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Policy Assessment for the Reconsideration of
the National Ambient Air Quality Standards for
Particulate Matter, External Review Draft
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EPA-452/P-21-001
October 2021
Policy Assessment for the Reconsideration of the National Ambient Air Quality Standards for
Particulate Matter, External Review Draft
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Health and Environmental Impacts Division
Research Triangle Park, NC
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DISCLAIMER
This draft Policy Assessment has been prepared by staff in the U.S. Environmental
Protection Agency's (EPA) Office of Air Quality Planning and Standards. Any findings and
conclusions are those of the authors and do not necessarily reflect the views of the EPA.
Questions or comments related to this document should be addressed to Dr. Lars Perlmutt, U.S.
Environmental Protection Agency, Office of Air Quality Planning and Standards, C539-06,
Research Triangle Park, North Carolina 27711 (email: perlmutt.lars@epa.gov).
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TABLE OF CONTENTS
1 Introduction 1-1
1.1 Purpose 1-2
1.2 Legislative Requirements 1-3
1.3 History of Reviews of the PM NAAQS 1-6
1.3.1 Reviews Completed in 1971 and 1987 1-8
1.3.2 Review Completed in 1997 1-8
1.3.3 Review Completed in 2006 1-10
1.3.4 Review Completed in 2012 1-12
1.3.5 Review Completed in 2020 1-12
1.4 Reconsideration of the 2020 PM NAAQS Final Action 1-14
1.4.1 Decision to Initiate a Reconsideration 1-14
1.4.2 Process for Reconsideration of the 2020 PM NAAQS Decision 1-15
1.4.3 Ongoing Litigation 1-18
References 1-19
2 PM Air Quality 2-1
2.1 Distribution of Particle Size in Ambient Air 2-1
2.1.1 Sources of PM Emissions 2-3
2.2 Ambient PM Monitoring Methods and Networks 2-14
2.2.1 Total Suspended Particulates (TSP) Sampling 2-15
2.2.2 PMio Monitoring 2-16
2.2.3 PM2.5 Monitoring 2-17
2.2.4 PM10-2.5 Monitoring 2-22
2.2.5 Additional PM Measurements and Metrics 2-23
2.3 Ambient Air Concentrations 2-25
2.3.1 Trends in Emissions of PM and Precursor Gases 2-25
2.3.2 Trends in Monitored Ambient Concentrations 2-26
2.3.3 Predicted Ambient PM2.5 Based on Hybrid Modeling Approaches 2-43
2.4 Background PM 2-53
2.4.1 Natural Sources 2-55
2.4.2 International Transport 2-57
2.4.3 Estimating Background PM with Recent Data 2-59
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References 2-62
3 Reconsideration of the Primary Standards for PM2.5 3-1
3.1 Background on the Current Standards 3-2
3.1.1 Considerations Regarding the Adequacy of the Existing Standards in the 2020
Review 3-4
3.2 General Approach and Key Issues in this Reconsideration of the 2020 Final Decision
3-12
3.3 Health Effects Evidence 3-15
3.3.1 Nature of Effects 3-18
3.3.2 Public Health Implications and At-Risk Populations 3-50
3.3.3 PM2.5 Concentrations in Key Studies Reporting Health Effects 3-57
3.3.4 Uncertainties in the Health Effects Evidence 3-125
3.4 Risk Information 3-128
3.4.1 Risk Assessment Overview 3-129
3.4.2 Results of the Risk Assessment 3-138
3.4.3 Conclusions of the Risk Assessment 3-156
3.5 Key Considerations Regarding the Adequacy of the Primary PM2.5 Standards.... 3-157
3.5.1 Evidence-based Considerations 3-157
3.5.2 Risk-based Considerations 3-171
3.5.3 Preliminary Conclusions 3-181
3.6 Areas for Future Research and Data Collection 3-203
References 3-206
4 Reconsideration of the Primary Standard for PM10 4-1
4.1 Background on the Current Standard 4-2
4.1.1 Considerations Regarding the Adequacy of the Existing Standards in the 2020
Review 4-4
4.2 General Approach and Key Issues in this Reconsideration of the 2020 Final Decision
4-5
4.3 Health Effects Evidence 4-8
4.3.1 Nature of Effects 4-9
4.4 Preliminary Conclusions on the Adequacy of the Current Primary PM10 Standard 4-16
4.5 Areas for Future Research and Data Collection 4-18
References 4-20
5 Reconsideration of the Secondary Standards for PM 5-1
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5.1 Background on the Current Standards 5-2
5.1.1 Non-Visibility Effects 5-2
5.1.2 Visibility Effects 5-5
5.2 General Approach and Key Issues in this Reconsideration of the 2020 Final Decision
5-11
5.3 Welfare Effects and Quantitative Information 5-14
5.3.1 Visibility Effects 5-16
5.3.2 Non-Visibility Effects 5-33
5.4 Preliminary Conclusions Regarding the Adequacy of the Secondary PM Standards ....
5-45
5.5 Areas for Future Research and Data Collection 5-49
References 5-51
in
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Appendix A.
Appendix B.
Appendix C.
Appendix D.
LIST OF APPENDICES
Supplemental Information on PM Air Quality Analyses
Supplemental Study Information: Selection Criteria, Study Methods and Details
Supplemental Information Related to the Human Health Risk Assessment
Quantitative Analyses for Visibility Impairment
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LIST OF TABLES
Table 1-1. Summary of NAAQS promulgated for particulate matter 1971-2012 1-7
Table 2-1. Percent Changes in PM and PM precursor emissions in the NEI for the time
periods 1990-2017 and 2002-2017 2-26
Table 2-2. Nationwide averages of ratios of maximum annual PM2.5 design values to average
composite monitor PM2.5 concentrations across CBSAs 2-45
Table 2-3. Mean 2011 PM2.5 concentration by region for predictions in Figure 2-29 2-52
Table 2-4. Average Annual PM2.5 Concentration (|ig/m3) by Year 2-58
Table 2-5. Three-Year Average of the Average Annual PM2.5 Concentrations (|ig/m3) 2-59
Table 2-6. Average Annual PM2.5 Concentrations and Ratios to Regulatory Design Values....
2-60
Table 3-1. Key causality determinations for PM2.5 and UFP exposures 3-19
Table 3-2. National demographic information, 2019 3-53
Table 3-3. Prevalence of cardiovascular and respiratory diseases among adults by age, race,
and ethnicity in the U.S. in 2018 3-56
Table 3-4. Summary of information from PM2.5 controlled human exposure studies 3-59
Table 3-5. Key U.S. Epidemiologic Studies: Monitor-Based Exposure 3-82
Table 3-6. Key Canadian Epidemiologic Studies: Monitor-Based Exposure 3-86
Table 3-7. Key U.S. Epidemiologic Studies: Model-Based Exposure 3-89
Table 3-8. Key Canadian Epidemiologic Studies: Model-Based Exposure 3-95
Table 3-9. PM2.5 Concentrations Metrics from Monitor and Modeled Data 13-00
Table 3-10. Epidemiologic studies examining the health impacts associated with ambient
PM2.5 concentrations when studies are conducted with restricted air quality
exposures 3-115
Table 3-11. Summary of information from studies that use causal modeling statistical
methods 3-119
Table 3-12. Epidemiologic studies examining the health impacts of long-term reductions in
ambient PM2.5 concentrations 3-122
Table 3-13. Epidemiologic studies used to estimate PM2.5-associated risk 3-130
Table 3-14. Estimates of PM2.5-associated mortality for air quality adjusted to just meet the
current or alternative standards (47 urban study areas) 3-140
Table 3-15. Estimated reduction in PM2.5-associated mortality for alternative annual and 24-
hour standards (47 urban study areas) 3-141
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Table 3-16. Estimates of PIVh.s-associated mortality for the current and potential alternative
annual standards in the 30 study areas where the annual standard is controlling.
3-143
Table 3-17. Estimated delta and percent reduction in PM2.5-associated mortality for the
current and potential alternative annual standards in the 30 study areas where
the annual standard is controlling 3-144
Table 3-18. Estimates of PIVh.s-associated mortality for the current 24-hour standard, and an
alternative, in the 11 study areas where the 24-hour standard is controlling
3-147
Table 3-19. Average national percent PM2.5 reduction in demographic populations aged 65
and over residing in the full set of 47 study areas and subset of 30 study areas
controlled by the annual standard 3-151
Table 3-20. Average national percent PM2.5 risk reduction in demographic populations aged
65 and over residing in the full set of 47 study areas and subset of 30 study
areas controlled by the annual standard 3-151
Table 4-1. Key Causality Determinations for PM10-2.5 Exposures 4-9
Table 5-1. Key causality determinations for PM-related welfare effects 5-15
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LIST OF FIGURES
Figure 2-1. Comparisons of PM2.5 and PM10 diameters to human hair and beach sand 2-2
Figure 2-2. Percent contribution of PM2.5 national emissions by source sectors 2-5
Figure 2-3. 2017 NEI PM2.5 Emissions Density Map, tons per square mile 2-6
Figure 2-4. Percent contribution of PM10 emissions by national source sectors 2-7
Figure 2-5. 2017 NEI PM10 Emissions Density Map, tons per square mile 2-7
Figure 2-6. Percent contribution to organic carbon (top panel) and elemental carbon (bottom
panel) national emissions by source sectors 2-9
Figure 2-7. 2017 NEI Elemental Carbon Emissions Density Map, tons per square mile.... 2-10
Figure 2-8. Percent contribution to sulfur dioxide (panel A), oxides of nitrogen (panel B),
ammonia (panel C), and volatile organic compounds (panel D) national
emissions by source sectors. (Source: 2017 NEI). All graphics only show
anthropogenic contributions 2-11
Figure 2-9. SO2 Emissions Density Map, tons per square mile 2-12
Figure 2-10. NOx Emissions Density Map, tons per square mile 2-12
Figure 2-11. NH3 Emissions Density Map, tons per square mile 2-12
Figure 2-12. Anthropogenic (including wildfires) VOC Emissions Density Map, tons per
square mile 2-13
Figure 2-13. PM Monitoring stations reporting to EPA's AQS database by PM size fraction,
1970-2020 2-15
Figure 2-14. National emission trends of PM2.5, PM10, and precursor gases from 1990 to
2017 2-26
Figure 2-15. Annual average and 98th percentile of 24-hour PM2.5 concentrations (in |ag/m3)
from 2017-2019 (top) and linear trends and their associated significance (based
on p-values) in PM2.5 concentrations from 2000-2019 (bottom) 2-28
Figure 2-16. Seasonally-weighted annual average PM2.5 concentrations in the U.S. from 2000
to 2019 (406 sites) 2-29
Figure 2-17. Pearson's correlation coefficient between annual average and 98th percentile of
24-hour PM2.5 concentrations from 2000-2019 2-30
Figure 2-18. Scatterplot of CBSA maximum annual versus daily design values (2017-2019)
with the solid black line representing the ratio of daily and annual NAAQS
values 2-31
Figure 2-19. Frequency distribution of 2017-2019 2-hour averages for sites meeting both or
violating either PM2.5 NAAQS for October to March (blue) and April to
September (red) 2-33
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Figure 2-20. Annual average PM2.5 sulfate, nitrate, organic carbon, and elemental carbon
concentrations (in |ig/m3) from 2017-2019 2-34
Figure 2-21. Annual average and 2nd highest PM10 concentrations (in |ag/m3) from 2017-2019
(top) and linear trends and their associated significance in PM10 concentrations
from 2000-2019 (bottom) 2-36
Figure 2-22. National trends in Annual 2nd Highest 24-Hour PM10 concentrations from 2000
to 2019 (262 sites) 2-37
Figure 2-23. Annual average PM2.5/PM10 ratio for 2017-2019 2-38
Figure 2-24. PM2.5/PM10 ratio on the date of the second highest PM10 concentrations for
2017-2019 2-38
Figure 2-25. Annual average and 98th percentile PM10-2.5 concentrations (|ag/m3) from 2017-
2019 (top) and linear trends and their associated significance in PM10-2.5
concentrations from 2000-2019 (bottom) 2-40
Figure 2-26. Average hourly particle number concentrations from three locations in the State
of New York for 2014 to 2015 2-41
Figure 2-27. Time series of annual average mass and number concentrations (left) and
scatterplot of mass vs. number concentration (right) between 2000-2019 in
Bondville, IL
2-42
Figure 2-28. Comparison of CBSA average annual design values and CBSA maximum
annual design values for 2017-2019 2-44
Figure 2-29. Comparison of CBSA average annual design values and CBSA maximum daily
design values for 2017-2019 2-46
Figure 2-30. R2 for ten-fold cross-validation of daily PM2.5 predictions in 2015 from three
methods for individual sites as a function of observed concentration 2-50
Figure 2-31. Comparison of 2011 annual average PM2.5 concentrations from four methods....
2-51
Figure 2-32. Comparison of 2011 annual average PM2.5 concentrations from four methods for
regions centered on the (a) California (b) New Jersey, and (c) Arizona 2-53
Figure 2-33. (a) Spatial distribution of the CV (i.e., standard deviation divided by mean) in
percentage units for the four models in Figure 2-29. (b) Boxplot distributions of
CV for grid cells binned by the average PM2.5 concentration for the four
models 2-54
Figure 2-34. Distance from the center of the 12-km grid cells to the nearest PM2.5 monitoring
site for PM2.5 measurements from the AQS database and IMPROVE network. ..
2-54
Figure 2-35. Location of PM2.5 predictions by range in annual average concentration for the
four prediction methods at their native resolution 2-55
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Figure 2-36. Annual mean PM2.5 from the VD2019 method (van Donkelaar et al., 2019) for
2001, 2006, 2011, and 2016 2-56
Figure 2-37. Spatial distribution of the annual average PM2.5 concentrations for 2015 using
the DI2019 surface nationwide (panel A) and for CBSAs only (panel B) 2-57
Figure 2-38. Smoke and fire detections observed by the MODIS instrument onboard the
Aqua satellite on August 4th, 2017 accessed through NASA Worldview 2-65
Figure 2-39. Fine PM mass time series during 2017 from the North Cascades IMPROVE site
in north central Washington state 2-65
Figure 2-40. Speciated annual average IMPROVE PM2.5 in |ig/m3 at select remote monitors
during 2004 and 2016 2-69
Figure 2-41. Site locations for the IMPROVE monitors in Figure 2-40 2-70
Figure 3-1. Overview of general approach for the reconsideration of the 2020 final decision
on the primary PM2.5 standards 3-14
Figure 3-2. Estimated concentration-response function and 95% confidence intervals between
PM2.5 and cardiovascular mortality in the Six Cities Study (1974-2009) 3-66
Figure 3-3. Epidemiologic studies examining associations between long-term PM2.5
exposures and mortality 3-73
Figure 3-4. Epidemiologic studies examining associations between long-term PM2.5
exposures and morbidity 3-74
Figure 3-5. Epidemiologic studies examining associations between short-term PM2.5
exposures and mortality 3-76
Figure 3-6. Epidemiologic studies examining associations between short-term PM2.5
exposures and morbidity 3-78
Figure 3-7. Estimated PM2.5 concentrations using the DI2019 hybrid approach and
monitoring locations and design values for the state of Georgia and the Atlanta-
Sandy Springs-Roswell, Georgia CBSA 3-99
Figure 3-8. Monitor-based PM2.5 concentrations in key U.S. epidemiologic studies 3-104
Figure 3-9. Monitor-based PM2.5 concentrations in key Canadian epidemiologic studies
3-105
Figure 3-10. Hybrid model-predicted PM2.5 concentrations in key U.S. epidemiologic
studies 3-106
Figure 3-11. Hybrid model-predicted PM2.5 concentrations in key Canadian epidemiologic
studies 3-107
Figure 3-12. Hybrid model-predicted PM2.5 concentrations in key U.S. epidemiologic studies,
subset by spatial scale 3-109
Figure 3-13. Hybrid model-predicted PM2.5 concentrations in key U.S. epidemiologic studies,
subset by method used to average grid cells in study-reported long-term mean
PM2.5 concentrations 3-110
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Figure 3-14. Hybrid model-predicted PM2.5 concentrations in key U.S. epidemiologic studies,
subset by spatial scale and method used to average grid cells in study-reported
long-term mean PM2.5 concentrations 3-111
Figure 3-15. Illustration of approach to adjusting air quality to simulate just meeting annual
standards with levels of 11.0, 9.0, and 8.0 |ig/m3 3-131
Figure 3-16. Map of 47 urban study areas included in risk modeling 3-134
Figure 3-17. Available epidemiologic associations between long-term PM2.5 exposure and
mortality outcomes in demographic populations 3-136
Figure 3-18. Distribution of total risk estimates (PM2.5-attibutable mortality) for the current
and alternative annual standards for the subset of 30 urban study areas where
the annual standard is controlling (blue and green bars represent the Pri-PM2.5
and Sec-PM2.5 estimates, respectively) 3-145
Figure 3-19. Distribution of the difference in risk estimates between the current annual
standard (level of 12.0 |ig/m3) and alternative annual standards with levels of
11.0, 10.0, 9.0, and 8.0 |ig/m3 for the subset of 30 urban study areas where the
annual standard is controlling 3-146
Figure 3-20. Average PM2.5 exposure concentration and PM2.5-attributable risk estimates by
demographic population when just meeting current or alternative PM2.5
standards 3-149
Figure 3-21. Average change in PM2.5 exposure concentration and PM2.5-attributable
mortality risk estimates by demographic population when moving from the
current to alternative PM2.5 standards 3-150
Figure 3-22. PM2.5 exposure concentrations and PM2.5-attributable mortality risk estimates by
demographic population when just meeting current or alternative PM2.5
standards 3-152
Figure 3-23. Change in PM2.5 exposure concentrations and PM2.5-attributable mortality risk
estimates by demographic population when moving from the current to
alternative PM2.5 standards 3-153
Figure 5-1. Overview of general approach for the reconsideration of the 2020 final decision
on the secondary PM standards 5-13
Figure 5-2. Relationship of viewer acceptability ratings to light extinction 5-24
Figure 5-3. Comparison of 90th percentile of daily light extinction, averaged over three years,
and 98th percentile of daily PM2.5 concentrations, averaged over three years, for
2017-2019 using the original IMPROVE equation 5-29
Figure 5-4. Comparison of 90th percentile of daily light extinction, averaged over three years,
and 98th percentile of daily PM2.5 concentrations, averaged over three years, for
2015-2017 using the Lowenthal and Kumar equation 5-31
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1 INTRODUCTION
This document, Policy Assessment for the Reconsideration of the National Ambient Air
Quality Standards for Particulate Matter, External Review Draft (hereafter referred to as the
draft PA), presents the draft policy assessment for the U.S. Environmental Protection Agency's
(EPA's) reconsideration of the review of the national ambient air quality standards (NAAQS) for
particulate matter (PM) completed in 2020.1 The overall plan for the 2020 review was presented
in the Integrated Review Plan for the National Ambient Air Quality Standards for Particulate
Matter (IRP; U.S. EPA, 2016). The IRP also identified key policy-relevant issues to be addressed
in the 2020 review and discussed the key documents that generally inform NAAQS reviews,
including an Integrated Science Assessment (ISA) and a Policy Assessment (PA). The key
considerations presented in this draft PA are intended to provide updates to the policy
information to support the reconsideration of the 2020 PM NAAQS final action, which retained
the primary and secondary PM2.5 and PM10 standards without revision (85 FR 82684, December
18, 2020). In reconsidering the 2020 final action, the EPA will consider the scientific and
technical analyses on which the December 2020 PM NAAQS final action was based, as well as
the newly available scientific information evaluated in the Supplement to the 2019 Integrated
Science Assessment for Particulate Matter (External Review Draft) (hereafter referred to as the
draft ISA Supplement; U.S. EPA, 2021) and the policy implications of the new scientific
evidence and updated quantitative analyses presented in this draft PA. Much of the information
in this draft PA is drawn directly from information included in the 2019 ISA (U.S. EPA, 2019)
and the 2020 PA (U.S. EPA, 2020).
This document is organized into five chapters. Chapter 1 presents introductory
information on the purpose of the PA, legislative requirements for reviews of the NAAQS, an
overview of the history of the PM NAAQS, including background information on prior reviews,
and a summary of the progress to date for the reconsideration of the 2020 final decision. Chapter
2 provides an overview of the available information on PM-related emissions, atmospheric
chemistry, monitoring and air quality. Chapter 3 focuses on policy-relevant aspects of the
currently available health effects evidence as presented in the 2019 ISA and draft ISA
Supplement, as well as updated exposure/risk information, and identifies and summarizes the key
considerations related to this reconsideration of the primary PM2.5 standards. Chapter 4 draws
substantially from the information presented in the 2020 PA on the policy-relevant aspects of the
1 On June 10, 2021, the Agency announced its decision to reconsider the 2020 PM NAAQS final action. The press
release for this announcement is available at: https://www. epa.gov/newsreleases/epa-reexamine-health-standards-
harmful-soot-previous-administration-left-unchanged
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health effects evidence presented in the 2019 ISA and identifies and summarizes the key
considerations related to this reconsideration of the primary standard PMio. Chapter 5 focuses on
policy-relevant aspects of the currently available welfare effects evidence as presented in the
2019 ISA and draft ISA Supplement, as well as updated quantitative analyses for visibility
effects, and identifies and summarizes the key considerations related to this reconsideration of
the secondary PM standards.2 More detail about the process for this reconsideration is described
in section 1.4.2 below, and the approach for considering the available information for this
reconsideration is presented within Chapters 3, 4, and 5 of this draft PA.
1.1 PURPOSE
The PA evaluates the potential policy implications of the available scientific evidence, as
assessed in the ISA, and the potential implications of the available air quality, exposure or risk
analyses. The role of the PA is to help "bridge the gap" between the Agency's scientific
assessments and quantitative technical analyses, and the judgments required of the Administrator
in determining whether it is appropriate to retain or revise the NAAQS.
In evaluating the question of adequacy of the current standards, and whether it may be
appropriate to consider alternative standards, the PA focuses on information that is most
pertinent to evaluating the standards and their basic elements: indicator, averaging time, form,
and level.3 These elements, which together serve to define each standard, must be considered
collectively in evaluating the health and welfare protection the standards afford.
The PA is also intended to facilitate advice to the Agency and recommendations to the
Administrator from an independent scientific review committee, the Clean Air Scientific
Advisory Committee (CASAC), as provided for in the Clean Air Act (CAA). As discussed below
in section 1.2, the CASAC is to advise on subjects including the Agency's assessment of the
relevant scientific information and on the adequacy of the current standards, and to make
2 The welfare effects considered in this review include visibility impairment, climate effects, and materials effects
(i.e., damage and soiling). Ecological effects associated with PM, and the adequacy of protection provided by the
secondary PM standards for them, are being addressed in the separate review of the secondary NAAQS for oxides
of nitrogen, oxides of sulfur and PM in recognition of the linkages between oxides of nitrogen, oxides of sulfur,
and PM with respect to atmospheric chemistry and deposition, and with respect to ecological effects. Information
on the current review of the secondary NAAQS for oxides of nitrogen, oxides of sulfur and PM can be found at
https://www. epa.gov/naaqs/nitrogen-dioxide-no2-and-sulfur-dioxide-so2-secondary-air-quality-standards.
3 The indicator defines the chemical species or mixture to be measured in the ambient air for the purpose of
determining whether an area attains the standard. The averaging time defines the period over which air quality
measurements are to be averaged or otherwise analyzed. The form of a standard defines the air quality statistic
that is to be compared to the level of the standard in determining whether an area attains the standard. For
example, the form of the annual NAAQS for fine particulate matter is the average of annual mean concentrations
for three consecutive years, while the form of the 8-hour NAAQS for carbon monoxide is the second-highest 8-
hour average in a year. The level of the standard defines the air quality concentration used for that purpose.
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recommendations as to any revisions of the standards that may be appropriate. The EPA
generally makes available to the CASAC and the public one or more drafts of the PA for
CASAC review and public comment.
In this draft PA, we4 take into account the available scientific evidence, as assessed in the
Integrated Science Assessment for Particulate Matter (Final Report) (2019 ISA [U.S. EPA,
2019]) and in the draft ISA Supplement (U.S. EPA, 2021), as well as additional policy-relevant
analyses of air quality and risks. The evaluation and preliminary conclusions presented in this
draft PA have been informed by the scientific evidence presented in the 2019 ISA and the draft
ISA Supplement, as well as the policy-relevant considerations and conclusions reached in the
2020 PA, along with updated quantitative analyses of air quality, risk, and exposure, where
available. Review and comments from the CASAC, as well as public comment, on this draft PA
will inform the final evaluation and conclusions in the final PA. The final PA is intended to help
the Administrator in considering the scientific and technical information, and in formulating
judgments regarding the adequacy of the current standards and regarding alternative standards,
as appropriate.
Beyond informing the Administrator and facilitating the advice and recommendations of
the CASAC, the PA is also intended to be a useful reference to all parties interested in the review
of the PM NAAQS. In these roles, it is intended to serve as a source of policy-relevant
information that informs the Agency's review of the NAAQS for PM, and it is written to be
understandable to a broad audience.
1.2 LEGISLATIVE REQUIREMENTS
Two sections of the Clean Air Act (CAA) govern the establishment and revision of the
NAAQS. Section 108 (42 U.S.C. 7408) directs the Administrator to identify and list certain air
pollutants and then to issue air quality criteria for those pollutants. The Administrator is to list
those pollutants "emissions of which, in his judgment, cause or contribute to air pollution which
may reasonably be anticipated to endanger public health or welfare"; "the presence of which in
the ambient air results from numerous or diverse mobile or stationary sources"; and for which he
"plans to issue air quality criteria...." (42 U.S.C. § 7408(a)(1)). Air quality criteria are intended
to "accurately reflect the latest scientific knowledge useful in indicating the kind and extent of all
identifiable effects on public health or welfare which may be expected from the presence of [a]
pollutant in the ambient air...." 42 U.S.C. § 7408(a)(2).
4 The terms "we," "our," and "staff' throughout this document refer to the staff in the EPA's Office of Air Quality
Planning and Standards (OAQPS).
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Section 109 [42 U.S.C. 7409] directs the Administrator to propose and promulgate
"primary" and "secondary" NAAQS for pollutants for which air quality criteria are issued [42
U.S.C. § 7409(a)], Section 109(b)(1) defines primary standards as ones "the attainment and
maintenance of which in the judgment of the Administrator, based on such criteria and allowing
an adequate margin of safety, are requisite to protect the public health."5 Under section
109(b)(2), a secondary standard must "specify a level of air quality the attainment and
maintenance of which, in the judgment of the Administrator, based on such criteria, is requisite
to protect the public welfare from any known or anticipated adverse effects associated with the
presence of [the] pollutant in the ambient air."6
In setting primary and secondary standards that are "requisite" to protect public health
and welfare, respectively, as provided in section 109(b), the EPA's task is to establish standards
that are neither more nor less stringent than necessary. In so doing, the EPA may not consider the
costs of implementing the standards. See generally, Whitman v. American Trucking Associations,
531 U.S. 457, 465-472, 475-76 (2001). Likewise, "[attainability and technological feasibility are
not relevant considerations in the promulgation of national ambient air quality standards."
American Petroleum Institute v. Costle, 665 F.2d 1176, 1185 (D.C. Cir. 1981). At the same time,
courts have clarified the EPA may consider "relative proximity to peak background ...
concentrations" as a factor in deciding how to revise the NAAQS in the context of considering
standard levels within the range of reasonable values supported by the air quality criteria and
judgments of the Administrator. American Trucking Associations, Inc. v. EPA, 283 F.3d 355, 379
(D.C. Cir. 2002).
The requirement that primary standards provide an adequate margin of safety was
intended to address uncertainties associated with inconclusive scientific and technical
information available at the time of standard setting. It was also intended to provide a reasonable
degree of protection against hazards that research has not yet identified. See Lead Industries
Association v. EPA, 647 F.2d 1130, 1154 (D.C. Cir 1980), cert, denied, 449 U.S. 1042 (1980);
American Petroleum Institute v. Costle, 665 F.2d at 1186 (D.C. Cir. 1981), cert, denied, 455 U.S.
1034 (1982); Coalition of Battery Recyclers Ass'n v. EPA, 604 F.3d 613, 617-18 (D.C. Cir.
2010); Mississippi v. EPA, 744 F.3d 1334, 1353 (D.C. Cir. 2013). Both kinds of uncertainties are
5 The legislative history of section 109 indicates that a primary standard is to be set at "the maximum permissible
ambient air level.. . which will protect the health of any [sensitive] group of the population," and that for this
purpose "reference should be made to a representative sample of persons comprising the sensitive group rather
than to a single person in such a group." S. Rep. No. 91-1196, 91st Cong., 2d Sess. 10 (1970).
6 Under CAA section 302(h) (42 U.S.C. § 7602(h)), effects on welfare include, but are not limited to, "effects on
soils, water, crops, vegetation, manmade materials, animals, wildlife, weather, visibility, and climate, damage to
and deterioration of property, and hazards to transportation, as well as effects on economic values and on personal
comfort and well-being."
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components of the risk associated with pollution at levels below those at which human health
effects can be said to occur with reasonable scientific certainty. Thus, in selecting primary
standards that include an adequate margin of safety, the Administrator is seeking not only to
prevent pollution levels that have been demonstrated to be harmful but also to prevent lower
pollutant levels that may pose an unacceptable risk of harm, even if the risk is not precisely
identified as to nature or degree. The CAA does not require the Administrator to establish a
primary NAAQS at a zero-risk level or at background concentration levels, see Lead Industries
v. EPA, 647 F.2d at 1156 n.51, Mississippi v. EPA, 744 F.3d at 1351, but rather at a level that
reduces risk sufficiently so as to protect public health with an adequate margin of safety.
In addressing the requirement for an adequate margin of safety, the EPA considers such
factors as the nature and severity of the health effects involved, the size of the sensitive
population(s), and the kind and degree of uncertainties. The selection of any particular approach
to providing an adequate margin of safety is a policy choice left specifically to the
Administrator's judgment. See Lead Industries Association v. EPA, 647 F.2d at 1161-62;
Mississippi v. EPA, 744 F.3d at 1353.
Section 109(d)(1) of the Act requires a review be completed every five years and, if
appropriate, revision of existing air quality criteria to reflect advances in scientific knowledge on
the effects of the pollutant on public health and welfare. Under the same provision, the EPA is
also to review every five years and, if appropriate, revise the NAAQS, based on the revised air
quality criteria.7
Section 109(d)(2) addresses the appointment and advisory functions of an independent
scientific review committee. Section 109(d)(2)(A) requires the Administrator to appoint this
committee, which is to be composed of "seven members including at least one member of the
National Academy of Sciences, one physician, and one person representing State air pollution
control agencies." Section 109(d)(2)(B) provides that the independent scientific review
committee "shall complete a review of the criteria.. .and the national primary and secondary
ambient air quality standards...and shall recommend to the Administrator any new... standards
and revisions of existing criteria and standards as may be appropriate...." Since the early 1980s,
this independent review function has been performed by the Clean Air Scientific Advisory
Committee (CAS AC) of the EPA's Science Advisory Board. A number of other advisory
functions are also identified for the committee by section 109(d)(2)(C), which reads:
Such committee shall also (i) advise the Administrator of areas in which
additional knowledge is required to appraise the adequacy and basis of existing,
new, or revised national ambient air quality standards, (ii) describe the research
7 This section of the Act requires the Administrator to complete these reviews and make any revisions that may be
appropriate "at five-year intervals."
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efforts necessary to provide the required information, (iii) advise the
Administrator on the relative contribution to air pollution concentrations of
natural as well as anthropogenic activity, and (iv) advise the Administrator of any
adverse public health, welfare, social, economic, or energy effects which may
result from various strategies for attainment and maintenance of such national
ambient air quality standards.
As previously noted, the Supreme Court has held that section 109(b) "unambiguously bars cost
considerations from the NAAQS-setting process" (Whitman v. Am. Trucking Associations, 531
U.S. 457, 471 [2001]). Accordingly, while some of these issues regarding which Congress has
directed the CASAC to advise the Administrator are ones that are relevant to the standard setting
process, others are not. Issues that are not relevant to standard setting may be relevant to
implementation of the NAAQS once they are established.8
1.3 HISTORY OF REVIEWS OF THE PM NAAQS
This section summarizes the PM NAAQS that have been promulgated in past reviews
(Table 1-1). Each of these reviews is discussed briefly below.
8 Some aspects of CASAC advice may not be relevant to EPA's process of setting primary and secondary standards
that are requisite to protect public health and welfare. Indeed, were EPA to consider costs of implementation
when reviewing and revising the standards "it would be grounds for vacating the NAAQS." Whitman, 531 U.S. at
471 n.4. At the same time, the Clean Air Act directs CASAC to provide advice on "any adverse public health,
welfare, social, economic, or energy effects which may result from various strategies for attainment and
maintenance" of the NAAQS to the Administrator under section 109(d)(2)(C)(iv). In Whitman, the Court
clarified that most of that advice would be relevant to implementation but not standard setting, as it "enable [s] the
Administrator to assist the States in carrying out their statutory role as primary implementers of the NAAQS." Id.
at 470 (emphasis in original). However, the Court also noted that CASAC's "advice concerning certain aspects of
'adverse public health ... effects' from various attainment strategies is unquestionably pertinent" to the NAAQS
rulemaking record and relevant to the standard setting process. Id. at 470 n.2.
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1 Table 1-1. Summary of NAAQS promulgated for particulate matter 1971-2012.
Review
Completed
Indicator
Averaging
Time
Level
Form
1971
Total
Suspended
Particles
(TSP)
24-hour
260 |jg/m3
(primary)
150 pg/m3
(secondary)
Not to be exceeded more than once per year
Annual
75 |jg/m3
(primary)
60 |jg/m3
(secondary)
Annual geometric mean
1987
PM10
24-hour
150 |jg/m3
Not to be exceeded more than once per year on
average over a 3-year period
Annual
50 |jg/m3
Annual arithmetic mean, averaged over 3 years
1997
PM2.5
24-hour
65 |jg/m3
98th percentile, averaged over 3 years
Annual
15.0 |jg/m3
Annual arithmetic mean, averaged over 3 years3
PM10
24-hour
150 |jg/m3
99th percentile, averaged over 3 yearsb
Annual
50 |jg/m3
Annual arithmetic mean, averaged over 3 years
2006
PM2.5
24-hour
35 |jg/m3
98th percentile, averaged over 3 years
Annual
15.0 |jg/m3
Annual arithmetic mean, averaged over 3 years0
PM10
24-hourd
150 |jg/m3
Not to be exceed more than once per year on average
over a 3-year period
2012
PM2.5
24-hour
35 |jg/m3
98th percentile, averaged over 3 years
Annual
12.0 pg/m3
(primary)
15.0 pg/m3
(secondary)
Annual mean, averaged over 3 yearse
PM10
24-hour
150 pg/m3
Not to be exceeded more than once per year on
average over 3 years
Note: When not specified, primary and secondary standards are identical.
a The level of the 1997 annual PM2.5 standard was to be compared to measurements made at the community-
oriented monitoring site recording the highest concentration or, if specific constraints were met, measurements
from multiple community-oriented monitoring sites could be averaged (i.e., "spatial averaging") (62 FR 38652,
July 18, 1997).
b When the 1997 standards were vacated (see below), the form of the 1987 standards remained in place (i.e., not
to be exceeded more than once per year on average over a 3-year period).
c The EPA tightened the constraints on the spatial averaging criteria by further limiting the conditions under which
some areas may average measurements from multiple community-oriented monitors to determine compliance (71
FR 61144, October 17, 2006).
d The EPA revoked the annual PM10 NAAQS in 2006 (71 FR 61144, October 17, 2006).
e In the 2012 decision, the EPA eliminated the option for spatial averaging (78 FR 3086, January 15, 2013).
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1.3.1 Reviews Completed in 1971 and 1987
The EPA first established NAAQS for PM in 1971 (36 FR 8186, April 30, 1971), based
on the original Air Quality Criteria Document (AQCD) (DHEW, 1969).9 The federal reference
method (FRM) specified for determining attainment of the original standards was the high-
volume sampler, which collects PM up to a nominal size of 25 to 45 micrometers (|im) (referred
to as total suspended particulates or TSP). The primary standards were set at 260 |ig/m3, 24-hour
average, not to be exceeded more than once per year, and 75 |ig/m3, annual geometric mean. The
secondary standards were set at 150 |ig/m3, 24-hour average, not to be exceeded more than once
per year, and 60 |ig/m3, annual geometric mean.
In October 1979 (44 FR 56730, October 2, 1979), the EPA announced the first periodic
review of the air quality criteria and NAAQS for PM. Revised primary and secondary standards
were promulgated in 1987 (52 FR 24634, July 1, 1987). In the 1987 decision, the EPA changed
the indicator for particles from TSP to PMio, in order to focus on the subset of inhalable particles
small enough to penetrate to the thoracic region of the respiratory tract (including the
tracheobronchial and alveolar regions), referred to as thoracic particles.10 The level of the 24-
hour standards (primary and secondary) was set at 150 |ig/m3, and the form was one expected
exceedance per year, on average over three years. The level of the annual standards (primary and
secondary) was set at 50 |ig/m3, and the form was annual arithmetic mean, averaged over three
years.
1.3.2 Review Completed in 1997
In April 1994, the EPA announced its plans for the second periodic review of the air
quality criteria and NAAQS for PM, and in 1997 the EPA promulgated revisions to the NAAQS
(62 FR 38652, July 18, 1997). In the 1997 decision, the EPA determined that the fine and coarse
fractions of PMio should be considered separately. This determination was based on evidence
that serious health effects were associated with short- and long-term exposures to fine particles in
areas that met the existing PMio standards. The EPA added new standards, using PM2.5 as the
indicator for fine particles (with PM2.5 referring to particles with a nominal mean aerodynamic
diameter less than or equal to 2.5 |im). The new primary standards were as follows: (1) an annual
standard with a level of 15.0 |ig/m3, based on the 3-year average of annual arithmetic mean
9 Prior to the review initiated in 2007 (see below), the AQCD provided the scientific foundation (i.e., the air quality
criteria) for the NAAQS. Beginning in that review, the ISA has replaced the AQCD.
10 PMio refers to particles with a nominal mean aerodynamic diameter less than or equal to 10 |im. More
specifically, 10 |im is the aerodynamic diameter for which the efficiency of particle collection is 50 percent.
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PM2.5 concentrations from single or multiple community-oriented monitors;11 and (2) a 24-hour
standard with a level of 65 |ig/m3, based on the 3-year average of the 98th percentile of 24-hour
PM2.5 concentrations at each monitor within an area. Also, the EPA established a new reference
method for the measurement of PM2.5 in the ambient air and adopted rules for determining
attainment of the new standards. To continue to address the health effects of the coarse fraction
of PM10 (referred to as thoracic coarse particles or PM10-2.5; generally including particles with a
nominal mean aerodynamic diameter greater than 2.5 |im and less than or equal to 10 |im), the
EPA retained the annual primary PM10 standard and revised the form of the 24-hour primary
PM10 standard to be based on the 99th percentile of 24-hour PM10 concentrations at each monitor
in an area. The EPA revised the secondary standards by setting them equal in all respects to the
newly established primary standards.
Following promulgation of the 1997 PM NAAQS, petitions for review were filed by
several parties, addressing a broad range of issues. In May 1999, the U.S. Court of Appeals for
the District of Columbia Circuit (D.C. Circuit) upheld the EPA's decision to establish fine
particle standards, holding that "the growing empirical evidence demonstrating a relationship
between fine particle pollution and adverse health effects amply justifies establishment of new
fine particle standards." American Trucking Associations v. EPA, 175 F. 3d at 1027, 1055-56
(D.C. Cir. 1999). The D.C. Circuit also found "ample support" for the EPA's decision to regulate
coarse particle pollution, but vacated the 1997 PM10 standards, concluding that the EPA had not
provided a reasonable explanation justifying use of PM10 as an indicator for coarse particles.
American Trucking Associations v. EPA, 175 F. 3d at 1054-55. Pursuant to the D.C. Circuit's
decision, the EPA removed the vacated 1997 PM10 standards, and the pre-existing 1987 PM10
standards remained in place (65 FR 80776, December 22, 2000). The D.C. Circuit also upheld
the EPA's determination not to establish more stringent secondary standards for fine particles to
address effects on visibility. American Trucking Associations v. EPA, 175 F. 3d at 1027.
The D.C. Circuit also addressed more general issues related to the NAAQS, including
issues related to the consideration of costs in setting NAAQS and the EPA's approach to
establishing the levels of NAAQS. Regarding the cost issue, the court reaffirmed prior rulings
holding that in setting NAAQS the EPA is "not permitted to consider the cost of implementing
those standards." American Trucking Associations v. EPA, 175 F. 3d at 1040-41. Regarding the
levels of NAAQS, the court held that the EPA's approach to establishing the level of the
11 The 1997 annual PM2.5 standard was to be compared with measurements made at the community-oriented
monitoring site recording the highest concentration or, if specific constraints were met, measurements from
multiple community-oriented monitoring sites could be averaged (i.e., "spatial averaging"). In the 2012 review,
the EPA replaced the term "community-oriented" monitor with the term "area-wide" monitor. Area-wide
monitors are those sited at the neighborhood scale or larger, as well as those monitors sited at micro - or middle-
scales that are representative of many such locations in the same CBSA (78 FR 3236, January 15, 2013).
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standards in 1997 (i.e., both for PM and for the ozone NAAQS promulgated on the same day)
effected "an unconstitutional delegation of legislative authority." American Trucking
Associations v. EPA, 175 F. 3d at 1034-40. Although the court stated that "the factors EPA uses
in determining the degree of public health concern associated with different levels of ozone and
PM are reasonable," it remanded the rule to the EPA, stating that when the EPA considers these
factors for potential non-threshold pollutants "what EPA lacks is any determinate criterion for
drawing lines" to determine where the standards should be set.
The D.C. Circuit's holding on the cost and constitutional issues were appealed to the
United States Supreme Court. In February 2001, the Supreme Court issued a unanimous decision
upholding the EPA's position on both the cost and constitutional issues. Whitman v. American
Trucking Associations, 531 U.S. 457, 464, 475-76. On the constitutional issue, the Court held
that the statutory requirement that NAAQS be "requisite" to protect public health with an
adequate margin of safety sufficiently guided the EPA's discretion, affirming the EPA's
approach of setting standards that are neither more nor less stringent than necessary.
The Supreme Court remanded the case to the Court of Appeals for resolution of any
remaining issues that had not been addressed in that court's earlier rulings. Id. at 475-76. In a
March 2002 decision, the Court of Appeals rejected all remaining challenges to the standards,
holding that the EPA's PM2.5 standards were reasonably supported by the administrative record
and were not "arbitrary and capricious" American Trucking Associations v. EPA, 283 F. 3d 355,
369-72 (D.C. Cir. 2002).
1.3.3 Review Completed in 2006
In October 1997, the EPA published its plans for the third periodic review of the air
quality criteria and NAAQS for PM (62 FR 55201, October 23, 1997). After the CASAC and
public review of several drafts, the EPA's NCEA finalized the AQCD in October 2004 (U.S.
EPA, 2004a, U.S. EPA, 2004b). The EPA's OAQPS finalized a Risk Assessment and Staff Paper
in December 2005 (Abt Associates, 2005, U.S. EPA, 2005).12 On December 20, 2005, the EPA
announced its proposed decision to revise the NAAQS for PM and solicited public comment on a
broad range of options (71 FR 2620, January 17, 2006). On September 21, 2006, the EPA
announced its final decisions to revise the primary and secondary NAAQS for PM to provide
increased protection of public health and welfare, respectively (71 FR 61144, October 17, 2006).
With regard to the primary and secondary standards for fine particles, the EPA revised the level
12 Prior to the review initiated in 2007, the Staff Paper presented the EPA staffs considerations and conclusions
regarding the adequacy of existing NAAQS and, when appropriate, the potential alternative standards that could
be supported by the evidence and information. More recent reviews present this information in the Policy
Assessment.
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of the 24-hour PM2.5 standards to 35 |ig/m3, retained the level of the annual PM2.5 standards at
15.0 |ig/m3, and revised the form of the annual PM2.5 standards by narrowing the constraints on
the optional use of spatial averaging. With regard to the primary and secondary standards for
PM10, the EPA retained the 24-hour standards, with levels at 150 |ig/m3, and revoked the annual
standards.13 The Administrator judged that the available evidence generally did not suggest a link
between long-term exposure to existing ambient levels of coarse particles and health or welfare
effects. In addition, a new reference method was added for the measurement of PM10-2.5 in the
ambient air in order to provide a basis for approving federal equivalent methods (FEMs) and to
promote the gathering of scientific data to support future reviews of the PM NAAQS.
Several parties filed petitions for review following promulgation of the revised PM
NAAQS in 2006. These petitions addressed the following issues: (1) selecting the level of the
primary annual PM2.5 standard; (2) retaining PM10 as the indicator of a standard for thoracic
coarse particles, retaining the level and form of the 24-hour PM10 standard, and revoking the
PM10 annual standard; and (3) setting the secondary PM2.5 standards identical to the primary
standards. On February 24, 2009, the U.S. Court of Appeals for the District of Columbia Circuit
issued its opinion in the case American Farm Bureau Federation v. EPA, 559F. 3d512 (D.C.
Cir. 2009). The court remanded the primary annual PM2.5 NAAQS to the EPA because the
Agency failed to adequately explain why the standards provided the requisite protection from
both short- and long-term exposures to fine particles, including protection for at-risk populations.
American Farm Bureau Federation v. EPA, 559 F. 3d 512, 520-27 (D.C. Cir. 2009). With regard
to the standards for PM10, the court upheld the EPA's decisions to retain the 24-hour PM10
standard to provide protection from thoracic coarse particle exposures and to revoke the annual
PM10 standard. American Farm Bureau Federation, 559 F. 2d at 533-38. With regard to the
secondary PM2.5 standards, the court remanded the standards to the EPA because the Agency
failed to adequately explain why setting the secondary PM standards identical to the primary
standards provided the required protection for public welfare, including protection from visibility
impairment. American Farm Bureau Federation, 559 F. 2d at 528-32. The EPA responded to the
court's remands as part of the next review of the PM NAAQS, which was initiated in 2007
(discussed below).
13 In the 2006 proposal, the EPA proposed to revise the 24-hour PM10 standard in part by establishing a new PMi0-2.5
indicator for thoracic coarse particles (i.e., particles generally between 2.5 and 10 |im in diameter). The EPA
proposed to include any ambient mix of PM10-2 5 that was dominated by resuspended dust from high density
traffic on paved roads and by PM from industrial sources and construction sources. The EPA proposed to exclude
any ambient mix of PMi 0-2.5 that was dominated by rural windblown dust and soils and by PM generated from
agricultural and mining sources. In the final decision, the existing PM10 standard was retained, in part due to an
"inability.. .to effectively and precisely identify which ambient mixes are included in the [PM10-2 5] indicator and
which are not" (71 FR 61197, October 17, 2006).
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1.3.4 Review Completed in 2012
In June 2007, the EPA initiated the fourth periodic review of the air quality criteria and
the PM NAAQS by issuing a call for information in the Federal Register (72 FR 35462, June 28,
2007). Based on the NAAQS review process, as revised in 2008 and again in 2009,14 the EPA
held science/policy issue workshops on the primary and secondary PM NAAQS (72 FR 34003,
June 20, 2007; 72 FR 34005, June 20, 2007), and prepared and released the planning and
assessment documents that comprise the review process (i.e., IRP (U.S. EPA, 2008), ISA (U.S.
EPA, 2009a), REA planning documents for health and welfare (U.S. EPA, 2009b, U.S. EPA,
2009c), a quantitative health risk assessment (U.S. EPA, 2010a) and an urban-focused visibility
assessment (U.S. EPA, 2010b), and PA (U.S. EPA, 2011)). In June 2012, the EPA announced its
proposed decision to revise the NAAQS for PM (77 FR 38890, June 29, 2012).
In December 2012, the EPA announced its final decisions to revise the primary NAAQS
for PM to provide increased protection of public health (78 FR 3086, January 15, 2013). With
regard to primary standards for PM2.5, the EPA revised the level of the annual PM2.5 standard15 to
12.0 |ig/m3 and retained the 24-hour PM2.5 standard, with its level of 35 |ig/m3. For the primary
PM10 standard, the EPA retained the 24-hour standard to continue to provide protection against
effects associated with short-term exposure to thoracic coarse particles (i.e., PM10-2.5). With
regard to the secondary PM standards, the EPA generally retained the 24-hour and annual PM2.5
standards16 and the 24-hour PM10 standard to address visibility and non-visibility welfare effects.
As with previous reviews, petitioners challenged the EPA's final rule. Petitioners argued
that the EPA acted unreasonably in revising the level and form of the annual standard and in
amending the monitoring network provisions. On judicial review, the revised standards and
monitoring requirements were upheld in all respects. NAMv EPA, 750 F.3d 921 (D.C. Cir.
2014).
1.3.5 Review Completed in 2020
In December 2014, the EPA announced the initiation of the periodic review of the air
quality criteria for PM and of the PM2.5 and PM10 NAAQS and issued a call for information in
the Federal Register (79 FR 71764, December 3, 2014). On February 9 to 11, 2015, the EPA's
NCEA and OAQPS held a public workshop to inform the planning for the current review of the
PM NAAQS (announced in 79 FR 71764, December 3, 2014). Workshop participants, including
14 The history of the NAAQS review process, including revisions to the process, is discussed at
https://www.epa.gov/naaqs/historical-information-naaqs-review-process.
15 The EPA also eliminated the option for spatial averaging.
16 Consistent with the primary standard, the EPA eliminated the option for spatial averaging with the annual
standard.
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a wide range of external experts as well as EPA staff representing a variety of areas of expertise
(e.g., epidemiology, human and animal toxicology, risk/exposure analysis, atmospheric science,
visibility impairment, climate effects), were asked to highlight significant new and emerging PM
research, and to make recommendations to the Agency regarding the design and scope of this
review. This workshop provided for a public discussion of the key science and policy-relevant
issues around which the EPA has structured the current review of the PM NAAQS and of the
most meaningful new scientific information that would be available in this review to inform our
understanding of these issues.
The input received at the workshop guided the EPA staff in developing a draft IRP,
which was reviewed by the CASAC Particulate Matter Panel and discussed on public
teleconferences held in May 2016 (81 FR 13362, March 14, 2016) and August 2016 (81 FR
39043, June 15, 2016). Advice from the CASAC, supplemented by the Particulate Matter Panel,
and input from the public were considered in developing the final IRP for this review (U.S. EPA,
2016). The final IRP discusses the approaches to be taken in developing key scientific, technical,
and policy documents in this review and the key policy-relevant issues that will frame the EPA's
consideration of whether the current primary and/or secondary NAAQS for PM should be
retained or revised.
In May 2018, the Administrator issued a memorandum describing a "back-to-basics"
process for reviewing the NAAQS (Pruitt, 2018). This memo announced the Agency's intention
to conduct the current review of the PM NAAQS in such a manner as to ensure that any
necessary revisions were finalized by December 2020. Following this memo, on October 10,
2018 the Administrator additionally announced that the role of reviewing the key science
assessments developed as part of the ongoing review of the PM NAAQS (i.e., drafts of the ISA
and PA) would be performed only by the seven-member chartered CASAC (i.e., without the
support of the CASAC Particulate Matter Panel that reviewed the draft IRP).17
The EPA released the draft ISA in October 2018 (83 FR 53471, October 23, 2018). The
draft ISA was reviewed by the chartered CASAC at a public meeting held in Arlington, VA in
December 2018 (83 FR 55529, November 6, 2018) and was discussed on a public teleconference
in March 2019 (84 FR 8523, March 8, 2019). The CASAC provided its advice on the draft ISA
in a letter to the EPA Administrator dated April 11, 2019 (Cox, 2019a). The EPA took steps to
address these comments in the final ISA, which was released in December 2019 (U.S. EPA,
2019).
The EPA released the draft PA in September 2019 (84 FR 47944, September 11, 2019).
The draft PA was reviewed by the chartered CASAC and discussed in October 2019 at a public
17 Announcement available at: https://www.regulations.gov/document/EPA-HQ-OAR-2015-0072-0223
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meeting held in Cary, NC. Public comments were received via a separate public teleconference
(84 FR 51555, September 30, 2019). A public meeting to discuss the chartered CASAC letter
and response to charge questions on the draft PA was held in Cary, NC in December 2019 (84
FR 58713, November 1, 2019), and the CASAC provided its advice on the draft PA, including its
advice on the current primary and secondary PM standards, in a letter to the EPA Administrator
dated December 16, 2019 (Cox, 2019b). With regard to the primary standards, the CASAC
recommended retaining the current 24-hour PM2.5 and PM10 standards but did not reach
consensus on the adequacy of the current annual PM2.5 standard. With regard to the secondary
standards, the CASAC recommended retaining the current standards. In response to the
CASAC's comments, the 2020 final PA incorporated a number of changes (U.S. EPA, 2020), as
described in detail in section I.C.5 of the 2020 proposal (85 FR 24100, April 30, 2020).
On April 14, 2020, the EPA proposed to retain all of the primary and secondary PM
standards, without revision. These proposed decisions were published in the Federal Register on
April 30, 2020 (85 FR 24094, April 30, 2020). The EPA's final decision on the PM NAAQS was
published in the Federal Register on December 18, 2020 (85 FR 82684, December 18, 2020). In
the 2020 rulemaking, the EPA retained the primary and secondary PM2.5 and PM10 standards,
without revision. The EPA received three petitions for judicial review (described in more detail
in section 1.4.3 below), as well as three petitions for reconsideration of the 2020 final action.
1.4 RECONSIDERATION OF THE 2020 PM NAAQS FINAL ACTION
On January 20, 2021, President Biden issued an "Executive Order on Protecting Public
Health and the Environment and Restoring Science to Tackle the Climate Crisis," (Executive
Order 13990; 86 FR 7037, January 25, 2021)18 which directed review of certain agency actions.
An accompanying fact sheet provides a non-exclusive list of agency actions that agency heads
will review in accordance with that order, including the 2020 Particulate Matter NAAQS
Decision.19
1.4.1 Decision to Initiate a Reconsideration
On June 10, 2021, the Agency announced its decision to reconsider the 2020 PM
NAAQS final action.20 The EPA is reconsidering the December 2020 decision because the
18 See https://www.whitehouse.gOv/briefing-room/presidential-actions/2021/01/20/executive-order-protecting-
public-health-and-environment-and-restoring-science-to-tackle-climate-crisis/
19 See https://www.whitehouse.gOv/briefing-room/statements-releases/2021/01/20/fact-sheet-list-of-agency-actions-
for-review/
20 The press release fortius announcement is available at: https://www.epa.gov/newsreleases/epa-reexamine-health-
standards-harmful-soot-previous-administration-left-unchanged
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available scientific evidence and technical information indicate that the current standards may
not be adequate to protect public health and welfare, as required by the Clean Air Act. We note
that the 2020 PA concluded that the scientific evidence and information supported revising the
level of the primary annual PM2.5 standard to below the current level of 12 |ig/m3 while retaining
the primary 24-hour PM2.5 standard (U.S. EPA, 2020). The EPA also notes that the 2020 PA
concluded that the available scientific evidence and information supported retaining the primary
PM10 standard and secondary PM standards without revision (U.S. EPA, 2020).
1.4.2 Process for Reconsideration of the 2020 PM NAAQS Decision
In its announcement of the reconsideration of the PM NAAQS, the Agency explained
that, in support of the reconsideration, it would develop a supplement to the 2019 ISA and a
revised PA. The EPA also explained that the draft ISA Supplement and draft PA would be
reviewed at a public meeting by the CASAC, and the public will have opportunities to comment
on these documents during the CASAC review process, as well as to provide input during the
rulemaking through the public comment process and public hearings on the proposed
rulemaking.
On March 31, 2021, the Administrator announced his decision to reestablish the
membership of the CASAC to "ensure the agency received the best possible scientific insight to
support our work to protect human health and the environment."21 Consistent with this
memorandum, a call for nominations of candidates to the EPA's chartered CASAC was
published in the Federal Register (86 FR 17146, April 1, 2021). On June 17, 2021, the
Administrator announced his selection of the seven members to serve on the chartered CASAC.22
23 Additionally, a call for nominations of candidates to a PM-specific panel was published in the
Federal Register (86 FR 33703, June 25, 2021). The members of the PM CASAC panel were
announced on August 30, 2021 24
The draft ISA Supplement was released in September 2021 (U.S. EPA, 2021). The
evidence presented within the 2019 ISA, along with the targeted identification and evaluation of
new scientific information in the draft ISA Supplement, provides the scientific basis for the
21 The press release fortius announcement is available at: https://www.epa.gov/newsreleases/administrator-regan-
directs-epa-reset-critical-science-focused-federal-advisory
22 The press release fortius announcement is available at: https://www.epa.gov/newsreleases/epa-announces-
selections-charter-members-clean-air-scientific-advisory-committee
23 The list of members of the chartered CASAC and their biosketches are available at:
https://yosemite.epa. gov/sab/sabpeople.nsf/WebExternalCommitteeRosters?OpenView&committee=CASAC&sec
ondname=Clean%20Air%20Scientific%20Advisory%20Committee%20
24 The list of members of the PM CASAC panel and their biosketches are available at:
https://casac. epa.gov/ords/sab/f?p=105:14:99 79229564047: ::14:P14COMMITTEEON:2021%20CASA C%20P
M%>20Panel
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reconsideration of the 2020 PM NAAQS final decision. The draft ISA Supplement focuses on a
thorough evaluation of some studies that became available after the literature cutoff date of the
2019 ISA that could either further inform the adequacy of the current PM NAAQS or address
key scientific topics that have evolved since the literature cutoff date for the 2019 ISA. In
selecting the health effects to evaluate within the draft ISA Supplement, the EPA focused on the
strongest causality determinations for health effects categories presented in the 2019 ISA, and
the subsequent use of the health effects evidence in the 2020 PA with respect to which were most
useful in informing staff conclusions (U.S. EPA, 2021).25 Specifically, within the draft ISA
Supplement, the focus is only on the health effects evidence where the 2019 ISA concluded a
"causal relationship" (U.S. EPA, 2021, section 1.2.1). Consistent with the rationale for the health
effects, the selection of the welfare effects to evaluate within the draft ISA Supplement were
based on the causality determinations reported in the 2019 ISA and the subsequent use of
scientific evidence in the 2020 PA.26 Specifically, for welfare effects, the focus within the draft
ISA Supplement is on visibility effects. The draft ISA Supplement also considers recent health
effects evidence that addresses key scientific topics where the literature has evolved since the
2020 review was completed, specifically since the literature cutoff date for the 2019 ISA.27
Building on the rationale presented in section 1.2.1, the draft ISA Supplement considered
peer-reviewed studies published from approximately January 2018 through March 2021 that
meet the following criteria:
• Health effects:
- Health effect categories where the 2019 PM ISA concluded a "causal
relationship" (i.e., short- and long-term PM2.5 exposure and cardiovascular effects
25 As described in section 1.2.1 of the draft ISA Supplement: "In considering the public health protection provided
by the current primary PM2 5 standards, and the protection that could be provided by alternatives, [the U.S. EPA,
within the 2020 PM PA] emphasized health outcomes for which the ISA determined that the evidence supports
either a "causal" or a "likely to be causal" relationship with PM2 5 exposures" (U.S. EPA, 2020). Although the
2020 PA initially focused on this broader set of evidence, the basis of the discussion on potential alternative
standards primarily focused on health effect categories where the 2019 PM ISA concluded a "causal
relationship" (i.e., short- and long-term PM2 5 exposure and cardiovascular effects and mortality) as reflected in
Figures 3-7 and 3-8 of the 2020 PA (U.S. EPA, 2020).
26 As described in section 1.2.1 of the draft ISA Supplement: The 2019 PM ISA concluded a "causal relationship"
for each of the welfare effects categories evaluated (i.e., visibility, climate effects and materials effects). While
the 2020 PA considered the broader set of evidence for these effects, for climate effects and material effects, it
concluded that there remained "substantial uncertainties with regard to the quantitative relationships with PM
concentrations and concentration patterns that limit[ed] [the] ability to quantitatively assess the public welfare
protection provided by the standards from these effects" (U.S. EPA, 2020).
27 These key scientific topics include experimental studies conducted at near-ambient concentrations, epidemiologic
studies that employed causal modeling methods or conducted accountability analyses, studies that assess the
relationship between PM2 5 exposure and Coronavirus Disease 2019 (COVID-19) infection and death; and in
accordance with recent EPA guidance on addressing environmental justice, studies that examine disparities in
PM2.5 exposure and the risk of health effects (U.S. EPA, 2021, section 1.2.1).
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and mortality). Additionally, for these health effect categories the recent studies
evaluated are limited to:
o U.S. and Canadian epidemiologic studies
o Epidemiologic studies that employed causal modeling methods or
conducted accountability analyses (i.e., examined the impact of a policy
on reducing PM2.5 concentrations)
• Welfare Effects:
- U.S. and Canadian studies that provide new information on public preferences for
visibility impairment and/or developed methodologies or conducted quantitative
analyses of light extinction
• Key Scientific Topics
- Experimental studies (i.e., controlled human exposure and animal toxicological)
conducted at near-ambient PM2.5 concentrations
- At-Risk Populations
o U.S. and Canadian-based epidemiologic or exposure studies examining
potential disparities in either PM2.5 exposures or the risk of health effects
by race/ethnicity or socioeconomic status (SES)
- U.S. and Canadian-based epidemiologic studies that examined the relationship
between PM2.5 exposures and COVID-19 infection and/or death
Given the narrow scope of the draft ISA Supplement, it is important to recognize that the
evaluation does not encompass the full multidisciplinary evaluation presented within the 2019
ISA that would result in weight-of-evidence conclusions on causality (i.e., causality
determinations). The draft ISA Supplement critically evaluates and provides key study specific
information for those recent studies deemed to be of greatest significance for informing
preliminary conclusions on the PM NAAQS in the context of the body of evidence and scientific
conclusions presented in the 2019 ISA.
This draft PA considers the scientific evidence presented in the 2019 ISA and draft ISA
Supplement. This draft PA additionally considers the quantitative and technical information
presented in the 2020 PA, along with updated and newly available analyses since the completion
of the 2020 review. For those health and welfare effects for which the draft ISA Supplement
evaluated recently available evidence and updated quantitative analyses were supported (i.e.,
PM2.5-related health effects and visibility effects), the draft PA includes consideration of this
newly available scientific and technical information in reaching preliminary conclusions. For
those health and welfare effects for which newly available scientific and technical information
were not evaluated (i.e., PMio-25-related health effects and non-visibility effects), the preliminary
conclusions presented in this draft PA rely heavily on the information that supported the
conclusions in the 2020 PA.
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1 1.4.3 Ongoing Litigation
2 Following publication of the 2020 final action, several parties filed petitions for review of
3 the EPA's final decision in the D.C. Circuit and the Court consolidated the cases. In order to
4 consider whether reconsideration of the 2020 final action was warranted, the EPA moved for two
5 90-day abeyances in these consolidated cases, which the Court granted. After the EPA
6 announced that is reconsidering the 2020 final decision, the EPA filed a motion with the Court to
7 hold the consolidated cases in abeyance until March 1, 2023. The court has not yet acted on the
8 EPA's motion, which the court granted on October 1, 2021.
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REFERENCES
Abt Associates, Inc. (2005). Particulate matter health risk assessment for selected urban areas:
Draft report. EPA Contract No. 68-D-03-002. U.S. Environmental Protection Agency.
Research Triangle Park, NC. Available at:
http://www3.epa.gov/ttn/naaqs/standards/pm/data/PMrisk2005122Q.pdf.
Cox, LA. (2019a). Letter from Louis Anthony Cox, Jr., Chair, Clean Air Scientific Advisory
Committee, to Administrator Andrew R. Wheeler. Re: CASAC Review of the EPA's
Integrated Science Assessment for Particulate Matter (External Review Draft - October
2018). April 11, 2019. EPA-CASAC-19-002. Office of the Administrator, Science
Advisory Board U.S. EPA HQ, Washington DC. Available at:
https://vosemite.epa.gov/sab/sabproduct.nsf/LookupWebReportsLastMonthCASAC/932
D1DF8C2A9043F852581000048170D?QpenDocument&TableRow=2.3#2.
Cox, LA. (2019b). Letter from Louis Anthony Cox, Jr., Chair, Clean Air Scientific Advisory
Committee, to Administrator Andrew R. Wheeler. Re: CASAC Review of the EPA's
Policy Assessment for the Review of the National Ambient Air Quality Standards for
Particulate Matter (External Review Draft - September 2019). December 16, 2019. EPA-
CASAC-20-001. Office of the Administrator, Science Advisory Board U.S. EPA HQ,
Washington DC. Available at:
https://vosemite.epa.gov/sab/sabproduct.nsf/264cbl227d55e02c852574020Q7446a4/E2F
6C7173 72016128525 84D20069DFB l/$File/EP A-C AS AC-20-001 .pdf.
DHEW (1969). Air Quality Criteria for Particulate Matter. National Air Pollution Control
Administration. Washington, D.C. U.S. Department of Health. January 1969.
Pruitt, E. (2018). Memorandum from E. Scott Pruitt, Administrator, U.S. EPA to Assistant
Administrators. Back-to-Basics Process for Reviewing National Ambient Air Quality
Standards. May 9, 2018. Office of the Administrator U.S. EPA HQ, Washington DC.
Available at: https://www.epa.gov/criteria-air-pollutants/back-basics-process-reviewing-
national-ambient-air-qualitv-standards.
U.S. EPA (2004a). Air Quality Criteria for Particulate Matter. (Vol I of II). Office of Research
and Development. Research Triangle Park, NC. U.S. EPA. EPA-600/P-99-002aF.
October 2004. Available at:
https://nepis.epa. gov/Exe/ZvPURL.cgi?Dockev=P 100LFIQ.txt.
U.S. EPA (2004b). Air Quality Criteria for Particulate Matter. (Vol II of II). Office of Research
and Development. Research Triangle Park, NC. U.S. EPA. EPA-600/P-99-002bF.
October 2004. Available at:
https://nepis.epa. gov/Exe/ZvPURL.cgi?Dockev=P 100LG7Q.txt.
U.S. EPA (2005). Review of the National Ambient Air Quality Standards for Particulate Matter:
Policy Assessment of Scientific and Technical Information, OAQPS Staff Paper. Office
of Air Quality Planning and Standards. Research Triangle Park, NC. U.S. EPA. EPA-
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452/R-05-005a. December 2005. Available at:
https://nepis.epa. gov/Exe/ZyPURL.cgi?Dockev=P 1009MZM.txt.
U.S. EPA (2008). Integrated Review Plan for the National Ambient Air Quality Standards for
Particulate Matter. Office of Research and Development, National Center for
Environmental Assessment; Office of Air Quality Planning and Standards, Health and
Environmental Impacts Division. Research Triangle Park, NC. U.S. EPA. EPA 452/R-08-
004. March 2008. Available at:
https://nepis.epa. gov/Exe/ZvPURL.cgi?Dockev=P 100 lFB9.txt.
U.S. EPA (2009a). Integrated Science Assessment for Particulate Matter (Final Report). Office
of Research and Development, National Center for Environmental Assessment. Research
Triangle Park, NC. U.S. EPA. EPA-600/R-08-139F. December 2009. Available at:
https://cfpub.epa. gov/ncea/risk/recordisplav.cfm?deid=216546.
U.S. EPA (2009b). Particulate Matter National Ambient Air Quality Standards: Scope and
Methods Plan for Health Risk and Exposure Assessment. Office of Air Quality Planning
and Standards, Health and Environmental Impacts Division. Research Triangle Park, NC.
U.S. EPA. EPA-452/P-09-002. February 2009. Available at:
https://nepis.epa. gov/Exe/ZvPURL.cgi?Dockev=P 100FLWP.txt.
U.S. EPA (2009c). Particulate Matter National Ambient Air Quality Standards: Scope and
Methods Plan for Urban Visibility Impact Assessment. Office of Air Quality Planning
and Standards, Health and Environmental Impacts Division. Research Triangle Park, NC.
U.S. EPA. EPA-452/P-09-001. February 2009. Available at:
https://nepis.epa. gov/Exe/ZvPURL.cgi?Dockev=P 100FLUX.txt.
U.S. EPA (2010a). Quantitative Health Risk Assessment for Particulate Matter (Final Report).
Office of Air Quality Planning and Standards, Health and Environmental Impacts
Division. Research Triangle Park, NC. U.S. EPA. EPA-452/R-10-005. June 2010.
Available at: https://nepis.epa.gov/Exe/ZvPURL.cgi?Dockev=P1007RFC.txt.
U.S. EPA (2010b). Particulate Matter Urban-Focused Visibility Assessment (Final Document).
Office of Air Quality Planning and Standards, Health and Environmental Impacts
Division. Research Triangle Park, NC. U.S. EPA. EPA-452/R-10-004. July 2010.
Available at: https://nepis.epa.gov/Exe/ZvPURL.cgi?Dockev=P 100FQ5D.txt.
U.S. EPA (2011). Policy Assessment for the Review of the Particulate Matter National Ambient
Air Quality Standards. Office of Air Quality Planning and Standards, Health and
Environmental Impacts Division. Research Triangle Park, NC. U.S. EPA. EPA-452/R-
11-003. April 2011. Available at:
https://nepis.epa. gov/Exe/ZvPURL.cgi?Dockev=P 100AUMY.txt.
U.S. EPA (2016). Integrated review plan for the national ambient air quality standards for
particulate matter. Office of Air Quality Planning and Standards. Research Triangle Park,
NC. U.S. EPA. EPA-452/R-16-005. December 2016. Available at:
https://www3.epa.gov/ttn/naaqs/standards/pm/data/201612-final-integrated-review-
plan.pdf.
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U.S. EPA (2019). Integrated Science Assessment (ISA) for Particulate Matter (Final Report).
U.S. Environmental Protection Agency, Office of Research and Development, National
Center for Environmental Assessment. Washington, DC. U.S. EPA. EPA/600/R-19/188.
December 2019. Available at: https://www.epa.gov/naaqs/particulate-matter-pm-
standards-integrated-science-assessments-current-review.
U.S. EPA (2020). Policy Assessment for the Review of the National Ambient Air Quality
Standards for Particulate Matter. Office of Air Quality Planning and Standards, Health
and Environmental Impacts Division. Research Triangle Park, NC. U.S. EPA. EPA-
452/R-20-002. January 2020. Available at: https://www.epa.gov/sites/default/files/202Q-
01/documents/final policy assessment for the review of the pm naaqs 01-2020.pdf.
U.S. EPA (2021). Supplement to the 2019 Integrated Science Assessment for Particulate Matter
(External Review Draft). U.S. Environmental Protection Agency, Office of Research and
Development, Center for Public Health and Environmental Assessment. Washington,
DC. U.S. EPA. EPA/600/R-21/198. December 2019. Available at:
https://www.epa.gov/naaqs/particulate-matter-pm-standards-integrated-science-
assessments-current-review.
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2 PM AIR QUALITY
This chapter provides an overview of recent ambient air quality with respect toPM. It
summarizes information on the distribution of particle size in ambient air, including discussions
about size fractions and components (section 2.1), ambient monitoring of PM in the U.S. (section
2.2), ambient concentrations of PM in the U.S. (section 2.3), and background PM (section 2.4).
2.1 DISTRIBUTION OF PARTICLE SIZE IN AMBIENT AIR
In ambient air, PM is a mixture of substances suspended as small liquid and/or solid
particles. Particle size is an important consideration for PM, as distinct health and welfare effects
have been linked with exposures to particles of different sizes. Particles in the atmosphere range
in size from less than 0.01 to more than 10 micrometers (|im) in diameter (U.S. EPA, 2019b,
section 2.2). When describing PM, subscripts are used to denote the aerodynamic diameter1 of
the particle size range in micrometers (|im) of 50% cut points of sampling devices. The EPA
defines PM2.5, also referred to as fine particles, as particles with aerodynamic diameters
generally less than or equal to 2.5 [j,m. The size range for PM10-2.5, also called coarse or thoracic
coarse particles, includes those particles with aerodynamic diameters generally greater than 2.5
[j,m and less than or equal to 10 [j,m. PM10, which is comprised of both fine and coarse fractions,
includes those particles with aerodynamic diameters generally less than or equal to 10 [j,m.
Figure 2-1 provides perspective on these particle size fractions. In addition, ultrafine particles
(UFP) are often defined as particles with a diameter of less than 0.1 [j,m based on physical size,
thermal diffusivity or electrical mobility (U.S. EPA, 2019b, section 2.2).
1 Aerodynamic diameter is thesize of a sphere of unit density (i.e., 1 g/cm3) that has the same terminal settling
velocity astheparticle ofinterest (U.S. EPA, 2018,U.S. EPA, 2019b,section 4.1.1).
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90 (im (microns) in diameter
FINE BEACH SAND
C-PMio
Dust, pollen, mold, etc.
<10|ilT) (microns) in diameter
C PM2.5
Combustion particles, organic
compounds, metals, etc.
<2.5 )J.m (microns) in diameter
Figure 2-1. Comparisons of PM2.5 and PM10 diameters to human hair and beach sand.
(Adapted from: httpsr'M'ww.epa.govpm-polhuion particiilate-matler-pm-basics)
Atmospheric distributions of particle size generally exhibit distinct modes that roughly
align with the PM size fractions defined above. The nucleation mode is made up of freshly
generated particles, formed either during combustion or by atmospheric reactions of precursor
gases. The nucleation mode is especially prominent near sources like heavy traffic, industrial
emissions, biomass burning, or cooking (Vu et al., 2015). While nucleation mode particles are
only a minor contributor to overall ambient PM mass and surface area, they are the main
contributors to ambient particle number (U.S. EPA, 2019b, section 2.2). By number, most
nucleation mode particles fall into the UFP size range, though some fraction of the nucleation
mode number distribution can extend above 0.1 pm in diameter. Nucleation mode particles can
grow rapidly through coagulation or uptake of gases by particle surfaces, giving rise to the
accumulation mode. The accumulation mode is typically the predominant contributor to PM2.5
mass and surface area, though only a minor contributor to particle number (U.S. EPA, 2019b,
section 2.2). PM2.5 sampling methods measure most of the accumulation mode mass, although a
small fraction of particles that make up the accumulation mode are greater than 2.5 |im in
diameter. Coarse mode particles are formed by mechanical generation, and through processes
like dust resuspension and sea spray formation (Whitby et al., 1972). Most coarse mode mass is
captured by PM10-2.5 sampling, but small fractions of coarse mode mass can be smaller than 2.5
pm or greater than 10 urn in diameter (U.S. EPA, 2019b, section 2.2).
Most particles are found in the lower troposphere, where they can have residence times
ranging from a few hours to weeks. Particles are removed from the atmosphere by wet
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deposition, such as when they are carried by rain or snow, or by dry deposition, when particles
settle out of suspension due to gravity. Atmospheric lifetimes are generally longest for PM2.5,
which often remains in the atmosphere for days to weeks (U.S. EPA, 2019b, Table 2-1) before
being removed by wet or dry deposition. In contrast, atmospheric lifetimes for UFP and PM10-2.5
are shorter. Within hours, UFP can undergo coagulation and condensation that lead to formation
of larger particles in the accumulation mode, or can be removed from the atmosphere by
evaporation, deposition, or reactions with other atmospheric components. PM10-2.5 are also
generally removed from the atmosphere within hours, through wet or dry deposition (U.S. EPA,
2019b, Table 2-1).
2.1.1 Sources of PM Emissions
PM is composed of both primary (directly emitted particles) and secondary chemical
components. Primary PM is derived from direct particle emissions from specific PM sources
while secondary PM originates from gas-phase chemical compounds present in the atmosphere
that have participated in new particle formation or condensed onto existing particles (U.S. EPA,
2019b, section 2.3). Primary particles, and gas-phase compounds contributing to secondary
formation PM, are emitted from both anthropogenic and natural sources.
Anthropogenic sources of PM include both stationary and mobile sources. Stationary
sources include fuel combustion for electricity production and other purposes, industrial
processes, agricultural activities, and road and building construction and demolition. Mobile
sources of PM include diesel- and gasoline-powered highway vehicles and other engine-driven
sources (e.g., ships, aircraft, and construction and agricultural equipment). Both stationary and
mobile sources directly emit primary PM to ambient air, along with secondary PM precursors
(e.g., SO2) that contribute to the secondary formation of PM in the atmosphere (U.S. EPA,
2019b, section 2.3, Table 2-2).
Natural sources of PM include dust from the wind erosion of natural surfaces, sea salt,
wildland fires, primary biological aerosol particles (PB AP) such as bacteria and pollen, oxidation
of biogenic hydrocarbons such as isoprene and terpenes to produce secondary organic aerosol
(SOA), and geogenic sources such as sulfate formed from volcanic production of SO2 (U.S.
EPA, 2009, section 3.3, Table 3-2). While most of the above sources release or contribute
predominantly to fine aerosol, some sources including windblown dust, and sea salt also produce
particles in the coarse size range (U.S. EPA, 2019b, section 2.3.3).
Generally, the sources of PM for different size fractions vary. While PM2.5 in ambient air
is largely emitted directly by sources such as those described above or through secondary PM
formation in the atmosphere, PM10-2.5 is almost entirely from primary sources (i.e., directly
emitted) and is produced by surface abrasion or by suspension of sea spray or biological
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materials such as microorganisms, pollen, and plant and insect debris (U.S. EPA, 2019b, section
2.3.2.1).
In sections 2.1.1.1 and 2.1.1.2 below, we describe the most recently available information
on sources contributing to PM2.5 and PM10-2.5 emissions into ambient air, respectively, based on
the 2017 National Emissions Inventory (NEI).2 In section 2.1.1.3, we describe information on
sources contributing to emissions of PM components and precursor gases, with a focus on the
2017 NEI. Section 2.3.1 discusses emission trends and identifies the sectors that have
experienced the most change in direct PM and precursor emissions from 1990 to 2017. It should
be noted that major decreases have been observed in NOx and SO2 emissions over this time, with
continued reductions observed from the 2014 NEI to the 2017 NEI. Foramore detailed review
of the changes in PM and PM precursor emissions from the 2014 NEI to the 2017 NEI, please
refer to the 2017 NEI Technical Support Document (U.S. EPA, 2021).
2.1.1.1 Sources Contributing to Primary PM2.5 Emissions
The National Emissions Inventory (NEI) is a comprehensive and detailed estimate of air
emissions of criteria pollutants, criteria pollutant precursors, and hazardous air pollutants from a
comprehensive set of air emissions sources, including point sources (e.g., electric generating
units, boilers, etc.), nonpoint (or area) sources (e.g., oil & gas, residential wood combustion, and
many other dispersed sources), mobiles sources, and events (large fires). There are over 3,000
sources for which the NEI is developed. The NEI is released every three years based primarily
upon data provided by state, local, and tribal air agencies for sources in their jurisdictions and
supplemented by data developed by the EPA. The NEI is built using the Emissions Inventory
System (EIS) first to collect the data from state, local, and tribal air agencies and then to blend
that data with other data sources.
Based on the 2017 NEI, approximately 5.7 million tons/year of PM2.5 were estimated to
be directly emitted to the atmosphere from a number of source sectors in the U.S. This total
excludes sources that are not a part of the NEI (e.g., windblown dust, geogenic sources). As
shown in Figure 2-2, nearly half of the total primary PM2.5 emissions nationally are contributed
by the dust and fire sectors together. Dust includes agricultural, construction, and road dust. Of
these, agricultural dust and road dust in sum make the greatest contributions to PM2.5 emissions
nationally. Fires include wildfires, prescribed fires, and agricultural fires, with wildfires and
prescribed fires accounting for most of the fire-related primary PM2.5 emissions nationally (U.S.
2 These sections do not provide a comprehensive list of all sources, nor do they provide estimates of emission rates
or emission factorsfor all source categories. Individual subsectors of source types were aggregated up to a sector
level as used in Figure 2-2 and Figure 2-4. More information about the sectors and subsectors can be found as a
part of the 2017 NEI (U.S. EPA, 2021).
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EPA, 2019b, section 2.3.1.1). Other lesser-contributing anthropogenic sources ofPlVh.s
emissions nationally include stationary fuel combustion and agriculture sources.
Mobile Sources
Miscellaneous
5%
6%
Agriculture
14%
Induslrial
Processes
5%
Stationary Fuel
Combustion
11%
Figure 2-2. Percent contribution of PM2.5 national emissions by source sectors. (Source:
2017NEI)
The relative contributions of specific sources to annual emissions of primary PM2.5 can
vary from location to location, with a notable difference in contributions of sources of PM2.5
emissions in urban areas compared to national emissions. For example, the 2019 ISA illustrates
this variation of primary PM2.5 emissions with data from five urban counties in the U.S. (U.S.
EPA, 2019b, Figure 2-3).3 Across the majority of these urban areas, the largest PM2.5-emitting
sectors are mobile sources and fuel combustion. This is in contrast to fires, which account for the
largest fraction of primary emissions nationally but make much smaller contributions in many
urban counties (U.S. EPA, 2019b, section 2.3.1.2, Figure 2-3). While primary PM2.5 from mobile
sources are a dominant contributor in some urban areas, accounting for an estimated 13 to 30%
of the total primary PM2.5 emissions, mobile sources contribute only about 5% to total primary
PM2.5 emissions nationally as shown in Figure 2-2.
Another way to examine the emissions data shown in Figure 2-2 is by county. Figure 2-3
presents county-based total PM2.5 emissions divided by the area of the county to normalize for
differences in county size. This "emissions density" map highlights regions of the country with
3 The five counties included in the 2019ISA analysis include Queens County, NY, Philadelphia County,PA, Los
Angeles County,CA, Sacramento County, CA, and Maricopa County (Phoenix), AZ (U.S. EPA, 2019b,section
2.3.1.2).
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the highest totalPlVh.s emissions by county accounting for county size. While Figure 2-3 shows
totalPM2.5 emissions, different sectors will contribute at different levels across the country.
%
i
-
-•W
Tons Per Sq IVIi
i
0.8316- 1.2793
0 - 0.8063
Figure 2-3. 2017 NEIPM2.5 Emissions Density Map, tons per square mile
2.1.1.2 Sources Contributing to Primary PIYlio Emissions
Although the NEI does not estimate emissions of PM10-2.5 (coarse PM) specifically,
estimates of PM10 emissions can provide insight into sources of coarse particles. Thus, the
discussion below focuses on PM10 emissions. The relative contributions of key sources to
national PM10 emissions, based on the 2017 NEI, are shown in Figure 2-4. Total PM10 emissions
are estimated to be about 17 million tons. National emi ssions of PM10 are dominated by dust and
agriculture, contributing a combined 70% of the total emissions. Current NEI estimates of dust
emissions across the U.S. are based on limited emissions profile and activity information. For a
number of reasons, quantification of dust emissions is highly uncertain. Much like wildfires, dust
emissions are common but intermittent emissions sources. Additionally, the suspension and
resuspension of dust is difficult to quantify. Moreover, some dust particles in the PM10-2.5 size
range are also transported internationally and are considered as a part of the background
component of PM as opposed to a primary emission of coarse PM (U.S. EPA, 2019b, section
2.3.3).
As withPM2.5, the relative contributions of sources to total PM10 emissions varies from
location to location (e.g., depending on local climate, geography, degree of urbanization, etc.).
However, unlike PM2.5, the sectors included in Figure 2-4 are expected to be among the most
important contributors to coarse PM emissions at both the national and more regional levels,
particularly given the sources of the particles in these source categories (e.g., mineral dust,
primary biological aerosols (including pollen), sea spray). As noted previously, the NEI does not
include sources such as pollen, sea spray, windblown dust, or geogenic sources, though those
sources also likely contribute to PM10 emissions. Figure 2-4 shows the national contributions to
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PMio emissions from particular source sectors and Figure 2-5 exhibits the corresponding
emissions density map forPMio.
Industrial
Processes
5%
Mobile Sources
Miscellaneous
2%
2%
Stationary Fuel
Combustion
4%
Figure 2-4. Percent contribution of PM)0 emissions by national source sectors. (Source:
2017 NEI)
', V,-V,
"~•T .. &&(&.:¦*. if
Tons Per Sq Mi
10.0785 - 528.8188
6.5071 - 9.7905
4.6178 - 6.3843
2.8936 - 4.5438
0-2.8108
Figure 2-5. 2017 NEIPMio Emissions Density Map, tons per square mile
2.1.1.3 Sources Contributing to Emissions of PM Components and Precursor Gases
Understanding the components of PM is particularly important for providing insight into
which sources contribute to PM mass, as well as to better understand the health and welfare
effects of particles. Major components of PM2.5 mass include sulfate (SO42"), nitrate (NO3"),
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elemental or black carbon (EC or BC), organic carbon (OC), and crustal materials. Some of these
PM components are emitted directly to the air (e.g., EC/BC) while others are formed secondarily
through reactions by gaseous precursors (e.g., sulfate, nitrate). The following sections
specifically discuss the sources that contribute to the specific PM2.5 components, including
particulate carbon (section 2.1.1.3.1) and precursor gases (section 2.1.1.3.2).
2.1.1.3.1 Sources Contributing to Emissions of Particulate Carbon
Of the directly emitted components of PM2.5, emissions of elemental (or black) carbon
and organic carbon often make up the largest percentage of directly emitted PM2.5 mass. Figure
2-6 illustrates the sources that contribute to national emissions of elemental and organic carbon
based on the 2017 NEI. The top panel of Figure 2-6 shows that fires account for most (i.e., 63%)
of the 1.8 million tons of particulate OC emissions estimated in the 2017 NEI, while the bottom
panel of Figure 2-6 shows that fires and mobile sources (mostly diesel sources) contribute 71%
of the estimated ~ 284,000 tons of particulate EC in the 2017 NEI. It should be noted that the
fraction of EC toPlVh.s was lower in the 2017 NEI compared to the 2014 NEI, owing to a
significantly lower contribution of EC from fires in the 2017 NEI compared to previous NEIs.
This change in the EC fraction resulted from an in-house research program to investigate the
PM2.5 chemical composition of the emissions from fires burning different fuels and in different
combustion phases. It should be noted that the OC contributions on a percentage basis increased
in accordance with the EC decreases. While these results have not yet been directly published,
this information has been acknowledged and used in other EPA analyses (Kelly et al., 2019b,
Figure 13).
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Organic Carbon
Mobile Sources
4%
Miscellaneous
9%
Agriculture qusj
5% ^ 3%
Induslrial
Processes
2%
Stationary Fuel
Combustion
14%
Elemental Carbon
Miscellaneous
Mobile Sources
42%
2
3
4
Industrial
Processes
2%
Stationary Fuel
Combustion
13%
Figure 2-6. Percent contribution to organic carbon (top panel) and elemental carbon
(bottom panel) national emissions by source sectors. (Source: 2017 NEI)
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Figure 2-7 shows the emissions density map for elemental carbon. This map illustrates
that the EC emissions signals are strong in the Southeast U.S, the central region of the U.S. (i.e.,
Kansas and Oklahoma), and parts of the West and Northwest U.S., where fires make substantial
contributions to PM2.5. In addition, areas where diesel off-road and on-road sources are a large
part of the emissions mix also stand out (urban and highway corridors). The OC density map (not
shown) shows the highest emissions density in locations with substantial biomass burning
activity, consistent with most of the OC emissions coming from fires (Figure 2-6).
Tons Per Sq Mi
0.1678-18.9127
0.0984 - 0.1609
0 0662 - 0.0965
0.0401 - 0.0650
0 - 0.0391
Figure 2-7. 2017 NEI Elemental Carbon Emissions Density Map, tons per square mile.
2.1.1.3.2 Sources Contributing to Emissions of Precursor Gases
As discussed further in the 2019 ISA (U.S. EPA, 2019b, section 2.3.2.1), secondary PM
is formed in the atmosphere by photochemical oxidation reactions of both inorganic and organic
gas-phase precursors. Precursor gases include S02,NOx, and volatile organic compound (VOC)
gases of anthropogenic or natural origin (U.S. EPA, 2019b, section 2.3.2.1). Anthropogenic SO2
and NOx:are the predominant precursor gases in the formation of secondary PM2.5, and ammonia
also plays an important role in the formation of nitrate PM by neutralizing sulfuric acid and nitric
acid. In addition, atmospheric oxidation of VOCs, both anthropogenic and biogenic, is an
important source of organic aerosols, particularly in summer. The semi-volatile and non-volatile
products of VOC oxidation reactions can condense onto existing particles or can form new
particles (U.S. EPA, 2009, section 3.3.2; U.S. EPA, 2019b, section 2.3.2).
Emissions of each of the precursor gases noted above are estimated in the NEI and have
unique source signatures at the national level. Figure 2-8 illustrates the source contributions at
the national level for these PM2.5 precursor gases. As shown in Panel A in Figure 2-8, stationary
fuel combustion sources contribute nearly 70% of the estimated total of 2.8 million tons of
national SO2 national emissions. Within this source category, nearly all of the SO2 emitted to the
atmosphere comes from electricity generating units, or EGUs. Anthropogenic NOx emissions,
shown in panel B, are emitted by a range of combustion sources, including mobile sources (59%)
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and stationary fuel combustion sources (25%). In the 2017 NEI, there is an estimated total of
10.3 million tons of NOx emitted. Of the total estimated 4.3 million tons of anthropogenic
ammonia (NH3) emissions shown in panel C of Figure 2-8, NH3 emissions are dominated by the
agriculture source categories. In these categories, NH3 is predominantly emitted by livestock
waste from animal husbandry operations (56%) and fertilizer application (25%). In urban areas,
on-road mobile sources may also contribute significantly toNH3 emissions (U.S. EPA, 2019b,
Figure 2-3; Sun et al., 2014; U.S. EPA, 2020). Of the estimated 17.2 million tons of VOC
emissions from anthropogenic sources, fires (39%) and "miscellaneous" (22%)4 are the highest
contributors, followed by mobile sources (17%) and industrial processes (18%), as shown in
Figure 2-8 panel D. It should be noted that as these traditional combustion sources of VOCs are
reduced by regulations and controls, new non-combustion sources, such as volatile chemical
products (solvents) are emerging as key contributors to anthropogenic VOC totals in some parts
of the country, and particularly in urban corridors. In addition, biogenic sources (not shown in
Figure 2-8) are significant contributors to both VOC and NOx emissions.
Figure 2-8. Percent contribution to sulfur dioxide (panel A), oxides of nitrogen (panel B),
ammonia (panel C), and volatile organic compounds (panel D) national emissions by
source sectors. (Source: 2017 NEI). All graphics only show anthropogenic contributions.
4 The "miscellaneous" category includes such things as solvents, commercial cooking and waste disposal.
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Figure 2-9 to Figure 2-12 below show the emissions density maps corresponding to each
of the PM2.5 precursors included in Figure 2-8.
i rM * •* r 75' 4 ¦
Figure 2-9. SO2 Emissions Density Map, tons per square mile.
Figure 2-10. NO\ Emissions Density Map, tons per square mile.
v,\_-
1
ra
% '
X
mm' w * "**
"!• I" >••*#! _ !ft» : M Av
:-V '
V.
Tons Per Sq Mi
0 6500 - 264.7683
0.1740 - 0.5923
0.0802 -0 1661
0.0370 - 0.0774
0 - 0.0348
Tons Per Sq Mi
7.2452 - 830.8265
3 8459 - 6 9474
2 4582 - 3.7804
1.5045-2.4218
0-1.4700
Tons Per Sq Mi
2.0750-138.1133
1.2897-2.0271
0.7747-1.2547
0.4820-8.7569
8-0.3784
Figure 2-11. NH3 Emissions Density Map, tons per square mile.
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ink v \ m-
V A ^
* 1 " '.--i&jjt
1 | < ,r ' v..
\-j - -¦ rr «*.¦'^afe4b
* *¦ V *
* Hi : * Jt . * wj&fe
55 * ** ;#il%
- %
tj ^
^ y
Figure 2-12. Anthropogenic (including wildfires) VOC Emissions Density Map, tons per
square mile.
2.1.1.3.3 Uncertainty in Emission Estimates
Accuracy in an emissions inventory reflects the extent to which the inventory represents
the actual emissions that occurred. Anthropogenic emissions of air pollutants result from a
variety of sources such as power plants, industrial sources, motor vehicles and agriculture. The
emissions from any individual source typically vary in both time and space. It is not practically
possible to monitor each of the emission sources individually and, therefore, emission
inventories necessarily contain assumptions, and must rely too on interpolation and extrapolation
from a limited set of sample data.
The NEI process is based on a "bottom up" approach to developing emission estimates.
This means that a combination of activity and an appropriate emissions factor is used to estimate
emissions for all processes, including accounting for controls as possible. For the thousands of
sources that make up the NEI, there is uncertainty in one or all of these factors. For some
sources, such as EGUs, direct emission measurements enable the emission factors to be more
certain than for sources without such direct measurements. For example, emission factors for
residential wood combustion are taken from information available in the literature, regardless of
its pedigree and direct applicability to the source in question. Many of these issues related to the
analysis of uncertainty in the NEI are discussed by Day et al. (2019).
It is not clear how uncertainties in emission estimates affect air quality modeling, as there
are no numerical empirical uncertainty estimates available for the NEI. However, by comparing
modeled concentrations to ambient measurements, overall uncertainty in model outputs can be
characterized. Some of this uncertainty in model outputs is likely due to uncertainty in emission
estimates. The EPA uses information from air quality models and feedback from modelers and
other stakeholders to help identify which sectors to prioritize for emissions data methods
improvements.
Tons Per Sq Mi
29.8035 - 707.2287
18.5502 - 29.1950
10.4074 - 18.1336
5.8970 - 10.1220
0 - 5.S357
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2.2 AMBIENT PM MONITORING METHODS AND NETWORKS
To promote uniform enforcement of the air quality standards set forth under the CAA and
to achieve the degree of public health and welfare protection intended fortheNAAQS, the EPA
established PM Federal Reference Methods (FRMs)5 for both PMio and PM2.5 (40 CFR
Appendix J and L to Part 50) and performance requirements for approval of Federal Equivalent
Methods (FEMs) (40 CFR Part 53). Amended following the 2006 and 2012 PMNAAQS
reviews, the current PM monitoring network relies on FRMs and automated continuous FEMs, in
part to support changes necessary for implementation of the revised PM standards. The
requirements for measuring ambient air quality and reporting ambient air quality data and related
information are the basis for 40 CFR Appendices A through E to Part 58.
The EPA and its partners at state, local, and tribal monitoring agencies manage and
operate the nation's ambient air monitoring networks. The EPA provides minimum monitoring
requirements for criteria pollutants and related monitoring (e.g., the Chemical Speciation
Network (CSN)), including identification of an FRM for criteria pollutants and guidance
documents to support implementation and operation of the networks. Monitoring agencies carry
out and perform ambient air monitoring in accordance with theEPA's requirements and
guidance as well as often meeting their own state monitoring needs that may go beyond the
minimum federal requirements. Data from the ambient air monitoring networks are available
from two national databases: 1) the Air Quality System (AQS) database, which is the EPA's
long-term repository of ambient air monitoring data and 2) the AirNow database, which provides
near real-time data used in public reporting and forecasting of the Air Quality Index (AQI).6
The EPA and monitoring agencies manage and operate robust national networks for both
PMioandPM2.5, as these are the two measurement programs directly supporting thePM
NAAQS. PM10 measurements are based on gravimetric mass, while PM2.5 measurements include
gravimetric mass and chemical speciation. A smaller network of stations is operating and
reporting dataforPMio-2.5 gravimetric mass and a few monitors are operated to support special
projects, including pilot studies, for continuous speciation and particle count data. Monitoring
networks and additional monitoring efforts for each of the various PM size fractions and for PM
5 FRMs provide the methodologicalbasis for comparison to the NAAQS and also serve as the "gold standard" for
the comparison of othermethods being reviewed forpotentialapprovalasequivalentmethods. The EPA keeps a
complete list of designated reference and equivalent methods available on its Ambient Monitoring Technology
InformationCenter(AMTIC) website fhttps://www.epa.gov/amtic/air-monitoring-methods-criteria-pollutants').
6 The AQI is an index forreporting daily air quality and translates air quality data into numbers and colors to help
people understand how clean or polluted the air is, and what associated health effects might be a concern,
especially for ozone and particle pollution.
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composition are discussed below.7 Section 2.2.1 provides information on monitoring for total
suspended particulates (TSP), section 2.2.2 provides information on monitoring for PMio, section
2.2.3 provides information on monitoring PM2.5, section 2.2.4 provides information on
monitoring for PMi0-2.5, and section 2.2.5 provides information on additional PM metrics. All
sampler and monitor counts provided in these sections are based on data submitted to the EPA
for calendar year 2020, unless otherwise noted. Figure 2-13 below illustrates the changes in PM
monitoring stations reporting to the EPA's AQS database by size fraction since 1970.
PM Monitoring Stations Reporting to EPA's AQS
database by Size Fraction 1970 - 2020
—.~SP FM10 Total 0-1 OumSTP — PM2.5-Local Conditions PM10-2.5
Figure 2-13. PM Monitoring stations reporting to EPA's AQS database by PM size
fraction, 1970-2020.
2.2.1 Total Suspended Particulates (TSP) Sampling
The EPA first established NAAQS forPM in 1971, based on the original air quality
criteria document (DHEW, 1969). The reference method specified for determining attainment of
the original standards was the high-volume sampler, which collects PM up to a nominal size of
25 to 45 [j,m (referred to as total suspended particles or TSP). TSP was replaced by PM10 as the
indicator for the PM NAAQS in the 1987 final rule (52 FR 24854, July 1, 1987). TSP sampling
remains in operation at a limited number of locations primarily to provide aerosol collection for
TSP lead (Pb) analysis as well as for instances where a state may continue to have state standards
forTSP. The size oftheTSP network peaked in the mid-1970s when over 4,300 TSP samplers
7 More information on ambient monitoringnetworks can be found at https://www.epa.gov/amtic
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were in operation. As of 2020, there were 104 TSP samplers still in operation as part of the Pb
monitoring program; of these, 25 also report TSP mass.
2.2.2 PMio Monitoring
To support the 1987 PMio NAAQS, the EPA and its state and local partners implemented
the first size-selective PM monitoring network in 1990 with the establishment of a PMio network
consisting of mainly high-volume samplers. The network design criteria emphasize monitoring at
middle8 and neighborhood9 scales to effectively characterize the emissions from both mobile and
stationary sources, although not ruling out microscale10 monitoring in some instances (40 CFR
Part 58 Appendix D, 4.6 (b)). The PMio monitoring network peaked in size in 1995 with 1,665
stations reporting data.
In 2020, there were 680 PMio stations in operation to support comparison of the PMio
data to the NAAQS, trends, and reporting and forecasting of the AQI. Though the PMio network
8 For PMio, middle-scale is defined asfollows: Much of the short-term public exposure to PMio is on this scale and
on the neighborhood scale. People moving through downtown areas or living nearmajorroadwaysor stationary
sources, may encounterparticulate pollution that would be adequately characterized by measurements of this
spatialscale. Middle scale PMio measurementscanbeappropriatefortheevaluationofpossible short-term
exposure public health effects. In many situations, monitoring sites that are representative of micro -scale or
middle-scale impacts are not unique and are representative of many similar situations. This can occur along traffic
corridors or other locations in a residential district. In this case, one location is representative of a neighborhood
of small-scale sites andis appropriate for evaluation of long-term or chronic effects. This scale also includes the
characteristic concentrations for other areas with dimensions of a few hundred meters such as the parking lot and
feeder streets associated with shopping centers, stadia, and office buildings. In the case of PMio, unpavedor
seldomly swept parking lots associated with these sources could be an important source in addition to the
vehicular emissions themselves.
9 For PMio, neighborhood scale is defined as follows: Measurement sin this category represent conditions
throughout some reasonably homogeneous urban sub-region with dimensions of a few kilometers and of
generally more regular shape than the middle scale. Homogeneity refers to the particulate matter concentrations,
as well as the land use and land surface characteristics. In some cases, a location carefully chosen to provide
neighborhood scale data would represent not only the immediate neighborhood but also neighborhoods of the
sametypein other parts of the city. Neighborhood scale PMio sites provide information about trends and
compliance with standards because they often represent conditions in areas where people commonly live and
work for extended periods. Neighborhood scale data could provide valuable informationfordeveloping, testing,
and revising models that describe the larger-scale concentration patterns, especially those models relying on
spatially smoothed emission fields for inputs. The neighborhood scale measurementscould also be used for
neighborhood comparisons within or between cities.
i° por PMio, microscale is defined asfollows: This scale would typify areas such as downtown street canyons, traffic
corridors, and fence line stationary source monitoring locations where the general public could be exposed to
maximum PMio concentrations. Microscale particulate matter sites should be located near inhabited buildings or
locations where the general public can be expected to be exposed to the concentration measured. Emissions from
stationary sources such as primary and secondary smelters, power plants, and other large industrial processes
may, under certain plume conditions, likewise result in high ground level concentrations at the microscale. In the
latter case, the microscale would represent an area impacted by the plume with dimensions extending up to
approximately 100 meters. Data collected at microscale sites provide information for evaluating and developing
hot spot control measures.
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is relatively stable, monitoring agencies may continue divesting of some of thePMio monitoring
stations where concentration levels are low relative to the NAAQS.
While the PMio network is national in scope, there are areas of the west, such as
California and Arizona, with substantially higher PMio station density than the rest of the
country. In the PMio mass network, 385 of the stations operate automated continuous mass
monitors approved as FEMs and 295 operate FRMs. About 30 of the PMio stations have
collocation with both continuous FEMs and FRMs. More than half of the PMio stations with
FRMs operate on a sample frequency of one in every sixth day, with about 55 stations operating
every third day and another 55 stations operating every day.
2.2.3 PM2.5 Monitoring
To support the 1997 PM2.5 NAAQS, the first PM standard withPM2.5 as an indicator, the
EPA and states implemented a PM2.5 network consisting of ambient air monitoring sites with
mass and/or chemical speciation measurements. Network operation began in 1999 with nearly
1,000 monitoring stations operating FRMs to measure fine particle mass. The PM2.5 monitoring
program remains one of the major ambient air monitoring programs operated across the country.
For most urban locations, PM2.5 monitors are sited at the neighborhood scale,11 where
PM2.5 concentrations are reasonably homogeneous throughout an entire urban sub-region. In each
CBS A with a monitoring requirement, at least onePM2.5 monitoring station representing area-
wide air quality is to be sited in an area of expected maximum concentration. Sites that represent
relatively unique microscale, localized hot-spot, or unique middle scale impact sites are only
eligible for comparison to the 24-hour PM2.5 NAAQS.
There are three main components of the current PM2.5 monitoring program: FRMs, PM2.5
continuous mass monitors, and CSN samplers. The FRMs are primarily used for comparison to
the NAAQS, but also serve other important purposes such as developing trends and evaluating
the performance of PM2.5 continuous mass monitors. PM2.5 continuous mass monitors are
automated methods primarily used to support forecasting and reporting of the AQI, but are also
used for comparison to the NAAQS where approved as FEMs. The CSN and related Interagency
Monitoring of Protected Visual Environments (IMPROVE) network are used to provide
11 For PM2.5, neighborhood scale is defined as follows: Measurements in this category would represent conditions
throughout some reasonably homogeneous urban sub-region with dimensions of a few kilometers and of
generally more regular shape than the middle scale. Homogeneity refers to the particulate matter concentrations,
aswell asthe landuse and land surface characteristics. Much of thePM2.5 exposures are expected to be associated
with this scale of measurement. In some cases, a location carefully chosen to provide neighborhood scale data
would represent the immediate neighborhood aswell as neighborhoods of the same type in other parts of the city.
PM2.5 sites of this kind provide good information about trends and compliance with standards because they often
represent conditions in areas where people commonly live and work for periods comparable to those specified in
the NAAQS. In general, most PM2.5 monitoring in urban areas should have this scale.
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chemical composition of the aerosol which serve a variety of objectives. This section provides an
overview of each of these components of the PM2.5 monitoring program and of recent changes to
PM2.5 monitoring requirements.
2.2.3.1 Federal Reference Method and Continuous Monitors
As noted above, thePlVh.s monitoring network began operation in 1999 with nearly 1,000
monitoring stations operating FRMs. The PM2.5 FRM network peaked in operation in 2001 with
over 1,150 monitoring stations. In the PM2.5 network for 2020 there were 527 FRM filter-based
samplers that provide 24-hour PM2.5 mass concentration data. Of these operating FRMs, 68 are
providing daily PM2.5 data, 340 every third day, and 119 every sixth day.
As of 2020, there are 950 continuous PM2.5 mass monitors that provide hourly data on a
near real-time basis reporting across the country. A total of 660 of the PM2.5 continuous monitors
are FEMs and therefore used both for comparison with the NAAQS and to report the AQI.
Another 290 monitors not approved as FEMs are operated primarily to report the AQI. These
legacy PM2.5 continuous monitors were largely purchased prior to the availability of PM2.5
continuous FEMs.
The first method approved as a continuous PM2.5 FEMwas the Met One BAM 1020. This
method, approved in 2008, accounts for just over a third of the operating PM2.5 continuous FEMs
in the country. The EPA has approved a total of 11 PM2.5 continuous methods as FEMs. Other
methods approved as continuous PM2.5 FEMs include beta attenuation from multiple instrument
manufacturers; optical methods such as the GRIMM and Teledyne T640; and methods
employing the Tapered Element Oscillating Microbalance (TEOM) with a Filter Dynamic
Measurement System (FDMS) manufactured by Thermo Fisher Scientific.
The quality of the data generated by PM2.5 FRMs and automated FEMs were analyzed for
years 2018-2020. Data quality terms for measurement uncertainty regularly assessed in thePM2.5
monitoring program include precision and bias. Precision is calculated by comparing data from
collocated methods of the same make and model operated by the same monitoring organization.
Bias is calculated by comparing data from routinely operated FRMs or automated FEMs by the
monitoring organization and comparing that to data from reference method audit samplers
temporarily collocated and operated independently from the staff in the monitoring organization.
Goals for measurement uncertainty are defined in Appendix A to 40 CFRPart 58. They state
"Measurement Uncertainty for Automated and Manual PM2.5 Methods. The goal for acceptable
measurement uncertainty is defined for precision as an upper 90 percent confidence limit for the
coefficient of variation (CV) of 10 percent and ±10 percent for total bias." The most recent three-
year average estimate of national aggregate PM2.5 FRM precision is 7.6% and bias is -7.5%.
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Automated PM2.5 FEMs include a wide variety of approved methods which can have
different measurement principles. Data aggregated across all automated FEMs for years 2018-
2020 result in a collocated precision of 12.8%. Bias can be calculated from the reference method
audit program and by comparing continuous FEMs to collocated FRMs run by the monitoring
agency. The 2018-2020 reference method audit program had a bias of -1.7% with a sample size
of 573 audits across all continuous FEMs. Continuous FEMs compared to collocated monitoring
agency FRMs were biased higher by 11.5% with a large sample size of 85,539 collocated pairs
for 2018-2020 (all cases where both the FRM and continuous FEM are at or above 3.0 (J,g/m3).
When evaluating automated FEMs as individual methods, only two of the seven methods with
available collocated precision data met the measurement uncertainty goal and six of the eleven
methods met the bias goal. However, for collocated precision data and when considering a
requirement for approval of candidate FEMs: "Statistical analyses based on the DQO model
show that the precision of a candidate method is not, statistically, very important to annual
concentration averages used for NAAQS attainment decisions, but would be important for a
daily standard" (71 FR2620, January 17, 2006) In summary, PM2.5 automated FEMs tend to
have higher collocated precision than FRMs and tend to have a positive bias relative to state and
local operated FRMs.
2.2.3.2 Chemical Speciation and IMPROVE Networks
Due to the complex nature of fine particles, the EPA and states implemented the CSN to
better understand the components of fine particle mass at selected locations across the country.
The CSN was first piloted at 13 sites in 2000, and after the pilot phase, the program continued
with deployment of the Speciation Trends Network (STN) later that year. The CSN ultimately
grew to 54 trends sites and peaked in operation in 2005 with 252 stations: the 54 trends stations
and nearly 200 supplemental stations. The original CSN program had multiple sampler
configurations including the Thermo Andersen RAAS, Met One SASS/SuperSASS, and URG
MASS. During the 2000s, the EPA and states worked to align the network to one common
sampler for elements and ions, which was the Met One SASS/SuperSASS. In 2005, the CAS AC
provided recommendations to the EPA for making changes to the CSN. These changes were
intended to improve data comparability with the rural IMPROVE carbon concentration data. To
accomplish this, the EPA replaced the existing carbon channel sampling and analysis methods
with a new modified IMPROVE version III module C sampler, the URG 3000N. Implementation
of the new carbon sampler and analysis was broken into three phases starting in May 2007
through October 2009.
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In the 2020 PM2.5 CSN, long-term measurements are made at about 75 largely urban
locations comprised of either the STN or the National Core (NCore) network.12 NCore is a
multipollutant network measuring particles, gases, and basic meteorology that has been in formal
operation since January 1, 2011. Particle measurements made at NCore include PM2.5 filter-based
mass, which is largely the FRM, except in some rural locations that utilize the IMPROVE
program PM2.5 mass filter-based measurement; PM2.5 speciation using either the CSN program or
IMPROVE program; and PM10-2.5 mass utilizing an FRM, FEM or IMPROVE for some of the
rural locations. As of 2020, the NCore network includes a total of 78 stations of which 63 are in
urban or suburban stations designed to provide representative population exposure and another
15 rural stations designed to provide background and transport information. The NCore network
is deployed in all 50 States, DC, and Puerto Rico with at least one station in each state and two or
more stations in larger population states (California, Florida, Illinois, Michigan, New York,
North Carolina, Ohio, Pennsylvania, and Texas).
Both the STN and NCore networks are intended to remain in operation indefinitely. The
CSN measurements at NCore and STN stations operate every third day. Six of these stations
have collocated sets of CSN samplers where the collocated samplers operate every sixth day to
provide precision calculations of each chemical species measured. Another approximately 70
CSN stations, known as supplemental sites, are intended to be potentially less permanent
locations used to support State Implementation Plan (SIP) development and other monitoring
objectives.13 Supplemental CSN stations typically operate every sixth day. In January 2015, 38
supplemental CSN stations that are largely located in the eastern half of the country stopped
operations to ensure a sustainable CSN network moving forward.14
Specific components of fine particles are also measured through the IMPROVE
monitoring program,15 which supports regional haze characterization and tracks changes in
12 Inmost cases where a city has an STN station, it is located at the same site as the NCore station. In a few cases, a
city may have an STN station located at a different location than the NCore station.
13 See https://www.epa.gov/amtic/chemical-speciation-network-csnformoreinformationonthe PM2.5 speciation
monitoring program.
14 Based on assessments of the CSN network andlMPROVE protocol sites, monitoring resources were redistributed
to focus on new or high priorities. More information on the CSN andlMPROVE protocolassessmentsis
available at https://www.epa.gov/amtic/csn-and-improve-protocol-network-assessment.
15 Recognizing the importance of visual air quality, Congress included legislation in the 1977 Clean Air Act to
prevent future and remedy existing visibility impairment in Class I areas. To aid the implementation of this
legislation, the IMPROVE program was initiated in 1985 and substantially expanded in 2000 -2003. This program
implemented an extensive long-term monitoring program to establish the current visibility conditions, track
changes in visibility and determine causalmechanismforthe visibility impairment in the National Parks and
Wilderness Areas. For more information, see https://vista.cira.colostate.edu/Improve/.
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visibility in Class I areas16 as well as many other rural and some urban areas. As of 2018, the
IMPROVE network includes 110 monitoring locations that are part of the base network
supporting regional haze and another 38 locations operated as IMPROVE protocol sites where a
monitoring agency has requested participation in the program. These IMPROVE protocol sites
operate the same way as the IMPROVE program, but they may serve several monitoring
objectives (i.e., the same objectives as the CSN) and are not explicitly tied to the Regional Haze
Program. Samplers at IMPROVE stations operate every third day. In January 2016, eight
IMPROVE protocol stations stopped operating to ensure a sustainable IMPROVE program
moving forward. Details on the process and outcomes of the CSN supplemental and IMPROVE
protocol assessments used to identify sites that would no longer be funded are available on a
website.17 Together, the CSN and IMPROVE data provide chemical species information for fine
particles that are critical for use in health and epidemiologic studies to help inform reviews of the
primary PM NAAQS. CSN and IMPROVE data can also be used to better understand visibility
through calculation of light extinction using the IMPROVE algorithm18 to support reviews of the
secondary PM NAAQS.
The quality of the data generated by the PM2.5 speciation networks (CSN and IMPROVE)
is assessed regularly, using a variety of metrics. Overall network precision, including
uncertainties associated with both field operations and laboratory analyses, is assessed using the
subset of sites with collocated samplers. Fractional uncertainty is one metric that both speciation
networks regularly calculates using collocated data pairs above the MDL and reflects the overall
percent uncertainty for the measurements. For CSN data collected between June 2016 and
December 2019, the fractional uncertainties range from 5.6% for sulfate to 36.4% for chlorine.19
For IMPROVE data collected in 2016 and 2017, the fractional uncertainties range from 2% for
sulfur and sulfate to 27% for phosphorous.20 In general, uncertainties are higher for species with
16 See Regional Haze rule text at50CFR Part 51.308(d)(4) and (f)(6) (pasted below) lists SIP requirements, oneof
which is a "Monitoring Strategy...". This part of the rule doesn't necessarily require IMPROVE, rather it simply
assures states that IMPROVE will meetthis requirement. Specifically, this text reads: "(6) Monitoring strategy
and other implementation plan requirements. The State must submit with the implementation plan a monitoring
strategy formeasuring, characterizing, and reporting of regional haze visibility impairment that is representative
of all mandatory ClassI Federal areaswithin the State. Compliance with this requirement may bemet through
participation in the Interagency Monitoring of Protected Visual Environments network."
17 See the CSN and IMPROVE Protocol Network Assessment Website at: https://www.epa.gov/amtic/csn-and-
improve-protocol-network-assessment
18 The IMPROVE algorithm is an equation to estimate light extinction based on themeasured concentration of
several PM components and is used to track visibility progress in the Regional Haze Rule. More information
about the IMPROVE algorithm is at available at: http://vista.cira.colostate.edu/Improve/the-improve-algorithm.
19 https://airaualitv.ucdavis.edu/sites/g/files/dgvnsk 1671/files/inline-
files/CSN AnnualReport 2016Data 03.06.2019 FINAL APPROVED.pdf
20 http://vista.cira.colostate.edU/improve/wp-content/uploads/2019/l 1/IMPROVE OAReport 11.15.2019.pdf
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concentrations near the detection limit. Bias for the speciation networks can be assessed using
reports from interlaboratory comparisons.21
2.2.3.3 Recent Changes to PM2.5 Monitoring Requirements
Key changes made to the EPA's monitoring requirements as a result of the 2012 PM
NAAQS review included the addition of PM2.5 monitoring at near-road locations in core-based
statistical areas (CBS As) over 1 million in population; the clarification of terms used in siting of
PM2.5 monitors and their applicability to the NAAQS; and the provision of flexibility on data
uses to monitoring agencies where their PM2.5 continuous monitors are not providing data that
meets the performance criteria used to approve the continuous method as an FEM. The addition
of PM2.5 monitoring at near-road locations was phased in from 2015 to 2017. On January 1,
2015, 22 CBSAswith a population of 2.5 million or more were required to have a PM2.5 FRM or
FEM operating at a near-road monitoring station. On January 1, 2017, 30 CBSAswith a
population between 1 million and 2.5 million were required to have a PM2.5 FRM or FEM
operating are a near-road monitoring station.
The terms clarified as a part of the 2012 rulemaking ensure consistency with all other
NAAQS and long-standing definitions used by the EPA (78 FR 3234, January 15, 2013). The
flexibility provided to monitoring agencies ensures that the incentives of utilizing PM2.5
continuous monitors (e.g., efficiencies in operation and availability of hourly data in near-real
time) are realized without having potentially poor performing data being used in situations where
the data is not applicable to theNAAQS (78 FR3241, January 15, 2013).
2.2.4 PM10-2.5 Monitoring
In the 2006 PM NAAQS review, the EPA promulgated a new FRM for the measurement
of PM10-2.5 mass in ambient air. Although the standard for coarse particles uses aPMio indicator,
a new FRM for PM10-2.5 mass was developed to provide a basis for approving FEMs and to
promote the gathering of scientific data to support future reviews of thePM NAAQS. The PM10-
2.5 FRM (or approved FEMs, where available) was implemented at required NCore stations by
January 1, 2011. In addition to NCore, there are other collocated PM10 and PM2.5 low-volume
FRMs operating across the country that are essentially providing the PM10-2.5 FRM measurement
by the difference method.
PM10-2.5 measurements are currently performed across the country at NCore stations,
IMPROVE monitoring stations, and at a few additional locations where state or local agencies
choose to operate a PM10-2.5 method. For urban NCore stations and other State and Local Air
21 https://www.epa.gov/amtic/chemical-speciation-network-interlaboratorv-performance-evaluation-comparison-
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Monitoring Stations (SLAMS) the method employed is either a PM10-2.5 FRM, which is
performed using a low-volume PM10 FRM collocated with a low volume PM2.5 FRM of the same
make and model, or FEMs for PMi0-2.5, including filter-based dichotomous methods and
continuous methods of which several makes and models are approved. Filter-based PM10-2.5
measurements atNCore (i.e., the FRM or dichotomous filter-based FEM) operate every third
day, while continuous methods have data available every hour of every day. PM10-2.5 filter-based
methods at other SLAMS typically operate every third or sixth day. For IMPROVE, which is
largely a rural network, PM10-2.5 measurements are made with two sample channels; one each for
PM10 and PM2.5. All IMPROVE program samplers operate every third day. All togetherthere
were 287 stations in 2020 where PM10-2.5 data were being reported to the AQS database.
There is no operating chemical speciation network for characterizing the specific
components of coarse particles. In 2015, Washington University at St. Louis, under contract to
the EPA, reported on a coarse particle speciation pilot study with several objectives aimed at
addressing this issue, such as evaluating a coarse particle species analyte list and evaluating
sampling and analytical methods (U.S. EPA, 2015). The coarse particle speciation pilot study
provides useful information for any organization wishing to pursue coarse particle speciation.
2.2.5 Additional PM Measurements and Metrics
There are additional PM measurements and metrics made at a much smaller number of
stations. These measurements may be associated with special projects or are complementary
measurements to other networks where the monitoring agency has prioritized having the
measurements. None of these measurements are required by regulation. They include PM
measurements such as particle counts, continuous carbon, and continuous sulfate.
The EPA and state and local agencies have also been working together to pilot additional
PM methods at near-road monitoring stations that may be of interest to data users. These
methods include such techniques as particle counters, particle size distribution, and black carbon
by aethalometer. These methods and their rationale foruse at near-road monitoring stations are
described in a Technical Assistance Document (TAD) on NO2 near-road monitoring (U.S. EPA,
2012, section 16).
Aethalometer measurements of the concentration of optically absorbing particles have
been submitted to AQS for many years. Data uses include characterizing black carbon and wood
smoke. Ambient air monitoring stations that may have aethalometers include some of the near-
road monitoring stations and National Air Toxics Trends Stations (NATTS). In 2020, data from
72 monitoring sites across the county were reported from aethalometers and other related
commercially available continuous carbon analyzers. While aethalometer and related continuous
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carbon data are available at high time resolutions (e.g., 5-minute data), they are typically
reported to the AQS database in 1-hour periods.
Continuous elemental and organic carbon data were monitored at select locations
participating in a pilot of the Sunset EC/OC analyzer as well as a few additional sites that were
already operating before the EPA initiated the pilot study.22 The Sunset EC/OC analyzer
provides high-time-resolution carbon data, typically every hour, but in some remote locations the
instrument is programmed to run every two hours to ensure collection of enough aerosol. The
data from the Sunset EC/OC analyzer was compared to filter-based carbon methods from the
carbon channel of the CSN program. The Sunset EC/OC analyzer was operated at each of the
study sites for at least three years. Results from this pilot study are available in an EPA report
(U.S. EPA, 2019a). A key finding from the study suggests that when the Sunset instrument was
working well, OC and optical EC were comparable to CSN OC and EC; however, the time and
resources needed to keep a Sunset analyzer operational did not merit replacement of CSN OC
and EC measurements.
As of 2020, continuous sulfate is measured at two remaining monitoring sites, one each
in Maine and North Carolina. Several other stations have historical data but are no longer
monitoring continuous sulfate. Discontinued monitoring efforts for continuous sulfate is likely an
outcome of the significantly lower sulfate concentrations throughout the east where these
methods were operated. The continuous sulfate analyzer provides hourly data and these data can
be readily compared to 24-hour sulfate data which are collected from the ion channel in both the
CSN and IMPROVE programs.
In addition, over the last few years, the EPA has investigated the use of several PM
sensor technologies as one of several areas of research intended to address the next generation of
air measurements. The investigation into air sensors is envisioned to work towards near real-time
or continuous measurement options that are smaller, cheaper, and more portable than traditional
FRM or FEM methods. These sensor devices have the potential to be used in several applications
such as identifying hotspots, informing network design, providing personal exposure monitoring,
supporting risk assessments, and providing background concentration data for permitting. The
EPA has hosted workshops and published several documents and peer-reviewed articles on this
work 23
22 The six sites that participated in the study were Washington, DC; Chicago, IL; St. Louis, MO; Houston, TX; Las
Vegas, NV; and Los Angeles, CA.
23 For more information, see https://www.epa.gov/sciencematters/epas-next-generation-air-measuring-research and
https://www.epa.gov/air-sensor-toolbox
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2.3 AMBIENT AIR CONCENTRATIONS
This section summarizes available information on recent ambient PM concentrations.
Section 2.3.1 presents trends in emissions of PM and precursor gases, while section 2.3.2
presents trends in monitored ambient concentrations of PM in the U.S. Section 2.3.3 discusses
approaches for predicting ambient PM2.5 by hybrid modeling approaches.
2.3.1 Trends in Emissions of PM and Precursor Gases
Direct emissions of PM have remained relatively unchanged in recent years, while
emissions of some precursor gases have declined substantially.24 As illustrated in Figure 2-14,25
from 1990 to 2017, SO2 emissions have undergone the largest declines while NH3 emissions
have undergone the smallest change. Declining SO2 emissions during this time period are
primarily a result of reductions at stationary sources such as EGUs, with substantial reductions
also from mobile sources (U.S. EPA, 2019b, section 2.3.2.1). Inmore recent years (i.e., 2002 to
2017), emissions of SO2 and NOx have undergone the largest declines, while direct PM2.5 and
NH3 emissions have undergone the smallest changes, as shown in Table 2-1. Regional trends in
emissions can differ from the national trends illustrated in Figure 2-14 and Table 2-1.26 For
example, Hand et al. (2012) studied reductions in EGU-related annual SO2 emissions during the
2001-2010 period and found that while SO2 emissions decreased throughout the U.S. by an
average of 6.2% per year, the amount of change varied across the U.S. with the largest percent
reductions in the western U. S. at 20.1% per year.
It should be noted that the reductions shown in PM2.5 and PM10 emissions in Figure 2-14,
a Table 2-1, and any subsequent discussions of emission trends are most likely due to changes in
the methods used by the EPA to estimate emissions for source sectors over time In all likelihood,
emissions from dust and fires have increased over this time, which has been noted earlier in this
document and mentioned broadly in the literature as well (Pu and Ginoux, 2017; Li et al., 2021;
Liu et al., 2014; Schoennagel et al., 2017). It should also be noted that these data (in Figure 2-14
and Table 2-1) do not include emissions from wildfires, and these emissions can fluctuate greatly
from year to year.
24 More information on these trends, including details on methods and explanations on the noted changes over time
is available at https://www.epa.gov/air-emissions-inventories/air-pollutant-emissions-trends-data.
25 Emission trends in Figure 2-14 do not include wildfire emissions.
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30000
Year
—NH3 —NOx —PM2.5 —PM10 ^^S02 ^^VOCs
Figure 2-14. National emission trends of PM2.5, PM10, and precursor gases from 1990 to
2017.
Table 2-1. Percent Changes in PM and PM precursor emissions in the NEI for the time
periods 1990-2017 and 2002-2017.
Pollutant
Percent Change
in Emissions:
1990 to 2017
Percent Change
in Emissions:
2002 to 2017
Majo r So u rces th at co ntrib u te to
changes overtime
nh3
-3.1%
+5.6%
Agricultural Sources (Fertilizer and
Livestock Waste), Fires
NOx
-62%
-60%
EGUs, Mobile Sources
S02
-90%
-84%
EGUs, other Stationary Sources
VOCs
-45%
-26%
Solvents, Fires, Mobile Sources
PM25
-36%
-14%
Dust, Fires
PM10
-43%
-25%
Dust, Fires
2.3.2 Trends in Monitored Ambient Concentrations
2.3.2.1 National Characterization of PM2.sMass
At long-term monitoring sites in the U.S., annual PM2.5 concentrations from 2017 to 2019
averaged 8.0 [j,g/m3 (with the 10th and 90th percentiles at 5.9 and 10.0 (j,g/m3, respectively) and
the 98th percentiles of 24-hour concentrations averaged 21.3 [j,g/m3 (with the 10th and 90th
percentiles at 14.0 and 29.7 (J,g/m3, respectively). Figure 2-15 (top panels) shows that the highest
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ambient PM2.5 concentrations occur in the west, particularly in California and the Pacific
northwest. Much of the eastern U.S. has lower ambient concentrations, with annual average
concentrations generally well below 12.0 [j,g/m3 and 98th percentiles of 24-hour concentrations
generally at or below 30 [j,g/m3.
These concentrations are distinct from design values in part because they include days
with episodic events like wildfires and dust storms which can have very high PM2.5 and/or PM10
concentrations. The EPA's Exceptional Events Rule (81 FR 68216, October 3, 2016), most
recently updated in 2016, describes the process by which these events can be excluded from the
design values used for comparison to theNAAQS. For the remainder of Chapter 2, episodic
events are included in the calculations of PM concentrations. When design values are discussed
in Chapter 2, regionally-concurred exceptional events (as of June 2021) have been excluded from
the analysis.27
27 Regionally-concurred exceptional events are unusual or naturally-occurring events such aswildfires or high wind
dust events that have 1) resulted in PM2.5 concentrations above the level of theNAAQS, 2) been submitted by
tribal, state or local air agencies under the EPA's Exceptional Events Rule to their respective EPA Region, and 3)
received concurrence.
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Analysis of monthly data indicate distinct peaks in national ambient PM2.5 concentrations
during the summer and the winter (U.S. EPA, 2019b, Figure 2-22). Through 2008, the summer
peaks reflected the highest national average PM2.5 concentrations. These summer peaks in
ambient PM2.5 concentrations were largely a consequence of summertime peaks in SO 2
emissions from power plants in the eastern U.S., and subsequent sulfate formation. However,
substantial reductions in SO2 emissions (see above and U.S. EPA, 2019b, sections 2.5.1.1.1 and
2.5.2.2.1) have changed this pattern. Starting in 2009, winter peaks in national average PM2.5
concentrations have been higher than those in the summer (U.S. EPA, 2019b, section 2.5.2.2.1).
This pattern is illustrated by data from 2013 to 2015, when average winter PM2.5 concentrations
were about 11 (J,g/m3, average summer concentrations were about 9 (j,g/m3, and average spring
and fall concentrations were about 7 [j,g/m3 (Chan et al., 2018).
The ambient PM2.5 concentrations in Figure 2-15 reflect the substantial reductions that
have occurred across much of the U.S. over recent years (Figure 2-15, bottom panels and Figure
2-16). From 2000 to 2019, national annual average PM2.5 concentrations have declined from 13.5
[j,g/m3 to 7.6 (j,g/m3, a 43% decrease (Figure 2-16).28 These declines have occurred at both urban
and rural monitoring sites, although urban PM2.5 concentrations remain consistently higher than
those in rural areas (Chan et al., 2018) due to the so-called "urban increment" of PM2.5 from
local sources in an urban area that is additive to the regional and natural background PM2.5
concentrations.
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—1—1—1—1—1—1—1—1—1—1—1—1—1—1—1—1—1—1—r
22222222222222222222
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012345678901 23456789
Figure 2-16. Seasonally-weighted annual average PM2.5 concentrations in the U.S. from
2000 to 2019 (406 sites). (Note: The white line indicates the mean concentration while the
gray shading denotes the 10th and 90th percentile concentrations.)
28 See https://www.epa.gov/air-trends/particulate-matter-pm25-trendsforup-to-datePMT s trends information.
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Analyses at individual monitoring sites indicate that declines in ambient PM2.5
concentrations have been most consistent across the eastern U.S. and in parts of coastal
California, where both annual average and 98th percentiles of 24-hour concentrations have
declined significantly (Figure 2-15, bottom panels). In contrast, trends in ambient PM2.5
concentrations have been less consistent over much of the western U.S., with no significant
changes since 2000 observed at some sites in the Pacific northwest, the northern Rockies and
plains, and the southwest, particularly for 98th percentiles of 24-hour concentrations (Figure 2-
15, bottom panels). Trends in annual average PM2.5 concentrations have been highly correlated
with trends in 98th percentiles of 24-hour concentrations at individual sites (Figure 2-17). Such
correlations are highest across the eastern U.S. and in coastal California, and are somewhat
lower, though still generally positive, at sites in the Central and Western U.S. (i.e., outside of
coastal California).
Figure 2-17. Pearson's correlation coefficient between annual average and 98th percentile
of 24-hour PM2.5 concentrations from 2000-2019.
2.3.2.2 Characterization of PM2.5 Mass at Finer Spatial and Temporal Scales
2.3.2.2.1 CBSA Maximum Annual Versus Daily Design Values
Analysis of recent air quality indicates that maximum annual and daily PM2.5 design
values within a CBSA are positively correlated with some noticeable regional variability (Figure
2-18). In the Southeast, Northeast, and Industrial Midwest regions, the annual design values are
high relative to the daily design values due in part to the infrequent impacts of episodic events
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like wildfire or dust storms. On the other hand, the Northwest region has very high daily design
values relative to the annual design values. This is due to episodically high PM2.5 concentrations
that affect the region, both from wintertime stagnation events and summer/fall wildfire smoke
events.29 The relatively small population and low emissions in the region result in much lower
PM2.5 concentrations during the other parts of the year not affected by these episodes.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
CBSA Maximum 2017-2019 Annual Design Value (|jg rrr3)
Figure 2-18. Scatterplot of CBSA maximum annual versus daily design values (2017-2019)
with the solid black line representing the ratio of daily and annual NAAQS values.
2.3.2.2.2 PM2.5 Near Major Roadways
Because of its longer atmospheric lifetime (U.S. EPA, 2019b, section 2.2), PM2.5 is
expected to exhibit less spatial variability on an urban scale than UFP or PM10-2.5 (U.S. EPA,
2019b, section 2.5.1.2.1). Analyses in the 2009 ISA for PM indicated that correlations between
29 Due to the recent time period shown in Figure 2-18, it is likely that some of the annualand daily design values are
affected by potential exceptional events associated with wildfire smoke that have yet to be regionally -concurred
and removed from the design value calculations. The EPA defines exceptional events as unusual or natural-
occurring events that that affect air quality but are not reasonably controllable using techniques that tribal, state,
or local air agencies may implement. This is especially likely for the daily design values in the Northwest region,
which experienced frequent wildfire smoke events during the 2017-2019 period.
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PM2.5 monitoring sites up to a distance of 100 km from each other were greater than 0.75 in most
urban areas. However, more substantial spatial variation has been reported for some urban areas,
due in part to proximity between monitors and emissions sources (U.S. EPA, 2019b, section
2.5.1.2.1). The recent deployment of PM2.5 monitors near major roads in large urban areas
provides some insight into this spatial variation.
As discussed above, in the 2012 review of the PM NAAQS, the EPA required monitoring
of PM2.5, along with NO2 and CO, near major roads in CBSAs with populations greater than 1
million. PM2.5 monitoring was required to start for the largest CBSAs at the beginning of 2015,
and several years of data are now available for analysis at these sites. DeWinter et al. (2018)
analyzed these data and found that the average near-road increment (difference between near-
road PM2.5 concentrations and the concentrations at other sites in the same CBS A) was 1.2 [j,g/m3
for 2014-2015. Gantt et al. (2021) found that this near-road increment has a diurnal cycle, with a
peak during the morning rush hour. This near-road increment likely is additive to the urban
increment of PM2.5 from local sources in the CBSA including mobile sources on the numerous
non-highway roads that are not monitored by the near-road network. For 2016-2018, Gantt et al.
(2021) also reported that 52% and 24% of the time the near-road sites reported the highest annual
and 24-hour PM2.5 design value in the CBSA, respectively. Of the CBSAs with the highest
annual design values at near-road sites reported by Gantt et al. (2021), those design values were,
on average, 0.8 |ig/m3 higher than at the highest measuring non-near-road sites (range is 0.1 to
2.1 |ig/m3 higher at near-road sites).
Although most near-road monitoring sites do not have sufficient data to evaluate long-
term trends in near-road PM2.5 concentrations, Gantt et al. (2021) analyzed data at one long-term
near-road-like site in Elizabeth, NJ,30 and found that the annual average increment has generally
decreased between 2001 and 2018 from about 2.0 [j,g/m3 to about 1.3 [j,g/m3. The trend in the
near-road increment of elemental carbon at the Elizabeth, NJ site has shown a similar reduction,
with values of -1.0 [j,g/m3 in 2001 decreasing to -0.5 [j,g/m3 in 2018. These data are consistent
with the timing of EPA emission standardsfor motor vehicles.31 Although long-term dataarenot
available at other near-road sites, the national scope of the diesel vehicle controls suggests the
near-road environment across the U.S. may have experienced similar decreasing trends in near-
road PM2.5 increments.
2.3.2.2.3 Sub-Daily Concentrations of PM2.5
30 The Elizabeth Lab site in Elizabeth, NJ is situated approximately 30 meters from travel lanes of the Interchange
13 toll plaza of the New Jersey Turnpike and within 200 meters of travel lanes for Interstate 278 and the New
Jersey Turnpike.
31 See https://www.epa.gOv/diesel-fuel-standards/diesel-fuel-standards-and-rulemakings#nonroad-diesel.
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Ambient PM2.5 concentrations can exhibit a diurnal cycle that varies due to impacts from
intermittent emission sources, meteorology, and atmospheric chemistry. The PM2.5 monitoring
network in the U.S. has an increasing number of continuous FEM monitors reporting hourly
PM2.5 mass concentrations that reflect this diurnal variation. The 2019 ISA describes a two-
peaked diurnal pattern in urban areas, with morning peaks attributed to rush-hour traffic and
afternoon peaks attributed to a combination of rush hour traffic, decreasing atmospheric dilution,
and nucleation (U.S. EPA, 2019b, section 2.5.2.3, Figure 2-32). Because a focus on annual
average and 24-hour average PM2.5 concentrations could mask sub-daily patterns, and because
some health studies examine PM exposure durations shorter than 24-hours, it is useful to
understand the broader distribution of sub-daily PM2.5 concentrations across the U.S. Figure 2-19
below presents the frequency distribution of 2-hour average PM2.5 mass concentrations from all
FEMPM2.5 monitors in the U.S. for2017-2019.32 At sites meeting the current primary PM2.5
standards, these 2-hour concentrations generally remain below 10 ng/m3, and virtually never
exceed 30 |ag/m3. Two-hour concentrations are higher at sites violating the current standards,
generally remaining below 16 ng/m3 and virtually never exceeding 80 ng/m3,
Sites meeting both NAAQS
Sites violating either NAAQS
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Figure 2-19. Frequency distribution of 2017-2019 2-hour averages for sites meeting both or
violating either PM2.5 NAAQS for October to March (blue) and April to September
(red).
32 As discussed further in section 3.2, PM2.5 controlled human exposure studies often examine 2-hour exposures.
Thus, when evaluatingthose studies in the context of the current primary PM2.5 standards, it is usefulto consider
the distribution of 2-hour PM2.5 concentrations. Similar analyses of 4-hour and 5-hourPM2.5 concentrations are
presented in Appendix A, Figure A-2 and Figure A-3, respectively.
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The extreme upper end of the distribution of 2-hour PM2.5 concentrations is shifted higher
during the warmer months (red in Figure 2-19), generally corresponding to the period of peak
wildfire frequency (April to September) in the U.S. At sites meeting the current primary
standards, the highest 2-hour concentrations measured virtually never occur outside of the period
of peak wild fire frequency. Most of the sites measuring these very high concentrations are in the
northwestern U.S. and California, where wildfires have been relatively common in recent years
(see Appendix A, Figure A-l). When the period of peak wildfire frequency is excluded from the
analysis (blue in Figure 2-19), the extreme upper end of the distribution is reduced.
2.3.2.3 Chemical Composition of PM2.5
Based on recent air quality data, the major chemical components of PM2.5 have distinct
spatial distributions. Sulfate concentrations tend to be highest in the eastern U.S., while in the
Ohio Valley, Salt Lake Valley, and California nitrate concentrations are highest and relatively
high concentrations of organic carbon are widespread across most of the Continental U.S., as
shown in Figure 2-20. Elemental carbon, crustal material, and sea-salt are found to have the
highest concentrations in the northeast U.S., southwest U.S., and coastal areas, respectively.
2017-2019 Sulfate 2017-2019 Nitrate
Figure 2-20. Annual average PM2.5 sulfate, nitrate, organic carbon, and elemental carbon
concentrations (in jug/m3) from 2017-2019.
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An examination of PM2.5 composition trends can provide insight into the factors
contributing to overall reductions in ambient PM2.5 concentrations. The biggest change in PM2.5
composition that has occurred in recent years is the reduction in sulfate concentrations due to
reductions in SO2 emissions. Between 2000 and 2015, the nationwide annual average sulfate
concentration decreased by 17% at urban sites and 20% at rural sites. This change in sulfate
concentrations is most evident in the eastern U.S. and has resulted in organic matter or nitrate
now being the greatest contributor toPlVh.s mass in many locations (U.S. EPA, 2019b, Figure 2-
19). The overall reduction in sulfate concentrations has contributed substantially to the decrease
in national average PM2.5 concentrations as well as the decline in the fraction of PM10 mass
accounted for by PM2.5 (U.S. EPA, 2019b, section 2.5.1.1.6; section 2.3.1 above).
2.3.2.4 National Characterization of PMioMass
At long-term monitoring sites in the U.S., the 2017-2019 average of 2nd highest 24-hour
PM10 concentration was 68 [j,g/m3 (with the 10th and 90th percentiles at 28 and 124 (J,g/m3,
respectively) (Figure 2-21, top panels).33 The highest PM10 concentrations tend to occur in the
western U.S. Seasonal analyses indicate that ambient PM10 concentrations are generally higher in
the summer months than at other times of year, though the most extreme high concentration
events are more likely in the spring (U.S. EPA, 2019b, Table 2-5). This is due to fact that the
major PM10 emission sources, dust and agriculture, are more active during the warmer and drier
periods of the year.
33 The form ofthe current 24-hour PM10 standard is one-expected-exceedance, averaged overthree years.
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Recent ambient PMio concentrations reflect reductions that have occurred across much of the
U.S. (Figure 2-21, bottom panels). From 2000 to 2019, 2nd highest 24-hour PMio concentrations
have declined by about 46% (Figure 2-22).34 Analyses at individual monitoring sites indicate that
annual average PMio concentrations have declined at most sites across the U.S., with much of the
decrease in the eastern U.S. associated with reductions in PM2.5 concentrations. Annual second
highest 24-hour PMio concentrations have generally declined in the eastern U.S., while
concentrations in the much of the midwest and western U.S. have remained unchanged or
increased since 2000 (Figure 2-21, bottom panels).
00000000000000000000
00000000001 1111 111 11
0 1234567890123456789
Figure 2-22. National trends in Annual 2nd Highest 24-Hour PMio concentrations from
2000 to 2019 (262 sites). (Note: The white line indicates the mean concentration while the
gray shading denotes the 10th and 90th percentile concentrations.)
Compared to previous reviews, data available from theNCore monitoring network in the
current reconsideration allows a more comprehensive analysis of the relative contributions of
PM2.5 and PMio-2.5toPMio mass. PM2.5 generally contributes more to annual average PMio mass
in the eastern U.S. than the western U.S. (Figure 2-23). At most sites in the eastern U.S., the
majority of PMio mass is comprised of PM2.5. As ambient PM2.5 concentrations have declined in
the eastern U.S. (section 2.3.2.2, above), the ratios of PM2.5 to PMio have also declined.
34 For more information,see https://www.epa.g0v/air-trends/particulate-matter-pmlO-trends#pmnat
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8 Figure 2-24. PM2.5/PM111 ratio on the date of the second highest P.Mni concentrations for
9 2017-2019.
2017-2019 PM2.5/PM10 Ratio (Annual Average)
Figure 2-23. Annual average PM2.5/PM10 ratio for 2017-2019.
For days with very high PM10 concentrations (Figure 2-24), the PM2.5/PIVI10 ratios are
typically higher than the annual average ratios. This is particularly true in the northwestern U.S.
where the high PM10 concentrations can occur during wildfires with high PM2.5.
• >0.7
2017-2019 PM2.5/PM10 Ratio (2nd Highest PM10)
• >0.7
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1 2.3.2.5 National Characterization of PMio-2.5 Mass
2 Since the 2012 review, the availability of PM10-2.5 ambient concentration data has greatly
3 increased. As illustrated in Figure 2-2535 (top panels), annual average and 98th percentile PM10-2.5
4 concentrations exhibit less distinct differences between the eastern and western U. S. than for
5 either PM2.5 or PM10. Additionally, compared to PM2.5 and PM10, changes in PM10-2.5
6 concentrations have been small in magnitude and inconsistent in direction (Figure 2-25, lower
7 panels).
35 The sites shown in Figure 2-25 have a data completeness of either 75% or >182 valid day sin each year.
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2.3.2.6 Characterization of the Ultrafine Fraction of PM2.5 Mass
Compared toPlVh.s mass, there is relatively little data on U.S. particle number
concentrations, which are dominated by UFP. In the published literature, annual average particle
number concentrations reaching about 20,000 to 30,000 cm3 have been reported in U.S. cities
(U.S. EPA, 2019b). In addition, based on UFP measurements in two urban areas (New York
City, Buffalo) and at a background site (Steuben County) in New York, there is a pronounced
difference in particle number concentration between different types of locations (Figure 2-26;
U.S. EPA, 2019b, Figure 2-18). Urban particle number counts were several times higher than at
the background site, and the highest particle number counts in an urban area with multiple sites
(Buffalo) were observed at a near-road location. Hourly data indicate that particle numbers
remain fairly constant throughout the day at the background site, that they peak around 8:00 a.m.
in Buffalo and New York City (NYC), and that they remain high into the evening hours with
distinct rush hour and early afternoon peaks.
OK
0123456789 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Hour of the Day
Figure 2-26. Average hourly particle number concentrations from three locations in the
State of New York for 2014 to 2015 (green is Steuben County, orange is Buffalo, red is
New York City). (Source: Figure 2-18 in U.S. EPA, 2019b).
Long-term trends in UFP are generally not available at U.S. monitoring sites. However,
data on number size distribution have been reported for an 8-year period from 2002 to 2009 in
Rochester, NY. Number concentrations averaged 4,730 cnT3 for 0.01 to 0.05 [j,m particles and
1,838 cnT3 for 0.05 to 0.1 [j,m particles (Wang et al., 2011). On average over the 8 years that
UFP data were collected in Rochester, total particle number concentrations declined from the
earlier period evaluated (i.e., 2001 to 2005) to the later period (2006 to 2009). This decline was
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most evident for particles between 0.01 and 0.1 [j,m and was attributed to changes in local
sources resulting from the 2007 Heavy Duty Highway Rule (66 FR 5002, January 18, 2001), a
reduction in local industrial activity, and the closure of a nearby coal-fired power plant (Wang et
al., 2011; U.S. EPA, 2019b, section 2.5.2.1.4).
In addition, at a site in Illinois the annual average particle number concentration declined
between 2000 and 2019, closely matching the reductions in annual PM2.5 mass over that same
period (Figure 2-27, below). Particle number concentrations at this site are closer to those of the
background site in Figure 2-27 than the urban sites. A recent study found that particle number
concentrations in an urban area (Pittsburgh, PA) decreased between 2001-2002 and 2016-2017
along with decreases in PM2.5 associated with SO2 emission reductions (Saha et al., 2018).
However, the relationship between changes in ambient PM2.5 and UFPs cannot be
comprehensively characterized due to the high variability and limited monitoring of UFPs.
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scatterplot of mass vs. number concentration (right) between 2000-2019 in Bondville, IL.
2.3.3 Characterizing Ambient PM2.5 Concentrations for Exposure
Epidemiologic studies use various methods to characterize exposure to ambient PM2.5.
The methods used to estimate PM2.5 concentrations can vary from traditional methods using
monitoring data from ground-based monitors to those using more complex hybrid modeling
approaches. Studies using hybrid modeling approaches aim to broaden the spatial coverage
of estimated PM2.5 concentrations by expanding beyond just those areas with monitors and
providing estimates in areas that do not have ground-based monitors (i.e., areas that are
generally less densely populated and tend to have lower PM2.5 concentrations). As such, the
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hybrid modeling approaches tend to broaden the areas captured in the exposure assessment, and
in doing so, the studies that utilize these methods tend to report lower mean PM2.5 concentrations
than monitor-based approaches. Further, other aspects of the method used to calculate PM2.5
concentrations (i.e. population weighting, trim mean) can also have an impact on the predicted
exposure and the related study-reported mean concentration.
2.3.3.1 Predicted Ambient PM2.5 and Exposure Based on Monitored Data
Ambient concentrations of PM2.5 are often characterized using measurements from
national monitoring networks due to the accuracy and precision of the measurements and the
public availability of data. For applications requiring PM2.5 characterizations across urban areas,
data averaging techniques such as area-wide and population-weighted averaging of monitors are
sometimes used to provide complete coverage from the site measurements (U.S. EPA, 2019b,
chapter 3).
For an area to meet theNAAQS, all valid design values in that area, including the highest
annual and 24-hour values, must be at or below the levels of the standards. Because monitors are
often required in locations with high PM2.5 concentrations (section 2.2.3), areas meeting an
annual PM2.5 standard with a particular level would be expected to have long-term average PM2.5
concentrations (i.e., averaged across space and overtime in the area) somewhat below that
standard level. Figure 2-28 and Figure 2-29 indicate that, based on recent air quality in U.S.
CBS As, maximum annual PM2.5 design values are often 10% to 20% higher than annual average
concentrations (i.e., averaged across multiple monitors in the same CBSA). The difference
between the maximum annual design value and average concentration in an area can be smaller
or larger than this range, likely depending on factors such as the number of monitors, monitor
siting characteristics, and the distribution of ambient PM2.5 concentrations. Given that higher
PM2.5 concentrations have been reported at some near-road monitoring sites, relative to the
surrounding area (section 2.3.2.2.2), recent requirements forPlVh.s monitoring at near-road
locations in large urban areas (section 2.2.3.3) may increase the ratios of maximum annual
design values to averaged concentrations in some areas. Such ratios may also depend on how the
average concentrations are calculated (i.e., averaged across monitors versus across modeled grid
cells). Compared to annual design values, Figure 2-29 indicates a more variable relationship
between maximum 24-hour PM2.5 design values and annual average concentrations.
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\ CBSA Average 2017-2019 Annual Design Value ((jg rrr3)
2 Figure 2-28. Comparison of CBSA average annual design values and CBSA maximum
3 annual design values for 2017-2019. (Note: Includes all CBSAs with at least 3 valid annual
4 DVs.)
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1 Table 2-2. Nationwide averages of ratios of maximum annual PM2.5 design values to
2 average composite monitor PM2.5 concentrations across CBSAs.
Years of
Monitoring Data
Numberof Monitors
perCBSA
Number
of CBSAs
Ratio of Maximum
Annual DVto CBSA
Average
Ratio of Maximum
24-hourDVtoCBSA
Average
2009-2011
3 or more
67
1.12
1.13
4 or more
33
1.14
1.16
5 or more
18
1.17
1.19
2012-2014
3 or more
60
1.15
1.15
4 or more
38
1.17
1.18
5 or more
23
1.19
1.21
2015-2017
3 or more
65
1.16
1.19
4 or more
38
1.19
1.21
5 or more
30
1.20
1.24
2017-2019
3 or more
67
1.16
1.22
4 or more
47
1.19
1.25
5 or more
32
1.21
1.26
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• M
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mm
• •
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• •
0 1
8 9 10 11 12 13 14 15 16 17 18
CBSA Average 2017-2019 Annual Design Value (pg rrr3)
Figure 2-29. Comparison of CBSA average annual design values and CBSA maximum
daily design values for 2017-2019. (Note: Dashed lines indicate the level of the current 24-
hour PM2.5 standard (35 |ig/m3) and the current annual PM standard (12 |ig/m3). Includes all
CBS As with at least 3 valid daily and 3 valid annual DVs.)
2.3.3.2 Predicted Ambient PM2.5 Based on Hybrid Modeling Approaches
Ambient concentrations of PM2.5 are often characterized using measurements from
national monitoring networks due to the accuracy and precision of the measurements and the
public availability of data. For applications requiring PM2.5 characterizations across urban areas,
data averaging techniques such as area-wide and population-weighted averaging of monitors are
sometimes used to provide complete coverage from the site measurements (U.S. EPA, 2019b,
chapter 3). Yet data averaging methods may not adequately represent the spatial heterogeneity of
PM2.5 within an area and are not practical for large unmonitored areas or time periods. As a
result, additional methods have been developed to improve PM2.5 characterizations in areas
where monitoring is relatively sparse or unavailable. Methods include interpolation of monitored
data, land-use regression models, chemical-transport models (CTMs), models based on satellite-
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derived aerosol optical depth (AOD), and hybrid spatiotemporal models that combine
information from the individual approaches (U.S. EPA, 2019b, chapter 3). A number of recent
studies have employed such methods to estimate PM2.5 air quality concentrations across the U.S.
and Canada, and to estimate population exposures foruse in epidemiologic analyses (U.S. EPA,
2019b, sections 3.3 and 3.4). Given the increasing availability and application of these methods,
in this section we provide an overview of recently developed hybrid modeling methods, their
predictions and performance, and how predictions from various methods compare to each other.
2.3.3.2.1 Overview of Hybrid Methods
Hybrid methods are broadly classified into four categories: (1) methods based primarily
on interpolation of monitor data, (2) Bayesian statistical downscalers, (3) methods based
primarily on satellite-derived AOD, and (4) methods based on machine-learning algorithms.
Each method is discussed briefly below.
Interpolation-based methods are the simplest approach for developing spatial fields of
PM2.5 concentrations and rely on the moderate degree of spatial autocorrelation in PM2.5 in many
areas of the U.S. Interpolation methods often use inverse-distance or inverse-distance-squared
weighted averaging of monitoring data to predict PM2.5 concentrations at unmonitored receptor
points. Examples include the Voronoi neighbor averaging (VNA) approach and the enhanced
VNA approach (eVNA). The VNA approach applies weighted averaging to the concentrations
monitored in the Voronoi cells neighboring the cell containing the prediction point (Abt
Associates, 2014). In the eVNA approach, monitored data are further weighted by the ratio of
CTM predictions in the grid-cell containing the prediction point to the grid-cell containing the
monitor (Abt Associates, 2014).
Bayesian statistical modeling has been used to calibrate CTM PM2.5 predictions or
satellite-derived AOD estimates to surface measurements (Berrocal et al., 2012; Wang et al.,
2018b, Berrocal et al., 2020). This approach, commonly referred to as a Bayesian downscaler
because it "downscales" grid-cell average values to points, first regresses the PM2.5 predictions
or AOD estimates on monitoring data. The resulting relationships are then used to develop a
gridded PM2.5 field from the CTM or AOD input field. Bayesian downscalers have been applied
to develop gridded daily PM2.5 fields at 12-km resolution for the conterminous U.S. (Wang et al.,
2018b; U.S. EPA, 2017). An ensemble technique that optimally combines predictions of CTM
and AOD downscalers has also been developed to predict PM2.5 at high resolution over Colorado
during the fire season (Geng et al., 2018).
Surface PM2.5 concentrations can also be predicted based on satellite retrievals of AOD
and the relationship between surface PM2.5 and AOD from CTM simulations (van Donkelaar et
al., 2010). For example, in van Donkelaar etal. (2015a), satellite-based approaches (van
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Donkelaar et al., 2010; van Donkelaar et al., 2013) were used to estimate a gridded field of
global mean PM2.5 concentration for the 2001-2010 period that was combined with information
from radiometrically stable satellite instruments (Boys et al., 2014) to develop global PM2.5
fields over the 1998-2012 period (van Donkelaar etal., 2015a). Motivated by the limited use of
surface measurements in this approach, van Donkelaar et al. (2015b) developed an updated
method that incorporates additional information from PM2.5 monitoring networks to improve
performance. Specifically, geographically weighted regression (GWR) of residual PM2.5 (i.e., the
difference between monitored PM2.5 and predictions based on satellite-derived AOD)with land-
use and other variables is performed to improve PM2.5 concentration estimates in areas such as
North America where monitoring is relatively dense (van Donkelaar et al., 2019; van Donkelaar
et al., 2015b). This approach has been used to create long-term PM2.5 fields globally and for
North America at about 1-km resolution. However, the developers caution thatPM2.5 gradients
may not be fully resolved at 1-km resolution due to the influence of coarser-scale data used in
the model36 and report that mean error variance decreases when averaging the 1-km fields to
coarser resolution (van Donkelaar et al., 2019).
Daily PM2.5 fields based on non-parametric (i.e., machine learning) methods have also
been developed to characterize PM2.5 over the U.S. Non-parametric methods facilitate the use of
large numbers of predictor variables that may have complex nonlinear relationships with PM2.5
concentrations that would be challenging to specify with a parametric method. For example, a
neural network algorithm was used to predict daily PM2.5 fields at 1-km resolution over the
conterminous U.S. during 2000-2012 using more than 50 predictor variables including satellite-
derived AOD, CTM predictions, satellite-derived absorbing aerosol index, meteorological data,
and land-use variables (Di et al., 2016). A random forest algorithm was also applied to develop
daily PM2.5 fields at 12-km resolution over the conterminous U.S. in 2011 and provide variable
importance information for about 40 predictor variables including CTM results and satellite-
derived AOD (Hu et al., 2017). Satellite-derived AOD and the convolution layer for nearby
PM2.5 measurements are ranked among the top five most important predictor variables for the
importance metrics considered. An ensemble model based on random forest, neural network, and
gradient boosting methods has also been recently applied to develop daily 1-km PM2.5
concentration fields over the U.S. for the 2000-2015 period (Di et al., 2019). A wide range of
parametric and non-parametric hybrid PM2.5 models have recently been reviewed in Chapter 3 of
the 2019 ISA (U.S. EPA, 2019b).
2.3.3.2.2 Performance of the Methods
36 See http://fizz.phvs.dal.ca/~atmos/martin/7page id=l 40
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The performance of hybrid modeling methods is often evaluated against surface
measurements using n-fold cross validation (i.e., 1/n of the data are reserved for validation with
the rest used for model training, and the process is repeated n times). Although model evaluation
methods are not consistent across studies, ten-fold cross-validation statistics are often reported
and support use of the hybrid methods just described. For example, the neural network achieved
total R2 of 0.84 and root-mean-square error (RMSE) of 2.94 |Lxg m"3 for daily PM2.5 predictions at
sites in the conterminous U.S. during 2000-2012 (Di et al., 2016). The random forest achieved
total R2 of 0.80 and RMSE of 2.83 |Lxg m"3 for daily PM2.5 predictions at U.S. sites in 2011 (Hu et
al., 2017). The satellite-derived AOD approach with GWR yielded an R2 of 0.79 and RMSE of
1.7 |Lxg nr3 in cross validation for longer-term PM2.5 predictions at sites in North America (van
Donkelaar et al., 2015b). The Bayesian downscalers had weaker performance in cross validation
(e.g., national R2: 0.66-0.70; Wang et al., 2018b; Kelly et al., 2019a) than the other methods,
possibly due to the relatively small number of predictor variables. However, the downscalers
have advantages of simplicity, computational efficiency, and lower potential for overfitting
compared with the machine learning methods.
Although model validation analyses often report favorable performance in terms of
aggregate cross-validation statistics, studies have reported heterogeneity in performance by
season, region, and concentration range. For example, several methods had relatively high cross-
validation R2 in summer compared with other seasons (Kelly et al., 2019a ; Hu et al., 2017; Di et
al., 2016; van Donkelaar et al., 2015b). Also, studies have noted relatively weak performance in
parts of the western U.S., possibly due to the sharp concentration gradients, complex terrain, low
concentrations (and therefore signal-to-noise ratio), less dense monitoring, prevalence of
wildfire, and challenges in satellite retrievals and CTM modeling (Di et al., 2016; Wang et al.,
2018b; Hu et al., 2017; Kelly et al., 2019a). Predictive capability in terms of cross-validation R2
has also been reported to weaken with decreasing PM2.5 concentration in several studies (e.g.,
Kelly et al., 2019a; Di et al., 2016; van Donkelaar et al., 2019). This trend could be due in part to
increases in the fraction of thePM2.5 distribution that is explained by less predictable stochastic
variation as PM2.5 concentrations decrease (Just et al., 2020). Trends in model performance
associated withPM2.5 concentration (e.g., Figure 2-30) could also be due to the relatively sparse
monitoring in remote areas, where PM2.5 concentrations tend to be low. Consistent with this
hypothesis, studies have reported degradation of model performance metrics with increasing
distance to the nearest in-sample monitor, suggesting that predictions are most reliable in densely
monitored urban areas (Jin et al., 2019; Huang et al., 2018; Kelly et al., 2019a; Berrocal et al.,
2020).
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1.00-
0.75-
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^ VNA
^ eVNA
0.00-
46 171 487 301 37
37
I I I I I
0-3 3-6 6-9 9-12 >12
Observed PM2.5 (ng m 3)
Figure 2-30. R2 for ten-fold cross-validation of daily PM2.5 predictions in 2015 from three
methods for individual sites as a function of observed concentration. Text indicates the
number of monitors in thePlVh.s concentration range. Downscaler: Bayesian downscaler of
CMAQ predictions; VNA: Voronoi Neighbor Averaging; eVNA: enhanced-VNA. From
Kelly etal., 2019a.
A limited number of studies have intercompared concentration predictions based on
different PM2.5 characterization methods. Huang et al. (2018) compared PM2.5 concentrations
from the method of Di et al. (2016) with concentrations from the CTM-based data fusion method
ofFriberg et al. (2016) and the satellite-derived AOD approach of Huet al. (2014) for North
Carolina. They reported general agreement in concentrations among methods, with some
differences along the coast and in forested regions where monitoring is less dense. Yu et al.
(2018) compared PM2.5 concentrations from fourteen approaches of varying complexity for
developing PM2.5 spatial fields over the Atlanta, Georgia region. They reported that predictions
of the methods can differ considerably, and the hybrid approaches that incorporate CTM
predictions generally outperformed the simpler techniques (e.g., monitor interpolation). Also,
model predictions appeared to be more reliable in the urban center based on relatively low cross
validation R2 for sites away from the urban core. Jin et al. (2019) reported increasing uncertainty
in hybrid model predictions with distance to the nearest AQS monitor. Keller and Peng (2019)
reported that a prediction model incorporating CTM output outperformed a monitor averaging
approach and error reduction could be achieved by restricting the study to areas near monitors.
Diao et al. (2019) reviewed publicly available PM2.5 products and identified inconsistencies in
PM2.5 predictions from several methods. Kelly et al. (2021) reported broad agreement among
model predictions at the national scale but differences in the intra-urban variations in PM2.5
concentrations.
2.3.3.2.3 Comparison of PM2.5 Fields Across Approaches
To illustrate features of the spatial fields reported in the literature, the annual mean PM2.5
concentrations for 2011 from four methods is shown in Figure 2-31, where predictions from the
methods were averaged to a common 12-km grid. The fields were developed using a Bayesian
downscaler (downscaler, Berrocal et al., 2012), neural network (DI2016, Di et al., 2016), random
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forest (HU2017, Huet al., 2017), and GWRof residuals from satellite-based PM2.5 estimates
(VD2019; van Donkelaar et al., 2019). Annual mean concentrations were developed from daily
PM2.5 predictions in the downscaler, DI2016, and HU2017 cases and from monthly PM2.5
predictions in the VD2019 case. General features of the 2011 fields are in reasonable agreement
across methods, with elevated concentrations across broad areas of the eastern U.S. and in the
San Joaquin Valley and South Coast Air Basin of California. The national mean PM2.5
concentration for the VD2019 case (7.06 jog nr3) is slightly lower than those of the other cases
(7.36-7.44 jjg nr3), possibly because the VD2019 fields were developed using monthly (rather
than daily) PM2.5 measurements. Use of monthly averages provides greater influence on the
annual mean of sites with less frequent monitoring that tend to be in rural areas with relatively
low concentrations. Mean PM2.5 concentrations predicted by the four methods in nine U.S.
climate regions (Karl and Koss, 1984) are provided in Table 2-3.
CD
"o
downscaler
Avg: 7.44 ug/m3
45-;
40-
35-
30-
Avg: 7.36 ug/m3
-120 -110
HU2017
"satr ^
-100
-90
r
-80
-70
Longitude
VD2019
mm
M Gulfof 1,. ,
1 1 1 1
I ,
Avg: 7.06 ug/m3
ug/m3
i
> 15
10
5
0
Figure 2-31. Comparison of 2011 annual average PIVI2.5 concentrations from four methods.
(Note: These four methods include: downscaler (Berrocal etal., 2012), DI2016(Di etal.,
2016), HU2017 (Hu et al., 2017), and VD2019 (van Donkelaar et al., 2019). Predictions have
been averaged to a common 12-km grid for this comparison.)
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Table 2-3. Mean 2011 PM2.5 concentration by region for predictions in Figure 2-29
Region1
downscaler
HU2017
DI2016
VD2019
Northeast
8.5
8.0
8.2
7.5
Southeast
9.9
10.0
9.4
9.8
Ohio Valley
10.7
9.6
9.8
10.0
Upper Midwest
8.8
7.9
7.9
7.1
South
8.8
8.9
9.0
8.7
Southwest
5.0
5.3
5.2
5.1
N. Rockies & Plains
5.6
5.9
5.6
4.5
Northwest
5.0
5.3
6.1
4.9
West
5.5
5.7
6.0
6.5
1 U.S. climate reaion: httDs://www.ncdc.noaa.aov/monitorina-references/maDs/us-climate-reaions.DhD.
In Figure 2-32, PM2.5 concentrations predicted by the four methods are shown at their
native resolution for regions centered on California, New Jersey, and Arizona. Predictions have
sharper spatial gradients and span a wider range of concentrations for the western regions
centered on California and Arizona (Figure 2-32, panels a and c) than the eastern region centered
on New Jersey (Figure 2-32, panel b). Despite general agreement among predictions for the
California and the eastern U.S. areas, the spatial texture of the concentration fields differs among
methods. For instance, the 12-km Bayesian downscaler produces the smoothest PM2.5
concentration field, and the 1-km neural network (DI2016) produces the field with the greatest
variance. Some of the largest differences in PM2.5 concentration among methods occurred over
southwest Arizona. The DI2016 and VD2019 methods predict higher concentrations in this area
than the downscaler and HU2017 methods, and the DI2016 approach predicts distinct spatial
features associated with Interstate 40,10, and 8 that are not apparent in the other fields (Figure 2-
32, panel c).
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1 downscaler
D12016
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D12016
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Avg: 6.62 ug/m3
&vg: 7.06 ug/m3
42-
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i
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o Flagstaff
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3:4.91 ug/m3
HU2017
Avg: 5.44 ug/m3
-114 -112 -110
c
VD2019
-114 -112 -110
Longitude
ug/m3
>16
I
Figure 2-32. Comparison of 2011 annual average P1M2.5 concentrations from four methods
for regions centered on the (a) California (b) New Jersey, and (c) Arizona. Predictions
are shown at their native resolution (i.e., about 1-km forDI2016 and VD2019 and 12-km for
downscaler and HU2017).
In Figure 2-33, the coefficient of variation (CV; i.e., the standard deviation divided by the
mean) among methods is shown in percentage units based on predictions that were averaged to a
common 12-km grid. The largest values occur in the western U.S. (Figure 2-33, panel a), where
spatial gradients are high, terrain is complex, wildfire is prevalent, monitoring is relatively
sparse, and PM2.5 concentrations are low on average. The distance from the grid-cell center to the
nearest monitor is greater than 100 km for broad areas of the west (Figure 2-34).
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(a)
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Moodaja ©2019 Gooalc; IfyEGI
-120 -110
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11442 13687 11302 14501
3-5 5-7 7-9 9-11
8
4.
11-16
Average PM2.5 (jig m )
Figure 2-33. (a) Spatial distribution of the CV (i.e., standard deviation divided by mean) in
percentage units for the four models in Figure 2-29. (b) Boxplot distributions of CV for
grid cells binned by the average PM2.5 concentration for the four models. (Note: The box
brackets the interquartile range (IQR), the horizontal line within the box represents the
median, the whiskers represent 1.5 times the IQR from either end of the box, and circles
represent individual values less than and greater than the range of the whiskers.)
** Gulf of
-100 -90
Longitude
Figure 2-34. Distance from the center of the 12-km grid cells to the nearest PM2.5
monitoring site for P1M2.5 measurements from the AQS database and IMPROVE
network.
Concentrations less than 5 (.ig/m3 occur exclusively in the western U.S. for the downscaler
and HU2017 methods, and the western U.S. plus a few areas along the northern U.S. border in
the eastern U.S. for the DI2016 and VD2019 methods (Figure 2-35, top row). Concentrations
between 5 and 7 jig/m3 are predicted in the western U.S. and parts of New England for all
methods and over Florida by the downscaler and DI2016 approaches (Figure 2-35, second row).
The CV among methods increases with decreasing concentration (Figure 2-33 above, panel b),
and the median CV is about 15% for grid cells with mean concentrations less than 7 |jg/m3. As
illustrated by Figure 2-33 and Figure 2-35, the low-concentration areas with relatively large C Vs
are in the western U.S. and along the northern and southern border of the eastern U.S.
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downscaler
•-
DI2016
¦*
HU2017
VD2019
*
trj ~r
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f f
A
oi
-120-110-100 -90 -80 -70-120-110-100 -90 -80 -70-120-110-100 -90 -80 -70-120-110-100-90 -80 -70
Longitude
Figure 2-35. Location of PM2.5 predictions by range in annual average concentration for
the four prediction methods at their native resolution. (Note: Concentration ranges: < 5
Lig/m\ 5-7 |J,g/m3, 7-9 ug/m3, 9-11 Lig/m3, and >11 |jg/m3.)
The comparison of PM2.5 concentrations across approaches was based on the 2011 period
due to the availability of predictions from multiple method s for that year. As discussed earlier in
this chapter, PM2.5 concentrations have declined over the U.S. in the last several decades. Annual
mean PM2.5 concentrations predicted by the VD2019 method for 2011 are compared with
predictions for 2001, 2006, and 2016 in Figure 2-36. The VD2019 fields capture the trend of
decreasing PM2.5 over the U.S. during this period, and the areas with annual mean PM2.5
concentration greater than 11 |jg/mJ in 2016 are limited to California and southwest Arizona.
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2001 2006 2011 2016
1- nr • nr • V
25~' r "i " i i i i ""'r'""'T'" i *iv i i i i i i i i i i i i i
-120-110-100 -90 -80 -70-120-110-100 -90 -80 -70-120-110-100 -90 -80 -70 -120-110-100 -90 -80 -70
Longitude 0
Figure 2-36. Annual mean PM2.5 from the VD2019 method (van Donkelaar et al., 2019) for
2001,2006, 2011, and 2016.
2.3.3.2.4 Comparison of PM2.5 Fields in Estimating Exposure and Relative to
Design Values
Two types of hybrid approaches that have been utilized in several key PM2.5
epidemiologic studies in the 2019 ISA and draft ISA Supplement include neural network
approaches and use of GWR of residual PM2.5 with land-use and other variables to improve
estimates of PM2.5 concentration in the US. As such, we further compare these two types of
approaches across various scales and taking into account population weighting approaches
utilized in epidemiologic studies when estimating PM2.5 exposure. Additionally, we assess how
average PM2.5 concentrations computed using these hybrid surfaces compare to the maximum
design values measured at ground-based monitors. For this assessment, we evaluate the DI201937
and HA202038 surfaces. This analysis may help to inform how the magnitude of the overall study
reported mean PM2.5 concentrations in epidemiologic studies may be influenced by the approach
used to compute that mean and how that value might compare to monitor reported
concentrations.
In estimating exposure, some studies focus on estimating concentrations in urban areas,
while others examine the entire U.S. or large portions of the countiy. Figure 2-37 shows the
spatial distribution of the annual average PM2.5 concentrations for 2015 using the DI2019 surface
nationwide (panel A) and for CBSAs only (panel B). As shown in the figure, the geographic
coverage is much less when estimating the annual average PM2.5 concentrations at the CBS A
scale compared to the national scale and tends to be primarily representative of areas that are
37 This analysis includes an updated version of the surface used in Di et al. (2016). Predictions in Di et al. (2016)
were for 2000 to 2012 using a neural network model. The Di et al. (2019) study improved on that effort in several
ways. First, a generalized additive model was used that accounted for geographic variations in performance to
combine predictions from three models (neural network, random forest, and gradient boosting) to make the final
optimalPMi.s predictions. Second, the data sets were updated that were used in model training and included
additional variables such as 12-km CMAQ modeling a s predictors. Finally, more recent years were included in
the Di et al. (2019) study.
The HA2020 field is based on the V4.NA.03 product available at: https: // sites, wustl. edu/a ca g/datasets/surf ace -
um2-5/. The name"HA2020" comes from the references for this product (Hammer et al., 2020; van Donkelaar et
a I.. 2019).
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more urban or densely populated. Further, the areas that are not included in the CBSA-only
analysis tend to have lower PM2.5 concentrations. These areas tend to be more rural or less
densely populated areas, and likely correspond to those locations where monitoring data
availability is limited or nonexistent.
-120 -110 -100 -90 -80 -70
Longitude
Longitude
Figure 2-37. Spatial distribution of the annual average PM2.5 concentrations for 2015 using
the D12019 surface nationwide (panel A) and for CBS As only (panel B).
Using the DI2019 and HA2020 surfaces, for each year of available data, the 1 km x 1 km
grid cells for each modeled surface within a CBSA were averaged, resulting in an estimated
average annual PM2.5 concentration at the CBSA spatial resolution. In addition, for each surface,
all 1 km x 1 km grid cells were averaged over the conterminous U.S., resulting in an estimated
average annual PM2.5 concentration at the national scale. These average annual PM2.5
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1 concentrations for each year from 2000-2016 for the DI2019 and HA2020 surfaces are shown in
2 Table 2-4. In addition, we also examined the average annual PM2.5 concentrations nationwide
3 and in CBS As in terms of a 3-year average, which is the averaging time of the annual standard.
4 These averages are shown in Table 2-5.
5 Table 2-4. Average Annual PM2.5 Concentration (jig/m3) by Year.
Year
DI2019
HA2020
Nationwide3
CBSAsb
Nationwide3
CBSAsb
2000
8.36
8.96
7.37
7.83
2001
7.88
8.49
7.08
7.61
2002
7.99
8.59
7.37
7.98
2003
8.25
8.72
7.03
7.51
2004
7.62
8.18
6.59
7.13
2005
7.98
8.51
7.34
7.92
2006
7.68
8.13
6.72
7.21
2007
7.90
8.41
7.26
7.69
2008
7.13
7.59
6.51
7.00
2009
6.52
6.94
6.02
6.45
2010
6.71
7.10
6.09
6.47
2011
6.72
7.13
6.31
6.74
2012
6.69
6.95
6.24
6.47
2013
6.15
6.50
5.75
6.14
2014
6.08
6.41
5.61
6.04
2015
6.00
6.25
5.43
5.76
2016
5.29
5.56
4.98
5.36
a Nationwide average annual PM2.5 concentrations include all 1 km x 1 km grid cells of the modeling surface.
b CBSA average annual PM2.5 concentrations include only those 1 km x 1 km grid cells that were located within a CBSA.
6
7
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Table 2-5. Three-Year Average of the Average Annual PM2.5 Concentrations (jig/m3).
Year
DI2019
HA2020
Nationwide3
CBSAsb
Nationwide®
CBSAsb
2000-2002
8.08
8.68
7.27
7.81
2001-2003
8.04
8.60
7.16
7.70
2002-2004
7.95
8.50
7.00
7.54
2003-2005
7.95
8.47
6.99
7.52
2004-2006
7.96
8.28
6.88
7.42
2005-2007
7.85
8.35
7.11
7.61
2006-2008
7.57
8.04
6.83
7.30
2007-2009
7.18
7.65
6.60
7.04
2008-2010
6.78
7.21
6.21
6.64
2009-2011
6.65
7.05
6.14
6.55
2010-2012
6.71
7.06
6.21
6.56
2011-2013
6.52
6.86
6.10
6.45
2012-2014
6.31
6.62
5.87
6.22
2013-2015
6.08
6.38
5.60
5.98
2014-2016
5.79
6.07
5.34
5.72
a Nationwide average annual PM2.5 concentrations include all 1 km x 1 km grid cells of the modeling surface.
b CBSA average annual PM2.5 concentrations include only those 1 km x 1 km grid cells that were located within a CBSA.
At the national scale, the average annual PM2.5 concentrations are slightly higher when
using theDI2019 surface compared to theHA2020 surface but are generally similar. The
average annual PM2.5 concentrations are also slightly lower using theHA2020 surface compared
to the DI2019 surface when the analyses are conducted for CBS As. However, regardless of
which surface is used, the average annual PM2.5 concentrations for the CBSA-only analyses are
somewhat higher than for the nationwide analyses (4-8% higher), likely reflecting the more
urban or densely populated areas in the CBSA-only analyses that typically have higher PM2.5 in
ambient air compared to more rural or less densely populated areas captured in the nationwide
analyses.
Similarly, as shown in Table 2-5, for both the DI2019 and HA2020 surfaces, the
nationwide average annual PM2.5 concentrations, averaged over three years, are lower than the
CBSA only average annual PM2.5 concentrations, averaged over three years. For the national
scale, 3-year averages of the average annual PM2.5 concentrations generally range from about 5.3
|ig/m3 to 8.1 |ig/m3, compared to the CBSA scale, which ranges from 5.7 |ig/m3 to 8.7 |ig/m3,
Overall, these analyses suggest that there are slight differences in the average annual
PM2.5 concentrations depending on the modeling method employed in a hybrid modeling study.
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It is important to recognize that the use of different methods in the hybrid modeling studies to
estimate mean PM2.5 concentrations may influence the comparability across studies
We next evaluate how the averages of the model surfaces compare to regulatory design
values and how population weighting influences the averages. For this analysis, we include
CBSAswith three or more valid design values for the 3-year period.39 The regulatory design
values for the CBS As were calculated for each 3-year period for the CBSAswith 3 or more
design values in each of the3-year periods. Using the maximum design value for each CBSA
and by each 3-year period, the ratio of maximum design values to modeled average annual PM2.5
concentrations were calculated, for each 3-year period. In addition, we evaluated the influence of
population weighting on the average annual PM2.5 concentrations using both the DI2019 and
HA2020 surfaces for 3-year periods in CBSAsthat also have available regulatory design value
data. These data are shown in Table 2-6.
Table 2-6. Average Annual PM2.5 Concentrations and Ratios to Regulatory Design Values.
Years of
Monitoring
Data
No. of
CBSAs3
Average
Annual PM2.5
Concentration
(|jg/m3)b
Population
Weighted
Average Annual
PM2.5
Concentration
(|jg/m3)b
Average
Maximum
Annual
DVs
(|jg/m3)b
Ratio of
Average
Maximum
Annual DVs to
Average Annual
PM25
Concentrations
Ratio of
Average
Maximum
Annual DVs to
Population
Weighted
Average Annual
PM2.5
Concentrations
DI2019 Surface from Di etal. (2019)
2008-2010
67
8.61
10.17
11.67
1.48
1.15
2011-2013
64
8.10
9.37
10.91
1.47
1.17
2014-2016
61
7.22
8.26
9.57
1.41
1.17
HA2020 Surface from Hammer etal. (2020) and van Donkelaaretal. (2019)
2008-2010
67
8.25
9.93
11.67
1.50
1.18
2011-2013
64
7.92
9.34
10.91
1.43
1.17
2014-2016
61
6.98
8.19
9.57
1.43
1.18
a The number of CBSAs with 3 or more valid design values for the 3-year period
b Averaged across CBSAs
As shown in Table 2-6, the results using the DI2019 and HA2020 surfaces are similar for
the average annual PM2.5 concentrations, by each 3-year period. When population weighting is
not applied, the average annual PM2.5 concentrations generally range from 7.0 to 8.6 |ig/m3.
When population weighting is applied, the average annual PM2.5 concentrations are slightly
higher, ranging from 8.2 to 10.2 |ig/m3. As with CBSAs versus the national comparison above,
39 More details about the analytical methods used for this analysis are described in section A.7 of Appendix A.
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population weighting results in a higher average PM2.5 concentration than when population
weighting is not applied.
For the CBSAs included in the population weighted analyses, the average maximum
annual design values generally range from 9.5 to 11.7 |ig/m3. As shown in Table 2-6, these
analyses show that the results are similar for both the DI2019 and HA2020 surfaces and the
maximum annual PM2.5 design values are often 40% to 50% higher than average annual PM2.5
concentrations when population weighting is not applied. However, when population weighting
is applied, the ratio of the maximum annual PM2.5 design values to the average annual PM2.5
concentrations are lower than when not population weighted, and generally range from 15% to
18%.
2.3.3.2.5 Summary
Hybrid PM2.5 modeling methods have improved the ability to estimate PM2.5 exposure for
populations throughout the conterminous U.S. compared with the earlier approaches based on
monitoring data alone. Excellent performance in cross-validation tests suggests that hybrid
methods are reliable for estimating PM2.5 exposure in many applications. As discussed in
Chapter 3 of this draft PA, good agreement in health study results between monitor- and model-
based methods for urban areas (McGuinn et al., 2017) and general consistency in results for the
conterminous U.S. (Jerrett et al., 2017; Di et al., 2016) also suggests that the fields are reliable
for use in health studies. However, there are also important limitations associated with the
modeled fields. First, performance evaluations for the methods are weighted toward densely
monitored urban areas at the scales of representation of the monitoring networks. Predictions at
different scales or in sparsely monitored areas are relatively untested. Second, studies have
reported heterogeneity in performance with relatively weak performance in parts of the western
U.S., at low concentrations, at greater distance to monitors, and under conditions where the
reliability and availability of key input datasets (e.g., satellite retrievals and air quality modeling)
are limited. Differences in predictions among different hybrid methods have also been reported
and tend to be most important under conditions with the performance issues just noted.
Differences in predictions could also be related to the different approaches used to create long-
term PM2.5 fields (e.g., averaging daily PM2.5 fields vs. developing long-term average fields),
which is important due to variable monitoring schedules. More work is warranted on identifying
the most appropriate model performance metrics and comprehensively characterizing model
performance to further inform our understanding of the implications of using these fields to
estimate PM2.5 exposures in health studies.
When additional analyses are done to further compare the DI2019 and HA2020 surfaces,
the results suggest the DI2019 and HA2020 surfaces predict similar average annual PM2.5
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concentrations at the national scale and on average across all CBS As in the U.S. The spatial scale
can affect the magnitude of the average annual PM2.5 concentration with somewhat higher
concentrations (4-8% higher) resulting from averaging across all CBS As in the U.S. versus
averaging across the entire U.S. Additionally, when average annual PM2.5 concentrations from
the hybrid modeled surfaces are compared to the average maximum annual design value
measured at ground-based monitors in a subset of CBSAs, the average of the maximum annual
design values tends to be a 40-50% higher than the average annual PM2.5 concentration estimated
from the hybrid modeling surfaces. When population weighting is introduced, the average of the
maximum annual design values tendsto only be 15-18%) higher than the average annual PM2.5
concentration estimated from the hybrid modeling surfaces. This analysis may help better
explain why reported study means from different epidemiologic studies can vary and why these
mean values tend to be lower than concentrations reported at ground-based monitors. However,
it is important to recognize that these results only reflect two surfaces and two types of
approaches and that the use of different hybrid methods to estimate mean PM2.5 concentrations
may influence the comparability across studies.
2.4 BACKGROUND PM
For the purposes of this assessment, we define background PM as all particles that are
formed by sources or processes that cannot be influenced by actions within the jurisdiction of
concern. For this document, U.S. background PM is defined as any PM formed from emissions
other than U.S. anthropogenic (i.e., manmade) emissions. Potential sources of U.S. background
PM include both natural sources (i.e., PM that would exist in the absence of any anthropogenic
emissions ofPM or PM precursors) and transboundary sources originating outside U.S. borders.
Ambient monitoring networks provide long-term records of speciated PM concentrations
across the U.S., which can inform estimates of individual source contributions to background PM
levels in different parts of the country. However, even the most remote monitors within the U.S.
can be periodically affected by U.S. anthropogenic emissions. Monitor data are also limited in
more remote areas due to a sparser monitoring network where PM concentrations are more likely
influenced by background sources. Chemical transport models (CTMs) offer complementary
information to ambient monitor networks by providing more spatially and temporally
comprehensive estimates of atmospheric composition. CTMs can also be applied to isolate
contributions from specific emission sources to PM concentrations in different areas via source
apportionment or "zero-out" modeling (i.e., estimating what the residual concentrations would be
were emissions from the emission source of interest to be entirely removed).
At annual and national scales, estimated background PM concentrations in the U.S. are
small compared to contributions from domestic anthropogenic emissions. For example, based on
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zero-out modeling in the 2012 review of thePMNAAQS, annual background PM2.5
concentrations were estimated to range from 0.5-3 |ig/m3 across the sites examined. The
magnitude and sources of background PM can vary widely by region and time of year. Coastal
sites may experience a consistent contribution of PM from sea spray aerosol, while other areas
covered with dense vegetation may be impacted by biogenic aerosol production during the
summertime. Sources of background PM also operate across a range of time scales. While some
sources like biogenic aerosol vary at monthly to seasonal scales, many sources of background
PM are episodic in nature. These episodic sources (e.g., large wildfires) can be characterized by
infrequent contributions to high-concentration events occurring over shorter periods of time (e.g.,
hours to several days). Such episodic events are sporadic and do not necessarily occur in all
years. While these exceptional episodes can lead to violations of the daily PM2.5 standard (35
|ig/m3) in some cases (Schweizer et al., 2017), such events are routinely screened for and usually
identifiable in the monitoring data. As described further below, contributions to background PM
in the U.S. result mainly from sources within North America. Contributions from
intercontinental events have also been documented (e.g., transport from dust storms occurring in
deserts in North Africa and Asia), but these events are less common and represent a relatively
small fraction of background PM in most places.
While the potential sources of background PM discussed above include sources of both
fine (PM2.5) and coarse (PM10-2.5) particles, background contributions to ambient UFP are less
well characterized and are not discussed here due to lack of information. Section 2.4.1 below
further discusses background PM from natural sources inside the U.S. Section 2.4.2 characterizes
the role of international transport of PM from sources outside U.S. borders.
2.4.1 Natural Sources
As noted in section 2.1.1, sources that contribute to natural background PM include dust
from the wind erosion of natural surfaces, sea salt, wildland fires, primary biological aerosol
particles (PBAP) such as bacteria and pollen, oxidation of biogenic hydrocarbons such as
isoprene and terpenes to produce SOA, and geogenic sources such as sulfate formed from
volcanic production of SO2 and oceanic production of dimethyl-sulfide (DMS). While most of
the above sources release or contribute predominantly to fine aerosol, some sources including
windblown dust, and sea salt also produce particles in the coarse size range (U.S. EPA, 2019b,
section 2.3.3).
Biogenic emissions from plants are perhaps the most ubiquitous sources of background
PM in the U.S. Certain species of plants and trees can release large amounts of VOCs such as
isoprene and monoterpenes that are oxidized in the atmosphere to form organic aerosol. SOA
production from biogenic emissions is largest in the southeastern U.S., where conditions are
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warm, humid, and sunny for much of the year. Many of the processes involved with biogenic
SOA formation are complex and remain highly uncertain. Results from radiocarbon techniques
applied to distinguish modern (biogenic or fires) from fossil (anthropogenic) carbon fractions in
organic aerosol have suggested comparable contributions from both carbon types in the
Southeast where SOA concentrations are high (Schichtel etal., 2008). However, SOA formation
from biogenic emission sources can also be facilitated by the presence of anthropogenic
precursors (Xu et al., 2015). More work characterizing the interactions of anthropogenic and
biogenic emissions is needed to determine the implications of such processes for background PM
concentrations.
Soil dust and sea salt have been estimated to account for less than 10% of urban PM2.5 on
average in the U.S. (Karagulian et al., 2015), although episodic contributions from these sources
can be much higher in some locations. For example, during a dust storm affecting Phoenix in
July of 2011, peak hourly average PM10 concentrations were greater than 5,000 |ig/m3, with area-
wide average hourly concentrations ranging from a few hundred to a few thousand |ig/m3
(Vukovic et al., 2014). Dust can also account for much of thePM that originates from outside the
U.S., which we discuss further below (U.S. EPA, 2019b, section 2.5.4.2). In addition to sea salt
aerosol, biological production of the sulfate precursor DMS can also occur in some marine
environments, although the impact of DMS emissions on annual mean sulfate concentrations is
likely very small in the U.S. (<0.2 |ig/m3) and confined to coastal areas (Sarwar et al., 2018).
Wildfires release large amounts of particles and gaseous PM precursors. Invasive species,
historical fire management practices, frequency of drought, and extreme heat have resulted in
longer fire seasons (Jolly et al., 2015) and more large fires (Dennison et al., 2014) overtime. In
addition to emissions from fires in the U.S., emissions from fires in other countries can be
transported to the U.S. Transport of smoke from fires in Canada, Mexico, Central America, and
Siberia have been documented in multiple studies (U.S. EPA, 2009). According to the NEI,
wildfire smoke contributes between 10 and 20% of primary PM emissions in the U.S. per year
(U.S. EPA, 2019b, section 2.3.1), with much higher localized contributions near fire-affected
areas.
To illustrate how episodic impacts from a large natural source can affect PM
concentrations in the U.S., Figure 2-38 and Figure 2-39 show an example from a recent wildfire
event. In summer 2017, smoke from wildfires in British Columbia, Canada led to severe air
quality degradation in parts of the Pacific Northwest. A NASA Worldview40 image from August
4, 2017 (Figure 2-38) shows smoke from multiple fire detections across southern British
Columbia crossing into northern Washington state. Smoke from these fires was also captured at
40 Available from https://worldview.earthdata.nasa.gov
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1 the North Cascades IMPROVE monitor (Figure 2-39), where daily fine PM concentrations were
2 increased from atypical baseline of less than 10 ug/'m3 to -100 ug/m5 during this time.
4 Figure 2-38. Smoke and fire detections observed by the MODIS instrument onboard the
5 Aqua satellite on August 4th, 2017 accessed through NASA Worldview.
6
100
U>~ 60
20
North Cascades
Daily Fine Mass Budgets, 2017
L
¦iL
c.
CO
\
Feb !
i
Mar !
i
Apr i
May !
|
Jun j
1
Jul j
J
Aug
Sep
Oct
:=¦
o
Z
o
CD
Q
IMPROVE Monitor ID: NOCA1, WA
M Sea Salt
¦ Soil
H Elemental Carbon
| Organic Mass
| Ammonium Nitrate
Ammonium Sulfate
Figure 2-39. Fine PM mass time series during 2017 from the North Cascades IMPROVE
site in north central Washington state.41
41 Available at http://views.cira.colostate.edu/fed/SiteBrowser/Def ault.a spx?appkev=SBCF PmHazeComp
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Later in August and September 2017, many other wildfires occurred in Washington state
and Oregon, making this fire season one of the worst for the Pacific Northwest in recent history.
The severe fires in British Columbia, Washington and Oregon during 2017 have been linked to
the combination of usually hot temperatures in August/September in the region following a very
wet preceding winter season. While many of the most severe wildfire events in the U.S. occur in
the western part of the country during the late summer, most of the contiguous U.S. is affected
by wildfire smoke during some part of the year (Kaulfus et al., 2017).
2.4.2 International Transport
Background PM contributions from international sources include PM that is both natural
and anthropogenic in origin crossing into U.S. borders from Canada and Mexico or from longer
range intercontinental transport. While in general the biggest contributions to U.S. background
PM from international sources come from nearby Canada and Mexico, large episodic events
from intercontinental sources can sometimes occur (e.g., windblown dust from Asia or Africa).
This section discusses transboundary PM transport within North America (section 2.4.2.1) as
well as long range intercontinental transport from anthropogenic (section 2.4.2.2) and natural
(section 2.4.2.3) sources.
2.4.2.1 Transboundary Transport in North America
As discussed above, some of the largest potential international sources of U.S.
background PM originate elsewhere in North America. PM produced from fires in both Canada
and Mexico can affect air quality in the U.S., particularly in border states (Park et al., 2007;
Miller et al., 2011; Wang etal., 2018a). Anthropogenic emissions from Canada and Mexico can
also influence U.S. PM air quality. An inverse modeling study by Henze et al. (2009) estimated
that in 2001 anthropogenic SOx emissions from Canada and Mexico accounted for 6% and 4%
respectively of total daily inorganic PM2.5 in the U.S. These authors also estimated that SOx
emissions related to international shipping accounted for approximately 2% of total inorganic
PM in the U.S.
2.4.2.2 Long Range Transport from Anthropogenic Sources
Due to the relatively short atmospheric lifetime of particles (-days to weeks), long range
transport of aerosols does not contribute significant PM mass to the U.S. Heald et al. (2006)
estimated that transport from Asia accounted for less than 0.2 |ig/m3 of sulfate PM2.5 in the
Northwestern U.S. in spring, and Leibensperger etal. (2011) estimated intercontinental
contributions from Asian anthropogenic SO2 andNOx emissions of 0.1 - 0.25 |ig/m3 annually in
the western U.S. Leibensperger et al. (2011) also concluded that much of the intercontinental
influence captured by the GEOS-Chem model was in fact local PM production attributable to
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domestic emissions in receptor countries arising from changes in global oxidant budgets, rather
than impacts from PM directly transported across geopolitical boundaries. The studies above are
also consistent with findings from other analyses. A report from the United Nations on global air
quality synthesizing results across many studies estimated an annual average contribution of
approximately 0.1 |ig/m3 sulfate PM in North America due to transport from East Asia
(TFHTAP, 2006).
2.4.2.3 Long Range Transport from Natural Sources
Long range transport of dust from both Asia (Vancuren and Cahill, 2002; Yu et al., 2008)
and North Africa (Prospero, 1999b; Prospero, 1999a; Chiapello et al., 2005; McKendry et al.,
2007) has been shown to occasionally contribute to surface PM concentrations in some regions
of the U.S. The likelihood of such long-range dust transport events depends on large-scale
meteorological patterns, which can vary significantly across seasons and between years. Yuet al.
(2015) found that the transport of North African dust across the Atlantic Ocean is strongly
negatively correlated with precipitation in the Sahel during the preceding year. Dust from Africa
has also shown a decreasing trend of approximately 10% per decade from 1982 to 2008 based on
measurements of aerosol optical depth and surface concentrations in Barbados. This trend was
attributed to a corresponding decrease in surface winds over source regions (Ridley et al., 2014).
Variability in springtime Asian dust transport to the U.S. has been linked to north-south shifts in
trans-Pacific flow modulated by the El Nino-Southern Oscillation (Achakulwisut et al., 2017), as
well as to variations in regional precipitation affecting both dust emissions in Asia and
atmospheric residence times during transport (Fischer et al., 2009).
On average, intercontinental dust transport is estimated to contribute about 1-2 |ig/m3 to
annual PM2.5 at some U.S. sites (Jaffe et al., 2005; TFHTAP, 2006; Creamean et al., 2014).
However, daily concentrations can be substantially larger for individual events, especially for
coarser particles. For example, Jaffe et al. (2003) found evidence of Asian dust events in 1998
and 2001 contributing 30-40 |ig/m3 to daily PM10 at sites throughout the U.S., although the
authors also note that large events of this scale are rare and only occurred twice during their 15-
year study period. Similar magnitudes have also been reported for individual North African
events; analysis of a multidecadal record of African dust reaching Miami indicated
concentrations of PMranging from -10 to 120 |ig/m3 (Prospero, 1999a; Prospero, 1999b).42 In
June 2020 a large dust transport episode originating in North Africa may have contributed up to
50 |ig/m3 for several days at multiple sites in the southeastern U.S. (Pu and Jin, 2021).
42 Sample collection beganin 1974, before network PM10 and PM2.5 samplers were developed, and no size cut was
specified (Prospero, 1999a).
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2.4.3 Estimating Background PM with Recent Data
As discussed above, the 2009 PM ISA estimated background PM concentrations at
several remote IMPROVE sites in different regions of the U.S. for 2004 using a combination of
monitor data and zero-out air quality modeling. Revisiting the speciated IMPROVE PM data at
the monitors included in the 2009 ISA assessment provides some insights into how contributions
from different PM sources may have changed, and what those changes (or lack thereof) mean for
our current understanding of background PM in the U.S.
Figure 2-40 shows observed annual average PM2.5 in 2004 and 2016 at the same remote
monitors examined in the 2009 ISA. The comparisons show decreases in both totalPM2.5 and
ammonium sulfate across all sites examined, consistent with decreases in anthropogenic SO2 and
other PM precursors observed over this time period. It is likely that most of the remaining
ammonium sulfate observed at these sites is also a result of domestic anthropogenic emissions
and therefore not relevant for assessments of background PM.
Sea salt and dust aerosol are likely natural in origin at these remote sites. With the
exception of REDW1, a coastal site in California, soil and sea salt aerosol together account for
less than about 0.5 |ig/m3 of the annual average PM2.5 at all monitors examined here, which is
below the values cited from the literature for long range dust contributions discussed above.
Contributions from ammonium nitrate and elemental carbon could be from either anthropogenic
or natural sources, but together represent less than about 0.5 |ig/m3 at most of the sites in 2016.
The largest contribution from nitrate occurs at the BRIG1 monitor in New Jersey and is likely
anthropogenic given the high density of NOx from vehicle emissions in that region.
After ammonium sulfate, the next largest contributing species for most of the sites is
organic matter, which for many of the monitors in Figure 2-40 represents 50% or more of total
PM in both 2004 and 2016. In addition to the IMPROVE sites from the 2019 ISA, Figure 2-40
also shows comparisons for three sites in the Southeast U.S. As a region, the Southeast has the
highest levels of biogenic aerosol production in the country, so the organic matter contribution at
these three sites likely represents an upper bound for the country of what natural biogenic
organic aerosol production could be under present atmospheric conditions. The organic aerosol
components shown in Figure 2-37 will also include the influence of fires for some monitors. The
highest organic matter contribution for any of the sites shown in Figure 2-40, including the three
Southeast monitors, is approximately 2 |ig/m3. While contributions from ammonium sulfate have
decreased substantially at some of the monitors, particularly the eastern sites, contributions from
organic aerosol are roughly consistent between 2004 and 2016, as are the contributions from the
other species assumed to be mostly natural in origin (soil and sea salt). Therefore, while no new
zero-out modeling was done for the reconsideration, revisiting these monitors with more recent
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data suggests that estimates of background concentrations at these monitors are still around 1-3
|ig/m3 and have not changed significantly since the 2012 PMNAAQS review.
While estimates of total annual background concentrations have generally not changed
significantly since the 2012 review, our scientific understanding of organic aerosol formation has
evolved. Organic aerosol can be produced from a variety of natural and anthropogenic processes,
which presents a challenge for source attribution techniques. Additionally, new research over the
past decade has identified a host of new sources and chemical pathways for SO A formation that
have only recently begun to be implemented into CTMs. Further research implementing these
new sources and pathways into CTMs is needed to understand l)the behavior of these different
algorithms under a range of possible atmospheric conditions, and 2) what the implications are for
understanding SO A formation in the U.S.
ACAD1
BRIG1
D0SO1
VOYA2
15-
10"
P, -
15"
10-
P -
u
On O C>1
1 1 1
15-
10-
P-
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¦ 1
0-
0-
0-
0-
2004
BRID1
2016
2004
CANY 1
2016
2004
GICL1
2016
2004
GLAC1
2016
15-
15"
15-
15-
10-
10-
10-
10-
5-
5-
5-
5-
_
—
0-
0-
^9
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2054
REDW1
2016
2054
OKEF1
2016
2054 2016
SHR01
2054
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2016
15-
15"
15"
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2016
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2054
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2054
20 1 a
|Amm. nit. EC Sea salt
Arrrr. OU Soil
Figure 2-40. Speciated annual average IMPROVE PM2.5 in jig/m3 at select remote monitors
during 2004 and 2016. (Note: Monitor locations are shown in Figure 2-41.)
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2 Figure 2-41. Site locations for the IMPROVE monitors in Figure 2-40. (Note: Monitors also
3 assessed in the 2009 ISA are shown in blue. Monitors only examined in this assessment are
4 shown in red.)
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3 RECONSIDERATION OF THE PRIMARY
STANDARDS FOR PM2 5
This chapter presents and evaluates the policy implications of the key aspects of the
scientific and technical information pertaining to this reconsideration of the primary PM2.5
standards. In so doing, this chapter presents key aspects of the evidence of health effects of
PM2.5, as documented in the 2019 ISA (U.S. EPA, 2019) and draft ISA Supplement (U.S. EPA,
2021a),1 with support from the prior IS As and AQCDs, and associated public health
implications. It also presents key aspects of updated quantitative risk analyses conducted for this
reconsideration, as detailed in the appendices associated with this chapter. Together this
information provides the basis for our evaluation of the scientific information regarding health
effects of PM2.5 in ambient air and the potential for effects to occur under air quality conditions
associated with the existing standard (or any alternatives considered), as well as the associated
implications for public health. Our evaluation is focused around key policy-relevant questions
derived from the IRP (U.S. EPA, 2016, section 2.1) for the review completed in 2020, and also
takes into account conclusions reached in previous reviews. In this way we identify key policy-
relevant considerations and summary conclusions regarding the public health protection provided
by the current standards for the Administrator's consideration in this reconsideration of the 2020
final decision on the primary PM2.5 standards.
Within this chapter, background information on the current standards is summarized in
section 3.1. The general approach for considering the available information in this
reconsideration, including policy-relevant questions identified to frame our policy evaluation, is
summarized in section 3.2. Key aspects of the available health effects evidence and associated
public health implications and uncertainties are addressed in section 3.3, and the current air
quality and risk information, with associated uncertainties, is addressed in section 3.4. Section
3.5 summarizes the key evidence- and risk-based considerations identified in our evaluation and
also presents associated preliminary conclusions on the adequacy of the current standards. Key
remaining uncertainties and areas for future research are identified in section 3.6.
1 As described in detail in section 1.4.2 above and section 3.3 below, the draft ISA Supplement focuses on a
thorough evaluation of some studies that became available after the literature cutoff date of the 2019 ISA that
could either further inform the adequacy of the current PM NAAQS or address key scientific topics that have
evolved since the literature cutoff date for the 2019 ISA (U.S. EPA, 2021a). The selection of the health effects to
evaluate within the draft ISA Supplement was based on the causality determinations reported in the 2019 ISA and
the subsequent use of scientific evidence in the 2020 PA. Specifically, for PM2 5-related health effects, the focus
within the draft ISA Supplement is on mortality and cardiovascular effects. The draft ISA Supplement does not
include an evaluation of studies for other PM2 5-related health effects (U.S. EPA, 2021a).
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3.1 BACKGROUND ON THE CURRENT STANDARDS
The current primary PM2.5 standards were retained in 2020 based on the Administrator's
judgments regarding the available scientific evidence, the available risk information regarding
the risk that may be allowed by such standards, and the appropriate degree of public health
protection provided by the existing standards (85 FR 82718, December 18, 2020). With the 2020
final decision, the EPA retained the primary 24-hour PM2.5, with its level of 35 |ig/m3, and the
primary annual PM2.5 standard, with its level of 12.0 |ig/m3. This decision drew upon the
scientific evidence assessed in the 2019 ISA, the evidence and quantitative risk information in
the 2020 PA, the advice and recommendations of the CASAC, and public comments on the
proposed decision (85 FR 24094, April 30, 2020).
The health effects evidence base available in the 2020 review included extensive
evidence from previous reviews as well as the evidence that had emerged since the prior review
had been completed in 2012. This evidence base, spanning several decades, documents the
relationship between short- and long-term PM2.5 exposure and mortality or serious morbidity
effects. The evidence available in the 2019 ISA reaffirmed, and in some cases strengthened, the
conclusions from the 2009 ISA regarding the health effects of PM2.5 exposures (U.S. EPA,
2009). Much of the evidence came from epidemiologic studies conducted in North America,
Europe, or Asia that demonstrated generally positive, and often statistically significant, PM2.5
health effect associations. Such studies reported associations between estimated PM2.5 exposures
and non-accidental, cardiovascular, or respiratory mortality; cardiovascular or respiratory
hospitalizations or emergency department visits; and other mortality/morbidity outcomes (e.g.,
lung cancer mortality or incidence, asthma development). Experimental evidence, as well as
evidence from panel studies, strengthened support for potential biological pathways through
which PM2.5 exposures could lead to health effects reported in many population-epidemiologic
studies, including support for pathways that could lead to cardiovascular, respiratory, nervous
system, and cancer-related effects (U.S. EPA, 2019). Based on this evidence, the 2019 ISA
concludes there to be a causal relationship between long- and short-term PM2.5 exposure and
mortality and cardiovascular effects, as well as likely to be causal relationships between long-
and short-term PM2.5 exposures and respiratory effects, as well as long-term PM2.5 exposures and
cancer and nervous system effects (U.S. EPA, 2019, section 1.7).
Epidemiologic studies reported PM2.5 health effect associations with mortality and/or
morbidity across multiple U.S. cities and in diverse populations, including in studies examining
populations and lifestages that may be at comparatively higher risk of experiencing a PM2.5-
related health effect (e.g., older adults, children). The 2019 ISA cited extensive evidence
indicating that "both the general population as well as specific populations and lifestages are at
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risk for PIVh.s-related health effects" (U.S. EPA, 2019, p. 12-1). In support of the causal and
likely to be causal determinations, the 2019 ISA cites substantial evidence for:
• PM-related mortality and cardiovascular effects in older adults (U.S. EPA, 2019, sections
11.1, 11.2, 6.1, and 6.2);
• PM-related cardiovascular effects in people with pre-existing cardiovascular disease (U.S.
EPA, 2019, section 6.1);
• PM-related respiratory effects in people with pre-existing respiratory disease, particularly
asthma (U.S. EPA, 2019, section 5.1);
• PM-related impairments in lung function growth and asthma development in children (U.S.
EPA, 2019, sections 5.1, 5.2, and 12.5.1.1).
The 2019 ISA also noted that stratified analyses (i.e., analyses that allow for comparison of PM-
related health effects in subgroups to health effects for full populations) provided strong
evidence for racial and ethnic differences in PM2.5 exposures and PM2.5-related health risk. Such
analyses indicated that certain racial and ethnic groups such as Hispanic and non-Hispanic Black
populations have higher PM2.5 exposures than non-Hispanic White populations, thus contributing
to risk of adverse health effects in non-white populations (U.S. EPA, 2019, section 12.5.4).
Stratified analyses focused on other groups also suggested that populations with pre-existing
cardiovascular or respiratory disease, populations that are overweight or obese, populations that
have particular genetic variants, and populations that are of low socioeconomic status could be at
increased risk for PM2.5-related adverse health effects (U.S. EPA, 2019, chapter 12).
The risk information available in the 2020 review included risk estimates for air quality
conditions just meeting the existing primary PM2.5 standards, and also for air quality conditions
just meeting potential alternative standards. The general approach to estimating PM2.5-associated
health risks combined concentration-response functions from epidemiologic studies with model-
based PM2.5 air quality surfaces, baseline health incidence data, and population demographics for
47 urban areas (U.S. EPA, 2020, section 3.3, Figure 3-10, Appendix C). The risk assessment
estimated that the existing primary PM2.5 standards could allow a substantial number of PM2.5-
associated deaths in the U.S. Uncertainty in risk estimates (e.g., in the size of risk estimates) can
result from a number of factors, including assumptions about the shape of the concentration-
response relationship with mortality at low ambient PM concentrations, the potential for
confounding and/or exposure measurement error, and the methods used to adjust PM2.5 air
quality. In light of the limitations and uncertainties, these risk estimates were given little weight
by the Administrator in his decision on the standards (85 FR 82717, December 18. 2020).
Consistent with the general approach routinely employed in NAAQS reviews, the initial
consideration in the 2020 review of the primary PM2.5 standards was with regard to the adequacy
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of protection provided by the then-existing standards. Key aspects of that consideration are
summarized in section 3.1.1 below.
3.1.1 Considerations Regarding the Adequacy of the Existing Standards in the 2020
Review
With the 2020 final decision, the EPA retained the primary 24-hour PM2.5 standard, with
its level of 35 |ig/m3, and the primary annual PM2.5 standard, with its level of 12.0 |ig/m3. The
Administrator's conclusions regarding the adequacy of the primary PM2.5 standards at the time of
the 2020 review was based on consideration of the evidence, analyses and conclusions contained
in the 2019 ISA; the quantitative risk assessment in the 2020 PA; advice from the CAS AC; and
public comments. Key considerations informing the Administrator's decisions that the 2012
standards should be retained are summarized below.
As an initial matter, the Administrator considered the range of scientific evidence
evaluating these effects, including studies of at-risk populations, to inform his review of the
primary PM2.5 standards, placing the greatest weight on evidence of effects for which the 2019
ISA determined there to be a causal or likely to be causal relationship with long- and short-term
PM2.5 exposures (85 FR 82714-82715, December 18, 2020).
With regard to indicator, the Administrator recognized that, consistent with the evidence
available in prior reviews, the scientific evidence in the 2020 review continued to provide strong
support for health effects following short- and long-term PM2.5 exposures. He noted the 2020 PA
conclusions that the information continued to support the PM2.5 mass-based indicator and
remained too limited to support a distinct standard for any specific PM2.5 component or group of
components, and too limited to support a distinct standard for the ultrafine fraction. Thus, the
Administrator concluded that it was appropriate to retain PM2.5 as the indicator for the primary
standards for fine particulates (85 FR 82715, December 18, 2020).
With respect to averaging time and form, the Administrator noted that the scientific
evidence continued to provide strong support for health effects associations with both long-term
(e.g., annual or multi-year) and short-term (e.g., mostly 24-hour) exposures to PM2.5, consistent
with the conclusions in the 2020 PA. In the 2019 ISA, epidemiologic and controlled human
exposure studies examined a variety ofPlVh.s exposure durations. Epidemiologic studies
continued to provide strong support for health effects associated with short-term PM2.5 exposures
based on 24-hour PM2.5 averaging periods, and the EPA noted that associations with sub-daily
estimates are less consistent and, in some cases, smaller in magnitude (U.S. EPA, 2019, section
1.5.2.1; U.S. EPA, 2020, section 3.5.2.2). In addition, controlled human exposure and panel-
based studies of sub-daily exposures typically examined subclinical effects, rather than the more
serious population-level effects that have been reported to be associated with 24-hour exposures
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(e.g., mortality, hospitalizations). Taken together, the 2019 ISA concludes that epidemiologic
studies did not indicate that sub-daily averaging periods were more closely associated with
health effects than the 24-hour average exposure metric (U.S. EPA, 2019, section 1.5.2.1).
Additionally, while controlled human exposure studies provided consistent evidence for
cardiovascular effects following PM2.5 exposures for less than 24 hours (i.e., < 30 minutes to 5
hours), exposure concentrations in the studies were well-above the ambient concentrations
typically measured in locations meeting the existing standards (U.S. EPA, 2020, section 3.2.3.1).
Thus, these studies also did not suggest the need for additional protection against sub-daily PM2.5
exposures (U.S. EPA, 2020, section 3.5.2.2). Therefore, the Administrator judged that the 24-
hour averaging time remained appropriate (85 FR 82715, December 18, 2020).
With regard to the form of the 24-hour standard (98th percentile, averaged over three
years), the Administrator noted that epidemiologic studies continued to provide strong support
for health effect associations with short-term (e.g., mostly 24-hour) PM2.5 exposures (U.S. EPA,
2020, section 3.5.2.3) and that controlled human exposure studies provided evidence for health
effects following single short-term "peak" PM2.5 exposures. Thus, the evidence supported
retaining a standard focused on providing supplemental protection against short-term peak
exposures and supported a 98th percentile form for a 24-hour standard. The Administrator further
noted that this form also provided an appropriate balance between limiting the occurrence of
peak 24-hour PM2.5 concentrations and identifying a stable target for risk management programs
(U.S. EPA, 2020, section 3.5.2.3). As such, the Administrator concluded to retain the form and
averaging time of the current 24-hour standard (98th percentile, averaged over three years) and
annual standard (annual average, averaged over three years) (85 FR 82715, December 18, 2020).
With regard to the level of the standards, in reaching his final decision, the Administrator
considered the large body of evidence presented and assessed in the 2019 ISA (U.S. EPA, 2019),
the policy-relevant and risk-based conclusions and rationales as presented in the 2020 PA (U.S.
EPA, 2020), advice from the CASAC, and public comments. In particular, in considering the
2019 ISA and 2020 PA, he considered key epidemiologic studies that evaluated associations
between PM2.5 air quality distributions and mortality and morbidity, including key accountability
studies; the availability of experimental studies to support biological plausibility; controlled
human exposure studies examining effects following short-term PM2.5 exposures; air quality
analyses; and the important uncertainties and limitations associated with the information (85 FR
82715, December 18, 2020).
As an initial matter, the Administrator considered the protection afforded by both the
annual and 24-hour standards together against long- and short-term PM2.5 exposures and health
effects. The Administrator recognized that the annual standard was most effective in controlling
"typical" PM2.5 concentrations near the middle of the air quality distribution (i.e., around the
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mean of the distribution), but also provided some control over short-term peak PM2.5
concentrations. On the other hand, the 24-hour standard, with its 98th percentile form, was most
effective at limiting peak 24-hour PM2.5 concentrations, but in doing so also had an effect on
annual average PM2.5 concentrations. Thus, while either standard could be viewed as providing
some measure of protection against both average exposures and peak exposures, the 24-hour and
annual standards were not expected to be equally effective at limiting both types of exposures.
Thus, consistent with previous reviews, the Administrator's consideration of the public health
protection provided by the existing primary PM2.5 standards was based on his consideration of
the combination of the annual and 24-hour standards. Specifically, he recognized that the annual
standard was more likely to appropriately limit the "typical" daily and annual exposures that are
most strongly associated with the health effects observed in epidemiologic studies. The
Administrator concluded that an annual standard (as the arithmetic mean, averaged over three
years) remained appropriate for targeting protection against the annual and daily PM2.5 exposures
around the middle portion of the PM2.5 air quality distribution. Further, recognizing that the 24-
hour standard (with its 98th percentile form) was more directly tied to short-term peak PM2.5
concentrations, and more likely to appropriately limit exposures to such concentrations, the
Administrator concluded that the current 24-hour standard (with its 98th percentile form,
averaged over three years) remained appropriate to provide a balance between limiting the
occurrence of peak 24-hour PM2.5 concentrations and identifying a stable target for risk
management programs. However, the Administrator recognized that changes in PM2.5 air quality
to meet an annual standard would likely result not only in lower short- and long-term PM2.5
concentrations near the middle of the air quality distribution, but also in fewer and lower short-
term peak PM2.5 concentrations. The Administrator further recognized that changes in air quality
to meet a 24-hour standard, with a 98th percentile form, would result not only in fewer and lower
peak 24-hour PM2.5 concentrations, but also in lower annual average PM2.5 concentrations (85
FR 82715-82716, December 18, 2020).
Thus, in considering the adequacy of the 24-hour standard, the Administrator noted the
importance of considering whether additional protection was needed against short-term
exposures to peak PM2.5 concentrations. In examining the scientific evidence, he noted the
limited utility of the animal toxicologic studies in directly informing conclusions on the
appropriate level of the standard given the uncertainty in extrapolating from effects in animals to
those in human populations. The Administrator noted that controlled human exposure studies
provided evidence for health effects following single, short-term PM2.5 exposures that
corresponded best to exposures that might be experienced in the upper end of the PM2.5 air
quality distribution in the U.S. (i.e., "peak" concentrations). However, most of these studies
examined exposure concentrations considerably higher than are typically measured in areas
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meeting the standards (U.S. EPA, 2020, section 3.2.3.1). In particular, controlled human
exposure studies often reported statistically significant effects on one or more indicators of
cardiovascular function following 2-hour exposures to PM2.5 concentrations at and above 120
[j,g/m3 (at and above 149 [j,g/m3 for vascular impairment, the effect shown to be most consistent
across studies). To provide insight into what these studies may indicate regarding the primary
PM2.5 standards, the 2020 PA (U.S. EPA, 2020, p. 3-49) noted that 2-hour ambient
concentrations of PM2.5 at monitoring sites meeting the current standards almost never exceeded
32 [j,g/m3. In fact, even the extreme upper end of the distribution of 2-hour PM2.5 concentrations
at sites meeting the primary PM2.5 standards remained well-below the PM2.5 exposure
concentrations consistently shown in controlled human exposure studies to elicit effects (i.e.,
99.9th percentile of 2-hour concentrations at these sites is 68 [j,g/m3 during the warm season).
Thus, the available experimental evidence did not indicate the need for additional protection
against exposures to peak PM2.5 concentrations, beyond the protection provided by the
combination of the 24-hour and the annual standards (U.S. EPA, 2020, section 3.2.3.1; 85 FR
82716, December 18, 2020).
With respect to the epidemiologic evidence, the Administrator noted that the studies did
not indicate that associations in those studies were strongly influenced by exposures to peak
concentrations in the air quality distribution and thus did not indicate the need for additional
protection against short-term exposures to peak PM2.5 concentrations (U.S. EPA, 2020, section
3.5.1). The Administrator noted that this was consistent with CAS AC consensus support for
retaining the current 24-hour standard. Thus, the Administrator concluded that the 24-hour
standard with its level of 35 |ig/m3 was adequate to provide supplemental protection (i.e., beyond
that provided by the annual standard alone) against short-term exposures to peak PM2.5
concentrations (85 FR 82716, December 18, 2020).
With regard to the level of the annual standard, the Administrator recognized that the
annual standard, with its form based on the arithmetic mean concentration, was most
appropriately meant to limit the "typical" daily and annual exposures that were most strongly
associated with the health effects observed in epidemiologic studies. However, the Administrator
also noted that while epidemiologic studies examined associations between distributions of PM2.5
air quality and health outcomes, they did not identify particular PM2.5 exposures that cause
effects and thus, they could not alone identify a specific level at which the standard should be
set, as such a determination necessarily required the Administrator's judgment. Thus, consistent
with the approaches in previous NAAQS reviews, the Administrator recognized that any
approach that used epidemiologic information in reaching decisions on what standards are
appropriate necessarily required judgments about how to translate the information from the
epidemiologic studies into a basis for appropriate standards. This approach included
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consideration of the uncertainties in the reported associations between daily or annual average
PM2.5 exposures and mortality or morbidity in the epidemiologic studies. Such an approach is
consistent with setting standards that are neither more nor less stringent than necessary,
recognizing that a zero-risk standard is not required by the CAA (85 FR 82716, December 18,
2020).
The Administrator emphasized uncertainties and limitations that were present in
epidemiologic studies in previous reviews and persisted in the 2020 review. These uncertainties
included exposure measurement error, potential confounding by copollutants, increasing
uncertainty of associations at lower PM2.5 concentrations, and heterogeneity of effects across
different cities or regions (85 FR 82716, December 18, 2020). The Administrator also noted the
advice given by the CASAC on this matter. The CASAC members who supported retaining the
annual standard expressed their concerns with the epidemiologic studies, asserting that these
studies did not provide a sufficient basis for revising the existing standards. They also identified
several key concerns regarding the associations reported in epidemiologic studies and concluded
that "while the data on associations should certainly be carefully considered, this data should not
be interpreted more strongly than warranted based on its methodological limitations" (Cox, 2019,
p. 8 consensus responses).
Taking into consideration the views expressed by the CASAC members who supported
retaining the annual standard, the Administrator recognized that epidemiologic studies examined
associations between distributions of PM2.5 air quality and health outcomes, and they did not
identify particular PM2.5 exposures that cause effects (U.S. EPA, 2020, section 3.1.2). While the
Administrator remained concerned about placing too much weight on epidemiologic studies to
inform conclusions on the adequacy of the primary standards, he noted the approach to
considering such studies in the 2012 review. In the 2012 review, it was noted that the evidence of
an association in any epidemiologic study was "strongest at and around the long-term average
where the data in the study are most concentrated" (78 FR 3140, January 15, 2013). In
considering the characterization of epidemiologic studies, the Administrator viewed that when
assessing the mean concentrations of the key short-term and long-term epidemiologic studies in
the U.S. that use ground-based monitoring (i.e., those studies where the mean is most directly
comparable to the current annual standard), the majority of studies had mean concentrations at or
above the level of the existing annual standard, with the mean of the study-reported means or
medians equal to 13.5 |ig/m3, a concentration level above the existing level of the primary annual
standard of 12 |ig/m3. The Administrator further noted his caution in directly comparing the
reported study mean values to the standard level given that study-reported mean concentrations,
by design, are generally lower than the design value of the highest monitor in an area, which
determines compliance. In the 2020 PA, analyses of recent air quality in U.S. CBSAs indicated
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that maximum annual PM2.5 design values for a given three-year period were often 10% to 20%
higher than average monitored concentrations (i.e., averaged across multiple monitors in the
same CBSA) (U.S. EPA, 2020, Appendix B, section B.7). He further noted his concern in
placing too much weight on any one epidemiologic study but instead judged that it was more
appropriate to focus on the body of studies together and therefore noted the calculation of the
mean of study-reported means (or medians). Thus, while the Administrator was cautious in
placing too much weight on the epidemiologic evidence alone, he noted that: (1) the reported
mean concentration in the majority of the key U.S. epidemiologic studies using ground-based
monitoring data were above the level of the existing annual standard; (2) the mean of the
reported study means (or medians) (i.e., 13.5 |ig/m3) was above the level of the current standard;2
(3) air quality analyses showed the study means to be lower than their corresponding design
values by 10-20%; and (4) these analyses must be considered in light of uncertainties inherent in
the epidemiologic evidence. When taken together, the Administrator judged that, even if it were
appropriate to place more weight on the epidemiologic evidence, this information did not call
into question the adequacy of the current standards (85 FR 82716-82717, December 18, 2020).
In addition to the evidence, the Administrator also considered the potential implications
of the risk assessment. He noted that all risk assessments have limitations and that he remained
concerned about the uncertainties in the underlying epidemiologic data used in the risk
assessment. The Administrator also noted that in previous reviews, these uncertainties and
limitations have often resulted in less weight being placed on quantitative estimates of risk than
on the underlying scientific evidence itself (e.g., 78 FR 3086, 3098-99, January 15, 2013). These
uncertainties and limitations included uncertainty in the shapes of concentration-response
functions, particularly at low concentrations; uncertainties in the methods used to adjust air
quality; and uncertainty in estimating risks for populations, locations and air quality distributions
different from those examined in the underlying epidemiologic study (U.S. EPA, 2020, section
3.3.2.4). Additionally, the Administrator noted similar concern expressed by some members of
the CASAC who support retaining the existing standards; they highlighted similar uncertainties
and limitations in the risk assessment (Cox, 2019). In light of all of this, the Administrator
judged it appropriate to place little weight on quantitative estimates of PIVh.s-associated mortality
risk in reaching conclusions about the level of the primary PM2.5 standards (85 FR 82717,
December 18, 2020).
The Administrator additionally considered an emerging body of evidence from
accountability studies that examined past reductions in ambient PM2.5 and the degree to which
2 The median of the study-reported mean (or median) PM2 5 concentrations is 13.3 |ig/ml which was also above the
level of the existing standard.
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those reductions resulted in public health improvements. While the Administrator agreed with
public commenters that well-designed and conducted accountability studies can be informative,
he viewed that interpreting such studies in the context of the primary PM2.5 standards was
complicated by the fact that some of the available studies had not evaluated PM2.5 specifically
(e.g., as opposed to PM10 or total suspended particulates), did not show changes in PM2.5 air
quality, or had not been able to disentangle health impacts of the interventions from background
trends in health (U.S. EPA, 2020, section 3.5.1). He further recognized that the small number of
available studies that did report public health improvements following past declines in ambient
PM2.5 had not examined air quality meeting the existing standards (U.S. EPA, 2020, Table 3-3).
This included U.S. studies that reported increased life expectancy, decreased mortality, and
decreased respiratory effects following past declines in ambient PM2.5 concentrations. Such
studies examined "starting" annual average PM2.5 concentrations (i.e., prior to the reductions
being evaluated) ranging from about 13.2 to > 20 |ig/m3 (i.e., U.S. EPA, 2020, Table 3-3). Given
the lack of available accountability studies reporting public health improvements attributable to
reductions in ambient PM2.5 in locations meeting the existing standards, together with his broader
concerns regarding the lack of experimental studies examining PM2.5 exposures typical of areas
meeting the existing standards, the Administrator judged that there was considerable uncertainty
in the potential for increased public health protection from further reductions in ambient PM2.5
concentrations beyond those achieved under the existing primary PM2.5 standards (85 FR 82717,
December 18, 2020).
When the above considerations were taken together, the Administrator concluded that the
scientific evidence assessed in the 2019 ISA, together with the analyses in the 2020 PA based on
that evidence and consideration of CASAC advice and public comments, did not call into
question the adequacy of the public health protection provided by the existing annual and 24-
hour PM2.5 standards. In particular, the Administrator judged that there was considerable
uncertainty in the potential for additional public health improvements from reducing ambient
PM2.5 concentrations below the concentrations achieved under the existing primary standards and
that, therefore, standards more stringent than the existing standards (e.g., with lower levels) were
not supported. That is, he judged that such standards would be more than requisite to protect the
public health with an adequate margin of safety. This judgment reflected the Administrator's
consideration of the uncertainties in the potential implications of the lower end of the air quality
distributions from the epidemiologic studies due in part to the lack of supporting evidence from
experimental studies and retrospective accountability studies conducted at PM2.5 concentrations
meeting the existing standards (85 FR 82717, December 18, 2020).
In reaching this conclusion, the Administrator judged that the existing standards provided
an adequate margin of safety. With respect to the annual standard, the level of 12 |ig/m3 was
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below the lowest "starting" concentration (i.e., 13.2 |ig/m3) in the available accountability
studies that showed public health improvements attributable to reductions in ambient PM2.5. In
addition, while the Administrator placed less weight on the epidemiologic evidence for selecting
a standard, he noted that the level of the annual standard was below the reported mean (and
median) concentrations in the majority of the key U.S. epidemiologic studies using ground-based
monitoring data (noting that these means tend to be 10-20% lower than their corresponding area
design values which is the more relevant metric when considering the level of the standard) and
below the mean of the reported means (or medians) of these studies (i.e., 13.5 |ig/m3). In
addition, the Administrator recognized that concentrations in areas meeting the existing 24-hour
and annual standards remained well-below the PM2.5 exposure concentrations consistently shown
to elicit effects in human exposure studies (85 FR 82717-82718, December 18, 2020).
In addition, based on the Administrator's review of the science, including controlled
human exposure studies examining effects following short-term PM2.5 exposures, the
epidemiologic studies, and accountability studies conducted at levels just above the existing
annual standard, he judged that the degree of public health protection provided by the existing
annual standard is not greater than warranted. This judgment, together with the fact that no
CASAC member expressed support for a less stringent standard, led the Administrator to
conclude that standards less stringent than the existing standards (e.g., with higher levels) were
also not supported (85 FR 82718, December 18, 2020).
In reaching his final decision, the Administrator concluded that the scientific evidence
and technical information continued to support the existing annual and 24-hour PM2.5 standards.
This conclusion reflected the Administrator's view that there were important limitations and
uncertainties that remained in the evidence. The Administrator concluded that these limitations
contributed to considerable uncertainty regarding the potential public health implications of
revising the existing primary PM2.5 standards. Given this uncertainty, and noting the advice from
some CASAC members, he concluded that the primary PM2.5 standards, including the indicators
(PM2.5), averaging times (annual and 24-hour), forms (arithmetic mean and 98th percentile,
averaged over three years) and levels (12.0 ng/m3, 35 |j,g/m3), when taken together, remained
requisite to protect the public health. Therefore, in the 2020 review, the Administrator reached
the conclusion that the primary 24-hour and annual PM2.5 standards, together, were requisite to
protect public health from fine particles with an adequate margin of safety, including the health
of at-risk populations, and retained the standards, without revision (85 FR 82718, December 18,
2020).
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3.2 GENERAL APPROACH AND KEY ISSUES IN THIS
RECONSIDERATION OF THE 2020 FINAL DECISION
As is the case for all such reviews, this reconsideration of the 2020 final decision on the
primary PM2.5 standards is most fundamentally based on the Agency's assessment of the
scientific evidence and associated quantitative analyses to inform the Administrator's judgments
regarding primary standards that are requisite to protect public health with an adequate margin of
safety. This draft PA is intended to help bridge the gap between the scientific evidence and
information assessed in the 2019 ISA and draft ISA Supplement and the judgments required of
the Administrator in determining whether it is appropriate to retain or revise the primary PM2.5
NAAQS. The approach for this reconsideration builds on the substantial assessments and
evaluations performed over the course of the prior reviews (U.S. EPA, 2011; U.S. EPA, 2020),
taking into account the more recent scientific information and air quality data now available to
inform our understanding of the key policy issues relevant in this reconsideration.
The evaluations in this draft PA of the scientific assessments in the 2019 ISA and the
draft ISA Supplement,3 augmented by the quantitative risk analyses, are intended to inform the
Administrator's public health policy judgments and conclusions, including his decisions as to
whether to retain or revise the primary PM2.5 standards. The draft PA evaluations consider the
potential implications of various aspects of the scientific evidence, the risk-based information,
and the associated uncertainties and limitations. In so doing, the approach for this draft PA
involves evaluating the scientific and technical information to address a series of key policy-
relevant questions using both evidence- and risk-based considerations. Together, consideration of
the full set of evidence and information available in this reconsideration will inform the answer
to the following initial overarching question for the reconsideration:
• Does the scientific evidence, air quality and quantitative risk information support or
call into question the adequacy of the public health protection afforded by the
current primary annual and 24-hour PM2.5 standards?
In reflecting on this question, we will consider the body of scientific evidence, assessed
in the 2019 ISA and draft ISA Supplement and used as a basis for developing or interpreting risk
analyses, including whether it supports or calls into question the scientific conclusions reached in
3 As described in detail in section 1.4.2, the draft ISA Supplement focuses on a thorough evaluation of some studies
that became available after the literature cutoff date of the 2019 ISA that could either further inform the adequacy
of the current PM NAAQS or address key scientific topics that have evolved since the literature cutoff date for
the 2019 ISA (U.S. EPA, 2021a). The selection of the health effects to evaluate within the draft ISA Supplement
were based on the causality determinations reported in the 2019 ISA and the subsequent use of scientific evidence
in the 2020 PA. Specifically, for PM2 5-related health effects, the focus within the draft ISA Supplement is on
mortality and cardiovascular effects. The draft ISA Supplement does not include an evaluation of studies for other
PM2 5-related health effects (U.S. EPA, 2021a).
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the 2020 review regarding health effects related to exposure to PM2.5 in ambient air. Information
available in this reconsideration that may be informative to public health judgments regarding
significance or adversity of key effects will also be considered. Additionally, the available risk
information, whether newly developed for this reconsideration or predominantly developed in
the past and interpreted in light of recent information, will be considered, including with regard
to the extent to which it may continue to support judgments made in the 2020 review. Further, in
considering this question with regard to the primary PM2.5 standards, as in all NAAQS reviews,
we give particular attention to exposures and health risks to at-risk populations (including at-risk
lifestages).4
If the information available in this reconsideration suggests that revision of the current
primary standards would be appropriate to consider, the draft PA will also evaluate how the
standards might be revised based on the scientific information, air quality assessments, and risk
information, and also considering what the information indicates as to the health protection
expected to be afforded by the current or potential alternative standards. Such an evaluation may
consider the effect of revising one or more elements of the standard (indicator, averaging time,
level, and form), with the impact evaluated being on the resulting potential standard and all of its
elements collectively. Based on such evaluations, the draft PA would then identify potential
alternative standards (specified in terms of indicator, averaging time, level, and form) intended to
reflect a range of alternative policy judgments as to the degree of protection that is requisite to
protect public health with an adequate margin of safety, and options for standards to achieve it.
The initial overarching policy-relevant question that frames such an evaluation of what revision
of the standard might be appropriate to consider is:
• What range of potential alternative standards could be supported by the available
scientific evidence, air quality and risk information?
The approach to reaching preliminary conclusions on the current primary PM2.5 standards
and, as appropriate, on potential alternative standards is summarized in general terms in Figure
3-1.
4 As used here and similarly throughout this document, the term population refers to persons having a quality or
characteristic in common, such as a specific pre-existing illness or a specific age or life stage. Identifying at-risk
populations involves consideration of susceptibility and vulnerability. Susceptibility refers to innate (e.g., genetic
or developmental aspects) or acquired (e.g., disease or smoking status) sensitivity that increases the risk of health
effects occurring with exposure to PM2 5. Vulnerability refers to an increased risk of PM2 5-related health effects
due to factors such as those related to socioeconomic status, reduced access to health care or exposure.
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Adequacy of Existing Primary PM^ Standards
Evidence-Based Considerations
Degree to which available evidence strengthens support for or calls
into question health effects attributable to fine particle exposures
*¦ Evidence for at-risk populations
*¦ Degree to which uncertainties have been addressed or nav
uncertainties identified
>Supportfor adverse effects at ambient concentrations meeting current
PM2,s standards
Risk-Based Considerations
^Nature. magnitude, and importance of
estimated risks associated with current
primary PVI; ; standards
Uncertainties in the risk estimates
Does information call
into question adequacy
of current Pnmarv PM;r
standards"?
NO
Appropriate to
consider retaining
current standards
jYES
Further evaluate the scientific evidence and risk assess nwntto
inform Identification of potential alternatives
Figure 3-1. Overview of general approach for the reconsideration of the 2020 final decision
on the primary PM2.5 standards.
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The Agency's approach in reconsidering the primary standards is consistent with
requirements of the provisions of the CAA related to the review of the NAAQS and with how the
EPA and the courts have historically interpreted the CAA. As discussed in section 1.1 above,
these provisions require the Administrator to establish primary standards that, in the
Administrator's judgment, are requisite (i.e., neither more nor less stringent than necessary) to
protect public health with an adequate margin of safety. Consistent with the Agency's approach
across all NAAQS reviews, the approach of this draft PA to informing these judgments is based
on a recognition that the available health effects evidence generally reflects continuums that
include ambient air exposures for which scientists generally agree that health effects are likely to
occur through lower levels at which the likelihood and magnitude of response become
increasingly uncertain. The CAA does not require the Administrator to establish a primary
standard at a zero-risk level or at background concentration levels, but rather at a level that
reduces risk sufficiently so as to protect public health, including the health of sensitive groups,5
with an adequate margin of safety.
The decisions on the adequacy of the current primary PM2.5 standards and on any
alternative standards considered in a reconsideration are largely public health policy judgments
made by the Administrator. The four basic elements of the NAAQS (i.e., indicator, averaging
time, form, and level) are generally considered collectively in evaluating the health protection
afforded by the current standards, and by any alternatives considered. The Administrator's final
decisions draw upon the scientific evidence for health effects, quantitative analyses of population
exposures and/or health risks, as available, and judgments about how to consider the
uncertainties and limitations that are inherent in the scientific evidence and quantitative analyses.
3.3 HEALTH EFFECTS EVIDENCE
In this section, we draw from the EPA's synthesis and assessment of the scientific
evidence presented in the 2019 ISA (U.S. EPA, 2019) and the draft ISA Supplement (U.S. EPA,
2021a) to consider the following policy-relevant question:
5 More than one population group may be identified as sensitive or at-risk in a NAAQS review. Decisions on
NAAQS reflect consideration of the degree to which protection is provided for these sensitive population groups.
To the extent that any particular population group is not among the identified sensitive groups, a decision that
provides protection for the sensitive groups would be expected to also provide protection for other population
groups.
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• To what extent does the currently available scientific evidence, as assessed in the
2019 ISA and the draft ISA Supplement, support or call into question the public
health protection afforded by the current suite of PM2.5 standards?
The 2019 ISA uses a weight-of-evidence framework for characterizing the strength of the
available scientific evidence for health effects attributable to PM exposures (U.S. EPA, 2015b,
Preamble, section 5). This framework provides the basis for robust, consistent, and transparent
evaluation of the scientific evidence, including its uncertainties, and for drawing conclusions on
PM-related health effects. As in previous reviews, the 2019 ISA adopts a five-level hierarchy to
classify the overall weight of evidence into one of the following categories: causal relationship;
likely to be a causal relationship; suggestive of, but not sufficient to infer, a causal relationship;
inadequate to infer a causal relationship; and not likely to be a causal relationship (U.S. EPA,
2015b, Preamble Table II). In using the weight-of-evidence approach to inform judgments about
the causal nature of relationships between PM exposure and health effects, evidence is evaluated
for major outcome categories or groups of related outcomes (e.g., respiratory effects), integrating
evidence from across disciplines, including epidemiologic, controlled human exposure, and
animal toxicological studies and evaluating the coherence of evidence across a spectrum of
related endpoints (U.S. EPA, 2015b, Preamble, section 5.c.). In this draft PA, we consider the
full body of health evidence, including evidence from the 2019 ISA and draft ISA Supplement,
placing the greatest emphasis on the health effects for which the evidence has been judged in the
2019 ISA to demonstrate a "causal" or a "likely to be causal" relationship with PM exposures.
The 2019 ISA defines these causality determinations as follows (U.S. EPA, 2019, p. p-20; U.S.
EPA, 2015b):
• Causal relationship: the pollutant has been shown to result in health effects at relevant
exposures based on studies encompassing multiple lines of evidence and chance,
confounding, and other biases can be ruled out with reasonable confidence.
• Likely to be a causal relationship: there are studies in which results are not explained by
chance, confounding, or other biases, but uncertainties remain in the health effects evidence
overall. For example, the influence of co-occurring pollutants is difficult to address, or
evidence across scientific disciplines may be limited or inconsistent.
While the 2019 ISA provides the broad scientific foundation for this reconsideration, we
recognize that additional literature has become available since the literature cutoff date of the
2019 ISA that expands the body of evidence that can inform the Administrator's judgments on
the adequacy of the current primary PM2.5 standards. As such, the draft ISA Supplement builds
on the information in the 2019 ISA with a targeted identification and evaluation of new scientific
information (U.S. EPA, 2021a, section 1.2). The draft ISA Supplement focuses on PM2.5 health
effects evidence where the 2019 ISA concludes a "causal relationship," because such health
effects are given the most weight in an Administrator's decisions in a NAAQS review. The draft
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ISA Supplement evaluates newly available evidence related to short- and long-term PM2.5
exposure and mortality and cardiovascular effects given the strength of the evidence available in
the 2019 ISA and past IS As and AQCDs, as well as the clear adversity of these endpoints.
Specifically, U.S. and Canadian epidemiologic studies for mortality and cardiovascular effects,
along with experimental studies related to cardiovascular effects, were considered to be of
greatest utility in informing the Administrator's conclusions on the adequacy of the current
primary PM2.5 standards. While the draft ISA Supplement does not include information for
health effects other than mortality and cardiovascular effects, the evidence as it was assessed in
the 2019 ISA is considered in this draft PA in reaching preliminary conclusions as a part of the
reconsideration of the 2020 final decision.
The draft ISA Supplement also assessed accountability studies because these types of
epidemiologic studies were part of the body of evidence that was a focus of the 2020 review.
Accountability studies inform our understanding of the potential for public health improvements
as ambient PM2.5 concentrations have declined over time. Further, the draft ISA Supplement
considered studies that employed causal modeling methods, given that such studies were
highlighted by the CASAC and identified in public comments in the 2020 review. Since the
literature cutoff date for the 2019 ISA, multiple accountability studies and studies that employ
causal modeling have become available for consideration in the draft ISA Supplement and in this
reconsideration.
The draft ISA Supplement also considered recent health effects evidence that addresses
key scientific issues where the literature has expanded since the completion of the 2019 ISA.6
Given the importance of identifying the populations at increased risk of PIVh.s-related effects, the
draft ISA Supplement also included epidemiologic or exposure studies examining exposure or
risk disparities by race/ethnicity or socioeconomic status. The draft ISA Supplement assessed
studies that examined the relationship between PM2.5 exposures and COVID-19 infection and/or
death, as these studies are a new area of research and were raised by a number of public
commenters in the 2020 review. These types of studies provide additional information related to
factors that may increase risk of PM2.5-related health effects and provide additional evidence for
consideration by the Administrator in reaching conclusions regarding the adequacy of the current
standards.
The evidence presented within the 2019 ISA, along with the targeted identification and
evaluation of new scientific information in the draft ISA Supplement, provides the scientific
basis for the reconsideration of the 2020 final decision on the primary PM2.5 standards. In the
6 As with the epidemiologic studies for long- and short-term PM2 5 exposure and mortality and cardiovascular
effects, epidemiologic studies of exposure or risk disparities and COVID-19 infection and/or death were limited
to those conducted in the U.S. and Canada.
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sections below, we consider the nature of the health effects attributable to long- and short-term
fine particle exposures (section 3.3.1), the public health implications and populations potentially
at increased risk for PM-related effects (section 3.3.2), and the PM2.5 concentrations at which
effects have been shown to occur (section 3.3.3).
3.3.1 Nature of Effects
In considering the available evidence for health effects attributable to PM2.5 exposures
presented in the 2019 ISA and the draft ISA Supplement, this section poses the following policy-
relevant questions:
• To what extent does the currently available scientific evidence strengthen, or
otherwise alter, our preliminary conclusions regarding health effects attributable to
long- or short-term fine particle exposures? Have previously identified uncertainties
been reduced? What important uncertainties remain and have new uncertainties
been identified?
In answering these questions, as noted above, we consider the full body of evidence assessed in
the 2019 ISA, along with the targeted evaluation of recent evidence in the draft ISA Supplement.
In so doing, we place particular emphasis on health outcomes for which the evidence in the 2019
ISA supports either a "causal" or a "likely to be causal" relationship. While the strongest
evidence focuses on PM2.5, the 2019 ISA also assesses the evidence for the ultrafine fraction of
PM2.5 (ultrafine particles or UFP), generally considered as particulates with a diameter less than
or equal to 0.1 [j,m7 (typically based on physical size, thermal diffusivity or electrical mobility)
(U.S. EPA, 2019, Preface, p. 11). Table 3-1 lists causality determinations for all of the health
effect categories and exposure durations for both PM2.5 and UFP, which we consider within this
chapter (adapted from U.S. EPA, 2019, Table 1-4).
7 Definitions of UFP vary across the scientific literature and, as discussed in sections 3.3.1.5 and 3.3.1.6, UFP
exposures in animal toxicological and controlled human exposure studies typically use a particle concentrator,
which can result in exposures to particles > 0.1 |im in diameter in some studies of UFP-related health effects.
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1 Table 3-1. Key causality determinations for PM2.5 and UFP exposures.
Health Outcome
Size
Fraction
Exposure
Duration
2009 ISA
2019 ISA
Mortality
PM2.5
Long-term
Short-term
Causal
Causal
Cardiovascular
effects
PM2.5
Long-term
Short-term
Causal
Causal
UFP
Short-term
Suggestive of, but not
sufficient to infer
Suggestive of, but not
sufficient to infer
Respiratory
effects
PM2.5
Long-term
Short-term
Likely to be causal
Likely to be causal
UFP
Short-term
Suggestive of, but not
sufficient to infer
Suggestive of, but not
sufficient to infer
Cancer
PM2.5
Long-term
Suggestive of, but not
sufficient to infer
Likely to be causal
Nervous System
effects
PM2.5
Long-term
—
Likely to be causal
Short-term
Inadequate
Suggestive of, but not
sufficient to infer
UFP
Long-term
...
Suggestive of, but not
sufficient to infer
Short-term
Inadequate
Suggestive of, but not
sufficient to infer
Metabolic effects
PM2.5
Long-term
...
Suggestive of, but not
sufficient to infer
Short-term
...
Suggestive of, but not
sufficient to infer
Reproduction
and Fertility
Pregnancy and
Birth Outcomes
PM2.5
Long-,
Short-term
Suggestive of, but not
sufficient to infer
Suggestive of, but not
sufficient to infer
Table 3-1 lists the health outcomes for which the 2019 ISA concludes the evidence supports either a causal, a likely to
be causal, or a suggestive relationship. For other health outcomes, the 2019 ISA concludes the evidence is inadequate
to infer a causal relationship (U.S. EPA, 2019, Table 1-4).
The 2009 ISA (U.S. EPA, 2009) made causality determinations for the broad category of "Reproductive and
Developmental Effects." Causality determinations for 2009 represent this broad category and not specifically for "Male
and Female Reproduction and Fertility" and "Pregnancy and Birth Outcomes".
For reproductive and developmental effects, the 2019 ISA's causality determinations reflect the combined evidence for
both short- and long-term exposures (U.S. EPA, 2019, Chapter 9).
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Sections 3.3.1.1 to 3.3.1.5 summarize the evidence supporting the 2019 ISA's "causal" and
"likely to be causal" determinations for PM2.5 (italics in Table 3-1) and integrates the recent
evidence assessed in the draft ISA Supplement, where available. Section 3.3.1.6 briefly
summarizes the evidence supporting the 2019 ISA's "suggestive" determinations, as well as
emerging evidence related to COVID-19 infection and death detailed in the draft ISA
Supplement. Each of these sections focuses on addressing the policy-relevant questions posed
above. Section 3.3.1.7 summarizes the evidence in preceding sections and revisits the policy-
relevant questions posed above. Section 3.3.2 describes the public health implications and at-risk
populations. In section 3.3.3, we present the PM2.5 concentrations in key studies reporting PM2.5-
related health effects, and section 3.3.4 summarizes the key uncertainties and limitations
associated with the health effects evidence.
3,3,1,1 Mortality
Long-term PM2.5 exposures
The 2009 ISA reported that the evidence was "sufficient to conclude that the relationship
between long-term PM2.5 exposures and mortality is causal" (U.S. EPA, 2009, p. 7-96). The
strongest evidence supporting this conclusion was provided by epidemiologic studies,
particularly those examining two seminal cohorts, the American Cancer Society (ACS) and the
Harvard Six Cities cohorts. Analyses of the Harvard Six Cities cohort included demonstrations
that reductions in ambient PM2.5 concentrations are associated with reduced mortality risk
(Laden et al., 2006) and with increases in life expectancy (Pope et al., 2009). Further support was
provided by other cohort studies conducted in North America and Europe that also reported
positive associations between long-term PM2.5 exposures and risk of mortality (U.S. EPA, 2009).
Cohort studies, assessed in the 2019 ISA, continue to provide consistent evidence of
positive associations between long-term PM2.5 exposures and mortality. These studies add
support for associations with total and non-accidental mortality,8 as well as with specific causes
of death, including cardiovascular disease and respiratory disease (U.S. EPA, 2019, section
11.2.2). Many of these studies have extended the follow-up periods originally evaluated in the
ACS and Harvard Six Cities cohorts and continue to observe positive associations between long-
term PM2.5 exposures and mortality (U.S. EPA, 2019, section 11.2.2.1; Figures 11-18 and 11-
19). Adding to the evaluations of the ACS and Six Cities cohorts, studies conducted in other
cohorts also demonstrate consistent, positive associations between long-term PM2.5 exposure and
mortality across various demographic groups (e.g., age, sex, occupation), spatial and temporal
extents, exposure assessment metrics, and statistical techniques (U.S. EPA, 2019, sections
8 The majority of these studies examined non-accidental mortality outcomes, though some Medicare studies lack
cause-specific death information and, therefore, examine total mortality.
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11.2.2.1, 11.2.5; U.S. EPA, 2021a, Table 11-8). This includes some of the largest cohort studies
conducted to date, with analyses of the U.S. Medicare cohort that include nearly 61 million
enrollees (Di et al., 2017b) and studies that control for a range of individual and ecological
covariates, such as race, age, socioeconomic status, smoking status, body mass index, and annual
weather variables (e.g., temperature, humidity).
Many recent North American cohort studies evaluated in the draft ISA Supplement
continue to examine the relationship between long-term PM2.5 exposure and mortality and report
positive and statistically significant associations. Recent studies continue to utilize large and
demographically diverse cohorts that are generally representative of the national populations in
both the U.S. and Canada, as well as focus on occupation-based specific cohorts. These "studies
published since the 2019 ISA support and extend the evidence base that contributed to the
conclusion of a causal relationship between long-term PM2.5 exposure and mortality" (U.S.
EPA, 2021a, section 3.2.2.2.1, Figure 3-19, Figure 3-20)
Furthermore, studies in the 2019 ISA and the draft ISA Supplement evaluating cause-
specific mortality build on previous research that found consistent, positive associations between
cardiovascular and respiratory mortality, as well as other mortality outcomes. For
cardiovascular-related mortality, the evidence assessed in the draft ISA Supplement is consistent
with the evidence assessed in the 2019 ISA with recent studies reporting positive associations
with long-term PM2.5 exposure. When evaluating cause-specific cardiovascular mortality, recent
studies report positive associations for a number of outcomes including ischemic heart disease
(IHD) and stroke mortality (U.S. EPA, 2021a, Figure 3-23). Recent studies also provide some
initial evidence that people with pre-existing health issues (such as heart failure and diabetes) are
at an increased risk of PM2.5-related effects (U.S. EPA, 2021a, section 3.2.2.4) and suggest that
these individuals have a higher risk of mortality overall, which was previously only examined in
studies that used stratified analyses rather than a cohort of people with an underlying health
condition (U.S. EPA, 2021a, section 3.2.2.4). With regard to respiratory mortality, epidemiologic
studies assessed in the 2019 ISA and draft ISA Supplement provide continued support for
associations between long-term PM2.5 exposure and respiratory mortality (U.S. EPA, 2019,
section 5.2.10; U.S. EPA, 2021a, Table 3-2).
A series of epidemiologic studies evaluated in the 2019 ISA tested the hypothesis that
past reductions in ambient PM2.5 concentrations have been associated with increased life
expectancy or a decreased mortality rate (U.S. EPA, 2019, section 11.2.2.5). In their original
study, Pope et al. (2009) used air quality data in a cross-sectional analysis from 51 metropolitan
areas across the U.S., beginning in the 1970s through the early 2000s, to demonstrate that a
10 |ig/m3 decrease in long-term PM2.5 concentration was associated with a 0.61-year increase in
life expectancy. In a subsequent analysis, these authors extended the period of analysis to include
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2000 to 2007 (Correia et al., 2013), a time period with lower ambient PM2.5 concentrations. In
this follow-up study, a decrease in long-term PM2.5 concentration continued to be associated with
an increase in life expectancy, though the magnitude of the increase was smaller than during the
earlier time period (i.e., a 10 |ig/m3 decrease in long-term PM2.5 concentration was associated
with a 0.35-year increase in life expectancy). Additional studies conducted in the U.S. or Europe
similarly report that reductions in ambient PM2.5 are associated with improvements in longevity
(U.S. EPA, 2019, section 11.2.2.5). Multiple epidemiologic studies that conducted accountability
analyses and were published after the literature cutoff date for the 2019 ISA were evaluated in
the draft ISA Supplement (U.S. EPA, 2021a, section 3.2.1.3). These studies are consistent with
and expand upon the body of evidence from the 2019 ISA. For example, Bennett et al. (2019)
reported that PM2.5 concentrations above the lowest observed concentration (2.8 |ig/m3) were
associated with a 0.15 year decrease in national life expectancy for women and 0.13 year
decrease in national life expectancy for men (U.S. EPA, 2021a, section 3.2.2.2.4, Figure 3-25).
Another study compared participants living in areas with PM2.5 concentrations >12 |ig/m3 to
participants living in areas with PM2.5 concentrations <12 |ig/m3 and reported that the number of
years of life lost due to living in areas with higher PM2.5 concentrations was 0.84 years over a 5-
year period (Ward-Caviness et al., 2020; U.S. EPA, 2021a, section 3.2.2.2.4).
Since the 2009 ISA there is an emerging group of studies that used causal modeling
statistical methods to further assess relationship between long-term PM2.5 exposure and mortality
(U.S. EPA, 2019, section 11.2.2.4). The goal of causal modeling methods is to "estimate the
difference (or ratio) in the expected value of [an] outcome in the population under the exposure
they received versus what it would have been had they received an alternative exposure"
(Schwartz et al., 2015). Multiple epidemiologic studies that implemented causal modeling
methods and were published since the literature cutoff date of the 2019 ISA were evaluated in
the draft ISA Supplement (U.S. EPA, 2021a, section 3.2.2.3). These studies use a variety of
statistical methods including generalized propensity score (GPS), inverse probability weighting
(IPW), and difference-in-difference (DID) to reduce uncertainties related to confounding bias in
the association between long-term PM2.5 exposure and mortality. Studies that employed these
causal modeling methods reported consistent positive associations that further inform the
relationship between long-term PM2.5 exposure and total mortality (U.S. EPA, 2021a, section
3.2.2.3). These studies provide further support of associations seen in cohort studies and
referenced just above.
The 2019 ISA and draft ISA Supplement also evaluate the degree to which recent studies
that examine the relationship between long-term PM2.5 exposure and mortality have addressed
key policy-relevant issues and/or previously identified data gaps in the scientific evidence,
including methods to estimate exposure, methods to control for confounding, like copollutant
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confounding, and the shape of the concentration-response curve. For example, based on its
assessment of the evidence, the 2019 ISA concludes that positive associations between long-term
PM2.5 exposures and mortality are robust across recent analyses using various approaches to
estimate PM2.5 exposures (e.g., based on monitors, modeling, satellites, or hybrid methods that
combine information from multiple sources) (U.S. EPA, 2019, section 11.2.5.1). This includes a
study Hart et al. (2015) reporting that correction for bias due to exposure measurement error
increases the magnitude of the hazard ratios (confidence intervals widen but the association
remains statistically significant), suggesting that failure to correct for exposure measurement
error could result in attenuation or underestimation of risk estimates.
The 2019 ISA additionally concludes that positive associations between long-term PM2.5
exposures and mortality are robust across statistical models that use different approaches to
control for confounders or different sets of confounders (U.S. EPA, 2019, sections 11.2.3 and
11.2.5), across diverse geographic regions and populations, and across a range of temporal
periods including the periods of declining PM concentrations (U.S. EPA, 2019, sections 11.2.2.5
and 11.2.5.3). Additional evidence further demonstrates that associations with mortality remain
robust in copollutants analyses (U.S. EPA, 2019, section 11.2.3), and that associations persist in
analyses restricted to long-term exposures below 12 |j,g/m3 (Di et al., 2017b) or 10 |j,g/m3 (Shi et
al., 2016) (i.e., indicating that risks are not disproportionately driven by the upper portions of the
air quality distribution). Recent studies further assess potential copollutant confounding as
reflected in the studies evaluated in the draft ISA Supplement that indicate while there is some
evidence of potential confounding of the PM2.5-mortality association by copollutants in the some
of the studies (i.e., those studies of the MAPLE cohort), this result is inconsistent with other
recent studies evaluated in the 2019 ISA that were conducted in the U.S. and Canada that found
associations in both single and copollutant models (U.S. EPA, 2019; U.S. EPA, 2021a, section
3.2.2.4 and 3.1.2.2.8). Additionally, a few studies use statistical techniques to reduce
uncertainties related to potential confounding in order to further inform conclusions on causality
for long-term PM2.5 exposure and mortality. For example, studies by Greven et al. (2011), Pun et
al. (2017), and Eum et al. (2018) decompose ambient PM2.5 into "spatial" and "spatiotemporal"
components in order to evaluate the potential for bias due to unmeasured spatial confounding.
Eum et al. (2018) and Wu et al. (2020a) also attempted to address long-term trends and
meteorological variables as potential confounders and found that not adjusting for temporal
trends could overestimate the association, while effect estimates in analyses that excluded
meteorological variables remained unchanged compared to main analyses. The results of these
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analyses suggest the presence of unmeasured confounding, though they do not indicate the
direction or magnitude of the bias that could result.9
An additional important consideration in characterizing the public health impacts
associated with PM2.5 exposure is whether concentration-response relationships are linear across
the range of concentrations or if nonlinear relationships exist along any part of this range. Studies
evaluated in the 2019 ISA and draft ISA Supplement examine this issue, and continue to provide
evidence of linear, no-threshold relationships between long-term PM2.5 exposures and all-cause
and cause-specific mortality (U.S. EPA, 2019, section 11.2.4; U.S. EPA, 2021a, section
3.2.2.2.7, Table 3-6). Across the studies evaluated in the 2019 ISA and draft ISA Supplement, a
variety of statistical methods have been used to assess whether there is evidence of deviations in
linearity (U.S. EPA, 2019, Table 11-7; U.S. EPA, 2021a, section 2.2.3.2). Studies have also
conducted cut-point analyses that focus on examining risk at specific ambient PM2.5
concentrations. These studies reported results that generally support a linear, no-threshold
relationships between long-term PM2.5 exposures and total (nonaccidental) mortality, especially
at lower ambient PM2.5 concentration, with confidence in the linear relationship as low as 5 to 8
|ig/m3 in some studies (U.S. EPA, 2019, section 11.2.4; U.S. EPA, 2021a, section 2.2.3.2). There
was also some limited evidence indicating that the slope of the concentration-response (C-R)
function may be steeper (supralinear) at lower concentrations for cardiovascular mortality (U.S.
EPA, 2021a, section 2.2.3.2).
The biological plausibility of PIVh.s-attributable mortality is supported by the coherence
of effects across scientific disciplines (i.e., animal toxicological, controlled human exposure
studies, and epidemiologic) when evaluating respiratory and cardiovascular morbidity effects,
which are some of the largest contributors to total (nonaccidental) mortality. The 2019 ISA
outlines the available evidence for biologically plausible pathways by which inhalation exposure
to PM2.5 could progress from initial events (e.g., pulmonary inflammation, autonomic nervous
system activation) to endpoints relevant to population outcomes, particularly those related to
cardiovascular diseases such as coronary heart disease (CHD), stroke and atherosclerosis (U.S.
EPA, 2019, section 6.2.1, Table 11-8), and metabolic effects, including diabetes (U.S. EPA,
2019, section 7.3.1). The 2019 ISA notes "more limited evidence from respiratory morbidity"
(U.S. EPA, 2019, p. 11-101) such as development of chronic obstructive pulmonary disease
9 In public comments on the 2019 draft PA, the authors of the Pun et al. study further note that "the presence of
unmeasured confounding.. .was expected given that we did not control for several potential confounders that may
impact PM2 5-mortality associations, such as smoking, socio-economic status (SES), gaseous pollutants, PM2 5
components, and long-term time trends in PM2 5" and that "spatial confounding may bias mortality risks both
towards and away from the null" (Docket ID EPA-HQ-OAR-2015-0072-0065; accessible in
https://www.regulations.gov/)
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(COPD) (U.S. EPA, 2019, section 5.2.1) to support the biological plausibility of mortality due to
long-term PM2.5 exposures (U.S. EPA, 2019, section 11.2.1).
Taken together, recent studies, i.e., those evaluated in the 2019 ISA and in the draft ISA
Supplement, reaffirm and further strengthen the body of evidence from the 2009 ISA for the
relationship between long-term PM2.5 exposure and mortality. Epidemiologic studies evaluated
in the 2019 ISA, including recent studies evaluated in the draft ISA Supplement, consistently
report positive associations between long-term PM2.5 exposure and mortality across different
geographic locations, populations, and analytic approaches (U.S. EPA, 2019; U.S. EPA, 2021a,
section 3.2.2.4).
As such, these studies reduce key uncertainties identified in the previous review,
including those related to potential copollutant confounding, and provide additional information
on the shape of the concentration-response curve. As assessed in the 2019 ISA, experimental and
epidemiologic evidence for cardiovascular effects, and respiratory effects to a more limited
degree, supports the plausibility of mortality due to long-term PM2.5 exposures. The 2019 ISA
concludes that, "collectively, this body of evidence is sufficient to conclude that a causal
relationship exists between long-term PM2.5 exposure and total mortality" (U.S. EPA, 2019,
section 11.2.7; p. 11-102) which is supported and extended by recent evidence evaluated in the
draft Supplement (U.S. EPA, 2021a, section 3.2.2.4).
Short-term PM2.5 exposures
The 2009 ISA concluded that "a causal relationship exists between short-term exposure
to PM2.5 and mortality" (U.S. EPA, 2009). This conclusion was based on the evaluation of both
multi- and single-city epidemiologic studies that consistently reported positive associations
between short-term PM2.5 exposure and non-accidental mortality. These associations were
strongest, in terms of magnitude and precision, primarily at lags of 0 to 1 days. Examination of
the potential confounding effects of gaseous copollutants was limited, though evidence from
single-city studies indicated that gaseous copollutants have minimal effect on the PM2.5-mortality
relationship (i.e., associations remain robust to inclusion of other pollutants in copollutant
models). The evaluation of cause-specific mortality found that effect estimates were larger in
magnitude, but also had larger confidence intervals, for respiratory mortality compared to
cardiovascular mortality. Although the largest mortality risk estimates were for respiratory
mortality, the interpretation of the results was complicated by the limited coherence from studies
of respiratory morbidity. However, the evidence from studies of cardiovascular morbidity
provided both coherence and biological plausibility for the relationship between short-term PM2.5
exposure and cardiovascular mortality.
Multicity studies evaluated in the 2019 ISA and draft ISA Supplement provide evidence
of primarily positive associations between daily PM2.5 exposures and mortality, with percent
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increases in total mortality ranging from 0.19% (Lippmann et al., 2013) to 2.80% (Kloog et al.,
2013)10 at lags of 0 to 1 days in single pollutant-models. Whereas most studies rely on assigning
exposures using data from ambient monitors, associations are also reported in studies that
employ hybrid modeling approaches using additional PM2.5 data (i.e., from satellites, land use
information, and air quality modeling, in addition to monitors), allowing for the inclusion of
more rural locations in analyses (Kloog et al., 2013, Shi et al., 2016). Consistent with the
evidence assessed in previous ISAs, recent studies report more variable results with wider
confidence intervals for respiratory mortality (Lavigne et al., 2018; Shin et al., 2021).
Some studies have expanded the examination of potential confounders, including long-
term temporal trends, weather, and co-occurring pollutants. Mortality associations were found to
remain positive, although in some cases were attenuated, when using different approaches to
account for temporal trends or weather covariates (U.S. EPA, 2019, section 11.1.5.1). For
example, Sacks et al. (2012) examined the influence of model specification using the approaches
for confounder adjustment from models employed in several multicity studies within the context
of a common data set (U.S. EPA, 2019, section 11.1.5.1). These models use different approaches
to control for long-term temporal trends and the potential confounding effects of weather. The
authors report that associations between daily PM2.5 and cardiovascular mortality were similar
across models, with the percent increase in mortality ranging from 1.5-2.0%) (U.S. EPA, 2019,
Figure 11-4). Thus, alternative approaches to controlling for long-term temporal trends and for
the potential confounding effects of weather may influence the magnitude of the association
between PM2.5 exposures and mortality but have not been found to influence the direction of the
observed association (U.S. EPA, 2019, section 11.1.5.1). Taken together, the 2019 ISA and the
draft ISA Supplement conclude that recent multicity studies conducted in the U.S., Canada,
Europe, and Asia continue to provide consistent evidence of positive associations between
short-term PM2.5 exposures and total mortality across studies that use different approaches to
control for the potential confounding effects of weather (e.g., temperature) (U.S. EPA, 2019,
section 1.4.1.5.1; U.S. EPA, 2021a, section 2.1.1.5.1).
With regard to copollutants, studies evaluated in the 2019 ISA provide additional
evidence that associations between short-term PM2.5 exposures and mortality remain positive and
relatively unchanged in copollutant models with both gaseous pollutants and PM10-2.5 (U.S. EPA,
2019, Section 11.1.4). Additionally, the low (r < 0.4) to moderate correlations (r = 0.4-0.7)
between PM2.5 and gaseous pollutants and PM10-2.5 increase the confidence in PM2.5 having an
independent effect on mortality (U.S. EPA, 2019, section 11.1.4). Consistent with the studies
10 As detailed in the Preface to the ISA, risk estimates are for a 10 |ig/m3 increase in 24-hour avg PM2 5
concentrations, unless otherwise noted (U.S. EPA, 2019).
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evaluated in the 2019 ISA, studies evaluated in the draft ISA Supplement that used data from
more recent years also indicate that associations between short-term PM2.5 exposure and
mortality remain unchanged in copollutant models. However, the evidence indicates that the
association could be larger in magnitude in the presence of some co-occuring pollutants such as
oxidant gases (Lavigne et al., 2018; Shin et al., 2021).
The generally positive associations reported with mortality are supported by a small group
of studies employing causal modeling methods or quasi-experimental statistical approaches (U.S.
EPA, 2019, section 11.1.2.1). For example, two studies by Schwartz et al. (Schwartz et al., 2015;
Schwartz et al., 2017) report associations between PM2.5 instrumental variables and mortality
(U.S. EPA, 2019, Table 11-2), including in an analysis limited to days with 24-hour average
PM2.5 concentrations <30 [j,g/m3 (Schwartz et al., 2017). In addition to the main analyses, these
studies conducted Granger-like causality tests as sensitivity analyses to examine whether there
was evidence of an association between mortality and PM2.5 after the day of death, which would
support the possibility that unmeasured confounders were not accounted for in the statistical
model. Neither study reports evidence of an association with PM2.5 after death (i.e., they do not
indicate unmeasured confounding). A quasi-experimental study examines whether a specific
regulatory action in Tokyo, Japan (i.e., a diesel emission control ordinance) resulted in a
subsequent reduction in daily mortality (Yorifuji et al., 2016). The authors report a reduction in
mortality in Tokyo due to the ordinance, compared to Osaka, which did not have a similar diesel
emission control ordinance in place. In another study, Schwartz et al. (2018b) utilized three
causal methods including instrumental variable analysis, a negative exposure control, and
marginal structural models to estimate the association between PM2.5 and daily mortality
(Schwartz et al., 2018b). Results from this study continue to support a relationship between
short-term PM2.5 exposure and mortality. Additional epidemiologic studies evaluated in the draft
ISA Supplement that employed causal modeling methods to examine the association between
short-term PM2.5 exposure and mortality also report consistent positive associations in studies
that examine effects across multiple cities in the U.S. (U.S. EPA, 2021a).
The positive associations for total mortality reported across the majority of studies
evaluated are further supported by analyses reporting generally consistent, positive associations
with both cardiovascular and respiratory mortality (U.S. EPA, 2019, section 11.1.3). Recent
multicity studies evaluated in the draft ISA Supplement add to the body of evidence indicating a
relationship between short-term PM2.5 exposure and cause-specific mortality, with more
variability in the magnitude and precision of associations for respiratory mortality (U.S. EPA,
2021a; Figure 3-14). For both cardiovascular and respiratory mortality, there has been a limited
assessment of potential copollutant confounding, though initial evidence indicates that
associations remain positive and relatively unchanged in models with gaseous pollutants and
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PMio-2.5. This evidence further supports the copollutant analyses conducted for total mortality.
The strong evidence for ischemic events and heart failure, as detailed in the assessment of
cardiovascular morbidity (U.S. EPA, 2019, Chapter 6), provides biological plausibility for
PM2.5-related cardiovascular mortality, which comprises the largest percentage of total mortality
(i.e., -33%) (NHLBI, 2017). Although there is evidence for exacerbations of COPD and asthma,
the collective body of respiratory morbidity evidence provides limited biological plausibility for
PM2.5-related respiratory mortality (U.S. EPA, 2019, Chapter 5).
In the 2009 ISA, one of the main uncertainties identified was the regional and city-to-city
heterogeneity in PM2.5-mortality associations. Recent studies examine both city-specific as well
as regional characteristics to identify the underlying contextual factors that could contribute to
this heterogeneity (U.S. EPA, 2019, section 11.1.6.3). Analyses focusing on effect modification
of the PM2.5-mortality relationship by PM2.5 components, regional patterns in PM2.5 components
and city-specific differences in composition and sources indicate some differences in the PM2.5
composition and sources across cities and regions, but these differences do not fully explain the
observed heterogeneity. Additional studies find that factors related to potential exposure
differences, such housing stock and commuting, as well as city-specific factors (e.g., land-use,
port volume, and traffic information), may explain some of the observed heterogeneity (U.S.
EPA, 2019, section 11.1.6.3). Collectively, studies evaluated in the 2019 ISA and the draft ISA
Supplement indicate that the heterogeneity in PM2.5-mortality risk estimates cannot be attributed
to one factor, but instead a combination of factors including, but not limited to, PM composition
and sources as well as community characteristics that could influence exposures (U.S. EPA,
2019, section 11.1.12; U.S. EPA, 2021a, section 3.2.1.2.1)).
A number of studies conducted systematic evaluations of the lag structure of associations
for the PM2.5-mortality relationship by examining either a series of single-day or multiday lags
and these studies continue to support an immediate effect (i.e., lag 0 to 1 days) of short-term
PM2.5 exposures on mortality (U.S. EPA, 2019, section 11.1.8.1; U.S. EPA, 2021a, section
3.2.1.1). Recent studies also conducted analyses comparing the traditional 24-hour average
exposure metric with a sub-daily metric (i.e., 1-hour max). These initial studies provide evidence
of a similar pattern of associations for both the 24-hour average and 1-hour max metric, with the
association larger in magnitude for the 24-hour average metric.
Multicity studies indicate that positive and statistically significant associations with
mortality persist in analyses restricted to short-term PM2.5 exposures below 35 |j,g/m3 (Lee et al.,
2015),11 below 30 |j,g/m3 (Shi et al., 2016), and below 25 |j,g/m3 (Di et al., 2017a), indicating that
11 Lee et al. (2015) also report that positive and statistically significant associations between short-term PM2 5
exposures and mortality persist in analyses restricted to areas with long-term concentrations below 12 |.ig/m\
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risks associated with short-term PM2.5 exposures are not disproportionately driven by the peaks
of the air quality distribution. Additional studies examine the shape of the C-R relationship and
whether a threshold exists specifically for PM2.5 (U.S. EPA, 2019, section 11.1.10). These
studies have used various statistical approaches and consistently demonstrate a linear
relationship with no evidence of a threshold. Moreover, recent studies evaluated in the draft ISA
Supplement provide additional support for a linear, no-threshold C-R relationship between short-
term PM2.5 exposure and mortality, with confidence in the shape decreasing at concentrations
below 5 |ig/m3 (Liu et al., 2019; Lavigne et al., 2018). Recent analyses provide initial evidence
indicating that PM2.5-mortality associations persist and may be stronger (i.e., a steeper slope) at
lower concentrations (e.g., Di et al., 2017a; Figure 11-12 in U.S. EPA, 2019). However, given
the limited data available at the lower end of the distribution of ambient PM2.5 concentrations,
the shape of the C-R curve remains uncertain at these low concentrations. Although difficulties
remain in assessing the shape of the PM2.5-mortality C-R relationship, to date, studies have not
conducted systematic evaluations of alternatives to linearity, and recent studies continue to
provide evidence of a no-threshold linear relationship, with less confidence at concentrations
lower than 5 |ig/m3.
Overall, recent epidemiologic studies build upon and extend the conclusions of the 2009
ISA for the relationship between short-term PM2.5 exposures and total mortality. Supporting
evidence for PM2.5-related cardiovascular morbidity, and more limited evidence from respiratory
morbidity, provides biological plausibility for mortality due to short-term PM2.5 exposures. The
primarily positive associations observed across studies conducted in diverse geographic locations
is further supported by the results from co-pollutant analyses indicating robust associations,
along with evidence from analyses of the concentration-response relationship. The 2019 ISA
states that, collectively, "this body of evidence is sufficient to conclude that a causal relationship
exists between short-term PM2.5 exposure and total mortality" (U.S. EPA, 2019, pp. 11-58).
Recent evidence evaluated in the draft ISA Supplement provides "additional support to the
evidence base that contributed to the conclusion of a causal relationship between short-term
PM2.5 exposure and mortality" (U.S. EPA, 2021a, section 3.2.1.4, pp 3-69).
3.3,1,2 Cardiovascular Effects
Long-term PM2.5 exposures
The scientific evidence reviewed in the 2009 ISA was "sufficient to infer a causal
relationship between long-term PM2.5 exposure and cardiovascular effects" (U.S. EPA, 2009).
The strongest line of evidence comprised findings from several large epidemiologic studies of
U.S. and Canadian cohorts that consistently showed positive associations between long-term
PM2.5 exposure and cardiovascular mortality (Krewski et al., 2009, Miller et al., 2007, et al.,).
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Studies of long-term PM2.5 exposure and cardiovascular morbidity were limited in number.
Biological plausibility and coherence with the epidemiologic findings were provided by studies
using genetic mouse models of atherosclerosis demonstrating enhanced atherosclerotic plaque
development and inflammation, as well as changes in measures of impaired heart function,
following 4- to 6-month exposures to PM2.5 concentrated ambient particles (CAPs), and by a
limited number of studies reporting CAPs-induced effects on coagulation factors, vascular
reactivity, and worsening of experimentally induced hypertension in mice (U.S. EPA, 2009).
Consistent with the evidence assessed in the 2009 ISA, the 2019 ISA concludes that
recent studies, together with the evidence available in previous reviews, support a causal
relationship between long-term exposure to PM2.5 and cardiovascular effects. Additionally,
recent epidemiologic studies published since the completion of the 2019 ISA and evaluated in
the draft ISA Supplement expands the body of evidence and further supports such a conclusion
(U.S. EPA, 2021a). As discussed above (section 3.3.1.1), results from U.S. and Canadian cohort
studies evaluated in the 2019 ISA consistently report positive associations between long-term
PM2.5 exposure and cardiovascular mortality (U.S. EPA, 2019, Figure 6-19) in evaluations
conducted at varying spatial scales and employing a variety of exposure assessment and
statistical methods (U.S. EPA, 2019, section 6.2.10). Positive associations between long-term
PM2.5 exposures and cardiovascular mortality are generally robust in copollutant models adjusted
for ozone, NO2, PM10-2.5, or SO2. In addition, most of the results from analyses examining the
shape of the concentration-response relationship for cardiovascular mortality support a linear
relationship with long-term PM2.5 exposures and do not identify a threshold below which effects
do not occur (U.S. EPA, 2019, section 6.2.16; Table 6-52).
The body of literature examining the relationship between long-term PM2.5 exposure and
cardiovascular morbidity has greatly expanded since the 2009 ISA, with positive associations
reported in several cohorts (U.S. EPA, 2019, section 6.2). Though results for cardiovascular
morbidity are less consistent than those for cardiovascular mortality (U.S. EPA, 2019, section
6.2), studies in the 2019 ISA and draft ISA Supplement provide some evidence for associations
between long-term PM2.5 exposures and the progression of cardiovascular disease. Positive
associations with cardiovascular morbidity (e.g., coronary heart disease, stroke, arrhythmias,
myocardial infarction (MI), and atherosclerosis progression) are observed in several
epidemiologic studies (U.S. EPA, 2019, sections 6.2.2. to 6.2.9; U.S. EPA, 2021a, section
3.1.1.4). Associations in such studies are supported by toxicological evidence for increased
plaque progression in mice following long-term exposure to PM2.5 collected from multiple
locations across the U.S. (U.S. EPA, 2019, section 6.2.4.2). A small number of epidemiologic
studies also report positive associations between long-term PM2.5 exposure and heart failure,
changes in blood pressure, and hypertension (U.S. EPA, 2019, sections 6.2.5 and 6.2.7).
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Associations with heart failure are supported by animal toxicological studies demonstrating
decreased cardiac contractility and function, and increased coronary artery wall thickness
following long-term PM2.5 exposure (U.S. EPA, 2019, section 6.2.5.2). Similarly, a limited
number of animal toxicological studies demonstrating a relationship between long-term exposure
to PM2.5 and consistent increases in blood pressure in rats and mice are coherent with
epidemiologic studies reporting positive associations between long-term exposure to PM2.5 and
hypertension. Moreover, a number of studies assessed in the draft ISA Supplement focusing on
morbidity outcomes, including those that focused on incidence of MI, atrial fibrillation (AF),
stroke, and congestive heart failure (CHF), expand the evidence pertaining to the shape of the C-
R relationship between long-term PM2.5 exposure and cardiovascular effects. Additionally,
studies evaluated in the draft ISA Supplement report positive associations among those with pre-
existing conditions, among patients followed after a cardiac event procedure, and among those
with a first hospital admission for heart attacks among older adults enrolled in Medicare (U.S.
EPA, 2021a, sections 3.1.1 and 3.1.2). A number of these studies use statistical techniques that
allow for departures from linearity (U.S. EPA, 2021a, Table 3-3, and generally support the
evidence characterized in the 2019 ISA showing linear, no-threshold C-R relationship for most
CVD outcomes. However, there is some evidence for a sublinear or supralinear C-R relationship
for some outcomes (U.S. EPA, 2021a, section 3.1.2.2.9).12 Moreover, several recent
epidemiologic studies evaluated in the draft ISA Supplement reported that the association
between long-term PM2.5 exposure with stroke persisted after adjustment for NO2 but was
attenuated in the model with O3 and oxidant gases represented by the redox weighted average of
NO2 and O3 (U.S. EPA, 2021a, section 3.1.2.2.8).
Longitudinal epidemiologic analyses also report positive associations with markers of
systemic inflammation (U.S. EPA, 2019, section 6.2.11), coagulation (U.S. EPA, 2019, section
6.2.12), and endothelial dysfunction (U.S. EPA, 2019, section 6.2.13). These results are coherent
with animal toxicological studies generally reporting increased markers of systemic
inflammation, oxidative stress, and endothelial dysfunction (U.S. EPA, 2019, section 6.2.12.2
and 6.2.14).
The 2019 ISA concludes that there is consistent evidence from multiple epidemiologic
studies illustrating that long-term exposure to PM2.5 is associated with mortality from
cardiovascular causes. Epidemiologic studies in the draft ISA Supplement support and extend the
findings characterized in the 2019 ISA, providing additional evidence of positive associations
between long-term PM2.5 exposure and cardiovascular morbidity (U.S. EPA, 2021a section
12 As noted above for mortality, uncertainty in the shape of the C-R relationship increases near the upper and lower
ends of the distribution due to limited data.
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3.1.1.4). Associations with CHD, stroke and atherosclerosis progression were observed in several
additional epidemiologic studies, providing coherence with the mortality findings.
Results from copollutant models generally support the independence of the PM2.5
associations (U.S. EPA, 2019, Table 3-2; U.S. EPA, 2021a). Additional evidence of the
independent effect of PM2.5 on the cardiovascular system is provided by experimental studies in
animals, which demonstrate biologically plausible pathways by which long-term inhalation
exposure to PM2.5 could potentially result in outcomes such as CHD, stroke, CHF and
cardiovascular mortality. The combination of epidemiologic and experimental evidence results in
the 2019 ISA conclusion that "a causal relationship exists between long-term exposure to PM2.5
and cardiovascular effects" (U.S. EPA, 2019, section 6.2.18). Studies evaluated in the draft ISA
Supplement support and extend the evidence that contributed to the conclusion of a causal
relationship between long-term PM2.5 exposure and cardiovascular effects (U.S. EPA, 2021a,
section 3.1.2.4).
Short-term PM2.5 exposures
The 2009 ISA concluded that "a causal relationship exists between short-term exposure
to PM2.5 and cardiovascular effects" (U.S. EPA, 2009). The strongest evidence in the 2009 ISA
was from epidemiologic studies of emergency department (ED) visits and hospital admissions
for IHD and HF, with supporting evidence from epidemiologic studies of cardiovascular
mortality (U.S. EPA, 2009). Animal toxicological studies provided coherence and biological
plausibility for the positive associations reported with myocardial ischemia ED visit and hospital
admissions. These included studies reporting reduced myocardial blood flow during ischemia
and studies indicating altered vascular reactivity. In addition, effects of PM2.5 exposure on a
potential indicator of ischemia (i.e., ST segment depression on an electrocardiogram) were
reported in both animal toxicological and epidemiologic panel studies.13 Key uncertainties from
the 2009 ISA resulted from inconsistent results across disciplines with respect to the relationship
between short-term exposure to PM2.5 and changes in blood pressure, blood coagulation markers,
and markers of systemic inflammation. In addition, while the 2009 ISA identified a growing
body of evidence from controlled human exposure and animal toxicological studies, uncertainties
remained with respect to biological plausibility.
Recent evidence assessed in the 2019 ISA and the draft ISA Supplement supports and
extends the evidence from the 2009 ISA indicating that there is a causal relationship between
short-term PM2.5 exposure and cardiovascular effects. This includes generally positive
associations observed in multicity epidemiologic studies of emergency department visits and
13 Some animal studies included in the 2009 ISA examined exposures to mixtures, such as motor vehicle exhaust or
woodsmoke. In these studies, it was unclear if the resulting cardiovascular effects could be attributed specifically
to the particulate components of the mixture.
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hospital admissions for IHD, heart failure (HF), and combined cardiovascular-related endpoints.
In particular, nationwide studies of older adults (65 years and older) using Medicare records
report positive associations between PM2.5 exposures and hospital admissions for HF (U.S. EPA,
2019, section 6.1.3.1). Moreover, recent multicity studies, published after the literature cutoff
date of the 2019 ISA, are coherent with studies evaluated in the 2019 ISA that report positive
association between short-term PM2.5 exposure and ED visits and hospital admission for IHD,
heart attacks, and HF (U.S. EPA, 2021a, section 3.1). Epidemiologic studies conducted in single
cities contribute some support, though associations reported in single-city studies are less
consistently positive than in multicity studies, and include a number of studies reporting null
associations (U.S. EPA, 2019, sections 6.1.2 and 6.1.3). When considered as a whole; however,
the recent body of IHD and HF epidemiologic evidence supports the evidence from previous
ISAs reporting mainly positive associations between short-term PM2.5 concentrations and
emergency department visits and hospital admissions.
Consistent with the evidence assessed in the 2019 ISA, some studies evaluated in the
draft ISA Supplement report no evidence of an association with stroke, regardless of stroke
subtype. Additionally, as in the 2019 ISA, evidence evaluated in the draft ISA Supplement
continues to indicate an immediate effect of PM2.5 on cardiovascular-related outcomes primarily
within the first few days after exposure, and that associations generally persisted in models
adjusted for copollutants (U.S. EPA, 2021a, section 3.1.1.2).
A number of controlled human exposure, animal toxicological, and epidemiologic panel
studies provide evidence that PM2.5 exposure could plausibly result in IHD or HF through
pathways that include endothelial dysfunction, arterial thrombosis, and arrhythmia (U.S. EPA,
2019, section 6.1.1). The most consistent evidence from recent controlled human exposure
studies is for endothelial dysfunction, as measured by changes in brachial artery diameter or flow
mediated dilation. All but one of the available controlled human exposure studies examining the
potential for endothelial dysfunction report an effect of PM2.5 exposure on measures of blood
flow (U.S. EPA, 2019, section 6.1.13.2). These studies report variable results regarding the
timing of the effect and the mechanism by which reduced blood flow occurs (i.e., availability vs
sensitivity to nitric oxide). Some controlled human exposure studies using CAPs report evidence
for small increases in blood pressure (U.S. EPA, 2019, section 6.1.6.3). In addition, although not
entirely consistent, there is also some evidence across controlled human exposure studies for
conduction abnormalities/arrhythmia (U.S. EPA, 2019, section 6.1.4.3), changes in heart rate
variability (HRV) (U.S. EPA, 2019, section 6.1.10.2), changes in hemostasis that could promote
clot formation (U.S. EPA, 2019, section 6.1.12.2), and increases in inflammatory cells and
markers (U.S. EPA, 2019, section 6.1.11.2). A recent study by Wyatt et al. (2020a) adds to the
limited evidence base of controlled human exposure studies conducted at near ambient PM2.5
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concentrations. The study, completed in healthy young adults subject to intermittent exercise,
found some significant cardiovascular effects (e.g., systematic inflammation markers, including
C-reactive protein (CRP), and cardiac repolarization).
Thus, when taken as a whole, controlled human exposure studies are coherent with
epidemiologic studies in that they demonstrate short-term exposures to PM2.5 may result in the
types of cardiovascular endpoints that could lead to emergency department visits and hospital
admissions in some people.
Animal toxicological studies published since the 2009 ISA also support a relationship
between short-term PM2.5 exposure and cardiovascular effects. A study demonstrating decreased
cardiac contractility and left ventricular pressure in mice is coherent with the results of
epidemiologic studies reporting associations between short-term PM2.5 exposure and heart failure
(U.S. EPA, 2019, section 6.1.3.3). In addition, and as with controlled human exposure studies,
there is generally consistent evidence in animal toxicological studies for indicators of endothelial
dysfunction (U.S. EPA, 2019, section 6.1.13.3). Studies in animals also provide evidence for
changes in a number of other cardiovascular endpoints following short-term PM2.5 exposure.
Although not entirely consistent, these studies provide some evidence of conduction
abnormalities and arrhythmia (U.S. EPA, 2019, section 6.1.4.4), changes in HRV (U.S. EPA,
2019, section 6.1.10.3), changes in blood pressure (U.S. EPA, 2019, section 6.1.6.4), and
evidence for systemic inflammation and oxidative stress (U.S. EPA, 2019, section 6.1.11.3).
In summary, recent evidence evaluated in the 2019 ISA and the draft ISA Supplement
further supports and extends the conclusions of the evidence base reported in the 2009 ISA. In
support of epidemiologic studies reporting robust associations in copollutant models, direct
evidence for an independent effect of PM2.5 on cardiovascular effects can be found in a number
of controlled human exposure and animal toxicological studies. Coherent with these results are
epidemiologic panel studies reporting that PM2.5 exposure is associated with some of the same
cardiovascular endpoints reported in experimental studies. For these effects, there are
inconsistencies in results across some animal toxicological, controlled human exposure, and
epidemiologic panel studies, though this may be due to substantial differences in study design
and/or study populations. Overall, the results from epidemiologic panel, controlled human
exposure, and animal toxicological studies, in particular those related to endothelial dysfunction,
impaired cardiac function, ST segment depression, thrombosis, conduction abnormalities, and
changes in blood pressure provide coherence and biological plausibility for the consistent results
from epidemiologic studies observing positive associations between short-term PM2.5
concentrations and IHD and HF, and ultimately cardiovascular mortality. The 2019 ISA
concludes that, overall, "there continues to be sufficient evidence to conclude that a causal
relationship exists between short-term PM2.5 exposure and cardiovascular effects" (U.S. EPA,
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2019, p. 6-138), which is further supported by recent studies evaluated in the draft ISA
Supplement (U.S. EPA, 2021a section 3.1.1.4 ).
3.3,1.3 Respiratory Effects
Long-term PM2.5 exposures
The 2009 ISA concluded that "a causal relationship is likely to exist between long-term
PM2.5 exposure and respiratory effects" (U.S. EPA, 2009). This conclusion was based mainly on
epidemiologic evidence demonstrating associations between long-term PM2.5 exposure and
changes in lung function or lung function growth in children. Biological plausibility was
provided by a single animal toxicological study examining pre- and post-natal exposure to PM2.5
CAPs, which found impaired lung development. Epidemiologic evidence for associations
between long-term PM2.5 exposure and other respiratory outcomes, such as the development of
asthma, allergic disease, and COPD; respiratory infection; and the severity of disease was
limited, both in the number of studies available and the consistency of the results. Experimental
evidence for other outcomes was also limited, with one animal toxicological study reporting that
long-term exposure to PM2.5 CAPs results in morphological changes in nasal airways of healthy
animals. Other animal studies examined exposure to mixtures, such as motor vehicle exhaust and
woodsmoke, and effects were not attributed specifically to the particulate components of the
mixture.
Cohort studies evaluated in the 2019 ISA provided additional support for the relationship
between long-term PM2.5 exposure and decrements in lung function growth (as a measure of lung
development), indicating a robust and consistent association across study locations, exposure
assessment methods, and time periods (U.S. EPA, 2019, section 5.2.13). This relationship was
further supported by a retrospective study that reports an association between declining PM2.5
concentrations and improvements in lung function growth in children (U.S. EPA, 2019,
section 5.2.11). Epidemiologic studies also examine asthma development in children (U.S. EPA,
2019, section 5.2.3), with prospective cohort studies reporting generally positive associations,
though several are imprecise (i.e., they report wide confidence intervals). Supporting evidence is
provided by studies reporting associations with asthma prevalence in children, with childhood
wheeze, and with exhaled nitric oxide, a marker of pulmonary inflammation (U.S. EPA, 2019,
section 5.2.13). Additionally, animal toxicological study showing the development of an allergic
phenotype and an increase in a marker of airway responsiveness provides biological plausibility
for allergic asthma (U.S. EPA, 2019, section 5.2.13). Other epidemiologic studies report a
PM2.5-related acceleration of lung function decline in adults, while improvement in lung function
was observed with declining PM2.5 concentrations (U.S. EPA, 2019, section 5.2.11). A
longitudinal study found declining PM2.5 concentrations are also associated with an improvement
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in chronic bronchitis symptoms in children, strengthening evidence reported in the 2009 ISA for
a relationship between increased chronic bronchitis symptoms and long-term PM2.5 exposure
(U.S. EPA, 2019, section 5.2.11). A common uncertainty across the epidemiologic evidence is
the lack of examination of copollutants to assess the potential for confounding. While there is
some evidence that associations remain robust in models with gaseous pollutants, a number of
these studies examining copollutant confounding were conducted in Asia, and thus have limited
generalizability due to high annual pollutant concentrations.
When taken together, the 2019 ISA concludes that the "epidemiologic evidence strongly
supports a relationship with decrements in lung function growth in children" and "with asthma
development in children, with increased bronchitis symptoms in children with asthma, with an
acceleration of lung function decline in adults, and with respiratory mortality and cause-specific
respiratory mortality for COPD and respiratory infection" (U.S. EPA, 2019, p. 1-34). In support
of the biological plausibility of such associations reported in epidemiologic studies of respiratory
health effects, animal toxicological studies continue to provide direct evidence that long-term
exposure to PM2.5 results in a variety of respiratory effects. Animal studies in the 2019 ISA show
pulmonary oxidative stress, inflammation, and morphologic changes in the upper (nasal) and
lower airways. Other results show that changes are consistent with the development of allergy
and asthma, and with impaired lung development. Overall, the 2019 ISA concludes that "the
collective evidence is sufficient to conclude that a causal relationship is likely to exist between
long-term PM2.5 exposure and respiratory effects" (U.S. EPA, 2019, section 5.2.13).
Short-term PM2.5 exposures
The 2009 ISA (U.S. EPA, 2009) concluded that a "causal relationship is likely to exist"
between short-term PM2.5 exposure and respiratory effects. This conclusion was based mainly on
the epidemiologic evidence demonstrating positive associations with various respiratory effects.
Specifically, the 2009 ISA described epidemiologic evidence as consistently showing
PM2.5-associated increases in hospital admissions and emergency department visits for chronic
obstructive pulmonary disease (COPD) and respiratory infection among adults or people of all
ages, as well as increases in respiratory mortality. These results were supported by studies
reporting associations with increased respiratory symptoms and decreases in lung function in
children with asthma, though the epidemiologic evidence was inconsistent for hospital
admissions or emergency department visits for asthma. Studies examining copollutants models
showed that PM2.5 associations with respiratory effects were robust to inclusion of CO or SO2 in
the model, but often were attenuated (though still positive) with inclusion of O3 or NO2. In
addition to the copollutants models, evidence supporting an independent effect of PM2.5 exposure
on the respiratory system was provided by animal toxicological studies of PM2.5 CAPs
demonstrating changes in some pulmonary function parameters, as well as inflammation,
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oxidative stress, injury, enhanced allergic responses, and reduced host defenses. Many of these
effects have been implicated in the pathophysiology for asthma exacerbation, COPD
exacerbation, or respiratory infection. In the few controlled human exposure studies conducted in
individuals with asthma or COPD, PM2.5 exposure mostly had no effect on respiratory
symptoms, lung function, or pulmonary inflammation. Available studies in healthy people also
did not clearly demonstrate respiratory effects following short-term PM2.5 exposures.
Epidemiologic studies evaluated in the 2019 ISA continue to provide strong evidence for
a relationship between short-term PM2.5 exposure and several respiratory-related endpoints,
including asthma exacerbation (U.S. EPA, 2019, section 5.1.2.1), COPD exacerbation (U.S.
EPA, 2019, section 5.1.4.1), and combined respiratory-related diseases (U.S. EPA, 2019, section
5.1.6), particularly from studies examining emergency department visits and hospital admissions.
The generally positive associations between short-term PM2.5 exposure and asthma and COPD
emergency department visits and hospital admissions are supported by epidemiologic studies
demonstrating associations with other respiratory-related effects such as symptoms and
medication use that are indicative of asthma and COPD exacerbations (U.S. EPA, 2019, sections
5.1.2.2 and 5.4.1.2). The collective body of epidemiologic evidence for asthma exacerbation is
more consistent in children than in adults. Additionally, epidemiologic studies examining the
relationship between short-term PM2.5 exposure and respiratory mortality provide evidence of
consistent positive associations, demonstrating a continuum of effects (U.S. EPA, 2019, section
5.1.9).
Building off the studies evaluated in the 2009 and 2019 ISA, epidemiologic studies
expand the assessment of potential copollutant confounding. There is some evidence that PM2.5
associations with asthma exacerbation, combined respiratory-related diseases, and respiratory
mortality remain relatively unchanged in copollutant models with gaseous pollutants (i.e., O3,
NO2, SO2, with more limited evidence for CO) and other particle sizes (i.e., PM10-2.5) (U.S. EPA,
2019, section 5.1.10.1).
In the 2019 ISA, the uncertainty related to whether there is an independent effect of PM2.5
on respiratory health is also partially addressed by findings from animal toxicological studies.
Specifically, short-term exposure to PM2.5 enhanced asthma-related responses in an animal
model of allergic airways disease and enhanced lung injury and inflammation in an animal model
of COPD (U.S. EPA, 2019, sections 5.1.2.4.4 and 5.1.4.4.3). The experimental evidence
provides biological plausibility for some respiratory-related endpoints, including limited
evidence of altered host defense and greater susceptibility to bacterial infection as well as
consistent evidence of respiratory irritant effects. Animal toxicological evidence for other
respiratory effects is inconsistent. A recent study by Wyatt et al. (2020a) was conducted at near
ambient PM2.5 concentrations and adds to the limited evidence base of controlled human
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exposure studies. The study, completed in healthy young adults subject to intermittent exercise,
found some significant respiratory effects (e.g., decrease in lung function).
The 2019 ISA concludes that "[t]he strongest evidence of an effect of short-term PM2.5
exposure on respiratory effects is provided by epidemiologic studies of asthma and COPD
exacerbation. While animal toxicological studies provide biological plausibility for these
findings, some uncertainty remains with respect to the independence of PM2.5 effects" (U.S.
EPA, 2019, p. 5-155). When taken together, the 2019 ISA concludes that this evidence "is
sufficient to conclude that a causal relationship is likely to exist between short-term PM2.5
exposure and respiratory effects" (U.S. EPA, 2019, p. 5-155).
3.3.1.4 Cancer - Long-term PM2.5 Exposures
The 2009 ISA concluded that the overall body of evidence was "suggestive of a causal
relationship between relevant PM2.5 exposures and cancer" (U.S. EPA, 2009). This conclusion
was based primarily on positive associations observed in a limited number of epidemiologic
studies of lung cancer mortality. The few epidemiologic studies that had evaluated PM2.5
exposure and lung cancer incidence or cancers of other organs and systems generally did not
show evidence of an association. Toxicological studies did not focus on exposures to specific
PM size fractions, but rather investigated the effects of exposures to total ambient PM, or other
source-based PM such as wood smoke. Collectively, results of in vitro studies were consistent
with the larger body of evidence demonstrating that ambient PM and PM from specific
combustion sources are mutagenic and genotoxic. However, animal inhalation studies found
little evidence of tumor formation in response to chronic exposures. A small number of studies
provided preliminary evidence that PM exposure can lead to changes in methylation of DNA,
which may contribute to biological events related to cancer.
Since the 2009 ISA, additional cohort studies provide evidence that long-term PM2.5
exposure is positively associated with lung cancer mortality and with lung cancer incidence, and
provide initial evidence for an association with reduced cancer survival (U.S. EPA, 2019, section
10.2.5). Re-analyses of the ACS cohort using different years of PM2.5 data and follow-up, along
with various exposure assignment approaches, provide consistent evidence of positive
associations between long-term PM2.5 exposure and lung cancer mortality (U.S. EPA, 2019,
Figure 10-3). Additional support for positive associations with lung cancer mortality is provided
by epidemiologic studies using individual-level data to control for smoking status, by studies of
people who have never smoked (though such studies generally report wide confidence intervals
due to the small number of lung cancer mortality cases within this population), and in analyses of
cohorts that relied upon proxy measures to account for smoking status (U.S. EPA, 2019, section
10.2.5.1.1). Although studies that have evaluated lung cancer incidence, including studies of
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people who have never smoked, are limited in number, studies in the 2019 ISA generally report
positive associations with long-term PM2.5 exposures (U.S. EPA, 2019, section 10.2.5.1.2). A
subset of the studies focusing on lung cancer incidence also examined histological subtype,
providing some evidence of positive associations for adenocarcinomas, the predominate subtype
of lung cancer observed in people who have never smoked (U.S. EPA, 2019, section 10.2.5.1.2).
Associations between long-term PM2.5 exposure and lung cancer incidence were found to remain
relatively unchanged, though in some cases confidence intervals widened, in analyses that
attempted to reduce exposure measurement error by accounting for length of time at residential
address or by examining different exposure assignment approaches (U.S. EPA, 2019, section
10.2.5.1.2).
The 2019 ISA evaluates the degree to which epidemiologic studies have addressed the
potential for confounding by copollutants and the shape of the concentration-response
relationship. To date, relatively few studies have evaluated the potential for copollutant
confounding of the relationship between long-term PM2.5 exposure and lung cancer mortality or
incidence. The small number of such studies have generally focused on O3 and report that PM2.5
associations remain relatively unchanged in copollutant models (U.S. EPA, 2019, section
10.2.5.1.3). However, available studies have not systematically evaluated the potential for
copollutant confounding by other gaseous pollutants or by other particle size fractions (U.S.
EPA, 2019, section 10.2.5.1.3). Compared to total (non-accidental) mortality (U.S. EPA, 2019,
section 10.2.4.1.4), fewer studies have examined the shape of the concentration-response curve
for cause-specific mortality outcomes, including lung cancer. Several studies of lung cancer
mortality and incidence have reported no evidence of deviations from linearity in the shape of
the concentration-response relationship (Lepeule et al., 2012; Raaschou-Nielsen et al., 2013;
Puett et al., 2014), though authors provided only limited discussions of results (U.S. EPA, 2019,
section 10.2.5.1.4).
In support of the biological plausibility of an independent effect of PM2.5 on lung cancer,
the 2019 ISA notes evidence from recent experimental and epidemiologic studies demonstrating
that PM2.5 exposure can lead to a range of effects indicative of mutagenicity, genotoxicity, and
carcinogenicity, as well as epigenetic effects (U.S. EPA, 2019, section 10.2.7). For example,
both in vitro and in vivo toxicological studies have shown that PM2.5 exposure can result in DNA
damage (U.S. EPA, 2019, section 10.2.2). Although such effects do not necessarily equate to
carcinogenicity, the evidence that PM exposure can damage DNA, and elicit mutations, provides
support for the plausibility of epidemiologic associations with lung cancer mortality and
incidence. Additional supporting studies indicate the occurrence of micronuclei formation and
chromosomal abnormalities (U.S. EPA, 2019, section 10.2.2.3), and differential expression of
genes that may be relevant to cancer pathogenesis, following PM exposures. Experimental and
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epidemiologic studies that examine epigenetic effects indicate changes in DNA methylation,
providing some support for PM2.5 exposure contributing to genomic instability (U.S. EPA, 2019,
section 10.2.3). Overall, there is limited evidence that long-term PM2.5 exposure is associated
with cancers in other organ systems, but there is some evidence that PM2.5 exposure may reduce
survival in individuals with cancer (U.S. EPA, 2019 section 10.2.7; U.S. EPA, 2021a, section
2.1.1.4.1).
Epidemiologic evidence for associations between PM2.5 and lung cancer mortality and
incidence, together with evidence supporting the biological plausibility of such associations,
contributes to the 2019 ISA's conclusion that the evidence "is sufficient to conclude that a causal
relationship is likely to exist between long-term PM2.5 exposure and cancer" (U.S. EPA, 2019,
section 10.2.7).
3,3,1,5 Nervous System Effects
Long-term PM2.5 exposures
Reflecting the very limited evidence available in the 2012 review, the 2009 ISA did not
make a causality determination for long-term PM2.5 exposures and nervous system effects (U.S.
EPA, 2009). Since the last review, this body of evidence has grown substantially (U.S. EPA,
2019, section 8.2). Animal toxicology studies assessed in the 2019 ISA report that long-term
PM2.5 exposures can lead to morphologic changes in the hippocampus and to impaired learning
and memory. This evidence is consistent with epidemiologic studies reporting that long-term
PM2.5 exposure is associated with reduced cognitive function (U.S. EPA, 2019, section 8.2.5).
Further, while the evidence is limited, the presence of early markers of Alzheimer's disease
pathology has been demonstrated in rodents following long-term exposure to PM2.5 CAPs. These
findings support reported associations with neurodegenerative changes in the brain
(i.e., decreased brain volume), all-cause dementia, or hospitalization for Alzheimer's disease in a
small number of epidemiologic studies (U.S. EPA, 2019, section 8.2.6). Additionally, loss of
dopaminergic neurons in the substantia nigra, a hallmark of Parkinson disease, has been reported
in mice (U.S. EPA, 2019, section 8.2.4), though epidemiologic studies provide only limited
support for associations with Parkinson's disease (U.S. EPA, 2019, section 8.2.6). Overall, the
lack of consideration of copollutant confounding introduces some uncertainty in the
interpretation of epidemiologic studies of nervous system effects, but this uncertainty is partly
addressed by the evidence for an independent effect of PM2.5 exposures provided by
experimental animal studies.
In addition to the findings described above, which are most relevant to older adults,
several studies of neurodevelopmental effects in children have also been conducted. Positive
associations between long-term exposure to PM2.5 during the prenatal period and autism
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spectrum disorder (ASD) are observed in multiple epidemiologic studies (U.S. EPA, 2019,
section 8.2.7.2), while studies of cognitive function provide little support for an association (U.S.
EPA, 2019, section 8.2.5.2). Interpretation of these epidemiologic studies is limited due to the
small number of studies, their lack of control for potential confounding by copollutants, and
uncertainty regarding the critical exposure windows. Biological plausibility is provided for the
ASD findings by a study in mice that found inflammatory and morphologic changes in the
corpus collosum and hippocampus, as well as ventriculomegaly (i.e., enlarged lateral ventricles)
in young mice following prenatal exposure to PM2.5 CAPs.
Taken together, the 2019 ISA concludes that studies indicate long-term PM2.5 exposures
can lead to effects on the brain associated with neurodegeneration (i.e., neuroinflammation and
reductions in brain volume), as well as cognitive effects in older adults (U.S. EPA, 2019, Table
1-2). Animal toxicology studies provide evidence for a range of nervous system effects in adult
animals, including neuroinflammation and oxidative stress, neurodegeneration, and cognitive
effects, and effects on neurodevelopment in young animals. The epidemiologic evidence is more
limited, but studies generally support associations between long-term PM2.5 exposure and
changes in brain morphology, cognitive decrements and dementia. There is also initial, and
limited, evidence for neurodevelopmental effects, particularly ASD. The consistency and
coherence of the evidence supports the 2019 ISA's conclusion that "the collective evidence is
sufficient to conclude that a causal relationship is likely to exist between long-term PM2.5
exposure and nervous system effects" (U.S. EPA, 2019, section 8.2.9).
3,3,1.6 Other Effects
Compared to the health outcomes discussed above, the 2019 ISA concludes that there is
greater uncertainty in the evidence linking PM2.5, or UFP, exposures with other health outcomes,
reflected in conclusions that the evidence is "suggestive of, but not sufficient to infer, a causal
relationship." The sections below summarize the 2019 ISA conclusions for these outcomes for
long-term (section 3.3.1.6.1) and short-term (section 3.3.1.6.2) PM2.5 and UFP exposures.
Section 3.3.1.6.3 summarizes information assessed in the draft ISA Supplement related to the
emerging area of COVID-19 infection and death.
3.3.1.6.1 Long-term Exposures
As indicated in Table 3-1 above, the 2019 ISA concludes that the evidence is "suggestive
of, but not sufficient to infer, a causal relationship" between long-term PM2.5 exposures and
metabolic effects and reproductive and developmental effects (reproduction and fertility;
pregnancy and birth outcomes). These conclusions reflect evidence that is "generally supportive
but not entirely consistent or is limited overall" where "[cjhance, confounding, and other biases
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cannot be ruled out" (U.S. EPA, 2019, Preface, p. P-20). The basis for these causality
determinations is summarized briefly below.
PM2.5 - Metabolic effects
There were no causality determinations for long-term PM2.5 exposure and metabolic
effects in the 2009 ISA (U.S. EPA, 2009). However, the literature pertaining to the effect of
long-term exposure to PM2.5 and metabolic effects has expanded substantially since the 2009
ISA, and consists of both epidemiologic and experimental evidence (U.S. EPA, 2019, section
7.2). Epidemiologic studies report positive associations between long-term PM2.5 exposure and
diabetes-related mortality. In addition, although results were not consistent across cohorts, there
is some evidence from epidemiologic studies for positive associations with incident diabetes,
metabolic syndrome, and alterations in glucose and insulin homeostasis. Consideration of
copollutant confounding was limited. In animal toxicologic studies, there is some support for a
relationship between long-term PM2.5 exposure and metabolic effects from experimental studies
demonstrating increased blood glucose, insulin resistance, and inflammation and visceral
adiposity but the experimental evidence was not entirely consistent. Based on this evidence, the
2019 ISA concludes that, "[ojverall, the collective evidence is suggestive of, but is not sufficient
to infer, a causal relationship between long-term PM2.5 exposure and metabolic effects" (U.S.
EPA, 2019, p. 7-52).
PM2.5 - Reproductive and developmental effects
The 2009 ISA determined that the evidence was "suggestive of a causal relationship" for
the association between long-term PM2.5 exposure and reproductive and developmental
outcomes. The body of literature characterizing these relationships has grown since the 2009
ISA, with much of the evidence focusing on reproduction and fertility or pregnancy and birth
outcomes, though important uncertainties persist (U.S. EPA, 2019, sections 9.1.1, 9.1.2, 9.1.5).
Effects of PM2.5 exposure on sperm have been studied in both epidemiology and
toxicology studies and shows the strongest evidence in epidemiologic studies for impaired sperm
motility and in animal toxicological studies for impaired spermiation. Epidemiologic evidence on
sperm morphology have reported inconsistent results. Evidence for effects of PM2.5 exposure on
female reproduction also comes from both epidemiology and toxicology studies. In the
epidemiologic literature, results on human fertility and fecundity are limited, but the evidence on
in vitro fertilization indicates a modest association of PM2.5 exposures with decreased odds of
becoming pregnant. Studies in rodents have shown ovulation and estrus are affected by PM2.5
exposure. Biological plausibility for outcomes related to male and female fertility and
reproduction comes from laboratory animal studies demonstrating genetic and epigenetic
changes in germ cells with PM2.5 exposure. The 2019 ISA concludes that, "[cjollectively, the
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evidence is suggestive of, but not sufficient to infer, a causal relationship between PM2.5
exposure and male and female reproduction and fertility" (U.S. EPA, 2019, p. 9-43).
With regard to pregnancy and birth outcomes, while the collective evidence for many of
the outcomes examined is not consistent, there are some animal toxicology and epidemiologic
studies that indicate an association between PM2.5 exposures and reduced fetal growth, low birth
weight and preterm birth. Most of the epidemiologic studies do not control for co-pollutant
confounding and do not identify a specific sensitive window of exposure, but results from animal
toxicologic studies provide biological plausibility for these outcomes, as well as support for
multiple sensitive windows for PM2.5 exposure-associated outcomes. There is also epidemiologic
evidence for congenital heart defects of different types, as well as biological plausibility to
support this outcome from the animal toxicology literature. However, evidence for a relationship
between PM2.5 exposure and various pregnancy-related pathologies, including gestational
hypertension, pre-eclampsia and gestational diabetes is inconsistent. Biological plausibility for
effects of PM2.5 exposure and various pregnancy and birth outcomes is provided by studies
showing that PM2.5 exposure in laboratory rodents resulted in impaired implantation and vascular
endothelial dysfunction. Coherence with toxicological studies is provided by epidemiologic
studies in humans reporting associations with epigenetic changes to the placenta and impaired
fetal thyroid function. When taken together, the 2019 ISA concludes that the available evidence,
including uncertainties that evidence, is "suggestive of, but not sufficient to infer, a causal
relationship between exposure to PM2.5 and pregnancy and birth outcomes" (U.S. EPA, 2019, p.
9-44).
UFP - Nervous System Effects
The 2009 ISA reported limited animal toxicological evidence of a relationship between
long-term exposure to UFP and nervous system effects, with no supporting epidemiologic
studies. Animal toxicological studies evaluated in the 2019 ISA substantially add to this evidence
base. Multiple toxicological studies of long-term UFP exposure conducted in adult mice provide
consistent evidence of brain inflammation and oxidative stress in the whole brain, hippocampus,
and cerebral cortex (U.S. EPA, 2019, section 8.6.3). Studies also found morphologic changes,
specifically neurodegeneration in specific regions of the hippocampus and pathologic changes
characteristic of Alzheimer's disease, and initial evidence of behavioral effects in adult mice
(U.S. EPA, 2019, sections 8.6.4 and 8.6.5). Toxicological studies examining pre- and post-natal
UFP exposures provide extensive evidence for behavioral effects, altered neurotransmitters,
neuroinflammation, and morphologic changes (U.S. EPA, 2019, section 8.6.6.2). Persistent
ventriculomegaly was observed in male, but not female, mice exposed postnatally to UFP (U.S.
EPA, 2019, section 8.6.6). Epidemiologic evidence is limited to a single study of school children
that provides support for the experimental results. This study, which did not consider copollutant
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confounding, reports an association between long-term exposure to UFP, which was measured at
the school, and decrements on tests of attention and memory. However, uncertainties remain as a
result of inadequate assessment of potential copollutant confounding, the spatial variation in UFP
concentrations, and exposure measurement error. Based primarily on the animal toxicological
evidence of neurotoxicity and altered neurodevelopment, the 2019 ISA concludes that the
evidence is "suggestive of, but not sufficient to infer, a causal relationship" between long-term
UFP exposure and nervous system effects (U.S. EPA, 2019, section 8.6.7).
3.3.1.6.2 Short-term Exposures
As indicated in Table 3-1 above, the 2019 ISA concludes that the evidence is "suggestive
of, but not sufficient to infer, a causal relationship" between short-term PM2.5 exposures and
metabolic effects and nervous system effects. Additionally, the 2019 ISA concludes that the
evidence is "suggestive" for short-term UFP exposures and cardiovascular effects, respiratory
effects, and nervous system effects. As for the outcomes related to long-term exposures,
discussed above, these conclusions reflect evidence that is "generally supportive but not entirely
consistent or is limited overall" where "[cjhance, confounding, and other biases cannot be ruled
out" (U.S. EPA, 2019, Preface, p.P-20). The basis for these causality determinations is
summarized briefly below.
PM2.5 - Metabolic effects
There were no studies of the effect of short-term PM2.5 exposure and metabolic effects
reviewed in the 2009 ISA (U.S. EPA, 2009). New evidence for a relationship between short-term
PM2.5 exposure and metabolic effects is based on a small number of epidemiologic and animal
toxicological studies reporting effects on glucose and insulin homeostasis and other indicators of
metabolic function such as inflammation in the visceral adipose tissue and liver (U.S. EPA,
2019, section 7.1). The 2019 ISA concludes that, overall, the collective evidence "is suggestive
of, but not sufficient to infer, a causal relationship between short-term PM2.5 exposure and
metabolic effects" (U.S. EPA, 2019, p. 7-11).
PM2.5 - Nervous system effects
The evidence reviewed in the 2009 ISA was characterized as "inadequate to infer" a
causal relationship between short-term PM2.5 exposure and nervous system effects (U.S. EPA,
2009), based on a small number of experimental animal studies. Studies assessed in the 2019
ISA provide additional evidence that short-term exposure to PM2.5 can affect the nervous system
(U.S. EPA, 2019, section 8.1). The strongest evidence is provided by experimental studies in
mice that show effects on the brain. These toxicological studies demonstrate changes in
neurotransmitters in the hypothalamus that are linked to sympathetic nervous system and
hypothalamic-pituitary-adrenal (HPA) stress axis activation, as well as upregulation of
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inflammation-related genes, changes in cytokine levels, and other changes that are indicative of
brain inflammation. In addition, an association of short-term PM2.5 exposure with hospital
admissions for Parkinson's disease was observed indicating the potential for exacerbation of
neurological diseases. The 2019 ISA concludes that, overall, the collective evidence "is
suggestive of, but not sufficient to infer, a causal relationship between short-term exposure to
PM2.5 and nervous system effects" (U.S. EPA, 2019, p. 8-15).
UFP - Cardiovascular effects
In the 2009 ISA, the evidence from toxicological studies, many of which examined
exposures to whole diesel exhaust or wood smoke rather than UFP alone, was suggestive of a
causal relationship between short-term UFP exposure and cardiovascular effects. Since the 2009
ISA, there have been only a limited number of studies published describing the relationship
between short-term UFP exposure and cardiovascular effects. This includes a small number of
epidemiologic panel studies that have observed positive associations between short-term
exposure to UFPs and measures of HRV (U.S. EPA, 2019, section 6.5.9.1) and markers of
coagulation (U.S. EPA, 2019, section 6.5.11.1) although there are also studies that did not report
such UFP-related effects. In addition, there is evidence from a single controlled human exposure
study indicating decreases in the anticoagulant proteins plasminogen and thrombomodulin in
individuals with metabolic syndrome (U.S. EPA, 2019, section 6.5.11.2). There is inconsistent
evidence from controlled human exposure and epidemiologic panel studies for endothelial
dysfunction, changes in blood pressure, and systemic inflammation following short-term
exposure to UFPs. Notably, there is little evidence of an effect when considering short-term UFP
exposure on other cardiovascular endpoints as well as cardiovascular-disease emergency
department visits or hospital admissions. The assessment of study results across experimental
and epidemiologic studies is complicated by differences in the size distributions examined
between disciplines and by the nonuniformity in the exposure metrics examined (e.g., particle
number concentration, surface area concentration, and mass concentration) (U.S. EPA, 2019,
section 1.4.3). When considered as a whole, the 2019 ISA concludes that the evidence is
"suggestive of, but not sufficient to infer, a causal relationship between short-term exposure UFP
exposure and cardiovascular effects" (U.S. EPA, 2019, p. 6-304).
UFP - Respiratory effects
A limited number of studies examining short-term exposure to UFPs and respiratory
effects were reported in the 2009 ISA, which concluded that the relationship between short-term
exposure to UFP and respiratory effects is "suggestive of a causal relationship." This conclusion
was based on epidemiologic evidence indicating associations with combined respiratory-related
diseases, respiratory infection, and asthma exacerbation. In addition, personal exposures to
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ambient UFP were associated with lung function decrements in adults with asthma. The few
available experimental studies provided limited coherence with epidemiologic findings for
asthma exacerbation. Studies assessed in the 2019 ISA add to this evidence base and support
epidemiologic evidence for asthma exacerbation and combined respiratory-related diseases but
do not rule out chance, confounding, and other biases (U.S. EPA, 2019, section 5.5). For
example, associations persist in one epidemiologic study with adjustment for NO2, but not in
another. Additional supporting evidence, showing decrements in lung function and enhancement
of allergic inflammation and other allergic responses, is provided by a controlled human
exposure study in adults with asthma and by animal toxicological studies in an animal model of
allergic airway disease. For combined respiratory-related diseases, recent findings add
consistency for hospital admissions and emergency department visits and indicate lung function
changes among adults with asthma or COPD. Uncertainty remains regarding the characterization
of UFP exposures and the potential for copollutant confounding in epidemiologic studies, which
limits inference about an independent effect of UFP exposures (U.S. EPA, 2019, section 5.5).
The 2019 ISA concludes that, overall, the evidence is "suggestive of, but not sufficient to infer, a
causal relationship between short-term UFP exposure and respiratory effects" (U.S. EPA, 2019,
p. 5-303).
UFP- Nervous system effects
The 2009 ISA reported limited animal toxicological evidence of a relationship between
short-term exposure to UFP and nervous system effects, without supporting epidemiologic
studies. Several experimental studies evaluated in the 2019 ISA add to this evidence base. In the
2019 ISA, the strongest evidence for a relationship between short-term UFP exposure and
nervous system effects is provided by animal toxicological studies that show inflammation and
oxidative stress in multiple brain regions following exposure to UFP. There is a lack of evidence
from epidemiologic studies (U.S. EPA, 2019, section 8.5). The 2019 ISA concludes that, overall,
the collective evidence is "suggestive of, but not sufficient to infer, a causal relationship between
short-term UFP exposure and nervous system effects" (U.S. EPA, 2019, p. 8-86).
3.3.1.6.3 COVID-19 Infection and Death
With the advent of the global COVID-19 pandemic, a number of recent studies evaluated
in the draft ISA Supplement examined the role of ambient air pollution, specifically PM2.5, on
COVID-19 infections and deaths, including a few studies within the U.S. and Canada (U.S. EPA,
2021a; section 3.3.2). While there is no exact corollary within the 2019 ISA for these types of
studies, the 2019 ISA presented evidence that evaluates the potential relationship between short-
and long-term PM2.5 exposure and respiratory infection (U.S. EPA, 2019, section 5.1.5 and
5.2.6). Studies assessed in the 2019 ISA report that some evidence of positive associations
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between short-term PM2.5 and hospital admissions and emergency department visits for
respiratory infections, however the interpretation of these studies is complicated by the
variability in the type of respiratory infection outcome examined (U.S. EPA, 2019, Figure 5-7).
In the 2019 ISA, studies of long-term PM2.5 exposure were limited and while there were some
positive associations reported, there was minimal overlap in respiratory infection outcomes
examined across studies. Exposure to PM2.5 has been shown to impair host defense, specifically
altering macrophage function, providing a biological pathway by which PM2.5 exposure could
lead to respiratory infection (U.S. EPA, 2019, sections 5.1.1 and 5.1.5.) There is some additional
evidence that PM2.5 exposure can lead to decreases in an individual's immune response, which
can subsequently facilitate replication of respiratory viruses (Bourdrel et al., 2021).
As assessed in the draft ISA Supplement, a number of studies examined whether daily
changes in PM2.5 can influence COVID-19 outcomes (ISA Supplement, section 3.3.2.1).
Additionally, several studies assessed in the draft ISA Supplement evaluates whether long-term
PM2.5 exposure is related to increased susceptibility to COVID-19 outcomes in North America
(U.S. EPA, 2021a, section 3.3.2.2). While some of the studies report positive associations,
overall, they were subjected to methodological issues that may influence the results, including:
(1) the use of ecological study design; (2) some of the studies were conducted during the ongoing
pandemic when the etiology of COVID-19 was still not well understood (e.g., specifically, there
are important differences in COVID-19 related outcomes by a variety of factors such as race and
socioeconomic status); and (3) studies did not account for crucial factors that could influence
results (e.g., stay-at-home orders, social distancing, use of masks, and testing capacity) (U.S.
EPA, 2021a, chapter 5). Taken together, there is limited evidence at this point in the COVID-19
pandemic to determine if short- or long-term exposure to air pollutants, such as PM2.5, influence
the spread or susceptibility of COVID-19 in the population.
3,3,1,7 Summary
Based on the evidence assessed in the 2019 ISA and the draft ISA Supplement (U.S.
EPA, 2019, U.S. EPA, ), and summarized in sections 3.3.1.1 to 3.3.1.6 above, we revisit the
policy-relevant questions posed at the beginning of this section:
• To what extent does the scientific evidence strengthen, or otherwise alter, our
preliminary conclusions regarding health effects attributable to long- or short-term
fine particle exposures? Have previously identified uncertainties been reduced?
What important uncertainties remain and have new uncertainties been identified?
We consider these questions in the context of the evidence for effects of long- and short-
term PM2.5 exposures. Studies reviewed in the 2019 ISA and the draft ISA Supplement expand
our understanding of the PM2.5-related health effects from long- and short- term exposures, as
well as reduced important uncertainties identified in prior reviews. Epidemiologic studies
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consistently report positive associations between PM2.5 exposures and a wide range of health
outcomes, including total and cause-specific mortality (e.g., cardiovascular and respiratory
mortality), cardiovascular and respiratory morbidity, lung cancer, and nervous system effects.
Such associations have been reported in analyses employing a variety of study designs,
approaches to estimating PM2.5 exposures, statistical models, and long-term exposure windows
(i.e., the exposure period that is associated with the health outcome). Recent U.S. and Canadian
epidemiologic studies evaluated in the draft ISA Supplement provide additional support for the
conclusions of the 2019 ISA. Overall, these studies support, and in some instances strengthen,
the evidence presented in the 2019 ISA of long-term PM2.5 exposures and health effects. Cohort
studies assessed in the draft ISA Supplement add to the large body of evidence exhibiting
consistent, positive associations between long-term PM2.5 exposure and mortality detailed in the
2019 ISA. While relatively fewer recent U.S. and Canadian epidemiologic studies examined
short-term PM2.5 exposure and mortality, these studies continue to provide evidence of positive
associations with all-cause and total (nonaccidental) mortality, in addition to cause-specific
mortality outcomes. Further, the 2019 ISA and draft ISA Supplement include retrospective
studies that demonstrate improvements in health outcomes, including increased life expectancy,
decreasing mortality, or decreasing respiratory effects, as a result of decreases in ambient PM2.5
concentrations over time. Lastly, the biological plausibility of PIVh.s-attributable mortality is
supported by the coherence of effects across scientific disciplines (i.e., animal toxicological,
controlled human exposure studies, and epidemiologic) when evaluating respiratory and
cardiovascular morbidity effects, which are some of the largest contributors to total
(nonaccidental) mortality.
Epidemiologic studies (for short-term and long-term exposure) evaluated in the 2019 ISA
and the draft ISA Supplement assessed the role potential uncertainties may have on the health-
effect associations, and examined various exposure windows, approaches to adjust for
confounding variables, and exposure assessment methods that used different sources of data and
were conducted at different spatial resolutions. These evaluations increased confidence in the
causal relationship between long-term PM2.5 exposure and mortality. Moreover, this evidence
further informs whether there is evidence of copollutant confounding, and although there were
some differences across studies, generally positive associations persisted in copollutant models.
Some studies reported that associations persisted in analyses that exclude PM2.5 exposures near
the upper end of the air quality distribution. Overall, the assessment of the C-R relationship
continues to generally support a linear, no-threshold relationship with some recent studies
providing evidence for either a sublinear, linear, or supralinear relationship at these lower
concentrations.
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Building on the evidence presented in the 2019 ISA, the evidence assessed in the draft
ISA Supplement provides additional information to address key uncertainties associated with the
health effects evidence. The draft ISA Supplement examined an expanded body of evidence
related to causal modeling methods, to further evaluate the causal nature of associations between
exposure to PM2.5 and mortality. Consistent with the 2019 ISA, this expanded body of evidence
reduces uncertainties related to confounding and provides robust support for positive and
significant associations seen in cohort studies of long-term exposure to PM2.5. Although there
were fewer more recent multicity studies conducted in the U.S. and Canada examining the
relationship between short-term exposure and mortality than for long-term exposure, the studies
assessed in the draft ISA Supplement add to the extensive evidence evaluated in the 2019 ISA.
Furthermore, these studies report consistent positive associations across studies that are using
different exposure assessment methods, statistical models, as well as different methods to control
for confounding effects.
Recent U.S. and Canadian epidemiologic studies examining short- and long-term PM2.5
exposure and cardiovascular effects provide evidence that is consistent with the evidence
evaluated in the 2019 ISA. Studies examining short-term PM2.5 exposure report consistent
positive associations for cardiovascular-related emergency department visits and hospital
admissions, specifically for ischemic heart disease, myocardial infarction, and heart failure. In
studies evaluating long-term exposures there remains strong evidence for cardiovascular-related
mortality with support from studies of cardiovascular morbidity outcomes, including coronary
heart disease, stroke, and atherosclerosis progression, among individuals with preexisting
diseases or patients followed after a cardiac event or procedure. In addition, the studies provide
evidence of an immediate effect of short-term-related PM2.5 exposure on cardiovascular-related
outcomes, especially during the first few days following exposure.
With respect to long-term PM2.5 exposure, the strongest evidence associated with
cardiovascular mortality is exhibited in studies that report positive associations with ischemic
heart disease and stroke mortality. Furthermore, recent studies examining association between
long-term PM2.5 exposure and cardiovascular morbidity, specifically coronary heart disease,
stroke, and atherosclerosis progression, most consistently report positive associations when
focusing on individuals with pre-existing diseases and patients followed after a cardiac event or
procedure, and not the general population as a whole, supporting and extending the evidence
presented in 2019 ISA. The 2019 ISA also assessed controlled human exposure studies that were
conducted in Europe at near-ambient PM2.5 concentrations and provide initial evidence of
vascular changes and reductions in heart rate as well as changes in cardiac and lung function as
well as inflammation.
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The draft ISA Supplement also evaluates epidemiologic studies that examine the
relationship between PM2.5 exposure and COVID-19 infection and mortality. While these studies
report positive associations, there a number of methodological limitations which include: (1)
employing an ecological study design, (2) conducting research while COVID-19 etiology was
poorly understood, and (3) the lack of accounting for key factors in disease transmission such as
use of mask, stay home orders, and testing capacity.
Thus, when taken together, the evidence available in the draft ISA Supplement reaffirms,
and in some cases strengthens, the conclusions from the 2019 ISA regarding long- and short-
term PM2.5 exposures and mortality and cardiovascular effects.
3.3.2 Public Health Implications and At-Risk Populations
The public health implications of the evidence regarding PM2.5 health effects, as for other
effects, are dependent on the type and severity of the effects, as well as the size of the population
affected. Such factors are discussed here in the context of our consideration of the health effects
evidence related to PM2.5 in ambient air. Additionally, we summarize the information on
population groups at risk of the effects of PM2.5 in ambient air.
• Does the evidence alter our understanding of populations that are particularly at
risk from PM2.5 exposures? What are important uncertainties in that evidence?
The information available in this reconsideration has not altered our understanding of
human populations at risk of health effects from PM2.5 exposures. As recognized in the 2020
review, the 2019 ISA cites extensive evidence indicating that "both the general population as
well as specific populations and lifestages are at risk for PIVh.s-related health effects" (U.S. EPA,
2019, p. 12-1). Factors that may contribute to increased risk of PIVh.s-related health effects
include lifestage (children and older adults), pre-existing diseases (cardiovascular disease and
respiratory disease), race/ethnicity, and socioeconomic status.14
Children make up a substantial fraction of the U.S. population and often have unique
factors that contribute to their risk of experiencing a health effect due to exposures to ambient air
pollutants because of their continuous growth and development.15 There is strong evidence that
demonstrates PM2.5 associated health effects in children, particularly from epidemiologic studies
of long-term PM2.5 exposure and impaired lung function growth, decrements in lung function,
and asthma development. However, there is limited evidence from stratified analyses that
children are at increased risk of PIVh.s-related health effects compared to adults. Additionally,
14 As described in the 2019 ISA, other factors that have the potential to contribute to increased risk include obesity,
diabetes, genetic factors, smoking status, sex, diet, and residential location (U.S. EPA, 2019, chapter 12).
15 Children, as used throughout this draft PA, generally refers to those younger than 18 years old.
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there is some evidence that indicates that children receive higher PM2.5 exposures than adults,
and dosimetric differences in children compared to adults can contribute to higher doses (U.S.
EPA, 2019, section 12.5.1.1).
In the U.S., older adults, often defined as adults 65 years of age and older, represent an
increasing portion of the population and often have pre-existing diseases or conditions that may
compromise biological function. While there is limited evidence to indicate that older adults
have higher exposures than younger adults, older adults may receive higher doses of PM2.5 due to
dosimetric differences. There is consistent evidence from studies of older adults demonstrating
generally consistent, positive associations in studies examining health effects from short- and
long-term PM2.5 exposure and cardiovascular or respiratory hospital admissions, emergency
department visits, or mortality (U.S. EPA, 2019, sections 6.1, 6.2, 11.1, 11.2, 12.5.1.2).
Additionally, several animal toxicological, controlled human exposure, and epidemiologic
studies did not stratify results by lifestage, but instead focused the analyses on older individuals,
and can provide coherence and biological plausibility for the occurrence among this lifestage
(U.S. EPA, 2019, section 12.5.1.2).
Individuals with pre-existing disease may be considered at greater risk of an air pollution-
related health effect than those without disease because they are likely in a compromised
biological state that can vary depending on the disease and severity. With regard to
cardiovascular disease, we first note that cardiovascular disease is the leading cause of death in
the U.S., accounting for one in four deaths, and approximately 12% of the adult population in the
U.S. has a cardiovascular disease (U.S. EPA, 2019, section 12.3.1). Strong evidence
demonstrates that there is a causal relationship between cardiovascular effects and long- and
short-term exposures to PM2.5. Some of the evidence supporting this conclusion is from studies
of panels or cohorts with pre-existing cardiovascular disease, which provide supporting evidence
but do not directly demonstrate an increase in risk (U.S. EPA, 2019, section 12.3.1).
Epidemiologic evidence indicates that individuals with pre-existing cardiovascular disease may
be at increased risk for PIVh.s-associated health effects compared to those without pre-existing
cardiovascular disease. While the evidence does not consistently support increased risk for all
pre-existing cardiovascular diseases, there is evidence that certain pre-existing cardiovascular
diseases (e.g., hypertension) may be a factor that increases PM2.5-related risk. Furthermore, there
is strong evidence supporting a causal relationship for long- and short-term PM2.5 exposure and
cardiovascular effects, particularly for IHD (U.S. EPA, 2019, chapter 6, section 12.3.1).
With regard to respiratory disease, we first note that the most chronic respiratory diseases
in the U.S. are asthma and COPD. Asthma affects a substantial fraction of the U.S. population
and is the leading chronic disease among children. COPD primarily affects older adults and
contributes to compromised respiratory function and underlying pulmonary inflammation. The
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body of evidence indicates that individuals with pre-existing respiratory diseases, particularly
asthma and COPD, may be at increased risk for PM2.5-related health effects compared to those
without pre-existing respiratory diseases (U.S. EPA, 2019, section 12.3.5). There is strong
evidence indicating PIVh.s-associated respiratory effects among those with asthma, which forms
the primary evidence base for the likely to be causal relationship between short-term exposures
to PM2.5 and respiratory health effects (U.S. EPA, 2019, section 12.3.5). For asthma,
epidemiologic evidence demonstrates associations between short-term PM2.5 exposures and
respiratory effects, particularly evidence for asthma exacerbation, and controlled human
exposure and animal toxicological studies demonstrate biological plausibility for asthma
exacerbation with PM2.5 exposures (U.S. EPA, 2019, section 12.3.5.1). For COPD,
epidemiologic studies report positive associations between short-term PM2.5 exposures and
hospital admissions and emergency department visits for COPD, with supporting evidence from
panel studies demonstration COPD exacerbation. Epidemiologic evidence is supported by some
experimental evidence of COPD-related effects, which provides support for the biological
plausibility for COPD in response to PM2.5 exposures (U.S. EPA, 2019, section 12.3.5.2).
There is strong evidence for racial and ethnic disparities in PM2.5 exposures and PM2.5-
related health risk, as assessed in the 2019 ISA and with even more evidence available since the
literature cutoff date for the 2019 ISA and evaluated in the draft ISA Supplement. There is strong
evidence demonstrating that Black and Hispanic populations, in particular, have higher PM2.5
exposures than non-Hispanic White populations (U.S. EPA, 2019, Figure 12-2; U.S. EPA,
2021a, Figure 3-38). Black populations or individuals that live in predominantly Black
neighborhoods experience higher PM2.5 exposures, in comparison to non-Hispanic White
populations. There is also consistent evidence across multiple studies that demonstrate increased
risk of PM2.5-related health effects, with the strongest evidence for health risk disparities for
mortality (U.S. EPA, 2019, section 12.5.4). There is also evidence of health risk disparities for
both Hispanic and non-Hispanic Black populations compared to non-Hispanic White populations
for cause-specific mortality and incident hypertension (U.S. EPA, 2021a, 3.3.3.2).
Socioeconomic status (SES) is a composite measure that includes metrics such as
income, occupation, or education, and can play a role in access to healthy environments as well
as access to healthcare. SES may be a factor that contributes to differential risk from PM2.5-
related health effects. Studies assessed in the 2019 ISA and draft ISA Supplement provide
evidence that lower SES communities are exposed to higher concentrations of PM2.5 compared to
higher SES communities (U.S. EPA, 2019, section 12.5.3; U.S. EPA, 2021a, section 3.3.3.1.1).
Studies using composite measures of neighborhood SES consistently demonstrated a disparity in
both PM2.5 exposure and the risk of PM2.5-related health outcomes. There is some evidence that
supports associations larger in magnitude between mortality and long-term PM2.5 exposures for
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those with low income or living in lower income areas compared to those with higher income or
living in higher income neighborhoods (U.S. EPA, 2019, section 12.5.3; U.S. EPA, 2021a,
section 3.3.3.1.1). Additionally, evidence supports conclusions that lower SES is associated with
cause-specific mortality and certain health endpoints (i.e., HI and CHF), but less so for all-cause
or total (non-accidental) mortality (U.S. EPA, 2021a, section 3.3.3.1).
• What does the available information indicate with regard to the size of at-risk
populations and their distribution in the U.S.?
The magnitude and characterization of a public health impact is dependent upon the size
and characteristics of the populations affected, as well as the type or severity of the effects. As
summarized above, lifestage (children and older adults), race/ethnicity and socioeconomic status
are factors that increase the risk of PM2.5-related health effects. The American Community
Survey (ACS) for 2019 estimates that approximately 22% and 16% of the U.S. population are
children (age <18) and older adults (age 65+), respectively. For all ages, non-Hispanic Black and
Hispanic populations are approximately 12% and 18% of the overall U.S. population in 2019.
Table 3-2 below considers the currently available information that helps to characterize key
features of these populations.
Table 3-2. National demographic information, 2019.
Characteristic1
Number
Percent of Total
Total
328,239,523
Child (Age <18)
72.967.785
22.2
Adult (Age 18+)
255.271.738
77.8
All Age Groups
0-4 years
19,404,835
5.9
5-14 years
41,113,916
12.5
15-19 years
21,353,524
6.5
20-24 years
21,468,680
6.5
25-34 years
45,578,475
13.9
35-64 years
125,246,065
38.1
65+ years
54,074,028
16.4
Race/Ethnicity
328.239.523
White NH 2
196,789,401
60
Black NH
40,596,040
12.4
American Indian or Alaska Native NH
2,236,348
0.7
Asian NH
18,427,914
5.6
Hispanic, all
60,481,746
18.4
Other NH
9,708,074
3
Household Income (past 12 months)3
Less than $10,000
5.8
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$10,000 to $14,999
4.0
$15,000 to $24,999
8.3
$25,000 to $34,999
8.4
$35,000 to $49,999
11.9
$50,000 to $74,999
17.4
$75,000 to $99,999
12.8
$100,000 to $149,999
15.7
$150,000 to $199,999
7.2
$200,000 or more
8.5
Educational Attainment4
Less than high school
25,618,541
11.4
High school graduate (or equivalent)
60,482,353
26.9
Some college, no degree
44,914,086
20
Associate's degree
19,381,937
8.6
Bachelor's degree
45,730,479
20.3
Graduate or professional degree
28,771,172
12.8
1 Numbers within selected characteristics may not sum to total due to rounding
2 NH = non-Hispanic
3 Household income in the past 12 months in 2019 inflation-adjusted dollars.
4 Educational attainment for population aged 25 years and older.
Adapted from the 2019 American Community Survey and Housing Survey. Available at:
DemoaraDhics: httDs://data.census.aov/cedsci/table?a=United%20States&tid=ACSDP1Y2019.DP05
Income: httos://data.census.aov/cedsci/table?q=United%20States&t=lncome%20and%20Povertv&tid=ACSST1Y2019.S1901
Education:
httos://data.census.aov/cedsci/table?q=United%20States&t=Education%3AEducational%20Attainment&tid=ACSST1Y2019.S1501
As noted above, individuals with pre-existing cardiovascular disease and pre-existing
respiratory disease may also be at increased risk of PIVh.s-related health effects. Table 3-3 below
considers the currently available information that helps to characterize key features of
populations with cardiovascular or respiratory diseases or conditions. The National Center for
Health Statistics data for 2018 indicate that, for adult populations, older adults (e.g., those 65
years and older) have a higher prevalence of cardiovascular diseases compared to younger adults
(e.g., those 64 years and younger). For respiratory diseases, older adults also have a higher
prevalence of emphysema than younger adults, and adults 44 years or older have a higher
prevalence of chronic bronchitis. However, the prevalence for asthma is generally similar across
all adult age groups.
With respect to race, American Indians or Alaskan Natives have the highest prevalence of
all heart disease and coronary heart disease, while Blacks have the highest prevalence of
hypertension and stroke. Hypertension has the highest prevalence across all racial groups
compared to other cardiovascular diseases or conditions, ranging from approximately 22% to
32% of each racial group. Overall, the prevalence of cardiovascular diseases or conditions is
lowest for Asians compared to Whites, Blacks, and American Indians or Alaskan Natives.
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1 Asthma prevalence is highest among Black and American Indian or Alaska Native populations,
2 while prevalence is generally similar across racial groups for chronic bronchitis and emphysema.
3 Overall, the prevalence for respiratory diseases is lowest for Asians compared to Whites, Blacks,
4 and American Indians or Alaskan Natives. With regard to ethnicity, cardiovascular and
5 respiratory disease prevalence across all diseases or conditions is generally similar between
6 Hispanic and non-Hispanic populations, although non-Hispanics have a slightly higher
7 prevalence compared to Hispanics.
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1 Table 3-3. Prevalence of cardiovascular and respiratory diseases among adults by age, race, and ethnicity in the U.S. in 2018.
Adults (18+)
Age (%)1
Race (%)2
Ethnicity (%)3
Chronic
Disease or
Condition
N (in
thousands)
18-44
44-64
65-74
75+
White
Black
American
Indian or
Alaska
Native
Asian
Hispanic
Non-
Hispanic
All (N, in
thousands)
249,456
115,008
83,038
30,809
20,601
193,454
30,813
2,810
15,960
40,749
208,706
Selected Cardiovascular Diseases/Conditions
All heart disease
30,252
4.8
11.8
23.6
37.3
11.5
10.0
14.6
7.7
8.2
11.7
Coronary heart
disease
15,780
1.0
6.0
15.5
23.9
5.7
5.4
8.6
4.4
5.1
5.7
Hypertension
67,856
00
CO
34.4
54.4
61.1
23.9
32.2
27.2
21.9
23.7
25.1
Stroke
7,801
0.6
3.1
6.9
11.8
2.6
3.9
3.0
2.7
2.5
2.9
Selected Respiratory Diseases
Asthma4
19,233
7.2
8.3
8.6
6.7
7.5
9.1
9.5
3.7
6.0
8.1
COPD - chronic
bronchitis
9,003
2.2
4.5
5.1
5.6
3.6
3.4
*
1.1
2.7
3.6
COPD-
emphysema
3,780
0.2
1.6
4.1
4.5
1.4
1.1
0.4
0.7
1.0
1.4
1 Percentage of individual adults within each age group with disease, based on N (at the top of each age column).
2 Percentage of individual adults within each race group with disease, based on N (at the top of each race column).
3 Percentage of individual adults within each ethnic group with disease, based on N (at the top of each ethnic column).
4 Asthma prevalence is reported for "still has asthma."
* Estimate does not meet NCHS standards of reliability.
Source: (Insert cites); National Center for Health Statistics, Summary Health Statistics, National Health Interview Survey, 2018; Tables A-1 and A-2.
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Taken together, this information indicates that the groups at increased risk of PM2.5-
related health effects represent a substantial portion of the total U.S. population. In evaluating the
primary PM2.5 standards, an important consideration is the potential PM2.5-related public health
impacts in these populations.
3.3.3 PM2.5 Concentrations in Key Studies Reporting Health Effects
To inform conclusions on the adequacy of the public health protection provided by the
current primary PM2.5 standards, this section evaluates the PM2.5 exposures and ambient
concentrations (i.e., used as surrogates for exposures in epidemiologic studies) in studies
reporting PM2.5-related health effects. We specifically consider the following overarching
questions:
• What are the short- or long-term PM2.5 exposures that have been associated with
health effects and to what extent does the evidence support the occurrence of such
effects for air quality meeting the current primary PM2.5 standards?
In addressing these questions, we emphasize health outcomes for which the 2019 ISA concludes
that the evidence supports a "causal" or a "likely to be causal" relationship with PM2.5 exposures.
As discussed above, this includes mortality, cardiovascular effects, and respiratory effects
associated with short- or long-term PM2.5 exposures and cancer and nervous system effects
associated with long-term PM2.5 exposures. While the causality determinations in the 2019 ISA
are informed by studies evaluating a wide range of PM2.5 concentrations, this section considers
the degree to which the evidence in the 2019 ISA and draft ISA Supplement supports the
occurrence of PM-related effects at concentrations relevant to informing conclusions on the
primary PM2.5 standards. Section 3.3.3.1 considers the exposure concentrations that have been
evaluated in experimental studies and section 3.3.3.2 considers the ambient concentrations in
locations evaluated by epidemiologic studies.
3,3.3.1 PM Exposure Concentrations Evaluated in Experimental Studies
As stated in the 2019 ISA, the evidence for a particular PIVh.s-related health outcome is
strengthened when results from experimental studies demonstrate biologically plausible
mechanisms through which adverse human health outcomes could occur (U.S. EPA, 2015b,
Preamble p. 20). Two types of experimental studies are of particular importance in understanding
the effects of PM exposures: controlled human exposure and animal toxicology studies. In such
studies, investigators expose human volunteers or laboratory animals to known concentrations of
air pollutants under carefully regulated environmental conditions and activity levels. Thus,
controlled human exposure and animal toxicology studies can provide information on the health
effects of experimentally administered pollutant exposures under highly controlled laboratory
conditions (U.S. EPA, 2015b, Preamble, p. 11).
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In this section, we consider the PM2.5 exposure concentrations shown to result effects in
controlled human exposure studies and in animal toxicology studies. We particularly consider
the consistency of specific PIVh.s-related effects across studies, the potential adversity of such
effects, and the degree to which exposures shown to cause effects are likely to occur in areas
meeting the current primary standards. To address these issues, we consider the following
question:
• To what extent does the evidence from controlled human exposure or animal
toxicology studies support the potential for adverse cardiovascular, respiratory, or
other effects following PM2.5 exposures likely to occur in areas meeting the current
or alternative primary standards?
Controlled Human Exposure Studies
As discussed in detail in the 2019 ISA (U.S. EPA, 2019, section 6.1), controlled human
exposure studies have reported that PM2.5 exposures lasting from less than one hour up to five
hours can impact cardiovascular function.16 The most consistent evidence from these studies is
for impaired vascular function (U.S. EPA, 2019, section 6.1.13.2). In addition, although less
consistent, the 2019 ISA notes that studies examining PM2.5 exposures also provide evidence for
increased blood pressure (U.S. EPA, 2019, section 6.1.6.3), conduction abnormalities/arrhythmia
(U.S. EPA, 2019, section 6.1.4.3), changes in heart rate variability (U.S. EPA, 2019, section
6.1.10.2), changes in hemostasis that could promote clot formation (U.S. EPA, 2019, section
6.1.12.2), and increases in inflammatory cells and markers (U.S. EPA, 2019, section 6.1.11.2).
The 2019 ISA concludes that, when taken as a whole, controlled human exposure studies
demonstrate that short-term exposure to PM2.5 may impact cardiovascular function in ways that
could lead to more serious outcomes (U.S. EPA, 2019, section 6.1.16). Thus, such studies can
provide insight into the potential for specific PM2.5 exposures to result in physiological changes
that could increase the risk of more serious effects.
Table 3-4 below summarizes information from the 2019 ISA and draft ISA Supplement
on available controlled human exposure studies that evaluate effects on markers of
cardiovascular function following exposures to PM2.5, either as concentrated ambient particles
(CAP) or in unfiltered versus filtered air.17
16 In contrast, controlled human exposure studies provide little evidence for respiratory effects following short-term
PM2.5 exposures (U.S. EPA, 2019, section 5.1, Table 5-18). Therefore, this section focuses on cardiovascular
effects evaluated in controlled human exposure studies of PM2 5 exposure.
17 Table 3-4 identifies controlled human exposure studies included in the 2019 ISA and draft ISA Supplement that
examine the potential for PM2 5 exposures to alter markers of cardiovascular function and is ordered by exposure
concentration. Studies that focus on specific components of PM25 (e.g., endotoxin), or studies that evaluated
PM2 5 exposures only in the presence of an intervention (e.g., dietary intervention) or other pollutant (e.g., ozone),
are not included.
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1 Table 3-4. Summary of information from PM2.5 controlled human exposure studies.
Study
Population
Exposure Details
(average concentration;
duration)
Results
Brauner et al.,
2008
Healthy adults
10.5 |jg/m3 PM2.5
(unfiltered) vs below
detection (filtered); 24 h
No significant effect on markers of vascular
function
Hemmingsen et
al., 2015a,
Hemmingsen et
al., 2015b
Healthy,
overweight
older adults
24 |jg/m3 (unfiltered) vs
3.0 |jg/m3 (filtered)
Copenhagen PM; 5 h
Impaired vascular function and altered heart rate
variability; no significant changes in blood
pressure or markers of inflammation or oxidative
stress
Wyattet al.,
2020a *
Healthy young
adults (18-35)
37.8 pg/m3 CAP vs2.1
|jg/m3 (filtered); 4h
Increased blood inflammatory markers;
Inconsistent changes in HRV
Urch et al., 2010
Non-asthmatic
and mild
asthmatic
adults
64 pg/m3 CAP (lower
exposure); 2 h
No significant change in blood markers of
inflammation or oxidative stress
Huang et al., 2012
Healthy adults
90 pg/m3 CAP; 2 h
No significant changes in heart rate variability
Devlin et al., 2003
Healthy older
adults
99 pg/m3 CAP1; 2 h
Decreased heart rate variability
Hazuchaet al.,
2013
Adult current
and former
smokers
109 pg/m3 CAP; 2 h
No significant changes in markers of
inflammation or coagulation
Ghio et al., 2000
Healthy young
adults
120 pg/m3 CAP; 2 h
Increased fibrinogen (coagulation)
Ghio et al., 2003
Healthy young
adults
120 pg/m3 CAP; 2 h
Increased fibrinogen; no significant effect on
markers of inflammation
Urch et al., 2010
Non-asthmatic
and mild
asthmatic
adults
140 pg/m3 CAP (higher
exposure); 2 h
Increased blood inflammatory markers
Brook et al., 2009
Healthy adults
149 pg/m3 CAP;2h
Impaired vascular function, increased blood
pressure; no significant change in markers of
inflammation (compared to filtered air)
Ramanathan et
al., 2016
Healthy adults
149 pg/m3 CAP;2h
Decreased anti-oxidant/anti-inflammatory
capacity when baseline capacity was low
Sivagangabalan et
al., 2011
Healthy adults
150 pg/m3 CAP; 2 h
Increase in indicator of possible arrhythmia; no
significant effect on heart rate
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Kushaet al., 2012
Healthy adults
154 |jg/m3 CAP;2h
No significant effect on indicator of possible
arrhythmia
Gong et al., 2003
Adults with and
without asthma
174 |jg/m3 CAP;2h
Increased heart rate; No significant effect on
indicators of arrhythmia, inflammation,
coagulation; inconsistent effects on blood
pressure
Gong et al., 2004
Older adults
with and
without COPD
200 |jg/m3 CAP; 2 h
Decreased heart rate variability, increase in
markers of inflammation (without COPD only);
inconsistent effect on arrhythmia; no significant
effect on markers of blood coagulation
Liu et al., 2015
Healthy adults
238|jg/m3 CAP; 130 min
Increase in urinary markers of oxidative stress
and vascular dysfunction; no significant effect on
blood markers of oxidative stress, vascular
function, or inflammation
Bellaviaet al.,
2013
Healthy adults
-242 pg/m3 CAP; 130 min
Increased blood pressure
Behbod et al.,
2013
Healthy adults
-250 pg/m3 CAP; 130 min
Increase in markers of inflammation
Tong et al., 2015
Healthy older
adults
253 pg/m3 CAP; 2 h
Impaired vascular function and increased blood
pressure; no significant change in markers of
inflammation or coagulation
Lucking et al.,
2011
Healthy young
men
320 pg/m3 (unfiltered) vs
7.2 pg/m3 (filtered); 1 h
Impaired vascular function and increased
potential for coagulation; no significant effect on
blood pressure, markers of inflammation, or
arterial stiffness
Vieira et al.,
2016a, Vieira et
al., 2016b
Healthy adults;
Heart failure
patients
325 pg/m3 (unfiltered) vs
25 pg/m3 (filtered) diesel
exhaust; 21-min
Increase in marker of potential impairment in
heart function, impaired vascular function (heart
failure patients); no significant effect on blood
pressure, heart rate or heart rate variability,
markers of inflammation, markers of coagulation,
or arterial stiffness
* Study newly assessed in the draft ISA Supplement
1 The published study reports an average CAP concentration of 41 pg/m3, but communication with the study authors revealed
an error in that reported concentration (Jenkins, 2016).
1
2 Most of the controlled human exposure studies in Table 3-4 exposed participants to
3 average PM2.5 concentrations at or above about 100 |ig/m3, with exposure durations typically up
4 to about two hours. Statistically significant effects on one or more indicators of cardiovascular
5 function are often, though not always, reported following 2-hour exposures to average PM2.5
6 concentrations at and above about 120 |ig/m3, with less consistent evidence for effects following
7 exposures to concentrations lower than 120 |ig/m3. Impaired vascular function, the effect
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identified in the 2019 ISA as the most consistent across studies (U.S. EPA, 2019, section
6.1.13.2), is shown following 2-hour exposures to PM2.5 concentrations at and above 149 |ig/m3.
Mixed results are reported in the three studies that evaluated longer exposure durations (i.e.,
longer than 2 hours) and lower (i.e., near-ambient) PM2.5 concentrations, with significant effects
for some outcomes reported following 5-hour exposures to 24 |ig/m3 in Hemmingsen et al.
(2015b), but not for other outcomes following 5-hour exposures in Hemmingsen et al. (2015a)
and not following 24-hour exposures to 10.5 |ig/m3 in Brauner et al. (2008). Wyatt et al. (2020a)
adds to this limited evidence base of controlled human exposure studies conducted at near
ambient concentrations. This study was a randomized double-blind crossover study in healthy
young participants (18-35 years, n=21) who were subject to intermittent moderate exercise and
found significant effects for some cardiovascular and (e.g., systematic inflammation markers,
cardiac repolarization, and decreased pulmonary function) following 4-hour exposures to 37.8
|ig/m3. The higher ventilation rate and longer exposure duration in this study compared to most
controlled human exposure studies is roughly equivalent to a 2-hour exposure of 75-100 |ig/m3
of PM2.5. Therefore, dosimetric consideration may explain the observed changes in lung function
and inflammation in young healthy individuals. While this study provides evidence of some
effects at lower PM2.5 concentrations, overall there is inconsistent evidence for changes in lung
function and inflammation in other controlled human exposure studies evaluated in the 2019 ISA
(U.S. EPA, 2019, sections 5.1.7., 5.1.2.3.3, and 6.1.11.2.1; U.S. EPA, 2021a, section 3.3.1).
Taken together, these controlled human exposure studies support biological plausibility
for the serious cardiovascular and respiratory effects that have been linked with ambient PM2.5
exposures and seen in epidemiologic studies (U.S. EPA, 2019, Chapter 6). However, while these
studies are important in establishing biological plausibility, it is unclear how the results alone
and the importance of the effects observed in these studies, particularly in studies conducted at
near-ambient PM2.5 concentrations, should be interpreted with respect to adversity to public
health. For example, impaired vascular function, the effect identified as most consistent across
studies (U.S. EPA, 2019, section 6.1.13.2), can signal an intermediate effect along the potential
biological pathways for cardiovascular effects following short-term exposure to PM2.5 and show
a role for exposure to PM2.5 leading to potential worsening of IHD and heart failure followed
potentially by ED visits, hospital admissions, or mortality (U.S. EPA, 2019, section 6.1 and
Figure 6-1). However, just observing the occurrence of impaired vascular function alone does
not clearly suggest an adverse health outcome. Additionally, associated judgments regarding
adversity or health significance of measurable physiological responses to air pollutants have been
informed by guidance, criteria or interpretative statements developed within the public health
community, including the American Thoracic Society (ATS) and the European Respiratory
Society (ERS), which cooperatively updated the ATS 2000 statement What Constitutes an
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Adverse Health Effect of Air Pollution (ATS, 2000) with new scientific findings, including the
evidence related to air pollution and the cardiovascular system (Thurston et al., 2017).18 With
regard to vascular function, the ATS/ERS statement considers the adversity of both chronic and
acute reductions in endothelial function. While the ATS/ERS statement concluded that chronic
endothelial and vascular dysfunction can be judged to be a biomarker of an adverse health effect
from air pollution, they also conclude that "The health relevance of acute reductions in
endothelial function induced by air pollution is less certain" (Thurston et al., 2017). This is
particularly informative to our consideration of the controlled human exposure studies which are
short-term in nature (i.e., ranging from 2- to 5-hours), including those studies that are conducted
at near-ambient PM2.5 concentrations.
Nonetheless, we note the findings in several of these controlled human exposure studies
conducted at near-ambient PM2.5 concentrations and the potential of these studies to provide
some insight into what these controlled human exposure studies may indicate regarding short-
term exposure to peak PM2.5 concentrations and how those relate to ambient PM2.5
concentrations in areas that meet the primary PM2.5 standards. As such, we focus on 2-hour
exposures (the exposure window most often utilized) and consider the degree to which 2-hour
ambient PM2.5 concentrations in locations meeting the current primary standards are likely to
exceed the 2-hour exposure concentrations at which statistically significant effects are reported
in multiple studies for one or more indicators of cardiovascular function. To this end, we refer to
Figure 2-19 (Chapter 2, section 2.3.2.2.3), which presents the frequency distribution of 2-hour
average PM2.5 concentrations from all FEM PM2.5 monitors in the U.S. for 2017-2019. At sites
meeting the current primary PM2.5 standards, most 2-hour concentrations are below 10 ng/m3,
and almost never exceed 30 |ag/m3. The extreme upper end of the distribution of 2-hour PM2.5
concentrations is shifted higher during the warmer months (April to September, denoted by red
bars in Figure 2-19), generally corresponding to the period of peak wildfire frequency in the U.S.
At sites meeting the current primary standards, the highest 2-hour concentrations measured
almost never occur outside of the period of peak wildfire frequency (i.e., 99.9th percentile of 2-
hour concentrations is 62 ng/in3 during the warm season). Most of the sites measuring these very
18 The ATS/ERS described its 2017 statement as one "intended to provide guidance to policymakers, clinicians and
public health professionals, as well as others who interpret the scientific evidence on the health effects of air
pollution for risk management purposes" and further notes that "considerations as to what constitutes an adverse
health effect, in order to provide guidance to researchers and policymakers when new health effects markers or
health outcome associations might be reported in future." The most recent policy statement by the ATS, which
once again broadens its discussion of effects, responses and biomarkers to reflect the expansion of scientific
research in these areas, reiterates that concept, conveying that it does not offer "strict rules or numerical criteria,
but rather proposes considerations to be weighed in setting boundaries between adverse and nonadverse health
effects," providing a general framework for interpreting evidence that proposes a "set of considerations that can
be applied in forming judgments" for this context (Thurston et al., 2017).
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high concentrations are in the northwestern U.S. and California (see Appendix A, Figure A-l),
where wildfires have been relatively common in recent years. When the typical fire season is
excluded from the analysis (blue in Figure 2-19), the extreme upper end of the distribution is
reduced (i.e., 99.9th percentile of 2-hour concentrations is 55 ng/in3),19 Given these results, we
conclude that PM2.5 exposure concentrations evaluated in most of these controlled human
exposure studies are well-above the 2-hour ambient PM2.5 concentrations typically measured in
locations meeting the current primary standards.
Animal Toxicology Studies
The 2019 ISA relies on animal toxicology studies to support the plausibility of a wide
range of PIVh.s-related health effects. While animal toxicology studies often examine more
severe health outcomes and longer exposure durations than controlled human exposure studies,
there is uncertainty in extrapolating the effects seen in animals, and the PM2.5 exposures and
doses that cause those effects, to human populations. We consider these uncertainties when
evaluating what the available animal toxicology studies may indicate with regard to the current
primary PM2.5 standards.
Most of the animal toxicology studies assessed in the 2019 ISA have generally examined
short-term exposures to PM2.5 concentrations from 100 to >1,000 |j,g/m3 and long-term exposures
to concentrations from 66 to >400 |j,g/m3 (e.g., see U.S. EPA, 2019, Table 1-2). Two exceptions
are a study reporting impaired lung development following long-term exposures (i.e., 24 hours
per day for several months prenatally and postnatally) to an average PM2.5 concentration of 16.8
Hg/m3 (Mauad et al., 2008) and a study reporting increased carcinogenic potential following
long-term exposures (i.e., 2 months) to an average PM2.5 concentration of 17.7 |j,g/m3 (Cangerana
Pereira et al., 2011). These two studies demonstrate serious effects following long-term
exposures to PM2.5 concentrations similar to the ambient concentrations reported in some PM2.5
epidemiologic studies (U.S. EPA, 2019, Table 1-2), though still above the ambient
concentrations likely to occur in areas meeting the current primary standards. However, noting
uncertainty in extrapolating the effects seen in animals, and the PM2.5 exposures and doses that
cause those effects to human populations, animal toxicology studies are of limited utility in
informing decisions on the public health protection provided by the current or alternative
primary PM2.5 standards. As such, the animal toxicological studies are most useful in providing
further evidence to support the biological mechanisms and plausibility of various adverse effects.
19 Similar analyses of 4-hour and 5-hour PM2 5 concentrations are presented in Appendix A, Figure A-2 and Figure
A-3, respectively.
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3.3,3,2 Ambient PM Concentrations in Locations of Epidemiologic Studies
As summarized in section 3.1.1 above, epidemiologic studies examining associations
between daily or annual average PM2.5 exposures and mortality or morbidity represent a large
part of the evidence base supporting several of the 2019 ISA's "causal" and "likely to be causal"
determinations and provide further support for these associations as assessed in the draft
ISA Supplement. In this section, we consider the ambient PM2.5 concentrations present in areas
where epidemiologic studies have evaluated associations with mortality or morbidity, and what
such concentrations may indicate regarding the primary PM2.5 standards. As noted in section 3.2,
the use of information from epidemiologic studies to inform conclusions on the primary PM2.5
standards is complicated by the fact that such studies evaluate associations between distributions
of ambient PM2.5 and health outcomes, and do not identify the specific exposures that can lead to
the reported effects. Rather, health effects can occur over the entire distribution of ambient PM2.5
concentrations evaluated, and epidemiologic studies do not identify a population-level threshold
below which it can be concluded with confidence that PM-associated health effects do not occur
(U.S. EPA, 2019, section 1.5.3). To address these issues, we consider the following question:
• To what extent does the evidence from epidemiologic studies that have evaluated
associations with mortality or morbidity provide support for adverse effects
occurring following PM2.5 exposures?
In the absence of discernible thresholds, we consider what information can be provided
from epidemiologic studies. In particular, to address the question above, we consider the study-
reported ambient PM2.5 concentrations reflecting estimated exposure with a focus on the middle
portion of the PM2.5 air quality distribution, which provides the strongest support for reported
health effect associations. The section below discusses the key epidemiologic studies available in
this reconsideration and observations from these studies to inform preliminary conclusions on the
primary PM2.5 standards.
3.3.3.2.1 PM2.5 Air Quality Distributions Associated with Mortality or Morbidity in Key
Epidemiologic Studies
In this section, we consider the PM2.5 air quality distributions associated with mortality or
morbidity in key epidemiologic studies. In previous reviews, the decision framework used to
judge adequacy of the existing PM2.5 standards, and what levels of any potential alternative
standards should be considered, placed significant weight on epidemiologic studies that assessed
associations between PM2.5 exposure and health outcomes that were most strongly supported by
the body of scientific evidence. In doing so, the decision framework recognized that while there
is no specific point in the air quality distribution of any epidemiologic study that represents a
"bright line" at and above which effects have been observed and below which effects have not
been observed, there is significantly greater confidence in the magnitude and significance of
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observed associations for the part of the air quality distribution corresponding to where the bulk
of the health events in each study have been observed, generally at or around the mean
concentration. This is the case both for studies of daily PM2.5 exposures and for studies of annual
average PM2.5 exposures.
Studies of daily PM2.5 exposures examine associations between day-to-day variation in
PM2.5 concentrations and health outcomes, often over several years. While there can be
considerable variability in daily exposures over a multi-year study period, most of the estimated
exposures reflect days with ambient PM2.5 concentrations around the middle of the air quality
distributions examined (i.e., "typical" days rather than days with extremely high or extremely
low concentrations). Similarly, for studies of annual PM2.5 exposures, most of the estimated
exposures reflect annual average PM2.5 concentrations around the middle of the air quality
distributions examined. In both cases, epidemiologic studies provide the strongest support for
reported health effect associations for this middle portion of the PM2.5 air quality distribution,
which corresponds to the bulk of the underlying data, rather than the extreme upper or lower
ends of the distribution. Consistent with this, as noted above in section 3.3.1.1, several
epidemiologic studies report that associations persist in analyses that exclude the upper portions
of the distributions of estimated PM2.5 exposures, indicating that "peak" PM2.5 exposures are not
disproportionately responsible for reported health effect associations.
An example of the relationship between data density and reported health effect
associations is illustrated in Figure 3-2 below (from Lepeule et al., 2012, Figure 1 in
supplemental material; U.S. EPA, 2019, Figure 6-26). For the years 1974 to 2009, Lepeule et al.
(2012) report a positive and statistically significant association between estimated long-term
PM2.5 exposures and cardiovascular mortality in six U.S. cities. Based on a visual inspection of
the concentration-response function reported in this study (i.e., presented in Figure 3-2), 95%
confidence intervals are narrowest for long-term PM2.5 concentrations near the overall mean
concentration reported in the study (i.e., 15.9 |j,g/m3). Confidence intervals widen at lower and
higher long-term PM2.5 concentrations, particularly at concentrations < -10 |j,g/m3 and > -20
Hg/m3. This widening in the confidence intervals is likely due in part to the comparative lack of
data at concentrations approaching the lower and upper ends of the air quality distribution (i.e.,
exposure estimates are indicated by hash marks on the horizontal axis).
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Cardiovascular mortality
10 15 20 25 30 35 40
PM2.s(ng/m3)-moving average
1-3 years
Figure 3-2. Estimated concentration-response function and 95% confidence intervals
between PM2.5 and cardiovascular mortality in the Six Cities Study (1974-2009) (from
Lepeule et al., 2012, supplemental material, figure 1; Figure 6-26 in U.S. EPA, 2019).
Similar to the information presented in Figure 3-2, other studies have also reported that
confidence intervals around concentration-response functions are relatively narrow at PM2.5
concentrations around the overall mean concentrations reported by those studies, likely reflecting
high data density in the middle portions of the distributions (e.g., Crouse et al., 2015; Villeneuve
et al., 2015; Shi et al., 2016 as discussed in U.S. EPA, 2019, section 11.2.4). Thus, consistent
with the approaches in the 2012 and 2020 reviews (78 FR 3161, January 15, 2013; U.S. EPA,
2011, sections 2.1.3 and 2.3.4.1; 85 FR 82716-82717, December 18, 2020; U.S. EPA, 2020,
sections 3.1.2 and 3.2.3), in this reconsideration, we use study-reported means (or medians) of
daily and annual average PM2.5 concentrations over the entire study period as proxies for the
middle portions of the air quality distributions, over which studies generally provide strong
support for reported associations. As described further below, when considering the PM2.5 air
quality distributions in epidemiologic studies in this section, we focus on PM2.5 concentrations
around these overall means (including concentrations somewhat below the means (e.g., 25th and
10th percentiles)).
In evaluating the overall study-reported means, the focus is on the form, averaging time
and level of the current annual PM2.5 standard. Consistent with the approaches used in the 2012
and 2020 reviews (78 FR 3161-3162, January 15, 2013; 85 FR 82716-82717, December 18,
2020), this is because the annual standard has been utilized as the primary means of providing
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public health protection against the bulk of the distribution of short- and long-term PM2.5
exposures. Thus, the evaluation of the study-reported mean concentrations from key
epidemiologic studies lends itself best to evaluating the adequacy of the annual PM2.5 standard
(rather than the 24-hour standard with its 98th percentile form). This is true for the study-reported
means from both long-term and short-term epidemiologic studies, recognizing that the overall
mean PM2.5 concentrations reported in studies of short-term (24-hour) exposures reflect averages
across the study population and over the years of the study. Thus, mean concentrations from
short-term studies reflect long-term averages of 24-hour PM2.5 exposure estimates. In this way,
our examination aims to evaluate the protection provided by the annual PM2.5 standard against
the exposures that provide strong support for associations with mortality and morbidity in key
epidemiologic studies. We note that the protection provided by the annual standard is evaluated
in partnership with that provided by the 24-hour standard, with its 98th percentile form, which
aims to provide supplemental protection against the short-term exposures to peak PM2.5
concentrations that can occur in areas with strong contributions from local or seasonal sources,
even when overall mean PM2.5 concentrations remain relatively low.
As in past reviews, application of a decision framework based on assessing means of key
epidemiologic studies must also consider how the study means were computed and how these
values compare to the annual standard metric (including the level, averaging time and form) and
the use of the monitor with the highest PM2.5 design value in an area for compliance. In the 2012
review, it was recognized that the key epidemiologic studies computed the study mean using an
average across monitor-based PM2.5 concentrations. As such, the Agency noted that this decision
framework applied an approach of using maximum monitor concentrations to determine
compliance with the standard, while selecting the standard level based on consideration of
composite monitor concentrations. Further, the Agency included analyses (Hassett-Sipple et al.,
2010; Frank, 2012) that examined the differences in these two metrics (i.e., maximum monitor
concentrations and composite monitor concentrations) across the U.S. and in areas included in
the key epidemiologic studies and found that the maximum design value in an area was generally
higher than the monitor average across that area, with that amount varying based on location and
concentration. This information was taken into account in the Administrator's final decision in
selecting a level for the primary annual PM2.5 standard the 2012 review and discussed more
specifically in her considerations on adequate margin of safety.
As an initial matter, in this reconsideration, we note that there are a substantial number of
different types of studies available since the 2012 review, included in both the 2019 ISA and the
draft ISA Supplement. While the key epidemiologic studies in the 2012 review were all monitor-
based studies, the newer studies include hybrid modeling approaches which have emerged in the
epidemiologic literature as an alternative to approaches that only use ground-based monitors to
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estimate exposure. As assessed in the 2019 ISA and draft ISA Supplement, a substantial number
of epidemiologic studies used hybrid model-based methods in evaluating associations between
PM2.5 exposure and health effects. Hybrid model-based studies employ various fusion techniques
that combine ground-based monitored data with air quality modeled estimates and/or information
from satellites to estimate PM2.5 exposures. While these studies provide a broader estimation of
PM2.5 exposures compared to monitor-based studies (i.e., PM2.5 concentrations are estimated in
areas without monitors), the hybrid modeling approaches result in study-reported means that are
more difficult to relate to the annual standard metric and to the use of maximum monitor design
values to assess compliance. In addition, to further complicate the comparison, when looking
across these studies, we find variations in how exposure is estimated between such studies, and
thus, how the study means are calculated. Two important variations across studies include: (1)
variability in spatial scale used (i.e., averages computed across the national (or large portions of
the country) versus a focus on only CBS As) and (2) variability in exposure assignment methods
(i.e., averaging across all grid cells, averaging across a scaled up area like a ZIP code, and
population weighting). Because of these differences, the application of any decision framework
in considering the study-reported mean PM2.5 concentrations, given the current state of the
science, is more complicated than the approaches used in past reviews. In the sections that
follow, we provide detailed analyses of the different air quality and exposure estimation methods
in the used in the key epidemiologic studies and consider how those differences translate into
comparisons between the mean PM2.5 concentrations reported in the studies and the level of the
primary annual PM2.5 standard.
• What are the epidemiologic studies assessed in the 2019 ISA and draft ISA
Supplement that have the potential to be most informative in reaching preliminary
conclusions on the primary PM2.5 standards?
To evaluate the PM2.5 air quality distributions in key studies in this draft PA
reconsideration, we first identify the epidemiologic studies assessed in the 2019 ISA and draft
ISA Supplement that have the potential to be most informative in reaching preliminary
conclusions on the primary PM2.5 standards. As with the experimental studies discussed above,
we focus on epidemiologic studies that provide strong support for "causal" or "likely to be
causal" relationships with PM2.5 exposures in the 2019 ISA. We focus on the health effect
associations that are determined in the 2019 ISA and draft ISA Supplement to be consistent
across studies, coherent with the broader body of evidence (e.g., including animal and controlled
human exposure studies), and robust to potential confounding by co-occurring pollutants and
other factors. We emphasize multicity/multistate studies that examine health effect associations
in the U.S. or Canada, as such studies examine potential associations over large geographic areas
with diverse atmospheric conditions and population demographics. Additionally, studies
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examining associations outside the U.S. or Canada reflect air quality and exposure patterns that
may be less typical of the U.S., and thus less likely to be informative for purposes of reviewing
the NAAQS.20 We note that, while we consider studies from Canada in our evaluation of the
epidemiologic evidence, there are considerable differences between studies conducted in the
U.S. and in Canada, particularly those related to population densities, PM2.5 concentration
gradients, and source distributions in the two countries. As a result, while we consider the
information from studies conducted in Canada, we generally place a greater emphasis on U.S.-
based studies.
Figure 3-3 to Figure 3-6 below summarize information from U.S. and Canadian studies
that are assessed in the 2019 ISA and draft ISA Supplement and that meet these criteria. For each
study, Figure 3-3 to Figure 3-6 present the cohort and/or geographic area examined, the approach
used to estimate PM2.5 exposures (i.e., monitored or predicted with hybrid modeling methods21),
the study years during which health events occurred, the years of PM2.5 air quality data used to
estimate exposures, and the effect estimate22 with 95% confidence intervals (per 5 |j,g/m3 for
long-term exposures; 10 |j,g/m3 for short-term exposures). When available, these figures also
include the overall means (or medians if means are not available) of the short- or long-term
PM2.5 exposure estimates reported by the study. Figure 3-3 and Figure 3-4 summarize
information from studies of long-term PM2.5 exposures. Figure 3-5 and Figure 3-6 summarize
information from studies of short-term PM2.5 exposures.
20 This emphasis on studies conducted in the U.S. or Canada is consistent with the approach in the 2012 and 2020
reviews of the PM NAAQS (U.S. EPA, 2011, section 2.1.3; U.S. EPA, 2020, section 3.2.3.2.1).
21 As discussed further below, and in Chapter 2, hybrid methods incorporate data from several sources, often
including satellites and models, in addition to ground-based monitors.
22 The effect estimates presented in the forest plot figures (Figure 3-3 to Figure 3-6) show the associations of long-
or short-term PM2 5 exposures with health endpoints presented either as hazard ratio or odds ratio or relative risk
(for which the bold dotted vertical line is at 1), or as per unit or percent change (for which the bold dotted vertical
line is at 0).
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All-cause mortality (U.S.)
Exposure Air Quality
Proxy Citation Cohort Health Data Data Reported PM Mean (Range)(ug/m3)
Modelled Dietal., 2017b Medicare 2000-2012 2000-2012 11.0 (5th and 95th: 6.21-15.64)
•
Dominicietal., 2019 Medicare 2000-2012 2000-2012 ll.O(NR)
•-
Nurses
Elliott et al., 2020 ,, _ 1988-2008 1988-2007 13.7 (NR)
Health v '
Nurses
Hart et a 1., 2015 , 2000-2006 1999-2006 12.0 (NR)
Health v
Lefler et al., 2019 NHIS 1987-2015 1988-2015 10.7 (NR)
~
Pope et a 1., 2015 ACSCPS-II 1982-2004 1999-2004 12.6(1.0-28.0)
-
Pope et al., 2019 NHIS 1986-2015 1999-2015 10.7(2.5-19.2)
—
Nurses
Puett etal., 2009 ,, _ 1992-2002 1988-2002 13.9(5.8-27.6)
Health v '
Puett etal., 2011 Health 1989-2003 1988-2003 17.8 (NR)
Professionals
Shi etal., 2016 Medicare 2003-2008 2003-2008 8.12(0.8-20.22)
—
Thurston et al., 2016 NIH-AARP 2000-2009 2000-2008 12.2(2.9-28.0)
Turner etal., 2016 ACSCPS-II 1982-2004 1999-2004 12.6(1.4-27.9)
-
Wang etal., 2017 Medicare 2000-2013 2000-2013 NR (Median: 10.7) (6.0-20.6)
-•
Wang et al., 2020 Medicare 2000-2008 2000-2008 10.3 (NR)
-•
Weichenthal et al.. Health 1993-2009 2001-2006 Iowa: 8.8; North Carolina: 11.1 (NR)
2014 y v '
Wu etal., 2020 Medicare 2000-2016 2000-2016 9.8 (NR)
•-
Monitor Eum et al., 2018 Medicare 2000-2012 2000-2012 Overall: 11.7 (NR)
Central region: 9.9 (NR)
Eastern region: 12.3 (NR)
Western region: 11.5 (NR)
•
•
•
•
Goss etal., 2004 U.S. Cystic 1999.2000 2000 13.7 (NR)
Fibrosis x '
Nurses
Hart et al., 2015 ,, _ 2000-2006 2000-2006 12.7 (NR)
Health v '
Kiomourtzoglou et al., Mecjjcare 2000-2010 2000-2010 12.0 (Mean Range: 9.0-13.0) (NR)
, Harvard „ 1974-2009:15.9; 2000 onwards mean
Lepeu e et a ., 2012 2001-2009 1979-2009 „ r
v Six-City range: <15-<18 (NR)
Lipfert etal., 2006 Veterans 1997-2001 1999-2001 14.3 (NR)
•
Zeger et al., 2008 MCAPS 2000-2005 2000-2005 Central region: NR (Median: 10.7) (NR)
Eastern region: NR (Median: 14.0) (NR)
Western region: NR (Median: 13.1) (NR)
0.95 1.00 1.05 1.10 1.15 1.20 1.25 1.30 1.35
Hazard Ratio (95% CI) *
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All-cause mortality (Canada)
Exposure Air Quality Reported PM Mean
Proxy Citation Cohort Health Data Data (Range)(ug/m3)
Modelled Cakmaketal.,2018 CanCHEC 1991-2011 1984-2011 6.5(1.2,24.1)
christidis et al., 2019 mCHHS 2000-2016 1998-2015 5.9(0.4-17.2)
Crouse et al., 2012 CanCHEC 1991-2001 2001-2006 8.7(1.9-19.2)
-
Crouse et al„ 2015 CanCHEC 1991-2006 1984-2006 8.9(0.9-17.6)
-
Crouse et al., 2019 CanCHEC 2001-2011 1998-2010 7.2 (1-year 1-km mean) (0.0-20.0)
7.4 (3-year 1-km mean) (0.0-20.0)
8.0 (8-year 1-km mean) (0.3-18.4)
—
Erickson et al., 2020 ^anCHEC 2001-2016 1998-2016 9.3 (Immigrant) (NR)
(Immigrant) 1 '
CanCHEC (Non- 2001-2016 1998-2016 7.5 (Non-Immigrant) (NR)
immigrant) v 'v '
—
Pappin et al„ 2019 CanCHEC 1991-2016 1988-2015 7.9 (Year 1991) (0.4-20.0)
7.2 (Year 1996) (0.4-20.0)
6.7 (Year 2001) (0.4-18.5)
-•
-•
-•
Pinaultetal.,2016 CCHS 2000-2011 1998-2011 6.3(1.0-13.0)
Pinault et al., 2017 CanCHEC 1991-2011 1998-2011 7.4 (<0.01-20.0)
—
Zhang et al., 2021 Ontario Health 2009_2017 2000-2016 7.8 (NR)
Monitor Crouse et al., 2012 CanCHEC 1991-2001 1987-2001 11.2 (NR)
—
Weichenthaletal., CanCHEC 1991-2009 1998-2009 9.8(4.74-13.62)
20169
0.90 0.95 1.00 1.05 1.10 1.15 1.20 1.25 1.30 1.35
Hazard Ratio (95% CI) *¦
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CVD mortality
Exposure Health Air Quality Reported PM Mean
Proxy Country Citation Cohort Data Data (Range)(ug/m3) Health Outcome
, „ , Hayes etal., 1995/96- , w , CVD mortality
Modelled U.S. * NIH-AARP „ ' 1980-2010 NR(Median: 13.3) (2.9-28.0) _
2020 2011 v 'v ' Age 50-71
-
Jerrettetal., , . IHD mortality
ACS CPS-11 1982-2004 2002-2004 12.0(1.5-26.6) .
2016 v ' Age 30+
—
Pope etal., ACSCPS-II 1982-2004 1999-2004 12.6(1.0-28.0) CVDmortality
2015 Age30+
IHD mortality
Age 30+
Other CVD-
CBVD Age 30+
—
Pope etal., , . CVDmortality
2QW19 NHIS 1986-2015 1999-2015 10.7(2.5-19.2) Age 18-84
—
Thurston et . . CVDmortality
. NIH-AARP 2000-2009 2000-2008 12.2(2.9-28.0) .
al., 2016 v ' Age 50-71
—
Turner etal., ACSCPS-II 1982-2004 1999-2004 12.6(1.4-27.9) CVDmortality
Age 30+
2016 y
IHD mortality
Age 30+
Other CVD-
CBVD Age 30+
-
Wang etal., .. . . CVDmortality
" Medicare 2000-2008 2000-2008 10.3 (NR) .
2020 x ' Age 65-120
*
Weichenthal Iowa: 8.8; North Carolina: 11.1
etal 2014 A9Health 1993-2009 2001-2006 CVDmortality
Chen etal., . , CVDmortality
Canada EFFECT RCT 1999-2011 2001-2010 10.7 (NR) .
2016 v ' Age 35+
Chen etal., , . CVDmortality
ONPHEC 2001-2016 2000-2016 8.6 (NR) .
2020 v ' Age 35-85
~
Crouseetal., , . CVDmortality
„„„ CanCHEC 1991-2001 2001-2006 8.7(1.9-19.2) A
2012 v ' Age 25+
-
Crouseetal., , . CVDmortality
2015 CanCHEC 1991-2006 1984-2006 8.9(0.9-17.6) Age 25-90
>
Crouseetal., , , . . . CVDmortality
2019 CanCHEC 2001-2011 1998-2010 7.4 (3-year 1-km mean) (0.0-20.0) Agg 25 8g
—
Pinaultetal., , . CVDmortality
CCHS 2000-2011 1998-2011 6.3(1.0-13.0) .
2016 x Age 25-90
Pinaultetal., , v CVDmortality
2017 CanCHEC 1991-2011 1998-2011 7.4 (<0.01-20.0) Age 25-89
-
, CVD mortality
Pinaultetal., CanCHEC 2001-2011 1998-2012 7.4(NR) .
Age 25-90
2018 a
-
CVD mortality
mCHHS 2001-2008 1998-2013 6.4 (NR) .
v ' Age 25-90
Villeneuveet CNBSS 1980-2005 1998-2006 9.1(1.3-17.6) ?!°!?l,ty
Age 40-59
al., 2015 a
IHD mortality
Age 40-59
Zhang etal., Ontario , . CVDmortality
,, n.u~ ^ 2009-2017 2000-2016 7.8 (NR) .
2021 Health Study v ' Age 30+
Monitor U.S. 2011^31 ' TriPS 1985-2000 2000 14.1 (NR) CVDmortality
Lepeule et al., Harvard 1974-2009:15.9; 2000 onwards CVD mortality
2012 Six-City 2001-2009 1979-2009 mean range: <15-<18 (NR) Age 25-74
Miller, etal. , . CVDmortality
2007 WHI 1994-2002 2000 13.5(3.4-28.3) Age 5Q.79
Weichenthal , , IHD mortality
Canada . , CanCHEC 1991-2009 1998-2009 9.8(4.74-13.62) . _c
etal., 2016a v ' Age 25-89
0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8
Hazard Ratio (95% CI) +
October 2021
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-------
Respiratory mortality
Exposure
Proxy
Country
Citation
Cohort
Health
Data
Air Quality
Data
Reported PM Mean
(Range)(ug/m3)
Modelled
U.S.
Pope etal., 2015
ACS CPS-II
1982-2004
1999-2004
12.6 (1.0-28.0)
Thurston etal., 2016
NIH-AARP
2000-2009
2000-2008
12.2 (2.9-28.0)
•—
Turner etal., 2016
ACS CPS-II
1982-2004
1999-2004
12.6 (1.4-27.9)
-
Canada
Crouseetal., 2015
CanCHEC
1991-2006
1984-2006
8.9 (0.9-17.6)
Crouse etal., 2019
CanCHEC
2001-2011
1998-2010
7.4 (3-year 1-km mean)
(0.0-20.0)
—
Pinaultetal., 2016
CCHS
2000-2011
1998-2011
6.3 (1.0-13.0)
Pinaultetal., 2017
CanCHEC
1991-2011
1998-2011
7.4 (<0.01-20.0)
~
Zhang etal., 2021
Ontario
2009-2017
2000-2016
7.8 (NR)
Health Study
Monitor
U.S.
Hart etal., 2011
TrlPS
1985-2000
2000
14.1 (NR)
Canada
Weichenthal etal.,
2016a
CanCHEC
1991-2009
1998-2009
9.8 (4.74-13.62)
0.8
1.0 1.2
1.4 1.6 1.8 2.0
Hazard Ratio (95% CI) +
2.2
Lung cancer mortality
Exposure Health Air Quality Reported PM Mean
Proxy Country Citation Cohort Data Data (Range)(ug/m3)
...... Turner etal.,
Modelled U.S. 2016 ACS CPS-II 1982-2004 1999-2004 12.6(1.4-27.9)
-
crouseetal., ^ .
Canada 2015 CanCHEC 1991-2006 1984-2006 8.9(0.9-17.6)
~-
Pinaultetal.,
2016 CCHS 2000-2011 1998-2011 6.3(1.0-13.0)
Villeneuveet
al 2015 CNBSS 1980-2005 1998-2006 9.1(1.3-17.6)
^ Hart etal., , ,
Monitor U.S. Trips 1985-2000 2000 14.1 (NR)
Krewski etal-, 1979-1983, 1979-1983: 21.2; 1999-2000:14.0
2009 ACSCPS-" 1982-2000 1999-2000 (NR)
-
Laden etal., Harvard 1979-1987; . . . .
2006 Six-City 1974-1998 lg85.19g8 16.4 (Mean Range: 10.2-29.0) (MR)
Lepeuleetal.. Harvard 1974-2009:15^9 2000onwards
2012 Six-City mean range: <15-<18 (NR)
Thurston et . .
al 2013 ACS CPS-II 1982-2004 2000-2005 14.2 (NR)
Weichenthal
Canada eta| 2016a CanCHEC 1991-2009 1998-2009 9.8(4.74-13.62)
0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6
Hazard Ratio (95% CI) *
Figure 3-3. Epidemiologic studies examining associations between long-term PM2.5
exposures and mortality.
Asthma incidence
Exposure
Proxy Health Air Quality Reported PM Mean
(group) Country Citation Cohort Data Data (Range)(ug/m3)
Modelled Canada et3QICDSS 1996-2011 2001-2006 9.86(NR)
•
Monitor U.S. McConnell et al., CHS 2003-2005 2003-2004 13.9(6.3-23.7)
2010 1 '
NishimuraetaL, Mean Range: 8.1-17.0
GALA 11/SAGE 11 1986-2003 1986-2003 y
2013 ' (NR)
1.0 1.5 2.0
Odds Ratio (95% Cl)
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Lung cancer incidence
Exposure Health Air Quality Reported PM Mean
Proxy Country Citation Cohort Data Data (Range)(ug/m3)
Modelled Canada Hi'STadetal-< NECSS 1994-1997 1975-1994 11.9 (NR)
2013 1 '
2016Zak8tal ' CNBSS 1980-2004 1993-2006 9.1(1.3-17.6)
Monitor U.S. Gharibv=nd 6^ AHSMOG-2 2002-2011 2000-2001 12.9 (NR)
a!.r 2016 v '
Nursss
Puett et ai„ 2014 , , 1994-2010 1988-2007 13.1 (NR)
Health
1.0 1.1 1.2 1.3 1.4
Hazard Ratio (95% CI)
Lung development
Exposure Health Air Quality Reported PM Mean
Proxy Country Citation Cohort Data Data (Range)(ug/m3) Health Outcome
Monitor U.S. Breton etal., CHS 1993-2000,1994-2004 Mean Range: Lung Development (Change)
2011 1996-2004 6.0-28.0 (NR) ™C Age 10-18
Lung Development (Change) -
MMEF Age 10-18
Lung Development (Change)-
FEV1 Age 10-18
Gail derm an CHS 1993-2000 1994-2000 Mean Range: Lung Development (Change)-
etal., 2004 6.0-28.0 (NR) ™C Age 10-18
Lung Development (Change) -
MMEF Age 10-18
Lung Development (Change)-
FEV1 Age 10-18
-80 -60 -40 -20 0
ml Change in Growth (95% CI)
Lung function
Exposure Health Air Quality Reported PM Mean
Proxy Country Citation Cohort Data Data (Range)(ug/m3) Health Outcome
Monitor U.S. Urmanetal., CHS 2002-2007 2002-2007 6.0-28.0 (NR) Lung Function Decline (%)-
2014 FEV1 Age 5-7
Lung Function Decline (%)-
FVC Age 5-7
-1.5 -1.0 -0.5 0.0
Percent Difference (95% CI)
Figure 3-4. Epidemiologic studies examining associations between long-term PM2.5
exposures and morbidity.
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All-cause mortality
Exposure
Proxy
Country
Citation
Cohort
Health Data
Reported PM Mean
(Range)(ug/m3)
Modelled
U.S.
Dietal., 2017a
Medicare
2000-2012
11.6 (5th and 95th: 6.21-
15.64)
—
Lee etal., 2015b
State Dept
2007-2011
11.1 (0.02- 86.2)
Shi etal., 2016
Medicare
2003-2008
8.21 (0.8-20.22)
Monitor
Canada
Burnett etal., 2003
Statistics Canada
1986-1996
13.3 (NR)
Burnett et al., 2004
Statistics Canada
1981-1999
12.8 (NR)
Lavigneetal., 2018
Canadian Mortality
Database
1998-2011
8.8 (<1-98.15)
Liu etal., 2019
MCC
1986-2011
9.3 (NR)
Shin etal., 2021
National Vital
Statistics Database
1984-2012
8.0 (Warm season); 6.0 (Cold
season) (NR)
U.S.
Baxter et al., 2017
NCHS
2001-2005
Cluster Mean Range: 12.2-14.1
(NR)
Dai et al., 2014
NCHS
2000-2006
13.3 (NR)
Dominici etal., 2007
NMMAPS
1999-2000
NR (NR)
Franklin etal., 2007
NCHS/State Dept
1997-2002
15.6 (NR)
Franklin etal., 2008
NCHS/State Dept
2000-2005
14.8 (NR)
Klemm etal., 2003
Harvard Six-City
1979-1988
14.7 (Median: 9.0) (NR)
Krall et al., 2013
NCHS
2000-2005
13.6 (NR)
Liu etal., 2019
MCC
1987-2006
12.4 (NR)
Zanobetti and Schwartz,
2009
NCHS
1999-2005
13.2 (NR)
Zanobetti etal., 2014
Medicare
1999-2010
Mean Range: 4.37-17.97 (NR)
0.0 0.5 1.0 1.5 2.0
2.5 3.0
Percent Increase (95% CI)
CVD mortality
Exposure
Proxy
Country
Citation
Cohort
Health Data
Reported PM Mean
(Range)(ug/m3)
Modelled
U.S.
Lee etal., 2015b
State Dept
2007-2011
11.1 (0.02- 86.2)
Monitor
U.S.
Dai et al., 2014
NCHS
2000-2006
13.3 (NR)
Franklin et al., 2007
NCHS/State
1997-2002
15.6 (NR)
Dept
Franklin etal., 2008
NCHS/State
2000-2005
14.8 (NR)
Dept
Zanobetti and
Schwartz, 2009
NCHS
1999-2005
13.2 (NR)
0.0 0.5 1.0 1.5 2.0
Percent Increase (95% CI)
2.5 3.0
October 2021
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-------
Respiratory mortality
Exposure Reported PM Mean
Proxy Country Citation Cohort Health Data (Range)(ug/m3)
Modelled U.S. Lee etal., 2015b State Dept 2007-2011 11.1(0.02-86.2)
Monitor U.S. Dai etal., 2014 NCHS 2000-2006 13.3 (NR)
Franklin etal., 2007 NCHS/State 1997.2002 15.6 (NR)
Dept v '
Franklin etal., 2008 NCHS/State 200o-2005 14.8 (NR)
Dept v '
Zanobetti and 1999-2005 13.2 (NR)
Schwartz, 2009 v '
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5
Percent Increase (95% CI)
2 Figure 3-5. Epidemiologic studies examining associations between short-term PM2.5
3 exposures and mortality.23
23 As noted above, the overall mean PM2.5 concentrations reported in studies of short-tenn (24-hour) exposures
reflect averages across the study population and over the years of the study. Thus, mean concentrations reflect
long-term averages of 24-hour PM2.5 exposure estimates.
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3-76
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CVD morbidity (U.S.)
Exposure Reported PM Mean
Proxy Endpoint Health Outcome Citation Cohort Health Data (Range)(ug/m3)
Modelled CVD All CVD (First HA) deSouza et al., Medjcajd adu|ts 2000-2012 11.5 (NR)
Age 18+ 2021 v '
All CVD Hospital Wyattetal., j-,SRDS
... ' .. haemodialysis 2008-2014 9.3(0.05-155.16)
re-admission 2020b N '
patients
•
CVD HA Age 65+ 2™0etal'' Medicare 2002-2006 12.3 (IMR)
—
2012 8tal" Medicare 2000-2006 9.6(0.01-72.59)
—
^!?°?eta'' Medicare 2000-2006 11.9 (NR)
2014 v '
—
CHF (First HA) Age deSouza etal., Medicaid adults 2000.2012 11>5(NR)
18+ 2021 v '
*
Kralletal., Mean Range: 10.8-15.4
CHF ED ED visit databases 2002-2008 .....
2018 (NR)
CHF Hospital Wyattetal., hemodialysis 2008-2014 9.3(0.05-155.16)
re-admission 2020b _
patients
—
¦ ¦._ « Kralletal., Mean Range: 10.8-15.4
HD HD ED ED visit databases 2002-2008
2018 (NR)
IHD HA Age 65+ I!'0,0? 8t 3l'' Medicare 2000-2006 11.9 (NR)
y 2014 v '
—
Ml (First HA) Age deSouza et al., .. .. .. , . „„„„„„„„ ^ ^ .
M iriv ' Medicaid adults 2000-2012 11.5 (NR)
18+ 2021 v '
WHI
Stroke ^S^cidence Ages 201^ (Postmenopausal 1993-2012 12.4 (case day) (NR)
women)
-
Fisher etal., nnrr 12.9 (IS: 13.1, HS: 11.9,
HS Ages 53-88 HPFS 1999-2010 ,, . V. . ' „__w' v
2019 Undertermined: 13.7) (NR)
—
IS (First HA) Age deSouzaetal., .. .. ,
„„v ' Medicaid adults 2000-2012 11.5 (NR)
18+ 2021 v '
-
«-.*«« Fisher etal., linrr 12.9 (IS: 13.1, HS: 11.9,
IS Ages 53-88 HPFS 1999-2010 ,, . V. . .
2019 Undertermined: 13.7) (NR)
—
„ , Kralletal., t Mean Range: 10.8-15.4
Stroke ED „ ED visit databases 2002-2008 ..
2018 (NR)
Total stroke Ages Fisher etal., 12.9 (IS: 13.1, HS: 11.9,
53-88 2019 Undertermined: 13.7) (NR)
-
Undetermined Fisher etal., 12.9 (IS: 13.1, HS: 11.9,
HPFS 1999-2010
stroke Ages 53-88 2019 Undertermined: 13.7) (NR)
Dpi 1 pt *i|
Monitor CVD CVD HA Age 65+ „ " Medicare 1999-2005 12.9 (NR)
y 2008 v '
—
Sell et al
" Medicare 2000-2004 14.0 (Median: 11.7) (NR)
2017°0tal" Medicare 2002-2006 12.5 (l\IR)
—
20098tal" Medicare 2000-2006 NR (Median: 11.8) (NR)
HF Heart Failure HA tal" Medicare 1999-2010 12.3(6.4 20.2)
Age 65+
—
2006^'^' Medicare 1999-2002 13.4 (NR)
Zfn™?'et Medicare 2000-2003 15.3 (IMR)
al., 2009 v
IHD IHD HA Age 65+ 2015"' Medicare 1999-2010 12.3(6.4-20.2)
^ooe"'0' 8t a'' Medicare 1999-2002 13.4 (NR)
Ml Ml HA Age 65+ zanobettiet Medicare 2000-2003 15.3 (NR)
al., 2009 v
-1 012 3456
Percent Increase (95% CI) *¦
October 2021 3-77 Draft - Do Not Quote or
-------
CVD morbidity (Canada)
Exposure Proxy
Endpoint
Health Outcome
Citation
Cohort
Health Data
Reported PM Mean
(Range)(ug/m3)
Monitor
Angina
Angina ED
Szyszkowicz et al.,
2009
Hospital
Database
1992-2003
8.3 (NR)
Angina/
Angina/Ml ED
Stieb et al., 2009
Hospital
1992-2003
8.2 (6.7-9.8)
Ml
Database
Ml
Ml ED
Weichenthal et al..
NACRS
2004-2011
6.9 (NR)
2016b
HF
Heart Failure ED
Stieb et al., 2009
Hospital
1992-2003
8.2 (6.7-9.8)
Database
0 2 4 6 8 10 12 14 16
Percent Increase (95% CI) *
Respiratory morbidity
Exposure Proxy Country Endpoint Health Outcome Citation
U.S. COPD COPD HA Age 65+ Kloog etal., 2014
Modelled
Monitor
Canada Asthma Asthma ED stiebetal., 2009
Weichenthal et al., 2016c
COPD COPD ED Stieb etal., 2009
Weichenthal et al., 2016c
U.S. Asthma Asthma ED Age 5-18 Alhanti etal., 2016
Asthma ED Age 65+ Alhanti et al., 2016
Asthma HA Age 65+ Bell etal., 2015
Asthma HA Age 1-9:
Central Valley
Asthma HA Age 1-9:
Yap et al, 2013
Yap et al, 2013
South Coast
Asthma ED & HA Malig et al., 2013
Ostro et al., 2016
COPD COPD HA Age 65+ Bell et al., 2015
Dominici etal., 2006
COPD ED 8 HA Malig et al., 2013
Ostro etal., 2016
Cohort
Medicare
Hospital Database
NACRS
Hospital Database
IMACRS
Hospital Database
Hospital Database
Medicare
Hospital Admissions
Hospital Admissions
Hospital Inpatient and
Outpatient visits
Hospital Inpatient and
Outpatient visits
Medicare
Medicare
Hospital Inpatient and
Outpatient visits
Hospital Inpatient and
Outpatient visits
Health Data
2000-2006
1992-2003
2004-2011
1993-2009
1993-2009
1999-2010
2000-2005
2000-2005
2005-2008
2005-2009
1999-2010
1999-2002
2005-2008
Reported PM Mean
(Range)(ug/m3)
11.9 (NR)
8.2 (6.7-9.8)
7.1 (NR)
8.2 (6.7-9.8)
7.1 (NR)
Mean Range:
11.1-14.1 (NR)
Mean Range:
11.1-14.1 (NR)
12.3 (6.4- 20.2)
Mean Range:
12.8-20.8 (NR)
Mean Range:
14.0-24.6 (NR)
Mean Range:
5.2-19.8 (NR)
16.5 (NR)
12.3 (6.4- 20.2)
13.4 (NR)
Mean Range:
5.2-19.8 (NR)
2005-2009 16.5 (NR)
0.95 1.00 1.05
Relative Risk/ Odds Ratio (95% CI) *
Figure 3-6. Epidemiologic studies examining associations between short-term PM2.5
exposures and morbidity.
• What are the key epidemiologic studies on which the draft PA should focus for
informing preliminary conclusions regarding the current and potential alternative
primary PM2.5 standards? For these key epidemiologic studies, how were the mean
PM2.5 concentrations calculated?
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Based on the information in Figure 3-3 to Figure 3-6, key epidemiologic studies indicate
generally positive and statistically significant associations between estimated PM2.5 exposures
(short- or long-term) and mortality or morbidity across a range of ambient PM2.5 concentrations.
Drawing from the multicity studies in Figure 3-3 to Figure 3-6, we identify the key
epidemiologic studies most informative to our understanding to evaluate the PM2.5 air quality
distributions in key studies in this reconsideration. Key epidemiologic studies are those that
report overall mean (or median) PM2.5 concentrations and for which the years of PM2.5 air quality
data used to estimate exposures overlap entirely with the years during which health events are
reported. For some studies of long-term PM2.5 exposures, exposure is estimated from air quality
data corresponding to only part of the study period, often including only the later years of the
health data, and are not likely to reflect the full ranges of ambient PM2.5 concentrations that
contributed to reported associations.24 While this approach can be reasonable in the context of an
epidemiologic study that is evaluating health effect associations with long-term PM2.5 exposures,
under the assumption that spatial patterns in PM2.5 concentrations are not appreciably different
during time periods for which air quality information is not available (e.g., Chen et al., 2016),
our interest is in understanding the distribution of ambient PM2.5 concentrations that could have
contributed to reported health outcomes. Therefore, we identify studies as key epidemiologic
studies when the years of air quality data and health data overlap in their entirety.
Additionally, for studies that estimate PM2.5 exposure using hybrid modeling approaches,
we also consider the approach used to estimate PM2.5 concentrations and the approach used to
validate hybrid model predictions when determining those studies that we identify as key
epidemiologic studies. Such studies are identified as those that use hybrid modeling approaches
for which recent methods and models were used (e.g., recent versions and configurations of the
air quality models); studies that are fused with PM2.5 data from national monitoring networks
(i.e., FRM/FEM data); and studies that reported a thorough model performance evaluation for
core years of the study.25 While numerous approaches to estimating PM2.5 concentrations in
hybrid modeling studies can be reasonable in the context of an epidemiologic study evaluating
health effect associations with PM2.5 exposures (e.g., in studies that use satellite data in fused
surfaces), our interest is in utilizing the most up to date methods based on surfaces fused with
24 The following studies do not have an overlap between the years of PM2 5 air quality data and the years during
which health effects are reported: Miller et al., 2007; Hart et al., 2011; Thurston et al., 2013; Weichenthal et al.,
2014;; Pope et al., 2015; Villeneuve et al., 2015; Turner et al., 2016; Weichenthal et al., 2016a; Pinault et al.,
2017; Parker et al., 2018; Pope et al., 2019; and Bevan et al., 2021.
25 The following studies do not meet these criteria: Bravo et al., 2017, Crouse et al., 2015; Puett et al., 2009, Puett et
al., 2011, Hystad et al., 2012; Hystad et al., 2013, Hayes et al., 2020; Elliott et al., 2020; Lefler et al., 2019;;
Pappin et al., 2019; Cakmak et al., 2018; Fisher et al., 2019; Sun et al., 2019; McClure et al., 2017; Loop et al.,
2018 ; and Honda etal., 2017.
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33
monitored PM2.5 data in order to inform the consideration of the PM NAAQS, as attainment of
the standards is determined based on PM2.5 monitoring data.
While all of the key epidemiologic studies in the 2012 review relied on ground-based
monitoring information to characterize PM2.5 exposure concentrations, as at the time of the 2020
review, a number of the more recent epidemiologic studies in Figure 3-3 to Figure 3-6 utilized
various "hybrid modeling" approaches that include fusion techniques that combine ground-based
monitored data with air quality modeled estimates and/or information from satellites to estimate
PM2.5 exposures. Furthermore, some studies use various mathematical approaches (e.g.,
population weighting, trimmed mean26) to compute the study-reported mean from the estimated
PM2.5 exposure concentrations. The fact that there are more and different techniques utilized to
characterize exposure in the key epidemiologic studies in this reconsideration highlights the
importance of understanding those techniques and how they compare to each other and to
consider how those differences translate into comparisons between the mean PM2.5
concentrations reported in the studies and the level of the primary annual PM2.5 standard.
As noted above, study-reported mean concentrations in Figure 3-3 to Figure 3-6 were
calculated using different methods. This is an important consideration when comparing mean
concentrations across studies, as the methods used to estimate PM2.5 concentrations can vary
from traditional methods using monitoring data from ground-based monitors to those using more
complex hybrid modeling approaches. Studies using hybrid modeling approaches aim to broaden
the spatial coverage of estimated PM2.5 concentrations by bringing in additional information to
provide estimates in areas that do not have ground-based monitors (i.e., areas that are generally
less densely populated and tend to have lower PM2.5 concentrations). As such, the hybrid
modeling approaches tend to broaden the areas captured in the exposure assessment, and in
doing so, the studies that utilize these methods tend to report lower mean PM2.5 concentrations
than monitor-based approaches because they include more suburban and rural areas where
concentrations are lower. Further, other aspects of the method used to calculate mean PM2.5
concentrations can also have an impact on the study-reported mean concentration (i.e.,
population weighting, trim mean).
In those studies that use ground-based monitors alone to estimate long- or short-term
PM2.5 concentrations, approaches include: (1) PM2.5 concentrations from a single monitor within
a city/county; (2) average of PM2.5 concentrations across all monitors within a city/county or
other defined study area (e.g., CBSA); or (3) population-weighted averages of exposures. Once
the study location average PM2.5 concentration is calculated, the study-reported long-term
26 A trimmed mean is a method of averaging that removes a small percentage of the largest and smallest values
before calculating the mean.
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1 average is derived by averaging daily/annual PM2.5 concentrations across all study locations over
2 the entire study period. Table 3-5 and Table 3-6 list the key U.S. and Canadian epidemiologic
3 studies, respectively, that use ground-based monitors to estimate exposure, gives the reported
4 study mean, and describes the method used to calculate the mean.
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1 Table 3-5. Key U.S. Epidemiologic Studies: Monitor-Based Exposure
Citation
Health
Endpoint
Geographic Area
Study
Design
Years and Method Used to Calculate Study-reported
Mean PM2.5 Concentrations
Reported Mean
(other percentiles)
|jg/m3
Short-term Exposure Studies
Bell et al.,
2008*
CVD HA
(65+)
202 U.S. Counties
(population >200,000)
Time-series
study
(MEDICARE
enrollees)
Trimmed mean: 1999-2005
Daily PM2 5 concentrations of 202 counties were averaged
to calculate overall mean PM2.5 exposure for the study
location (all and region specific) and study period
12.9
(10th: 9.8, 25th: 11.5)
Bell et al.,
2014
CVD,
asthma,
and
COPD HA
(65+)
4 Counties in MA and CT
Time-series
study
(MEDICARE
enrollees)
2000-2004
Daily PM2.5 concentrations for all four counties (three with
single monitor and one with two monitors that used
population weighted approach) were used to calculate the
overall mean PM2 5for the study location and period
14.0
Bell et al.,
2015
HF HA
(65+)
213 U.S. Counties
Time-series
study
(MEDICARE
enrollees)
1999-2010
Daily PM2 5 concentrations of 213 counties were averaged
to calculate overall and region-specific mean
PM2.5PM2.5for the study location and period.
12.3
Bravo et al.,
2017
CVD HA
(65+)
418 U.S. Counties
(population >50,000)
Time-series
study
(MEDICARE
enrollees)
2002-2006
Daily PM2 5 concentration of 418 counties were averaged
to calculate overall mean PM2.5for the study location and
period.
12.3
Dai et al., 2014
All-cause,
CVD, and
respiratory
mortality
75 U.S. Cities (available
daily mortality data and
PM2.5 data for at least
400 days 2000-2006)
Time-series
study (NCHS)
2000-2006
Daily PM2.5 concentration of 75 cities were averaged to
calculate overall mean PM2.5for the study location and
period
13.3
Dominici et al.,
2006*
HF and
COPD HA
(65+)
204 Urban U.S. Counties
(population >200,000)
Time-series
study
(MEDICARE
enrollees)
Trimmed mean: 1999-2002
Daily PM2 5 concentrations for 204 US counties were
averaged to calculate overall mean PM2.5 concentration
for the study regions and period.
13.4
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Citation
Health
Endpoint
Geographic Area
Study
Design
Years and Method Used to Calculate Study-reported
Mean PM2.5 Concentrations
Reported Mean
(other percentiles)
|jg/m3
Franklin et al.,
2007*
All-cause,
CVD, and
respiratory
mortality
27 U.S. Communities in
Boston area (with PM2.5
monitoring and daily
mortality data for at least
2 years of 6-year study
period 1997-2000)
Case-
crossover
study (NCHS)
1997-2000
Daily PM2.5 concentrations (from monitors that are highly
correlated in the counties and thus representing general
population exposure) for 27 communities were averaged
to calculate overall mean PM2.5 concentration for the
study location and period.
15.6
(10th: 10.4, 25th: 12.9)
Franklin et al.,
2008*
All-cause,
CVD, and
respiratory
mortality
25 U.S. Communities for
Boston area (with PM2.5
monitoring and daily
mortality data for at least
4 years of 6-year
period 2000-2005)
Case-
crossover
study (NCHS)
2000-2005
Daily PM2.5 concentrations (from monitors that are highly
correlated in the counties and thus representing general
population exposure) for 25 communities were averaged
to calculate overall mean PM2.5 concentration for the
study location and period.
14.8
Klemm and
Mason, 2003 *
All-cause
mortality
Harvard Six-City study
reanalysis
Time-series
study
1979-1988
Daily PM2 5 concentration of six cities were used to
calculate overall mean PM2.5 exposure for the study
location (all and by study center) and period.
Median: 14.7:
(25th: 9.0)
Krall et al.,
2013
All-cause
mortality
72 Urban U.S.
Communities
Time-series
study (NCHS)
2000-2005
Daily PM2.5 concentration (including only the source-
oriented monitors representative of typical population
exposures) of 72 urban communities were used to
calculate overall mean PM2.5 exposure for the study
location and period
13.6
Liu et al., 2019
All-cause
and
cause-
specific
mortality
107 U.S. Cities
Time-series
study (MCC
Collaborative
Research
Network)
1987-2006
Daily PM25 concentration averaged across stations within
each city was used to calculate an average 2-day moving
average PM2.5 concentrations for the city. These data
were then used to calculate overall mean concentration
for the study location and period.
12.4
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Citation
Health
Endpoint
Geographic Area
Study
Design
Years and Method Used to Calculate Study-reported
Mean PM2.5 Concentrations
Reported Mean
(other percentiles)
|jg/m3
Ostro et al.,
2016
Asthma
and
COPD ED
8 Metropolitan
Areas/Counties in CA
Case-
crossover
study
2005-2009
Daily PM2.5 concentrations for eight metropolitan counties
were used to calculate an overall mean PM2.5
concentration for the study location and period.
16.5
Peng et al.,
2009b
CVD HA
(65+)
119 U.S. Urban
Counties>150,000
populations
Time-series
study
(MEDICARE
enrollees)
2000-2006
Daily PM2 5 concentrations for 119 counties were used to
calculate an overall median PM2.5 concentration for the
study location and period.
Median: 11.9
Zanobetti et
al., 2009
CVD, HF,
Ml HA
(65+)
26 U.S. Cities
Time-series
study
(MEDICARE
enrollees)
2000-2003
Daily average PM2.5 data for each county was calculated
using an algorithm that accounts for monitor-specific
means and variances. Monitors that were not well
correlated with other monitors were excluded.
15.3
Zanobetti and
Schwartz,
2009*
All-cause,
CVD and
respiratory
mortality
112 U.S. Cities
Time-series
study (NCHS)
1999-2005
Daily PM2.5 concentrations (from monitors that are highly
correlated in the counties and thus representing general
population exposure) for 112 cities were averaged to
calculate overall mean PM2.5 concentration for the study
location and period.
13.2
(10th: 10.3, 25th: 12.5)
Long-term Exposure Studies
Eum et al.,
2018
All-cause
mortality
U.S. Geographic regions:
"East" of the Mississippi
River, "Center" between
the Mississippi River and
the Sierra Nevada
mountain range, and
"West" of the Sierra
Nevada mountain range
Cohort study
(MEDICARE
enrollees)
2000-2012
Annual average PM2.5 concentrations assigned to
individuals living in zip codes with centroids within 6 miles
of a valid monitor (monitors with daily measurements for
at least 8 calendar years, with each year having 9+
months, and with 4+ daily measurements) were used to
calculate overall mean PM2.5 concentration for the study
location (all and by study region) and study period.
Overall: 11.65
Central: 9.9
Eastern: 12.3
West: 11.5
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Citation
Health
Endpoint
Geographic Area
Study
Design
Years and Method Used to Calculate Study-reported
Mean PM2.5 Concentrations
Reported Mean
(other percentiles)
|jg/m3
Gharibvand et
al., 2016
Lung
cancer
incidence
U.S. Nationwide
Cohort study
(AHSMOG-2
study)
2000-2001
Monthly PM2.5 concentrations (calculated using at least
75% valid daily data) assigned to study participants based
on residential address were used to calculate overall
mean PM2.5 for the study period.
12.9
Hart et al.,
2015
All-cause
mortality
U.S. Nationwide
Cohort study
(Nurses'
Health study)
2000-2012
Monthly PM2.5 concentrations assigned to study
participants based on the nearest monitor to residence
locations were used to calculate overall mean for the
study period
12.7
Kioumourtzogl
ou et al., 2016
All-cause
mortality
(65+)
207 U.S. cities
Cohort study
(MEDICARE
enrollees)
2000-2010
Annual PM2.5 concentrations for 207 cities were averaged
to calculate overall mean PM2.5 exposure for the study
location (all and region specific) and study period.
12.0
McConnell et
al., 2010
Asthma
Incidence
13CA Communities
Cohort study
(CHS)
2003-2004
Average annual PM2.5 concentrations assigned to study
participants based on their community of residence were
used to calculate overall mean PM2.5 exposure for the
study location and period.
13.9
Zeger et al.,
2008*
All-cause
mortality
65+
668 U.S. Urban Counties
Cohort Study
of
MEDICARE
enrollees
(MCAPS)
2000-2005
Average annual PM2.5 concentrations of ZIP codes (for zip
code centroids within 6 miles of a monitor and with >10
months of data per year) were used to calculate overall
mean PM2.5 exposure for the study location (all and by
region) and the study period.
Central Region
median: 10.7
Eastern Region
median: 14.0
Western region
median: 13.1
* Evaluated in 2012 review
1
2
3
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1 Table 3-6. Key Canadian Epidemiologic Studies: Monitor-Based Exposure
Citation
Health
Endpoint
Geographic
Area
Study Design
Years and Method Used to Calculate Study-reported Mean
PM2.5 Concentrations
Reported Mean
(other percentiles)
|jg/m3
Short-term Exposure Studies
Burnett and
Goldberg,
2003*
All-cause
mortality
8 Canadian
cities
Time-series
study
1986-1996
Daily PM25 concentrations (day before the death) for 8 Canadian
cities were averaged to get overall mean for the study area and
period
13.3
Burnett et al.,
2004*
All-cause
mortality
12 Canadian
cities
Time-series
study (data from
Statistics
Canada)
1981-1999
PM2 sDaily PM2.5 concentrations for 12 cities (calculated by
averaging all monitors within each city) were used along with
population information to calculate an overall population weighted
PM2 5 concentration for the study location and period
12.8
Lavigne et al.,
2018
Non-
accidental,
CVD, and
respiratory
mortality
24 Canadian
cities
Case-crossover
study
1998-2011
Daily average PM2.5 concentrations assigned to participants based
on closest monitor(s) to participant's city of residence. Daily PM2.5
concentrations in 24 Canadian cities were used to calculate overall
mean PM2.5 concentration over the study location and period.
8.8
(Median: 7.1)
Liu et al., 2019
All-cause
and
cause-
specific
mortality
25 Canadian
cities
Time-series
Study (MCC
Collaborative
Research
Network)
1986-2011
PM25 concentration averaged across stations within each city was
used to calculate an average 2-day moving average PM2.5
concentrations for the city. These data were then used to calculate
overall mean concentration for the study location and period.
9.3
Stieb et al.,
2009
Cardiac
and
respiratory
ED visits
7 Canadian
cities
Time-series
study (Hospital
cases)
1992-2003
Daily PM2 5 concentrations of the cities (calculated by averaging all
monitors within city) were used to calculate the overall mean PM2.5
exposure for the study location (by site) and study period.
8.2
(10th: 6.7, 25th: 6.8)
Szyszkowicz,
2009
Angina ED
7 Canadian
cities
Time-series
study (Hospital
cases)
1992-2003
Daily PM2 5 concentrations of the cities (calculated by averaging all
monitors within city) were used to calculate the overall mean PM2.5
exposure for the study location (all and by cities) and study period.
8.3
(10th: 6.4, 25th: 6.5)
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Citation
Health
Endpoint
Geographic
Area
Study Design
Years and Method Used to Calculate Study-reported Mean
PM2.5 Concentrations
Reported Mean
(other percentiles)
|jg/m3
Weichenthal et
al., 2016b
Ml ED
16 cities in
Ontario
Case-crossover
Design (cases
extracted from
NACRS
database)
2004-2011
Daily PM2 5 concentrations in Ontario were used to calculate the
overall mean PM2.5 exposure for the study location and period
6.9
Weichenthal et
al., 2016c
Asthma
and
COPD ED
15 cities in
Ontario
Case-crossover
design (cases
extracted from
NACRS
database)
2004-2011
Daily PM2 5 concentrations in Ontario were used to calculate the
overall mean PM2.5 exposure for the study location and period.
Asthma: 7.1
COPD: 7.1
Long-term Exposure Studies
Crouse et al.,
2012
All-cause
mortality
11 Canadian
Cities
Cohort study
1987-2001
Annual PM2.5 concentrations from monitors and assigned to
study participants based on the census division of the residence
were used to calculate overall mean PM2.5 for the study population
and duration.
8.7
* Evaluated in 2012 review
1
2
3
4
5
6
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2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
In the studies that use hybrid modeling approaches to estimate long- or short-term PM2.5
concentrations, data can be incorporated from several different sources, including satellites and
air quality models, in addition to ground-based monitors, as described in section 2.3.3 above.
Compared to ground-based monitors alone, hybrid modeling methods have the potential to
improve the characterization of PM2.5 concentrations in areas with relatively sparse monitoring
networks. These approaches also tend to have lower study-reported mean PM2.5 concentrations
since they often include estimates of PM2.5 concentrations in less populated areas compared to
those methods using only ground-based monitored. Studies that use hybrid modeling approaches
can estimate PM2.5 concentrations at different spatial resolutions, including at 1 km x 1 km grid
cells (i.e., Di et al., 2017b and Di et al., 2017a), at 10 km x 10 km grid cells (i.e., Kloog et al.,
2014), or at the census tract level (i.e., Bravo et al., 2017). Estimated PM2.5 concentrations are
then generally averaged up to a larger spatial resolution that corresponds to the spatial resolution
for which health data exists (e.g., ZIP code level). These values are then averaged across all
study locations at the larger spatial resolution (e.g., averaged across all ZIP codes in the study)
over the study period, resulting in the study-reported mean 24-hour average or annual average
PM2.5 concentration. Table 3-7 and Table 3-8 list the key U.S. and Canadian epidemiologic
studies, respectively, that use hybrid modeling approaches to estimate exposure and give the
reported study mean and describes the method used to calculate the mean. Studies included in
these tables are those that report overall mean (or median) PM2.5 concentrations and for which
the years of PM2.5 air quality data used to estimate exposures overlap entirely with the years
during which health events are reported. In addition, studies included in Table 3-7 and Table 3-8
are those for which recent methods and models were used (e.g., recent versions and
configurations of the air quality models); studies that are fused with PM2.5 data from national
monitoring networks (i.e., FRM/FEM data); and studies that reported a thorough model
performance evaluation for core years of the study.
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1 Table 3-7. Key U.S. Epidemiologic Studies: Model-Based Exposure
Citation1
Health
Endpoint
Geographic
Area
Study Design
Years, Model Type, and Method Used to Calculate
Study-reported Mean PM2.5 Concentrations
Reported
Mean
(other
percentiles)
Mg/m3
Short-term Exposure Studies
deSouza et al., 2021
First CVD HA
Continental
U.S.
Time-stratified
case-
crossover
design
(Medicaid
Adults)
2000-2012
Ensemble model (integrating machine learning
algorithms)
Daily PM25 estimates of all grid cells averaged at ZIP
code were assigned to study participants based on the
ZIP code of residence. Daily PM2.5 concentration from
case days were used to calculate overall case day
mean PM2.5 concentration for the study location and
period.
11.5 (case days
mean)
Di et al., 2017a
All-cause
mortality (65+)
U.S.
Nationwide
Case-
crossover
study
(MEDICARE
enrollees)
2000-2012
Artificial Neural Network (Hybrid method)
Daily PM2 5 concentrations for case and control days
assigned to participants based on ZIP code of
residence were used to calculate overall mean PM2.5
for the study location and period.
11.6
(10th: 4.7, 25th:
6.7)
Kloog et al., 2012
CVD HA (65+)
New
England
Area with 6
U.S. States
Mixed study
design (with
time series and
cohort
components)
2000-2006
Spatiotemporal model
Daily PM2.5 concentration of all grids within the NE
area for acute exposure (0 day lag) were used
to calculate overall mean for short-term PM2.5
exposure, for the study location and period.
9.6
(25th: 6.4)
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Citation1
Health
Endpoint
Geographic
Area
Study Design
Years, Model Type, and Method Used to Calculate
Study-reported Mean PM2.5 Concentrations
Reported
Mean
(other
percentiles)
|jg/m3
Kloog et al., 2014
CVD and
COPD HA
(65+)
7 U.S. Mid-
Atlantic
States and
D.C.
Case-
crossover
design
(MEDICARE
enrollees)
2000-2006
Spatiotemporal model
2-day moving average of PM2.5 concentration of all
grids within the mid-Atlantic states were used
to calculate overall mean (all area and rural/urban
areas) PM2.5 exposure for the study location and
period.
11.9
(25th: 7.9)
Lee et al., 2015
All-cause,
cardiovascular
, respiratory
mortality
3 U.S.
Southeast
States
Case-
crossover
design (Dept.
of Pub Health
data)
2007-2011
Spatiotemporal model PM2.5
Daily PM2 5 concentrations for ZIP codes (calculated
as averages of all grids within ZIP code or the closest
grid cell) within 3 SE states were averaged to
calculate overall mean PM2.5 concentration (all and by
state).
11.1
Case-
2000-2012
Neural network (using machine learning algorithm)
Qiu et al., 2020
CVD HA
New
England
(VT, NH,
CT, MA, Rl
and ME)
crossover
study applying
causal
modeling
approach
(MEDICARE)
Daily PM2 5 concentration at grid cells were averaged
to estimate exposure at ZIP code level and were
assigned to study participants based on ZIP code of
residence. Case and control days PM2.5 concentration
were used to calculate overall mean PM2.5
concentration (all, and separately for case and control
days) for the study period.
10.0
(AMI: 10.13
CHF: 10.08
IS: 10.10)
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Citation1
Health
Endpoint
Geographic
Area
Study Design
Years, Model Type, and Method Used to Calculate
Study-reported Mean PM2.5 Concentrations
Reported
Mean
(other
percentiles)
|jg/m3
Shi et al., 2016
Total mortality
(65+)
New
England
Area with 6
U.S. States
Open Cohort
study
(MEDICARE
enrollees)
2003-2008
Predicted from 3-stage statistical model
Lag01 PM2.5 concentrations of all grid cells in the
study area were used to calculate overall mean PM2.5
exposure for the study location and period.
8.2
(25th: 4.6)
2008-2014
Spatiotemporal prediction model
Wyattet al., 2020c
All-cause,
CVD, RD 30-
day hospital
readmissions
530 U.S.
counties
Cohort study
(USRDS
hemodialysis
patients)
Daily PM2.5 concentrations for grid cells were
converted to population-weighted county-level PM2.5
estimates using 2010 census tract population
estimates. Participants were assigned daily PM2.5
based on the county of their last dialysis visit. Daily
estimates at county-level were then used to calculate
overall PM2.5 concentration for the study location and
period.
9.29
Long-term Exposure Studies
Diet al., 2017b
All-cause
mortality (65+)
U.S.
Nationwide
Cohort study
(MEDICARE
enrollees)
2000-2012
Artificial Neural Network (Hybrid method)
Daily PM2 5 concentrations for all ZIP codes were used
to calculate overall mean PM2.5 for the study location
and period.
11.0
(10th: 7.3, 25th:
9.1)
Dominici et al., 2019
All-cause
mortality (65+)
U.S.
Nationwide
Cohort study
(MEDICARE
enrollees)
2000-2012
Artificial Neural Network (Hybrid method)
Daily PM2 5 concentrations for all ZIP codes were used
to calculate overall mean PM2.5 for the study location
and period.
11.0
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Citation1
Health
Endpoint
Geographic
Area
Study Design
Years, Model Type, and Method Used to Calculate
Study-reported Mean PM2.5 Concentrations
Reported
Mean
(other
percentiles)
|jg/m3
Hart et al., 2015
All-cause
mortality
U.S.
Nationwide
Cohort study
(Nurses' Health
study)
2000-2012
Spatiotemporal model
Monthly PM2.5 concentrations assigned to study
participants at residence locations were used to
calculate overall mean for the study period.
12.0
Kloog et al., 2012
CVD HA (65+)
New
England
Area with 6
U.S. States
Mixed study
design (with
time series and
cohort
components)
2000-2006
Spatiotemporal model
Daily PM2.5 concentration of all grids within the NE
area for chronic exposure (365 day moving average)
were used to calculate overall mean for long-term
PM25 exposure, for the study location and period.
9.7
(25th: 9.2)
Shi et al., 2016
Total mortality
(65+)
New
England
Area with 6
U.S. States
Open Cohort
study
(MEDICARE
enrollees)
2003-2008
Predicted from 3-stage statistical model
Average annual PM2.5 concentrations of all grid cells in
the study area were used to calculate overall mean
PM25 exposure for the study location and period.
8.1 (25th: 6.2)
Thurston et al., 2016
All-cause,
CVD and
respiratory
mortality
6 U.S.
States and
2 MS As
Cohort study
(NIH_AARP
cohort)
2000-2008
Spatiotemporal model
Average annual PM2.5 concentrations of census tract
estimates assigned to participants based on the
census tract of residence used to calculate overall
mean PM2.5 exposure for the study location and
period.
12.2
Mean range:
2.9-28.0
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Citation1
Health
Endpoint
Geographic
Area
Study Design
Years, Model Type, and Method Used to Calculate
Study-reported Mean PM2.5 Concentrations
Reported
Mean
(other
percentiles)
|jg/m3
2000-2013
Three stage Hybrid model PM2.5
Wang et al., 2017
Total mortality
(65+)
7 U.S.
Southeast
States
Cohort study
(MEDICARE
enrollees)
Average annual PM2.5 concentrations of ZIP code
tabulation areas were calculated by averaging annual
mean PM2.5 concentration of all grids in the ZCTA and
then used to calculate overall median PM2.5 exposure
for the study location (overall and by state), and period
(overall and by year).
Median: 10.7
Range: 6.0-20.6
(25th: 9.1)
Wang et al., 2020
Non-
accidental
cause-specific
(respiratory,
CVD, cancer)
mortality
U.S.
Nationwide
Cohort study
(MEDICARE)
2000-2008
Spatiotemporal prediction model
Daily PM2 5 concentrations of grids were matched to
study participants based on the grid point closest to
their residential ZIP code centroid. The estimates
were used to calculate overall annual mean PM2.5
exposure for the study period.
10.3
New
2000-2012
Spatiotemporal Prediction model
Wu et al., 2019
All-cause
mortality
England
(VT, NH,
CT, MA, Rl
and ME);
2202 ZIP
codes
Causal
modeling study
(MEDICARE)
Daily PM2 5 exposures determined at grid cells were
aggregated using area-weighted average of PM2.5
concentrations of all grid cells within the ZIP code and
assigned to individuals based on their ZIP code of
residence. Annual concentrations were used to
calculate overall mean PM2.5 concentration over the
study period.
9.3
(Trimmed
population: 9.4)
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Citation1
Health
Endpoint
Geographic
Area
Study Design
Years, Model Type, and Method Used to Calculate
Study-reported Mean PM2.5 Concentrations
Reported
Mean
(other
percentiles)
|jg/m3
Wu et al., 2020a
All-cause
mortality
U.S.
Nationwide
Cohort study
(MEDICARE)
2000-2016
Ensemble model (integrating machine learning
algorithms)
Daily PM2.5 concentration at grid cells whose centroids
were inside the ZIP code boundary were averaged for
each year and assigned to participants based on the
ZIP code of residence. These data were used to
calculate overall mean PM2.5 concentration for the
study period.PM2.5PM2.5
9.8
(<12 ug/m3:
8.4)
1 None of the studies presented in this table were evaluated in the 2012 review.
1
2 Table 3-8. Key Canadian Epidemiologic Studies: Model-Based Exposure
Citation1
Health
Endpoint
Geographic
Area
Study
Design
Years, Model Type, and Method Used to Calculate Study-reported
Mean PM2.5 Concentrations
Reported Mean
(other percentiles)
|jg/m3
Long-term Exposure Studies
Bai et al.,
2019
CHF and
AMI
incidence
Ontario
Cohort study
(ONPHEC)
1998-2012
Fused surface (AOD, GEOS-Chem & geographically weighted regression)
Annual estimates of PM2.5 concentrations assigned to participants based on
postal code of residence used to calculate 3-year moving average PM2.5
concentration for each year of follow-up in the study. The 3-year moving
averages for study participants at the baseline residence location was used
to calculate overall mean PM2.5 concentration at the beginning of the follow-
up period in 2001.
9.6
(25th: 7.9)
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Citation1
Health
Endpoint
Geographic
Area
Study
Design
Years, Model Type, and Method Used to Calculate Study-reported
Mean PM2.5 Concentrations
Reported Mean
(other percentiles)
|jg/m3
2000-2016
Fused surface (AOD, GEOS-Chem & geographically weighted regression)
Chen et al.,
2020
CVD
mortality
Ontario
Cohort study
(ONPHEC)
Annual estimates of PM2.5 concentrations were assigned to participants
based on postal code of residence. Annual PM2.5 concentrations in the
Ontario region were then used to calculate overall mean PM2.5
concentration for the study location and period.
8.61
1998-2015
Fused surface (AOD, GEOS-Chem & geographically weighted regression)
Christidis et
al., 2019
Non-
accidental
mortality
Canada
Nationwide
Cohort study
(mCHHS)
Annual PM2.5 estimates from the postal code and assigned to study
participants based on the postal code for residence were used to calculate
3-year moving average based on the location and year of follow-up. The
average PM2.5 concentrations were then used to calculate overall mean
PM2.5 concentration for the study period.
5.9
(Median: 5.5; 25th:
4.3)
Crouse et al.,
2019
Non-
accidental,
CVD,
respiratory
mortality
and lung
cancer
Canada
Nationwide
Cohort study
(CanCHEC)
1998-2010
Fused surface (AOD, GEOS-Chem & geographically weighted regression)
Annual PM2.5 estimates from the postal code and assigned to study
participants based on the postal code for residence were used to calculate
moving average at various temporal and spatial scales based on the
location and year of follow-up. The average PM2.5 concentrations were then
used to calculate overall mean PM2.5 concentration for the study period at
various temporal and spatial scales.
1-year in 1 km:
Mean: 7.2,
3-year in 1 km:
Mean: 7.4,
8-year in 1 km:
Mean: 8.0
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Citation1
Health
Endpoint
Geographic
Area
Study
Design
Years, Model Type, and Method Used to Calculate Study-reported
Mean PM2.5 Concentrations
Reported Mean
(other percentiles)
|jg/m3
Erickson et
al., 2020
Non-
accidental,
CVD, and
respiratory
mortality
and
cancer
Canada
Nationwide
Cohort study
(CanCHEC)
1998-2016
Fused surface (AOD, GEOS-Chem & geographically weighted regression)
Annual PM2.5 estimates from the postal code and assigned to study
participants based on the postal code for residence were used to calculate
3-year moving average based on the location and year of follow-up. The
average PM2.5 concentrations were then used to calculate overall mean
PM2 5 concentration for the study period by immigrant status and duration in
Canada.
Non-immigrant: 7.5
Immigrant: 9.3
Pre-1971: 9.1
1971-1980: 9.3
1981-1990: 9.5
1991-2001:9.7
All-cause,
CVD,
respiratory
, and lung
cancer
mortality
1998-2012
Fused surface (AOD, GEOS-Chem & geographically weighted regression)
Erickson et
al., 2020
Canada
Nationwide
Cohort study
(CCHS)
Annual PM2.5 estimates from the postal code and assigned to study
participants based on the postal code for residence were used to calculate
3-year moving average based on the location and year of follow-up. The
average PM2.5 concentrations were then used to calculate overall mean
PM2.5 concentration for the study period.
6.3
Pinault et al.,
2018
CVD
mortality
Canada
Nationwide
Cohort study
(CanCHEC,
mCHHS)
1998-2012
Fused surface (AOD, GEOS-Chem & geographically weighted regression)
Annual PM2.5 estimates from the postal code and assigned to study
participants based on the postal code for residence were used to calculate
3-year moving average based on the location and year of follow-up. The
average PM2.5 concentrations were then used to calculate overall mean
PM2.5 concentration for the study period.
CanCHEC: 7.4
mCHHS: 6.4
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Citation1
Health
Endpoint
Geographic
Area
Study
Design
Years, Model Type, and Method Used to Calculate Study-reported
Mean PM2.5 Concentrations
Reported Mean
(other percentiles)
|jg/m3
Shin et al.,
2019
AF and
Stroke (1st
HA)
Ontario
Cohort study
(ONPHEC)
1998-2012
Fused surface (AOD, GEOS-Chem & geographically weighted regression)
Annual PM2.5 estimates from the postal code and assigned to study
participants based on the postal code for residence were used to calculate
5-year moving average based on the location and year of follow-up. The
average PM2.5 concentrations were then used to calculate overall mean
PM2.5 concentration for the study period.
9.8
(25th: 8)
Zhang et al.,
2021
Non-
accidental,
CVD, and
respiratory
mortality
Ontario
Cohort study
(Ontario
Health
Study)
Modeled from AOD satellite retrievals
2000-2016
Annual PM2.5 estimates from the postal code and assigned to study
participants based on the postal code for residence was used to calculate 3-
year and 5-year moving averages based on the location and year of follow-
up. The 5-year average PM2.5 concentrations were then used to calculate
overall mean PM2.5 concentration for the baseline year.
Baseline: 7.8
(Median: 8.0; 25th:
6.7)
1 None of the studies presented in this table were evaluated in the 2012 review.
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
As noted above, the key epidemiologic studies use differing approaches to estimate mean
PM2.5 concentrations. Approaches differ not only between monitor-based studies and model-
based studies, but also between studies using the same types of air quality information. It is
important to recognize the differences between the techniques used for estimating mean PM2.5
concentrations in epidemiologic studies, in particular when comparing the results across the
studies and considering what the study reported means represent and how that information
informs our consideration of the form, averaging time and level of the current annual PM2.5
standard. To further understand these differences, we seek to answer the following question:
• How can the approaches used in key epidemiologic studies to estimate exposure
affect the study-reported mean PM2.5 concentrations? How do these approaches and
the resulting means compare to one another?
In answering this question, we first utilize a simplified example to show differences in
the mean concentrations depending on the methods used to estimate exposure. In Figure 3-7
below, we exhibit the state of Georgia and the CBSA of Atlanta-Sandy Springs-Roswell. In this
Figure, the gradient of PM2.5 concentrations are shown for 1 km x 1 km grid cells using one of
the hybrid approaches described in more detail in Chapter 2, referred to as the DI201927 hybrid
approach, from 2014-2016, as well as the monitor locations within the Atlanta-Sandy Springs-
Roswell CBSA and their annual PM2.5 design values for 2016. Using these data, several metrics
were calculated and shown in Table 3-9 below. For all monitors within the CBSA, the average
PM2.5 concentration is 9.3 |ig/m3, while the design value (based on the highest monitored PM2.5
concentration in the area) is 10.4 |ig/m3. This comparison helps to illustrate the fact that
composite monitor values tend to be somewhat lower than the highest area monitor values,
consistent with the key points made in the 2012 review. This example also communicates how
monitors are sited to represent the higher concentrations within the area and that the area's
annual design value, which is used for compliance with the standard, is calculated based on the
highest monitor in the area.
Next, we evaluate the average estimated PM2.5 concentrations from 2014-2016 using the
D12019 hybrid approach and calculate: (1) the average concentration across the entire state; (2)
the population weighted average across the entire state; (3) average concentration across the
CBSA; and (4) the population weighted average across the CBSA. In doing this, we have
focused on using some of the main approaches used in epidemiologic studies to compute study
means. At the urban level (e.g., Atlanta-Sandy Springs-Roswell CBSA), the average PM2.5
concentration when taking the mean of all grid cells is 9.2 |ig/m3, whereas the population-
27 As discussed above in section 2.3.3.2.4, DI2019 refers to estimated PM2 5 concentrations from a hybrid modeling
approach developed by Di et al. (2019b), which estimates Nationwide PM2 5 concentrations from 2000-2016.
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
weighted mean is 9.6 j.ig/m\ Across Georgia, the average PM2.5 concentration using the hybrid
approach is 8.3 jig/m3, lower than the population-weighted statewide average of 9.1 iig/m\
While this is a simple example evaluated in just one state and one CBSA, it suggests that the
lowest mean values tend to result from the approaches that use concentrations from all or most
grid cells, both urban and rural, across the study area to compute the mean. Higher mean values
are observed when the approach focuses on the urban areas alone or when the approach
incorporates population weighting. Overall, this example suggests that the means from studies
using hybrid modeling approaches are generally lower than the means from monitor-based
approaches, and both are lower than the annual design values for the same area. Population-
weighting tends to increase the calculated mean, likely because more densely populated areas
also tend to have higher PM2.5 concentrations. Table 3-9shows how the different approaches
affect mean concentration estimates for the example discussed above. Note that while the
statewide average using the hybrid approach is quite a bit lower than the mean from either the
monitor-based approach or the Atlanta-only hybrid approach, population-weighting the statewide
average brings the value closer to the other approaches.
6 6.5 7 7.5 8 8.5 9 9.5 10 10.5 11
Figure 3-7. Estimated PM2.5 concentrations using the DI2019 hybrid approach and
monitoring locations and design values for the state of Georgia and the Atlanta-Sandy
Springs-Roswell, Georgia CBSA. (Note: Additional information on the DI2019 hybrid
approach is described in section 2.3.3.1.4 and in Di et al., 2019a.)
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
Table 3-9. PM2.5 Concentrations Metrics from Monitor and Modeled Data28
Description of Metric
PM2.5 Concentrations (mq/ti3)
Atlanta highest monitor
10.4
Atlanta monitored average
9.3
Atlanta spatial average
9.2
Atlanta population-weighted average
9.6
Georgia spatial average
8.3
Georgia population-weighted average
9.1
To expand upon this example in answering our question, we look to the analyses in
Chapter 2 which compared area annual design values, composite monitor PM2.5 concentrations
and mean concentrations from two hybrid approaches. The analyses also included population-
weighted mean metrics. In the air quality analyses comparing composite monitored PM2.5
concentrations with annual PM2.5 design values in U.S. CBS As, maximum annual PM2.5 design
values were approximately 10% to 20% higher than annual average concentrations (i.e.,
averaged across multiple monitors in the same CBSA) (section 2.3.3.1, Figure 2-28 and Table 2-
2). The difference between the maximum annual design value and average concentration in an
area can be smaller or larger than this range, depending on factors such as the number of
monitors, monitor siting characteristics, and the distribution of ambient PM2.5 concentrations.29
Such ratios may also depend on how the average concentrations are calculated (i.e., averaged
across monitors versus across modeled grid cells). Compared to annual design values, Figure 2-
29 indicates a more variable relationship between maximum 24-hour PM2.5 design values and
annual average concentrations.
In addition, the air quality analyses in Chapter 2 looked at data from two hybrid modeling
approaches. While hybrid modeling approaches are not universal and the various hybrid
approaches all have their different nuances, the analysis in Chapter 2 focused on the DI2019 and
HA2020 approaches, which have been used in several of the key epidemiologic studies in Table
3-7 and Table 3-8. Section 2.3.3.2.4 details a comparison of PM2.5 fields in estimating exposure
relative to design values using these two hybrid modeling surfaces. PM2.5 concentrations are
28 "Spatial average" as used in Table 3-9 refers to the average across all grid cells in Atlanta or Georgia using the
DI2019 hybrid modeling approach, while "population-weighted average" uses the DI2019 hybrid modeling
approach and applies population-weighting to calculate the mean PM2 5 concentration.
29 Given that higher PM2 5 concentrations have been reported at some near-road monitoring sites, relative to the
surrounding area (section 2.3.2.2.2), recent requirements for PM2 5 monitoring at near-road locations in large
urban areas (section 2.2.3.3) may increase the ratios of maximum annual design values to averaged concentrations
in some areas. In the Georgia example above, a near-road monitor was not included in our analysis. The near-
road monitor was not added until 2015, and data related to DI2019 ended in 2016. For purposes of developing
three-year average concentrations using the most recent data for which we had monitored and modeled data,
2014-2016 data was selected for monitors as well, for which data from 2014-2016 was not available for the near-
road monitor.
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
estimated per year at a 1 km x 1 km spatial resolution. As exhibited in Figure 2-37, the means
vary when one estimates PM2.5 exposures in urban areas only (CBSAs) versus when the averages
used all or most grid cells nationwide. This is likely indicative of the fact that areas included
outside of CBSAs tend to be more rural and have lower estimated PM2.5 concentrations. This is
important to note since, which study area is included in the calculation of the mean (Table 3-7
and Table 3-8 above), and more specifically whether a study is focused on nationwide, regional,
or urban areas, will affect the calculation of the study mean based on how many rural areas are
included with lower estimated PM2.5 concentrations. While the determination of what spatial
scale to use to estimate PM2.5 concentrations does not inherently affect the quality of the
epidemiologic study, the spatial scale can affect the calculation of the long-term mean
concentration across the study area and period. As exhibited in Table 2-4, regardless of the
hybrid modeling approach assessed, the annual average PM2.5 concentrations in CBSA-only
analyses are 4-8% higher than for nationwide analyses, likely as a result of higher PM2.5
concentrations in more densely populated areas. When evaluating comparisons between surfaces
that estimate exposure using population-weighting versus surfaces that do not calculate means
using population-weighting, surfaces that calculate long-term mean PM2.5 concentrations with
population-weighted averages have higher average annual PM2.5 concentrations, ranging from
8.2-10.2 |ig/m3, compared to annual PM2.5 concentrations that range from 7.0-8.6 |ig/m3 in
analyses that do not apply population weighting. Average maximum annual design values, on the
other hand, exhibit a range from 9.5 to 11.7 |ig/m3. Analyses exhibit that average maximum
annual design values are 40 to 50% higher when compared to annual average PM2.5
concentrations estimated without population-weighting and are 15% to 18% higher when
compared to average annual PM2.5 concentrations with population weighting applied.
The comparisons discussed above show a trend generally observed across the various
methods employed to calculate the mean. First, the area annual design values tend to be 10-20%
higher than composite monitor values. Additionally, when assessing means from hybrid
modeling data, the lowest mean values tend to result from the approaches that use estimated
PM2.5 concentrations from all or most grid cells, both urban and rural, across the study area to
compute the mean. When compared to the area annual design values, these annual design values
are higher than means by 40-50%. However, when the approach instead employs methods that
population-weight the mean (e.g. average up the grid cells to a ZIP code spatial level), the
calculated mean PM2.5 concentrations are higher, regardless of the hybrid method employed, and
when compared to the area annual design values, design values are only 15-18% higher than
means (similar to the differences observed for the composite monitor comparison values for the
monitor-based epidemiologic studies). We note that our comparisons used only two hybrid
modeling approaches, and while both modeling approaches are popular in the key epidemiologic
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7
8
9
10
11
12
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14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
studies, they are only just two of the hybrid approaches being used in the literature to estimate
PM2.5 concentrations. Research groups also continue to develop and improve prediction models
to estimate PM2.5 concentrations in epidemiologic studies. We also note that different
epidemiologic studies use different methods to assign a population weighted average PM2.5
concentration to their study population and our comparisons do not assess them all.
Additionally, while these analyses focus on the relationships between study reported
means and area annual design values, some studies also provide information on the broader
distributions of exposure estimates and/or health events and the PM2.5 concentrations
corresponding to the lower percentiles of those data (e.g., 25th and/or 10th). We note that this air
quality analysis does not provide a similar comparison for these lower percentiles, and that
caution should be placed upon any direct comparison of these study reported concentration
values corresponding to lower percentiles and annual design values.
In assessing these analyses, we note that these results are most relevant to interpreting
U.S. epidemiologic studies. Using information from the U.S.-based analyses for Canadian
studies would introduce additional uncertainties, given the differences between U.S. and
Canadian studies with respect to population densities, source distributions, and PM2.5
concentration gradients. Given these important differences between studies conducted in the two
countries and the fact that we lack data and information that would allow us to do similar
analyses for Canada, we are unable to provide insight into how the study reported means in the
Canadian studies would compare to area design values in the U.S.
To further expand our evaluation of study-reported mean PM2.5 concentrations, we
specifically consider the following questions:
• What are the overall mean PM2.5 concentrations reported by key epidemiologic
studies? For studies with available information on the broader distributions of
exposure estimates and/or health events, what are the PM2.5 concentrations
corresponding to the lower percentiles of those data (e.g., 25th and/or 10th)?
Figure 3-8 and Figure 3-9 highlight the overall mean (or median) PM2.5 concentrations
reported in key U.S. and Canadian studies, respectively, that use ground-based monitors alone to
estimate long- or short-term PM2.5 exposures. For the small subset of studies with available
information on the broader distributions of underlying data, Figure 3-8 and Figure 3-9 also
identify the study-period mean PM2.5 concentrations corresponding to the 25th and 10th
percentiles of health events30 (see Appendix B, Section B.2 for more information).
30 That is, 25% of the total health events occurred in study locations with mean PM2 5 concentrations (i.e., averaged
over the study period) below the 25th percentiles identified in Figure 3-8 and Figure 3-9 and 10% of the total
health events occurred in study locations with mean PM2 5 concentrations below the 10th percentiles identified.
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1 Figure 3-10 and Figure 3-11 present overall means of predicted PM2.5 concentrations for
2 key U.S. and Canadian model-based epidemiologic studies, respectively, and the concentrations
3 corresponding to the 25th and 10th percentiles of estimated exposures or health events31 when
4 available (see Appendix B, section B.3 for additional information).
31 For most studies in Figure 3-10 and Figure 3-11, 25th percentiles of exposure estimates are presented. The
exception is Di et al., 2017b, for which Figure 3-10 presents the short-term PM2 5 exposure estimates
corresponding to the 25th and 10th percentiles of deaths in the study population (i.e., 25% and 10% of deaths
occurred at concentrations below these concentrations). In addition, the authors of Di et al., 2017b provided
population-weighted exposure values (Chan, 2019). The 10th and 25th percentiles of these population-weighted
exposure estimates are 7.9 and 9.5 |ig/ml respectively.
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ST Exposure
and Mortality
ST Exposure
and Morbidity
LT Exposure
and Mortality
LT Exposure
and Morbidity
Franklin 2007 (US: 27 Cities)
Franklin 2008 (US: 25 Cities)
Klemm 2003 (US: Harvard 6 City)
Krall 2013 (US: 72 Cities)
Dai 2014 (US: 75 Cities)
Zanobetti and Schwartz 2009 (US: 112 Cities)
Liu 2019 (US: 107 cities; ST Exposure)*
Ostro 2016 (US: 8 California Counties)
Zanobetti 2009 (US: 26 cities)
Bell 2014 (US: 4 Counties in MA & CT)
Dominici 2006 (US: 204 Urban Counties)
Bell 2008 (US: 202 Counties)
Bravo 2017 (US: 418 Counties)
Bell 2015 (US: 70 Urban Counties)
Peng 2009 (US: 119 Urban Counties)
Zeger 2008 (US: 421 Eastern Region Counties)
Zeger 2008 (US: 62 Western Region Counties)
Hart 2015 (US: Nationwide)
Eum 2018 (US: Eastern Geographic Region; LT Exposure)*
Kioumourtzoglou 2016 (US: 207 Cities)
Eum 2018 (US: 3 Geographic Regions overall; LT Exposure)*
Eum 2018 (US: Western Geographic Region; LT Exposure)*
Zeger 2008 (US: 185 Central Region Counties)
Eum 2018 (US: Central Geographic Region; LT Exposure)*
McConnell 2010 (US: 13 California Communities)
Gharibvand 2016 (US: Nationwide)
Study Type
| ST Exposure and Mortality
| ST Exposure a ad Morbidity
| LT Exposure and Mortality
| LT Exposure and Morbidity
Summary Statistics
O 10th percentile
£ 25th percentile
¦ Mean
9.5 10.0 10.5 11.0
11.5 12.0 12.5 13.0 13.5 14.0 14.5 15.0
Overall PM2.5 Concentration for the Study Period (ng/m3)
15.5 16.0 16.5
Figure 3-8. Monitor-based PM2.5 concentrations in key U.S. epidemiologic studies. (Asterisks denote studies included in the draft
ISA Supplement).
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ST Exposure and
Mortality
Burnett 2003 (Canada: 8 Cities)
¦
Burnett 2004 (Canada: 12 Cities)
¦ NS
Liu 2019 (Canada: 25 cities; ST Exposure)*
¦
Lavigne 2018 (Canada: 24 Cities; ST Exposure)*
¦
ST Exposure and
Morbidity
Szyszkowicz 2009 (Canada: 6 Cities)
m
Stieb 2009 (Canada: 6 Cities)
0# ¦
Weichenthal 2016c (Canada: 15 Ontario Cities)
¦
Weichenthal 2016b (Canada: 16 Ontario Cities)
¦ NS
LT Exposure and
Mortality
Crouse 2012 (Canada: 11 Cities)
¦
6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0 10.5 11.0 11.5 12.0 12.5 13.0 13.5
Overall PM2.5 Concentration for the Study Period (tig/m3)
Study Type
| ST Exposure and Mortality
ST Exposure and Morbidity
| LT Exposure and Mortality
Summary Statistics
O 10th percentile
£ J5th percentile
¦ Mean
Figure 3-9. Monitor-based PM2.5 concentrations in key Canadian epidemiologic studies. (Asterisks denote studies included in the
draft ISA Supplement).
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ST Exposure & Mortality Di 2017a (US: Nationwide; ST Exposure)
Lee 2015b (US: 3 SE States; ST Exposure)
ST exposure & Morbidity
Shi 2016 (US: 6 NE States; ST Exposure)
Kioog 2014 (US: 7 Mid-Atlantic States and D.C.; ST
Exposure)
deSouza 2021 (US: Nationwide; ST Exposure)*
Qiu 2020 (US: New England Area; ST Exposure)*
Kloog 2012 (US: 6 NE States; ST Exposure)
Wyatt 2020b (US: 530 US counties; ST Exposure)*
LTexposure & Mortality Thurston 2016 (US: 6 States and 2 MSAs; LT Exposure)
Hart 2015 (US: Nationwide; LT Exposure)
Di 2017b (US: Nationwide; LT Exposure)
Dominici 2019 (US: Nationwide; LT Exposure)*
Wang 2017 (US: 7 SE States; LT Exposure)
Wang 2020 (US: Nationwide; LT Exposure)*
Wu 2020a (US: Nationwide; LT Exposure)*
Wu 2019 (US: New England Area; LT Exposure)*
Shi 2016 (US: 6 NE States; LT Exposure)
LT Exposure & Morbidity Kloog 2012 (US: 6 NE States; LT Exposure)
Study Type
| ST Exposure & Mortality
I] ST exposure & Morbidity
|H LT exposure & Mortality
([I LT Exposure & Morbidity
Summary Statistics
O 10th percentile
£ 25th percentile
¦ Mean
4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0 10.5 11.0 11.5 12.0 12.5
Overall PM2.5 Concentration for the Study Period (ng/m3)
Figure 3-10. Hybrid model-predicted PM2.5 concentrations in key U.S. epidemiologic studies. (Asterisks denote studies included
in the draft ISA Supplement).
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Erickson 2020 (Canada: Nationwide; LT Exposure;
LT exposure , . , , , . .
& Mortality lmmigrant residence length < 10 years)*
Erickson 2020 (Canada: Nationwide; LT Exposure;
immigrants residence length 11-20 years)*
Erickson 2020 (Canada: Nationwide; LT Exposure;
immigrants residence length 21-30 years)*
Erickson 2020 (Canada: Nationwide; LT
Exposure;lmmigrant residence length > 30 years)*
Chen 2020 (Canada: Ontario; LT Exposure)*
Crouse 2019 (Canada: Nationwide; LT Exposure;
8-year)*
Zhang 2021 (Canada: Ontario; LT Exposure)*
Erickson 2020 (Canada: Nationwide; LT Exposure;
Non-immigrant)*
Crouse 2019 (Canada: Nationwide; LT Exposure;
3-year)*
Pinault 2018 (Canada: Nationwide; LT Exposure;
CanCHEC)*
Crouse 2019 (Canada: Nationwide; LT Exposure;
1-year)*
Pinault 2018 (Canada: Nationwide; LT Exposure;
mCHHS)*
Pinault 2016 (Canada: Nationwide; LT Exposure)
Christidis 2019 (Canada: Nationwide; LT Exposure)*
LT Exposure Shin 2019 (Canada: Ontario; LT Exposure)*
& Morbidity
Bai 2019 (Canada: Ontario; LT Exposure)*
Study Type
| LT exposure & Mortality
¦ LT Exposure & Morbidity
Summary Statistics
0 2?th percentile
¦ Mean
5.5 6.0 6.5 7.0 7.5 8.0 8.5
Overall PM2.5 Concentration for the Study Period (pg/m3)
Figure 3-11. Hybrid model-predicted PM2.5 concentrations in key Canadian epidemiologic studies. (Asterisks denote studies
included in the draft ISA Supplement).
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In further examining these data, we also ask:
• For the key epidemiologic studies using hybrid modeling approaches, what are the
study reported means for the general categories of methods of calculating the study
mean and how do the study-reported means vary and compare to each other?
Figure 3-12 and Figure 3-13 present the same key model-based epidemiologic studies
from the figures above but focus on the U.S. studies and group them based on their approach to
calculating the study-reported mean. For Figure 3-12, the studies are grouped by the
geographical spatial scale at which the modeling was conducted (i.e., nationwide, regional,
rural). Figure 3-13 presents the same key U.S. model-based epidemiologic studies, but subset by
the method used to average grid cells in study-reported long-term mean PM2.5 concentrations.
For the key U.S. model-based epidemiologic studies, the various methods include the average of
all grid cells; grid cells averaged up to ZIP code, postal code or census tract; or population-
weighted grid cell averaged up to ZIP code or census tract. Lastly, Figure 3-14 subsets the key
U.S. epidemiologic studies that used hybrid exposure models by both spatial scale and the
method used to average grid cells in study-reported long-term mean PM2.5 concentrations.
Grouping the key epidemiologic studies in such ways allows for visual comparisons of the study-
reported mean PM2.5 concentrations across the different spatial scales and methods of averaging
the grid cells.
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Nationwide US
Regional US
Hart 2015 (US: Nationwide; LT Exposure)
Di 2017a (US: Nationwide; ST Exposure)
deSouza 2021 (US: Nationwide; ST Exposure)*
Di 2017b (US: Nationwide; LT Exposure)
Dominici 2019 (US: Nationwide; LT Exposure)*
Wang 2020 (US: Nationwide; LT Exposure)*
Wu 2020a (US: Nationwide; LT Exposure)*
Thurston 2016 (US: 6 States and 2 MSAs; LT
Exposure)
Kloog 2014 (US: 7 Mid-Atlantic States and D.C.; ST
Exposure)
Lee 2015b (US: 3 SE States; ST Exposure)
Wang 2017 (US: 7 SE States; LT Exposure)
Qiu 2020 (US: New England Area; ST Exposure)*
Kloog 2012 (US: 6 NE States; LT Exposure)
Kloog 2012 (US: 6 NE States; ST Exposure)
Wu 2019 (US: New England Area; LT Exposure)*
Wyatt 2020b (US: 530 US counties; ST Exposure)*
Shi 2016 (US: 6 NE States; ST Exposure)
Shi 2016 (US: 6 NE States; LT Exposure)
Study Type
| LT Exposure & Morbidity
| LT exposure S Mortality
| ST exposure a Morbidity
| ST Exposure a Mortality
Summary Statistics
O 10th percentile
0 25th percentile
¦ Mean
4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0 10.5 11.0 11.5 12.0 12.5
Overall PM2.5 Concentration for the Study Period (jig/m3)
Figure 3-12. Hybrid model-predicted PM2.5 concentrations in key U.S. epidemiologic studies, subset by spatial scale. (Asterisks
denote studies included in the draft ISA Supplement).
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Avg of all grid cells
Grid cell averaged up to zip, US
postal code, or census tract
Kloog 2014 (US: 7 Mid-Atlantic States and D.C.; ST
Exposure)
Kloog 2012 (US: 6 NE States; LT Exposure)
Kloog 2012 (US: 6 NE States; ST Exposure)
Shi 2016 (US: 6 NE States; ST Exposure)
Shi 2016 (US: 6 NE States; LT Exposure)
Thurston 2016 (US: 6 States and 2 MSAs; LT
Exposure)
Hart 2015 (US: Nationwide; LT Exposure)
Study Type
I ST Exposure & Mortality
I ST exposure & Morbidity
| LT exposure & Mortality
| LT Exposure & Morbidity
Summary Statistics
O 10th percentile
• 25th percentile
¦ Mean
Di 2017a (US: Nationwide; ST Exposure)
deSouza 2021 (US: Nationwide; ST Exposure)*
Lee 2015b (US: 3 SE States; ST Exposure)
Di 2017b (US: Nationwide; LT Exposure)
Dominici 2019 (US: Nationwide; LT Exposure)*
Wang 2017 (US: 7 SE States; LT Exposure)
Wang 2020 (US: Nationwide; LT Exposure)*
Qiu 2020 (US: New England Area; ST Exposure)*
Wu 2020a (US: Nationwide; LT Exposure)*
Population weighted grid US Wu 2019 (US: New England Area; LT Exposure)*
cell averaged upto census
tract or zip code Wyatt 2020b (US: 530 US counties; ST Exposure)*
5 6 7 8 9 10 11 12
Overall PMZ.S Concentration for the Study Period (ng/m')
Figure 3-13. Hybrid model-predicted PM2.5 concentrations in key U.S. epidemiologic studies, subset by method used to average
grid cells in study-reported long-term mean PM2.5 concentrations. (Asterisks denote studies included in the draft ISA
Supplement).
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Nationwide Grid cell averaged up to zip, postal US
code,or census tract
Regional Avg of all grid cells
Grid cell averaged up to zip, postal US
code,or census tract
Population weighted grid cell US
averaged up to census tract or zip code
Hart 2015 (US: Nationwide; LT Exposure)
Di 2017a (US: Nationwide; ST Exposure)
deSouza 2021 (US: Nationwide; ST Exposure)*
Di 2017b (US: Nationwide; LT Exposure)
Dominici 2019 (US: Nationwide; LT Exposure)*
Wang 2020 (US: Nationwide; LT Exposure)*
Wu 2020a (US: Nationwide; LT Exposure)*
Kloog 2014 (US: 7 Mid-Atlantic States and D.C.; ST Exposure)
Kloog 2012 (US: 6 NE States; LT Exposure)
Kloog 2012 (US: 6 NE States; ST Exposure)
Shi 2016 (US: 6 NE States; ST Exposure)
Shi 2016 (US: 6 NE States; LT Exposure)
Thurston 2016 (US: 6 States and 2 MSAs; LT Exposure)
Lee 2015b (US: 3 SE States; ST Exposure)
Wang 2017 (US: 7 SE States; LT Exposure)
Qiu 2020 (US: New England Area; ST Exposure)*
Wu 2019 (US: New England Area; LT Exposure)*
Wyatt 2020b (US: 530 US counties; ST Exposure)*
o
Study Type
| ST Exposure & Mortality
ST exposure & Morbidity
| LT exposure & Mortality
| LT Exposure & Morbidity
y Statistics
O 10th percentile
0 25th percentile
¦ Mean
Overall PM;.S Concentration for the Study Period (|ig/m3)
Figure 3-14. Hybrid model-predicted PM2.5 concentrations in key U.S. epidemiologic studies, subset by spatial scale and
method used to average grid cells in study-reported long-term mean PM2.5 concentrations. (Asterisks denote studies included
in the draft ISA Supplement).
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32
33
34
35
36
37
38
Based on the information above with regard to the key U.S. and Canadian epidemiologic
studies, we summarize some of our observations:
• For key U.S. epidemiologic studies that use monitors to estimate PM2.5 exposures (Figure 3-
8), overall mean PM2.5 concentrations are generally at or above 9.9 |j,g/m3.32 Based on our air
quality analyses, we would generally expect these values to be 10-20% lower than the
corresponding area annual design value.
• For key U.S. epidemiologic studies that use hybrid model-predicted exposure (Figure 3-10),
mean PM2.5 concentrations range from just above 8.0 |j,g/m3 to just above 12.0 |j,g/m3.The
majority of these studies estimate PM2.5 exposure by averaging up from the grid cell spatial
resolution used in the modeling approach to the spatial resolution of health study data (e.g.,
ZIP code or census tract). This incorporates an aspect of population weighting in the
calculation of the mean. Based on our air quality analyses, we would expect these
epidemiologic studies to report means similar to those from monitor-based studies and to
generally be about 14-18% less than the area annual design value.
In studies that average up from the grid cell level to the ZIP code, postal code,
or census tract level, mean PM2.5 concentrations range from 9.8 |j,g/m3 to 12.2
Hg/m3.
The one study that population weighted the grid cell prior to averaging up to
the ZIP code or census tract level report mean PM2.5 concentrations of 9.3
Hg/m3.
• The other set of key U.S. epidemiologic studies averaged up from the grid cell spatial
resolution across the entire study area, whether that be the nation or a region of the country.
Based on our air quality analyses (i.e., suggesting these means are 40-50% lower than the
area annual design value), we would expect these epidemiologic studies to report some of the
lowest mean values.
- For these studies, the reported mean PM2.5 concentrations range from 8.1
[j,g/m3 to 11.9 [j,g/m3.
• Of the key epidemiologic studies evaluated in the 2019 ISA and draft ISA Supplement, a
subset of studies report PM2.5 concentrations corresponding to the 25th and 10th percentiles of
health data or exposure estimates to provide insight into the concentrations that comprise the
lower quartiles of the air quality distributions.
In key U.S. epidemiologic studies that use monitors to estimate PM2.5
exposures, 25th percentiles of health events correspond to mean PM2.5
concentrations (i.e., averaged over the study period for each study city) at or
above 11.5 |j,g/m3 and 10th percentiles of health events correspond to mean
PM2.5 concentrations at or above 9.8 |j,g/m3 (i.e., 25% and 10% of health
events, respectively, occur in study locations with mean PM2.5 concentrations
below these values).
32 This is generally consistent with, but slightly below, the lowest study-reported mean PM2 5 concentration from
monitor-based studies available in the 2020 PA, which was 10.7 |ig'/m3 (U.S. EPA, 2020, Figure 3-7).
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39
Of the key U.S. epidemiologic studies that use hybrid modeling approaches to
estimate long-term PM2.5 exposures, the ambient PM2.5 concentrations
corresponding to 25th percentiles of estimated exposures are 6.2 and 9.1
Hg/m3.
In key U.S. epidemiologic studies that use hybrid modeling approaches to
estimate short-term PM2.5 exposures, the ambient concentrations
corresponding to 25th percentiles of estimated exposures, or health events, are
generally at or above 6.4 |j,g/m3. In the one study with lower concentrations,
the ambient PM2.5 concentration corresponding to the 25th percentile of
estimated exposures is 4.7 |j,g/m3.33 In the one study with information
available on the 10th percentile of health events, the ambient PM2.5
concentration corresponding to that 10th percentile is 4.7 |j,g/m3.
• Generally, the study-reported mean concentrations in Canadian studies are lower than those
reported in the U.S. studies for both monitor-based and hybrid model methods. However,
based on our lack of information about how best to compare air quality gradients between
the two countries, it is unclear how to view these Canadian study mean values in the context
of a level of the annual standard in the U.S.
- For the majority of key Canadian epidemiologic studies that use monitor-
based exposure (Figure 3-9), mean PM2.5 concentrations generally ranged
from 7.0 |j,g/m3 to 9.0 |j,g/m3. For these studies, 25th percentiles of health
events correspond to mean PM2.5 concentrations at or above 6.5 |j,g/m3 and
10th percentiles of health events correspond to mean PM2.5 concentrations at
or above 6.4 |j,g/m3.
- For the key Canadian epidemiologic studies that use hybrid model-predicted
exposure (Figure 3-11), the mean PM2.5 concentrations are generally lower
than in U.S. model-based studies (Figure 3-10), ranging from approximately
6.0 |j,g/m3 to just below 10.0 |j,g/m3.
The majority of the key Canadian epidemiologic studies that used hybrid
modeling were completed at the nationwide scale, while four studies were
completed at the regional geographic spatial scale. In addition, all the key
Canadian epidemiologic studies, average up from the grid cell level to the
spatial resolution of health study data (e.g., postal code).
The majority of studies estimating exposure nationwide range between just
below 6.0 |j,g/m3 to 8.0 |j,g/m3. One study (Erickson et al. (2020)) presents an
analysis related immigrant status and length of residence in Canada versus
non-immigrant populations, which accounts for the four highest mean PM2.5
concentrations in Figure 3-11, ranging between 9.0 |j,g/m3 and 10.0 |j,g/m3.
The four studies that estimate exposure at the regional scale report mean PM2.5
concentrations that range from 7.8 |j,g/m3 to 9.8 |j,g/m3.
33 As noted above, in this study (Shi et al., 2016), the authors report that most deaths occurred at or above the 75th
percentile of annual exposure estimates (i.e., 10 |.ig/m3). The short-term exposure estimates accounting for most
deaths are not presented in the published study.
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26
In two Canadian studies with information available on the 25th percentile of
health events, the ambient PM2.5 concentration corresponding to that 25th
percentile is approximately 8.0 |j,g/m3 in two studies, and 4.3 |j,g/m3 in a third
study.
In addition to the key epidemiologic studies, the 2019 ISA and draft ISA Supplement also
include a subset of studies that assess the relationship between PM2.5 exposure and health effects
that have emerged and so we ask:
• To what extent has information emerged to further inform our understanding of
PM2.5 in ambient air and associations with health effects? Are there studies that
explore alternative methods for assessing the relationship between PM2.5 exposure
and health effects or studies that observe changes in health effects with changes in
PM2.5 concentrations in ambient air over time?
In addition to the expanded body of evidence from the key epidemiologic studies
discussed above, there are also a subset of studies that have emerged that further inform our
understanding of the relationship between PM2.5 exposure and health effects (U.S. EPA, 2019,
U.S. EPA, ).
The first type are studies that examine health effect associations in analyses with the
highest exposures excluded, restricting analyses to daily exposures less than the 24-hour primary
PM2.5 standard and annual exposures less than the annual PM2.5 standard. The restricted analyses
can be informative in assessing the nature of the association between long-term exposures (e.g.,
< 12.0 |ig/m3) or short-term exposures (e.g., < 35 |ig/m3) when looking only at exposures to
lower concentrations, including whether the association persists in such restricted analyses
compared to the same analyses for all exposures, as well as whether the association is stronger,
in terms of magnitude and precision, than when completing the same analysis for all exposures.
These studies, as assessed in the 2019 ISA and draft ISA Supplement, are summarized in Table
3-10 below.
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1 Table 3-10. Epidemiologic studies examining the health impacts associated with ambient PM2.5 concentrations when studies
2 are conducted with restricted air quality exposures.
3
Citation
Study Area
(health
endpoint)
Years of
PM2.5 Air
Quality
(monitored)
AQ in restricted
analysis
(Mg/m3)
Study-
reported
Mean in
restricted
analysis
(ug/m3)
Study-
reported
Mean in
main
analysis
(ug/m3)
Effect Estimate in
restricted analysis
(95% CI)
Effect Estimate in main
analysis (95% CI)
U.S.-based Studies and Long-term Exposure (per 5 ng/m3)
Di et al.,
2017b
Nationwide
(All-cause
mortality 65+)
2000-2012
< 12.0
9.6
11.0
1.07 (1.06-1.07)
1.04(1.04-1.04)
Dominici et al.,
2019
Nationwide
(All-cause
mortality)
2000-2012
< 12
9.6
11.0
1.06 (1.06-1.07)
1.03(1.03-1.04)
Shi et al., 2016
6 NE States
2003-2008
< 10.0
NR
8.1
1.04 (1.00, 1.09)
1.04(1.01, 1.06)
Yazdi et al.,
2019
7 SE States
(CVD
morbidity)
2000-2012
< 12
NR
NR
Stroke: 1.29 (1.27-
1.31)
Ml: 1.18(1.16-1.20)
HF: 1.44 (1.43-1.46)
Stroke: 1.16(1.16-1.17)
Ml: 1.14(1.13-1.15)
HF: 1.29 (1.29-1.30)
Canadian Studies and Long-term Exposure (per 5 ng/m3)
Zhang et al.,
2021
Ontario (Non-
accidental and
CVD mortality)
2000-2016
< 10.0 and
<8.8
NR
7.8
Non-accidental
mortality: < 10.0:1.22
(1.10-1.36); and <8.8:
1.04(0.91-1.17)
CVD mortality: < 10.0:
1.38 (1.10-1.73); and <
8.8: 1.05(0.80-1.38)
Non-accidental mortality: 1.20
(1.09-1.32)
CVD mortality: 1.49 (1.22-1.83)
U.S. Studies and Short-term Exposure (per 10 Mg/m3)
deSouza et al.,
2021
Nationwide
(First CVD HA)
2000-2012
<25
NR
11.5
1.3% (0.9-1.6%)
0.9% (0.6-1.1 %)
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Di et al.,
2017a
Nationwide
(All-cause
mortality 65+)
2000-2012
<25.0
NR
11.6
1.61 (1.48-1.74)
1.18(1.09-1.28)
Lee et al.,
2015 1
3 SE States
(Non-
accidental)
2007-2011
In ZIP codes
where annual
average <12.0
and only on
days < 35.0
NR
11.1
Non-accidental: 2.08%
(1.99-2.17)%
Non-accidental 1.56% (1.19-
1.94%)
Lee et al.,
20152
3 SE States
(Non-
accidental)
2007-2011
In ZIP codes
where annual
average < 12.0
NR
11.1
Non-accidental: 2.06%
(1.97-2.15%)
Non-accidental 1.56% (1.19-
1.94%)
Shi et al., 2016
6 NE States
2003-2008
<30.0
NR
8.2
2.14% (1.34-2.95%)
2.14% (1.38, 2.89%)
Wei et al.,
2019
Nationwide
(CVD HA)
2000-2012
<25
(WHO air quality
guideline value
for daily PM2.5)
NR
NR
Relative increase in
risk for HA with 1
|jg/m3 increase in
Iag0-1 PM25:
Ml: 0.16 (0.09, 0.24)
CHF: 0.16(0.11,0.22)
Relative increase in risk for HA
with 1 pg/m3 increase in Iag0-1
PM25:
Ml: 0.11 (0.07, 0.16)
CHF: 0.14(0.10, 0.17)
1 First, restricted ZIP code areas to where the annual average of predicted PM2.5 is < 12 |jg/m3 to assess the acute effect of PM2.5 on mortality only areas with annual average
concentrations < 12 |jg/m3.
2 In terms of daily standard, conducted analysis on the days < 35 |jg/m3 and only in ZIP codes with annual average concentrations < 12 |jg/m3.
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40
There are a number of U.S. and Canadian studies that examine health effect associations
in analyses with the highest exposures excluded. These restricted analyses provide support for
positive and statistically significant effect estimates at lower mean PM2.5 concentrations than
their main effect analysis means as shown in Table 3-10 and in many cases, exhibit greater effect
estimates in magnitude than their corresponding main analyses. With regard to these studies, we
particularly note the following:
• In the four U.S. studies that estimate effects associated with long-term exposure to PM2.5, the
effect estimates are greater in the restricted analyses than in the main analyses.
o Di et al. (2017a) and Dominici et al. (2019) report positive and statistically significant
associations in analyses restricted to concentrations less than 12.0 |j,g/m3 for all-cause
mortality Di et al. (2017b) and stroke, MI, and HF Dominici et al. (2019), and effect
estimates are greater in the restricted analyses than effect estimates reported in main
analyses. In addition, both studies report mean PM2.5 concentrations of 9.6 |j,g/m3
o Shi et al. (2016) and Yazdi et al. (2019) report positive and statistically significant
associations in analyses restricted to concentrations less than 10.0 |j,g/m3 and 12.0
Hg/m3, respectively. Shi et al. (2016) does not report overall mean PM2.5
concentrations in restricted analyses, though such means are presumably somewhat
below the main analysis reported mean of 8.1 |j,g/m3. Yazdi et al. (2019) does not
report the overall mean PM2.5 concentration in either the restricted analysis or main
analysis, but the effect estimates for stroke, MI, and HF are all higher in the restricted
analyses compared to main analyses.
• While none of the U.S. studies of short-term exposure present mean PM2.5 concentrations for
the restricted analyses, these studies generally have mean 24-hour average PM2.5
concentrations in the main analyses below 12.0 |ag/m\ and report increases in the effect
estimates in the restricted analyses compared to the main analyses.
o With the exception of Wei et al. (2019), short-term exposure studies report mean 24-
hour average PM2.5 concentration in main analyses all below 12.0 ng/m3, and ranging
from 8.2 |j,g/m3 Shi et al. (2016) to 11.6 (Di et al. (2017a).
o These studies, except for Shi et al. (2016), report increases in effect estimates in
restricted analyses compared to main analyses. Shi et al. (2016) reports the same effect
estimates for both the restricted and main analyses.
• In the one Canadian study of long-term PM2.5 exposure, Zhang et al. (2021) conducted
analyses where annual PM2.5 concentrations were restricted to concentrations below 10.0
Hg/m3 and 8.8 |ag/m\ which presumably have lower mean concentrations than the mean of
7.8 |j,g/m3 reported in the main analyses, though restricted analysis mean PM2.5
concentrations are not reported.
o Effect estimates for non-accidental mortality are greater in analyses restricted to PM2.5
concentrations less than 10.0 |ag/m\ but less in analyses restricted to < 8.8 |j,g/m3.
Effect estimates for CVD mortality are lower in restricted analyses than the main
analysis.
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Overall, these studies provide additional information on the nature of the association
between long- or short-term exposures when analyses are restricted to lower PM2.5
concentrations. Further, these studies indicate that effect estimates are generally greater in
magnitude in the restricted analyses for long- and short-term PM2.5 exposure compared to the
main analyses.
The second type of studies that have recently emerged and can further inform our
understanding of the relationship between PM2.5 exposure and health effects are those that
employ causal modeling methods. Causal modeling methods seek to mimic randomized
experiments through the use of study design and statistical methods, which reduces the potential
bias of effects due to confounding. The studies that employ causal modeling methods assessed in
the 2019 ISA and draft ISA Supplement are summarized in Table 3-11 below.
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1 Table 3-11. Summary of information from studies that use causal modeling statistical methods.
Study
Reference
Statistical
Method1
Study Area
AQ Years
Health
Endpoint
(populatio
n)
Study-reported
Mean (ng/m3)
Results
Awad et
al., 2019
IPW
U.S.
Nationwide
2000-
2012
LT
mortality
(65+)
Mean change in
exposure the year
before move and
the second year
after move:
Whites: -0.73
Blacks: -0.90
Per a 10 pg/m3 increase in annual PM2 5 concentrations:
White individuals: HR= 1.21 (95% CI: 1.20,1.22)
Black individuals: HR = 1.12 (95% CI: 1.08,1.15)
All-cause mortality: HR = 1.12 (95% CI: 1.08,1.15)
Awad et
al., 2019
(restricted
IPW
U.S.
Nationwide
2000-
2012
LT
mortality
(65+)
Restricted <12.0:
NR
Per a 10 pg/m3 increase in annual PM2 5 concentrations:
White individuals: HR = 1.25 (95% CI: 1.24,1.27)
Black individuals: HR = 1.08 (95% CI: 1.01,1.14)
Higbee et
al., 2020
IPW
U.S.
Nationwide
1986-
2015
LT
mortality
(18+)
10.7
For a 10 pg/m3 increase in annual PM2 5 concentrations:
All-cause mortality: HR = 1.12 (95% CI: 1.08,1.15)
Cardiopulmonary mortality: HR = 1.23 (95% CI: 1.17,
1.29)
Qiu et al.,
2020
IPW
New England
2000-
2012
ST CVD
HA (65+)
AMI: 10.3
CHF: 10.08
IS: 10.1
Percent increase HA rate for a 10 pg/m3 increase in
PM25 concentrations
AMI: 4.31% (95% CI: 2.21, 6.42)
CHF: 3.95% (95% CI: 2.37,5.53)
IS: 2.56% (95% CI: 0.44, 4.69)
Schwartz
et al.,
2018a
3 approaches:
Instrumental
approach
Marginal structural
models
Time-series
analysis
135 U.S.
Cities
1999-
2010
ST
mortality
(18+)
12.8
Percent change in daily mortality per 10 pg/m3 increase
in PM2.5 concentrations
Instrumental approach: 1.54% (95% CI: 1.12,1.97)
Marginal structural models: 0.75% (95% CI: 0.35,1.15)
Time-series: 0.60%: (95% CI: 0.34, 0.85%)
Schwartz
et al.,
2018a
3 approaches:
Instrumental
approach
135 U.S.
Cities
1999-
2010
ST
mortality
(18+)
Restricted < 25.0:
NR
Percent change in daily mortality per 10 pg/m3 increase
in PM2.5 concentrations
Instrumental approach: 1.70% (95% CI: 1.11, 2.29)
Marginal structural models: 0.83% (95% CI: 0.39,1.27)
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(restricted
analysis)
Marginal structural
models
Time-series
analysis
Time-series: 0.62%: (95% CI: 0.32, 0.93)
Schwartz
et al.,
2018b
GPS IPW
Northeastern
and Mid-
Atlantic States
2000-
2012
Life
expectancy
NA
Estimated mean age at death for an annual average
exposure of 12 pg/m3 was 0.89 years (95% CI:
0.88,0.91) than estimated for a counterfactual PM2.5
exposure of 7.5 |jg/m3
Schwartz
et al.,
2021
DID
U.S.
Nationwide
2000-
2016
LT
probability
of dying
(65+)
10.3
Probability of dying in each year increased by 3.85x104
(95% CI 1.95x104, 5.76x104) for each 1 pg/m3 increase
in annual PM2.5 concentrations
Schwartz
et al.,
2021
(restricted
analysis)
DID
U.S.
Nationwide
2000-
2016
LT
probability
of dying
(65+)
NR
Probability of dying in each year increased by 4.26x104
(95% CI 1.43x104, 7.09x104) for each 1 pg/m3 increase
in annual PM2.5 concentrations
Wu et al.,
2019
RC-GPS and 3
GPS approaches:
Subclassification
GPS
IPTW GPS
GPS matching
New England
2000-
2012
(modeled)
LT
mortality
(65+)
NA
Exposure levels of low (< 8.0 pg/m3) versus moderate
PM2.5 concentrations (8.0-10.0 Dg/m3) to low exposure
Subclassification: 1.025 (95% CI: 1.006,1.045)
IPTW GPS: 1.022 (95% CI: 1.007, 1.038)
Matching GPS: 1.028 (1.012,1.045)
Comparison of exposure levels of < 8.0 pg/m3 vs. >
10.0 pg/m3
Subclassification: 1.035 (95% CI: 0.999,1.072)
IPTW GPS: 1.030 (95% CI: 1.005, 1.056)
Matching GPS: 1.035 (95% CI: 1.015,1.055)
Wu et al.,
2020b
Three GPS
approaches:
GPS matching
GPS weighting
GPS adjustment
U.S.
Nationwide
2000-
2016
(modeled)
LT
mortality
(65+)
9.8
Reported hazard ratios for a decrease in mortality risk
per 10 pg/m3 decrease in annual PM2.5
GPS matching: HR = 1.068 (95% CI: 1.054,1.083)
GPS weighting: HR = 1.076 (95% CI: 1.065,1.088)
GPS adjustment: HR = 1.072 (95% CI: 1.061,1.082)
Wu et al.,
2020a
(restricted
analysis)
Three GPS
approaches:
GPS matching
GPS weighting
U.S.
Nationwide
2000-
2016
(modeled)
LT
mortality
(65+)
Restricted <12.0:
8.4
Reported hazard ratios for a decrease in mortality risk
per 10 pg/m3 decrease in annual PM2.5
GPS matching: HR = 1.261 (95% CI: 1.233,1.289)
GPS weighting: HR = 1.268 (95% CI: 1.237,1.300)
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GPS adjustment
GPS adjustment: HR = 1.231 (95% CI: 1.180,1.284)
Yazdi et
al., 2021
Doubly Robust
Additive Model
(DRAM)
U.S.
Nationwide
2000-
2016
(modeled)
LT
Cardiovasc
ular
hospitalizat
ion
outcomes
(65+)
10.21
% increase in the risk with 1 pg/m3 increase in PM2.5:
Ml: 0.002; Stroke: 0.009; Al: 0.006
Yitshak-
Sade et
al., 2019
DID
Northeastern
and mid-
Atlantic States
(14 U.S.
States)
2000-
2013
LT
mortality
(65+)
Range: 6.5-14.5
4.04% (95% CI: 3.49,4.59) increase in mortality rates for
an IQA (3 pg/m3) increase in annual PM2.5
concentrations
1 GPS: generalized propensity score; IPW: inverse probability weighting; DID: Difference-in-difference; HR: hazard ratio; IRR: incidence rate ratio; IPTW: inverse probability
treatment weighting; IV: instrument variable; OLS: Ordinary Least Squares; RC: regression calibration
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The 2019 ISA and draft ISA Supplement assess epidemiologic studies that implemented
causal modeling methods. As presented in Table 3-11 above, these studies employ a variety of
statistical methods, such as GPS, IPW, and DID. We particularly note the following:
• These studies reported consistent results among large study populations across the U.S. The
results from studies that use causal modeling methods further inform the relationship
between long- and short-term PM2.5 exposure and total mortality.
• Studies that employ causal methods to assess the association between long-term exposure to
PM2.5 and mortality provide additional support for the associations reported in the broader
body of cohort studies that examined long-term PM2.5 exposure and mortality.
- For example, Wu et al., 2020a used three different causal modeling statistical
approaches, in addition to two more traditional statistical method methods
(Cox proportional hazards modeling and Poisson time-series regression
model), finding consistent positive and statistically significant results between
the five statistical methods and with HRs per a 10 |j,g/m3 increase in PM2.5
ranging from 1.062 (95% CI: 1.055,1.069) using the poisson statistical
method to 1.076 (95% CI: 1.065, 1.088) with the GPS matching statistical
method.
Lastly, there is also a smaller subset of epidemiologic studies, accountability analyses,
that evaluated the potential for improvements in public health as ambient PM2.5 concentrations
have declined over time. Given the nature of these studies, the majority tend to focus on time
periods in the past during which ambient PM2.5 concentrations were substantially higher than
those measured more recently (e.g., see Chapter 2, Figure 2-16). These studies, as assessed in the
2019 ISA and draft ISA Supplement, are summarized in Table 3-12 below.
Table 3-12. Epidemiologic studies examining the health impacts of long-term reductions in
ambient PM2.5 concentrations.
Study Reference
Study Area
Years of
PM2.5 Air
Quality
(monitored)
Starting Mean
PM2.5
Concentration
(ug/m3)
Ending Mean
PM2.5
Concentration
(ug/m3)
Study Results
Pope et al. (2009)
211 U.S.
counties
1979-1983
compared to
1999-2000
20.6
14.1
Statistically
significant
association
between declining
ambient PM2.5 and
increasing life
expectancy
Correia et al.
(2013)
545 U.S.
counties
2000
compared to
2007
13.2
11.6
Statistically
significant
association
between declining
ambient PM2.5 and
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increasing life
expectancy
Berhane et al.
(2016)
4,602 children
in 8 California
communities
1992-2000;
1995-2003;
2002-2011
20.5
14.4
Statistically
significant decrease
in bronchitic
symptoms in
10-year old children
with and without
asthma
Gauderman et al.
(2015)
2,120 children
in 5 California
communities
1994-1997;
1997-2000;
2007-2010
21.3-31.5
11.9-17.8
Statistically
significant
improvements in 4-
year growth of lung
function
Wyatt et al., 2020b
2132 counties
in the U.S.
(population
>20,000)
1990-2010
NR
NR
The annual change
in cardiovascular
mortality rate
ranged from 6.5-7.6
fewer deaths/year
(per 100,000
person-years) per 1
|jg/m3 decrease in
PM2.5 over time.
Bennett et al.,
2019
U.S.
Nationwide
and 1339
U.S. counties
1999-2015
13.6 (Pop-
weighted mean)
8.0 (Population-
weighted mean;
Mean range in
counties: 2.8-
13.2)
Reductions in PM2.5
since 1999 have
increased life
expectancy in men
and women in all
but 14 counties
where PM2.5
increased slightly
Corrigan et al.,
2018
619 U.S.
counties
2000-2010
2000-2004: 12.0
2005-2010: 10.8
Fewer CV deaths
per year for each 1
|jg/m3 decrease in
PM2.5.
Henneman et al.,
2019
Multiple U.S.
states
2005-2012
2005: 10.0
2012: 7.2
Reduced exposure
to total PM2.5 and
coal emissions led
to reduced rates
total mortality and
CVD HA.
Sanders et al.,
2020
600-700 U.S.
counties
2000-2013
Before 2006:
Non-attainment:
15.3 and
Attainment: 11.0
After 2006:
Non-attainment:
12.0
Attainment: 9.3
By 2005 PM2.5
designation status
(attainment or non-
attainment), PM2.5
levels and
corresponding
mortality rates
Fan and Wang,
2020
Eastern US
1999-2013
NR
NR
Fewer CVD deaths
per year for each 1
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|jg/m3 reduction in
annual PM2.5
concentrations
Peterson et al.,
2020
2132 counties
1990-2010
NR
NR
Fewer CVD deaths
for each 1 pg/m3
reduction in annual
PM2.5
concentrations
The accountability studies assessed in the 2019 ISA and draft ISA Supplement provide
support for the conclusion that public health benefits are associated with decreases in ambient
PM2.5 concentrations. In particular, we note the following key observations from these studies:
• Of the new studies evaluated in the 2019 ISA and draft ISA Supplement, Corrigan et al.
(2018), Henneman et al. (2019) and Sanders et al. (2020) present analyses with starting
concentrations below 12.0 |ig/m3.
- Henneman et al. (2019) explored the changes in modeled PM2.5 concentrations
following the retirement of coal fired power plants in the U.S., and found that
reductions from mean annual PM2.5 concentrations of 10.0 |ig/m3 in 2005 to
mean annual PM2.5 concentrations of 7.2 |ig/m3 in 2012 from coal-fueled
power plants resulted in corresponding reductions in the number of
cardiovascular-related hospital admissions and total mortality in those aged 65
and older.
Corrigan et al. (2018) examined whether there was a change in the
cardiovascular mortality rate before (2000-2004) and after (2005-2010)
implementation of the first annual PM2.5 NAAQS implementation based on
mortality data from the National Center for Health Statistics. They reported
1.10 (95% CI: 0.37, 1.82) fewer cardiovascular deaths per year per 100,000
people for each 1 [j,g/m3 reduction in annual PM2.5 concentrations. When
comparing whether counties met the annual PM2.5 standard, there were 1.96
(95% CI: 0.77, 3.15) fewer cardiovascular deaths for each 1 [j,g/m3 reduction
in annual PM2.5 concentrations between the two periods for attainment
counties, whereas for non-attainment counties, there were 0.59 (95% CI:
-0.54, 1.71) fewer cardiovascular deaths between the two periods.
Sanders et al. (2020) examined whether policy actions (i.e., the first annual
PM2.5 NAAQS implementation rule in 2005 for the 1997 annual PM2.5
standard with a 3-year annual average of 15 |ig/m3) reduced PM2.5
concentrations and mortality rates in Medicare beneficiaries between 2000-
2013. They found evidence of changes in associations with mortality (a
decreased mortality rate of - 0.5 per 1,000 in attainment and non-attainment
areas) due to changes in annual PM2.5 concentrations in both attainment and
non-attainment areas, which had starting concentrations below 12.0 |ig/m3
following implementation of the annual PM2.5 NAAQS in 2005. In addition,
following implementation of the annual PM2.5 NAAQS, annual PM2.5
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concentrations decreased by 1.59 [j,g/m3 (95% CI: 1.39, 1.80) which
corresponded to a reduction in mortality rates among individuals 65 years and
older (0.93% [95% CI: 0.10%, 1.77%]) in non-attainment counties relative to
attainment counties.
• Bennett et al. (2019) reports increases in life expectancy in all but 14 counties (1325 of 1339
counties) that have exhibited reductions in PM2.5 concentrations from 1999 to 2015.
• While Fan and Wang (2020), Peterson et al. (2020), and Wyatt et al. (2020a) do not report
starting and ending concentrations, these studies lend support to the conclusions that
reductions in PM2.5 concentrations lead to public health improvements, including reductions
in cardiovascular mortality.
The information in Table 3-10, Table 3-11, and Table 3-12 provide additional support to
inform the relationship between long- and short-term PM2.5 exposure and total mortality.
Analyses that are restricted only to concentrations at or below the levels of the current primary
PM2.5 standards find positive and significant associations with exposure to PM2.5 and health
outcomes. These restricted analyses often report greater effect estimates compared to effect
estimates in the main analysis that uses the full distribution of PM2.5 concentrations. Studies that
use causal modeling methods to assess the relationship between PM2.5 and health outcomes
provide additional support for the associations reported in other epidemiologic studies. Finally,
new studies assessed in the draft ISA Supplement evaluate the relationship between declines in
ambient PM2.5 concentrations over time and the potential for improvements in public health, and
support the conclusion in the 2020 PA; improvements in air quality are associated with
improvements in public health. Some of these new studies have lower starting concentrations
than similar studies included in the 2019 ISA.
3.3.4 Uncertainties in the Health Effects Evidence
• To what extent have important uncertainties identified in prior reviews been
reduced and/or have additional uncertainties emerged?
We have not identified any new uncertainties in the evidence since the 2020 review.
However, we continue to recognize uncertainties that persist from the previous reviews. This
array of important areas of uncertainty related to the current health effects evidence, including
that assessed in the 2019 ISA and the draft ISA Supplement, is summarized below.
Although the epidemiologic studies clearly demonstrate associations between long- and
short-term PM2.5 exposures and health outcomes, as in previous reviews, we continue to
recognize several uncertainties and limitations in the health effects evidence remain.
Epidemiologic studies evaluating short-term PIVh.sexposure and health effects have reported
heterogeneity in associations between cities and geographic regions within the U.S.
Heterogeneity in the associations observed across epidemiologic studies may be due in part to
exposure error related to measurement-related issues, the use of central fixed-site monitors to
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represent population exposure to PM2.5, and our limited understanding of factors that could be
due to a number of factors including exposure error related to measurement-related issues,
variability in PM2.5 composition regionally, and factors that result in differential exposures (e.g.,
topography, the built environment, housing characteristics, personal activity patterns).
Heterogeneity is expected when the methods or the underlying distribution of covariates vary
across studies (U.S. EPA, 2019, p. 6-221). Studies assessed in the 2019 ISA and draft ISA
Supplement have advanced the state of exposure science by presenting innovative methodologies
to estimate PM exposure, detailing new and existing measurement and modeling methods, and
further informing our understanding of the influence of exposure measurement error due to
exposure estimation methods on the associations between PM2.5 and health effects reported in
epidemiologic studies (U.S. EPA, 2019, section 1.2.2; U.S. EPA, 2021a). Data from PM2.5
monitors continue to be commonly used in health studies as a surrogate for PM2.5 exposure, and
often provide a reasonable representation of exposures throughout a study area (U.S. EPA, 2019,
section 3.4.2.2; U.S. EPA, 2021a, section 3.2.2.2.2). However, an increasing number of studies
employ hybrid modeling methods to estimate PM2.5 exposure using data from several sources,
often including satellites and models, in addition to ground-based monitors. These hybrid models
typically have good cross-validation, especially for PM2.5, and have the potential to reduce
exposure measurement error and uncertainty in the health effect estimates from epidemiologic
models of long-term exposure (U.S. EPA, 2019, section 3.5; U.S. EPA, 2021a, section 2.3.3).
While studies using hybrid modeling methods have demonstrated reduced exposure
measurement error and uncertainty in the health effect estimates, these studies use a variety of
approaches to estimate PM2.5 concentrations and to assign exposure to assess the association
between health outcomes and PM2.5 exposure. This variability in methodology has inherent
limitations and uncertainties, as described in more detail in section 2.3.3.1.5, and the
performance of the modeling approaches depends on the availability of monitoring data which
varies by location. Factors likely contributing to poorer model performance often coincide with
relatively low ambient PM2.5 concentrations, in areas where predicted exposures are at a greater
distance to monitors, and under conditions where the reliability and availability of key datasets
(e.g., air quality modeling) are limited. Thus, uncertainty in hybrid model predictions becomes
an increasingly important consideration as lower predicted concentrations are considered.
Regardless of whether a study uses monitoring data or a hybrid modeling approach when
estimating PM2.5 exposures, one key limitation that persists is associated with the interpretation
of the study-reported mean PM2.5 concentrations and how they compare to design values, the
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metric that describe the air quality status of a given area relative to the NAAQS.34 As discussed
above, the overall mean PM2.5 concentrations reported by key epidemiologic studies reflect
averaging of short- or long-term PM2.5 exposure estimates across location (i.e., across multiple
monitors or across modeled grid cells) and over time (i.e., over several years). For monitor-based
studies, the comparison is somewhat more straightforward than for studies that use hybrid
modeling methods, as the monitors used to estimate exposure in the epidemiologic studies are
generally the same monitors that are used to calculate design values for a given area. It is
expected that areas meeting a PM2.5 standard with a particular level would be expected to have
average PM2.5 concentrations (i.e., averaged across space and over time in the area) somewhat
below that standard level. Analyses of recent air quality in U.S. CBS As indicate that maximum
annual PM2.5 design values for a given three-year period are often 10% to 20% higher than
average monitored concentrations (i.e., averaged across multiple monitors in the same CBSA
(U.S. EPA, 2020, Appendix B, section B.7). The difference between the maximum annual design
value and average concentration in an area can be smaller or larger than this range, likely
depending on factors such as the number of monitors, monitor siting characteristics, and the
distribution of ambient PM2.5 concentrations. For studies that use hybrid modeling methods to
estimate PM2.5 concentrations, the comparison between study-reported mean PM2.5
concentrations and design values is more complicated given the variability in the modeling
methods, temporal scales (i.e., daily versus annual), and spatial scales (i.e., nationwide versus
urban) across studies. A recent comparison between two hybrid modeling surfaces explored the
impact of these factors on the resulting mean PM2.5 concentrations and provided additional
information about the relationship between mean concentrations from studies using hybrid
modeling methods and design values (see section 2.3.3.1.4). However, the results of those
analyses only reflect two surfaces and two types of approaches, so uncertainty remains in
understanding the relationship between estimated modeled PM2.5 concentrations and design
values more broadly across hybrid modeling studies. Moreover, this analysis was completed
using two hybrid modeling methods that estimate PM2.5 concentrations in the U.S., thus an
additional uncertainty includes understanding the relationship between modeled PM2.5
concentrations and design values reported in Canada.
In addition, where PM2.5 and other pollutants (e.g., ozone, nitrogen dioxide, and carbon
monoxide) are correlated, it can be difficult to distinguish whether attenuation of effects in some
studies results from copollutant confounding or collinearity with other pollutants in the ambient
mixture (U.S. EPA, 2019, section 1.5.1; U.S. EPA, 2021a, section 2.2.1). Studies evaluated in
34 For the annual PM2 5 standard, design values are calculated as the annual arithmetic mean PM2 5 concentration,
averaged over 3 years. For the 24-hour standard, design values are calculated as the 98th percentile of the annual
distribution of 24-hour PM2 5 concentrations, averaged over three years (Appendix N of 40 CFR Part 50).
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the 2019 ISA and draft ISA Supplement further examined the potential confounding effects of
both gaseous and particulate copollutants on the relationship between long- and short-term PM2.5
exposure and health effects. The studies continue to provide evidence indicating that associations
with PM2.5 are relatively unchanged in copollutants models (U.S. EPA, 2019, section 1.5.1; U.S.
EPA, 2021a, section 2.2.1). Another area of uncertainty is associated with other potential
confounders, beyond copollutants. Some studies have expanded the examination of potential
confounders to not only include copollutants, but also systematic evaluations of the potential
impact of inadequate control from long-term temporal trends and weather (U.S. EPA, 2019,
section 11.1.5.1). Analyses examining these covariates further confirm that the relationship
between PM2.5 exposure and mortality is unlikely to be biased by these factors. Other studies
have explored the use of causal modeling statistical techniques to reduce uncertainties related to
potential confounding that can further inform the causality determination for long-term and
short-term PM2.5 and mortality and cardiovascular effects (U.S. EPA, 2019, section 11.2.2.4, and
U.S. EPA 2021, sections 3.1.1.3, 3.1.2.3, 3.2.1.2, and 3.2.2.3). These studies indicate that bias
from unmeasured confounders can occur in either direction, although controlling for these
confounders did not result in the elimination of the association, but instead provided additional
support for associations between long-term PM2.5 exposure and mortality when accounting for
additional confounders (U.S. EPA, 2021a, section 3.2.2.2.6).
Another important limitation associated with the evidence is that, while epidemiologic
studies indicate associations between PM2.5 and health effects, they do not identify particular
PM2.5 exposures that cause effects. Rather, health effects can occur over the entire distribution of
ambient PM2.5 concentrations evaluated, and epidemiologic studies do not identify a population-
level threshold below which it can be concluded with confidence that PIVh.s-related effects do not
occur. Overall, evidence assessed in the 2019 ISA and draft ISA Supplement continues to
indicate a linear, no-threshold concentration-response relationship for long- and short-term PM2.5
exposure and all-cause or cause specific mortality. There is less certainty in the shape of the
concentration-response curve at mean annual PM2.5 concentrations generally below 8 |ig/m3,
although some studies characterize the concentration-response function with certainty in the
linear relationship below 8 |ig/m3 and down to as low as 5 |ig/m3 (U.S. EPA, 2019, section
11.2.4; U.S. EPA, 2021a, section 2.2.3.2).
3.4 RISK INFORMATION
To inform conclusions regarding the primary PM2.5 standards that are "requisite" to
protect public health (i.e., neither more nor less stringent than necessary; section 1.2), it is
important to consider the health risks that would be allowed under those standards. For the
current standards, this means evaluating PM2.5-related health risks in locations with three-year
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annual PM2.5 design values of 12.0 |j,g/m3 and/or three-year 24-hour design values of 35 |j,g/m3
(i.e., neither above nor below the levels of the current standards). Therefore, in addition to our
evaluation of PM2.5 concentrations in locations of key epidemiologic studies (which are based on
existing air quality; section 3.3.3.2), we assess PIVh.s-attributable risk associated with either:
• PM2.5 air quality that has been adjusted to simulate "just meeting" the current standards (i.e.,
design values equal to 12.0 |j,g/m3 and/or 35 |ag/m3) or lower alternative annual and/or 24-
hour standards.
• The change in risk associated with moving from PM2.5 air quality "just meeting" the current
standards to "just meeting" alternative annual and/or 24-hour standards.
These risk estimates, when considered alongside analyses of the evidence discussed in
section 3.3.3, are meant to inform conclusions on the primary standards that would be requisite
to protect the public health against long- and short-term PM2.5 exposures. Our consideration of
estimated risks focuses on addressing the following policy-relevant questions:
• What are the estimated PM2.5-associated health risks for air quality just meeting the
current primary PM2.5 standards?
• To what extent are risks estimated to decline when air quality is adjusted to just
meet potential alternative standards with lower levels?
• What are the uncertainties and limitations in these risk estimates?
The sections below summarize our approach to estimating risks (section 3.4.1) and the
results of the risk assessment (section 3.4.1.8). Additional detail on the risk assessment is
provided in Appendix C.
3.4.1 Risk Assessment Overview
Risk assessments combine data from multiple sources and involve various assumptions
and uncertainties. Below we summarize key aspects of the risk modeling approach. Input data for
these analyses includes concentration-response functions from epidemiologic studies (section
3.4.1.1) for each health outcome (section 3.4.1.2) and ambient annual or 24-hour PM2.5
concentrations (sections 3.4.1.3 and 3.4.1.4) for the study areas (section 3.4.1.5) utilized in the
risk assessment. Quantitative and qualitative methods used to characterize variability and
uncertainty in the risk estimates are discussed in section 3.4.1.7.
Information on other data inputs, such as baseline health incidence rate and population
demographic information, can be found in the Estimating PM2.5 and Ozone-Attributable Health
Benefits Technical Support Document (TSD) (U.S. EPA, 2021b; associated with the 2021
Revised Cross-State Air Pollution Rule Update (86 FR 23054, April 30, 2021). Additional detail
on the risk assessment approach is provided in Appendix C (section C. 1).
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3.4.1.1 Concentration-Response Functions
Concentration-response functions used in this risk assessment are from large, multicity
U.S. epidemiologic studies that evaluate the relationship between PM2.5 exposures and mortality.
Specific epidemiologic studies and concentration-response functions used here to estimate risk
were identified using criteria that take into account factors such as study design, geographic
coverage, demographic populations, and health endpoints. Information about the studies used in
this risk assessment is summarized in Table 3-13 and additional detail regarding the selection of
epidemiologic studies and specification of concentration-response functions can be found in
Appendix C (section C. 1.1) and the EstimatingPM2.5 and Ozone-Attributable Health Benefits
TSD (U.S. EPA, 2021b).
3.4.1.2 Health Outcomes
Consistent with the overall approach for this reconsideration, this risk assessment has a
targeted scope that focuses on all-cause or nonaccidental mortality associated with long-term and
short-term PM2.5 exposures (Table 3-13 and Appendix C, section C. 1.1).35 Evidence for these
outcomes supports the determination of a "causal relationship" in the 2019 ISA (U.S. EPA,
2019).36
Table 3-13. Epidemiologic studies used to estimate PM2.5-associated risk.
Epidemiology Study
Study Population3
Age Range
(years)
Mortality Categories Covered
Long-term mortality studies
Di et al., 2017b
Medicare
65+
All-cause
Turner et al., 2016
ACS
+
O
CO
All-cause
Short-term mortality
Baxter et al., 2017
77 cities
All ages
Non-accidental
Ito et al., 2013
NPACT
All ages
All cause
Zanobetti et al., 2014
121 communities
65+
All cause
aACS (American Cancer Survey), NPACT (National Particle Components Toxicity). See Appendix C Table C-1 for
additional study details.
35 Epidemiologic studies tend to attribute risk to either long- or short-term PM2 5 exposures, but rarely to both,
leading to uncertainties in the relationship between health effects from long- and short-term exposures. When
biologically plausible pathways leading to health effects are similar, estimates of impacts from long-term
exposures may include impacts due to short-term exposures and vice-versa. However, if pathways diverge,
impacts due to long- and short-term exposures may be the sum, or even greater than the sum, of the two exposure
durations.
36 While the 2019 ISA also found that evidence supports the determination of a "causal relationship" between long-
and short-term exposures and cardiovascular effects, cardiovascular mortality was not included as a health
outcome as it will be captured in the estimates of all-cause mortality.
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3.4.1.3 Air Quality Scenarios
We first estimate health risks associated with air quality adjusted to simulate "just
meeting" the current primary PM2.5 standards (i.e., the annual standard with its level of 12.0
|ig/m3 and the 24-hour standard with its level of 35 |ig/m3). We then use air quality modeling to
simulate air quality just meeting an alternative standard with a level of 10.0 |ig/m3 (annual) and
30 |ig/m3 (24-hour). In addition to the model-based approach, for the subset of 30 areas
controlled by the annual standard we also employ linear interpolation and extrapolation to
simulate just meeting alternative annual standards with levels of 11.0 (interpolated between 12.0
and 10.0 |jg/m3), 9.0 |ag/m\ and 8.0 |j,g/m3 (both extrapolated from 12.0 and 10.0 |ag/m3) ,37
Figure 3-15 provides an example of the interpolation and extrapolation calculations performed
for a single grid cell. In this example grid cell, modeled annual PM2.5 concentrations are 11.23
when the corresponding design value monitor just meets the current annual standard and 9.87
when the corresponding design value monitor just meets the alternative annual standard of 10.0
Hg/m3. The interpolated and extrapolated values for the example grid cells are provided in green
and blue text, respectively.38
11.23
10.55
8.51
Linear slope
Modeled
attainment of
current
standard (12)
Interpolated
attainment of
alternate
standard (11)
Modeled
attainment of
alternative
standard (10)
Extrapolated
attainment of
alternate
standard (9)
Extrapolated
attainment of
alternate
standard (8)
Figure 3-15. Illustration of approach to adjusting air quality to simulate just meeting
annual standards with levels of 11.0, 9.0, and 8.0 jig/m3.
37 Modeled air quality surfaces are simulated to just meet standards at the design value monitors and not necessarily
in all grid cells. As the extrapolated alternative annual standard decreases, the proportion of grid cells at or above
the modeled standard increases. Appendix Figure C-31 provides the full distribution of grid cell concentrations at
each modeled and extrapolated standard.
38 Modeling to "just meet" annual standards involves adjusting the design value monitor to the standard, and not
necessarily all grid cells modeled. Therefore, it is possible to have estimated PM2 5 concentrations above the
annual standard modeled in individual grid cells.
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There is greater uncertainty regarding whether a revised 24-hour standard (i.e., with a
lower level) is needed to further limit "peak" PM2.5 concentration exposure39 and whether a
lower 24-hour standard level would most effectively reduce PM2.5-associated health risks
associated with "typical" daily exposures. However, we do estimate health risks associated with
air quality adjusted to meet a revised 24-hour standard with a level of 30 |ig/m3, in conjunction
with estimating the health risks associated with meeting a revised annual standard with a level of
10 |ig/m3 40'41
3,4,1,4 Model-Based Approaches to Adjusting Air Quality
Air quality modeling was used to develop 12 km gridded PM2.5 concentration fields for
the risk assessment in the 2020 PM PA, and the same air quality simulations used in that
assessment are used here (U.S. EPA, 2020). A PM2.5 concentration field for 2015 was developed
using a Bayesian statistical model (Downscaler) that calibrates chemical transport model (CTM)
predictions of PM2.5 to surface measurements (section 2.3.3). The 2015 PM2.5 concentration field
was then adjusted using response factors developed from CTM modeling with emission changes
relative to 2015. The modeling approach applies realistic spatial response patterns from CTM
modeling to a concentration field, similar to those used in a number of recent epidemiologic
studies, to characterize PM2.5 concentration fields at 12 km resolution for study areas. The
adjusted concentration fields correspond to:
(1) Just meeting the existing annual and 24-hour standards of 12.0 |ig/m3 and 35 |ig/m3, and
(2) Just meeting potential alternative annual and 24-hour standards of 10.0 |ig/m3 and 30 |ig/m3.
The adjustments to simulate just meeting the current standards and alternative standards
are approximations of these air quality scenarios. In reality, changes in PM2.5 in an area will
depend on what emissions changes occur and the concentration gradients of PM2.5 will vary
across an area accordingly. In this risk assessment, two different adjustment approaches were
applied to provide two outcomes that could represent potential bounding scenarios of PM2.5
39 As noted in section 3.3.2.1, while controlled human exposure studies provided consistent evidence for
cardiovascular effects following PM2 5 exposures for less than 24 hours (i.e., < 30 minutes to 5 hours), exposure
concentrations in the studies were well-above the ambient concentrations typically measured in locations meeting
the existing standards.
40 The simulated air quality surface, which just meets both an alternative annual standard of 10.0 |ig/m3 and
alternative 24-hour standard of 30 |ig/m3. was subset into areas that are controlled by either the alternative annual
standard of 10.0 |ig/m3 or 24-hour standard of 30 |ig/m3 to assess risk associated with just meeting each
alternative standard.
41 We also estimate population risks for recent (i.e., unadjusted) ambient PM2.5 concentrations (Appendix C).
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concentrations changes across the study area. The two adjustment approaches used to guide the
generation of these modeled surfaces were:
• Reductions in primary PM2.5 (Pri-PM)\ This modeling approach simulates air quality
scenarios of interest by preferentially adjusting direct/primary PM emissions. As such, the
changes in PM2.5 tend to be more localized near the direct emissions sources of PM.42
• Reductions in secondary PM2.5 (Sec-PM)\ This modeling approach simulates air quality
scenarios of interest by preferentially adjusting SO2 and NOx precursor emissions to simulate
changes in secondary PM2.5. In this case, the reductions in PM2.5 tend to be more evenly
spread across a study area.43
The air quality surfaces generated using these two approaches are not additive. Rather,
they should be viewed as reflecting two different broad strategies for adjusting ambient PM2.5
concentrations.
3.4.1.5 Study Area Selection
The following factors were considered most important when selecting U.S. study areas
for inclusion in the risk assessment:
• Available Ambient Monitors : We have greater confidence in estimating and simulating air
quality concentrations over areas with relatively dense ambient monitoring networks, as the
modeled air quality surfaces can be compared with monitored concentrations (additional
detail available in Appendix C, section C.1.4).
• Geographical Diversity. Risk assessments including areas that represent a variety of regions
across the U.S. and a substantial portion of the U.S. population can be more representative.
• Ambient PM2.5 Air Quality Concentrations: Based on 2014-2016 design values, only 16
CBSAs44, also called urban study areas here, exceeded either or both the current annual and
24-hour PM2.5 NAAQS. To include a larger portion of the U.S. in this risk assessment, we
also identified CBSAs with ambient PM2.5 concentrations below, but near, the current annual
and/or 24-hour PM2.5 NAAQS. Inclusion of such areas in the risk assessment necessitates an
upward adjustment to PM2.5 air quality concentrations in order to simulate just meeting the
current standards. Given uncertainty in how such increases could potentially occur, we select
areas requiring a relatively modest upward adjustment (i.e., no more than 2.0 |j,g/m3 for the
annual standard and 5 |j,g/m3 for the 24-hour standard, based on the 2014-2016 design value
period). Areas that appeared to be strongly influenced by exceptional events were also
excluded (section C.1.4). Using these criteria, 47 urban study areas were identified, which
42 In locations for which air quality scenarios cannot be simulated by adjusting modeled directly emitted PM alone,
modeled SO2 and NOx precursor emissions are additionally adjusted to simulate changes in secondarily formed
PM2 5 (Appendix C, section C.1.4).
43 In locations for which air quality scenarios cannot be simulated by adjusting modeled precursor emissions alone, a
proportional adjustment of air quality is subsequently applied. This behavior occurs in areas where emission
changes in addition to NOx and S02 would be needed to adjust design values to just meet the standard.
(Appendix C, Figure C-19).
44 CBSAs (core-based statistical areas) can include one or more counties. Each CBS A selected included at least one
monitor with valid design values and several CBSAs had more than 10 monitors. See Table C-3 in Appendix C.
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30 Annual (Blue) ~50M
11 Daily (Green) ~4M
6 Mixed (Grey) ~5M
Total: 47 ~60M
7
8 Figure 3-16. Map of 47 urban study areas included in risk modeling.
9
1 include nearly 60 million people aged 30-99, or approximately 30% of the U.S population in
2 this age range (Figure 3-16 and Appendix C, section C.1.3). Of the 47 study areas, there were
3 30 study areas where just meeting the current standards is controlled by the annual
4 standard,4d 11 study areas where just meeting the current standards is controlled by the daily
5 standard,46 and 6 study areas where the controlling standard differed depending on the air
6 quality adjustment approach (Figure 3-16).47
Number of Urban Study Controlling Population (>30
Areas (CBSAs) Standard years old)
45 For these areas, the annual standard is the "controlling standard" because when air quality is adjusted to simulate
just meeting the current or potential alternative annual standards, that air quality also would meet the 24-hour
standard being evaluated.
46 For these areas, the 24-hour standard is the controlling standard because when air quality is adjusted to simulate
just meeting the current or potential alternative 24-hour standards, that air quality also would meet the annual
standard being evaluated. Some areas classified as being controlled by the 24-hour standard also violate the
annual standard.
47 In these 6 areas, the controlling standard depended on the air quality adjustment method used and/or the standard
scenarios evaluated.
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3,4,1.6 At-Risk Analysis
To inform conclusions regarding the primary PM2.5 standards that are "requisite" to
protect public health (i.e., neither more nor less stringent than necessary; section 1.2) and provide
an adequate margin of safety, it is important to consider the health risks of specific populations
identified as at increased risk (at-risk) that would be allowed under current and alternative
standards, recognizing associated uncertainties (section 3.4.1.8). Our consideration of estimated
risks among potentially at-risk populations focuses on addressing the following policy-relevant
questions:
• How does PM2.5 exposure and risk compare between demographic groups when air
quality just meets the current and potential alternative primary PM2.5 annual
standards?
• To what extent are impacts estimated to change within each demographic group
when air quality is adjusted to just meet potential alternative annual standards with
lower levels?
Assessing PM2.5-attributable risk stratified by the value of another covariate (e.g., race or
ethnicity) can provide insight into population-specific risk. As described in section 3.3.2, the
2019 ISA and draft ISA Supplement cite extensive evidence indicating that "both the general
population as well as specific populations and lifestages are at-risk for PM2.5-related health
effects" (U.S. EPA, 2019, p. 12-1; U.S. EPA, 2021a). Factors that may contribute to increased
risk of PM2.5-related health effects include lifestage (children and older adults), pre-existing
diseases (cardiovascular disease and respiratory disease), race/ethnicity, and socioeconomic
status. In considering the strength of the available scientific evidence and recognizing that this
risk assessment is focused on the health endpoint of mortality, we assess long-term PM2.5-
attributable exposure and mortality risk, stratified by racial/ethnic demographics. Specifically,
we evaluate exposure and risk, stratified by race-specific concentration-response functions when
available, of White, Black, Asian, Native American, Non-Hispanic, and Hispanic individuals.
Concentration-response functions used in this at-risk analysis are from large, multicity
U.S. epidemiologic studies that evaluate the relationship between PM2.5 exposures and mortality.
Eight epidemiologic long-term exposure studies of PM2.5 exposure and all-cause, nonaccidental,
or total mortality in nonwhite populations were identified in the 2019 ISA and draft ISA
Supplement (U.S. EPA, 2019; U.S. EPA, 2021a). Associations from those eight studies relating
long-term PM2.5 exposure and mortality outcomes in nonwhite populations are available in
Figure 3-17.
Specific epidemiologic studies and concentration-response functions used here to
estimate risk were identified using criteria that take into account factors such as study design,
geographic coverage, demographic populations, and health endpoints. Of the studies available
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from the 2019 ISA, Di et al., 2017b was identified as best characterizing potentially at-risk non-
White populations across the U.S.48 Additional information on input parameters used in the at-
risk analysis can be found in Appendix C, section C.3.
At-risk estimates presented in section 3.4.2.4, when considered alongside estimates of
risk across all populations in the 47 study areas (sections 3.4.2.1, 3.4.2.2, and 3.4.2.3) are meant
to inform conclusions on the primary annual PM2.5 standards that would be requisite to protect
the public health of nonwhite populations potentially at increased risk of long-term PIVh.s-related
mortality effects.
Demographic Population
Citation
Cohort
Location
White
Awad et al., 2019
Medicare
National US
•
Di et al., 2017
Medicare
National US
•
Lipfert et al., 2020
Veterans
31 VA clinics in 27 states
Parker etal., 2018
NHIS
National US
Son etal., 2020
North Carolina
North Carolina
~
Wang etal., 2017
Medicare
7 southeastern states
•
Wang et al., 2020
Medicare
National US |
•
White (75th percentile cities) Kioumourtzoglou et al., 2016
Medicare
National US (207 cities)
Black
Awad et at., 2019
Medicare
National US
: —
Di et al., 2017
Medicare
National US
~
Lipfert et al., 2020
Veterans
31 VA clinics in 27 states
Parker etal., 2018
NHIS
National US
Son et al., 2020
North Carolina
North Carolina
Wang etal., 2017
Medicare
7 southeastern states
•
Wang etal., 2020
Medicare
National US 1
•—
Black (75th percentile cities)
Kioumourtzoglou et al., 2016
Medicare
National US (207 effles)
Asian
Di etal., 2017
Medicare
National US
Son et al., 2020
North Carolina
North Carolina
Wang et al., 2020
Medicare
National US J
—• :
Asian (75th percentile cities)
Kioumourtzoglou et al., 2016
Medicare
National US (207 cities)
Hispanic
Di etal., 2017
Medicare
National US
•-
Parker etal., 2018
NHIS
National US
Son et al., 2020
North Carolina
North Carolina
Wang etal., 2020
Medicare
National US
0.85 0.90 0.95 1.00 1.05 1.10 1.15
Hazard/Risk/Odds Ratio (95% CI)
Figure 3-17. Available epidemiologic associations between long-term PM2.5 exposure and
mortality outcomes in demographic populations.49
3.4.1.7 Characterization of Variability and Uncertainty in the Risk Assessment
Both quantitative and qualitative methods have been used to characterize variability and
uncertainty in the risk estimates (Appendix C, section C.3), including:
48 Additional details on concentration-response function identification can be found in Appendix C, section C.3.2. Di
et al.. 2017b was identified as best characterizing potentially at-risk non-White populations across the U.S. using
study and risk estimate criteria described in the Estimating PM2.5 and Ozone-Attributable Health Benefits TSD
(U.S. EPA, 2021b). Additional information on all available at-risk epidemiologic studies is available in Appendix
C, section C.3.2.
49 All studies estimated median or average long-term PM2 5 exposures between 10-12 ug/iii3. other than Lipfert and
Wvzga (2020), which reported an approximate average exposure concentration of 14 ng/m3. Kioumourtzoglou
et al., 2016 reported associations in cities ranking at or about the 75th percentile proportionally with regards to
demographic population only. VA, Veterans Affairs: NI-IIS, National Health Insurance Service.
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• 95th percentile confidence intervals: We use an iterative Monte Carlo simulation that samples
from the standard error associated with each epidemiologic concentration-response function.
We present the resulting 2.5th and 97.5th percentile values from this distribution as a 95th
percentile confidence interval around the risk estimate. Monte Carlo methods are a well-
established means of characterizing random sampling error associated with concentration-
response functions.
• Health endpoint sensitivity analyses: We include multiple concentration-response functions
reflecting epidemiology studies differing in various ways, such as the population (e.g.,
geographic locations and demographics), exposure estimation methods (e.g., monitor-based
or hybrid techniques), and potential confounders included in the epidemiologic model (e.g.,
ozone).50
• Air quality adjustment sensitivity analyses: We simulate just meeting the current and
alternative standards using two approaches, which represent potential bounding scenarios of
PM2.5 concentration changes across the study areas. The Pri-PM adjustment method
preferentially adjusts direct (i.e., primary, directly-emitted) PM2.5 emissions, whereas the
Sec-PM method preferentially adjusts SO2 and NOx precursor emissions to simulate changes
in secondarily formed PM2.5.
• Qualitative uncertainty assessment: We perform additional qualitative evaluations of the
potential for key sources of uncertainty to impact the magnitude and direction of risk
estimates (Appendix C, section C.3.2).
3.4.1.8 Characterization of Variability and Uncertainty in the At-Risk Analysis
While considering exposure and health risks of individual at-risk racial and ethnic
populations can be policy-relevant, these estimates will be more uncertain than similar estimates
from the overall risk assessment (sections 3.4.2.1 and 3.4.2.2). This is due to additional sources
of uncertainty specific to the at-risk analysis, such as using concentration-response functions
derived from smaller epidemiologic sample sizes, being combined with the sources of
uncertainty that apply to the overall risk assessment. The augmentation of existing uncertainness
is exemplified by the exposure estimates in the White populations in the simulated air quality
scenarios. White populations make up a greater proportion of rural areas (-60% vs -80%,
USD A, 2018), and rural areas tend to have lower ambient PM2.5 concentrations. Therefore, as
these scenarios are restricted to the 47 urban study areas, we expect that the average exposure
estimated in this assessment is an over-estimate of the overall national average exposure in the
White population.
For characterizing risk in at-risk populations, we used air quality fields from the Pri-PM
adjustment case alone, because the Pri-PM air quality adjustments are largely associated with
emission reductions within the study areas, due to the local nature of air quality impacts from
50 Additional information on long-term epidemiologic study identification can be found in the Estimating PM2.5 and
Ozone-Attributable Health Benefits TSD (U.S. EPA, 2021b). Specifically, additional information on the identified
long-term epidemiologic studies can be found in the Study Information Table (U.S. EPA, 2021b).
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22
23
24
25
26
27
primary PM sources51. In contrast, Sec-PM air quality adjustments may be strongly associated
with sources located outside of the study areas. Since the at-risk analyses are performed for
population groups within the 47 areas alone, the Pri-PM adjustment case (in which air quality
adjustments are primarily associated with emission sources within the 47 areas) is most
appropriate for this at-risk analysis. However, limiting the analysis to a single simulation
decreases the potential representativeness of simulated PM2.5 concentrations changes across the
study area.
3.4.2 Results of the Risk Assessment
This section presents estimates of PM2.5-associated mortality risks for populations in the
identified urban study areas (additional results available in Appendix C, section C.2). Results are
shown as point estimates with 95th percentile confidence intervals for air quality adjusted to
simulate just meeting the current, and potential alternative, standards. We provide tables that
include the total mortality risk associated with air quality just meeting the current or potential
alternative standards, the change in mortality risk (also called delta risk) when moving from air
quality just meeting the current standard to just meeting potential alternative standards, and the
percent risk reduction when moving from air quality just meeting the current standard to just
meeting potential alternative standards.52 We also quantify the percent of baseline incidence,
which estimates the percent of total incidence that is associated with ambient PM2.5 exposure
(e.g., percent of mortality attributable to PM2.5 exposure out of all deaths in the specified
population).53 In addition to tables, we provide figures to illustrate how risks are distributed
across annual average ambient PM25 concentrations. Figures present results for all-cause
mortality associated with long-term PM2.5 exposures, based on a key epidemiologic study by
(Turner et al., 2016). Additional results are presented in Appendix C (section C.2).
The sections below present risk estimates for the full set of 47 urban study areas (section
3.4.2.1), the subset of 30 areas for which the annual PM2.5 standard is controlling (section
3.4.2.2), and the subset of 11 areas for which the 24-hour PM2.5 standard is controlling (section
3.4.2.3). Risk estimates from populations potentially at increased risk of PM-related effects are
51 The Pri-PM and Sec-PM adjustment approaches are described in section 3.4.1.4.
52 Total risk refers to risk associated with the full increment of exposure associated with each air quality scenario.
Both delta risk and percent risk reduction reflect the change in risk in going from the current standard to a
specific alternative standard, with delta risk referring to the change in incidence (i.e., premature PM2 5-attributable
mortality) and percent risk reduction referring to the percent change when comparing risk under the current
standard to risk under simulation of an alternative standard. Percent risk reduction is calculated by dividing the
delta risk by the total risk.
53 In other words, the percent of the health effect attributable to PM2 5 exposure. For example, risk results estimate
that 6-8% of all-cause mortality in 2015 was associated withPM25 exposure (Table 3-14).
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
available in section 3.4.2.4. Uncertainties in the risk assessment are summarized in section
3.4.2.5.
3.4,2.1 Summary of Risk Estimates for the Full Set of 47 Urban Study Areas
Risk estimates for the 47 urban study areas are presented in Table 3-14 and Table 3-15.
Table 3-14 presents all-cause and non-accidental mortality risk estimates attributable to PM2.5
when just meeting the current primary PM2.5 standards and just meeting either an alternative
modeled annual standard of 10.0 |j,g/m3 or an alternative modeled 24-hour standard of 30 |j,g/m3.
Table 3-14 also provides the percent of total all-cause mortality attributable to PM2.5 in 2015
estimated by each epidemiologic concentration-response function.
Table 3-15 presents the reduction in estimated risk when moving from air quality
scenarios just meeting the current standard to air quality just meeting alternative standards. Areas
are again subset into those just meeting either an alternative annual standard of 10.0 |j,g/m3 or an
alternative 24-hour standard of 30 |ag/m\ based on which standard is controlling in that study
area. Smaller reductions estimated for the alternative 24-hour standard reflect the reduced
number of study areas controlled by the 24-hour standard and the lesser population in those
areas.
Key observations for the full set of 47 study areas from Table 3-14 and Table 3-15, which
include approximately 30% of the U.S. population aged 30-99, are as follows:
• Substantially larger risk reductions are associated with lowering the annual standard than
with lowering the 24-hour standard (Table 3-15). Impacts are estimated to decrease by 13-
17% when air quality is adjusted to just meet an alternative annual standard with a level of
10.0 |j,g/m3 or by 1-2% when adjusted to just meet an alternative 24-hour standard with a
level of 30 |j,g/m3. This corresponds to up to 7,440 (5,040-9,830) fewer deaths per year
attributable to long-term PM2.5 exposures.54
• Up to 45,100 deaths in 2015 are attributable to long-term PM2.5 exposures associated with air
quality just meeting the current annual and 24-hour PM2.5 standards, with a 95th percentile
confidence interval of 30,800-59,000. This constitutes up to 8% of total baseline mortality in
adults age 30-99 (Table 3-14).
54 In most study areas, the risk reductions presented for an annual standard with a level of 10.0 |ig/m3 reflect the
difference between air quality with a maximum three-year annual PM2 5 design value of 12.0 |ig/m3 and air
quality with a maximum three-year annual PM2 5 design value of 10.0 |ig/m3. Similarly, in most study areas, the
risk reduction presented for a 24-hour standard with a level of 30 |ig/m3 reflects the difference between air quality
with a maximum three-year 24-hour PM2 5 design value of 35 |ig/m3 and air quality with a maximum three-year
24-hour PM2 5 design value of 30 |ig/m3. However, in a small number of study areas, the "starting concentration"
for the annual standard are below 12.0 |ig/m3 (four study areas: Riverside-San Bernardino-Ontario, CA; Stockton-
Lodi, CA; Bakersfield, CA; and Hanford-Corcoran, CA) or the starting concentration for the 24-hour standard are
below 35 |ig/m3 (two study areas Pittsburgh, PA and South Bend-Mishawaka, IN-MI:). This is because, in these
areas, the controlling standard for air quality adjusted to just meet the current standards is different from the
controlling standard for air quality adjusted to simulate just meeting the alternative standards evaluated.
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1 • Short-term PM2.5 exposures are estimated to be associated with up to 3,870 (2,570-5,160)
2 deaths annually. This accounts for between 0.2-0.7% of mortality in adults age 30-99 in
3 2015.
4
5 Table 3-14. Estimates of PM2.5-associated mortality for air quality adjusted to just meet the
6 current or alternative standards (47 urban study areas).
Exposure
Study &
Ages
Simulation Method
Total Mortality
Under the Current
Standard (12/35-0)
% of Baseline
Mortality
Attributable to the
Current Standard
Total Mortality
Under an
Alternative Annual
Standard (10-0)
Total Mortality
Under an
Alternative 24-Hr
Standard (30-0)
Long-Term
Di
(65-99)
Pri PM
40,600
(39,600 to 41,700)
7.4
35,400
(34,400 to 36,300)
40,100
(39,100 to 41,200)
SecPM
41,200
(40,200 to 42,300)
7.5
34,800
(33,900 to 35,700)
40,600
(39,500 to 41,600)
Turner
(30-99)
Pri PM
44,400
(30,300 to 58,200)
6.1
38,600
(26,300 to 50,700)
43,900
(30,000 to 57,500)
SecPM
45,100
(30,800 to 59,000)
6.2
38,000
(25,900 to 49,900)
44,400
(30,300 to 58,200)
Short-Term
Baxter
(0-99)
Pri PM
2,490
(982 to 3,990)
0.4
2,160
(850 to 3,460)
2,460
(970 to 3,950)
SecPM
2,530
(997 to 4,050)
0.4
2,120
(837 to 3,400)
2,490
(982 to 3,990)
Ito
(0-99)
Pri PM
1,180
(-15.8 to 2,370)
0.2
1,020
(-13.7 to 2,050)
1,160
(-15.6 to 2,340)
SecPM
1,200
(-16.0 to 2,400)
0.2
1,000
(-13.5 to 2,020)
1,180
(-15.8 to 2,370)
Zanobetti
(65-99)
Pri PM
3,810
(2,530 to 5,080)
0.7
3,300
(2,190 to 4,400)
3,760
(2,500 to 5,020)
SecPM
3,870
(2,570 to 5,160)
0.7
3,250
(2,160 to 4,330)
3,810
(2,530 to 5,070)
8
9
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
Table 3-15. Estimated reduction in PM2.5-associated mortality for alternative annual and
24-hour standards (47 urban study areas).
Exposure
Study &
Ages
Simulation
Method
Risk Change When
Moving from the Current
to an Alternative Annual
Standard of 10
Risk Change When
Moving fromthe Current
to an Alternative 24-Hr
Standard of 30
% Risk Reduction When
Moving fromthe Current
to an Alternative Annual
Standard of 10
Risk Change When
Moving fromthe Current
to an Alternative 24-Hr
Standard of 30
Long-Term
Di
(65-99)
Pri PM
5,630
(5,490 to 5,780)
501
(488 to 514)
13.9
1.2
Sec PM
6,820
(6,640 to 7,000)
675
(657 to 692)
16.6
1.6
T urner
(30-99)
Pri PM
6,120
(4,140 to 8,090)
555
(375 to 734)
13.8
1.2
Sec PM
7,440
(5,040 to 9,830)
714
(483 to 943)
16.5
1.6
Short-Term
Baxter
(0-99)
Pri PM
335
(132 to 537)
30.2
(11.9 to 48.4)
13.4
1.2
Sec PM
408
(160 to 654)
38.7
(15.2 to 62.1)
16.1
1.5
Ito
(0-99)
Pri PM
158
(-2.12 to 317)
14.4
(-0.194 to 29.0)
13.4
1.2
Sec PM
192
(-2.58 to 386)
18.4
(-0.246 to 36.9)
16.1
1.5
Zanobetf
(65-99)
Pri PM
513
(341 to 684)
45.5
(30.2 to 60.7)
13.5
1.2
Sec PM
622
(413 to 830)
61.5
(40.8 to 82.0)
16.1
1.6
3.4.2.2 Summary of Risk Estimates for the 30 Areas Controlled by the Annual
Standard
This section presents the results for the range of alternative annual standard levels for the
30 urban study areas for which the annual standard is controlling under all air quality scenarios
evaluated.55'56 Table 3-16 presents total all-cause and non-accidental mortality risk estimates
attributable to PM2.5 when just meeting the current standard of 12.0 |ig/m3 and just meeting
potential alternative annual standards with levels of 11.0, 10.0, 9.0, and 8.0 |ig/m3. It also
provides the percent of baseline risk attributable to PM2.5 when just meeting the current annual
standard. Table 3-17 presents the reduction in estimated mortality incidence and percent of risk
reduction when moving from air quality scenarios just meeting the current annual standard to air
quality just meeting the various alternative annual standards.
After presenting mortality impact results from the various epidemiologic studies in Table
3-16 and Table 3-17, we focus on a single epidemiologic concentration-response function from
55 These 30 areas controlled by the annual standard under all scenarios evaluated include a population of
approximately 48 million adults aged 30-99, which corresponds to about 75% of the population included in the
full set of 47 areas or approximately 25% of the total U.S. population.
56 Alternative annual air quality surfaces in addition to the modeled surface just meeting 10.0 |ig/m3 were developed
using interpolation and extrapolation of modeled PM2.5 concentrations (section 3.4.1.4 and Appendix C section
C.1.4).
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1 Turner et al. (2016) to provide additional insight into the distribution of health impacts across
2 long-term ambient PM2.5 concentrations.57 Figure 3-18 presents distributions of total risk
3 attributable to annual PM2.5 concentration bins of 1 |ig/m3 when just meeting the current and
4 alternative annual standards.58 Figure 3-19 presents distributions as a heat map, again binned in 1
5 |ig/m3increments, associated with moving from just meeting the current standard to just meeting
6 each alternative annual standard.59
7 Drawing from the information in Table 3-16, Table 3-17, Figure 3-18, and Figure 3-19
8 for the subset of 30 study areas (approximately 25% of the U.S. population) in which the annual
9 standard is controlling, we note the following key observations:
10 • There is a potential for significant public health impacts in locations just meeting the current
11 primary PM2.5 standards. The majority of PIVh.s-associated deaths fall well-within the range
12 of long-term average concentrations over which key epidemiologic studies provide strong
13 support for reported positive and statistically significant PM2.5 health effect associations.
14 • Compared to the current annual standards, air quality adjusted to meet alternative annual
15 standards with lower levels is associated with reductions in estimated all-cause mortality
16 impacts (i.e., 7-9% reduction for an alternative annual level of 11.0 |ig/m3, 15-19% reduction
17 for a level of 10.0 |ig/m3, 22-28% reduction for a level of 9.0 |ig/m3, and 30-37%) reduction
18 for a level of 8.0 |ig/m3) (Table 3-17 and Figure 3-18).
19 • The magnitude of estimated risk reduction increases as alternative annual standards with
20 lower levels are simulated, and these estimated risk reductions are associated with lower
21 ambient PM2.5 concentrations. Specifically, for air quality adjusted to simulate just meeting
22 an alternative annual standard, the majority of risk reduction occurs in grid cells with
23 ambient PM2.5 concentrations between the alternative standard and 2 |ig/m3 lower (e.g., for
24 air quality adjusted to simulate just meeting an annual standard with a level of 8.0 |ig/m3, the
57 The Estimating PM2.5 and Ozone- Attributable Health Benefits TSD details the approach and criteria used to
identify studies and concentration-response functions from the 2019 ISA used in this risk assessment (U.S. EPA,
2021b). Briefly, two studies were again identified as best characterizing mortality risk across the U.S., Di et al.,
2017b and Turner et al., 2016. While both studies used sophisticated techniques to relate PM2 5 exposure and all-
cause mortality across large portions of the U.S population, Di et al., 2017b evaluated Medicare beneficiaries
aged 65+, whereas Turner et al., 2016 included adults ages 30+ from the ACS cohort. The concentration-response
function identified in the Estimating PM2.5 and Ozone- Attributable Health Benefits TSD (U.S. EPA, 2021b) from
Turner et al., 2016 was selected for use in this risk assessment due to the broader age range, although it should be
noted that the concentration-response function from Di et al., 2017b typically generates mortality risk estimates
within approximately 5% of the Turner et al., 2016 concentration-response function.
58 Bins correspond to the lower whole number and include up to, but not including the next whole number. For
example, the bin for 8 |ig/ml includes all risk occurring at PM2 5 concentrations from 8.00 |ig/m3 to 8.99 |ig/m3.
Previously this data was presented as a line graph, which can be found in Appendix C, Figure C-30.
59 As noted above, Figure 3-18 and Figure 3-19 present estimates of all-cause mortality associated with long-term
PM2 5 exposures, based on the study by Turner et al., 2016.
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1 majority of risk reduction occurs in grid cells with ambient PM2.5 concentrations between 6
2 and 8 |ig/m3) (Figure 3-18 and Figure 3-19).60
3 • For air quality just meeting the current annual standard, long-term PM2.5 exposures are
4 estimated to be associated with as many as 39,000 (26,000-51,000) total deaths from long-
5 term exposure annually, accounting for approximately 6-8% of baseline mortality.
6 Table 3-16. Estimates of PM2.5-associated mortality for the current and potential
7 alternative annual standards in the 30 study areas where the annual standard is
8 controlling.
% of
Total Risk Under
the Current
Standard (12/35-0)
Baseline
Risk
Attributable
to the
Current
Standard
Total Risk Under
Total Risk Under
Total Risk Under
Total Risk Under
Exposure
Study &
Simulation
an Alternative
an Alternative
an Alternative
an Alternative
Ages
Method
Annual Standard
(11-0)
Annual Standard
(10-0)
Annual Standard
(9-0)
Annual Standard
(8-0)
Pri PM
34.900
7.6
32.400
29.900
27.400
24.900
Di
(34.000 to 35.800)
(31.600 to 33.300)
(29.200 to 30.700)
(26.700 to 28.100)
(24.200 to 25.500)
(65-SS)
SecPM
35.600
7.7
32.500
29.400
26.300
23.100
Long-Term
(34.700 to 36.500)
(31.700 to 33.300)
(28.600 to 30.100)
(25.600 to 26.900)
(22.500 to 23.700)
Pri PM
38.200
6.3
35.500
32.700
29.900
27,200
Turner
(26.100 to 50.100)
(24.200 to 46.500)
(22.300 to 42.900)
(20.400 to 39.300)
(18.500 to 35.700)
(30-SS)
SecPM
38.900
(26.600 to 51.000)
6.4
35.500
(24.200 to 46.600)
32.100
(21.900 to 42.100)
(19.500 to 37.600)
25.200
(17.100 to 33.100)
Pri PM
2.150
0.4
1.990
1.830
1.670
1.510
Baxter
(846 to 3.440)
(784 to 3.190)
(721 to 2.930)
(658 to 2.680)
(595 to 2,420)
(0-SS)
SecPM
2.190
(862 to 3.510)
0.4
1.990
(785 to 3.190)
1.790
(707 to 2,880)
1.600
(630 to 2.560)
1.400
(552 to 2.250)
Pri PM
1.010
0.2
939
864
789
713
Short-Term
Ito
(-13.6 to 2.040)
(-12.6 to 1.880)
(-11.6 to 1.730)
(-10.6 to 1.580)
(-9.57 to 1.430)
(0-95)
SecPM
1.030
0.2
940
847
754
661
(-13.9 to 2.070)
(-12.6 to 1.890)
(-11.4 to 1.700)
(-10.1 to 1.510)
(-8.87 to 1,330)
Pri PM
3.280
0.7
3.040
2.790
2.550
2.310
Zanobetti
(2.180 to 4.370)
(2.020 to 4.050)
(1.860 to 3.730)
(1.700 to 3.400)
(1.540 to 3.080)
(65-99)
SecPM
3 340
0.7
3 040
2 740
2 440
2140
(2.220 to 4.450)
(2.020 to 4.050)
(1.820 to 3.650)
(1.620 to 3.260)
(1,420 to 2.860)
60 Compared to adjusting primary PM2 5 emissions, adjustment of PM precursor emissions resulted in substantially
larger estimated risk reductions at 7 |ig/m3.
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Table 3-17. Estimated delta and percent reduction in PM2.5-associated mortality for the
current and potential alternative annual standards in the 30 study areas where the
annual standard is controlling.
Exposure
Study &
Ages
Simulation
Method
Risk Change
When Moving
from the Current
to an Alternative
Annual Standard
of 11
Risk Change
When Moving
from the Current
to an Alternative
Annual Standard
of 10
Risk Change
When Moving
from the Current
to an Alternative
Annual Standard
of 9
Risk Change
When Moving
from the Current
to an Alternative
Annual Standard
of 8
% Risk Reduction
When Moving
from the Current
to an Alternative
Annual Standard
of 11
% Risk Reduction
When Moving
from the Current
to an Alternative
Annual Standard
of 10
% Risk Reduction
When Moving
from the Current
to an Alternative
Annual Standard
of 9
% Risk Reduction
When Moving
from the Current
to an Alternative
Annual Standard
of 8
Long-Term
Di
(65-99)
Pri PM
2,680
(2,610 to 2,750)
5,350
(5,210 to 5,490)
8,000
(7,790 to 8,210)
10,600
(10,400 to 10,900)
7.7
15.3
22.9
30.5
Sec PM
3,320
(3,230 to 3,400)
6,610
(6,440 to 6,780)
9,880
(9,620 to 10,100)
13,100
(12,800 to 13,500)
9.3
18.6
27.8
36.9
Turner
(30-99)
Pri PM
2,920
(1,970 to 3,860)
5,830
(3,940 to 7,700)
8,720
(5,900 to 11,500)
11,600
(7,860 to 15,300)
7.6
15.2
22.8
30.3
Sec PM
3,610
(2,440 to 4,770)
7,200
(4,870 to 9,510)
10,800
(7,290 to 14,200)
14,300
(9,710 to 18,900)
9.3
18.5
27.7
36.8
Short-Term
Baxter
(0-99)
Pri PM
160
(62.8 to 256)
319
(126 to 512)
478
(188 to 767)
638
(251 to 1,020)
7.4
14.9
22.3
29.7
Sec PM
197
(77.6 to 316)
394
(155 to 632)
592
(233 to 948)
789
(310 to 1,260)
9.0
18.0
27.0
36.0
Ito
(0-99)
Pri PM
75.2
(-1.01 to 151)
150
(-2.02 to 302)
226
(-3.03 to 453)
301
(-4.03 to 604)
7.4
14.8
22.3
29.7
Sec PM
93.1
(-1.25 to 187)
186
(-2.49 to 374)
279
(-3.74 to 561)
372
(-4.99 to 748)
9.0
18.0
27.0
36.0
Zanobetti
(65-99)
Pri PM
244
(162 to 325)
487
(324 to 650)
731
(486 to 975)
974
(647 to 1,300)
7.4
14.9
22.3
29.7
Sec PM
301
(200 to 402)
603
(400 to 804)
904
(600 to 1,210)
1,200
(800 to 1,610)
9.0
18.0
27.0
36.0
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Annual PM Concentration (1 (jg/m3 bins) / Simulation Method
2
3
4
5
6
7
8
9
10
11
12
13
Just meeting
12 |jg/m3
15K
10K
5K
¦ —
ll
I
1
ll
Interpolated
to 11 pg/m3
15K
10K
5K
II
I
1
1
1
Just meeting
10 ijg/m3
15K
10K
5K
¦
1
ll
1
1
Extrapolated
to 9 pg/m3
15K
10K
5K
.1
1
1
ii
Extrapolated
to 8 pg/m3
15K
10K
5K
_l
II
1
1
Pri PM
Sec PM
Pri PM
3ec PM
II
-c 0
CL V
¦ CD
Pri PM
3ec PM
Pri PM
3ec PM
lAJd 09S
lAld Md
lAld Md
3ec PM
Pri PM
3ec PM
Pri PM
Sec PM
Pri PM
Sec PM
Pri PM
Sec PM
Pri PM
Sec PM
2 Figure 3-18. Distribution of total risk estimates (PM2.5-attibutable mortality) for the
3 current and alternative annual standards for the subset of 30 urban study areas where
4 the annual standard is controlling (blue and green bars represent the Pri-PMi.s and
5 Sec-PM2.5 estimates, respectively).61
6
7
61 Risk is estimated in this figure using Turner et al., 2016. Risk estimates are rounded toward zero into whole PM2.5
concentration values (e.g., risk estimate at 10 pg/nf includes risk occurring at 10.0-10.9 iig/m3). For each
standard, a small amount of risk is estimated at concentrations higher than the level of the annual standard (e.g.,
some risk is estimated at an average concentration of 13 |ig/m3 when air quality is adjusted to just meet the
current standard). This can result because risk estimates are for a single year (i.e., 2015) within the 3-year design
value period (i.e., 2014 to 2016). While the three-year average design value is 12.0 [ig/in3. a single year can have
grid cells with annual average concentrations above or below 12.0 |ig/m\
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Annual PM Concentration of Lower Standard (1 |jg/m3 bins)
Annual
Standard
Change
Simulation
Method
2
3
4
5
6
7
8
9
10
11
12
Sum
12-11
(interpolated)
Mg/m3
Pri PM
Sec PM
0
0
3
4
11
9
17
26
39
40
110
122
381
628
1,534
1,836
763
858
62
89
2,920
3,611
12-10 |jg/m3
Pri PM
Sec PM
1
1
18
23
12
23
81
89
116
287
569
1,632
3,205
3,377
1,720
1,681
103
87
5,826
7,201
12-9
Pri PM
3
27
82
106
596
4,467
3,252
185
8,718
(extrapolated)
Mg/m3
Sec PM
0
5
48
98
529
2,754
4,953
2,334
47
10,768
12-8
Pri PM
0
11
85
161
368
5,324
5,408
238
11,595
(extrapolated)
Mg/m3
Sec PM
0
50
129
1,116
3,527
6,390
3,101
14,314
Figure 3-19. Distribution of the difference in risk estimates between the current annual
standard (level of 12.0 jig/m3) and alternative annual standards with levels of 11.0,10.0,
9.0, and 8.0 jig/m3 for the subset of 30 urban study areas where the annual standard is
controlling.62
3.4.2.3 Summary of Risk Estimates for the 11 Areas Controlled by the 24-Hour
Standard
Table 3-18 presents annual risk information for the subset of 11 urban study areas in
which the 24-hour standard controls the simulated attainment of all modeled standard levels.63
For air quality just meeting the current 24-hour standard, PM2.5 exposures are estimated to be
associated with as many as 2,570 (1,750-3,370) deaths annually, accounting for up to 7% of the
baseline mortality in those 11 areas. Compared to the current standard, air quality just meeting an
alternative 24-hr standard with a level of 30 |ig/m3 is associated with reductions in estimated risk
of 9-13%.
62 Risks are presented as integers rounded to three significant digits and aggregated into 1 ng/m3 bins. Bins begin at
the whole number value indicated and include values up to, but not including the next whole number (e.g., risk
occurring at PM concentrations of 6.00 to 6.99 are shown in the bin at 6). Risk is estimated in this figure using
Turner et al., 2016.
63 These 11 areas controlled by the 24-hour standard under all scenarios evaluated include a population of
approximately 10 million adults aged 30-99, or about 17% of the population included in the full set of 47 areas.
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1 Table 3-18. Estimates of PM2.5-associated mortality for the current 24-hour standard, and
2 an alternative, in the 11 study areas where the 24-hour standard is controlling.
Exposure
Study &
Ages
Simulation
Method
Total Risk
Under the
Current
Standard (12/35-
0)
%of
Baseline
Total Risk Under
an Alternative
Annual Standard
(30-0)
Risk Change When
Moving from the
Current to an
Alternative 24-Hr
Standard of 30
% Risk Reduction
When Moving from
the Current to an
Alternative 24-Hr
Standard of 30
Di
(65-99)
Pri PM
2,320
(2,260 to 2,380)
6.7
2,040
(1,990 to 2,090)
304
(296 to 312)
13.1
SecPM
2,300
(2,250 to 2,360)
6.7
2,100
(2,050 to 2,150)
218
(212 to 224)
9.4
Long-Term
Turner
(30-99)
Pri PM
2,570
(1,750 to 3,370)
5.6
2,250
(1,530 to 2,960)
334
(226 to 442)
13.0
SecPM
2,550
(1,740 to 3,340)
5.6
2,320
(1,580 to 3,050)
241
(163 to 318)
9.4
Short-Term
Baxter
(0-99)
Pri PM
142
(56.1 to 228)
0.3
124
(49.0 to 199)
18.1
(7.11 to 29.0)
12.7
SecPM
141
(55.6 to 226)
0.3
128
(50.5 Id 206)
13.0
(5.12 to 20.9)
9.2
Ito
(0-99)
Pri PM
68.6
(-0.920 to 138)
0.1
59.9
(-0.803 to 120)
8.70
(-0.117 to 17.5)
12.7
SecPM
68.0
(-0.912 to 137)
0.1
61.8
(-0.828 to 124)
6.25
(-0.0838 to 12.6)
9.2
Zanobetti
(65-99)
Pri PM
217
(145 Id 290)
0.6
190
(126 to 253)
27.7
(18.4 to 36.9)
12.7
SecPM
216
(143 Id 287)
0.6
196
(130 to 261)
19.8
(13.1 to 26.4)
9.2
4
5 3.4.2.4 Summary of Risk Estimates for At-Risk Populations
6 Potential at-risk populations are summarized in section 3.3.2. Given that this risk and
7 exposure assessment focuses on mortality endpoints, a quantitative assessment is supported by
8 evidence in the 2019 ISA and draft ISA Supplement for racial and ethnic differences in PM2.5
9 exposures and in PM2.5-related health risk supports a quantitative assessment (U.S. EPA, 2019,
10 section 12.5.4, U.S. EPA, 2021a, section 3.3.3.2).64 Evidence strongly supports that non-White
11 populations, such as Black and Hispanic populations, have higher PM2.5 exposures than White
12 and non-Hispanic populations, respectively, thus contributing to increased risk of PM-related
13 effects. Additionally, Di et al., 2017b provides race- and ethnicity-stratified concentration-
14 response functions for ages 65 and over. Therefore, we quantitatively assess risk for certain
64 For characterizing risk in at-risk populations, we used air quality fields from the Pri-PM adjustment case alone. In
the Pri-PM case, the air quality adjustments for a given area are largely associated with emission reductions
within that area due to the local nature of air quality impacts from primary PM sources. For the Sec-PM case, the
air quality adjustments may be strongly associated with sources located outside of the area. Since the at-risk
analyses are performed for population groups within the 47 areas alone, the Pri-PM adjustment case (in which air
quality adjustments are primarily associated with emission sources within the 47 areas) is most appropriate for the
at-risk analysis.
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racial and ethnic populations of older adults in the full set of 47 areas and the subset of 30 areas
controlled by the annual PM2.5 standard under all Pri-PM air quality simulations evaluated.65
Additional information on this at-risk analysis is available throughout Appendix C, section C.2.
For this analysis, we first compare the estimated changes in air quality occurring within
each demographic population when just meeting current and alternative annual PM2.5 standards
(Figure 3-20, left side).66 Across all simulated air quality scenarios in the full set of 47 and subset
of 30 study areas, Blacks experience the highest average PM2.5 concentrations of the
demographic groups analyzed. This increase was typically around 2-5% and was highest in
modeling scenarios just meeting the current suite of standards. Native American populations
typically experienced the lowest average PM2.5 concentrations, especially in the full set of 47
study areas. White, Hispanic, and Asian populations were exposed to fairly similar average PM2.5
concentrations, although White populations tended to be at the higher end of that range in the
subset of 30 areas and the lower end of that range in the full set of 47 areas. Additionally, there is
comparatively less disproportionate exposure between demographic populations as the
alternative annual standard decreases.
While exposure is an important aspect to evaluate when considering potentially
disproportionate impacts, risk estimates provide additional information. Notably, risk estimates
also generate information regarding:
• The number of people affected by the air pollution reduction. In this instance, the population
is further divided by demographic group.
• The relationship between exposure and health impact baseline incidence rates, or more
specifically, the percentage change in the risk of an adverse health effect due to a one-unit
change in ambient air pollution. These concentration-response functions are generally
derived from epidemiologic studies.
• The average number of people who die in a given population over a given period of time.
This is commonly referred to as the baseline mortality incidence rate.
For this quantitative analysis of demographic populations potentially at increased risk of PM2.5
exposure, we utilize race-specific, or race-stratified, concentration-response functions and
65 Each individual is categorized by both race and ethnicity in this analysis. In other words, the sum of White, Black,
Asian, and Native American individuals equals the total population, as well as the sum of Hispanic and non-
Hispanic individuals. Though Di et al., 2017b did not provide a non-Hispanic concentration-response
relationship, results for non-Hispanics appears similar to Whites when the overall concentration-response
relationship was applied to non-Hispanics (Appendix C Figures C-33 and C-34).
66 Changes in air quality are estimated using the same approach used in the general risk assessment (sections 3.4.2.1,
3.4.2.2, and 3.4.2.3), summarized in section 3.4.1.4 and detailed in Appendix C.
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6
7
8
9
10
11
12
baseline incidence rates, to more accurately estimate risk within each demographic group.67
Population-normalized mortality risk occurring within each demographic population is available
on the right side of Figure 3-20. Across all scenarios and demographic groups evaluated, Black
populations are associated with the largest PIVb.s-attributable mortality risk rate per 100,000
people. An example of the 95th percentile confidence interval is available in Appendix Figure C-
32.
Study Modeling
Areas Scenario
47 areas Just meeting
12/35 pg/m3
•White
Black •
• Hispanic
Asian
Native American
• White
Black •
• Hispanic
Asian
• Native American
Just meeting
10/30 (jg/m3
•White
• Black
• Hispanic
Asian
Native American
•White
• Black
• Hispanic
Asian
Native American
30 areas Just meeting
12/35 pg/m3
•White
Black •
Hispanic*
Asian
Native American
•White
Black •
• Hispanic
Asian
Native American
Interpolated
to 11 |jg/m3
•White
• Black
• Hispanic
• Native American
•White
• Black
• Hispanic
Native American
Just meeting
10/30 pg/m3
•White
• Black
• Hispanic
Asian
Native American
•White
• Black
• Hispanic
Asian
Native American
Extrapolated
to 9 |jg/m3
•White
• Black
• Hispanic
Asian
1 Native American
•White
• Black
• Hispanic
Asian
• Native American
Extrapolated
to 8 |jg/m3
•White
• Black
• Hispanic
Asian
Native American
•White
• Black
• Hispanic
Asian
Native American
7.5 8^0 8.5 9.0 9.5 10.0 10.5 11.0 11 5
Average PM Concentration (pg/m3)
200 300 400 500 600 700 800
Average Mortality Risk Rate (per 100k)
Figure 3-20. Average P1VI2.5 exposure concentration and PM2.5-attributable risk estimates
by demographic population when just meeting current or alternative P1VI2.5 standards.
We next estimate demographic-specific average exposure and risk changes when
modeled air quality shifts from just meeting the current annual standard to just meeting potential
67 Information on how the race-stratified concentration-response functions and baseline incidence rates impact the
results can be found in Appendix C, section C.4. Briefly, race-stratified concentration-response functions
increased risk estimated in nonwhite populations, with the greatest magnitude increase occurring in Black
populations, and decreased risk estimated in White populations. Race-stratified baseline incidence rates decreased
risk estimated in all demographic populations analyzed, with the greatest magnitude decreases occurring in White
and Black populations.
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22
alternative annual standard scenarios (Figure 3-21). Simulated PM2.5 concentration reductions
are shown on the left side of the figure and reductions in population-normalized mortality risk
are shown on the right side. As the alternative annual PM standard decreases in the subset of 30
areas controlled by the annual standard, the average reduction in PM2.5 concentration and
mortality risk rates increase across all demographic populations assessed.
Study Modeling
Areas Scenario
47 areas 12/35-10/30
|jg/m3
•White
• Black
• Hispanic
Asian
Native American
•White
• Black
• Hispanic
Asian
Native American
30 areas 12/35-11
(interpolated)
pg/m3
•White
• Black
• Hispanic
Asian
Native American
•White
• Black
• Hispanic
Asian
• Native American
12/35-10/30
|jg/m3
•White
• Black
• Hispanic
Asian
Native American
•White
• Black
• Hispanic
Asian
i Native American
12/35-9
(extrapolated)
Hg/m3
•White
• Black
• Hispanic
Asian
Native American
•White
• Black
• Hispanic
Asian
Native American
12/35-8
(extrapolated)
|jg/m3
•White
Black •
Hispanic*
Asian
Native American
•White
Black •
• Hispanic
Asian
Native American
10 15 2.0 2.5 3^0 3^5
Average PM Concentration Reduction (^g/m3)
50 100 150 200 250
Average Mortality Risk Rate Reduction (per 100k)
Figure 3-21. Average change in PM2.5 exposure concentration and PiVh.s-attributable
mortality risk estimates by demographic population when moving from the current to
alternative PM2.5 standards.
We also directly compare the reductions in average national PM2.5 concentrations and
risk rates within each demographic population. Table 3-19 and Table 3-20 provide the percent of
national average PM2.5-attributable exposures and risk reductions, when shifting from the current
annual PM2.5 standard (12.0 ug/m') to potential alternative annual PM2.5 standards (11.0 |.ig/'m\
10.0 ug/m3, 9.0 ug/nr, and 8.0 ug/m '). The percent PM2.5 and risk reductions are greater in the
Black population than in the White population for each alternative standard evaluated for both
the full set of study areas and the subset controlled by the annual standard. Additionally, the
difference in percent risk reduction increases more in Blacks than in Whites as the potential
alternative annual standard decreases. In other words, Blacks will experience proportionally
greater benefit from successively lower annual standards, although even at an annual standard of
8 (ig/m 5 Blacks will experience higher rates of premature mortality risk from PM2.5 exposure
than Whites.
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1 Table 3-19. Average national percent PM2.5 reduction in demographic populations aged 65
2 and over residing in the full set of 47 study areas and subset of 30 study areas controlled
3 by the annual standard.
Ethnicity & Race
% PM Reduction
from 12 pg/m3 to 11
(interpolated) |jg/m3
% PM Reduction from
12 pg/m3 to 10 pg/m3
% PM Reduction from
12 pg/m3 to 9
(extrapolated) |jg/m3
% PM Reduction
from 12 pg/m3 to 8
(extrapolated) |jg/m3
30 areas
47 areas
30 areas
30 areas
30 areas
White
7
14
15
22
29
Black
8
15
15
23
31
Hispanic
8
15
16
23
31
Asian
8
15
15
23
31
Native American
8
14
15
23
30
4
5 Table 3-20. Average national percent PM2.5 risk reduction in demographic populations
6 aged 65 and over residing in the full set of 47 study areas and subset of 30 study areas
7 controlled by the annual standard.
% Risk Reduction
% Risk Reduction
% Risk Reduction
% Risk Reduction
Ethnicity & Race
from 12 pg/m3 to 11
from 12 pg/m3 to 10
from 12 pg/m3 to 9
from 12 pg/m3 to 8
(interpolated) |jg/m3
|jg/m3
(extrapolated) |jg/m3
(extrapolated) |jg/m3
30 areas
47 areas
30 areas
30 areas
30 areas
White
8
15
15
23
30
Black
9
17
17
25
33
Hispanic
8
16
16
25
33
Asian
8
16
16
24
32
Native American
8
15
16
24
32
8
9 While average exposure concentrations and risk estimates across demographic
10 populations can convey some insight regarding whether certain populations may be
11 disproportionately impacted, distributional information, while more complex, can provide a more
12 comprehensive understanding of the analytical results. As such, we compare both estimated
13 PM2.5 exposures and mortality risk rates per 100k individuals to the running sum of each
14 demographic population. To permit the direct comparison of demographic populations with
15 different absolute numbers, populations are expressed as a percentage in Figure 3-22 and Figure
16 3-23.68
17 In both Figure 3-22 and Figure 3-23, PM2.5 concentration information is on the left side
18 and mortality risk estimates are on the right side. Recent conditions (2015) information for both
19 exposure and risk can be found in Appendix C, section C.4, as well as sensitivity analyses
68 Information on the absolute number of all-cause premature mortality cases within each racial and ethnic
population demographic can be found in Appendix C Tables C-12 and C-13.
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1 investigating the impact of race-stratified concentration-response functions and baseline
2 incidence rates on the results. Cumulative distribution plots of PM2.5 concentrations and
3 population-normalized mortality risk reductions when shifting from the current to an alternative
4 annual standard are available in Figure 3-23.
5
g PM Concentration (pg/m3) * Mortality Risk Rate (per 100k) *
7 Figure 3-22. PM2.5 exposure concentrations and PM2.5-attributable mortality risk estimates
8 by demographic population when just meeting current or alternative PM2.5 standards.
9
10
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Study Modeling
Areas Scenario
47 12/35-10/30
areas pg/m3
12/35-11
30 (interpolated)
areas pg/m3
12/35-10/30
pg/m3
12/35-9
(extrapolated)
pg/m3
§ C 75%-
03 .2
Q_ "c5
qj "5 50%-
S Q.
15 f?
| 25%-
3 0%-
¦s 100%-
! c 75%-
s s
« -§ 50%
1 °
| ^ 25%
" 0%-
•S 100%
§ = 75%-
03 J
w 3 50%
> CL
15 °
| ^ 25%
" 0%
"o 100%
§ c 75%-
25%-
"o 100%-
I c 75%
12/36-8 cl -s
(extrapolated)
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30
31
32
33
34
35
36
• Characterizing the 95th percentile confidence intervals associated with risk estimates: There
is considerable variation in the range of confidence intervals associated with the point
estimates generated for this analysis (Table 3-14), with some concentration-response
functions displaying substantially greater variability than others (e.g., short-term PM2.5
exposure and all-cause mortality based on effect estimates from Ito et al. (2013) versus long-
term PM2.5 exposure all-cause mortality estimates based on Turner et al., 2016. There are a
number of factors potentially responsible for the varying degrees of statistical precision in
effect estimates, including sample size, exposure measurement error, degree of control for
confounders/effect modifiers, and variability in PM2.5 concentrations evaluated in the original
epidemiologic study.
• Qualitative assessment of additional sources of uncertainty: Based in part on WHO (2008)
guidance and on guidance documents developed by the EPA (U.S. EPA, 2001, U.S. EPA,
2004), we also completed a qualitative characterization of sources of uncertainty including an
assessment of both the magnitude and direction of impact of those uncertainties on risk
estimates. The classification of the magnitude of impact for sources of uncertainty includes
three levels: (a) low (unlikely to produce a sufficient impact on risk estimates to affect their
interpretation), (b) medium (potential to have a sufficient impact to affect interpretation), and
(c) high (likely to have an impact sufficient to affect interpretation). For several of the
sources, we provide a classification between these levels (e.g., low-medium, medium-high).69
The below uncertainties, as well as various additional sources of uncertainty, are detailed in
the EstimatingPM2.5 and Ozone- Attributable Health Benefits TSD (U.S. EPA,
202 lb). Sources of uncertainty with at least a low classification as to the magnitude of
potential impacts include the following (from Appendix C, Table C-32):70
- Use of air quality modeling to adjust PM2.5 concentrations: The baseline and
adjusted air quality concentration fields were developed using modeling to fill
spatial and temporal gaps in monitoring and explore "what if' scenarios.
State-of-the-science modeling methods were used, but modeling-related biases
and errors introduce uncertainty into the PM2.5 concentration estimates. In
addition, due to the national scale of the assessment, scenarios are based on
changing modeled emissions of primary PM2.5 or NOx and SO2 from all
anthropogenic sources throughout the U.S. by fixed percentages. Although
this approach tends to target key emission sources in each study area, it does
not tailor emission changes to specific sources. The two adjustment cases span
a wide range of emission conditions, but these cases are necessarily a subset
of the full set of possible emission scenarios that could be used to adjust PM2.5
concentrations to simulate "just meeting" standards.
69 Additional information is available in Appendix C, section C.3.
70 We also identified several additional factors judged to have less than a medium classification of impact on the risk
estimates generate, including: (a) the temporal mismatch between ambient air quality data characterizing
exposure and mortality in long-term exposure-related epidemiology studies, (b) compositional and source
differences in PM, (c) exposure measurement error in epidemiology studies assessing the relationship between
mortality and exposure to ambient PM2 5, (d) lag structure in short-term expo sure-related mortality epidemiology
studies, and (e) assumed causal association between PM and mortality that supports modeling changes in risk
associated with future changes in ambient PM2 5. See Table C-32 in Appendix C for additional discussion of these
sources of uncertainty.
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32
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46
- Use of linear interpolation/extrapolation to adjust air quality: The use of
interpolation and extrapolation to simulate just meeting annual standards with
levels of 11.0, 9.0, and 8.0 |j,g/m3 does not fully capture potential non-
linearities associated with real-world changes in air quality.
- Potential confounding of the PM2.5-mortality effect: Factors are considered
potential confounders if demonstrated in the scientific literature to be related
to the health effect and correlated with PM2.5. Omitting potential confounders
from analyses could either increase or decrease the magnitude of PM2.5 effect
estimates (e.g., Di et al., 2017b, supplemental Figure S2). Thus, not
accounting for confounders can introduce uncertainty into effect estimates
and, consequently, into the estimated impacts generated using those effect
estimates. Confounders vary according to study design, exposure duration,
and health effect. For studies of short-term exposures, confounders may
include meteorology (e.g., temperature, humidity), day of week, season,
medication use, allergen exposure, and long-term temporal trends. For studies
of long-term exposures, confounders may include socioeconomic status, race,
age, medication use, smoking status, stress, noise, and occupational
exposures. While various approaches to control for potential confounders have
been adopted across the studies used in the risk assessment, and across the
broader body of PM2.5 epidemiologic studies assessed in the 2019 ISA, no
individual study adjusts for all potential confounders (U.S. EPA, 2019, Table
A-l).
- Potential for exposure error: Epidemiologic studies have employed a variety
of approaches to estimate population-level PM2.5 exposures (e.g., stationary
monitors and hybrid modeling approaches). These approaches are based on
using measured and/or predicted ambient PM2.5 concentrations as surrogates
for population exposures. As such, exposure estimates in epidemiologic
studies are subject to exposure error. The 2019 ISA notes that, while bias in
either direction can occur, exposure error tends to result in underestimation of
health effects in epidemiologic studies of PM exposure (U.S. EPA, 2019,
section 3.5). Consistent with this, Hart et al. (2015) reports that correction for
PM2.5 exposure error using personal exposure information results in a
moderately larger effect estimate for long-term PM2.5 exposure and mortality,
though with wider confidence intervals. Error in the underlying epidemiologic
studies contributes to uncertainty in the risk estimates based on concentration-
response relationships in those studies. Beyond the exposure error in
concentration-response functions, the use of a different approach to represent
exposures in the risk assessment (i.e., 12 x 12 km gridded surface based on
modeling) could introduce additional error into risk estimates.
Shape of the concentration-response relationship at low ambient PM
concentrations: Interpreting the shapes of concentration-response
relationships, particularly at PM2.5 concentrations near the lower end of the air
quality distribution, can be complicated by relatively low data density in the
lower concentration range, the possible influence of exposure measurement
error, and variability among individuals with respect to air pollution health
effects. These sources of variability and uncertainty tend to smooth and
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"linearize" population-level concentration-response functions, and thus could
obscure the existence of a threshold or nonlinear relationship (U.S. EPA,
2015b, section 6.c).
Additional uncertainties are associated with the at-risk analysis. Importantly, the smaller
population within each demographic group reduces statistical power. As this risk and exposure
assessment focuses on urban areas, demographic groups that primarily reside in rural areas, such
as Native Americans, are underrepresented.
3.4.3 Conclusions of the Risk Assessment
Although limitations in the underlying data and approaches lead to some uncertainty
regarding estimates of PIVh.s-associated risk (summarized in section 3.4.1.7), the risk assessment
estimates that the current primary PM2.5 standards could allow a substantial number of PM2.5-
associated deaths in the U.S. For example, when air quality in the 47 study areas is adjusted to
simulate just meeting the current standards, the risk assessment estimates 40,600-45,100 long-
term PM2.5 exposure-related deaths in a single year, with confidence intervals ranging from
30,300-59,000 deaths (Table 3-14). Additionally, the at-risk assessment estimated that Black
populations may experience disproportionally higher exposures and risk under simulated air
quality conditions just meeting the current primary PM2.5 annual standard as compared to White
populations (section 3.4.2.4).71
Compared to the current annual standard, meeting a revised annual standard with a lower
level is estimated to reduce PIVh.s-associated health risks in the 30 annually-controlled study
areas by about 7-9% for a level of 11.0 |ig/m3, 15-19% for a level of 10.0 |ig/m3, 22-28% for a
level of 9.0 |ig/m3, and 30-37% for a level of 8.0 |ig/m3. (Table 3-17)72 Meeting a revised annual
standard with a lower level may also reduce exposure and risk in Black populations slightly more
so than in White populations in simulated scenarios just meeting alternative annual standards
(section 3.4.2.4).
Revising the level of the 24-hour standard to 30 |j,g/m3 is estimated to lower PM2.5-
associated risks across a more limited population and number of areas then revising the annual
standard (section 3.4.2.3). Risk reduction predictions are largely confined to areas located in the
71 Risk estimates in Black populations are largely due to race-specific concentration-response functions.
72 Importantly, as the magnitude of estimated risk reductions increases with lower alternative annual standards,
estimated risk reductions are associated with lower ambient PM2 5 concentrations. Lower PM2 5 concentrations
may less closely align with those observed in the epidemiologic study from which the concentration-response
function was obtained, contributing to uncertainty. Additional information on estimated ambient concentrations of
the original Medicare and ACS cohorts evaluated by Di et al., 2017b and Turner et al., 2016, respectively, can be
found in section 6.1.2.1 of the Estimating PM2.5 and Ozone- Attributable Health Benefits TSD (U.S. EPA, 2021b).
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western U.S., several of which are also likely to experience risk reductions upon meeting a
revised annual standard.
3.5 KEY CONSIDERATIONS REGARDING THE ADEQUACY OF THE
PRIMARY PM2 s STANDARDS
In considering the adequacy of the primary PM2.5 standards, the overarching question we
consider is:
• Does the scientific evidence and risk-based information support or call into question
the adequacy of the protection afforded by the current primary PM2.5 standards?
To assist us in interpreting the scientific evidence and the results of recent quantitative
risk analyses to address this question, we have focused on a series of more specific questions, as
detailed in sections 3.5.1 and 3.5.2 below. In considering the scientific and technical information,
we consider both the information available at the time of the 2012 and 2020 reviews and
information available in this reconsideration, which have been critically assessed in the 2019 ISA
and the draft ISA Supplement. In so doing, a key consideration is whether the information in this
reconsideration alters our overall conclusions from the 2020 review regarding health effects
associated with PM2.5 in ambient air.
3.5.1 Evidence-based Considerations
In considering the evidence with regard to the overarching question posed above
regarding the adequacy of the current PM2.5 standards, we address a series of more specific
questions that focus on policy-relevant aspects of the evidence. These questions begin with
consideration of the available evidence on health effects associated with exposure to PM2.5.
(section 3.5.1.1). The subsequent questions consider identification of populations at-risk of
PM2.5-related health effects (section 3.5.1.2), and the exposure durations and levels of PM2.5
associated with health effects (section 3.5.1.3). Important uncertainties associated with the
evidence are considered in section 3.5.1.4.
3.5.1.1 Health Effects Associated with Exposure to PM2.5
In answering the overarching question above, we begin by considering the following
question:
• Is there newly available evidence that indicates the importance of certain particle
characteristics (i.e., components or size fractions) other than PM2.5 mass with regard
to concentrations in ambient air, and potential for human exposures and health
effects?
No newly available evidence has been identified in this reconsideration regarding particle
characteristics, such as components or size fractions, other than PM2.5 mass with regard to
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concentrations in ambient air, and potential for health effects. While some studies evaluate the
health effects of particular sources of fine particles, or of particular fine particle components,
evidence from these studies does not identify any one source or component that is a better
predictor of health effects than PM2.5 mass (U.S. EPA, 2019, section 1.5.4). The 2019 ISA
specifically notes that "results of these studies confirm and further support the conclusion of the
2009 ISA that many PM2.5 components and sources are associated with many health effects and
that the evidence does not indicate that any one source or component is consistently more
strongly related with health effects than PM2.5 mass" (U.S. EPA, 2019, section 1.5.4). In
addition, the evidence for health effects following exposures specifically to the ultrafine fraction
of fine particles continues to be far more limited than the evidence for PM2.5 mass as a whole. As
discussed in the 2019 ISA, the lack of a consistent UFP definition in health studies and across
disciplines, together with a variety of approaches to administering and measuring UFP in those
studies, contribute to such limitations (U.S. EPA, 2019, section 1.4.3). Thus, as was the case for
previous reviews, the evidence base for health effects of fine particles does not support
consideration of other PM characteristics, such as components, or size fractions. For these
reasons, we continue to focus on the health effects associated with PM2.5 mass.
• Does the available scientific evidence alter our conclusions regarding the nature of
health effects attributable to human exposure to PM2.5 from ambient air?
The scientific evidence, including that assessed in the 2019 ISA and draft ISA
Supplement, is consistent with the conclusion reached in the previous reviews regarding health
effects and PM exposures where a causal relationship was concluded. Specifically, as in prior
reviews, it was concluded that there is a causal relationship between short- and long-term PM2.5
exposures and mortality and cardiovascular effects (U.S. EPA, 2019, sections 11.1, 11.2, 6.1,
6.2; U.S. EPA, 2021a, sections 3.2.1, 3.2.2, 3.1.1, and 3.1.2). Further, a likely to be causal
relationship was concluded for short- and long-term PM2.5 exposures and respiratory effects
(U.S. EPA, 2019, sections 5.1 and 5.2). Additionally, conclusions reached in the 2019 ISA differ
with regard to cancer and nervous systems effects and long-term PM2.5 exposure, based on
evidence assessed in the 2019 ISA and it was concluded that there is a likely to be causal
relationship (U.S. EPA, 2019, sections 10.2 and 8.2). The evidence base is concluded to be
suggestive of, but not sufficient to infer, causal relationships between short- and long-term PM2.5
exposures and metabolic effects (U.S. EPA, 2019, sections 7.1 and 7.2), reproduction and
fertility (U.S. EPA, 2019, section 9.1.1), and pregnancy and birth outcomes (U.S. EPA, 2019,
section 9.1.2). In addition, effects associated with short-term exposure to UFP and cardiovascular
(U.S. EPA, 2019, section 6.5), respiratory (U.S. EPA, 2019, section 5.5), and nervous system
effects (U.S. EPA, 2019, section 8.5), as well as long-term exposure to UFP and nervous system
effects (U.S. EPA, 2019, section 8.6) are concluded to be suggest of, but not sufficient to infer,
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causal relationship. As in the 2020 review, the strongest evidence, including with regard to
quantitative characterizations of relationships between PM2.5 exposure and effects, is for
mortality and cardiovascular effects.
3,5.1,2 Populations At-Risk of PM2.5-related Health Effects
Populations or lifestages can be at increased risk of an air pollutant-related health effect
due to one or more factors. These factors can be intrinsic, such as physiological factors that may
influence the internal dose or toxicity of a pollutant, or extrinsic, such as sociodemographic, or
behavioral factors. The questions considered in this section address what the available evidence
indicates regarding which populations are particularly at risk of health effects related to exposure
to PM2.5 in ambient air.
• Does the current evidence alter our understanding of populations that are
particularly at risk from PM2.5 exposures? Is there evidence that suggests additional
at-risk populations that should be given increased focus for this reconsideration?
The current evidence does not alter our understanding of which populations are
potentially at greater risk from health effects of PM2.5 exposures. As in previous reviews, the
2019 ISA continues to provide support that factors that may contribute to increased risk of
PM2.5-related health effects include lifestage (children and older adults), pre-existing diseases
(cardiovascular disease and respiratory disease), race/ethnicity, and socioeconomic status. Other
factors that have the potential to contribute to increased risk, but for which the evidence is less
clear, include obesity, diabetes, genetic factors, smoking status, sex, diet, and residential location
(U.S. EPA, 2019, chapter 12).
In addition to these population groups, the 2019 ISA and draft ISA Supplement note that
there is strong evidence for racial and ethnic differences in PM2.5 exposures and PIVh.s-related
health risk. There is strong evidence demonstrating that Black and Hispanic populations, in
particular, have higher PM2.5 exposures than non-Hispanic White populations (U.S. EPA, 2019,
Figure 12-2; U.S. EPA, 2021a, Figure 3-38). Further, there is consistent evidence across multiple
studies that demonstrate increased risk of PIVh.s-related health effects, with the strongest
evidence for health risk disparities for mortality (U.S. EPA, 2019, section 12.5.4).
Studies assessed in the 2019 ISA and draft ISA Supplement also provide evidence of
exposure and health risk disparities based on SES. The evidence indicates that lower SES
communities are exposed to higher concentrations of PM2.5 compared to higher SES
communities (U.S. EPA, 2019, section 12.5.3; U.S. EPA, 2021a, section 3.3.3.1.1). Additionally,
evidence supports the conclusions that lower SES is associated with cause-specific mortality and
certain health endpoints (i.e., MI and CHF), but less so for all-cause or total (non-accidental)
mortality (U.S. EPA, 2019, section 12.5.3; U.S. EPA, 2021a, section 3.3.3.1).
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3,5,1,3 Exposure Concentrations Associated with Health Effects
In answering the overarching question with regard to the adequacy of the primary PM2.5
standards, as described above, we next consider the scientific evidence and the support it
provides for the occurrence of adverse public health effects and the associated exposure
concentrations at which such effects occur. In so doing, we ask the following questions:
• Does the current evidence alter our conclusions regarding the exposure duration and
concentrations associated with health effects? To what extent does the scientific
evidence indicate health effects attributable to exposures to PM2.5 concentrations
lower than previously reported and what are important uncertainties in that
evidence?
The evidence available in this reconsideration regarding PM2.5 exposures associated with
health effects affirms and strengthens the evidence available at the time of the 2020 review,
taking into account studies that have become available since that time. Consistent with the
evidence available in the 2020 review, and as assessed in the 2019 ISA and the draft ISA
Supplement, the strong evidence base of epidemiologic studies report associations between long-
and short-term PM2.5 exposures and a variety of outcomes, including mortality and
cardiovascular effects. Additionally, as detailed in section 3.3.1, animal toxicological studies and
controlled human exposure studies continue to provide support understanding the effects of
exposure to PM2.5, and support for biologically plausible mechanisms through which adverse
human health outcomes could occur. In addition, controlled human exposure studies have
consistently reported that PM2.5 exposures lasting from less than one hour up to five hours can
impact cardiovascular function and provide some insight into how short-term exposure to PM2.5
may impact cardiovascular function in ways that could lead to more serious outcomes.
The controlled human exposure studies, as discussed in detail in the 2019 ISA (U.S. EPA,
2019, section 6.1) and summarized above in section 3.3.3.1, have demonstrated effects on
cardiovascular function following PM2.5 exposures ranging from one to five hours, with the most
consistent evidence for impaired vascular function (U.S. EPA, 2019, section 6.1.13.2). In
addition, although less consistent, the 2019 ISA notes that studies examining PM2.5 exposures
also provide evidence for increased blood pressure (U.S. EPA, 2019, section 6.1.6.3), conduction
abnormalities/arrhythmia (U.S. EPA, 2019, section 6.1.4.3), changes in heart rate variability
(U.S. EPA, 2019, section 6.1.10.2), changes in hemostasis that could promote clot formation
(U.S. EPA, 2019, section 6.1.12.2), and increases in inflammatory cells and markers (U.S. EPA,
2019, section 6.1.11.2). The 2019 ISA concludes that, when taken as a whole, controlled human
exposure studies demonstrate that exposure to PM2.5 may impact cardiovascular function in ways
that could lead to more serious outcomes (U.S. EPA, 2019, section 6.1.16). Thus, such studies
can provide insight into the potential for specific PM2.5 exposures to result in physiological
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changes that could increase the risk of more serious effects, though the health relevance of the
occurrence of these acute effects is less certain.
To provide some insight into what these studies may indicate regarding the primary PM2.5
standards, air quality analyses examine monitored 2-hour PM2.5 concentrations at sites meeting
the current primary PM2.5 standards (as described in section 2.3.2 and section A.3 of Appendix
A).73 The 2-hour PM2.5 concentrations to which individuals were exposed in most of these
studies are well-above the ambient concentrations typically measured in locations meeting the
current primary standards. For example, at air quality monitoring sites meeting the current
primary PM2.5 standards (i.e., the 24-hour standard and the annual standard), the 2-hour
concentrations generally remain below 10 |ig/m\ and virtually never exceed 30 |ig/m3. Two-hour
concentrations are higher at monitoring sites violating the current standards, but generally remain
below 16 [j,g/m3 and virtually never exceeding 80 [j,g/m3. Thus, while controlled human exposure
studies provide support for the biological mechanisms and plausibility of the serious
cardiovascular effects associated with ambient PM2.5 exposures in epidemiologic studies (U.S.
EPA, 2019, chapter 6), the exposures evaluated in most of these studies are well-above the
ambient concentrations typically measured in locations meeting the current primary standards,
and the results are variable across some of the controlled human exposure studies evaluated at
near ambient PM2.5 concentrations.
While controlled human exposure studies provide insight on the exposure concentrations
that directly elicit health effects in humans, uncertainty exists in translating the observations in
animal toxicology studies to potential adverse health effects in humans. The interpretation of the
animal toxicology studies with regard to the potential implications for human health is
complicated by the fact that the concentrations of PM2.5 in animal toxicologic studies are much
higher than those shown to elicit effects in human populations, and there are also significant
anatomical and physiological differences between animal models and humans. Most of the
animal toxicology studies have generally examined short-term exposures to PM2.5 concentrations
from 100 to >1,000 [j,g/m3 and long-term exposures to concentrations from 66 to >400 [j,g/m3
(e.g., see U.S. EPA, 2019, Table 1-2). Two exceptions are a study reporting impaired lung
development following long-term exposures (i.e., 24 hours per day for several months prenatally
and postnatally) to an average PM2.5 concentration of 16.8 [j,g/m3 (Mauad et al., 2008) and a
study reporting increased carcinogenic potential following long-term exposures (i.e., 2 months)
to an average PM2.5 concentration of 17.7 [j,g/m3 (Cangerana Pereira et al., 2011). These two
studies report serious effects following long-term exposures to PM2.5 concentrations close to the
73 In addition, 4-hour and 5-hour PM2 5 concentrations at monitoring sites meeting or violating the current primary
PM2 5 standards were also evaluated (as described in section 2.3.2 and section A.3 of Appendix A).
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ambient concentrations reported in some PM2.5 epidemiologic studies (U.S. EPA, 2019, Table 1-
2), though still above the ambient concentrations likely to occur in areas meeting the current
primary standards. Thus, as is the case with controlled human exposure studies, animal
toxicology studies support the plausibility of various adverse effects that have been linked to
ambient PM2.5 exposures (U.S. EPA, 2019) ).
Epidemiologic studies in the U.S. and Canada, assessed in the 2019 ISA and draft ISA
Supplement, continue to report positive and statistically significant associations between long-
and short-term exposure to PM2.5 and mortality and morbidity, including both new studies
evaluated in the draft ISA Supplement related to total mortality and cardiovascular mortality and
morbidity and studies that examined populations and lifestages that may be at comparatively
higher risk of experiencing a PIVh.s-related health effects (e.g., older adults). Such studies
employ various designs and examine a variety of health outcomes, geographic areas, and
approaches to controlling for confounding variables. With regard to controlling for potential
confounders in particular, key epidemiologic studies use a wide array of approaches. Time-series
studies control for potential confounders that vary over short time intervals (e.g., including
temperature, humidity, dew point temperature, and day of the week) while cohort studies control
for community- and/or individual-level confounders that vary spatially (e.g., including income,
race, age, socioeconomic status, smoking, body mass index, and annual weather variables such
as temperature and humidity) (Appendix B, Table B-4). Sensitivity analyses indicate that adding
covariates to control for potential confounders can either increase or decrease the magnitude of
PM2.5 effect estimates, depending on the covariate, and that none of the covariates examined can
fully explain the association with mortality (e.g., Di et al., 2017b, Figure S2 in Supplementary
Materials). Thus, while no individual study adjusts for all potential confounders, a broad range of
approaches have been adopted across studies to examine confounding, supporting the robustness
of reported associations.
Available studies additionally indicate that PM2.5 health effect associations are robust
across various approaches to estimating PM2.5 exposures and across various exposure windows.
This includes recent studies that estimate exposures using ground-based monitors alone and
studies that estimate exposures using data from multiple sources (e.g., satellites, land use
information, modeling), in addition to monitors. While none of these approaches eliminates the
potential for exposure error in epidemiologic studies, such error does not call into question the
fundamental findings of the broad body of PM2.5 epidemiologic evidence. In fact, the 2019 ISA
notes that while bias in either direction can occur, exposure error tends to lead to underestimation
of health effects in epidemiologic studies of PM exposure (U.S. EPA, 2019, section 3.5).
Consistent with this, a recent study reports that correction for PM2.5 exposure error using
personal exposure information results in a moderately larger effect estimate for long-term PM2.5
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exposure and mortality (Hart et al., 2015). While most PM2.5 epidemiologic studies have not
employed similar corrections for exposure error, several studies report that restricting analyses to
populations in close proximity to a monitor (i.e., in order to reduce exposure error) result in
larger PM2.5 effect estimates (e.g., Willis et al., 2003; Kloog et al., 2013). The consistent
reporting of PM2.5 health effect associations across exposure estimation approaches, even in the
face of exposure error, together with the larger effect estimates reported in some studies that
have attempted to reduce exposure error, provides further support for the robustness of
associations between PM2.5 exposures and mortality and morbidity.
Consistent findings from the broad body of epidemiologic studies are also supported by
an emerging body of studies employing causal modeling methods to further inform the causal
nature of the relationship between long- or short-term term PM2.5 exposure and mortality (U.S.
EPA, 2019, sections 11.1.2.1, 11.2.2.4, U.S. EPA, 2021a, sections 3.1.1.3, 3.1.2.3, 3.2.1.3, and
3.2.2.3). These studies, summarized above in Table 3-11, used a variety of statistical methods to
control for confounding bias and consistently report positive associations, which support the
positive and significant effects seen in cohort studies associated with short- and long-term
exposure to PM2.5 and mortality.
In addition to broadening our understanding of the health effects that can result from
exposures to PM2.5 and strengthening support for some key effects (e.g., nervous system effects,
cancer), recent epidemiologic studies strengthen support for health effect associations at
relatively low ambient PM2.5 concentrations. Studies that examine the shapes of concentration-
response functions over the full distribution of ambient PM2.5 concentrations have not identified
a threshold concentration, below which associations no longer exist (U.S. EPA, 2019, section
1.5.3, U.S. EPA, 2021a, sections 2.2.3.1 and 2.2.3.2). While such analyses are complicated by
the relatively sparse data available at the lower end of the air quality distribution (U.S. EPA,
2019, section 1.5.3), analyses that assess the concentration-response relationship support a linear,
no-threshold effect down to 5.0 (J,g/m3, though uncertainties increase at concentrations of less
than 8.0 [j,g/m3.
There are a number of U.S. and Canadian studies that examine health effect associations
in analyses with the highest exposures excluded and report positive and statistically significant
associations in analyses restricted to annual average PM2.5 exposures at or below 12 |j,g/m3 and
or to daily exposures below 35 |j,g/m3 (Table 3-10). While mean PM2.5 concentrations for these
restricted analyses may not be reported in most studies, we can presume that the mean PM2.5
concentrations in the restricted analyses are less than the study-reported mean PM2.5
concentrations in the main analyses, which range from 8.1 |ig/m3 to 11.6 |ig/m3 in the U.S., and
was 7.8 |ig/m3 for the one study in Canada that included restricted analysis. It is important to
note that even if we had information on PM2.5 mean concentrations reported in restricted
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analysis, we would not necessarily be able to use these means in a similar decision framework as
was used in past reviews (section 3.3.3.2.1). given uncertainties associated with identifying the
relationship between a calculated mean concentration that excludes specific daily or annual
average concentrations above a certain threshold and the design value used to determine
compliance with a standard (annual or 24-hour). However, restricted analyses do provide support
for effects at lower concentrations, exhibiting associations for mean concentrations presumably
below the mean concentrations for the main analyses.
Finally, accountability studies evaluate whether changes in air quality are associated with
improvements in public health and a number of recent studies are evaluated in the draft ISA
Supplement (summarized in Table 3-12 above). These studies exhibit positive and significant
associations, including some studies that report starting PM2.5 concentrations below 12.0 |ig/m3,
indicating that public health improvements may occur following PM2.5 reductions in areas that
already meet the current annual PM2.5 standard. For example, studies by Corrigan et al. (2018)
and Sanders et al. (2020) both found improvements in mortality rates due to improvements in air
quality in both attainment and nonattainment areas following implementation of the 1997
primary annual PM2.5 NAAQS.74 Other recent studies additionally report that declines in ambient
PM2.5 concentrations over a period of years have been associated with decreases in mortality
rates and increases in life expectancy, improvements in respiratory development, and decreased
incidence of respiratory disease in children, further supporting the robustness of PM2.5 health
effect associations reported in the epidemiologic evidence.
Consistent with previous reviews, we note that the use of information from epidemiologic
studies to inform conclusions on the primary PM2.5 standards is complicated by the fact that such
studies evaluate associations between distributions of ambient PM2.5 and health outcomes, and
do not identify the specific exposures that can lead to the reported effects. Rather, health effects
can occur over the entire distribution of ambient PM2.5 concentrations evaluated, and
epidemiologic studies do not identify a population-level threshold below which it can be
concluded with confidence that PM-associated health effects do not occur (U.S. EPA, 2019,
section 1.5.3). However, the study-reported ambient PM2.5 concentrations reflecting estimated
exposure in the middle portion of the PM2.5 air quality distribution, which corresponds to the
bulk of the underlying data, which provide the strongest support for reported health effect
associations and can inform our preliminary conclusions on the current and potential alternative
standards. In using this information to inform our preliminary conclusions, we recognize that the
mean PM2.5 concentrations reported by key epidemiologic studies differ in how mean
74 We note that the studies by Corrigan et al. (2018) and Sanders et al. (2020) report monitor-based average PM2 5
concentrations, and that these studies do not report design values.
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concentrations were calculated (Table 3-5, Table 3-6, Table 3-7, Table 3-8), as well as their
interpretation in what means represent in the context of the current standards. To frame our
evaluation of study-reported mean PM2.5 concentrations, we specifically consider the following
question:
• How do the study-reported means from the key epidemiologic studies and the related
air quality analyses that compare study means to area design values inform our
consideration of the level of the current annual PM2.5 standard?
In the 2012 review, the Administrator recognized that evidence of an association between PM2.5-
related health effects and long- and short-term exposures in the epidemiologic studies were
strongest at and around the long-term average where the data in the study are most concentrated.
In so doing, she noted that the long-term mean PM2.5 concentrations were available for the
studies considered and represented the most robust data set to inform decisions on appropriate
levels for the annual primary PM2.5 standard, while also recognizing that this approach did not
provide a bright line for reaching this decision (78 FR 3140, January 15, 2013). As detailed in
section 3.3.3.2.1, the reported mean PM2.5 concentrations derived from monitored observations
are not the same as the mean PM2.5 concentrations estimated using hybrid modeling methods,
which are also not the same as design values used to determine whether an area meets or exceeds
the PM2.5 NAAQS. Additional analyses, new in this draft PA though similar to those in the 2012
review, examine how the calculation of the study mean varies across studies and how these
metrics compare to the annual design value. The analysis indicates that study means from
methods that use hybrid models to estimate exposures are generally lower in areas where urban
and rural PM2.5 concentrations are estimated, compared to hybrid modeled PM2.5 concentrations
in urban areas or concentrations that have been population-weighted. Moreover, the analysis
indicates that hybrid modeling mean estimates are generally lower than the average of monitored
PM2.5 concentrations, which are both below the concentration measured at the highest monitor
(i.e., the approach used to calculate the design value). In the national-scale analysis, where air
quality analyses compared composite monitored PM2.5 concentrations with annual PM2.5 design
values in the U.S., annual PM2.5 design values were approximately 10% to 20% higher than
concentrations averaged across multiple monitors in the same CBSA (section 2.3.3.1, Figure 2-
28 and Table 2-2).
Further, with the expansion of studies that employ hybrid modeling methods to estimate
PM2.5 concentrations, Section 2.3.3.2.4 details a comparison of PM2.5 fields in estimating
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exposure relative to design values using the DI2019 and HA202075 surfaces, which are two air
quality surfaces included in several of the key epidemiologic studies. This analysis illustrates that
population-weighting the PM2.5 concentrations in the hybrid modeling approaches has an effect
on the resulting study-reported mean. Specifically, the analysis shows that area annual design
values are 40% to 50% higher compared to the study-reported means when population-weighting
is not employed. Additionally, when population-weighting is applied in studies using hybrid
modeling approaches, average annual PM2.5 design values are only 15% to 18% higher than the
study-reported means. This suggests that whether a study using a hybrid modeling approach
incorporated population-weighting is very important for understanding how to interpret the
estimated PM2.5 exposure concentrations, particularly for purposes of comparing those estimated
concentrations to actual design values.
Thus, given the potentially large differences between study reported means and area
annual design values, it is important to consider the manner in which PM2.5 concentrations are
estimated (e.g., monitored concentrations versus modeled concentrations) and the method by
which means are calculated and reported as the overall mean PM2.5 concentration (e.g., averaging
across all grid cells in an urban area versus population-weighting). Additional analyses, new in
this draft PA though similar to those in the 2012 review, suggest that area annual design values
higher than the study-reported means by 10-20% (monitor-based studies), 14-18%) (hybrid
modeling with population-weighting) or 40-50% (hybrid modeling without population
weighting). Grouping studies based on the approach used to estimate the mean, we note that the
overall mean PM2.5 concentrations in key U.S. epidemiologic studies are as follows:
• Range of monitor-based mean PM2.5 concentrations is from 9.9 [j,g/m3 to 16.5 [j,g/m3 (range in
2020 PA: 10.7 [j,g/m3 to 16.5 (J,g/m3)
• Range of mean PM2.5 concentrations in studies that use hybrid modeling and apply
population-weighting: 9.3 [j,g/m3 to 12.3 [j,g/m3
• Range of mean PM2.5 concentrations in studies that use hybrid modeling and do not apply
population-weighting: 8.1 [j,g/m3 to 11.9 [j,g/m3
The mean PM2.5 concentrations in Canadian studies are more difficult to compare to the
annual design value used to determine compliance in the U.S. As we note above, the air quality
analyses in section 3.3.3.2.1 are most relevant for interpreting U.S. epidemiologic studies. Given
that we are lacking important pieces of information that allow us to do similar analyses for
75 As discussed above in section 2.3.3.2.4, HA2020 refers to estimated PM2 5 concentrations from a hybrid modeling
approach developed by Hammer et al. (2020) and van Donkelaar et al. (2019), and which estimates Nationwide
PM2 5 concentrations from 2000-2016.
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Canada, we are unable to provide specific quantitative insight into how the study reported means
in the Canadian studies would compare to area design values in the U.S. However, we note that
the overall mean PM2.5 concentrations in key Canadian epidemiologic studies are similar to,
though somewhat lower than, those from the U.S. studies:
• Range of monitor-based mean PM2.5 concentrations: 6.9 |ig/m3 to 13.3 |ig/m3
• Range of mean PM2.5 concentrations in studies that use hybrid modeling (all of which
average up to postal codes and thus include some aspects of population-weighting): 5.9
|ig/m3 to 9.8 |ig/m3
In the context of evaluating whether the newly available scientific information alters our
conclusions from the 2020 review regarding the nature of health effects attributable to human
exposure to PM2.5 from ambient air, while the causality determinations have not changed, the
number of studies that use hybrid modeling approaches has expanded. When using the
information from the new air quality analyses to interpret key epidemiologic studies in the
context of the primary standards, we note that they suggest that epidemiologic studies that use
monitor-based estimates for PM2.5 exposure or that calculate population-weighted averages from
hybrid modeling approaches generally report mean concentrations that are more easily compared
to an area annual design value (i.e., area annual design values are 10-20% greater than mean
PM2.5 concentrations). However, we also note that area annual design values tend to be
substantially greater than mean concentrations in epidemiologic studies that use hybrid
approaches and do not include population weighting (e.g. 40-50% greater). Thus, when
evaluating what the mean PM2.5 concentrations reported by key epidemiologic studies may
indicate regarding the current or alternative PM2.5 standards, we emphasize the importance of
considering the broader relationships between mean PM2.5 concentrations, averaged across space
and over time using a variety of approaches, and PM2.5 design values.
• How do the study-reported PM2.5 concentrations corresponding to the 25th and 10th
percentiles of health data or exposure estimates provide insight to inform our
consideration of the level of the current annual PM2.5 standard?
In the 2012 review, the 2011 PA noted the interrelatedness of the distributional statistics
and a range of one standard deviation around the mean which contains approximately 68% of
normally distributed data, in that one standard deviation below the mean falls between the 25th
and 10th percentiles (U.S. EPA, 2011 p. 2-71). Given this, the 2011 PA provided information, as
available for a subset of key epidemiologic studies, on the study-reported PM2.5 concentrations
corresponding to the 25th and 10th percentiles of health data or exposure estimates.
In that review, the Administrator placed some weight on studies that provided mean
PM2.5 concentrations around the 25th percentile of the distributions of deaths and cardiovascular-
related hospitalizations and judged the region around the 25th percentile as a reasonable part of
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the distribution to guide the decision on the appropriate standard level (78 FR 3161, January 15,
2013). Given the potential for consideration of this information in this reconsideration with
regard to the adequacy of the standard level, we note that of the key epidemiologic studies
evaluated in the 2019 ISA and draft ISA Supplement, a subset of studies report PM2.5
concentrations corresponding to the 25th and 10th percentiles of health data or exposure estimates
to provide insight into the concentrations that comprise the lower quartiles of the air quality
distributions. In the key U.S. epidemiologic studies that report the 25th and 10th percentiles of
health events corresponding to mean PM2.5 concentrations (i.e., averaged over the study period
for each study city), we note:
• Monitor-based 25th percentiles of health events correspond to mean PM2.5 concentrations
(i.e., averaged over the study period for each study city): at or above 11.5 |j,g/m3
• Monitor-based 10th percentiles of health events correspond to mean PM2.5 concentrations: at
or above 9.8 |j,g/m3
• PM2.5 concentrations corresponding to 25th percentiles of estimated exposures that use hybrid
modeling approaches to estimate long-term PM2.5 exposures range from 6.2 to 9.2 |j,g/m3
• PM2.5 concentrations corresponding to 25th percentiles of estimated exposures in studies that
uses hybrid modeling to estimate short-term exposures: at or above 6.4 |j,g/m3
• PM2.5 concentration corresponding to the 25th percentile of estimated exposures in one study
with lower concentrations is 4.6 |j,g/m3
• PM2.5 concentration corresponding to the 10th percentile in the two studies with available
information on this percentile range from 4.7 |j,g/m3 to 7.3 |j,g/m3.
In thinking about these values relative to an area annual design value, we emphasize that
the 25th and 10th percentiles provide information about the lower quartiles of the air quality
distributions, while the study reported mean provides information about the average or typical
exposures, and the corresponding area annual design value provides the highest average annual
PM2.5 concentration being measured. In this way, all of these metrics (i.e. lower percentiles,
study mean, annual design value) have a relationship relative to the other.
3,5,1,4 Uncertainties in the Health Effects Evidence
A number of key uncertainties and limitations were identified in the previous review with respect
to health effects evidence, as described in the 2020 PA. This section considers the currently
available information, including that newly available in this reconsideration, with regard to such
areas of uncertainty.
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• To what extent have previously identified uncertainties in the health effects evidence
been reduced and/or have new uncertainties emerged?
We continue to recognize uncertainties that persist from previous reviews. First, we note
uncertainties related to the susceptibility of different population groups for which evidence is not
as clear (e.g., based on differences in underlying factors such as obesity, smoking status and
residential location). For human exposures studies, there are uncertainties related to mixed
results seen at concentrations near ambient PM2.5 levels. It is also unclear how the results alone
and the importance of the effects observed in these studies, particularly in studies conducted at
near-ambient PM2.5 concentrations, should be interpreted with respect to adversity to public
health. With respect to animal toxicology studies, while these studies also help establish
biological plausibility, uncertainty exists in extrapolating the effects seen in animal toxicology
studies, and the PM2.5 concentrations that cause those effects to human populations.
Uncertainties associated with the epidemiologic evidence (e.g., the potential for
copollutant confounding and exposure measurement error) remain, though new studies assessed
in the draft ISA Supplement employ statistical methods like causal modeling methods, which
have reduced some uncertainties related to potential confounding of effects. In so doing,
however, we note the strength in the epidemiologic evidence in its support for determination of a
causal relationship for mortality and cardiovascular effects as summarized in section 3.3.1 above.
With regard to controlling for potential confounders in particular, key epidemiologic
studies use a wide array of approaches. Time-series studies control for potential confounders that
vary over short time intervals (e.g., including temperature, humidity, dew point temperature, and
day of the week), while cohort studies control for community- and/or individual-level
confounders that vary spatially (e.g., including income, race, age, socioeconomic status,
smoking, body mass index, and annual weather variables such as temperature and humidity)
(Appendix B, Table B-4). Sensitivity analyses indicate that adding covariates to control for
potential confounders can either increase or decrease the magnitude of PM2.5 effect estimates,
depending on the covariate, and that none of the covariates examined can fully explain the
association with mortality (e.g., Di et al., 2017b, Figure S2 in Supplementary Materials). Thus,
while no individual study adjusts for all potential confounders, a broad range of approaches have
been adopted across studies to examine confounding, supporting the robustness of reported
associations. Available studies additionally indicate that PM2.5 health effect associations are
robust across various approaches to estimating PM2.5 exposures and across various exposure
windows. This includes recent studies that estimate exposures using ground-based monitors
alone and studies that estimate exposures using data from multiple sources (e.g., satellites, land
use information, modeling), in addition to monitors. While none of these approaches eliminates
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the potential for exposure error in epidemiologic studies, such error does not call into question
the fundamental findings of the broad body of PM2.5 epidemiologic evidence.
Additionally, studies that examine the shapes of concentration-response functions over
the full distribution of ambient PM2.5 concentrations have not identified a threshold
concentration, below which associations no longer exist (U.S. EPA, 2019, section 1.5.3, U.S.
EPA, 2021a, sections 2.2.3.1 and 2.2.3.2). While such analyses are complicated by the relatively
sparse data available at the lower end of the air quality distribution (U.S. EPA, 2019, section
1.5.3), analyses that assess the concentration-response relationship support a linear, no-threshold
effect down to 5.0 (J,g/m3, though uncertainties increase at concentrations of less than 8.0 [j,g/m3.
While studies using hybrid modeling methods have demonstrated reduced exposure
measurement error and uncertainty in the health effect estimates, these methodologies have
inherent limitations and uncertainties, as described in more detail in section 2.3.3.1.5 and above
in 3.3.4, and the performance of the modeling approaches depends on the availability of
monitoring data which varies by location. Factors likely contributing to poorer model
performance often coincide with relatively low ambient PM2.5 concentrations, in areas where
predicted exposures are at a greater distance to monitors, and under conditions where the
reliability and availability of key datasets (e.g., air quality modeling) are limited. Thus,
uncertainty in hybrid model predictions becomes an increasingly important consideration as
lower predicted concentrations are considered.
In addition, limitations and or uncertainties exist in the analysis (section 2.3.3.2.4)
evaluating the comparison of estimated PM2.5 concentrations using hybrid modeling surfaces and
their relationship to design values that should be considered. While design values in general are
higher than estimated PM2.5 concentrations using these two hybrid modeling approaches, it is
important to recognize that these are just two hybrid modeling approaches and other
models/approaches/spatial scales may result in somewhat different values. This analysis
estimates PM2.5 concentrations by CBSAs, but not every health study uses PM2.5 estimates at this
spatial scale, and spatial scales for exposure estimates can vary by study. As an example of this
variation, in Di et al. (2016), an annual average PM2.5 concentration was assigned to a person at-
risk of death according to the ZIP code of the person's residence. The analysis completed was a
nationwide analysis and ratios are based on national estimates. However, not all health studies
are national studies and ratios in different parts of the country could be higher or lower,
depending on factors like population, as well as rural versus urban areas. This analysis used
specific air quality years (2000-2016) and other air quality year could result in higher or lower
ratios.
Regardless of whether an epidemiologic study uses monitoring data or a hybrid modeling
approach when estimating PM2.5 exposures, one important challenge that persists is associated
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with the interpretation of the study reported mean PM2.5 concentrations and how they compare to
design values. This is particularly true given the variability that exists across the various
approaches to estimate exposure and to calculate the study reported mean. Further, with respect
to interpreting the study reported mean concentrations from Canadian studies, using U.S. based
analyses of hybrid modeling and their relationship to design values is complicated by differences
between the U.S. and Canada as it relates to population densities, PM2.5 concentration gradients,
and source distributions in the two countries.
3.5.2 Risk-based Considerations
Our consideration of the scientific evidence available in this reconsideration, as at the
time of the 2020 review, is informed by results from a quantitative analysis of risk. The
overarching consideration in this section is whether the current risk information alters our overall
conclusions regarding health risk associated with exposure to PM2.5 in ambient air. As in our
consideration of the evidence in section 3.5.1 above, we have focused the discussion regarding
the risk information around key questions related to air quality conditions simulated to just meet
existing and alternative primary PM2.5 standards.
Prior to addressing the key risk questions, we provide a summary of important aspects of
the assessment, including the study areas, air quality scenarios, and risk metrics (section 3.5.2.1).
We then consider aspects of the questions beginning with the magnitude of risk estimated by
both the overall assessment and for certain at-risk populations, followed by the key uncertainties
associated with the quantitative analyses with regard to drawing conclusions as to the adequacy
of protection afforded by the current primary PM2.5 standards (section 3.5.2.2 and 3.5.2.3). We
also consider uncertainties associated with the risk assessment (section 3.5.2.4). Lastly, we
consider the risk estimates from the quantitative assessments with regard to the extent to which
such estimates may be judged to be important from a public health perspective (section 3.5.2.5).
3,5.2,1 Risk Assessment Analyses
In the risk assessment conducted for this reconsideration, described in detail in section
3.4 above and Appendix C, we have estimated PM2.5 health risks associated with air quality
conditions that just meet the current primary PM2.5 standards and potential alternative standard
levels. These analyses inform our understanding of the health risks for all-cause or nonaccidental
mortality associated with long- and short-term PM2.5 exposures. These analyses estimate
exposure and risk for populations in 47 urban study areas, as well as subsets of those study areas
depending on which of the primary PM2.5 standards is controlling in a given study area.
The 47 urban study areas were identified as they required relatively small adjustments
(<20%) to just meet the current primary PM2.5 standards and present a variety of circumstances
with regard to risk associated with long- and short-term exposures to PM2.5 in ambient air. This
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set of study areas and the associated populations are intended to be informative to the EPA's
consideration of potential risks that may be associated with the air quality conditions that meet
the current and potential alternative primary PM2.5 standards. The 47 study areas include nearly
60 million people ages 30 years or older and illustrate the differences likely to occur across
various locations with such air quality as a result of area-specific differences in emissions,
meteorological, and population characteristics. While the same conceptual air quality scenarios
are simulated in all study areas (i.e., conditions that just meet the existing or alternate standards),
source, meteorological and population characteristics in the study areas contribute to variability
in the estimated magnitude of risk across study areas.
As an initial matter, we note that, consistent with the overall approach for this
reconsideration, the risk assessment has a target scope that focuses on all-cause or nonaccidental
mortality associated with long- and short-term PM2.5 exposures (section 3.4.1.2). As noted in
section 3.5.1 above, the evidence assessed in the 2019 ISA and draft ISA Supplement support a
causal relationship between long- and short-term PM2.5 exposures and mortality. Concentration-
response functions used in the risk assessment are from large, multicity U.S. epidemiologic
studies that evaluate the relationship between PM2.5 exposures and mortality and were identified
using criteria that take into account factors such as study design, geographic coverage,
demographic populations, and health endpoints (U.S. EPA, 2021b, section 2.1).
In the risk assessment, air quality modeling was used to develop a PM2.5 concentration
field for 2015 (described in more detail in section 3.4.1.4 and Appendix C). The 2015 PM2.5
concentration field was adjusted to simulate just meeting the existing annual and 24-hour
standards of 12.0 |ig/m3 and 35 |ig/m3 and to just meeting potential alternative annual and 24-
hour standards of 10.0 |ig/m3 and 30 |ig/m3. The adjustments made to the PM2.5 concentration
field are based on assumptions. Changes in PM2.5, in reality, require specific information
regarding emissions changes, with concentration gradients of PM2.5 varying accordingly across
an area. The risk assessment used two adjustment approaches to serve as bounding scenarios for
the various ways an alternative standard may be met: (1) preferentially adjusting direct/primary
PM emissions, for which changes in PM2.5 tend to be more localized near the direct emissions
sources of PM (Pri-PM), and (2) preferentially adjusting SO2 and NOx precursor emissions to
simulate changes in secondarily formed PM2.5, for which reductions in PM2.5 tend to be more
evenly spread across a study area (Sec-PM). In addition to the air quality modeling approach,
linear interpolation and extrapolation were used to simulate just meeting alternative annual
standards with levels of 11.0 (interpolated between 12.0 and 10.0 |j,g/m3), 9.0 ng/m3, and 8.0
Hg/m3 (both extrapolated from 12.0 and 10.0 |ag/m3) in the subset of study areas controlled by
the annual standard.
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Evidence strongly supports that different racial and ethnic groups, such as Black and
Hispanic populations, have higher PM2.5 exposures than White and non-Hispanic populations,
respectively, thus contributing to increased risk of PM-related effects. In addition to the risk
assessment described above, quantitative analyses for this reconsideration also assess long-term
PM2.5-attributable exposure and mortality risk, stratified by racial/ethnic demographics.
Consistent with the overall risk assessment approach, the specific epidemiologic studies and
concentration-response functions used in the at-risk analyses were selected to take into account
factors such as study design, geographic coverage, demographic populations, and health
endpoints. Of the available studies, Di et al., 2017b was identified as best characterizing
populations potentially at increased risk of long-term exposure and all-cause mortality and
provides race- and ethnicity-stratified concentration-response functions for ages 65 and over
(section 3.4.1.6 and Appendix C). Risk is quantitatively assessed within racial and ethnic
minority populations of older adults in the full set of 47 areas and the subset of 30 areas
controlled by the annual PM2.5 standard under Pri-PM air quality simulations. This analysis,
when considered alongside estimates of risk across all populations in the 47 study areas, can help
to inform preliminary conclusions on the annual primary PM2.5 standards that would be requisite
to protect the public health of nonwhite populations potentially at increased risk of long-term
PM2.5-related mortality effects.
3.5.2.2 Estimating Risk under the Current and Alternative Primary PM2.5 Standards
In this section, we summarize the risk estimates associated with air quality scenarios just
meeting the current primary PM2.5 standards and potential alterative standard levels.
• What are the estimated PM2.5-associated health risks for air quality just meeting the
current primary PM2.5 standards?
In considering the risk results, we focus first on estimates for the full set of 47 urban
study areas. The risk assessment estimates that the current primary PM2.5 standards could allow a
substantial number of deaths in the U.S., with the large majority of those deaths associated with
long-term PM2.5 exposures. For example, when air quality in the 47 study areas is adjusted to just
meet the current standards, the risk assessment estimates about 41,000 to 45,000 deaths from all-
cause mortality in a single year (i.e., for long-term exposures; confidence intervals range from
about 30,000 to 59,000) (section 3.4.2.1). For the 30 study areas76 where just meeting the current
76 These 30 areas controlled by the annual standard under all scenarios evaluated include a population of
approximately 48 million adults aged 30-99, or about 75% of the population included in the full set of 47 areas.
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standards is controlled by the annual standard,77 long-term PM2.5 exposures are estimated to be
associated with as many as 39,000 (confidence intervals range from about 26,000 to 51,000)
deaths from all-cause mortality in a single year (section 3.4.2.2). For the 11 study areas78 where
just meeting the current standards is controlled by the daily standard,79 long-term PM2.5
exposures are estimated to be associated with as many as 2,600 (confidence intervals ranging
from 1,700 to 3,400) deaths in a single year (section 3.4.2.3). The risk assessment estimates far
fewer deaths in a single year for short-term PM2.5 exposures as compared to long-term PM2.5
exposures, across all of the study area subsets.
While the absolute numbers of estimated deaths vary across exposure durations,
populations, and concentration-response functions, the general magnitude of risk estimates
supports the potential for significant public health impacts in locations meeting the current
primary PM2.5 standards. This is particularly the case given that the large majority of PM2.5-
associated deaths for air quality just meeting the current standards are estimated at annual
average PM2.5 concentrations from about 10 to 12 |ig/m3. These annual average PM2.5
concentrations fall within the range of long-term average concentrations over which key
epidemiologic studies provide strong support for reported positive and statistically significant
health effect associations.
• To what extent are risks estimated to decline when air quality is adjusted to just
meet potential alternative standards with lower levels?
In the 47 urban study areas, when air quality is simulated to just meet alternative
standards, there are substantially larger risk reductions associated with lowering the annual
standard then with lowering the 24-hour standard. Risks are estimated to decrease by 13-17%
when air quality is adjusted to just meet an alternative annual standard with a level of 10.0 |j,g/m3
or by 1-2% when adjusted to just meet an alternative 24-hour standard with a level of 30 |j,g/m3
(section 3.4.2.1). The percentage decrease when just meet an alternative annual standard with a
level of 10.0 |j,g/m3 corresponds to approximately 7,400 fewer deaths per year (confidence
intervals ranging from about 4,100 to 9,800) attributable to long-term PM2.5 exposures.
77 For these areas, the annual standard is the "controlling standard" because when air quality is adjusted to simulate
just meeting the current or potential alternative annual standards, that air quality also would meet the 24-hour
standard being evaluated.
78 These 11 areas controlled by the 24-hour standard under all scenarios evaluated include a population of
approximately 10 million adults aged 30-99, or about 17% of the population included in the full set of 47 areas.
79 For these areas, the 24-hour standard is the controlling standard because when air quality is adjusted to simulate
just meeting the current or potential alternative 24-hour standards, that air quality also would meet the annual
standard being evaluated. Some areas classified as being controlled by the 24-hour standard also violate the
annual standard.
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In the 30 study areas where just meeting the current and alternative standards is
controlled by the annual standard, air quality adjusted to meet alternative annual standards with
lower levels is associated with reductions in estimated all-cause mortality risk. These reductions
in risk for alternative annual levels are as follows: 7-9% reduction for an alternative annual level
of 11.0 |ig/m3, 15-19% reduction for a level of 10.0 |ig/m3, 22-28%) reduction for a level of 9.0
|ig/m3, and 30-37%) reduction for a level of 8.0 |ig/m3 (section 3.4.2.2). For each of these
standards, most of the risk remaining is estimated at annual average PM2.5 concentrations that
fall somewhat below the alternative standard levels.
3,5,2,3 At-Risk Analyses
As noted above, in addition to the risk assessment described in sections 3.4.1.1-3.4.1.5
and 3.4.2.1-3.4.2.3, risk was quantitatively assessed within racial and ethnic minority populations
of older adults in the full set of 47 areas and the subset of 30 areas controlled by the annual PM2.5
standard under all air quality simulations evaluated (sections 3.4.1.6 and 3.4.2.4).
• What is the magnitude of population risk in at-risk populations in areas simulated to
just meet the current primary PM2.5 standards? To what extent are risks estimated
to decline within each demographic group when air quality is adjusted to just meet
potential alternative annual standards with lower levels?
The at-risk analysis first compares the average estimated PM2.5 exposure concentrations
for each demographic population when just meeting the current and alternative annual PM2.5
standards. Across all simulated air quality for both the full set of 47 and the subset of 30 study
areas, Blacks experience the highest average PM2.5 concentrations of the demographic groups
analyzed. Native Americans experienced the lowest average PM2.5 concentrations, particularly in
the full set of 47 study areas. White, Hispanic, and Asian populations were exposed to similar
average PM2.5 concentrations. Additionally, as the levels of potential alternative annual PM2.5
standards decrease, there is comparatively less disproportionate exposure between demographic
populations (section 3.4.2.4).
Risk estimates can provide additional information beyond the exposure information to
inform our understanding of potentially disproportionate impacts, in this instance by including
demographic-specific information on baseline incidence and the relationship between exposure
and health effect. Across all air quality scenarios and demographic groups evaluated, Black
populations are associated with the largest PIVh.s-attributable mortality risk rate per 100,000
people, while White populations are associated with the smallest PM2.5-attributative mortality
risk rate (section 3.4.2.4, Figure 3-20). Generally, as the levels of potential alternative annual
PM2.5 standards decrease in the 30 areas controlled by the annual standard, the average reduction
in PM2.5 concentration and mortality risk rates increase across all demographic populations
(section 3.4.2.4, Figure 3-21).
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In comparing the reductions in average national PM2.5 concentrations and risk rates
within each demographic population, we note that the average percent PM2.5 concentrations and
risk reductions are slightly greater in the Black population than in the White population for each
alternative standard evaluated (11.0 |ig/m3, 10.0 |ig/m3, 9.0 |ig/m3, and 8.0 |ig/m3), when shifting
from the current annual PM2.5 standard (12.0 |ig/m3) in the full set of 47 areas and the subset of
30 areas controlled by the annual standard. We further note that the difference in average percent
risk reductions increases slightly more in Blacks than in Whites as the level of the potential
alternative annual standard decreases (section 3.4.2.4, Table 3-19 and Table 3-20).
3,5,2,4 Uncertainties
In this section, we consider uncertainties associated with the quantitative estimates of risk
in the overall risk assessment and from risk rates and exposure estimates in the at-risk analysis
(sections 3.4.2.5, 3.4.1.7, and 3.4.1.8). Variability and uncertainty associated with the risk
estimates are assessed using several quantitative and qualitative approaches, as described in more
detail in section C.3 of Appendix C. Generally, the quantitative uncertainty characterization
approaches include the following: (1) evaluating multiple concentration-response functions for
the same health endpoint; (2) evaluating multiple methods for simulating air quality scenarios;
and (3) characterizing the 95% confidence intervals associated with risk estimates. The
qualitative uncertainty characterization approach is based on WHO (2008) guidance and on
guidance documents developed by the EPA (U.S. EPA, 2001, U.S. EPA, 2004). This qualitative
approach includes an assessment of both the magnitude and direction of impact of those
uncertainties on risk estimates, including three levels of classification for the magnitude: low,
medium, and high.80
• What are the key uncertainties associated with the risk estimates and at-risk
analysis, including those of particular significance with regard to drawing
conclusions as to the adequacy of the protection afforded by the current primary
PM2.5 standards?
Based on the uncertainty characterization and associated analyses in the risk assessment
and consideration of associated policy implications, we recognize several areas of uncertainty as
particularly important in our consideration of the risk estimates, as was also the case in previous
reviews, and in the risk rates and exposure and risk reductions in the at-risk analysis.
80 The classification of the magnitude of impact for sources of uncertainty includes three levels: (a) low (unlikely to
produce a sufficient impact on risk estimates to affect their interpretation), (b) medium (potential to have a
sufficient impact to affect interpretation), and (c) high (likely to have an impact sufficient to affect interpretation)
For several of the sources, a classification was provided between these levels (e.g., low-medium, medium-high).
More information is available in Appendix C, section C.3.
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With regard to the concentration-response relationships, we recognize that the degree to
which different concentration-response functions result in different risk estimates could reflect
differences in study design and/or populations evaluated, as well as other factors. We also note
uncertainty in the risk assessment associated with the interpretation of the shapes of
concentration-response relationships, particularly at PM2.5 concentrations near the lower end of
the air quality distribution. This interpretation is complicated by relatively low data density in the
lower concentration range, the possible influence of exposure measurement error, and variability
among individuals with respect to air pollution health effects. These sources of variability and
uncertainty tend to smooth and "linearize" population-level concentration-response functions,
and thus could obscure the existence of a threshold or nonlinear relationship (U.S. EPA, 2015b,
section 6.c). As described in section 3.3.1, the 2019 ISA concludes and the draft ISA Supplement
provides further support that the majority of evidence of long-term PM2.5 exposure and mortality
supports a linear, no-threshold concentration-response relationship, though there is initial
evidence indicating that the slope of the concentration-response curve may be steeper at lower
concentrations for cardiovascular mortality (U.S. EPA, 2019, section 1.5.3.2; U.S. EPA, 2021a,
section 3.2.2.2). The 2019 ISA and draft ISA Supplement note that there is less certainty in the
shape of the concentration-response curve at mean annual PM2.5 concentrations generally below
8 [j,g/m3 because data density is reduced below this concentration (U.S. EPA, 2019, section
11.2.4; U.S. EPA, 2021a, section 3.2.2.2.7). As described in more detail in section 3.4.2.5 above
and Appendix C, a portion of risk modeling in the risk assessment does include locations with
annual ambient PM2.5 concentrations adjusted to below 8 ug/m3, so there is the potential for
significant uncertainty being introduced into the risk assessment (particularly for that portion of
risk modeled at or below 8 ug/m3). With regard to short-term PM2.5 exposure and mortality, the
2019 ISA concludes and the draft ISA Supplement provides additional support that, while
difficulties remain in assessing the shape of the PM2.5-mortality concentration-response
relationship and studies have not conducted systematic evaluations of alternatives to linearity,
recent studies continue to provide evidence of a no-threshold linear relationship, with less
confidence at concentrations lower than 5 [j,g/m3 (U.S. EPA, 2021a, section 3.2.1.2.6). However,
we note that in most instances in the risk assessment for this reconsideration, the concentration-
response function used had only a small impact on the risk estimates.
With regard to the method for simulating air quality scenarios, the approach used to
adjust air quality (i.e., adjusting primary PM emissions or secondary PM emission precursors)
had some impact on the overall risk estimates. We also note that there may be uncertainty
associated with the methods used to simulate air quality scenarios just meeting the current and
potential alternative primary PM2.5 standards. The model-based methods for simulating air
quality scenarios that just meet the current and alternative standards could contribute to
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uncertainties associated with the PM2.5 concentration estimates used in the risk assessment and
at-risk analyses. While state-of-the-science modeling methods were used to fill in the spatial and
temporal gaps in monitoring data, model-related biases and errors can introduce uncertainties.
Additionally, the modeling scenarios are based on "across-the-board" changes in primary PM2.5
or NOx and SO2 emissions from all anthropogenic sources throughout the U.S. by fixed
percentages. While this approach tends to target the key sources in each area, emission changes
are not tailored to specific periods or sources. Furthermore, while the two adjustment approaches
that were applied span a wide range of emissions conditions, they represent a subset of the
possible emissions cases that could be used to adjust PM2.5 concentrations. In addition, when
simulating air quality scenarios that just meet potential alternative annual PM2.5 standards using
linear extrapolation/interpolation, we recognize that this approach does not fully capture the
potential non-linearities associated with real-world changes in air quality. However, it is
important to note that the adjustment approach had a larger impact on the distribution of risk
reductions, particularly for potential alternative annual standard levels of 9.0 and 8.0 |j,g/m3.
It is important to note that the air quality adjustment approaches applied in the risk
assessment differ from the development and modeling of emission control strategies that would
occur in implementing a standard. In implementing a standard, an appropriately defined
nonattainment area would reduce emissions of primary PM and/or PM precursors selected
through analysis of site-specific conditions to meet a standard that is exceeded. In the risk
assessment, gridded concentration fields over CBSAs were adjusted to higher or lower
concentrations to correspond to just meet standards based on emission changes applied
throughout the U.S. Two emission adjustment cases (primary PM and NOx and SO2) were used
to provide concentration fields that span a wide range of realistic spatial patterns, but the air
quality modeling for the risk assessment is not designed to reflect emission changes that might
occur in implementing a standard. The Regulatory Impact Analysis (RIA) associated with
NAAQS revisions provides illustrative estimates of emission changes needed to meet potential
alternative standards and more closely reflects implementation considerations (U.S. EPA, 2013,
U.S. EPA, 2015a).
We further note that there is considerable variation in the range of confidence intervals
associated with the point estimates generated in the risk assessment, with some concentration-
response functions displaying greater variability than others. A number of factors could
potentially influence the varying degrees of statistical precision in effect estimates, including
sample size, exposure measurement error, degree of control for confounders/effect modifiers,
and variability in PM2.5 concentrations evaluated in the original epidemiologic study.
There may also be uncertainty associated with the potential confounding of the PM2.5-
mortality effect and the omission of potential confounders from analyses could either increase or
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decrease the magnitude of PM2.5 effect estimates. Not accounting for confounders can introduce
uncertainty into the effect estimates, and thereby introduce uncertainty into the risk estimates that
are generated using those effect estimates. While various approaches to control for potential
confounders have been adopted across the epidemiologic studies assessed in the 2019 ISA and
draft ISA Supplement, and those used in the risk assessment, no individual study adjusts for all
potential confounders.
In addition to the uncertainty associated with the risk assessment estimates, additional
uncertainties are associated with the risk rates, exposure estimate, and risk reductions in the at-
risk analysis. As an initial matter, we note that this analysis is based on race- and ethnicity-
stratified concentration-response functions only for ages 65 and over (Di et al., 2017b). The use
of one study in such an analysis introduces uncertainties and limitations in the broad applicability
of such results in the context of the national U.S. population across demographic groups and age
ranges. In addition, each non-White demographic group analyzed in the study comprised a
smaller percentage of the full study population, which reduces analytical power. Finally, the risk
and exposure assessment focuses on urban areas. This means that demographic groups that
preferentially reside in rural areas, such as Native Americans, are underrepresented in this
analysis. Additionally, average exposure concentrations estimated for demographic groups with
substantial rural populations, such as Whites, may be overestimated in this urban analysis.
In summary, here we recognize several particularly important uncertainties that affect the
quantitative estimates of risk rates and exposure in the at-risk analysis and their interpretation in
the context of considering the current primary PM2.5 standards. These include uncertainties
related to the modeling and adjustment methods for simulating air quality scenarios; the potential
influence of confounders on the relationship between PM2.5 exposure and mortality; the
interpretation of the shapes of concentration-response functions, particularly at lower
concentrations; and limited availability of studies to inform the at-risk analysis.
3.5.2.5 Potential Public Health Implications
In considering the public health implications of the quantitative risk assessment and at-
risk analysis that may inform the Administrator's judgments in this area, this section discusses
the information pertaining to the following questions.
• To what extent are the estimates of risk important from a public health perspective?
What does the information available in this reconsideration indicate with regard to
the size of the at-risk populations?
Several factors are important to consideration of public health implications. These
include the magnitude or severity of the effects associated with the estimated exposures, as well
as their adversity at the individual and population scales. Other important considerations include
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the size of the population estimated to experience such effects or to experience exposures
associated with such effects. Thus, the discussion here reflects consideration of the risk-based
evidence in the context of potential health implications in previous NAAQS decisions.
With regard to PM2.5 concentrations in ambient air, the public health implications and
potential public health impacts of interest in this reconsideration relate to those effects where a
causal relationship with PM2.5 exposure was concluded. These are mortality and cardiovascular
effects related to both long- and short-term exposures, as summarized in section 3.3.1 above.
Such effects, including more serious effects such as mortality, can be considered severe from a
public health perspective.
In considering public health implications, it is important to consider impacts on
population groups of differing susceptibility. The size of the at-risk populations (children, older
adults, those with pre-existing cardiovascular or respiratory diseases) in the U.S. is substantial.
As summarized in section 3.3.2, more than 22% of the population are children (<18 years old;
approximately 73 million people) and about 16% are older adults (65+ years old; approximately
54 million people). For adults in the U.S. 18 years old and older, cardiovascular diseases are
most prevalent in adult populations over the age of 65, with 29% of this age group reporting
some type of heart disease (Table 3-3 above). Similarly, adults over the age of 65 also have a
greater prevalence of respiratory diseases, particularly COPD reported as chronic bronchitis or
emphysema, while the asthma prevalence is generally consistent across all adult age groups for
those 18 years or older (Table 3-3). It is important to note that for older adults, the increased risk
in this lifestage can likely be attributed to the gradual decline in physiological processes that
occurs with aging, and some overlap exists between populations considered to be at-risk because
of pre-existing disease and lifestage (U.S. EPA, 2019, p. 12-25).
Another factor that may contribute to differences PM2.5 exposures and PIVh.s-related
health risk is race/ethnicity. As described above in section 3.3.2 and in the 2019 ISA and draft
ISA Supplement, there is strong evidence demonstrating that Black and Hispanic populations, in
particular, have higher PM2.5 exposures and health risk disparities compared to non-Hispanic
White populations. In the U.S., more than 12% of the U.S. population (more than 40.5 million
people) are Blacks and more than 18% are Hispanics (more than 60 million people), while 60%
of the population (nearly 197 million people) are non-Hispanic Whites (Table 3-2). Black and
Hispanic individuals of all ages make up a substantial portion of the population.
In considering the public health implications of the risk estimates across the study areas,
we note the purpose for the study areas is to illustrate circumstances that may occur in areas that
just meet the current or potential alternative standards, and not to estimate risk associated with
conditions occuring in those specific locations currently. We note that some areas across the U.S.
have air quality for PM2.5 that is near or above the existing standards. Thus, the air quality and
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exposure circumstances assessed in the study areas in the risk assessment are of particular
importance in considering whether the currently available information calls into question the
adequacy of the public health protection afforded by the current standards.
The risk estimates for the study areas assessed in this reconsideration reflect differences
in exposure circumstances among those areas and illustrate the exposures and risks that might be
expected to occur in other areas with such circumstances under air quality conditions that just
meet the current standards or the alternative standards assessed. Thus, the exposure and risk
estimates indicate the magnitude of exposure and risk that might be expected in many areas of
the U.S. with PM2.5 concentrations at or near the current or alternative standards. Although the
methodologies and data used to estimate risks in this reconsideration differ in several ways from
what was used in the 2020 review, the findings and considerations summarized here present a
pattern of exposure and risk that is generally similar to that considered in the 2020 review, and
indicate a level of protection generally consistent with that described in the 2020 PA.
In summary, the considerations raised here are important to conclusions regarding the
public health significance of the risk assessment results. Specifically, we note that available
evidence and information suggests that both long- and short-term PM2.5 exposures are associated
with adverse health effects, including more severe effects such as mortality. In addition, we note
that such effects impact large segments of the U.S. population, including those populations that
may have other factors that influence risk (i.e., lifestage, pre-existing cardiovascular and
respiratory diseases, race/ethnicity), as well as disparities in PM2.5 exposures and health risks
based on race and ethnicity. Therefore, we recognize that the air quality allowed by the current
primary PM2.5 standards could be judged to be associated with significant public health risk. We
recognize that such conclusions also depend in part on public health policy judgments that will
weigh in the Administrator's decision in this reconsideration with regard to the adequacy of
protection afforded by the current standards. Such judgments that are common to NAAQS
decisions include those related to public health implications of effects of differing severity. Such
judgments also include those concerning the public health significance of effects at exposures for
which evidence is limited or lacking, such as effects at lower concentrations than those
demonstrated in the key epidemiologic studies and in those population groups for which
population-specific information, such as concentration-response functions, are not available from
the epidemiologic literature.
3.5.3 Preliminary Conclusions
This section describes our preliminary conclusions for the Administrator's consideration
in this reconsideration of the primary PM2.5 standards. These preliminary conclusions are based
on considerations described in the sections above, and in the discussion below regarding the
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scientific evidence (as summarized in the 2019 ISA (U.S. EPA, 2019) and the draft ISA
Supplement (U.S. EPA, 2021a)), the quantitative assessments of PM2.5-associated health risks,
and analyses of PM2.5 air quality.
3,5.3,1 Current Standards
In taking into consideration the discussions responding to specific questions above in this
chapter, this section addresses the following overarching policy question.
• Does the currently available scientific evidence and risk-based information support
or call into question the adequacy of the public health protection afforded by the
current annual and 24-hour PM2.5 standards?
In considering this question, we recognize that, as is the case with NAAQS reviews in
general, the extent to which the current primary PM2.5 standards are judged to be adequate will
depend on a variety of factors, including science policy judgments and public health policy
judgments to be made by the Administrator. These factors include public health policy
judgments concerning the appropriate PM2.5 concentrations on which to place weight, as well as
judgments on the public health significance of the effects that have been observed at the
exposures evaluated in the health effects evidence. The factors relevant to judging the adequacy
of the standards also include the interpretation of, and decisions as to the weight to place on,
different aspects of the results of the risk assessment for the study areas included and the
associated uncertainties. Thus, we recognize that the Administrator's conclusions regarding the
adequacy of the current standards will depend in part on judgments regarding aspects of the
evidence and risk estimates, and judgments about the degree of protection that is requisite to
protect public health with an adequate margin of safety.
Our response to the overarching question above takes into consideration the discussions
that address the specific policy-relevant questions in prior sections of this document (sections
3.3, 3.4, 3.5.1, and 3.5.2) and builds on the approach from previous reviews (summarized in
section 3.1 above). We focus first on consideration of the evidence, including that assessed in the
2019 ISA and the draft ISA Supplement, and the extent to which it alters key conclusions
supporting the current standards. We then turn to consideration of the quantitative estimates of
risk developed in this reconsideration, including associated uncertainties and limitations, and the
extent to which they indicate differing conclusions regarding the magnitude of risk, as well as
level of protection from adverse effects, associated with the current standards. We additionally
consider the key aspects of the evidence and risk estimates emphasized in establishing the
current standards, and the associated public health policy judgments and judgments about the
uncertainties inherent in the scientific evidence and quantitative analyses that are integral to
decisions on the adequacy of the current primary PM2.5 standards.
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We first note that our approach recognizes that the current annual standard (based on
arithmetic mean concentrations) and 24-hour standard (based on 98th percentile concentrations),
together, are intended to provide public health protection against the full distribution of short-
and long-term PM2.5 exposures. In general, the annual standard is most effective at controlling
exposures to "typical" daily PM2.5 concentrations that are experienced over the year, while the
24-hour standard, with its 98th percentile form, is most effective at limiting peak daily or 24-
hour PM2.5 concentrations. In considering the combined effects of these standards, we recognize
that changes in PM2.5 air quality designed to meet an annual standard would likely result not only
in lower short- and long-term PM2.5 concentrations near the middle of the air quality distribution,
but also in fewer and lower short-term peak PM2.5 concentrations. Additionally, changes
designed to meet a lower 24-hour standard, with a 98th percentile form, would most effectively
result in fewer and lower peak 24-hour PM2.5 concentrations, but also have an effect on lowering
the annual average PM2.5 concentrations. Thus, our focus in evaluating the current primary
standards is on the protection provided by the combination of the annual and 24-hour standards
against the distribution of both short- and long-term PM2.5 exposures.
As an initial matter, we note the longstanding body of health evidence supporting
relationships between PM2.5 exposures (short- and long-term) and mortality or serious morbidity
effects. The evidence available in this reconsideration (i.e., assessed in U.S. EPA, 2019 and U.S.
EPA, 2021a) and summarized above in section 3.3.1 and section 3.5.1) reaffirms, and in some
cases strengthens, the conclusions from the 2009 ISA regarding the health effects of PM2.5
exposures (U.S. EPA, 2009). As noted above, epidemiologic studies conducted in North
America, Europe, or Asia demonstrate generally positive, and often statistically significant,
PM2.5 health effect associations. Such studies report associations between estimated PM2.5
exposures and non-accidental, cardiovascular, or respiratory mortality; cardiovascular or
respiratory hospitalizations or emergency room visits; and other mortality/morbidity outcomes
(e.g., lung cancer mortality or incidence, asthma development). Recent experimental evidence, as
well as evidence from panel studies, strengthens support for potential biological pathways
through which PM2.5 exposures could lead to the serious effects reported in many population-
level epidemiologic studies, including support for pathways that could lead to cardiovascular,
respiratory, nervous system, and cancer-related effects.
Epidemiologic studies in the U.S. report health effect associations with mortality and/or
morbidity across multiple cities and in diverse populations, including in studies examining
populations and lifestages that may be at comparatively higher risk of experiencing a PM2.5-
related health effect (e.g., older adults, children). Further, these studies use a variety of statistical
designs, and employ a variety of methods to examine exposure measurement error as well as to
control for confounding effects, including more recent causal modeling studies. Results of these
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analyses support the robustness of the reported associations. Additional findings from an
expanded body of studies that employ causal modeling and accountability methods further
inform the causal nature of the relationship between long- or short-term term PM2.5 exposure and
mortality (U.S. EPA, 2019, sections 11.1.2.1, 11.2.2.4,U.S. EPA, 2021a, sections 3.1.1.3, 3.1.2.3,
3.2.1.3, and 3.2.2.3). These studies, summarized above in Table 3-11 and Table 3-12, examine
both short- and long-term PM2.5 exposure and cardiovascular effects and mortality, and using a
variety of statistical methods to control for confounding bias, consistently report positive
associations, which further supports the broader body of epidemiologic evidence for both
cardiovascular effects and mortality. Moreover, recent epidemiologic studies strengthen support
for health effect associations at relatively low ambient PM2.5 concentrations. Studies that
examine the shapes of concentration-response relationships over the full distribution of ambient
PM2.5 concentrations have not identified a threshold concentration, below which associations no
longer exist (U.S. EPA, 2019, section 1.5.3, U.S. EPA, 2021a, sections 2.1.1.5.1 and 2.1.1.5.2).
While such analyses are complicated by the relatively sparse data available at the lower end of
the air quality distribution (U.S. EPA, 2019, section 1.5.3), several studies report positive and
statistically significant associations in additional analyses restricted to annual average PM2.5
exposures below 12 |j,g/m3 or to daily exposures below 35 |j,g/m3 as exhibited in Table 3-10.
These and other recent studies provide support for health effect associations at lower
ambient PM2.5 concentrations than in previous reviews. In this reconsideration, a large number of
key studies report positive and statistically significant associations for air quality distributions
with lower overall mean PM2.5 concentrations (i.e., Figure 3-8, Figure 3-9, Figure 3-10, Figure 3-
11). Consistent with the 2012 review, it is important to consider the manner in which PM2.5 mean
concentrations are estimated (e.g., monitored concentrations versus modeled concentrations) and
the method by which means are calculated and reported as the overall mean PM2.5 concentration
(e.g., averaging across all grid cells in an urban area versus population-weighting). Additional
analyses, new in this draft PA though similar to those in the 2012 review, suggest that the area
annual design value is generally greater than the study mean by 10-20% (monitor-based studies),
14-18% (hybrid modeling with population-weighting) or 40-50% (hybrid modeling without
population weighting). We note this information relative to the overall mean PM2.5
concentrations in key U.S. epidemiologic studies which are: 9.9 [j,g/m3 to 16.5 [j,g/m3 for monitor-
based studies; 9.3 [j,g/m3 to 12.3 [j,g/m3 for studies that use hybrid modeling and apply
population-weighting; and 8.1 [j,g/m3 to 11.9 [j,g/m3 for studies that use hybrid modeling and do
not apply population-weighting. The study reported mean concentrations in Canadian studies are
more difficult to compare to the area annual standard design value but are lower than those
reported in the U.S. studies for both monitor-based and hybrid model methods, ranging from 7.0
Hg/m3 to 9.0 |j,g/m3 in monitor-based studies, and 6.0 |j,g/m3 to 10.0 |j,g/m3 in model-based
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studies. These mean values are consistent with the mean PM2.5 concentrations reported in studies
available at the time of the 2020 review (U.S. EPA, 2020, Figure 3-8).
In assessing the adequacy of the current standard, we examine a subset of studies, many
of which are newly available in this reconsideration, that employ causal modeling methods to
control for confounding bias (Table 3-11), which report positive and significant associations for
a variety of health outcomes and support the positive and significant associations in analyses
identified as key epidemiologic studies above. We also evaluate what the accountability studies
may indicate with respect to improvements in public health with improvements in air quality. In
so doing, we take note of two accountability studies (Sanders et al., 2020 and Corrigan et al.,
2018) newly available in this reconsideration with starting concentrations at or below 12.0 |ig/m3
that indicate positive and significant associations with mortality and reductions in ambient PM2.5
(Table 3-12). We further evaluate studies with analyses that restrict annual or daily PM2.5
concentrations to values below the annual or daily PM2.5 standard, respectively (Table 3-10).
These restricted analyses indicate positive and significant associations, including mean PM2.5
concentrations presumably below the mean reported PM2.5 in the main cohort, where long-term
mean PM2.5 concentrations range from 8.2 |ig/m3 to 11.5 |ig/m3, as well as effect estimates that
are generally greater in magnitude than effect estimates seen in main analyses.
In addition to the epidemiologic evidence, we examine experimental studies, including
controlled human exposure studies and animal toxicological studies. As detailed in above in
section 3.3.3.1 and section 3.5.1.3, these studies provide support for the effects of exposure to
PM2.5, and support for biologically plausible mechanisms through which adverse human health
outcomes could occur. Exposures in controlled human exposure studies last from less than one
hour and up to five hours, and indicate that the most consistent evidence is associated with
cardiovascular effects, and more specifically, impaired vascular function. PM2.5 exposures
evaluated in most of these studies are well-above the ambient concentrations typically measured
in locations meeting the current primary standards. For example, at air quality monitoring sites
meeting the current primary PM2.5 standards (i.e., the 24-hour standard and the annual standard),
the 2-hour concentrations generally remain below 10 (J,g/m3, and virtually never exceed 30
[j,g/m3. Two-hour concentrations are higher at monitoring sites violating the current standards,
but generally remain below 16 |ig/m3 and virtually never exceed 80 [j,g/m3. In addition, as noted
earlier in section 3.3.3.1, chronic vascular dysfunction can be judged to be a biomarker of an
adverse health effect from air pollution, but the health relevance of acute reductions in vascular
function are less certain (Thurston et al., 2017). Thus, while these studies are important in
establishing biological plausibility, it is unclear how the results alone and the importance of the
effects observed in these studies, particularly in studies conducted at near-ambient PM2.5
concentrations, should be interpreted with respect to adversity to public health..
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In addition to the evidence above, we also consider what the risk assessment indicates
with regard to the adequacy of the current primary PM2.5 standards. The risk assessment
estimates that the current primary PM2.5 standards could allow a substantial number of deaths in
the U.S., with the large majority of those deaths associated with long-term PM2.5 exposures. For
example, when air quality in the 47 study areas is adjusted to simulate just meeting the current
standards, the risk assessment estimates 40,600-45,100 long-term PM2.5 exposure-related deaths
in a single year, with confidence intervals ranging from 30,300-59,000. While the absolute
numbers of estimated deaths vary across exposure durations, populations, and concentration-
response functions, the general magnitude of risk estimates supports the potential for significant
public health impacts in locations meeting the current primary PM2.5 standards. This is
particularly the case given that the large majority of PIVh.s-associated deaths for air quality just
meeting the current standards are estimated at annual average PM2.5 concentrations from about
10 to 12 |j,g/m3. These annual average PM2.5 concentrations fall well-within the range of long-
term average concentrations over which key epidemiologic studies provide strong support for
reported positive and statistically significant PM2.5 health effect associations.
Based on the information summarized above, and discussed in more detail in sections 3.3,
3.4, and 3.5 of this draft PA, we particularly note the following in reaching preliminary
conclusions on the current primary PM2.5 standards:
• There is a long-standing body of strong health evidence demonstrating relationships between
long- or short-term PM2.5 exposures and a variety of outcomes, including mortality and
serious morbidity effects. Studies assessed in the 2019 ISA and the draft ISA Supplement
have reduced key uncertainties and broadened our understanding of the health effects that
can result from exposures to PM2.5.
- Recent U.S. and Canadian epidemiologic studies provide support for generally
positive and statistically significant health effect associations across a broad
range of ambient PM2.5 concentrations, including for air quality distributions
with overall mean concentrations lower than in the previous reviews.
Controlled human exposure studies and animal toxicological studies provide
support for the effects of exposure to PM2.5, and support for biologically
plausible mechanisms through which adverse human health outcomes could
occur.
- Epidemiologic studies that use causal modeling methods have expanded since
the 2020 PA and further inform the causal nature of the relationship between
short- and long-term exposure to PM2.5 and mortality and cardiovascular
effects. These studies use a variety of statistical methods to reduce
uncertainties with respect to confounding bias.
• Recent U.S. accountability studies provide support for improvements in public health,
including reductions in mortality in studies with starting PM2.5 concentrations at or below the
current primary PM2.5 annual standard. Some epidemiologic studies (Corrigan et al., 2018
and Sanders et al., 2020) that employ accountability methods using monitored data evaluate
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the effect of the implementation of the 1997 annual PM2.5 standard, finding evidence of
reductions in mortality in areas with starting PM2.5 concentrations at or below 12.0 |j,g/m3.
• Studies that restrict analyses to air quality below the current daily or annual PM2.5 standard
exhibit positive and significant associations, which are often greater in magnitude than main
analyses. Di et al. (2017b) and Dominici et al. (2019) report positive and statistically
significant associations that are greater in analyses restricted below 12.0 |ig/m3 and report
mean concentrations of 9.6 |ig/m3. In studies that restrict analyses < 35.0 |ig/m3 or lower,
mean PM2.5 concentrations are not reported, though such means are presumably somewhat
below those based on the overall cohort, which range from 8.2 |ig/m3 to 11.5 |ig/m3, and
effect estimates are generally great than those in the overall cohort. More specifically, one
U.S. study by Shi et al. (2016) reports positive and statistically significant associations in
analyses restricted to relatively low annual or 24-hour PM2.5 exposure estimates.
• Exposures in controlled human exposure studies last from less than one hour and up to five
hours and indicate that the most consistent evidence is associated with cardiovascular effects,
and more specifically, impaired vascular function. Further, air quality analyses suggest that
the ambient concentrations in these studies typically do not occur in locations meeting the
current primary standards, thus suggesting that the current primary PM2.5 standards provide
protection against these "peak" concentrations.
• We note the decision framework used in previous reviews that places significant weight on
key epidemiologic studies and consider whether the mean concentrations in these studies
would be allowed in areas meeting the current primary standard.
Such a decision framework placed significant weight on epidemiologic studies
that assessed associations between PM2.5 exposure and health outcomes that
were most strongly supported by the body of scientific evidence and
recognized there is significantly greater confidence in the magnitude and
significance of observed associations for the part of the air quality distribution
corresponding to where the bulk of the health events in each study have been
observed, generally at or around the mean concentration.
Additional analyses, new in this draft PA though similar to analyses in the
2012 review, suggest that the area annual design value is greater than the
study reported mean values by 10-20% (monitor-based studies), 14-18%
(hybrid modeling with population-weighting) or 40-50% (hybrid modeling
without population weighting).
- Focusing on the key epidemiologic studies available in this reconsideration,
the overall mean PM2.5 concentrations in key U.S. epidemiologic studies are
as follows:
o Range of monitor-based mean PM2.5 concentrations is from 9.9 [j,g/m3
to 16.5 [j,g/m3 (range in 2020 PA: 10.7 [j,g/m3 to 16.5 (J,g/m3)
o Range of mean PM2.5 concentrations in studies that use hybrid
modeling and apply population-weighting: 9.3 [j,g/m3 to 12.3 [j,g/m3
o Range of mean PM2.5 concentrations in studies that use hybrid
modeling and do not apply population-weighting: 8.1 [j,g/m3 to 11.9
[j,g/m3
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Though the Canadian studies are more difficult to utilize for comparison to the
annual design value used to determine compliance in the U.S., the overall
mean PM2.5 concentrations in key Canadian epidemiologic studies are within
the range, though somewhat lower than those from the U.S. studies, and are as
follows:
o Range of monitor-based mean PM2.5 concentrations is from 6.9 |ig/m3
to 13.3 |ig/m3
o Range of mean PM2.5 concentrations in studies that use hybrid
modeling (all of which average up to postal codes and thus include
some aspects of population-weighting) is 5.9 |ig/m3 to 9.8 |ig/m3
- Past decision frameworks also placed some weight on considering the annual
standard level relative to the 25th and 10th percentile of health events while
also noting that epidemiologic studies provide more limited support for health
effect associations based on air quality distributions at these lower PM2.5
concentration percentiles.
o In key U.S. epidemiologic studies that use monitors to estimate PM2.5
exposures, 25th percentiles of health events correspond to mean PM2.5
concentrations (i.e., averaged over the study period for each study city)
at or above 11.5 |ig/m3 and 10th percentiles of health events correspond
to mean PM2.5 concentrations at or above 9.8 |ig/m3
o Of the key U.S. epidemiologic studies that use hybrid modeling
approaches to estimate long-term PM2.5 exposures and do not apply
population-weighting, the ambient PM2.5 concentrations corresponding
to 25th percentiles of estimated exposures range from 4.6 |ig/m3 to 9.2
|ig/m3, while in studies that do apply population-weighting, 25th
percentiles range from 6.7 |ig/m3 to 9.1 |ig/m3. In the two studies (each
apply population-weighting) with information available on the 10th
percentile of health events, the ambient PM2.5 concentrations
corresponding to the 10th percentile are 4.7 |ig/m3 and 7.3 |ig/m3.
• The risk assessment estimates that the current primary PM2.5 standards could allow a
substantial number of PIVh.s-associated deaths in the U.S. The large majority of these
estimated deaths are associated with the annual average PM2.5 concentrations near (and above
in some cases) the average concentrations in key epidemiologic studies reporting positive and
statistically significant health effect associations. Further, the risk assessment estimated that
Black populations may experience disproportionally higher exposures and risk under
simulated air quality conditions just meeting the current primary PM2.5 annual standard as
compared to White populations.
When taken together, we reach the conclusion that the available scientific evidence, air quality
analyses, and the risk assessment, as summarized above, can reasonably be viewed as calling
into question the adequacy of the public health protection afforded by the combination of the
current annual and 24-hour primary PM2.5 standards. In particular, we note the information and
analyses new to this reconsideration (and discussed in detail above) in reaching this conclusion.
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3,5.3,2 Potential Alternative Standards
In this section, we consider the potential alternative primary PM2.5 standards that could be
supported by the evidence and quantitative information available in this reconsideration. These
considerations are framed by the following overarching policy-relevant question, posed at the
beginning of this chapter:
• What is the range of potential alternative standards that could be supported by the
available scientific evidence and risk-based information to increase public health
protection against short- and long-term fine particle exposures?
In answering this question, we consider each of the elements of the annual and 24-hour PM2.5
standards: indicator, averaging time, form, and level. The sections below discuss our
consideration of these elements, and our conclusions that (1) it is appropriate to consider revising
the level of the current annual standard, in conjunction with retaining the current indicator,
averaging time, and form of that standard, to increase public health protection against fine
particle exposures and (2) depending on the decision made on the annual standard, consideration
could be given to either retaining or revising the level of the 24-hour PM2.5 standard.
3.5.3.2.1 Indicator
In initially setting standards for fine particles in 1997, the EPA concluded it was
appropriate to control fine particles as a group, rather than singling out any particular component
or class of fine particles. The Agency noted that community health studies had found significant
health effect associations using various indicators of fine particles, and that health effects in a
large number of areas had significant mass contributions from differing components or sources
of fine particles. In addition, a number of toxicological and controlled human exposure studies
had reported health effects following exposures to high concentrations of numerous fine particle
components (62 FR 38667, July 18, 1997). In establishing a size-based indicator in 1997 to
distinguish fine particles from particles in the coarse mode, the EPA noted that the available
epidemiologic studies of fine particles were based largely on PM2.5 mass. The selection of a 2.5
|j,m size cut additionally reflected the regulatory importance of defining an indicator that would
more completely capture fine particles under all conditions likely to be encountered across the
U.S. and the monitoring technology that was generally available (62 FR 38666 to 38668, July 18,
1997).
Since the 1997 review, studies that evaluate fine particle-related health effects continue to
provide strong support for such effects using PM2.5 mass as the metric for fine particle exposures.
Subsequent reviews have recognized the strength of this evidence, concluding that it has
continued to support a PM2.5 mass-based indicator for a standard meant to protect against fine
particle exposures. In the 2012 review, some studies had additionally examined health effects of
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exposures to particular sources or components of fine particles, or to the ultrafine fraction of fine
particles. Based on limitations in such studies, together with the continued strong support for
effects of PM2.5 exposures, the Agency retained PM2.5 mass as the indicator for fine particles and
did not supplement the PM2.5 standards with standards based on particle composition or on the
ultrafine fraction (78 FR 3123, January 15, 2013).
As in the 2012 review, studies assessed the 2019 ISA continue to provide strong support
for health effects following long- and short-term PM2.5 exposures (U.S. EPA, 2019). While some
studies evaluate the health effects of particular sources of fine particles, or of particular fine
particle components, evidence from these studies does not identify any one source or component
that is a better predictor of health effects than PM2.5 mass (U.S. EPA, 2019, section 1.5.4). As
summarized in section 3.5.1 above, the 2019 ISA the evidence confirms and further supports that
many PM2.5 components and sources are associated with health effects, and does not indicate that
any one source or component is consistently more strongly related with health effects than PM2.5
mass (U.S. EPA, 2019, section 1.5.4). Further, the evidence for health effects following
exposures specifically to the ultrafine fraction of fine particles continues to be far more limited
than the evidence for PM2.5 mass, and the varying definitions of UFP, as well as differences in
approaches to administering and measuring UFP, contribute to such limitations (U.S. EPA, 2019,
section 1.4.3). Thus, for reasons similar to those discussed in the 2020 review (85 FR 82715,
December 18, 2020), we reach the preliminary conclusion that the available information
continues to support the PM2.5 mass-based indicator and remains too limited to support a distinct
standard for any specific PM2.5 component or group of components, and too limited to support a
distinct standard for the ultrafine fraction.
3.5.3.2.2 Averaging Time
In 1997, the EPA initially set an annual PM2.5 standard to protect against health effects
associated with both long- and short-term PM2.5 exposures, and a 24-hour standard to supplement
the protection afforded by the annual standard (62 FR 38667 to 38668, July 18, 1997). In
subsequent reviews, the EPA retained both annual and 24-hour averaging times, largely
reflecting the strong evidence for health effects associated with annual and daily PM2.5 exposure
estimates (71 FR 61164, October 17, 2006; 78 FR 3123 to 3124, January 15, 2013).
In this reconsideration, epidemiologic and controlled human exposure studies have
examined a variety of PM2.5 exposure durations. Epidemiologic studies continue to provide
strong support for health effects associated with both long- and short-term PM2.5 exposures based
on annual (or multiyear) and 24-hour PM2.5 averaging periods, respectively.
With regard to short-term exposures in particular, a smaller number of epidemiologic
studies examine associations between sub-daily PM2.5 exposures and respiratory effects,
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cardiovascular effects, or mortality. Compared to 24-hour PM2.5 exposure estimates, associations
with sub-daily estimates are less consistent and, in some cases, smaller in magnitude (U.S. EPA,
2019, section 1.5.2.1). In addition, studies of sub-daily exposures typically examine subclinical
effects, rather than the more serious population-level effects that have been reported to be
associated with 24-hour exposures (e.g., mortality, hospitalizations). Taken together, the 2019
ISA concludes that epidemiologic studies do not indicate sub-daily averaging periods are more
closely associated with health effects than the 24-hour average exposure metric (U.S. EPA, 2019,
section 1.5.2.1).
Additionally, while recent controlled human exposure studies provide consistent evidence
for cardiovascular effects following PM2.5 exposures for less than 24 hours (i.e., < 30 minutes to
5 hours), exposure concentrations in these studies are well-above the ambient concentrations
typically measured in locations meeting the current standards (section 3.3.3.1). Thus, these
studies also do not suggest the need for additional protection against sub-daily PM2.5 exposures,
beyond that provided by the current primary standards.
Drawing from the evidence assessed in the 2019 ISA, and the observations noted above,
we reach the conclusion that the available evidence continues to provide strong support for
consideration of retaining the current annual and 24-hour averaging times. The available
evidence suggests that PM2.5 standards with these averaging times, when coupled with
appropriate forms and levels, can protect against the range of long- and short-term PM2.5
exposures that have been associated with health effects. Thus, as in the 2020 review (78 FR
82715, December 18, 2020), we reach the preliminary conclusion that the currently available
evidence does not support considering alternatives to the annual and 24-hour averaging times for
standards meant to protect against long- and short-term PM2.5 exposures.
3.5.3.2.3 Form
The form of a standard defines the air quality statistic that is to be compared to the level
in determining whether an area attains that standard. As in other recent reviews, our foremost
consideration in reaching preliminary conclusions on form is the adequacy of the public health
protection provided by the combination of the form and the other elements of the standard.
As noted above, in 1997 the EPA initially set an annual PM2.5 standard to protect against
health effects associated with both long- and short-term PM2.5 exposures and a 24-hour standard
to provide supplemental protection, particularly against the short-term exposures to "peak" PM2.5
concentrations that can occur in some areas (62 FR 38667 to 38668, July 18, 1997). The EPA
established the form of the annual PM2.5 standard as an annual arithmetic mean, averaged over 3
years, from single or multiple community-oriented monitors. That is, the level of the annual
standard was to be compared to measurements made at each community-oriented monitoring site
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or, if specific criteria were met, measurements from multiple community-oriented monitoring
sites could be averaged together (i.e., spatial averaging) (62 FR 38671 to 38672, July 18, 1997).
In the 1997 review, the EPA also established the form of the 24-hour PM2.5 standard as the 98th
percentile of 24-hour concentrations at each monitor within an area (i.e., no spatial averaging),
averaged over three years (62 FR at 38671 to 38674, July 18, 1997). In the 2006 review, the EPA
retained these standard forms but tightened the criteria for using spatial averaging with the
annual standard (71 FR 61117, October 17, 2006).81
In the 2012 review, the EPA's consideration of the form of the annual PM2.5 standard
again included a focus on the issue of spatial averaging. An analysis of air quality and population
demographic information indicated that the highest PM2.5 concentrations in a given area tended
to be measured at monitors in locations where the surrounding populations were more likely to
live below the poverty line and to include larger percentages of racial and ethnic minorities (U.S.
EPA, 2011, p. 2-60). Based on this analysis, the 2011 PA concluded that spatial averaging could
result in disproportionate impacts in minority populations and populations with lower SES. The
Administrator concluded that public health would not be protected with an adequate margin of
safety in all locations, as required by law, if disproportionately higher PM2.5 concentrations in
low income and minority communities were averaged together with lower concentrations
measured at other sites in a large urban area. Therefore, she concluded that the form of the
annual PM2.5 standard should be revised to eliminate spatial averaging provisions (78 FR 3124,
January 15, 2013).
In the 2012 review, the EPA also considered the form of the 24-hour PM2.5 standard. The
Agency recognized that the existing 98th percentile form for the 24-hour standard was originally
selected to provide a balance between limiting the occurrence of peak 24-hour PM2.5
concentrations and identifying a stable target for risk management programs. Updated air quality
analyses in the 2012 review provided additional support for the increased stability of the 98th
percentile PM2.5 concentration, compared to the 99th percentile (U.S. EPA, 2011, Figure 2-2, p.
2-62). Thus, the Administrator concluded that it was appropriate to retain the 98th percentile form
for the 24-hour PM2.5 standard (78 FR 3127, January 15, 2013).
In the 2020 review, the Administrator noted that the scientific evidence continued to
provide strong support for health effect associations for both long-term (e.g., annual or multi-
year) and short-term (e.g., mostly 24-hour) exposures to PM2.5 and judged that the evidence did
not support considering alternative averaging times (85 FR 82715, December 18, 2020). For
81 Specifically, the Administrator revised spatial averaging criteria such that "(1) [t]he annual mean concentration at
each site shall be within 10 percent of the spatially averaged annual mean, and (2) the daily values for each
monitoring site pair shall yield a correlation coefficient of at least 0.9 for each calendar quarter (71 FR 61167,
October 17, 2006).
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reasons consistent with those in the 2012 review, the Administrator judged that the current
annual and 24-hour averaging times remained appropriate.
The information available in this reconsideration continues to support the current forms
of the annual and 24-hour PM2.5 standards. As discussed above (section 3.3.1), epidemiologic
studies continue to provide strong support for health effect associations with both long-term
(e.g., annual or multi-year) and short-term (e.g., mostly 24-hour) PM2.5 exposures. These studies
provide the strongest support for such associations for the part of the air quality distribution
corresponding to the bulk of the underlying data, typically around the overall mean
concentrations reported (section 3.3.3.2.1). The form of the current annual standard (i.e.,
arithmetic mean, averaged over three years) remains appropriate for targeting "typical" daily and
annual exposures around these means of the PM2.5 air quality distribution. In addition, controlled
human exposure studies provide evidence for health effects following single short-term PM2.5
exposures near the peak concentrations measured in the ambient air (section 3.3.3.1). Thus, the
evidence also supports retaining a standard focused on providing supplemental protection against
short-term peak exposures. The information available in this reconsideration continues to support
the decision to use a 98th percentile form for a 24-hour standard that is meant to provide a
balance between limiting the occurrence of such peak 24-hour PM2.5 concentrations and
identifying a stable target for risk management programs. Thus, when the information
summarized above is taken together, we reach the preliminary conclusion that it is appropriate to
consider retaining the forms of the current annual and 24-hour PM2.5 standards, in conjunction
with a revised level as discussed below.
3.5.3.2.4 Level
With regard to level, we specifically address the following policy-relevant question:
• For primary PM2.5 standards defined in terms of the current averaging times and
forms, what potential alternative levels are appropriate to consider in order to
increase public health protection against long- and short-term exposures to PM2.5 in
ambient air?
In answering this question, we consider key epidemiologic studies that evaluate associations
between PM2.5 air quality distributions and mortality or morbidity, controlled human exposure
studies examining effects following short-term PM2.5 exposures, air quality analyses that help to
place these studies into a policy-relevant context, and the risk assessment estimates of PM2.5-
associated mortality under various alternative standard scenarios.
Consideration of the evidence and analyses, as summarized in this chapter, informs our
evaluation of the public health protection that could be provided by alternative annual and 24-
hour standards with revised levels. There are various ways to combine an annual standard (based
on arithmetic mean concentrations) and a 24-hour standard (based on 98th percentile
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concentrations), to achieve an appropriate degree of public health protection. In particular, we
recognize that changes in PM2.5 air quality designed to meet an annual standard would likely
result not only in lower short- and long-term PM2.5 concentrations near the middle of the air
quality distribution (i.e., around the mean of the distribution), but also in fewer and lower short-
term peak PM2.5 concentrations. Additionally, changes designed to meet a 24-hour standard, with
a 98th percentile form, would result not only in fewer and lower peak 24-hour PM2.5
concentrations, but also in lower average PM2.5 concentrations.
However, while either standard could be viewed as providing some measure of protection
against both average exposures and peak exposures, the 24-hour and annual standards are not
expected to be equally effective at limiting both types of exposures. Specifically, the 24-hour
standard (with its 98th percentile form) is more directly tied to short-term peak PM2.5
concentrations, and thus more likely to appropriately limit exposures to such concentrations, than
the more typical concentrations that make up the middle portion of the air quality distribution.
Therefore, compared to a standard that is directly tied to the middle of the air quality distribution,
the 24-hour standard is less likely to appropriately limit the "typical" daily and annual exposures
that are most strongly associated with the health effects observed in epidemiologic studies. In
contrast, the annual standard, with its form based on the arithmetic mean concentration, is more
likely to effectively limit the PM2.5 concentrations that comprise the middle portion of the air
quality distribution, affording protection against the daily and annual PM2.5 exposures that
strongly support associations with the most serious PIVh.s-related effects in epidemiologic studies
(e.g., mortality, hospitalizations).
For these reasons, we focus on alternative levels of the annual PM2.5 standard as the
principle means of providing increased public health protection against the bulk of the
distribution of short- and long-term PM2.5 exposures, and thus protecting against the exposures
that provide strong support for associations with mortality and morbidity in key epidemiologic
studies. We additionally consider the 24-hour standard, with its 98th percentile form, primarily as
a means of providing supplemental protection against the short-term exposures to peak PM2.5
concentrations that can occur in some areas (e.g., those with strong contributions from local or
seasonal sources), even when overall mean PM2.5 concentrations remain relatively low.
To inform our consideration of potential alternative annual and 24-hour standard levels,
we specifically note the key observations in section 3.5.3.1 (rather than repeating them here) and
note more specifically, related to those observations that:
Mean PM2.5 Concentrations in Key Epidemiologic Studies and Relationships between Mean
PM2.5 Concentrations and Annual Design Values
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• Areas meeting a particular annual PM2.5 standard would be expected to have average PM2.5
concentrations (i.e., averaged across the area and over time) somewhat below the level of that
standard (which is measured at the peak monitor). This is supported by analyses of
monitoring data in CBSAs across the U.S., which show that maximum annual PM2.5 design
values are often 10% to 20% higher than long-term mean PM2.5 concentrations in an area
(section 2.3.3.1, Figure 2-28; Table 2-2). Additional analyses also support differences
between annual PM2.5 design values and long-term mean PM2.5 concentrations in hybrid
modeling studies, with the extent of the difference depending on the methods used to
estimate mean PM2.5 concentrations. These analyses suggest that the area annual design
values are generally higher than the study mean by 14-18% (hybrid modeling with
population-weighting) or 40-50% higher (hybrid modeling without population-weighting)
(section 2.3.3.2.4, Table 2-4).
• Most key U.S. epidemiologic studies indicate consistently positive and statistically
significant health effect associations based on air quality distributions with overall mean
PM2.5 concentrations at or above 9.3 |ig/m3 (9.9 |ig/m3 based on U.S. studies that use
monitors to estimate PM2.5 exposures). Other key epidemiologic studies (which do not
incorporate population-weighting into their calculation of the study mean) report mean PM2.5
concentrations to be as low as 8.1 |ig/m3 with the air quality analyses suggesting that areas
included in these studies would have corresponding area annual design values generally 40-
50%) higher than the study reported mean concentrations.
• Though the mean PM2.5 concentrations from Canadian studies are more difficult to directly
compare to the annual design value used to determine compliance in the U.S., the overall
mean PM2.5 concentrations in key Canadian epidemiologic studies are close to, though
somewhat lower than, those from the U.S. studies. The range of monitor-based mean PM2.5
concentrations is from 6.9 |ig/m3 to 13.3 |ig/m3 while the range of mean PM2.5 concentrations
in studies that use hybrid modeling (all of which average up to postal codes and thus include
some aspects of population-weighting) is 5.9 |ig/m3 to 9.8 |ig/m3.
• Epidemiologic studies provide more limited support for health effect associations based on
air quality distributions at lower PM2.5 percentile concentrations. In assessing the 25th
percentile of data, PM2.5 concentrations in key U.S. epidemiologic studies that use hybrid
modeling methods and do not apply some aspects of population-weighting range from 4.6
|ig/m3 to 9.2 |ig/m3, while those that apply some aspects of population weighting range from
6.7 |ig/m3 to 9.1 |ig/m3. In U.S. studies that use monitored values have 25th percentiles
ranging from 11.5 |ig/m3 to just below 13.0 |ig/m3. In Canada two monitored studies report
25th percentile concentrations around 6.5 |ig/m3, while hybrid modeled studies in Canada, all
of which average up to postal codes and thus include some aspects of population-weighting,
report 25th percentile concentrations around 8.0 |ig/m3 in two studies, and 4.3 |ig/m3 in one
study.
Scientific Evidence and Associated Uncertainties Supporting Associations at Lower
Concentrations
• Recent evidence further demonstrates that associations with mortality remain robust in
copollutants analyses (U.S. EPA, 2019, section 11.2.3), and that associations persist in
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analyses restricted to long-term exposures below 12 |j,g/m3 (Di et al., 2017b) or 10 |j,g/m3
(Shi et al., 2016) (i.e., indicating that risks are not disproportionately driven by the upper
portions of the air quality distribution).
• Studies that examine the shapes of concentration-response functions over the full distribution
of ambient PM2.5 concentrations have not identified a threshold concentration, below which
associations no longer exist (U.S. EPA, 2019, section 1.5.3, U.S. EPA, 2021a, section 2.2.3.1
and 2.2.3.2). While such analyses are complicated by the relatively sparse data available at
the lower end of the air quality distribution (U.S. EPA, 2019, section 1.5.3), analyses that
assess the concentration-response relationship support a linear, no-threshold effect down to
5.0 (J,g/m3, though uncertainties increase at concentrations of less than 8.0 [j,g/m3.
• While there is no specific point in the air quality distribution of any epidemiologic study that
represents a "bright line" at and above which effects have been observed and below which
effects have not been observed, there is significantly greater confidence in the magnitude and
significance of observed associations for the part of the air quality distribution corresponding
to where the bulk of the health events in each study have been observed, generally at or
around the mean concentration, with more limited support for health effect associations
based on air quality distributions at lower PM2.5 percentile concentrations.
• Controlled human exposure studies demonstrate consistent evidence of effects at higher
concentrations (e.g., > 120 |ig/m3) and provide support for biological plausibility for more
serious effects (e.g., hospital admissions) (U.S. EPA, 2019, Figure 6-1).
Scientific Evidence on Short-term Exposures andPM2.5 Exposures Shown to Cause Effects
• While controlled human exposure studies support the plausibility of the serious
cardiovascular effects that have been linked with ambient PM2.5 exposures (U.S. EPA, 2019,
chapter 6), the PM2.5 exposure concentrations evaluated in most of these studies are well-
above the ambient concentrations typically measured in locations meeting the current
primary standards (and thus well-above those likely to be measured in locations that would
meet revised standards with lower annual or 24-hour levels) (Figure 2-19, Figure A-2, Figure
A-3).
PM2.5-AssociatedRisk Estimates
• The risk assessment estimates that, compared to the current standards, potential alternative
annual standards with levels from 11.0 down to 8.0 |j,g/m3 could reduce PIVh.s-associated
mortality broadly across the United States. Meeting a revised annual standard with a lower
level is estimated to reduce PIVh.s-associated health risks in the 30 annually-controlled study
areas by about 7-9% for a level of 11.0 |ig/m3, 15-19% for a level of 10.0 |ig/m3, 22-28% for
a level of 9.0 |ig/m3, and 30-37% for a level of 8.0 |ig/m3, compared to the current annual
standard.
• Revising the level of the 24-hour standard to 30 |j,g/m3 is estimated to lower PIVh.s-associated
risks across a more limited population and number of areas than revising the annual standard
(section 3.4.2.3). Risk reduction predictions are largely confined to areas located in the
western U.S., several of which are also likely to experience risk reductions upon meeting a
revised annual standard.
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• The at-risk assessment estimated that Black populations may experience disproportionally
higher exposures and risk under simulated air quality conditions just meeting the current
primary PM2.5 annual standard as compared to White populations. Meeting a revised annual
standard with a lower level may also proportionally reduce exposure and risk in Black
populations slightly more so than in White populations in simulated scenarios just meeting
alternative annual standards.
• Uncertainties in risk estimates (e.g., in the size of risk estimates) can result from a number of
factors, including assumptions about the shape of the C-R relationship with mortality at low
ambient PM concentrations, the potential for confounding and/or exposure measurement
error in the underlying epidemiologic studies, and the methods used to adjust PM2.5 air
quality. In considering such uncertainties, we recognize that the risk estimates can help to
place the evidence for specific effects into a broader public health context, but should be
considered along with the inherent uncertainties and limitations of such analyses when
informing judgments about the potential for additional public health protection associated
with PM2.5 exposure and related health effects.
The information summarized in these key observations could support various decisions on
the levels of the annual and 24-hour PM2.5 standards, depending on the weight given to different
aspects of the evidence, air quality and risk information, including its uncertainties. In this draft
PA we seek to provide as broad an array of policy options as is supportable by the available
evidence and quantitative information, recognizing that the selection of a specific approach to
reaching final decisions on the primary PM2.5 standards will reflect the judgments of the
Administrator as to what weight to place on the various types of evidence and information, and
on associated uncertainties. Potential approaches to considering support for particular alternative
annual and 24-hour standard levels are discussed below.
Alternative Annual Standard Levels
As discussed above, the degree to which particular alternative annual standard levels
below 12.0 |j,g/m3 are supported will depend on the weight placed on various aspects of the
scientific evidence, air quality and risk information, and its associated uncertainties. In selecting
a particular level from 10.0 |j,g/m3 to < 12.0 |ag/m3, consideration of the evidence could take into
account individual study characteristics such as study design and statistical approaches, precision
of reported associations, study size and location, and uncertainties in the study itself or in our
analyses of study area air quality. For example, a level below 12 |j,g/m3 and as low as about 10.0
Hg/m3 could be supported to the extent weight is placed on the following:
• Setting a standard expected to maintain the PM2.5 air quality distributions below those
present in most key epidemiologic studies, recognizing the general relationships
demonstrated in the air quality analyses between study mean calculation and the annual
standard and noting the values of the study reported means as listed below:
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o The monitor-based key epidemiologic studies report mean PM2.5 concentrations from
9.9 [j,g/m3 to 16.5 (J,g/m3;
o The key epidemiologic studies that incorporate hybrid modeling and population-
weight study mean PM2.5 concentrations report means from 9.3 [j,g/m3 to 12.2 [j,g/m3.
• Noting that given the differences between population densities, PM2.5 concentration
gradients, and source distributions between the U.S. and Canada, it may be inappropriate to
draw a direct comparison between the Canadian study means and the annual design value
metric used for compliance in the U.S., but also noting that the study reported means from
the Canadian studies are similar, though somewhat lower, than those in the U.S.
• Setting a standard level within the starting range of the mean PM2.5 concentrations evaluated
in accountability studies, recognizing that some of the studies that report public health
improvements with improvements to air quality have starting concentrations that range
between 10.0 |ig/m3 to 12.0 |ig/m3 (Table 3-12).
• Setting a standard estimated to reduce PIVh.s-associated health risks, such that a substantial
portion of the risk reduction that would be accomplished is estimated at annual average PM2.5
concentrations within the range of overall means for which key epidemiologic studies
indicate consistently positive and statistically significant health effect associations (> about 8
Hg/m3) while also noting important uncertainties inherent in the risk assessment as described
in detail in sections 3.4.1.7 and 3.4.1.8. Further, the at-risk analyses indicate that the average
percent reduction in PM2.5 concentrations and risk are slightly greater in the Black population
than in the White population for each alternative standard evaluated (11.0 |ig/m3 and 10.0
|ig/m3), when shifting from the current annual PM2.5 standard (12.0 |ig/m3) in the full set of
47 areas and the subset of 30 areas controlled by the annual standard (section 3.4).
• Noting a number of uncertainties associated with the scientific evidence and risk information
including: (1) there are few key epidemiologic studies (and only one key U.S. study) that
report positive and statistically significant health effect associations for PM25 air quality
distributions with overall mean concentrations below 9.6 |ag/m\ and areas meeting a standard
with a level of 10.0 ng/m3 would generally be expected to have lower long-term mean
PM25 concentrations (and potentially around 8.0 ng/m3 in some areas) (section 3.3.3.2.1); (2)
there is increasing uncertainty in PM25 exposure estimates in some of the largest key studies
at lower ambient concentrations (i.e., those that use hybrid model predictions to estimate
exposures), given the more limited information available to develop and validate model
predictions (sections 2.3.3 and 3.3.3.2.1); and (3) there is increasing uncertainty in
quantitative estimates of PM25-associated mortality risk for standard levels below 10.0 |ag/m\
given that a substantial proportion of the risk reductions estimated for lower standard
levels occur at annual average PM25 concentrations below 8 |ag/m\ and thus below the lower
end of the range of overall mean PM25 concentrations in key epidemiologic studies that
consistently report positive and statistically significant associations (section 3.4.1.7).
In contrast, an annual standard with a level below 10.0 |j,g/m3 and as low as 8.0 ng/m3,
could be supported to the extent greater weight is placed on the potential public health
improvements that could result from additional reductions in ambient PM2.5 concentrations (i.e.,
beyond those achieved by a standard with a level of 10.0 |ag/m3) and less weight is placed on the
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limitations in the evidence that contribute to greater uncertainty at lower concentrations. For
example, a level below 10.0 |j,g/m3 could be supported to the extent greater weight is placed on
the following:
• Setting the annual standard at or below most or all of the study reported means, including
means of hybrid modeling studies that did not use population weighted approaches, such that
the standard would be expected to maintain the PM2.5 air quality distributions further below
those present in most key epidemiologic studies and noting that the relationships between
study mean calculation and the annual standard in the draft PA analyses are approximations
and less weight should be placed on them and the mathematical approach used to calculate
the mean.
• Results of the key Canadian epidemiologic studies, which report mean PM2.5 concentrations
that are lower than those reported in U.S. studies and for which the PM2.5 concentrations
generally range from 7.0 |ig/m3 to 9.0 |ig/m3 (monitor-based) and 6.0 |ig/m3 to 10.0 |ig/m3
(hybrid model-based and all of which apply some aspects of population-weighting) (section
3.3.3.2.1);
• Consideration of the air quality distribution below the mean for which key epidemiologic
studies have reported associations with health effects. The ambient PM2.5 concentrations
around the 25th percentile of underlying data, which range from 11.5 |ig/m3 to 12.9 |ig/m3 in
U.S. monitor-based studies, from 6.5 |ig/m3 to 6.8 |ig/m3 in Canadian monitor-based studies,
from 4.6 |ig/m3 to 9.2 |ig/m3. In key U.S. epidemiologic studies that use hybrid modeling
methods and do not apply some aspects of population-weighting range from 4.6 |ig/m3 to 9.2
|ig/m3, while those that apply some aspects of population weighting range from 6.7 |ig/m3 to
9.1 |ig/m3 while hybrid modeled studies in Canada, all of which average up to postal codes
and thus include some aspects of population-weighting, report 25th percentile concentrations
around 8.0 |ig/m3 in two studies, and 4.3 |ig/m3 in one study (section 3.3.3.2.1);
• Noting studies that examined the shapes of concentration-response functions over the full
distribution of ambient PM2.5 concentrations and concluded that while the concentration-
response relationship support a linear, no-threshold effect down to 5.0 (J,g/m3, uncertainties
increase at concentrations of less than 8.0 (j,g/m3; and also noting that the PM2.5 exposure
concentrations in an area with a design value of less than 8.0 [j,g/m3 would reflect a
distribution of air quality that would be mostly associated with average daily concentrations
below 8.0 [j,g/m3.
• The potential for continued public health improvements with improvements in air quality
below the lowest starting concentration evaluated in accountability studies, which was
approximately 10.0 |ig/m3 (Table 3-12);
• Studies that restrict analyses to air quality associated with levels below the current annual
standard and report positive and significant associations, often with effect estimates that are
greater in magnitude than those reported in the main analysis. Although the mean of the
restricted analyses are generally not reported, in one key U.S. epidemiologic study, the mean
concentration when restricting annual average PM2.5 concentrations to below 12.0 |ig/m3 was
presumably lower than the overall mean concentration of 8.1 |ig/m3 reported in the main
analysis (Shi et al., 2016) (Table 3-10);
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• The potential public health importance of the additional reductions in PIVh.s-associated health
risks estimated for a level below 10.0 |ig/m3 |j,g/m3 and the potential for continued
improvements below the lowest level examined in the risk assessment (8.0 |j,g/m3). Further,
the at-risk analyses indicate that the average percent reduction in PM2.5 concentrations and
risk are slightly greater in the Black population than in the White population for each
alternative standard evaluated (9.0 |ig/m3 and 8.0 |ig/m3), when shifting from the current
annual PM2.5 standard (12.0 |ig/m3) in the full set of 47 areas and the subset of 30 areas
controlled by the annual standard (section 3.4).
Alternative 24-Hour Standard Levels
We additionally evaluate the degree to which the evidence supports considering potential
alternative levels for the 24-hour PM2.5 standard, in conjunction with the current 98th percentile
form of that standard. With respect to current and recent air quality relationships, we note that
the risk assessment indicates that the annual standard is the controlling standard across most of
the urban study areas evaluated and revising the level of the 24-hour standard to 30 |j,g/m3 would
be estimated to lower PIVh.s-associated risks, compared to the current standards, largely in a few
study areas located in the western U.S. (several of which are also likely to experience risk
reductions upon meeting a revised annual standard). Additionally, recent air quality analyses
indicate that almost all CBSAs with maximum annual PM2.5 design values at or below 12.0
Hg/m3 also have maximum 24-hour PM2.5 design values below 35 |j,g/m3 (and below 30 |j,g/m3 in
most areas) (chapter 2, Figure 2-18). The exceptions are a few CBSAs in the western U.S.
As in previous reviews, we recognize that the annual standard would generally be the
controlling standard across much of the U.S., except for certain areas where there are high
seasonal emissions (e.g., wood smoke) and conducive meteorology (e.g., temperature inversions)
or where there are more unique source-oriented influences (e.g., near manufacturing sources). In
such areas, the 24-hour standard is the generally controlling standard, though the number of these
areas in the U.S. is small. Thus, as was the approach in multiple recent reviews, we focus on the
annual standard as the principle means of limiting both long- and short-term PM2.5
concentrations, recognizing that the 24-hour standard, with its 98th percentile form, would
provide supplemental protection against short-term peak exposures, particularly for areas with
high peak-to-mean ratios (e.g., areas with strong seasonal sources). Compared to the annual
standard, we recognize that the 24-hour standard is less likely to appropriately limit the more
typical PM2.5 exposures (i.e., corresponding to the middle portion of the air quality distribution)
that are most strongly associated with the health effects observed in epidemiologic studies. Thus,
as in previous reviews (78 FR 3161-3162, January 15, 2013; 85 FR 82715, December 18, 2020),
we focus on the 24-hour standard as a means of providing supplemental protection against the
short-term exposures to "peak" PM2.5 concentrations, such as can occur in areas with strong
contributions from local or seasonal sources.
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Taking into account this approach, an important consideration is whether additional
protection is needed against short-term exposures to peak PM2.5 concentrations in areas meeting
both the current 24-hour standard and the current, or a revised, annual standard. To the extent
that the evidence indicates that such exposures can lead to adverse health effects, it would be
appropriate to consider alternative levels for the 24-hour standard. In considering this issue, we
evaluate the evidence from key health studies. With regard to these studies, we particularly note
the following:
• Controlled human exposure studies provide evidence for health effects following single,
short-term PM2.5 exposures to concentrations that typically correspond to upper end of the
PM2.5 air quality distribution in the U.S. (i.e., "peak" concentrations). In the studies evaluated
at near ambient PM2.5 concentrations, results are mixed but they do report statistically
significant effects on one or more indicators of cardiovascular function following 2-hour
exposures to PM2.5 concentrations at and above 120 [j,g/m3 (at and above 149 [j,g/m3 for
vascular impairment, the effect shown to be most consistent across studies).
• Animal toxicologic studies provide evidence of effects related to short-term exposures to
PM2.5 at concentrations ranging from 100 to > 1,000 [j,g/m3 and providing further evidence to
support the biological mechanisms and plausibility of various adverse effects associated with
short-term exposures.
• The body of epidemiologic evidence provides limited support forjudging adequacy of the
level of the 24-hour standard. As discussed in detail above (section 3.3.3.2.1), epidemiologic
studies provide the strongest support for reported health effect associations for the part of the
air quality distribution corresponding to the bulk of the underlying data (i.e., estimated
exposures and/or health events), often around the overall mean concentrations evaluated
rather than near the upper end of the distribution. Additionally, the magnitudes of the
associations in restricted analyses are similar to or larger than the magnitudes of the
associations based on the full cohorts (Table 3-10), suggesting that, at a minimum, short-term
exposures to peak PM2.5 concentrations are not disproportionately responsible for reported
health effect associations.
Based on the evidence above, we assessed the protection provided by the current
standards against the concentrations seen in the human exposure studies. The air quality analyses
included in this draft PA show that 2-hour ambient concentrations of PM2.5 at monitoring sites
meeting the current standards almost never exceed 30 [j,g/m3 (Figure 2-19). In fact, even the
extreme upper end of the distribution of 2-hour PM2.5 concentrations at sites meeting the current
standards remain well-below the PM2.5 exposure concentrations consistently shown to elicit
effects (i.e., 99.9th percentile of 2-hour concentrations at these sites is 62 [j,g/m3 during the warm
season). We also note some caution in placing too much weigh on the need to provide protection
against any of the exposures observed in the clinical studies given that it is unclear how the
results alone and the importance of the effects observed in these studies, particularly in the
studies conducted at near-ambient PM2.5 concentrations, should be interpreted with respect to
adversity to public health.
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When the information summarized above is considered in the context of the 24-hour
standard, we reach the preliminary conclusion that, in conjunction with a lower annual standard
level intended to increase protection against average short- and long-term PM2.5 exposures across
the U.S., the evidence does not support the need for additional protection against short-term
exposures to peak PM2.5 concentrations. In particular, while the epidemiologic studies do support
the need to consider increasing protection against the typical daily and annual PM2.5 exposures
that provide strong support for reported health effect associations, these studies do not provide
the same support for a need for increasing protection against short-term, peak exposures. Further,
the epidemiologic studies do not indicate that the reported health effect associations in these
studies are strongly influenced by exposures to the peak concentrations in the air quality
distribution. Also, while animal toxicologic studies provide evidence to support the biological
mechanisms and plausibility of various adverse effects associated with short-term exposures,
they provide limited support forjudging adequacy of the level of the 24-hour standard. Human
clinical studies support the occurrence of effects following single short-term exposures to PM2.5
concentrations that correspond to the peak of the air quality distribution, though these
concentrations are well above those typically measured in areas meeting the current standards,
suggesting that the current standards are providing protection against these exposures. As such,
the available evidence supports the need for the current 24-hour standard to protect against peak
concentrations but does not clearly support the need for a lower level of that standard. Thus, in
the context of a 24-hour standard that is meant to provide supplemental protection (i.e., beyond
that provided by the annual standard alone) against short-term exposures to peak PM2.5
concentrations, the evidence supports consideration of retaining the current 24-hour standard
with its level of 35 |j,g/m3.
However, we also recognize that a different policy approach than that described above
could be applied to considering the level of the 24-hour standard. For example, consideration
could be given to lower 24-hour standard levels in order to increase protection across the U.S.
against the broader PM2 5 air quality distribution. If such an approach is evaluated in this
reconsideration, consideration of 24-hour standard levels as low as 30 |j,g/m3 could be supported
(either alone or in conjunction with a lower annual standard level). The risk assessment estimates
that a level of 30 |j,g/m3 would increase protection compared to the current standards, though
only in a small number of study areas largely confined to the western U.S. (section 3.4.2).
If this alternative approach to revising the primary PM2.5 standards is adopted, the
uncertainty inherent in using the 24-hour standard to increase protection against the broad
distribution of PM2.5 air quality should be carefully considered. Specifically, the degree of
protection provided by any particular 24-hour standard against the typical PM2 5 exposures
corresponding to the middle portion of the air quality distribution will vary across locations and
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over time, depending on the relationship between those typical concentrations and the short-term
peak PM25 concentrations that are directly targeted by the 24-hour standard (i.e., with its 98th
percentile form). Thus, lowering the level of the 24-hour standard is likely to have a more
variable impact on public health than lowering the level of the annual standard. Depending on
the 24-hour standard level set, some areas could experience reductions that are greater than
warranted, based on the evidence, while others could experience reductions that are less than
warranted. Therefore, the rationale supporting this approach would need to recognize and
account for the uncertainty inherent in using 24-hour standard, with a 98th percentile form, to
increase protection against the broad distribution of PM2.5 air quality.
3.6 AREAS FOR FUTURE RESEARCH AND DATA COLLECTION
In this section, we identify key areas for additional research and data collection for fine
particles, based on the uncertainties and limitations that remain in the evidence and technical
information. Additional research in these areas could reduce uncertainties and limitations in
future reviews of the primary PM2.5 standards. Important areas for future research include the
following:
• Further elucidating the physiological pathways through which exposures to the PM2.5
concentrations present in the ambient air across much of the U.S. could be causing mortality
and the morbidity effects shown in many epidemiologic studies. This could include the
following:
Controlled human exposure studies that examine exposures near ambient
PM2.5 concentrations (e.g., Wyatt et al. (2020a) longer exposure periods (e.g.,
24-hour as in Brauner et al. (2008); 5-hour as in Hemmingsen et al. (2015b)),
or repeated exposures, to concentrations typically measured in the ambient air
across the U.S.
Studies that evaluate the health impacts of decreasing PM2.5 exposures (e.g.,
due to changes in policies or behavior, shifts in important emissions sources,
or targeted interventions).
Additional animal toxicological studies that evaluate exposures to near
ambient PM2.5 concentrations.
• Additional research into "causal inference" methods in epidemiologic studies to evaluate the
causal nature of relationships between PM2.5 exposure and mortality or morbidity.
• Additional research into "accountability" or "quasi-experimental" epidemiologic studies with
'starting PM2.5 concentrations' below 12.0 |ig/m3,
• Improving our understanding of the PM2.5 concentration-response relationships near the
lower end of the PM2.5 air quality distribution, including the shapes of concentration-
response functions and the uncertainties around estimated functions for various health
outcomes and populations (e.g., older adults, people with pre-existing diseases, children).
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• Understanding of the potential for particle characteristics, other than size-fractionated mass,
to influence PM toxicity (e.g., composition, oxidative potential, etc.) and the PM health
effect associations observed in epidemiologic studies.
• Improving our understanding of the uncertainties inherent in the various approaches used to
estimate PM2.5 exposures in epidemiologic studies, including how those uncertainties may
vary across space and time, and over the PM2.5 air quality distribution. Approaches to
incorporating these uncertainties into quantitative estimates of PM2.5 concentration-response
relationships should also be explored.
• Additional health research on ultrafine particles, with a focus on consistently defining UFPs
across studies and across disciplines (i.e., animal, controlled human exposure, and
epidemiologic studies), on using consistent exposure approaches in experimental studies, and
on improving exposure characterizations in epidemiologic studies. Also, further examine the
potential for translocation of ultrafine particles from the respiratory tract into other
compartments (i.e., blood) and organs (e.g., heart, brain), with particular emphasis on studies
conducted in humans.
• Additional work to measure ultrafine particle emissions and the composition of ultrafine
particles, using comparable methods to measure emissions from various types of sources
(e.g., mobile sources, fires, etc.).
• Further evaluate the potential for some groups to be at higher risk of PM2.5-related effects
than the general population and the potential for PM2.5 exposures to contribute to the
development of underlying conditions that may then confer higher risk of PM2.5-related
effects. For example, research to address this latter need could include efforts to understand
the potential for long-term PM exposures to contribute to the development and progression of
atherosclerosis in adults and/or asthma in children. It could also include research to
understand the potential role of PM exposures in developmental outcomes (e.g.,
neurodevelopmental effects, reproductive and birth outcomes).
• Research to further evaluate the combination of factors that contribute to differences in risk
estimates between cities, potentially including differences in exposures, demographics,
particle characteristics.
• Research to improve our understanding of variability in PM2.5 exposures within and across
various populations (e.g., defined by life stage, pre-existing condition, etc.), the most health-
relevant exposure durations, as well as the temporal and spatial variability in ambient PM2.5
that is not captured by existing ambient monitors.
• Future research to examine PM2.5 exposure and associated effects in pregnant women, and
birth outcomes, as well as future research and data collection to examine developmental
outcomes and different life stages
In addition to research and data collection, additional information that could be reported
in epidemiologic studies may help to reduce uncertainties and limitations in future reviews of the
primary PM2.5 standards. This information includes:
• Descriptive statistics of PM2.5 concentrations that are used in epidemiologic studies to
evaluate associations between PM2.5 and health effects (e.g., minimum, maximum, 10th
percentile, 25th percentile, mean, median, 75th percentile).
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• More detailed information on the methods used to calculate the mean PM2.5 concentrations
that are reported in the study (e.g., whether population-weighting was applied, how the PM2.5
concentrations estimated from hybrid modeling are averaged prior to being assigned to health
events).
- Noting whether the mean PM2.5 concentration reported is the concentration
across the area evaluated or if the mean PM2.5 concentration reported is based
only PM2.5 concentrations used in analyses to assess the association between
health outcomes and PM2.5.
• In analyses restrict PM2.5 concentrations below specific concentrations (e.g., below annual
averages of 12.0 |ig/m3 or below daily averages of 35 |ig/m3) reporting of the Mean PM2.5
concentrations in the restricted analysis could be helpful.
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4 RECONSIDERATION OF THE PRIMARY STANDARD
FOR PMio
This chapter presents and evaluates the policy implications of the scientific and technical
information pertaining to reconsideration of the 2020 final decision on the primary PMio
standard. In so doing, the chapter presents key aspects of the health effects evidence of PM10-2.5,
as documented in the 2019 ISA, with support from the prior ISA and AQCDs, and associated
public health implications. This information provides the basis for our evaluation of the scientific
information regarding health effects of PMio in ambient air and the potential for effects to occur
under air quality conditions associated with the existing standard, as well as the associated
implications for public health. Our evaluation is framed around key policy-relevant questions
derived from the IRP (U.S. EPA, 2016, section 2.1) for the review completed in 2020, and the
scientific conclusions regarding the relationship between short- and long-term PM10-2.5 exposure
and health effects detailed in the 2019 ISA, while also taking into account conclusions reached in
previous reviews. In this way, we identify key policy-relevant issues and summary conclusions
regarding the public health protection provided by the current standard as the Administrator
reconsiders the final 2020 decision on the primary PMio standard.
As described in Chapter 1, the scope of the updated scientific evaluation of the health
effects evidence for PMio is based on those health effects categories where the 2019 ISA
concluded a causal relationship exists. Therefore, the draft ISA Supplement does not include an
evaluation of additional studies for PM10-2.5 and the 2019 ISA continues to serve as the scientific
foundation for assessing the adequacy of the primary PMio standard in this reconsideration of the
2020 final decision (U.S. EPA, 2019, section 1.7; U.S. EPA, 2021). As such, this chapter draws
heavily from the 2020 PA in identifying and summarizing key issues related to this
reconsideration of the primary PMio standard.
Within this chapter, background information on the current standard is summarized in
section 4.1. The general approach for evaluating the available information in this
reconsideration, including policy-relevant questions identified to frame our policy evaluation, is
summarized in section 4.2. Key aspects of the available health effects evidence presented in the
2019 ISA and considered in the 2020 PA are addressed in section 4.3. Section 4.4 summarizes
the key evidence-based considerations identified in our evaluation and presents associated
preliminary conclusions on the adequacy of the current standard. Key remaining uncertainties
and areas for future research are identified in section 4.5.
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4.1 BACKGROUND ON THE CURRENT STANDARD
With the 2020 final decision on the PM NAAQS, the EPA retained the existing 24-hour
primary PMio standard, with its level of 150 |ig/m3 and its one-expected-exceedance form on
average over three years, to continue to provide public health protection against short-term
exposures to PM10-2.5 (85 FR 82725, December 18, 2020). This decision was based on the
scientific information available at that time, as well as the Administrator's judgments regarding
the health effects evidence and the appropriate degree of public health protection for the existing
standard.
The health effects evidence assessed in the 2019 ISA included an expanded body of
scientific evidence linking short-term PM10-2.5 to health outcomes such as premature death and
hospital visits (U.S. EPA, 2009, U.S. EPA, 2019). This evidence base assessed the causal nature
of relationships between short-term exposure to PM10-2.5 and a broad range of health effects (U.S.
EPA, 2020, section 1.4.2). These effects associated with short-term exposure ranged from
hospital admissions and emergency department visits for cardiovascular effects (documented in
epidemiologic studies that reported PM10-2.5 associations with cardiovascular hospital admissions
and emergency department visits in study locations with mean 24-hour average PM10-2.5
concentrations ranging from 7.4 to 13 |ig/m3) and respiratory effects (documented in
epidemiologic studies that reported PM10-2.5 associations with respiratory hospital admissions and
emergency department visits in study locations with mean 24-hour average concentrations
ranging from 5.6 to 16.2 |ig/m3) to mortality (documented in epidemiologic studies that reported
PM10-2.5 associations with mortality in study areas with mean 24-hour average concentrations
ranging from 6.1 |ig/m3 to 16.4 |ig/m3). In addition to the epidemiologic studies, the evidence
base included few controlled human exposure studies and animal toxicologic studies that
provided insight into the biological plausibility of these effects. Collectively, the epidemiologic
studies, controlled human exposure, and animal toxicological studies, with their inherent
uncertainties, contributed to the causality determinations of "suggestive of, but not sufficient to
infer, a causal relationship" between short-term exposures to PM10-2.5 and cardiovascular effects,
respiratory effects, and mortality (U.S. EPA, 2009, U.S. EPA, 2019, section 1.4.2).
Building on the evidence considered in the 2012 review, the primary focus in the 2020
review was on multi-city and single-city epidemiologic studies that evaluated associations
between short-term PM10-2.5 and mortality, cardiovascular effects (hospital admissions and
emergency department visits), and respiratory effects. Despite differences in the approaches used
to estimate ambient PM10-2.5 concentrations, the majority of the studies reported positive, though
often not statistically significant, associations with short-term PM10-2.5 exposures. Most PM10-2.5
effect estimates remained positive in copollutant models that included either gaseous pollutants
or other particulate matter size fractions (e.g., PM2.5). In U.S. study locations likely to have met
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the PMio standard during the study period, a few studies reported positive associations between
PMio-2.5 and mortality that were statistically significant and remained so in copollutant models
(U.S. EPA, 2009, U.S. EPA, 2019).
In addition to the epidemiologic studies, there were a small number of controlled human
exposure studies assessed in the 2019 ISA that reported alterations in heart rate variability or
increased pulmonary inflammation following short-term exposure to PM10-2.5, providing some
support for the associations in the epidemiologic studies. Toxicological studies that examined the
effects of PM10-2.5 used intratracheal instillation as opposed to inhalation. Therefore, these studies
provided limited evidence for the biological plausibility of PMio-2 5-induced effects (U.S. EPA,
2009, U.S. EPA, 2019).
Although the scientific evidence available in the 2019 ISA expanded the understanding of
health effects associated with PM10-2.5 exposures, a number of important uncertainties remained.
These uncertainties, and their implications for interpreting the scientific evidence, include the
following:
• The potential for confounding by copollutants, notably PM2.5, was addressed with
copollutant models in a relatively small number of PM10-2.5 epidemiologic studies (U.S.
EPA, 2009, U.S. EPA, 2019). This was particularly important given the relatively small
body of experimental evidence (i.e., controlled human exposure and animal toxicological
studies) available to support the independent effect of PM10-2.5 on human health. This
increases the uncertainty regarding the extent to which PM10-2.5 itself, rather than one or
more cooccurring pollutants, is responsible for the mortality and morbidity effects
reported in epidemiologic studies.
• There was greater spatial variability in PM10-2.5 concentrations than PM2.5 concentrations,
resulting in increased exposure error for PM10-2.5 (U.S. EPA, 2009, U.S. EPA, 2019).
Available measurements did not provide sufficient information to adequately characterize
the spatial distribution of PM10-2.5 concentrations (U.S. EPA, 2009, U.S. EPA, 2019). The
limitations in estimates of ambient PM10-2.5 concentrations "would tend to increase
uncertainty and make it more difficult to detect effects of PM10-2.5 in epidemiologic
studies" (U.S. EPA, 2009, U.S. EPA, 2019).
• The distributions of PM10-2.5 concentrations over which reported health outcomes occur
remain highly uncertain. Only a relatively small number of PM10-2.5 monitoring sites were
operating at the time of the 2012 review and such sites had only been in operation for a
relatively short period of time, limiting the spatial and temporal coverage for routine
measurement of PM10-2.5 concentrations. Given these limitations in routine monitoring,
epidemiologic studies employed a number of different approaches for estimating PM10-2.5
concentrations. Given the relatively small number of PM10-2.5 monitoring sites, the
relatively large spatial variability in ambient PM10-2.5 concentrations, the use of different
approaches to estimating ambient PM10-2.5 concentrations across epidemiologic studies,
and the limitations inherent in such estimates, the distributions of PM10-2.5 concentrations
over which reported health outcomes occur remain highly uncertain (U.S. EPA, 2009,
U.S. EPA, 2019).
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• There was relatively little information on the chemical and biological composition of
PMio-2.5 and the effects associated with the various components (U.S. EPA, 2019).
Without more information on the chemical speciation of PM10-2.5, the apparent variability
in associations with health effects across locations was difficult to characterize (U.S.
EPA, 2009, U.S. EPA, 2019).
Consistent with the general approach routinely employed in NAAQS reviews, the initial
consideration in the 2020 review of the primary PM10 standard was with regard to the adequacy
of protection provided by the then-existing standard. Key aspects of that consideration are
summarized in section 4.1.1 below.
4.1.1 Considerations Regarding the Adequacy of the Existing Standards in the 2020
Review
In the 2020 final decision, the EPA retained the existing 24-hour primary PM10 standard
with its level of 150 |ig/m3 and its one-expected-exceedance form on average over three years to
continue to provide public health protection against exposures to PM10-2.5 (85 FR 82727,
December 18, 2020). In reaching his decision, the Administrator specifically noted that, while
the health effects evidence was somewhat expanded since the prior reviews, the overall
conclusions in the 2019 ISA, including uncertainties and limitations, were generally consistent
with what was considered in the 2012 review (85 FR 82725, December 18, 2020). In addition,
the Administrator recognized that there were still a number of uncertainties and limitations
associated with the available evidence.
With regard to the evidence on PM10-2.5-related health effects, the Administrator noted
that epidemiologic studies continued to report positive associations with mortality and morbidity
in cities across North America, Europe, and Asia, where PM10-2.5 sources and composition were
expected to vary widely. While significant uncertainties remained in the 2020 review, the
Administrator recognized that this expanded body of evidence had broadened the range of effects
that have been linked with PM10-2.5 exposures. The studies evaluated in the 2019 ISA expanded
the scientific foundation presented in the 2009 ISA and led to revised causality determinations
(and new determinations) for long-term PM10-2.5 exposures and mortality, cardiovascular effects,
metabolic effects, nervous system effects, and cancer (85 FR 82726, December 18, 2020).
Drawing from his consideration of this evidence, the Administrator concluded that the scientific
information available since the time of the last review supported a decision to maintain a primary
PM10 standard to provide public health protection against PM10-2.5 exposures, regardless of
location, source of origin, or particle composition (85 FR 82726, December 18, 2020).
With regard to uncertainties in the available evidence, the Administrator first noted that a
number of limitations were identified in the 2012 review related to: (1) estimates of ambient
PM10-2.5 concentrations used in epidemiologic studies; (2) limited evaluation of copollutant
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models to address the potential for confounding; and (3) limited experimental studies supporting
biological plausibility for PMio-25-related effects. Despite the expanded body of evidence for
PMio-2.5 exposures and health effects, the Administrator recognized that uncertainties in the 2020
review continued to include those associated with the exposure estimates used in epidemiologic
studies, the independence of the PM10-2.5 health effect associations, and the biologically plausible
pathways for PM10-2.5 health effects (85 FR 82726, December 18, 2020). These uncertainties
contributed to the 2019 ISA determinations that the evidence is "suggestive of, but not sufficient
to infer" causal relationships (85 FR 82726, December 18, 2020).
Further, consistent with the approach in reaching the 2012 decision, the approach for the
2020 PM NAAQS review did not include quantitative assessments of estimated exposures or
risks allowed by the existing standard or potential alternative standards. Further, the available
evidence in the 2019 ISA did not provide support for evaluating air quality distributions in
locations of individual epidemiologic studies as was done in the 2012 review (78 FR 3176,
January 15, 2013). The substantial uncertainty in such analyses, if conducted based on the
available PM10-2.5 health studies, would have been of limited utility for informing conclusions on
the primary PM10 standard.
In the 2020 decision, for all of the reasons discussed above and recognizing the CASAC
conclusion that the evidence provided support for retaining the current standard, the
Administrator concluded that it was appropriate to retain the existing primary PM10 standard,
without revision. His decision was consistent with the CASAC advice related to the primary
PM10 standard. Specifically, the CASAC agreed with the 2020 PA conclusions that, while these
effects are important, the "evidence does not call into question the adequacy of the public health
protection afforded by the current primary PM10 standard" and "supports consideration of
retaining the current standard in this review" (Cox, 2019a, p. 3 of letter). Thus, the Administrator
concluded that the primary PM10 standard (in all of its elements) was requisite to protect public
health with an adequate margin of safety against effects that have been associated with PM10-2.5.
In light of this conclusion, the EPA retained the existing PM10 standard.
4.2 GENERAL APPROACH AND KEY ISSUES IN THIS
RECONSIDERATION OF THE 2020 FINAL DECISION
As is the case for all such reviews, this reconsideration of the 2020 final decision on the
primary PM10 standard is most fundamentally based on using the Agency's assessment of the
scientific evidence and quantitative information, if available, to inform the Administrator's
judgments regarding a primary standard that is requisite to protect public health with an adequate
margin of safety. The approach for this reconsideration builds on the substantial assessments and
evaluations performed over previous reviews (U.S. EPA, 2011, U.S. EPA, 2020). As noted
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above, the draft ISA Supplement does not include an evaluation of studies for PM10-2.5 and the
2019 ISA continues to serve as the scientific foundation for this reconsideration. Given that there
is no new evidence for PMio-25-related health effects assessed in the draft ISA Supplement that
would inform quantitative assessments or preliminary conclusions on the current primary PM10
standard since the completion of the 2020 review, this draft PA draws from the evaluation of the
health effects evidence for PMio-25-related effects in the 2019 ISA and considerations of such
effects in the 2020 PA (U.S. EPA, 2020).
The evaluations in this draft PA of the health effects evidence assessed in the 2019 ISA
are intended to inform the Administrator's public health policy judgments and conclusions as a
part of this reconsideration of the 2020 final decision, including his decision as to whether to
retain or revise the primary PM10 standard. The draft PA evaluations consider the potential
implications of various aspects of the scientific evidence and the associated uncertainties and
limitations. In so doing, the approach for this draft PA involves evaluating the available scientific
and technical information to address a series of key policy-relevant questions using evidence-
based considerations. Consideration of the full set of evidence in this reconsideration will inform
the answer to the following initial overarching question:
• Does the scientific evidence support or call into question the adequacy of the
protection afforded by the current 24-hour primary PM10 standard against health
effects associated with exposures to PM10-2.5?
In reflecting on this question, we consider the body of scientific evidence, assessed in the
2019 ISA, including whether it supports or calls into question the scientific conclusions reached
in previous reviews regarding health effects related to exposure to PM10-2.5 in ambient air.
Information available in the 2019 ISA that may be informative to public health judgments
regarding significance or adversity of key effects will also be considered. Further, in considering
this question with regard to the primary PM10 standard, as in all NAAQS reviews, we give
particular attention to exposures and health risks to at-risk populations (including at-risk
lifestages). Evaluation of the scientific information with regard to this consideration of the
current standard will focus on key policy-relevant issues by addressing a series of questions
including the extent to which the available scientific evidence supports retaining or altering the
conclusions in the prior reviews regarding health effects attributed to PM10-2.5 exposures.
Furthermore, this draft PA will examine whether the previously identified uncertainties have
been reduced and if new uncertainties have been identified.
The general approach to reaching preliminary conclusions on the current primary PM10
standard is summarized in Figure 4-1:
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OoesinfeBBafefi cal
i® queston adequacy
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Appropriate t® consider
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Further evaluate the scientific evidence and exposure risk assessments to
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Identify appropriaterangc of potential alternative standards for consideration
1
2 Figure 4-1. Overview of general approach for the reconsideration of the 2020 final decision
3 on the primary PM10 standard.
4
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The Agency's approach to reviewing the primary standards is consistent with the
requirements of the provisions of the CAA related to the review of the NAAQS and with how the
EPA and the courts have historically interpreted the CAA. As discussed in section 1.1 above,
these provisions require the Administrator to establish primary standards that, in the
Administrator's judgment, are requisite (i.e., neither more nor less stringent than necessary) to
protect public health with an adequate margin of safety. Consistent with the Agency's approach
across all NAAQS reviews, the approach of this draft PA to informing these judgments is based
on a recognition that the available health effects evidence generally reflects continuums that
include ambient air exposures for which scientists generally agree health effects are likely to
occur through lower levels at which the likelihood and magnitude of response become
increasingly uncertain. The CAA does not require the Administrator to establish a primary
standard at a zero-risk level or at background concentration levels, but rather at a level that
reduces risk sufficiently so as to protect public health, including the health of sensitive groups,1
with an adequate margin of safety.
The decisions on the adequacy of the current primary PMio standard and on any
alternative standards considered in a review are largely public health policy judgments made by
the Administrator. The four basic elements of the NAAQS (i.e., indicator, averaging time, form,
and level) are generally considered collectively in evaluating the health protection afforded by
the current standard, and by any alternatives considered. The Administrator's final decisions in a
review draw upon the scientific evidence for health effects, quantitative analyses of population
exposures and/or health risks, as available, and judgments about how to consider the
uncertainties and limitations that are inherent in the scientific evidence and quantitative analyses.
4.3 HEALTH EFFECTS EVIDENCE
This section draws from the EPA's synthesis and assessment of the scientific evidence
presented in the 2019 ISA (U.S. EPA, 2019) to consider the following policy-relevant questions:
• To what extent does the available scientific evidence strengthen, or otherwise alter, our
conclusions from previous reviews regarding health effects attributable to long- or
short-term PM10-2.5 exposures? Have previously identified uncertainties been reduced?
What important uncertainties remain and have new uncertainties been identified?
Answers to these questions will inform our response to the overarching question on the adequacy
of the current primary PM10 standard, posed at the beginning of this chapter. In section 4.3.1
1 More than one population group may be identified as sensitive or at-risk in a NAAQS review. Decisions on
NAAQS reflect consideration of the degree to which protection is provided for these sensitive population groups.
To the extent that any particular population group is not among the identified sensitive groups, a decision that
provides protection for the sensitive groups would be expected to also provide protection for other population
groups.
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below, we consider the nature of the effects attributable to long-term and short-term PM10-2.5
exposures.
4.3.1 Nature of Effects
As noted above, for the health effect categories and exposure duration combinations
evaluated, the 2019 ISA concludes that the evidence supports causality determinations for
PM10-2.5 that are "suggestive of, but not sufficient to infer, a causal relationship." These health
effect categories, along with their corresponding causality determinations from the 2009 ISA, are
highlighted below in Table 4-1 (adapted from U.S. EPA, 2019, Table 1-4).
Table 4-1. Key Causality Determinations for PM10-2.5 Exposures
Health Outcome
Exposure
Duration
2009 PM ISA
2019 PM ISA
Mortality
Long-term
Inadequate
Short-term
Suggestive of, but not sufficient to infer
Cardiovascular
Long-term
Inadequate
effects
Short-term
Suggestive of, but not sufficient to infer
Respiratory effects
Short-term
Suggestive of, but not sufficient to infer
Suggestive of, but not
sufficient to infer
Cancer
Long-term
Inadequate
Nervous System
effects
Long-term
—
Metabolic effects
Long-term
—
While the evidence supporting the causal nature of relationships between exposure to
PM10-2.5 has been strengthened for some of the health effect categories listed in Table 4-1 since
the 2009 ISA, the 2019 ISA concludes that overall "the uncertainties in the evidence identified in
the 2009 PM ISA have, to date, still not been addressed" (U.S. EPA, 2019, section 1.4.2, p. 1-
41). Specifically, epidemiologic studies available in the 2012 review relied on various methods
to estimate PM10-2.5 concentrations, and these methods had not been systematically compared to
evaluate spatial and temporal correlations in PM10-2.5 concentrations. Methods included (1)
calculating the difference between PM10 and PM2.5 concentrations at co-located monitors, (2)
calculating the difference between county-wide averages of monitored PM10- and PIVh.s-based on
monitors that are not necessarily co-located, and (3) direct measurement of PM10-2.5 using a
dichotomous sampler (U.S. EPA, 2019, section 1.4.2). As described in the 2019 ISA, there
continues to be variability across epidemiologic studies in the approaches used to estimate PM10-
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2.5 concentrations. Additionally, some studies estimate long-term PM10-2.5 exposures as the
difference between PM10 and PM2.5 concentrations based on information from spatiotemporal or
land use regression (LUR) models, in addition to monitors. The various methods used to estimate
PM10-2.5 concentrations have not been systematically evaluated (U.S. EPA, 2019, section
3.3.1.1), contributing to uncertainty regarding the spatial and temporal correlations in PM10-2.5
concentrations across methods and in the PM10-2.5 exposure estimates used in epidemiologic
studies (U.S. EPA, 2019, section 2.5.1.2.3). Given the greater spatial and temporal variability of
PM10-2.5 and the lower number of PM10-2.5 monitoring sites, compared to PM2.5, this uncertainty is
particularly important for the coarse size fraction.
Beyond the uncertainty associated with PM10-2.5 exposure estimates in epidemiologic
studies, the limited information on the potential for confounding by copollutants and the limited
support available for the biological plausibility of health effects following PM10-2.5 exposures
also continue to contribute to uncertainty in the PM10-2.5 health evidence. Uncertainty related to
potential confounding stems from the relatively small number of epidemiologic studies that have
evaluated PM10-2.5 health effect associations in copollutants models with both gaseous pollutants
and other PM size fractions. On the other hand, uncertainty related to the biological plausibility
of effects attributed to PM10-2.5 exposures results from the small number of controlled human
exposure and animal toxicology2 studies that have evaluated the health effects of experimental
PM10-2.5 inhalation exposures. The evidence supporting the 2019 ISA's "suggestive of, but not
sufficient to infer, a causal relationship" causality determinations for PM10-2.5, including
uncertainties in this evidence, is summarized in sections 4.3.1.1 to 4.3.1.6 below.
4.3.1.1 Mortality
Long-term exposures
Due to the dearth of studies examining the association between long-term PM10-2.5
exposure and mortality, the 2009 ISA concluded that the evidence was "inadequate to determine
if a causal relationship exists" (U.S. EPA, 2009, U.S. EPA, 2019). As reported in the 2019 ISA,
some recent cohort studies conducted in the U.S. and Europe report positive associations
between long-term PM10-2.5 exposure and total (nonaccidental) mortality, though results are
inconsistent across studies (U.S. EPA, 2019, Table 11-11). The examination of copollutant
models in these studies remains limited and, when included, PM10-2.5 effect estimates were often
attenuated after adjusting for PM2.5 (U.S. EPA, 2019, Table 11-11). Across studies, PM10-2.5
exposure concentrations were estimated using a variety of approaches, including direct
2 Compared to humans, rats and mice have small nasal passages, allowing smaller fractions of inhaled PMi 0-2.5 to
penetrate into the thoracic regions of the lungs of rats and mice (U.S. EPA, 2019, section 4.1.6), contributing to
the relatively limited evaluation of PMi0-2.5 exposures in animal studies.
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measurements from dichotomous samplers, calculating the difference between PMio and PM2.5
concentrations measured at collocated monitors, and calculating the difference between area-
wide concentrations of PM10 and PM2.5. As discussed above, temporal and spatial correlations
between these approaches have not been evaluated, contributing to uncertainty regarding the
potential for exposure measurement error (U.S. EPA, 2019, section 3.3.1.1 and Table 11-11).
The 2019 ISA concludes that this uncertainty "reduces the confidence in the associations
observed across studies" (U.S. EPA, 2019, p. 11-125). The 2019 ISA additionally concludes that
the evidence for long-term PM10-2.5 exposures and cardiovascular effects, respiratory morbidity,
and metabolic disease evidence provides limited biological plausibility for PM10-2.5-related
mortality (U.S. EPA, 2019, sections 11.4.1 and 11.4). Taken together, the 2019 ISA concludes
that, "this body of evidence is suggestive of, but not sufficient to infer, a causal relationship
between long-term PM10-2.5 exposure and total mortality" (U.S. EPA, 2019, p. 11-125).
Short-term exposures
The 2009 ISA concluded that the evidence is "suggestive of a causal relationship between
short-term exposure to PM10-2.5 and mortality" (U.S. EPA, 2009). The 2019 ISA included
multicity epidemiologic studies conducted primarily in Europe and Asia which continue to
provide consistent evidence of positive associations between short-term PM10-2.5 exposure and
total (nonaccidental) mortality (U.S. EPA, 2019, Table 11-9). Although these studies contribute
to increasing confidence in the PMio-2 5-mortality relationship, the use of a variety of approaches
to estimate PM10-2.5 exposures continues to contribute uncertainty to the associations observed.
Studies considered in the 2019 ISA continue to expand the assessment of potential copollutant
confounding of the PMio-2 5-mortality relationship and provide evidence that PM10-2.5
associations generally remain positive in copollutant models, though associations are attenuated
in some instances (U.S. EPA, 2019, section 11.3.4.1, Figure 11-28, Table 11-10). The 2019 ISA
concludes that, overall, the assessment of potential copollutant confounding is limited due to the
lack of information on the correlation between PM10-2.5 and gaseous pollutants and the small
number of locations in which copollutant analyses have been conducted. Associations with
cause-specific mortality provide some support for associations with total (nonaccidental)
mortality, though associations with cause-specific mortality, particularly respiratory mortality,
are more uncertain (i.e., wider confidence intervals) and less consistent (U.S. EPA, 2019, section
11.3.7). The 2019 ISA concludes that the evidence for PMio-25-related cardiovascular and
respiratory effects provides only limited support for the biological plausibility of a relationship
between short-term PM10-2.5 exposure and cardiovascular mortality (U.S. EPA, 2019, section
11.3.7). Based on the overall evidence, the 2019 ISA concludes that, "this body of evidence is
suggestive of, but not sufficient to infer, a causal relationship between short-term PM10-2.5
exposure and total mortality" (U.S. EPA, 2019, p. 11-120).
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4.3.1.2 Cardiovascular Effects
Long-term exposures
In the 2009 ISA, the evidence describing the relationship between long-term exposure to
PMio-2.5 and cardiovascular effects was characterized as "inadequate to infer the presence or
absence of a causal relationship." The limited number of epidemiologic studies reported
contradictory results and experimental evidence demonstrating an effect of PM10-2.5 on the
cardiovascular system was lacking (U.S. EPA, 2019, section 6.4).
The evidence relating long-term PM10-2.5 exposures to cardiovascular mortality remains
limited, with no consistent pattern of associations across studies and, as discussed above,
uncertainty stemming from the use of various approaches to estimate PM10-2.5 concentrations
(U.S. EPA, 2019, Table 6-70). The evidence for associations with cardiovascular morbidity has
grown since the 2009 ISA and, while results across studies are not entirely consistent, some
epidemiologic studies report positive associations with ischemic heart disease (IHD) and
myocardial infarction (MI) (U.S. EPA, 2019, Figure 6-34); stroke (U.S. EPA, 2019, Figure 6-
35); atherosclerosis; venous thromboembolism (VTE); and blood pressure and hypertension
(U.S. EPA, 2019, Section 6.4.6). PM10-2.5 cardiovascular mortality effect estimates are often
attenuated, but remain positive, in models that adjust for PM2.5. For morbidity outcomes,
associations are inconsistent in models that adjust for PM2.5, NO2, and chronic noise pollution
(U.S. EPA, 2019, p. 6-276). The lack of toxicological evidence for long-term PM10-2.5 exposures
represents a substantial data gap (U.S. EPA, 2019, section 6.4.10), resulting in the 2019 ISA
conclusion that "evidence from experimental animal studies is of insufficient quantity to
establish biological plausibility" (U.S. EPA, 2019, p. 6-277). Based largely on the observation of
positive associations in some epidemiologic studies, the 2019 ISA concludes that "evidence is
suggestive of, but not sufficient to infer, a causal relationship between long-term PM10-2.5
exposure and cardiovascular effects" (U.S. EPA, 2019, p. 6-277).
Short-term exposures
The 2009 ISA concluded that the available evidence for short-term PM10-2.5 exposure and
cardiovascular effects was "suggestive of a causal relationship." This conclusion was based on
several epidemiologic studies reporting associations between short-term PM10-2.5 exposure and
cardiovascular effects, including IHD hospitalizations, supraventricular ectopy, and changes in
heart rate variability (HRV). In addition, dust storm events resulting in high concentrations of
crustal material were linked to increases in total cardiovascular disease emergency department
visits and hospital admissions. However, the prior reviews noted the potential for exposure
measurement error and copollutant confounding in these epidemiologic studies. In addition, there
was only limited evidence of cardiovascular effects from a small number of experimental studies
(e.g., animal toxicological studies and controlled human exposure studies) that examined short-
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term PM10-2.5 exposures (U.S. EPA, 2009, U.S. EPA, 2019). In the 2019 ISA, key uncertainties
include the potential for exposure measurement error, copollutant confounding, and limited
evidence of biological plausibility for cardiovascular effects following inhalation exposure (U.S.
EPA, 2019, section 6.3.13).
The evidence for short-term PM10-2.5 exposure and cardiovascular outcomes has expanded
since the 2009 ISA, though important uncertainties remain. The 2019 ISA notes that there are a
small number of epidemiologic studies reporting positive associations between short-term
exposure to PM10-2.5 and cardiovascular-related morbidity outcomes. However, the evidence is
limited to suggest that these associations were biologically plausible, or independent of
copollutant confounding. The 2019 ISA also concludes that it remains unclear how the
approaches used to estimate PM10-2.5 concentrations in epidemiologic studies may impact
exposure measurement error. Taken together, the 2019 ISA concludes that "the evidence is
suggestive of, but not sufficient to infer, a causal relationship between short-term PM10-2.5
exposures and cardiovascular effects" (U.S. EPA, 2019, p.6-254).
4.3.1.3 Respiratory Effects
Short-term exposures
Based on a small number of epidemiologic studies observing associations with some
respiratory effects and limited evidence from experimental studies to support biological
plausibility, the 2009 ISA concluded that the relationship between short-term exposure to PM10-
2.5 and respiratory effects is "suggestive of a causal relationship" (U.S. EPA, 2009).
Epidemiologic findings were consistent for respiratory infection and combined respiratory-
related diseases, but not for COPD. Studies were characterized by overall uncertainty in the
exposure assignment approach and limited information regarding potential copollutant
confounding. Controlled human exposure studies of short-term PM10-2.5 exposures found no lung
function decrements and inconsistent evidence for pulmonary inflammation. Animal
toxicological studies were limited to those using non-inhalation (e.g., intra-tracheal instillation)
routes of PM10-2.5 exposure.
Recent epidemiologic findings consistently link PM10-2.5 exposure to asthma exacerbation
and respiratory mortality, with some evidence that associations remain positive (though
attenuated in some studies of mortality) in copollutant models that include PM2.5 or gaseous
pollutants. Studies provide limited evidence for positive associations with other respiratory
outcomes, including COPD exacerbation, respiratory infection, and combined respiratory-related
diseases (U.S. EPA, 2019, Table 5-36). As noted above for other endpoints, one source of
uncertainty in these epidemiologic studies is the lack of a systematic evaluation of the various
methods used to estimate PM10-2.5 concentrations as well as the resulting uncertainty in the
spatial and temporal variability in PM10-2.5 concentrations compared to PM2.5 (U.S. EPA, 2019,
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sections 2.5.1.2.3 and 3.3.1.1). Taken together, the 2019 ISA concludes that "the collective
evidence is suggestive of, but not sufficient to infer, a causal relationship between short-term
PMio-2.5 exposure and respiratory effects" (U.S. EPA, 2019, p. 5-270).
4.3.1.4 Cancer
Long-term exposures
In the 2012 review, few studies examined cancer following inhalation exposures to PMio-
2.5. Thus, the 2009 ISA determined the evidence was "inadequate to assess the relationship
between long-term PM10-2.5 exposures and cancer" (U.S. EPA, 2009). The scientific information
assessed in the 2019 ISA of long-term PM10-2.5 exposure and cancer remains limited, with a few
recent epidemiologic studies reporting positive, but imprecise, associations with lung cancer
incidence (U.S. EPA, 2019). Additionally, uncertainty remains in these studies with respect to
exposure measurement error due to the use of PM10-2.5 predictions that have not been validated
by monitored PM10-2.5 concentrations (U.S. EPA, 2019, sections 3.3.2.3 and 10.3.4). Relatively
few experimental studies of PM10-2.5 have been conducted, though available studies indicate that
PM10-2.5 exhibits two key characteristics of carcinogens: genotoxicity and oxidative stress. While
limited, such experimental studies provide some evidence of biological plausibility for the
findings in a small number of epidemiologic studies (U.S. EPA, 2019, section 10.3.4). Taken
together, the small number of epidemiologic and experimental studies, along with uncertainty
with respect to exposure measurement error, contribute to the determination in the 2019 ISA that,
"the evidence is suggestive of, but not sufficient to infer, a causal relationship between long-term
PM10-2.5 exposure and cancer" (U.S. EPA, 2019, p. 10-87).
4.3.1.5 Metabolic Effects
Long-term exposures
The 2009 ISA did not make a causality determination for PMio-25-related metabolic
effects. One epidemiologic study is assessed in the 2019 ISA that reports an association between
long-term PM10-2.5 exposure and diabetes incidence, while additional cross-sectional studies
report associations with effects on glucose or insulin homeostasis (U.S. EPA, 2019, section 7.4).
As discussed above for other outcomes, uncertainties with the epidemiologic evidence include
the potential for copollutant confounding and exposure measurement error (U.S. EPA, 2019,
Tables 7-14 and 7-15). The evidence base to support the biological plausibility of metabolic
effects following PM10-2.5 exposures is limited, but a cross-sectional study that investigated
biomarkers of insulin resistance and systemic and peripheral inflammation may support a
pathway leading to type 2 diabetes (U.S. EPA, 2019, sections 7.4.1 and 7.4.3). Based on the
expanded, though still limited evidence base, the 2019 ISA concludes that, "[ojverall, the
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evidence is suggestive of, but not sufficient to infer, a causal relationship between [long]-term
PMio-2.5 exposure and metabolic effects" (U.S. EPA, 2019, p. 7-56).
4.3.1.6 Nervous system effects
Long-term exposures
The 2009 ISA did not make a causality determination for PMio-25-related nervous system
effects. In the 2019 ISA, available epidemiologic studies report associations between PM10-2.5
and impaired cognition and anxiety in adults in longitudinal analyses (U.S. EPA, 2019, Table 8-
25, section 8.4.5). Associations of long-term exposure with neurodevelopmental effects are not
consistently reported in children (U.S. EPA, 2019, sections 8.4.4 and 8.4.5). Uncertainties in
these studies include the potential for copollutant confounding, as no studies examined
copollutants models (U.S. EPA, 2019, section 8.4.5), and for exposure measurement error, given
the use of various model-based subtraction methods to estimate PM10-2.5 concentrations (U.S.
EPA, 2019, Table 8-25). In addition, there is only limited animal toxicological evidence
supporting the biological plausibility of nervous system effects (U.S. EPA, 2019, sections 8.4.1
and 8.4.5). Overall, the 2019 ISA concludes that, "the evidence is suggestive of, but not
sufficient to infer, a causal relationship between long-term PM10-2.5 exposure and nervous system
effects (U.S. EPA, 2019, p. 8-75).
4.3.1.7 Preliminary Conclusions Drawn from the Evidence
With the evidence available in this reconsideration, as assessed in the 2019 ISA (U.S.
EPA, 2019) and summarized in subsections 4.3.1.1 to 4.3.1.6 above, we revisit the policy-
relevant questions posed at the beginning of this section:
• To what extent does the available scientific evidence strengthen, or otherwise alter, our
conclusions from previous reviews regarding health effects attributable to long- or
short-term PM10-2.5 exposures? Have previously identified uncertainties been reduced?
What important uncertainties remain and have new uncertainties been identified?
For each of these categories of effects listed above, the 2019 ISA concludes that the
evidence is "suggestive of, but not sufficient to infer, a causal relationship" (U.S. EPA, 2019).
As summarized in the sections above, key uncertainties in the evidence result from limitations in
the approaches used to estimate ambient PM10-2.5 concentrations in epidemiologic studies, limited
examination of the potential for confounding by co-occurring pollutants, and limited support for
the biological plausibility of the serious effects reported in many epidemiologic studies. The
evidence base for several PM10-2.5-related health effects has expanded over time, broadening our
understanding of the range of health effects linked to PM10-2.5 exposures. This includes additional
evidence for the relationships between long-term exposures and cardiovascular effects, metabolic
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effects, nervous system effects, cancer, and mortality. However, the 2019 ISA identifies a
number of key limitations in the evidence, including the following:
• The use of a variety of methods to estimate PM10-2.5 exposures in epidemiologic studies
and the lack of systematic evaluation of these methods, together with the relatively high
spatial and temporal variability in ambient PM10-2.5 concentrations and the small number
of monitoring sites, results in uncertainty in exposure estimates.
• The limited number of studies that evaluate PM10-2.5 health effect associations in
copollutant models, together with evidence from some studies for attenuation of
associations in such models, results in uncertainty in the independence of PM10-2.5 health
effect associations from co-occurring pollutants.
• The limited number of controlled human exposure and animal toxicology studies of
PM10-2.5 inhalation contribute to uncertainty in the biological plausibility of the PM10-2.5-
related effects reported in epidemiologic studies.
These uncertainties contribute to the conclusions in the 2019 ISA that the evidence for the PM10-
2.5-related health effects discussed in this section for both short- and long-term exposures is
"suggestive of, but not sufficient to infer, a causal relationship."
4.4 PRELIMINARY CONCLUSIONS ON THE ADEQUACY OF THE
CURRENT PRIMARY PM10 STANDARD
This section describes our preliminary conclusions regarding the adequacy of the current
primary PM10 standard. Our approach to reaching preliminary conclusions considers the EPA's
assessment of the scientific evidence for PMio-25-related health effects in the 2019 ISA. We
revisit the overarching question for this chapter:
• Does the available scientific evidence support or call into question the adequacy of
the protection afforded by the current primary PM10 standard against health effects
associated with exposures to PM10-2.5?
As an initial matter, we note that the scope of the updated scientific evaluation of the
health effects evidence for PM10 is based on those health effects categories where the 2019 ISA
concludes a causal relationship exists. Therefore, the draft ISA Supplement does not include an
evaluation of additional studies for PM10-2.5 and the 2019 ISA continues to serve as the scientific
foundation for assessing the adequacy of the primary PM10 standard in this reconsideration of the
2020 final decision (U.S. EPA, 2019, section 1.7; U.S. EPA, 2021). As such, this section
describing our preliminary conclusions regarding the adequacy of the current primary PM10
standard draws heavily from the conclusions in the 2020 PA related to the primary PM10
standard (U.S. EPA, 2020, section 4.4). Lastly, we recognize that a final decision on the primary
PM10 standard in this reconsideration will be largely a public health policy judgement in which
the Administrator weighs the evidence, including its associated uncertainties.
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With respect to the indicator, we note that the evidence continues to support retaining the
PMio indicator given that the varying concentrations of PM10-2.5 permitted in urban versus non-
urban areas under a PM10 standard, based on the varying levels of PM2.5 present (i.e., lower
PM10-2.5 concentrations allowed in urban areas, where PM2.5 concentrations tend to be higher),
appropriately reflect differences in the strength of PM10-2.5 health effects evidence.
Regarding evidence for PMio-2 5-related health effects, we note that the evidence for
several PMio-2 5-related health effects has expanded, particularly for long-term exposures,
broadening our understanding of the range of effects linked to PM10-2.5 exposures. The
epidemiologic studies considered in the 2019 ISA continue to report positive associations with
mortality or morbidity in cities across North America, Europe, and Asia, where PM10-2.5 sources
and composition are expected to vary widely. Such studies provide an important part of the body
of evidence supporting the strengthened causality determinations (and new determinations) for
long-term PM10-2.5 exposures and mortality, cardiovascular effects, metabolic effects, nervous
system effects and cancer (U.S. EPA, 2019). Although most of these studies examined PM10-2.5
health effect associations in urban areas, some studies have also linked mortality and morbidity
with relatively high ambient concentrations of particles of non-urban crustal origin from dust
storm events (U.S. EPA, 2019). Drawing from this evidence, we note continued support for
maintaining a standard that provides some measure of protection against exposures to PM10-2.5,
regardless of location, source of origin, or particle composition (78 FR 3176, January 15, 2013).
Thus, the scientific evidence evaluated for this reconsideration does not call into question the
decision in the 2020 review to maintain a primary standard that provides some measure of public
health protection against PM10-2.5 exposures, regardless of location, source of origin, or particle
composition.
With regard to uncertainties, the 2019 ISA notes that important uncertainties remain in the
evidence base for PMio-25-related health effects. As summarized in section 4.3.1 above, these
include uncertainties in the PM10-2.5 exposure estimates used in epidemiologic studies, in the
independence of PM10-2.5 health effect associations, and in the biological plausibility of the
PMio-2 5-related effects. Thus, the evidence available in the 2019 ISA for consideration in
reaching preliminary conclusions in this reconsideration is subject to the same broad
uncertainties present in the 2012 review (U.S. EPA, 2019). Consistent with the assessment of the
evidence in the 2009 ISA, these uncertainties contribute to the determinations in the 2019 ISA
that the evidence for key PMio-2 5-related health effects is "suggestive of, but not sufficient to
infer" causal relationships (U.S. EPA, 2019). Drawing from this information, we reach the
preliminary conclusion that, as in previous reviews, such uncertainties raise questions regarding
the degree to which additional public health improvements would be achieved by revising the
existing PM10 standard.
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When the above information is taken together, we reach the preliminary conclusion that
the available evidence does not call into question the scientific judgments that informed the
decision in the 2020 review to retain the current primary PMio standard in order to protect
against PM10-2.5 exposures. Specifically, while the evidence supports maintaining a PM10
standard to provide some measure of protection against PM10-2.5 exposures, uncertainties in the
evidence lead to questions regarding the potential public health implications of revising the
existing PM10 standard. Thus, consistent with the approach taken in the previous reviews, we
reach the preliminary conclusion that the evidence does not call into question the adequacy of the
public health protection afforded by the current primary PM10 standard. Furthermore, the
available evidence in this reconsideration of the 2020 final decision supports retaining the
current standard. As such, we have not evaluated alternative standards in this updated PA.
4.5 AREAS FOR FUTURE RESEARCH AND DATA COLLECTION
As discussed above, a number of key uncertainties and limitations in the health evidence
have been considered, consistent with those identified in the 2009 ISA and 2019 ISA. In this
section, we highlight areas for future health-related research and data collection activities from
the 2020 PA to address these uncertainties and limitations in the evidence (U.S. EPA, 2020,
section 4.5). These efforts, if undertaken, could provide important evidence for informing future
reviews of the PM NAAQS. Key areas for future research efforts are summarized below.
• The body of experimental inhalation studies of exposure to PM10-2.5 (e.g., controlled
human exposure and animal toxicology studies) is relatively sparse. While coarse PM
inhalation studies in rats and mice are complicated by substantial differences in dosimetry
(i.e., compared to humans), additional experimental studies of short- or long-term PM10-
2.5 exposures could play an important role in weight of evidence judgments in future
ISAs. Experimental evaluation of effects that are plausibly related to the serious health
outcomes documented in epidemiologic studies could be particularly informative. Such
effects could include changes in markers of cardiovascular or respiratory function, similar
to the effects that have been evaluated following PM2.5 exposures (e.g., vascular function,
blood pressure, heart rate and heart rate variability, markers of potential for coagulation,
systemic and respiratory inflammation, respiratory function, etc.).
• The potential for exposure error is of particular concern for PM10-2.5, given its less
homogeneous atmospheric distribution compared to fine particles (U.S. EPA, 2019, U.S.
EPA, 2009 section 1.2.1.5) and the relatively sparse PM10-2.5 monitoring network.
Therefore, efforts to develop and validate new exposure estimation approaches, or to
further validate existing approaches, would be informative.
• Existing epidemiologic studies have rarely examined associations with PM10-2.5 in
copollutant models, contributing to uncertainty in the degree to which reported health
effect associations are independent of potential confounding variables. Additional
epidemiologic studies that evaluate copollutants models would be informative.
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• Epidemiologic studies use a variety of approaches to measure/estimate PM10-2.5
concentrations, including: (1) difference method with co-located monitors, (2) difference
method with area-wide averages of monitored PM10 and PM2.5, (3) difference method
with area-wide averages of modeled PM10 and PM2.5 or (4) direct measurement of
PM10-2.5 using a dichotomous sampler. It is important that we better understand how these
methods compare to one another, both in terms of absolute estimated concentrations and
in terms of the spatial and temporal correlations in those estimated concentrations
between methods.
• Measurement capabilities and the availability of PM10-2.5 ambient concentration data have
greatly increased since the 2009 ISA (U.S. EPA, 2019, U.S. EPA, 2009, section
2.5.1.1.3). Starting in 2011, PM10-2.5 has been monitored atNCore stations, IMPROVE
stations, and several sites run by State and local agencies. Furthermore, there has been an
increase in the deployment of PM2.5 FEM monitors that also measure PM10-2.5. To date,
epidemiologic studies have used a variety of approaches to measure/estimate PM10-2.5
concentrations but have not used direct measurements from NCore or IMPROVE stations
to evaluate health effects associations with PM10-2.5 exposure. A body of epidemiologic
studies that evaluate health effect associations using monitoring data from these stations
could allow more direct comparisons of results across studies.
• Evaluate and expand the PM10-2.5 network, along with speciation of PM10-2.5 including
multi-elements, major ions, carbon (including carbonate carbon), and bioaerosols.
• Characterize PM10-2.5 in different health-relevant exposure environments (e.g., city center,
suburban, roadside, agricultural, and rural areas) for mass, elements (including potential
toxic species), carbonaceous materials (including selected organic compounds and
carbonate), water-soluble ions, and bioaerosols (including endotoxins, 1,3 beta glucans,
and total protein).
• Additional areas of interest for future research include:
o Further evaluation of the potential for particular PM10-2.5 components, groups of
components, or other particle characteristics to contribute to exposure-related
health effects.
o Research to improve our understanding of concentration-response relationships
and the confidence bounds around these relationships, especially at lower ambient
PM10-2.5 concentrations.
o Identifying novel populations that could be at-risk of PMio-2 5-related health
effects.
o Modeling to estimate PM10-2.5 mass and composition in areas with sparse or less-
than-daily monitoring.
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REFERENCES
U.S. EPA (2009). Integrated Science Assessment for Particulate Matter (Final Report). Office of
Research and Development, National Center for Environmental Assessment. Research
Triangle Park, NC. U.S. EPA. EPA-600/R-08-139F. December 2009. Available at:
https://cfpub.epa. gov/ncea/risk/recordisplav.cfm?deid=216546.
U.S. EPA (2011). Policy Assessment for the Review of the Particulate Matter National Ambient
Air Quality Standards. Office of Air Quality Planning and Standards, Health and
Environmental Impacts Division. Research Triangle Park, NC. U.S. EPA. EPA-452/R-
11-003. April 2011. Available at:
https://nepis. epa.gov/Exe/ZyPURL. cgi?Dockev=P 100AUMY.txt.
U.S. EPA (2016). Integrated review plan for the national ambient air quality standards for
particulate matter. Office of Air Quality Planning and Standards. Research Triangle Park,
NC. U.S. EPA. EPA-452/R-16-005. December 2016. Available at:
https://www3.epa.gov/ttn/naaqs/standards/pm/data/201612-final-integrated-review-
plan.pdf.
U.S. EPA (2019). Integrated Science Assessment (ISA) for Particulate Matter (Final Report).
U.S. Environmental Protection Agency, Office of Research and Development, National
Center for Environmental Assessment. Washington, DC. U.S. EPA. EPA/600/R-19/188.
December 2019. Available at: https://www.epa.gov/naaqs/particulate-matter-pm-
standards-integrated-science-assessments-current-review.
U.S. EPA (2020). Policy Assessment for the Review of the National Ambient Air Quality
Standards for Particulate Matter. Office of Air Quality Planning and Standards, Health
and Environmental Impacts Division. Research Triangle Park, NC. U.S. EPA. EPA-
452/R-20-002. January 2020. Available at: https://www.epa.gov/sites/default/files/202Q-
01/documents/final policy assessment for the review of the pm naaqs 01-2020.pdf.
U.S. EPA (2021). Supplement to the 2019 Integrated Science Assessment for Particulate Matter
(External Review Draft). U.S. Environmental Protection Agency, Office of Research and
Development, Center for Public Health and Environmental Assessment. Washington,
DC. U.S. EPA. EPA/600/R-21/198. December 2019. Available at:
https://www.epa.gov/naaqs/particulate-matter-pm-standards-integrated-science-
assessments-current-review.
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5 RECONSIDERATION OF THE SECONDARY
STANDARDS FOR PM
This chapter presents and evaluates the policy implications of the scientific and technical
information pertaining to this reconsideration of the 2020 final decision on the secondary PM
standards. In so doing, the chapter presents key aspects of the evidence for the welfare effects of
PM documented in the 2019 ISA and draft ISA Supplement, with support from the prior ISA and
AQCDs, and associated public welfare implications, as well as key aspects of quantitative
analyses of recent air quality that is presented in the appendix associated with this chapter. As
described in detail in section 1.4.2, the draft ISA Supplement focuses on a thorough evaluation of
some studies that became available after the literature cutoff date of the 2019 ISA that could
either further inform the adequacy of the current PM NAAQS or address key scientific topics
that have evolved since the literature cutoff date for the 2019 ISA. The selection of the welfare
effects to evaluate within the draft ISA Supplement were based on the causality determinations
reported in the 2019 ISA and the subsequent use of scientific evidence in the 2020 PA.
Specifically, for welfare effects, the focus within the draft ISA Supplement is on visibility
effects. The draft ISA Supplement does not include an evaluation of studies on climate or
materials effects. Together, the scientific evidence and quantitative information provides the
foundation for our evaluation of the scientific information regarding welfare effects of PM in
ambient air and the potential for welfare effects to occur under air quality conditions associated
with the current standards, as well as the associated public welfare implications. Our evaluation
is framed around key policy-relevant questions derived from the questions included in the IRP
(U.S. EPA, 2016) for the review completed in 2020 and also takes into account the conclusions
reached in the review. In this way we identify key policy-relevant considerations and summary
conclusions regarding the public welfare protection provided by the currents standards for the
Administrator's consideration in this reconsideration of the 2020 final decision on the secondary
PM standards.
Within this chapter, background information on the current standards, including key
considerations in reaching the final decision in the 2020 review, is summarized in section 5.1.
The general approach for considering the information in this reconsideration of the 2020 final
decision, including policy-relevant questions identified to frame our policy evaluation, is
summarized in section 5.2. Key aspects of the welfare effects evidence, quantitative information,
and associated public welfare implications and uncertainties are addressed in section 5.3. Section
5.3.1 presents our consideration of the available scientific evidence and quantitative information
for visibility effects, while section 5.3.2 considers the scientific evidence for each of the non-
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visibility welfare effects (climate effects and materials effects) separately.1 Section 5.4
summarizes the key evidence- and quantitative-based considerations identified in our evaluation
and presents associated summary conclusions of this analysis. Key remaining uncertainties and
areas for future research are identified in section 5.5.
5.1 BACKGROUND ON THE CURRENT STANDARDS
The current secondary PM standards were affirmed in 2020 based on the scientific and
technical information available at that time, as well as the Administrator's judgments regarding
the available welfare effects evidence, the appropriate degree of public welfare protection for the
existing standards, and available air quality information on visibility impairment that may be
allowed by such a standard (85 FR 82684, December 18, 2020). The welfare effects evidence
base available in the 2020 review included several decades of extensive research on the visibility
and non-visibility effects (climate effects, materials effects, and ecological effects) of PM,
conducted both in and outside of the U.S., that documents the impacts of PM (U.S. EPA, 2019;
U.S. EPA, 2009; U.S. EPA, 2004b; U.S. EPA, 2004a). With the 2020 decision, the EPA retained
the secondary 24-hour PM2.5 standard, with its level of 35 |ig/m3, the annual PM2.5 standard, with
its level of 15.0 |ig/m3, and the 24-hour PM10 standard, with its level of 150 |ig/m3. The sections
below focus on the key considerations, and the Administrator's conclusions, for climate and
materials effects (section 5.1.1) and visibility effects (section 5.1.2) in the 2020 review.
5.1.1 Non- Visibility Effects
In light of the robust evidence base, the 2019 ISA concluded there to be causal
relationships between PM and climate effects and material effects (U.S. EPA, 2019, sections
13.3.9 and 13.4.2). For climate effects, the 2019 ISA concluded that aerosols2 alter climate
processes directly through radiative forcing and by indirect effects on cloud brightness, changes
1 Other welfare effects of PM, such as ecological effects, are being considered in the separate, on-going review of
the secondary NAAQS for oxides of nitrogen, oxides of sulfur and PM. Accordingly, the public welfare
protection provided by the secondary PM standards against ecological effects such as those related to deposition
of nitrogen- and sulfur-containing compounds in vulnerable ecosystems is being considered in that separate
review. Thus, the Administrator's conclusion in this reconsideration of the 2020 final decision will be focused
only and specifically on the adequacy of public welfare protection provided by the secondary PM standards from
effects related to visibility, climate, and materials.
2 In the climate sciences research community, PM is encompassed by what is typically referred to as aerosol. An
aerosol is defined as a solid or liquid suspended in a gas, but PM refers to the solid or liquid phase of an aerosol.
In this reconsideration of the 2020 final decision on the secondary PM NAAQS the discussion on climate effects
of PM uses the term PM throughout for consistency with the 2019 ISA (U.S. EPA, 2019) as well as to emphasize
that the climate processes altered by aerosols are generally altered by the PM portion of the aerosol. Exceptions to
this practice include the discussion of climate effects in the 2012 review, when aerosol was used when discussing
suspending aerosol particles, and for certain acronyms that are widely used by the climate community that include
the term aerosol (e.g., aerosol optical depth, or AOD).
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in precipitation, and possible changes in cloud lifetimes (U.S. EPA, 2019, section 13.3.9).
Additionally, the major aerosol components with the potential to affect climate processes (i.e.,
black carbon (BC), organic carbon (OC), sulfates, nitrates and mineral dusts) vary in their
reflectivity, forcing efficiencies, and direction of climate forcing (U.S. EPA, 2019, section
13.3.5). For materials effects, the 2019 ISA considered effects associated with the deposition of
PM (i.e., dry and wet deposition), including both physical damage (materials effects) and
aesthetic qualities (soiling effects). The deposition of PM can physically affect materials, adding
to the effects of natural weathering processes, by promoting or accelerating the corrosion of
metals; by degrading paints; and by deteriorating building materials such as stone, concrete, and
marble (U.S. EPA, 2019, section 13.4.2). Additionally, the deposition of PM from ambient air
can reduce the aesthetic appeal of buildings and objects through soiling.
The 2020 decision on the adequacy of the secondary standards for climate and materials
effects was a public welfare policy judgment made by the Administrator, which drew upon the
available scientific evidence for PM-attributable climate and materials effects and recognized
that the evidence did not support a quantitative assessment of exposures and public welfare risks
based on impacts to climate and materials. Noting the strong evidence indicating that aerosols
affect climate, the Administrator further considered what the available information indicated
regarding the adequacy of protection provided by the secondary PM standards. He noted that a
number of uncertainties in the scientific information affected our ability to quantitatively
evaluate the standards in this regard. For example, the 2019 ISA and 2020 PA noted the spatial
and temporal heterogeneity of PM components that contribute to climate forcing, uncertainties in
the measurement of aerosol components, inadequate consideration of aerosol impacts in climate
modeling, insufficient data on local and regional microclimate variations and heterogeneity of
cloud formations (U.S. EPA, 2019, section 13.3.9). In light of these uncertainties and the lack of
sufficient data, the 2020 PA concluded that "the data remain insufficient to conduct quantitative
analyses for PM effects on climate in the current review" (U.S. EPA, 2020, pp. 5-34 to 5-35) and
that there was insufficient information available to base a national ambient air quality standard
on climate impacts associated with ambient air concentrations of PM or its constituents (U.S.
EPA, 2020, section 5.4).
With regard to materials effects, the Administrator noted that the 2020 PA noted that
quantitative relationships were lacking between characteristics of PM and frequency of
repainting and repair of surfaces and that considerable uncertainty exists in the contributions of
co-occurring pollutants to materials damage and soiling processes (U.S. EPA, 2020, p. 5-35).
The 2020 PA concluded that none of the evidence available called into question the adequacy of
the existing secondary PM standards to protect against material effects (U.S. EPA, 2020, section
5.4).
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The 2020 final decision was based on a thorough review in the 2019 ISA of the scientific
information on PM-induced climate and materials effects. The decision also took into account:
(1) assessments in the 2020 PA of the most policy-relevant information in the 2019 ISA
regarding evidence of adverse effects of PM to climate and materials, (2) uncertainties in the
available evidence to inform a quantitative assessment of PM-related climate and materials
effects, (3) CASAC advice and recommendations, and (4) public comments received during the
development of these documents and on the proposal notice.
Consistent with the general approach routinely employed in NAAQS reviews, the initial
consideration in the 2020 review of the secondary standards was with regard to the adequacy of
protection provided by the then-existing standards. Key aspects of that consideration are
summarized in section 5.1.1.1 below.
5.1.1.1 Considerations Regarding Adequacy of the Existing Standards for Non-
Visibility Effects in the 2020 Review
In considering non-visibility welfare effects in the 2020 review, as discussed above, the
Administrator concluded that, while it is important to maintain an appropriate degree of control
of fine and coarse particles to address non-visibility welfare effects, "it is generally appropriate
to retain the existing standards and that there is insufficient information to establish any distinct
secondary PM standards to address climate and materials effects of PM" (85 FR 82744,
December 18, 2020).
With regard to climate, the Administrator recognized that there were a number of
improvements and refinements to climate models since the 2012 review. However, while the
evidence continued to support a causal relationship between PM and climate effects, the
Administrator noted that significant limitations continued to exist related to quantifying the
contributions of direct and indirect effects of PM and PM components on climate forcing (U.S.
EPA, 2020, sections 5.2.2.1.1 and 5.4). He also recognized that the models continued to exhibit
considerable variability in estimates of PM-related climate impacts as regional scales (e.g., -100
km) as compared to simulations at global scales. Therefore, the resulting uncertainty led the
Administrator to conclude that the available scientific information in the 2020 review remained
insufficient to quantify climate impacts associated with particular concentrations of PM in
ambient air (U.S. EPA, 2020, section 5.2.2.2.1) or to evaluate or consider a level of PM air
quality in the U.S. to protect against climate effects and that there was insufficient information
available to base a national ambient standard on climate impacts (85 FR 82744, December 18,
2020).
With regard to materials effects, the Administrator noted that the evidence available in
the 2019 ISA continued to support a causal relationship between materials effects and PM
deposition (U.S. EPA, 2019, section 13.4). He recognized that the deposition of fine and coarse
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particles to materials can lead to physical damage and/or impaired aesthetic qualities. Particles
can contribute to materials damage by adding to the natural weathering processes and by
promoting the corrosion of metals, the degradation of building materials, and the weakening of
material components. While some new information was available in the 2019 ISA, the
information was from studies primarily conducted outside of the U.S. in areas where PM
concentrations in ambient air are typically higher than those observed in the U.S. (U.S. EPA,
2020, section 13.4). Additionally, the information assessed in the 2019 ISA did not support
quantitative analyses of PM-related materials effects in the 2020 review (U.S. EPA, section
5.2.2.2.2). Given the limited amount of information available and its inherent uncertainties and
limitations, the Administrator concluded that he was unable to relate soiling or damage to
specific levels of PM in ambient air or to evaluate or consider a level of air quality to protect
against such materials effects, and that there was insufficient information available to support a
distinct national ambient standard based on materials effects (85 FR 82744, December 18, 2020).
In the 2020 decision, for all of the reasons discussed above and recognizing the CASAC
conclusion that the evidence provided support for retaining the current secondary PM standards,
the Administrator concluded that it was appropriate to retain the existing secondary PM
standards, without revision. His decision was consistent with the CASAC advice related to non-
visibility effects. Specifically, the CASAC agreed with the 2020 PA conclusions that, while these
effects are important, "the available evidence does not call into question the protection afforded
by the current secondary PM standards" and recommended that the secondary standards "should
be retained" (Cox, 2019a, p. 3 of letter). For climate and materials effects, this conclusion
reflected his judgment that, although it remains important to maintain secondary PM2.5 and PM10
standards to provide some degree of control over long- and short-term concentrations of both
fine and coarse particles, there was insufficient information to establish distinct secondary PM
standards to address non-visibility PM-related welfare effects (85 FR 82744, December 18,
2020). Thus, the Administrator concluded that it was appropriate to retain all aspects of the
existing 24-hour PM2.5, annual PM2.5, and 24-hour PM10 secondary standards. With regard to the
secondary annual PM2.5 standard, the Administrator concluded that it was appropriate to retain a
level of 15.0 |ig/m3 while revising only the form of the standard to remove the option for spatial
averaging (85 FR 82744, December 18, 2020).
5.1.2 Visibility Effects
Visibility refers to the visual quality of a human's view with respect to color rendition
and contrast definition. It is the ability to perceive landscape form, colors, and textures. Visibility
involves optical and psychophysical properties involving human perception, judgment, and
interpretation. Light between the observer and the object can be scattered into or out of the sight
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path and absorbed by PM or gases in the sight path. Given the strength of the evidence base, the
2019 ISA concluded that, "the evidence is sufficient to conclude that a causal relationship exists
between PM and visibility impairment" (U.S. EPA, 2019, section 13.2.6). Visibility impairment
is caused by light scattering and absorption by suspended particles and gases, including water
content of aerosols.3 The available evidence in the 2012 review indicated that specific
components of PM have been shown to contribute to visibility impairment. For example, at
sufficiently high relative humidity values, sulfate and nitrate are the PM components that scatter
more light and thus contribute most efficiently to visibility impairment. Elemental carbon (EC)
and OC are also important contributors, especially in the northwestern U.S. where their
contribution to PM2.5 mass is higher. Crustal materials can be significant contributors to visibility
impairment, particularly for remote areas in the arid southwestern U.S. (U.S. EPA, 2009, section
2.5.1; 2019 ISA, section 13.2.4.1).
Visibility impairment can have implications for people's enjoyment of daily activities
and for their overall sense of well-being (U.S. EPA, 2009, section 9.2). Consistent with the
evidence available in the 2012, the 2019 ISA evaluated available visibility preference studies that
were part of the overall body of evidence, and these preference studies were considered in the
2020 PA (U.S. EPA, 2020, pp. 5-15 to 5-17). These preference studies provided information
about the potential public welfare implications of visibility impairment from surveys in which
participants were asked questions about their preferences or the values they placed on various
visibility conditions, as displayed to them in scenic photographs or in images with a range of
known light extinction levels.4
The 2020 decision on the adequacy of the secondary standards with regard to visibility
effects was a public welfare policy judgment made by the Administrator, which drew upon the
available scientific evidence for PM-related visibility effects and on analyses of visibility
impairment, as well as judgments about the appropriate weight to place on the range of
uncertainties inherent in the evidence and analyses. Consistent with the approach in the 2012
review, the analyses utilized a PM2.5 visibility index based on an algorithm, known as the
3 All particles scatter light and, although a larger particle scatters more light than a similarly shaped smaller particle
of the same composition, the light scattered per unit of mass is greatest for particles with diameters from -0.3-1.0
|im (U.S. EPA, 2009, section 2.5.1; 2019 ISA, section 13.2.1). Particles with hygroscopic components (e.g.,
particulate sulfate and nitrate) contribute more to light extinction at higher relative humidity than at lower relative
humidity because they change size in the atmosphere in response to relative humidity.
4 Preference studies were available in four urban areas. Three western preference studies were available, including
one in Denver, Colorado (Ely et al., 1991), one in the lower Fraser River valley near Vancouver, British
Columbia, Canada (Pryor, 1996), and one in Phoenix, Arizona (BBC Research & Consulting, 2003). A pilot focus
group study was also conducted for Washington, DC (Abt Associates, 2001), and a replicate study with 26
participants was also conducted for Washington, DC (Smith and Howell, 2009). More details about these studies
are available in Appendix D.
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IMPROVE algorithm,5 that provides for the estimation of light extinction (bext), in units of Mm"1,
using routinely monitored components of fine (PM2.5) and coarse (PM10-2.5) PM. The quantitative
analyses focused on PM2.5 based on conclusions in the 2019 ISA that fine particles scatter more
light than coarse particles on a per unit mass basis and include sulfates, nitrates, organics, light-
absorbing carbon, and soil (Malm et al., 1994). The 2019 ISA also concluded that hygroscopic
particles like ammonium sulfate, ammonium nitrate, and sea salt increase in size as relative
humidity increases, leading to increased light scattering (U.S. EPA, 2019, section 13.2.3).
Included in this decision were judgments on the weight to place on the visibility preference
studies; on the weight to give associated uncertainties, including those related to variability in
visibility preferences across the studies in different areas of the U.S.; variability in in occurrence
of visibility impairment in areas of the U.S., especially in urban areas; and on the extent to which
such effects in such areas may be considered adverse to public welfare.
The 2020 final decision was based on a thorough review in the 2019 ISA of the scientific
information on PM-related visibility effects. The decision also took into account: (1) assessments
in the 2020 PA of the most policy-relevant information in the 2019 ISA regarding evidence of
adverse effects of PM on visibility; (2) air quality analyses of the PM2.5 visibility index and
design values based on the form and averaging time of the existing standard; (3) CASAC advice
and recommendations; and (4) public comments received during the development of these
documents and on the 2020 proposal notice.
Consistent with the general approach routinely employed in NAAQS reviews, the initial
consideration in the 2020 review of the secondary PM standards was with regard to the adequacy
of the protection provided by the then-existing standards. Key aspects of that consideration are
summarized in section 5.1.2.1 below.
5.1.2.1 Consideration Regarding the Adequacy of the Existing Standards for
Visibility Effects in the 2020 Review
In considering the visibility effects in the 2020 review, the Administrator noted the long-
standing body of evidence for PM-related visibility impairment. This evidence, which is based
on the fundamental relationship between light extinction and PM mass, demonstrated that
ambient PM can impair visibility in both urban and remote areas, and had changed very little
since the 2012 review (U.S. EPA, 2019, section 13.1; U.S. EPA, 2009a, section 9.2.5). The
evidence related to public perception of visibility impairment was from studies from four areas in
North America. These studies provided information to inform our understanding of levels of
5 The algorithm is referred to as the IMPROVE algorithm as it was developed specifically to use monitoring data
generated at IMPROVE network sites and with equipment specifically designed ot support the IMPROVE
program and was evaluated using IMPROVE optical measurements at the subset of monitoring sites that make
those measurements (Malm et al., 1994).
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visibility impairment that the public judged to be "acceptable" (U.S. EPA, 2010b; 85 FR 24131,
April 30, 2020). In considering these public preference studies, the Administrator noted that, as
described in the 2019 ISA, no new visibility studies had been conducted in the U.S. and there
was little newly available information with regard to acceptable levels of visibility impairment in
the U.S. The Administrator recognized that visibility impairment can have implications for
people's enjoyment of daily activities and their overall well-being, and therefore, considered the
degree to which the current secondary standards protect against PM-related visibility
impairment.
Consistent with the 2012 review, in the 2020 review, the Administrator first concluded
that a target level of protection for a secondary PM standard is most appropriately defined in
terms of a visibility index that directly takes into account the factors (i.e., species composition
and relative humidity) that influence the relationship between PM2.5 in ambient air and PM-
related visibility impairment. In defining a target level of protection, the Administrator
considered the specific aspects of such an index, including the appropriate indicator, averaging
time, form and level (78 FR 82742-82744, December 18, 2020).
First, with regard to indicator, the Administrator noted that in the 2012 review, the EPA
used an index based on estimates of light extinction by PM2.5 components calculated using an
adjusted version of the IMPROVE algorithm, which allows the estimation of light extinction
using routinely monitored components of PM2.5 and PM10-2.5, along with estimates of relative
humidity. The Administrator recognized that, while there have been some revisions to the
IMPROVE algorithm since the time of the 2012 review, our fundamental understanding of the
relationship between PM in ambient air and light extinction had changed little and the various
IMPROVE algorithms appropriately reflected this relationship across the U.S. In the absence of
a monitoring network for direct measurement of light extinction, he concluded that calculated
light extinction indicator that utilizes the IMPROVE algorithms continued to provide a
reasonable basis for defining a target level of protection against PM-related visibility impairment
(78 FR 82742-82744, December 18, 2020).
In further defining the characteristics of a visibility index, the Administrator next
considered the appropriate averaging time, form, and level of the index. Given the available
scientific information in the review, and in considering the CASAC's advice and public
comments, the Administrator concluded that, consistent with the decision in the 2012 review, a
visibility index with a 24-hour averaging time and a form based on the 3-year average of annual
90th percentile values remained reasonable. With regard to the averaging time and form of such
an index, the Administrator noted analyses conducted in the last review that demonstrated
relatively strong correlations between 24-hour and subdaily (i.e., 4-hour average) PM2.5 light
extinction (78 FR 3226, January 15, 2013), indicating that a 24-hour averaging time is an
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appropriate surrogate for the sub-daily time periods of the perception of PM-related visibility
impairment and the relevant exposure periods for segments of the viewing public. This decision
in the 2020 review also recognized that a 24-hour averaging time may be less influenced by
atypical conditions and/or atypical instrument performance (78 FR 3226, January 15, 2013). The
Administrator recognized that there was no new information to support updated analyses of this
nature, and therefore, he believed these analyses continued to provide support for consideration
of a 24-hour averaging time for a visibility index in this review. With regard to the statistical
form of the index, the Administrator noted that, consistent with the 2012 review: (1) A multi-
year percentile form offers greater stability from the occasional effect of interannual
meteorological variability (78 FR 3198, January 15, 2013; U.S. EPA, 2011, p. 4-58); (2) a 90th
percentile represents the median of the distribution of the 20 percent worst visibility days, which
are targeted in Federal Class I areas by the Regional Haze Program; and (3) public preference
studies did not provide information to identify a different target than that identified for Federal
Class I areas (U.S. EPA, 2011, p. 4-59). Therefore, the Administrator judged that a visibility
index based on estimates of light extinction, with a 24-hour averaging time and a 90th percentile
form, averaged over three years, remained appropriate (78 FR 82742-82744, December 18,
2020).
With regard to the level of a visibility index, consistent with the 2012 review, the
Administrator judged that it was appropriate to establish a target level of protection of 30
deciviews (dv),6 7 reflecting the upper end of the range of visibility impairment judged to be
acceptable by at least 50% of study participants in the available public preference studies (78 FR
3226, January 15, 2013). As described above, the 2011 PA identified a range of levels from 20 to
30 dv based on the responses in the public preference studies available at that time. At the time
of the 2012 review, the Administrator noted a number of uncertainties and limitations in public
preference studies, including the small number of stated preference studies available, the
relatively small number of study participants, the extent to which the study participants may not
be representative of the broader study area population in some of the studies, and the variations
in the specific materials and methods used in each study. In considering the available preference
studies, with their inherent uncertainties and limitations, the prior Administrator concluded that
the substantial degree of variability and uncertainty in the public preference studies should be
reflected in a target level of protection based on the upper end of the range of candidate
protection levels (CPLs).
6 Deciview (dv) refers to a scale for characterizing visibility that is defined directly in terms of light extinction. The
deciview scale is frequently used in the scientific and regulatory literature on visibility.
7 For comparison, 20 dv, 25 dv, and 30 dv are equivalent to 64, 112, and 191 megameters (Mm1), respectively.
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Given that there were no new preference studies available in 2020 review, the
Administrator's judgments were based on the same studies, with the same range of levels,
available in the 2012 review. As identified in the 2020 PA (U.S. EPA, 2020, section 5.5), there
were a number of limitations and uncertainties associated with these studies, including the
following:
• Available studies may not represent the full range of preferences for visibility in the U.S.
population, particularly given the potential variability in preferences based on the
conditions commonly encountered and the scenes being viewed.
• Available preference studies were conducted 15 to 30 years ago and may not accurately
represent the current day preferences of people in the U.S.
• The variety of methods used in the preference studies may potentially influence the
responses as to what level of impairment is deemed acceptable.
• Factors that are not captured in the methods of the preference studies, such as the time of
day when light extinction is the greatest or the frequency of impairment episodes, may
influence people's judgment on acceptable visibility (U.S. EPA, 2020, section 5.2.1.1).
Therefore, in considering the scientific information, with its uncertainties and limitations,
as well as public comments on the level of the target level of protection against visibility
impairment, the Administrator concluded that it is appropriate to again use a level of 30 dv for
the visibility index (78 FR 82742-82744, December 18, 2020).
Having concluded that the protection provided by a standard defined in terms of a PM2.5
visibility index, with a 24-hour averaging time, and a 90th percentile form, averaged over 3 years,
set at a level of 30 dv, was requisite to protect public welfare with regard to visual air quality, the
Administrator next considered the degree of protection from visibility impairment afforded by
the existing suite of secondary PM standards.
In this context, the Administrator considered the updated analyses of visibility
impairment presented in the 2020 PA (U.S. EPA, 2020, section 5.2.1.2), which reflected a
number of improvements since the 2012 review. Specifically, the updated analyses examined
multiple versions of the IMPROVE equation, including the version incorporating revisions since
the time of the 2012 review. These updated analyses provided a further understanding of how
variation in the inputs to the algorithms affect the estimates of light extinction (U.S. EPA, 2020,
Appendix D). Additionally, for a subset of monitoring sites with available PM10-2.5 data, the
updated analyses better characterized the influence of coarse PM on light extinction than in the
2012 review (U.S. EPA, 2020, section 5.2.1.2).
The results of the updated analyses in the 2020 PA were consistent with those from the
2012 review. Regardless of which version of the IMPROVE equation was used, the analyses
demonstrated that, based on 2015-2017 data, the 3-year visibility metric was at or below about
30 dv in all areas meeting the current 24-hour PM2.5 standard, and below 25 dv in most of those
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areas. In locations with available PM10-2.5 monitoring, which met both the current 24-hour
secondary PM2.5 and PM10 standards, 3-year visibility index metrics were at or below 30 dv
regardless of whether the coarse fraction was included as an input to the algorithm for estimating
light extinction (U.S. EPA, 2020, section 5.2.1.2). While the inclusion of the coarse fraction had
a relatively modest impact on the estimates of light extinction, the Administrator recognized the
continued importance of the PM10 standard given the potential for larger impacts on light
extinction in areas with higher coarse particle concentrations, which were not included in the
analyses in the 2020 PA due to a lack of available data (U.S. EPA, 2019, section 13.2.4.1; U.S.
EPA, 2020, section 5.2.1.2). He noted that the air quality analyses showed that all areas meeting
the existing 24-hour PM2.5 standard, with its level of 35 |ig/m3, had visual air quality at least as
good as 30 dv, based on the visibility index. Thus, the secondary 24-hour PM2.5 standard would
likely be controlling relative to a 24-hour visibility index set at a level of 30 dv. Additionally,
areas would be unlikely to exceed the target level of protection for visibility of 30 dv without
also exceeding the existing secondary 24-hour standard. Thus, the Administrator judged that the
24-hour PM2.5 standard provided sufficient protection in all areas against the effects of visibility
impairment, i.e., that the existing 24-hour PM2.5 standard would provide at least the target level
of protection for visual air quality of 30 dv which he judged appropriate (78 FR 82742-82744,
December 18, 2020).
5.2 GENERAL APPROACH AND KEY ISSUES IN THIS
RECONSIDERATION OF THE 2020 FINAL DECISION
This reconsideration of the 2020 final decision on the secondary PM standards is most
fundamentally based on using the Agency's assessment of the scientific evidence and associated
quantitative analyses to inform the Administrator's judgments regarding secondary standards that
are requisite to protect public welfare from known or anticipated adverse effects. This draft PA is
intended to help bridge the gap between the scientific evidence and information assessed in the
2019 ISA and draft ISA Supplement and the judgments required of the Administrator in
determining whether it is appropriate to retain or revised the secondary PM NAAQS. The
approach planned for this reconsideration of the 2020 final decision on the secondary PM
standards will build on previous reviews, including the substantial assessments and evaluations
performed in those reviews, and taking into account scientific information and air quality data to
inform our understanding of the key policy-relevant issues in this reconsideration.
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The evaluations in this draft PA, of the scientific assessments in the 2019 ISA and draft
ISA Supplement8 augmented by quantitative air quality analyses, are intended to inform the
Administrator's public welfare policy judgments and conclusions, including his decisions as to
whether to retain or revise these standards. The draft PA considers the potential implications of
various aspects of the scientific evidence, the air quality information, and the associated
uncertainties and limitations. In so doing, the approach for this draft PA involves evaluating the
scientific and technical information to address a series of key policy-relevant questions using
both evidence- and quantitative-based considerations. Together, consideration of the full set of
evidence and information in this reconsideration will inform the answer to the following initial
overarching question for the reconsideration:
• Do the scientific evidence and quantitative information support or call into question
the adequacy of the protection afforded by the current secondary PM standards?
In reflecting on this question in the remaining sections of this chapter, we consider the
body of scientific evidence assessed in the 2019 ISA and draft ISA Supplement and considered
as basis for developing or interpreting air quality analyses, including whether it supports or calls
into question the scientific conclusions reached in the 2020 review regarding welfare effects
related to exposure to PM in ambient air. Information in this reconsideration of the 2020 final
decision that may be informative to public policy judgments on the significance or adversity of
key effects on the public welfare is also considered. Additionally, the quantitative information,
whether newly developed in this reconsideration or predominantly developed in the past and
interpreted in light of current information, is considered, including with regard to the extent to
which it may continue to support judgments made in previous reviews.
The approach to reaching conclusions on the current secondary PM standards and, as
appropriate, on potential alternative standards, including consideration of policy-relevant
questions that frame the current reconsideration, is illustrated in Figure 5-1.
8 As noted above and described in detail in section 1.4.2, the draft ISA Supplement focuses on a thorough evaluation
of some studies that became available after the literature cutoff date of the 2019 ISA that could either further
inform the adequacy of the current PM NAAQS or address key scientific topics that have evolved since the
literature cutoff date for the 2019 ISA. The selection of the welfare effects to evaluate within the draft ISA
Supplement were based on the causality determinations reported in the 2019 ISA and the subsequent use of
scientific evidence in the 2020 PA. Specifically, for welfare effects, the focus within the draft ISA Supplement is
on visibility effects. The draft ISA Supplement does not include an evaluation of studies on climate or materials
effects.
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Does information "all
into question acfequan
of the ftel+are protection
provided b\ tne current
PM standards'?
, Consider retaining
— «~! current secondary
N0 1, PM standard^)
\
*fE5
Indicator
>Supportfor PM;s and/or PM-;*?
> Support for indicator's) based
on other size fraction. PM
components light extinction, etc ?
Averaging Time
>Support for current 24-hour
and,.or annual?
>SuDDortforsub-daih
seasonal or other averaging
timet sr>
Form
>Support for retaining existing
forms'?
>Support for alternative form based
on daylight hours or other metric?
Level
> Support for PM-attributaWe visibility, climate, or materials impacts at PM concentrations corresponding
to various potential standard levels?
> Support from quantitative exposure antfor risk assessments for public welfare improvements with
various potential standard levels''
>l'ncertainties and limitations in the extent to which ret, ised standard levels could result in public
welfare improvements, compared to existing standards
i
Identify range of potential alternative secondary standards for consideration
1 : 1Z :
2 Figure 5-1. Overview of general approach for the reconsideration of the 2020 final decision
3 on the secondary PM standards.
4
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The Agency's approach in its reconsideration of the 2020 final decision on the secondary
standards is consistent with the requirements of the provisions of the CAA related to the review
of NAAQS and with how the EPA and the courts have historically interpreted the CAA. As
discussed in section 2.1 above, these provisions require the Administrator to establish secondary
standards that, in the Administrator's judgment, are requisite (i.e., neither more nor less stringent
than necessary) to protect the public welfare from known or anticipated adverse effects
associated with the presence of the pollutant in ambient air. In so doing, the Administrator
considers advice from the CASAC and public comment.
Consistent with the Agency's approach across all NAAQS reviews, the approach of this
draft PA to informing the Administrator's judgments in this reconsideration of the 2020 final
decision on the secondary PM standards is based on a recognition that the evidence generally
reflects continuums that include ambient air exposures for which scientists generally agree that
effects are likely to occur through lower levels at which the likelihood and magnitude of
response become increasingly uncertain. The CAA does not require that standards be set at a
zero-risk level, but rather at a level that reduces risk sufficiently so as to protect the public
welfare from known or anticipated adverse effects. The Agency's decisions on the adequacy of
the current secondary standards and, as appropriate, on any potential alternative standards
considered in a review, are largely public welfare policy judgments made by the Administrator.
The four basic elements of the NAAQS (i.e., indicator, averaging time, form, and level) are
considered collectively in evaluating the protection afforded by the current standard, or any
alternative standards considered. Thus, the Administrator's final decisions in such reviews draw
upon the scientific information and analyses about welfare effects, environmental exposures and
risks, and associated welfare significance, as well as judgments about how to consider the range
and magnitude of uncertainties that are inherent in the scientific evidence and analyses.
5.3 WELFARE EFFECTS AND QUANTITATIVE INFORMATION
In considering the evidence for welfare effects attributable to PM presented in the 2019
ISA and the draft ISA Supplement, this section poses the following policy-relevant questions:
• Does the scientific evidence and quantitative information support or call into
question the adequacy of the welfare protection afforded by the current secondary
PM standards?
In answering this question, we have posed a series of more specific questions to aid in
considering the scientific evidence and quantitative information, as discussed below. In
considering the scientific and technical information, we reflect upon both the information in
previous reviews and information that is assessed and presented in the 2019 ISA (U.S. EPA,
2019) and in the draft ISA Supplement (U.S. EPA, 2021), focusing on welfare effects for which
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the evidence supports either a "causal" or a "likely to be causal" relationship as described in the
Preamble to the ISA (U.S. EPA, 2015). Table 5-1 lists such causality determinations from the
2019 ISA for welfare effects. As in previous reviews, the evidence is sufficient to support a
causal relationship between PM and visibility effects (section 5.3.1), climate effects (section
5.3.2) and materials effects (section 5.3.2).
While the 2019 ISA provides the broad scientific foundation for this reconsideration, we
recognized that additional literature has become available since the cutoff date of the 2019 ISA
that expands the body of evidence related to visibility effects that can inform the Administrator's
judgments on the adequacy of the current secondary PM standards. As such, the draft ISA
Supplement builds on the information in the 2019 ISA with a target identification and evaluation
of new scientific information regarding visibility effects (U.S. EPA, 2021, section 1.2). As
described in chapter 1, the selection of the welfare effects to evaluate within the draft ISA
Supplement were based on the causality determinations reported in the 2019 ISA and the
subsequent use of scientific evidence in the 2020 PA. The draft ISA Supplement focuses on U.S.
and Canadian studies that provide new information on public preference for visibility impairment
and/or developed new methodologies or conducted quantitative analyses of light extinction (U.S.
EPA, 2021, section 1.2). Such studies of visibility effects and quantitative relationships between
visibility impairment and PM in ambient air were considered to be of greatest utility in informing
the Administrator's conclusions on the adequacy of the current secondary PM standards. The
visibility effects evidence presented within the 2019 ISA, along with the targeted identification
and evaluation of new scientific information in the draft ISA Supplement, provides the scientific
basis for the reconsideration of the 2020 final decision on the primary PM2.5 standards. For
climate and materials effects, the 2020 PA concluded that there were substantial uncertainties
associated with the quantitative relationships with PM concentrations and the concentration
patterns that limited the ability quantitatively assess the public welfare protection provided by
the standards from these effects. Therefore, for climate and materials effects, we draw heavily
from the 2020 PA in our evaluation of the information related to these effects and in reaching
preliminary conclusions in this draft PA.
Table 5-1. Key causality determinations for PM-related welfare effects.
Effect
2009 PM ISA
2019 PM ISA
Visibility effects
Causal
Causal
Climate effects
Causal
Causal
Materials effects
Causal
Causal
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5.3.1 Visibility Effects
In the sections below, we consider the nature of visibility-related effects attributable to
PM (section 5.3.1.1) and the quantitative information (section 5.3.1.2).
5.3.1.1 Nature of Effects
In considering the evidence of visibility welfare effects attributable to PM as presented in
the 2019 ISA and the draft ISA Supplement, this section addresses the following policy-relevant
question:
• Does the available scientific evidence alter our conclusions from the 2020 review
regarding the nature of visibility effects attributable to PM in ambient air?
Visibility refers to the visual quality of a human's view with respect to color rendition
and contrast definition. It is the ability to perceive landscape form, colors, and textures. Visibility
involves optical and psychophysical properties involving human perception, judgment, and
interpretation. Light between the observer and the object can be scattered into or out of the sight
path and absorbed by PM or gases in the sight path. As recognized above, the conclusion of the
2019 ISA that "the evidence is sufficient to conclude that a causal relationship exists between
PM and visibility impairment" is consistent with conclusions of causality in the 2012 review
(U.S. EPA, 2019, section 13.2.6). These conclusions are based on strong and consistent evidence
that ambient PM can impair visibility in both urban and remote areas (U.S. EPA, 2009, section
9.2.5).
These subsequent questions consider the characterization and quantification of light
extinction and preferences associated with varying degrees of visibility impairment.
• To what extent is information available that changes or enhances our understanding
of the physics of light extinction and/or its quantification (e.g., through light
extinction or other monitoring methods or through algorithms such as IMPROVE)?
Our understanding of the relationship between light extinction and PM mass has changed
little since the 2009 ISA (U.S. EPA, 2009). The combined effect of light scattering and
absorption by particles and gases is characterized as light extinction, i.e., the fraction of light that
is scattered or absorbed per unit of distance in the atmosphere. Light extinction is measured in
units of 1/distance, which is often expressed in the technical literature as visibility per
megameter (abbreviated Mm"1). Higher values of light extinction (usually given in terms of Mm"1
or dv) correspond to lower visibility. When PM is present in the air, its contribution to light
extinction is typically much greater than that of gases (U.S. EPA, 2019, section 13.2.1). The
impact of PM on light scattering depends on particle size and composition, as well as relative
humidity. All particles scatter light, as described by the Mie theory, which relates light scattering
to particle size, shape and index of refraction (U.S. EPA, 2019, section 13.2.3; Van de Hulst,
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1981; Mie, 1908). Fine particles scatter more light than coarse particles on a per unit mass basis
and include sulfates, nitrates, organics, light-absorbing carbon, and soil (Malm et al., 1994).
Hygroscopic particles like ammonium sulfate, ammonium nitrate, and sea salt increase in size as
relative humidity increases, leading to increased light scattering (U.S. EPA, 2019, section
13.2.3).
Direct measurements of PM light extinction, scattering, and absorption are considered
more accurate for quantifying visibility impairment than PM mass-based estimates because they
do not depend on assumptions about particle characteristics (e.g., size, shape, density, component
mixture, etc.). Measurements of light extinction can be made with high time resolution, allowing
for characterization of subdaily temporal patterns of visibility impairment. Measurement
methods include transmissometers for measurement of light extinction and the determination of
visual range and integrating nephelometers for measurement of light scattering, as well as
teleradiometers and telephotometers, and photography and photographic modeling (U.S. EPA,
2009; U.S. EPA, 2004b). While some recent research confirms and adds to the body of
knowledge regarding direct measurements as is described in the 2019 ISA and draft ISA
Supplement, no major new developments have been made with these measurement methods
since prior reviews (U.S. EPA, 2019, section 13.2.2.2; U.S. EPA, 2021, section 4.2).
A theoretical relationship between light extinction and PM characteristics has been
derived from Mie theory (U.S. EPA, 2019, Equation 13-5) and can be used to estimate light
extinction by combining mass scattering efficiencies of particles with particle concentrations
(U.S. EPA, 2019, section 13.2.3; U.S. EPA, 2009, sections 9.2.2.2 and 9.2.3.1). However,
routine ambient air monitoring rarely includes measurements of particle size and composition
information with sufficient detail for these calculations. Accordingly, a much simpler algorithm
has been developed to make estimating light extinction more practical.
The algorithm, known as the IMPROVE algorithm,9 estimates light extinction (bext,
measured in units of Mm"1), using routinely monitored components of fine (PM2.5) and coarse
(PM10-2.5) PM. Relative humidity data are also needed to estimate the contribution by liquid
water that is in solution with the hygroscopic components of PM. To estimate each component's
contribution to light extinction, their concentrations are multiplied by extinction coefficients and
are additionally multiplied by a water growth factor that accounts for their expansion with
moisture. Both the extinction efficiency coefficients and water growth factors of the IMPROVE
algorithm have been developed by a combination of empirical assessment and theoretical
9 The algorithm is referred to as the IMPROVE algorithm as it was developed specifically to use monitoring data
generated at IMPROVE network sites and with equipment specifically designed ot support the IMPROVE
program and was evaluated using IMPROVE optical measurements at the subset of monitoring sites that make
those measurements (Malm et al., 1994).
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calculation using particle size distributions associated with each of the major aerosol components
(U.S. EPA, 2019, section 13.2.3.1, section 13.2.3.3).
The original IMPROVE algorithm (Equation D-l in Appendix D), so referenced here to
distinguish it from subsequent variations developed later, was found to underestimate the highest
light scattering values and overestimate the lowest values at IMPROVE monitors throughout the
U.S. (Malm and Hand, 2007; Ryan et al., 2005; Lowenthal and Kumar, 2004) and at sites in
China (U.S. EPA, 2019, section 13.2.3.3). To resolve these biases, a revised IMPROVE equation,
shown in Equation D-2 in Appendix D, was developed (Pitchford et al., 2007) that divides PM
components into smaller and larger sizes of particles in PM2.5, with separate mass scattering
efficiencies and hygroscopic growth functions for each size category. The revised IMPROVE
equation was described in detail in the 2009 ISA (U.S. EPA, 2009) and at that time, it both
reduced bias at the lowest and highest scattering values and improved the accuracy of the
calculated light bext. However, poorer precision was observed with the revised IMPROVE
equation compared to the original IMPROVE equation (U.S. EPA, 2009).10 Recent research
suggests that changes in PM composition in ambient air can impact the accuracy of estimating
light extinction using the IMPROVE algorithms (U.S. EPA, 2021, section 4.2.2). As an example,
a study by Prenni et al. (2019) found that the relationship between directly measured light
scattering and estimated light scattering using the revised IMPROVE equation has changed over
time in recent years. In particular, Prenni et al. (2019) compared estimated light extinction using
the revised IMPROVE equation with measured light extinction using nephelometers from 2001-
2016 and found that the revised IMPROVE equation underestimated light extinction at many
sites, especially for locations that experienced large decreases in sulfate and organic mass
concentrations. They further found that the underestimation results from splitting the components
into smaller and larger sizes of particles, with too much of the mass being allocated to the
smaller size fraction which has a lower dry mass scattering efficiency (U.S. EPA, 2021, section
4.2.2; Prenni et al., 2019).
Since the 2012 review, Lowenthal and Kumar (2016) have tested and evaluated a number
of modifications to the revised IMPROVE equation based on evaluations of monitoring data
from remote IMPROVE sites. In these locations, they observed that the multiplier to estimate the
concentration of organic matter, [OM], from the concentration of organic carbon, [OC], was
10 In the most recent IMPROVE report, a combination of the original and revised IMPROVE equations (the
modified original IMPROVE equation) was used (Hand et al., 2011). This equation uses the sea salt term of the
revised equation but does not subdivide the components into two size classes. Further, it uses a factor of 1.8 to
estimate organic matter from organic carbon concentrations and also replaces the constant value of 10 Mm1 used
for Rayleigh scattering in the original and revised equations with a site-specific term based on elevation and mean
temperature.
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closer to 2.1 than the value of 1.8 used in the revised IMPROVE equation.11 They also observed
that water soluble organic matter absorbs water as a function of relative humidity, which is not
accounted for in either the original or revised IMPROVE equations and was therefore
underestimated in these equations. They further suggested that light scattering by sulfate was
overestimated because the assumption that all sulfate is fully neutralized ammonium sulfate is
not always true (U.S. EPA, 2019, section 13.2.3.3). Modifications based on these points are
reflected in Equation D-3 in Appendix D.
In summary, rather than altering our understanding from previous reviews, we continue
to recognize that direct measurements are better at characterizing light extinction than estimating
light extinction with an algorithm. However, in the absence of advances in the monitoring
methods and/or network for directly measuring light extinction, the use of the IMPROVE
equation for estimating light extinction continues to be supported by the evidence, with some
refinements to the inputs of the IMPROVE equation. Accordingly, as in previous reviews, this
reconsideration focuses on calculated light extinction when quantifying visibility impairment
resulting from recent concentrations of PM in ambient air.
• What does the information indicate with regard to factors that influence light
extinction and visibility, as well as variation in these factors and resulting light
extinction across the U.S.?
The 2019 ISA provides a comprehensive discussion of the spatial and temporal patterns
of PM2.5 composition and its contribution to light extinction from IMPROVE and CSN
monitoring sites, which are mostly rural and urban, respectively.12 The data from these sites for
the periods of 2005-2008 and 2011-2014 were used in the 2019 ISA to identify differences in
species contributing to light extinction in urban and rural areas by region and season. This is an
expansion over the analysis in the 2009 ISA, in that the measurements at that time were
primarily based measurements from monitors located in rural areas and at remote sites (U.S.
EPA, 2019, section 13.2.4.1, Figures 13-1 through 13-14).
Focusing on the more recent time period of 2011-2014, some major differences in
estimated light extinction are apparent among regions of the U.S. Annual average calculated b e%t
was considerably greater in the East and Midwest than in the Southwest. Based on IMPROVE
11 In areas near sources, PM is often less oxygenated, and therefore, in these locations, much of the organic PM mass
is present as OC (Jimenez et al., 2009). In areas further away from PM sources, organic PM mass is often more
oxygenated as a result of photochemical activity and interactions with other PM and gaseous components in the
atmosphere (Jimenez et al., 2009). Under these conditions, the multiplier to convert OC to OM may be higher
than in locations with less aged organic PM.
12 Monitors were grouped into 28 IMPROVE regions and 31 CSN regions based on site location and PM
concentrations for major species. For comparison purposes, and where possible, CSN regions were defined
similarly to those forthe IMPROVE network (Hand et al., 2011; U.S. EPA, 2019, section 13.2.4.1).
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data, annual average bext was greater than 40 Mm"1 in the Southeast, East Coast, Mid-South,
Central Great Plains, and Appalachian regions, with the highest annual average bext (greater than
50 Mm"1) in the Ohio River Valley,13 while annual average bext was below 40 Mm"1 for all
Western IMPROVE regions. Annual average bext values were also generally higher in the East
than the West based on CSN data, although the highest annual average bext was in the
Sacramento/San Joaquin Valley and Los Angeles areas (U.S. EPA, 2019, section 13.2.4.1, Figure
13-1, Figure 13-3, Figure 13-5).
Consistent with the analysis in the 2019 ISA, a recent study analyzed national and
regional trends in light extinction based on reconstructed total light extinction estimated from
IMPROVE data using 5-year aggregates of annual mean b ext (Mm"1) for 2000-2004 and 2014-
2018 (U.S. EPA, 2021, section 4.2.2). Hand et al. (2020) found that, for 2000-2004, the highest
levels of bext occurred in the Appalachian Mountains and Ohio River valley (-100 Mm"1 or
greater), with decreasing values in the central U.S (-70 Mm"1). Values of bext in the East
significantly decreased over time, reduced to -50 Mm"1 in the 2014-2018 time period, likely
corresponding to decreases in sulfate concentrations over time. However, for 2014-2018, the
highest values of bext were in the central U.S. (50-60 Mm"1), which is an area with high
agricultural activity and nitrate and ammonium concentrations. During both time periods, lower
bext occurred in the western U.S. (20-30 Mm"1), with improvements in bext closer to the West
Coast in 2014-2018 compared to 2004-2008.
Moreover, Hand et al. (2020) also explored changes in bext over time as relative trends (%
yr"1) and found spatial variability in long-term and short-term trends. Generally, similar
magnitudes and spatial variability were found for both long-term and short-term trends, with the
strongest reductions in bext across the eastern U.S. (-4% yr"1 or greater) and along the West Coast,
particularly in Southern California. There was less improvement in the Intermountain West14 (-
2% yr"1), although air quality in these areas have been increasingly impacted by wildfire activity
and biomass smoke in recent years (Hand et al., 2020). Decreased trends also occurred across the
Southwest, but at a lower rate than in the Eastern U.S. Over the entire continental U.S., on
average, bext decreased at a rate of -2.8% yr"1 from 2002 to 2018 and -18% yr"1 from 1992 to
2018, with much of the improvement occurring in the eastern U.S. (U.S. EPA, 2021, section
4.2.2; Hand et al., 2020).
Components of PM2.5 contributing to light extinction vary regionally. For example, in the
analysis completed in the 2019 ISA, in the Eastern regions, ammonium sulfate accounted for
approximately 35 to 60% of the annual average bext, with the greatest contributions typically
13 A bext value of 40 Mm1 corresponds to a visual range of about 100 km.
14 The Intermountain West area includes Idaho, Montana, northern Wyoming, and portions of northern California.
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occurring in the summer (U.S. EPA, 2019, section 13.2.4.1). The second greatest contribution to
light extinction came from particulate organic matter (POM), ranging from about 20 to 30% of
annual average bext with less seasonal variation on average than ammonium sulfate. Ammonium
nitrate also contributed approximately 10% to 35% of annual average bext, with much higher
concentrations in the winter than in the summer (U.S. EPA, 2019, section 13.2.4.1). In the
Northwest, POM was the largest contributor to annual average bext, up to 70%, in most urban and
rural regions with the greatest contributions in the fall. This seasonal contribution of POM may
be related to wildfires. A few exceptions included Boise and sites in North Dakota, where
ammonium nitrate was the greatest contributor, and sites in the Alaska IMPROVE region, where
ammonium sulfate was the greatest contributor (U.S. EPA, 2019, section 13.2.4.1). In the
Southwest, based on IMPROVE data, ammonium sulfate or POM were generally the greatest
contributors to annual average bext, with nearly equivalent contributions in several regions. Based
on CSN data, ammonium nitrate was often the greatest contributor, with especially high bext
contributions in the winter. While PM10-2.5 mass scattering was relatively small in the eastern and
northwestern U.S., in the Southwest, PM10-2.5 mass scattering contributed to more than 20% of
light extinction (U.S. EPA, 2019, section 13.2.4.1).
Differences also exist between the urban CSN and the mainly rural IMPROVE data.
Light extinction is generally higher in CSN regions than the geographically corresponding
IMPROVE regions. Annual average bext was greater than 50 Mm"1 in 11 CSN regions, compared
to only one IMPROVE region, and was greater than 20 Mm"1 in all CSN regions, compared to
just over half of the IMPROVE regions. Light absorbing carbon was the greatest contributor to
light extinction in several Western CSN regions but was not a large contributor in any of the
IMPROVE regions (U.S. EPA, 2019, Figure 13-11). Ammonium nitrate also accounted for more
light extinction in the CSN regions, while it was only a top contributor to bext in one IMPROVE
region (U.S. EPA, 2019, section 13.2.4.1).
From the 2005-2008 time period to the 2011-2014 time period, the annual average bext in
most CSN regions in the Eastern U.S. decreased by more than 20 Mm"1. This corresponds to an
improvement in average visual range in most Eastern U.S. regions of more than 6 Mm"1 (or 15
km) from 2005-2008 to 2011-2014. Additionally, the contribution of ammonium sulfate to light
extinction has also changed over this period. Due to decreased atmospheric sulfate
concentrations, the impact on visibility impairment is evident with a smaller fraction of the total
bext accounted for by ammonium sulfate in 2011-2014 compared to 2005-2008 (U.S. EPA, 2019,
section 13.2.4.1).
Additionally, Hand et al. (2020) observed that changes in PM composition in ambient air
also affect trends for annual, regional mean speciated bext at IMPROVE monitoring locations
across the U.S. In the East, annual mean total bext decreased by -4.3% yr"1 during from 2002 to
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2018, much of which is attributable to reductions of light extinction from ammonium sulfate.
Light extinction was also decreased for ammonium nitrate, although at a lower rate and a lower
magnitude than ammonium sulfate. Light extinction by POM, EC, and fine dust also decreased
over time, while light extinction by coarse PM increased slightly. In the Intermountain West and
Southwest, annual mean total be%t decreased by -0.9% yr"1 from 2002 to 2018. The composition
of PM in these regions are different than in the East, and while light extinction from ammonium
sulfate and ammonium nitrate generally decreased over these time periods, their contribution to
light extinction in the Intermountain West and Southwest is less than in the East. Light extinction
by POM, EC, and fine dust decreased over time, while the trend for coarse PM remained
relatively the same, although the composition of the particles responsible for light extinction in
these areas shifted towards a more carbon-dominated composition over time. It is also important
to note that the trends observed in the Intermountain West and Southwest regions are likely
influenced by biomass smoke, as wildfire smoke emissions are the largest contributor to light
extinction by POM and the impacts of wildfires on air quality in these regions has increased in
recent years (Hand et al., 2020). Light extinction levels in the West Coast region were higher
than in the Intermountain West and Southwest regions, but generally decreased over time (-1.5%
yr"1). Light extinction by ammonium nitrate decreased at the highest rate in the West Coast
region, and was the only area were the rate decreased at a greater rate than ammonium sulfate.
Light extinction by EC and fine dust also decreased, while the trend for POM generally remained
flat and light extinction by coarse mass increased slightly. The mix of positive and negative
trends in the West Coast region are likely due to the influence of biomass smoke in northern
California and Oregon, in particular during 2017 and 2018, as well as reductions in NOx
emissions in Southern California and reductions in light extinction by ammonium sulfate across
the region (U.S. EPA, 2021, section 4.2.2; Hand et al., 2020).
Since the completion of the 2019 ISA, additional research has emerged that explores the
impact of wildfire smoke and biomass smoke on PM composition in the U.S. The increases in
PM emissions from these sources coincides with decreases in SO2 and NOx emissions, which
influences the contribution of different PM species to light extinction. The evidence suggests that
PM emissions from wildfire and biomass smoke can impact visibility impairment due to general
changes in the dominant PM species in the ambient air during these events, as well as the
influence of particle size and aging of the PM over time (U.S. EPA, 2021, section 4.2.2; Laing et
al., 2016; Kleinman et al., 2020).
In summary, the spatial and temporal analysis of PM monitoring network data in the
2019 ISA and recent evidence presented in the draft ISA Supplement emphasize that the extent
of light extinction by PM2.5 depends on PM2.5 composition and relative humidity. Regional
differences in PM2.5 composition greatly influence light extinction spatially and temporally.
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Changes in PM2.5 composition over time can also affect light extinction based on concentrations
of specific PM components in ambient air.
• To what extent are recent studies available that might inform judgments about the
potential adversity to public welfare of PM-attributable visibility impairment and
the nature of the relationship between PM-attributable visibility impairment and
public perceptions of such impairment?
In the 2012 review, visibility preference studies were available from four areas in North
America,15 as described in section 5.1.2 above. Study participants were queried regarding
multiple images that, depending on the study, were either photographs of the same location and
scenery that had been taken on different days on which measured extinction data were available
or digitized photographs onto which a uniform "haze" had been superimposed. Results of these
studies indicated a wide range of judgments on what study participants considered to be
acceptable visibility across the different study areas, depending on the setting depicted in each
photograph. As a part of the 2010 UFVA, each study was evaluated separately, and figures were
developed to display the percentage of participants that rated the visual air quality depicted as
"acceptable" (U.S. EPA, 2010). Figure 5-2 represents a graphical summary of the results of the
studies in the four cities and identifies a range encompassing the PM2.5 visibility index values
from images that were judged to be acceptable by at least 50% of study participants across all
four of the urban preference studies (U.S. EPA, 2010, p. 4-24).16 As shown in Figure 5-2, much
lower visibility (considerably more haze resulting in higher values of light extinction) was
considered acceptable in Washington, D.C. than was in Denver. The median judgment for the
study groups in the two areas differed by 9.2 dv (which roughly corresponds to about 30 |ig/m3
ofPM)(U.S. EPA, 2010).
15 As noted above, preference studies were available in four urban areas in the last review: Denver, Colorado (Ely et
al., 1991, Pryor, 1996), Vancouver, British Columbia, Canada (Pryor, 1996), Phoenix, Arizona (BBC Research &
Consulting, 2003), and Washington, DC (Abt Associates, 2001; Smith and Howell, 2009). More details about
these studies are available in Appendix D.
16 Figure 5-2 shows the results of a logistical regression analysis using a logit model of the acceptable or
unacceptable ratings from participants of the studies. The logit model is a generalized linear model used for
binomial regression analysis which fits explanatory data about binary outcomes (in this case, a person rating an
image as acceptable or unacceptable) to a logistic function curve. A detailed description is available in Appendix
J of the 2010 UFVA (U.S. EPA, 2010).
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Light Extinction (Mm'1)
100 200
20 25 30
PM2.5 Visibility Index (dv)
£k Denver
~ Phoenix
~ BC
~ Washington
Denver Logit
Phoenix Logit
BC Logit
DC Logit
Figure 5-2. Relationship of viewer acceptability ratings to light extinction. (Source: U.S.
EPA, 2011, Figure 4-2; U.S. EPA, 2010, Figure 2-16)
Since the completion of the 2012 review, there has been very little research on visibility
preferences, with one visibility preference study conducted in the Grand Canyon, AZ (Malm et
al., 2019) and one in Beijing, China (Fajardo et al., 2013). The Grand Canyon study reported a
lower range of acceptable visibility impairment among participants than was found in preference
studies previously conducted in the U.S. (Malm et al., 2019). The Malm et al. (2019) study
design is similar to that used in the public preference studies discussed above, but differs from
those studies in that this study was conducted in a Federal Class I area, as opposed to in an urban
area, with a scene depicted in the photographs that did not include urban features.17 The Malm et
al. (2019) study also used a much lower range of superimposed "haze" than the preference
studies discussed above, which may bias the participant responses given the generally lower
visibility range presented compared to the other studies.18
17 The Grand Canyon study used a single scene looking west down the canyon with a small landscape feature of a
100-km-distant mountain (Mount Trumbull), along with other closer landscape features. The scenes presented in
the previously available visibility preference studies are presented in more detail in Table D-9 in Appendix D.
18 The Grand Canyon study superimposed light extinction ranging from 3 dv to 20 dv on the image slides shown to
participants compared to the previously available preference studies. In those studies, the visibility ranges
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The study conducted in Beijing found a higher range of acceptable visibility impairment
among participants than was found in preference studies previously conducted in the U.S. This
finding may be related to the common occurrence of higher PM2.5 concentrations in Beijing (with
associated visibility impairment) than is typical in the U.S. (U.S. EPA, 2019, section 13.2.5).
Similarly, there is little recent information regarding acceptable levels of visibility
impairment in the U.S. One study explored alternate methods for evaluating "acceptable" levels
of visual air quality from the preference studies, including the use of scene-specific visibility
indices as potential indicators of visibility levels as perceived by the observer (Malm et al.,
2019). In addition to measures of atmospheric haze, such as atmospheric extinction, used in
previously available preference studies, other indices for visual air quality include color and
achromatic contrast of single landscape figures, average and equivalent contrast of an entire
scene, edge detection algorithms such as the Sobel index, and just-noticeable difference or
change indexes. The results reported by Malm et al. (2019) suggest that scene-dependent metrics,
such as contrast, may be useful alternate predictors of preference levels compared to universal
metrics like light extinction (U.S. EPA, 2021, section 4.2.1). This is because extinction alone is
not a measure of "haze," but of light attenuation per unit distance, and visible "haze" is
dependent on both light extinction and distance to a landscape feature (U.S. EPA, 2021, section
4.2.1).
• To what extent have important uncertainties in the evidence from the last review
been addressed, and have new uncertainties emerged?
Since the 2012 review, some refinements have been made to the IMPROVE equation to
better estimate light extinction, but there has been no expansion of monitoring efforts for direct
measurement of light extinction. At the time of the 2012 review, it was noted that a PM2.5 light
extinction monitoring program could help with characterizing visibility conditions and the
relationships between PM component concentrations and light extinction.
Little new research is available that helps to expand our understanding of visibility
preferences or our characterization of visibility conditions. Uncertainties and limitations
consistent with those identified in the past reviews persist in this reconsideration.
• Given the potential for people to have different preferences based on the visibility they are
used to based on conditions that they commonly encounter, and the potential for them to
also have different preferences for different types of scenes, the preference studies may
not capture the range of preferences of people in the U.S.
• Most of t7he preference studies were conducted 15 to 30 years ago and may not reflect the
visibility preferences of the U.S. population today. Given that air quality has improved
presented were as low as 9 dv and as high as 45 dv. The visibility ranges presented in the previously available
visibility preference studies are described in more detail in Table D-9 in Appendix D.
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over the last several decades, the older studies may not reflect current preferences of
people in the U.S. Newer studies may not capture the extent to which preferences may be
changing over time.
• The preference studies have used different methods to evaluate what level of visibility
impairment is acceptable. Variability in study methodology may influence an individual's
response as to what level of visibility impairment is deemed acceptable, and thereby
influence the results of the study.
• Many factors that are not captured by the methods used in the preference studies may
influence people's judgments on acceptable visibility. For example, an individual's
perception of an acceptable level of visibility impairment could be influenced by the
duration of visibility impairment experienced, the time of day during which light
extinction is greatest, and the frequency of episodes of visibility impairment, as well as
the intensity of the visibility impairment (i.e., the focus of the studies).
• Methods for quantitatively evaluating people's judgments on acceptability are evolving
but are still inconsistent in their application across studies. Variability in quantitative
methods for comparing visual air quality in public preference studies may influence the
consistency and comparability of results and the interpretation of these results in the
context of regional or national preferences for visibility impairment in urban, non-urban,
and Federal Class I areas.
Overall, the body of evidence regarding visibility effects remains largely unchanged since
the time of the 2012 review. While one new study provides refinements to the methods for
estimating light extinction, uncertainties and limitations in the scientific evidence during the
previous reviews remain.
5.3.1.2 Quantitative and Air Quality Information
Beyond our consideration of the scientific evidence, discussed in section 5.3.1.1 above,
we have also considered quantitative analyses of PM air quality and visibility impairment with
regard to the extent they could inform conclusions on the adequacy of the public welfare
protection provided by the current secondary PM standards. In the 2012 review, quantitative
analyses focused on daily visibility impairment, given the short-term nature of PM-related
visibility effects. Such quantitative analyses conducted as part of the 2012 review informed the
decision on the secondary standards in that review (U.S. EPA, 2010, U.S. EPA, 2011; 78 FR
3189-3192, January 15, 2013). The information available since the 2012 review includes an
updated equation for estimating light extinction, summarized in section 5.3.1.1 above and
described in the 2019 ISA, as well as more recent air monitoring data, that together allow for
development of an updated assessment with the potential to substantially add to our
understanding of PM-related visibility impairment. Thus, we have conducted updated analyses
for this reconsideration based on the technical information, tools, and methods.
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• How much visibility impairment is estimated to occur in areas that meet the current
secondary PM standards? What are the factors contributing to the estimates in areas
with higher values?
Consistent with the analyses conducted in the 2012 and 2020 reviews, we have conducted
analyses examining the relationship between PM mass concentrations and calculated light
extinction using the 3-year design values19 for the current secondary standards and a 3-year
average visibility metric based on light extinction estimated using IMPROVE equations using air
quality data for 2017 to 2019.20 These analyses are intended to inform our understanding of
visibility impairment in the U.S. under recent air quality conditions, particularly those conditions
that meet the current standards, and the relative influence of various factors on light extinction.
Given the relationship of visibility with short-term PM, we focus particularly on the short-term
PM standards.
Given that visibility-related effects are often associated with short-term PM
concentrations, and recognizing the relatively larger role of PM2.5 and its components in light
extinction and as inputs to the IMPROVE equation, we have given somewhat more attention to
consideration of the 24-hour PM2.5 standard. Analyses were conducted using three versions of
the IMPROVE equation (Equations D-l through D-3 in Appendix D) to estimate light extinction
to better understand the influence of variability in inputs across the three equations. This analysis
included 60 monitoring sites that are geographically distributed across the U.S. in both urban and
rural areas (see Figure D-l in Appendix D). These sites are those that have a valid 24-hour PM2.5
design value for the 2017-2019 period and met strict criteria for PM species for this analysis.21
We present results for these 60 sites using the original IMPROVE equation, with modifications
to the equation consistent with those made in evaluating light extinction in the 2012 review
(described in detail in section D.l of Appendix D). We then present results for these 60 sites with
light extinction calculated using the Lowenthal and Kumar (2016) IMPROVE equation described
in section 5.3.1.1 above.
19 A design value is a statistic that summarizes the air quality data for a given area in terms of the indicator,
averaging time, and form of the standard. Design values can be compared to the level of the standard and are
typically used to designate areas as meeting or not meeting the standard and assess progress towards meeting the
NAAQS.
20 This is the 3-year visibility metric that was used to evaluate visibility impairment in the 2012 and 2020 reviews.
Given that there has been little new research since the time of the 2012 review to better inform our understanding
of visibility preferences in the U.S., there is no new information available to inform selection of a visibility metric
for evaluating visibility impairment in the current review different from the one identified in the 2012 review.
21 For this analysis, completeness criteria for speciated PM data at these sites included having all 12 quarters in the
2017-2019 period with at least 11 days in each quarter with a valid PM2 5 and PMi 0-2.5 mass, sulfate, nitrate,
organic carbon, elemental carbon, sea salt (chlorine or chloride), and fine soil (aluminum, silica, calcium, iron,
and titanium) measurement.
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In considering the relationship between the 24-hour PM2.5 mass-based design value and
the 3-year visibility metric using recent air quality data, we first examine the relationship using
the original IMPROVE equation, consistent with the methods used in the 2012 review (Kelly et
al., 2012; 78 FR 3201, January 15, 2013; Appendix D). In those areas that meet the current 24-
hour PM2.5 standard, all sites have light extinction estimates at or below 26 dv (Figure 5-3; 78 FR
3218, January 15, 2013). For the four locations that exceed the current 24-hour PM2.5 standard,
light extinction estimates range from 22 dv to 29 dv (Figure 5-3). These findings are consistent
with the findings of the analysis using the same IMPROVE equation in the 2012 review with
data from 102 sites with data from 2008-2010 and in the 2020 review with data from 67 sites
with data from 2015-2017. This indicates similar findings from this analysis as was the case with
the similar analysis in the 2012 and 2020 reviews, i.e., the updated quantitative analysis shows
that the 3-year visibility metric was no higher than 30 dv22 at sites meeting the current secondary
PM standards, and at most such sites the 3-year visibility index values are much lower (e.g., an
average of 20 dv across the 60 sites).
22 For comparison purposes in these air quality analyses, we use a 3 -year visibility metric with a level of 30 dv,
which is the highest level of visibility impairment judged to be acceptable by at least 50 percent of the
participants in the preference studies that were available at the time of the 2012 review (78 FR 3191, January 15,
2013).
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35-
£ 30-
25-
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• •
• Northeast (n = 13)
• Southeast (n = 8)
• IndustMidwest (n = 15)
• UpperMidwest (n=9)
Southwest (n = 2)
• Northwest (n=8)
• SoCal (n=4)
• Alaska (n = l)
40 45 50 55 60
¦25 concentration,
0 5 10 15 20 25 30 35
98th percentile of daily PM-
averaged over 3 years (pg nrf3)
Figure 5-3. Comparison of 90th percentile of daily light extinction, averaged over three
years, and 98th percentile of daily PM2.5 concentrations, averaged over three years, for
2017-2019 using the original IMPROVE equation. (Note: Dashed lines indicate the level
of current 24-hour PM2.5 standard (35 |ig/m3) and the target level of protection identified for
the 3-year visibility metric (30 dv).)
When light extinction was calculated using the refined equation from Lowenthal and
Kumar (2016), the resulting 3-year visibility metrics are slightly higher at all sites compared to
light extinction estimates calculated using the original IMPROVE equation (Figure 5-4). As
noted in section 5.3.1.1, this version of the IMPROVE equation uses a multiplier of 2.1 to
convert the measured OC to OM for input into the equation and also accounts for water
absorption by water soluble organic matter as a function of relative humidity, likely contributing
to the slightly higher estimates of light extinction. As noted in section 5.3.1.1, the Lowenthal and
Kumar (2016) refinements to the IMPROVE equation are based on evaluations of monitoring
data from remote IMPROVE sites. More remote areas tend to have more aged organic particles
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than urban areas, and these adjustments to the IMPROVE equation account for the higher
concentration of organic matter as a result of more aged organic particles at these sites. It is
important to note that, since the Lowenthal and Kumar (2016) refinements to the IMPROVE
equation likely result in one of the higher estimates of light extinction, this equation may
overestimate light extinction in non-remote areas, including those urban areas in our analyses.
Using the Lowenthal and Kumar (2016) equation, for those sites that meet the current 24-
hour PM2.5 standard, the 3-year visibility metric is at or below 28 dv when light extinction is
calculated. For those sites that exceed the current 24-hour PM2.5 standard, three of these sites
have a 3-year visibility metric ranging between 26 dv and 30 dv, while one site in Fresno,
California that exceeds the current 24-hour PM2.5 standard and has a 3-year visibility index value
of 32 dv (compared to 29 dv when light extinction is calculated with the original IMPROVE
equation) (see Table D-3 in Appendix D). At this site, it is likely that the 3-year visibility metric
using the Lowenthal and Kumar (2016) equation would be below 30 dv if PM2.5 concentrations
were reduced such that the 24-hour PM2.5 level of 35 |ig/m3 was attained.
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98th percentile of daily PM2 5 concentration,
averaged over 3 years (pg nrr3)
Figure 5-4. Comparison of 90th percentile of daily light extinction, averaged over three
years, and 98th percentile of daily PM2.5 concentrations, averaged over three years, for
2015-2017 using the Lowenthal and Kumar equation. (Note: Dashed lines indicate the
level of current 24-hour PM2.5 standard (35 |ig/m3) and the target level of protection
identified for the 3-year visibility metric (30 dv).)
In considering visibility impairment under recent air quality conditions, we recognize that
the differences in the inputs to equations estimating light extinction can influence the resulting
values. For example, given the varying chemical composition of emissions from different
sources, the 2.1 multiplier in the Lowenthal and Kumar (2016) equation may not be appropriate
for all source types. At the time of the 2012 review, the EPA judged that a 1.6 multiplier for
converting OC to OM was more appropriate, for the purposes of estimating visibility index at
sites across the U.S., than the 1.4 or 1.8 multipliers used in the original and revised IMPROVE
equations, respectively. A multiplier of 1.8 or 2.1 would account for the more aged and
oxygenated organic PM that tends to be found in more remote regions than in urban regions,
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whereas a multiplier of 1.4 may underestimate the contribution of organic PM found in remote
regions when estimating light extinction (78 FR 3206, January 15, 2013; U.S. EPA, 2012a, p.
IV-5). The information and analyses indicate that it may be appropriate to select inputs to the
IMPROVE equation (e.g., the multiplier for OC to OM) on a regional basis rather than a national
basis when calculating light extinction. This is especially true when comparing sites with
localized PM sources (such as sites in urban or industrial areas) to sites with PM derived largely
from biogenic precursor emissions (that contribute to widespread secondary organic aerosol
formation), such as those in the southeastern U.S. We note, however, that conditions involving
PM from such different sources have not been well studied in the context of applying a multiplier
to estimate light extinction, contributing uncertainty to estimates of light extinction for such
conditions.
At the time of the 2012 review, the EPA noted that PM2.5 is the size fraction of PM
responsible for most of the visibility impairment in urban areas (77 FR 38980, June 29, 2012).
Data available at the time of the 2012 review suggested that, generally, PM10-2.5 was a minor
contributor to visibility impairment most of the time (U.S. EPA, 2010) although the coarse
fraction may be a major contributor in some areas in the desert southwestern region of the U.S.
Moreover, at the time of the 2012 review, there were few data available from PM10-2.5 monitors
to quantify the contribution of coarse PM to calculated light extinction. Since that time, an
expansion in PM10-2.5 monitoring efforts has increased the availability of data for use in
estimating light extinction with both PM2.5 and PM10-2.5 concentrations included as inputs in the
equations. The analysis in the 2020 review addressed light extinction at 20 of the 67 PM2.5 sites
where collocated PM10-2.5 monitoring data were available. Since the 2020 review, PM10-2.5
monitoring data are available at more locations and the analyses presented in this draft PA
include those for light extinction estimated with coarse and fine PM at all 60 sites. Generally, the
contribution of the coarse fraction to light extinction at these sites is minimal, contributing less
than 1 dv to the 3-year visibility metric (U.S. EPA, 2020, section 5.2.1.2). However, we note that
in our analysis, only a few sites were in locations that would be expected to have high
concentrations of coarse PM, such as the Southwest. These results are consistent with those in
the analyses in the 2019 ISA, which found that mass scattering from PM10-2.5 was relatively
small (less than 10%) in the eastern and northwestern U.S., whereas mass scattering was much
larger in the Southwest (more than 20%) particularly in southern Arizona and New Mexico (U.S.
EPA, 2019, section 13.2.4.1, p. 13-36).
In summary, the findings of these updated quantitative analyses are generally consistent
with those in the 2012 and 2020 reviews. The 3-year visibility metric was generally below 26 dv
in most areas that meet the current 24-hour PM2.5 standard. Small differences in the 3-year
visibility metric were observed between the variations of the IMPROVE equation, which may
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suggest that it may be more appropriate to use one version over another in different regions of
the U.S. based on PM characteristics such as particle size and composition to more accurately
estimate light extinction.
5.3.2 Non- Visibility Effects
5.3.2.1 Nature of Effects
In considering the evidence for non-visibility welfare effects attributable to PM as
presented in the 2019 ISA, this section poses the following policy-relevant questions:
• To what extent has the scientific evidence improved our understanding of the nature
and magnitude of non-visibility welfare effects of PM in ambient air, including the
variability associated with such effects? To what extent have important uncertainties
in the evidence from the last review been addressed, and have new uncertainties
emerged?
As an initial matter, we note that the draft ISA Supplement does not include an evaluation
of additional studies for climate and materials effects and the causality determinations from PM-
related climate and materials effects presented in the 2019 ISA continue to serve as the scientific
foundation for these effects. As such, the sections below that address these questions for PM and
climate effects (section 5.3.2.1.1) and materials effects (section 5.3.2.1.2) draw from the
evaluation of the welfare effects evidence for PM-related climate and materials effects in the
2019 ISA and considerations of such effects in the 2020 PA (U.S. EPA, 2020).
5.3.2.1.1 Climate Effects
In considering the evidence of climate effects attributable to PM, this section poses the
following policy-relevant question:
• To what extent is information available that changes or enhances our understanding
of the climate impacts of PM-related aerosols, particularly regarding a quantitative
relationship between PM concentrations and effects on climate (e.g., through
radiative forcing)?
In the 2012 review, the 2009 PM ISA concluded that there was "sufficient evidence to
determine a causal relationship between PM and climate effects - specifically on the radiative
forcing of the climate system, including both direct effects of PM on radiative forcing and
indirect effects that involve cloud feedbacks that influence precipitation formation and cloud
lifetimes" (U.S. EPA, 2009, section 9.3.10).23 Since the 2012 review, climate impacts have been
23 Radiative forcing (RF) for a given atmospheric constituent is defined as the perturbation in net radiative flux, at
the tropopause (or the top of the atmosphere) caused by that constituent, in watts per square meter (Wm~2), after
allowing for temperatures in the stratosphere to adjust to the perturbation but holding all other climate responses
constant, including surface and tropospheric temperatures (Fiore et al., 2015, Myhre et al., 2013). A positive
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extensively studied and the 2019 ISA concludes that "overall the evidence is sufficient to
conclude that a causal relationship exists between PM and climate effects" (U.S. EPA, 2019,
section 13.3.9). Recent research reinforces and strengthens the evidence evaluated in the 2009
ISA. Recent evidence provides greater specificity about the details of these radiative forcing
effects and increased understanding of additional climate impacts driven by PM radiative effects.
The Intergovernmental Panel on Climate Change (IPCC) assesses the role of anthropogenic
activity in past and future climate change. In the 2012 review, the 2009 ISA relied heavily on the
Fourth IPCC Assessment Report (AR4); since the 2012 review, the IPCC issued an updated
report, as described in the 2019 ISA. The Fifth IPCC Assessment Report (AR5; IPCC, 2013)
reports on the key scientific advances in understanding the climate effects of PM since AR4. The
2019 ISA draws substantially upon AR5 in summarizing these effects.
Atmospheric PM has the potential to affect climate in multiple ways, including absorbing
and scattering of incoming solar radiation, alterations in terrestrial radiation, effects on the
hydrological cycle, and changes in cloud properties (U.S. EPA, 2019, section 13.3.1).
Atmospheric PM interacts with incoming solar radiation. Many species of PM (e.g., sulfate and
nitrate) efficiently scatter solar energy. By enhancing reflection of solar energy back to space,
scattering PM exerts a cooling effect on the surface below. Certain species of PM such as black
carbon (BC), brown carbon (BrC), or dust can also absorb incoming sunlight. A recent study
found that whether absorbing PM warms or cools the underlying surface depends on several
factors, including the altitude of the PM layer relative to cloud cover and the albedo of the
surface (Ban-Weiss et al., 2014). PM also perturbs incoming solar energy by influencing cloud
cover and cloud lifetime. For example, PM provides nuclei upon which water vapor condenses,
forming cloud droplets. Finally, absorbing PM deposited on snow and ice can diminish surface
albedo and lead to regional warming (U.S. EPA, 2019, section 13.3.2).
PM has direct and indirect effects on climate processes. PM interactions with solar
radiation through scattering and absorption, collectively referred to as aerosol-radiation
interactions (ARI), are also known as the direct effects of PM on climate, as opposed to the
indirect effects that involve aerosol-cloud interactions (ACI). The direct effects of PM on climate
result primarily from particles scattering light away from Earth and sending a fraction of solar
energy back into space, decreasing the transmission of visible radiation to the surface of the
Earth and resulting in a decrease in the heating rate of the surface and the lower atmosphere. The
IPCC AR5, taking into account both model simulations and satellite observations, reports a
radiative forcing from aerosol-radiation interactions (RFari) from anthropogenic PM of -0.35 ±
forcing indicates net energy trapped in the Earth system and suggests warming of the Earth's surface, whereas a
negative forcing indicates net loss of energy and suggests cooling (U.S. EPA, 2019, section 13.3.2.2).
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0.5 watts per square meter (Wm"2) (Boucher, 2013), which is slightly reduced compared to AR4.
Estimates of effective radiative forcing24 from aero sol-radiation interactions (ERFari), which
include the rapid feedback effects of temperature and cloud cover, rely mainly on model
simulations, as this forcing is complex and difficult to observe (U.S. EPA, 2019, section
13.3.4.1). The IPCC AR5 best estimate for ERFari is -0.45 ± 0.5 Wm"2, which reflects this
uncertainty (Boucher, 2013).
By providing cloud condensation nuclei, PM increases cloud droplet number, thereby
increasing cloud droplet surface area and albedo (Twomey, 1977). The climate effects of these
perturbations are more difficult to quantify than the direct effects of aerosols with RF but likely
enhance the cooling influence of clouds by increasing cloud reflectivity (traditionally referred to
as the first indirect effect) and lengthening cloud lifetime (the second indirect effect). These
effects are reported as the radiative forcing from aerosol-cloud interactions (RFaci) and the
effective radiative forcing from aerosol-cloud interactions (ERFaci) (U.S. EPA, 2019, section
13.3.3.2). IPCC AR5 estimates ERFaci at -0.45 Wm"2, with a 90% confidence interval of-1.2 to
0 Wm"2 (U.S. EPA, 2019, section 13.3.4.2).25 Studies have also calculated the combined
effective radiative forcing from aerosol-radiation and aerosol-cloud interactions (ERFari+aci)
(U.S. EPA, 2019, section 13.3.4.3). IPCC AR5 reports a best estimate of ERFari+aci of -0.90 (-
1.9 to -0.1) Wm"2, consistent with these estimates (Boucher, 2013).
PM can also strongly reflect incoming solar radiation in areas of high albedo, such as
snow- and ice-covered surfaces. The transport and subsequent deposition of absorbing PM such
as BC to snow- and ice-covered regions can decrease the local surface albedo, leading to surface
heating. The absorbed energy can then melt the snow and ice cover and further depress the
albedo, resulting in a positive feedback loop (U.S. EPA, 2019, section 13.3.3.3; Bond et al.,
2013; U.S. EPA, 2012b). Deposition of absorbing PM, such as BC, may also affect surface
temperatures over glacial regions (U.S. EPA, 2019, section 13.3.3.3). The IPCC AR5 best
estimate of RF from the albedo effect is +0.04 Wm"2, with an uncertainty range of +0.02 to +0.09
Wm"2 (Boucher, 2013).
While research on PM-related effects on climate has expanded since the 2012 review,
there are still significant uncertainties associated with the accurate measurement of PM
contributions to the direct and indirect effects of PM on climate.
24 Effective radiative forcing (ERF), new in the IPCC AR5, takes into account not just the instantaneous forcing but
also a set of climate feedbacks, involving atmospheric temperature, cloud cover, and water vapor, that occur
naturally in response to the initial radiative perturbation (U.S. EPA, 2019, section 13.3.2.2).
25 While the 2019 ISA includes estimates of RFaci and ERFaci from a number of studies (U.S. EPA, 2019, sections
13.3.4.2, 13.3.4.3, 13.3.3.3), this draft PA focuses on the single best estimate with a range of uncertainty, as
reported in IPCC AR5 (Boucher, 2013).
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• To what extent does the information provide evidence of a quantitative relationship
between specific PM constituents (i.e., BC, OC, sulfate) and climate-related effects?
Since the 2012 review, a number of studies have examined the individual climate effects
associated with key PM components, including sulfate, nitrate, OC, BC, and dust, along with
updated quantitative estimates of the radiative forcing associated with the individual species.
Sulfate particles form through oxidation of SO2 by OH in the gas phase and in the
aqueous phase by a number of pathways, including in particular those involving ozone and H2O2
(U.S. EPA, 2019, section 13.3.5.1). The main source of anthropogenic sulfate is from coal-fired
power plants, and global trends in the anthropogenic SO2 emissions are estimated to have
increased dramatically during the 20th and early 21st centuries, although the recent
implementation of more stringent air pollution controls on sources has led to a reversal in such
trends in many places (U.S. EPA, 2019, section 13.3.5.1). Sulfate particles are highly reflective.
Consistent with other recent estimates, on a global scale, the IPCC AR5 estimates that sulfate
contributes more than other PM types to RF, with RFari of -0.4 (-0.6 to -0.2) Wm"2, where the
5% and 95% uncertainty range is represented by the numbers in the parentheses (Myhre et al.,
2013). This uncertainty range indicates the challenges associated with estimating SO2 from
sources in developing regions and estimating the lifetime of sulfate against wet deposition.
Sulfate is also a major contributor to the influence of PM on clouds (Takemura, 2012). A total
effective radiative forcing (ERFari+aci) for anthropogenic sulfate has been estimated to be nearly
-1.0 Wm"2 (Adams et al., 2001, Zelinka et al., 2014).
Nitrate particles form through the oxidation of nitrogen oxides and occur mainly in the
form of ammonium nitrate. Ammonium preferentially associates with sulfate rather than nitrate,
leading to formation of ammonium sulfate at the expense of ammonium nitrate (Adams et al.,
2001). As anthropogenic emissions of SO2 decline, more ammonium will be available to react
with nitrate, potentially leading to future increases in ammonium nitrate particles in the
atmosphere (U.S. EPA, 2019, section 13.3.5.2; Hauglustaine et al., 2014; Lee et al., 2013;
Shindell et al., 2013). Warmer global temperatures, however, may decrease nitrate abundance
given that it is highly volatile at higher temperatures (Tai et al., 2010). The IPCC AR5 estimates
RFari of nitrate of -0.11 (-0.3 to -0.03) Wm"2 (Boucher, 2013), which is one-fourth of the RFari
of sulfate.
Primary organic carbonaceous PM, including BrC, are emitted from wildfires,
agricultural fires, and fossil fuel and biofuel combustion. Secondary organic aerosols (SOA)
form when anthropogenic or biogenic nonmethane hydrocarbons are oxidized in the atmosphere,
leading to less volatile products that may partition into PM (U.S. EPA, 2019, section 13.3.5.3).
Organic particles are generally reflective, but in the case of BrC, a portion is significantly
absorbing at shorter wavelengths (<400 nm). The IPCC AR5 estimates an RFari for primary
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organic PM from fossil fuel combustion and biofuel use of -0.09 (-0.16 to -0.03) Wm"2 and an
RFari estimate for SOA from these sources of -0.03 (-0.27 to +0.20) Wm"2 (Myhre et al., 2013).
The wide range in these estimates, including inconsistent signs for forcing, reflect uncertainties
in the optical properties of organic PM and its atmospheric budgets, including the production
pathways of anthropogenic SOA (Scott et al., 2014; Myhre et al., 2013; McNeill et al., 2012;
Heald et al., 2010). The IPCC AR5 also estimates an RFari of -0.2 Wm"2 for primary organic PM
arising from biomass burning (Boucher, 2013).
Black carbon (BC) particles occur as a result of inefficient combustion of carbon-
containing fuels. Like directly emitted organic PM, BC is emitted from biofuel and fossil fuel
combustion and by biomass burning. BC is absorbing at all wavelengths and likely has a large
impact on the Earth's energy budget (Bond et al., 2013). The IPCC AR5 estimates a RFari from
anthropogenic fossil fuel and biofuel use of +0.4 (+0.5 to +0.8) Wm"2 (Myhre et al., 2013).
Biomass burning contributes an additional +0.2 (+0.03 to +0.4) Wm"2 to BC RFari, while the
albedo effect of BC on snow and ice adds another +0.04 (+0.02 to +0.09) Wm"2 (Myhre et al.,
2013; U.S. EPA, 2019, section 13.3.5.4, section 13.3.4.4).
Dust, or mineral dust, is mobilized from dry or disturbed soils as a result of both
meteorological and anthropogenic activities. Dust has traditionally been classified as scattering,
but a recent study found that dust may be substantially coarser than currently represented in
climate models, and thus more light-absorbing (Kok et al., 2017). The IPCC AR5 estimates
RFari as -0.1 ± 0.2 Wm"2 (Boucher, 2013), although the results of the study by Kok et al. (2017)
would suggest that in some regions dust may have led to warming, not cooling (U.S. EPA, 2019,
section 13.3.5.5).
Recent research expands upon the evidence from the 2012 review. Consistent with the
evidence in the 2012 review, the key PM components, including sulfate, nitrate, OC, BC, and
dust, that contribute to climate processes vary in their reflectivity, forcing efficiencies, and
direction of forcing.
• To what extent does the evidence change or improve our understanding of the spatial
and temporal variation in climate responses to PM?
Radiative forcing due to PM elicits a number of responses in the climate system that can
lead to significant effects on weather and climate over a range of spatial and temporal scales,
mediated by a number of feedbacks that link PM and climate. Since the 2012 review, the
evidence base has expanded with respect to the mechanisms of climate responses and feedbacks
to PM radiative forcing, described below, although considerable uncertainties continue to exist.
We focus our discussion primarily on the climate impacts in the U.S.
Unlike well-mixed, long-lived greenhouse gases in the atmosphere, PM has a very
heterogenous distribution across the Earth. As such, patterns of RFari and RFaci tend to correlate
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with PM loading, with the greatest forcings centralized over continental regions. The climate
response is more complicated since the perturbation to one climate variable (e.g., temperature,
cloud cover, precipitation) can lead to a cascade of effects on other variables. While the initial
PM radiative forcing may be concentrated regionally, the eventual climate response can be much
broader spatially or be concentrated in remote regions (U.S. EPA, 2019, section 13.3.6). The
complex climate system interactions lead to variation among climate models, with some studies
showing relatively close correlation between forcing and surface response temperatures (e.g.,
Leibensperger et al., 2012), while other studies show much less correlation (e.g., Levy et al.,
2013). Many studies have examined observed trends in PM and temperature in the U.S. Climate
models have suggested a range of factors which can influence large-scale meteorological
processes and may affect temperature, including local feedback effects involving soil moisture
and cloud cover, changes in the hygroscopicity of the PM, and interactions with clouds alone
(U.S. EPA, 2019, section 13.3.7). While evidence described in the 2019 ISA suggests that PM
influenced temperature trends across the southern and eastern U.S. in the 20th century,
uncertainties continue to exist and further research is needed to better characterize the effects of
PM on regional climate in the U.S.
• To what extent have important uncertainties identified in prior reviews been
reduced and/or have new uncertainties emerged?
Since 2009, significant progress has been made in evaluating PM-related climate effects
and uncertainties. The IPCC AR5 states that "climate-relevant aerosol processes are better
understood, and climate-relevant aerosol properties are better observed, than at the time of the
AR4" (Boucher, 2013). However, significant uncertainties remain that make it difficult to
quantify the climate effects of PM. Such uncertainties include those related to our understanding
of:
• The magnitude of PM radiative forcing and the portion of that associated with
anthropogenic emissions;
• The contribution of regional differences in PM concentrations, and of individual
components, to radiative forcing;
• The mechanisms of climate responses and feedbacks resulting from PM-related radiative
forcing; and,
• The process by which PM interacts with clouds and how to represent such interactions in
climate models.
While research has progressed significantly since the 2012 review, substantial
uncertainties still remain with respect to key processes linking PM and climate, because of the
small scale of PM-relevant atmospheric processes compared to the resolution of state-of-the-art
models, and because of the complex cascade of indirect impacts and feedbacks in the climate
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system that result from an initial PM-related radiative perturbation (U.S. EPA, 2019, section
13.3.9).
5.3.2.1.2 Materials Effects
In considering the evidence on materials effects attributable to PM, this section poses the
following policy-relevant question:
• To what extent is information available to link PM to materials effects, including
degradation of surfaces, and deterioration of materials such as metal, stone, concrete
and marble?
In the 2012 review, the 2009 ISA concluded that there was "a causal relationship between
PM and effects on materials" (U.S. EPA, 2009, sections 2.5.4 and 9.5.4). Rather than altering our
conclusions from the 2012 review, the evidence in the 2019 ISA continues to support prior
conclusions regarding materials effects associated with PM deposition. Effects of deposited PM,
particularly sulfates and nitrates,26 to materials include both physical damage and impaired
aesthetic qualities. Because of their electrolytic, hygroscopic, and acidic properties and their
ability to sorb corrosive gases, particles contribute to materials damage by adding to the effects
of natural weathering processes, by potentially promoting or accelerating the corrosion of metals,
degradation of painted surfaces, deterioration of building materials, and weakening of material
components. The majority of the evidence on materials effects of PM are from outside the U.S.
on buildings and other items of cultural heritage; however, they provide limited new data for
consideration. (U.S. EPA, 2019, section 13.4).
Materials damage from PM generally involves one or both of two processes: soiling and
corrosion (U.S. EPA, 2019, section 13.4.2). Soiling and corrosion are complex, interdependent
processes, typically beginning with deposition of atmospheric PM or SO2 to exposed surfaces.
Constituents of deposited PM can interact directly with materials or undergo further chemical
and/or physical transformation to cause soiling, corrosion, and physical damage. Weathering,
including exposure to moisture, ultraviolet (UV) radiation and temperature fluctuations, affects
the rate and degree of damage (U.S. EPA, 2019, section 13.4.2).
Soiling is the result of PM accumulation on an object that alters its optical characteristics
or appearance. These soiling effects can affect the aesthetic value of a structure or result in
reversible or irreversible damage to the surface. The presence of air pollution can increase the
frequency and duration of cleaning and can enhance biodeterioration processes on the surface of
materials. For example, deposition of carbonaceous components of PM can lead to the formation
26 In the case of materials effects, it is difficult to isolate the effects of gaseous and particulate nitrogen and sulfur
wet deposition so both will be considered along with other PM-related deposition effects on materials.
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of black crusts on surfaces, and the buildup of microbial biofilms27 can discolor surfaces by
trapping PM more efficiently (U.S. EPA, 2009, p. 9-195; U.S. EPA, 2019, section 13.4.2). The
presence of PM may alter light transmission or change the reflectivity of a surface. Additionally,
the organic or nutrient content of deposited PM may enhance microbial growth on surfaces.
Since the 2012 review, very little evidence has become available related to deposition of
SO2 to materials such as limestone, granite, and metal. Deposition of SO2 onto limestone can
transform the limestone into gypsum, resulting in a rougher surface, which allows for increased
surface area for accumulation of deposited PM (Camuffo and Bernardi, 1993; U.S. EPA, 2019,
section 13.4.2). Oxidation of deposited SO2 that contributes to the transformation of limestone to
gypsum can be enhanced by the formation of surface coatings from deposited carbonaceous PM
(both elemental and organic carbon) (Grossi et al., 2007, McAlister et al., 2008). Ozga et al.
(2011) characterized damage to two concrete buildings in Poland and Italy. Gypsum was the
main damage product on surfaces of these buildings that were sheltered from rain runoff, while
PM embedded in the concrete, particularly carbonaceous particles, were responsible for
darkening of the building walls (Ozga et al., 2011).
Building on the evidence in the 2009 ISA, research has progressed on the theoretical
understanding of soiling of cultural heritage in a number of studies. Barca et al. (2010)
developed and tested a new methodological approach for characterizing trace elements and
heavy metals in black crusts on stone monuments to identify the origin of the chemicals and the
relationship between the concentrations of elements in the black crusts and local environmental
conditions. Recent research has also used isotope tracers to distinguish between contributions
from local sources versus atmospheric pollution to black crusts on historical monuments in
France (Kloppmann et al., 2011). A study in Portugal found that biological activity played a
major role in soiling, specifically in the development of colored layers and in the detachment
process (de Oliveira et al., 2011). Another study found damage to cement renders, often used for
restoration, consolidation, and decorative purposes on buildings, following exposure to sulfuric
acid, resulting in the formation of gypsum (Lanzon and Garcia-Ruiz, 2010).
Corrosion of stone and the decay of stone building materials by acid deposition and
sulfate salts were described in the 2009 ISA (U.S. EPA, 2009, section 9.5.3). Since that time,
advances have been made on the quantification of degradation rates and further characterization
of the factors that influence damage of stone materials (U.S. EPA, 2019, section 13.4.2). Decay
rates of marble grave stones were found to be greater in heavily polluted areas compared to a
relatively pristine area (Mooers et al., 2016). The time of wetness and the number of
27 Microbial biofilms are communities of microorganisms, which may include bacteria, algae, fungi and lichens, that
colonize an inert surface. Microbial biofilms can contribute to biodeterioration of materials via modification of
the chemical environment.
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dissolution/crystallization cycles were identified as hazard indicators for stone materials, with
greater hazard during the spring and fall when these indicators are relatively high (Casati et al.,
2015).
A study examining the corrosion of steel as a function of PM composition and particle
size found that changes in the composition of resulting rust gradually changed with particle size
(Lau et al., 2008). In a study of damage to metal materials under in Hong Kong, which generally
has much higher PM concentrations than those observed in the U.S., Liu et al. (2015) found that
iron and steel were corroded by both PM and gaseous pollutants (SO2 and NO2), while copper
and copper alloys were mainly corroded by gaseous pollutants (SO2 and O3) and aluminum and
aluminum alloy corrosion was mainly attributed to PM and NO2.
A number of studies have also found materials damage from PM components besides
sulfate and black carbon and atmospheric gases besides SO2. Studies have characterized impacts
of nitrates, NOx, and organic compounds on direct materials damage or on chemical reactions
that enhance materials damage (U.S. EPA, 2019, section 13.4.2). Other studies have found that
soiling of building materials can be attributed to enhanced biological processes and colonization,
including the development and thickening of biofilms, resulting from the deposition of PM
components and atmospheric gases (U.S. EPA, 2019, section 13.4.2).
Since the 2012 review, other materials have been studied for damage attributable to PM,
including glass and photovoltaic panels. Soiling of glass can affect its optical and thermal
properties, and can lead to increased cleaning costs and frequency. The development of haze28 on
modern glass has been measured and modeled, with a strong correlation between the size
distribution of particles and the evolution of the mass deposited on the surface of the glass.
Measurements showed that, under sheltered conditions, mass deposition accelerated regularly
with time in areas closest to sources of PM (i.e., near roadways) and coarse mineral particles
were more prevalent compared to other sites (Alfaro et al., 2012). Model predictions were found
to correctly simulate the development of haze at site locations when compared with
measurements (Alfaro et al., 2012).
Soiling of photovoltaic panels can lead to decreased energy efficiency. For example,
soiling by carbonaceous PM decreased solar efficiency by nearly 38%, while soil particles
reduced efficiency by almost 70% (Radonjic et al., 2017). The rate of photovoltaic power output
can also be degraded by soiling and has been found to be related to the rate of dust accumulation.
28 In this discussion of non-visibility welfare effects (section 5.3.2), haze is used as it has been defined in the
scientific literature on soiling of glass, i.e., the ratio of diffuse transmitted light to direct transmitted light
(Lombardo et al., 2010). This differs from the definition of haze as used in the discussion of visibility welfare
effects in section 5.3.1, where it is used as a qualitative description of the blockage of sunlight by dust, smoke,
and pollution.
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In five sites in the U.S. representing different meteorological and climatological conditions,29
photovoltaic module power transmission was reduced by approximately 3% for every g/m2 of
PM deposited on the cover plate of the photovoltaic panel, independent of geographical location
(Boyle et al., 2017). Another study found that photovoltaic module power output was reduced by
40% after 10 months of exposure without cleaning, although a number of anti-reflective coatings
can generally mitigate power reduction resulting from dust deposition (Walwil et al., 2017).
Energy efficiency can also be impacted by the soiling of building materials, such as light-colored
marble panels on building exteriors, that are used to reflect a large portion of solar radiation for
passive cooling and to counter the urban heat island effect. Exposure to acidic pollutants in urban
environments have been found to reduce the solar reflectance of marble, decreasing the cooling
effect (Rosso et al., 2016). Highly reflective roofs, or cool roofs, have been designed and
constructed to increase reflectance from buildings in urban areas, to both decrease air
conditioning needs and urban heat island effects, but these efforts can be impeded by soiling of
materials used for constructing cool roofs. Methods have been developed for accelerating the
aging process of roofing materials to better characterize the impact of soiling and natural weather
on materials used in constructing cool roofs (Sleiman et al., 2014).
• To what extent has information emerged for quantifying material damage
attributable to PM through dose-response relationships or damage functions? Are
there studies linking perceptions of reduced aesthetic appeal of buildings and other
objects to PM or wet deposition of nitrogen and sulfur species?
Some progress has been made since the 2012 review in the development of dose-response
relationships for soiling of building materials, although some key relationships remain poorly
characterized. The first general dose-response relationships for soiling of materials were
generated by measuring contrast reflectance of a soiled surface to the reflectance of the unsoiled
substrate for different materials, including acrylic house paint, cedar siding, concrete, brick,
limestone, asphalt shingles, and window glass with varying total suspended particulate (TSP)
concentrations (Beloin and Haynie, 1975; U.S. EPA, 2019, section 13.4.3). Continued efforts to
develop dose-response curves for soiling have led to some advancements for modern materials,
but these relationships remain poorly characterized for limestone. One study quantified the dose-
response relationships between PMio and soiling for painted steel, white plastic, and
polycarbonate filter material, but there was too much scatter in the data to produce a dose-
response relationship for limestone (Watt et al., 2008). A dose-response relationship for silica-
29 Of the five sites studied, three were in rural, suburban, and urban areas representing a semi-arid environment
(Front Range of Colorado), one site represented a hot and humid environment (Cocoa, Florida), and one
represented a hot and arid environment (Albuquerque, New Mexico) (U.S. EPA, 2019, section 13.4.2; Boyle et
al., 2017).
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soda-lime window glass soiling by PMio, NO2, and SO2 was quantified based on 31 different
locations (Lombardo et al., 2010; U.S. EPA, 2019, section 13.4.3, Figure 13-32, Equation 13-8).
The development of this dose-response relationship required several years of observation time
and had inconsistent data reporting across the locations.
Since the 2012 review, there has also been progress in developing methods to more
rapidly evaluate soiling of different materials by PM mixtures. Modern buildings typically have
simpler lines, less detailed surfaces, and a greater use of glass, tile, and metal, which are easier to
clean than stone. There have also been major changes in the types of materials used for
buildings, including a variety of polymers available for use as coatings and sealants. New
economic and environmental considerations beyond aesthetic appeal and structural damage are
emerging (U.S. EPA, 2019, section 13.4.3). Changes in building materials and design, coupled
with new approaches in quantifying the dose-response relationship between PM and materials
effects, may reduce the amount of time needed for observations to support the development of
material-specific dose-response relationships.
In addition to dose-response functions, damage functions have also been used to quantify
material decay as a function of pollutant type and load. Damage can be determined from sample
surveys or inspection of actual damage and a damage function can be developed to link the rate
of material damage to time of replacement or maintenance. A cost function can then link the time
for replacement and maintenance to a monetary cost, and an economic function links cost to the
dose of pollution based on the dose-response relationship (U.S. EPA, 2019, section 13.4.3).
Damage functions are difficult to assess because it depends on human perception of the level of
soiling deemed to be acceptable and evidence in this area remains limited. As described in the
2019 ISA, damage functions for a wide range of building materials (i.e., stone, aluminum, zinc,
copper, plastic, paint, rubber, stone) have been developed and reviewed (Brimblecombe and
Grossi, 2010). One study estimated long-term deterioration of building materials and found that
damage to durable building material (such as limestone, iron, copper, and discoloration of stone)
is no longer controlled by pollution as was historically documented but rather that natural
weathering is a more important influence on these materials in modern times (Brimblecombe and
Grossi, 2009). Even as PM-attributable damage to stone and metals has decreased over time, it
has been predicted that there will be potentially higher degradation rates for polymeric materials,
plastic, paint, and rubber due to increased oxidant concentrations and solar radiation
(Brimblecombe and Grossi, 2009).
• To what extent have important uncertainties identified in prior reviews been
reduced and/or have new uncertainties emerged?
While there are a number of studies in the 2019 ISA that investigate the effect of PM on
newly studied materials and further characterize the effects of PM on previously studied
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materials, there remains insufficient evidence to relate soiling or damage to specific PM levels or
to establish a quantitative relationship between PM in ambient air and materials degradation.
Uncertainties that were identified in the 2012 review still largely remain with respect to
quantitative relationships between particle size, concentration, chemical concentrations, and
frequency of repainting and repair. No new studies are assessed in the 2019 ISA that link
perceptions of reduced aesthetic appeal of buildings and other objects to PM-related materials
effects. Moreover, uncertainties about the deposition rates of airborne PM to surfaces and the
interaction of co-pollutants still remain.
5.3.2.2 Quantitative Information
Beyond our consideration of the scientific evidence, discussed above in section 5.3.2.1
Error! Reference source not found, above, we also consider the extent to which quantitative
analyses of PM air quality and quantitative assessments for climate and materials effects could
inform conclusions on the adequacy of the public welfare protection provided by the current
secondary PM standards. We have evaluated the potential support for conducting new analyses
of PM air quality concentrations and non-visibility welfare effects.
5.3.2.2.1 Climate Effects
While expanded since the 2012 review, our current understanding of PM-related climate
effects is still limited by significant uncertainties. Large spatial and temporal heterogeneities in
direct and indirect PM climate forcing can occur for a number of reasons, including the
frequency and distribution of emissions of key PM components contributing to climate forcing,
the chemical and microphysical processing that occurs in the atmosphere, and the atmospheric
lifetime of PM relative to other pollutants contributing to climate forcing (U.S. EPA, 2019,
section 13.3). These issues particularly introduce uncertainty at the local and regional scales in
the U.S. that would likely be most relevant to a quantitative assessment of the potential effects of
a national PM standard on climate in this review. Limitations and uncertainties in the evidence
make it difficult to quantify the impact of PM on climate and in particular how changes in the
level of PM mass in ambient air would result in changes to climate in the U.S. Thus, as in the
2012 review, the data remain insufficient to conduct quantitative analyses for PM effects on
climate.
5.3.2.2.2 Materials Effects
As at the time of the 2012 review, sufficient evidence is not available to conduct a
quantitative assessment of PM-related soiling and corrosion effects. While soiling associated
with PM can lead to increased cleaning frequency and repainting of surfaces, no quantitative
relationships have been established between characteristics of PM or the frequency of cleaning
or repainting that would help inform our understanding of the public welfare implications of
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soiling (U.S. EPA, 2019, section 13.4). Similarly, while some information is available with
regard to microbial deterioration of surfaces and the contribution of carbonaceous PM to the
formation of black crusts that contribute to soiling, the available evidence does not support
quantitative analyses (U.S. EPA, 2019, section 13.4). While some evidence is available with
respect to PM-attributable materials effects, the data are insufficient to conduct quantitative
analyses for PM effects on materials.
5.4 PRELIMINARY CONCLUSIONS REGARDING THE ADEQUACY OF
THE SECONDARY PM STANDARDS
This section discusses preliminary staff conclusions for the Administrator's consideration
in judging the adequacy of the current secondary PM standards. These preliminary conclusions
are based on consideration of the assessment and integrative synthesis of evidence presented in
the 2019 ISA and draft ISA Supplement, as well as analyses of recent air quality. Taking into
consideration the responses to specific questions discussed above, we revisit the overarching
policy question for this chapter:
• Does the scientific evidence and quantitative information support or call into
question the adequacy of the protection afforded by the current secondary PM
standards?
As provided in section 109(b)(2) of the CAA, the secondary standard is to "specify a
level of air quality the attainment and maintenance of which in the judgment of the
Administrator.. .is requisite to protect public welfare from any known or anticipated adverse
effects associated with the presence of such air pollutant in the ambient air." Effects on welfare
include, but are not limited to, "effects on soils, water, crops, vegetation, man-made materials,
animals, wildlife, weather, visibility, and climate, damage to and deterioration of property, and
hazards to transportation, as well as effects on economic values and on personal comfort and
well-being" (CAA section 302(h)). The secondary standards are not meant to protect against all
known or anticipated PM-related effects, but rather those that are judged to be adverse to the
public welfare (78 FR 3212, January 15, 2013). Similarly, the extent to which secondary
standards are concluded to provide adequate protection from such effects also depends on
judgments by the Administrator.
Therefore, we recognize that, as is the case in NAAQS reviews in general, the extent to
which the current secondary PM standards are judged to be adequate will depend on a variety of
factors and judgments to be made by the Administrator. Such judgments include those
concerning the extent or severity of welfare effects that may be considered adverse to the public
welfare, and accordingly, what level of protection from such known or anticipated effects may be
judged requisite. In general, the public welfare significance of PM-related effects for different air
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quality conditions and in different locations depend upon the type and severity of the effects, as
well as the strength of the underlying information and associated uncertainties. Thus, in the
discussion below, our intention is to focus on such aspects of the evidence and quantitative
analyses.
With regard to visibility, climate, and materials effects of PM, our response to the
question above takes into consideration the discussions that address the specific policy-relevant
questions in prior sections of this chapter (see sections 5.3.1 and 5.3.2) and the approach
described in section 5.2 that builds on the approach from previous reviews. With respect to the
evidence-based considerations, we note that the evidence, while somewhat expanded since
previous reviews, does not include evidence of effects at lower concentrations or other welfare
effects of PM than those identified at the time of prior reviews. There continue to be significant
uncertainties related to quantifying the relationships between PM mass concentrations in ambient
air and welfare effects, including visibility impairment, climate effects, and materials effects.
With respect to the visibility effects of PM, the evidence continues to support a causal
relationship. With respect to evidence for visibility effects of PM, we note that the evidence,
while somewhat expanded since the 2012 review, does not include evidence of effects at lower
concentrations than those identified at the time of the 2012 review. Consistent with the evidence
available at the time of the 2012 review, significant limitations remain in directly measuring light
extinction. However, a number of small refinements have been made to the algorithm commonly
used to estimate light extinction (U.S. EPA, 2019, section 13.2.3.3; section 5.3.1.1 above). Light
extinction by PM2.5 is dependent on PM2.5 composition and relative humidity, which varies
regionally, with component contributions to light extinction also changing over time with
changes in emissions, as can be seen in analyses of recent air quality. We also note that limited
new research is available on methods of characterizing visibility or on how visibility is valued by
the public, such as visibility preference studies. Thus, while limited new research has further
informed our understanding of the influence of atmospheric components of PM2.5 on light
extinction, the available evidence to inform consideration of the public welfare implications of
PM-related visibility impairment remains relatively unchanged.
With respect to quantitative-based considerations, analyses using recent air quality and
considering updated and alternative methods for estimating visibility impairment provide results
generally similar to those given a focus in the decision for the 2012 and 2020 reviews. We
recognize that conclusions reached regarding visibility in previous reviews were based primarily
on the quantitative analyses that considered the relationship of estimated visibility impairment
(light extinction) with design values for the secondary 24-hour PM2.5 standard. These analyses
demonstrated that visibility index values were below 30 dv - the value identified as the target
level of protection for visibility-related welfare effects - at all locations that met the daily
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standard. In our evaluation in this chapter, we have considered the information regarding the
equations to estimate light extinction and the inputs to the equations and regarding identification
of the target level of protection. With regard to the equations, consistent with the approach in the
2020 review, we have utilized both the most recently published equations as well as alternatives
considered in the 2012 review in recognition of the uncertainties inherent in the quantitative
relationship between PM and light extinction and the variability in applicability to different
locations. Further, we have considered key coefficients in estimating and adjusting
concentrations of specific PM2.5 components, a key example of which is the multiplier used to
estimate the concentration of organic matter from the concentration of organic carbon. For
consistency with the analyses on which the decisions were based in the 2012 and 2020 reviews,
we have focused on a 3-year average of the 90th percentile of daily light extinction (calculated
using old and new algorithms) in considering visibility impairment at the analyzed locations.
In reaching a conclusion in the 2012 and 2020 reviews with regard to the adequacy of
visibility protection provided by the secondary PM standards, both Administrators identified 30
dv as an appropriate target level of protection. We have not identified new information available
since the completion of the 2020 review in this reconsideration of the 2020 final decision that
would challenge this public policy. Thus, in our consideration of the current information and
analyses in this document, we have compared the results of the updated analyses to the value of
30 dv, finding that all sites meet this target level of protection while also meeting the current
daily standards. In so finding, we additionally note the uncertainties recognized above regarding
estimation of OM for use in the IMPROVE equations, and also the variability across sites in
characteristics that affect the relationship between PM in ambient air and light extinction, and in
characteristics that affect human visibility and preferences in that regard. Based on the findings
of this comparison, in light of all of these considerations, we find it reasonable to conclude that
the quantitative information available in this reconsideration of the 2020 final decision does not
call into question the adequacy of visibility-related public welfare protection provided by the
current secondary PM standards. As a result, we have not conducted additional analyses to
evaluate the level of visibility protection that might be afforded by potential alternative
standards.
With respect to the non-visibility welfare effects of PM, the available evidence continues
to support causal relationships between climate effects and PM and materials effects and PM.
The evidence related to climate effects and PM, while expanded since previous reviews, has not
appreciably improved our understanding of the spatial and temporal heterogeneity of PM
components that contribute to climate forcing. We note that, as at the time of the 2012 review,
the evidence describes differences among individual PM components in their reflective
properties and direction of climate forcing. We also note that, while climate research has
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continued, there are still significant limitations in our ability to quantify contributions of PM, and
of individual PM components, to the direct and indirect effects of PM on climate (e.g. changes to
the pattern of rainfall, changes to wind patterns, effects on vertical mixing in the atmosphere).
While climate models have been improved and refined since the 2012 review, climate models
simulating aerosol-climate interactions on regional scales (e.g., -100 km) tend to have more
variability in estimates of the PM-related climate effects than simulations at the global scale, and
fewer studies are available that simulate specific regions (e.g., the U.S.) than that provide global-
scale simulations. While recent research has added to the understanding of climate forcing on a
global scale, there remain significant limitations to quantifying potential adverse effects from
PM on climate in the U.S. and how they would vary in response to changes in PM concentrations
in the U.S. That is, the information with regard to climate does not provide a clear understanding
of a quantitative relationship between concentrations of PM mass in ambient air and associated
climate-related effects, and consequently, precludes a quantitative evaluation of the level of
protection provided by a PM concentration-based secondary standard from adverse climate-
related effects on the public welfare in the U.S. Thus, on the whole, we do not find the
information to provide support for different conclusions than were reached in the 2012 and 2020
reviews with regard to climate-related effects of PM in ambient air.
In considering the evidence related to materials effects and PM, we note that there is
some evidence that informs our understanding on the soiling process and types of materials
affected, and provides limited information on dose-response relationships and damage functions,
although most of the recent evidence comes from studies outside of the U.S. In particular, there
is a growing body of research on PM and energy efficiency-related materials, such as solar
panels and passive cooling building materials, affecting the optical and thermal properties,
thereby impacting the intended energy efficiency of these materials. While recent research has
added to the understanding of PM-related materials effects, there remains a lack of research
related to quantifying materials effects and understanding the public welfare implications of such
effects.
In summary, with regard to the two main non-visibility effects - climate effects and
materials effects - the available evidence, as in previous reviews, documents a causal role for
PM in ambient air. This evidence, however, as in the 2012 and 2020 reviews, also includes
substantial uncertainties with regard to quantitative relationships with PM concentrations and
concentration patterns that limit our ability to quantitatively assess the public welfare protection
provided by the standards from these effects. Thus, as a whole, the available information does
not call into question the adequacy of protection provided by the current standards for these
effects.
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Based on all of the above considerations, we find that the available evidence does not call
into question the protection afforded by the current secondary PM standards against PM-related
welfare effects. Thus, our preliminary conclusion for the Administrator's consideration is that it
is appropriate to consider retaining the current secondary PM standards, without revision. In so
concluding, we recognize, as noted above, that the final decision on this reconsideration of the
secondary PM standards to be made by the Administrator is largely a public welfare judgment,
based on his judgment as to the requisite protection of the public welfare from any known or
anticipated adverse effects. This final decision will draw upon the available scientific evidence
and quantitative analyses on PM-attributable welfare effects, and on judgments about the
appropriate weight to place on the range of uncertainties inherent in the evidence and analyses.
5.5 AREAS FOR FUTURE RESEARCH AND DATA COLLECTION
In this section, we highlight key uncertainties in the available information related to the
effects of PM on public welfare. Such key uncertainties and areas for future research, model
development, and data gathering are outlined below. We note, however, that a full set of research
recommendations is beyond the scope of this discussion. Rather, listed below are key
uncertainties, research questions and data gaps that have been thus far highlighted in this review
of the secondary PM standards.
• A critical aspect of our consideration of the evidence and quantitative information for
visibility impairment is our understanding of human perception of visibility impairment
in the preference studies. This is essential to the Administrator's consideration of the
public welfare implications of visibility effects and to decisions on the adequacy of
protection provided by the secondary PM standards from them. Additional information
related to several areas would reduce uncertainty in in our interpretation of the available
information for purposes of characterizing visibility impairment. These areas include the
following:
- Expanding the number and geographic coverage of preference studies in urban,
rural and Class I areas to account for the potential for people to have different
preferences based on the conditions that they commonly encounter and potential
differences in preferences based on the scene types;
- Evaluating visibility preferences of the U.S. population today, given that the
preference studies were conducted more than 15 years ago, during which time air
quality in the U.S. has improved;
- Accounting for the influence that varying study methods may have on an
individual's response as to what level of visibility impairment is acceptable; and
- Providing insights regarding people's judgments on acceptable visibility based on
those factors that can influence an individual's perception of visibility
impairment, including the duration of visibility impairment experiences, the time
of day during which light extinction is greatest, and the frequency of episodes of
visibility impairment, as well as the intensity of the visibility impairment.
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• The development and implementation of direct monitoring of PM2.5 light extinction would
help to characterize visibility and the relationships between PM component
concentrations and light extinction and to evaluate and refine light extinction calculation
algorithms for use in areas near anthropogenic sources, and would provide measurements
for future visibility effects assessments.
• Substantial uncertainties still remain with respect to key processes linking PM and
climate, because of the small scale of PM-relevant atmospheric processes compared to
the resolution of state-of-the-art models, and because of the complex cascade of indirect
impacts and feedbacks in the climate system that result from an initial PM-related
radiative perturbation. Such uncertainties include those related to our understanding of:
- The magnitude of PM radiative forcing and the portion of that associated with
anthropogenic emissions;
- The contribution of regional differences in PM concentrations, and of individual
components, to radiative forcing; and,
- The process by which PM interacts with clouds and how to represent such
interactions in climate models.
• Research on more accurate U.S. and global emission inventories would provide source-
specific data on PM and PM component contributions to climate effects, particularly
those effects resulting from climate forcing.
• Insufficient evidence is available to relate soiling or damage to specific PM
concentrations or to establish a quantitative relationship between PM concentrations in
ambient air and materials degradation. Additional information would reduce uncertainty
in in our interpretation of the available information, including in the following areas:
- Identifying quantitative relationships between particle size, PM concentration,
chemical concentrations, and frequency of repainting and repair;
- Understanding human perceptions of reduced aesthetic appeal of buildings, and
other objects to PM-related materials effects; and
- Characterizing deposition rates of airborne PM to surfaces and the interaction of
co-pollutants.
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http://vosemite.epa.gov/sab/sabprodiict.iiht' I I W'0796E5) ' . ^\)6FB737/$Fi
le/CI f+Robustness+Studv+3+30+09+final.pdf.
Tai, APK, Mickley, LJ and Jacob, DJ (2010). Correlations between fine particulate matter
(PM2.5) and meteorological variables in the United States: Implications for the sensitivity
of PM2.5 to climate change. Atmospheric Environment 44(32): 3976-3984.
Takemura, T (2012). Distributions and climate effects of atmospheric aerosols from the
preindustrial era to 2100 along Representative Concentration Pathways (RCPs) simulated
using the global aerosol model SPRINTARS. Atmospheric Chemistry and Physics
12(23): 11555-11572.
Twomey, S (1977). The influence of pollution on the shortwave albedo of clouds. Journal of the
Atmospheric Sciences 34(7): 1149-1152.
U.S. EPA (2004a). Air Quality Criteria for Particulate Matter. (Vol II of II). Office of Research
and Development. Research Triangle Park, NC. U.S. EPA. EPA-600/P-99-002bF.
October 2004. Available at:
https://nepis.epa. eov/Exe/ZvPIJR.L.cei?Dockev=P 100LG7Q.txt.
U.S. EPA (2004b). Air Quality Criteria for Particulate Matter. (Vol I of II). Office of Research
and Development. Research Triangle Park, NC. U.S. EPA. EPA-600/P-99-002aF.
October 2004. Available at:
https://nepis.epa. eov/Exe/ZvPIJR.L.cei?Dockev=P 100LFIQ.txt.
U.S. EPA (2009). Integrated Science Assessment for Particulate Matter (Final Report). Office of
Research and Development, National Center for Environmental Assessment. Research
Triangle Park, NC. U.S. EPA. EPA-600/R-08-139F. December 2009. Available at:
https://cfpub.epa.gov/ncea/risk/recordisplav.cfm?deid=216546.
U.S. EPA (2010). Particulate Matter Urban-Focused Visibility Assessment (Final Document).
Office of Air Quality Planning and Standards, Health and Environmental Impacts
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Division. Research Triangle Park, NC. U.S. EPA. EPA-452/R-10-004. July 2010.
Available at: https://nepis.epa.eov/Exe/ZyPI y i?Dockey=Pl 00 TU^D tvt.
U.S. EPA (2011). Policy Assessment for the Review of the Particulate Matter National Ambient
Air Quality Standards. Office of Air Quality Planning and Standards, Health and
Environmental Impacts Division. Research Triangle Park, NC. U.S. EPA. EPA-452/R-
11-003. April 2011. Available at:
https://nepis.epa. gov/Exe/ZyPURL.cgi?Dockey= fY.txt.
U.S. EPA (2012a). Responses to Significant Comments on the 2012 Proposed Rule on the
National Ambient Air Quality Standards for Particulate Matter (June 29, 2012; 77 FR
38890). Research Triangle Park, NC. U.S. EPA. Docket ID No. EPA-HQ-OAR-2007-
0492. Available at: https://www3.epa.eov/ttn/naaqs/standards/pm/date pdf.
U.S. EPA (2012b). Report to Congress on Black Carbon. U.S. Environmental Protection
Agency, Office of Air and Radition. Washington, DC. U.S. EPA. EPA-450/R-12-001.
March 2012. Availble at: http://www.epa.eov/blackcarbon/2012report/fullreport.pdf.
U.S. EPA (2015). Preamble to the integrated science assessments. U.S. Environmental Protection
Agency, Office of Research and Development, National Center for Environmental
Assessment, RTP Division. Research Triangle Park, NC. U.S. EPA. EPA/600/R-15/067.
November 2015. Available at:
https://cfpub.epa. gov/ncea/isa/recordisplay.cfm?dei(
U.S. EPA (2016). Integrated review plan for the national ambient air quality standards for
particulate matter. Office of Air Quality Planning and Standards. Research Triangle Park,
NC. U.S. EPA. EPA-452/R-16-005. December 2016. Available at:
https://www3 .epa.gov/ttn/naaqy standards/pm/data/^ al-integrated-review-
plan.pdf.
U.S. EPA (2019). Integrated Science Assessment (ISA) for Particulate Matter (Final Report).
U.S. Environmental Protection Agency, Office of Research and Development, National
Center for Environmental Assessment. Washington, DC. U.S. EPA. EPA/600/R-19/188.
December 2019. Available at: https://www.epa.eov/naaqs/particulate-rnatter-pm-
standards-inteerated-science-assessments-current-review.
U.S. EPA (2020). Policy Assessment for the Review of the National Ambient Air Quality
Standards for Particulate Matter. Office of Air Quality Planning and Standards, Health
and Environmental Impacts Division. Research Triangle Park, NC. U.S. EPA. EPA-
452/R-20-002. January 2020. Available at: https://www.epa.gov/sites/default/files/2020-
01 /documents/fmal policy assessment for the review of the pm naaq Q2Q.pdf.
U.S. EPA (2021). Supplement to the 2019 Integrated Science Assessment for Particulate Matter
(External Review Draft). U.S. Environmental Protection Agency, Office of Research and
Development, Center for Public Health and Environmental Assessment. Washington,
DC. U.S. EPA. EPA/600/R-21/198. December 2019. Available at:
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assessments-current-review.
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1 Van de Hulst, H (1981). Light scattering by small particles. Dover Publications, Inc. New York.
2 Walwil, HM, Mukhaimer, A, Al-Sulaiman, FA and Said, SAM (2017). Comparative studies of
3 encapsulation and glass surface modification impacts on PV performance in a desert
4 climate. Solar Energy 142: 288-298.
5 Watt, J, Jarrett, D and Hamilton, R (2008). Dose-response functions for the soiling of heritage
6 materials due to air pollution exposure. Science of the Total Environment 400(1-3): 415-
7 424.
8 Zelinka, MD, Andrews, T, Forster, PM and Taylor, KE (2014). Quantifying components of
9 aerosol-cloud-radiation interactions in climate models. Journal of Geophysical Research:
10 Atmospheres 119(12): 7599-7615.
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APPENDIX A. SUPPLEMENTAL INFORMATION ON
PM AIR QUALITY ANALYSES
This appendix provides supplemental information on the data sources and methods used
to generate the figures and table presented in Chapter 2 of this draft PA. Sections A. 1 to A.4
describe the data sources and methods used to generate figures and tables in section 2.3.2.
Section A.5 describes the data sources and methods used to generate figures and tables in section
2.3.3. Section A.6 describes the data sources and methods used to generate figures and tables in
section 2.4. Section A.7 described the methods used for the comparison on PM2.5 fields in
estimating exposure and relative to design values.
A.l DATA SOURCES AND METHODS FOR GENERATING NATIONAL
PM2 5, PM10, PM10-2 5, AND PM2.5 SPECIATION FIGURES
• PM2.5 annual average and 98th percentile mass concentrations: calculated from regulatory-
quality (Federal Reference Method or Federal Equivalent Method) 24-hour average
values from monitors with at least 75% completeness for each year. When a single site
has multiple monitors, the figure shows the average of the annual averages and 98th
percentiles from each monitor at the site. We downloaded the monitor-level
concentrations for all sites in the United States for all available days (including potential
exceptional events) for 2000-2019 from the EPA's Air Quality System (AQS,
https ://www. epa. gov/aq s)
• PM10 annual average and 98th percentile mass concentrations: calculated from regulatory-
quality (Federal Reference Method or Federal Equivalent Method) 24-hour average
values from monitors with at least 75% completeness for each year. When a single site
has multiple monitors, the figure shows the average of the annual averages and 98th
percentiles from each monitor at the site. We downloaded the monitor-level
concentrations for all sites in the United States for all available days (including potential
exceptional events) for 2000-2019 from the EPA's Air Quality System (AQS,
https ://www. epa. gov/aq s)
• PMi0-2.5 annual average and 98th percentile mass concentrations: calculated from both
regulatory and non-regulatory methods using 24-hour average values from monitors with
at least 75% completeness for each year. When a single site has multiple monitors, the
figure shows the average of the annual averages and 98th percentiles from each monitor at
the site. We downloaded the monitor-level concentrations for all sites in the United
States for all available days (including potential exceptional events) for 2000-2019 from
the EPA's Air Quality System (AQS, https://www.epa.gov/aqs)
• PM2.5 speciated annual average mass concentrations: calculated from filter-based, 24-hour
averages from monitors with at least 75% completeness for each year. We downloaded
data from monitors that are part of the Interagency Monitoring of Protected Visual
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Environments (IMPROVE) network, Chemical Speciation Network (CSN), and the
NCore Multipollutant Monitoring Network for 2017-2019.
• The 2000-2019 trends are calculated from the Pearson correlation coefficient for monitors
having at least 75% of the available years with 75% completeness within each year.
When a single site has multiple monitors, the average of the annual averages and 98th
percentiles from each monitor at the site is taken prior to calculation of the Pearson
correlation coefficient.
A.2 DATA SOURCES AND METHODS FOR GENERATING NEAR-
ROAD PM2 s DESIGN VALUE TABLE AND INCREMENT FIGURES
• PM2.5 design values: calculated using the data handling described by 40 CFR Appendix N
to Part 50 - Interpretation of the National Ambient Air Quality Standards for PM2.5. We
downloaded the design values for all sites in the United States for all available days
(including potential exceptional events) for 2017-2019 from the EPA's Air Quality
System (AQS, https ://www. epa. gov/aq s)
• PM2.5 hourly, daily, and annual average mass concentrations: calculated from regulatory-
quality (Federal Reference Method or Federal Equivalent Method) monitors. When a
single site has multiple monitors, the figures show the average from all monitors at the
site. We downloaded the monitor-level concentrations for all sites in the United States
for all available days (including potential exceptional events) for 2000-2019 from the
EPA's Air Quality System (AQS, https://www.epa.gov/aqs)
A.3 DATA SOURCES FOR SUB-DAILY PM2 5 CONCENTRATION
FIGURE
• PM2.5 hourly average mass concentrations: calculated from regulatory-quality Federal
Equivalent Method monitors. The 2-hour and 5-hour averages were calculated for periods
with each hourly average available. Only sites with a valid annual or 24-hour design
value for 2017-2019 are shown in the figure. The percentages of 2-hour average PM2.5
mass concentrations above 140 ng/in3 at individual sites are illustrated in Figure A-l.
Frequency distributions of 5-hour averages are presented in Figure A-2.
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Figure A-l. Percentages of 2017-2019 2-hour average PM2.5 mass concentrations above 140
Hg/m3.
Sites meeting both NAAQS
Sites violating either NAAQS
Concentration (|ig m"3)
30 60 90 120 150
Concentration (|ig m"3)
6 Figure A-2. Frequency distribution of 2017-2019 4-hour averages for sites meeting both or
7 violating either PM2.5 NAAQS for October to March (blue) and April to September
8 (red).
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Sites meeting both NAAQS
Percentiles (|jg nr3)
Percentiles (|jg m 3)
Sites violating either NAAQS
O
c
CD
3
cr
CD
180
Concentration (jug m"3)
1 (r
10'
10°
10"'
10"
10"
Concentration (|ig m )
Figure A-3. Frequency distribution of 2017-2019 5-hour averages for sites meeting both or
violating either PM2.5 NAAQS for October to March (blue) and April to September
(red).
A.4 DATA SOURCES FOR ULTRAFINE FRACTION OF PM2.5 MASS
FIGURE
• Annual average particle number and mass concentrations for Bondville. IL: calculated
from 24-hour average values for years with 66% data completion in 75% of the months
of the year from 2000-2019. We downloaded the mass concentrations from the EPA's
Air Quality System (AQS, https://www.epa.gov/aqs) and particle number concentrations
from NOAA's Earth System Research Laboratory's Global Monitoring Division
(https://www.esrl.noaa.gov/gmd).
A.5 METHODS FOR PREDICTING AMBIENT PM2.5 BASED ON HYBRID
MODELING APPROACHES
A.5.1 Data Sources for 2011 PM2.5 Spatial Fields
• The "HU2017" fields were provided by Professor Yang Liu of Emory University in the
form of comma-separated-values files (*.csv) of daily average PM2.5 on a national grid.
• The "DI2016" fields were provided by Dr. Qian Di of Harvard in the form of MATLAB
files (*.mat) of daily average PM2.5 on a national grid.
• The "VD2019" fields were provided by Dr. Aaron van Donkelaar in the form of netCDF
files (*.nc) of annual average concentration. These files are also available at:
http://fizz.phvs.dal.ca/~atmos/martin/7page id=140.
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• The "downscaler" files were developed in terms of daily average Downscaler predictions
on a national grid following methods described in the risk assessment appendix.
A.5.2 Data Averaging and Coefficient of Variation
PM2.5 concentration fields were loaded into R version 3.4.4, and daily fields were
averaged to the annual period. Concentrations for each method at prediction points were
then averaged to the corresponding CMAQ grid cells to enable consistent comparisons
for Figure 2-28, Figure 2-29, and Table 2-2.
The coefficient of variation (CoV) was calculated for each grid cell using the following
formula
100
CoV(%) = -=-
F N
if=i (Pi-py
N
where P is the prediction for each of the four methods (i.e., N=4).
A.6 ANALYSES OF BACKGROUND PM
Data sources for Figure 2-38: Smoke and fire detections observed by MODIS in August
2017
- Image was produced using the NASA Worldview platform
(https://worldview.earthdata.nasa.eov/). Layers selected were 1) Corrected
Reflectance and 2) Fires and Thermal Anomalies, both from Aqua/MODIS. Day
selected was August 4, 2017.
Data sources for Figure 2-39: Fine PM mass time series during 2017 from North Cascades
IMPROVE site
- Image was archived from the IMPROVE website
("http://views.cira.colostate.edu/fed/SiteBrowser/Default.aspx?appkev=SBCF Pm
HazeComp; hosted by CIRA/CSU and sponsored by NPS and USFS) for the
North Cascades (NOCA1) site in 2017.
Data sources for Figure 2-40: Speciated annual average fine PM mass from IMPROVE at
select remote monitors in 2004 and 2016
- Speciated IMPROVE data from 2004 and 2016
("http://views.cira.colostate.edu/fed/SiteBrowser/Default.aspx?appkev=SBCF
HazeComp) were averaged annually for each monitor. Corresponding monitor
locations are shown in Figure 2-41.
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A.7 COMPARISON OF PM2 5 FIELDS IN ESTIMATING EXPOSURE AND
RELATIVE TO DESIGN VALUES: METHODS
Section 2.3.3.2.4 outlines analyses comparing the PM2.5 concentrations in estimating
exposure relative to design values. Below details the data sources and methods used.
To calculate annual average concentrations over the U.S. for 2000-2016, gridded
concentration fields were obtained based on the DI2019 (Di et al., 2019) and the HA2020
(Hammer et al., 2020) and (Van Donkelaar et al., 2019) methods. The DI2019 concentrations
were acquired from a Google Drive and the HA2020 concentrations (version V4.NA.03) were
acquired from a web link. To identify grid cells that fall within the contiguous U.S. and Core
Based Statistical Areas (CBSAs) boundaries, cartographic boundary shapefiles
("cb_2017_us_state_5m" and "cb_2017_us_cbsa_5m") were downloaded from the census.gov
website. The concentration data and shapefiles were read into R version 3.62 (R Core Team,
2019), and grid cells within the contiguous U.S. and CBS As were identified using the Simple
Features package version 0.8-0 (Pebesma, 2018) in R. Average concentrations were then
calculated for each year and for each region (i.e., contiguous U.S. and CBSAs within the
contiguous U.S.) using the dplyr package version 0.8.3 (Wickham et al., 2019) in R.
To generate the population-weighting for the DI2019 and HA2020 PM2.5 concentrations,
2015 gridded population counts at 0.05x0.05° from the fourth version of the Gridded Population
of the World (GPWv4; https://sedac.ciesin.columbia.edu/data/collection/gpw-v4) were spatially-
collocated with the PM2.5 concentrations surfaces after conversion to latitude-longitude
coordinates. A similar CBS A filtering was performed for the gridded population and spatially-
collocated PM2.5 surfaces from DI2019 and HA2020 and the fractional population for each grid
was multiplied by the PM2.5 concentrations within each CBS A.
Regulatory design values were calculated using the data handling described by 40 CFR
Appendix N to Part 50 - Interpretation of the National Ambient Air Quality Standards for PM2.5,
by CBS A, for each 3-year period of available hybrid modeling surface data from the EPA's Air
Quality System (AQS, https://www.epa.gov/aqs). Within each CBSA, by each 3-year period, the
ratio of design values to estimated PM2.5 concentrations was calculated.
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REFERENCES
Di, Q, Amini, H, Shi, L, Kloog, I, Silvern, R, Kelly, J, Sabath, MB, Choirat, C, Koutrakis, P and
Lyapustin, A (2019). An ensemble-based model of PM2.5 concentration across the
contiguous United States with high spatiotemporal resolution. Environment International
130: 104909.
Hammer, MS, van Donkelaar, A, Li, C, Lyapustin, A, Sayer, AM, Hsu, NC, Levy, RC, Garay,
MJ, Kalashnikova, OV and Kahn, RA (2020). Global estimates and long-term trends of
fine particulate matter concentrations (1998-2018). Environmental Science &
Technology 54(13): 7879-7890.
Pebesma, EJ (2018). Simple features for R: standardized support for spatial vector data. R J.
10(1): 439.
R Core Team (2019). R: A language and environment for statistical computing R Foundation for
Statistical Computing Vienna, Austria.
Van Donkelaar, A, Martin, RV, Li, C and Burnett, RT (2019). Regional estimates of chemical
composition of fine particulate matter using a combined geoscience-statistical method
with information from satellites, models, and monitors. Environmental science &
technology 53(5): 2595-2611.
Wickham, H, Francis, R, Henry, L and Miiller, K (2019). dplyr: A Grammar of Data
Manipulation. R package version 0.8.3.
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APPENDIX B. SUPPLEMENTAL STUDY
INFORMATION: SELECTION CRITERIA, STUDY
METHODS AND DETAILS
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1 TABLE OF CONTENTS
2 B.l Forest Plots B-l
3 B.2 Monitored PM2.5 Concentrations in Key Epidemiologic Studies B-l
4 B.3 Hybrid Model Predicted PM2.5 Concentrations in Key Epidemiologic Studies B-3
5 B.4 Details of Key Epidemiologic Studies, Including Study Design, Exposure Metric, and
6 Statistical Analysis B-8
7 References B-8 5
8
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This appendix presents supplemental information on the key epidemiologic studies
evaluated in section 3.3.3 of this draft PA. Section B.l provides supplemental information on the
forest plots presented in Figures 3-3 to 3-6. Section B.2 provide supplemental information on the
study-reported PM2.5 concentrations presented in Figure 3-8, Figure 3-9, while section B.3
provides supplemental information on studies presented Figure 3-10 and Figure 3-11. Section
B.4 provides details on key elements of epidemiologic studies, including the study design and
details on the statistical analyses employed, including control for confounding effects.
B.l FOREST PLOTS
Figure 3-3 through 3-6 in Chapter 3 present forest plots that include the effect estimates
and 95% confidence intervals from 92 epidemiologic studies that were assessed in the 2019 ISA
and draft ISA Supplement that have the potential to be most informative in reaching conclusions
on the adequacy of the current primary PM2.5 standards. Epidemiologic studies included in these
figures support "causal" or "likely to be causal" relationships with PM exposures in the 2019
ISA and include mortality (all-cause mortality, cardiovascular (CVD) mortality, respiratory
mortality, lung cancer mortality), and morbidity (asthma incidence, lung cancer incidence, lung
function and lung development, CVD and respiratory emergency room visit or hospital
admission) health endpoints. Further, studies included in Figure 3-3 to Figure 3-6 were restricted
to multi-city studies in the United States or Canada. Multi-city studies within a single State were
not included, with the exception of respiratory morbidity endpoints, where multi-city studies
were limited (U.S. EPA, 2019). For some of the major cohort studies included in the 2009 ISA,
like the American Cancer Society (ACS) cohort, we included more recent studies that reanalyze
epidemiologic associations for multiple mortality endpoints (e.g. lung cancer mortality and IHD
mortality) and an extension of follow-up periods (e.g., Pope et al., 2015a, Turner et al. (2016),
Jerrett et al. (2016), and Thurston et al. (2016b)), as well as a reanalysis (Krewski et al. (2009) of
the original ACS dataset, including an extended follow-up period, that was evaluated in the 2009
ISA (U.S. EPA, 2009)).
B.2 MONITORED PM2.5 CONCENTRATIONS IN KEY EPIDEMIOLOGIC
STUDIES
Based on the 92 key studies identified in Figure 3-3 to Figure 3-6, a subset of studies are
depicted in Figure 3-8 and Figure 3-9 and includes key epidemiologic studies that report an
overall study mean or median concentration of PM2.5 (as opposed to a study mean/median range
across study area locations) and based on ambient PM2.5 monitored data. The plots include
studies that report significant effect estimates (29 studies) and studies that report non-significant
effect estimates (4 studies). Further, to be included, only key studies for which the years of air
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quality data used to estimate exposures overlap entirely with the years during which health
events are reported were included. The PM2.5 concentrations reported by studies that estimate
exposures from air quality corresponding to only part of the study period, often including only
the later years of the health1 are not likely to reflect the full ranges of ambient PM2.5
concentrations that contributed to reported associations.2
Some of the key epidemiologic studies assessed in the 2019 ISA also provide city-
specific study mean concentrations and city-specific health events, but this information was not
available in studies evaluated in the draft ISA Supplement. PM2.5 exposure estimates
corresponding to the 10th and 25th percentiles of those events were calculated in the following
manner. City-specific cases and PM2.5 concentrations were input in ascending order by PM2.5
concentration. The city-specific percent of cases was calculated as a proportion of the total study
cases and the cumulative percent of cases was determined. The PM2.5 concentration associated
with the cumulative percent closest to the 10th and 25th percentiles are presented in Figure 3-8
and Figure 3-9 and the cumulative percent values closest to the associated 10th and 25th percentile
values are shown in Table B-l.3 Data for Bell et al. (2008) and Zanobetti and Schwartz (2009)
were previously provided by the study authors, as described in Raj an (2011).
Table B-l. PM2.5 concentrations corresponding to the 25th and 10th percentiles of estimated
health events.
Citation
10th Percentile PM2.5
(|jg/m3) (Cumulative
percent value closest)
25th Percentile PM2.5
(|jg/m3) (Cumulative
percent value closest)
Bell et al. (2008)
9.8
11.5
Franklin et al. (2007)
10.4(11.1%)
12.9 (25.3%)
Stieb et al. (2009)
6.7(16.5%)
6.8 (20.5%)
Szyszkowicz (2009)
6.4(4.1%)
6.5(18.6%)
Zanobetti and Schwartz (2009)
10.3
12.5
1 The following studies do not have an overlap between the years of PM2 5 air quality data and the years during
which health effects are reported: Miller et al., 2007 ; Hart et al., 2011 ; Thurston et al., 2013; Weichenthal et al.,
2014; Pope et al., 2015b ; Villeneuve et al., 2015; Turner et al., 2016; Weichenthal et al., 2016a; Pinault et al.,
2017; Parker et al., 2018; and Pope et al., 2019.
2 This is an issue only for some studies of long-term PM2 5 exposures. While this approach can be reasonable in the
context of an epidemiologic study evaluating health effect associations with long-term PM2 5 exposures, under the
assumption that spatial patterns in PM2 5 concentrations are not appreciably different during time periods for
which air quality information is not available (e.g., Chen et al., 2016), our interest is in understanding the
distribution of ambient PM2 5 concentrations that could have contributed to reported health outcomes.
3 That is, 25% of the total health events occurred in study locations with mean PM2 5 concentrations (i.e., averaged
over the study period) below the 25th percentiles identified in Figure 3-8 and Figure 3-9 and 10% of the total
health events occurred in study locations with mean PM2 5 concentrations below the 10th percentiles identified.
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B.3 HYBRID MODEL PREDICTED PM2 s CONCENTRATIONS IN KEY
EPIDEMIOLOGIC STUDIES
Figure 3-10 and Figure 3-11 focus on multicity/multistate studies in the U.S. and Canada,
that are part of the evidence supporting "causal" or "likely to be causal" determinations in the
2019 ISA and that use hybrid modeling methods to estimate PM2.5 exposures, as well as studies
assessed in the draft ISA Supplement. In addition, as detailed in section 3.2.3.2.1, for studies
included in Figure 3-10 and Figure 3-11 we also consider the approach used to estimate PM2.5
concentrations and the approach used to validate hybrid model predictions when determining
those studies that we identify as key epidemiologic studies. Such studies are identified as those
that use hybrid modeling approaches for which recent methods and models were used (e.g.,
recent versions and configurations of the air quality models); studies that are fused with PM2.5
data from national monitoring networks (i.e., FRM/FEM data); and studies that reported a
thorough model performance evaluation for core years of the study.4
Figure 3-10 and Figure 3-11 present overall means of hybrid model-predicted PM2.5
concentrations for key studies, and the concentrations corresponding to the 25th and 10th
percentiles of estimated exposures or health events, when available. For Di et al. (2017b), we
present 25th and 10th percentiles of annual PM2.5 concentrations by zip code corresponding to
long-term exposure estimates, while for Di et al. (2017a), we present daily air pollution
concentrations (short-term exposure estimates) corresponding to the 25th and 10th percentiles of
deaths at the zip-code level. These values, along with other percentiles, are illustrated in Figure
B-l and Figure B-2 (Jenkins, 2019a, Jenkins, 2019b). The study authors for Di et al. (2017b)
additionally provided information on population weighted percentile values corresponding to
long-term PM2.5 exposure (Chan, 2019). These are presented in Table B-2. For other studies
included in Figure 3-10 and 3-11 [Bai et al., 2019, Erickson et al., 2019, Kloog et al. (2012),
Kloog et al. (2014), Shi et al. (2016), U.S. EPA, 2021, and Wang et al. (2017)], 25th percentiles
of exposure estimates were derived from study manuscripts of air quality descriptive statistics
and can be found in Table B-3.
4 The following studies do not meet these criteria: Bravo et al., 2017, Crouse et al., 2015; Puett et al., 2009, Puett et
al., 2011, Hystad et al., 2012; Hystad et al., 2013, Hayes et al., 2020; Elliott et al., 2020; Lefler et al., 2019;;
Pappin et al., 2019; Cakmak et al., 2018; Fisher et al., 2019; Sun et al., 2019; McClure et al., 2017; Loop et al.,
2018 ; and Honda etal., 2017.
October 2021
B-3
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Percentiles of PM2.5 By Zip Code
Fhreashokfe defining percentiles of Ptvfcj exposure for each zip code.
Percentile of PM2 5, Based on ZIP code PM2 5 Value
0% I c:.>:n:5
71741:46
8.0043245
; 4.ja^c,n;,
9.8273901
1 U Z I. 7 * b j 1
tC°: V SoL'Cu'J-
85% 12.0707952
70% l2 4:ne:7-
7:". 12 ji.'5
opu. i; 41.-J 1.16
85% 13,9765291
90% 14.6375324
I.:": if -I-.1 It DP-
MI0.-
Figure B-l. Percentiles of annual PM2.5 concentrations by zip code corresponding to long-
term exposure estimates in Di et al., 2017b.
October 2021
B-4
Draft - Do Not Quote or Cite
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1 Table B-2. Population weighted percentiles of annual PM2.5 concentrations by zip code
2 corresponding to long-term exposure estimates in Di et al., 2017b.
3
Percentile
Population Weighted PM2.5
(Hg/m3)
0.0
0.0
5.0
7.1
10.0
7.9
15.0
8.6
20.0
9.1
25.0
9.5
30.0
9.9
35.0
10.3
40.0
10.6
45.0
11.0
50.0
11.4
55.0
11.7
60.0
12.1
65.0
12.5
70.0
12.9
75.0
13.4
80.0
13.9
85.0
14.4
90.0
15.1
95.0
16.1
100.0
32.6
4
October 2021
B-5
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Percentiles of PM2.5 By Zip Code
Threasholds defining percentiles of Daily PM2.5 exposure for each zip code.
Percentile of Daily PM2.s, Based on ZIP code PM2.5 Value
0%
0.0006378
5%
3.8286960
10%
4.7224770
15%
5.4309290
20%
6.0727840
25%
6.6863868
30%
7.2922285
35%
7.9031599
40%
8.5292050
45%
9.1836408
50%
9.8740436
55%
10.6124979
60%
11.4111824
65%
12.2910351
70%
13.2835707
75%
14.4301324
80%
15.8159815
85%
17.5894591
90%
20.0959732
95%
24.4759063
100% 201.3071287
Figure B-2. Daily air pollution concentrations (short-term exposure estimates)
corresponding to various percentiles of deaths at the zip-county level in Di et al., 2017a.
October 2021
B-6
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1 Table B-3. PM2.5 concentrations corresponding to the 25th and 10th percentiles of
2 estimated exposures in Figure 3-8.
Citation
10th Percentile PM2.5 (HEJ/m3)
25th Percentile PM2.5 (HE)/™3)
Di et al. (2017a)
4.7
6.7
Di et al. (2017b)
7.3
9.1
Kloog et al. (2012)
6.4
Kloog et al. (2014)
7.9
Shi et al. (2016)
4.6
Shi et al. (2016)
6.2
Wang et al. (2017)
9.1
Bai et al. (2019)
7.9
Christidis etal. (2019)
4.3
Shinet al. (2019)
8
3
October 2021
B-7
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1 B.4 DETAILS OF KEY EPIDEMIOLOGIC STUDIES, INCLUDING STUDY DESIGN, EXPOSURE
2 METRIC, AND STATISTICAL ANALYSIS
3 Table B-4 below summarizes additional details related to the designs of the U.S. and Canadian epidemiologic studies included
4 in Figure 3-3 to 3-6, and Figure 3-8 to Figure 3-11, as well as studies included in the risk assessment (Table 3-13).
5 Table B-4. Study characteristics from key studies.
6
7
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
October 2021
B-8
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-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Baxter etal.,
ST
All-cause
77 US Cities
Time
EPA's National and State
Poisson regression model and
Average daily
2017
mortality
Series
Local Ambient Monitoring
meta-regression
PM2.5 values
study
Stations providing
were calculated
(NCHS)
integrated daily
In stage 1, ran single city
for each city.
measurements and
Poisson time-series models;
First, a global
operated more than 6
adjusted for temperature and
mean and
months or had more than
dew point temperature,
variance were
30 observations (2001-
including variables for previous
created within
2005) considered.
day temperature, temporal
each city for the
Monitors representing the
trends, and trends by age.
entire time
general population
In stage 2, meta-regression with
period. Using the
exposure in the cities
cluster analysis (5 clusters)
valid monitor
were selected. For this
based on characteristics of
measurements.
correlation was assessed
residential infiltration.
Next, all values
between each pair of
were
monitors within the county
standardized and
and the ones uncorrelated
average PM2.5
(coefficients.8 with
within a given
majority of other monitors)
day in each city
were excluded. Once
was calculated.
appropriate valid monitors
Finally, the
were identified the
standardized
summary measure of
daily value was
PM2.5 concentration over
reversed to
the county was calculated.
calculate
average daily
2-day moving average
PM2.5 for each
(lag 0-1 days) of PM2.5
city.
cone included in the
model.
October 2021
B-9
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-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Bell et al., 2008
ST
CVD HA
Age 65+
202 US
Counties with
populations>
200,000
Time
Series
study
(MEDICA
RE
enrollees)
PM2.5 concentrations
obtained from EPA
monitors providing data
daily or every 3 days for
the period 1999-2005.
Used 10% trimmed mean
to calculate daily average
across monitors after
correction for yearly
monitor averages (to
protect against outliers as
applied in Dominici et al.
2006).
Used lagO PM2.5 in the
model.
2-stage Bayesian hierarchical
model
In stage 1, adjusted for
temperature and dew point
temperature, including variables
for previous day's conditions,
day-of-the-week, temporal
trends, and differential temporal
trends by age. In stage 2,
county-specific estimates were
combined, accounting for their
statistical uncertainty.
Average daily
PM2.5
concentrations
for each county
used to calculate
overall mean for
the study area
and duration.
October 2021
B-10
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-------
Bell et al., 2014
ST
CVD,
Asthma,
and
COPD HA
Age 65+
4 Counties in
MA and CT
Time-
series
study
(MEDICA
RE
enrollees)
October 2021
PM2.5 Teflon filter samples
(measuring PM2.5 total
mass) obtained from CT
and MA DEP for the
period of 2000-2004.
Used data from five
monitoring locations
(providing daily or every
third day data) within four
county regions. Assigned
daily PM2.5 concentration
from a single monitor to
three counties. For
Fairfield County with two
monitors: daily PM2.5
concentration was
calculated by using
population-weighted
averaging of census tract
PM2.5 concentrations.
First, each census tract in
the Fairfield county (209
tracts in total) was
assigned the PM2.5
exposure of the nearest
monitor. Then, PM2.5
exposures for all tracts
were averaged and
weighted by each tract's
2000 U.S. census
population to calculate a
county-level exposure for
the Fairfield county.
Log-linear Poisson regression
analysis
Adjusted for temperature and
dew point temperature,
including previous day's
temperature and dew point
temperature, day-of-the-week
temporal trends, and region.
Daily PM2.5
concentrations
for all four
counties (three
with single
monitor and one
with two monitors
that used
population
weighted
approach) over
the period of
2000-2004 were
used to calculate
the overall mean
PM2.5for the
study location
and period.
B-ll
Draft - Do Not Quote or Cite
-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Explored various lags and
presented lagO PM2.5
model.
Bell et al., 2015
ST
HF HA
65+
213 U.S.
Counties
Time-
series
study
(MEDICA
RE
enrollees)
Daily monitored PM2.5
data from the US EPA
AQS monitors for the
period of 1999-2010. On
average, county-level
PM2.5 data was available
for 56.5% of study days
(range: 7.8%-99.9%; no
imputation done for
missing data). For each
county, daily PM2.5
measurement was
calculated by averaging
the PM2.5 values from all
monitors within a county
in a given day.
Explored various lags and
presented lagO PM2.5
model.
2-stage Bayesian hierarchical
model
The stage 1 model included
county-specific model adjusted
for weather (temperature, dew
point, previous days'
temperature, and dew point),
day-of-the-week, and temporal
trends. In stage 2 county-
specific effect estimates were
pulled together to present
overall association.
Daily PM2.5
concentrations
for 213 counties
over the period
of 1999-2010
were used to
calculate region-
specific mean
PM2.5, and
overall mean
PM2.5 for the
study location
and period.
October 2021
B-12
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-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Bravo et al.,
2017
ST
CVD HA
Age 65+
418 U.S.
Counties
Time-
series
study
(MEDICA
RE
enrollees)
Daily (24-hr) monitored
PM2.5 data from the US
EPA AQS monitors
(NAMS/SLAMS) obtained
for the period of 2002-
2006. Approximately 80%
of PM2.5 monitors
recorded observation
once every 3 days. For
each county (>=50K
population), daily (24-hr)
PM2.5 concentration was
calculated by averaging
multiple monitor
measurements for the
same day.
Explored various lags and
distributed lags of PM2.5
exposure.
2-stage Bayesian hierarchical
model
The stage 1 included log-linear
Poisson regression models with
over-dispersion fit at county-
level. Model adjusted for same-
day temperature and dew point
temperature, 3-day moving
average of temperature and
dew point temperature,
temporal trends in
hospitalizations, day-of-the-
week, and age. Fitted
distributed lag model with
multiple lags (0- to 7-day lags)
of PM2.5 cone simultaneously in
the county-specific model.
The stage 2 estimated the
association for the entire study
area using two-level normal
independent sampling
estimation with priors thus
allowing to combine risk
estimates across counties while
accounting for within county SE
and between-county variability
in the true RR.
Daily PM2.5
concentrations
for 418 counties
over the period
of 2002-2006
were used to
calculate overall
mean PM2.5 for
the study
location and
period.
October 2021
B-13
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-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Bravo et al.,
2017
ST
CVD HA
Age 65+
708 U.S.
Counties
Time-
series
study
(MEDICA
RE
enrollees)
Daily PM2.5 concentrations
were estimated at census
tract centroids using the
downscaler method (input
from the US EPAAQS
NAMS/SLAMS monitoring
data, and gridded 12x12
km CMAQ) for the period
of 2002-2006. County-
level daily PM2.5
exposures were
calculated from a pop-
weighted averages of
PM2.5 concentrations
predicted at census tract
within each county using
2000 U.S. Census data.
CMAQds was generated
for all days in the study
period 2002-2006.
CMAQds-subset was
calculated by taking
population-weighted
county level exposures
only for counties and days
with monitoring data
(n=418 counties.
Explored various lags and
distributed lags of PM2.5
exposure.
2-stage Bayesian hierarchical
model
The stage 1 included log-linear
Poisson regression models with
over-dispersion fit at county-
level. Model adjusted for same-
day temperature and dew point
temperature, 3-day moving
average of temperature and
dew point temperature,
temporal trends in
hospitalizations, day-of-the-
week, and age. Fitted
distributed lag model with
multiple lags (0- to 7-day lags)
of PM2.5 cone simultaneously in
the county-specific model.
The stage 2 estimated the
association for the entire study
area using two-level normal
independent sampling
estimation with priors thus
allowing to combine risk
estimates across counties while
accounting for within county SE
and between-county variability
in the true RR.
24-hr average
PM2.5
concentrations
for 708 counties
over the period
of 2002-2006
were used to
calculate overall
mean PM2.5 for
the study
location and
period.
October 2021
B-14
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-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Burnett and
Goldberg, 2003
ST
All-cause
mortality
8 Canadian
Cities
Time-
series
study
PM2.5 data obtained from
dichotomous sampler with
Teflon filters operating on
6-day schedule for the
period of 1986-1996.
Each city had one
sampler and two cities
have two samplers. If two
samplers then data was
averaged between the
samplers and assigned to
the city.
Lag 1 explored.
Generalized additive model
(GAM) analysis to generate
pooled estimate of air pollution
effect among the eight cities.
The model adjusted for day-of-
the-week, temporal trends, and
weather variables (daily
average temperature, daily
average relative humidity, and
barometric pressure lagged 0
and 1 days).
Daily PM2.5
concentrations
(day before the
death) for 8
Canadian cities
over the period
of 1986-1996
were averaged to
get overall mean
for the study
area and period
Burnett et al.,
2004
ST
All-cause
mortality
12 Canadian
Cities
Time-
series
study
(data from
Statistics
Canada)
Monitoring data available
for 12 cities from the
Statistics Canada for the
period of 1981-1999.
PM2.5 data available every
6th-day sampling
schedule. Daily PM2.5
concentrations were
calculated for each city by
averaging data over all
monitors with each city.
Explore various lags and
moving average and
presented data for lag 1
for PM2.5.
Random-effects regression
model.
Adjusted for temporal trends in
mortality and effects of weather
using humidex index at lag 0
and lag 1 (a measure of
combined effect of temperature
and humidity)
Daily PM2.5
concentrations
for all 12 cities
over the period
of 1981-1999
were used along
with population
information to
calculate an
overall
population
weighted PM2.5
concentration for
the study
location and
period.
October 2021
B-15
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-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Cakmak et al.,
2018
LT
Non-
accidental,
CVD,
respiratory
and lung
cancer
mortality
Canada
Nationwide
Cohort
study
(CanCHE
C)
PM2.5 estimates obtained
from median satellite-
derived concentrations for
the period of 1998-2011.
The concentration was
determined at 10 km2
resolution as detailed in
(van Donkelaar, 2010).
Changes in PM2.5
between 1998 and 2006
was inferred using
satellite instruments,
MISR and SeaWiFS
(Boys, 2014). Annual
estimates of PM2.5
concentration was
assigned to participants
based on postal code of
residence and was used
to calculate 7-year moving
average (at least 4 out of
7 years of data is
available) PM2.5
concentration for each
year of follow-up in the
study.
Cox proportional hazards
models to estimate the
relationship between long-term
exposure and date of death
accounting for residential
mobility.
Model adjusted for individual-
level covariates (aboriginal
ancestry, minority status,
marital status, education,
immigrant status and income)
The 7-year
moving averages
for study
participants were
then used to
calculate overall
mean PM2.5
concentration (all
and by
geographic
zones)
October 2021
B-16
Draft - Do Not Quote or Cite
-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Chen et al.,
2020
LT
CVD
mortality
Ontario,
Canada
Cohort
study
(ONPHEC
)
PM2.5 concentration
estimated from multiple
satellite retrievals of AOD
combined with
geophysical relationship
between AOD and PM2.5
simulated by GEOS-
Chem, which were then
calibrated with surface
measurements by GWR
as detailed in (van
Donkelaar, 2019). Annual
estimates of
exposure to PM2.5 and the
composition for each
participant was estimated
by interpolating the annual
mean concentrations of
PM2.5 and the
corresponding proportion
of PM2.5 attributed to the
seven major components
to the centroid of their
residential postal code for
that year, thereby
accounting for residential
mobility.
Component-adjusted approach
that jointly estimated the health
impacts of PM2.5 and its major
components while allowing for a
potential nonlinear
PM2.5-outcome relationship.
Compared this approach with
three traditional approaches
using Cox Hazard models.
Adjusted for individual-level
covariates, four time-varying
variables for neighborhood-level
SES, area-level indicators.
Annual PM2.5
concentrations in
the Ontario
region were
then used to
calculate overall
mean PM2.5
concentration for
the study
location and
period.
October 2021
B-17
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-------
Christidis et al.
2019
LT
Non-
accidental
mortality
Canada
Nationwide
Cohort
study
(mCHHS)
October 2021
PM2.5 exposures derived
from AOD retrievals using
GEOS-Chem calibrated to
surface measurements by
GWR (van Donkelaar,
2015). Spatial variation
from modeled surface
used with simulate PM2.5
and constrained with local
ground-based monitors to
estimate PM2.5
concentrations through
2015 (Meng, 2019).
Linked postal codes to
PM2.5 concentrations
using points of latitude
and longitude. When
multiple points of latitude
and longitude was
available for a single
urban postal code, equal
weighting of the multiple
air pollutant values was
used to provide a singular
value. In rural
communities, population-
weighted average of
the values associated with
duplicate postal codes
was used. Used
population-weighing to
average multiple values to
create inputs for partial
postal codes (2 to 5 digit).
For each individual and
year of follow-up, PM2.5
Cox proportional hazard models
to assess the relationship
between PM2.5 exposure and
non-accidental death in low-
exposure environment.
C-R relationship observed using
Shape constrained health
impact function (SCHIF Model)
Adjusted for socio-economic,
behavioral, and time-varying
contextual covariates
3-year moving
average PM2.5
concentrations
were for the
participants used
to calculate
overall mean
PM2.5 concentrati
on for the study
periodr
B-18
Draft - Do Not Quote or Cite
-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
estimates was calculated
as 3-year moving average
with one-year lag.
October 2021
B-19
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-------
Crouse et al.
2012
LT
All-cause
mortality
11 Canadian
Cities
National
Cohort
study
(Subset of
Canadian
census
mortality
follow-up
study;
43%; non-
immigrant
population
October 2021
Monitor data from ground-
based stations available
for 11 cities for the 15-yr
period including the 5-yr
prior to baseline and 10-yr
of follow-up (1987-2001)
from Statistics Canada.
PM2.5 data available every
6th-day sampling
schedule. To address
missing monthly PM2.5
data for some stations,
data from all stations
within 6-km of each other
were pooled to calculate
monthly, seasonal, annual
and five-yr (1987-1991,
1992-1996, 1997-2001)
means at each monitored
location. Mean annual
concentration (averaged
over 1987-2001) from
ground-based monitors
was then assigned to the
cohort member based on
the 11 census divisions of
their residence.
A second set of exposure
(10x10 km) was created
using estimates of PM2.5
from remote sensing
during period 2001-2006
to calculate 6-yr average.
The mean concentration
of PM2.5 within boundaries
of each enumeration area
2 different modelling approach.
Approach 1: Cox proportional
hazards model, and Approach
2: nested, spatial random-
effects Cox model with spatial
clusters.
Models adjusted for individual-
level covariates, urban/rural
indicator, and ecological
covariates (% unemployed, %
without high school diploma,
lowest income quintile, and
rural/urban indicator).
AnnualPM2.5
concentrations
for the study
participants were
used to calculate
overall mean
PM2.5 for the
study population
and duration.
B-20
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-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
was calculated by
overlaying PM surface
over the surface of
enumeration area across
country. Satellite derived
PM2.5 estimate was then
assigned to participants
based on their
enumeration area of
residence in 1991.
October 2021
B-21
Draft - Do Not Quote or Cite
-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Crouse et al.,
2019
LT
Non-
accidental,
CVD,
respiratory
mortality,
and lung
cancer
Canada
Nationwide
Cohort
study
(CanCHE
C)
PM2.5 concentrations
derived from AOD
retrievals using GEOS-
Chem calibrated to
surface measurements by
GWR (van Donkelaar,
2015). Spatial variation
from modeled surface
used with simulate PM2.5
and constrained with local
ground-based monitors to
estimate PM2.5
concentrations through
2015 as detailed in
(Meng, 2019). Linked
postal codes to PM2.5
concentrations from grid
cells. Annual PM2.5
estimates from the postal
code and assigned to
study participants based
on the postal code for
residence was used to
calculate moving
average at various
temporal and spatial
scales based on the
location and year of
follow-up.
Cox Hazard model to assess
the relationship between PM2.5
exposure at different temporal
and spatial scales.
Adjusted for individual-level
variables (aboriginal identity,
visible minority status, marital
status, highest level of
education, employment status,
and household income
adequacy quintiles)
The average
annual PM2.5
concentrations
were used to
calculate overall
mean PM2.5
concentration for
the study
period at various
temporal and
spatial scales.
October 2021
B-22
Draft - Do Not Quote or Cite
-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Dai et al., 2014
ST
All-cause,
75 U.S. Cities
Time-
Monitored data obtained
Two stage: Stage 1. City-
Daily PM2.5
CVD, and
(with
series
from US EPA AQS for the
specific season-stratified time-
concentrations
Respirator
available daily
study
period of 2000-2006. Daily
series analysis using Poisson
for all 75 cities
y mortality
mortality data
(NCHS)
PM2.5 concentrations from
regression in GAM
over the period
and PM2.5
each monitor assigned to
of 2000-2006
data for at
corresponding city. For
Model adjusted for 24-hr
were used to
least 400
cities with more than one
average temperature from
calculate an
days between
sampling site,
closest weather station to the
overall mean
2000 and
concentration data were
city center at lagO and Iag1,
PM2.5
2006)
averaged across all
temporal trends, and day-of-
concentration for
monitors within the city.
the-week. Stage 2. Multivariate
the study
random effects meta-analysis to
location and
Used average of 2-day
combined 300 (i.e. 75 cities * 4
period.
lag (lag 01) PM2.5.
seasons) effect estimates to
obtain overall association.
October 2021
B-23
Draft - Do Not Quote or Cite
-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
deSouza et al.,
2021
ST
First CVD
HA
US
Nationwide
Time-
stratified
case-
crossover
design (M
EDICAID)
PM2.5 concentration were
derived for 1 km2 grid
cells in the continental
United States by
integrating remote
sensing, outputs from a
chemical transport model,
and other variables such
as meteorological and
land-use variables (Di et
al. 2019); from an
ensemble-based model
that integrated multiple
machine learning
algorithms for the period
of 2000-2012. Daily PM2.5
estimates of all grid
cells averaged at-Zip code
were assigned to study
participants based on
the zip code of residence.
Used Iag01 average
exposure in the model.
Conditional logistic regression
models to estimate the
associations between short-
term exposure to PM2.5 and
CVD hospitalization rates.
Adjusted for individual-level
covariates, air and dew-point
temperature.
Daily PM2.5
concentration fro
m case days
were then used
to calculate
overall case
day mean
PM2.5 concentra
tion for the study
location and
period.
October 2021
B-24
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-------
Diet al.,2017b
LT
All-cause
mortality
65+
US
Nationwide
Cohort
(MEDICA
RE
enrollees)
October 2021
Artificial neural network
that incorporated satellite-
based measurements,
simulation outputs from a
chemical transport model,
land-use terms,
meteorological data, and
other data to predict daily
concentrations of PM2.5.
The neural network was fit
with monitored PM2.5 data
and daily PM2.5
concentrations were
predicted for nationwide
grids that were 1x1 km.
While not explicitly
detailed in the study, it
was assumed that the 1
km x 1 km grid cells were
averaged up to the zip
code spatial resolution.
For each calendar year
during which a person
was at risk of death the
annual average PM2.5
concentration was
assigned according to the
ZIP Code of the person's
residence. As part of a
sensitivity analysis,
monitored PM2.5 data was
matched with each person
in the study within a
distance of 50 km of the
nearest monitoring site.
Two-pollutant Cox proportional
hazards model with generalized
estimating equation to account
for correlation between ZIP
codes.
Accounted for individual
variables, (sex, race, Medicaid
eligibility, and average age at
study entry), zip code-level
variables (% Hispanic, % Black,
median household income,
median value of housing, % >
65 living below poverty level, %
> 65 with less than high school
education, % of owner-occupied
housing units, and population
density), county-level variables
(county-level BMI and % ever
smokers), hospital service area-
level variables (% low-density
lipoprotein level measured, %
glycated hemoglobin level
measured, and % >1
ambulatory visits), 32 km2
gridded weather and 1 km2
gridded pollution variables
(annual average PM2.5
concentration, annual average
temperature, and annual
average humidity), monitor level
air pollution variables (PM2.5
monitored data), and a regional
dummy variable.
Average PM2.5
concentrations
for all Zip Codes
(entire US or ZIP
codes with study
participants only)
from 2000 to
2012 were used
to calculate
overall mean
PM2.5 for the
study location
and period.
B-25
Draft - Do Not Quote or Cite
-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Dietal., 2017b
(< 12 ug/m3)
Analysis restricted to
persons-years with PM2.5
exposures lower than 12
ug/m3
October 2021
B-26
Draft - Do Not Quote or Cite
-------
Di et al„ 2017a
ST
All-cause
mortality
65+
US
Nationwide
Case-
crossover
study
(MEDICA
RE
enrollees)
October 2021
Artificial neural network
that incorporated satellite-
based measurements,
simulation outputs from a
chemical transport model,
land-use terms,
meteorological data, and
other data to predict daily
concentrations of PM2.5.
The neural network was fit
with monitored PM2.5 data
and daily PM2.5
concentrations were
predicted for nationwide
grids that were 1x1 km.
For each case day (date
of death) and its control
days, the 24-hour PM2.5
concentrations were
assigned based on zip
code of residence of the
individual.
As part of a sensitivity
analysis, monitored PM2.5
data was matched with
each person in the study
within a distance of 50 km
of the nearest monitoring
site, and cross-validation
was performed between
predicted and monitored
concentrations.
Used average of 2-day lag
(lag 01) PM2 5.
Conditional logistic regression.
"Case Day" defined as death.
For the same person, compared
daily air pollution exposure on
the case day vs. daily air
pollution exposure on "control
days." Control days were
chosen (1) on the same day of
the week as the case day to
control for potential confounding
effect by day of week; (2)
before and after the case day to
control for time trend; and (3)
only in the same month as the
case day to control for seasonal
and sub-seasonal patterns.
Individual-level covariates and
zip code-level covariates that
did not vary day to day (e.g.,
age, sex, race/ethnicity, SES,
smoking, and other behavioral
risk factors) were not
considered to be confounders
as they remain constant when
comparing case days vs control
days.
The regression model adjusted
for air and dew point
temperature.
The case and
control days
PM2.5
concentrations
for study
participants were
averaged to get
mean PM2.5
concentration for
the study area
and period.
B-27
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-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Dominici et al.,
2006
ST
HF and
COPD HA
65+
204 Urban
U.S. counties
Time-
series
study
(MEDICA
RE
enrollees)
Monitored PM2.5
concentrations available
from US EPA AQS for the
period of 1999-2002. Of
the 204 counties
(>200,000 population), 90
counties had daily PM2.5
data across the study
period and the remaining
counties had PM2.5 data
collected once every 3
days for at least 1 full
year. To protect against
consequences of outliers,
used 10% trimmed mean
to calculate daily average
across monitors after
correction for yearly
averages for each
monitor.
Various lags (lag 0,1, 2
days) and distributed lags
assessed and presented.
2-stage Bayesian hierarchical
models to estimate county-
specific, region-specific, and
national-average associations.
Stage 1 model included single
lag and distributed lag over-
dispersed Poisson regression
models to estimate county-
specific risk. Models adjusted
for temperature and dew point
on the same day and the 3
previous days, calendar time to
control for seasonality and other
time-varying influences, daily
numbers of individuals at risk,
and day-of-the-week. In Stage
2, to produce a national
average estimate, Bayesian
hierarchical models were used
to combine RRs across
counties and accounting for
within-county statistical error
and for between-county
variability or heterogeneity. To
produce regional estimates.
The Stage 2 hierarchical
models described above was
used for 7 regions separately.
Daily PM2.5
concentrations
for all 204 US
counties over the
period of 1999-
2002 were used
to calculate an
overall mean
PM2.5
concentration for
the study regions
and period.
October 2021
B-28
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-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Dominici et al.,
2019
LT
Non-
accidental
mortality
Nationwide
Cohort
study
(MEDICA
RE)
Artificial neural network
that incorporated satellite-
based measurements,
simulation outputs from a
chemical transport model,
land-use terms,
meteorological data, and
other data was used to
predict daily
concentrations of PM2.5 (Di
et al. 2017). Daily PM2.5
concentrations were
predicted for nationwide
grids at1 km2 resolution
for the period 2000-2012.
Survival analyses using the
Andersen-Gill method, a variant
of the traditional Cox
proportional hazards model
C-R relationship assessed
fitting a log-linear model with
thin-plate splines.
Adjusted for individual-level
covariates, county-and ZIP
code-level variables,
meteorological variables, and
other area-level variables.
Daily PM2.5
concentrations
over all ZIP
codes were used
to calculate
overall mean
PM2.5 for the
study location
and period.
October 2021
B-29
Draft - Do Not Quote or Cite
-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Erickson et al.,
LT
Non-
Canada
Cohort
PM2.5 exposures derived
Cox proportional hazards
The average
2020
accidental,
Nationwide
study
from AOD retrievals using
models to examine the
PM2.5
CVD,
(CanCHE
GEOS-Chem calibrated to
associations between ambient
concentrations
respiratory
C)
surface measurements by
PM2.5 exposure and non-
were used to
mortality,
GWR (van Donkelaar,
accidental and cause-specific
calculate overall
and lung
2015). Linked postal
mortality.
mean PM2.5
cancer
codes to PM2.5
Adjusted for individual-level and
concentration for
concentrations from grid
contextual-level covariates.
the study
cells. Annual PM2.5
period by
estimates from the postal
immigrant status
code and assigned to
and duration in
study participants based
Canada.
on the postal code for
residence was used to
calculate 3-year moving
average based on the
location and year of
follow-up.
October 2021
B-30
Draft - Do Not Quote or Cite
-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Eum et al., 2018
LT
All-cause
Geographic
Cohort
PM2.5 concentration
Age-stratified log-linear model
Annual average
mortality
regions:
study
obtained from US EPA's
including offset terms for the
PM2.5 concentrati
"East" of the
(MEDICA
AQS for the period of
size of the population as a base
ons were used to
Mississippi
RE)
2000-2012. Monitoring
model. Also included the
calculate overall
River,
sites with daily
temporal and spatiotemporal
mean
"Center"
measurements for at least
components. Ran base model
concentration for
between the
8 calendar years with
using data for entire 13-year
the study
Mississippi
each year having 9+
study period (2000-2012) and
location (all and
River and the
months and with 4+daily
for shorter periods ranging
by study region)
Sierra
measurements included.
between 3 and 12 years and
and study
Nevada
798 sites then were used
compared MRRs to assess
period.
mountain
to calculate long-term
temporal confounding. In
range, and
concentration (yearly
addition to base model, also
"West" of the
moving average with 350+
assessed temporal confounding
Sierra
days of valid data) using
using three approaches
Nevada
Greven et al. Annual
(decomposition-based, residual-
mountain
average assigned to
based, and spline models)
range
individuals that lived in
Adjusted for individual-
ZIP codes with centroids
covariates, as well as county-
within 6 miles of a valid
level behavioral covariates, %
monitor.
of non-whites, smoking status,
comorbidities, access to health
care, income, and BMI.
October 2021
B-31
Draft - Do Not Quote or Cite
-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Fisher et al.,
ST
Stroke
Nationwide
Time-
Validated national-scale,
Conditional logistic regression
Daily PM2.5
2019
(Self-
stratified
log-normal ordinary
models
concentration on
reported
case-
kriging model for PM2.5
Adjusted for mean daily
the case day
stroke
crossover
were used to estimate
temperature, and stratified
were used to
adjudicate
study
daily PM2.5 concentration.
models to examine effect
calculate overall
d by
(HPFS)
US EPA's AQS data used
modification by individual-level
case day PM2.5
physician
to calculate monitor
characteristics.
mean for the
medical
specific daily averages
study period.
record)
(monitors >=18 hours
measures). These inputs
were then used to
produced kriged surfaces
of daily mean PM2.5
concentrations at the
geocoded residential
addresses of all HPFS
participants for the period
1999-2010.
Lag periods up to 3 days
prior to the stroke event
and a 4-day average used
in model.
October 2021
B-32
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-------
Franklin et al.
2007
ST
All-cause,
CVD, and
Respirator
y mortality
27 U.S.
communities
(with PM2.5
monitoring
and daily
mortality data
for at least 2
years of 6-
year study
period 1997-
2000)
Case-
crossover
study
(NCHS)
October 2021
Monitored daily PM2.5
concentrations available
from US EPAAQS
(NAMS/SLAMS) for the
period of 1997-2000. Data
for Boston area available
from Harvard University.
To determine which
monitors in the county are
representative of
exposure for a general
population in the county,
correlation was assessed
between monitor pairs
and excluded the monitors
with r<0.8 for 2 or more
monitor pairs. Once
appropriate monitors were
identified then a summary
measure of PM2.5 cone for
the county was calculated
using alternate averaging
method described in
Schwartz 2000 to account
for data availability
variation (daily vs 3-6
days for each monitors in
the county) and calculate
daily average PM2.5 cone
for each of the 27
counties and
corresponding
communities.
2-stage time-stratified analysis:
1) Conditional logistic
regression analysis to generate
community specific estimates;
2) Meta-regression analysis to
combined community specific
estimates to generate overall
pooled effect estimate.
Stage 1 of the model adjusted
for day-of-the-week, as well as
apparent temperature at lagO
and lagl Cases were defined
as "deaths" and control days for
a particular subject were
chosen to be every third day
within the same month and year
that death occurred. Effect
modification of age and gender
was examined using interaction
terms in stage 1, while effect
modification of community-
specific characteristics including
geographic location, annual
PM2.5 concentration > 15 ug/m3
and central AC prevalence was
used in stage 2.
Daily PM2.5
concentrations
for all 27 US
communities
over the period
of 1997-2000
were used to
calculate an
overall mean
PM2.5
concentration for
the study
location and
period.
B-33
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-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Calculated and presented
various lags and averages
for PM2.5.
October 2021
B-34
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-------
Franklin et al.
2008
ST
All-cause,
CVD, and
Respirator
y mortality
25U.S
communities
(with PM2.5
monitoring
and daily
mortality data
for at least 4
years of 6-
year period
between
2000-2005)
Case-
crossover
study
(NCHS)
October 2021
Monitored daily PM2.5
concentrations available
from US EPAAQS
(NAMS/SLAMS) for the
period of 2000-2005. Data
for Boston area available
from Harvard University.
To determine which
monitors in the county are
representative of
exposure for a general
population in the county,
correlation was assessed
between monitor pairs
and excluded the monitors
with r<0.8 for 2 or more
monitor pairs. Once
appropriate monitors were
identified then a summary
measure of PM2.5 cone for
the county was calculated
using alternate averaging
method described in
Schwartz 2000 to account
for data availability
variation (daily vs 3-6
days for each monitors in
the county) and calculate
daily average PM2.5 cone
for each of the 27
counties and
corresponding
communities.
2-stage time-stratified analysis:
1) Conditional logistic
regression analysis to generate
community specific estimates;
2) Meta-regression analysis to
combined community specific
estimates to generate overall
pooled effect estimate.
Stage 1 of the model adjusted
for day-of-the-week, as well as
apparent temperature at lagO
and lagl Cases were defined
as "deaths" and control days for
a particular subject were
chosen to be every third day
within the same month and year
that death occurred. Effect
modification of age and gender
was examined using interaction
terms in stage 1.
Daily PM2.5
concentrations
for all 25 US
communities
over the period
of 2000-2005
were used to
calculate an
overall mean
PM2.5
concentration for
the study
location and
period (overall
and by seasons).
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-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Calculated and presented
various lags and averages
for PM2.5.
Gharibvand et
al., 2016
LT
Lung
cancer
incidence
US
Nationwide
Cohort
study
(AHSMOG
-2 study)
Monitor data obtained
from US EPA AQS for the
period of 2000-2001 (2-
year prior to start of the
study). Using monitored
PM2.5 data, inverse
distance weighted
interpolations methods,
monthly pollution surfaces
for PM2.5 were created for
the US. Monthly exposure
averages were based on
daily PM2.5
measurements. Only
months with at least 75%
valid data were included
in the exposure
estimation. Participants
were assigned monthly
exposure based on their
baseline residential
address.
Cox proportional hazards model
Covariates included sex, race,
smoking status, years since
participant quit smoking,
average number of cigarettes
per day during all smoking
years, and education level.
Additional covariates included
calendar time, alcohol
consumption, family income,
BMI, physical activity, and
marital status. 3 variables
identified a priori as either as
confounders or effect modifiers:
hours/day spent outdoors,
years of pre-study residence
length at enrollment address,
and moving distance from
enrollment address during
follow-up.
Monthly PM2.5
concentrations
for study
participants were
used to calculate
overall 2-yr mean
PM2.5 for the
study period
2000-2001.
October 2021
B-36
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-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Hart et al., 2015
LT
All-cause
US
Cohort
Monitored data obtained
Cox proportional hazards
Monthly PM2.5
(monitored)
mortality
Nationwide
study
from US EPA AQS for the
model.
concentrations at
(Nurses'
period 1999-2006.
residence
Health
Monthly average PM2.5
Information on potential
locations during
study)
concentration calculated
confounders was available
the follow-up
from the nearest
every two years (4 years for diet
period of 2000 to
monitoring location for all
information) and each woman
2012 were
addresses. The monthly
was assigned updated
averaged to
data was again averaged
covariate values for each
calculate overall
to get the previous 12-
questionnaire cycle.
mean PM2.5
month moving average at
Confounders examined include
exposure for the
each residential address
age, race, region, season,
participants
prior to mortality.
physical activity, BMI,
included in the
Nearest monitor
hypercholesterolemia, family
study.
exposures were validated
history
against personal
of Ml, smoking history, Current
exposures to PM2.5 of
smoking status, diet, SES
ambient origin.
(education level, occupation of
October 2021
B-37
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-------
Hart et al., 2015
(modeled)
LT
All-cause
mortality
US
Nationwide
Cohort
study
(Nurses'
Health
study)
October 2021
Spatio-temporal models
(developed using
monitored data from US
EPAAQS, the IMPROVE
network, and also
included meteorological
and GIS-derived
covariates, such as urban
land use within 1 km,
elevation, tract- and
county-level population
density, distance to the
nearest road for road
classes A1-A3 and point-
source emission density
within 7.5 km) was used
to estimate monthly PM2.5
exposures at each
geocoded address.
The monthly data was
again averaged to get the
previous 12-month
moving average prior to
mortality for each
residential address.
Modeled exposures were
validated against personal
exposures to PM2.5 of
ambient origin.
Previous 12-month
moving average of
exposure either from
nearest monitor or spatio-
temporal models were
both of the nurses' parents
when she was 16, marital
status, and husband's
education if applicable). Also
adjusted for area-level SES
(census tract level median
income and house value), and
long-term temporal trends.
Risk set regression calibration
for time-varying exposures was
used to correct for bias due to
exposure measurement error in
the hazard ratios of all-cause
mortality using the personal
exposure validation data.
B-38
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-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
assigned to study
participants.
Hayes etal.,
2020
LT
CVD
mortality
6 US states
(California,
Florida,
Louisiana,
New Jersey,
North
Carolina, and
Pennsylvania)
and 2 urban
areas
(Atlanta, GA,
and Detroit,
Ml,)
Cohort
study
(NIH-
AARP Diet
and
Health
study)
Modelled (Hybrid land use
regression geostatistical
model developed by Kim
et al. 2017) for the period
of 1980-2010. Mean
annual estimates of
PM2.5 for each census
tract in the US from
spatio-temporal model
were used till 1998. For
period 1999-2010,
monitored US
EPA monitor and
IMPROVE network was
used to derive annual
average estimates.
Annual average PM2.5
concentrations assigned
at census tract level
lagged by 1 year in time-
dependent manner.
Annual PM2.5 exposure
analyzed as continuous
and categorical <8, 8-<12,
12-<20, and 20+ ug/m3
variables.
Cox regression modelling with
time-dependent covariates.
Adjusted for individual-level
variables (age, race/ethnicity,
education, marital status, BMI,
alcohol, and smoking status),
as well as census tract
variables.
Annual PM2.5
concentrations of
the study
participants for
the year 2000
was used to
calculate overall
mean PM2.5
concentration for
the period 2000.
October 2021
B-39
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-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Ito et al., 20135
ST
All-cause
mortality
150 U.S.
cities
Time-
series
study
24-hr average PM2.5 mass
data in a given city, and
when data from multiple
monitors were available in
a given city, computed the
average of the daily
values after standardizing
each site's data using the
mean and standard
deviation of the sites data.
Pollutant concentration is
expressed in the model as
a deviation from the
monthly mean to reduce
the influence of the
seasonal cycles of the
pollutants on the overall
associations and help
focus on the short-term
associations.
Poisson regression analysis
First city- and season-specific
Poisson regression was run,
and then city-specific estimates
were combined using random
effects approach
Adjusted for temporal trends
(annual cycles and influenza
epidemics), immediate and
delayed temperature, and day-
of-week pattern, for entire years
(2001-2006) and for warm
(April-September) and cold
(October-March) seasons.
In second stage, assessed
effect modification using land-
use variables and average air
pollution levels.
5 This study is not referenced individually in the ISA, but is study 3 of the National Particle Component Toxicity (NPACT) Initiative published in HEI
(Lippmann et al., 2013).
October 2021
B-40
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-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Jerrettet al.,
2016
LT
IHD
mortality
30+
U.S.
Nationwide
Cohort
study
(ACS
Cancer
Prevention
Study II)
Multiple exposure
estimation approaches
evaluated within the study
- risk assessment uses
results based on an
ensemble approach that
incorporates chemical
transport modeling, land
use data, satellite data,
and data from ground-
based monitors
Cox proportional hazards
regression
Covariates included current and
former smoking status as well
as smoking duration, amount,
age started, second hand
cigarette smoke (hours/day
exposed), exposure to PM2.5 in
the workplace for each of the
subject's major lifetime
occupation, self-reported
exposure to dust/fumes at work,
marital status, level of
education, BMI, alcohol
consumption, dietary
vegetable/fruit/fiber index,
dietary fat index, missing
nutrition information. Ecologic
characteristics included median
household income, percentage
of people with < 125% of
poverty-level income,
percentage of persons > 16
who are unemployed,
percentage of adults with < 12th
grade education, and
percentage of population who
were Black or Hispanic.
October 2021
B-41
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-------
Kioumourtzoglou
LT
All-cause
207 U.S.
Open
Monitored data available
2-stage approach for modelling.
Annual PM2.5
et al., 2016
mortality
cities
Cohort
from US EPA AQS for the
concentrations
65+
study
(MEDICA
RE
enrollees)
period of 2000-2010. City-
specific annual and 2-year
PM2.5 averages was
calculated using data from
all available monitors in
each city.
In Stage 1, Cox proportional
hazards model was fit for each
city stratified by age, gender,
race, and follow-up time in
study. Control for slowly varying
potential confounders (e.g.,
SES) and confounders that vary
across subjects, city, and time.
City-characteristics for:
proportion of city population >
65, median household income,
proportion in poverty, proportion
of city families in poverty,
proportion of white, black, and
Asian residents, proportion of
residents with/without high-
school degrees and a college
degree, and city-specific
smoking and obesity rates.
Population-weighted city
averages were developed
based on census data at the
county level. Also included
average annual temperature in
the model.
In stage 2, combined the city-
specific estimates using a
random effects meta-analysis to
generate region-specific effects.
Assessed effect modification by
annual temperature levels, and
population and city
characteristics (greenness,
for 207 cities
during the period
of 2000 to 2010
were averaged to
calculate overall
mean PM2.5
exposure for the
study location (all
and region
specific) and
study period.
October 2021
B-42
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-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
poverty, racial composition,
etc.).
Klemm and
Mason, 2003
ST
All-cause
mortality
Harvard Six-
City study
reanalysis
Time-
series
study
24-hour PM2.5
concentration obtained
from Dichotomous
samplers placed at the
central residential
monitoring sites in each of
the six cities. Integrated
24-hour samples were
collected daily for part of
the study periods but were
collected at least every
other day until the late
1980s.
Generalized additive and
Generalized linear models
Model adjusted for temporal
trends, day-of-the-week,
weather (average daily
temperature and average daily
dew point temperature).
Daily PM2.5
concentration of
six cities over the
period of 1979-
1988 were used
to calculate
overall mean,
median and
percentiles of
PM2.5 exposure
for the study
location (all and
by study center)
and period.
October 2021
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-------
Kloog et al.,
2012
ST, LT
CVD HA
Age 65+
New England
Area with 6
U.S. States
Mixed
study
design
(with time
series and
cohort
componen
ts)
October 2021
Spatiotemporal model:
Used day-specific
calibrations of aerosol
optical depth (AOD) data,
using ground PM2.5
measurements.
Incorporated land use
regressions and
meteorological variables
(temperature, wind speed,
visibility, elevation,
distance to major road,
percent of open space,
point emissions and area
emissions) for the period
of 2000-2006. Model
predicted daily PM2.5
concentrations at a 10 x
10 km spatial resolution.
The PM2.5 concentration
then was matched to ZIP
codes based on spatial
location and date.
Short-term exposure:
used the mean of PM2.5on
the day of admission and
day before admission.
Long-term exposure:
calculated as the mean
exposure in each zip-code
across the 7-year study
period. Short term
exposure was defined as
the difference between
the two-day average and
the long-term average.
Equivalence between Poisson
regression and the piecewise
constant proportional hazard
model to model the time to a
hospital admission as a function
of both long-term and short-
term exposure simultaneously
and enabling simultaneously
examination of short term and
long-term associations with
hospital admissions
(Hierarchical mixed Poisson
regression model).
The model adjusts for
temperature, age, percent
minorities, median income, and
percent of people with no high
school education.
Daily PM2.5
concentration of
all grids within
the NE area for
the acute (0 day
lag) and chronic
(365 day moving
average) were
used to calculate
overall mean
short- and long-
term PM2.5
exposure
respectively, for
the study
location and
period.
B-44
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-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Kloog et al.,
2014
ST
CVD and
COPD HA
Age 65+
7 U.S. Mid-
Atlantic
States and
D.C.
Case-
crossover
design
(MEDICA
RE
enrollees)
Spatiotemporal model:
Used day-specific
calibrations of aerosol
optical depth (AOD) data,
using ground PM2.5
measurements.
Incorporated land use
regression (elevation,
distance to major roads,
percent of open space,
point emissions and area
emissions) and
meteorological variables
(temperature, wind speed,
relative humidity, and
visibility) for the period of
2000-2006. Model used
to predict daily PM2.5
concentrations at a 10 x
10 km spatial resolution.
Daily predicted PM2.5
exposure estimates at
grids were matched to zip
codes.
Average of 2-day lag (lag
0 and 1) PM2.5 used.
Conditional logistic regression
analysis
Temperature with the same
moving average as PM2.5 was
included in the model as a
potential confounder.
Study design samples only
cases and compares each
subject's exposure experience
in a time period just before a
case-defining event with the
subject's exposure at other
times, eliminating confounding
(unmeasured or measured) that
do not vary over time. Cases
were matched on day of the
week and defined the relevant
exposure time window as the
mean exposure of the day of
and day before the patient's
hospital admission. Effect
modification: 1) assessed
whether subject residence
within 30 km of a monitor or
farther modified the PM2.5
association; 2) examined
interaction between exposure
and income level and gender.
2-day moving
average of PM2.5
concentration of
all grids within
the mid-Atlantic
states were used
to calculate
overall mean (all
area and
rural/urban
areas) PM2.5
exposure for the
study location
and period.
October 2021
B-45
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-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Krall et al., 2013
ST
All-cause
mortality
72 Urban
U.S.
Communities
Time-
series
study
(NCHS)
Monitored data available
from US EPA AQS for the
period of 2000-2005.
Excluded data from
source-oriented monitors
that may not be
representative of typical
population exposures.
Daily community-level
pollutant exposure as the
arithmetic mean of daily
monitor observations
within the community. For
communities with single
monitor pollutant
concentration represented
concentrations recorded
by that monitor.
Used lag 1 PM2.5 in
model.
Log-linear Poisson Regression
Model
Model adjusted for temperature
and previous day's
temperature, long-term and
seasonal trends, age, and day-
of-the-week. Also included
interaction term for pollutant
concentration and seasons.
Daily PM2.5
concentration of
72 US urban
communities
were used to
calculate overall
mean PM2.5
exposure for the
study location
and period.
October 2021
B-46
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-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Krall et al., 2018
ST
ED Visits
Multi-city (5
Time-
PM2.5 concentrations
Poisson time- series regression
Daily (24-hr) PM
for CVD
Metropolitan
series
obtained from ambient
model accounting for over-
concentrations
(CHF,
areas)
study
monitoring stations
dispersion (Peng et al. 2009;
for specific cities
Cardiac
(Electronic
located within each of the
Krall et al. 2013) to calculate
were used to
dysrhythm
billing of
metropolitan areas were
city specific associations. To
calculate overall
ia, IHD,
ED visits)
fused with Community
calculate overall and posterior
PM2.5
Stroke) or
Multi-Scale Air Quality
city-specific associations,
concentration by
RD
model estimates (Friberg
applied Bayesian hierarchical
city for the study
(asthma/w
et al, 2016, 2017) to
models (Everson and Morris
period.
heeze,
obtain population-
2000).
COPD,
weighted average
pneumoni
estimates of the 24-hour
Adjusted for weekday, season,
a, URI)
average PM2.5
holidays, metrology, temporal
concentrations.
trends.
October 2021
B-47
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-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Lavigne et al.,
2018
ST
Non-
accidental,
CVD, and
respiratory
mortality
24 Canadian
cities
Case-
crossover
study
Daily (24-hour) average
PM2.5 concentrations
obtained from monitors in
Canada's NAPS network
and were used to estimate
PM2.5 concentrations for
the period of 1998-2011.
Exposure estimates were
assigned to each study
participant based on the
monitoring station(s)
located in participants' city
of residence. If PM2.5
measurements were
available from multiple
monitors in a single city,
daily concentrations were
averaged across
monitors.
Conditional logistic regression
analysis.
Performed stratified analyses
examining the relationship
between PM2.5 and mortality
across tertiles of Oxidant
capacity.
Daily PM2.5
concentrations in
24 Canadian
cities were used
to
calculate overall
mean PM2.5
concentration
over the study
location and
period.
October 2021
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-------
Lee et al., 2015
ST
All-cause,
Cardiovas
cular,
respiratory
mortality
3 U.S.
Southeast
States
Case-
crossover
design
(Dept. of
Pub
Health
data)
October 2021
Spatio-temporal model
that used satellite AOD
data to predict daily PM2.5
at 1X1 km resolution for
the period of 2007-2011.
Daily PM2.5 concentration
at 1km grids were
aggregated into the zip
code level. For this, 1 km
grid cells were matched to
zip code area by
assigning the centroid of
each 1 km grid cell to the
centroid of the closest zip
code. Zip code areas that
contained one or more 1
km grid cells were given
the averaged PM2.5 and
zip codes that were
smaller than 1 km2 were
given the predictions from
the closest grid cell.
Finally, PM2.5
concentrations from zip
codes were assigned to
the study participants
based on their residence
zip code and for specific
days.
Conditional logistic regression
Model adjusted for temperature
and day of the week
Also ran stratified analysis by
age, sex, race, education, and
primary cause of death.
Analysis also restricted for zip
codes where annual average of
PM2.5 <12 or daily average <35
separately.
Sensitivity analysis: potential
non-linear relationship between
temp and mortality modelled
using natural spline to the
temperature term.
Daily PM2.5
concentrations
for ZIP code from
2007-2011 were
averaged to get
overall mean
PM2.5 (all states
and by state)
For sensitivity: Daily
monitored PM2.5
concentrations from the
nearest EPA and
IMPROVE monitors from
B-49
Draft - Do Not Quote or Cite
-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
resident zip code (no
distance limit) were
identified and assigned to
individuals.
Used lagO and Iag1 in
model.
October 2021
B-50
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-------
Lefler et al.
2019
LT
All-cause
mortality,
Cardiopul
monary
mortality
Nationwide
Cohort
study
(NHIS)
October 2021
Annual average PM2.5 was
modeled using regulatory
monitors and land use
data as described in (Kim,
2018). PM2.5 exposure
prior to 1999 were
estimated using PM10
data. Estimates for each
pollutant-year through
2015
were generated at the
census-block level using
year-2010
Census block centroids.
Tract-level estimates for
year 2000 Census tracts
and year-2010 Census
tracts were estimated by
mapping year-2010
Census blocks to census
tracts and then calculating
a population-weighted
average of the census
blocks within a census
tract. PM2.5 exposure
estimates were assigned
to home census tracts as
either 2-year (i.e., cohort
year and previous year) or
5-year (i.e., cohort year
and previous 4 years)
average PM2.5
concentrations, 17-year
average PM2.5
concentrations (1999 -
2015), or 28-year average
Cox hazard model 2 versions:
Basic PH model, and complex
PH model using
SURVEYPHREG.
Basic model adjusted for age,
sex, and race/ethnicity.
Complex model adjusted for
complex survey design. Both
models controlled for marital
status, household income,
education, smoking status, BMI,
urban/rural, census regions and
survey year.
Annual PM2.5
concentration
were for
participants were
used to calculate
overall mean
concentration for
thel7-year study
period 1999-
2015.
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-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
PM2.5 concentrations
(1988-2015).
Lepeule et al.,
2012
LT
All-cause,
Cardiovas
cular, lung
cancer
mortality
HARVARD 6
cities
Prospectiv
e
Cohort/Lo
ngitudinal
follow-up
study
(HARVAR
D 6 cities
data)
PM2.5 data from monitors
in the participant's city.
PM2.5 data 1979-
1986/1988 from monitors,
end of monitoring to 1998
estimated from PM10
using US EPA monitors,
1999-2009 direct PM2.5
measurement from US
EPA monitors. 1-yror 1-
3yr or 1-5 yr. moving
PM2.5 averages were
assigned to participants
based on city of
residence.
Cox proportional hazard
models, Poisson survival
analysis
Stratified analysis by sex, age,
and time in the study (1-yr
interval). Confounders included:
Baseline information on
smoking status, smoking pack-
years, education, linear and
quadratic term for BMI.
Also explored effect
modification of PM2.5 on
mortality by smoking status at
enrollment, as well as time
period in study.
October 2021
B-52
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-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Liu et al., 2019
ST
All-cause
107 U.S.
Time-
Monitored PM2.5
Used two-stage analytic
Daily PM2.5
and
Cities
series
concentration obtained
protocol, which had been
concentration
cause-
study
from MCC database for
developed and widely applied in
were used to
specific
(MCC
the period of 1987-2006.
previous
calculate overall
mortality
Collaborati
Hourly data was used to
multicity time-series studies.
mean
ve
calculate 24-hr daily
First stage estimated city-
concentration for
Research
average. Daily PM2.5
specific association using
the study
Network)
concentrations were
quasi-Poisson generalized
location and
averaged across stations
additive models. Second stage
period.
within each city. Finally, 2-
used random-effects models to
day moving average for
pool the estimates of the city-
the city was calculated.
specific associations. Two-
2-day moving average
stage regional analysis was
(Iag01) was used in
also performed by WHO
model.
regions.
Also explored the shape of the
relationship using C-R curves
with PM term appearing with a
B-spline function with two knots
at 25th and 75th percentiles.
October 2021
B-53
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-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Malig et al.,
ST
Respirator
35 CA
Case-
PM2.5 data obtained from
County-level conditional logistic
2013
y
counties
crossover
California Air Resources
regression analysis. Overall
morbidity
(9 counties
design
Board. Same day lag and
estimate was then calculated by
(Asthma
included for
(CA Office
various days lags average
combining county-level
and
PM2.5
of
were calculated for PM2.5.
estimates using a random-
COPD ED
analysis)
Statewide
Participants were
effects meta-analysis
and HA)
Health
assigned exposure from
Planning
the closest monitor from
Time-invariant confounders and
and
the residential population-
seasonal trends were controlled
Developm
weighted zip code
for given the study design.
ent Data)
centroid. Only participants
living in zip codes within
Other confounders included in
20 km of PM2.5 monitors
the models were: other gaseous
were included to increase
pollutants including ozone,
validity of pollution
linear and squared term for
exposure metrics.
daily average temperature.
Stratified analysis also by
distance to monitor within 10
km vs. 10-20 km
October 2021
B-54
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-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
McConnell et al.,
2010
LT
Asthma
Incidence
13 CA
communities
Cohort
Study
(CHS)
PM2.5 concentration data
measured in central site
monitors in each
community (for 9 of 13
communities since 1994
and others different time
period). This study
considered 2003-2004
PM2.5 measurements at
each community monitor.
Average annual PM2.5
concentration from each
community was assigned
to study participants
based on their community
of residence.
Multi-level Cox proportional
hazard model accounting for
residual variation in time to
asthma onset and clustering of
children around schools and
communities
Models adjusted for:
secondhand smoke, pets in
home, race/ethnicity, age at
study entry, sex, and random
effects for community and
school.
Average annual
PM2.5
concentrations
assigned to
study
participants were
used to calculate
overall mean
PM2.5 exposure
for the study
location and
period.
October 2021
B-55
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-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Ostro et al.,
ST
Asthma
8
Case-
PM2.5 (24-hour average)
County-level conditional logistic
Daily PM2.5
2016
and
metropolitan
crossover
data obtained from U.S.
regression analysis. Overall
concentrations
COPD ED
areas/countie
design
EPA provided by
estimate was then calculated by
for all 8
s in CA
(CA Office
California Air Resources
combining county-level
metropolitan
of
Board for the period of
estimates using a random-
counties over the
Statewide
2005-2009. Participants
effects meta-analysis
period of 2005-
Health
were assigned exposure
2009 were used
Planning
from the closest monitor
Time-invariant confounders and
to calculate an
and
from the residential
seasonal trends were controlled
overall mean
Developm
population-weighted zip
for given the study design.
PM2.5
ent Data)
code centroid. Only
concentration for
participants living in zip
Other confounders included in
the study
codes within 20 km of
the models were: linear and
location and
PM2.5 monitors were
squared term for lagO
period.
included to increase
temperature, day of the week.
validity of pollution
exposure metrics.
Used lagO, Iag1 and Iag2
in model.
October 2021
B-56
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-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Pappin et al.,
2019
LT
Non-
accidental
mortality
Canada
Nationwide
Cohort
study
(CanCHE
C)
PM2.5 exposures derived
from AOD retrievals using
GEOS-Chem calibrated to
surface measurements by
GWR (van Donkelaar,
2015). For PM2.5
concentrations prior to
1998, back casting
method employed that
applied observed trends in
ground monitoring data for
PM2.5 to adjust pre-
gridded PM2.5 estimates
(Meng, 2019). Annual
M2.5 estimates from the
postal code and assigned
to study participants
based on the postal code
for residence was used to
calculate 3-year moving
average based on the
location and year of
follow-up.
Cox Hazard model and DAG
approach. Also performed C-R
analysis using a 3 step
approaches: (1) fit the data
using restricted cubic splines
(RCS) with a large number of
knots; (2) smooth potential
erratic predictions from the
large number of knots using
monotonically increasing
smoothing splines (MISS); and
(3) fit the shape constrained
health impact function (SCHIF)
to the MISS predictions.
Cox model stratified by age,
sex, and immigration status
separately by CanCHEC
cohorts. Two covariate
adjustment models. First based
on DAG and controlled for
airshed, urban form, CMA/CA
size. Second model "full" model
adjusted for individual-level
variables (income, education,
occupation, marital status).
The annual
average PM2.5
concentrations
were used to
calculate overall
mean PM2.5
concentration for
the study cohorts
and periods.
October 2021
B-57
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Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Peng et al.,
2009
ST
CVD HA
Age 65+
119 U.S.
Urban
counties>150,
000
populations
Time-
series
analysis
(MEDICA
RE
enrollees)
PM2.5 data (daily or every
3 days) obtained from US
EPA's AQS and STN for
the period of 2000-2006.
Countywide PM2.5 total
mass concentration was
calculated by averaging
the daily PM2.5 values
from all the monitors in a
county.
Used lagO, Iag1 and Iag2
in model.
Log-linear Poisson Regression
analysis
Adjusted for potential
confounders including weather,
day of the week, unobserved
seasonal factors. In county-
specific regression model,
following indicators were
included: indicator for the day of
the weeks, a smooth function of
time per calendar year to
control for seasonality and long-
term trends, a smooth function
of current-day temperature, a
smooth function of the 3-day
running mean temperature, a
smooth function of current-day
dew-point temperature, and a
smooth function of the 3-day
running mean dew-point
temperature. To model smooth
functions, we used a natural
spline basis.
Daily PM2.5
concentrations
for all 119
counties over the
period of 2000-
2006 were used
to calculate an
overall median
PM2.5
concentration for
the study
location and
period.
October 2021
B-58
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Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Pinault et al.,
2016
LT
All-cause,
CVD and
lung
cancer
mortality
Multicity
Canada
Prospectiv
e Cohort
Study
(subset of
participant
s of the
Canadian
Communit
y Health
Survey)
PM2.5 concentration
derived from MODIS.
Geographically weighted
regression including
monitoring and land use
data was applied to the
estimates from MODIS to
produce average PM2.5
concentration at 1 km2
resolution. These model
estimates extended to
1998-2003 using inter-
annual variation of Boys
et al.
Participants were
assigned exposure based
on their postal code of
residence. For each year
in the cohort, respondents
were assigned a PM2.5
concentration
corresponding to the
mean of the three
previous years to the
follow-up year.
Cox proportional hazards
models
Models were stratified by age
(5-yr interval) and sex. Models
adjusted for individual
socioeconomic covariates and
behavioral (BMI, smoking and
alcohol consumption, fruit, and
vegetable consumption)
covariates, ecological variables
including neighborhood
socioeconomic status (both
social and material deprivation).
Annual 3-year
PM2.5 average
concentration for
the study
participants were
used to calculate
overall PM2.5
concentration for
the study period.
October 2021
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-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Pinault et al.,
LT
Non-
Canada
Cohort
PM2.5 exposures derived
Cox survival models. Also
The annual 3-
2017
accidental,
Nationwide
study
from AOD retrievals using
estimated Shape Constrained
year moving
CVD,
(CanCHE
GEOS-Chem calibrated to
Health Impact Functions (a
average PM2.5
respiratory
C)
surface measurements by
concentration-response
concentrations
and lung
GWR (van Donkelaar,
function) for selected causes of
for study
cancer
2015). PM2.5
death.
participants were
mortality
concentrations extended
Adjusted for individual
used to calculate
back to 1998 by applying
demographic and
overall mean
interannual variation of a
socioeconomic variables at
PM2.5
publishing PM2.5 dataset
baseline (on Census day):
concentration for
(Boys, 2014). Annual
Aboriginal identity, visible
the study period
PM2.5 estimates from the
minority status, marital status,
postal code was assigned
educational attainment, income
to study participants
adequacy quintile, and labor
based on the postal code
force status, and contextual
for residence and used to
variables at the census division
calculate 3-year moving
scale.
average based on the
location and year of
follow-up for years 1998 -
2012.
October 2021
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-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Pinault et al.,
2018
LT
CVD
mortality
Canada
Nationwide
Cohort
study
(CanCHE
C, mCHH
S)
PM2.5 exposures derived
from AOD retrievals using
GEOS-Chem calibrated to
surface measurements by
GWR (van Donkelaar,
2015). PM2.5
concentrations extended
back to 1998 by applying
interannual variation of a
publishing PM2.5 dataset
(Boys, 2014). Annual
PM2.5 estimates from the
postal code was assigned
to study participants
based on the postal code
for residence and used to
calculate 3-year moving
average based on the
location and year of
follow-up for years 1998 -
2012.
Cox proportional hazard
models.
Considered co-occurring
diabetes with and without other
contributing causes of death:
hypertension, dementia or
Alzheimer's disease, and
chronic kidney disease, as
these comorbidities are
medically related to diabetes.
Also considered diabetes status
at baseline as effect modifier
using CCHS-mortality cohort.
Adjusted model for individual-
level variables (aboriginal
identity, visible minority status,
education, labor force status
and income adequacy), and
neighborhood-level variables.
The annual 3-
year moving
average PM2.5
concentrations
for study
participants were
used to calculate
overall mean
PM2.5
concentration for
the study period
October 2021
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Pope et al.,
2015b
LT
All-cause,
IHD
mortality
(30+)
U.S.
Nationwide
Cohort
study
(ACS
Cancer
Prevention
Study II)
October 2021
Monthly exposure to PM2.5
was estimated by linking
geocoded home
addresses
of the study participants to
ambient PM2.5
concentrations derived
using
a national-level hybrid
land use regression (LUR)
and Bayesian
Maximum Entropy (BME)
interpolation model (LUR-
BME) that incorporated
data from ground-based
monitors for the study
period of 1999-2004.
Cox proportional hazards
models
The individual-level covariates
incorporated in the models
included 13
variables that characterized
current and former smoking
habits (including
smoking status of never,
former, or current smoker,
linear and
squared terms for years
smoked, and cigarettes smoked
per day, indicator
for starting smoking at aged
<18 years, and pipe/cigar
smoker).
1 continuous variable that
assessed exposure to second-
hand cigarette
smoke (hours/d exposed); 7
variables that reflected
workplace PM2.5
exposure in each subject's main
lifetime occupation; a variable
that
indicated self-reported
exposure to dust and fumes in
the workplace;
variables that represented
marital status
(separated/divorced/widowed
or single versus married);
variables that characterized the
level
Monthly mean
PM2.5
concentration for
study
participants were
used to calculate
overall mean
concentration for
the study period.
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October 2021
of education (high school, more
than high school versus less
than
high school); 2 body mass
index variables (linear and
squared terms
for body mass index); variables
that characterized the
consumption
of alcohol (beer, missing beer,
wine, missing wine, liquor, and
missing
liquor); and variables that
indicated quartile ranges of
dietary fat
index and quartile ranges of a
dietary vegetable/fruit/fiber
index.
Ecological covariates included
median household income;
percentage
of people with <125% of
poverty-level income;
percentage of
unemployed individual aged
>16 years; percentage of adults
with
<12th grade education; and
percentage of the population
who were
black or Hispanic. These
ecological covariates were
included in the
models using both zip code
level data and zip code
deviations from
Draft - Do Not Quote or Cite
-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
the county means.
Pope et al.,
2019
LT
All-cause
and
cause-
specific
mortality
Nationwide
Cohort
study
PM2.5 concentration
estimated for census
block using regulatory
monitoring data from
1999-2015 within a
universal kriging
framework employing
land-use regression
methods and other
variables (Kim 2018).
Pop-weighted annual
averages were
calculated for all 17 years
for each 2000 and 2010
census tract. Individual
were assigned air
pollution cone based on
their census tract of
residency at the time of
survey, e.g.: using year-
2000 census tract for
individuals surveyed
1986-2010 and using
year-2010 census tract for
individuals surveyed
2011-2014. For primary
analysis: PM2.5 exposure
is an average
concentration over the 17
yrs.
Cox Hazard models. Ran 2
models: one accounting for
complex survey design and
sampling strategy including
sample weights
(SURVEYPHREG) and another
without accounting for complex
survey design (PHREG). Ran
model using full-cohort and sub-
cohort with additional data on
BMI and smoking. The shape of
the PM2 5—mortality relationship
was also explored using an
integrated modeling approach.
Adjusted for age, sex, and race-
ethnicity, income inflation-
adjusted to 2015, education
levels, marital status, urban vs
rural, US regions, survey years,
smoking status.
Annual PM2.5
average
concentrations
over the 17-
years (1999-
2015) were used
to calculate
overall PM2.5
concentration.
October 2021
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Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Shi et al., 2016
ST and LT
Total
New England
Open
Daily PMzsfor the New
Chronic effects of air pollution
Long-term
mortality
Area with 6
Cohort
England area was
assessed using Cox
average:
(65+)
U.S. States
study
predicted at 1-km2 spatial
proportional hazard models.
Average annual
(MEDICA
resolution from novel 3-
Acute effects of air pollution
PM2.5
RE
stage statistical models
assessed using Poisson log-
concentrations of
enrollees)
for the period of 2003-
linear models.
all grid cells in
2008.
Both acute and chronic effects
the study area
365-day moving average
were assessed using Poisson
were used to
(for long-term exposure)
survival analysis. Analysis
calculate overall
and average Iag0-1 (for
performed in full-cohort as well
mean PM2.5
short-term exposure) were
as low exposure cohorts.
exposure for the
calculated for each grid
study location
cell. The long-term and
Poisson survival models were
and period.
short-term averages at
adjusted for smooth function of
grid-cells were matched to
time, temporal covariates such
ZIP codes by linking the
as temperatures and day of the
Short-term
ZIP code centroid to the
week, spatial covariates such
average: Lag01
nearest PM2.5 grid.
as zip code-level socio-
PM2.5
Participants were
economic variables.
concentrations of
assigned PM2.5
all grid cells in
concentrations based on
the study area
the ZIP codes of
were used to
residence.
calculate overall
mean PM2.5
Used Iag0-1 average for
exposure for the
short-term exposure
study location
analysis in model.
and period.
October 2021
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-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Shin et al., 2019
LT
AF and
Stroke (1st
HA)
Ontario,
Canada
Cohort
study
(ONPHEC
)
PM2.5 concentrations
estimated using AOD and
PM2.5 simulated by the
GEOS-Chem chemical
transport model (i.e.
individual's exposure in
2001 was estimated as
mean exposure from
1996-2000). Final surface
with 1 x 1 km resolution
was generated for
Ontario. Annual PM2.5
estimates was calculated
for the postal code and
assigned to study
participants based on the
postal code for residence.
PM2.5 concentration was
used to calculate 5-year
moving average based on
the location and year of
follow-up.
Cox proportional hazards
models.
Adjusted for individual-level
variables (age and sex),
neighborhood-level SES
variables, and geographic
indicators.
The 5-year
moving average
PM2.5
concentrations
for study
participants were
used to calculate
overall mean
PM2.5
concentration for
the study period
October 2021
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-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Shin et al., 2021
ST
All-cause
hospitaliza
tion and
all-cause
mortality
22 Canadian
cities
Time
series
study
(Statistics
Canada)
Daily (24-hour) average
PM2.5 concentrations were
calculated for each study
city using ambient
monitoring data available
from Canada's NAPS for
the period 2001-2012.
Daily PM2.5 concentrations
were averaged across
monitors within a city
when multiple monitoring
sites were present.
Generalized additive Poisson
model and Bayesian
hierarchical model. Static
approach to estimate the
nationwide overall
associations between air
pollution and health outcomes
for all
years combined. A two-stage
hierarchical model was
employed: firstly, a
generalized additive Poisson
model for city-specific
associations
between individual health
outcomes and individual air
pollutants,
respectively, and secondly a
Bayesian random effects model
to
combine the city-specific
associations to obtain
nationwide associations.
Daily PM2.5
concentrations of
22 Canadian
cities were used
to calculate
overall mean
OM2.5
concentration for
the study
location and
period.
October 2021
B-67
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-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Stieb et al.,
2009
ST
Cardiac
and
Respirator
y ED visits
Seven
Canadian
Cities
Time
series
study
(Hospital
cases)
PM data obtained from
National Air Pollution
Surveillance (NAPS)
system for the period of
the 1990s and early
2000s. City averages of
the PM2.5 exposure were
calculated by averaging
all monitoring stations
within the city.
Used lag 0, land 2, in
model.
Generalized Linear Models with
natural spline functions of time
to adjust for seasonal cycles in
air pollution and health
Confounders included: Mean
daily temperature and relative
humidity at lag 0,1, and 2 days,
day of the week and holidays.
Daily PM2.5
concentrations of
the cities were
used to calculate
the overall mean
PM2.5 exposure
for the study
location (by site)
and study period.
Sun et al., 2019
ST
Incident
stroke:
Total, HS
and IS
(self-
reported)
Nationwide
Time-
stratified
case-
crossover
(WHI)
PM2.5 concentration
obtained from log-normal
ordinary kriging model as
previously described (Liao
et al., 2006). This model
estimates daily air
pollutants at each address
based on weighted
average of measurement
from nearby monitors
(Legendre and Fortin,
1989). Daily mean PM2.5
concentrations were
estimated atgeocoded
participant address for the
period of 1993-2012.
Conditional logistic regression.
Adjusted for time-varying
variables (daily mean ambient
temperature, dew point
temperature, and relative
humidity)
Various lags assessed (1-day
moving average to 6-day
moving average)
Daily PM2.5
concentrations
on the case days
for the
participants were
used to calculate
the overall mean
PM2.5
concentration for
the study period.
October 2021
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-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Szyszkowicz,
2009
ST
Angina ED
Seven
Canadian
Cities
Time
series
study
(Hospital
cases)
PM data obtained from
National Air Pollution
Surveillance (NAPS)
system. City averages of
the exposure were
calculated by averaging
stations within the city.
Used lag 0,1 and 2, in
model.
Generalized Linear Mixed
models
Models adjusted for
meteorological variables such
as relative humidity,
temperature, and atmospheric
pressure (a daily 24-hr average
measurements were
calculated). Temperature and
relative humidity in models were
represented by natural splines.
Stratified analysis by season as
well as combined for the whole
period.
Daily PM2.5
concentrations of
the cities were
used to calculate
the overall mean
PM2.5 exposure
for the study
location (all and
by cities) and
study period.
October 2021
B-69
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-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Thurston et al.,
2016a
LT
All-cause,
CVD and
respiratory
mortality
6 U.S. States
and 2 MSAs
Cohort
study
(NIH_AAR
P cohort)
PM data obtained from
US EPA AQS for the
period of 2000-2008.
Census-tract estimates
were generated using
hybrid LUR and BME
models, which were
combined to generate
monthly estimates of
PM2.5. Participants
exposure was estimated
at census-tract of
residence and included
annual mean
concentration in prior year
of mortality.
Cox proportional hazard models
Stratified analysis by age, sex,
regions (6 states and 2 MSAs).
Confounders adjusted included:
race, education, marital status,
BMI, alcohol consumption,
smoking history, contextual
variables such as median
household income and % pop
with less than high school
education. Several interactions
between PM2.5 and socio-
demographics were also tested.
Average annual
PM2.5
concentrations of
census tract
estimates were
used to calculate
overall mean
PM2.5 exposure
for the study
location and
period.
October 2021
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Turner et al.
2016
LT
Lung
cancer
mortality
(30+)
U.S.
Nationwide
Cohort
study
(ACS
Cancer
Prevention
Study II)
October 2021
Estimated PM2.5
concentrations were
obtained using a national-
level hybrid land use
regression (LUR) and
Bayesian
maximum entropy (BME)
interpolation
model. Monthly PM2.5
monitoring data were
collected from 1,464
sites from 1999 through
2008, with 10%
reserved for cross-
validation. The base LUR
model that predicted PM2.5
concentrations
included traffic within 1 km
and green space within
100 m3. Residual
spatiotemporal variation in
PM2.5 concentrations was
interpolated with
a BME interpolation
model. The two estimates
were then combined. The
cross validation
R2 was approximately
0.79. Mean PM2.5 (1999-
2004) concentrations
were used here.
Cox proportional hazards model
Models were adjusted for
education; marital status; BMI
and BMI squared; cigarette
smoking status; cigarettes per
day and
cigarettes per day squared;
years smoked, and years
smoked squared; started
smoking at younger than 18
years of age; passive smoking
(hours); vegetable, fruit, fiber,
and fat intake; beer, wine, and
liquor consumption;
occupational exposures; an
occupational
dirtiness index; and six
sociodemographic
ecological covariates at both
the postal code and postal code
minus county-level mean
derived from the 1990 U.S.
Census (median household
income and percentage
of African American residents,
Hispanic residents, adults with
postsecondary education,
unemployment, and poverty).
Potential confounding examined
by elevation, MSA size, annual
average daily maximum air
temperature, mean county-level
residential radon
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Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
concentrations, and 1980
percentage of air conditioning.
Urman et al.,
2014
LT
Lung-
function
decline
8 Southern
CA
communities/
counties
Cohort
study
(CHS)
Central monitors in each
community provided data
on air pollutants. Each
child was assigned
exposure based on the
child's resident
community.
Linear Regression model
Models were adjusted for
demographic, socio-economic
and anthropometric variables
(BMI, height), study community.
Wang et al.,
2017
LT
Total
mortality
(65+)
7 U.S.
Southeast
States
Cohort
study
(MEDICA
RE
enrollees)
Three stage Hybrid model
to predict daily PM2.5
concentration at 1km X
1km resolution for the
period 2000-2013.Annual
average of PM2.5 for each
grid cell calculated and
took arithmetic mean of
the annual average PM2.5
across all grids in each of
the zip code tabulation
area (ZCTA). Participants
were assigned annual
averages of PM2.5 based
on their ZCTA of
residence.
Cox Proportional hazard
models
Models were stratified by age
groups, sex, race. Adjusted for
variables: year of enrollment,
previous admission due to CHF,
COPD, Ml and diabetes,
numbers of days spent in ICU
and CCU, state, ZCTA level
socio-demographic variables
such as % pop below poverty,
urbanicity, lower education,
median income and median
home value, and behavioral
variables such as % smokers
and obesity at county level.
Further model also included
yearly mean summer
temperature at ZCTA level.
Average annual
PM2.5
concentrations of
ZCTAs were
used to calculate
overall median
PM2.5 exposure
for the study
location (overall
and by state),
and period
(overall and by
year).
October 2021
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Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Wang et al.,
2020
LT
Non-
accidental
cause-
specific
mortality
(Resp,
CVD,
cancer)
Nationwide
Cohort
study
(MEDICA
RE)
Daily PM2.5 was estimated
on a 6-km grid using a
spatio-temporal model
described in (Yanosky,
2014) for the period of
2000-2008. Model inputs
included monitored PM2.5,
meteorological and
geospatial covariates, and
traffic-related PM
estimated using a
Gaussian line-source
dispersion model.
Medicare beneficiaries
were matched to the grid
point closest to their ZIP
code centroid and PM2.5
concentrations were
averaged for the 12-
month period prior to
death.
Cox hazard models. Also fit
models using restricted cubic
splines (RCS) with three knots
to characterize non-linearity.
Effect-modification assess for
age, sex, race and urbanicity.
Adjusted for SES variables.
Annual average
PM2.5
concentration for
participants were
used to calculate
overall annual m
ean PM2.5
exposure for the
study period.
October 2021
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Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Weichenthal et
al., 2016c
ST
Asthma
and
COPD ED
15 cities in
Ontario
Case-
crossover
Design
(cases
extracted
from
NACRS
database)
Daily average
concentration of PM2.5
collected from fixed-
monitoring stations for the
period of 2004-2011 in
Ontario, which is part of
Canada's National Air
Pollution Data. PM data
obtained from 19 sites
located in 15 cities. 2
years of data available for
3 cities and remaining had
5-8 years of daily air
pollution data. Case and
control days of study
participants were
assigned PM2.5
concentration based on
the city of residence and
based on monitoring
station closest to the
population-weighted
centroid of each subject's
3-digit postal code (if
multiple monitors
available in participants
city such as Toronto and
Hamilton).
Various lags assessed:
lagO, Iag1, Iag2 and mean
of lagO-2.
Conditional logistic regression
models
Models adjusted for 3-day
mean temperature and relative
humidity using cubic splines.
Daily PM2.5
concentrations in
Ontario over the
period of 2004-
2011 were used
to calculate the
overall mean
PM2.5 exposure
for the study
location and
period.
October 2021
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Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Weichenthal et
ST
Ml ED
16 cities in
Case-
Daily average
Conditional logistic regression
Daily PM2.5
al., 2016b
Ontario
crossover
concentration of PM2.5
models
concentrations in
Design
collected from fixed-
Ontario over the
(cases
monitoring stations for the
Models adjusted for 3-day
period of 2004-
extracted
period of 2004-2011 in
mean temperature and relative
2011 were used
from
Ontario, which is part of
humidity using cubic splines.
to calculate the
NACRS
Canada's National Air
overall mean
database)
Pollution Data. PM data
PM2.5 exposure
obtained from 20
for the study
provincial monitoring sites
location and
located in 16 cities. Case
period.
and control days of study
participants were
assigned PM2.5
concentration based on
the monitoring station
closest to the population-
weighted centroid of each
subject's 3-digit postal
code.
Various lags assessed:
lagO, Iag1, Iag2 and mean
of lagO-2.
October 2021
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Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Wu et al., 2020
LT
All-cause
mortality
(65+)
US
Nationwide
Cohort
study
(MEDICA
RE)
Annual PM2.5 exposure.
Modeled PM2.5 exposure
at 1km2 grid cells across
the US using well-
validated ensemble
models (Di et al. 2019a,
Di 2019b) for the period of
2000-2016. Daily
concentration in grid cells
were then averaged
to estimate
annual concentration at
ZIP code and then
assigned to individual
based on ZIP code of
residence.
Five statistical approaches: 2
regression approach (Cox
Hazard, Poisson reg); 3 causal
inference approach (GPSs)
Stratified by individual-level
characteristics. Further adjusted
for community-level factors
such as smoking and BMI, zip
code-level census variables and
meteorological variables,
geographic regions, and
calendar years (2000-2016).
Annual average
PM2.5
concentration for
participants were
used to calculate
overall mean
PM2.5
concentration for
the study period.
October 2021
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Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Wyattet al.,
ST
All-cause,
530 US
Case-
PM2.5 concentration
The relative risks of hospital
Daily
2020
CVD, RD
counties
crossover
estimates from AOD
admissions associated with
estimates at
30-day
and
integrated with chemical
daily PM2.5 were estimated with
county-level were
hospital
Cohort stu
transport model
conditional Poisson models for
used to calculate
readmissi
dy designs
predictions, meteorology,
each of the three health
overall PM2.5
ons
(USRDS h
land use variables for 1
outcomes separately. Cox
concentration for
emodialysi
km grid cells (Di,2016).
proportional hazards models
the study
s patients)
Gridded PM2.5 estimates
were used to assess the
location and
were subsequently
relative risk of early (1-7 days
period.
converted to population-
post discharge) and late (8-30
weighted
days post discharge)
county-level estimates
readmission associated with
using 2010 Census tract
daily PM2.5 following all-cause
population values. Daily
and cause-specific index
PM2.5 was linked to patient
hospitalizations.
hospitalizations based on
Cox model adjusted for time-
the county of their last
dependent (daily PM2.5, daily
dialysis visit.
temperature, daily RH, and day
of the week) and time-
Examined Lag 0 and
independent (patient-specific
unconstrained distributive
hospitalization event and county
lag model.
SES) risk factors.
October 2021
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Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Yap et al., 2013
ST
Asthma
HA
12 CA
counties
Time
Series
study
(Hospital
admission
s)
PM2.5 data for the period
of 2000-2005 obtained
from California Air
Resources Board that
maintains information
from the National Air
Monitoring Stations.
PM2.5 reported was 24-hr
average mass
concentration based on
measurements taken
every 1, 3, or 6 days. For
counties with more than 1
monitoring site, daily
average PM2.5 was
calculated by taking the
average across monitors
within the county. Missing
values were computed
based on data from other
monitoring stations.
PM at various lags lagO-
Iag6 were assessed.
Generalized Additive Poisson
Regression analysis were run at
county-level
Models adjusted for: long-term
time trends and seasonality,
day of the week and smoothing
splines within different lags for
temperature. Effect modification
by single or composite area-
based SES assessed.
Daily PM2.5
concentrations in
12CA counties
over the period
of 2000-2005
were used to
calculate the
overall mean
PM2.5 exposure
for the study
location and
period.
October 2021
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-------
Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Yazdi et al.,
2019
LT
First HA:
Stroke,
COPD,
pneumoni
a, Ml, lung
cancer,
and HF
7
Southeastern
states: FL,
AL, MS, GA,
NC, SC, and
TN
Cohort
study
(MEDICA
RE)
PM2.5 concentration
estimated from spatio-
temporal prediction model
at 1-km2 grid cell (Di et al.
2017) for the period of
2000-2012. Daily PM2.5
concentrations for grid
cells were averaged to
create annual PM2.5
concentration at zip code
level and assigned to
study participants based
on the zip code of
residence
Marginal structural Cox
proportional hazards models
which was weighted with
stabilized IPWs (to approximate
a causal model).
Adjusted for individual-level
variables (sex, race, year, state,
Medicaid eligibility), as well as
census SES.
NR
October 2021
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Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Zanobetti et al.,
2009
ST
Heart
Failure
and Ml HA
65+
26 US
communities
Time
Series
study
(MEDICA
RE
enrollees
data)
PM2.5 data obtained from
US EPA AQS for the
period of 2000-2003. For
majority of cities,
metropolitan counties
encompassed the city and
its suburbs but some
cities like Boston,
Minneapolis-St Paul
included multiple counties.
Daily PM2.5 data available
for various monitors were
averaged over the county
and community (Monitors
ranged from 1-4). Before
averaging, however,
monitors were tested for
correlations and those
with correlation <0.8 with
2 or more monitor pairs
within a county were
excluded considering it
does not represent
exposure for general
population.
Generated 2-day moving
average (Iag01)
concentration
Poisson regression analysis
Models stratified by season.
Controlled for long-term trend
with natural cubic spline for
each season and year, day of
the week, three-day average
temperature and dew point
temperature.
October 2021
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Zanobetti and
Schwartz, 2009
ST
All-cause,
CVD and
respiratory
mortality
112 US cities
Time
Series
study
(NCHS
data)
October 2021
PM25 data obtained from
US EPAAQS (NAMS and
SLAMS providing daily
PM2.5 concentration) for
the period of 1999-2005.
For majority of cities,
counties encompassed
the city but some cities
like Boston, Atlanta,
Washington DC, the city
included multiple counties.
Daily PM2.5 (24-hr) data
available for various
monitors were averaged
over the county and city.
Before averaging,
however, monitors were
tested for correlations and
those with correlation <0.8
with 2 or more monitor
pairs within a county were
excluded considering it
does not represent
exposure for general
population. Used
standardized method to fill
in the missing data in
some monitors with at
least 265 days of data in
at least one year.
Poisson regression analysis
First city- and season-specific
Poisson regression was run,
and then city-specific estimates
were combined using random
effects approach in total by
season and region.
Controlled for long-term trend
with natural cubic spline for
each season and year, day of
the week, same day, and
previous day temperature.
Daily PM2.5
concentrations in
112 US cities
over the period
of 1999-2005
were used to
calculate the
overall mean
PM2.5 exposure
for the study
location and
period???
Generated 2-day moving
average (lag 01)
concentration
B-81
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Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Zanobetti et al.,
2014
ST
All-cause
mortality
65+
121 US
communities/
cities
Case-
Crossover
Design
(MEDICA
RE
enrollees)
PM2.5 data obtained from
US EPAAQS. Daily PM2.5
data available for various
monitors were averaged
over the communities.
Participants were
assigned 2-day moving
average (lag 0 and 1)
based on community of
residence.
Conditional logistic regression
models at community level. In a
second stage of analysis, the
community specific results were
combined using the multivariate
meta-analysis techniques
Conditional logistic regression
controlled for confounders such
as average temp for the same
and previous day. Temperature
was modelled using spline to
account for nonlinear
relationship. Effect modification
tested for cause of prior
admission due to neurological
disorders or diabetes, primary
or secondary hospitalization for
other disease conditions.
Stratified analysis by sex, age,
or race.
October 2021
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Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Zeger et al.,
2008
LT
All-cause
mortality
65+
668 U.S
Urban
counties
Retrospec
tive
Cohort
Study of
MEDICAR
E
enrollees
(MCAPS)
PM2.5 data (every 6th day
at many locations)
available from US EPA's
AirData Database for the
period of 2000-2005.
Calculated mean annual
PM2.5 concentration for all
4,568 ZIP code centroids
within 6 miles of a monitor
with >10 months of data
per year. Given the focus
of study on long-term
exposure, ZIP code 6-
year average of PM2.5 was
calculated and assigned
to study participants living
within a zip code both
during the 6 years of
follow-up and some time
before cohort enrollment.
Log-linear Regression model
ran for specific US regions
separately
Models adjusted for individual
socio-demographic variables
and ZIP code level SES
variables (education, income,
poverty etc.). Also included
standardized mortality ratio for
COPD as a surrogate indicator
of long-term smoking pattern of
its residents.
Average annual
PM2.5
concentrations of
ZIP codes were
used to calculate
overall mean
PM2.5 exposure
for the study
location (all and
by region) for the
study period
2000-2005.
October 2021
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Citation
Long-term
(LT)/Short
-term (ST)
Health
Endpoint
Geographic
Area
Study
Design
Exposure Metric
Statistical Analysis Including
Confounding Variables
Addressed
Calculation of
study reported
mean PM2.5
concentrations
Zhang et al.,
2021
LT
Non-
accidental,
CVD and
respiratory
mortality
Ontario,
Canada
Cohort
study
(Ontario
Health
Study)
PM2.5 exposures derived
from AOD retrievals using
GEOS-Chem calibrated to
surface measurements by
GWR (van Donkelaar,
2015). PM2.5 estimates at
1 km2 were used to
estimate annual PM2.5
average and then 3-year
and 5-year moving
averages. These annual
estimates were then
assigned to participants
based on postal code of
residence (updated
annually to account for
residential mobility).
Cox proportional hazard
models.
Basic model stratified by age,
sex, ethnicity, enrollment year
to control for baseline risks.
Models were adjusted for born
in Canada, education, marital
status, household income, BMI,
fruits, and vegetable intake,
smoking and drinking, physical
activity, urban/rural, and various
neighborhood level SES
indicators.
The 5-
year average
PM2.5
concentrations
were used to
calculate overall
mean PM2.5
concentration for
the baseline
year.
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APPENDIX C. SUPPLEMENTAL INFORMATION
RELATED TO THE HUMAN HEALTH RISK
ASSESSMENT
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TABLE OF CONTENTS
C. 1 Additional Technical Detail on the Risk Assessment Approach C-l
C. 1.1 Selection of Key Health Endpoints and Specification of Concentration-Response
Functions from Epidemiologic Studies C-2
C. 1.2 Specification of Demographic and Baseline Incidence Data Inputs C-l 1
C. 1.3 Study Area Selection C-l 1
C.1.4 Generation of Air Quality Inputs to the Risk Assessment C-17
C. 1.5 Risk Modeling Approach C-46
C.2 Supplemental Risk Results C-48
C.2.1 Results from Full Set of 47 Study Areas C-50
C.2.2 Results from Set of 30 Study Areas controlled by the Annual Standard C-55
C.2.3 Key Observations from the Suppmental Risk Results C-56
C.3 Additional Technical Detail on the At-Risk Analysis C-57
C.3.1 Race/Ethnicity C-58
C.3.2 Concentration-Response Functions C-59
C.3.3 Age C-61
C.3.4 Baseline Incidence Rates C-61
C.3.5 Selection of Air Quality Simulation Approach C-63
C.4 Supplemental At-Risk Results C-63
C.5 Characterizing Variability and Uncertainty in Risk Estimates C-70
C. 5.1 Quantitative Assessment of Uncertainty C-72
C.5.2 Qualitative Uncertainty Analysis C-73
C.5.3 Conclusion C-81
C.6 PM2.5 Design Values for the Air Quality Projections C-82
References C-l 06
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This appendix provides supplemental information related to the risk assessment described
in section 3.4 of this draft PA for the reconsideration of the 2020 final decision on the particulate
matter (PM) National Ambient Air Quality Standards (NAAQS), including:
• Additional technical detail on the risk assessment approach, including sources and
derivation of key inputs to the risk modeling process (section C. 1).
• Supplemental risk results (section C.2) intended to provide additional context for the
summary risk estimates presented in sections 3.4.2.1-3.4.2.3.
• Additional technical detail on the at-risk analytic approach, including sources and
derivation of key inputs to the risk modeling process (section C.3).
• Supplemental at-risk analytics (section C.3.4.2) intended to provide additional context for
the summary risk estimates presented in section 3.4.2.4.
• Characterization of variability and uncertainty related to the risk assessment (section C.5)
intended to provide additional context for section 3.4.2.5.
C.l ADDITIONAL TECHNICAL DETAIL ON THE RISK ASSESSMENT
APPROACH
As discussed in section 3.4, our general approach to estimating PM2.5-associated human
health risks in this reconsideration utilizes concentration-response (CR) functions obtained from
epidemiologic studies to link ambient PM2.5 exposure to risk in the form of mortality incidence
(counts). The derivation and use of this type of CR function in modeling PM2.5-attributable risk
is well documented both in previous PM NAAQS-related risk assessments (section 3.1.2 of U.S.
EPA, 2010) and section C.l.l of this appendix. Inputs required to model risk using CR functions
are identified below (Figure C-l) and include
(1) the CR functions themselves, obtained from epidemiologic studies (section C.l.l and
C.3.2),
(2) baseline health incidence data and information on population demographics (section 0
and C.3.4),
(3) study areas (section C. 1.3), and
(4) modeled ambient PM2.5 concentrations corresponding to air quality scenarios of interest
(section C.1.4).
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Specification of
demographic
and baseline
incidence inputs
Selection of Health
Endpoints and
Specification of CR
functions (including
selection of epi studies)
Risk modeling
(including generation
of risk metrics)
Using BenMAP-CE
Model
1
Risk estimates
(metrics)
Study area
selection
I
Air quality
characterization
Figure C-l. Key inputs to the risk assessment.
C.l.l Selection of Key Health Endpoints and Specification of Concentration-Response
Functions from Epidemiologic Studies
In selecting specific CR functions for the risk assessment, we began by considering
health outcomes for which the 2019 Integrated Science Assessment (ISA) determined the
evidence supports either a "causal" or a "likely to be causal" relationship with short- or long-
term PM2.5 exposures (U.S. EPA, 2019). As discussed in Chapter 3 (Table 3-1), these outcomes
include the following:
• mortality (resulting from long- and short-term exposure),
• cardiovascular effects (resulting from long- and short-term exposure),
• respiratory effects (resulting from long- and short-term exposure),
• cancer (resulting from long-term exposure), and
• nervous system effects (resulting from long-term exposure).
We focused the risk assessment on short- and long-term PM exposure-related mortality,
reflecting its clear public health importance, the large number of epidemiologic studies available
for consideration, and the broad availability of baseline incidence data. The specific set of health
effect endpoints included in the risk assessment are:
• Long-term PM exposure-related mortality, all-cause
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• Short-term PM exposure-related mortality, all-cause and non-accidental
To identify specific epidemiologic studies for potential inclusion in the risk assessment,
we focus on U.S. multicity studies assessed in the 2019 ISA. These studies are identified in
section 3.4.1.5 of this draft PA. Of these, we used the following criteria to identify the specific
set of studies for inclusion in the risk assessment:
• National-scale geographic coverage: We focus on epidemiologic studies reporting
national-level CR functions. Epidemiologic studies that focus on individual cities or
regions were excluded. Focusing on national-level epidemiologic studies has the benefit
of characterizing PIVh.s-associted risks broadly across the U.S. and in relatively large
populations (compared with single-city or regional studies), which tends to improve
precision in the CR functions generated.
• Evaluation of relatively lower ambient PM concentrations: In selecting epidemiology
studies, to the extent possible, we focus on those studies which characterized the ambient
PM2.5-mortality relationship at levels at or near the current NAAQS, given that the risk
assessment would be focusing on evaluating risk associated with the current NAAQS.
• Populations with available baseline incidence data: For some populations (e.g., diesel
truck drivers), it can be challenging to model risk at the national-level given uncertainties
associated with specifying key inputs for risk modeling (i.e., baseline incidence rates for
mortality endpoints and detailed national-level demographics). For that reason, we focus
on those epidemiology studies providing CR functions for populations readily
generalizable to the broader U.S. population (e.g., specific age groups not differentiated
by additional socio-economic, or employment attributes).
• Estimates of long-term PM2.5 exposures based on hybrid modeling approaches: For long-
term PM2.5 exposures, we focus on epidemiologic studies that estimate exposures with
hybrid modeling approaches. The rationale for this decision is the agreement between the
design of these epidemiology studies (i.e., their use of hybrid-based modeling approaches
in characterizing ambient PM) and the hybrid air quality surfaces we are using in this risk
assessment. This general agreement between the air modeling surfaces used in long-term
mortality epidemiology studies and our air quality modeling reduces uncertainty in the
risk assessment.
• Estimates of short-term PM2.5 exposures based on composite monitor data: Short-term
mortality epidemiology studies utilizing hybrid modeling approaches, which are fewer in
number compared with long-term mortality studies, tend to be regional in scope and did
not meet the criterion of providing national-scale effect estimates. For that reason, in
modeling short-term mortality, epidemiology studies utilizing composite-monitor based
exposure surrogates were used as the basis for deriving CR functions. We recognize the
uncertainty introduced into the modeling of short-term mortality due to the use of CR
functions obtained from studies utilizing composite monitors. However, we felt these use
of national-scale epidemiology studies was a more important criterion for selection.
• Evaluation of potential confounders and effect modifiers: To the extent possible,
preference was given to studies which more fully address potential confounders and
effect modifiers and to those studies which utilize individual (rather than ecological)
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measures in representing those confounders/effect modifiers. Recognizing that both
single- and co-pollutant models have advantages and disadvantages in characterizing the
ambient PM-mortality relationship, to the extent possible, we include epidemiology
studies (and associated CR functions) based on either single- or co-pollutant models that
include ozone. Additional information available in the Estimating PM2.5 and Ozone-
Attributable Health Benefits TSD associated with the 2021 Revised Cross-State Air
Pollution Rule Update (RCU) (U.S. EPA, 2021b).
• Exploration of multiple approaches for estimating exposures: For studies that estimate
PM2.5 exposures using hybrid modeling approaches, preference was given to studies that
also explore additional methods for estimating exposures (i.e., multiple hybrid methods
or hybrid methods plus monitor-based methods) and compare health effect associations
across approaches.
Application of the criteria listed above resulted in the selection of the epidemiology
studies presented in Table C-l for inclusion in the risk assessment as sources of effect estimates.
Table C-l includes summary information on study design, details on the selection of effect
estimates, the derivation of beta values, and specification of CR functional form based on those
effect estimates for use in the risk assessment. The procedure used to derive CR functions
(including specification of beta values and mathematical forms for those functions) is described
below.
The remainder of this section describes the method used in specifying the CR functions
used in the PM NAAQS HHRA. Information presented in this section is drawn from the EPA's
Environmental Benefits Mapping and Analysis Program - Community Edition (BenMAP-CE)
Manual, Appendix C.1 CR functions translate changes in ambient PM2.5 into changes in baseline
incidence rates for specific disease endpoints utilizing beta (P) values obtained from
epidemiology studies studying the association between ambient PM2.5 exposure and specific
health endpoints. P values (and associated standard errors) are based on effect estimates obtained
from the underlying epidemiology studies. In addition, the mathematical forms for the health
impact functions specified for use in this risk assessment reflect the models used in the
epidemiology studies providing those effect estimates. Consequently, derivation of the P values
based on effect estimates from underlying epidemiology studies (and specification of the form of
the health impact functions) represents a key step in the design of the HHRA.
The majority of the epidemiology studies providing effect estimates for this PM HHRA
utilized either Poisson or Cox proportional hazard models which result in exponential (or log-
linear) forms for the CR functions, where the natural logarithm of mortality incidence is a linear
1 https://www.epa.gov/benmap/benmap-ce-manual-and-appendices
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function of PM2.5 2 If we let xo denote the baseline (starting) PM2.5 level, and xi denote the
control (ending) PM2.5 level, yo denote the baseline incidences rate of the health effect, and Pop
the underlying population count for the applicable demographic group in the spatial unit of
analysis3 we can derive the following CR function specifying the relationship between the
change in x, Ax= (xo- xi) and the corresponding change in y, Ay (mortality incidence):
Ay = y0[l — e~@Ax] * Pop
Given that the epidemiology studies providing effect estimates for long-term exposure-
related mortality and short-term exposure-related mortality in the context of the current PM
HHRA (Table C-l) use different categories of models (Cox proportional hazard and
Poisson/Logistic, respectively) we describe the process of deriving the betas and specifying CR
functional forms separately for each of these endpoint categories. As noted earlier, the logit
model utilized in Zanobetti et al., 2014, is discussed at the end of the section covering short-term
PM2.5-related mortality.
Derivation of betas for long-term PM2.5 exposure-related mortality
Cox proportional hazard models used to evaluate mortality associated with long-term
PM2.5 exposure are designed to model effects on population survival. This class of epidemiology
model is based on a hazard function, defined as the probability that an individual dies at time t,
conditional on that individual having survived up to time t. As such, the hazard function
represents a time-specific snapshot of the rate of mortality (events per unit time) within a study
population. While the risk can vary over time, in the case of the Cox proportional hazard model,
it is assumed that the hazard ratio is constant. The proportional hazard model takes the form:
h(X,t) = h0(t)ex'P
Where X is a vector of explanatory variables, P is a vector of associated coefficients and
ho(t) is the baseline hazard (the risk when all covariates (X) are set to zero).
Epidemiology studies utilizing the Cox proportional hazard model in characterizing
ambient PM2.5-health effects typically report hazard ratios (HRs) as the effect estimate. HRs
represent the ratio of hazard functions for the baseline and control scenarios reflecting a specific
2 One study. Zanobetti et al., 2014, supporting the modeling of short-term PM2 5 exposure-related mortality provided
a logistic-based model form, which is discussed at the end of this section.
3 Spatial unit of analysis refers to the geographic scale at which the CR function is applied in generating a risk
(incidence) estimate (e.g., zip code, county, 12km grid cell). Typically, the spatial unit of analysis used in a REA
is based on the spatial scale reflected in the epidemiology study(s) supplying the effect estimates. For this REA,
the spatial unit of analysis is the 12km grid cell.
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difference in ambient PM2.5 exposure (often a 10 |ig/m3 increment). The HR simplifies as shown
(with the baseline hazard ratio dropping out), allowing us to readily derive the P value from this
effect estimate:
un _ h(.Xo,t) _ h0{t)ex°'P _ apm»[3
h(Xc,t) h0(t)exc'P
It is then possible to calculate the beta as follows:
_ InjHR)
" APM
As noted in Sutradhar and Austin, 2018, the HR associated with a Cox-proportional
hazard model may approximate the RR when the effect estimate (and consequently the P) is
relatively small. This is the case with the effect on mortality modeled for long-term exposure to
ambient PM2.5 (i.e., the size of the effect estimate supports an assumed equivalency between HR
and RR). The near equivalency between the HR and RR, allows us to utilize the P derived above
in a CR function based on a log-linear functional form of the type presented earlier, to model
changes in mortality related to changes in ambient PM.
Derivation of betas for short-term PM2.5 exposure-related mortality
The epidemiology studies selected for use in modeling short-term PM2.5 exposure-related
mortality utilize both the Poisson (log-linear) model form (Baxter et al., 2017) and the logit
model form (Zanobetti et al., 2014).4 In both cases, the epidemiology studies provide effects in
terms of percent increase in mortality.
The log-linear (Poisson) model is used to evaluate effects associated with continuous
(count) events. With the log-linear (Poisson) model, the relative risk is simply the ratio of the
two risks:
RR = Z° = eP'APM
yc
The derivation of the beta with a Poisson model specified RR is as follows. Taking the
natural log of both sides, the beta coefficient in the CR function underlying the relative risk can
be derived as:
„ _ Jtt(RR)
" APM
4 Note that the Ito et al., 2013 study also utilizes a Poisson model. However, that study provides beta values
(including standard errors) and for that reason the results of this study are directly applicable in modeling changes
in mortality without any of the derivations presented here for the other studies.
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The beta derived in this fashion can then be used with a log-linear functional form (as
presented earlier) to model changes in mortality related to changes in ambient PM.
The logistic model form is used to model dichotomous events. With the logistic model
form, when we are provided with a RR value, as is the case here, we can make a similar
assumption to that used above with the Cox proportional hazard function (i.e., that the OR and
RR approach equivalency under conditions of relatively small effect levels). That observation in
turn allows us to assume that
RR=y^={l-yQ)xe-APM'P+yQ
yc
Then, assuming (based on the relatively small size of the baseline incidence) that:
e-APM'P = (l-yo)xe-APM'P +y0
=> RR = e~APM'P
It is then possible to calculate the underlying beta coefficient as follows:
In(RR) _ „
-APM ~ "
Since the derivation of the beta is based on the assumption of a log linear functional
form, we can apply the beta in a log-liner CR function of the form described earlier:
Ay = y0[l — e~PAx] * Pop
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1 Table C-l. Details regarding selection of epidemiology studies and specification of concentration-response functions for the
2 risk assessment.
Reference
and Title
Study Description
Exposure
Estimation
Approach
CR Function
Location of CR
Function(s) in
Article
Additional
Notes on CR
Function(s)
Selection
Epidemio-
logic
Statistic
Mortality
Endpoint
Selected
Effect
Estimate
Selected
Beta
Selected
Beta
Standard
Error (SE)
Long-term exposure-related mortality studies
Di et al., 2017
Air Pollution
and Mortality
in the
Medicare
Population
Exploring relationship between
air pollution (ozone, PM2.5) and
mortality
Key details:
- Medicare population (65+)
- ecological control for
confounders
- all-cause mortality only
- provides CR function slopes
for areas above and below the
current PM NAAQS level (but
model for areas below current
standard only done for low
ozone cells)
Exposures estimated
at zip code of
residence based on
a neural network
model that
incorporates satellite
data, chemical
transport modeling,
land-use terms,
meteorology data,
monitoring data, and
other data
Cox proportional-
hazards model
with a
generalized
estimating
equation to
account for the
correlation
between ZIP
codes
Table 2
Risk of death
associated with an
increase of 10 |jg/m3
PM2.5 or an increase
of 10 ppb in ozone
concentration. Uses
single pollutant
model for full
analysis.
Using single
pollutant, full PM
range model
(model for <12
|jg/m3 applicable
to only low-
ozone days)5
Hazard ratio
(95 percent
CI)
All-cause
1.073
(1.071,
1.075)
7.0E-03
1E-04
Turner et al.,
2016
Long-Term
Ozone
Exposure and
Mortality in a
Large
Prospective
Study
Evaluates the relationship
between long-term exposure
to ambient PM2.5 and all-cause
and cause-specific mortality.
Also, estimated the
association between PM2.5,
regional PM2.5, and near-
source PM2.5 and mortality in
single-pollutant, copollutant
and multipollutant models.
- ACS (30+)
- Includes lung cancer
(otherwise similar results to
Pope et al., 2015)
- county-level assessment
Exposures estimated
at residential
locations based on
land use data and
ground-based
monitors
Cox proportional
hazard model
Table E4. Adjusted
HRs (95th percentile
CI) for all-cause and
cause-specific
mortality in relation to
each 10 unit increase
in PM2.5 LUR-BME
concentrations,
follow-up 1982-2004,
CPS-II cohort, United
States (n = 669,046).
Note that the
non-cancer
mortality
endpoints
provided in table
E4 appear to
mirror those
provided in Table
1 of Pope et al.,
2015-so will use
long-cancer
effect estimate
from this study
only.
Hazard ratio
(95 percent
CI)
All-cause
1.06
(1.04-
1.08)
5.8E-03
9.6E-04
Short-term exposure-related mortality studies
5 We note that Di et al., 2017 does include a copollutant model-based effect estimate (HR 1.073, 95th%CI 1.071-1.075). Had this effect estimate been used in risk
modeling (which would translate into a beta value of 7.05E-3), we would anticipate the risk estimates for all-cause mortality to be slightly less f 13% lower based
on comparison of calculated betas) than those estimated based on the single-pollutant model used in this risk assessment.
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Reference
and Title
Study Description
Exposure
Estimation
Approach
CR Function
Location of CR
Function(s) in
Article
Additional
Notes on CR
Function(s)
Selection
Epidemio-
logic
Statistic
Mortality
Endpoint
Selected
Effect
Estimate
Selected
Beta
Selected
Beta
Standard
Error (SE)
Baxter etal.,
Uses cluster-based approach
Exposure estimates
Poisson (log-
Obtained from
Percent
24-hr non-
2017
to evaluate the impact of
based on data from
linear) at city-
results section in the
increase in
accidental
residential infiltration factors
ground-based
level then
text. After pooling the
24-hr
mortality
Influence of
on inter-city heterogeneity in
monitors
aggregated
city-specific effect
mortality (95
exposure
short-term PM-mortality
estimates into an
percent CI)
differences in
associations.
overall effect
city-to-city
- Mortality data from NCHS -
estimate, short-term
0.33
heterogeneity
77 U.S. CBSAs (all ages)
PM2.5 exposure was
NA
(0.13-
3.29E-04
1.02E-04
in PM2.5-
- non-accidental mortality
found to increase 24-
0.53)
mortality
- CBSA-level assessment
hr non-accidental
associations
mortality by 0.33%
in U.S. cities
(95% CI:
0.13,0.53). Based
on lag 2 (day 0-1)
Ito etal., 2013
Use factor analysis to
Exposure estimates
Poisson GLM
Appendix G, Table
Utilized lag-1 (all
Betas with
24-hr all-
characterize pollution sources,
based on data from
G.6 for Figure 4 - use
year) beta
SE (no
cause
NPACT study
assess the association
ground-based
all-year lag 1 Beta:
because that had
conversion
mortality
3. Time-series
between PM2.5 and PM2.5
monitors
Regression
the strongest
required)
analysis of
components with morbidity
coefficients (beta)
effect for CVD
mortality,
and mortality outcomes. Also
and their SE for air
mortality and
Study
provided
beta and
SE
hospitalization
evaluates pollution levels,
pollutants at lag 0
wanted our all-
s, and
land-use, and other variables
through 3 days used
cause to reflect
1.45E-04
7.47E-05
ambient PM2.5
as modifiers that may explain
to compute percent
that stronger lag-
and its
inter-city variation in PM-
excess risks in
association for
components
mortality effect estimates.
- Mortality data from NCHS -
150 and 64 U.S. cities (two
analyses) (all ages)
- MSA-level assessment
figures shown in the
main text and in
Appendices B and G
(corresponding
figures are noted).
the CVD effect
(even though
focusing on all-
cause)
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Reference
and Title
Study Description
Exposure
Estimation
Approach
CR Function
Location of CR
Function(s) in
Article
Additional
Notes on CR
Function(s)
Selection
Epidemio-
logic
Statistic
Mortality
Endpoint
Selected
Effect
Estimate
Selected
Beta
Selected
Beta
Standard
Error (SE)
Zanobetti et
al„ 2014
A national
case-
crossover
analysis of
the short-term
effect of PM2.5
on
hospitalization
sand
mortality in
subjects with
diabetes and
neurological
disorders
Estimates the effect of short-
term exposure to PM2.5 on all-
cause mortality. Additionally,
assesses the potential for pre-
existing diseases to modify the
association between PM2.5 and
mortality (neurological
disorders and diabetes)
- Medicare cohort -121 U.S.
communities (65+)
- Community-level assessment
(community defined as the
county or contiguous counties
encompassing a city's
population)
Exposure estimates
based on data from
ground-based
monitors
Logistic
regression
Table 2. Percent
increase for 10 |jg/m3
increase in the two
days average PM2.5:
Combined across the
121 communities
NA
Percent
increase (95
percent CI)
All deaths
0.64
(0.42-
0.85)
6.38E-04
1.09E-04
1
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C.1.2 Specification of Demographic and Baseline Incidence Data Inputs
This risk analysis requires both demographic and baseline-incidence data for the mortality
endpoint categories evaluated. For our analyses, these data are for the year 2015 since the hybrid
surfaces included in the analyses are based on a 2015 model year.6 The BenMAP-CE model7 is
used in this risk assessment and the relevant demographic and baseline incidence data for the
contiguous U.S., from the sources described below, is readily available within the current version
of BenMAP-CE:
• Demographic data: BenMAP-CE includes 2010 U.S. Census block-level age, race,
ethnicity, and gender-differentiated data which the program can aggregate to various
grid-level definitions selected by the user, including the 12 km grid coverage used for
risk modeling in this analysis. In addition, BenMAP-CE has the ability to project future
demographics using county-level projections provided by Woods & Poole, 2015. See
BenMAP-CE manual Appendix J and the EstimatingPM2.5 and Ozone- Attributable
Health Benefits TSD associated with the 2021 RCU for additional detail (U.S. EPA,
2021b).
• Baseline incidence data for mortality endpoints: County-level mortality and population
data from 2012-2014 for seven causes of death in the contiguous U.S. was obtained from
the Centers for Disease Control (CDC) WONDER database. To estimate values for 2015,
we applied annual adjustment factors, based on a series of Census Bureau projected
national mortality rates for all-cause mortality. See BenMAP-CE manual Appendix D for
additional detail.
C.1.3 Study Area Selection
In selecting U.S. study areas for inclusion in the risk assessment, we focus on the
following characteristics:
• Available Ambient Monitors: We have greater confidence in estimating and simulating air
quality concentrations over areas with relatively dense ambient monitoring networks, as
the modeled air quality surfaces can be compared with monitored concentrations (air
quality adjustments are described below in section C.1.4).
• Geographical Diversity. Risk assessments including areas that represent a variety of
regions across the U.S. and a substantial portion of the U.S. population can be more
representative.
6 The 2015 model year was the most recent CMAQ modeling platform available at the time of the design of the risk
assessment and represents the central year of the 2014-2016 design value (DV) period. A single modeling year
was used in the risk assessment, rather than modeling risk for the full three-year design value period, because
model inputs for the 2016 period were not available at the time of the study (section C. 1.4.3).
7 https://www.epa.gov/benmap
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• AmbientPM2.5 Air Quality Concentrations: Based on 2014-2016 design values, 16 CBSA8
areas exceeded either or both the current annual and 24-hr PM2.5 NAAQS. To include a
larger portion of the U.S. in this risk assessment, we also identified CBSA areas with ambient
PM2.5 concentrations below, but near, the current annual and/or 24-hr PM2.5 NAAQS.
Inclusion of such areas in the risk assessment necessitates an upward adjustment to PM2.5 air
quality concentrations in order to simulate just meeting the current standards. Given
uncertainty in how such increases could potentially occur, we select areas requiring a
relatively modest upward adjustment (i.e., no more than 2.0 |j,g/m3 for the annual standard
and 5 |j,g/m3 for the 24-hour standard, based on the 2014-2016 design value period). Areas
that appeared to be strongly influenced by exceptional events were also excluded (section
C.1.4). Using these criteria, 47 urban study areas were identified (PA Figure 3-16 and
Appendix section C.1.3), including 30 study areas where just meeting the current standards is
controlled by the annual standard,9 11 study areas where just meeting the current standards is
controlled by the daily standard,10 and 6 areas where the controlling standard differed
depending on the air quality adjustment approach (PA Figure 3-16).11
Applying these criteria resulted in the inclusion of 47 core-based statistical areas
(CBSAs). These 47 study areas are identified in Figure C-2, with colors indicating whether they
meet either or both the design value cutoffs. Please note, meeting the criteria for inclusion does
not mean the areas exceed the current annual and/or 24-hr PM NAAQS standards. Green
indicates areas that only exceed a 24-hr design value of 30 |ig/m3, blue indicates areas that only
exceed an annual design value of 10 |ig/m3, and red indicates areas that exceed both the 24-hr
and annual design values.
8 CBSAs (core-based statistical areas) can include one or more counties. Each CBSA selected included at least one
monitor with valid design values and several CBSAs had more than 10 monitors. See Table C-3 in Appendix C.
9 For these areas, the annual standard is the "controlling standard" because when air quality is adjusted to simulate
just meeting the current or potential alternative annual standards, that air quality also would meet the 24-hour
standard being evaluated.
10 For these areas, the 24-hour standard is the controlling standard because when air quality is adjusted to simulate
just meeting the current or potential alternative 24-hour standards, that air quality also would meet the annual
standard being evaluated. Some areas classified as being controlled by the 24-hour standard also violate the
annual standard.
11 In these 6 areas, the controlling standard depended on the air quality adjustment method used and/or the standard
scenarios evaluated.
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1
2 Figure C-2. Map of the areas modeled in the risk assessment, colored by 2014-2016 PM2.5
3 design values (DV).
4
5 These 47 urban study areas include many highly populated CBSAs (Figure C-3 and
6 Figure C-4). The population at or above the age of 30 in these areas includes roughly 58.4
7 million people, or approximately 30% of the total U.S. population above the age of 30.
8 Additional age-specific population information corresponding to each identified mortality study
9 can be found in Table C-2.
10
Longitude
_Mao data
-------
Q>
o%M ¥
o
z?Q
%
2018 Population
¦ 0 to 8,880
¦ 8.880 to 18.600
18.600 to 36,800
36,800 to 93.800
'¦ 93.800 to 10.300.000
Figure C-3. Map of the 2018 U.S. population by CBSA, with the selected urban study areas
outlined.
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CBSA
New York-Newark-Jersey City, NY-NJ-PA
Los Angeles-Long Beach-Anaheim, CA
Chicago-Naperville-Elgin, IL-IN-WI
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD
Houston-The Woodlands-Sugar Land, TX
Atlanta-Sandy Springs-Rosweil, GA
Detroit-Warren-Dearborn, Ml
Riverside-San Bernardino-Ontario, CA
St. Louis, MO-IL
Pittsburgh, PA
Sacramento-Roseville-Arden-Arcade, CA
Cincinnati, OH-KY-IN
Las Vegas-Henderson-Paradise, NV
Indianapolis-Carmel-Anderson, IN
Cleveland-Elyria, OH
Louisville-Jefferson County, KY-IN
Birmingham-Hoover, AL
Salt Lake City, UT
Fresno, CA
Akron, OH
Bakersfield, CA
Little Rock-North Little Rock-Conway, AR
Stockton-Lodi, CA
McAllen-Edinburg-Mission, TX
Lancaster, PA
Ogden-Clearfield, UT
Modesto, CA
Visalia-Porterville, CA
Canton-Massillon, OH
Provo-Orem, UT
Evansville, IN-KY
South Bend-Mishawaka, IN-MI
San Luis Obispo-Paso Robles-Arroyo Grande, CA
Merced, CA
Macon, GA
Elkhart-Goshen, IN
Napa, CA
Madera, CA
El Centro, CA
Wheeling, WV-OH
Johnstown, PA
Hanford-Corcoran, CA
Altoona, PA
Lebanon, PA
Weirton-Steubenville, WV-OH
Logan, UT-ID
Prineville, OR
OM 1M 2M 3M 4M 5M 6M 7M 8M 9M 10M 11M 12M
Population
Figure C-4. Population counts for ages 30 and above from each of the 47 CBSAs included
in the risk assessment.
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1 Table C-2. Population of the 47 urban study areas by age range.
Population Age
Range (Years)
Studies Using Age Range
Study Area Groupings (
Millions)
47
30 (Annual-
Controlled)
11 (24-hr-
Controlled)
0-99
Baxter et al., 2017 and Ito et al., 2013
98.5
82.5
7.2
30-99
Turner et al., 2016
58.4
49.5
3.9
65-99
Di et al., 2017 and Zanobetti et al., 2014
13.2
11.1
0.8
2
3 As noted in section 3.4 of the draft PA and illustrated in Figure C-5, the 47 urban study
4 areas include 30 study areas where just meeting the simulated standards is controlled by the
5 current annual standard (12.0 |ig/m3), 11 study areas where just meeting the simulated standards
6 is controlled by the current 24-hr standard (35 |ig/m3), and 6 study areas where just meeting the
7 simulated standards is controlled by either the annual or 24-hr standard, depending on the air
8 quality scenario and adjustment strategy (discussed more fully in section C. 1.4).
9
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Number of Urban Study
Areas (CBSAs)
Controlling
Standard
Population (>30
years old)
30 Annual (Blue) ~50M
11 Daily (Green) ~4M
6 Mixed (Grey) ~5M
Total: 47 -60M
Figure C-5. Map of 47 Urban Study Areas Reflected in Risk Modeling Identifying Subsets
Reflected in Risk Modeling (population estimates in millions of people).
C.1.4 Generation of Air Quality Inputs to the Risk Assessment
As described in detail below, air quality modeling was used to develop gridded PM2.5
concentration fields for the risk assessment. A PM2.5 concentration field for 2015 was developed
using a Bayesian statistical model that calibrates chemical transport model (CTM) predictions of
PM2.5 to surface measurements (Chapter 2). The 2015 PM2.5 concentration field was then
adjusted to correspond to just meeting the existing and potential alternative standards using
response factors developed from CTM modeling with emission changes relative to 2015. The
modeling approach applies realistic spatial response patterns from CTM modeling to a
concentration field, similar to those used in a number of recent epidemiologic studies, to
characterize PM2.5 fields at 12 km resolution for study areas.
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The adjustments to simulate just meeting the current standards and alternative standards
are approximations of these air quality scenarios. In reality, changes in PM2.5 in an area will
depend on what emissions changes occur and the concentration gradients of PM2.5 will vary
across an area accordingly. For our analyses, two different adjustment approaches were applied
to provide two outcomes that could represent potential bounding scenarios of PM2.5
concentrations changes across the study area. The two adjustment approaches used to guide the
generation of these modeled surfaces were:
• Primary PM-based modeling approach (Pri-PM) \ This modeling approach simulates air
quality scenarios of interest by preferentially adjusting direct (i.e., primary, directly-
emitted) PM emissions. As such, the changes in PM2.5 tend to be more localized near the
direct emissions sources of PM. In locations for which air quality scenarios cannot be
simulated by adjusting modeled primary emissions alone, SO2 and NOx precursor
emissions are additionally adjusted to simulate changes in secondarily formed PM2.5.
• Secondary PM-based modeling approach (Sec-PM): This modeling approach simulates
air quality scenarios of interest by preferentially adjusting SO2 and NOx precursor
emissions to simulate changes in secondarily formed PM2.5. In this case, the reductions in
PM2.5 tend to be more evenly spread across a study area. In locations for which air quality
scenarios cannot be simulated by adjusting precursor emissions alone, a proportional
adjustment of air quality is subsequently applied.
The air quality surfaces generated using these two approaches are not additive. Rather, they
should be viewed as reflecting two different broad strategies for adjusting ambient PM2.5 levels.
In addition, we also employed linear interpolation and extrapolation to simulate air
quality under two additional alternative annual standard levels, 11.0, 9.0, and 8.0 |ig/m3,
respectively (section 3.4.1.3 of the draft PA, Figure 3-15). Interpolation and extrapolation were
only performed for grid cells in the subset of 30 urban study areas where the annual standard was
controlling in both Pri-PM and Sec-PM simulated air quality scenarios of both 12/35 and 10/30
standard combinations. The interpolation and extrapolation were completed at the grid-cell level
based on values simulated using hybrid air quality modeling to just meet the current annual
standard of 12.0 ug/m3 and alternative annual standard of 10.0 ug/m3 (section 3.4.1.3 of the draft
PA, Figure 3-15). A similar linear extrapolation/interpolation was not conducted for additional
24-hr standards due to the weaker relationship between the 98th percentile of 24-hr PM2.5
concentrations, which are most relevant for simulating air quality that just meets the 24-hour
standard, and the concentrations comprising the middle portion of the PM2.5 air quality
distribution, which are most relevant for estimating risks based on information from
epidemiologic studies (i.e., discussed further in sections 3.1.2 and 3.2.3.2 in the draft PA).
The sections below provide more detailed information on the air quality modeling
approach used to adjust air quality to simulate just meeting the current or alternative primary
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25
PM2.5 standards. Tables containing PM2.5 DVs for the air quality projections can be found in
section C.6.
C.l.4.1 Overview of the Air Quality Modeling Approach
To inform risk calculations, recent PM2.5 measurements were analyzed to characterize the
magnitude and spatial distribution of PM2.5 concentrations. These data were then coupled with
air quality modeling data to project ambient air quality levels corresponding to just meeting the
existing and alternative PM2.5 NAAQS12 in specific areas. An overview of the approach is
provided in Figure C-6. The process starts by acquiring PM2.5 monitoring data from EPA's Air
Quality System (AQS)13 and simulating PM2.5 concentrations with the Community Multiscale
Air Quality (CMAQ)14 model for base case and emission-sensitivity scenarios (Figure C-6, Box
1). The monitored and modeled data are then fused using the Downscaler model and the
Software for Model Attainment Test-Community Edition (SMAT-CE)15 to develop a baseline
spatial field of PM2.5 concentrations and relative response factors (RRFs) for projecting PM2.5
concentrations, respectively (Figure C-6, Box 2). PM2.5 concentrations are projected in two main
steps using output from Downscaler and SMAT-CE (Figure C-6, Box 3). First, the PM2.5
concentrations measured at monitoring sites in an area are iteratively projected using the RRFs to
identify the percent change in anthropogenic emissions required for the highest monitored DV in
the area to just meet the controlling standard. Second, gridded spatial fields of PM2.5
concentrations are projected using the area-specific percent emission change16 that corresponds
to just meeting the standard at the controlling ambient data site. Additional details on the method
are provided in (Kelly et al., 2019a; application of the method to the PM NAAQS risk
assessment is described in the remainder of this appendix.
12 The phrase, "just meeting the PM2.5 NAAQS" is defined as the conditions where the highest design value (DV) for
the controlling standard in the area equals the existing or alternative NAAQS level under consideration. DVs are
statistics used in judging attainment of the NAAQS (www, epa. gov/ai r-t rends/ai r-ona ti tv -design-values').
13 www.epa.gov/aas
14 www.epa.gov/cniaa
15 www.epa.gov/scram/photochemical-modeling-tools
16 Scenarios based on a statistical projection approach were also developed for certain cases as discussed below.
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5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
Figure C-6. Overview of the system for projecting PM2.5 concentrations to correspond to
just meeting NAAQS. See section C. 1.4.6 and Kelly et al., 2019a for more details.
C.l.4.2 PM2.5 Monitoring Data and Area Selection
The 2014-2016 DV period was the most recent period having a complete set of total and
speciated PM2.5 observations available at the time of the study. PM2.5 concentrations from the
2014-2016 DV period were used in selecting study areas and as the starting point for air quality
projections (Figure C-6, Box 1, "AQS"). Total and speciated PM2.5 concentrations for the 2014-
2016 DV period were acquired from AQS. For sites in Los Angeles and Chicago, DVs were
invalid during the 2014-2016 period. Los Angeles and Chicago have large populations, recent
valid DVs for sites in Los Angeles are above existing standards, and Chicago is part of a CBSA
that includes sites with valid 2014-2016 DVs in Indiana. For these reasons, invalid data for sites
in these areas were replaced with valid data from other recent periods to enable DVs to be
approximated for inclusion in the assessment. Specifically, for sites in Los Angeles and Orange
Counties in California, observations from April - October 2014 were replaced with observations
from the same months in 2013. For sites in Cook, DuPage, Kane, McHenry, and Will Counties in
Illinois, observations from January to mid-July 2014 were replaced with observations from the
same months in 2015.
Of the 56 areas initially identified as above the 10/30 selection threshold17, DVs for seven
areas18 appeared to meet the threshold due to the influence of wildfires. The influence of
17 "10/30" indicates an annual standard level of 10 ng/ m3 and a 24-hr standard level of 3 ng m 3
18 Butte-Silver Bow, MT; Helena, MT; Kalispell, MT; Knoxville, TN; Medford, OR; Missoula, MT; and Yakima,
WA
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20
21
wildfires on DVs for these areas was estimated in part by recalculating 2014-2016 DVs with
days removed that were clearly associated with summertime wildfires in the northwest. Since
wildfire influence is often excluded when judging NAAQS attainment, these seven areas were
excluded from further consideration. Additionally, the Eugene, OR CBS A was excluded. One
monitor in the Eugene CBSA has a 24-hr 2014-2016 DV slightly above the 10/30 selection
threshold19, but the monitor is in a small valley in Oakridge with very local high concentrations
of PM2.5 in winter that are distinct from conditions in the broader CBSA. Finally, the Phoenix-
Mesa-Scottsdale, AZ CBSA was excluded. This CBSA had one monitor slightly above the 10/30
DV threshold20, but projecting concentrations for the CBSA was judged to be relatively uncertain
because the annual DV is invalid at the only site that exceeded the threshold and the 24-hr DV is
just above the threshold.
The remaining 47 CBSAs were selected for the risk assessment. These areas are shown in
Figure C-7. The maximum 2014-2016 DVs and associated sites for each CBSA are provided in
Table C-3, and the counties associated with the CBSAs are listed in Table C-4. DVs were
calculated to an extra digit of precision for the air quality projections compared with official
DVs. This approach is consistent with DV calculations in previous air quality projections (e.g.,
USEPA, 201221) and provides a precise target for the iterative projection calculations.
19 The 410392013 monitor in Oakridge has a 24-hr 2014-2016 DV of 31 |ig m 3
20 The 040213015 monitor in the Phoenix-Mesa-Scottsdale, AZ CBSA has 24-hr 2014-2016 DV of 31 |ig m 3
21 USEPA (2012) Regulatory Impact Analysis for the Final Revisions to the National Ambient Air Quality
Standards for Particulate Matter. Office of Air Quality Planning and Standards, Health and Environmental
Impacts Division, Research Triangle Park, NC 27711. EPA-452/R-12-005 Available:
fat tps ://www3. epa. gov/tt n/eeas/re gdata/KI As/fi naif ia. pdf
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]
2
3
4
2 ^ —Wan date l2"1H Geooifi. inegi
-120 -110
-100 -90
Longitude
Above 10 annual and 30 daily
Above 30 daily
Above 10 annual
Figure C-7. CBSAs selected for the risk assessment. Colors indicate whether the maximum
2014-2016 DVs in the CBSA are above the annual (10 fig/m3) and/or 24-hr (30 jag/m3)
selection criteria.
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1 Table C-3. Maximum annual and 24-hr PM2.5 DVs for 2014-2016 and associated sites for
2 selected CBSAs.
CBSA Name
# of
Annual
Annual
Max 14-16
DV
24-hr Max
24-hr Max
Sites
Max Site
Site
14-16 DV
Akron, OH
2
391530017
10.99
391530017
23.7
Altoona, PA
1
420130801
10.11
420130801
23.8
Atlanta-Sandy Springs-Roswell, GA
6
131210039
10.38
131210039
19.7
Bakersfield, CA
5
060290016
18.45
060290010
70.0
Birmingham-Hoover, AL
4
010732059
11.25
010730023
22.8
Canton-Massillon, OH
2
391510017
10.81
391510017
23.7
Chicago-Naperviiie-Elgin, IL-IN-Wia
22
170313103
11.10
170310057
26.8
Cincinnati, OH-KY-IN
9
390610014
10.70
390170020
24.2
Cleveland-Elyria, OH
8
390350065
12.17
390350038
25.0
Detroit-Warren-Dearborn, Ml
11
261630033
11.30
261630033
26.8
El Centra, CA
3
060250005
12.63
060250005
33.5
Elkhart-Goshen, IN
1
180390008
10.24
180390008
28.6
Evansville, IN-KY
4
181630023
10.11
181630016
22.0
Fresno, CA
4
060195001
14.08
060190011
53.8
Hanford-Corcoran, CA
2
060310004
21.98
060310004
72.0
Houston-The Woodlands-Sugar Land, TX
4
482011035
11.19
482011035
22.4
Indianapolis-Carmel-Anderson, IN
7
180970087
11.44
180970043
26.0
Johnstown, PA
1
420210011
10.68
420210011
25.8
Lancaster, PA
2
420710012
12.83
420710012
32.7
Las Vegas-Henderson-Paradise, NV
4
320030561
10.28
320030561
24.5
Lebanon, PA
1
420750100
11.20
420750100
31.4
Little Rock-North Little Rock-Conway, AR
2
051191008
10.27
051191008
21.7
Logan, UT-ID
1
490050007
6.95
490050007
34.0
Los Angeles-Long Beach-Anaheim, CAa
9
060371103
12.38
060371103
32.8
Louisville/Jefferson County, KY-IN
7
180190006
10.64
180190006
23.9
Macon, GA
2
130210007
10.13
130210007
21.2
Madera, CA
1
060392010
13.30
060392010
45.1
McAllen-Edinburg-Mission, TX
1
482150043
10.09
482150043
25.0
Merced, CA
2
060470003
11.81
060472510
39.8
Modesto, CA
2
060990006
13.02
060990006
45.7
Napa, CA
1
060550003
10.36
060550003
25.1
New York-Newark-Jersey City, NY-NJ-PA
17
360610128
10.20
340030003
24.5
Ogden-Clearfield, UT
3
490570002
8.99
490110004
32.6
Philadelphia-Camden-Wilmington, PA-NJ-DE-
MD
10
420450002
11.46
421010055
27.5
Pittsburgh, PA
10
420030064
12.82
420030064
35.8
Prineville, OR
1
410130100
8.60
410130100
37.6
Provo-Orem, UT
3
490494001
7.74
490494001
30.9
Riverside-San Bernardino-Ontario, CA
2
060658005
14.48
060658005
43.2
Sacramento-Roseville-Arden-Arcade, CA
6
060670006
9.31
060670006
31.4
Salt Lake City, UT
3
490353006
7.62
490353010
41.5
San Luis Obispo-Paso Robles-Arroyo Grande,
CA
3
060792007
10.70
060792007
25.9
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CBSA Name
# of
Sites
Annual
Max Site
Annual
Max 14-16
DV
24-hr Max
Site
24-hr Max
14-16 DV
South Bend-Mishawaka, IN-MI
1
181410015
10.45
181410015
32.5
St. Louis, MO-IL
6
290990019
10.12
295100007
23.7
Stockton-Lodi, CA
2
060771002
12.23
060771002
38.7
Visalia-Porterville, CA
1
061072002
16.23
061072002
54.0
Weirton-Steubenville, WV-OH
4
390810017
11.75
390810017
27.2
Wheeling, WV-OH
2
540511002
10.24
540511002
22.5
a DVs for Chicago-Naperville-Elgin, IL-IN-WI and
described in section C. 1.4.2.
.os Angeles-Long Beach-Anaheim, CA were approximated as
1
2 Table C-4. Counties associated with selected CBSAs
CBSA Name
Associated Counties
Akron, OH
Portage, Summit
Altoona, PA
Blair
Atlanta-Sandy Springs-Roswell, GA
Barrow, Bartow, Butts, Carroll, Cherokee, Clayton, Cobb, Coweta,
Dawson, DeKalb, Douglas, Fayette, Forsyth, Fulton, Gwinnett,
Haralson, Heard, Henry, Jasper, Lamar, Meriwether, Morgan,
Newton, Paulding, Pickens, Pike, Rockdale, Spalding, and Walton
Bakersfield, CA
Kern
Birmingham-Hoover, AL
Bibb, Blount, Chilton, Jefferson, St. Clair, Shelby, and Walker
Canton-Massillon, OH
Carroll, Stark
Chicago-Naperville-Elgin, IL-IN-WI
Cook, DeKalb, DuPage, Grundy, Kane, Kendall, Lake, McHenry,
Will, Jasper, Lake, Newton, Porter, and Kenosha
Cincinnati, OH-KY-IN
Dearborn, Ohio, Union, Boone, Bracken, Campbell, Gallatin,
Grant, Kenton, Pendleton, Brown, Butler, Clermont, Hamilton, and
Warren
Cleveland-Elyria, OH
Cuyahoga, Geauga, Lake, Lorain, and Medina
Detroit-Warren-Dearborn, Ml
Lapeer, Livingston, Macomb, Oakland, St. Clair, and Wayne
El Centra, CA
Imperial
Elkhart-Goshen, IN
Elkhart
Evansville, IN-KY
Posey, Vanderburgh, Warrick, and Henderson
Fresno, CA
Fresno
Hanford-Corcoran, CA
Kings
Houston-The Woodlands-Sugar Land, TX
Austin, Brazoria, Chambers, Fort Bend, Galveston, Harris,
Liberty, Montgomery, and Waller
Indianapolis-Carmel-Anderson, IN
Boone, Brown, Hamilton, Hancock, Hendricks, Johnson, Madison,
Marion, Morgan, Putnam, and Shelby
Johnstown, PA
Cambria
Lancaster, PA
Lancaster
Las Vegas-Henderson-Paradise, NV
Clark
Lebanon, PA
Lebanon
Little Rock-North Little Rock-Conway, AR
Faulkner, Grant, Lonoke, Perry, Pulaski, and Saline
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CBSA Name
Associated Counties
Logan, UT-ID
Franklin, Cache
Los Angeles-Long Beach-Anaheim, CA
Los Angeles and Orange
Louisville/Jefferson County, KY-IN
Clark, Floyd, Harrison, Scott, Washington, Bullitt, Henry,
Jefferson, Oldham, Shelby, Spencer, and Trimble
Macon, GA
Bibb, Crawford, Jones, Monroe, and Twiggs
Madera, CA
Madera
McAllen-Edinburg-Mission, TX
Hidalgo
Merced, CA
Merced
Modesto, CA
Stanislaus
Napa, CA
Napa
New York-Newark-Jersey City, NY-NJ-PA
Bergen, Essex, Hudson, Hunterdon, Middlesex, Monmouth,
Morris, Ocean, Passaic, Somerset, Sussex, Union, Bronx,
Dutchess, Kings, Nassau, New York, Orange, Putnam, Queens,
Richmond, Rockland, Suffolk, Westchester, and Pike
Ogden-Clearfield, UT
Box Elder, Davis, Morgan, and Weber
Philadelphia-Camden-Wilmington, PA-NJ-
DE-MD
New Castle, Cecil, Burlington, Camden, Gloucester, Salem,
Bucks, Chester, Delaware, Montgomery, and Philadelphia
Pittsburgh, PA
Allegheny, Armstrong, Beaver, Butler, Fayette, Washington, and
Westmoreland
Prineville, OR
Crook
Provo-Orem, UT
Juab and Utah
Riverside-San Bernardino-Ontario, CA
Riverside and San Bernardino
Sacramento-Roseville-Arden-Arcade, CA
El Dorado, Placer, Sacramento, and Yolo
Salt Lake City, UT
Salt Lake, and Tooele
San Luis Obispo-Paso Robles-Arroyo
Grande, CA
San Luis Obispo
South Bend-Mishawaka, IN-MI
St. Joseph and Cass
St. Louis, MO-IL
Bond, Calhoun, Clinton, Jersey, Macoupin, Madison, Monroe, St.
Clair, Franklin, Jefferson, Lincoln, St. Charles, St. Louis, Warren,
and St. Louis city
Stockton-Lodi, CA
San Joaquin
Visalia-Porterville, CA
Tulare
Weirton-Steubenville, WV-OH
Jefferson, Brooke, and Hancock
Wheeling, WV-OH
Belmont, Marshall, and Ohio
1
2 C.l.4.3 Air Quality Modeling
3 Air quality modeling was conducted using version 5.2.1 of the CMAQ modeling system
4 (Appel, 2018) to develop a continuous national field of PM2.5 concentrations and estimates of
5 how concentrations would respond to changes in PM2.5 and PM2.5 precursor emissions (Figure C-
6 6, "CMAQ"). The CMAQ modeling domain (Figure C-9) covered the contiguous U.S. with 12
7 km horizontal resolution and 35 vertical layers. Since 2015 was the most recent modeling
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platform available at the time of the study and represents the central year of the 2014-2016 DV
period, 2015 was selected as the baseline modeling year for the PM2.5 projections. A single
modeling year was used due to the time and resources needed to conduct photochemical grid
modeling, and because model inputs for the 2016 period were not available at the time of the
study.
Information on the CMAQ model configuration for the 2015 modeling is provided in
Table C-5. The 2015 model simulation and its evaluation against network measurements of
speciated and total PM2.5 has been described in detail previously (Kelly et al., 2019b). Model
performance statistics for PM2.5 organic carbon, sulfate, and nitrate were generally similar to or
improved compared to the performance for other recent national 12 km model simulations. One
exception to the generally good model performance was identified for the Northwest region (OR,
WA, and ID). Model performance statistics for this region were generally not as good as in our
recent modeling due to issues related to unusually high fire influences in 2015, atmospheric
mixing over sites near the Puget Sound, and other factors. However, model performance issues
in the Northwest have minimal influence on the risk assessment, because only two of the 47
CBSAs are in the Northwest region (i.e., Prineville, OR and part of the Logan, UT-ID, CBSA).
Also, the analysis uses ratios of model predictions rather than absolute modeled concentrations,
and systematic biases associated with mixing height and fire impact estimates may largely cancel
in the ratios. Moreover, fusion of monitor data with model predictions in developing PM2.5 RRFs
and the baseline concentration field helps mitigate the influence of biases in model predictions
(as discussed below). Overall, the model performance evaluation (Kelly et al., 2019b) indicates
that the 2015 CMAQ simulation provides concentration estimates that are generally as good or
better than in other recent applications and are reliable for use in projecting PM2.5 in the risk
assessment. Model performance statistics for PM2.5 by U.S. climate region and season are
provided in Table C-6 and statistic definitions can be found in Table C-7.
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2 Figure C-9. CMAQ modeling domain.
3
4 Table C-5. CMAQ model configuration.
Category
Description
Grid resolution
12 km horizontal; 35 vertical layers
Gas-phase chemistry
Carbon Bond 2006 (CB6r3)
Organic aerosol
Non-volatile treatment for primary organic aerosol; secondary organic
aerosol from anthropogenic and biogenic sources
Inorganic aerosol
ISORROPIAII
NH3 surface exchange
Bi-directional NH3 surface exchange
Windblown dust emissions
Simulated online
Sea-spray emissions
Simulated online
Meteorology
Version 3,8 of Weather Research & Forecasting (WRF) Skamarock et
al.. 2005 model
5
6 Table C-6. Model performance statistics22'23 for PM2.5 at AQS sites for the 2015 base case.
Region23
Season
N
< 0 ™
Avg.
Mod.
(Kg m-3)
MB22
(M9 m"3)
NMB22
(%)
RMSE22
(M9 m"3)
NME22
(%)
r22
Winter
13001
10.04
12.74
2.71
27.0
7.33
48.0
0.68
Spring
13538
7,97
8.83
0.86
10.8
5.19
44.0
0,59
Northeast
Summer
13660
8,38
8.02
-0.36
-4,3
4.06
35.2
0.67
Fall
13270
7.18
9.08
1.90
26,5
5.40
50,0
0.73
Annual
53469
8.38
9,64
1.26
15,0
5.60
44,2
0.67
Winter
11190
8.07
10.28
2.21
27,4
5.65
47,4
0.58
Southeast
Spring
11961
8,06
8,25
0.18
2.3
4,08
33.6
0,55
Summer
11641
9,78
8.45
-1.33
-13.6
4,86
35.3
0,47
Fall
11365
6,93
8.13
1.20
17.3
4.32
41,7
0.70
22 See Table C-7 for definition of statistics.
23 See Figure C-10 for definition of regions.
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Region23
Season
N
<0 a
Avg.
Mod.
(ug m 3)
MB22
(H9 m"3)
NMB22
(%)
RMSE22
(H9 m"3)
NME22
(%)
r22
Annual
46157
8.22
8.76
0.54
6.6
4.75
39.1
0.55
Ohio Valley
Winter
10323
9.49
11.60
2.10
22.1
5.75
43.2
0.63
Spring
10867
8.90
9.85
0.95
10.6
4.60
36.3
0.65
Summer
10714
10.95
10.56
-0.39
-3.6
5.55
34.3
0.55
Fall
10568
8.41
10.96
2.54
30.2
6.23
47.1
0.65
Annual
42472
9.44
10.73
1.29
13.6
5.56
39.8
0.59
Upper Midwest
Winter
6478
8.79
9.72
0.92
10.5
4.75
38.2
0.70
Spring
6643
7.32
8.27
0.96
13.1
4.30
41.9
0.67
Summer
6718
7.88
7.85
-0.03
-0.4
5.26
40.8
0.56
Fall
6664
6.81
9.14
2.33
34.2
4.92
49.3
0.75
Annual
26503
7.69
8.74
1.04
13.6
4.82
42.2
0.64
South
Winter
8041
7.53
10.13
2.60
34.5
11.81
56.6
0.36
Spring
8369
8.08
7.12
-0.96
-11.9
4.24
36.3
0.51
Summer
8440
10.80
8.31
-2.49
-23.0
6.04
40.3
0.34
Fall
8340
7.55
7.99
0.44
5.9
3.76
35.5
0.63
Annual
33190
8.50
8.37
-0.13
-1.6
7.15
41.8
0.34
Southwest
Winter
4911
7.46
7.90
0.45
6.0
6.50
55.9
0.52
Spring
4998
4.88
5.88
1.00
20.6
3.60
48.4
0.44
Summer
5069
6.12
4.85
-1.27
-20.8
4.15
43.1
0.59
Fall
5091
5.31
5.90
0.59
11.1
4.35
52.2
0.49
Annual
20069
5.93
6.12
0.19
3.2
4.77
50.2
0.52
N. Rockies &
Plains
Winter
4987
5.57
3.60
-1.98
-35.5
6.80
63.4
0.23
Spring
5380
4.57
5.00
0.44
9.6
29.58
61.6
0.20
Summer
5260
9.98
7.68
-2.30
-23.1
17.61
57.4
0.57
Fall
5010
5.57
5.42
-0.15
-2.7
5.65
56.4
0.44
Annual
20637
6.43
5.45
-0.99
-15.3
18.06
59.2
0.34
Northwest
Winter
8994
7.90
7.82
-0.08
-1.0
10.20
80.9
0.25
Spring
9306
5.02
6.84
1.82
36.2
6.65
71.5
0.48
Summer
9993
9.17
11.12
1.95
21.2
32.40
67.7
0.46
Fall
9868
7.03
9.39
2.37
33.7
15.33
78.3
0.31
Annual
38161
7.31
8.85
1.55
21.2
19.26
74.3
0.43
West
Winter
10462
11.67
9.58
-2.08
-17.8
8.09
43.3
0.68
Spring
10989
7.52
6.95
-0.57
-7.6
4.17
38.3
0.55
Summer
11065
8.95
8.53
-0.43
-4.8
6.36
43.5
0.51
Fall
10587
8.61
9.11
0.50
5.8
16.85
46.9
0.37
Annual
43103
9.16
8.52
-0.64
-7.0
10.02
43.1
0.44
1
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Table C-7. Definition of statistics used in the CMAQ model performance evaluation.
Statistic
Description
MB (® m-3)=-JJl=1{Pi-Oi)
Mean bias (MB) is defined as the average difference between
predicted (P) and observed (0) concentrations for the total number
of samples (n)
(Pj-OOVn
Root mean-squared error (RMSE)
CD
-J?
0*^
II
M
M cj
0 i
0
X
0
0
The normalized mean bias (NMB) is defined as the sum of the
difference between predictions and observations divided by the
sum of observed values
NME(%)=2M x 100
Li °i
Normalized mean error (NME) is defined as the sum of the
absolute value of the difference between predictions and
observations divided by the sum of observed values
r _ S?=1(Pi-P)(Oi-o)
jzhCPi-PfJxl^Ot-d)*
Pearson correlation coefficient
CD
¦o 40-
13
¦+—•
ra
-1 35-
25-GOC)a|e (Mao data©2018 Gooa'e. inegi (
-120 -100
Mpyirn
Northeast
Northern Rockies & Plains
Northwest
Ohio Valley
South
Southeast
Southwest
Upper Midwest
West
Longitude
Figure C-10. U.S. climate regions24 used in the CMAQ model performance evaluation.
In addition to the national model performance evaluation just described, CMAQ
predictions of PM2.5 concentrations were evaluated specifically for the CBSAs considered in the
risk assessment. In Table C-8, model performance statistics are provided for predictions at
monitors in the 47 CBSAs in 2015. Predictions generally agree well with observations over the
full set of areas, with NMBs less than 10% in all seasons except Fall (NMB: 23.6%) and
correlation coefficients greater than 0.60 in all seasons except Summer (r: 0.56). Model
predictions are compared with observations by CBSA in Figure C-l 1, and NMBs at individual
sites in the CBSAs are shown in Figure C-12. Predictions generally agree well with observations
in the individual CBSAs, although underpredictions occurred in the Chicago-Naperville-Elgin
m https://www.ncdc.noaa.gov/monitoring-references/maps/us-climate-regions.php
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33
CBSA when observed PM2.5 concentrations were > 40 |Lxg m"3. The high observed values in
Chicago were associated with the 4th of July holiday, and the underpredictions on July 4th and 5th
have small influence on the annual PM2.5 projections in the risk assessment. The NMB is highest
for model predictions in the Birmingham-Hoover CBSA (NMB: 66%). As mentioned above, the
effects of model bias are mitigated in part by use of relative response factors (i.e., the ratio model
predictions from a base and emission control simulation is used in projecting PM2.5
concentrations, and some model bias likely cancels in the ratio). For the risk assessment
projections, the key aspect of the CMAQ modeling is the spatial of pattern of PM2.5 response to
changes in emissions. The spatial response pattern was examined in the 47 CBS As and found to
be reasonable even in areas with relatively high bias, such as Birmingham. In Figure C-13, the
spatial response pattern associated with the 10/30 projection case for the Birmingham-Hoover
CBSA is compared for the proportional projection method and the primary PM projection case
based on CMAQ modeling. Relatively high PM2.5 responsiveness occurred in the urban part of
Birmingham and along arterial roads in the CMAQ-based approach. This spatial pattern is
consistent with the location of PM2.5 emission sources in Birmingham and provides a realistic
spatial response pattern despite the relatively high bias in the concentration predictions. Overall,
both the national model performance evaluation and the evaluation for the 47 CBS As of the risk
assessment support use of the CMAQ modeling in this application.
To inform PM2.5 projections, annual CMAQ modeling was conducted using the same
configuration and inputs as the 2015 base case simulation but with anthropogenic emissions of
primary PM2.5 or NOx and SO2 scaled by fixed percentages. Specifically, seven simulations were
conducted with changes in anthropogenic NOx and SO2 emissions (i.e., combined NOx and SO2,
not separate NOx and SO2 simulations) of-100%, -75%, -50%, -25%, +25%, +50%, and +75.
Two simulations were conducted with changes in anthropogenic PM2.5 emissions of -50% and
+50%). The sensitivity simulations were based on emission changes applied to all anthropogenic
sources throughout the year. These "across-the-board" emission changes facilitate projecting the
baseline concentrations to just meet a relatively wide range of standards in areas throughout the
U.S. using a feasible number of national sensitivity simulations.
October 2021 C-30 Draft - Do Not Quote or Cite
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1 Table C-8. Performance statistics for CMAQ predictions at monitoring sites in the 47
2 CBSAs considered in the risk assessment.
Season
Average
Observed
(ng m 3)
Average
Modeled
(ng m 3)
MB
(ng m-3)
NMB
(%)
RMSE
(ng m 3)
NME (%)
r
Winter
12.40
13.45
1.05
8.5
8.03
42.4
0.61
Spring
9.17
9.94
0.77
8.4
5.15
38.6
0.62
Summer
10.35
10.08
-0.27
-2.6
5.51
34.6
0.56
Fall
9.00
11.11
2.12
23.6
6.26
45.6
0.67
October 2021
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O)
3
_cd
CD
"D
o
120
80
40
0
120
80
40
0
120-
NMB: 5%
MB: 0.47
80
RMSE: 4.2
r: 0.64
40
0'
AkronO
120'
NMB: -13%
MB:-1.59
80
RMSE: 4.96
n 0.71
40
_
0
if-
120
Chicag
NMB: 45 %
MB: 4.47 ~
80
RMSE: 8.69
r:0j54 .
40
M - •
0
W
Evansv
Lancas
NMB: 7 %
MB: 0.79
RMSE: 5.8?
r: 0.72
*
Louisv
NMB: 9%
MB: 0.94
RMSE: 4.89
r: 0.65
120
NapaCA
NMB: -34 %
MB: -3.61 /
80
RMSE: 7.75
r: 0.39
40
&
0
120-
ProvoO
NMB: -31 %
MB: -2.2
80
RMSE: 5.42
r: 0.45
40
• ~
0'
A'.
St. Lou
120
NMB: 21 %
MB: 2.16 ~
80
RMSE: 6.15
r: 0.60
40
JL;-.
0
Altoon
NMB: -32 %
MB: -3.5
RMSE: 4.89
r: 0.77
Cincin
NMB: 24 %
MB: 2.35
RMSE: 5.06
r: 0.72
Fresno
NMB:-13%
MB: -J .73
RMSE: 8.48
r: 0.€*r .
k*
LasVeg
NMB: 2 %
MB: 0.17
RMSE: 3.66
r: 0.74
MaconG
NMB: 10%
MB: 0.88
RMSE: 4.66
r: 0.60
NewYor
NMB: 52 %
MB: 4.61
RMSf: 8.17..
'.
Modest
NMB: -3 %
MB: -0.3
RMS£.5.43
r: 0.82* .
Prinev
NMB: -30 %
MB: -2.6
RMSE: 9.7 ,
r: 0.43 f '
%
SouthB
NMB: -4 %
MB: -0.39
RMSE: 4.32,
r: 0.72
40 80 120
120 0 40 80 120
2 Figure C-l 1. Comparison of CMAQ predictions and observations at monitoring sites in the
3 47 CBS As considered in the risk assessment.
4
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2
3
4
5
6
7
8
9
10
11
12
Spring
Summer
Winter
50-
45-
40-
35-
30-
CD ?5 -
"O
1 50-
Figure C-12.
season in
by
-120 -100 -80 -120 -100 -80
Longitude
NMB for CMAQ PM2.5 predictions at monitoring sites in the 47 CBSAs
2015.
0
"O
3
• Controlling
^ NotCntling
% Chg
¦
-5
-6
-7
-8
-9
-10
Tallade
nsboro I
Map data®2fl?9.Google, INEGf
Proportional
National Forest Cullman w.
Primary PM
Bankhead „ ^ j
Bankhead
National Forest
33.5
33.0-
I Talladec :aloosa
—j National F< o
:aloosa
ihbco
-87.6 -87.2 -86.8 -86.4 -87.6 -87.2 -86.8 -86.4
Longitude
Figure C-13. Percent change in 2015 annual average PM2.5 over the Birmingham CBS A
associated with projecting 2014-2016 DVs at monitors to just meet an alternative
NAAQS of 10/30 using the proportional projection method and the primary PM2.5,
CMAQ-based projection method.
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The two emission sensitivity scenarios (primary PM2.5 and NOx and SO2) were selected
to span a wide range of possible PM2.5 spatial response patterns. NOx and SO2 emission changes
influence concentrations of ammonium nitrate and ammonium sulfate, which are secondary
pollutants that often have broad spatial distributions. Primary PM2.5 emission changes have the
greatest influence on PM2.5 concentrations close to emission sources. The two distinctly different
PM2.5 response patterns for primary PM2.5 and NOx and SO2 emission changes enable PM2.5 to
be projected for a wide range of conditions. Projecting PM2.5 for a wide range of conditions is
desirable in this study because many PM2.5 spatial response patterns can cause PM2.5
concentrations to just meet NAAQS.
C.l.4.4 Relative Response Factors for PM2.5 Projection
The 2015 base case and sensitivity modeling results were used to develop RRFs for
projecting PM2.5 concentrations to correspond to just meeting NAAQS (Figure C-6, Box 2,
"SMAT-CE"). Baseline PM2.5 concentrations are projected by multiplication with RRFs. The
RRF for a PM2.5 species is calculated as the ratio of the concentration in the sensitivity
simulation to that in the base case:
nr>r? Csensitivity,species
KKrspecies ~~ ^ (¦*¦)
^base,species
where Csensitivity,species is the concentration of the PM2.5 species in the sensitivity
simulation, and Cbase,species is the concentration of the PM2.5 species in the base case simulation.
RRFs were calculated for each monitor, grid cell, calendar quarter, standard (annual or 24-hr),
species, and sensitivity simulation using SMAT-CE version 1.2.1. RRFs are used in projecting
air quality to help mitigate the influence of systematic biases in model predictions (National
Resources Council, U.S. EPA, 2018b). More details on the RRF projection method are provided
in EPA's modeling guidance document (U.S. EPA, 2018b) and the user's guide for the
predecessor to the SMAT-CE software (Abt Associates, 2014).
To apply the RRF approach for the risk assessment projections, RRFs for total PM2.5
were calculated from RRFs for the individual PM2.5 species using observation-based estimates of
PM2.5 species concentrations in SMAT-CE output. Specifically, total PM2.5 RRFs (RRFrot,PM2.5)
were calculated as the weighted average of the speciated RRFs using the observation-based
species concentrations (C species) as weights:
nnr? ^RRFspecies^species
KKrrot, PM2.5 ~ ^7 U)
Z. uspecies
Total PM2.5 RRFs were used to project base-case PM2.5 concentrations as follows:
^^2.5, projected ~ ^^^Tot,PM2.S^^2.S,base (3)
The species concentrations used in calculating the total PM2.5 RRFs were generally based
on application of the Sulfate, Adjusted Nitrate, Derived Water, Inferred Carbonaceous material
balance approacH (SANDWICH) (Frank, 2006) to measurements of PM2.5 species
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24
concentrations from the Chemical Speciation Network (CSN)25 and the Interagency Monitoring
of Protected Visual Environments (IMPROVE)26 network. The SANDWICH method corrects for
different artifacts in the measurements for PM2.5 species and total PM2.5. An alternative approach
to calculating total PM2.5 RRFs was applied for monitors and grid cells in California due to
factors including missing data at the Bakersfield speciation monitor27 throughout 2014 and part
of 2015. For projections in California, RRFs were calculated directly from the ratio of CMAQ
PM2.5 concentration predictions in the sensitivity simulation to the base simulation.
By default, PM2.5 RRFs for the annual standard are calculated using average
concentrations over all modeled days in the quarter, and RRFs for the 24-hr standard are
calculated using average concentrations over days with the top 10% of modeled PM2.5
concentration in the quarter. The default approach was generally followed here, with exceptions
for counties in the San Joaquin Valley (SJV) of California and Utah. In these counties28, the
average concentration over all days in the quarter was used to calculate RRFs for both the 24-hr
and annual standards for sites with valid 24-hr and annual DVs. This approach was used to
provide stability in projections of annual fields due the variability in the 24-hr and annual
RRFs29. Also, RRFs were set to one30 in the third quarter (July-September) for select counties in
the San Joaquin Valley and Utah31 to better reflect the seasonal nature of PM2.5 in these areas
(i.e., PM2.5 concentrations are relatively high in winter).
RRFs were calculated for each combination of emission sensitivity simulation and the
2015 base case. RRFs corresponding to the percent change in emissions for each sensitivity
simulation were then interpolated across the range of emission changes from -100 to +100% to
facilitate iterative projections of PM2.5 concentrations to the nearest percent emission change.
PM2.5 RRFs are shown in Figure C-14 and Figure C-15 as a function of changes in anthropogenic
primary PM2.5 and NOx and SO2 emissions for monitors in the U.S. during the first and third
25 www.epa.gov/aintlc/cheniical-speciation-network-csn
26 http://vista.cira.colostate.edu/Improve/
27 Site identification number: 060290014
28 SJV counties: Fresno, Stanislaus, Kern, Merced, Madera, Tulare, San Joaquin, and Kings; Utah counties: Cache,
Box Elder, Davis, Morgan, Weber, Juab, Utah, Salt Lake, and Tooele.
29 This variability is less of an issue in regional modeling applications where emission changes can be targeted to
time periods of elevated PM2 5 concentrations in the area.
30 When the RRF is 1, the projected concentration equals the base concentration (Equation 3).
31 SJV counties: Fresno, Stanislaus, Kern, Merced, and Madera; Utah counties: Cache, Box Elder, Davis, Morgan,
Weber, Juab, Utah, Salt Lake, and Tooele. This approach was not applied for Kings, Tulare, and San Joaquin
counties in SJV because the percent exceedance of the annual standard was within 10% of the exceedance of the
24-hr standard suggesting that relatively uniform PM2 5 concentrations occur throughout the year compared with
the other SJV counties.
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calendar quarters. Spatial fields of PM2.5 RRFs for 50% reductions in anthropogenic primary
PM2.5 and NOx and SO2 emissions are shown in Figure C-16.
(a) July-September
(b) January-March
DC
oc
LO
OJ
-100 -50
0 50 100-100 -50 0
Emission Change (%)
100
Figure C-14. Annual standard P1VI2.5 RRFs for quarters 1 and 3 as a function of the percent
change in anthropogenic primary PM2.5 emissions for monitoring sites in the contiguous
U.S.
1.50-
0.75-
(a) July-September
(b) January-March
¦ ^HH
-50 0 50 100-100 -50 0
Emission Change (%)
50 100
Figure C-15. Annual standard PM2.5 RRFs for quarters 1 and 3 as a function of the percent
change in anthropogenic NOx and SO2 emissions for monitoring sites in the contiguous
U.S.
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(b) 50% Primary PM25 Reduction
Q)
"O 40
MHDdat^©20l8 Goocile ITvJEG
-120 -110 -100 -90 -80 -70 -120 -110
Longitude
Figure C-16. Annual average PM2.5 RRFs at CMAQ grid-cell centers for 50% reductions in
anthropogenic (a) NOx and SO2 and (b) primary PM2.5 emissions.
C. 1.4.5 2015 PM2.5 Concentration Fields
To develop a baseline gridded PM2.5 concentration field for projection with PM2.5 RRFs,
a Bayesian statistical model (i.e., Downscaler) was applied (Figure C-6, Box 2, "Downscaler")
(Berrocal et al., 2012). Downscaler makes predictions of PM2.5 concentrations to a spatial field
of receptor points using PM2.5 monitoring data and CMAQ model predictions as inputs.
Downscaler takes advantage of the accuracy of the monitoring data and the spatial coverage of
the CMAQ predictions to develop new predictions of PM2.5 concentration over the U.S.
The Downscaler model is routinely applied by U.S. EPA to predict 24-hr average PM2.5
concentrations at the centroids of census tracts in the contiguous U.S. (U.S. EPA, 2018a). The
model configuration used here is generally consistent with the previous applications, but here
predictions were made to the centers of the CMAQ model grid cells rather than to census-tract
centroids. Also, PM2.5 measurements from the IMPROVE monitoring network were used in
addition to measurements included in the AQS database. 24-hr average PM2.5 concentrations
were predicted for the 2015 period, and the 24-hr PM2.5 fields were averaged to the quarterly
periods of the PM2.5 RRFs for use in projection.
Annual average PM2.5 concentrations from the monitoring network and CMAQ
simulation that were used in model fitting are shown in Figure C-17 along with the resulting
Downscaler predictions. Cross-validation statistics are provided in Table C-9 based on
comparisons of Downscaler predictions against the 10% of the observations that were randomly
withheld from model fitting.
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Observed
CMAQ
A
\2yh
Tn \—ft
Gulf of \J
Downscaler
¦i
1
I—| r—ft ft 3
^ Gulf of \j
1 1 1 1
-120 -110 -100 -90 -80 -70 -120 -110 -100 -90 -80 -70 -120 -110 -100 -90 -80 -70
Longitude
ug/m3
I
>20
15
m
10
5
0
Figure C-17. Annual average of the 2015 PM2.5 observations and CMAQ predictions used
in the Downscaler model, and the annual average of the Downscaler PM2.5 predictions.
Table C-9. Cross-validation statistics associated with the 2015 Downscaler predictions.
Number of Monitors
1101
Mean Bias3
(U9 rn -)
0.37
Root Mean Squared Errorb
(BJg m3)
3.17
Mean Coverage0
0.95
aThe mean of all biases across the CV cases, where the bias of each prediction is the downscaler
prediction minus the observed value.
bThe bias is squared for each CV prediction, then the square root of the mean of all squared
biases across all CV predictions is obtained.
CA value of 1 is assigned if the measured value lies in the 95th percentile CI of the Downscaler
prediction (the Downscaler prediction ± the Downscaler standard error), and 0 otherwise. This
column is the mean of all those O's and 1's,
C. 1.4.6 Projecting PM2.5 to Just Meet the Standards
PM2.5 was projected from baseline concentrations to levels corresponding to just meeting
NAAQS using the monitoring data (section C. 1.4.2), RRFs (section C.l.4.4), and baseline
concentration fields (section C.l.4.5) described above. The projection was done in two steps as
shown in Box 3 of Figure C-6. Projections were performed for the existing (12/35)32 and
alternative (10/30)33 standards.
First, monitors in the CBSA of interest were identified, and concentrations from these
monitors were subset from the national monitoring dataset. The measured concentrations were
then projected using the corresponding PM2.5 RRF. PM2.5 DVs were calculated using the
projected concentrations, and the difference between the maximum projected DV and target
standard was determined. DY projections over the complete range of percent emission changes (-
100 to 100%) were performed using bisection iteration until the difference between the
32 Annual standard level of 12 jig rrr3 and 24-hr standard level of 35 jig nr3
33 Annual standard level of 10 |tg nr3 and 24-hr standard level of 30 jig in 3
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maximum projected DV in the CBS A and the standard level was zero or within the difference
associated with a 1% emission change. Iterative projections of annual and 24-hr DVs were
performed separately, and the controlling standard was determined as the standard requiring the
greater percent emission change34. In cases where the emission change needed to just meet the
target annual or 24-hr standard was outside of the ± 100% range, the standard could not be met
using the modeled air quality scenarios. If neither the annual nor 24-hr standard could be just met
with emission changes within ± 100%, then an alternative projection approach was used
(discussed below).
Second, 2015 PM2.5 concentration fields developed with Downscaler were projected
according to the percent emission change required for the maximum projected DV to just meet
the controlling standard. The projection was done by multiplying the gridded spatial fields of
quarterly average PM2.5 concentrations based on Downscaler modeling with the gridded spatial
fields of quarterly PM2.5 RRFs corresponding to the percent emission change required to just
meet the controlling standard. The projected fields of quarterly average PM2.5 concentrations
were then averaged to produce the annual average projected field.
Since PM2.5 concentrations can be projected in multiple ways to just meet a standard,
projections were done for two scenarios that provide results for a range of PM2.5 conditions. The
first scenario is referred to as "Primary PM" or Pri-PM because projections were largely based
on RRFs developed using CMAQ sensitivity simulations with primary PM2.5 emission changes.
For three CBSAs35, standards could not be met using primary PM2.5 emission reductions alone.
PM2.5 concentrations were projected for these areas using a combination of primary PM2.5 and
NOx and SO2 emission reductions in the Primary PM scenario36 (Figure C-18).
34 Note that calculations are performed in terms of percent emission reduction. Therefore, in cases where DVs are
projected to just meet standards greater than the baseline DVs, the required percent emission reduction is negative
(i.e., an emission increase is required), and the smaller absolute percent emission change is selected as the
controlling case. For example, the annual standard would be selected as controlling in a case where a 10%
emission increase is needed to meet the annual standard and a 50% emission increase is needed to meet the 24-hr
standard (because -10 is greater than -50).
35 Bakersfield, Hanford-Corcoran, and Visalia-Porterville (all in California)
36 This approach was applied by using RRFs from the NOx and SO2 emission sensitivity simulations to eliminate a
fraction of the difference between the maximum base DV and the standard level and then using RRFs from the
primary PM2 5 emission sensitivity simulations to eliminate the remainder of the difference. The fraction of the
difference eliminated with NOx and SO2 emission reductions was as follows: 0.4 for Bakersfield, 0.5 for Visalia-
Porterville, and 0.6 for Hanford-Corcoran
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10
>- W;
*
i - .?
|TWr
45-'
0 40-
"O
13
03
35-
30-
%
25 _Mao dat^ C2019 Gaoole INEG?^>,
-120 -110
Primary PM
j-
v*
T- 1*.
#_v
Gulf of
IT"
-100 -90
Longitude
-80
-70
Primary N0xS02+Primary
Figure C-18. Projection method used for each CBSA in the "Primary PM" projection case.
See text for details.
The second scenario is referred to as "Secondary PM" or Sec-PM because projections
were largely based on RRFs developed using CMAQ modeling with NOx and SO2 emission
changes, which affect concentrations of secondary PM components such as ammonium nitrate
and ammonium sulfate. For 22 CBSAs37, standards could not be just met using NOx and SO2
emission changes alone. These areas were projected using the proportional scaling method38
(Figure C-19). The proportional method was selected to gap-fill the Secondary PM case because
37 Altoona, PA; Atlanta-Sandy Springs-Roswell, GA: Bakersfield, CA; Chicago-Naperville-Elgin. IL-IN-WI; El
Centra, CA; Elkhart-Goshen, IN; Fresno, CA; Hanford-Corcoran, CA; Las Vegas-Henderson-Paradise, NV: Los
Angeles-Long Beach-Anaheim, CA; Macon, GA: Madera, CA; McAllen-Edinburg-Mission. TX: Modesto, CA;
Napa, CA; New York-Newark-Jersey City, NY-NJ-PA; Prineville, OR; Riverside-San Bernardino-Ontario, CA;
St. Louis, MO-IL; San Luis Obispo-Paso Robles-Arroyo Grande. CA; Visalia-Porterville, CA; Wheeling, WV-
OH
38 In the proportional method, the spatial field is uniformly scaled by a fixed percentage that corresponds to the
percent difference between the controlling standard level and maximum PM2.5 DV for the controlling standard.
The controlling standard (annual or 24-hr) is identified as the one with the greater percent difference between the
maximum DV and the standard level.
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it is based on a spatially uniform percent change in PM2.5 over the area that is like the
conceptually broad spatial response pattern of PM2.5 to changes in secondary PM2.5 components.
The proportional method has been used previously in the Risk and Exposure Assessment for the
2012 PM NAAQS review (U.S. EPA, 2010).
30-
Secondary PM
?
* 1»
1*
J,
", *
\
25 _Mao Oa'.j) CSOtOGDOQle INEGIj.
•120
-110
¦100
Sulfof
-90
-80
Longitude
-70
NOxS02 Proportional
Figure C-19. Projection method used for each CBSA in the "Secondary PM" projection
case.
The baseline 2015 concentration in the 47 CBSAs is shown in Figure C-20. These
concentrations are the same as those in Figure C-17 but are shown only for the CBSAs included
in the projections. In Figure C-2L the difference in annual concentration projected for the 12/35
case and the 2015 baseline concentration is shown. The positive and negative differences reflect
areas where concentrations were projected to higher and lower levels to just meet the standard,
respectively. In Figure C-22, the difference between the annual concentration projected for the
10/30 case and the and 2015 baseline concentration. Negative values indicate that concentrations
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1 were projected to lower levels in all cases for the areas. The difference in projected
2 concentrations for the 10/30 and 12/35 fields is shown in Figure C-23. Baseline and projected
3 PM2.5 DVs for monitors in the 47 CBSAs are provided in Table C-19, Table C-20, Table C-21,
4 and Table C-22 in section C.6.39
5
2015 PM2.5
CD
-o 40 H
-4^
03
ug/m3
I
Q
6
7
8
9
Figure C-20. Annual average 2015 PM2.5 concentrations in the 47 CBSAs based on
Downscaler modeling.
-100 -90
Longitude
39 The tables report the percent emission reduction associated with just meeting standards in the current modeling.
These values should not be interpreted as the percent emission reductions that would be required to meet the
standards in other application (e.g., attaimnent demonstrations for state implementation plans). The modeling
done here was designed to quickly project PM2 5 fields throughout the U.S. with a broad range of model response
patterns, rather than to apply model configurations and emission scenarios specific to just meeting standards most
efficiently in particular regions.
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2
3
4
Primary PM
ug/m3
3
Figure C-21. Difference between the annual average projected PM2.5 concentrations and
the 2015 baseline concentrations for the 12/35 projection cases (i.e., 12/35 - baseline).
-100 -90
Longitude
Secondary PM
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ug/m3
3
2
1
o
-1
-2
<-3
Primary PM
1
2
3
Secondary PM
Figure C-22. Difference between the annual average projected P1VI2.5 concentrations and
the 2015 baseline concentrations for the 10/30 projection cases (i.e., 10/30 - baseline).
-100 -90
Longitude
October 2021 C -44 Draft - Do Not Quote or Cite
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Primary PM
ug/m3
6 C.l.4.7 Limitations
7 There are several limitations associated with the air quality projections. First, the baseline
8 and projected concentrations rely on model predictions. Although state-of-the-science modeling
9 methods were applied, and model performance was generally good, there is uncertainty
10 associated with the model predictions. Second, due to the national scale of the assessment, the
Secondary PM
1
2
3
4
Figure C-23. Difference between the annual average projected P1VI2.5 concentrations in the
10/30 and 12/35 cases (i.e., 10/30 - 12/35) for the Primary PM and Secondary PM
projection cases.
-100 -90
Longitude
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modeling scenarios are based on "across-the-board" emission changes in which emissions of
primary PM2.5 or NOx and SO2 from all anthropogenic sources throughout the U.S. are scaled by
fixed percentages. Although this approach tends to target the key sources in each area, it does not
tailor emission changes to specific periods or sources. More refined emission scenarios could be
beneficial for projections in areas with relatively large seasonal and/or spatial variability in
PM2.5. Similarly, fine scale simulations (e.g., 4 km or less), which are not possible due to the
national scale of the assessment, would be beneficial in areas with complex terrain and relatively
large spatial gradients in PM2.5. A third limitation arises because many emission cases could be
applied to project PM2.5 concentrations to just meet standards. We applied two projection cases
that span a wide range of possible conditions, but these cases are necessarily a subset of the full
set of possible projection cases.
C.1.5 Risk Modeling Approach
Risk modeling for this assessment was completed using BenMAP-CE version 1.5.40
BenMAP-CE was used to estimate risk at the 12 km grid cell level for grid cells intersected by
the 47 urban study area CBSAs included in risk modeling. BenMAP-CE is an open-source
computer program that calculates the number and economic value of air pollution-related deaths
and illnesses. The software incorporates a database that includes many of the CR relationships,
population files, and health and economic data needed to quantify these impacts. BenMAP-CE
also allows the user to import customized datasets for any of the inputs used in modeling risk.
For this analysis, CR functions developed specifically for this assessment were imported into
BenMAP-CE (section C.l.l). The BenMAP-CE tool estimates the number of health impacts
resulting from changes in air quality. BenMAP-CE can also translate these incidence estimates
into monetized benefits, although that functionality was not employed for this risk assessment.
Inputs to BenMAP-CE used for this risk assessment are identified above in Figure C-l and
described in detail in sections C.l.l, C.1.2C.1.3, and C.1.4.
An overall flow diagram of the risk assessment approach is provided in Figure C-24.
Application of this approach resulted in separate sets of risk estimates being generated for the
following three groupings of urban study areas:
- the full set of 47,
- the 30 areas controlled by the annual standard, and
- the 11 areas controlled by the 24-hr standard.
Available air quality modeling surfaces for each of the three study area groupings are
summarized in Table C-10.
40 BenMAP-CE is a free program which can be downloaded from: https://www.epa.gov/benmap.
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Estimated risk in 30 annual- Estimated risk in 11
controlled study areas (~50M daily-controlled study
people 30+) for RC, 12.0, 11.0, areas (~4M people 30+)
10.0, 9.0, and 8.0 ug/m3 for RC, 35, and 30 ug/m3
2 Figure C-24. Flow diagram of risk assessment technical approach.
3
4 Table C-10. Summary of available air quality scenarios for each study area set
47 Study Areas
(full set)
30 Study Areas
(annually controlled)
11 Study Areas
(daily controlled)
Recent Conditions (2015)
X
X
X
3
Just meeting 12/35 pg/m
X
X
X
3
11 |jg/m (interpolated)
X
3
Just meeting 10/30 pg/m
X
X
X
3
9 |jg/m (extrapolated)
X
3
8 |jg/m (extrapolated)
X
5
6 Risk estimates are presented and discussed for each of these groupings in draft PA
7 section 3.4.2, with greater emphasis being placed on results generated for the full set of 47 urban
8 study areas and 30 annual-controlled study areas, given interest in national representation and on
Estimated risk in all
47 study areas for RC,
12/35, and 10/30 ug/m3
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35
those study areas where we could also consider the alternative annual standards of 8.0, 9.0 and
11.0 |ig/m3.
C.2 SUPPLEMENTAL RISK RESULTS
As noted earlier, this appendix also presents additional granular risk results that supplement the
aggregated risk estimates presented and discussed in section 3.4.2 of the draft PA. The
supplemental results are intended to provide additional context for the interpretation of summary
risk estimates presented in draft PA section 3.4.2 and include additional line plots, maps and
scatter plots illustrating the distribution of the grid-level risk estimates across ambient PM2.5
concentrations (section C.2). Graphics provide insight into various aspects of the grid-level data
underlying the summary tables presented in the draft PA, such as the spatial distribution of risk
across the cities included in the risk assessment and how the distribution of grid-cell level risk
estimates shifts as lower alternative standards are considered.
It can be challenging to understand how patterns of risk are changing under air quality
simulated to just meet the current or alternative standards, due to differences in underlying
demographics (e.g., size and age of population), health status (e.g., underlying death rates) and
exposure (air quality conditions). To better illustrate the distribution of risk under the current
standards and how that distribution changes under potential alternative standards, this section
presents graphics depicting these changes both in aggregate and at the grid-cell level.
As the pattern of risk and risk reduction is similar across mortality endpoints, we focus on
a single CR function to illustrate the changes graphically. Consequently, as with the graphics
presented in draft PA section 3.4.2, the graphics presented in this section are also based on long-
term exposure-related all-cause mortality modeled using a CR function obtained from Turner et
al., 2016. The first set of graphics presented in this section (Figure C-25, Figure C-26, Figure C-
27, Figure C-28, and Figure C-29) include results for the full set of 47 urban study areas and the
second set (Figure C-30 and Figure C-31) include results for the 30 annual-controlled study
areas. Graphical plots include:
• Histograms showing the distribution of 12 km gridded risk estimates across annual-
averaged PM2.5 concentrations (Figure C-25 and Figure C-30). These figures allow
consideration of how the distribution of risk shifts when simulating air quality that just
meets the current standards (12/35 |ig/m3) relative to 2015 recent conditions and
subsequently how that distribution of risk shifts downward when simulating air quality
that just meets alternative standards of 10/30 |ig/m3.
• Maps showing the 12 km grid-level risk estimates associated with each of the 47 urban
study areas. In these representative maps each grid cell is shown as a square, with the
color of the square going from green (lower risk estimates) to red (higher risk estimate)
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colors. The center of the color scales (the beginning of yellow) has been set to a risk
estimate of two premature deaths. This means that green squares represent grid cells
where 0-1 premature deaths are estimated, yellow squares represent grid cells in which at
least two premature deaths are estimated, and as the color graduation approaches red the
number of estimated premature deaths increases. Separate maps are presented for
(a) the unadjusted 2015 recent conditions simulation (Figure C-26),
(b) simulation of the current standards (12/35 |ig/m3) (Figure C-27), and
(c) simulation of the change (delta) in risk between the current and alternative
standards (10/30 |ig/m3) (Figure C-28).
• Scatter plots depicting the distribution of modeled risk by annual-average PM2.5
concentration (Figure C-29 and Figure C-31). While these scatter plots present similar
distributional information as the line graphs, the scatter plots allow for a more detailed
consideration of the nature of the risk distribution in relation to ambient PM2.5 levels. In
these figures, each grid cell is shown as a dot, with the frequency of dots shown on a
color scale from cool (green - lower frequency) to hot (red - higher frequency) colors.41
Consequently, it is possible to consider whether, for example, a shift in risk involves a
change in the magnitude of risk across higher-risk cells, or in a change in the density of
lower risk cells.
Key observations resulting from review of these graphics are presented below the figures.
41 For adjusted air quality, a small amount of risk is estimated at concentrations higher than the level of the annual
standard (e.g., some risk is estimated at an average concentration of 13 |ig/m3 when air quality is adjusted to just
meet the current standard). This can result because risk estimates are for a single year (i.e., 2015) within the 3 -
year design value period (i.e., 2014 to 2016). While the three-year average design value is 12.0 |ig/ml a single
year can have grid cells with annual average concentrations above or below 12.0 |ig/m3.
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1 C.2.1 Results from Full Set of 47 Study Areas
Annual PM Concentration (1 pg/m3 bins) / Simulation Method
3
4
5
6
7
8
9
10
11
12
13
14
15
16
20K
Recent 15K
conditions 10K
(2015)
5K
OK
¦
1
1
1
¦
1 . ,¦ 20K
Just meeting
12 Ljg/m3 15K
annual and
35 Ljg/m3 10K
24-hr 5K
standards
OK
¦
¦
I
1
I
I
1
1
, . .¦ 20K
Just meeting
10 ^ig/m3 15K
annual and
30 ^ig/m3
24-hr 5K
standards
OK
¦ ¦
ll
I
I
1
1
Pri PM
Sec PM
Pri PM
Sec PM |
Pri PM |
Sec PM |
Pri PM |
Sec PM |
5 5
CL Q_
"EI O
0_ a>
-------
2 0.0 830.0
3 Figure C-26. Estimated number of premature deaths (by 12 km grid cell) under 2015 recent conditions in all 47 study areas.
4
ipbox © OpenStreetMap
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< IV
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x>x © OpenStreelMap
o.o
830.0
Figure C-28. Estimated reduction in the number of premature deaths (by 12 km grid cell) when going from just meeting the
current standards (12/35) to just meeting the alternative standards (10/30) in all 47 study areas (Pri-PM simulation).
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800
Recent
conditions 400
(2015)
0
800
Just meeting Pri PM 400
12 |jg/m3
annual and 35 o
- - i 1 li 1 •' • ».
Mg/m3 24-hr 800
standards
Sec PM 400
0
800
Just meeting Pri PM 400
10 |jg/m3
annual and 30 0
- • # •
Mg/m3 24-hr 800
standards
Sec PM 400
0
3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Annual PM Concentration (|jg/n"i3)
2 Figure C-29. Distribution of estimated premature death (by 12 km grid cell) for the current
3 standards (12/35 jig/m3), alternative standards (10/30 jtig/m3), and recent conditions
4 (2015) for all 47 urban study areas (Pri-PM simulation).
5
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I C.2.2 Results from Set of 30 Study Areas controlled by the Annual Standard
Recent 15K
conditions 10K
(2015) 5K
OK
¦ ¦ ¦ ¦
15K
Just meeting ^
12|jg/m3 5K
OK
¦ ¦ ¦ ¦ ¦ ¦
15K
Interpolated
to 11 pg/m3 5[<
OK
¦—¦—¦—¦—
15K
Just meeting ^ q^
10 (jg/m3 5[<
OK
. . .
15K
Extrapolated ^ q^
to 9 pg/m3 5K
OK
15K
Extrapolated ^
to 8 pg/m3 5[<
OK
. . .
2 3 4 5 6 7 8 9 10 11 12 13
Annual PM Concentration {1 pg/m3 bins)
3 Figure C-30. Distribution of estimated PM2.5-associated mortality for recent conditions
4 (2015), the current annual standard (12/35 jig/m3), and alternative standards (8.0, 9.0,
5 10.0, and 11.0 jig/m3) simulated for the 30 annual-controlled urban study areas (blue
6 and green bars represent the Pri-P.\l2.5 and Sec-PiVb.? estimates, respectively).43
7
8
43 Risk is rounded toward zero into whole PM2 5 concentration values (e.g., risk estimate at 10 jig/in3 includes risk
occurring at 10.0-10.9 iig/in'). Blue lines represent the Pri-PM risk estimates, green lines represent the Sec-PM risk
estimates, and black lines represent the 2015 recent conditions risk estimates.
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Recent 800
conditions 400
(2015) 0
800
Just meeting Pri PM 400
12 pg/m3 0
800
Sec PM 400
0
800
Interpolated Pri PM 400
to 11 pg/m3 0
800
Sec PM 400
0
800
Just meeting Pri PM 400
10 |jg/m' 0
*
1
«
!
•
f
1
800
Sec PM 400
0
800
Extrapolated Pri PM 400
to 9 ijg/m3 0
800
Sec PM 400
0
800
Extrapolated Pri PM 400
to 8 pg/m3 0
800
Sec PM 400
0
.1
•
•
t
I
2 3 4 5 6 7 8 9 10 11 12 13 14
Annual PM Concentration (pg/m3)
Figure C-31. Distribution of estimated premature death (by 12 km grid cell) 47 urban
study areas (Pri-PM simulation) for recent conditions (2015), the current annual
standard (12.0 jig/m3), alternative annual standards (8.0, 9.0,10.0,11.0 jig/m3).
C.2.3 Key Observations from the Suppmental Risk Results
Review of the distributional risk estimates presented in section C.2 further support the
key observations presented in draft PA section 3.4.2. Briefly, these observations include:
• Across the full set of alternative annual standards modeled including 11.0, 10.0, 9.0, and
8.0 jig/m3 (each evaluated for the 30 annually-controlled study areas), we see a consistent
reduction in mortality (Figure C-30 and Figure C-31). In addition, we note that these risk
reductions are associated with iteratively lower ambient PM2.5 concentrations, such that
with the lowest annual standard considered (8.0 fig/m !) the majority of remaining risk
occurs in grid cells with ambient PM2.5 concentrations between 6 and 9 Lig/irr In
contrast, most of the risk occurring under the current standard occurs in grid cells with
ambient concentrations in the range of 10-12 ag/iir' (Figure C-29).
• Patterns of risk reduction seen in the summary (aggregated) risk results tables presented
both in draft PA section 3.4 and this appendix are driven by considerable underlying
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variability across both CBSAs and across the 12km grid-level risk estimates. Specifically,
if we consider the maps and scatter plots presented in section C.2, we see considerable
spread (i.e., variability) in the grid-level risk estimates. We note that this underlying
variability in risk reflects local patterns of population density, baseline incidence, and
modeled ambient PM2.5 levels. However, it is important to also note that the underlying
variability does not result from differences in CR functions, since for all mortality
endpoints modeled in this analysis, national-level effect estimates were utilized.
• When considering the shift in the distribution of risks for the alternative standards (Figure
C-29 and Figure C-31), we note that risk reductions are estimated in grid cells
encompassing a wide range of PM2.5 concentrations. This includes grid cells with typical
(i.e., frequently occurring) concentrations (orange and red dots) as well as cells with
concentrations that occur relatively infrequently (green dots). Furthermore, these shifts
reflect reductions both in areas with relatively few estimated premature deaths (as
represented by points near the bottom of each of the scatter plots) and in areas with much
larger numbers of estimated deaths (points higher on the y-axis in these scatter plots).
C.3 ADDITIONAL TECHNICAL DETAIL ON THE AT-RISK ANALYSIS
Our consideration of estimated risks among potentially at-risk populations in the draft PA
focuses on addressing the following policy-relevant questions:
• How does PM2.5 exposure and risk compare between demographic groups when air quality just
meets the current and potential alternative primary PM2.5 annual standards?
• To what extent are risks estimated to decline within each demographic group when air quality
is adjusted to just meet potential alternative annual standards with lower levels?
Estimating PM2.5 exposure and risk within various demographic populations when just
meeting the current or alternative annual standard or moving from the current annual standard to
an alternative annual standard requires multiple input parameters and several simplifying
assumptions. An overall summary of the analytical components is provided in Table C-l 1 and
below we discuss in detail the various data inputs and assumptions associated with the at-risk
analysis presented in the draft PA.
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Table C-ll. Summary of At-Risk Analysis Variables"
Race/Ethnicity
Concentration-
Response Function
Baseline Incidence Rate
1. White
2. Black
3. Asian
4. Hispanic
5. (Non-Hispanic)
6. (All)
1. (Overall function)
2. Race/Ethnicity-
stratified
functions
1. (Overall baseline incidence rate)
2. Race/ethnicity-stratified
baseline incidence rates
a Parentheses indicate the variable was used in sensitivity analyses only.
C.3.1 Race/Ethnicity
As the 2019 ISA and the draft ISA Supplement noted strong support for non-White
populations, and particularly Black/African American populations, being at increased risk from
PM2.5-related health effects, in part due to disparities in exposure, we focused on comparing
exposure and risk in Black and White populations. We also included exposure and risk
information from Asians, Native Americans, and Hispanics, although there is less evidence in the
PM IS As that those demographic groups are at increased risk of PM2.5 -related health effects or
experience disparities in PM2.5 exposure (U.S. EPA, 2019, U.S. EPA, 2021a).
Population information for each demographic group from both the at-risk assessment
population and the original cohort population can be found in Table C-12. In general, the
proportions of White, Black, and Native American people in the Di et al., 2017 study were
comparable to the proportions in the 47 urban study areas, though a slightly higher proportion of
the population in the 47 areas was White. In contrast, the Asian and Hispanic subpopulations
represented a smaller proportion of the Di et al., 2017 cohort than the respective population
proportions in the 47 areas. Importantly, the 0.3% of Native Americans assessed by Di et al.,
2017 equates to approximately 180,000 individuals, which is nearly a third of the ACS cohort
(Turner et al., 2016).
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1 Table C-12. Demographic populations aged 65 and over residing in the full set of 47 study
2 areas, the subset of 30 study areas controlled by the annual standard, and the
3 original cohort.
Percent of
Population
in 47 Areas
Percent of
Population
in 30 Areas
Percent of
Ethnicity &
Race
Population
in 47 Areas
Population
in 30 Areas
Population
in Di et al.,
2017 cohort
White
10,560,891
80.0
8,756,815
78.6
85.4
Black
1,655,695
12.6
1,551,743
13.9
8.7
Asian
927,966
7.0
801,487
7.2
1.8
Native American
51,263
0.4
36,477
0.3
0.3
Non-Hispanic
11,647,164
88.3
9,897,164
88.8
-
Hispanic
1,548,639
11.7
1,249,353
11.2
1.9
4
5 C.3.2 Concentration-Response Functions
6 The following eight epidemiologic long-term exposure studies of PM2.5 exposure and all-
7 cause/nonaccidental/total mortality in nonwhite populations were identified in the 2019 ISA and
8 draft ISA Supplement, met the minimum criteria discussed in the EstimatingPM2.5 and Ozone-
9 Attributable Health Benefits TSD (U.S. EPA, 2019, U.S. EPA, 2021a, U.S. EPA, 2021b), and
10 were considered for inclusion in the at-risk assessment: Awad et al., 2019, Di et al., 2017,
11 Kioumourtzoglou et al., 2016, Parker et al., 2018, Lipfert and Wyzga, 2020, Son et al., 2020,
12 Wang et al., 2017, and Wang et al., 2020. Summary information regarding these eight studies is
13 available in Table C-13. Consistent with the main risk assessment, we focused on long-term
14 exposure studies so as to not double-count effects of short-term exposures. No mortality studies
15 for the at-risk group of children met the initial screening criteria.
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1 Table C-13. Summary information for available epidemiology studies of nonwhite populations considered for the at-risk
2 assessment.
Study
Cohort
Study Location
Health
Outcome
Study Size
Health
Years
Air Quality
Years
Ages
Exposure
Method
Awad etal.,
2019
Medicare
enrollees
National US
All-cause
mortality
12,095,504 movers
2000-2012
2000-2012
>64
Hybrid
Di etal., 2017
Medicare
enrollees
National US
All-cause
mortality
60,925,443 persons;
22,567,924 deaths
2000-2012
2000-2012
>64
Hybrid or
Monitor
Kioumourtzogl
ou et al., 2016
Medicare
enrollees
National US (207
US cities)
All-cause
mortality
35,295,005 subjects;
11,411,282 deaths
2000-2010
2000-2010
>64
Monitor
Lipfert and
Wyzga, 2020
Veterans
31 VA clinics
across 27 states
Mortality
risk
Approximately
700,000 males
1976-2001
1999-2001
Average age at entry
approximately 52
Hybrid or
Monitor
Parker etal.,
2018
NHIS
National US
All-cause
mortality
657,238 adults
1997-2009
2004
>24
Hybrid
Son etal.,
2020
North
Carolina
residents
North Carolina
Total
mortality
775,338 cases (i.e.,
total deaths) with
3,410,015 control days
2002-2013
2002-2013
All
Hybrid or
Monitor
Wang etal.,
2017
Medicare
enrollees
7 U.S. southeast
states: AL, FL, GA,
MS, NC, SC, TN
All-cause
mortality
13.1 million Medicare
beneficiaries; 4.7
million deaths
2000-2013
2000-2013
>64
Hybrid
Wang etal.,
2020
Medicare
enrollees
National US
Non-
accidental
mortality
52,954,845 Medicare
beneficiaries;
15,324,059 deaths
2000-2008
2000-2008
>64
Hybrid
3
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We evaluated the available studies and concentration-response functions to determine if
sufficient information exists for use in a quantitative analysis and to determine which study or
studies best characterizes at-risk populations across the U.S. Of the available studies from the
2019 ISA, Di et al., 2017 was the largest nationwide study, covered one of the most recent and
longest time spans, used a sophisticated exposure estimation technique, and provided sufficient
information to apply risk models quantifying increased risks to the following demographic
groups: White, Black, Asian, Native American, and Hispanic (Table C-14). Although effect
estimates from Di et al., 2017 were derived from a cohort aged 65 and older and the study did
not provide a non-Hispanic concentration-response function to directly compare to the Hispanic
concentration-response function, it was identified as best characterizing populations potentially
at increased risk of long-term PM2.5-attributable all-cause mortality. Health impact functions,
including beta parameters and standard errors (SE), were developed for each at-risk population
demographic described by Di et al., 2017 and are available in Table C-14.
Table C-14. At-risk hazard ratios, beta coefficients, and standard errors from Di et al.,
2017 used in this at-risk assessment.
Demographic
Population
Risk of Death Associated with
3
10 |jg/m Increase in PM^
Beta Coefficient
(SE)
White
1.063(1.060, 1.065)
0.0061 (0.0001)
All
1.073(1.071, 1.075)
0.0070 (0.0001)
Hispanic
1.116 (1.100, 1.133)
0.0110 (0.0008)
Black
1.208(1.199, 1.217)
0.0189 (0.0004)
Asian
1.096 (1.075, 1.117)
0.0092 (0.0010)
Native American
1.100 (1.060, 1.140)
0.0095 (0.0019)
C.3.3 Age
Concentration-response functions stratified by race and ethnicity from Di et al., 2017
were only available for ages 65-99. Therefore, this at-risk analysis only evaluated a single age
range group of 65-99 years.
C.3.4 Baseline Incidence Rates
BenMAP-CE includes baseline incidence rates at the most geographically- and age-
specific levels available for each health endpoint assessed. For many locations within the U.S.,
these data are resolved at the county- or state-level, providing a better characterization of the
geographic distribution of mortality rates than the national-level rates. Race- and ethnicity-
stratified baseline incidence rates from 2007-2016 Census data were recently improved for the
all-cause mortality health endpoint, by adding the geographic level option of rural/urban state
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between county-level and state-level (sections C.3.4.1 and C.3.4.2). Both overall and
race/ethnicity-stratified baseline rates are used in this at-risk analysis (section C.3.4.2).
C.3.4.1 Race-Stratified Baseline Incidence Rates
To estimate race-stratified and age-stratified incidence rates at the county level, we
downloaded all-cause and respiratory mortality data from 2007 to 2016 from the CDC
WONDER mortality database.44 Race-stratified incidence rates were calculated for the following
age groups: < 1 year, 1-4 years, 5-14 years, 15-24 years, 25-34 years, 35-44 years, 45-54 years,
55-64 years, 65-74 years, 75-84 years, and 85+ years. To address the frequent county-level data
suppression for race-specific death counts, we stratified the county-level data into two broad race
categories, White and Non-White populations. In a later step, we stratified the non-White
incidence rates by race (Black, Asian, Native American) using the relative magnitudes of
incidence values by race at the regional level, described in more detail below.
We followed methods outlined in Section D. 1.1 of the BenMAP User Manual with one
notable difference in methodology; we included an intermediate spatial scale between county and
state for imputation purposes.45 We designated urban and rural counties within each state using
CDC WONDER and, where possible, imputed missing data using the state-urban and state-rural
classifications before relying on broader statewide data. We followed methods for dealing with
suppressed and unreliable data at each spatial scale as described in Section D.l.l.
A pooled non-White incidence rate masks important differences in mortality risks by
race. To estimate county-level mortality rates by individual race (Black, Asian, Native
American), we applied regional race-specific incidence relationships to the county-level pooled
non-White incidence rates. We calculated a weighted average of race-specific incidence rates
using regional incidence rates for each region/age/race group normalized to one reference
population (the Asian race group) and county population proportions based on race-specific
county populations from CDC WONDER where available. In cases of population suppression
across two or more races per county, we replaced all three race-specific population proportions
derived from CDC WONDER with population proportions derived from 2010 Census data in
BenMAP-CE (e.g., 50 percent Black, 30 percent Asian, 20 percent Native American).
C.3.4.2 Ethnicity-Stratified Baseline Incidence Rates
To estimate ethnicity-stratified and age-stratified incidence rates at the county level, we
downloaded all-cause and respiratory mortality data from 2007 to 2016 from the CDC
44 https://wonder.cdc.gov/
45 https://www.epa.gov/sites/default/files/2015-04/documents/benmap-ce_user_manual_march_2015.pdf
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WONDER mortality database.46 Ethnicity-stratified incidence rates were calculated for the
following age groups: < 1 year, 1-4 years, 5-14 years, 15-24 years, 25-34 years, 35-44 years, 45-
54 years, 55-64 years, 65-74 years, 75-84 years, and 85+ years. We stratified county-level data
by Hispanic origin (Hispanic and non-Hispanic). We followed the methods outlined in Section
D.l.l to deal with suppressed and unreliable data. We also included an intermediate spatial scale
between county and state designating urban and rural counties for imputation purposes,
described in detail in Section D. 1.3 of the BenMAP User Manual. 47
C.3.5 Selection of Air Quality Simulation Approach
Concentration fields associated with just meeting the current and alternative standards in
the 47 urban study areas were based on adjusting 2015 modeled concentrations using CMAQ
sensitivity modeling with emission reductions applied throughout the modeling domain. This
approach was applied to develop realistic concentration fields that correspond to just meeting
standards in the 47 areas. Two distinctly different emission cases were used (Pri-PM and Sec-
PM) to examine the sensitivity of results to the air quality adjustment approach. For
characterizing risk in at-risk populations, we used air quality fields from the Pri-PM adjustment
case alone. In the Pri-PM case, the air quality adjustments for a given area are largely associated
with emission reductions within that area due to the local nature of air quality impacts from
primary PM sources. For the Sec-PM case, the air quality adjustments may be strongly
associated with sources located outside of the area. Since the at-risk calculations are performed
for population groups within the 47 urban study areas alone, the Pri-PM adjustment case (in
which air quality adjustments are primarily associated with emission sources within the 47 areas)
is most appropriate for the at-risk analysis.
C.4 SUPPLEMENTAL AT-RISK RESULTS
Absolute numbers of all-cause premature mortality cases within each racial and ethnic
population demographic are available in Table C-15 for total attributable burden under either the
current or alternative standards and Table C-16 for the change in risk estimates when moving
from the current to a potential alternative annual standard for both the full set of 47 urban study
areas and the subset of 30 annually-controlled areas.
46 https://wonder.cdc.gov/
47 https://www.epa.gov/sites/default/files/2015-04/documents/benmap-ce_user_manual_march_2015.pdf
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Table C-15. Estimates of total PM2.5-associated mortality by demographic population for
air quality adjusted to just meet the current or alternative standards.
Study Areas
Modeling Scenario
Ethnicity & Race
White
Black
Hispanic
Asian
Native American
47 areas
Just meeting 12/35 |jg/m3
29,400
13,600
4,850
1,930
125
(28,200 to 30,400)
(13,100 to 14,100)
(4,220 to 5,460)
(1,530 to 2,300)
(77.9 to 169)
Just meeting 10/30 |jg/m3
25,200
11,700
4,160
1,650
108
(24,300 to 26,200)
(11,300 to 12,100)
(3,610 to 4,680)
(1,310 to 1,970)
(66.9 to 146)
30 areas
Just meeting 12/35 |jg/m3
24,900
12,800
3,970
1,640
87.9
(23,900 to 25,800)
(12,400 to 13,300)
(3,450 to 4,460)
(1,300 to 1,960)
(54.6 to 119)
Interpolated to 11 pg/m3
23,100
11,900
3,680
1,520
81.5
(22,200 to 24,000)
(11,500 to 12,400)
(3,200 to 4,140)
(1,210 to 1,820)
(50.6 to 110)
Just meeting 10/30 |jg/m3
21,300
11,000
3,380
1,400
75.1
(20,500 to 22,100)
(10,600 to 11,400)
(2,940 to 3,810)
(1,110 to 1,670)
(46.5 to 102)
Extrapolated to 9 pg/m3
19,600
10,100
3,090
1,280
68.6
(18,800 to 20,300)
(9,740 to 10,500)
(2,680 to 3,480)
(1,010 to 1,530)
(42.4 to 93.0)
Extrapolated to 8 jjg/m3
17,800
9,180
2,790
1,150
62.0
(17,100 to 18,400)
(8,840 to 9,510)
(2,420 to 3,140)
(913 to 1,380)
(38.3 to 84.3)
Table C-16. Change in PM2.5-associated mortality by demographic population for air
quality adjusted to just meet the current or alternative standards.
Study Areas Modeling Scenario
47 areas 12/35-10/30 jjg/m3
30 areas
12/35-11 (interpolated) ijg/m3
12/35-10/30 Mg/m3
12/35-9 (extrapolated) |jg/m3
12/35-8 (extrapolated) |jg/m3
White
4,380
(4,200 to 4,540)
1,890
(1,810 to 1,960)
3,760
(3,610 to 3,900)
5,630
(5,410 to 5,840)
7,490
(7,190 to 7,770)
Black
2,280
(2,190 to 2,370)
1,090
(1,050 to 1,130)
2,170
(2,080 to 2,250)
3,220
(3,100 to 3,340)
4,260
(4,090 to 4,420)
Ethnicity & Race
Hispanic
771
(665 to 872)
327
(282 to 371)
652
(563 to 737)
973
(840 to 1,100)
1,290
(1,120 to 1,460)
Asian
302
(238 to 364)
133
(104 to 160)
264
(208 to 319)
395
(311 to 476)
525
(414 to 631)
Native American
18.9
(11.6 to 26.0)
7.04
(4.29 to 9.68)
14.0
(8.57 to 19.3)
21.0
(12.8 to 28.7)
27.8
(17.0 to 38.1)
For visual purposes only the central risk estimates are included in the at-risk results
presented in chapter 3 of the draft PA (section 3.4.2), but an example of the 95th percentile
confidence interval (CI) risk estimate spans resulting from the epidemiologic concentration-
response functions are provided in Figure C-32. The lower open circle represents the 2.5th
percentile and the higher open circle represents the 97.5th percentile CI for each population
demographic. CIs are derived from the concentration-response relationships presented in Di et
al., 2017 (Figure C-13). While the Hispanic and Native American risk rate CIs often overlap, the
Black risk rate estimates are consistently higher than the White risk rates, and the Asian risk
rates are consistently lower than the White risk rates (Figure C-32).
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Study Modeling
Areas Scenario Ethnicity & Race
47 areas Recent White
Conditions Hispanic
f?015l Asian
' Native American
o o
o o
o o
Just meeting White
,„„c , ? Black
12/35 [jg/m Hispanic
Asian
Native American
oo
O 0
o o
Just meeting White
10/30 |jg/m3 Hispanic
Asian
Native American
oo
o o
O 0
30 areas Recent White
Conditions Hispanic
O 0151 Asian
' Native American
oo
O 0
o o
Just meeting White
12/35 ng/m3 H,spamc
Asian
Native American
o o
O 0
O 0
Interpolated White
to 11 (jg/m3 Hispanic
Asian
Native American
oo
0 o
o o
Just meeting White
, ? Black
10/30 |jg/m Hispanic
Asian
Native American
oo
O 0
o o
Extrapolated White
, „ , 3 Black
to 9 |jg/m3 Hispanic
Asian
Native American
oo
o o
o o
Extrapolated White
to 8 ^ig/m3 Hispanic
Asian
Native American
oo
o o
O 0
0 100 200 300 400 500 600 700 800 900
95th Percentile Mortality Risk Rate Confidence Intervals (per 100k)
Figure C-32. Race- and ethnicity-stratified 95th percentile (2.5th percentile to 97.5th
percentile) confidence interval risk estimates for recent conditions (2015), the current
standard, and potential alternative standard air quality surfaces.
As the risk rate calculation integrates both population-specific baseline incidence rates
and concentration-response relationships with exposure information, we wanted to separate the
impacts of each data input. To distinguish the impacts of race-stratified concentration-response
functions from baseline incidence rates on the results, we provide the average PIVb.s-attributable
risk by demographic population in the full set of 47 urban study areas for the current standards,
potential alternative standards, and recent condition (2015) air quality surfaces within each
demographic group. Figure C-33 and Figure C-34 provide this information when just meeting
current and alternative standards or shifting between the current and potential alternative annual
standards, respectively.
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Generally, race-stratified concentration-response functions increased the population-
normalized risk estimated in nonwhite populations, with the greatest magnitude increase
occuring in Black populations, followed by Hispanic populations, and decreased risk estimated
in White populations. Di et al., 2017 did not provide a concentration-response function for the
non-Hispanic population, so only the overall concentration-response function was applied to
non-Hispanics in these supplemental analyses.
Many factors effect race/ethnicity-stratified baseline incidence rates, such as access to
medical care, socioeconomic status, and underlying health issues. As such, race/ethnicity-
stratified baseline incidence rates impacted by each race and ethnicity differently. Race/ethnicity-
stratified baseline incidence rates increased risk estimates substantially in Black populations and
slightly in White and non-Hispanic populations. In contrast, race/ethnicity-stratified baseline
incidence rates decreased risk rates estimated in Hispanic, Asian, and Native American
populations.
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Study
Areas
Modeling
Scenario
Ethnicity &
Race
CR Function
Baseline
Incidence
Just meeting White
Overall
Overall
Race-Stratified
•
•
v/jo |jg/nr
Race-Stratified
Overall
Race-Stratified
•
•
Black
Overall
Overall
Race-Stratified
•
•
Race-Stratified
Overall
Race-Stratified
•
•
Hispanic
Overall
Overall
Race-Stratified
•
•
Race-Stratified
Overall
Race-Stratified
•
•
Asian
Overall
Overall
Race-Stratified
Race-Stratified
Overall
Race-Stratified
Native
American
Overall
Overall
Race-Stratified
Race-Stratified
Overall
Race-Stratified
Non-Hispanic Overall
Overall
Race-Stratified
•
•
Just meeting White
Overall
Overall
Race-Stratified
•
•
iumu |jg/mj
Race-Stratified
Overall
Race-Stratified
•
•
Black
Overall
Overall
Race-Stratified
•
•
Race-Stratified
Overall
Race-Stratified
•
•
Hispanic
Overall
Overall
Race-Stratified
•
».
Race-Stratified
Overall
Race-Stratified
•
•
Asian
Overall
Overall
Race-Stratified
Race-Stratified
Overall
Race-Stratified
Native
American
Overall
Overall
Race-Stratified
Race-Stratified
Overall
Race-Stratified
Non-Hispanic Overall
Overall
Race-Stratified
•
•
Recent
Conditions
(2015)
White
Overall
Overall
Race-Stratified
•
•
Race-Stratified
Overall
Race-Stratified
•
•
Black
Overall
Overall
Race-Stratified
•
•
Race-Stratified
Overall
Race-Stratified
•
•
Hispanic
Overall
Overall
Race-Stratified
•
•
Race-Stratified
Overall
Race-Stratified
•
•
Asian
Overall
Overall
Race-Stratified
Race-Stratified
Overall
Race-Stratified
Native
American
Overall
Overall
Race-Stratified
Race-Stratified
Overall
Race-Stratified
Non-Hispanic Overall
Overall
Race-Stratified
•
•
100
200 300
400 500
600 700 800
Average Mortality Risk Rate (per 100k)
2 Figure C-33. Effect of race-stratified concentration-response (CR) functions and baseline
3 incidence rates on the average PM2.5-attributable risk by demographic population in
4 the 47 study areas for the current standard, potential alternative standard, and recent
5 conditions (2015) air quality surfaces within each demographic group.
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Study Modeling Ethnicity & Baseline
Areas Scenario Race CR Function incidence
47 areas 12/35-10/30 White Overall Overall
I ig/m3 Race-Stratified
•
•
Race-Stratified Overall
Race-Stratified
•
•
Black Overall Overall
Race-Stratified
•
•
Race-Stratified Overall
Race-Stratified
•
•
Hispanic Overall Overall
Race-Stratified
•
•
Race-Stratified Overall
Race-Stratified
•
•
Asian Overall Overall
Race-Stratified
Race-Stratified Overall
Race-Stratified •
Native Overall Overall
American Race-Stratified
Race-Stratified Overall
Race-Stratified
Non-Hispanic Overall Overall
Race-Stratified
•
•
30 40 50 70 100 150
Average Mortality Rate Risk Reduction (per 100k)
Figure C-34. Effect of race-stratified CR functions and baseline incidence rates on the
average PM2.5-attributable risk reductions by demographic population in the 47 study
areas when shifting front the current to the potential alternative standards within each
demographic group.
As the annual design values for many study areas required rolling up to just meet the
current standard (section C.l.4.6), for informational purposes we provide cumulative distribution
plots of PM2.5 exposure and PMb.s-attributable mortality risk per 100,000 people by demographic
group for the recent condition year 2015, along with the plots for just meeting the current
standards for direct comparison (Figure C-35). Several caveats should be noted when comparing
the recent conditions air quality surface to those adjusted to just meet current or recent air quality
conditions. Importantly, the at-risk analysis focuses on the Pri-PM adjustment approach (section
C.3.4.2), in which emission increases in areas below the current standard occur predominately at
and around the urban cores of the study areas. This could lead to a simulated increase of
disproportionate PM2.5 exposures in demographic populations that frequently reside at and
around the urban core. Conversely, disproportionate PM2.5 concentrations in demographic
populations residing in areas above the current standards may be obscured when concentrations
are adjusted downward to just meet the current standard.
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3
4
5
6
7
Study Modeling
Areas Scenario
47 Just meeting
areas 12/35 M9'm5
Recent
Conditions
(2015)
Just meeting
areas
Recent
Conditions
(2015)
Ethnicity S Race
¦ White
¦ Black
¦ Hispanic
Asian
Native Amencan
7 8 9 10 11 12
PM Concentration ((jg/in1) *
13 14
200 400 600 800
Mortality Risk Rate (per 100k) *
Figure C-35. PM2.5 concentrations and PMi.s-attributable risk by demographic population
for recent air quality conditions (2015) and air quality simulated to just meet the
current PM standards.
Another aspect of lowering the annual PM2.5 standard is the percent of overall risk
attributable to PM2.5 exposure. Table C-17 shows that the percent of baseline risk is higher in
racial/ethnic minority demographics in all scenarios analyzed. Additionally, some minority
populations may experience a greater decrease in the percent of baseline PIVh.s-attributable risk.
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Table C-17. Percent of mortality baseline incidence attributable to PM2.5 under the current
and potential alternative standards.
Ethnicity
& Race
% of B
PM2.5-Att
Risk Ur
Current
(12
47
areas
aseline
ributable
derthe
Standard
(35)
30
areas
% of Baseline
PM2.5-
Attributable
Risk Under an
Alternative
Standard (11)
30 areas
% of B
PM2.5-Att
Risk Ut
Alterr
Standar
47
areas
aseline
ributable
ideran
lative
d (10/30)
30
areas
% of Baseline
PM2.5-
Attributable
Risk Under an
Alternative
Standard (9)
30 areas
% of Baseline
PM2.5-
Attributable
Risk Under an
Alternative
Standard (8)
30 areas
White
6
7
6
5
6
5
5
Black
19
20
18
17
17
15
14
Hispanic
11
12
11
10
10
9
8
Asian
10
10
9
8
8
8
7
Native
American
9
10
9
8
9
8
7
C.5 CHARACTERIZING VARIABILITY AND UNCERTAINTY IN RISK
ESTIMATES
An important component of the risk assessment is the characterization of variability and
uncertainty. Variability refers to the heterogeneity of a variable of interest within a population or
across different populations. Variability is inherent and cannot be reduced through further
research. Hence, the design of a population-level risk assessment is often focused on effectively
characterizing variability in estimated risks across populations. Uncertainty refers to the lack of
knowledge regarding the actual values of inputs to an analysis. In contrast to variability,
uncertainty can be reduced through improved measurement of key variables and ongoing model
refinement. This section discusses our approaches to addressing key sources of variability and
uncertainty in the PM2.5 risk assessment.
Variability in the risk of PIVh.s-associated mortality could result from a number of factors.
These can include variation in PM2.5 exposures within and across populations (e.g., due to
differences in behavior patterns, building characteristics, air quality patterns etc.) and in the
health responses to those exposures (e.g., because some groups are at increased risk of PM-
related health effects). There is also variation over space and time in both PM2.5 itself (e.g.,
concentrations, air quality patterns) and in the ambient pollutants that co-occur with PM2.5. In the
PM2.5 risk assessment discussed in this draft PA, we account for these and other sources of
variability, in part, by estimating risks based on CR functions from a number of epidemiologic
studies. These studies evaluate PM2.5 health effect associations for either annual or daily PM2.5
exposures across various time periods; in numerous geographic locations, encompassing much or
all of the U.S.; in various populations, including some with the potential to be at higher risk than
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the general population (e.g., older adults); and using a variety of methods to estimate PM2.5
exposures (e.g., hybrid modeling approaches and monitors) and to control for potential
confounders. In selecting areas in which to estimate PIVh.s-associated risks, we include areas that
cover multiple regions of the U.S., with varying population demographics. Additionally, we use
two different strategies for adjusting PM2.5 air quality, reflecting the potential for changes in
ambient PM2.5 concentrations to be influenced by changes in primary PM2.5 emissions and by
changes in precursor emissions that contribute to secondary particle formation.
Beyond the reliance on information from multiple epidemiologic studies to account for
the variability in key risk assessment inputs, we use a combination of quantitative and qualitative
approaches to characterize the remaining risk estimates uncertainty more explicitly. The
characterization of uncertainty associated with risk assessments is often addressed in the
regulatory context using a tiered approach in which progressively more sophisticated methods
are used to evaluate and characterize sources of uncertainty depending on the overall complexity
of the risk assessment (WHO, 2008). Guidance documents developed by the EPA for assessing
air toxics-related risk and Superfund Site risks (U.S. EPA, 2004 U.S. EPA, 2001) as well as
recent guidance from the World Health Organization (WHO, 2008) specify multitiered
approaches for addressing uncertainty. The WHO guidance presents a four-tiered approach,
where the decision to proceed to the next tier is based on the outcome of the previous tier's
assessment. The four tiers described in the WHO guidance include:
• Tier 0 - recommended for routine screening assessments, uses default uncertainty factors
(rather than developing site-specific uncertainty characterizations);
• Tier 1 - the lowest level of site-specific uncertainty characterization, involves qualitative
characterization of sources of uncertainty (e.g., a qualitative assessment of the general
magnitude and direction of the effect on risk results);
• Tier 2 - site-specific deterministic quantitative analysis involving sensitivity analysis,
interval-based assessment, and possibly probability bound (high- and low-end)
assessment; and
• Tier 3 - uses probabilistic methods to characterize the effects on risk estimates of sources
of uncertainty, individually and combined.
With this four-tiered approach, the WHO framework provides a means for systematically
linking the characterization of uncertainty to the sophistication of the underlying risk assessment.
Ultimately, the decision as to which tier of uncertainty characterization to include in a risk
assessment will depend both on the overall sophistication of the risk assessment and the
availability of information for characterizing the various sources of uncertainty. EPA staff used
the WHO guidance as a framework for developing the approach used for characterizing
uncertainty in this risk assessment. The overall analysis in the PM NAAQS risk assessment is
relatively complex, thereby warranting consideration of a full probabilistic (WHO Tier 3)
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uncertainty analysis. However, limitations in available information prevent this level of analysis
from being completed at this time. In particular, the incorporation of uncertainty related to key
elements of CR functions (e.g., alternative functional forms, etc.) into a full probabilistic WHO
Tier 3 analysis would require that probabilities be assigned to each competing specification of a
given model element (with each probability reflecting a subjective assessment of the probability
that the given specification is the "correct" description of reality). However, for many model
elements there is insufficient information on which to base these probabilities. One approach that
has been taken in such cases is expert elicitation; however, this approach is resource- and time-
intensive and consequently, it was not feasible to use this technique in the current PM NAAQS
reconsideration to support a WHO Tier 3 analysis.
For most elements of this risk assessment, rather than conducting a full probabilistic
uncertainty analysis, we have included qualitative discussions of the potential impact of
uncertainty on risk results (WHO Tierl) and/or completed sensitivity analyses assessing the
potential impact of sources of uncertainty on risk results. The remainder of this section is
organized as follows. Those sources of uncertainty addressed quantitively in the risk assessment
are discussed in section C.5.1. Those sources of uncertainty addressed qualitatively in the risk
assessment are discussed in section C.5.2. Below we summarize key findings from both the
qualitative and quantitative assessments of variability and uncertainty in the context of assessing
overall confidence in the risk assessment and its estimates.
C.5.1 Quantitative Assessment of Uncertainty
The risk assessment includes three components which allow us to quantitatively evaluate
the impact of potentially important sources of uncertainty on the risk estimates generated. Each
of these is discussed below including conclusions drawn from each assessment regarding the
potential importance of each source of uncertainty:
• 95 percent CIs around point estimates of mortality risk: Each of the point estimates
presented in the results section includes 95 percent CIs generated by BenMAP-CE,
reflecting the standard error associated with the underlying effect estimate (i.e., a
measure of the statistical precision of the effect estimate). There is variation in the range
of 95 percent CIs associated with the point estimates generated for this analysis, with
some CR functions displaying substantially greater variability than others (e.g., Ito et al.,
2013, tables in section 3.4.2 of the draft PA). There are a number of factors potentially
responsible for the varying degrees of statistical precision in effect estimates, including
sample size, exposure measurement error, degree of control for confounders/effect
modifiers, and variability in PM2.5 concentrations.
• Inclusion of multiple mortality estimates reflecting variation in CR functions across
studies: For mortality endpoints, we include risk estimates reflecting multiple
epidemiology studies and associated study designs (e.g., age ranges, methods for
controlling potential confounders). In some instances, we find that the CR function used
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has only a small impact on risk estimates(e.g., Turner et al., 2016 and Di et al., 2017).
The degree to which different CR functions result in different risk estimates could reflect
differences in study design and/or study populations evaluated, as well as other factors. In
most instances in this risk assessment, the CR function used has only a small impact on
risk estimates (e.g., Di et al., 2017). Details regarding the design of epidemiology studies
providing effect estimates for this risk assessment are presented in Table C-l.
• Evaluation of two different strategies for simulating air quality scenarios: Two methods
are employed to adjust air quality in order to simulate just meeting the current and
alternative standards, which could represent potential bounding scenarios of PM2.5
concentrations changes across the study area (i.e., the Pri-PM-based method and the Sec-
PM based method). Our evaluation of these methods reflects the fact that there is both
variability and uncertainty in how emissions in a particular area could change such that
the area "just meets" either the current or alternative standards. By modeling risks based
on adjusted primary PM2.5 emissions and based on adjusted precursor emissions that
contribute to secondary PM2.5 formation, the risk assessment provides insight into the
potential significance of this source of uncertainty. As discussed in section 3.4.2 of this
draft PA, the approach to adjusting air quality had relatively modest impacts on overall
risk estimates. Specifically, the difference between the absolute risk estimates from two
air quality modeling approach methods was generally less than 5% (draft PA section
3.4.2).
C.5.2 Qualitative Uncertainty Analysis
While the methods described above address some of the potentially important sources of
uncertainty and variability in the risk assessment, there are a range of additional sources that
cannot be analyzed quantitatively due to limitations in data, methods and/or resources. We have
addressed these additional sources of uncertainty qualitatively (Table C-l 8).
In describing each source of uncertainty, we attempt to characterize both the magnitude
and direction of impact on mortality risk estimates, including our rationale for these
characterizations. The categories used in describing the potential magnitude of impact (i.e., low,
medium, or high) reflect EPA staff judgments on the degree to which a particular source of
uncertainty could produce a sufficient impact on risk estimates to influence the interpretation of
those estimates in the context of the PM NAAQS reconsideration. Sources classified as having a
low impact would not be expected to influence conclusions from the risk assessment. Sources
classified as having a medium impact have the potential to affect such conclusions and sources
classified as high are likely to influence conclusions. Because this classification of the potential
magnitude of impact of sources of uncertainty is qualitative, it is not possible to place a
quantitative level of impact on each of the categories.
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1 Table C-18. Qualitative analysis of sources of uncertainty and assessment of potential impact on risk assessment.
Source of Uncertainty
Description
Direction
Magnitude
Comments
Shape and
corresponding statistical
uncertainty around the
CR function for long-term
and short-term exposure-
related mortality
(especially at lower
ambient PM levels)
Interpreting the shapes of concentration-
response relationships, particularly at PM2.5
concentrations near the lower end of the air
quality distribution, can be complicated by
relatively low data density in the lower
concentration range, the possible influence
of exposure measurement error, and
variability among individuals with respect to
air pollution health effects. These sources
of variability and uncertainty tend to
smooth and "linearize" population-level
concentration-response functions, and thus
could obscure the existence of a threshold
or nonlinear relationship (U.S. EPA, 2015,
section 6.c).
Both
Medium-
High
With regard to long-term exposure-related
(nonaccidental) mortality, the ISA concludes that the
majority of evidence supports a linear, no-threshold
concentration-response relationship, though there is
initial evidence indicating that the slope of the
concentration-response curve may be steeper at
lower concentrations for cardiovascular mortality
(U.S. EPA, 2019, section 1.5.3.2). For long-term
exposure-related mortality, the ISA notes that there
is less certainty in the shape of the concentration-
response curve at mean annual PM2.5 concentrations
generally below 8 |jg/m3 because data density is
reduced below this concentration (2019 ISA, section
11.2.4). Given that a portion of risk modeling in the
risk assessment does involve locations with ambient
PM2.5 concentrations below 8 ug/m3 (although most
of the population modeled is associated with level
above this), we note the potential for significant
uncertainty being introduced into the risk
assessment (particularly for that portion of risk
modeled at or below 8 ug/m3). With regard to short-
term exposure-related mortality, the ISA concludes
that, while difficulties remain in assessing the shape
of the PM2 5-mortality concentration-response
relationship, as identified in the 2009 PM ISA, and
studies have not conducted systematic evaluations
of alternatives to linearity, recent studies continue to
provide evidence of a no-threshold linear
relationship, with less confidence at concentrations
lower than 5 |jg/m3.
Representing
population-level
exposure with 12 km grid
As with long-term exposure-related
mortality, short-term exposure-related
mortality endpoints were also modeled
Both
Medium-
High
Three studies providing effect estimates for short-
term exposure-related mortality in the risk
assessment all utilized some form of urban-level
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Source of Uncertainty
Description
Direction
Magnitude
Comments
cell spatial framework (in
context of modeling
short-term exposure-
related mortality)
using the same 12 km grid cell template.
The disconnect between the spatial
template used in the underlying short-term
epidemiology studies and the 12 km grid
template used in the risk assessment
introduces uncertainty into risk estimates.
spatial unit in characterizing exposure (e.g., Baxter
et al. (2017) utilizes the CBSA, Ito et al. (2013),
utilizes the MSA), which are larger (less spatially
differentiated) in general than the 12 km grid cells
used in modeling risk. This means that we are
generally modeling short-term exposure-related
mortality at a finer level of spatial resolution in the
risk assessment than reflected in the epidemiology
studies supplying the effect estimates, which does
introduce uncertainty into the analysis.
Representing population-
level exposure with 12
km grid cell spatial
framework (in context of
modeling long-term
exposure-related
mortality)
The risk assessment utilizes a 12 km grid
structure in modeling risk. A source of
uncertainty associated with this approach
is the mismatch between the 12 km grid
cell framework and the exposure
estimation approaches used in the
epidemiology studies providing effect
estimates for the risk assessment. This
mismatch can introduce additional
exposure error to risk estimates, beyond
the error inherent to the underlying
epidemiologic study.
Both
Medium
There are a variety of spatial templates used across
the epidemiology studies providing CR functions
used in the risk assessment and none of them are
an exact match with the 12 km grid cell template
used in the risk assessment. Jerrett et al. (2013),
Pope et al. (2015)Differences between the exposure
metric used in the risk assessment and those used
in the underlying epidemiologic studies introduce
uncertainty into risk estimates.
Simulating just meeting
alternative annual
standards with levels of
8.0, 9.0, and 11.0 ug/m3
using linear
extrapolation/
interpolation
The use of extrapolation/ interpolation in
simulating just meeting annual standards
introduces uncertainty into the risk
assessment since this approach does not
fully capture potential non-linearities
associated with the formation of secondary
PM2.5.
Both
Medium
Extrapolation to generate the surfaces for 9.0 and
8.0 |jg/m3 are subject to greater uncertainty than
interpolation to 11.0 pg/m3 (i.e., since the former
estimates concentrations below those in modeled
surfaces, while the latter estimates a surface
between two sets of modeled results). In addition,
linear extrapolation/interpolation based on the
primary-PM modeled surfaces (for current standard
and 10.0 pg/m3) is likely subject to less uncertainty
than extrapolation/interpolation based on the
secondary-PM modeled surfaces since the latter
focus on secondary formation which could involve a
higher degree of non-linearity.
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Source of Uncertainty
Description
Direction
Magnitude
Comments
Simulating just meeting
current and alternative
standards using model-
based (Downscaler)
methods
The baseline and adjusted concentration
fields were developed using modeling to fill
spatial and temporal gaps in monitoring
and to explore air quality scenarios of
policy interest. State-of-the-science
modeling methods were used, but model-
related biases and errors can introduce
uncertainty into the PM2.5 concentration
estimates.
b) Due to the national scale of the
assessment, the modeling scenarios are
based on "across-the-board" emission
changes in which emissions of primary
PM2.5or NOx and SO2 from all
anthropogenic sources throughout the U.S.
are scaled by fixed percentages. Although
this approach tends to target the key
sources in each area, it does not tailor
emission changes to specific periods or
sources.
c) Two adjustment cases were applied that
span a wide range of emission conditions,
but these cases are necessarily a subset of
the full set of possible emission cases that
could be used to adjust PM2.5
concentrations to just meet standards.
This source of
uncertainty
could bias
results in
either
direction.
Medium
Use of state-of-the-science modeling systems with
the relative response factor adjustment approach
provides confidence in the broad features of the
simulated national PM2.5 distributions and how the
distributions shift with changing standards levels.
Due to challenges in modeling local features in the
national annual simulations, quantitative results for
individual areas or small subsets of grid cells are
relatively uncertain compared with broad features of
the national PM2 5 distributions.
Potential confounding of
the PM2.5-mortalty effect
Factors are considered potential
confounders if demonstrated in the
scientific literature to be related to health
effects and correlated with PM. Omitting
potential confounders from analyses could
either increase or decrease the magnitude
of PM2.5 effect estimates (e.g., Di et al.,
2017, Figure S2 in Supplementary
Materials). Thus, not accounting for
Both
Medium
Long-term PM2.5 exposure and mortality studies: For
studies of long-term exposures, potential
confounders are those that vary spatially or
temporally. These may include socioeconomic
status, race, age, medication use, smoking status,
stress, noise, occupational exposures, and
copollutant concentrations. Cohort studies used to
characterize the PM2.5 -mortality relationship used a
variety of approaches to account for these and other
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Source of Uncertainty
Description
Direction
Magnitude
Comments
confounders can introduce uncertainty into
effect estimates and, consequently, into the
risk estimates generated using those effect
estimates. Confounders vary according to
study design, exposure duration, and
health effect. While a range of approaches
to control for potential confounders have
been adopted across the studies used in
the risk assessment, and across the
broader body of PM2.5 epidemiologic
studies assessed in the ISA, no individual
study adjusts for all potential confounders.
potential confounders (e.g., see Appendix B). Across
studies, a variety of study designs and statistical
approaches have been used to account for potential
confounding in the PM2 5-mortality relationship. The
fact that across this diverse body of evidence
epidemiologic studies continue to report consistently
positive associations that are often similar in
magnitude, adds support the conclusion that the
PM2 5-mortality association is robust. Specifically
regarding copollutants, the final PM ISA notes that,
overall, associations remained relatively unchanged
in copollutant models for total (nonaccidental)
mortality, cardiovascular, and respiratory adjusted
for ozone. Studies focusing on copollutant models
with NO2, PM10-2.5, SO2 and benzene were examined
in individual studies, and across these studies the
PM2 5-mortality association was relatively
unchanged.
Short-term PM2.5 exposure and mortality studies: For
studies of short-term exposures, potential
confounders are those that vary temporally. These
may include meteorology (e.g., temperature,
humidity), day of week, season, medication use,
allergen exposure, copollutant concentrations, and
long-term temporal trends. Some recent studies
have expanded the examination of potential
confounders, including long-term temporal trends,
weather, and copollutants. Overall, the ISA
concludes that alternative approaches to controlling
for long-term temporal trends and for the potential
confounding effects of weather may influence the
magnitude of the association between PM2.5
exposures and mortality, but have not been found to
influence the direction of the observed association
(U.S. EPA, 2019, section 11.1.5.1). With regard to
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Source of Uncertainty
Description
Direction
Magnitude
Comments
copollutants, recent studies conducted outside the
U.S. provide additional evidence that associations
between short-term PM2.5 exposures and mortality
remain positive and relatively unchanged in
copollutant models with both gaseous pollutants and
PM10-2.5 (U.S. EPA, 2019, Section 11.1.4).
Lag structure in short-
term exposure-related
mortality epidemiology
studies
It can be challenging to characterize the
timing associated with specific PM2.5-
related health effects and consequently
specify the lag-structure that should be
used in modeling those health effects. This
can introduce uncertainty into the modeling
of risk for short-term exposure-related
endpoints.
Both
Low-
Medium
Given the emphasis placed in the risk assessment
on mortality, we focus here on lags associated with
all-cause mortality.
Compositional and
source differences in PM
The composition of PM2.5 can differ across
study areas reflecting underlying
differences in primary and secondary PM2.5
sources (both natural and anthropogenic).
If these compositional differences lead to
differences in public health impacts (per
unit concentration in ambient air) for PM2.5,
then uncertainty may be introduced into
risk estimates that are based on
concentration-response relationships for
PM2.5 mass.
Both
Low
The Integrated Synthesis chapter of the final ISA
(Chapter 1, U.S. EPA, 2019) states that, the
assessment of PM sources and components
confirms and continues to support the conclusion
from the 2009 PM ISA: Many PM2.5 components and
sources are associated with health effects, and the
evidence does not indicate that any one source or
component is more strongly related with health
effects than PM2.5 mass.
Temporal mismatch
between ambient air
quality data
characterizing exposure
and mortality in long-term
exposure-related
epidemiology studies
Several of the epidemiology studies for
long-term exposure-related mortality have
a mismatch between the time period
associated with ambient PM2.5
concentrations used to characterize
population-level exposure and mortality
data Jerrett et al. (2016), Pope et al.
(2015).
Both
Low
This approach can be reasonable in the context of
an epidemiologic study evaluating health effect
associations with long-term PM2.5 exposures, under
the assumption that spatial patterns in PM2.5
concentrations are not appreciably different during
time periods for which air quality information is not
available (e.g., Chen et al. (2016)), Thus, as long as
the overall spatial pattern of ambient PM2.5 levels in
relation to population-level exposure and mortality
rates has held relatively stable over time, then a
October 2021
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Source of Uncertainty
Description
Direction
Magnitude
Comments
temporal disconnect between the time-period
associated with mortality and the ambient PM2.5 level
used in characterizing exposure would not be
expected to introduce significant uncertainty into the
epidemiology studies and associated effect
estimates.
Exposure measurement
error in epidemiologic
studies assessing the
relationship between
mortality and exposure to
ambient PM2.5
Epidemiologic studies have employed a
variety of approaches to estimate
population-level PM2.5 exposures (e.g.,
stationary monitors, hybrid modeling
approaches). These approaches are based
on using measured or predicted ambient
PM2.5 concentrations as surrogates for
population exposures. As such, exposure
estimates in epidemiologic studies are
subject to exposure error. This error in the
underlying epidemiologic studies
contributes to uncertainty in the risk
estimates that are based on concentration-
response relationships in those studies.
Both
Low
Available studies indicate that PM2.5 health effect
associations are robust across various approaches
to estimating PM2 5 exposures. This includes recent
studies that estimate exposures using ground-based
monitors alone and studies that estimate exposures
using data from multiple sources (e.g., satellites,
land use information, modeling), in addition to
monitors. While none of these approaches
eliminates the potential for exposure error in
epidemiologic studies, such error does not call into
question the findings of key PM2.5 epidemiologic
studies. The ISA notes that, while bias in either
direction can occur, exposure error tends to result in
underestimation of health effects in epidemiologic
studies of PM exposure (U.S. EPA, 2019, section
3.5). Consistent with this, a recent study Hart et al.
(2015) reports that correction for PM2.5 exposure
error using personal exposure information results in
a moderately larger effect estimate for long-term
PM2.5 exposure and mortality (though with wider
confidence intervals). While most PM2.5
epidemiologic studies have not employed similar
corrections for exposure error, several studies report
that restricting analyses to populations in close
proximity to a monitor (i.e., in order to reduce
exposure error) result in larger PM2.5 effect estimates
(e.g., Willis et al., 2003; Kloog et al., 2013). Thus, to
the extent key PM2.5 epidemiologic studies are
subject to exposure error, correction for that error
October 2021
C-79
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Source of Uncertainty
Description
Direction
Magnitude
Comments
would likely result in larger effect estimates, and thus
larger estimates of PM2 5-associated mortality
incidence in the risk assessment.
Use of associations
reported in epidemiologic
studies to estimate how
mortality incidence may
change with changing
PM2.5 air quality.
The ISA's determination that the evidence
supports a causal relationship between
PM2.5 exposure and mortality is based on
assessing a broad body of evidence from
epidemiologic and experimental studies.
Thus, the use of the concentration-
response relationship from any individual
epidemiologic study to estimate how
mortality incidence may change with
changing PM2.5 air quality is subject to
uncertainty.
Both
Low
The ISA assesses a longstanding body of health
evidence supporting relationships between PM2.5
exposures (short- and long-term) and mortality.
Much of this evidence comes from epidemiologic
studies conducted in North America, Europe, or Asia
that demonstrate generally positive, and often
statistically significant, associations between PM2.5
exposures and total or cause-specific mortality. In
addition, recent experimental evidence, as well as
evidence from panel studies, strengthens support for
potential biological pathways through which PM2.5
exposures could lead to serious health outcomes,
including mortality. While this broad body of
evidence from across disciplines provides the
foundation for the ISA's conclusions, the risk
assessment necessarily focuses on a small number
of individual studies. Although the studies selected
for the risk assessment are part of the evidence
base supporting the ISA's causality determinations
for mortality, the concentration-response relationship
in any given study reflects the particular time period,
locations, air quality distribution and populations
evaluated in that study. Thus, the use of the
concentration-response relationship from any
individual epidemiologic study to estimate mortality
incidence across the U.S. for populations, locations
and PM2.5 air quality distributions different from those
present during the study period is subject to
uncertainty.
October 2021
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1
2
3
4
5
6
7
8
9
10
11
12
13
C.5.3 Conclusion
To increase overall confidence in the risk assessment, a deliberative process has been
used in specifying each of the analytical elements comprising the risk model, including selection
of urban study areas as well as specification of other inputs such as CR functions. This
deliberative process involved rigorous review of available literature addressing both PM2.5
exposure and risk combined with the application of a formal set of criteria to guide development
of each of the key analytical elements in the risk assessment. The application of this deliberative
process increases overall confidence in the risk estimates by ensuring that the estimates are based
on the best available science and data characterizing PM2.5 exposure and risk, and that they
reflect consideration of input from experts on PM exposure and risk through CASAC and public
reviews.
October 2021
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1 C.6 PM2 5 DESIGN VALUES FOR THE AIR QUALITY PROJECTIONS
2
3 Table C-19. PM2.5 DVs for the Primary PM projection case and 12/35 standard level.
CBSAa
Site
Controlling
Standard
Controlling
Site?
NOx &
S02
Reduction
(%)b
Primary
PM2.5
Reduction
(%)c
Base
Annual
DV
(ug m 3)
Projected
Annual
DV
(ug m-3)
Base 24-
hr DV
(H9 m"3)
Projected
24-hr DV
(H9 m"3)
AkronO
391530017
Annual
Yes
0
-18
10.99
11.99
23.7
25.4
AkronO
391530023
Annual
No
0
-18
9.16
9.90
20.2
21.4
Altoon
420130801
Annual
Yes
0
-41
10.11
12.02
23.8
29.5
Atlant
131210039
Annual
Yes
0
-27
10.38
11.99
19.7
22.6
Atlant
132230003
Annual
No
0
-27
7.82
8.62
16.2
17.5
Atlant
131350002
Annual
No
0
-27
8.84
10.05
17.9
20.2
Atlant
130890002
Annual
No
0
-27
9.34
10.63
19.2
21.7
Atlant
130670003
Annual
No
0
-27
9.51
10.79
18.6
21.0
Atlant
130630091
Annual
No
0
-27
9.86
11.19
19.1
21.6
Bakers
060290010
24-hr
Yes
79
77
16.52
10.23
70.0
35.4
Bakers
060290016
24-hr
No
79
77
18.45
11.45
61.3
31.7
Bakers
060290015
24-hr
No
79
77
5.15
3.97
15.8
13.6
Bakers
060290014
24-hr
No
79
77
16.53
9.81
61.4
31.7
Bakers
060290011
24-hr
No
79
77
6.06
4.84
19.6
16.6
Birmin
010732059
Annual
Yes
0
-10
11.25
12.00
22.3
23.9
Birmin
010732003
Annual
No
0
-10
10.08
10.70
19.0
20.1
Birmin
010731010
Annual
No
0
-10
9.78
10.30
19.2
20.1
Birmin
010730023
Annual
No
0
-10
10.94
11.66
22.8
24.2
Canton
391510017
Annual
Yes
0
-23
10.81
12.04
23.7
26.1
Canton
391510020
Annual
No
0
-23
9.91
10.96
22.0
23.6
Chicag
170313103
Annual
Yes
0
-15
11.10
12.00
22.6
24.2
Chicag
550590019
Annual
No
0
-15
8.04
8.56
20.4
21.5
Chicag
181270024
Annual
No
0
-15
9.51
10.30
22.4
24.1
Chicag
180892004
Annual
No
0
-15
9.84
10.71
24.7
26.7
Chicag
180890031
Annual
No
0
-15
10.12
11.01
23.6
25.6
Chicag
180890026
Annual
No
0
-15
-
-
25.2
27.1
Chicag
180890022
Annual
No
0
-15
-
-
22.7
24.8
Chicag
180890006
Annual
No
0
-15
10.03
10.93
23.1
25.2
Chicag
171971011
Annual
No
0
-15
8.36
8.85
18.4
19.3
Chicag
171971002
Annual
No
0
-15
7.69
8.23
20.0
21.2
Chicag
170890007
Annual
No
0
-15
8.94
9.55
19.2
20.5
Chicag
170890003
Annual
No
0
-15
-
-
19.2
20.0
Chicag
170434002
Annual
No
0
-15
8.87
9.48
19.9
20.7
Chicag
170316005
Annual
No
0
-15
10.79
11.66
24.1
26.1
October 2021
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CBSAa
Site
Controlling
Standard
Controlling
Site?
NOx &
S02
Reduction
(%)b
Primary
PM2.5
Reduction
(%)c
Base
Annual
DV
(ug m-3)
Projected
Annual
DV
(ug m-3)
Base 24-
hr DV
(H9 m"3)
Projected
24-hr DV
(H9 m"3)
Chicag
170314201
Annual
No
0
-15
9.00
9.61
21.4
22.6
Chicag
170314007
Annual
No
0
-15
9.49
10.17
-
-
Chicag
170313301
Annual
No
0
-15
10.37
11.18
23.5
25.2
Chicag
170310076
Annual
No
0
-15
10.18
10.96
22.5
24.0
Chicag
170310057
Annual
No
0
-15
11.03
11.89
26.8
28.4
Chicag
170310052
Annual
No
0
-15
10.00
10.78
23.3
24.9
Chicag
170310022
Annual
No
0
-15
10.38
11.30
22.4
23.9
Chicag
170310001
Annual
No
0
-15
10.13
10.88
21.7
23.4
Cincin
390610014
Annual
Yes
0
-24
10.70
12.02
22.9
24.7
Cincin
390610042
Annual
No
0
-24
10.29
11.47
22.6
24.5
Cincin
390610040
Annual
No
0
-24
9.45
10.53
21.0
22.9
Cincin
390610010
Annual
No
0
-24
9.43
10.41
21.3
22.9
Cincin
390610006
Annual
No
0
-24
9.46
10.56
20.3
21.8
Cincin
390170020
Annual
No
0
-24
-
-
24.2
26.5
Cincin
390170019
Annual
No
0
-24
10.24
11.51
22.0
23.8
Cincin
390170016
Annual
No
0
-24
9.79
10.91
22.1
23.7
Cincin
210373002
Annual
No
0
-24
9.06
10.00
20.9
22.6
Clevel
390350065
Annual
Yes
0
2
12.17
12.03
24.9
24.6
Clevel
391030004
Annual
No
0
2
8.73
8.66
19.6
19.5
Clevel
390933002
Annual
No
0
2
8.10
8.03
20.2
20.1
Clevel
390850007
Annual
No
0
2
7.88
7.82
17.4
17.3
Clevel
390351002
Annual
No
0
2
8.86
8.78
19.5
19.4
Clevel
390350045
Annual
No
0
2
10.61
10.50
22.9
22.7
Clevel
390350038
Annual
No
0
2
11.38
11.25
25.0
24.8
Clevel
390350034
Annual
No
0
2
8.87
8.79
20.4
20.2
Detroi
261630033
Annual
Yes
0
-15
11.30
12.04
26.8
28.4
Detroi
261630039
Annual
No
0
-15
9.11
9.63
22.3
23.7
Detroi
261630036
Annual
No
0
-15
8.68
9.13
21.8
23.2
Detroi
261630025
Annual
No
0
-15
8.98
9.54
24.1
25.2
Detroi
261630019
Annual
No
0
-15
9.18
9.75
22.4
24.1
Detroi
261630016
Annual
No
0
-15
9.62
10.19
24.4
25.4
Detroi
261630015
Annual
No
0
-15
11.19
11.91
25.5
27.0
Detroi
261630001
Annual
No
0
-15
9.50
10.14
23.3
24.9
Detroi
261470005
Annual
No
0
-15
8.89
9.34
24.3
25.4
Detroi
261250001
Annual
No
0
-15
8.86
9.41
24.2
25.7
Detroi
260990009
Annual
No
0
-15
8.80
9.29
26.2
27.6
ElCent
060250005
Annual
Yes
0
12
12.63
12.00
33.5
31.3
ElCent
060251003
Annual
No
0
12
7.44
7.01
19.8
18.5
October 2021
C-83
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CBSAa
Site
Controlling
Standard
Controlling
Site?
NOx &
S02
Reduction
(%)b
Primary
PM2.5
Reduction
(%)c
Base
Annual
DV
(ug m-3)
Projected
Annual
DV
(ug m-3)
Base 24-
hr DV
(H9 m"3)
Projected
24-hr DV
(H9 m"3)
ElCent
060250007
Annual
No
0
12
8.37
7.99
21.5
20.8
Elkhar
180390008
Annual
Yes
0
-47
10.24
12.01
28.6
33.2
Evansv
181630023
Annual
Yes
0
-44
10.11
12.03
21.5
24.0
Evansv
211010014
Annual
No
0
-44
9.64
11.32
20.7
22.3
Evansv
181630021
Annual
No
0
-44
9.84
11.68
21.6
23.3
Evansv
181630016
Annual
No
0
-44
10.02
11.91
22.0
24.0
Fresno
060195001
24-hr
Yes
0
70
14.08
10.87
49.3
35.4
Fresno
060195025
24-hr
No
0
70
13.63
9.98
47.9
31.7
Fresno
060192009
24-hr
No
0
70
8.47
7.26
31.3
25.1
Fresno
060190011
24-hr
No
0
70
14.07
10.01
53.8
34.4
Hanfor
060310004
24-hr
Yes
65
79
21.98
11.79
72.0
35.4
Hanfor
060311004
24-hr
No
65
79
16.49
9.68
58.9
30.7
Housto
482011035
Annual
Yes
0
-14
11.19
12.04
22.4
24.0
Housto
482011039
Annual
No
0
-14
9.22
9.82
21.7
23.1
Housto
482010058
Annual
No
0
-14
9.67
10.37
22.3
23.8
Housto
481671034
Annual
No
0
-14
7.36
7.57
20.3
20.8
Indian
180970087
Annual
Yes
0
-10
11.44
12.01
25.9
26.8
Indian
180970083
Annual
No
0
-10
11.06
11.59
23.9
24.9
Indian
180970081
Annual
No
0
-10
11.07
11.61
25.0
26.0
Indian
180970078
Annual
No
0
-10
10.14
10.60
24.4
24.9
Indian
180970043
Annual
No
0
-10
-
-
26.0
26.4
Indian
180950011
Annual
No
0
-10
9.05
9.40
21.8
22.3
Indian
180570007
Annual
No
0
-10
9.02
9.39
21.4
22.1
Johnst
420210011
Annual
Yes
0
-25
10.68
12.03
25.8
30.3
Lancas
420710012
Annual
Yes
0
12
12.83
12.00
32.7
30.4
Lancas
420710007
Annual
No
0
12
10.57
9.88
29.8
27.4
LasVeg
320030561
Annual
Yes
0
-22
10.28
11.98
24.5
29.4
LasVeg
320032002
Annual
No
0
-22
9.79
11.38
19.8
23.4
LasVeg
320031019
Annual
No
0
-22
5.18
5.70
11.5
12.2
LasVeg
320030540
Annual
No
0
-22
8.80
10.21
21.7
25.9
Lebano
420750100
Annual
Yes
0
-15
11.20
12.02
31.4
33.9
Little
051191008
Annual
Yes
0
-41
10.27
12.03
21.7
24.7
Little
051190007
Annual
No
0
-41
9.78
11.76
20.5
24.0
Loganll
490050007
24-hr
Yes
0
-7
6.95
7.15
34.0
35.4
LosAng
060371103
Annual
Yes
0
5
12.38
12.03
32.8
32.1
LosAng
060592022
Annual
No
0
5
7.48
7.33
15.3
15.0
LosAng
060590007
Annual
No
0
5
9.63
9.37
-
-
LosAng
060374004
Annual
No
0
5
10.25
9.97
27.3
26.7
October 2021
C-84
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CBSAa
Site
Controlling
Standard
Controlling
Site?
NOx &
S02
Reduction
(%)b
Primary
PM2.5
Reduction
(%)c
Base
Annual
DV
(ug m-3)
Projected
Annual
DV
(ug m-3)
Base 24-
hr DV
(H9 m"3)
Projected
24-hr DV
(H9 m"3)
LosAng
060374002
Annual
No
0
5
11.06
10.76
29.2
28.6
LosAng
060371602
Annual
No
0
5
11.86
11.52
32.3
31.5
LosAng
060371302
Annual
No
0
5
11.99
11.64
31.5
30.8
LosAng
060371201
Annual
No
0
5
9.46
9.24
25.6
25.0
LosAng
060370002
Annual
No
0
5
10.52
10.27
29.2
28.6
Louisv
180190006
Annual
Yes
0
-27
10.64
12.04
23.9
26.2
Louisv
211110075
Annual
No
0
-27
10.42
11.84
22.3
24.3
Louisv
211110067
Annual
No
0
-27
9.55
10.78
21.4
23.6
Louisv
211110051
Annual
No
0
-27
10.29
11.48
21.8
23.7
Louisv
211110043
Annual
No
0
-27
10.37
11.72
22.0
24.1
Louisv
180431004
Annual
No
0
-27
9.96
11.20
22.0
24.2
Louisv
180190008
Annual
No
0
-27
8.72
9.69
20.1
21.5
MaconG
130210007
Annual
Yes
0
-39
10.13
12.01
21.2
24.8
MaconG
130210012
Annual
No
0
-39
7.68
8.90
16.6
18.6
Madera
060392010
24-hr
Yes
0
56
13.30
11.03
45.1
35.3
McAlle
482150043
Annual
Yes
0
-67
10.09
12.02
25.0
27.4
Merced
060470003
24-hr
Yes
0
28
11.81
10.97
39.0
35.4
Merced
060472510
24-hr
No
0
28
11.68
10.57
39.8
35.1
Modest
060990006
24-hr
Yes
0
51
13.02
10.70
45.7
35.3
Modest
060990005
24-hr
No
0
51
-
-
38.8
32.5
NapaCA
060550003
Annual
Yes
0
-47
10.36
12.03
25.1
29.1
NewYor
360610128
Annual
Yes
0
-26
10.20
12.00
23.9
27.8
NewYor
361030002
Annual
No
0
-26
7.18
8.10
18.8
21.0
NewYor
360810124
Annual
No
0
-26
7.52
8.65
19.5
22.4
NewYor
360710002
Annual
No
0
-26
6.95
7.81
17.5
19.6
NewYor
360610134
Annual
No
0
-26
9.70
11.38
21.6
25.0
NewYor
360610079
Annual
No
0
-26
8.42
9.82
22.8
25.6
NewYor
360470122
Annual
No
0
-26
8.66
10.10
20.5
23.7
NewYor
360050133
Annual
No
0
-26
9.05
10.53
24.0
28.0
NewYor
360050110
Annual
No
0
-26
7.39
8.56
19.4
22.8
NewYor
340392003
Annual
No
0
-26
8.59
9.87
23.6
26.3
NewYor
340390004
Annual
No
0
-26
9.87
11.40
24.2
27.3
NewYor
340310005
Annual
No
0
-26
8.42
9.63
22.2
24.7
NewYor
340292002
Annual
No
0
-26
7.23
8.04
18.1
19.8
NewYor
340273001
Annual
No
0
-26
6.78
7.56
17.1
18.8
NewYor
340171003
Annual
No
0
-26
8.79
10.15
23.4
26.9
NewYor
340130003
Annual
No
0
-26
8.89
10.21
23.8
27.3
NewYor
340030003
Annual
No
0
-26
8.90
10.22
24.5
27.4
October 2021
C-85
Draft - Do Not Quote or Cite
-------
CBSAa
Site
Controlling
Standard
Controlling
Site?
NOx &
S02
Reduction
(%)b
Primary
PM2.5
Reduction
(%)c
Base
Annual
DV
(ug m-3)
Projected
Annual
DV
(ug m-3)
Base 24-
hr DV
(H9 m"3)
Projected
24-hr DV
(H9 m"3)
OgdenC
490110004
24-hr
Yes
0
-18
7.28
7.77
32.6
35.4
OgdenC
490570002
24-hr
No
0
-18
8.99
9.73
-
-
OgdenC
490030003
24-hr
No
0
-18
6.35
6.76
-
-
Philad
420450002
Annual
Yes
0
-8
11.46
12.04
26.0
27.2
Philad
421010057
Annual
No
0
-8
10.86
11.37
27.0
28.4
Philad
421010055
Annual
No
0
-8
11.43
12.03
27.5
29.0
Philad
421010048
Annual
No
0
-8
10.27
10.77
25.6
27.0
Philad
420290100
Annual
No
0
-8
9.64
10.03
23.9
25.1
Philad
340150004
Annual
No
0
-8
8.33
8.69
20.6
21.5
Philad
340071007
Annual
No
0
-8
8.84
9.23
21.0
22.0
Philad
340070002
Annual
No
0
-8
10.19
10.61
23.5
24.6
Philad
240150003
Annual
No
0
-8
8.70
9.02
22.6
23.4
Philad
100031012
Annual
No
0
-8
9.04
9.40
23.0
23.8
Pittsb
420030064
Annual
Yes
0
13
12.82
12.00
35.8
32.8
Pittsb
421290008
Annual
No
0
13
8.65
8.15
19.6
18.9
Pittsb
421255001
Annual
No
0
13
8.35
7.89
17.8
17.2
Pittsb
421250200
Annual
No
0
13
8.95
8.44
19.3
18.2
Pittsb
421250005
Annual
No
0
13
11.02
10.38
22.7
21.2
Pittsb
420070014
Annual
No
0
13
10.11
9.48
21.9
20.5
Pittsb
420050001
Annual
No
0
13
11.03
10.30
21.9
20.5
Pittsb
420031301
Annual
No
0
13
11.00
10.30
24.8
23.0
Pittsb
420031008
Annual
No
0
13
9.78
9.16
20.5
19.3
Pittsb
420030008
Annual
No
0
13
9.50
8.85
20.5
19.0
Prinev
410130100
24-hr
Yes
0
10
8.60
8.17
37.6
35.3
ProvoO
490494001
24-hr
Yes
0
-30
7.74
8.57
30.9
35.3
ProvoO
490495010
24-hr
No
0
-30
6.73
7.52
-
-
ProvoO
490490002
24-hr
No
0
-30
7.41
8.31
28.9
33.2
Rivers
060658005
24-hr
Yes
0
36
14.48
11.51
43.2
35.3
Rivers
060658001
24-hr
No
0
36
-
-
36.5
29.6
Sacram
060670006
24-hr
Yes
0
-23
9.31
10.40
31.4
35.4
Sacram
061131003
24-hr
No
0
-23
6.62
7.19
15.8
17.3
Sacram
060670012
24-hr
No
0
-23
7.30
8.01
19.8
21.2
Sacram
060670010
24-hr
No
0
-23
8.67
9.65
26.5
29.9
Sacram
060610006
24-hr
No
0
-23
7.58
8.47
20.3
22.3
Sacram
060610003
24-hr
No
0
-23
6.71
7.26
19.3
20.2
SaltLa
490353010
24-hr
Yes
0
44
-
-
41.5
35.3
SaltLa
490353006
24-hr
No
0
44
7.62
6.19
36.8
30.2
SaltLa
490351001
24-hr
No
0
44
7.07
5.85
32.1
25.8
October 2021
C-86
Draft - Do Not Quote or Cite
-------
CBSAa
Site
Controlling
Standard
Controlling
Site?
NOx &
S02
Reduction
(%)b
Primary
PM2.5
Reduction
(%)c
Base
Annual
DV
(ug m-3)
Projected
Annual
DV
(ug m-3)
Base 24-
hr DV
(H9 m"3)
Projected
24-hr DV
(H9 m"3)
SanLui
060792007
Annual
Yes
0
-46
10.70
12.04
25.9
29.4
SanLui
060798002
Annual
No
0
-46
5.71
6.33
-
-
SanLui
060792004
Annual
No
0
-46
8.25
9.26
19.8
21.4
SouthB
181410015
24-hr
Yes
0
-23
10.45
11.37
32.5
35.4
St.Lou
290990019
Annual
Yes
0
-39
10.12
12.02
22.8
24.9
St.Lou
295100094
Annual
No
0
-39
9.57
11.38
23.3
25.9
St.Lou
295100093
Annual
No
0
-39
-
-
23.7
26.6
St.Lou
295100085
Annual
No
0
-39
10.10
12.01
23.6
26.2
St.Lou
295100007
Annual
No
0
-39
9.78
11.52
23.7
26.4
St.Lou
291893001
Annual
No
0
-39
9.85
11.72
22.4
25.2
Stockt
060771002
24-hr
Yes
0
17
12.23
11.30
38.7
35.4
Stockt
060772010
24-hr
No
0
17
10.74
9.96
37.3
34.3
Visali
061072002
24-hr
Yes
48
56
16.23
10.93
54.0
35.4
Weirto
390810017
Annual
Yes
0
-5
11.75
12.02
27.2
27.8
Weirto
540090011
Annual
No
0
-5
9.75
9.95
22.8
23.5
Weirto
540090005
Annual
No
0
-5
10.52
10.74
22.4
22.9
Weirto
390810021
Annual
No
0
-5
9.29
9.47
22.2
22.6
Wheeli
540511002
Annual
Yes
0
-44
10.24
12.02
22.5
25.4
Wheeli
540690010
Annual
No
0
-44
9.61
11.32
19.7
22.6
a CBSA names are the first six characters of the full CBSAs names in Table C-3.
b Percent reduction in NOx and SO2 emissions associated with just meeting the standard in this case.
c Percent reduction in Primary PM2.5 emissions associated with just meeting the standard in this case.
1
2
October 2021
C-87
Draft - Do Not Quote or Cite
-------
1 Table C-20. PM2.5 DVs for the Secondary PM projection case and 12/35 standard level.
CBSAa
Site
Controlling
Standard
Controlling
Site?
NOx &
S02
Reduction
(%)b
Primary
PM2.5
Reduction
(%)c
Base
Annual
DV
(ug m 3)
Projected
Annual
DV
(ug m-3)
Base 24-
hr DV
(H9 m"3)
Projected
24-hr DV
(H9 m"3)
AkronO
391530017
Annual
Yes
-67
0
10.99
12.04
23.7
26.8
AkronO
391530023
Annual
No
-67
0
9.16
10.20
20.2
21.8
Altoon
420130801
Annual
Yes
N/A
N/A
10.11
12.04
23.8
28.3
Atlant
131210039
Annual
Yes
N/A
N/A
10.38
12.04
19.7
22.9
Atlant
132230003
Annual
No
N/A
N/A
7.82
9.07
16.2
18.8
Atlant
131350002
Annual
No
N/A
N/A
8.84
10.25
17.9
20.8
Atlant
130890002
Annual
No
N/A
N/A
9.34
10.83
19.2
22.3
Atlant
130670003
Annual
No
N/A
N/A
9.51
11.03
18.6
21.6
Atlant
130630091
Annual
No
N/A
N/A
9.86
11.44
19.1
22.2
Bakers
060290010
24-hr
Yes
N/A
N/A
16.52
10.40
70.0
35.4
Bakers
060290016
24-hr
No
N/A
N/A
18.45
11.61
61.3
31.0
Bakers
060290015
24-hr
No
N/A
N/A
5.15
3.24
15.8
8.0
Bakers
060290014
24-hr
No
N/A
N/A
16.53
10.40
61.4
31.1
Bakers
060290011
24-hr
No
N/A
N/A
6.06
3.81
19.6
9.9
Birmin
010732059
Annual
Yes
-56
0
11.25
12.03
22.3
24.2
Birmin
010732003
Annual
No
-56
0
10.08
10.86
19.0
21.5
Birmin
010731010
Annual
No
-56
0
9.78
10.68
19.2
21.4
Birmin
010730023
Annual
No
-56
0
10.94
11.73
22.8
25.3
Canton
391510017
Annual
Yes
-78
0
10.81
12.04
23.7
26.1
Canton
391510020
Annual
No
-78
0
9.91
11.14
22.0
24.8
Chicag
170313103
Annual
Yes
N/A
N/A
11.10
12.04
22.6
24.5
Chicag
550590019
Annual
No
N/A
N/A
8.04
8.72
20.4
22.1
Chicag
181270024
Annual
No
N/A
N/A
9.51
10.32
22.4
24.3
Chicag
180892004
Annual
No
N/A
N/A
9.84
10.67
24.7
26.8
Chicag
180890031
Annual
No
N/A
N/A
10.12
10.98
23.6
25.6
Chicag
180890026
Annual
No
N/A
N/A
-
-
25.2
27.3
Chicag
180890022
Annual
No
N/A
N/A
-
-
22.7
24.6
Chicag
180890006
Annual
No
N/A
N/A
10.03
10.88
23.1
25.1
Chicag
171971011
Annual
No
N/A
N/A
8.36
9.07
18.4
20.0
Chicag
171971002
Annual
No
N/A
N/A
7.69
8.34
20.0
21.7
Chicag
170890007
Annual
No
N/A
N/A
8.94
9.70
19.2
20.8
Chicag
170890003
Annual
No
N/A
N/A
-
-
19.2
20.8
Chicag
170434002
Annual
No
N/A
N/A
8.87
9.62
19.9
21.6
Chicag
170316005
Annual
No
N/A
N/A
10.79
11.70
24.1
26.1
Chicag
170314201
Annual
No
N/A
N/A
9.00
9.76
21.4
23.2
Chicag
170314007
Annual
No
N/A
N/A
9.49
10.29
-
-
Chicag
170313301
Annual
No
N/A
N/A
10.37
11.25
23.5
25.5
October 2021
C-88
Draft - Do Not Quote or Cite
-------
CBSAa
Site
Controlling
Standard
Controlling
Site?
NOx &
S02
Reduction
(%)b
Primary
PM2.5
Reduction
(%)c
Base
Annual
DV
(ug m-3)
Projected
Annual
DV
(ug m-3)
Base 24-
hr DV
(H9 m"3)
Projected
24-hr DV
(H9 m"3)
Chicag
170310076
Annual
No
N/A
N/A
10.18
11.04
22.5
24.4
Chicag
170310057
Annual
No
N/A
N/A
11.03
11.96
26.8
29.1
Chicag
170310052
Annual
No
N/A
N/A
10.00
10.85
23.3
25.3
Chicag
170310022
Annual
No
N/A
N/A
10.38
11.26
22.4
24.3
Chicag
170310001
Annual
No
N/A
N/A
10.13
10.99
21.7
23.5
Cincin
390610014
Annual
Yes
-72
0
10.70
12.04
22.9
26.1
Cincin
390610042
Annual
No
-72
0
10.29
11.66
22.6
26.2
Cincin
390610040
Annual
No
-72
0
9.45
10.79
21.0
25.4
Cincin
390610010
Annual
No
-72
0
9.43
10.75
21.3
24.4
Cincin
390610006
Annual
No
-72
0
9.46
10.75
20.3
24.3
Cincin
390170020
Annual
No
-72
0
-
-
24.2
27.8
Cincin
390170019
Annual
No
-72
0
10.24
11.40
22.0
24.5
Cincin
390170016
Annual
No
-72
0
9.79
11.06
22.1
25.1
Cincin
210373002
Annual
No
-72
0
9.06
10.42
20.9
25.1
Clevel
390350065
Annual
Yes
6
0
12.17
12.04
24.9
24.7
Clevel
391030004
Annual
No
6
0
8.73
8.61
19.6
19.2
Clevel
390933002
Annual
No
6
0
8.10
7.99
20.2
19.9
Clevel
390850007
Annual
No
6
0
7.88
7.78
17.4
17.1
Clevel
390351002
Annual
No
6
0
8.86
8.74
19.5
19.2
Clevel
390350045
Annual
No
6
0
10.61
10.49
22.9
22.6
Clevel
390350038
Annual
No
6
0
11.38
11.26
25.0
24.7
Clevel
390350034
Annual
No
6
0
8.87
8.75
20.4
20.1
Detroi
261630033
Annual
Yes
-56
0
11.30
12.04
26.8
30.2
Detroi
261630039
Annual
No
-56
0
9.11
9.88
22.3
24.8
Detroi
261630036
Annual
No
-56
0
8.68
9.39
21.8
23.4
Detroi
261630025
Annual
No
-56
0
8.98
9.75
24.1
26.5
Detroi
261630019
Annual
No
-56
0
9.18
9.97
22.4
24.1
Detroi
261630016
Annual
No
-56
0
9.62
10.38
24.4
27.4
Detroi
261630015
Annual
No
-56
0
11.19
11.97
25.5
28.2
Detroi
261630001
Annual
No
-56
0
9.50
10.20
23.3
25.0
Detroi
261470005
Annual
No
-56
0
8.89
9.50
24.3
26.1
Detroi
261250001
Annual
No
-56
0
8.86
9.65
24.2
26.7
Detroi
260990009
Annual
No
-56
0
8.80
9.48
26.2
28.4
ElCent
060250005
Annual
Yes
N/A
N/A
12.63
12.04
33.5
31.9
ElCent
060251003
Annual
No
N/A
N/A
7.44
7.09
19.8
18.9
ElCent
060250007
Annual
No
N/A
N/A
8.37
7.98
21.5
20.5
Elkhar
180390008
Annual
Yes
N/A
N/A
10.24
12.04
28.6
33.6
Evansv
181630023
Annual
Yes
-89
0
10.11
12.03
21.5
32.5
October 2021
C-89
Draft - Do Not Quote or Cite
-------
CBSAa
Site
Controlling
Standard
Controlling
Site?
NOx &
S02
Reduction
(%)b
Primary
PM2.5
Reduction
(%)c
Base
Annual
DV
(ug m-3)
Projected
Annual
DV
(ug m-3)
Base 24-
hr DV
(H9 m"3)
Projected
24-hr DV
(H9 m"3)
Evansv
211010014
Annual
No
-89
0
9.64
11.58
20.7
30.2
Evansv
181630021
Annual
No
-89
0
9.84
11.79
21.6
32.4
Evansv
181630016
Annual
No
-89
0
10.02
11.95
22.0
32.8
Fresno
060190011
24-hr
Yes
N/A
N/A
14.07
10.46
53.8
35.4
Fresno
060195025
24-hr
No
N/A
N/A
13.63
10.13
47.9
31.5
Fresno
060195001
24-hr
No
N/A
N/A
14.08
10.47
49.3
32.4
Fresno
060192009
24-hr
No
N/A
N/A
8.47
6.30
31.3
20.6
Hanfor
060310004
24-hr
Yes
N/A
N/A
21.98
10.81
72.0
35.4
Hanfor
060311004
24-hr
No
N/A
N/A
16.49
8.11
58.9
29.0
Housto
482011035
Annual
Yes
-91
0
11.19
12.04
22.4
25.2
Housto
482011039
Annual
No
-91
0
9.22
10.16
21.7
24.9
Housto
482010058
Annual
No
-91
0
9.67
10.52
22.3
24.8
Housto
481671034
Annual
No
-91
0
7.36
8.27
20.3
23.3
Indian
180970087
Annual
Yes
-24
0
11.44
12.02
25.9
27.5
Indian
180970083
Annual
No
-24
0
11.06
11.64
23.9
25.2
Indian
180970081
Annual
No
-24
0
11.07
11.65
25.0
26.7
Indian
180970078
Annual
No
-24
0
10.14
10.72
24.4
26.2
Indian
180970043
Annual
No
-24
0
-
-
26.0
27.6
Indian
180950011
Annual
No
-24
0
9.05
9.51
21.8
23.1
Indian
180570007
Annual
No
-24
0
9.02
9.52
21.4
22.8
Johnst
420210011
Annual
Yes
-86
0
10.68
12.04
25.8
27.9
Lancas
420710012
Annual
Yes
40
0
12.83
12.03
32.7
31.6
Lancas
420710007
Annual
No
40
0
10.57
9.78
29.8
28.5
LasVeg
320030561
Annual
Yes
N/A
N/A
10.28
12.04
24.5
28.7
LasVeg
320032002
Annual
No
N/A
N/A
9.79
11.47
19.8
23.2
LasVeg
320031019
Annual
No
N/A
N/A
5.18
6.07
11.5
13.5
LasVeg
320030540
Annual
No
N/A
N/A
8.80
10.31
21.7
25.4
Lebano
420750100
Annual
Yes
-61
0
11.20
12.04
31.4
32.4
Little
051191008
Annual
Yes
-98
0
10.27
12.04
21.7
26.7
Little
051190007
Annual
No
-98
0
9.78
11.40
20.5
25.5
Loganll
490050007
24-hr
Yes
-28
0
6.95
7.12
34.0
35.4
LosAng
060371103
Annual
Yes
N/A
N/A
12.38
12.04
32.8
31.9
LosAng
060592022
Annual
No
N/A
N/A
7.48
7.27
15.3
14.9
LosAng
060590007
Annual
No
N/A
N/A
9.63
9.37
-
-
LosAng
060374004
Annual
No
N/A
N/A
10.25
9.97
27.3
26.6
LosAng
060374002
Annual
No
N/A
N/A
11.06
10.76
29.2
28.4
LosAng
060371602
Annual
No
N/A
N/A
11.86
11.53
32.3
31.4
LosAng
060371302
Annual
No
N/A
N/A
11.99
11.66
31.5
30.6
October 2021
C-90
Draft - Do Not Quote or Cite
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CBSAa
Site
Controlling
Standard
Controlling
Site?
NOx &
S02
Reduction
(%)b
Primary
PM2.5
Reduction
(%)c
Base
Annual
DV
(ug m-3)
Projected
Annual
DV
(ug m-3)
Base 24-
hr DV
(H9 m"3)
Projected
24-hr DV
(H9 m"3)
LosAng
060371201
Annual
No
N/A
N/A
9.46
9.20
25.6
24.9
LosAng
060370002
Annual
No
N/A
N/A
10.52
10.23
29.2
28.4
Louisv
180190006
Annual
Yes
-65
0
10.64
12.04
23.9
28.4
Louisv
211110075
Annual
No
-65
0
10.42
11.76
22.3
26.4
Louisv
211110067
Annual
No
-65
0
9.55
10.84
21.4
25.4
Louisv
211110051
Annual
No
-65
0
10.29
11.67
21.8
25.9
Louisv
211110043
Annual
No
-65
0
10.37
11.71
22.0
26.1
Louisv
180431004
Annual
No
-65
0
9.96
11.32
22.0
25.8
Louisv
180190008
Annual
No
-65
0
8.72
10.07
20.1
24.3
MaconG
130210007
Annual
Yes
N/A
N/A
10.13
12.04
21.2
25.2
MaconG
130210012
Annual
No
N/A
N/A
7.68
9.13
16.6
19.7
Madera
060392010
24-hr
Yes
N/A
N/A
13.30
11.15
45.1
35.4
McAile
482150043
Annual
Yes
N/A
N/A
10.09
12.04
25.0
29.8
Merced
060472510
24-hr
Yes
32
0
11.68
10.79
39.8
35.4
Merced
060470003
24-hr
No
32
0
11.81
10.89
39.0
34.1
Modest
060990006
24-hr
Yes
N/A
N/A
13.02
10.82
45.7
35.4
Modest
060990005
24-hr
No
N/A
N/A
-
-
38.8
30.1
NapaCA
060550003
Annual
Yes
N/A
N/A
10.36
12.04
25.1
29.2
NewYor
360610128
Annual
Yes
N/A
N/A
10.20
12.04
23.9
28.2
NewYor
361030002
Annual
No
N/A
N/A
7.18
8.48
18.8
22.2
NewYor
360810124
Annual
No
N/A
N/A
7.52
8.88
19.5
23.0
NewYor
360710002
Annual
No
N/A
N/A
6.95
8.20
17.5
20.7
NewYor
360610134
Annual
No
N/A
N/A
9.70
11.45
21.6
25.5
NewYor
360610079
Annual
No
N/A
N/A
8.42
9.94
22.8
26.9
NewYor
360470122
Annual
No
N/A
N/A
8.66
10.22
20.5
24.2
NewYor
360050133
Annual
No
N/A
N/A
9.05
10.68
24.0
28.3
NewYor
360050110
Annual
No
N/A
N/A
7.39
8.72
19.4
22.9
NewYor
340392003
Annual
No
N/A
N/A
8.59
10.14
23.6
27.9
NewYor
340390004
Annual
No
N/A
N/A
9.87
11.65
24.2
28.6
NewYor
340310005
Annual
No
N/A
N/A
8.42
9.94
22.2
26.2
NewYor
340292002
Annual
No
N/A
N/A
7.23
8.53
18.1
21.4
NewYor
340273001
Annual
No
N/A
N/A
6.78
8.00
17.1
20.2
NewYor
340171003
Annual
No
N/A
N/A
8.79
10.38
23.4
27.6
NewYor
340130003
Annual
No
N/A
N/A
8.89
10.49
23.8
28.1
NewYor
340030003
Annual
No
N/A
N/A
8.90
10.51
24.5
28.9
OgdenC
490110004
24-hr
Yes
-53
0
7.28
7.65
32.6
35.4
OgdenC
490570002
24-hr
No
-53
0
8.99
9.37
-
-
OgdenC
490030003
24-hr
No
-53
0
6.35
6.70
-
-
October 2021
C-91
Draft - Do Not Quote or Cite
-------
CBSAa
Site
Controlling
Standard
Controlling
Site?
NOx &
S02
Reduction
(%)b
Primary
PM2.5
Reduction
(%)c
Base
Annual
DV
(ug m-3)
Projected
Annual
DV
(ug m-3)
Base 24-
hr DV
(H9 m"3)
Projected
24-hr DV
(H9 m"3)
Philad
420450002
Annual
Yes
-75
0
11.46
12.04
26.0
27.4
Philad
421010057
Annual
No
-75
0
10.86
11.54
27.0
28.1
Philad
421010055
Annual
No
-75
0
11.43
12.03
27.5
28.8
Philad
421010048
Annual
No
-75
0
10.27
10.91
25.6
27.4
Philad
420290100
Annual
No
-75
0
9.64
10.38
23.9
25.2
Philad
340150004
Annual
No
-75
0
8.33
8.94
20.6
23.2
Philad
340071007
Annual
No
-75
0
8.84
9.51
21.0
21.9
Philad
340070002
Annual
No
-75
0
10.19
10.95
23.5
24.6
Philad
240150003
Annual
No
-75
0
8.70
9.47
22.6
23.7
Philad
100031012
Annual
No
-75
0
9.04
9.81
23.0
23.6
Pittsb
420030064
Annual
Yes
30
0
12.82
12.02
35.8
34.8
Pittsb
421290008
Annual
No
30
0
8.65
8.06
19.6
18.0
Pittsb
421255001
Annual
No
30
0
8.35
7.78
17.8
16.4
Pittsb
421250200
Annual
No
30
0
8.95
8.32
19.3
18.2
Pittsb
421250005
Annual
No
30
0
11.02
10.30
22.7
21.7
Pittsb
420070014
Annual
No
30
0
10.11
9.52
21.9
20.6
Pittsb
420050001
Annual
No
30
0
11.03
10.45
21.9
20.4
Pittsb
420031301
Annual
No
30
0
11.00
10.28
24.8
23.6
Pittsb
420031008
Annual
No
30
0
9.78
9.20
20.5
19.0
Pittsb
420030008
Annual
No
30
0
9.50
8.89
20.5
19.2
Prinev
410130100
24-hr
Yes
N/A
N/A
8.60
8.10
37.6
35.4
ProvoO
490494001
24-hr
Yes
-76
0
7.74
8.29
30.9
35.4
ProvoO
490495010
24-hr
No
-76
0
6.73
7.21
-
-
ProvoO
490490002
24-hr
No
-76
0
7.41
7.95
28.9
33.2
Rivers
060658005
24-hr
Yes
N/A
N/A
14.48
11.87
43.2
35.4
Rivers
060658001
24-hr
No
N/A
N/A
-
-
36.5
29.9
Sacram
060670006
24-hr
Yes
-99
0
9.31
10.04
31.4
35.3
Sacram
061131003
24-hr
No
-99
0
6.62
7.08
15.8
19.0
Sacram
060670012
24-hr
No
-99
0
7.30
7.85
19.8
21.3
Sacram
060670010
24-hr
No
-99
0
8.67
9.30
26.5
30.2
Sacram
060610006
24-hr
No
-99
0
7.58
8.08
20.3
22.2
Sacram
060610003
24-hr
No
-99
0
6.71
7.04
19.3
20.7
SaltLa
490353010
24-hr
Yes
58
0
-
-
41.5
35.4
SaltLa
490353006
24-hr
No
58
0
7.62
6.91
36.8
31.5
SaltLa
490351001
24-hr
No
58
0
7.07
6.30
32.1
25.8
SanLui
060792007
Annual
Yes
N/A
N/A
10.70
12.04
25.9
29.1
SanLui
060798002
Annual
No
N/A
N/A
5.71
6.43
-
-
SanLui
060792004
Annual
No
N/A
N/A
8.25
9.28
19.8
22.3
October 2021
C-92
Draft - Do Not Quote or Cite
-------
CBSAa
Site
Controlling
Standard
Controlling
Site?
NOx &
S02
Reduction
(%)b
Primary
PM2.5
Reduction
(%)c
Base
Annual
DV
(ug m-3)
Projected
Annual
DV
(ug m-3)
Base 24-
hr DV
(H9 m"3)
Projected
24-hr DV
(H9 m"3)
SouthB
181410015
Annual
Yes
-92
0
10.45
12.04
32.5
34.8
St. Lou
290990019
Annual
Yes
N/A
N/A
10.12
12.04
22.8
27.1
St. Lou
295100094
Annual
No
N/A
N/A
9.57
11.39
23.3
27.7
St. Lou
295100093
Annual
No
N/A
N/A
-
-
23.7
28.2
St. Lou
295100085
Annual
No
N/A
N/A
10.10
12.02
23.6
28.1
St. Lou
295100007
Annual
No
N/A
N/A
9.78
11.64
23.7
28.2
St. Lou
291893001
Annual
No
N/A
N/A
9.85
11.72
22.4
26.6
Stockt
060771002
24-hr
Yes
42
0
12.23
11.41
38.7
35.4
Stockt
060772010
24-hr
No
42
0
10.74
9.96
37.3
34.3
Visali
061072002
24-hr
Yes
N/A
N/A
16.23
10.64
54.0
35.4
Weirto
390810017
Annual
Yes
-14
0
11.75
12.03
27.2
27.5
Weirto
540090011
Annual
No
-14
0
9.75
10.02
22.8
23.6
Weirto
540090005
Annual
No
-14
0
10.52
10.80
22.4
23.1
Weirto
390810021
Annual
No
-14
0
9.29
9.55
22.2
22.8
Wheeli
540511002
Annual
Yes
N/A
N/A
10.24
12.04
22.5
26.5
Wheeli
540690010
Annual
No
N/A
N/A
9.61
11.30
19.7
23.2
a CBSA names are the first six characters of the full CBSAs names in Table C-3.
b Percent reduction in NOx and SO2 emissions associated with just meeting the standard in this case; N/A indicates 'not
applicable' where proportional projection was used.
c Percent reduction in Primary PM2.5 emissions associated with just meeting the standard in this case; N/A indicates 'not
applicable' where proportional projection was used.
1
2
October 2021
C-93
Draft - Do Not Quote or Cite
-------
1 Table C-21. PM2.5 DVs for the Primary PM projection case and 10/30 standard level.
CBSAa
Site
Controlling
Standard
Controlling
Site?
NOx &
S02
Reduction
(%)b
Primary
PM2.5
Reduction
(%)c
Base
Annual
DV
(ug m 3)
Projected
Annual
DV
(ug m-3)
Base 24-
hr DV
(H9 m"3)
Projected
24-hr DV
(H9 m"3)
AkronO
391530017
Annual
Yes
0
17
10.99
10.03
23.7
22.6
AkronO
391530023
Annual
No
0
17
9.16
8.46
20.2
19.1
Altoon
420130801
Annual
Yes
0
2
10.11
10.02
23.8
23.5
Atlant
131210039
Annual
Yes
0
6
10.38
10.01
19.7
19.0
Atlant
132230003
Annual
No
0
6
7.82
7.64
16.2
15.9
Atlant
131350002
Annual
No
0
6
8.84
8.57
17.9
17.3
Atlant
130890002
Annual
No
0
6
9.34
9.04
19.2
18.7
Atlant
130670003
Annual
No
0
6
9.51
9.22
18.6
18.2
Atlant
130630091
Annual
No
0
6
9.86
9.56
19.1
18.5
Bakers
060290016
Annual
Yes
91
100
18.45
10.01
61.3
29.1
Bakers
060290015
Annual
No
91
100
5.15
3.66
15.8
13.6
Bakers
060290014
Annual
No
91
100
16.53
8.37
61.4
26.0
Bakers
060290011
Annual
No
91
100
6.06
4.58
19.6
15.9
Bakers
060290010
Annual
No
91
100
16.52
8.87
70.0
27.9
Birmin
010732059
Annual
Yes
0
16
11.25
10.03
22.3
19.8
Birmin
010732003
Annual
No
0
16
10.08
9.06
19.0
17.2
Birmin
010731010
Annual
No
0
16
9.78
8.94
19.2
17.7
Birmin
010730023
Annual
No
0
16
10.94
9.77
22.8
20.6
Canton
391510017
Annual
Yes
0
15
10.81
10.01
23.7
22.6
Canton
391510020
Annual
No
0
15
9.91
9.21
22.0
21.0
Chicag
170313103
Annual
Yes
0
18
11.10
10.01
22.6
21.0
Chicag
550590019
Annual
No
0
18
8.04
7.42
20.4
18.8
Chicag
181270024
Annual
No
0
18
9.51
8.55
22.4
20.4
Chicag
180892004
Annual
No
0
18
9.84
8.78
24.7
22.8
Chicag
180890031
Annual
No
0
18
10.12
9.05
23.6
21.1
Chicag
180890026
Annual
No
0
18
-
-
25.2
22.8
Chicag
180890022
Annual
No
0
18
-
-
22.7
20.4
Chicag
180890006
Annual
No
0
18
10.03
8.93
23.1
20.5
Chicag
171971011
Annual
No
0
18
8.36
7.78
18.4
17.4
Chicag
171971002
Annual
No
0
18
7.69
7.04
20.0
18.7
Chicag
170890007
Annual
No
0
18
8.94
8.21
19.2
17.8
Chicag
170890003
Annual
No
0
18
-
-
19.2
18.1
Chicag
170434002
Annual
No
0
18
8.87
8.13
19.9
18.9
Chicag
170316005
Annual
No
0
18
10.79
9.73
24.1
21.7
Chicag
170314201
Annual
No
0
18
9.00
8.25
21.4
19.9
Chicag
170314007
Annual
No
0
18
9.49
8.66
-
-
Chicag
170313301
Annual
No
0
18
10.37
9.38
23.5
21.3
October 2021
C-94
Draft - Do Not Quote or Cite
-------
CBSAa
Site
Controlling
Standard
Controlling
Site?
NOx &
S02
Reduction
(%)b
Primary
PM2.5
Reduction
(%)c
Base
Annual
DV
(ug m-3)
Projected
Annual
DV
(ug m-3)
Base 24-
hr DV
(H9 m"3)
Projected
24-hr DV
(H9 m"3)
Chicag
170310076
Annual
No
0
18
10.18
9.24
22.5
20.7
Chicag
170310057
Annual
No
0
18
11.03
9.99
26.8
25.1
Chicag
170310052
Annual
No
0
18
10.00
9.06
23.3
21.4
Chicag
170310022
Annual
No
0
18
10.38
9.28
22.4
20.9
Chicag
170310001
Annual
No
0
18
10.13
9.22
21.7
19.7
Cincin
390610014
Annual
Yes
0
12
10.70
10.04
22.9
21.8
Cincin
390610042
Annual
No
0
12
10.29
9.69
22.6
21.6
Cincin
390610040
Annual
No
0
12
9.45
8.91
21.0
20.0
Cincin
390610010
Annual
No
0
12
9.43
8.93
21.3
20.5
Cincin
390610006
Annual
No
0
12
9.46
8.91
20.3
19.5
Cincin
390170020
Annual
No
0
12
-
-
24.2
23.3
Cincin
390170019
Annual
No
0
12
10.24
9.60
22.0
21.1
Cincin
390170016
Annual
No
0
12
9.79
9.22
22.1
21.2
Cincin
210373002
Annual
No
0
12
9.06
8.58
20.9
20.0
Clevel
390350065
Annual
Yes
0
33
12.17
10.00
24.9
21.3
Clevel
391030004
Annual
No
0
33
8.73
7.57
19.6
17.8
Clevel
390933002
Annual
No
0
33
8.10
6.95
20.2
18.7
Clevel
390850007
Annual
No
0
33
7.88
6.84
17.4
15.4
Clevel
390351002
Annual
No
0
33
8.86
7.64
19.5
17.5
Clevel
390350045
Annual
No
0
33
10.61
8.84
22.9
20.1
Clevel
390350038
Annual
No
0
33
11.38
9.37
25.0
22.0
Clevel
390350034
Annual
No
0
33
8.87
7.58
20.4
18.2
Detroi
261630033
Annual
Yes
0
26
11.30
10.00
26.8
24.9
Detroi
261630039
Annual
No
0
26
9.11
8.21
22.3
20.3
Detroi
261630036
Annual
No
0
26
8.68
7.88
21.8
19.8
Detroi
261630025
Annual
No
0
26
8.98
7.99
24.1
21.7
Detroi
261630019
Annual
No
0
26
9.18
8.18
22.4
19.7
Detroi
261630016
Annual
No
0
26
9.62
8.63
24.4
22.6
Detroi
261630015
Annual
No
0
26
11.19
9.94
25.5
22.8
Detroi
261630001
Annual
No
0
26
9.50
8.39
23.3
20.4
Detroi
261470005
Annual
No
0
26
8.89
8.11
24.3
22.4
Detroi
261250001
Annual
No
0
26
8.86
7.90
24.2
22.2
Detroi
260990009
Annual
No
0
26
8.80
7.94
26.2
23.8
ElCent
060250005
Annual
Yes
0
50
12.63
10.01
33.5
25.0
ElCent
060251003
Annual
No
0
50
7.44
5.67
19.8
14.6
ElCent
060250007
Annual
No
0
50
8.37
6.80
21.5
18.5
Elkhar
180390008
Annual
Yes
0
6
10.24
10.01
28.6
27.8
Evansv
181630023
Annual
Yes
0
2
10.11
10.02
21.5
21.5
October 2021
C-95
Draft - Do Not Quote or Cite
-------
CBSAa
Site
Controlling
Standard
Controlling
Site?
NOx &
S02
Reduction
(%)b
Primary
PM2.5
Reduction
(%)c
Base
Annual
DV
(ug m-3)
Projected
Annual
DV
(ug m-3)
Base 24-
hr DV
(H9 m"3)
Projected
24-hr DV
(H9 m"3)
Evansv
211010014
Annual
No
0
2
9.64
9.56
20.7
20.7
Evansv
181630021
Annual
No
0
2
9.84
9.76
21.6
21.5
Evansv
181630016
Annual
No
0
2
10.02
9.94
22.0
21.9
Fresno
060195001
24-hr
Yes
0
100
14.08
9.49
49.3
30.3
Fresno
060195025
24-hr
No
0
100
13.63
8.41
47.9
26.4
Fresno
060192009
24-hr
No
0
100
8.47
6.74
31.3
22.2
Fresno
060190011
24-hr
No
0
100
14.07
8.27
53.8
27.1
Hanfor
060310004
Annual
Yes
82
98
21.98
10.00
72.0
29.5
Hanfor
060311004
Annual
No
82
98
16.49
8.36
58.9
25.2
Housto
482011035
Annual
Yes
0
19
11.19
10.01
22.4
20.2
Housto
482011039
Annual
No
0
19
9.22
8.40
21.7
19.6
Housto
482010058
Annual
No
0
19
9.67
8.70
22.3
20.3
Housto
481671034
Annual
No
0
19
7.36
7.07
20.3
19.6
Indian
180970087
Annual
Yes
0
25
11.44
10.01
25.9
24.2
Indian
180970083
Annual
No
0
25
11.06
9.72
23.9
22.5
Indian
180970081
Annual
No
0
25
11.07
9.71
25.0
23.4
Indian
180970078
Annual
No
0
25
10.14
8.97
24.4
22.8
Indian
180970043
Annual
No
0
25
-
-
26.0
24.6
Indian
180950011
Annual
No
0
25
9.05
8.17
21.8
20.7
Indian
180570007
Annual
No
0
25
9.02
8.07
21.4
20.0
Johnst
420210011
Annual
Yes
0
12
10.68
10.02
25.8
23.5
Lancas
420710012
Annual
Yes
0
41
12.83
9.98
32.7
25.5
Lancas
420710007
Annual
No
0
41
10.57
8.20
29.8
22.0
LasVeg
320030561
Annual
Yes
0
4
10.28
9.97
24.5
23.6
LasVeg
320032002
Annual
No
0
4
9.79
9.50
19.8
19.2
LasVeg
320031019
Annual
No
0
4
5.18
5.08
11.5
11.3
LasVeg
320030540
Annual
No
0
4
8.80
8.55
21.7
20.9
Lebano
420750100
Annual
Yes
0
21
11.20
10.04
31.4
28.0
Little
051191008
Annual
Yes
0
6
10.27
10.00
21.7
21.3
Little
051190007
Annual
No
0
6
9.78
9.48
20.5
20.1
Loganll
490050007
24-hr
Yes
0
19
6.95
6.40
34.0
30.3
LosAng
060371103
Annual
Yes
0
34
12.38
9.99
32.8
27.8
LosAng
060592022
Annual
No
0
34
7.48
6.43
15.3
13.3
LosAng
060590007
Annual
No
0
34
9.63
7.84
-
-
LosAng
060374004
Annual
No
0
34
10.25
8.36
27.3
23.7
LosAng
060374002
Annual
No
0
34
11.06
9.02
29.2
24.9
LosAng
060371602
Annual
No
0
34
11.86
9.55
32.3
26.5
LosAng
060371302
Annual
No
0
34
11.99
9.64
31.5
27.0
October 2021
C-96
Draft - Do Not Quote or Cite
-------
CBSAa
Site
Controlling
Standard
Controlling
Site?
NOx &
S02
Reduction
(%)b
Primary
PM2.5
Reduction
(%)c
Base
Annual
DV
(ug m-3)
Projected
Annual
DV
(ug m-3)
Base 24-
hr DV
(H9 m"3)
Projected
24-hr DV
(H9 m"3)
LosAng
060371201
Annual
No
0
34
9.46
7.93
25.6
21.6
LosAng
060370002
Annual
No
0
34
10.52
8.81
29.2
25.0
Louisv
180190006
Annual
Yes
0
12
10.64
10.01
23.9
22.8
Louisv
211110075
Annual
No
0
12
10.42
9.79
22.3
21.4
Louisv
211110067
Annual
No
0
12
9.55
8.99
21.4
20.5
Louisv
211110051
Annual
No
0
12
10.29
9.76
21.8
21.2
Louisv
211110043
Annual
No
0
12
10.37
9.77
22.0
21.2
Louisv
180431004
Annual
No
0
12
9.96
9.41
22.0
21.0
Louisv
180190008
Annual
No
0
12
8.72
8.29
20.1
19.5
MaconG
130210007
Annual
Yes
0
2
10.13
10.03
21.2
21.0
MaconG
130210012
Annual
No
0
2
7.68
7.61
16.6
16.5
Madera
060392010
24-hr
Yes
0
84
13.30
9.89
45.1
30.4
McAile
482150043
Annual
Yes
0
2
10.09
10.03
25.0
24.9
Merced
060470003
24-hr
Yes
0
65
11.81
9.87
39.0
30.4
Merced
060472510
24-hr
No
0
65
11.68
9.11
39.8
28.8
Modest
060990006
24-hr
Yes
0
77
13.02
9.52
45.7
30.3
Modest
060990005
24-hr
No
0
77
-
-
38.8
29.2
NapaCA
060550003
Annual
Yes
0
9
10.36
10.04
25.1
24.6
NewYor
360610128
Annual
Yes
0
3
10.20
9.99
23.9
23.5
NewYor
361030002
Annual
No
0
3
7.18
7.07
18.8
18.6
NewYor
360810124
Annual
No
0
3
7.52
7.39
19.5
19.1
NewYor
360710002
Annual
No
0
3
6.95
6.84
17.5
17.2
NewYor
360610134
Annual
No
0
3
9.70
9.51
21.6
21.2
NewYor
360610079
Annual
No
0
3
8.42
8.26
22.8
22.5
NewYor
360470122
Annual
No
0
3
8.66
8.49
20.5
20.2
NewYor
360050133
Annual
No
0
3
9.05
8.87
24.0
23.6
NewYor
360050110
Annual
No
0
3
7.39
7.25
19.4
19.1
NewYor
340392003
Annual
No
0
3
8.59
8.44
23.6
23.2
NewYor
340390004
Annual
No
0
3
9.87
9.69
24.2
23.8
NewYor
340310005
Annual
No
0
3
8.42
8.28
22.2
21.9
NewYor
340292002
Annual
No
0
3
7.23
7.13
18.1
17.9
NewYor
340273001
Annual
No
0
3
6.78
6.69
17.1
16.9
NewYor
340171003
Annual
No
0
3
8.79
8.64
23.4
22.9
NewYor
340130003
Annual
No
0
3
8.89
8.73
23.8
23.4
NewYor
340030003
Annual
No
0
3
8.90
8.75
24.5
24.1
OgdenC
490110004
24-hr
Yes
0
15
7.28
6.89
32.6
30.3
OgdenC
490570002
24-hr
No
0
15
8.99
8.39
-
-
OgdenC
490030003
24-hr
No
0
15
6.35
6.02
-
-
October 2021
C-97
Draft - Do Not Quote or Cite
-------
CBSAa
Site
Controlling
Standard
Controlling
Site?
NOx &
S02
Reduction
(%)b
Primary
PM2.5
Reduction
(%)c
Base
Annual
DV
(ug m-3)
Projected
Annual
DV
(ug m-3)
Base 24-
hr DV
(H9 m"3)
Projected
24-hr DV
(H9 m"3)
Philad
420450002
Annual
Yes
0
20
11.46
9.99
26.0
22.9
Philad
421010057
Annual
No
0
20
10.86
9.56
27.0
23.4
Philad
421010055
Annual
No
0
20
11.43
9.94
27.5
24.2
Philad
421010048
Annual
No
0
20
10.27
9.00
25.6
22.7
Philad
420290100
Annual
No
0
20
9.64
8.66
23.9
21.2
Philad
340150004
Annual
No
0
20
8.33
7.43
20.6
18.2
Philad
340071007
Annual
No
0
20
8.84
7.86
21.0
18.8
Philad
340070002
Annual
No
0
20
10.19
9.11
23.5
20.6
Philad
240150003
Annual
No
0
20
8.70
7.90
22.6
20.5
Philad
100031012
Annual
No
0
20
9.04
8.15
23.0
21.1
Pittsb
420030064
Annual
Yes
0
44
12.82
10.04
35.8
26.2
Pittsb
421290008
Annual
No
0
44
8.65
6.96
19.6
16.9
Pittsb
421255001
Annual
No
0
44
8.35
6.78
17.8
15.7
Pittsb
421250200
Annual
No
0
44
8.95
7.22
19.3
15.7
Pittsb
421250005
Annual
No
0
44
11.02
8.85
22.7
18.0
Pittsb
420070014
Annual
No
0
44
10.11
7.98
21.9
17.5
Pittsb
420050001
Annual
No
0
44
11.03
8.58
21.9
17.8
Pittsb
420031301
Annual
No
0
44
11.00
8.64
24.8
18.7
Pittsb
420031008
Annual
No
0
44
9.78
7.68
20.5
16.1
Pittsb
420030008
Annual
No
0
44
9.50
7.30
20.5
16.3
Prinev
410130100
24-hr
Yes
0
33
8.60
7.19
37.6
30.4
ProvoO
490494001
24-hr
Yes
0
3
7.74
7.65
30.9
30.4
ProvoO
490495010
24-hr
No
0
3
6.73
6.65
-
-
ProvoO
490490002
24-hr
No
0
3
7.41
7.32
28.9
28.4
Rivers
060658005
24-hr
Yes
0
58
14.48
9.69
43.2
30.4
Rivers
060658001
24-hr
No
0
58
-
-
36.5
25.4
Sacram
060670006
24-hr
Yes
0
6
9.31
9.02
31.4
30.4
Sacram
061131003
24-hr
No
0
6
6.62
6.47
15.8
15.4
Sacram
060670012
24-hr
No
0
6
7.30
7.11
19.8
19.4
Sacram
060670010
24-hr
No
0
6
8.67
8.41
26.5
25.7
Sacram
060610006
24-hr
No
0
6
7.58
7.34
20.3
19.9
Sacram
060610003
24-hr
No
0
6
6.71
6.56
19.3
19.0
SaltLa
490353010
24-hr
Yes
0
85
-
-
41.5
30.4
SaltLa
490353006
24-hr
No
0
85
7.62
4.85
36.8
23.8
SaltLa
490351001
24-hr
No
0
85
7.07
4.72
32.1
21.0
SanLui
060792007
Annual
Yes
0
22
10.70
10.04
25.9
24.9
SanLui
060798002
Annual
No
0
22
5.71
5.42
-
-
SanLui
060792004
Annual
No
0
22
8.25
7.76
19.8
19.2
October 2021
C-98
Draft - Do Not Quote or Cite
-------
CBSAa
Site
Controlling
Standard
Controlling
Site?
NOx &
S02
Reduction
(%)b
Primary
PM2.5
Reduction
(%)c
Base
Annual
DV
(ug m-3)
Projected
Annual
DV
(ug m-3)
Base 24-
hr DV
(H9 m"3)
Projected
24-hr DV
(H9 m"3)
SouthB
181410015
24-hr
Yes
0
18
10.45
9.72
32.5
30.3
St. Lou
290990019
Annual
Yes
0
2
10.12
10.02
22.8
22.7
St. Lou
295100094
Annual
No
0
2
9.57
9.48
23.3
23.2
St. Lou
295100093
Annual
No
0
2
-
-
23.7
23.5
St. Lou
295100085
Annual
No
0
2
10.10
10.00
23.6
23.4
St. Lou
295100007
Annual
No
0
2
9.78
9.69
23.7
23.6
St. Lou
291893001
Annual
No
0
2
9.85
9.76
22.4
22.3
Stockt
060771002
24-hr
Yes
0
43
12.23
9.86
38.7
30.3
Stockt
060772010
24-hr
No
0
43
10.74
8.75
37.3
29.6
Visali
061072002
24-hr
Yes
58
74
16.23
9.67
54.0
30.4
Weirto
390810017
Annual
Yes
0
33
11.75
10.00
27.2
22.6
Weirto
540090011
Annual
No
0
33
9.75
8.42
22.8
19.8
Weirto
540090005
Annual
No
0
33
10.52
9.07
22.4
19.8
Weirto
390810021
Annual
No
0
33
9.29
8.06
22.2
19.3
Wheeli
540511002
Annual
Yes
0
5
10.24
10.03
22.5
22.1
Wheeli
540690010
Annual
No
0
5
9.61
9.42
19.7
19.4
a CBSA names are the first six characters of the full CBSAs names in Table C-3.
b Percent reduction in NOx and SO2 emissions associated with just meeting the standard in this case.
c Percent reduction in Primary PM2.5 emissions associated with just meeting the standard in this case.
1
October 2021
C-99
Draft - Do Not Quote or Cite
-------
1 Table C-22. PM2.5 DVs for the Secondary PM projection case and 10/30 standard level.
CBSAa
Site
Controlling
Standard
Controlling
Site?
NOx &
S02
Reduction
(%)b
Primary
PM2.5
Reduction
(%)c
Base
Annual
DV
(ug m 3)
Projected
Annual
DV
(ug m-3)
Base 24-
hr DV
(H9 m"3)
Projected
24-hr DV
(H9 m"3)
AkronO
391530017
Annual
Yes
45
0
10.99
10.04
23.7
20.8
AkronO
391530023
Annual
No
45
0
9.16
8.24
20.2
17.7
Altoon
420130801
Annual
Yes
N/A
N/A
10.11
10.04
23.8
23.6
Atlant
131210039
Annual
Yes
N/A
N/A
10.38
10.04
19.7
19.1
Atlant
132230003
Annual
No
N/A
N/A
7.82
7.56
16.2
15.7
Atlant
131350002
Annual
No
N/A
N/A
8.84
8.55
17.9
17.3
Atlant
130890002
Annual
No
N/A
N/A
9.34
9.03
19.2
18.6
Atlant
130670003
Annual
No
N/A
N/A
9.51
9.20
18.6
18.0
Atlant
130630091
Annual
No
N/A
N/A
9.86
9.54
19.1
18.5
Bakers
060290010
24-hr
Yes
N/A
N/A
16.52
8.99
70.0
30.4
Bakers
060290016
24-hr
No
N/A
N/A
18.45
10.04
61.3
26.6
Bakers
060290015
24-hr
No
N/A
N/A
5.15
2.80
15.8
6.9
Bakers
060290014
24-hr
No
N/A
N/A
16.53
9.00
61.4
26.7
Bakers
060290011
24-hr
No
N/A
N/A
6.06
3.30
19.6
8.5
Birmin
010732059
Annual
Yes
71
0
11.25
10.04
22.3
20.2
Birmin
010732003
Annual
No
71
0
10.08
8.86
19.0
16.1
Birmin
010731010
Annual
No
71
0
9.78
8.39
19.2
16.6
Birmin
010730023
Annual
No
71
0
10.94
9.72
22.8
20.3
Canton
391510017
Annual
Yes
36
0
10.81
10.04
23.7
21.7
Canton
391510020
Annual
No
36
0
9.91
9.13
22.0
19.4
Chicag
170313103
Annual
Yes
N/A
N/A
11.10
10.04
22.6
20.4
Chicag
550590019
Annual
No
N/A
N/A
8.04
7.27
20.4
18.5
Chicag
181270024
Annual
No
N/A
N/A
9.51
8.60
22.4
20.3
Chicag
180892004
Annual
No
N/A
N/A
9.84
8.90
24.7
22.3
Chicag
180890031
Annual
No
N/A
N/A
10.12
9.15
23.6
21.3
Chicag
180890026
Annual
No
N/A
N/A
-
-
25.2
22.8
Chicag
180890022
Annual
No
N/A
N/A
-
-
22.7
20.5
Chicag
180890006
Annual
No
N/A
N/A
10.03
9.07
23.1
20.9
Chicag
171971011
Annual
No
N/A
N/A
8.36
7.56
18.4
16.6
Chicag
171971002
Annual
No
N/A
N/A
7.69
6.96
20.0
18.1
Chicag
170890007
Annual
No
N/A
N/A
8.94
8.09
19.2
17.4
Chicag
170890003
Annual
No
N/A
N/A
-
-
19.2
17.4
Chicag
170434002
Annual
No
N/A
N/A
8.87
8.02
19.9
18.0
Chicag
170316005
Annual
No
N/A
N/A
10.79
9.76
24.1
21.8
Chicag
170314201
Annual
No
N/A
N/A
9.00
8.14
21.4
19.4
Chicag
170314007
Annual
No
N/A
N/A
9.49
8.58
-
-
Chicag
170313301
Annual
No
N/A
N/A
10.37
9.38
23.5
21.3
October 2021
C-100
Draft - Do Not Quote or Cite
-------
CBSAa
Site
Controlling
Standard
Controlling
Site?
NOx &
S02
Reduction
(%)b
Primary
PM2.5
Reduction
(%)c
Base
Annual
DV
(ug m-3)
Projected
Annual
DV
(ug m-3)
Base 24-
hr DV
(H9 m"3)
Projected
24-hr DV
(H9 m"3)
Chicag
170310076
Annual
No
N/A
N/A
10.18
9.21
22.5
20.4
Chicag
170310057
Annual
No
N/A
N/A
11.03
9.98
26.8
24.2
Chicag
170310052
Annual
No
N/A
N/A
10.00
9.05
23.3
21.1
Chicag
170310022
Annual
No
N/A
N/A
10.38
9.39
22.4
20.3
Chicag
170310001
Annual
No
N/A
N/A
10.13
9.16
21.7
19.6
Cincin
390610014
Annual
Yes
28
0
10.70
10.03
22.9
21.2
Cincin
390610042
Annual
No
28
0
10.29
9.61
22.6
20.8
Cincin
390610040
Annual
No
28
0
9.45
8.78
21.0
19.0
Cincin
390610010
Annual
No
28
0
9.43
8.78
21.3
19.6
Cincin
390610006
Annual
No
28
0
9.46
8.82
20.3
18.4
Cincin
390170020
Annual
No
28
0
-
-
24.2
22.5
Cincin
390170019
Annual
No
28
0
10.24
9.66
22.0
20.6
Cincin
390170016
Annual
No
28
0
9.79
9.16
22.1
20.1
Cincin
210373002
Annual
No
28
0
9.06
8.38
20.9
18.9
Clevel
390350065
Annual
Yes
79
0
12.17
10.04
24.9
20.5
Clevel
391030004
Annual
No
79
0
8.73
6.75
19.6
13.9
Clevel
390933002
Annual
No
79
0
8.10
6.28
20.2
13.8
Clevel
390850007
Annual
No
79
0
7.88
6.10
17.4
12.9
Clevel
390351002
Annual
No
79
0
8.86
6.81
19.5
14.4
Clevel
390350045
Annual
No
79
0
10.61
8.50
22.9
17.0
Clevel
390350038
Annual
No
79
0
11.38
9.33
25.0
19.7
Clevel
390350034
Annual
No
79
0
8.87
6.90
20.4
15.4
Detroi
261630033
Annual
Yes
60
0
11.30
10.03
26.8
24.3
Detroi
261630039
Annual
No
60
0
9.11
7.82
22.3
18.8
Detroi
261630036
Annual
No
60
0
8.68
7.43
21.8
19.1
Detroi
261630025
Annual
No
60
0
8.98
7.63
24.1
19.1
Detroi
261630019
Annual
No
60
0
9.18
7.83
22.4
20.3
Detroi
261630016
Annual
No
60
0
9.62
8.33
24.4
21.3
Detroi
261630015
Annual
No
60
0
11.19
9.88
25.5
22.0
Detroi
261630001
Annual
No
60
0
9.50
8.26
23.3
20.1
Detroi
261470005
Annual
No
60
0
8.89
7.81
24.3
20.6
Detroi
261250001
Annual
No
60
0
8.86
7.49
24.2
20.5
Detroi
260990009
Annual
No
60
0
8.80
7.57
26.2
21.8
ElCent
060250005
Annual
Yes
N/A
N/A
12.63
10.04
33.5
26.6
ElCent
060251003
Annual
No
N/A
N/A
7.44
5.91
19.8
15.7
ElCent
060250007
Annual
No
N/A
N/A
8.37
6.65
21.5
17.1
Elkhar
180390008
Annual
Yes
N/A
N/A
10.24
10.04
28.6
28.0
Evansv
181630023
Annual
Yes
3
0
10.11
10.03
21.5
21.2
October 2021
C-101
Draft - Do Not Quote or Cite
-------
CBSAa
Site
Controlling
Standard
Controlling
Site?
NOx &
S02
Reduction
(%)b
Primary
PM2.5
Reduction
(%)c
Base
Annual
DV
(ug m-3)
Projected
Annual
DV
(ug m-3)
Base 24-
hr DV
(H9 m"3)
Projected
24-hr DV
(H9 m"3)
Evansv
211010014
Annual
No
3
0
9.64
9.56
20.7
20.3
Evansv
181630021
Annual
No
3
0
9.84
9.76
21.6
21.2
Evansv
181630016
Annual
No
3
0
10.02
9.95
22.0
21.7
Fresno
060190011
24-hr
Yes
N/A
N/A
14.07
9.48
53.8
30.4
Fresno
060195025
24-hr
No
N/A
N/A
13.63
9.18
47.9
27.1
Fresno
060195001
24-hr
No
N/A
N/A
14.08
9.49
49.3
27.9
Fresno
060192009
24-hr
No
N/A
N/A
8.47
5.71
31.3
17.7
Hanfor
060310004
24-hr
Yes
N/A
N/A
21.98
9.28
72.0
30.4
Hanfor
060311004
24-hr
No
N/A
N/A
16.49
6.96
58.9
24.9
Housto
482011035
Annual
Yes
84
0
11.19
10.04
22.4
19.6
Housto
482011039
Annual
No
84
0
9.22
8.09
21.7
18.7
Housto
482010058
Annual
No
84
0
9.67
8.57
22.3
19.1
Housto
481671034
Annual
No
84
0
7.36
6.29
20.3
17.8
Indian
180970087
Annual
Yes
48
0
11.44
10.03
25.9
21.8
Indian
180970083
Annual
No
48
0
11.06
9.64
23.9
21.4
Indian
180970081
Annual
No
48
0
11.07
9.66
25.0
20.8
Indian
180970078
Annual
No
48
0
10.14
8.73
24.4
19.9
Indian
180970043
Annual
No
48
0
-
-
26.0
20.9
Indian
180950011
Annual
No
48
0
9.05
7.86
21.8
18.3
Indian
180570007
Annual
No
48
0
9.02
7.75
21.4
17.8
Johnst
420210011
Annual
Yes
31
0
10.68
10.04
25.8
25.1
Lancas
420710012
Annual
Yes
98
0
12.83
10.01
32.7
26.2
Lancas
420710007
Annual
No
98
0
10.57
7.81
29.8
23.4
LasVeg
320030561
Annual
Yes
N/A
N/A
10.28
10.04
24.5
23.9
LasVeg
320032002
Annual
No
N/A
N/A
9.79
9.56
19.8
19.3
LasVeg
320031019
Annual
No
N/A
N/A
5.18
5.06
11.5
11.2
LasVeg
320030540
Annual
No
N/A
N/A
8.80
8.59
21.7
21.2
Lebano
420750100
Annual
Yes
53
0
11.20
10.03
31.4
28.6
Little
051191008
Annual
Yes
11
0
10.27
10.04
21.7
21.1
Little
051190007
Annual
No
11
0
9.78
9.57
20.5
19.9
Loganll
490050007
24-hr
Yes
56
0
6.95
6.51
34.0
30.4
LosAng
060371103
Annual
Yes
N/A
N/A
12.38
10.04
32.8
26.6
LosAng
060592022
Annual
No
N/A
N/A
7.48
6.07
15.3
12.4
LosAng
060590007
Annual
No
N/A
N/A
9.63
7.81
-
-
LosAng
060374004
Annual
No
N/A
N/A
10.25
8.31
27.3
22.1
LosAng
060374002
Annual
No
N/A
N/A
11.06
8.97
29.2
23.7
LosAng
060371602
Annual
No
N/A
N/A
11.86
9.62
32.3
26.2
LosAng
060371302
Annual
No
N/A
N/A
11.99
9.72
31.5
25.5
October 2021
C-102
Draft - Do Not Quote or Cite
-------
CBSAa
Site
Controlling
Standard
Controlling
Site?
NOx &
S02
Reduction
(%)b
Primary
PM2.5
Reduction
(%)c
Base
Annual
DV
(ug m-3)
Projected
Annual
DV
(ug m-3)
Base 24-
hr DV
(H9 m"3)
Projected
24-hr DV
(H9 m"3)
LosAng
060371201
Annual
No
N/A
N/A
9.46
7.67
25.6
20.8
LosAng
060370002
Annual
No
N/A
N/A
10.52
8.53
29.2
23.7
Louisv
180190006
Annual
Yes
24
0
10.64
10.02
23.9
22.0
Louisv
211110075
Annual
No
24
0
10.42
9.83
22.3
20.3
Louisv
211110067
Annual
No
24
0
9.55
8.96
21.4
19.9
Louisv
211110051
Annual
No
24
0
10.29
9.68
21.8
20.2
Louisv
211110043
Annual
No
24
0
10.37
9.77
22.0
20.2
Louisv
180431004
Annual
No
24
0
9.96
9.37
22.0
20.4
Louisv
180190008
Annual
No
24
0
8.72
8.13
20.1
18.3
MaconG
130210007
Annual
Yes
N/A
N/A
10.13
10.04
21.2
21.0
MaconG
130210012
Annual
No
N/A
N/A
7.68
7.61
16.6
16.5
Madera
060392010
24-hr
Yes
N/A
N/A
13.30
10.04
45.1
30.4
McAile
482150043
Annual
Yes
N/A
N/A
10.09
10.04
25.0
24.9
Merced
060472510
24-hr
Yes
68
0
11.68
9.74
39.8
30.4
Merced
060470003
24-hr
No
68
0
11.81
9.82
39.0
29.8
Modest
060990006
24-hr
Yes
N/A
N/A
13.02
9.75
45.7
30.4
Modest
060990005
24-hr
No
N/A
N/A
-
-
38.8
25.8
NapaCA
060550003
Annual
Yes
N/A
N/A
10.36
10.04
25.1
24.3
NewYor
360610128
Annual
Yes
N/A
N/A
10.20
10.04
23.9
23.5
NewYor
361030002
Annual
No
N/A
N/A
7.18
7.07
18.8
18.5
NewYor
360810124
Annual
No
N/A
N/A
7.52
7.40
19.5
19.2
NewYor
360710002
Annual
No
N/A
N/A
6.95
6.84
17.5
17.2
NewYor
360610134
Annual
No
N/A
N/A
9.70
9.55
21.6
21.3
NewYor
360610079
Annual
No
N/A
N/A
8.42
8.29
22.8
22.4
NewYor
360470122
Annual
No
N/A
N/A
8.66
8.52
20.5
20.2
NewYor
360050133
Annual
No
N/A
N/A
9.05
8.91
24.0
23.6
NewYor
360050110
Annual
No
N/A
N/A
7.39
7.27
19.4
19.1
NewYor
340392003
Annual
No
N/A
N/A
8.59
8.46
23.6
23.2
NewYor
340390004
Annual
No
N/A
N/A
9.87
9.72
24.2
23.8
NewYor
340310005
Annual
No
N/A
N/A
8.42
8.29
22.2
21.9
NewYor
340292002
Annual
No
N/A
N/A
7.23
7.12
18.1
17.8
NewYor
340273001
Annual
No
N/A
N/A
6.78
6.67
17.1
16.8
NewYor
340171003
Annual
No
N/A
N/A
8.79
8.65
23.4
23.0
NewYor
340130003
Annual
No
N/A
N/A
8.89
8.75
23.8
23.4
NewYor
340030003
Annual
No
N/A
N/A
8.90
8.76
24.5
24.1
OgdenC
490110004
24-hr
Yes
29
0
7.28
7.01
32.6
30.4
OgdenC
490570002
24-hr
No
29
0
8.99
8.71
-
-
OgdenC
490030003
24-hr
No
29
0
6.35
6.10
-
-
October 2021
C-103
Draft - Do Not Quote or Cite
-------
CBSAa
Site
Controlling
Standard
Controlling
Site?
NOx &
S02
Reduction
(%)b
Primary
PM2.5
Reduction
(%)c
Base
Annual
DV
(ug m-3)
Projected
Annual
DV
(ug m-3)
Base 24-
hr DV
(H9 m"3)
Projected
24-hr DV
(H9 m"3)
Philad
420450002
Annual
Yes
86
0
11.46
10.04
26.0
22.3
Philad
421010057
Annual
No
86
0
10.86
9.12
27.0
22.5
Philad
421010055
Annual
No
86
0
11.43
9.95
27.5
23.9
Philad
421010048
Annual
No
86
0
10.27
8.70
25.6
21.1
Philad
420290100
Annual
No
86
0
9.64
7.87
23.9
19.5
Philad
340150004
Annual
No
86
0
8.33
6.99
20.6
16.9
Philad
340071007
Annual
No
86
0
8.84
7.23
21.0
17.1
Philad
340070002
Annual
No
86
0
10.19
8.40
23.5
20.2
Philad
240150003
Annual
No
86
0
8.70
6.90
22.6
17.5
Philad
100031012
Annual
No
86
0
9.04
7.21
23.0
17.7
Pittsb
420030064
24-hr
Yes
100
0
12.82
9.22
35.8
30.4
Pittsb
421290008
24-hr
No
100
0
8.65
6.04
19.6
12.9
Pittsb
421255001
24-hr
No
100
0
8.35
5.90
17.8
11.1
Pittsb
421250200
24-hr
No
100
0
8.95
6.10
19.3
13.7
Pittsb
421250005
24-hr
No
100
0
11.02
7.78
22.7
18.1
Pittsb
420070014
24-hr
No
100
0
10.11
7.38
21.9
15.2
Pittsb
420050001
24-hr
No
100
0
11.03
8.39
21.9
15.5
Pittsb
420031301
24-hr
No
100
0
11.00
7.79
24.8
19.7
Pittsb
420031008
24-hr
No
100
0
9.78
7.11
20.5
14.7
Pittsb
420030008
24-hr
No
100
0
9.50
6.81
20.5
14.2
Prinev
410130100
24-hr
Yes
N/A
N/A
8.60
6.95
37.6
30.4
ProvoO
490494001
24-hr
Yes
6
0
7.74
7.68
30.9
30.4
ProvoO
490495010
24-hr
No
6
0
6.73
6.68
-
-
ProvoO
490490002
24-hr
No
6
0
7.41
7.36
28.9
28.4
Rivers
060658005
Annual
Yes
N/A
N/A
14.48
10.04
43.2
30.0
Rivers
060658001
Annual
No
N/A
N/A
-
-
36.5
25.3
Sacram
060670006
24-hr
Yes
18
0
9.31
9.11
31.4
30.4
Sacram
061131003
24-hr
No
18
0
6.62
6.50
15.8
15.1
Sacram
060670012
24-hr
No
18
0
7.30
7.17
19.8
19.3
Sacram
060670010
24-hr
No
18
0
8.67
8.50
26.5
25.5
Sacram
060610006
24-hr
No
18
0
7.58
7.45
20.3
19.9
Sacram
060610003
24-hr
No
18
0
6.71
6.63
19.3
18.9
SaltLa
490353010
24-hr
Yes
79
0
-
-
41.5
30.3
SaltLa
490353006
24-hr
No
79
0
7.62
6.46
36.8
29.3
SaltLa
490351001
24-hr
No
79
0
7.07
5.88
32.1
23.2
SanLui
060792007
Annual
Yes
N/A
N/A
10.70
10.04
25.9
24.3
SanLui
060798002
Annual
No
N/A
N/A
5.71
5.36
-
-
SanLui
060792004
Annual
No
N/A
N/A
8.25
7.74
19.8
18.6
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CBSAa
Site
Controlling
Standard
Controlling
Site?
NOx &
S02
Reduction
(%)b
Primary
PM2.5
Reduction
(%)c
Base
Annual
DV
(ug m-3)
Projected
Annual
DV
(ug m-3)
Base 24-
hr DV
(H9 m"3)
Projected
24-hr DV
(H9 m"3)
SouthB
181410015
24-hr
Yes
30
0
10.45
9.68
32.5
30.4
St. Lou
290990019
Annual
Yes
N/A
N/A
10.12
10.04
22.8
22.6
St. Lou
295100094
Annual
No
N/A
N/A
9.57
9.49
23.3
23.1
St. Lou
295100093
Annual
No
N/A
N/A
-
-
23.7
23.5
St. Lou
295100085
Annual
No
N/A
N/A
10.10
10.02
23.6
23.4
St. Lou
295100007
Annual
No
N/A
N/A
9.78
9.70
23.7
23.5
St. Lou
291893001
Annual
No
N/A
N/A
9.85
9.77
22.4
22.2
Stockt
060771002
Annual
Yes
97
0
12.23
10.04
38.7
29.7
Stockt
060772010
Annual
No
97
0
10.74
8.69
37.3
28.4
Visali
061072002
24-hr
Yes
N/A
N/A
16.23
9.14
54.0
30.4
Weirto
390810017
Annual
Yes
62
0
11.75
10.02
27.2
23.8
Weirto
540090011
Annual
No
62
0
9.75
8.14
22.8
19.9
Weirto
540090005
Annual
No
62
0
10.52
8.82
22.4
18.8
Weirto
390810021
Annual
No
62
0
9.29
7.68
22.2
18.5
Wheeli
540511002
Annual
Yes
N/A
N/A
10.24
10.04
22.5
22.1
Wheeli
540690010
Annual
No
N/A
N/A
9.61
9.42
19.7
19.3
a CBSA names are the first six characters of the full CBSAs names in Table C-3.
b Percent reduction in NOx and SO2 emissions associated with just meeting the standard in this case; N/A indicates 'not
applicable' where proportional projection was used.
c Percent reduction in Primary PM2.5 emissions associated with just meeting the standard in this case; N/A indicates 'not
applicable' where proportional projection was used.
1
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APPENDIX D. QUANTITATIVE ANALYSES FOR
VISIBILITY IMPAIRMENT
D.l BACKGROUND
To inform the EPA's decision in the 2012 review on the adequacy of protection provided
by the secondary PM standards the EPA conducted a technical analysis of the relationships
between a 3-year average daily visibility metric and the 24-hour PM2.5 mass-based standard
(Kelly et al., 2012). The 3-year visibility metric was calculated as the 3-year average of the 90th
percentile of daily visibility index values.1 Light extinction coefficient (bext) values for the
visibility index were calculated using the original IMPROVE equation (Equation D-l in section
D.2.2 below), which at the time of the 2012 review, the EPA considered to be better suited to
urban sites that were the focus of the analysis than other versions of the IMPROVE equation,
with a few modifications to the equation: excluding the coarse mass2 and sea salt3 terms in the
equation and using a multiplier of 1.6 for converting OC to OM.4
1 The visibility index is a logarithmic transformation of the light extinction coefficient, tw, the use of which ensures
that increases or decreases in light extinction coefficient always produce, respectively, increases or decreases in
visibility index (Kelly et al., 2012).
2 PM2 5 is the size fraction of PM responsible for most of the visibility impairment in urban areas (U.S. EPA, 2009,
section 9.2.2.2). Data available at the time of the 2012 review suggested that, generally, PM10-2.5 was a minor
contributor to visibility impairment most of the time (U.S. EPA, 2010) although the coarse fraction may be a
major contributor in some areas in the desert southwestern region of the country. Moreover, at the time of the
2012 review, there were few data available from continuous PMi0-2.5 monitors to quantify the contribution of
coarse PM to calculated light extinction.
3 In estimating light extinction in the 2012 review, the EPA did not consider it appropriate to include the term for
hygroscopic sea salt in evaluating urban light extinction, given that sea salt is not a major contributor to light
extinction in urban areas compared with more remote coastal locations. In particular, Pitchford (2010) estimated
that the contribution of sea salt to PM2 5 light extinction was generally well below 5% for PM2 5 light extinction
greater than 24 dv (U.S. EPA, 2010, p. 3-22; U.S. EPA, 2012, p. IV-5).
4 At the time of the 2012 review, the EPA considered the multiplier of 1.8 recommended by Pitchford et al. (2007)
to convert OC to OM for use in the revised IMPROVE equation (Equation D-2 below) to be too high for urban
environments. The composition of, and the mix of emission sources contributing to, PM2 5 differ between urban
and remote areas, and consequently, the light extinction may differ between urban and remote areas. Organic
mass in urban areas is often from local and regional sources and would have a greater percentage of fresh
emissions compared with aged emissions, which tend to be more prominent in rural areas, and a different PM
mass to OC ratio than in urban areas. The EPA also considered the multiplier of 1.4 used with the original
IMPROVE equation to be too low to adequately account for the contribution of OM to visibility impairment,
particularly in urban areas where OM concentrations tend to be higher. Based on these considerations, along with
an evaluation of the OC to OM relationship at CSN sites (2011 PA, Appendix F, section F.6), the EPA chose to
use a multiplier of 1.6 to convert OC to OM in the light extinction calculations used in the 2012 review (U.S.
EPA, 2012, pages IV-5-IV-8).
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Using 2008-2010 air quality data for 102 CSN network sites,5 the 2012 analysis explored
the relationship between the 3-year design values for the existing 24-hour PM2.5 standard and
values of the 3-year visibility metric.6 The analysis indicated that increases in 24-hour PM2.5
design values generally correspond to increases in the 3-year visibility metric values, and vice-
versa (78 FR 3201, January 15, 2013). The analysis also found linear correlations between the
24-hour PM2.5 design values and the 3-year visibility metric with an average r2 value of 0.75
across all of the sites (Kelly et al., 2012). A key implication of this analysis was that for the level
proposed by the EPA for a visibility index-based standard, the 24-hour PM2.5 standard of 35
|ig/m3 would be controlling in almost all or all instances (78 FR 3202, January 15, 2013).
D.2 ANALYSIS: METHODS AND INPUTS
Consistent with the analyses conducted in the 2012 review described above and the 2020
review described in the 2020 PA ({U.S. EPA, 2020 #285}, section 5.2.1.2), we have conducted
analyses examining the relationship between PM mass concentrations and estimated light
extinction in terms of a PM visibility metric. These analyses are intended to inform our
understanding of visibility impairment in the U.S. under recent air quality conditions,
particularly those conditions that meet the current standards, and our understanding of the
relative influence of various factors on light extinction. These analyses were conducted using
three versions of the IMPROVE equation (Equations D-l through D-3 below) to estimate light
extinction to better understand the influence of variability in inputs across the three equations.
This analysis included 60 monitoring sites that are geographically distributed across the U.S. in
both urban and rural areas (see Figure D-l). The data set is comprised of sites with data for the
2017-2019 period that supported a valid 24-hour PM2.5 design value7 and met strict criteria for
PM species. Light extinction calculations at these 60 monitoring sites also included the coarse
fraction in the IMPROVE equations.8 Results for these analyses are presented in Figures 5-3 and
5-4 and discussed in section 5.2.1.2 of Chapter 5 and presented in Table D-7 and Figure D-2 in
section D.3 below.
5 The 102 sites included in the Kelly et al. (2012) analysis were those sites that met the data completeness criteria
used for that analysis (Kelly et al., 2012, p. 15).
6 The EPA used monthly average relative humidity values rather than shorter-term (e.g., hourly) values to estimate
light extinction in the 2012 review in order to capture seasonal variability of relative humidity and its effects on
visibility impairment. This was intended to focus more on the underlying aerosol contributions to visibility
impairment and less on the day-to-day variations in humidity (U.S. EPA, 2012, p. IV-10).
7 The design value (DV) for the standard is the metric used to determine whether areas meet or exceed the NAAQS.
A design value is a statistic that describes the air quality status of a given area relative to the NAAQS.
8 In the 2020 analyses, PM10 data were available for only a subset of 20 of the 67 monitoring sites included in the
analysis ({U.S. EPA, 2020 #285}, section 5.2.1.2).
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• Alaska (n=l)
sites violating NAAQS
Figure D-l. Locations of monitoring sites with data for 2017-2019 with a valid PM2.5 design
value and meeting completeness criteria for PM species.
D.2.1 Data Sources for Inputs to Estimate Light Extinction
D.2.1.1 Relative Humidity
Relative humidity data were downloaded from the North American Regional Reanalysis
(NARR). NARR is the National Centers for Environmental Prediction's (NCEP) high resolution
combined model and assimilated meteorological dataset. NARR is an extension of the NCEP
Global Reanalysis which is run over North American using the Eta Model (32 km) together with
the Regional Data Assimilation System. Files for 3-hour average 10 m relative humidity data for
2017-2019 are available at https://esrl.noaa.gov/psd/data/gridded/data.narr.html.
Using NARR latitudes, relative humidity data were reassigned to each grid cell from
coordinated universal time (UTC) to their closest time zone and the 3-hour relative humidity data
were then averaged to 24-hour local time averages in order to approximate the 24-hour averaging
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time (midnight-midnight) of the daily PM2.5 measurements. The PM2.5 and PM2.5 component
daily mass data (described in subsequent sections) were temporally and spatially matched with
the closest 24-hour average relative humidity grid cell.
D.2.1.2 PM2.5 Concentrations
The raw data for PM2.5 site-level daily mass concentrations came from an Air Quality
System (AQS)9 query of the daily site-level concentrations. Data files used were for 24-hour
average values from regulatory monitors for all sites in the U.S. for all available days (including
potential exceptional events) for 2017-2019. When a single site had multiple monitors, the
previously-determined primary monitor concentration was used. If the primary monitor value
was missing, the average of the collocated monitors was used. These data were screened so that
all days either had a valid filter-based 24-hour concentration measurement10 or at least 18 valid
hourly concentrations measurements.
D.2.1.3 Coarse PM Concentrations
The raw data for PM10-2.5 monitor-level daily mass concentrations came from an AQS
query of the daily monitor-level concentrations. Data files used were for 24-hour average
concentrations from monitors mainly in the Interagency Monitoring of Protected Visual
Environments (IMPROVE) network and NCore Multipollutant Monitoring Network. Data were
included for sites with >11 valid days for each quarter of 2017-2019.
D.2.1.4 PM2.5 Component Concentrations
The raw data for PM2.5 component concentrations for the components listed in Table D-l
came from an AQS query of the daily monitor-level concentrations. Data files used were for
filter-based, 24-hour average concentrations from monitors in the Interagency Monitoring of
Protected Visual Environments (IMPROVE) network, Chemical Speciation Network (CSN), and
NCore Multipollutant Monitoring Network. Data were included for days with valid data for all
chemical components listed in Table D-l below and for sites with >11 valid days for each
quarter of 2017-2019.
9 The Air Quality System is an EPA database of ambient air quality monitoring data (https://www.epa. gov/aasi.
10 A valid filter-based 24-hour concentration measurement is one collected via FRM, and that has undergone
laboratory equilibration (at least 24 hours at standardized conditions of 20-23°C and 30-40% relative humidity)
prior to analysis (see Appendix L of 40 CFR Part 50 for the 2012 NAAQS for PM).
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23
24
25
Table D-l. PM2.5 components from AQS used in IMPROVE equations.
PM2.5 Component Drawn from AQS
AQS Parameter Code
Sulfate
88403
Nitrate
88306
OC (TORs)
88320, 88370
EC (TORs)
88321,88380
Aluminum (Al), Silica (Si), Calcium (Ca), Iron
(Fe), Titanium (Ti)
88104 (Al), 88165 (Si), 88111 (Ca), 88126
(Fe), 88161 (Ti)
Chloride, Chlorine
88115 (Chlorine), 88203 (Chloride)
a OC and EC values are based on the thermal optical reflectance (TOR) analytical method,
which replaced the NIOSH 5040-like thermal optical transmittance (TOT) method in the CSN
network after 2009 (Spada and Hyslop, 2018).
D.2.1.5 24-Hour PM2.5 Design Values
Files for 24-hour PM2.5 design values for 2017-2019 are located at
https://www.epa.gov/air-trends/air-qualitv-design-values. Data handling of the 2017-2019 PM2.5
design values is described in Appendix N of 40 CFR Part 50 for the 2012 National Ambient Air
Quality Standards (NAAQS) for Particulate Matter (PM).
D.2.1.6 24-Hour PM10 Design Values
Files for 24-hour PM10 design values for 2017-2019 are located at
https://www.epa.gov/air-trends/air-qualitv-design-values. Data handling of the 2017-2019 PM10
design values is described in Appendix K of 40 CFR Part 50.
D.2.1.7 Annual PM2.5 Design Values
Files for annual PM2.5 design values for 2017-2019 are located at
https://www.epa.gov/air-trends/air-qualitv-design-values. Data handling of the 2017-2019 PM2.5
design values is described in Appendix N of 40 CFR Part 50 for the 2012 National Ambient Air
Quality Standards (NAAQS) for Particulate Matter (PM).
D.2.2 Calculating Light Extinction for Visibility Impairment Analyses
For all days with a valid relative humidity value, PM2.5 mass concentration, and all
chemical components listed in Table D-l, daily light extinction was calculated using three
versions of the IMPROVE equation, as shown below. Formulas for derivation of the equation
variables from the AQS parameters are presented in Table D-6.
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Original IMPROVE Equation (Malm et al., 1994):
bext = 3f(RH)([AS] + [AN]) + 4 [OM] + 10 [EC] + 1[FS] + 0.6 [CM] + 10
Equation D-l
where:
[AS] is concentration in |ig/m3 of ammonium sulfate,
[AN] is concentration in |ig/m3 of ammonium nitrate,
[OM] is concentration in |ig/m3 of organic matter,
[EC] is concentration in |ig/m3 of elemental carbon,
[FS] is concentration in |ig/m3 of fine soil,
[CM] is concentrations in |ig/m3 of coarse mass, and
f(RH) is the relative-humidity-dependent water growth function, assigned values as shown
in Table D-2:
Table D-2. Relatively-humidity-dependent water growth function for use in the original
IMPROVE equation.
RH (%)
1-36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
f(RH)
1
1.02
1.04
1.06
1.08
1.1
1.13
1.15
1.18
1.2
1.23
1.26
1.28
1.31
1.34
1.37
1.41
1.44
1.47
1.51
1.54
RH (%)
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
f(RH)
1.58
1.62
1.66
1.7
1.74
1.79
1.83
1.88
1.93
1.98
2.03
2.08
2.14
2.19
2.25
2.31
2.37
2.43
2.5
2.56
2.63
RH (%)
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98a
f(RH)
2.7
2.78
2.86
2.94
3.03
3.12
3.22
3.33
3.45
3.58
3.74
3.93
4.16
4.45
4.84
5.37
6.16
7.4
9.59
14.1
26.4
Note: See fRHOriainallMPROVE.csv file from http://vista.cira.colostate.edu/lmprove/the-improve-alaorithm/ (Maim et al., 1994).
a For our application, any relative humidity values greater than 98% were assigned the f(RH) value associated with 98%, the highest
value available for the relative humidity function.
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The various coefficients are the empirically derived extinction efficiency (mass scattering and
absorption) coefficients, as originally specified by Malm et al. (1994).
Revised IMPROVE Equation (Pitchford et al., 2007):
bext = 2.2fs(RH)[small sulfate] + 4.8/L(RH)[large sulfate] + 2.4fs(RH)[small nitrate]
+ 5.IfL(RH) [large nitrate] + 2.8[small OM] + 6.1 [large OM] + 10 [EC]
+ 1[FS] + 1.7 fss(RH)[SS] + 0.6 [CM] + 10
Equation D-2
where:
[small sulfate], [large sulfate], [small nitrate], [large nitrate], [small OM] and [large OM]
are defined as follows in Table D-3:
Table D-3. Values for use in the revised IMPROVE equation for small and large sulfate,
nitrate, and organic matter concentrations.
If [ ] >20
If [ ] <20
Large sulfate
[AS1
[AS1+20
Small sulfate
0
[AS1 - (fASl+20)
Large nitrate
[ANl
[AN1+20
Small nitrate
0
[ANl - (fANl-20)
Large OM
fOMl
[OM1+20
Small OM
0
[OM1 - ([OM1+20)
Note: [AS], [AN] and [OM] are defined as for Equation D-1.
[SS] is sea salt; and,
fss(RH), fs(RH), and fiXRH) are defined as shown in Table D-4:
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1 Table D-4. Relatively-humidity-dependent water growth function for sea salt, small
2 particles, and large particles for use in the revised IMPROVE equation.
RH (%)
1-36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
fss(RH)
1
1
1
1
1
1
1
1
1
1
1
2.3584
2.3799
2.4204
2.4488
fs(RH)
1
1.38
1.4
1.42
1.44
1.46
1.48
1.49
1.51
1.53
1.55
1.57
1.59
1.62
1.64
fi_(RH)
1
1.31
1.32
1.34
1.35
1.36
1.38
1.39
1.41
1.42
1.44
1.45
1.47
1.49
1.5
RH (%)
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
fss(RH)
2.4848
2.5006
2.5052
2.5279
2.5614
2.5848
2.5888
2.616
2.6581
2.6866
2.7341
2.7834
2.8272
2.8287
2.8594
fs(RH)
1.66
1.68
1.71
1.73
1.76
1.78
1.81
1.83
1.86
1.89
1.92
1.95
1.99
2.02
2.06
fi_(RH)
1.52
1.54
1.55
1.57
1.59
1.61
1.63
1.65
1.67
1.69
1.71
1.73
1.75
1.78
1.8
RH (%)
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
fss(RH)
2.8943
2.9105
2.9451
3.0105
3.0485
3.1269
3.1729
3.2055
3.2459
3.2673
3.3478
3.4174
3.5202
3.5744
3.6329
fs(RH)
2.09
2.13
2.17
2.22
2.26
2.31
2.36
2.41
2.47
2.54
2.6
2.67
2.75
2.84
2.93
fi_(RH)
1.83
1.86
1.89
1.92
1.95
1.98
2.01
2.05
2.09
2.13
2.18
2.22
2.27
2.33
2.39
RH (%)
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95 *
fss(RH)
3.6905
3.808
3.9505
4.0398
4.1127
4.2824
4.494
4.6078
4.8573
5.1165
5.3844
5.7457
6.1704
6.7178
7.3492
fs(RH)
3.03
3.15
3.27
3.42
3.58
3.76
3.98
4.23
4.53
4.9
5.35
5.93
6.71
7.78
9.34
fi_(RH)
2.45
2.52
2.6
2.69
2.79
2.9
3.02
3.16
3.33
3.53
3.77
4.06
4.43
4.92
5.57
Note: See fRHRevisedlMPROVE.csv file from htto://vista.cira.colostate.edu/lmprove/the-improve-alaorithm/ (Pitchford et al„
2007).
a For our application, any relative humidity values greater than 95% were assigned the f(RH) value associated with 95%, the
highest value available for the relative humidity function.
3
4 and
5 [EC], [FS] and [CM] are defined as for Equation D-l.
6 This equation is generally dividing PM components into small and large particle sizes11 with
7 separate mass scattering efficiencies and hygroscopic growth functions for each size (included in
8 the equation as fs(RH) for small particles, fiXRH) for large particles, and fss(RH) for sea salt).
9
11 The large mode for sulfate, nitrate, and OM represents aged and/or cloud processed particles, whereas the small
mode represents freshly formed particles. These size modes are described by log-normal mass size distributions
with geometric mean diameters and geometric standard deviations of 0.2 |im and 2.2 for small mode and 0.5 |im
and 1.5 for the large mode, respectively.
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Lowenthal and Kumar (2016) Equation:
bext = 2.2 fs(RH) [small sulfate] + 4.8/L(RH)[large sulfate] + 2.4fs(RH)[small nitrate]
+ 5.IfL(RH) [large nitrate] + 2.8fs(RH)0M [small OM]
+ 6.lfL(RH)0M [large OM] + 10 [EC] + 1[F5] + 1.7fss(RH) [55] + 0.6[CM]
+ 10
Equation D-3
where:
fs(RH)oM and fL(RH)oM are the relative-humidity-dependent water growth function for small and
large organic matter, respectively, as defined in Table D-5 below.
Table D-5. Relatively-humidity-dependent water growth function for small organic matter
and large organic matter for use in the original IMPROVE equation.
RH (%)
0-29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
fs(RH)oM
1.000
1.321
1.325
1.329
1.333
1.337
1.340
1.343
1.346
1.349
1.352
1.354
1.356
1.358
1.360
1.362
1.364
fL(RH)oM
1.000
1.267
1.271
1.274
1.278
1.280
1.283
1.286
1.288
1.290
1.292
1.294
1.296
1.297
1.299
1.300
1.302
RH (%)
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
fs(RH)oM
1.366
1.368
1.369
1.371
1.373
1.75
1.377
1.379
1.382
1.384
1.387
1.390
1.393
1.397
1.400
1.404
1.409
fs(RH)oM
1.303
1.305
1.306
1.308
1.309
1.311
1.306
1.308
1.309
1.311
1.313
1.314
1.316
1.318
1.320
1.323
1.325
RH (°-o)
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
fs(RH)oM
1.413
1.419
1.424
1.430
1.437
1.444
1.452
1.460
1.469
1.478
1.489
1.500
1.511
1.524
1.537
1.51
1.566
fs(RH)oM
1.328
1.331
1.334
1.338
1.342
1.346
1.350
1.355
1.385
1.393
1.401
1.409
1.418
1.428
1.438
1.449
1.461
RH (%)
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95 a
fs(RH)oM
1.582
1.599
1.617
1.637
1.657
1.679
1.703
1.727
1.754
1.782
1.812
1.843
1.877
1.912
1.950
1.989
fs(RH)oM
1.473
1.486
1.500
1.515
1.531
1.548
1.566
1.585
1.605
1.626
1.648
1.672
1.696
1.722
1.750
1.779
Note: See Table 1 in Lowenthal and Kumar (2016).
a For our application, any relative humidity values greater than 95% were assigned the f(RH) value associated with 95%, the highest
value available for the relative humidity function.
and
[small sulfate], [large sulfate], [small nitrate], [large nitrate], [small OM], [large OM], [EC],
[FS], [SS], [CM], fs(RH), fiXRH) and fss(RH) are defined as above for Equation D-2.
This equation updates the multiplier for estimating the concentration organic matter, [OM], from
the concentration of organic carbon to 2.1 and incorporates fs(RH)oM and fL(RH)oM representing
water absorption by soluble organic matter as a function of relative humidity for small and large
organic matter, respectively.
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18
Based on each equation, site-specific visibility metrics were derived for each site as
follows. Daily light extinction values were derived for 2017, 2018, and 2019, the 90th percentile
of daily values for each year was calculated, and the three years of values were averaged. The 3-
year averages of the 90th percentiles of daily light extinction values were paired with the 2017-
2019 PM2.5 24-hour design values for each site having valid data for both statistics.
Table D-6. Derivation of equation variables from AQS PM2.5 component concentrations.
Equation Variable
How Calculated from AQS Parameter Values
Ammonium Sulfate
All three equations: 1.375x[Sulfate]A
Ammonium Nitrate
All three equations: 1.29x[Nitrate]B
Organic Matter
Original IMPROVE equation: 1.6x[OC]c
Revised IMPROVE equation: 1.6x[OC]c
Lowenthal and Kumar (2016) equation: 2.1x[OC]
Elemental Carbon
[ECl
Fine Soil
All three equations:D
2.2x[All+2.49x[Sil+1,63x[Cal+2.42x[Fel+1,94x[Til
Sea Salt
Revised IMPROVE and Lowenthal and Kumar, 2016 equations:0
1.8x[Chloride]
1.8x[Chlorinej (if chloride is missing)
A This formula is based on molar molecular weights of ammonium sulfate and sulfate (Malm et al., 1994).
B This formula is based on molar molecular weights of ammonium nitrate and nitrate (Malm et al., 1994).
c See footnote 4 earlier in this appendix.
D This formula is documented in Malm et al. (1994).
D.3 SUMMARY OF RESULTS
Results for the visibility impairment analyses are discussed in section 5.2.1.2 of Chapter
5. Table D-7 presents the 24-hour PM2.5 design values, 24-hour PM10 design values, annual
PM2.5 design values, and 3-year visibility metrics based on light extinction calculations using the
three versions of the IMPROVE equation with the coarse mass fraction included in the analyses.
Figure 5-3 and 5-4 in Chapter 5 show a comparison of the 3-year visibility metric and the 24-
hour PM2.5 design values for the 60 monitoring sites in the analyses where light extinction was
calculated using the original IMPROVE equation12 and the Lowenthal and Kumar IMPROVE
equation. Figure D-2 below presents the 3-year visibility metric and the 24-hour PM2.5 design
values for the 60 monitoring sites with light extinction calculated using the revised IMPROVE
equation.13
12 For this analysis, the original IMPROVE equation in Equation D-l was modified to use a 1.6 multiplier to convert
OC to OM from the light extinction calculation, consistent with the modifications in the 2012 and 2020t review.
13 For this analysis, the revised IMPROVE equation in Equation D-2 was modified to use a 1.6 multiplier to convert
OC to OM, consistent with the modifications in the 2012 and 2020 reviews.
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1 Table D-7. Summary of 24-hour PM2.5, 24-hour PM10, and annual PM2.5 design values, and 3-year visibility metrics at 60
2 monitoring sites (2017-2019).
Monitor ID
State
Region
24-hour PM2.5
Design Value
(Hg/m3)A
24-hour PM10
Design Value
(number of
exceedances)BC
Annual
PM25
Design
Value
(Hg/m3)D
3-year Visibility Metric (deciviews)E
Original
IMPROVE
Equation F
Revised
IMPROVE
Equation G
Lowenthal &
Kumar
IMPROVE
Equation
010730023
Alabama
Southeast
21
0
10.0
23
23
26
020900034
Alaska
Alaska
40
1.4
8.9
24
25
27
040139997
Arizona
Southwest
21
0.7
7.4
21
21
24
051190007
Arkansas
Southeast
19
0
9.3
21
21
24
060270002
California
Northwest
23
3
5.6
13
14
15
060190011
California
SoCal
56
1
14.1
29
27
32
060371103
California
SoCal
31
11.9
26
25
28
060658001
California
SoCal
31
0
12.1
26
25
28
060670006
California
Northwest
37
4.1
10.2
25
25
30
060731022
California
SoCal
19
0
9.3
21
21
24
060850005
California
Northwest
43
0
10.5
22
22
26
090050005
Connecticut
Northeast
12
4.1
15
16
18
090090027
Connecticut
Northeast
18
0
6.9
23
23
26
110010043
District Of
Columbia
Northeast
20
0
8.9
23
23
25
120573002
Florida
Southeast
18
7.9
18
19
21
130890002
Georgia
Southeast
19
0
8.4
20
20
24
160010010
Idaho
Northwest
29
7.4
23
22
26
170191001
Illinois
IndustrialMidwest
18
7.8
22
22
23
180970078
Indiana
IndustrialMidwest
20
0
9.0
24
24
26
181630021
Indiana
IndustrialMidwest
17
0
8.2
22
22
24
191370002
Iowa
UpperMidwest
16
6.6
21
22
22
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191630015
Iowa
IndustrialMidwest
20
0
8.0
23
23
25
191770006
Iowa
UpperMidwest
16
0
7.0
21
22
23
201950001
Kansas
UpperMidwest
14
5.0
16
17
18
202090021
Kansas
UpperMidwest
26
9.4
23
23
26
220330009
Louisiana
Southeast
21
0
00
CO
22
23
25
230090103
Maine
Northeast
11
0
3.2
16
18
18
240230002
Maryland
IndustrialMidwest
13
5.7
16
17
18
240330030
Maryland
Northeast
15
0
6.7
19
20
23
250250042
Massachusetts
Northeast
18
7.4
19
20
21
261630001
Michigan
IndustrialMidwest
22
00
CO
23
23
25
270031002
Minnesota
UpperMidwest
20
0
7.3
23
23
25
270750005
Minnesota
IndustrialMidwest
13
3.8
16
16
17
280490020
Mississippi
Southeast
17
9.1
20
20
24
295100085
Missouri
IndustrialMidwest
21
8.7
24
24
26
300490004
Montana
Northwest
23
3.9
19
18
22
330115001
New Hampshire
Northeast
10
3.0
13
14
15
330150018
New Hampshire
Northeast
12
4.9
17
17
19
340010006
New Jersey
Northeast
15
6.6
18
19
20
340130003
New Jersey
Northeast
20
0
8.4
23
23
25
350010023
New Mexico
Southwest
15
0
5.6
16
17
19
360810124
New York
Northeast
18
0
7.0
21
22
24
371190041
North Carolina
Southeast
16
8.1
19
20
23
371830014
North Carolina
Southeast
13
0
7.7
19
19
23
380070002
North Dakota
UpperMidwest
15
3.9
18
18
20
380130004
North Dakota
UpperMidwest
16
0
3.6
20
20
21
390350060
Ohio
IndustrialMidwest
24
0
9.9
25
25
27
390610040
Ohio
IndustrialMidwest
20
0
9.4
22
23
24
391351001
Ohio
IndustrialMidwest
18
8.1
22
22
23
420030008
Pennsylvania
IndustrialMidwest
20
9.1
23
23
25
460330132
South Dakota
UpperMidwest
14
0
3.8
13
14
15
460710001
South Dakota
UpperMidwest
14
0
4.1
14
15
17
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490353006
Utah
Northwest
30
7.5
26
26
28
500070007
Vermont
Northeast
12
0
4.3
15
16
17
510870014
Virginia
Northeast
15
0
7.1
19
20
23
530330080
Washington
Northwest
26
6.3
21
22
24
540390020
West Virginia
IndustrialMidwest
15
7.9
21
21
24
550270001
Wisconsin
IndustrialMidwest
21
0
7.0
24
24
26
550410007
Wisconsin
IndustrialMidwest
15
4.7
19
19
21
560210100
Wyoming
Northwest
11
0
3.2
13
14
15
A The 24-hour PM2.5 design value is the 3-year average of the 98th percentile of daily PM2.5 mass concentrations. The current 24-hour PM2.5 NAAQS is set at a level of 35
|jg/m3.
B The 24-hour PM10 design value is not to be exceeded more than once per year on average over three years. The current 24-hour PM10 NAAQS is set at a level of 150
|jg/m3.
c For some monitoring locations, PM10 design values are not available because of a lack of collocated PM10 monitoring at the site or insufficient data after applying
completeness criteria for calculating PM10 design values.
D The annual PM2.5 design value is the annual mean, averaged over three years. The current secondary annual PM2.5 NAAQS is set at a level of 15.0 |jg/m3.
E The 3-year visibility metric is the 3-year average of the 90th percentile of daily light extinction. In the 2012 and 2020 reviews, the target level of protection identified for the 3-
year visibility metric was 30 deciviews.
FThe original IMPROVE equation in Equation D-1 was modified to use a 1.6 multiplier to convert OC to OM from the light extinction calculation, consistent with the
modifications in the 2012 and 2020 reviews.
G The revised IMPROVE equation in Equation D-2 was modified to use a 1.6 multiplier to convert OC to OM, consistent with the modifications in the 2012 and 2020 reviews.
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40 i
35-
£ 30-
25-
20-
15-
10-
0
• Northeast (n = 13)
• Southeast (n = 8)
• IndustMidwest (n = 15)
• UpperMidwest (n=9)
• Southwest (n = 2)
• Northwest (n = 8)
• SoCal (n=4)
• Alaska (n = l)
0 5 10 15 20 25 30 35 40 45 50 55 60
98th percentile of daily PM2 5 concentration,
averaged over 3 years (pg nrf3)
Figure D-2. Comparison of 90th percentile of daily light extinction, averaged over three
years, and 98th percentile of daily PM2.5 concentrations, averaged over three years, for
2017-2019 using the revised IMPROVE equation. (Note: Dashed lines indicate the level of
current 24-hour PM2.5 standard (35 |ig/m3) and the target level of protection identified for the
3-year visibility metric (30 dv).)
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2
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9
10
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21
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25
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27
28
29
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31
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33
34
35
36
37
38
REFERENCES
Abt Associates, Inc. (2001). Assessing public opinions on visibility impairment due to air
pollution: Summary report. U.S. Environmental Protection Agency. Research Triangle
Park, NC.
Kelly, J, Schmidt, M, Frank, N, Timin, B, Solomon, D and Venkatesh, R. (2012). Memorandum
to PM NAAQS Review Docket (EPA-HQ-OAR-2007-0492). Technical Analyses to
Support Surrogacy Policy for Proposed Secondary PM2.5 NAAQS under NSR/PSD
Programs. June 14, 2012. Docket ID No. EPA-HQ-OAR-2007-0492. Office of Air
Quality Planning and Standards Research Triangle Park, NC. Available at:
https://www3.epa.gov/ttn/naaqs/standards/pm/data/20120614Kellv.pdf.
Lowenthal, DH and Kumar, N (2016). Evaluation of the IMPROVE Equation for estimating
aerosol light extinction. Journal of the Air and Waste Management Association 66(7):
726-737.
Malm, WC, Sisler, JF, Huffman, D, Eldred, RA and Cahill, TA (1994). Spatial and seasonal
trends in particle concentration and optical extinction in the United States. Journal of
Geophysical Research 99(D1): 1347-1370.
Pitchford, M. (2010). Memorandum to PM NAAQS Review Docket (EPA-HQ-OAR-2007-
0492). Assessment of the Use of Speciated PM2.5 Mass-Calculated Light Extinction as a
Secondary PM NAAQS Indicator of Visibility. . November 17, 2010. Docket ID No.
EPA-HQ-OAR-2007-0492. Office of Air Quality Planning and Standards Research
Triangle Park, NC. Available at:
https://www3.epa.gov/ttn/naaqs/standards/pm/data/Pitchfordlll72010.pdf.
Pitchford, M, Maim, W, Schichtel, B, Kumar, N, Lowenthal, D and Hand, J (2007). Revised
algorithm for estimating light extinction from IMPROVE particle speciation data. Journal
of the Air and Waste Management Association 57(11): 1326-1336.
Spada, NJ and Hyslop, NP (2018). Comparison of elemental and organic carbon measurements
between IMPROVE and CSN before and after method transitions. Atmos Environ 178:
173-180.
U.S. EPA (2009). Integrated Science Assessment for Particulate Matter (Final Report). Office of
Research and Development, National Center for Environmental Assessment. Research
Triangle Park, NC. U.S. EPA. EPA-600/R-08-139F. December 2009. Available at:
https://cfpub.epa. gov/ncea/risk/recordisplav.cfm?deid=216546.
U.S. EPA (2010). Particulate Matter Urban-Focused Visibility Assessment (Final Document).
Office of Air Quality Planning and Standards, Health and Environmental Impacts
Division. Research Triangle Park, NC. U.S. EPA. EPA-452/R-10-004. July 2010.
Available at: https://nepis.epa.gov/Exe/ZvPURL.cgi?Dockev=P 100FQ5D.txt.
U.S. EPA (2012). Responses to Significant Comments on the 2012 Proposed Rule on the
National Ambient Air Quality Standards for Particulate Matter (June 29, 2012; 77 FR
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1 38890). Research Triangle Park, NC. U.S. EPA. Docket ID No. EPA-HQ-OAR-2007-
2 0492. Available at: https://www3.epa.gov/ttn/naaqs/standards/pm/data/20121214rtc.pdf.
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1 ATTACHMENT: SUMMARY OF VISIBILITY PREFERENCE
2 STUDIES
3
4 The preference studies available at the time of the 2012 and 2020 reviews were
5 conducted in four urban areas. Three western preference studies were available, including one in
6 Denver, Colorado (Ely et al., 1991), one in the lower Fraser River valley near Vancouver, British
7 Columbia, Canada (Pryor, 1996), and one in Phoenix, Arizona (BBC Research & Consulting,
8 2003). A pilot focus group study was also conducted for Washington, DC (Abt Associates,
9 2001), and a replicate study with 26 participants was also conducted for Washington, DC (Smith
10 and Howell, 2009).14 Study specific details for these preference studies are shown in Table D-8.
14 The replicate study with 26 participants was one test group of three included in Smith and Howell (2009). This
study also included two additional test groups to assess varying light extinction conditions using the same scene
as was used in the first test group. Study details in Table D-8 reflect all three test groups included in the study.
However, for reasons described in section 2.5.2 of U.S. EPA (2010), results from the other two test groups were
not included in the EPA's evaluation of levels of acceptable visibility impairment from the preference studies.
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1 Table D-8. Summary of visibility preference studies. (Adapted from Table 9-2 in U.S. EPA,
2 2009).
Denver, CO
Phoenix, AZ
Vancouver, British
Columbia
Washington, DC
Washington, DC
Report Date
1991
2003
1996
2001
2009
Duration of
45 minutes
50 minutes
2 hours
session
Compensation
None
$50
None
$50
None
# focus group
16a
27 b
4
1
3 tests
sessions
# participants
214
385
180
9
64
Age range
Adults
18-65+
University students
27-58
Adults
Annual or
Wintertime
Annual
Summertime
Annual
Annual
seasonal
# and type of
scene
Single scene of
downtown
Single scene of
downtown
Single scene from
each of two suburbs in
Single scene of
Potomac River,
Single scene of
DC Mall and
presented
Denver with the
Phoenix with the
the lower Fraser River
Washington Mall
downtown, 8 km
mountains in the
Estrella
valley - Chilliwack and
and downtown
maximum sight
south in the
Mountains in the
Abbotsfordc
Washington, DC,
background
background, 42
km max. distance
8 km max. sight
# total visibility
20 conditions (+
21 conditions (+
20 conditions (10 from
20 conditions (+
22 conditions
conditions
presented
5 duplicates)
4 duplicates)
each city)
5 duplicates)
Source of
Actual photos
Win Haze
Actual photos taken at
Win Haze
WinHaze
slides
taken between
9am and 3pm
1pm or 4pm
Medium of
presentation
Slide projection
Slide projection
Slide projection
Slide projection
Slide projection
Ranking scale
used
7 point scale
7 point scale
7 point scale
7 point scale
7 point scale
Visibility range
presented (dv)
11-40
15-35
Chilliwack: 13-25
Abbotsford: 13.5-31.5
9-38
9-45
Health issue
directions
Ignore potential
health impacts;
visibility only
Judge solely on
visibility, do not
consider health
Judge solely on
visibility, do not
consider health
Health never
mentioned,
"Focus only on
visibility"
Health never
mentioned,
"Focus only on
visibility"
Key questions
•Rank VAQ (1-7
•Rank VAQ (1-7
•Rank VAQ (1-7
•Rank VAQ (1-7
•Rank VAQ (1-7
asked
scale)
•Is each slide
scale)
•Is each slide
scale)
•Is each slide
scale)
•Is each slide
scale)
•Is each slide
"acceptable"
"acceptable"
"acceptable"
"acceptable"
"acceptable"
•"How much
haze is too
much?"
•How many days
a year would this
picture be
"acceptable"
•If this hazy, how
many hours
would it be
acceptable (3
slides only)
•Valuation
question
Mean dv found
20.3
23-25
Chilliwack: -23
-20
O
CO
I
"acceptable"
Abbotsford: -19
(range 20-25)
a No preference data were collected at a 17th focus group session due ot a slide projector malfunction.
b The 27 focus groups were conducted in 6 neighborhood locations in Phoenix, with 3 focus groups held in Spanish.
c Chilliwack scene includes downtown buildings in the foreground with mountains in the background up to 65 km away. Abbotsford scene
has fewer manmade objects in the foreground and is primarily a more rural scene with mountains in the background up to 55 km away.
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1
2
3
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5
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7
8
9
10
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12
13
14
15
16
REFERENCES
Abt Associates, Inc. (2005). Particulate matter health risk assessment for selected urban areas:
Draft report. Research Triangle Park, NC, U.S. Environmental Protection Agency: 164.
BBC Research & Consulting (2003). Phoenix area visibility survey. Denver, CO.
Ely, DW, Leary, JT, Stewart, TR and Ross, DM (1991). The establishment of the Denver
Visibility Standard. Denver, Colorado, Colorado Department of Health.
Pryor, SC (1996). Assessing public perception of visibility for standard setting exercises. Atmos
Environ 30(15): 2705-2716.
U.S. EPA. (2009). Integrated Science Assessment for Particulate Matter (Final Report). Research
Triangle Park, NC. Office of Research and Development, National Center for
Environmental Assessment. U.S. EPA. EPA-600/R-08-139F. December 2009. Available
at: https://cfpub.epa.gov/ncea/risk/recordisplav.cfm?deid=216546.
U.S. EPA. (2010). Particulate Matter Urban-Focused Visibility Assessment (Final Document).
Research Triangle Park, NC. Office of Air Quality Planning and Standards, Health and
Environmental Impacts Division. U.S. EPA. EPA-452/R-10-004 July 2010. Available at:
https://nepis.epa. gov/Exe/ZvPURL.cgi?Dockev=P 100FQ5D.txt.
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United States Office of Air Quality Planning and Standards Publication No. EPA-452/P-21-001
Environmental Protection Health and Environmental Impacts Division October 2021
Agency Research Triangle Park, NC
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