxics Assessment
NMB is used as a normalization to facilitate a range of concentration magnitudes. This statistic averages the
difference (model - observed) over the sum of observed values. NMB is a useful model performance indicator
because it avoids over-inflating the observed range of values, especially at low concentrations.
NME is similar to NMB, where the performance statistic is used as a normalization of the ME. NME indicates the
absolute value of the difference (model - observed) over the sum of observed values.
Correlation Coefficient (r; unitless):
The value of r provides an indication of the strength of linear relationship and is signed positive or negative based
on the slope of the linear regression.
Fractional Bias (FB) and Fractional Error (FE) (both unitless):
FB is a useful model performance indicator because it has the advantage of equally weighting positive and
negative bias estimates. The single largest disadvantage in this estimate of model performance is that the
estimated concentration (i.e., M) is found in both the numerator and denominator.
FE is similar to FB except the absolute value of the difference is used to that the error is always positive.
Standard Deviation (o, ppb):
a is a measure of the amount of variation of the observed and predicted values.
Coefficient of Variation (CoV, unitless):
a
CoV is the ratio of a to the mean and shows the extent of variation in relation to the mean.
Index of Agreement (unitless):
Index of Agreement = 1
E?(0 - M)2
.£?(|M-0| + |0-0|)2
F-3
-------
EPA's National-scale Air Toxics Assessment
Index of Agreement provides a sense of the strength ofthe relationship between model estimates and observations
that have been paired in time and space.
Systematic RMSE (RMSEs) and Unsystematic RMSE (RMSEu) (both ppb):
RMSEs= E^V-CJ2
rmse»= Sc*-cp)2
Where:
C* = a + bC0
a = least squares regression coefficient of Cp
b = least squares regression coefficient of C0
Cp = predicted (modeled) concentration
C0 = observed concentration
RMSEs measures the difference between the regression line ofthe observed and predicted values, while RMSEu
measures the random error about the regression line of the predicted values.
Skewness (unitless):
Measures the asymmetry of the probability distribution of a random value about its mean. For this assessment,
skewness was calculated as simply median/mean.
F.4 Hybrid Evaluation
We conducted an annual operational model performance evaluation for hybrid air toxics, resulting in comparisons
between CMAQ and HEM-3 predictions as well as an evaluation ofthe ability ofthe hybrid model to replicate the
2011 observed ambient concentrations. Inclusion of all three model results was intended to demonstrate the
merged attributes ofthe hybrid model used for the 2011 NATA. Statistical assessments of modeled versus
observed concentrations were paired in time and space and aggregated on an annual basis. Exhibit F-1 contains a
list of air toxics evaluated in the hybrid model performance evaluation and the number of paired sites (based on
completeness criteria of observations, Section F-2) used in the annual average. Exhibit F-2 is a map ofthe 2011
monitoring locations for hybrid air toxics. Acrolein and ethylene dibromide were excluded in the model evaluation
given the data uncertainty and sampling. Annual averages of xylene species (m-, o-, and p-) were summed
together to calculate a "Xylenes, total" air toxic group based on the individual risk for each species being the same.
Exhibit F-1. Hybrid Air Toxics Evaluated
Hybrid Air Toxic
Number of Paired Sites
1,3-Butadiene
83
1,3-Dichloropropene
5
1,4-Dichlorobenzene
22
Acetaldehyde
110
Acrylonitrile
18
Hybrid Air Toxic
Number of Paired Sites
Formaldehyde
110
Lead PM10
33
Manganese PM10
40
Methylene chloride
123
Naphthalene
36
F-4
-------
EPA's National-scale Air Toxics Assessment
Hybrid Air Toxic
Number of Paired Sites
Arsenic PMio
34
Benzene
214
Cadmium PM10
27
Chlorine
123
Chloroform
92
Ethylene dichloride
40
Hybrid Air Toxic
Number of Paired Sites
Nickel PM10
29
Propylene dichloride
5
Tetrachloroethylene
72
Toluene
211
Trichloroethylene
13
Xylenes
163
Exhibit F-2. 2011 Monitoring Locations for the Evaluation of Hybrid Air Toxics
\ * qRo»m ONTARIO
\ jWmnpeg
_ ^ J\v
MONIANA
t
4 * ~ T
IDAHO
OREGON f
I «- *
VlORTH DAKOTA ? %
MINNESOTA ?, .. ^
9, 9 - V>v"
fe. Ollflwa;. o Y-
t ^ Isconsu, MICHIGAN i
T. 1 T f' V Tt-^T " "«» * ?
' WYOMING T' ~ « RoflBslero HEW V
V T ' ? t . „v
"¦"W ?• V-f % T- t- ^ r "T'
-^EtPa,o TEXAS o V t-
^ %l nuiM ?' '' A T A"T' oJjcloon»llle
Hwm Monterey-3*owntv,lle pliant
r'..i.-rAr. XF ° 7$, W .
The annual model performance results for seven of these key hybrid air toxics are presented below in Exhibit F-3.
Exhibit F-3. 2011 Annual Air Toxics Performance Statistics for the Hybrid, CMAQ, and HEM-3 Models
Hybrid Air Toxic
Model
MB (|jg/m3)
ME (|jg/m3)
NMB (%)
NME (%)
Acetaldehyde
Hybrid
0.5
0.7
30.9
43.9
CMAQ
0.4
0.7
27.1
41.4
HEM-3
-1.3
1.3
-80.4
80.6
Formaldehyde
Hybrid
-0.8
1.0
-30.8
37.3
CMAQ
-0.9
1.0
-34.2
38.8
HEM-3
-2.2
2.2
-78.8
79.3
Benzene
Hybrid
0.0
0.5
1.6
60.7
CMAQ
-0.2
0.5
-22.7
57.8
HEM-3
-0.3
0.5
-33.1
60.2
-------
EPA's National-scale Air Toxics Assessment
Hybrid Air Toxic
Model
MB (|jg/m3)
ME (|jg/m3)
NMB (%)
NME (%)
1,3-butadiene
Hybrid
0.0
0.1
1.6
78.5
CMAQ
0.0
0.1
-21.2
77.2
HEM-3
0.0
0.1
5.4
77.3
Toluene
Hybrid
1.4
2.0
91.9
128.0
CMAQ
0.8
1.6
52.8
103.0
HEM-3
1.2
1.8
74.9
116.0
Lead PM10
Hybrid
0.0
0.0
48.1
208.0
CMAQ
0.0
0.0
-10.4
152.0
HEM-3
0.0
0.0
-11.2
168.0
Arsenic PM10
Hybrid
0.0
0.0
-74.9
79.0
CMAQ
0.0
0.0
-80.2
81.8
HEM-3
0.0
0.0
-81.8
85.6
Boxplots showing model distribution (units of |jg/m3) and bias differences (units of |jg/m3) as compared to ambient
observations are presented below. The boxplots use boxed interquartile ranges of 25th-to-75th percentile, along with
whiskers from the 5th to 95th percentiles, and they also contain summary statistics of r, RMSE, NMB, NME, MB, and
ME. Likewise, regional spatial maps which show the mean bias and error calculated at individual
As evidenced by Exhibits F-4 through F-24, CMAQ and hybrid model predictions of annual formaldehyde,
acetaldehyde, and benzene (three key air toxics in NATA) showed relatively small-to-moderate bias and error
percentages when compared to observations. HEM-3 showed larger biases and errors, with underestimates for
secondarily formed air toxics (e.g., -80.4 percent for acetaldehyde and -78.8 percent for formaldehyde), as expected
given HEM-3's exclusion of atmospheric chemistry. Differences in bias and error statistics between the hybrid and
CMAQ models were negligible for formaldehyde and acetaldehyde. Technical issues in the air toxics data consisted
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)
emissions- and science-uncertainty issues potentially affecting model performance, and (5) limited data for estimating
intercontinental transport that effects the estimation of boundary conditions (i.e., boundary estimates for some
species were much higher than predicted values inside the domain).
Exhibits F-25 through F-53 contain the box plots and regional spatial maps for the remaining evaluated hybrid air
toxics.
F-6
-------
EPA's National-scale Air Toxics Assessment
Exhibit F-4. Acetaldehyde: 2011 Boxplots of Observed and Modeled Concentrations (top) and Modeled-
Observed Bias Difference in Concentrations (bottom) for the Hybrid, CMAQ, and HEM-3 Models
2011v2eg_NATA_CMAQ_annual Acetaldehyde for 20110101 to 20110131
e
8
<
¦ Toxics
~ 2011v2eg_NATA_CMAQ_annual
¦ 2011v2eg_NATA_AERMOD_annual
¦ 2011v2eg_NATA_Hybrid_annual
r
0.12
-0.04
0.12
RMSE
0.67
1.5
0.91
NMB
27.1
-80.4
30.9
NME
41.4
80.6
43.9
MB
0.43
-1.33
0.49
ME
0.66
1.33
0.7
—I 1
Toxics 2011v2eg NATA CMAQ annual
T
2011v2eg_NATAHybrid_an nual
2011v2eg_NATA_CMAQ_annual Acetaldehyde for 20110101 to 20110131
~ 2011 v2eg_NATA_CM AQ_an nual
¦ 2011 v2eg_NATA_AERMOD_an nual
¦ 2011 v2eg_NATA_Hybrid_annual
E
<9
CO
0
<
8
r
0.12
-0.04
0.12
RMSE
0.87
1.5
0.91
NMB
27.1
-80.4
30.9
NME
41.4
80.6
43.9
MB
0.43
-1.33
0.49
ME
1.33
0.7
2011v2eg NATA CMAQ annual
T
2011v2eg NATA Hybrid annual
F-7
-------
EPA's National-scale Air Toxics Assessment
Exhibit F-5. Acetaldehyde: 2011 Mean Bias (%) at Monitoring Sites in the Hybrid Modeling Domain
Acetaldehyde MB (ug/m3) for run2011eg_NATA_HYBRID_annual_allHAPs for 20111001 to 20111031
CIRCLE=Toxics;
Exhibit F-6. Acetaldehyde: 2011 Mean Error (%) at Monitoring Sites in the Hybrid Modeling Domain
CIRCLE=Toxics;
F-8
-------
EPA's National-scale Air Toxics Assessment
units = ug/m3
coverage limit =
>2
1.8
1.6
1.4
12
1
0.8
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
-0.8
-1
-1.2
-1.4
-1.6
-1.8
<-2
Exhibit F-7. Acetaldehyde: 2011 Mean Bias (%) at Monitoring Sites in the CMAQ Modeling Domain
CIRCLE=Toxics;
Exhibit F-8. Acetaldehyde: 2011 Mean Error (%) at Monitoring Sites in the CMAQ Modeling Domain
Acetaldehyde ME (ug/m3) for run201 leg NATA CMAQ annual allHAPs for 20111001 to 20111031
units = ug/m3
coverage limit =
CIRCLE=Toxics;
F-9
-------
EPA's National-scale Air Toxics Assessment
Exhibit F-9. Acetaldehyde: 2011 Mean Bias (%) at Monitoring Sites in the HEM-3 Modeling Domain
Ac*<«kfehr<»» MB (ugffl3)fcx iun2011*
-------
EPA's National-scale Air Toxics Assessment
Exhibit F-11. Formaldehyde: 2011 Boxplots of Observed and Modeled Concentrations (top) and Modeled-
Observed Bias Difference in Concentrations (bottom) for the Hybrid, CMAQ, and HEM-3 Models
2011v2eg NATACMAQ annual Formaldehyde for 20110101 to 20110131
5 _ ¦ Toxics
~ 2011 v2eg_NATA_CMAQ_annual
¦ 2011 v2eg_NATA AERMODannual
¦ 2011 v2eg NATA Hybrid annual
r
-0.02
0.37
RMSE
2.37
1.16
NMB
-78.8
-30.8
NME
79.3
37.3
MB
-2.16
-0.81
ME
2.17
0.98
Toxics 2011v2eg NATA CMAQ annual 20l1v2eg NATA Hybrid annual
2011v2eg_NATA_CMAQ_annual Formaldehyde for 20110101 to 20110131
2 -
CO
E
O)
3
0
w
ca
CO
0)
"O
0)
¦a
Li-
8
X
_4 -
-6 -
2011v2eg NATA CMAQ annual 20l1v2eg NATAHybrid annual
~ 2011 v2eg_N ATA_CM AQ_an nual
¦ 2011 v2eg_NATA_AE RMOD_annual
¦ 2011 v2eg_NATA_Hybrid_annual
O
O
r
-0.02
0.37
RMSE
2.37
1.16
NMB
-78.8
-30.8
NME
79.3
37.3
MB
-2.16
-0.81
ME
2.17
0.98
F-11
-------
EPA's National-scale Air Toxics Assessment
Exhibit F-12. Formaldehyde: 2011 Mean Bias (%) at Monitoring Sites in the Hybrid Modeling Domain
Formaldehyde MB (ug,'m3) for run2011eg_NATA_HYBRID_annual_allHAPs for 20111001 to 20111031
Exhibit F-13. Formaldehyde: 2011 Mean Error (%) at Monitoring Sites in the Hybrid Modeling Domain
Formaldehyde ME
-------
EPA's National-scale Air Toxics Assessment
Exhibit F-14. Formaldehyde: 2011 Mean Bias (%) at Monitoring Sites in the CMAQ Modeling Domain
units = ug/m3
coverage limit =
> 2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
-0.8
-1
-1.2
-1.4
-1.6
-1.8
< -2
CIRCLE=Toxics;
Formaldehyde MB (ug.'m3) tor run2011 eg NATA CMAQ anriual allHAPs tor 20111001 to 20111031
Exhibit F-15. Formaldehyde: 2011 Mean Error (%) at Monitoring Sites in the CMAQ Modeling Domain
Formaldehyde ME (ug/m3) for run2011eg NATA CMAQ annual allHAPs for 20111001 to 20111031
units = ug/m3
coverage limit = %
2
-
1.8
-
1,
1.4
,2
-
<
-
0.8
.
0.6
-
0.4
J
0.2
0
CIRCLE=Toxics;
F-13
-------
EPA's National-scale Air Toxics Assessment
Exhibit F-16. Formaldehyde: 2011 Mean Bias (%) at Monitoring Sites in the HEM-3 Modeling Domain
Fonmtdaftyd* MB (up Till «Q» Iun3011«g NATA AERMOO annual allHAPt tot 20111001 lo 10111031
CiRCLE-toiuc8
Exhibit F-17. Formaldehyde: 2011 Mean Error (%) at Monitoring Sites in the HEM-3 Modeling Domain
FomuMlMlvd* ME luQffU: tor fia\2011«g KATA AERMOO annual allnAP* to< SOU 1001 to 30111031
CiRClE-Tokics:
F-14
-------
EPA's National-scale Air Toxics Assessment
Exhibit F-18. Benzene: 2011 Boxplots of Observed and Modeled Concentrations (top) and Modeled-Observed
Bias Difference in Concentrations (bottom) for the Hybrid, CMAQ, and HEM-3 Models
2011 v2eg_NATA_CMAQ_annual Benzene for 20110101 to 20110131
¦ AQS_Daily_PM
~ 2011 v2eg_N ATA_C M AQ_an n ual
¦ 2011 v2eg_NATA_AERMOD_annual
2.0 - ¦ 2011v2eg NATA Hybrid annual
co 1.5 -
E
O)
r
0.18
0.22
RMSE
0.83
0.8
NMB
-33,1
1.6
NME
60.2
60.7
MB
-0.28
0.01
ME
0.52
0.52
-0.5 -
AOS Daily PM 2011v2eg NATA AERMOD annual
2011 v2eg NATA CMAQ annual Benzene for 20110101 to 20110131
4 - ~ 2011v2eg_NATA_CMAQ_annual
¦ 2011 v2eg_NATA_AERMOD_annual
¦ 2011 v2eg_NATA_Hybrid_annual
o o
r
0.06
0.18
0.22
RMSE
0.81
0.83
0.8
NMB
-22-7
-33.1
1.6
NME
57.8
60.2
60.7
MB
-02
-0.28
0.01
ME
0.52
0.52
1 1 1
2011 v2eg NATA CMAQ annual 2011v2eg NATA Hybrid annual
F-15
-------
EPA's National-scale Air Toxics Assessment
Exhibit F-19. Benzene: 2011 Mean Bias (%) at Monitoring Sites in the Hybrid Modeling Domain
Benzene MB (ug/m3) for run2011eg_NATA_HYBRID_annual_allHAPs for 20111001 to 20111031
CIRCLE=Toxics;
Exhibit F-20. Benzene: 2011 Mean Error (%) at Monitoring Sites in the Hybrid Modeling Domain
Benzene ME
-------
EPA's National-scale Air Toxics Assessment
Exhibit F-21. Benzene: 2011 Mean Bias (%) at Monitoring Sites in the CMAQ Modeling Domain
units = ug/'m3
coverage limit
>2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
-0.8
-1
-1.2
-1.4
-1.6
-1.8
I < -2
units - ug/m
coverage lirr
CIRCLE=Toxics:
CIRCLE=Toxics;
Exhibit F-22. Benzene: 2011 Mean Error (%) at Monitoring Sites in the CMAQ Modeling Domain
Benzene MB (ug/m3) for run2011eg_NATA_CMAQ_annual_allHAPs for 20111001 to 20111031
F-17
-------
EPA's National-scale Air Toxics Assessment
Exhibit F-23. Benzene: 2011 Mean Bias (%) at Monitoring Sites in the HEM-3 Modeling Domain
Benzene MB (ug/m3)for run2011eg_NATA_AERMOD_annual_allHAPs for 20111001 to 20111031
units = ug/m:
coverage lim
1
>2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
-0.8
-1
-1.2
-1.4
-1.6
-1.8
<-2
CIRCLE=Toxics:
Exhibit F-24. Benzene: 2011 Mean Error (%) at Monitoring Sites in the HEM-3 Modeling Domain
Benzene ME (ug/m3) for run2011eg_NATA_AERMOD annual allHAPs for 20111001 to 20111031
units = ug/m
coverage lirr
I
> 2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
CIRCLE=Toxics;
F-18
-------
EPA's National-scale Air Toxics Assessment
Exhibit F-25.1,3-butadiene: 2011 Boxplots of Observed and Modeled Concentrations (top) and Modeled-
Observed Bias Difference in Concentrations (bottom) for the Hybrid, CMAQ, and HEM-3 Models
2011v2eg_NATA_CMAQ annual Butadienel 3 for 20110101 to 20110131
w
o
<
¦ AQS_Daily_PM
~ 2011 v2eg_NATA_CMAQ_annual
¦ 2011 v2eg_N ATAAE RMODannual
¦ 2011 v2eg_NATA_Hybrid annual
f
026
0.63
0.6
RMSE
0.16
0.13
0.13
NMB
-21.2
5.4
1.6
NME
77.2
77.3
78.5
MB
-0.02
0
0
ME
0.07
0.07
0.07
AOS Daily PM
2011v2eg NATA AERMOD annual
2011 v2eg NATA CMAQ annual Butadienel 3 for 20110101 to 20110131
~ 2011 v2eg_N ATA_C M AQ_an n ual
¦ 2011 v2eg_NATA_AERMOD_annual
¦ 2011 v2eg_NATA Hybrid annual
X
ra
bo
CO
c/)
?
r
0.25
0.63
0.6
RMSE
0.16
0.13
0.13
NMB
-21.2
5.4
1.6
NME
772
77.3
78.5
MB
-0.02
0
0
ME
0.07
0.07
2011 v2eg NATA CM AQ annual
2011v2eg NATA Hybrid annual
F-19
-------
EPA's National-scale Air Toxics Assessment
Exhibit F-26.1,3-butadiene: 2011 Mean Bias (%) at Monitoring Sites in the Hybrid Modeling Domain
units = ug/m3
coverage limit
>2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
CIRCLE=Toxics;
Exhibit F-27.1,3-butadiene: 2011 Mean Error (%) at Monitoring Sites in the Hybrid Modeling Domain
Butadiene"! 3 ME (ug/m3) for run2011eg_NATA_HYBRID_annual_allHAPs for 20111001 to 20111031
CIRCLE=Toxics;
Butadiene13 MB (ug/m3) for run201 leg NATA HYBRID annual allHAPs for 20111001 to 20111031
F-20
-------
EPA's National-scale Air Toxics Assessment
Exhibit F-28.1,3-butadiene: 2011 Mean Bias (%) at Monitoring Sites in the CMAQ Modeling Domain
Butadiene13 MB (ug/m3) for run2011eg_NATA_CMAQ_annual_allHAPs for 20111001 to 20111031
CIRCLE=Toxics;
Exhibit F-29.1,3-butadiene: 2011 Mean Error (%) at Monitoring Sites in the CMAQ Modeling Domain
CIRCLE=Toxics;
F-21
-------
EPA's National-scale Air Toxics Assessment
Exhibit F-3G. 1,3-butadiene: 2011 Mean Bias (%) at Monitoring Sites in the HEM-3 Modeling Domain
Butadiene13 MB (ug/m3) for run2011eg_NATA_AERMOD_annual_allHAPs for 20111001 to 20111031
CIRCLE=Toxics;
Exhibit F-31.1,3-butadiene: 2011 Mean Error (%) at Monitoring Sites in the HEM-3 Modeling Domain
Butadiene13 ME (ug'm3) for run2011eg NATA AERMOD annuaLallHAPs for 20111001 to 20111031
CIRCLE=Toxics;
F-22
-------
EPA's National-scale Air Toxics Assessment
Exhibit F-32. Toluene: 2011 Boxplots of Observed and Modeled Concentrations (top) and Modeled-Observed
Bias Difference in Concentrations (bottom) for the Hybrid, CMAQ, and HEM-3 Models
2011v2eg_NATA_CMAQ_annual Toluene for 20110101 to 20110131
¦ Toxics
~ 2011 v2eg_NATA_CMAQ_annual
¦ 2011 v2eg_N ATAAE RMODannual
¦ 2011 v2eg_NATA_Hybrid annual
r
0.11
0.08
RMSE
3.6
3.74
NMB
52-8
64.2
91,9
NME
103
105
128
MB
0.81
1.04
1.41
ME
1.69
1.97
NATA CMAQ annual
2011v2eg NATA Hybrid annual
2011 v2eg_NATA_CMAQ annual Toluene for 20110101 to 20110131
~ 2011 v2eg_NATA_CM AQ_an nual
¦ 2011 v2eg_NATA_AERMOD_annual
¦ 2011 v2eg_NATA_Hybrid_annual
E
D>
_8_
©
r
0.11
0.08
RMSE
3.6
3.74
NMB
64.2
91.9
NME
105
128
MB
1.04
1.41
ME
1.69
1.97
2011 v2eg NATA CM AQ annual
2011v2eg NATA Hybrid annual
F-23
-------
EPA's National-scale Air Toxics Assessment
Exhibit F-33. Toluene: 2011 Mean Bias (%) at Monitoring Sites in the Hybrid Modeling Domain
Toluene MB (ug/m3) for run2011eg_NATA_HYBRID_annual_allHAPs for 20111001 to 20111031
CIRCLE=Toxics:
Exhibit F-34. Toluene: 2011 Mean Error (%) at Monitoring Sites in the Hybrid Modeling Domain
Toluene ME (ug/m3) for run2011eg_NATA_HYBRID_annual_allHAPsfor 20111001 to 20111031
units = Lig/rr
coverage lirr
CIRCLE=Toxics;
>2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
F-24
-------
EPA's National-scale Air Toxics Assessment
Exhibit F-35. Toluene: 2011 Mean Bias (%) at Monitoring Sites in the CMAQ Modeling Domain
Toluene MB (ugm3) for ruri2011eg_N ATA_CMAQ_annual_allHAPs for 20111001 to 20111031
CIRCLE=Toxics:
Exhibit F-36. Toluene: 2011 Mean Error (%) at Monitoring Sites in the CMAQ Modeling Domain
Toluene ME (ug/m3) for run2011eg_NATA_CMAQ_annual_allHAPs for 20111001 to 20111031
CIRCLE=Toxics;
F-25
-------
EPA's National-scale Air Toxics Assessment
Exhibit F-37. Toluene: 2011 Mean Bias (%) at Monitoring Sites in the HEM-3 Modeling Domain
Toluene MB (ug,'m3) for ru n2011 eg_N ATAAE RM O Dan nua l a 11HAPs for 20111001 to 20111031
CIRCLE=Toxics;
Exhibit F-38. Toluene: 2011 Mean Error (%) at Monitoring Sites in the HEM-3 Modeling Domain
Toluene ME (ug/m3) for run2011eg_NATA_AERWIOD_annual_allHAPs for 20111001 to 20111031
CIRCLE=Toxics;
F-26
-------
EPA's National-scale Air Toxics Assessment
Exhibit F-39. Lead PM10: 2011 Boxplots of Observed and Modeled Concentrations (top) and Modeled-
Observed Bias Difference in Concentrations (bottom) for the Hybrid, CMAQ, and HEM-3 Models
2011 eg NATA CMAQ annual allHAPs Lead PM10 for 20111001 to 20111031
£ 0.004
¦ Toxics
Q 2011 eg_NATA_CMAQ_annual_allHAPs
¦ 201 leg NATA AERMOD annual allHAPs
¦ 2011 eg NATA HYBRID annuaLallHAPs
I
f
-0.07
-0.11
-0.1
RMSE
0
0
0
NMB
-10.4
-11.2
48,1
NME
152
168
208
MB
0
0
0
ME
0
0
1 r
201 leg NATA AERMOD annual allHAPs
2011eg_NATA_CMAQ_annual_allHAPs Lead_PM10 for 20111001 to 20111031
E
CO
bo
I o.oo
~ 2011 eg_NATA_CMAQ_annual_allHAPs
¦ 2011 eg_NATA_AERMOD_annual_allHAPs
¦ 2011eg_NATA HYBRID_annual_allHAPs
r
-0.07
-0.11
-0.1
RMSE
0
0
NMB
-11.2
48.1
NME
152
168
208
MB
0
0
0
ME
0
0
201 leg^NATA CMAQ annual allHAPs
201 leg NATA HYBRID annual_ allHAPs
F-27
-------
EPA's National-scale Air Toxics Assessment
CIRCLE=Toxics;
Exhibit F-41. Lead PM10: 2011 Mean Error (%) at Monitoring Sites in the Hybrid Modeling Domain
units = ug/m3
coverage limit =
>0.02
0.018
0.016
0.014
0.012
0.01
0.008
0.006
0.004
0,002
0
units = ug/m3
coverage limit = 7:
> 0.02
0.015
0.01
0.005
0
-0.005
-0.01
-0.015
<-0.02
Exhibit F-40. Lead PMio: 2011 Mean Bias (%) at Monitoring Sites in the Hybrid Modeling Domain
CIRCLE=Toxics;
F-28
-------
EPA's National-scale Air Toxics Assessment
Exhibit F-42. Lead PMio: 2011 Mean Bias (%) at Monitoring Sites in the CMAQ Modeling Domain
Lead_PM10 MB (ug/m3) for run2011eg_NATA_CMAQ_annual_allHAPs for 20111001 to 20111031
units = ug/m3
coverage limit = %
CIRCLE=Toxics;
Exhibit F-43. Lead PM10: 2011 Mean Error (%) at Monitoring Sites in the CMAQ Modeling Domain
Lead PM10 ME (ug/m3) for run2011 eg_NATA _CMAQ_ annual_allHAPs for 20111001 to 20111031
units = ug/m3
coverage limit =
CIRCLE=Toxics;
F-29
-------
EPA's National-scale Air Toxics Assessment
Exhibit F-44. Lead PMio: 2011 Mean Bias (%) at Monitoring Sites in the HEM-3 Modeling Domain
Lead PM10 MB (uq/m3) for run2011eq NATA AERMOD annual allHAPs for 20111001 to 20111031
units = ug/m3
coverage limit =
>0.02
0.018
0.016
0.014
0.012
0.01
0.008
0.006
0.004
0.002
0
units = ug/m3
coverage limit =
> 0.02
0.015
0.01
0.005
0
-0.005
-0.01
-0.015
< -0.02
CIRCLE=Toxics;
Exhibit F-45. Lead PM10: 2011 Mean Error (%) at Monitoring Sites in the HEM-3 Modeling Domain
CIRCLE=Toxics:
F-30
-------
EPA's National-scale Air Toxics Assessment
Exhibit F-46. Arsenic PM10: 2011 Boxplots of Observed and Modeled Concentrations (top) and Modeled-
Observed Bias Difference in Concentrations (bottom) for the Hybrid, CMAQ, and HEM-3 Models
201 leg NATACMAQ annuaLallHAPs Arsenic_PM10 for 20111001 to 20111031
¦ Toxics
D 2011 eg_ NATACM AQannual allHAPs
¦ 2011 eg NATA AERMOD annual allHAPs
¦ 2011 eg NATA HYBRID annual allHAPs
e
^3)
wm
—¦—
r
0.45
0.32
0.42
RMSE
0
0
0
NMB
-80.2
-81.8
-74.9
NME
81.8
85.6
79
MB
0
0
0
ME
0
0
1 r
201 leg NATA AERMOD annual allHAPs
2011eg_NATA_CMAQ_annual_allHAPs Arsenic_PM10 for 20111001 to 20111031
~ 2011 eg_NATA_CMAQ_annual_allHAPs
¦ 2011 eg_NATA_AERMOD_annual_allHAPs
¦ 2011 eg NATA HYBRID_annual_allHAPs
8
o
O
o
r
0.32
0.42
RMSE
0
0
NMB
-81.8
-74.9
NME
85.6
79
MB
0
0
ME
0
0
1 1 1
201 teg NATA_CMAQ annual allHAPs 201 leg NATA HYBRID^annual_ allHAPs
F-31
-------
EPA's National-scale Air Toxics Assessment
Exhibit F-47. Arsenic PMio: 2011 Mean Bias (%) at Monitoring Sites in the Hybrid Modeling Domain
Arsen!c_PM10 MB (ug/m3) tor run2011eg_NATA_HYBRID_annual_allHAPs for 20111001 to 20111031
units = ug/m3
coverage limit = 75
CIRCLE=Toxics;
> 0.002
0.0015
0.001
5e-04
0
-5e-04
-0.001
-0.0015
< -0.002
Exhibit F-48. Arsenic PM10: 2011 Mean Error (%) at Monitoring Sites in the Hybrid Modeling Domain
Arsenic_PM10 ME (ug/m3) for run2011eg_NATA_HYBRID_annual_allHAPs for 20111001 to 20111031
units = ug/m3
coverage limit =
CIRCLE=Toxics;
B
> 0.002
0.0018
0.0016
0.0014
0.0012
0.001
8e-04
6e-04
4e-04
2e-04
0
F-32
-------
EPA's National-scale Air Toxics Assessment
Exhibit F-49. Arsenic PMio: 2011 Mean Bias (%) at Monitoring Sites in the CMAQ Modeling Domain
Arsenic_PM10 MB (ug/m3) for run2011eg_NATA_CMAQ_annual_allHAPs for 20111001 to 20111031
units = ug/m3
coverage limit = 75*
CIRCLE=Toxics;
Exhibit F-50. Arsenic PM10: 2011 Mean Error (%) at Monitoring Sites in the CMAQ Modeling Domain
Arsenic_PM10 ME (ug/m3) for run2011eg_NATA_CMAQ_annual_allHAPs for 20111001 to 20111031
units = ug/m3
coverage limit =
CIRCLE=Toxics;
F-33
-------
EPA's National-scale Air Toxics Assessment
Exhibit F-51. Arsenic PMio: 2011 Mean Bias (%) at Monitoring Sites in the HEM-3 Modeling Domain
Arsenic_PM10 MB (ug/m3) for rim2011eg_NATA_AERMOD_annual_allHAPs for 20111001 to 20111031
units = ug/m3
coverage limit - 75
> 0.002
0.0015
0.001
5e-04
5e-04
-0.001
-0.0015
< -0.002
CIRCLE=Toxics;
Exhibit F-52. Arsenic PMio: 2011 Mean Error (%) at Monitoring Sites in the HEM-3 Modeling Domain
= ug/m3
rage limit «
>0.002
0.0018
0.0016
0.0014
0.0012
0.001
8e-04
6e-04
4e-04
2e-04
0
Arsenic_PM10 ME (ug/m3) tor run2011 eg_NATA_AERMOD_annual_allHAPs for 20111001 to 20111031
CIRCLE=Toxics;
F-34
-------
EPA's National-scale Air Toxics Assessment
Exhibit F-53. Manganese PM10 (a), Cadmium PM10 (b), Nickel PM10 (c), Naphthalene (d), Dichlorproprene [1,3-
dichloropropene] (e), Dichlorobenzene [1,4-dichlorobenzene] (f), Tetrachloroethylene (g), Trichloroethylene
(h), Methylene Chloride (i), 1,2-dichloroethane [ethylene dichloride] (j), Chlorine (k), Chloroform (I),
Acrylonitrile (m), Propdichloride [propylene dichloride] (n), and Xylenes (o): 2011 Boxplots of Modeled-
Observed Bias Difference in Concentrations for the Hybrid, CMAQ, and HEM-3 Models
(a) Manganese PM10:
2011eg_NATA_CMAQ annual allHAPs Manganese_PM10 for 20111001 to 2011103
~ 2011 eg_NATA_CMAQ_annual_allHAPs
¦ 2011eg_NATA_AERMOD_annual_allHAPs
¦ 2011 eg_NATA_HYBRID_annual_allHAPs
—8—
r
-0.11
-0.14
RMSE
0.01
0.01
NMB
-19.2
-8.5
NME
171
177
MB
0
0
ME
0
0
201 leg NATA CMAQ annual allHAPs
201 leg NATA HYBRID annual allHAPs
F-35
-------
EPA's National-scale Air Toxics Assessment
(b) Cadmium PM10:
201 leg NATA CMAO annual alIHAPs Cadmium PM10 for 20111001 to 20111031
2011eg„NATA_CMAG_annual_aIIHAPs
2011 eg_N ATA_AERMOD annual_a!IHAPs
2011eg NATA. HYBRID,annuaLallHAPs
30,0005 ¦
E
NMB
N
201 leg NATA CMAO annual alSHAPs
201 teg NATA HYBRID annual allHAPs
(c) Nickel PM10:
2011 eg_NATA_CMAQ_annuaLaHHAPs Nlckel_PM10 for 20111001 to 20111031
2011 eg NATA CMAQ annual allHAPs
2011 eg_NATA_AERMOD_annual_ailHAPs
2011 eg^NATA HYBRiD^annual _allHAPs
H -0.06 '
m.V-T
-iVi 1
118
0
8
201 leg NATA. CMAQ annual allHAPs
201 teg NATA HYBRID annual allHAPs
F-36
-------
EPA's National-scale Air Toxics Assessment
(d) Naphthalene:
2011eg_NATA_CMAQ_annual_allHAPs Naphthalene for 20111001 to 20111031
£
~ 2011 eg_NATA_CMAQ_annual_allHAPs
¦ 2011 eg_NATA_AERMOD_annual_allHAPs
B 2011 eg_NATA_HYBRID_annual_allHAPs
r
-0.35
-0.31
RMSE
0.06
0.07
NMB
1940
2210
NME
2050
2280
MB
0.04
0.05
ME
0.05
0.05
1
2011eg_NATA CMAQ annual allHAPs
201 leg NATA HYBRID annual allHAPs
(e) Dichloroproprene:
2011eg_NATA_CMAQ_annual_allHAPs Dlchloropropene for 20111001 to 20111031
~ 2011 eg_NATA_CMAQ_annual_allHAPs
¦ 2011 eg_NATA_AERMOD_annual_allHAPs
n 2011 eg_NATA_HYBRID_annual_allHAPs
E
53
CQ
r
-0.06
-0.07
RMSE
0.18
0.18
NMB
-38.1
-36.8
NME
156
156
MB
-0,01
-0.01
ME
0.06
0.06
2011eg_NATA CMAQ annual allHAPs
201 leg NATA HYBRID annual allHAPs
F-37
-------
EPA's National-scale Air Toxics Assessment
(f) Dichlorobenzene:
2011eg_NATA_CMAQ_annual_allHAPs Dichlorobenzene for 20111001 to 20111031
~ 2011 eg_NATA_CMAQ_annual_allHAPs
¦ 2011 eg_NATA_AERMOD_annual_allHAPs
B 2011 eg_NATA_HYBRID_annual_allHAPs
1
2011eg_NATA CMAQ annual allHAPs
o
©
o
©
O
§
r
-0.14
-0.13
RMSE
0.33
0.33
NMB
-91.6
-90.3
NME
101
101
MB
-0,16
-0.16
ME
0.18
0.18
201 leg NATA HYBRID annual allHAPs
(g) Tetrachloroethylene:
2011eg_NATA_CMAQ_annual_allHAPs CL4ETHE for 20111001 to 20111031
frj -0.5 "
~ 2011 eg_NATA_CMAQ_annual_allHAPs
¦ 2011 eg_NATA_AERMOD_annual_allHAPs
n 2011 eg_NATA_HYBRID_annual_allHAPs
T
T
T
8
8
0
o
o
©
o
8
o
o
i
0
o
o
o
r
0.2
0.2
RMSE
0.28
0.28
NMB
-44.5
-38.8
NME
90.1
90.6
MB
-0.08
-0.07
ME
0.16
0.16
1
2011eg_NATA CMAQ annual allHAPs
201 leg NATA HYBRID annual allHAPs
F-38
-------
EPA's National-scale Air Toxics Assessment
(h) Trichloroethylene:
2011eg_NATA_CMAQ_annual_allHAPs CL3ETHE for 20111001 to 20111031
~ 2011 eg_NATA_CMAQ_annual_allHAPs
¦ 2011 eg_NATA_AERMOD_annual_allHAPs
B 2011 eg_NATA_HYBRID_annual_allHAPs
i
CO
LU
o
O
x
.0
r
0.23
0.28
RMSE
0.19
0.19
NMB
-75.8
-70.6
NME
88.4
86.3
MB
-0.06
-0.05
ME
0.07
0.07
2011eg_NATA CMAQ annual allHAPs
201 leg NATA HYBRID annual allHAPs
(i) Methylene Chloride:
2011eg_NATA_CMAQ_annual_allHAPs CL2_ME lor 20111001 to 20111031
~ 2011 eg_NATA_CMAQ_annual_allHAPs
¦ 2011 eg_NATA_AERMOD_annual_allHAPs
n 2011 eg_NATA_HYBRID_annual_allHAPs
CD
LU
o
8
o
o
o
r
0
0
RMSE
4.36
4.36
NMB
-92.6
-90.8
NME
93.8
92.5
MB
-0.9
-0.88
ME
0.91
0.9
2011eg_NATA CMAQ annual allHAPs
201 leg NATA HYBRID annual allHAPs
F-39
-------
EPA's National-scale Air Toxics Assessment
(j) 1,2-dichloroethane:
2011eg_NATA_CMAQ_annual_allHAPs CL2_C2_12 for 20111001 to 20111031
~ 2011 eg_NATA_CMAQ_annual_allHAPs
¦ 2011 eg_NATA_AERMOD_annual_allHAPs
¦ 2011 eg_NATA_HYBRID_annual_allHAPs
o
o
o
o
o
o
f
0.26
0.27
RMSE
0.16
0.15
NMB
-97.4
-94.8
NME
97.5
95.1
MB
-0.06
-0.06
ME
0.06
0.06
2011eg_NATA CMAQ annual allHAPs
201 leg NATA HYBRID annual allHAPs
(k) Chlorine:
2011eg_NATA_CMAQ_annual_allHAPs CL2 tor 20111001 to 20111031
~ 2011 eg_NATA_CMAQ_annual_allHAPs
¦ 2011 eg_NATA_AERMOD_annual_allHAPs
IB 2011 eg NATA_HYBRID_annual_allHAPs
<9
CD
C\J
O -0.4
3
T
O
o
o
r
0.05
0.08
RMSE
0.06
0.06
NMB
-90.3
-90.8
NME
98.4
98.2
MB
-0.02
-0.02
ME
0.02
0.02
2011eg_NATA CMAQ annual allHAPs
201 leg NATA HYBRID annual allHAPs
F-40
-------
EPA's National-scale Air Toxics Assessment
(I) Chloroform:
2011eg_NATA_CMAQ_annual_allHAPs CHCL3for 20111001 to 20111031
~ 2011 eg_NATA_CMAQ_annual_allHAPs
¦ 2011 eg_NATA_AERMOD_annual_allHAPs
B 2011 eg_NATA_HYBRID_annual_allHAPs
TO
in
0
1
o
o
o
o
r
-0.01
-0.01
RMSE
0.5
0.5
NMB
-95.3
-94
NME
95.8
95
MB
-0.17
-0.17
ME
0.17
0.17
2011eg_NATA CMAQ annual allHAPs
201 leg NATA HYBRID annual allHAPs
(m) Acrylonitrile:
2011eg_NATA_CMAQ_annual_allHAPs Acrylonitrile for 20111001 to 20111031
£
03
CQ
-------
EPA's National-scale Air Toxics Assessment
(n) Propdichloride:
2011eg_NATA_CMAQ_annual_allHAPs Propdichloride for 20111001 to 20111031
°-2 ~ 201 leg NATA CMAQ_annual_allHAPs
¦ 2011 eg_NATA_AERMOD_annual_allHAPs
¦ 2011 eg_NATA_HYBRID_annual_allHAPs
o
O
o
o
o
o
f
0.01
0.01
RMSE
0.17
0.17
NMB
-99.7
-99.3
NME
99.7
99.4
MB
-0.05
-0.05
ME
0.05
0.05
1 l 1
2011eg_NATA_CMAQ_annual allHAPs 2011eg_NATA_ HYBRID annual allHAPs
(o) Xylenes:
2011eg_NATA_CMAQ_annual_allHAPs Xylenes for 20111001 to 20111031
~ 2011eg_NATA_CMAQ_annual_allHAPs
¦ 2011eg_NATA AERMOD_annual allHAPs
¦ 2011 eg_NATA_H YBRID_annual_allHAPs
E
O)
O
O
O
r
0.1
0.04
RMSE
1.85
1.88
NMB
15.7
14,8
NME
73.3
76.6
MB
0.18
0.17
ME
0.83
0.86
2011 eg_NATA_CMAQ_annual_allHAPs
2011 eg_N ATA H YB RI D an nual a 11H A Ps
F-42
-------
EPA's National-scale Air Toxics Assessment
F.5 Non-hybrid Evaluation
To estimate the ability of HEM-3 to replicate the 2011 observed ambient concentrations of air toxics, we conducted
an annual operational model performance evaluation for air toxics used in the non-hybrid model calculation.
Statistical assessments of modeled versus observed concentrations were paired in time and space and aggregated
on an annual basis. Exhibit F-54 contains a list of air toxics evaluated in the non-hybrid model performance
evaluation and the number of paired sites (based on completeness criteria of observations, Section F-20) used in
the annual average. Exhibit F-55 is a map of the 2011 monitoring locations for non-hybrid air toxics. Paired values
of the monitored and modeled data are provided in the spreadsheet "nonhybrid_polls-
model_evaluation_paired.xlsx" in the SupplementalData folder.
Exhibit F-54. Non-hybrid Air Toxics Evaluated
Non-hybrid Air Toxic
Number of Paired Sites
4-Methyl-2-pentanone
35
Acetonitrile
36
Antimony (TSP) LC
11
Antimony PM10 LC
18
Bromomethane
26
Carbon disulfide
23
Carbon tetrachloride
163
Chloromethane
155
Cobalt (TSP) LC
12
Cobalt PM10 LC
15
Ethylbenzene
170
Non-hybrid Air Toxic
Number of Paired Sites
Hexane
125
Isopropylbenzene
23
Methyl tert-butyl ether
6
Propanal
68
Selenium (TSP) LC
11
Selenium PM10 LC
14
Selenium PM2.5 LC
58
Styrene
71
Tribromomethane
5
Vinyl Acetate
25
Exhibit F-55. 2011 Monitoring Locations for the Evaluation of Non-hybrid Air Toxics
0Reglna ONTARIO
-tfr
4^
NORTH DAKOTA
MINNESOTA
TA -
q l^Piwidence
GREAT
S IN
NEVADA
-a
° ^ Columbus V dn~ '
CALI F C^LnI A
T"
""""s Tr ^oiumous y off'
COLORADO I'amac ? „S50U„, ffif. ^ «»'
h
UNITED S T?A T H S
. '¦¦COLOR ADO
PL ATE^U
ARIZONA
Kentucky
^ ?
NEW MEXiqO
V-
~ ~°EI Paso
9 .. • y
OK,Aia»IA-J ^Tibau "»*"'«<.
*»£¦ c^:
Birmingham
A LA BA Mj
TEXAS
Kbslin V : 1
o„
SSan
Torreon > .Monterrey-^ov'
Tlanta * It!
9 CAHoq^NA
OR^ ^
Ia ^ i"4 1 * ^ ^Jacksonville
jffL >%»'«
^sir^rKlo
rv
C: V4 F VirO"
The exhibits below (Exhibits F-54 through F-59) are boxplots containing ratio comparisons of model-to-monitor
(ambient observations) concentrations for the evaluated non-hybrid air toxics. All air toxics shown below (except
F-43
-------
EPA's National-scale Air Toxics Assessment
hexane) showed model underpredictions compared to ambient measurements. The modeled and monitored data
are provided in the SupplementalData folder in "nonhybrid_polls-model_evaluation_paired.xlsx"
Exhibit F-56. Metal HAPs (Antimony PM10 LC, Antimony TSP LC, Cobalt PM10 LC, Cobalt TSP LC, Selenium
PM10 LC, Selenium PM2.5 LC, and Selenium TSP LC): 2011 Model/Monitor Ratios
Model-to-Monitor metal
=4j8=
14
15
12
11
Antimony Pm1D Lc Antimony (Tsp) Lc Cobalt PmID Lc Cobalt (Tsp) Lc Selenium Pm10 Lc Selenium Pm2.5 Lc Selenium (Tsp) Lc
AQ S_PARA M ETE R_N AM E
F-44
-------
EPA's National-scale Air Toxics Assessment
Exhibit F-57. Carbon Tetrachloride and Chloromethane: 2011 Model/Monitor Ratios
Model-to-Monitor
1
i
¦
1
»
*
I
1
1
t
L
' 1
1 t
i
r 1
• 1
•
•
Carbon tetrachloride Chloromethane
AQS_PARAMETER_NAME
F-45
-------
EPA's National-scale Air Toxics Assessment
Exhibit F-58. Set #1 of Gaseous Air Toxics (Ethylbenzene, Hexane, Isopropylbenzene, Methyl Tert-butyl Ether,
Propanal, and Styrene): 2011 Model/Monitor Ratios
Model-to-Monitor commonVOC
•
•
• •
•
*
!
125
i
>
170
9 1
2
Ethylbenzene
Hexane
IsopropylbenzerMethyl tert-butyl ether
Propanal
Styrene
AQS_PARAM ETER_NAM E
F-46
-------
EPA's National-scale Air Toxics Assessment
Exhibit F-59. Set #2 of Gaseous Air Toxics (4-methyl-2-pentanone, Acetonitrile, Bromomethane, Carbon
Disulfide, Tribromomethane, and Vinyl Acetate): 2011 Model/Monitor Ratios
Model-to-Monitor othervoc
.9
"S
35
2
6
36
23
-26-
ethyl-2-pentanone
Acetonitrile
Bromomethane Carbon disulfide Tribromomethane
AQS_PARAMETE R_N AM E
Vinyl Acetate
F-47
-------
EPA's National-scale Air Toxics Assessment
This page intentionally left blank.
F-48
-------
EPA's National-scale Air Toxics Assessment
Appendix G
Exposure Factors for the 2011 NATA
As noted in the memorandum contained within this Appendix (after Table G-1 below), ratios comparing exposure
concentrations (ECs) predicted in HAPEM to air concentrations predicted in HEM-3 or CMAQ were developed for
seven ofthe airtoxic modeled for the 2011 NATA and applied to the remaining NATA air toxic s based on phase in
ambient air (i.e., particulate, gaseous, or both; see Table 1 ofthe embedded memorandum in this appendix) and
prevalence in emissions by NATA category. The ratios (or exposure factors) varied by airtoxic and census tract,
and the values were capped at maximum values determined as median plus one standard deviation (across all
census tracts), with values 100 or larger treated as outliers and not part ofthe cap determinations (see Table 5 of
the embedded memorandum in this appendix). Exhibit G-1 contains the factors (averaged across census tract) for
each airtoxic and each emission source sector (i.e., point, nonpoint, onroad mobile, nonroad mobile, and
background), and the larger memorandum describing the HAPEM modeling for NATA follows Exhibit G-1.
Although these averaged factors were not actually used in NATA, they provide a general summary ofthe tract-level
ratios that were used. These ratios account for the difference between the ambient outdoor concentration at a
location and the EC that individuals were assumed to actually inhale in the risk assessment. Most of these
averaged factors were less than 1, meaning that ECs tended to be lower than air concentrations. From among
these averaged factors, predictions of EC ranged from 57 percent smaller than predictions of air concentration (for
chromium (VI) from point sources) to 6 percent larger (for 1,3-butadiene from onroad mobile sources; note that
exposure factors for 1,3-butadiene were not used as surrogates for any other NATA pollutants). The overall
averaged exposure factor was 0.73 (i.e., ECs 27 percent smaller than air concentrations), likely due to the inability
of many chemicals to penetrate efficiently into indoor environments.
A proximity term was required to adjust the predicted ambient air concentration (which was assumed to be
representative ofthe census-tract centroid) to the level that we would expect immediately outside ofthe
microenvironment. This proximity term was set to unity (i.e., 1) for most microenvironments. For the transportation-
related microenvironments, however, ambient concentrations immediately outside the vehicle (i.e., very close to
the pollutant source) were assumed to be considerably higher than at the predicted value at the census-tract
centroid. Thus, we developed default proximity factors for HAPEM and used them for NATA. Separate factors also
adjusted concentrations based on proximity to major roadways, as air concentrations will generally be higher near
roadways than not near roadways.
G-1
-------
EPA's National-scale Air Toxics Assessment
Exhibit G-1. Averages of the Tract-level Exposure Factors Used in the 2011 NATA
Pollutant
Average Exposure Factor, by NATA
Categoryab
Overall
Average
Across
Categories
at Left
Used as Surrogate For
Point
Nonpoint
On road
Mobile
Nonroad
Mobile
Benzene
0.88
0.87
1.04
0.88
0.92
All gaseous pollutants except 1,3-
butadiene
1,3-butadiene
0.88
0.87
1.06
0.88
0.92
None
Coke oven
emissions
1.00
-
-
-
1.00
None
Chromium
(VI)
0.43
0.45
-
-
0.44
All particulate pollutants from point and
nonpoint categories, except diesel PM
Diesel PM
—
0.67
0.56
0.46
0.56
None
Nickel
-
-
0.53
0.45
0.49
All particulate pollutants from mobile
sources, except diesel PM
PAH, total
0.66
0.65
0.77
0.64
0.68
All pollutants that can be gaseous and
particulate, except coke oven
emissions
a Background and event sources not modeled in HAPEM
b Gray shading indicates a combination of pollutant and NATA category whose exposure factors were not applied as surrogates for
other NATA pollutants, as noted in the "Used as Surrogate For" column.
G-2
-------
ICF
INTERNATIONAL
MEMORANDUM
To: Ted Palma and Terri Hollingsworth
U.S. EPA, Office of Air Quality Planning and Standards
From: Chris Holder, Chris Stevens, Isaac Warren, Autumn Bordner, and Heidi Hubbard
ICF International
Date: 04/08/2015
Re: Running HAPEM7 for the 2011 National-scale Air Toxics Assessment (NATA)
The Hazardous Air Pollutant Exposure Model (HAPEM) is a U.S. Environmental Protection Agency (EPA)
model used to perform screening-level assessments of long-term inhalation exposures to hazardous air
pollutants (HAPs). The National-scale Air Toxics Assessment (NATA) is a nationwide EPA modeling
assessment of the air concentrations, exposure concentrations, and potential human health cancer and
non-cancer risks and hazards related to HAPs emitted by manmade and natural sources. For the
purposes of the 2011 NATA, ICF ("we") used Version 7 of HAPEM (HAPEM7) with its default files and air-
concentration outputs provided by EPA to model exposure concentrations for seven surrogate HAPs,
stratified by census tract and source category. We used the modeled annual-exposure concentrations to
estimate lifetime-exposure concentrations and divided those values by the corresponding annualized air
concentrations (creating "exposure factors"). EPA can then use the exposure factors along with the
NATA air concentrations to estimate exposure to each NATA HAP in each census tract.
In this memorandum, we discuss HAPEM7, how we identified the gas or particulate phase of the NATA
HAPs, how EPA selected the seven HAPs to be modeled in HAPEM, how we set up the HAPEM runs
(including linking to proximity and penetration factors), and how we developed the exposure factors. A
tab-delimited text file ("NATA 2011 Exposure Factors_20150330") provided with this memorandum
contains the exposure factors, and the Microsoft® Excel™ file "NATA 2011 Exposure Factors_Addl
lnformation_20150330" contains additional information used to develop those factors.
2635 Meridian Pkwy., Suite 200 ¦ Durham, NC 27713 ¦ 919.293.1620 ¦ 919.293.1645 fax ¦ icfi.com
G-3
-------
Page 2
1, Introduction to HAPEM and its Use in NATA
HAPEM is a model used by EPA to perform screening-level assessments of long-term inhalation
exposures to HAPs. We completed updating HAPEM and its default, ancillary files in early 2015, as
discussed in the ICF Memorandum "Updating the Hazardous Air Pollutant Exposure Model (HAPEM) for
Use in the 2011 National-scale Air Toxics Assessment (NATA)" (from April 8, 2015, addressed to Ted
Palma and Terri Hollingsworth of EPA's Office of Air Quality Planning and Standards [OAQPS]). This latest
version of HAPEM (HAPEM7)1 estimates exposure concentrations using demographic and behavior data
from the 2010 U.S. Census (covering all 50 states in the US, the District of Columbia, Puerto Rico, and
the U.S. Virgin Islands), compiled activity data from a recent version of EPA's Consolidated Human
Activity Database (CHAD), and other updated spatial data. The exposure concentrations are stratified by
location (i.e., U.S. Census tract), time of day, age group, and the individual emission source categories
and HAPs being modeled.
NATA is a nationwide modeling assessment of air concentrations, exposure concentrations, and
potential, chronic human health risks and hazards associated with HAP emissions from man-made and
naturally occurring sources. As described in this memorandum, we used HAPEM7 with its default files
and with modeled air concentrations to model exposure concentrations for the 2011 NATA, for every
census tract in the US, Puerto Rico, and the U.S. Virgin Islands.
2. Air Quality Inputs to HAPEM7
•s Assessed for the 2011 NATA
For the 2011 NATA, EPA compiled an inventory of the annual mass emitted of 273 HAPs from point, non-
point, on-road mobile, and non-road mobile sources nationwide, typically at spatial resolutions of
counties down to specific points. These HAPs are shown in Table 1, along with other information
discussed later in this memorandum. Using appropriate hourly meteorology data and temporal emission
profiles based on source category, EPA modeled these emissions in AERMOD (the atmospheric
dispersion model developed by the American Meteorological Society and the EPA Regulatory Model
Improvement Committee) and in CMAQ (EPA's Community Multiscale Air Quality model). The air-
modeling results were annual-average air concentrations at the level of census tracts, stratified by HAP,
source type, and hour of day.
2.2. Specifying Chemical Phases for HAPEM Modeling
For all source types, HAPEM7 models microenvironment (ME) concentrations for three chemical phases.
The model uses phase-specific penetration and proximity factors to estimate the ME chemical
concentration for HAPs present in the gaseous ("G") or particulate ("P") phase as well as HAPs for which
the phase can vary for typical atmospheric conditions ("G/P"). In addition, for on-road mobile sources of
three HAPs (benzene, 1,3-butadiene, and diesel particulate matter [DPM]) and a fourth category
•1
As of January 20, 2015, HAPEM6 is available for download from http://vvww2.epa.qov/fera/download-hazardous-air-
pollutant-exposure-model-hapem. We anticipate HAPEM7 and its User's Guide will be made available by EPA
online in Spring 2015. G-4
-------
Page 3
representing all other HAPs, HAPEM7 uses chemical-specific penetration and proximity factors to
estimate ME chemical concentrations.
Consequently, to prepare for NATA exposure modeling, we categorized each of the 273 NATA HAPs as G,
P, or G/P based on available boiling-point data, as defined in Table 2. We provide each HAP's boiling
point and assigned HAPEM7 HAP phase in Table 1. We obtained the vast majority of boiling-point values
from either the Centers for Disease Control (CDC), the National Institutes of Health (NIH), or the Royal
Society of Chemistry using their ChemSpider web site. These were judged to be the most reputable,
comprehensive, and user-friendly sources of chemical boiling-point data readily available to us. Each
allows the user to search by chemical name or Chemical Abstract Service number. For HAPs whose
boiling points we could not identify using these three sources, we searched a variety of additional data
sources. The source of each chemical's boiling-point value is provided in the "Source" column of Table 1.
We could not identify empirical boiling-point data for some of the HAPs. In many of these cases, we
identified predictive boiling points from ChemSpider, which generates estimated boiling point using
three software modules: EPA's EPIsuite, 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 2.
Note that the boiling point ranges in Table 1 have imprecise endpoints (e.g., the high end of boiling
points for G HAPs covers a range of 240 to 260 °C). A relatively small number (i.e., 41) of NATA HAPs
have boiling points within these imprecise endpoints, depending on the source of the data, meaning
there was some uncertainty associated with assigning the phases for these HAPs. In order to make
accurate 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 the HAPEM5
designations allowed us to make a reasonable phase designation for these HAPs. Nineteen HAPs have
boiling points within the 240-260 °C range; based on the literature review and HAPEM5 designations,
we categorized 15 as G and the remaining four as G/P. Twenty-two HAPs have boiling points within the
400-480 °C range; we categorized 16 as P and the remaining six as G/P.
In addition to the above 41 HAPs, boiling-point data were widely varying for three HAPs (see the "V"
designations in the boiling-point-value column in Table 1). 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
o
The HAP categorizations for HAPEM5 for the purposes of the 1999 NATA were discussed in an ICF and TRJ
Memorandum "Development of Penetration and Proximity Microenvironment Factor Distributions for the HAPEM5 in
Support of the 1999 National-Scale Air Toxics Assessment (NATA)." from April 5, 2004, addressed to Ted Palma of
EPA-OAQPS. G-5
-------
Page 4
this pollutant as consisting of a mixture of particulates, volatiles, and semi-volatiles
(http://www.epa.gov/ttnatw01/hlthef/cokeoven.html). 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) (http://www.atsdr.cdc.gov/). The third HAP with widely-varying boiling-point data
was diesel particulate matter and was assumed to be largely present as particulate.
For eight 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 1); three were fibers, which we categorized as P,
and we left the remaining five uncategorized pending potential resolution by EPA. The five HAPS
currently without phase designations are as follows and are likely not emitted in large amounts:
(Ethylenebis(Oxyethylenenitrilo)) Tetraacetic Acid
Extractable Organic Matter (EOM)
Propoxur
Quinone
Toxaphene
As noted previously, HAPEM7 does not use penetration and proximity factors specific to each NATA
HAP, and the temporal emission profiles used in NATA air-concentration modeling vary only by the four
source categories. Therefore, the level of effort to conduct exposure modeling on all 273 NATA HAPs is
not justified. EPA identified a small subset of NATA HAPs for which to conduct HAPEM exposure
modeling. NATA will use the HAPEM results of this modeled subset as-is, and these results will be used
as surrogates for the remaining NATA HAPs not modeled in HAPEM. EPA used a subjective combination
of decision points in identifying this subset, including
1. the subset must include at least one HAP per phase (i.e., at least one G HAP, one P HAP, and one
G/P HAP);
2. collectively among the HAP(s) representing a phase, each emitting source category must be
represented;
3. it is preferred that the selected HAPs pose high potential, relative risks to human health
nationwide (using comparisons of air concentrations to health benchmarks, in a screening way
without accounting for factors affecting exposure); and
4. it is preferred that the selected HAPs be emitted in many spatially-diverse locations across the
US.
Using the above general criteria, EPA selected the seven HAPs listed below (and shown in Table 3) for
exposure modeling.
Benzene and 1,3-butadiene, which are emitted by many processes (and all four modeled source
categories) in nearly all U.S. locations. Benzene was selected to be the surrogate for all other G
HAPs (EPA considers benzene modeling in NATA to be more reliable than 1,3-butadiene
modeling).
G-6
-------
Page 5
Unspeciated, generic PAHs ("PAH, total") are emitted by all four source categories and from a
wide variety of processes, so it was selected to be the surrogate for all other G/P HAPs, except
coke oven emissions which is a special case that was modeled by itself for point sources.
Chromium (VI) is a highly toxic HAP that was selected as the surrogate for all other P HAPs
emitted by point or non-point sources except for DPM, which was modeled as itself for non-
point and mobile sources. Note that the NATA air-concentration modeling included chromium
(VI) emissions from all four source categories, but its use as an exposure surrogate only included
point and non-point sources because those are its major emitters.
For P HAPs, besides DPM modeled as itself for non-point and mobile sources, and besides
chromium (VI) being a surrogate for all other point and non-point P HAPs, nickel was selected as
the surrogate for all other mobile-source P HAPs. Nickel is emitted by a variety of processes
spread across the US. Note that NATA air-concentration modeling included nickel emissions
from all four source categories, but its use as an exposure surrogate only included mobile
sources because chromium (VI) was designated as the surrogate for point and non-point
sources.
Whether a given HAP was modeled explicitly in HAPEM or is matched to a surrogate is indicated in Table
1 (final four columns). We used the air-concentration modeling outputs for these seven HAPs, stratified
by source type, hour of day, and census tract, as the air-quality input files for seven HAPEM7 runs for
NATA.
3. HAPEM7 Runs
3.1. Design
For each of the seven HAPEM7 runs (corresponding to each of the seven HAPs assessed for human
exposure), we used the HAPEM7 default census- and CHAD-based files.3 Each run assessed the 18
HAPEM7 MEs and all populated census tracts in the US, Puerto Rico, and the U.S. Virgin Islands. Though
the air-quality inputs were by hour of day (i.e., 24 values per HAP, tract, and source category), HAPEM7
evaluated exposure in three-hour periods (i.e., 8 values per HAP, tract, and source category). We used
the six default HAPEM7 age groups4 and three day types.5 We linked each HAP to its appropriate
HAPEM7 penetration- and proximity-factors files, and we used the recommended setting of 30
replicates evaluated per HAP and tract.
3.2. Quality Control and Quality Assurance
We reviewed the HAPEM7 control files (i.e., "parameters" files) for accuracy, and then we reviewed the
log, "counter," and "mistract" HAPEM7 output files to identify any potential errors in the modeling. We
identified no errors in the inputs or outputs. We present below, and in Table 4, some statistics gleaned
from the HAPEM7 log, counter, and mistract output files.
3
The HAPEM7 default input files are described in the ICF Memorandum "Updating the Hazardous Air Pollutant
Exposure Model (HAPEM) for Use in the 2011 National-scale Air Toxics Assessment (NATA)" from April 8, 2015,
addressed to Ted Palma and Terri Hollingsworth of EPA-OAQPS.
4 The HAPEM7 default age groups: 0-1, 2-4, 5-15, 16-17, 18-64, and 65 and older
5 The HAPEM7 default day types: summer weekday, non-summer weekday, and weekend.
G-7
-------
Page 6
202 tracts (i.e., less than 1 percent of U.S. tracts) were not modeled for air concentrations
because the population data EPA was using in air-concentration modeling indicated zero
residents (the HAPEM7 population file had two to three residents for two of these tracts).
An additional 377 tracts were not modeled in HAPEM7 because HAPEM7's population data
indicated zero residents.
1,027 areas modeled for air concentrations were not modeled in HAPEM7 because they were
not census tracts (in addition to census tracts, EPA modeled air concentrations in some areas
that corresponded to CMAQ grids; we did not model these areas for exposure).
In total, 1,404 of the areas modeled for air concentrations were not modeled for exposure
concentrations; 73,832 tracts were modeled for exposure concentrations for the 2011 NATA.
3.3. Post-processing into Exposure Factors
As has been done in previous NATAs, we utilized the HAPEM7 outputs for the seven assessed HAPs to
estimate exposure factors that EPA will then apply to all HAPs assessed in NATA, based on HAP phase
and source category. For each HAPEM7 run, and within that for each tract and source category, we
calculated the estimated lifetime-average exposure concentration for each modeled replicate. We
calculated these 70-year-average concentrations as the time-weighted average of exposures for the six
HAPEM7 age groups, as shown below.
Lifetime average
expo
sure cone
=
[age
group
0
- 1
exposure
cone.
X
2
/
70
+
[age
group
2
- 4
exposure
cone.
X
3
/
70
+
[age
group
5
- 15
exposure
cone.
X
11
/
70
+
[age
group
16
- 17
exposure
cone.
X
2
/
70
+
[age
group
18
- 64
exposure
cone.
X
47
/
70
+
[age
group
65 +
exposure
cone.
X
5
/
70
We then calculated the median lifetime-exposure concentration from the set of 30 replicates for each
tract and source category for a given HAP. The sum of these medians across source categories yields the
cumulative (i.e., from all modeled sources) "typical" lifetime-average exposure concentrations per HAP
and tract.
For each assessed HAP, and then for each tract and source category (including the cumulative from all
modeled sources), we divided these median lifetime-exposure concentrations by the corresponding
annual air concentrations, resulting in an exposure factor. EPA can then multiply these exposure factors
by the air concentrations of any appropriate HAP, resulting in estimated lifefime-exposure
concentrations for that HAP. For example, for a given census tract, to estimate the exposure
concentrations of a particular G/P HAP emitted by non-point sources, EPA will multiply the HAP's non-
point-source air concentrations by the non-point-source exposure factors for "PAH, total."
The median and average exposure factors were between approximately 0.4 and 1.1 (larger factors
typically for on-road mobile sources and gases; smaller factors typically for the other source categories
and particulates). However, for a relatively small number of tracts, exposure factors were larger than 10
and, for approximately 100 tracts, exposure factors were larger than 100. Such large exposure factors
are likely due to modeled people working in tracts with much larger air concentrations than their home
G-8
-------
Page 7
tracts, so that their exposure factors account for home- and work-tract air quality but the air-
concentration denominator in the exposure-factor calculation only accounts for home-tract air quality.
EPA considers these larger exposure factors to not reasonably represent average exposure scenarios
across individual tracts. To ensure representative exposure factors, we have limited exposure factors to
the maximum values shown in Table 5. These "caps", specified per HAP and source category, correspond
to the median exposure factor plus one standard deviation (taken across all tracts). The calculations for
medians and standard deviations did not consider exposure factors 100 or larger, which we considered
to be outliers. All applications of the exposure factors for the 2011 NATA use these caps, including for
the HAPs explicitly modeled in HAPEM7.
A tab-delimited text file ("NATA 2011 Exposure Factors_20150330") provided with this memorandum
contains the exposure factors. The Microsoft® Excel™ file "NATA 2011 Exposure Factors_Addl
lnformation_20150330" contains additional useful information such as the exposure and air
concentrations as well as the calculations used to cap the exposure factors. The latter file includes
warning flags for any situations where we did not calculate exposure factors. These situations
correspond to air concentrations of 0, or exposure concentrations of 0 (indicating zero residents), or
exposure concentrations not calculated (the 1,027 CMAQ grids). In all these flagged situations, we
forced the exposure factor to be a value of 1, indicating that the air concentration equals the exposure
concentration.
We quality-assured this post-processing by scrutinizing the SAS code used to accomplish it, spot-
checking its calculations manually, and other broad checks to ensure all records were properly read in
and the flagged records were properly processed.
G-9
-------
Page 8
Table 1. HAPs Assessed in the 2011 NATA, with their HAPEM7 HAP Phases
Exposure Factors Developed
Emission Source
for This HAP ("~"),
or
HAP
Modeled for NATA
Surrogate Used Instead
Num.
Air Concentrations3
Boiling Point
HAPEM7
(Surrogate HAP Name)e
for
M-
M-
Value
HAP
M-
M-
NATA
HAP Name
P
NP
OR
NR
(CT
Source0
Phased
P
NP
OR
NR
147
(Ethylenebis(Oxyethylenenitrilo))
Tetraacetic Acid
V
NA
NA
NA
NA
255
1,1,2,2-Tetrachloroethane
V
V
147
CDC
G
Benz
Benz
266
1,1,2-Trichloroethane
V
V
114
CDC
G
Benz
Benz
119
1,1-Dimethyl Hydrazine
V
64
CDC
G
Benz
176
1,2,3,4,5,6-
Hexachlorocyclohexane
V
V
323
CDC
G/P
PAH
PAH
263
1,2,4-Trichlorobenzene
V
V
213
CDC
G
Benz
Benz
92
l,2-Dibromo-3-Chloropropane
V
196
CDC
G
Benz
110
1,2-Dimethoxyethane
V
82
NIH
G
Benz
126
1,2-Diphenylhydrazine
V
293
NIH
G/P
PAH
128
1,2-Epoxybutane
V
V
63
NIH
G
Benz
Benz
246
1,2-Propylenimine
V
66
NIH
G
Benz
41
1,3-Butadiene
V
V
V
V
138
NIH
G
V
V
V
V
98
1,3-Dichloropropene
V
V
108
NIH
G
Benz
Benz
240
1,3-Propanesultone
V
180
NIH
G
Benz
94
1,4-Dichlorobenzene
V
V
173
CDC
G
Benz
Benz
195
12-Methylbenz(a)Anthracene
V
410 P
CS
P
Cr6
203
1-Methylnaphthalene
V
240
NIH
G
Benz
205
1-Methylphenanthrene
V
359
http://www.nature.nps.gov/hazardssafety/toxi
c/phenlmet.pdf
G/P
PAH
206
1-Methylpyrene
372
http://www.chemicalbook.com/ChemicalProdu
ctProperty EN CB7421679.htm
G/P
219
1-Nitropyrene
V
445 P
CS
P
Cr6
166
2-(Hexyloxy)Ethanol
V
258
NIH
G
Benz
274
2,2,4-Trimethylpentane
V
V
V
V
99
NIH
G
Benz
Benz
Benz
Benz
264
2,4,4'-Trichlorobiphenyl (PCB-28)
V
V
164
CS
G
Benz
Benz
268
2,4,5-Trichlorophenol
V
247
NIH
G
Benz
269
2,4,6-Trichlorophenol
V
V
246
NIH
G
Benz
Benz
G-10
-------
Page 9
Table 1. HAPs Assessed in the 2011 NATA, with their HAPEM7 HAP Phases
HAP
Num.
for
NATA
HAP Name
Emission Source
Modeled for NATA
Air Concentrations3
Boiling Point
HAPEM7
HAP
Phased
Exposure Factors Developed
for This HAP ("~"), or
Surrogate Used Instead
(Surrogate HAP Name)e
P
NP
M-
OR
M-
NR
Value
(CT
Source0
P
NP
M-
OR
M-
NR
78
2,4-Dichlorophenoxy Acetic Acid
V
V
345 P
CS
G/P
PAH
PAH
121
2,4-Dinitrophenol
V
V
312
CS
G/P
PAH
PAH
124
2,4-Dinitrotoluene
V
V
300
NIH
G/P
PAH
PAH
260
2,4-Toluene Diisocyanate
V
V
251
NIH
G
Benz
Benz
7
2-Acetylaminofluorene
V
400 P
CS
G/P
PAH
42
2-Butoxyethyl Acetate
V
V
192
CDC
G
Benz
Benz
58
2-Chloroacetophenone
V
V
244
CDC
G
Benz
Benz
238
2-Chlorobiphenyl (PCB-1)
V
V
290
NIH
G/P
PAH
PAH
63
2-Chloronaphthalene
V
V
256
http://www.chemicalbook.com/ChemicalProdu
ctProperty_EN_CB8854627.htm
G
Benz
Benz
204
2-Methylnaphthalene
V
V
V
241
http://www.speclab.com/compound/c91576.ht
m
G
Benz
Benz
Benz
218
2-Nitropropane
V
V
121
CDC
G
Benz
Benz
154
2-Propoxyethyl Acetate
V
184
CS
G
Benz
95
3,3'-Dichlorobenzidine
V
400
NIH
G/P
PAH
109
3,3'-Dimethoxybenzidine
V
391
CS
G/P
PAH
117
3,3'-Dimethylbenzidine
V
300
CDC
G/P
PAH
197
3-Methylcholanthrene
V
V
178
http://www.speclab.com/compound/c50328.ht
m
G
Benz
Benz
96
4,4'-Dichlorobiphenyl (PCB-15)
V
V
144
CS
G
Benz
Benz
199
4,4'-Methylenebis(2-
Chloraniline)
V
209
NIH
G
Benz
202
4,4'-Methylenedianiline
V
V
397
CDC
G/P
PAH
PAH
201
4,4'-Methylenediphenyl
Diisocyanate
V
V
313
CS
G/P
PAH
PAH
120
4,6-Dinitro-o-Cresol
V
312
CDC
G/P
PAH
13
4-Aminobiphenyl
V
302
CDC
G/P
PAH
114
4-Dimethylaminoazobenzene
V
371
CS
G/P
PAH
214
4-Nitrobiphenyl
V
340
CDC
G/P
PAH
217
4-Nitrophenol
V
V
279
NIH
G/P
PAH
PAH
G-11
-------
Page 10
Table 1. HAPs Assessed in the 2011 NATA, with their HAPEM7 HAP Phases
Exposure Factors Developed
Emission Source
for This HAP ("~"),
or
HAP
Modeled for NATA
Surrogate Used Instead
Num.
Air Concentrations3
Boiling Point
HAPEM7
(Surrogate HAP Name)e
for
M-
M-
Value
HAP
M-
M-
NATA
HAP Name
P
NP
OR
NR
(CT
Source0
Phased
P
NP
OR
NR
198
5-Methylchrysene
V
V
449 P
CS
P
Cr6
Cr6
116
7,12-Dimethylbenz[a]Anthracene
V
V
122
http://www.sigmaaldrich.com/catalog/product
/supelco/442425?lang=en®ion=US
G
Benz
Benz
90
7H-Dibenzo[c,g]carbazole
V
544 P
CS
P
Cr6
184
9-Methyl Anthracene
V
196
CS
G
1
Acenaphthene
V
V
V
V
279
NIH
G/P
PAH
PAH
PAH
PAH
2
Acenaphthylene
V
V
V
V
265
NIH
G/P
PAH
PAH
PAH
PAH
3
Acetaldehyde
V
V
V
V
20
CDC
G
Benz
Benz
Benz
Benz
4
Acetamide
V
V
165
CDC
G
Benz
Benz
5
Acetonitrile
V
V
82
CDC
G
Benz
Benz
6
Acetophenone
V
V
202
NIH
G
Benz
Benz
8
Acrolein
V
V
V
V
53
CDC
G
Benz
Benz
Benz
Benz
9
Acrylamide
V
V
175
CDC
G
Benz
Benz
10
Acrylic Acid
V
V
141
CDC
G
Benz
Benz
11
Acrylonitrile
V
V
77
CDC
G
Benz
Benz
12
Allyl Chloride
V
V
45
CDC
G
Benz
Benz
14
Aniline
V
V
184
CDC
G
Benz
Benz
16
Anthracene
V
V
V
V
342
NIH
G/P
PAH
PAH
PAH
PAH
17
Antimony
V
V
1,587
CDC
P
Cr6
Cr6
18
Arsenic
V
V
V
V
612
CDC
P
Cr6
Cr6
Ni
Ni
19
Asbestos
V
V
600
CDC
P
Cr6
Cr6
20
Benz[a]Anthracene
V
V
V
V
438
NIH
P
Cr6
Cr6
Ni
Ni
21
Benzene
V
V
V
V
80
CDC
G
V
V
V
V
22
Benzidine
V
400
CDC
G/P
PAH
23
Benzo(a)Fluoranthene
V
V
295
NIH
G/P
PAH
PAH
24
Benzo(c)phenanthrene
430 P
CS
P
26
Benzo(g,h,i)Fluoranthene
V
V
406 P
CS
P
Cr6
Cr6
28
Benzo[a]Pyrene
V
V
V
V
360
http://www.speclab.com/compound/c50328.ht
m
G/P
PAH
PAH
PAH
PAH
G-12
-------
Page 11
Table 1. HAPs Assessed in the 2011 NATA, with their HAPEM7 HAP Phases
HAP
Num.
for
NATA
HAP Name
Emission Source
Modeled for NATA
Air Concentrations3
Boiling Point
HAPEM7
HAP
Phased
Exposure Factors Developed
for This HAP ("~"), or
Surrogate Used Instead
(Surrogate HAP Name)e
P
NP
M-
OR
M-
NR
Value
(CT
Source0
P
NP
M-
OR
M-
NR
29
Benzo[b]Fluoranthene
V
V
V
V
4,665 P
CS
P
Cr6
Cr6
Ni
Ni
25
Benzo[e]Pyrene
V
V
465 P
CS
P
Cr6
Cr6
27
Benzo[g,h,i,]Perylene
V
V
V
V
550
NIH
P
Cr6
Cr6
Ni
Ni
30
Benzo[j]fluoranthene
V
480 E
CS
P
Cr6
31
Benzo[k]Fluoranthene
V
V
V
V
480
http://www.speclab.com/compound/c207089.
htm
P
Cr6
Cr6
Ni
Ni
32
Benzofluoranthenes
V
V
406 P
CS
P
Cr6
Cr6
33
Benzotrichloride
V
221
NIH
G
Benz
34
Benzyl Chloride
V
V
179
NIH
G
Benz
Benz
35
Beryllium
V
V
2,500
NIH
P
Cr6
Cr6
36
Beta-Propiolactone
V
162
http://www.cdc.gov/niosh/docs/81-
123/pdfs/0528.pdf
G
Benz
37
Biphenyl
V
V
256
NIH
G
Benz
Benz
38
Bis(2-Ethylhexyl)Phthalate
V
V
386
CDC
G/P
PAH
PAH
39
Bis(Chloromethyl) Ether
V
106
CDC
G
Benz
40
Bromoform
V
V
149
CDC
G
Benz
Benz
43
Butyl Carbitol Acetate
V
V
245
NIH
G
Benz
Benz
44
Cadmium
V
V
765
CDC
P
Cr6
Cr6
45
Calcium Cyanamide
V
V
>2,444
CDC
P
Cr6
Cr6
46
Captan
V
V
314
CS
G/P
PAH
PAH
47
Carbaryl
V
V
315
CS
G/P
PAH
PAH
48
Carbazole
V
V
355
http://www.sigmaaldrich.com/catalog/product
/sigma/c5132?lang=en®ion=US
G/P
PAH
PAH
49
Carbitol Acetate
V
219
NIH
G
Benz
50
Carbon Disulfide
V
V
47
CDC
G
Benz
Benz
51
Carbon Tetrachloride
V
V
77
CDC
G
Benz
Benz
52
Carbonyl Sulfide
V
V
-50
NIH
G
Benz
Benz
53
Catechol
V
245
CDC
G
Benz
139
Cellosolve Acetate
V
V
145
CDC
G
Benz
Benz
138
Cellosolve Solvent
V
V
124
CDC
G
Benz
Benz
G-13
-------
Page 12
Table 1. HAPs Assessed in the 2011 NATA, with their HAPEM7 HAP Phases
HAP
Num.
for
NATA
HAP Name
Emission Source
Modeled for NATA
Air Concentrations3
Boiling Point
HAPEM7
HAP
Phased
Exposure Factors Developed
for This HAP ("~"), or
Surrogate Used Instead
(Surrogate HAP Name)e
P
NP
M-
OR
M-
NR
Value
(CT
Source0
P
NP
M-
OR
M-
NR
54
Chloramben
V
312
CS
G/P
PAH
55
Chlordane
V
175
NIH
G
Benz
56
Chlorine
V
V
-33
CDC
G
Benz
Benz
57
Chloroacetic Acid
V
106
CDC
G
Benz
59
Chlorobenzene
V
V
132
CDC
G
Benz
Benz
60
Chlorobenzilate
V
146
NIH
G
Benz
61
Chloroform
V
V
62
CDC
G
Benz
Benz
62
Chloromethyl Methyl Ether
V
59
CDC
G
Benz
64
Chloroprene
V
V
59
CDC
G
Benz
Benz
65
Chromic Acid (VI)
V
250
CDC
G/P
PAH
67
Chromium (VI)
V
V
V
V
2,642
CDC
P
V
V
Ni
Ni
66
Chromium III
V
V
V
V
2,672
http://boo ks. google, com/boo ks?id=SFD30BvPB
hoC&pg=PA123&lpg=PA123&dq=chromium+lll
+melting+point&source=bl&ots=upHljDrKMy&s
ig=dlSMKFL5z0sVI0z8Z4NhlsFHggE&hl=en&sa=
X&ei=4nklVPLvJ4LS8AGbiYD4DA&ved=0CFkQ6A
EwCQ#v=onepage&q=chromium%20lll%20melt
ing%20point&f=false
P
Cr6
Cr6
Ni
Ni
68
Chromium Trioxide
V
250
CDC
G/P
PAH
69
Chrysene
V
V
V
V
448
http://www.speclab.com/compound/c218019.
htm
P
Cr6
Cr6
Ni
Ni
70
Coal Tar
V
>250
http://www.inchem.org/documents/icsc/icsc/ei
csl415.htm
G/P
PAH
71
Cobalt
V
V
3,100
CDC
P
Cr6
Cr6
72
Coke Oven Emissions
V
V
CDC
G/P
V
76
Cresol/Cresylic Acid (Mixed
Isomers)
V
V
202
CDC
G
Benz
Benz
77
Cumene
V
V
152
CDC
G
Benz
Benz
283
Cyanide
V
V
V
CDC
G/P
PAH
PAH
80
Decachlorobiphenyl (PCB-209)
V
V
460 P
CS
P
Cr6
Cr6
G-14
-------
Page 13
Table 1. HAPs Assessed in the 2011 NATA, with their HAPEM7 HAP Phases
Emission Source
Exposure Factors Developed
for This HAP ("~"), or
HAP
Num.
Modeled for NATA
Air Concentrations3
Boiling Point
HAPEM7
Surrogate Used Instead
(Surrogate HAP Name)e
for
M-
M-
Value
HAP
M-
M-
NATA
HAP Name
P
NP
OR
NR
[C)b
Source0
Phased
P
NP
OR
NR
81
Di(Ethylene Glycol Monobutyl
Ether) Phthalate
V
283
CDC
G/P
PAH
82
Diazomethane
V
-23
CDC
G
Benz
83
Dibenz[a,h]acridine
V
534 P
CS
P
Cr6
85
Dibenzo[a,e]Pyrene
V
552 P
CS
P
Cr6
86
Dibenzo[a,h]Anthracene
V
V
V
V
262
http://www.sigmaaldrich.com/catalog/product
/supelco/48574?lang=en®ion=US
G/P
PAH
PAH
PAH
PAH
87
Dibenzo[a,h]Pyrene
V
308 E
CS
G/P
PAH
88
Dibenzo[a,i]Pyrene
V
552 P
CS
P
Cr6
84
Dibenzo[a,j]Acridine
V
534 P
CS
P
Cr6
89
Dibenzo[a,l]Pyrene
V
552 P
CS
P
Cr6
91
Dibenzofuran
V
V
287
NIH
G/P
PAH
PAH
93
Dibutyl Phthalate
V
V
340
CDC
G/P
PAH
PAH
97
Dichloroethyl Ether
V
177
CDC
G
Benz
99
Dichlorvos
V
140 at
40
mmHG
NIH
G
Benz
284
Diesel PM
V
V
V
V
http://www.epa.gov/regionl/eco/airtox/diesel.
html
P
V
V
V
100
Diethanolamine
V
V
268
NIH
G/P
PAH
PAH
101
Diethyl Sulfate
V
210
NIH
G
Benz
103
Diethylene Glycol Diethyl Ether
V
189 E
CS
G
Benz
104
Diethylene Glycol Dimethyl Ether
V
161 E
CS
G
Benz
106
Diethylene Glycol Monobutyl
Ether
V
V
230
NIH
G
Benz
Benz
107
Diethylene Glycol Monoethyl
Ether
V
V
196
NIH
G
Benz
Benz
108
Diethylene Glycol Monomethyl
Ether
V
V
194 E
CS
G
Benz
Benz
112
Dimethyl Phthalate
V
V
284
NIH
G/P
PAH
PAH
G-15
-------
Page 14
Table 1. HAPs Assessed in the 2011 NATA, with their HAPEM7 HAP Phases
Exposure Factors Developed
Emission Source
for This HAP ("~"),
or
HAP
Modeled for NATA
Surrogate Used Instead
Num.
Air Concentrations3
Boiling Point
HAPEM7
(Surrogate HAP Name)e
for
M-
M-
Value
HAP
M-
M-
NATA
HAP Name
P
NP
OR
NR
(CT
Source0
Phased
P
NP
OR
NR
113
Dimethyl Sulfate
V
V
188
NIH
G
Benz
Benz
118
Dimethylcarbamoyl Chloride
V
167
NIH
G
Benz
127
Epichlorohydrin
V
V
118
NIH
G
Benz
Benz
130
Ethyl Acrylate
V
V
99
CDC
G
Benz
Benz
131
Ethyl Benzene
V
V
V
V
136
CDC
G
Benz
Benz
Benz
Benz
132
Ethyl Carbamate
V
185
NIH
G
Benz
133
Ethyl Chloride
V
V
-139
NIH
G
Benz
Benz
134
Ethylene Dibromide
V
V
131
CDC
G
Benz
Benz
135
Ethylene Dichloride
V
V
83
CDC
G
Benz
Benz
136
Ethylene Glycol
V
V
197
CDC
G
Benz
Benz
140
Ethylene Glycol Methyl Ether
V
V
124
CDC
G
Benz
Benz
141
Ethylene Glycol Monomethyl
Ether Acetate
V
145
CDC
G
Benz
142
Ethylene Glycol Mono-Sec-Butyl
Ether
V
192
CS
G
Benz
145
Ethylene Oxide
V
V
11
CDC
G
Benz
Benz
146
Ethylene Thiourea
V
230
CDC
G
Benz
144
Ethyleneimine
V
56
CDC
G
Benz
148
Ethylidene Dichloride
V
V
-17
CDC
G
Benz
Benz
149
Extractable Organic Matter
(EOM)
V
NA
NA
NA
NA
150
Fine Mineral Fibers
V
NA
http://www.usg.com/content/dam/USG_Mark
eting_Communications/united_states/product_
promotional_materials/finished_assets/usg-
mineral-wool-300a-msds-en-75850002.pdf
P
Cr6
151
Fluoranthene
V
V
V
V
384
NIH
G/P
PAH
PAH
PAH
PAH
152
Fluorene
V
V
V
V
295
NIH
G/P
PAH
PAH
PAH
PAH
153
Formaldehyde
V
V
V
V
-21
CDC
G
Benz
Benz
Benz
Benz
G-16
-------
Page 15
Table 1. HAPs Assessed in the 2011 NATA, with their HAPEM7 HAP Phases
HAP
Num.
Emission Source
Modeled for NATA
Air Concentrations3
Boiling Point
HAPEM7
Exposure Factors Developed
for This HAP ("~"), or
Surrogate Used Instead
(Surrogate HAP Name)e
for
NATA
HAP Name
P
NP
M-
OR
M-
NR
Value
(CT
Source0
HAP
Phased
P
NP
M-
OR
M-
NR
154
Glycol Ethers
V
V
120-240
http://msdssearch.dow.com/PublishedLiteratur
eDOWCOM/dh_012d/0901b8038012d976.pdf?
filepath=oxysolvents/pdfs/noreg/110-
00977. pdf&fromPage=Get Doc
G
Benz
Benz
155
Heptachlor
V
392
cs
G/P
PAH
156
Heptachlorobiphenyl
V
V
415 P
cs
P
Cr6
Cr6
157
Hexachlorobenzene
V
V
325
NIH
G/P
PAH
PAH
158
Hexachlorobiphenyl
V
V
396 P
CS
G/P
PAH
PAH
159
Hexachlorobutadiene
V
V
215
CDC
G
Benz
Benz
160
Hexachlorocyclopentadiene
V
V
238
CDC
G
Benz
Benz
161
Hexachloroethane
V
187
CDC
G
Benz
162
Hexamethylene Diisocyanate
V
V
212
NIH
G
Benz
Benz
163
Hexamethylphosphoramide
V
233
NIH
G
Benz
164
Hexane
V
V
V
V
69
CDC
G
Benz
Benz
Benz
Benz
167
Hydrazine
V
V
113
CDC
G
Benz
Benz
168
Hydrochloric Acid
V
V
-85
CDC
G
Benz
Benz
170
Hydrogen Cyanide
V
V
26
CDC
G
Benz
Benz
169
Hydrogen Fluoride
V
V
19
CDC
G
Benz
Benz
172
Hydroquinone
V
V
285
CDC
G/P
PAH
PAH
173
lndeno[l,2,3-c,d]Pyrene
V
V
V
V
530
http://www.speclab.com/compound/cl93395.
htm
P
Cr6
Cr6
Ni
Ni
174
Isophorone
V
V
215
CDC
G
Benz
Benz
175
Lead
V
V
1,740
CDC
P
Cr6
Cr6
177
Maleic Anhydride
V
V
202
CDC
G
Benz
Benz
178
Manganese
V
V
V
V
1,962
CDC
P
Cr6
Cr6
Ni
Ni
74
m-Cresol
V
V
202
CDC
G
Benz
Benz
179
Mercury
V
V
V
V
356
CDC
G/P
PAH
PAH
PAH
PAH
180
Methanol
V
V
64
CDC
G
Benz
Benz
182
Methoxychlor
V
89
NIH
G
Benz
G-17
-------
Page 16
Table 1. HAPs Assessed in the 2011 NATA, with their HAPEM7 HAP Phases
Exposure Factors Developed
Emission Source
for This HAP ("~"),
or
HAP
Modeled for NATA
Surrogate Used Instead
Num.
Air Concentrations3
Boiling Point
HAPEM7
(Surrogate HAP Name)e
for
M-
M-
Value
HAP
M-
M-
NATA
HAP Name
P
NP
OR
NR
[C)b
Source0
Phased
P
NP
OR
NR
183
Methoxytriglycol
V
249
http://msdssearch.dow.com/PublishedLiteratur
eDOWCOM/dh_012d/0901b8038012d976.pdf?
filepath=oxysolvents/pdfs/noreg/110-
00977. pdf&fromPage=Get Doc
G
Benz
185
Methyl Bromide
V
V
3
CDC
G
Benz
Benz
187
Methyl Chloride
V
V
-98
CDC
G
Benz
Benz
265
Methyl Chloroform
V
V
74
CDC
G
Benz
Benz
189
Methyl Iodide
V
V
43
CDC
G
Benz
Benz
190
Methyl Isobutyl Ketone
V
V
116
CDC
G
Benz
Benz
191
Methyl Isocyanate
V
39
CDC
G
Benz
192
Methyl Methacrylate
V
V
101
CDC
G
Benz
Benz
193
Methyl Tert-Butyl Ether
V
V
55
NIH
G
Benz
Benz
194
Methylanthracene
V
360 P
CS
G/P
PAH
196
Methylbenzopyrene
479 P
CS
G/P
198
Methylchrysene
449 P
CS
P
200
Methylene Chloride
V
V
39
CDC
G
Benz
Benz
188
Methylhydrazine
V
V
88
CDC
G
Benz
Benz
279
m-Xylene
V
V
V
V
139
CDC
G
Benz
Benz
Benz
Benz
115
N,N-Dimethylaniline
V
V
192
CDC
G
Benz
Benz
111
N,N-Dimethylformamide
V
V
153
CDC
G
Benz
Benz
207
Naphthalene
V
V
V
V
260
CDC
G
Benz
Benz
Benz
Benz
165
N-Hexyl Carbitol
V
260 E
CS
G
Benz
208
Nickel
V
V
V
V
2,913
CDC
P
Cr6
Cr6
V
V
209
Nickel Oxide
V
1,955
NIH
P
Cr6
210
Nickel Refinery Dust
V
2,730
http://www.cdc.gov/niosh/docs/81-
123/pdfs/0445.pdf
P
Cr6
213
Nitrobenzene
V
V
211
CDC
G
Benz
Benz
222
N-Nitrosodimethylamine
V
152
CDC
G
Benz
223
N-Nitrosomorpholine
V
224
NIH
G
Benz
221
N-Nitroso-N-Methylurea
V
164 P
CS
G
Benz
G-18
-------
Page 17
Table 1. HAPs Assessed in the 2011 NATA, with their HAPEM7 HAP Phases
Exposure Factors Developed
Emission Source
for This HAP ("~"),
or
HAP
Modeled for NATA
Surrogate Used Instead
Num.
Air Concentrations3
Boiling Point
HAPEM7
(Surrogate HAP Name)e
for
M-
M-
Value
HAP
M-
M-
NATA
HAP Name
P
NP
OR
NR
[C)b
Source0
Phased
P
NP
OR
NR
15
o-Anisidine
V
225
CDC
G
Benz
73
o-Cresol
V
V
191
CDC
G
Benz
Benz
261
o-Toluidine
V
V
200
CDC
G
Benz
Benz
280
o-Xylene
V
V
V
V
144
CDC
G
Benz
Benz
Benz
Benz
224
PAH, total
V
V
V
V
240-400
http://www.epa.gov/reg3hwmd/bf-
Ir/regional/analytical/semi-volatile.htm
G/P
•/
•/
•/
•/
239
PAH/POM - Unspecified
V
V
100-450
http://www.epa.gov/reg3hwmd/bf-
Ir/regional/analytical/semi-volatile.htm
G/P
PAH
PAH
225
Parathion
V
375
CDC
G/P
PAH
75
p-Cresol
V
V
202
CDC
G
Benz
Benz
125
p-Dioxane
V
V
101
CDC
G
Benz
Benz
226
Pentachlorobiphenyl
V
V
365 E
CS
G/P
PAH
PAH
227
Pentachloronitrobenzene
V
V
328
NIH
G/P
PAH
PAH
228
Pentachlorophenol
V
V
309
CDC
G/P
PAH
PAH
229
Perylene
V
V
276
http://www.sigmaaldrich.com/catalog/product
/aldrich/394475?lang=en®ion=US
G/P
PAH
PAH
230
Phenanthrene
V
V
V
V
340
NIH
G/P
PAH
PAH
PAH
PAH
231
Phenol
V
V
182
CDC
G
Benz
Benz
232
Phenyl Cellosolve
V
V
245 E
CS
G
Benz
Benz
234
Phosgene
V
V
8
CDC
G
Benz
Benz
235
Phosphine
V
V
88
CDC
G
Benz
Benz
236
Phosphorus
V
V
280
CDC
G/P
PAH
PAH
237
Phthalic Anhydride
V
V
295
CDC
G/P
PAH
PAH
238
Polychlorinated Biphenyls
V
V
365 E
CS
G/P
PAH
PAH
233
p-Phenylenediamine
V
267
CDC
G/P
PAH
241
Propionaldehyde
V
V
V
V
48
NIH
G
Benz
Benz
Benz
Benz
242
Propoxur
V
D
CDC
NA
NA
243
Propyl Cellosolve
V
V
150
http://msdssearch.dow.com/PublishedLiteratur
eDOWCOM/dh_012d/0901b8038012d976.pdf?
filepath=oxysolvents/pdfs/noreg/110-
00977. pdf&fromPage=Get Doc
G
Benz
Benz
G-19
-------
Page 18
Table 1. HAPs Assessed in the 2011 NATA, with their HAPEM7 HAP Phases
Exposure Factors Developed
Emission Source
for This HAP ("~"),
or
HAP
Modeled for NATA
Surrogate Used Instead
Num.
Air Concentrations3
Boiling Point
HAPEM7
(Surrogate HAP Name)e
for
M-
M-
Value
HAP
M-
M-
NATA
HAP Name
P
NP
OR
NR
[C)b
Source0
Phased
P
NP
OR
NR
244
Propylene Dichloride
V
V
97
CDC
G
Benz
Benz
245
Propylene Oxide
V
V
34
CDC
G
Benz
Benz
281
p-Xylene
V
V
V
V
138
CDC
G
Benz
Benz
Benz
Benz
247
Pyrene
V
V
V
V
404
NIH
P
Cr6
Cr6
Ni
Ni
248
Quinoline
V
V
238
NIH
G
Benz
Benz
249
Quinone
V
S
CDC
NA
NA
150
Rockwool (Man-Made Fibers)
V
NA
http://www.usg.com/content/dam/USG_Mark
eting_Communications/united_states/product_
promotional_materials/finished_assets/usg-
mineral-wool-300a-msds-en-75850002.pdf
P
Cr6
250
Selenium
V
V
685
CDC
P
Cr6
Cr6
150
Slagwool (Man-Made Fibers)
V
NA
http://www.usg.com/content/dam/USG_Mark
eting_Communications/united_states/product_
promotional_materials/finished_assets/usg-
mineral-wool-300a-msds-en-75850002.pdf
P
Cr6
251
Styrene
V
V
V
V
145
CDC
G
Benz
Benz
Benz
Benz
252
Styrene Oxide
V
V
194
http://www.sigmaaldrich.com/catalog/product
/aldrich/s5006?lang=en®ion=US
G
Benz
Benz
254
Tetrachlorobiphenyl
V
V
360 P
CS
G/P
PAH
PAH
256
Tetrachloroethylene
V
V
121
CDC
G
Benz
Benz
257
Titanium Tetrachloride
V
V
136
http://www.sigmaaldrich.com/catalog/product
/aldrich/697079?lang=en®ion=US
G
Benz
Benz
258
Toluene
V
V
V
V
111
CDC
G
Benz
Benz
Benz
Benz
259
Toluene-2,4-Diamine
V
292
CDC
G/P
PAH
262
Toxaphene
V
D
CDC
NA
NA
267
Trichloroethylene
V
V
87
CDC
G
Benz
Benz
270
Triethylamine
V
V
89
CDC
G
Benz
Benz
271
Triethylene glycol
V
V
285
NIH
G/P
PAH
PAH
154
Triethylene Glycol Dimethyl
Ether
V
215 E
CS
G
Benz
272
Trifluralin
V
V
140
http://www.speclab.com/compound/cl582098
.htm
G
Benz
Benz
G-20
-------
Page 19
Table 1. HAPs Assessed in the 2011 NATA, with their HAPEM7 HAP Phases
HAP
Num.
for
NATA
HAP Name
Emission Source
Modeled for NATA
Air Concentrations3
Boiling Point
HAPEM7
HAP
Phased
Exposure Factors Developed
for This HAP ("~"), or
Surrogate Used Instead
(Surrogate HAP Name)e
P
NP
M-
OR
M-
NR
Value
(CT
Source0
P
NP
M-
OR
M-
NR
273
Triglycol Monobutyl Ether
V
278
NIH
G/P
PAH
275
Vinyl Acetate
V
V
72
CDC
G
Benz
Benz
276
Vinyl Bromide
V
16
CDC
G
Benz
277
Vinyl Chloride
V
V
-14
CDC
G
Benz
Benz
278
Vinylidene Chloride
V
V
32
CDC
G
Benz
Benz
282
Xylenes (Mixed Isomers)
V
V
V
V
139
NIH
G
Benz
Benz
Benz
Benz
aThe emission sources modeled for air concentrations for each HAP in NATA are shown here for informational purposes. P =point; NP=non-point; M-OR=mobile on-road; M-NR=mobile
non-road.
b D=decomposes; E=experimental; NA=not available; P=predicted; S=sublimes; V=varies depending on compound.
cCDC=http://www.cdc.eov/niosh/npe: CS=http://www.chemspider.com: NIH=http://pubchem.ncbi.nlm.nih.eov/.
d G=gaseous; G/P=gaseous or particulate depending on conditions; P=particulate; NA=unknown.
e Benz=benzene; PAH=PAH, total; Ni=Nickel; Cr6=Chromium (VI).
G-21
-------
Page 20
Table 2. Boiling-point Defintions Used to Classify HAPs for HAPEM7 Modeling for the
2011 NATA
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 Organic Pollutants" table at EPA's Volatile
Organic Compound page (available as of February 12, 2015 at
http://www.epa.gov/iaq/voc2.html). 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.
Table 3. HAPs Modeled in HAPEM7 for the 2011 NATA
Emission Source Modeled for
NATA Exposure Concentrations3
NATA HAP
HAPEM7 HAP Phase"
P
NP
M-OR
M-NR
Benzene
G
V
V
V
V
1,3-butadiene
G
V
V
V
V
Coke oven emissions
G/P
V
PAH, total
G/P
V
V
V
V
(i.e., aggregate mass of polycyclic aromatic
hydrocarbons, where congeners were not specified)
Chromium (VI)
P
V
V
(i.e., compounds of hexavalent chromium)
Diesel PM
P
V
V
V
(i.e., Diesel particulate matter, or DPM)
Nickel
P
V
V
a For PAH total, chromium (VI), and nickel, we did not model exposure concentrations for the full set of source
categories they were modeled with for air concentrations. As discussed in the text, EPA selected these seven HAPs
to model and these specific source categories per HAP. P=point; NP=non-point; M-OR=mobile on-road; M-
NR=mobile non-road.
b G=gaseous; G/P=gaseous or particulate depending on conditions; P=particulate.
G-22
-------
Page 21
Table 4. Modeling Statistics for the 2011 NATA HAPs Modeled in HAPEM7
1,3-
Coke oven
Chromium
Diesel
PAH,
Benzene
butadiene
emissions
(VI)
PM
Nickel
total
In
Num. Counties
3,224
HAPEM7:
Num. Tracts
74,034
Num. Tracts with 0 Population (i.e., not modeled)3
579
In 2011
Num.
Counties
3,224
NATA:
Modeled:
"Tracts" For Air Concentrations'3
74,859
"Tracts" For Air Concentrations but not Exposure13
1,027
Tracts For Exposure
73,832
Num. Not
Tracts3
202 for air concentrations, an additional 377 for exposure
Modeled:
Instances of a Work Tractc
3,202
3,202
3,149
3,149
3,149
3,149
3,149
a EPA did not model air concentrations for 202 tracts which had zero residents according to the population data EPA was using at that time. In the HAPEM7 population data, two of
these tracts have two to three residents, but they were not modeled in HAPEM7 because they were not modeled in AERMOD and CMAQ; the other 200 tracts had zero residents
in HAPEM7. The HAPEM7 population data indicate that another 379 tracts also have zero residents; though EPA modeled air concentrations for these tracts, HAPEM7 did not
model these tracts.
b Air-concentration modeling included census tracts and some areas unrelated to census tracts. These non-tract areas were not modeled for exposure.
c Each home tract's collection of work tracts were randomly sampled with each run of HAPEM. A tract can be a work tract for multiple home tracts, and thus it can be sampled
multiple times as a work tract. Work tracts were not modeled for exposure if they had no air concentrations; tracts had no air concentrations if they had no people in them
according to the 2010 Census.
Table 5. "Caps" Applied to Exposure Factors for the 2011 NATA
HAP
Point
Non-point
Mobile On-road
Mobile Non-road
Total
Benzene
1.00
0.98
1.28
0.97
0.98
1,3-butadiene
1.09
0.97
1.30
0.99
1.00
Coke oven emissions
1.02
NA
NA
NA
1.02
PAH, total
0.93
0.71
0.89
0.73
0.73
Chromium (VI)
0.53
0.55
NA
NA
0.52
Diesel PM
NA
1.94
0.66
0.57
0.57
Nickel
NA
NA
0.64
0.54
0.58
G-23
-------
EPA's National-scale Air Toxics Assessment
This page intentionally left blank.
G-24
-------
EPA's National-scale Air Toxics Assessment
Appendix H
Toxicity Values Used in the 2011 NATA
Exhibit H-1 contains the toxicity values and supporting information for cancer and noncancer effects used in the
2011 NATA. The "target organ" column contains the organs or organ systems adversely affected at the lowest
dose in human or animal studies of noncancer effects. Hazard indices were calculated only for the respiratory
system (see Sections 5.2.2 and 6.3 of this document for the definitions of hazard quotients and hazard indices and
an explanation of how they are used in NATA). Other information on individual substances is shown in footnotes.
Abbreviations used for the sources of the unit risk estimates (UREs) and reference concentrations (RfCs) are as
follows:
IRIS = Integrated Risk Information System
ATSDR = Agency for Toxic Substances and Disease Registry
CAL = California Office of Environmental Health Hazard Assessment
HEAST = EPA Health Effects Assessment Tables
OAQPS = EPA Office of Air Quality Planning and Standards
H-1
-------
EPA's National-scale Air Toxics Assessment
Exhibit H-1. Toxicity Values Used in the 2011 NATA
NATA Pollutant
CAS
Number
Inhalation Unit Risk
Estimate (URE), 1/(|jg/m3)
Reference
Concentration (RfC),
mg/m3
Target Organ(s) or
System(s)
Noncancer
Impacts a
Value
Source
Value
Source
1,1,1-Trichloroethane
71556
5
IRIS
neurological
1,1,2,2-Tetrachloroethane
79345
1,1,2-Trichloroethane
79005
0.000016
IRIS
0.4
CAL
liver
1,1 -Dimethylhydrazine
57147
1,2,3,4,5,6-
Hexachlorocyclyhexane b
58899
0.00053
IRIS
0.002
CAL
liver
reproductive
kidney
1,2,4-Trichlorobenzene
120821
0.2
HEAST
liver
1,2-Dibromo-3-Chloropropane
96128
0.002
CAL
0.0002
IRIS
reproductive
1,2-Diphenylhydrazine
122667
0.00022
IRIS
1,2-Epoxybutane
106887
0.02
IRIS
respiratory
1,2-Propyleneimine
75558
1,3-Butadiene
106990
0.00003
IRIS
0.002
IRIS
reproductive
1,3-Dichloropropene
542756
0.000004
IRIS
0.02
IRIS
respiratory
1,3-Propane Sultone
1120714
0.00069
CAL
1,4-Dichlorobenzene
106467
0.000011
CAL
0.8
IRIS
liver
1,4-Dioxane
123911
0.000005
IRIS
0.03
IRIS
respiratory
liver
2,2,4-Trimethylpentane
540841
2,4,5-Trichlorophenol
95954
2,4,6-Trichlorophenol
88062
0.0000031
IRIS
2,4-D, salts and esters
94757
2,4-Dinitrophenol
51285
2,4-Dinitrotoluene
121142
0.000089
CAL
0.007
CAL
liver
neurological
2,4-Toluene Diamine
95807
0.0011
CAL
2,4-Toluene Diisocyanate
584849
0.000011
CAL
0.00007
IRIS
respiratory
2-Acetylaminofluorene
53963
0.00208
CAL
2-Chloroacetophenone
532274
0.00003
IRIS
respiratory
2-Nitropropane
79469
0.0000056 c
EPA
OAQPS
0.02
IRIS
liver
3,3'-Dichlorobenzidine
91941
0.00034
CAL
3,3'-Dimethoxybenzidine
119904
3,3'-Dimethylybenzidine
119937
4,4'-Methylene Bis(2-
Chloroaniline)
101144
0.00043
CAL
4,4'-Methylenedianiline
101779
0.00046
CAL
0.02
CAL
ocular
4,4'-Methylenediphenyl
Diisocyanate (MDI)
101688
0.0006
IRIS
respiratory
4,6-Dinitro-o-Cresol (Including
Salts)
534521
4-Aminobiphenyl
92671
4-Dimethylaminoazobenzene
60117
0.0013
CAL
4-Nitrobiphenyl
92933
4-Nitrophenol
100027
Acetaldehyde
75070
0.0000022
IRIS
0.009
IRIS
respiratory
Acetamide
60355
0.00002
CAL
Acetonitrile
75058
0.06
IRIS
whole body
Acetophenone
98862
H-2
-------
EPA's National-scale Air Toxics Assessment
NATA Pollutant
CAS
Number
Inhalation Unit Risk
Estimate (URE), 1/(|jg/m3)
Reference
Concentration (RfC),
mg/m3
Target Organ(s) or
System(s)
Noncancer
Impacts a
Value
Source
Value
Source
Acrolein
107028
0.00002
IRIS
respiratory
Acrylamide
79061
0.00016 d
IRIS
0.006
IRIS
neurological
Acrylic Acid
79107
0.001
IRIS
respiratory
Acrylonitrile
107131
0.000068
IRIS
0.002
IRIS
respiratory
Allyl Chloride
107051
0.000006
CAL
0.001
IRIS
neurological
Aniline
62533
0.0000016
CAL
0.001
IRIS
spleen
Anisidine
90040
Antimony Compounds
7440360
0.0002
IRIS
respiratory
Arsenic Compounds(inorganic
including Arsine)
7440382
0.0043
IRIS
0.000015
CAL
developmental
Benzene
71432
0.0000078 e
IRIS
0.03
IRIS
immune
Benzidine
92875
0.1072 d
IRIS
0.01
CAL
liver
neurological
Benzotrichloride
98077
Benzyl Chloride
100447
0.000049
CAL
Beryllium Compounds
7440417
0.0024
IRIS
0.00002
IRIS
respiratory
Beta-Propiolactone
57578
Biphenyl
92524
Bis(2-Ethylhexyl)Phthalate
(DEHP)
117817
0.0000024
CAL
0.01
CAL
respiratory
liver
Bis(Chloromethyl) Ether
542881
0.062
IRIS
Bromoform
75252
0.0000011
IRIS
Cadmium Compounds
7440439
0.0018
IRIS
0.00001
ATSDR
kidney
Calcium Cyanamide
156627
0.0008
IRIS
neurological
thyroid
Captan
133062
Carbaryl
63252
Carbon Disulfide
75150
0.7
IRIS
neurological
Carbon Tetrachloride
56235
0.000006
0.1
IRIS
liver
Carbonyl Sulfide
463581
0.163
EPA ORD
f
neurological
Catechol
120809
Chloramben
133904
Chlordane
57749
0.0001
IRIS
0.0007
IRIS
liver
Chlorine
7782505
0.00015
ATSDR
respiratory
Chloroacetic Acid
79118
Chlorobenzene
108907
1
CAL
liver
reproductive
kidney
Chlorobenzilate
510156
0.000078
HEAST
Chloroform
67663
0.098
ATSDR
liver
Chloromethyl Methyl Ether
107302
Chloroprene
126998
0.00048 d
IRIS
0.02
IRIS
respiratory
Chromium VI (Hexavalent)g
Multiple
0.012
IRIS
0.0001
IRIS
respiratory
Cobalt Compounds
7440484
0.0001
ATSDR
respiratory
Coke Oven Emissions
NA
0.00099 d
IRIS
Cresol/Cresylic Acid (Mixed
Isomers)h
Multiple
0.6
CAL
neurological
whole body
Cumene
98828
0.4
IRIS
kidney
endocrine
H-3
-------
EPA's National-scale Air Toxics Assessment
NATA Pollutant
CAS
Number
Inhalation Unit Risk
Estimate (URE), 1/(|jg/m3)
Reference
Concentration (RfC),
mg/m3
Target Organ(s) or
System(s)
Noncancer
Impacts a
Value
Source
Value
Source
Cyanide Compounds
57125
0.0008
IRIS
neurological
thyroid
Dibenzofuran
132649
Dibutylphthalate
84742
Dichloroethyl Ether (Bis[2-
Chloroethyl]Ether)
111444
0.00033
IRIS
Dichlorvos
62737
0.0005
IRIS
neurological
Diesel Particulate Matter
0.005
IRIS
respiratory
Diethanolamine
111422
0.003
CAL
respiratory
Diethyl Sulfate
64675
Dimethyl Formamide
68122
0.03
IRIS
liver
Dimethyl Phthalate
131113
Dimethyl Sulfate
77781
Dimethylcarbamoyl Chloride
79447
Epichlorohydrin
106898
0.0000012
0.001
IRIS
respiratory
Ethyl Acrylate
140885
Ethyl Carbamate (Urethane)
Chloride (Chloroethane)
51796
0.000464 d
Ethyl Chloride
75003
10
IRIS
developmental
Ethylbenzene
100414
0.0000025
CAL
1
IRIS
developmental
Ethylene Dibromide
(Dibromoethane)
106934
0.0006
IRIS
0.009
IRIS
respiratory
reproductive
Ethylene Dichloride (1,2-
Dichloroethane)
107062
0.000026
IRIS
2.4
ATSDR
liver
Ethylene Glycol
107211
0.4
CAL
respiratory
Ethylene Oxide
75218
0.000088
CAL
0.03
CAL
neurological
Ethylene Thiourea
96457
0.000013
CAL
0.003
CAL
endocrine
Ethyleneimine (Aziridine)
151564
Ethylidene Dichloride (1,1-
Dichloroethane)
75343
0.0000016
CAL
0.5
HEAST
kidney
Formaldehyde
50000
0.000013
IRIS
0.0098
ATSDR
respiratory
Glycol Ethers '
Multiple
0.02
IRIS
reproductive
Heptachlor
76448
0.0013
IRIS
Hexachlorobenzene
118741
0.00046
IRIS
0.003
CAL
liver
Hexachlorobutadiene
87683
0.000022
IRIS
0.09
CAL
reproductive
Hexachlorocyclopentadiene
77474
0.0002
IRIS
respiratory
Hexachloroethane
67721
0.03
IRIS
liver
neurological
kidney
Hexamethylene Diisocyanate
822060
0.00001
IRIS
respiratory
Hexane
110543
0.7
IRIS
neurological
Hydrazine
302012
0.0049
IRIS
0.0002
CAL
liver
thyroid
Hydrochloric Acid (Hydrogen
Chloride [Gas Only])
7647010
0.02
IRIS
respiratory
Hydrogen Fluoride (Hydrofluoric
Acid)
7664393
0.014
CAL
skeletal
Hydroquinone
123319
Isophorone
78591
2
CAL
liver
developmental
H-4
-------
EPA's National-scale Air Toxics Assessment
NATA Pollutant
CAS
Number
Inhalation Unit Risk
Estimate (URE), 1/(|jg/m3)
Reference
Concentration (RfC),
mg/m3
Target Organ(s) or
System(s)
Noncancer
Impacts a
Value
Source
Value
Source
Lead Compoundsj
7439921
0.00015
EPA
OAQPS
neurological
developmental
Maleic Anhydride
108316
0.0007
CAL
respiratory
Manganese Compounds
7439965
0.0003
ATSDR
neurological
Mercury Compounds
7439976
0.0003 k
IRIS
neurological
Methanol
67561
20
IRIS
developmental
Methoxychlor
72435
Methyl Bromide
(Bromomethane)
74839
0.005
IRIS
respiratory
Methyl Chloride
(Chloromethane)
74873
0.09
IRIS
neurological
Methyl Iodide (lodomethane)
74884
Methyl Isobutyl Ketone
(Hexone)
108101
3
IRIS
developmental
Methyl Isocyanate
624839
0.001
CAL
respiratory
whole body
Methyl Methacrylate
80626
0.7
IRIS
respiratory
Methyl Tert-Butyl Ether
1634044
0.00000026
CAL
3
IRIS
liver
kidney
ocular
Methylene Chloride
75092
0.000000016 d
IRIS
0.6
IRIS
respiratory
liver
Methylhydrazine
60344
N,N-Dimethylaniline
121697
Naphthalene
91203
0.000034
CAL
0.003
IRIS
respiratory
Nickel Compounds 1
1313991
0.00048
EPA
OAQPS
0.00009
ATSDR
respiratory
immune
Nitrobenzene
98953
0.00004
IRIS
0.009
IRIS
respiratory
N-Nitrosodimethylamine
62759
0.022 d
IRIS
N-Nitrosomorpholine
59892
0.0019
CAL
N-Nitroso-N-Methylurea
684935
o-Toluidine
95534
0.000051
CAL
PAH_000E0 (PAHPOM) m
Multiple
PAH_176E5 (PAHPOM)
Multiple
0.0000176 d
EPA
OAQPS
PAH_880E5 (PAHPOM)
Multiple
0.000088 d
EPA
OAQPS
PAH_176E4 (PAHPOM)
Multiple
0.000176 d
EPA
OAQPS
PAH_176E3 (PAHPOM)
Multiple
0.00176 d
EPA
OAQPS
PAH_192E3 (PAHPOM)
Multiple
0.00192 d
EPA
OAQPS
PAH_101E2 (PAHPOM)
Multiple
0.01008 d
EPA
OAQPS
PAH_176E2 (PAHPOM)
Multiple
0.0176 d
EPA
OAQPS
PAH_114E1 (PAHPOM)
Multiple
0.1136 d
EPA
OAQPS
Parathion
56382
H-5
-------
EPA's National-scale Air Toxics Assessment
CAS
Inhalation Unit Risk
Estimate (URE), 1/(|jg/m3)
Reference
Concentration (RfC),
mg/m3
Target Organ(s) or
System(s)
Noncancer
NATA Pollutant
Number
Value
Source
Value
Source
Impacts a
Pentachloronitrobenzene
(Quintobenzene)
82688
Pentachlorophenol
87865
0.0000051
CAL
0.1
CAL
liver
kidney
Phenol
108952
0.2
CAL
liver
Phosgene
75445
0.0003
IRIS
respiratory
Phosphine
7803512
0.0003
IRIS
whole bod
Phosphorus
7723140
Phthalic Anhydride
85449
0.02
CAL
respiratory
ocular
Polychlorinated Biphenyls
(Aroclors)
1336363
0.0001
IRIS
p-Phenylenediamine
106503
Propionaldehyde
123386
0.008
IRIS
respiratory
Propoxur (Baygon)
114261
Propylene Dichloride (1,2-
Dichloropropane)
78875
0.004
IRIS
respiratory
Propylene Oxide
75569
0.0000037
IRIS
0.03
IRIS
respiratory
Quinoline
91225
Quinone (P-Benzoquinone)
106514
Selenium Compounds
7782492
0.02
CAL
liver
neurological
hematologic
Styrene
100425
1
IRIS
neurological
Styrene Oxide
96093
0.006
CAL
respiratory
Tetrachloroethylene
127184
0.00000026
IRIS
0.04
IRIS
neurological
Titanium Tetrachloride
7550450
0.0001
ATSDR
respiratory
Toluene
108883
5
IRIS
neurological
Toxaphene (Chlorinated
Camphene)
8001352
0.00032
IRIS
liver
neurological
Trichloroethylene
79016
0.0000048 "
IRIS
0.002
IRIS
developmental
reproductive
kidney
immune
Triethylamine
121448
0.007
IRIS
respiratory
Trifluralin
1582098
Vinyl Acetate
108054
0.2
IRIS
respiratory
Vinyl Bromide
593602
0.000032
HEAST
0.003
IRIS
liver
Vinyl Chloride
75014
0.0000088
IRIS
0.1
IRIS
liver
Vinylidene Chloride
75354
0.2
IRIS
liver
Xylenes °
Multiple
0.1
IRIS
neurological
a For pollutants with more than one target organ or system listed, the order presented in this table does not represent priority or
significance of the noncancer impact.
b Includes all 4 lindane isomers. The modeling used the toxicity values of the most toxic isomer for cancer (CAS 319857) to estimate
risk.
0 The URE for 2-nitropropane derived by the Health Council of the Netherlands in 1999 was used in preference to the value in the
Health Effects Assessment Summary Tables, which does not reflect the most recent studies and analysis methods.
d This carcinogen acts via a mutagenic mode of action; therefore, the URE was adjusted by factor of 1.6 to account for the increased
risk during childhood exposures.
H-6
-------
EPA's National-scale Air Toxics Assessment
NATA Pollutant
CAS
Number
Inhalation Unit Risk
Estimate (URE), 1/(|jg/m3)
Reference
Concentration (RfC),
mg/m3
Target Organ(s) or
System(s)
Noncancer
Impacts a
Value
Source
Value
Source
e The IRIS assessment for benzene contains a range of UREs for inhalation exposure. The values that bracket this range are based
on different interpretations of the human-exposure information. As a health-protective national screening assessment, NATA used
the upper end of the range.
f A chronic screening level of 0.163 mg/m3 was developed for carbonyl sulfide by EPA ORD from a No Observed Adverse Effects
Level of 200 ppm based on brain lesions and neurophysiological alteration in rodents.
9 All hexavalent chromium compounds (including chromium (IV) trioxide) were modeled using the toxicity values for hexavalent
chromium.
h The individual cresol isomers were combined and noncancer impacts were estimated using the RfC for their mixture.
' The RfC for ethylene glycol methyl ether was used for all glycol eithers.
' The RfC for lead compounds is equivalent to the lead NAAQS.
k The RfC for mercury compounds is the value derived by IRIS for elemental mercury.
' The IRIS assessments for nickel compounds provided a range of plausible UREs. NATA used the highest value in that range which
is equal to the URE for nickel subsulfide. The low end of the range is equal to 50% of the URE for nickel subsulfide. The RfC value
for nickel subsulfide was also used all nickel compounds (including nickel oxide).
m See Section 2.1.1.2 of the TSD for a description of the PAH/POM grouping.
n Although trichloroethylene is carcinogenic by a mutagenic mode of action, the age-dependent adjust factor for the URE only
applies to the portion of the slope factor reflecting risk of kidney cancer. As such, the URE is adjusted by a factor of 1.12 (rather
than the typical factor of 1.6).
0 The individual xylene isomers were combined and noncancer impacts were estimated using the RfC for their mixture.
H-7
-------
EPA's National-scale Air Toxics Assessment
Final Review Draft - October 20,2010
This page intentionally left blank.
H-8
-------
EPA's National-scale Air Toxics Assessment
Appendix I
Adjustments from the 2011 Emissions/Modeling Approach
In a small number of situations, tract modeling results were adjusted due to errors or anomalies that had impacts
on the resultant tract risks.
For a few of these tracts, the modelled data were adjusted due to uncertainty with respect to the surrogate used to
allocate the county-level off-network (parking) emissions to the tract. These few tracts were each in a highly urban
core and were allocated based on square footage of industrial, commercial, institutional, and residential as an
indicator of parked cars. However, the surrounding tracts appeared very similar with respect to parking areas.
Given the uncertainty at this resolution, we chose to substitute the onroad light-duty values from a nearby tract. We
chose the next-highest onroad light-duty risk and associated concentrations and exposures.
Exhibit 1-1 contains the adjustments and the rationale.
Exhibit 1-1. Adjustments to Tract-level data
County,
State
Tract
HEM
Run
Groups
Pollut-
ants
Data to be
Adjusted
Adjustment
Reason
Kern, CA
06029001600
OR-LD
OR-HD
all
Cone.,
exposures,
poll.-
specific
risks
(cancer and
noncancer)
by HEM run
group and
source
group
Recompute as
the county mean
using all tracts in
the county
except for
06029001600
Faulty Surrogate 200
(urban primary road miles)
puts 100% of the
emissions in this tract, yet
there appears to be no
primary roads in the tract
Jefferson,
IL
17081051000,
17081050900
OR-LD
OR-HD
all
Same as
above
Recompute as
the county mean
using all tracts in
the county
except for
1708105100 and
17081050900
Faulty Surrogate 200
(urban primary road miles)
puts 100% of the
emissions in these two
tracts, yet there appears
to be no primary roads
King, WA
53033007402,
53033007401
OR-HD
NONRO
AD
all
Same as
above
Recompute as
the county mean
using all tracts in
the county
except for
53033007402
and
53033007401
Unreasonable emissions
density in high-population
tracts from nonroad due to
surrogates 140 and 100
(used for 520 due to gap
fill issue) and from
surrogate 221 in onroad.
These two tracts were
split up since last NATA.
San
Francisco,
CA
06075011700,
06075061500
NR-
Gas/Oth
er
Cancer
all
Same as
above
Recompute as
the county mean
using all tracts in
the county
except for
06075011700
and
06075061500
Surrogate 520 created
large emission densities
for commercial lawn and
garden emissions that are
likely not prevalent in this
tract
1-1
-------
EPA's National-scale Air Toxics Assessment
County,
State
Tract
HEM
Run
Groups
Pollut-
ants
Data to be
Adjusted
Adjustment
Reason
Du Page
County, IL
17043843900
OR-
HD_Die
sel
(source
group)
all
Same as
above
Recompute as
the county mean
using all tracts in
the county
except for
17043843900
The risk is due to
surrogate 205 (truck
stops). The underlying
truck-stop data shapefile
shows the weigh station to
be no longer in operation.
The satellite data do not
show any truck stops in
this tract.
St. Louis
County,
MO
2918922142
2
OR-
HD_Die
sel
(source
group)
all
Same as
above
Recompute as
the county mean
using all tracts in
the county
except for
29189221422
The risk is due to
surrogate 205 (truck
stops). The underlying
truck stop data shapefile
shows the major truck
stop that caused 71% of
the county emissions to
be allocated into this tract
is actually in the
neighboring tract to the
east, and thus this tract
should not have gotten the
emissions.
Orange
County,
CA
06059075514
NR-
Gas/Oth
er
Cancer
Risk
all
same as
above
Recompute as
the county mean
using all tracts in
the county
except for
06059075514
Very high emission
densities caused by two
surrogates: 520
(commercial + industrial +
institutional) and 510
(commercial + industrial)
used for allocating
commercial lawn and
garden equipment and
commercial equipment
that do not appear
consistent with the land
use in that tract
Lehigh
County,
PA
42077001000
OR-
Light
Duty
all
Total
Set onroad-light-
duty risk to next-
highest
neighboring tract
in the county
(42077000800)
This is the only tract with
risk > 100-in-1 million in
an urban area due to
onroad risk from parking-
area emissions. It appears
to have similar
characteristics (with
respect to parking areas)
as neighboring tracts
which are about 10-in-1
million or so lower risk.
Lancaster
County,
PA
42071000100
OR-
Light
Duty
all
Total
Set onroad-light-
duty risk to next-
highest
neighboring tract
in the county
(42071000700)
Same as above
Hennepin,
MN
27053104400
OR-
Light
Duty
all
Total
Set onroad-light-
duty risk to next-
highest
neighboring tract
in the county
(27053126100)
Same as above
1-2
-------
EPA's National-scale Air Toxics Assessment
County,
State
Tract
HEM
Run
Groups
Pollut-
ants
Data to be
Adjusted
Adjustment
Reason
Hamilton,
OH
39061000700
OR-
Light
Duty
all
Total
Set onroad-light-
duty risk to next-
highest
neighboring tract
in the county
(39061000900)
Same as above
Tulsa, OK
40143002500
OR-
Light
Duty
all
Total
Set onroad-light-
duty risk to next-
highest
neighboring tract
in the county
(40143003300)
Same as above.
Additionally, the primary
road on the perimeter of
the tract boundary may
also be in partly-adjacent
tract.
Puerto
Rico: 2
Municipios
: San Juan
72127 and
Ponce
72113.
T racts
impacted by
CMV
CMV -
ports
all
Emissions,
tract
concentrati
ons
Use same
emissions and
concentration as
2005 NATA,
recompute risks
based on the
concentrations
2011 NEI did not have
port emissions, so gap-fill
with 2005 NATA
1-3
------- |