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Policy Assessment for the Review of the
Primary National Ambient Air Quality
Standards for Oxides of Nitrogen
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EPA-452/R-17-003
April 2017
Policy Assessment for the Review of the Primary National Ambient Air Quality Standards for
Oxides of Nitrogen
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 document has been prepared by staff in the U.S. Environmental Protection Agency's
Office of Air Quality Planning and Standards (OAQPS). Conclusions do not necessarily reflect
the views of the Agency. Mention of trade names or commercial products is not intended to
constitute endorsement or recommendation for use. Questions or comments related to this
document should be addressed to Ms. Breanna Alman, U.S. Environmental Protection Agency,
Office of Air Quality Planning and Standards, C539-06, Research Triangle Park, North Carolina
27711 (email: alman.breanna@epa.gov) and Dr. Stephen Graham, U.S. Environmental
Protection Agency, Office of Air Quality Planning and Standards, C539-06, Research Triangle
Park, North Carolina 27711 (email: graham.stephen@epa.gov).
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TABLE OF CONTENTS
LIST OF FIGURES iv
LIST OF TABLES v
LIST OF ACRONYMS AND ABBREVIATIONS vi
Executive Summary ES-1
1 Introduction 1-1
1.1 PURPOSE 1-1
1.2 BACKGROUND 1-3
1.2.1 Legislative Requirements 1-3
1.2.2 Previous NO2 NAAQS Reviews 1-5
1.2.3 Current Review of the Primary NO2 NAAQS 1-8
1.3 SCOPE 01 THE CURRENT REVIEW 1-9
1.4 GENERAL APPROACH FOR REVIEW OF THE STANDARDS 1-10
1.4.1 Approach Used in the Last Review 1-11
1.4.2 General Approach for the Current Review 1-18
1.5 REFERENCES 1-20
2 NO2 air quality 2-1
2.1 N02 ATMOSPHERIC CHEMISTRY AND NOx EMISSIONS 2-1
2.1.1 Atmospheric Chemistry 2-1
2.1.2 Emissions 2-4
2.2 AMBIENT N02 MONITORING 2-7
2.2.1 N02 Methods 2-7
2.2.2 Ambient Monitoring Network 2-8
2.3 N02 MONITORING DATA TRENDS AND AIR QUALITY
RELATIONSHIPS 2-9
2.3.1 National Trends in Ambient NO2 Concentrations 2-10
2.3.2 Near-Road NO2 Air Quality 2-14
2.3.3 Relationships between Hourly and Annual NO2 Concentrations 2-21
2.3.4 Background NO2 Concentrations 2-23
2.4 REFERENCES 2-24
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3 Consideration of evidence for NCh-related health effects 3-1
3.1 WEIGHT 01 EVIDENCE IN THE ISA 3-1
3.2 EFFECTS OF SHORT-TERM NO: EXPOSURES 3-3
3.2.1 Nature of Effects 3-4
3.2.2 Consideration of NO2 Concentrations: Health Effects of Short-Term NO2
Exposures 3-11
3.3 EFFECTS OF LONG-TERM NO: EXPOSURES 3-26
3.3.1 Nature of Effects 3-26
3.3.2 Consideration of NO2 Concentrations: Health Effects of Long-Term NO2
Exposures 3-33
3.4 POTENTIAL PUBLIC HEALTH IMPLICATIONS 3-42
3.4.1 People with Asthma 3-44
3.4.2 Children 3-44
3.4.3 Older adults 3-45
3.4.4 Conclusions 3-45
3.5 REFERENCES 3-47
4 Consideration of NO2 air quality-, exposure- and risk-based information 4-1
4.1 APPROACH TO CONSIDERING POTENTIAL SUPPORT FOR UPDATED
QUANTITATIVE ANALYSES 4-2
4.2 COMPARISON OF N02 AIR QUALITY TO HEALTH-BASED
BENCHMARKS 4-5
4.2.1 Updated Analyses Comparing NO2 Air Quality with Health-Based
Benchmarks 4-5
4.3 MODEL-BASED EXPOSURE ASSESSMENT 4-21
4.4 EPIDEMIOLOGY-BASED RISK ASSESSMENT 4-22
4.4.1 Short-Term Epidemiologic-Based Risk Assessment 4-22
4.4.2 Long-Term Epidemiologic-Based Risk Assessment 4-24
4.5 REFERENCES 4-34
5 Conclusions on the adequacy of the current primary NO2 standards 5-1
5.1 EVIDENCE-BASED CONSIDERATIONS 5-2
5.2 AIR QUALITY-, EXPOSURE- AND RISK-BASED CONSIDERATIONS . 5-12
5.3 CASAC ADVICE 5-15
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5.4 STAFF CONCLUSION ON THE ADEQUACY OF THE CURRENT
STANDARDS 5-16
5.5 AREAS FOR FUTURE RESEARCH AND DATA COLLECTION 5-18
5.6 REFERENCES 5-23
APPENDICES
Appendix A: NO2 Air Quality A-l
Appendix B: Comparisons of NO2 Air Quality and Health-Based Benchmarks B-l
111
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LIST OF FIGURES
Figure 1-1. Overview of the Approach to Reviewing the Primary NO2 NAAQS 1-19
Figure 2-1 Schematic diagram of the cycle of reactive, oxidized nitrogen species in the
atmosphere 2-2
Figure 2-2. U.S. national average NOx emissions from 1980 to 2016 2-5
Figure 2-3. Major source sectors of NOx emissions in the U.S. from the 1980 and 2014 National
Emissions Inventories 2-6
Figure 2-4. Distributions of NO2 design values across the U.S. from 1980- 2015 2-11
Figure 2-5. Trend directions of NO2 design values for 1980-2015 at U.S. sampling sites 2-13
Figure 2-6. Distributions by decade of NO2 design values for six different bins of distances from
major roads in CBS As 2-15
Figure 2-7. Distributions of the near-road and non-near-road maximum 1-hr daily NO2
concentrations from 2013 2-17
Figure 2-8. Distributions of the near-road and non-near-road maximum 1-hr daily NO2
concentrations from 2014 2-18
Figure 2-9. Distributions of the near-road and non-near-road maximum 1-hr daily NO2
concentrations from 2015 2-19
Figure 2-10. Distributions of the available near-road and non-near-road maximum 1-hr daily
NO2 concentration data from 2016 2-20
Figure 2-11. Relationships between annual and hour DVs from 1980 to 2015 2-22
Figure 3-1. U.S. and Canadian epidemiologic studies of short-term NO2 exposures and asthma
hospital admissions and emergency department visits 3-24
Figure 3-2. U.S. and Canadian epidemiologic studies of long-term NO2 exposures and asthma
incidence 3-38
Figure 4-1. Risk characterization models employed in NAAQS Reviews 4-2
Figure 4-2. Key considerations for updated quantitative analyses 4-4
IV
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LIST OF TABLES
Table 1-1. Primary NO2 NAAQS since 1971 1-5
Table 2-1. Select Programs and Rules that have contributed to NOx reductions over time 2-5
Table 3-1. ISA causal determinations for health effects related to short-term nitrogen dioxide
(NO2) exposures 3-5
Table 3-2. Resting exposures to nitrogen dioxide and airway responsiveness in individuals with
asthma 3-13
Table 3-3. Exercising exposures to nitrogen dioxide and airway responsiveness in individuals
with asthma 3-14
Table 3-4. Causal determinations for long-term nitrogen dioxide (NO2) exposure and health
effects evaluated in the ISA for Oxides of Nitrogen in the previous and current
review 3-27
Table 4-1. Average and maximum number of days per year with non-near road NO2
concentrations at or above benchmarks 4-11
Table 4-2. Number of days in 2014 and 2015 with near-road NO2 concentrations at or above
benchmarks 4-13
Table 4-3. Factors to Consider in Conducting a Risk Assessment 4-28
V
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LIST OF ACRONYMS AND ABBREVIATIONS
AADT
Annual Average Daily Traffic
Act
Clean Air Act
AQI
Air Quality Index
AQS
Air Quality System
AR
Airway responsiveness
ATS
American Thoracic Society
AT SDR
Agency for Toxic Substances and Disease Registry
BC
Black carbon
CAA
Clean Air Act
CASAC
Clean Air Scientific Advisory Committee
CBS A
Core-based statistical area
CDC
Centers for Disease Control
CFR
Code of Federal Regulations
CHS
Children's Health Study
CI
Confidence interval
CO
Carbon monoxide
COPD
Chronic obstructive pulmonary disease
DV
Design value
EC
Elemental carbon
ED
Emergency department
EGU
Electric power generating unit
eNO
Exhaled nitric oxide
EPA
Environmental Protection Agency
FEM
Federal Equivalent Method
FEVi
Forced Expiratory Volume for 1 second
FR
Federal Register
FRM
Federal Reference Method
HA
Hospital Admission
HNO3
Nitric acid
HNO4
Peroxynitric acid
HONO
Nitrous acid
HPMS
Highway Performance Monitoring System
IDW
Inverse distance weighted
IgE
Immunoglobulin E
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IRP Integrated Review Plan
ISA Integrated Science Assessment
LUR Land use regression
m meter
Max maximum
MSA Metropolitan Statistical Area
NAAQS National ambient air quality standards
NAB North American background
NCEA National Center for Environmental Assessment
NEI National Emissions Inventory
NO Nitric oxide
NO2 Nitrogen Dioxide
NO3" Nitrate radicals
NOx Nitrogen Oxides
O3 Ozone
OAR Office of Air and Radiation
OAQPS Office of Air Quality Planning and Standards
OC Organic carbon
OMB Office of Management and Budget
ORD Office of Research and Development
PA Policy Assessment
PD Provocative dose
PAMS Photochemical Assessment Monitoring Stations
PANs Peroxyacetyl nitrates
ppb Parts per billion
PM Particulate matter
PM2.5 Particles generally less than or equal to 2.5 |im in diameter
PM10 Particles generally less than or equal to 10 micrometers (|im) in diameter
REA Risk and Exposure Assessment
RTP Research Triangle Park
SLAMS State and Local Monitoring Stations
SO2 Sulfur Dioxide
sRAW Specific airways resistance
Th2 Type 2 T helper cell
TBD To be determined
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U.S. United States
VOCs Volatile Organic Compounds
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EXECUTIVE SUMMARY
This Policy Assessment (PA) has been prepared by staff in the Environmental Protection
Agency's (EPA) Office of Air Quality Planning and Standards (OAQPS) as part of the Agency's
ongoing review of the primary (health-based) national ambient air quality standards (NAAQS)
for oxides of nitrogen (referred to herein as the NO2 NAAQS). It presents analyses and staff
conclusions regarding the policy implications of the key scientific and technical information that
informs this review. The PA is intended to "bridge the gap" between the relevant scientific
evidence and technical information and the judgments required of the EPA Administrator in
determining whether to retain or revise the current standards. Development of the PA is also
intended to facilitate advice and recommendations on the standards 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).
Staffs conclusions in this PA are informed by consideration of the scientific evidence
summarized and assessed in the Integrated Science Assessment for Oxides of Nitrogen - Health
Criteria (ISA) and updated analyses comparing ambient nitrogen dioxide (NO2) concentrations to
health-based benchmarks, included herein. Emphasis is given to considering the extent to which
the evidence newly available since the last review alters conclusions drawn in the last review
with regard to health effects related to NO2 exposures, the exposure concentrations at which
those effects occur, and populations that may be at increased risk for effects.
The overarching questions in this review, as in other NAAQS reviews, focus on the
support provided by the available scientific and technical information for the adequacy of the
current standards, and on the extent to which that scientific and technical information supports
consideration of potential alternative standards. The analyses presented in this PA to address
such questions lead to the staff conclusion that it is appropriate to consider retaining the current
primary NO2 standards, without revision, in this review. Accordingly, staff have not identified
potential alternative standards for consideration. Advice and recommendations from CASAC,
based on its review of the draft PA, and input from the public on the draft PA, have informed
staff conclusions and the presentation of information in this final document.
History of the Primary NO2 NAAQS
The NO2 NAAQS was initially promulgated in 1971. At that time, the Administrator set a
standard with an annual averaging time and a level of 53 ppb to protect against respiratory
disease in children that had been reported in the available studies. In subsequent reviews of the
NO2 NAAQS, completed in 1985 and 1996, the annual standard was retained without revision.
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The last review of the primary NO2 NAAQS was completed in 2010. In that review, the
EPA supplemented the existing annual NO2 standard by establishing a new 1-hour standard.
After considering an integrative synthesis of the body of evidence on human health effects
related to NO2 exposures and the available information from quantitative assessments of NO2
exposures and health risks, the Administrator determined that the annual standard alone was not
sufficient to protect the public health from the array of effects that could occur following short-
term exposures to ambient NO2. To increase protection against such exposures, the 1-hour NO2
standard was set with a level of 100 ppb, based on the 3-year average of the 98th percentile of
the annual distribution of daily maximum 1-hour concentrations. The EPA also retained the
existing annual NO2 standard with its level of 53 ppb.
In that review, the Administrator particularly noted the potential for adverse health
effects to occur following exposures to elevated NO2 concentrations that can occur around major
roadways. Accordingly, the revisions to the primary NO2 NAAQS in 2010 were accompanied by
revisions to the ambient air monitoring and reporting requirements. States were required to locate
monitors within 50 meters of heavily trafficked roadways in large urban areas and in other
locations where maximum NO2 concentrations were expected to occur. Near-road NO2 monitors
were initially required to become operational between January 1, 2014 and January 1, 2017.
Currently, there are approximately 70 near-road monitors in operation in urban areas across the
U.S., with approximately one to two years of data available from most of these monitors.
Scope and Approach in the Current Review
Consistent with the review completed in 2010, this review focuses on health effects
associated with gaseous oxides of nitrogen and the protection afforded by the current primary
NO2 standards. The gaseous oxides of nitrogen include NO2 and nitric oxide (NO), together
referred to as NOx, and their gaseous reaction products. Health effects and non-ecological
welfare effects associated with particulate species (e.g., nitrates) are addressed in the review of
the NAAQS for particulate matter (PM). Additionally, the EPA is separately reviewing the
ecological welfare effects associated with oxides of nitrogen, oxides of sulfur, and PM, and the
protection provided by the secondary NO2, SO2 and PM standards.
Staffs approach to reviewing the primary NO2 NAAQS in the current review is focused
on addressing a series of key policy-relevant questions. Consideration of these questions is
intended to inform the Administrator's decisions as to whether, and if so, how, to revise the
current primary NO2 standards. In addressing these questions in this PA, we are mindful that the
Administrator's ultimate judgments on the primary standards will most appropriately reflect an
interpretation of the available scientific evidence and information that neither overstates nor
understates the strengths and limitations of that evidence and information. This approach is
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consistent with the requirements of sections 108 and 109 of the CAA, as well as with how the
EPA and the courts have historically interpreted the CAA.
Characterization of NOx Emissions Sources and Trends in Ambient NO2 Concentrations
As was the case in previous reviews, the major sources of NOx emissions include
highway vehicles, off-highway vehicles, and fuel combustion from utilities and other sources.
Estimates indicate a 61% reduction in NOx emissions across all source categories since 1980,
and emissions are expected to decrease further as existing regulatory programs continue to be
implemented. Reductions in NOx emissions over past decades have occurred largely as the result
of substantial decreases in emissions from mobile sources and from fuel combustion at utilities
and other sources. Based on recent estimates, mobile sources remain the largest source of NOx
emissions in the U.S., with highway vehicles accounting for 34% of total NOx emissions and
off-highway vehicles and engines accounting for 23% of those emissions.
Consistent with reductions in NOx emissions, ambient NO2 concentrations have declined
substantially since 1980 (i.e., by about 65% and 50% for annual and hourly concentrations,
respectively). Based on recent data, all NO2 monitors measure ambient concentrations that meet
the existing NAAQS. Analyses of historical data indicate that monitoring sites meeting the
current 1-hour NO2 standard generally have corresponding annual average NO2 concentrations
below 35 ppb. Based on ongoing reductions in NOx emissions, we anticipate that ambient NO2
concentrations will continue to decline across most of the U.S.
Because mobile sources remain the largest contributors to NOx emissions in the U.S., an
important part of the current review is the evaluation of monitoring data from recently deployed
near-road NO2 monitors. Depending on local conditions, ambient NO2 concentrations can be
higher near roadways than at sites in the same area but farther removed from the road (and from
other sources of NOx emissions). Analyses included in this PA indicate that NO2 concentrations
are generally highest at sampling sites nearest to the road and decrease as distance from the road
increases. This pattern of decreasing concentrations with increasing distance from the road has
persisted over recent decades, though the absolute difference (in terms of ppb) between NO2
concentrations close to roads and those farther from roads has declined over time.
Consistent with this analysis of historical air quality information, the limited amount of
data available from recently deployed near-road monitors indicates that daily maximum 1-hour
NO2 concentrations are generally higher at near-road monitors than at the non-near-road
monitors in the same area. This is the case in most of the CBS As with near-road monitors,
though these relationships vary across CBSAs and over the years with available data, particularly
at the upper ends of the distributions of NO2 concentrations (i.e., 98th, 99th percentiles). As more
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years of data from near-road monitors become available, we expect to gain an improved
understanding of these relationships.
Health Effects Evidence and Review of the Primary NO2 NAAQS
In this PA, we evaluate what the health effects evidence can tell us with regard to the
adequacy of the public health protection provided by the current primary NO2 NAAQS. In doing
so, we consider the strength of the evidence for various effects and the extent to which that
evidence indicates adverse effects attributable to NO2 exposures at concentrations lower than
previously identified or below the current standards.
As in the last review, the strongest evidence continues to come from studies examining
respiratory effects following short-term NO2 exposures (e.g., minutes up to one month). In
particular, the ISA concludes that "[a] causal relationship exists between short-term NO2
exposure and respiratory effects based on evidence for asthma exacerbation." (US EPA, 2016a,
p. 1-17). The strongest support for this conclusion comes from controlled human exposure
studies examining the potential for N02-induced increases in airway responsiveness (AR) (i.e., a
hallmark of asthma) in individuals with asthma. Most of these studies were available in the last
review. Together with an updated meta-analysis of their individual-level data, these studies
indicate increases in AR in some people with asthma following resting exposures to NO2
concentrations from 100 to 530 ppb. Important limitations in this evidence include the lack of an
apparent dose-response relationship between NO2 and AR and uncertainty in the adversity of the
reported increases in AR. In addition, within the range of 100 to 530 ppb, the evidence for NO2-
induced increases in AR becomes less consistent across studies that examined the lower
exposure concentrations, particularly 100 ppb.
Evidence supporting the ISA conclusion also comes from epidemiologic studies reporting
associations between short-term NO2 exposures and an array of respiratory outcomes related to
asthma exacerbation. Such studies consistently report associations with several asthma-related
outcomes, including asthma-related hospital admissions and emergency department visits in
children and adults. The epidemiologic evidence that is newly available in the current review is
consistent with evidence from the last review and does not fundamentally alter our understanding
of respiratory effects related to short-term NO2 exposures. While our fundamental understanding
of such effects has not changed, recent epidemiologic studies do reduce some uncertainty from
the last review regarding the extent to which effects may be independently related to short-term
NO2 exposures. This reduced uncertainty results from recent studies reporting health effect
associations with short-term NO2 exposures in co-pollutant models and from recent studies using
improved exposure metrics.
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In addition to the effects of short-term exposures, the ISA concludes that there is "likely
to be a causal relationship" between long-term NO2 exposures and respiratory effects, based on
the evidence for asthma development in children. The strongest evidence supporting this
conclusion comes from recent epidemiologic studies demonstrating associations between long-
term NO2 exposures and asthma incidence. Important uncertainties in these studies result from
the methods used to assign NO2 exposures, the high correlations between NO2 and other traffic-
related pollutants, and the lack of information regarding the extent to which reported effects are
independently associated with NO2 rather than the overall mixture of traffic-related pollutants.
Additional support for the ISA conclusion comes from experimental studies supporting the
biological plausibility of a potential mode of action by which NO2 exposures could cause asthma
development. These include studies that support a potential role for repeated short-term NO2
exposures in the development of asthma.
While the overall evidence for N02-related respiratory effects supports a "causal"
relationship with short-term NO2 exposures and a "likely to be causal" relationship with long-
term exposures, these studies do not provide evidence that calls into question the adequacy of the
public health protection provided by current primary NO2 NAAQS. In particular, compared to
the last review when the 1-hour standard was set, evidence from controlled human exposure
studies has not altered our understanding of the NO2 exposure concentrations that cause
increased AR. In addition, there remains uncertainty in this evidence due to the lack of an
apparent dose-response relationship and uncertainty in the adversity of the response. These
uncertainties are increasingly important for the lower NO2 exposure concentrations evaluated
(i.e., at and near 100 ppb), where the evidence across individual studies is less consistent. In
addition, while epidemiologic studies report associations with asthma-related outcomes, these
associations are generally in locations that would likely have violated one or both of the existing
standards over at least part of the study periods. In the absence of studies reporting associations
in locations meeting the current NO2 standards, there is greater uncertainty regarding the extent
to which serious asthma exacerbations (short-term exposures) or the development of asthma
(long-term exposures) are caused by the NO2 exposures that occur with air quality meeting those
standards.
Comparisons of Ambient NO2 Concentrations with Health-Based Benchmarks
Beyond our consideration of the scientific evidence, we also consider the extent to which
quantitative analyses can inform conclusions on the adequacy of the public health protection
provided by the current primary NO2 standards. In particular, we have conducted updated
analyses comparing NO2 air quality with health-based benchmarks from 100 to 300 ppb to
estimate the potential for exposures of public health concern that could be allowed by the current
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standards. Benchmarks are based on information from controlled human exposure studies
indicating NCh-induced increases in AR and on the meta-analysis of individual-level data from
these studies.
Overall, these analyses indicate little potential for exposures to ambient NO2
concentrations that would be of public health concern in locations meeting the current 1-hour
standard. In particular, based on recent ambient measurements, all of which meet the current
standards, analyses indicate almost no potential for 1-hour exposures to NO2 concentrations at or
above any of the benchmarks examined, even the lowest benchmark (i.e., 100 ppb). When air
quality is adjusted upwards to simulate just meeting the current 1-hour NO2 standard, there is
also virtually no potential for exposures to the NO2 concentrations that have been shown most
consistently to increase AR in people with asthma (i.e., greater than 200 ppb), even under worst-
case conditions across a variety of study areas with among the highest NOx emissions in the U.S.
Such NO2 concentrations are not estimated to occur, even at monitoring sites adjacent to some of
the most heavily trafficked roadways in the country. In addition, the current standard is estimated
to limit exposures to NO2 concentrations that have the potential to exacerbate asthma symptoms,
but for which the evidence across studies is less consistent (i.e., 100 ppb). The results of these
analyses, and the uncertainties inherent in their interpretation, suggest that there is little potential
for exposures to ambient NO2 concentrations that would be of public health concern in locations
meeting the current 1-hour standard.
Staff Conclusions
Staff has reached the conclusion that the available scientific evidence, in combination
with the available information from quantitative analyses, supports the adequacy of the public
health protection provided by the current primary NO2 standards. Staff further reaches the
conclusion that it is appropriate to consider retaining the current standards, without revision, in
this review. In light of this conclusion, we have not identified potential alternative standards for
consideration in this PA. In its review of the draft PA, CASAC agreed with these conclusions,
stating that it "concurs with the EPA that the current scientific literature does not support a revision
to the primary NAAQS for nitrogen dioxide".
Staff additionally notes that the final decision on the adequacy of the current standards is
a public health policy judgment to be made by the Administrator, drawing upon the scientific
information as well as judgments about how to consider the range and magnitude of uncertainties
that are inherent in this information. In this context, we recognize that the uncertainties and
limitations associated with the estimated relationships between NO2 exposures and adverse
respiratory effects are amplified with consideration of increasingly lower NO2 concentrations. In
staffs view, there is appreciable uncertainty regarding the degree to which reductions in asthma
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exacerbations or asthma development would result from alternative NO2 standards with levels
lower than those of the current standards. Thus, the basis for any consideration of alternative
lower standard levels would likely reflect different public health policy judgments as to the
appropriate approach for weighing uncertainties in the evidence.
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1 INTRODUCTION
1.1 PURPOSE
The U.S. Environmental Protection Agency (EPA) is conducting a review of the primary
national ambient air quality standards (NAAQS) for Oxides of Nitrogen (referred to herein as the
NO2NAAQS). An overview of the approach to reviewing the primary NO2 NAAQS is presented
in the Integrated Review Plan for the Primary NAAQS for Nitrogen Dioxide (IRP, U.S. EPA,
2014). The IRP discusses the key policy-relevant issues that frame the EPA's consideration of
whether the current NO2 NAAQS should be retained or revised and the planned approaches to be
taken in developing key scientific, technical, and policy documents.
As part of the current review of the primary NO2 NAAQS, staff in the EPA's Office of
Air Quality Planning and Standards (OAQPS) has prepared this Policy Assessment (PA). The
PA is intended to help bridge the gap between the relevant scientific and technical information
and the judgments required of the EPA Administrator in determining whether, and if so how, it is
appropriate to revise the NAAQS. This PA for NO2 integrates and interprets information from
the Integrated Science Assessment for Oxides of Nitrogen - Health Criteria (IS A, U. S. EPA,
2016a) and from available quantitative assessments (Chapter 4, below) to frame policy options
for consideration by the Administrator. In doing so, we recognize that the selection of a specific
approach to reaching final decisions on the primary NO2 standards will reflect the judgments of
the Administrator.
The development of 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.1 As
discussed below in section 1.2.1, the CASAC is to advise not only on the Agency's assessment
of the relevant scientific information, but also on the adequacy of the existing standards, and to
make recommendations as to any revisions of the standards that may be appropriate. The EPA
facilitates the CASAC's advice and recommendations, as well as public input, by requesting
CASAC review and public comment on one or more drafts of the PA.2 As such, the
1 Beyond informing the EPA Administrator and facilitating the advice and recommendations of CASAC and the
public, this PA is also intended to be a useful reference to all parties interested in the review of the primary NO2
NAAQS. It is intended to serve as a single source of the most policy-relevant information that informs the Agency's
review of the primary NO2 NAAQS, and it is written to be understandable to a broad audience.
2 The decision whether to prepare more than one draft of the PA is influenced by staff conclusions and associated
CASAC advice and public input. Typically, a second draft PA is prepared in cases where the available information
calls into question the adequacy of the current standard(s) and where staff analyses of potential alternative standards
are developed. In such cases, a second draft PA includes staff conclusions regarding potential alternative standards
and undergoes review by the CASAC and public comment prior to preparation of the final PA. When analyses of
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considerations and conclusions in the final PA are informed by the advice and recommendations
provided by CASAC, as well as by public input provided as part of the CASAC review process.
In this final PA, we take into account the available scientific and technical information as
assessed in the ISA (U.S. EPA, 2016a). In so doing, we focus on information that is most
relevant to evaluating the basic elements of NAAQS: indicator3, averaging time, form4, and
level. These elements, which together serve to define each standard, must be considered
collectively in evaluating the health protection afforded by the primary NO2 standards. This final
PA builds upon staffs preliminary conclusions, as presented in the draft PA (U.S. EPA, 2016b)
and in the document titled Review of the Primary National Ambient Air Quality Standards for
Nitrogen Dioxide: Risk and Exposure Assessment Planning Document (REA Planning
Document, U.S. EPA, 2015). Staffs final conclusions in this PA have been informed by the
advice received from the CASAC, based on its review of the draft PA and the REA Planning
document (Diez Roux and Frey, 2015; Diez Roux and Sheppard, 2017), and by public input on
those documents.
The remainder of this chapter summarizes the NAAQS legislative requirements and
provides an overview of the history of the NO2 NAAQS (Section 1.2), summarizes the scope of
the review (Section 1.3), and provides an overview of the approach used to reaching decisions in
the last review of the primary NO2 standard and of our planned approach to reviewing the
primary NO2 standards in the current review (Section 1.4). Following Chapter 1, this PA presents
an overview of the NO2 monitoring network and of the available information on ambient NO2
concentrations and trends (Chapter 2); staffs consideration of the available evidence for NO2-
attributable health effects and the NO2 concentrations associated with those effects (Chapter 3);
staffs consideration of quantitative analyses (Chapter 4); and staffs conclusions regarding the
adequacy of the existing primary NO2 standards (Chapter 5).
potential alternative standards are not undertaken, as is the case in this review of the primary NO2 NAAQS (see
Chapter 4 below), a second draft PA may not be warranted. For this PA, CASAC advised that, with its
recommended revisions, another CASAC review of the draft PA would not be needed (Diez Roux and Sheppard,
2017). Staff similarly determined that a second draft PA is not warranted.
3The "indicator" of a standard defines the chemical species or mixture that is to be measured in determining whether
an area attains the standard.
4The "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.
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1.2 BACKGROUND
1.2.1 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 that are listed. The
Administrator is to list those air pollutants that, "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... [the Administrator] plans to issue air quality criteria..." 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). 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. Section 109(b)(1) defines a
primary standard as one "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 A secondary standard, as defined in section 109(b)(2), must "specify
a level of air quality the attainment and maintenance of which, in the judgment of the
Administrator, based on such criteria, is requisite to protect the public welfare from any known
or anticipated adverse effects associated with the presence of such air pollutant in the ambient
air."6 The secondary NO2 standard is being reviewed separately.7
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, e.g., State of
Mississippi v. EPA, 744 F. 3d 1334, 1353 (D.C. Cir. 2012); Lead Industries Association v. EPA,
5The 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)].
6Effects on welfare as defined in section 302(h) (42 U.S.C. 7602(h)) 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."
7 https://www.epa.gov/naaqs/nitrogen-dioxide-no2-and-sulfur-dioxide-so2-secondary-air-quality-standards
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647 F.2d 1130, 1154 (D.C. Cir 1980), cert, denied, 449 U.S. 1042 (1980); American Petroleum
Institute v. Costle, 665 F.2d 1176, 1186 (D.C. Cir. 1981), cert, denied, 455 U.S. 1034 (1982).
Both types of uncertainties are 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 provide 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 Association v. EPA, 647 F.2d at 1156 n. 51, 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 population(s) at
risk, and the kind and degree of the uncertainties that must be addressed. The selection of any
particular approach to providing an adequate margin of safety is a policy choice left specifically
to the Administrator's judgment. Lead Industries Association v. EPA, 647 F.2d at 1161-62; State
of Mississippi, 744 F. 3d at 1353.
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 for these purposes. In so doing, the EPA
may not consider the costs of implementing the standards. See generally, Whitman v. America
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 at 1185.
Section 109(d)(1) requires that "not later than December 31, 1980, and at 5-year intervals
thereafter, the Administrator shall complete a thorough review of the criteria published under
section 108 and the national ambient air quality standards . . . and shall make such revisions in
such criteria and standards and promulgate such new standards as may be appropriate ..."
Section 109(d)(2) requires that an 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 . . . ." This independent review function is now
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performed by the Clean Air Scientific Advisory Committee (CASAC) of EPA's Science
Advisory Board.8
1.2.2 Previous NO2 NAAQS Reviews
In 1971, the EPA added nitrogen oxides to the list of criteria pollutants under section
108(a)(1) of the CAA and issued the initial air quality criteria (36 FR 1515, January 30, 1971;
U.S. EPA, 1971). Based on these air quality criteria, the EPA promulgated NAAQS for nitrogen
oxides using NO2 as the indicator (36 FR 8186, April 30, 1971). Both primary and secondary
standards were set at 53 parts per billion (ppb),9 annual average. Since then, the Agency has
completed multiple reviews of the air quality criteria and primary NO2 standards, as summarized
in Table 1-1.
Table 1-1. Primary NO2 NAAQS since 1971.
Final
Rule/Decision
Indicator
Averaging
Time
Level
Form
1971
36 FR 8186
NO2
Annual
53 ppb10
Annual arithmetic average
April 30, 1971
1985
50 FR 25532
June 19, 1985
Primary NO2
standards retained, without revision.
1996
61 FR 52852
Primary NO2 standards retained, without revision.
October 8, 1996
2010
75 FR 6474
February 9, 2010
NO2
1 -hour
100 ppb
3-year average of the 98lh
percentile of the annual
distribution of daily
maximum 1-hour
concentrations
3rimary annual NO2 standard retained, without revision.
8 Lists of CASAC members and of members of the CASAC N02 Review Panel are available at:
http://yosemite.epa.gov/sab/sabpeople.nsf/WebExternalCommitteeRosters?OpenView&committee=CASAC&secondname=Clea
n%20Air%20Scientific%20Advisory%20Committee and
http://yosemite.epa.gov/sab/sabpeople.nsfAVebCommitteesSubcommittees/CASAC%200xides%20of%20Nitrogen%20Primary
%20NAAQS%20Review%20Panel%20(2013-2016), respectively.
9 In 1971, primary and secondary NO2 NAAQS were set at levels of 100 micrograms per cubic meter (|ig/m3).
which equals 0.053 parts per million (ppm) or 53 ppb.
10 The official level of the annual NO; standard is 0.053 ppm. equal to 53 ppb, which is shown here for the purpose
of clearer comparison to the 1 -hour standard.
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The EPA retained the primary NO2 standard, without revision, in reviews completed in
1985 and 1996 (50 FR 25532, June 19, 1985; 61 FR 52852, October 8, 1996). In the latter of the
two decisions, the EPA concluded that "the existing annual primary standard appears to be both
adequate and necessary to protect human health against both long- and short-term NO2
exposures" and that "retaining the existing annual standard is consistent with the scientific data
assessed in the Criteria Document (U.S. EPA, 1993), the Staff Paper (U.S. EPA, 1995), and the
advice and recommendations of [the] CASAC" (61 FR 52854, October 8, 1996).
The last review of the air quality criteria for oxides of nitrogen (health criteria) and the
primary NO2 standard was initiated in December 2005 (70 FR 73236, December 9, 2005).u'12
The EPA's plans for conducting that review were presented in the Integrated Review Plan for the
Primary National Ambient Air Quality Standardfor Nitrogen Dioxide (2007 IRP, U.S. EPA,
2007a), which included consideration of comments received during a CASAC consultation as
well as public comment on a draft IRP. The scientific assessment for the review was described in
the 2008 Integrated Science Assessment for Oxides of Nitrogen - Health Criteria (2008 ISA,
U.S. EPA, 2008a), multiple drafts of which received review by the CASAC and the public. The
EPA also conducted quantitative human risk and exposure assessments after consultation with
the CASAC and after receiving public comment on an analysis plan (U.S. EPA, 2007b). These
technical analyses were presented in the Risk and Exposure Assessment to Support the Review of
the NO 2 Primary National Ambient Air Quality Standard (2008 REA, U.S. EPA, 2008b),
multiple drafts of which received CASAC and public review.
In the course of reviewing the second draft REA in the last review, the CASAC expressed
the view that the document would be incomplete without the addition of a policy assessment
chapter presenting an integration of evidence-based considerations and risk and exposure
assessment results. The CASAC stated that such a chapter would be "critical for considering
options for the NAAQS for NO2" (Samet, 2008a, p.4). In addition, within the period of the
CASAC's review of the second draft REA, the EPA's Deputy Administrator indicated in a letter
to the CASAC chair, addressing earlier CASAC comments on the NAAQS review process, that
the risk and exposure assessment would include "a broader discussion of the science and how
uncertainties may affect decisions on the standard" and "all analyses and approaches for
considering the level of the standard under review, including risk assessment and weight of
11 Documents related to the current review as well as reviews complete in 2010 and 1996 are available at:
https://www.epa.gov/naaqs/nitrogen-dioxide-no2-primary-air-quality-standards
12 The EPA conducted a separate review of the secondary NO2 NAAQS jointly with a review of the secondary SO2 NAAQS. The
Agency retained those secondary standards, without revision, to address the direct effects on vegetation of exposure to gaseous
oxides of nitrogen and sulfur (77 FR 20218, April 3,2012).
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evidence methodologies" (Peacock, 2008, p. 3). Accordingly, the final 2008 REA included a
policy assessment chapter that considered the scientific evidence in the 2008 ISA and the
exposure and risk results presented in other chapters of the 2008 REA as they related to the
adequacy of the then current primary annual NO2 standard and potential alternative standards for
consideration (U.S EPA, 2008b, chapter 10).13 The CAS AC discussed the final version of the
2008 REA, with an emphasis on the policy assessment chapter, during a public teleconference on
December 5, 2008 (73 FR 66895, November 12, 2008). Following that teleconference, the
CASAC offered comments and advice on the primary NO2 standard in a letter to the
Administrator (Samet, 2008b)
In a notice published in the Federal Register on July 15, 2009, the EPA proposed to
supplement the existing primary annual NO2 standard by establishing a new short-term standard
(74 FR 34404, July 15, 2009). After considering an integrative synthesis of the body of evidence
on human health effects associated with the presence of NO2 in the air and the exposure and risk
information, the Administrator determined that the existing primary NO2 NAAQS, based on an
annual arithmetic average, was not sufficient to protect the public health from the array of effects
that could occur following short-term exposures to ambient NO2. In so doing, the Administrator
particularly noted the potential for adverse health effects to occur following exposures to
elevated NO2 concentrations that can occur around major roads (75 FR 6482, February 9, 2012).
In a notice published in the Federal Register on February 9, 2010, the EPA finalized a new short-
term NO2 standard with a level of 100 ppb, based on the 3-year average of the 98th percentile of
the annual distribution of daily maximum 1-hour concentrations. The EPA also retained the
existing primary annual NO2 standard with a level of 53 ppb, annual average (75 FR 6474,
February 9, 2010). The Agency's final decision included consideration of the CASAC's advice
(Samet, 2009) and public comments on the proposed rule. The EPA's final rule was upheld
against challenges in a decision issued by the U.S. Court of Appeals for the District of Columbia
Circuit on July 17, 2012. API v. EPA, 684 F.3d 1342 (D.C. Cir. 2012).
Revisions to the NAAQS were accompanied by revisions to the data handling
procedures, the ambient air monitoring and reporting requirements, and the Air Quality Index
(AQI).14 As described in Chapter 2, one aspect of the new monitoring network requirements
13 Subsequent to the completion of the 2008 REA, the EPA Administrator Jackson called for additional key changes to the
NAAQS review process including reinstating a policy assessment document that contains staff analysis of the scientific bases for
alternative policy options for consideration by senior EPA management prior to rulemaking (Jackson, 2009).
14 The current federal regulatory measurement methods for NO2 are specified in 40 CFR part 50, Appendix F and 40 CFR part 53.
Consideration of ambient air measurements with regard to judging attainment of the standards is specified in 40 CFR part 50,
Appendix S. The NO2 monitoring network requirements are specified in 40 CFR part 58, Appendix D, section 4.3. The EPA
revised the AQI for NO2 to be consistent with the revised primary NO2 NAAQS as specified in 40 CFR part 58, Appendix G.
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included requirements for states to locate monitors near heavily trafficked roadways in large
urban areas and in other locations where maximum NO2 concentrations can occur. Subsequent to
the 2010 rulemaking, the Agency revised the deadlines by which the near-road monitors were to
be operational in order to implement a phased deployment approach (78 FR 16184, March 14,
2013). The near-road NO2 monitors were required to become operational between January 1,
2014 and January 1, 2017.
1.2.3 Current Review of the Primary NO2 NAAQS
In February 2012, the EPA announced the initiation of the current periodic review of the
air quality criteria for oxides of nitrogen and of the Primary NO2 NAAQS and issued a call for
information in the Federal Register (77 FR 7149, February 10, 2012). 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, statistics, risk/exposure analysis, atmospheric science, and
biology) participated in a workshop held by the EPA on February 29 to March 1, 2012 in
Research Triangle Park, NC. The workshop provided an opportunity for a public discussion of
the key policy-relevant issues around which the Agency would structure this primary NO2
NAAQS review and the most meaningful new science that would be available to inform our
understanding of these issues.
Based in part on the workshop discussions, the EPA developed a draft plan for the ISA
and a draft IRP outlining the schedule, process, and key policy-relevant questions that would
guide the evaluation of the air quality criteria for NO2 and the review of the primary NO2
NAAQS. The draft plan for the ISA was released in May of 2013 (78 FR 26026) and was the
subject of a consultation with the CAS AC on June 5, 2013 (78 FR 27234). Comments received
from that consultation were considered in the preparation of first draft ISA, and preliminary
drafts of key ISA chapters were reviewed by subject matter experts at a public workshop hosted
by the EPA's National Center for Environmental Assessment (NCEA) in May 2013 (78 FR
27374). The first draft ISA was released in November 2013 (78 FR 70040). During this time, the
draft IRP was also in preparation and was released in February 2014 (79 FR 7184). Both the
draft IRP and first draft ISA were reviewed by the CASAC at a public meeting held in March
2014 (79 FR 8701), and the first draft ISA was further discussed at an additional teleconference
held in May 2014 (79 FR 17538). The CASAC finalized its recommendations on the first draft
Certain topics related to implementation of the new standard were also discussed in the Federal Register notices for the proposed
and final rules (74 FR 34404; 75 FR 6474).
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ISA and the draft IRP in letters dated June 10, 2014, and the final IRP was released in June 2014
(79 FR 36801).
The EPA released the second draft ISA in January 2015 (80 FR 5110) and the REA
Planning Document in May 2015 (80 FR 27304). These documents were review by the CASAC
at a public meeting held in June 2015 (80 FR 22993). A follow-up teleconference with the
CASAC was held in August 2013 (80 FR 43085) to finalize recommendations on the second
draft ISA. The final ISA was released in January 2016 (81 FR 4910). The CASAC's
recommendations on the draft REA Plan were provided to the EPA in a letter dated September 9,
2015.
After considering CASAC's advice and public comments, the EPA prepared a draft PA,
which was released on September 23, 2016 (81 FR 65353). The draft PA was reviewed by the
CASAC on November 9-10, 2016 (81 FR 68414), and a follow-up teleconference was held on
January 24, 2017 (81 FR 95137). The CASAC's recommendations, based on its review of the
draft PA, were provided in a letter to the EPA Administrator dated March 7, 2017 (Diez Roux
and Sheppard, 2017). The EPA staff took into account these recommendations, as well as public
comments provided on the draft PA, when developing this final PA.
In addition, in July 2016, several groups filed suit against the EPA for failure to complete
its review of the primary NO2 NAAQS within five years, as required by the CAA. Center for
Biological Diversity etal. v. McCarthy, (No. 4:16-cv-03796-VC, N.D. Cal., July 7, 2016). A
notice of a proposed consent decree to resolve this litigation was published in the Federal
Register on January 17, 2017 (82 FR 4866). We anticipate that, as a result of this litigation, the
court will establish deadlines for the EPA to take action in this review.
1.3 SCOPE OF THE CURRENT REVIEW
Consistent with the review completed in 2010, this review will focus on health effects
associated with gaseous oxides of nitrogen and the protection afforded by the primary NO2
standards. The gaseous oxides of nitrogen include NO2 and nitric oxide (NO) as well as their
gaseous reaction products. Total oxides of nitrogen include these gaseous species as well as
particulate species (e.g., nitrates). Collectively, we refer to the total set of species as NOy (U.S.
EPA, 2013b, Section 2.2, Figure 2-1). Health effects and non-ecological welfare effects
associated with the particulate species are addressed in the review of the NAAQS for particulate
matter (PM) (78 FR 30866, January 15, 2013; U.S. EPA, 2009).15 The EPA is separately
15 Additional information on the PM NAAQS is available at: https://www.epa.gov/naaqs/particulate-matter-pm-air-
quality-standards.
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reviewing the ecological welfare effects associated with oxides of nitrogen, oxides of sulfur, and
PM, and the protection provided by the secondary NO2, SO2 and PM standards. (78 FR 53452,
August 29, 2013).16
When referring to the group of gaseous oxidized nitrogen compounds as a whole, the ISA
and other assessment documents developed in this review use the term "oxides of nitrogen." In
the last review, the EPA used "NOx" as the abbreviation for oxides of nitrogen. However, based
on the definition commonly used in the scientific literature, in this review, the abbreviation NOx
will refer specifically to the sum of NO2 and NO concentrations, rather than all oxides of
nitrogen (U.S. EPA, 2016).17
1.4 GENERAL APPROACH FOR REVIEW OF THE STANDARDS
As described in Section 1.1 above, this PA presents a transparent evaluation of the
available scientific and technical information and staffs conclusions regarding the adequacy of
the current primary NO2 standards. Staffs considerations and conclusions in this document are
based on the available body of scientific evidence assessed in the ISA (U.S. EPA, 2016a) and on
the results of quantitative analyses comparing NO2 air quality to NO2 benchmarks based on the
available health evidence (see Chapter 4 of this PA). Staffs considerations and conclusions have
also been informed by the advice and recommendations received from CASAC during its review
of the draft PA, and by public input received. Staffs considerations and conclusions in this final
PA are intended to inform the Administrator's decision as to whether the existing primary NO2
standards should be retained or revised.
Section 1.4.1 below summarizes the approach used by the Administrator in reaching
conclusions in the last review of the primary NO2 NAAQS. Building on this approach from the
last review, section 1.4.2 summarizes staffs approach to informing the Administrator's decisions
on the primary NO2 NAAQS in the current review.
16 Additional information on the ongoing and previous review of the secondary NO2 and SO2 NAAQS is available
at: https://www.epa.gov/naaqs/nitrogen-dioxide-no2-and-sulfur-dioxide-so2-secondary-air-quality-standards.
17 "... [T]he term "oxides of nitrogen" (NOy) refers to all forms of oxidized nitrogen (N) compounds, including nitric
oxide (NO), nitrogen dioxide (NO2), and all other oxidized N-containing compounds formed from NO and NO2"
(U.S. EPA, 2016, p. 2-1). "A large number of oxidized nitrogen species in the atmosphere are formed from the
oxidation of NO and NO2. These include nitrate radicals (NO3), nitrous acid (HONO), nitric acid (HNO3), dinitrogen
pentoxide (N2O5), nitryl chloride (CINO2), peroxynitric acid (HNO4), PAN and its homologues (PANs), other
organic nitrates like alkyl nitrates [including isoprene nitrates(IN)], and PNO3" (U.S. EPA, 2016, p. 2-2).
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1.4.1 Approach Used in the Last Review
As noted above (Section 1.2.2), the last review of the primary NO2 NAAQS was
completed in 2010 (75 FR 6474, February 9, 2010). In that review, the EPA established a new 1-
hour standard to provide increased public health protection, including for people with asthma and
other at-risk populations,18 against an array of adverse respiratory health effects that had been
linked to short-term NO2 exposures (75 FR 6498 to 6502; U.S. EPA, 2008a, Sections 3.1.7 and
5.3.2.1; Table 5.3-1). Specifically, the EPA established a short-term standard defined by the 3-
year average of the 98th percentile of the annual distribution of daily maximum 1-hour NO2
concentrations, with a level of 100 ppb. In addition to setting the new 1-hour standard, the EPA
retained the existing annual standard with its level of 53 ppb (75 FR 6502, February 9, 2010).
The Administrator concluded that, together, the two standards provide protection against adverse
respiratory health effects associated with short-term exposures to NO2 and effects potentially
associated with long-term exposures. As discussed further in Chapter 2 below, in conjunction
with the revised primary NO2 NAAQS, the EPA also established a two-tiered monitoring
network composed of (1) near-road monitors which would be placed near heavily trafficked
roads in urban areas and (2) monitors located to characterize areas with the highest expected NO2
concentrations at the neighborhood and larger spatial scales (also referred to as "area-wide"
monitors) (75 FR 6505 to 6506, February 9, 2010).
Key aspects of the Administrator's approach to reaching these decisions are described
below. Section 1.4.1.1 summarizes her approach to reaching the conclusion that it was
appropriate to revise the primary NO2 NAAQS. Section 1.4.1.2 summarizes her approach to
considering the elements of a revised standard. Section 1.4.1.3 discusses the key uncertainties in
the evidence and information identified in the last review.
1.4.1.1 Approach to Considering the Needfor Revision
The 2010 decision to revise the existing primary NO2 standard was based largely on the
body of scientific evidence published through early 2008 and assessed in the 2008 ISA (U.S.
EPA, 2008a); the quantitative exposure and risk analyses and the assessment of the policy-
18 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 lifestage. Lifestage refers to a
distinguishable time frame in an individual's life characterized by unique and relatively stable behavioral and/or
physiological characteristics that are associated with development and growth (i.e., children and older adults).
Identifying at-risk populations includes consideration of intrinsic (e.g., genetic or developmental aspects) or
acquired (e.g., disease or smoking status) factors that increase the risk of health effects due to exposure to oxides of
nitrogen as well as extrinsic factors such as those related to socioeconomic status, reduced access to health care, or
exposure. The ISA characterizes the strength of the evidence for various at-risk populations (U.S. EPA, 2016,
Chapter 7).
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relevant aspects of the evidence presented in the REA (U.S. EPA, 2008b);19 the advice and
recommendations of the CASAC (Samet, 2008); and public comments on the proposal.
As an initial consideration in reaching this decision, the Administrator noted that the
evidence relating short-term (minutes to hours) NO2 exposures to respiratory morbidity was
judged in the ISA to be "sufficient to infer a likely causal relationship" (75 FR 6489, February 9,
2010; U.S. EPA, 2008a, Sections 3.1.7 and 5.3.2.1).20 The scientific evidence included controlled
human exposure studies providing evidence of increases in airway responsiveness in people with
asthma following short-term exposures to NO2 concentrations as low as 100 ppb21 and
epidemiologic studies reporting associations between short-term NO2 exposures and respiratory
effects in locations that would have met the annual standard.
The quantitative analyses presented in the 2008 REA included exposure and risk estimates
for air-quality adjusted to just meet the annual standard. The Administrator took note of the REA
conclusion that risks estimated for air quality adjusted upward to simulate just meeting the
current standard could reasonably be concluded to be important from a public health perspective,
while additionally recognizing the uncertainties associated with adjusting air quality in such
analyses (75 FR 6489, February 9, 2010). For air quality adjusted to just meet the existing annual
standard, the REA findings given particular attention by the Administrator included the
following: "a large percentage (8 to 9%) of respiratory-related emergency department visits in
Atlanta could be associated with short-term NO2 exposures; most asthmatics in Atlanta could be
exposed on multiple days per year to NO2 concentrations at or above 300 ppb; and most
locations evaluated could experience on-/near-road NO2 concentrations above 100 ppb on more
than half of the days in a given year" (75 FR 6489, February 9, 2010; U.S. EPA, 2008b, Section
10.3.2).
In reaching the conclusion on adequacy of the annual standard alone, the Administrator
also considered advice received from the CASAC. In its advice, the CASAC agreed that the
primary concern in the review was to protect against health effects that have been associated
with short-term NO2 exposures. The CASAC also agreed that the annual standard alone was not
19 As discussed in the IRP forNCh (U.S. EPA, 2014, section 1.3), due to changes in the NAAQS process, the last
review of the NO2 NAAQS did not include a separate Policy Assessment document. Rather, the REA for that review
included a policy assessment chapter.
20 In contrast, the evidence relating long-term (weeks to years) NO2 exposures to adverse health effects was judged
to be either "suggestive but not sufficient to infer a causal relationship" (respiratory morbidity) or "inadequate to
infer the presence or absence of a causal relationship" (mortality, cancer, cardiovascular effects,
reproductive/developmental effects) (75 FR 6478, February 9, 2010). The causal framework used in the ISA for the
current review is discussed below in Chapter 3.
21 Transient increases in airway responsiveness have the potential to increase asthma symptoms and worsen asthma
control (74 FR 34415, July 15, 2009; U.S. EPA, 2008a, sections 5.3.2.1 and 5.4).
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sufficient to protect public health against the types of exposures that could lead to these health
effects. As noted in its letter to the EPA Administrator, "[The] CAS AC concurs with EPA's
judgment that the current NAAQS does not protect the public's health and that it should be
revised" (Samet, 2008, p. 2).
Based on the considerations summarized above, the Administrator concluded that the
annual NO2 NAAQS alone was not requisite to protect public health with an adequate margin of
safety and that the standard should be revised in order to provide increased public health
protection against respiratory effects associated with short-term exposures, particularly for at-
risk populations and lifestages such as asthmatics, children, and older adults (75 FR 6490,
February 9, 2010). Upon consideration of approaches to revising the standard, the Administrator
concluded that it was appropriate to set a new short-term standard, in addition to the existing
annual standard with its level of 53 ppb, as described below.
1.4.1.2 Approach to Considering the Elements of a Revised Standard
In considering appropriate revisions in the last review, each of the four basic elements of
the NAAQS (indicator, averaging time, level, and form) was evaluated. The sections below
summarize the approaches used by the Administrator, and her final decisions, on each of those
elements.
Indicator
In the review completed in 2010, as well as in previous reviews, the EPA focused on NO2
as the most appropriate indicator for oxides of nitrogen because the available scientific
information regarding health effects was largely indexed by NO2. Controlled human exposure
studies and animal toxicological studies provided specific evidence for health effects following
exposures to NO2. In addition, epidemiologic studies typically reported effects associated with
NO2 concentrations22 (75 FR 6490, February, 9, 2010; U.S. EPA 2008b, Section 2.2.3). Based on
the information available in the last review, and consistent with the views of the CASAC (Samet,
2008, p.2; Samet, 2009, p.2), the EPA concluded it was appropriate to continue to use NO2 as the
indicator for a standard that was intended to address effects associated with exposure to NO2,
alone or in combination with other gaseous oxides of nitrogen. In so doing, the EPA recognized
that measures leading to reductions in population exposures to NO2 will also reduce exposures to
other oxides of nitrogen (75 FR 6490, February, 9, 2010).
Averaging time
22 The degree to which monitored NO2 reflected actual NO2 concentrations, as opposed to NO2 plus other gaseous
oxides of nitrogen, was recognized as an uncertainty (75 FR 6490, February, 9, 2010; U.S. EPA 2008b, section
2.2.3).
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In considering the most appropriate averaging time(s) for the primary NO2 NAAQS, the
Administrator noted the available scientific evidence as assessed in the ISA, the air quality
analyses presented in the REA, the conclusions of the policy assessment chapter of the REA, and
recommendations from the CASAC.23 Her key considerations are summarized below.
When considering averaging time, the Administrator first noted that the evidence relating
short-term (minutes to hours) NO2 exposures to respiratory morbidity was judged in the ISA to
be "sufficient to infer a likely causal relationship" (U.S. EPA, 2008a, section 5.3.2.1) while the
evidence relating long-term (weeks to years) NO2 exposures to adverse health effects was judged
to be either "suggestive but not sufficient to infer a causal relationship" (respiratory morbidity)
or "inadequate to infer the presence or absence of a causal relationship" (mortality, cancer,
cardiovascular effects, reproductive/developmental effects) (U.S. EPA, 2008a, Sections 5.3.2.4-
5.3.2.6). The Administrator concluded that these judgments most directly supported an averaging
time that focused protection on effects associated with short-term exposures to NO2.
In considering the level of support available for specific short-term averaging times, the
Administrator noted that the policy assessment chapter of the REA considered evidence from
both experimental and epidemiologic studies. Controlled human exposure studies and animal
toxicological studies provided evidence that NO2 exposures from less than 1 hour up to 3 hours
can result in respiratory effects such as increased airway responsiveness and inflammation (U.S.
EPA, 2008a, Section 5.3.2.7). She specifically noted the ISA conclusion that exposures of
asthmatic adults to 100 ppb NO2 for 1-hour (or 200 to 300 ppb for 30 minutes) can result in
small but significant increases in nonspecific airway responsiveness (U.S. EPA, 2008a, Section
5.3.2.1). In addition, the epidemiologic evidence provided support for short-term averaging times
ranging from approximately 1 hour up to 24 hours (U.S. EPA, 2008a, Section 5.3.2.7). Based on
this, the Administrator concluded that a primary concern with regard to averaging time is the
degree of protection provided against effects associated with 1-hour NO2 concentrations. Based
on REA analyses of ratios between 1-hour and 24-hour NO2 concentrations (U.S. EPA, 2008b,
Section 10.4.2), she further concluded that a standard based on 1-hour daily maximum NO2
concentrations could also be effective at protecting against effects associated with 24-hour NO2
exposures.
Based on the above, the Administrator judged that it was appropriate to set a new NO2
standard with a 1-hour averaging time. She concluded that such a standard would be expected to
effectively limit short-term (e.g., 1- to 24-hours) exposures that have been linked to adverse
respiratory effects. She also retained the existing annual standard to continue to provide
protection against effects potentially associated with long-term exposures to oxides of nitrogen
23 She also considered public comments received on the proposal (75 FR 6490, February, 9, 2010)
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(75 FR 6502, February, 9, 2010). These decisions were consistent with CASAC advice to
establish a short-term primary standard for oxides of nitrogen based on using 1-hour maximum
NO2 concentrations and to retain the current annual standard (Samet, 2008, p. 2; Samet, 2009, p.
2).
Level
With consideration of the available health effects evidence, exposure and risk analyses,
and air quality information, the Administrator set the level of the new 1-hour NO2 standard at
100 ppb. This standard was focused on limiting the maximum 1-hour NO2 concentrations in
ambient air (75 FR 6474, February, 9, 2010).24 In establishing this new standard, the
Administrator emphasized the importance of protecting against exposures to peak concentrations
of NO2, such as those that can occur around major roadways. Available evidence and
information suggested that roadways account for the majority of exposures to peak NO2
concentrations and, therefore, are important contributors to N02-associated public health risks
(U.S. EPA, 2008b, Figures 8-17 and 8-18).
In setting the level of the new 1-hour standard at 100 ppb, the Administrator noted that
there is no bright line clearly directing the choice of level. Rather, the choice of what is
appropriate is a public health policy judgment entrusted to the Administrator. This judgment
must include consideration of the strengths and limitations of the evidence and the appropriate
inferences to be drawn from the evidence and the exposure and risk assessments.
The Administrator judged that the existing evidence from controlled human exposure
studies supported the conclusion that the N02-induced increase in airway responsiveness at or
above 100 ppb presented a risk of adverse effects for some asthmatics, especially those with
more serious (i.e., more than mild) asthma. The Administrator noted that the risks associated
with increased airway responsiveness could not be fully characterized based on available
controlled human exposure studies, and thus she was not able to determine whether the increased
airway responsiveness experienced by asthmatics in these studies was an adverse health effect.
However, the Administrator concluded that asthmatics, particularly those suffering from more
severe asthma, warrant protection from the risk of adverse effects associated with the NO2-
induced increase in airway responsiveness. Therefore, the Administrator concluded that the
controlled human exposure evidence supported setting a standard level no higher than 100 ppb to
reflect a cautious approach to the uncertainty regarding the adversity of the effect. However,
those uncertainties led her to also conclude that this evidence did not support setting a standard
level lower than 100 ppb (75 FR 6500-6501, February, 9, 2010).
24 In conjunction with this new standard, the Administrator established a 2-tiered monitoring network that included
monitors sited to measure the maximum NO2 concentrations near major roadways, as well as monitors sited to
measure maximum area-wide NO2 concentrations.
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The Administrator also considered the more serious health effects reported in NO2
epidemiologic studies. She noted that a new standard focused on protecting against maximum 1-
hour NO2 concentrations in ambient air anywhere in an area, with a level of 100 ppb and an
appropriate form (as discussed below), would be expected to limit area-wide25 NO2
concentrations to below those in locations where epidemiologic studies had reported associations
with respiratory-related hospital admissions or emergency department visits. The Administrator
also concluded that such a 1-hour standard would be consistent with the REA conclusions based
on the NO2 exposure and risk information (75 FR 6501, February, 9, 2010).
Given the above considerations and the comments received on the proposal, and
considering the entire body of evidence and information before her, as well as the related
uncertainties, the Administrator judged it appropriate to set a 1-hour standard reflecting the
maximum allowable NO2 concentrations that can occur anywhere in an area, with a level of 100
ppb. Specifically, she concluded that such a standard, with an appropriate form as discussed
below, would provide a significant increase in public health protection compared to that provided
by the annual standard alone and would be expected to protect against the respiratory effects that
have been linked with NO2 exposures in both controlled human exposure and epidemiologic
studies. This includes limiting exposures at and above 100 ppb for the vast majority of people,
including those in at-risk groups, and maintaining area-wide NO2 concentrations below those in
locations where key U.S. epidemiologic studies had reported that ambient NO2 was associated
with clearly adverse respiratory health effects, as indicated by increased hospital admissions and
emergency department visits. The Administrator also noted that a standard level of 100 ppb was
consistent with the consensus recommendation of the CASAC. (75 FR 6501, February, 9, 2010).
In setting the standard level at 100 ppb rather than at a lower level, the Administrator also
acknowledged the uncertainties associated with the scientific evidence. She noted that a 1-hour
standard with a level lower than 100 ppb would only result in significant further public health
protection if, in fact, there is a continuum of serious, adverse health risks caused by exposure to
NO2 concentrations below 100 ppb and/or associated with area-wide NO2 concentrations well
below those in locations where key U.S. epidemiologic studies had reported associations with
respiratory-related emergency department visits and hospital admissions. Based on the available
evidence, the Administrator did not believe that such assumptions were warranted. Taking into
account the uncertainties that remained in interpreting the evidence from available controlled
human exposure and epidemiologic studies, the Administrator observed that the likelihood of
obtaining benefits to public health with a standard set below 100 ppb decreased, while the
25As discussed below in Chapter 2, area-wide concentrations refer to those measured by monitors that have been
sited to characterize ambient concentrations at the neighborhood and larger spatial scales.
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likelihood of requiring reductions in ambient concentrations that go beyond those that are needed
to protect public health increased. (75 FR 6501-02, February, 9, 2010).
Form
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. The Administrator recognized that
for short-term standards, concentration-based forms that reflect consideration of a statistical
characterization of an entire distribution of air quality data, with a focus on a single statistical metric
such as the 98th or 99th percentile, can better reflect pollutant-associated health risks than forms based
on expected exceedances. This is the case because concentration-based forms give proportionally
greater weight to days when pollutant concentrations are well above the level of the standard than to
days when the concentrations are just above the level of the standard.26 In addition, she concluded
that when averaged over three years, these concentration-based forms provide an appropriate balance
between limiting peak pollutant concentrations and providing a stable regulatory target, facilitating
the development of stable implementation programs (75 FR 6492, February, 9, 2010).
In the last review, the EPA considered two specific concentration-based forms (i.e., the 98th
and 99th percentile concentrations), averaged over 3 years, for the new 1 -hour NO2 standard. The
focus on the upper percentiles of the distribution was based, in part, on evidence of health effects
associated with short-term NO2 exposures from experimental studies which provided information on
specific exposure concentrations that were linked to respiratory effects. In a letter to the
Administrator following issuance of the Agency's proposed rule, the CAS AC recommended a form
based on the 3-year average of the 98th percentile of the distribution of 1-hour daily maximum NO2
concentrations (Samet, 2009, p. 2). In making this recommendation, the CASAC noted the potential
for instability in the higher percentile concentrations and the absence of data from the near-road
monitoring network.
Given the limited available information on the variability in peak NO2 concentrations near
important sources of NO2 such as near major roadways, and given the recommendation from the
CASAC regarding the potential for instability in the 99th percentile concentrations, the Administrator
judged it appropriate to set the form based on the 3-year average of the 98th percentile of the annual
distribution of daily maximum 1-hour NO2 concentrations. In addition, consistent with the CASAC's
advice (Samet, 2008, p. 2; Samet, 2009, p.2), the EPA retained the form of the annual standard (75
FR 6502, February, 9, 2010).
1.4.1.3 Areas of Uncertainty in Last Review
While the available scientific information informing the last review was stronger and
more consistent than in previous reviews and provided a strong basis for decision making in that
26 Compared to an exceedance-based form, a concentration-based form reflects the magnitude of the exceedance of a
standard level not just the fact that such an exceedance occurred.
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review, the Agency recognized that areas of uncertainty remained. These were generally related
to the following: (1) understanding the role of NO2 in the complex ambient mixture which
includes a range of co-occurring pollutants (e.g., PM2.5, CO and other traffic-related pollutants;
ozone (O3), SO2,) (e.g., 75 FR 6485 February 9, 2010); (2) understanding the extent to which
monitored ambient NO2 concentrations used in epidemiologic studies reflect exposures in study
populations and the range of ambient concentrations over which we continue to have confidence
in the health effects observed in the epidemiologic studies (e.g., 75 FR 6501, February 9, 2010);
(3) understanding the magnitude and potential adversity of N02-induced respiratory effects
reported in controlled human exposure studies (e.g., 75 FR 6500, February 9, 2010); and (4)
understanding the NO2 concentration gradients around important sources, such as major roads,
and relating those gradients to broader ambient monitoring concentrations (e.g., 75 FR 6479,
February 9, 2010).
1.4.2 General Approach for the Current Review
Staffs approach to reviewing the primary NO2 standards in the current review builds off
the approach taken in the last review and reflects the updated scientific and technical information
now available, as assessed in the 2016 ISA. Our considerations and conclusions related to the
primary NO2 standards in the current review are framed by a series of key policy-relevant
questions, expanding upon those presented in the IRP at the outset of this review (U.S. EPA,
2014). Our consideration of these questions is intended to inform the Administrator's decisions
as to whether, and if so how, to revise the current NO2 standards.
In reaching conclusions on options for the Administrator's consideration, we note that the
final decision to retain or revise the current primary NO2 standard is a public health policy
judgment to be made by the Administrator. This final decision by the Administrator will draw
upon the available scientific evidence for N02-attributable health effects and on information
from available quantitative analyses, including judgments about the appropriate weight to assign
the range of uncertainties inherent in the evidence and analyses. Our general approach in the
current review to informing these decisions recognizes that the available health effects evidence
reflects a continuum from relatively higher NO2 concentrations, at which scientists generally
agree that health effects are likely to occur, through lower concentrations, at which the likelihood
and magnitude of a response become increasingly uncertain. In developing conclusions in this
PA, we are mindful that the Administrator's ultimate judgments on the primary standard will
most appropriately reflect an interpretation of the available scientific evidence and information
that neither overstates nor understates the strengths and limitations of that evidence and
information. This approach is consistent with the requirements of sections 108 and 109 of the
CAA, as well as with how the EPA and the courts have historically interpreted the CAA.
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Figure 1-1 below provides an overview of our approach in this review. We believe that
the general approach outlined in Figure 1-1 provides a comprehensive basis to help inform the
judgments required of the Administrator in reaching decisions about the current and, if
appropriate, potential alternative primary NO2 standards.
Adequacy of Existing Primary NO, Standard*
Exposure-and Riak-Based
Considerations
>To what extent could the current
standards alto*- NC= exposures of public
health importance-'
>What information on NO: health risks is
available from the risk assessment
conducted 111 the last review
>What are the uncertainties and
limitations tn quantitative assessments'?
Evidence-Based Considerations
>Does currer#, available evidence strengthen support for, or call into question the
nature of Nth -attributable health effects'? Dees it provide an improved
understanding of at-risk populations'?
>Have uncertainties in tne ewdencefrcm previous reviews been addressed
have new uncertainties been identified''
>Does the evidence indicate MO; -attributable adverse effects at NO:
concentrations below the current standards''
>To what extent have adverse effects been reported following exposures to
NO; concentrations at;bek»1he current standard levels'?
>Tq wttat extent have health elect associations been reported at ambient NO;.
concentrations meeting the current standards? Are these associations specific to
NO; and vvhat are the uncertainties'?
Does information call
into question adequacy
of current Primary NO,
standards"?
NO
Consider retaining
current NO? standards)
|YES
ConsiderPotenlal Alternative Standards
1
Elements of Potential Alternative
Standards
J* Indicator
> Averaging time
>pafm
> Level
I
Identify' range of potential alternative standards for consideration
Figure 1-1. Overview of the Approach to Reviewing the Primary NO2 NAAQS.
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1.5 REFERENCES
Diez Roux A and Frey H.C. (2015). Letter from Drs. Ana Diez Roux, Chair and H. Christopher Frey, Immediate
Past Chair, Clean Air Scientific Advisory Committee to EPA Administrator Gina McCarthy. CASAC
Review of the EPA's Review of the Primary National Ambient Air Quality Standards for Nitrogen
Dioxide: Risk and Exposure Assessment Planning Document. EPA-CASAC-15-002. September 9, 2015.
Available at:
https://vosemite.epa.gov/sab/sabproduct.nsiy264cbl227d55e02c85257402007446a4/A7922887D5BDD8D4
85257EBB0071 A3 AD/$Fite/EPA~CAS AC-15-002+unsigned.pdf
Diez Roux A and Sheppard, E (2017). Letter form Dr. Elizabeth A. (Lianne) Sheppard, Chair, Clean Air Scientific
Advisory Committee to EPA Administrator E. Scott Pruitt. CASAC Review of the EPA's Policy
Assessment for the Review of the Primary National Ambient Air Quality Standards for Nitrogen Dioxide
(External Review Draft- September 2016). EPA-CASAC-17-001. March 7th, 2017. Available at:
https://yosemite.epa.gov/sab/sabproduct.nsf/LookupWebProjectsCurrentCASAC/7C2807D0D9BB4CC885
2580DD004EB C3 2/$File/EP A-C AS AC-17-001 .pdf
Peacock M (2008). Letter from Marcus Peacock, Deputy Administrator, U.S. EPA to Dr. Rogene Henderson, Chair,
Clean Air Scientific Advisory Committee. September 8, 200. Available at:
http://yosemite.epa.gov/sab/sabproduct.nsf/WebCASAC/CASAC_09-08-
08/$File/CASAC%20Letter%20to%20Dr%20Rogene%20Henderson.pdf.
Samet J (2008a). Letter from Dr. Jonathan M. Samet, Chair, Clean Air Scientific Advisory Committee to EPA
Administrator Stephen Johnson. Clean Air Scientific Advisory Committee's (CASAC) Peer Review of
Draft Chapter 8 of EPA's Risk and Exposure Assessment to Support the Review of the NO2 Primary
National Ambient Air Quality Standard. EPA-CASAC-09-001. October 28, 2008. Available at:
http://yosemite.epa.gov/sab/sabproduct.nsf/264cbl227d55e02c85257402007446a4/87D38275673D66B885
2574F00069D45E/$File/EPA-CASAC-09-001-unsigned.pdf.
Samet J (2008b). Letter from Dr. Jonathan M. Samet, Chair, Clean Air Scientific Advisory Committee to EPA
Administrator Stephen Johnson. Clean Air Scientific Advisory Committee's (CASAC) Review comments
on EPA's Risk and Exposure Assessment to Support the Review of the NO2 Primary National Ambient Air
Quality Standard. EPA-CASAC-09-003. December 16, 2008. Available at:
http://yosemite.epa.gov/sab/sabproduct.nsf/264cbl227d55e02c85257402007446a4/9C4A540D86BFB67A8
52575210074A7AE/$File/EP A-CASAC-09-003-unsigned.pdf.
Samet J (2009). Letter from Dr. Jonathan M. Samet, Chair, Clean Air Scientific Advisory Committee to EPA
Administrator Lisa P. Jackson. Comments and Recommendations Concerning EPA's Proposed Rule for the
Revision of the National Ambient Air Quality Standards (NAAQS) for Nitrogen Dioxide. EPA-CAS AC-
09-014. September 9, 2009. Available at:
http://yosemite.epa.gov/sab/sabproduct.nsf/264cbl227d55e02c85257402007446a4/0067573718EDA17F85
25762C0074059E/$File/EPA-CAS AC-09-014-unsigned.pdf.
U.S. EPA (1971). Air Quality Criteria for Nitrogen Oxides. U.S. Environmental Protection Agency. Air Pollution
Control Office, Washington, D.C. January 1971. Air Pollution Control Office Publication No. AP-84.
U.S. EPA (1993). Air Quality Criteria for Oxides of Nitrogen. Office of Health and Environmental Assessment,
Environmental Criteria and Assessment Office. Research Triangle Park, NC. EPA-600/8-91-049aF-cF,
August 1993. Available at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=40179.
U.S. EPA (1995). Review of the National Ambient Air Quality Standards for Nitrogen Oxides: Assessment of
Scientific and Technical Information, OAQPS Staff Paper. US EPA, Office of Air Quality Planning and
Standards, Research Triangle Park, NC. EPA-452/R-95-005, September 1995. Available at:
http://www.epa.gov/ttn/naaqs/standards/nox/data/noxspl995.pdf.
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U.S. EPA (2007a). Integrated Review Plan for the Primary National Ambient Air Quality Standard for Nitrogen
Dioxide. U.S. EPA. National Center for Environmental Assessment and Office of Air Quality Planning and
Standards. Research Triangle Park, NC. August 2007. Available at:
http://www.epa.gOv/ttn/naaqs/standards/nox/data/20070823_nox_review_plan_final.pdf.
U.S. EPA (2007b). Nitrogen Dioxide Health Assessment Plan: Scope and Methods for Exposure and Risk
Assessment. U.S. EPA, Office of Air Quality Planning and Standards, Research Triangle Park, NC. Draft
September 2007. Available
at:http://www.epa.gov/ttn/naaqs/standards/nox/data/20070927_risk_exposure_scope.pdf.
U.S. EPA (2008a). Integrated Science Assessment for Oxides of Nitrogen - Health Criteria. U.S. EPA, National
Center for Environmental Assessment and Office, Research Triangle Park, NC. EPA/600/R-08/071. July
2008. Available at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid= 194645.
U.S. EPA (2008b). Risk and Exposure Assessment to Support the Review of the NO2 Primary National Ambient Air
Quality Standard. U.S. EPA, Office of Air Quality Planning and Standards. Research Triangle Park, NC.
EPA 452/R-08-008a/b. November 2008. Available at:
http://www.epa.gOv/ttn/naaas/standards/nox/s nox cr rea.fatnit.
U.S. EPA. (2014). Integrated Review Plan for the Primary National Ambient Air Quality Standards for Nitrogen
Dioxide. U.S. EPA, National Center for Environmental Assessment and Office of Air Quality Planning and
Standards, Research Triangle Park, NC. EPA-452/R-14-003. June 2014. Available at:
http://www.epa.gov/ttn/naaas/standards/nox/data/201406finalirpprimarvno2.pdf.
U.S. EPA (2015). Review of the Primary National Ambient Air Quality Standards for Nitrogen Dioxide: Risk and
Exposure Assessment Planning Document. U.S. EPA, Office of Air Quality Planning and Standards,
Research Triangle Park, NC. EPA-452/D-15-001. May 13, 2015. Available at:
https://www3.epa.gov/ttii/naaas/standards/nox/data/20150504reapiaiining.pdf
U.S. EPA. (2016a). Integrated Science Assessment for Oxides of Nitrogen - Health Criteria (2016 Final Report).
U.S. EPA, National Center for Environmental Assessment, Research Triangle Park, NC. EPA/600/R-
15/068. January 2016. Available at: https://cfpnb.epa.gov/ncea/isa/recordisplav.cfm?deid=3.1.0879.
U.S. EPA. (2016b/ Policy Assessment for the Review of the Primary National Ambient Air Quality Standards for
Nitrogen Dioxide - External Review Draft. U.S. EPA, Office of Air Quality Planning and Standards,
Research Triangle Park, NC. EPA-452/P-16-001. September 2016. Available at:
https://www3.epa.gov/ttn/naaqs/standards/nox/data/20160927-no2-pa-external-review-draft.pdf
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2 no2 air quality
This chapter presents information on NO2 atmospheric chemistry, monitoring, and
ambient concentrations,27 with a focus on information that is most relevant for our review of the
primary NO2 standards. It is intended as a prologue for detailed discussions on the evidence for
health effects and exposures to NO2 that follow in the subsequent chapters, and as a source of
information to help interpret those effects in the context of air quality. We generally focus on
NO2 in this chapter, as this is the indicator for oxides of nitrogen and most relevant to the
evaluation of health evidence. The ISA presents a more thorough characterization of air quality
for the broader category of oxides of nitrogen (U.S. EPA, 2016, Chapter 2).
In this chapter, Section 2.1 provides an overview of the atmospheric chemistry of NO2
formation and the NOx emissions that contribute to ambient NO2. Section 2.2 discusses NO2
ambient monitoring methods and provides an overview of the U.S. ambient monitoring network
for NO2. Section 2.3 summarizes information on recent ambient concentrations of NO2,
including information from the near-road monitoring network, and on long-term temporal trends
in NO2 air quality.
2.1 NOi ATMOSPHERIC CHEMISTRY AND NOx EMISSIONS
2.1.1 Atmospheric Chemistry
The overall chemistry of reactive, oxidized nitrogen compounds in the atmosphere is
summarized in Figure 2-1. Ambient concentrations of NO2 are influenced by both direct NO2
emissions and by emissions of nitric oxide (NO), with the subsequent conversion of NO to NO2
primarily though reaction with ozone (O3). The initial reaction between NO and O3 to form NO2
occurs fairly quickly during the daytime, with reaction times on the order of minutes. However,
NO2 can also be photolyzed to reform NO, creating new O3 in the process (U.S. EPA, 2016,
Section 2.2). A large number of oxidized nitrogen species in the atmosphere are formed from the
oxidation of NO and NO2. These include nitrate radicals (NO3), nitrous acid (HONO), nitric acid
(HNO3), dinitrogen pentoxide (N2O5), nitryl chloride (CINO2), peroxynitric acid (HNO4),
peroxyacetyl nitrate and its homologues (PANs), other organic nitrates, such as alkyl nitrates
(including isoprene nitrates), and pN03. The sum of these reactive oxidation products
27 This chapter focuses on monitored ambient NO2 concentrations. In addition, we have developed an approach to
adjust ambient NO2 concentrations in order to estimate the concentrations that could occur if urban areas were to
"just meet" the current NO2 NAAQS. These estimated concentrations are based on statistical adjustments of existing
air quality concentrations and are discussed in detail in chapter 4 and appendix B of this PA.
2-1
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(collectively referred to as NOz) and NO plus NO2 (i.e., referred to as NOx) comprise NOv
(Figure 2-1).
Long-range transport to remote
regions at low temperatures
CINO
HNO
PANS
>0,
RC(0)00
NO
MPP
NO
MPP
HONO
Isoprene nitrates
alkyt nitrates
HO
,ro!
RO
NOz
NOz = NOy - NO;
NO
NO]
deposition
deposition
emissions
Note: The inner shaded box contains NOx (= NO + N02). The outer box contains other species (NOz) formed from reactions of NOx.
All species shown in the outer and inner boxes are collectively referred to as NOY by the atmospheric sciences community.
hv = solar photon, M = species transferring/removing enough energy to cause a molecule to decompose/stabilize, MPP =
multiphase processes, R = organic radical.
Source: National Center for Environmental Assessment,
Figure 2-1 Schematic diagram of the cycle of reactive, oxidized nitrogen species in the
atmosphere.
Due to the close relationship between NO and NO2, and their ready interconversion, these
species are often grouped together and referred to as NOx. The majority of NOx emissions are in
the form of NO. For example, 90% or more of tail-pipe NOx emissions are in the form of NO,
with only about 2 to 10% emitted as NO2 (Itano et al., 2014; Kota et al., 2013; Jimenez et al.,
2000; Richmond-Bryant et al., 2016). As noted above, NOx emissions require time and sufficient
O3 concentrations for the conversion of NO to NO2. Higher temperatures and concentrations of
reactants result in shorter conversion times (e.g., less than one minute under some conditions),
while dispersion and depletion of reactants results in longer conversion times. The time required
2-2
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to transport emissions away from a roadway can vary from less than one minute (e.g., under
open conditions) to about one hour (e.g., for certain urban street canyons) (During et al., 2011;
Richmond-Bryant and Reff, 2012). These factors can affect the locations where the highest NO2
concentrations occur. In particular, while ambient NO2 concentrations are often elevated near
important sources of NOx emissions, such as major roadways, the highest measured ambient
concentrations in a given urban area may not always occur immediately adjacent to those
00
sources.
The near-road environment provides a clear example of the interplay between NOx
emissions, meteorology, and the atmospheric chemistry that impacts ambient NO2
concentrations. Vehicular emissions tend to peak during the morning and afternoon commutes,
while peak O3 concentrations generally occur in the late morning to early evenings. In addition,
atmospheric mixing tends to be the strongest during the daytime, rapidly diluting roadway
emissions. Given the relative timing of O3 availability and peak atmospheric mixing conditions,
the highest near-road NO2 concentrations often occur during the early morning hours (i.e., before
atmospheric mixing can rapidly dilute emissions) (Kimbrough et al., 2016; Richmond-Bryant et
al., 2016).29
Oxidized nitrogen compounds are ultimately lost from the atmosphere by wet and dry
deposition to the Earth's surface. Soluble species are taken up by aqueous aerosols and cloud
droplets and are removed by wet deposition by rainout (i.e., incorporation into cloud droplets
that eventually coagulate into falling rain drops). Both soluble and insoluble species are removed
by washout (i.e., impaction with falling rain drops, another component of wet deposition), and by
dry deposition (i.e., impaction with the surface and gas exchange with plants). NO and NO2 are
not very soluble, and therefore wet deposition is not a major removal process for them. However,
a major NOx reservoir species, HNO3, is extremely soluble, and its deposition (both wet and dry)
represents a major sink for NOy.
28 Ambient NO2 concentrations around stationary sources of NOx emissions are similarly impacted by the
availability of O3 and by meteorological conditions, although surface-level NO2 concentrations can be less impacted
in cases where stationary source NOx emissions are emitted from locations elevated substantially above ground
level.
29 The conversion of NOx into the species that make up NOz typically takes place on a much longer time scale than
do interconversions between NO and NO2 (e.g., Ren et al., 2013). NOx emitted during morning rush hour by
vehicles can be converted almost completely to products by late afternoon during warm, sunny conditions.
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2.1.2 Emissions
The National Emissions Inventory (NEI)30 is a national compilation of emissions sources
collected from state, local, and tribal air agencies, as well as emission estimates developed by the
EPA from data on specific source sectors. According to the NEI, anthropogenic sources account
for a large majority of NOx emissions in the U.S., with highway vehicles, off-highway vehicles,
and fuel combustion identified the largest contributors. More specifically, highway vehicles
include all on-road vehicles, including light duty as well as heavy duty vehicles, both gasoline-
and diesel-powered. Off-highway vehicles and engines include aircraft, commercial marine
vessels, locomotives, and non-road equipment. Fuel combustion-utilities includes electric power
generating units (EGUs), which derive their power generation from all types of fuels. EGU
emissions are dominated by coal combustion, which accounts for 86% of all NOx emissions from
utilities in the 2014 NEI. The fuel combustion-other category includes commercial/institutional,
industrial, and residential combustion of biomass, coal, natural gas, oil, and other fuels. Other
anthropogenic sources include field burning, prescribed fires, and various industrial processes
(e.g., cement manufacturing, oil and gas production). On a national scale, agricultural field
burning and prescribed fires are the greatest contributors to the Other Anthropogenic sources
category. Biogenics and Wildfires include emissions estimates for plants and soil (i.e., biogenics)
and for wildfires.
Nationwide estimates indicate a 61% decrease in total NOx emissions from 1980 to 2016
(Figure 2-2) as a result of multiple regulatory programs. These include an assortment of key rules
implemented over multiple decades, some of which are highlighted in Table 2-1.
"The NEI may be found at: https://www.epa.gov/air-eniissions-inventories
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Figure 2-2. U.S. national average NOx emissions from 1980 to 2016.31
Table 2-1. Select Programs and Rules that have contributed to NOx reductions over time.
Kev Programs Leading to Reductions in NOx Emissions
Year Program or
Rule was Created
Clean Air Act - included NOx reduction mandates
1970
Energy Policy Conservation Act - Established Corporate Average
Fuel Economy (CAFE) standards
1975
1990 Clean Air Act amendments - Reasonably Available Control
Technology for stationary sources; Lowered emissions standards for
mobile sources; Acid Rain program
1990
NOx SIP Call
1997
Ozone Transport Commission - NOx Budget Program
1999
Tier 2 Light Duty emissions rule
1999
Clean Air Nonroad Diesel rule
2004
Clean Air Interstate Rule
2009
Cross State Air Pollution Rule
2011
Tier 3 Light duty vehicle emission and fuel standards
2014
31 Emissions trends information is based on a rolling 5-year average to smooth out variance caused by methodology
differences across certain transition years. This figure reflects an update to the information included in Figure 2-2 of
the ISA (U.S. EPA, 2016). Underlying data can be found at: http://www.epa.gov/air-emissions-inventories/air-
pollutant-emissions-trends-data
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The overall decrease in NOx emissions has been driven primarily by decreases from the
four largest emissions sectors. Specifically, compared to the 1980 NEI, estimates for 2016
indicate a 69% reduction in NOx emissions from highway vehicles, a 28% reduction from off-
highway vehicles and engines, an 85% reduction from fuel combustion-utilities, and a 60%
reduction from fuel combustion-other (see Figure 2-3, below).
14,000
12,000
10,000
8,000
6,000
4,000
2,000
0
I ll I. I
..if
.A*
OS
c°
.c/
JO
-------
can be much higher. For example, estimates in New Mexico, Oklahoma, and Texas indicate that
up to about 18 to 20% of state NOx emissions come from oil and gas operations.
2.2 AMBIENT NOi MONITORING
2.2.1 NO2 Methods
Ambient NO2 concentrations are measured by monitoring networks operated by state,
local, and tribal air agencies, which are typically funded in part by the EPA. The main network
of monitors providing ambient data for use in implementation activities related to the NAAQS is
the State and Local Air Monitoring Stations (SLAMS) network. This network relies on a
chemiluminescent Federal Reference Method (FRM) and on Federal Equivalent Methods (FEM)
that use either chemiluminescence or direct measurement methods of NO2. Chemiluminescent-
based FRMs only detect NO in the sample stream. Therefore, a two-step process is employed to
measure NO2, based on the subtraction of NO from oxidized nitrogen.34 Data produced by
chemiluminescent analyzers include NO, NO2, and NOx measurements, which are all routinely
logged by state and local air monitoring agencies. Hourly average values are typically reported to
the EPA's Air Quality System (AQS).
As discussed in the ISA (U.S. EPA, 2016, p. 2-24) the traditional chemiluminescence
FRM is subject to potential measurement biases resulting from interference by NOz species.
These potential biases are measurement uncertainties that can impact exposure analyses.
However, within metropolitan areas, where a majority of the NO2 monitoring network is located
and is influenced by strong NOx sources, the potential for NOz related bias is relatively small.
There have been recent advances in methods that provide measurements of NO2 with less
potential for interference. These newer methods include photolytic-chemiluminescent methods
that rely on photodissociation of NO2 using specific wavelengths of light, and direct
measurements of NO2, including cavity attenuated phase shift [CAPS] spectrometry and cavity
ring-down spectroscopy. It should be noted that the direct measurement methods do not provide
34 First, the analyzer determines the amount of NO in the sample air. Second, the analyzer re-routes air flow so that
the sample air stream passes over a heated molybdenum oxide catalytic converter reducing a large majority (if not
all) of the oxidized nitrogen species present in the sample stream to NO, before again measuring the amount of NO
in the sample. The analyzer then subtracts the measured, actual ambient NO, determined in the first step, from the
amount measured in the second step, allowing for the determination of NO, NO2, and NOx (where NOx = NO +
NO2). The catalytic converter can convert nitric acid (HNO3) and peroxyacetyl nitrate to NO, which would
subsequently be counted as NO2. Photolytic-chemiluminescence FEM carries out the reduction of NO2 to NO in a
photolytic converter with a known converter efficiency rate, which is specific to NO2 and, thus, is not subject to the
same positive bias potential as the chemiluminescent FRM.
2-7
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NO or NOx data (U.S. EPA, 2016, Section 2.4). These newer methods are expected to gradually
replace the older FRMs as the older monitors age.
2.2.2 Ambient Monitoring Network
Ambient NO2 monitors in the SLAMS network began operating in the late 1970s and
have been used to make measurements supporting NAAQS compliance, the Photochemical
Assessment Monitoring Station (PAMS) program, and other objectives at the national, state, and
local levels. As of January 2016, approximately 484 NO2 monitors were in operation across the
nation and reporting data to AQS. The network has grown and contracted in size since its
initiation in the 1970's, in response to changing objectives, priorities of federal, state, and local
air monitoring agencies, and resources. Currently, the network is growing due to the addition of
near-road monitors (discussed below) and as part of the revisions to the PAMS requirements (80
FR 65291, December 28, 2015).
In consideration of the location and measurements taken, each monitor is assigned a
spatial scale associated with the size of the area that it represents. The monitor spatial scales are
defined in 40 CFR 58 appendix D as:
1. Microscale: area dimensions ranging from several meters up to about 100 meters.
2. Middle scale: areas up to several city blocks in size with dimensions ranging from
about 100 meters to 0.5 kilometer.
3. Neighborhood scale: extended city area with relatively uniform land use and
dimensions in the 0.5 to 4.0 kilometers range.
4. Urban scale: area of city-like dimensions, on the order of 4 to 50 kilometers. Within
a city, the geographic placement of sources may result in there being no single site
that can be said to represent air quality on an urban scale.
5. Regional scale: rural area of reasonably homogeneous geography without large
sources, and extends from tens to hundreds of kilometers.
6. National and global scales: concentrations characterizing the nation and the globe as
a whole.
At the time of the last review of the primary NO2 NAAQS, the majority of NO2 monitors were
sited to represent the neighborhood scale. We used the term "area-wide" to refer to monitors
sited at neighborhood, urban, and regional scales, as well as those monitors sited at either micro-
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or middle-scale that are representative of many such locations in the same core-based statistical
area (CBSA)35 (75 FR 6474, February 9, 2010).
In the 2010 review of the primary NO2 NAAQS, consideration of population exposures
was focused on major roadways. Due to the lack of monitors specifically sited near major
roadways, new near-road monitoring requirements were promulgated (75 FR 6474, February 9,
2010). At that time, one near-road monitor was required in any CBSA with a population of
500,000 or more. An additional near-road monitor was required in CBSAs with populations of at
least 2,500,000 and in CBSAs with populations of at least 500,000 with roadway segments
carrying traffic volumes of at least 250,000 vehicles per day.
The near-road network has been implemented in phases. The first phase included CBSAs
with populations greater than 1,000,000 and was required to be operational as of January 2014.
The second phase included CBSAs with populations greater than 2,500,000 and CBSAs having
at least 500,000 people that also had one or more road segments with an AADT of at least
250,000, and was required to be in operation starting in January 2015.36 As of January 2017, the
EPA estimates that 70 near-road monitors are in operation and reporting data to AQS. These
monitors are sited as close as two meters from the target road, with several monitors within 10
m. The EPA maintains a database containing the meta-data for near-road monitors,37 including
the relevant CBSA, population estimates, the AQS ID of the near-road site, latitude and
longitude, site installation date, the name of the target road of individual near-road monitors,
traffic volume data of the target road, the distance to the target road, the height of the monitor
probe, and information on other pollutant monitoring occurring at the site.
2.3 NOi MONITORING DATA TRENDS AND AIR QUALITY RELATIONSHIPS
This section presents information on ambient NO2 concentrations. Section 2.3.1 presents
data on national trends in ambient NO2 concentrations, section 2.3.2 presents data on the NO2
concentrations measured by recently deployed near-road monitors, section 2.3.3 presents data on
the relationships between 1-hour and annual NO2 concentrations, and section 2.3.4 discusses
background NO2 concentrations.
35 A CBSA is a geographic area defined by the Office of Management and Budget (OMB) that consists of one or
more counties anchored by an urban core with a population >10,000. CBSAs have replaced metropolitan statistical
areas (MSAs) that were previously used by OMB.
36 The EPA, through public notice and comment rulemaking, removed requirements for Phase 3 near-road monitors,
effective December 30, 2016 (81 FR 96381, December 30, 2016).
37 The database is found at: fattp://www3.epa. gov/ttn/amtic/nearroactfatinl
2-9
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2.3.1 National Trends in Ambient NO2 Concentrations
The metric used to determine whether areas meet or exceed the NAAQS is called a
design value (DV).38 In the case of NO2, there are 2 types of DVs: the annual DV and the hourly
DV. The annual DV for a particular year is the average of all hourly values within that calendar
year. The hourly DV is the three-year average of the 98th percentiles of the annual distributions
of daily maximum 1-hour NO2 concentrations. DVs are considered to be valid if the monitoring
data used to calculate them meet completeness criteria described in 40 CFR 50.11 and appendix
S to Part 50.39
The long-term trends in DVs across the U.S. are displayed in Figures 2-4 and 2-5. The
distributions of valid40 DVs across the country as a function of time are shown in Figure 2-4.
Figure 2-4 shows that DVs across the country have been, on average, declining since 1980.
TO
A design value is a statistic that describes the air quality status of a given area relative to the NAAQS. Design
values are typically used to classify nonattainment areas, assess progress towards meeting the NAAQS, and develop
control strategies. See http://epa.gov/airtrends/values.html for guidance on how these values are defined.
39 See 40 CFR 50.11 (available at http://www.ecfr.gov/cgi-bin/text-
idx?SID=86b930e674d72c8e0el4bb65c51a0047&mc=true&node=se40.2.50 lll&rgn=div8') and appendix S to
Part 50 for more information on the calculation of DVs.
40 40 CFR part 50 appendix S states that a year is considered complete when all 4 quarters have at least 75 percent of
the sampling days, with a sampling day requiring coverage of 75 percent of the hours in the day. The 1-hour DV
requires 3 years of complete data.
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Annual Design Values
Hourly Design Values
1990
2000
2010
250-
200-
150-
100-
0- ,
1980
1990
2000
2010
0-
1980
O
Z 20-
Figure 2-4. Distributions of NO2 design values across the U.S. from 1980- 2015. The middle
lines represent the median the middle white band extends from the 25th to the 75th percentile, and the outer colored
band extends from the 5th to the 95th percentile.
Figure 2-5 shows maps of the NO2 monitoring network, with the direction of the symbol and
color of each point indicating the long term (1980-2015) trend direction.41 Since 1980, there has
been data collected at 2099 sites in the U.S., cumulatively, with individual sites having a wide
range in duration of operations across multiple decades. However, only sampling sites with data
sufficient to produce at least 5 valid DVs, whenever they might have occurred, were considered
in this analysis. After this screen, 647 and 433 monitors were used to determine trends of annual
and hourly DVs, respectively.
41 These directions were determined using the sign of spearman correlation coefficient between DV and year. Only
DVs determined to be valid by the completeness criteria in CFR 40 Appendix S were included in the calculation.
Trend directions were determined to be insignificant if the associated p-values were greater than 0.05 (95%
confidence level).
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Figure 2-5 shows that the majority of sampling sites have observed statistically
significant downward trends in ambient NO2 concentrations, with the annual and hourly DVs
showing downward trends at 61.5% and 74.8% of monitoring sites, respectively.42 At 3.9% and
1.8% of sites, the annual and hourly DVs trended upward,43 and at the remaining 34.6% and
23.3% sites no significant trend was found. Even considering the fact there are a handful of sites
where upward trends in NO2 concentrations have occurred, the maximum DVs in 2015 for the
whole network were well-below the NAAQS, with the highest values being 30 ppb (annual) and
72 ppb (hourly).
42 Since this analysis required 5 valid DVs, and near-road monitors have not been in operation long enough to
calculate 5 DVs, these trends do not reflect the near-road monitoring network.
43 It is not clear what specific sources may be responsible for these upward trends in ambient NO2 concentrations. As
discussed above (section 2.1.2), since 1980 increases inNOx emissions have been observed for several types of
sources, including oil and gas production, agricultural field burning, prescribed fires and mining. Though relatively
small contributors nationally, emissions from these sources can be substantial in some areas (e.g., see Table 2-2 in
U.S. EPA, 2016).
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Annual
M
Hourly
v
V:
v \ J
v % ov
rW
3 \ LSfc
X7 n"
V Ox
»
¦"v
V
• First Valid DV from 2000 or later First Valid DV from before 2000
Direction of DV Trend: v Decreasing A Increasing ° Insignificant
Figure 2-5. Trend directions of NCh design values for 1980-2015 at U.S. sampling sites.
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2.3.2
Near-Road NO2 Air Quality
As discussed above, the largest single source of NOx emissions is on-road vehicles, and
emissions are primarily in the form of NO, with NO2 formation requiring both time and
sufficient O3 concentrations. Depending on local meteorological conditions and O3
concentrations, ambient NO2 concentrations can be higher near roadways than at sites in the
same area but farther removed from the road (and from other sources of NOx emissions). To
better understand the historical relationships between distributions of NO2 concentrations at
monitors near roadways and monitors further away from roads,44 the annual and hourly DVs
from 1980 to 2015 are plotted by decade, as a function of distance from road in Figures 2-6.45
This analysis focused on monitors located inside the boundary of CBSAs. In all graphs, the color
is mapped to the number of sites included in each boxplot.
44 As defined by the 2012 HPMS shapefile used to determine road locations, located at
http://www.fliwa.dot.gov/policvinfonnatioti/hpms/shapefiles.cfm. This file contains main roads that are part of the
National Highway System. See Appendix A for more details.
45 NO2 monitors meeting the near-road siting requirements set forth in the 2010 NO2 NAAQS were not available in
most CBSAs prior to 2014. In particular, monitors were not sited within 50 m of the most heavily trafficked roads in
an area. Thus, the historical relationships reflected in Figure 2-6 do not reflect the relationships that existed between
NO2 concentrations and distance from the most heavily trafficked roads. However, for the 2010 to 2015 bin, data
from recently deployed near-road monitors are included, to the extent available, even when completeness criteria for
calculating DVs are not met.
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[1980,1990) [1990,2000) [2000,2010) [2010,2015]
50-
Distance from road (m)
Figure 2-6. Distributions by decade of NO2 design values for six different bins of distances
from major roads in CBSAs. The middle lines represent the median, box edges represent the
25th and 75th percentiles, and whisker ends represent the 5th and 95th percentiles.
Figure 2-6 indicates that NO2 DVs are generally highest at sampling sites nearest to the
road (less than 50 meters) and decrease as distance from the road increases. This relationship is
more pronounced for annual DVs than for hourly DVs. The general pattern of decreasing DVs
with increasing distance from the road has persisted over time, though the absolute difference (in
terms of ppb) between NO2 concentrations close to roads and those farther from roads has
generally decreased over time (i.e., compare 1980-1990 DVs with more recent DVs). This
decrease is likely due to the concurrent decrease in mobile source NOx emissions that occurred
over the same time period discussed above (Figure 2-3).
Figures 2-7, 2-8, 2-9, and 2-10 further explore the relationships between NO2
concentrations measured by newly deployed near-road monitors and those measured by non-
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near-road monitors (generally area-wide46) in the same CBSA. For the years 2013, 2014, and
2015, we identified CBSAs with complete NO2 data from at least one near-road and one non-
near-road monitor.47 For the year 2016 we used all available data in AQS, as 4th quarter data
were not yet due into AQS at the time of the analysis. Each near-road monitor was paired with
the non-near-road monitor in the same CBSA that measured the highest 98111 percentile NO2
concentrations.48 Distributions of daily maximum 1-hour NO2 concentrations from these monitor
pairs are presented for 2013 (Figure 2-7), 2014 (Figure 2-8), 2015 (Figure 2-9), and the partial
data available for 2016 (Figure2-10).49
46 Non-near-road monitors can generally be considered area-wide, but in some cases, non-near-road monitors can be
located close to other sources of NOx emissions (e.g., ports, railyards).
47 As indicated, 40 CFR part 50 appendix S states that a year is considered complete when all 4 quarters have at least
75 percent of the sampling days, with a sampling day requiring coverage of 75 percent of the hours in the day.
48 98th percentiles from non-near-road monitors were based on the same years that the near-road monitor was in
operation.
49 The data from 2016 is based on what was in AQS in January 2017 and the data have not been certified.
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• Petl98 * Pctl99
NearRoad $ NonNearRoad
Figure 2-7. Distributions of the near-road and non-near-road maximum 1-hr daily NO2
concentrations from 2013. The middle lines represent the median, box edges represent the 25th
and 75th percentiles, and whisker ends represent the 5th and 95th percentiles. Circles represent
98th percentiles and triangles represent 99th percentiles.
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60-
?40-
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a
a
i
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6
4
i
=
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£
f
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3
j
s
ft
,©
W
4
f
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• Pctl98 1 Pctl99
NearRoad $ NonNearRoad
Figure 2-8. Distributions of the near-road and non-near-road maximum 1-hr daily NCh
concentrations from 2014. The middle lines represent the median, box edges represent the 25th
and 75th percentiles, and whisker ends represent the 5th and 95th percentiles. Circles represent
98th percentiles and triangles represent 99th percentiles.
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60-
£i *
• Pctl98 * Pctl99
NearRoad $ NonNearRoad
Figure 2-9. Distributions of the near-road and non-near-road maximum 1-hr daily NO2
concentrations from 2015. The middle lines represent the median, box edges represent the 25th
and 75th percentiles, and whisker ends represent the 5th and 95th percentiles. Circles represent
98th percentiles and triangles represent 99th percentiles.
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60-
•S 40 -
&
a
s
2
20-
3
af
#
9
a:
/
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i
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* Petl98 *¦ Pctl99
NearRoad F?3 NonNearRoad
Figure 2-10. Distributions of the available near-road and non-near-road maximum 1-hr
daily NOi concentration data from 2016. The middle lines represent the median, box edges
represent the 25th and 75th percentiles, and whisker ends represent the 5th and 95th percentiles.
Circles represent 98th percentiles and triangles represent 99th percentiles.
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For the 4 years of available data, Figures 2-7 to 2-10 indicate that daily maximum 1-hour
NO2 concentrations are generally higher at near-road monitors than at non-near-road monitors in
the same CBSA. The 98th percentiles of 1-hour daily maximum concentrations (the statistic most
relevant to the current standard) were highest at near-road monitors (i.e., higher than all non-
near-road monitors in the same CBSA) in 58-77% of the CBSAs evaluated, depending on the
year. Near road monitors reported higher annual average NO2 concentrations in virtually all
instances (Appendix A, Figure A-l).
2.3.3 Relationships between Hourly and Annual NO2 Concentrations
As discussed above, control programs have resulted in substantial reductions in NOx
emissions since the 1980s. These reductions in NOx emissions have decreased both short-term
peak NO2 concentrations and annual average concentrations. Figure 2-10 illustrates the
relationship between 1-hour and annual DVs at individual monitors across the U.S., with data
segregated by decade
When considering the change from the 1980-1990 bin to the 2010-2015 bin, the median
annual DV has decreased by about 65% (i.e., from -23 ppb to ~8 ppb) and the median 1-hour
DV has decreased by about 50% (i.e., from -74 ppb to -37 ppb) (Figure 2-10). At various times
in the past, a number of sites would have violated the 1-hour standard without violating the
annual standard; however, no sites would have violated the annual standard without also
violating the 1-hour standard. Furthermore, these data indicate that 1-hour DVs at or below 100
ppb generally correspond to annual DVs below 35 ppb. CASAC noted this relationship, stating
that "attainment of the 1-hour standard corresponds with annual design value averages of 30 ppb
NO2" (Diez Roux and Sheppard, 2017). Thus, meeting the 1-hour standard with its level of 100
ppb would be expected to maintain annual average NO2 concentrations well-below the 53 ppb
level of the annual standard.
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1980:1990
100-
75-
NAAQS = 100 ppb
CP
50-
)° o
25-
>
Q
19
3
c
3
2011:2015
NAAQS = 100 ppb
NAAQS = 53 ppb
150
Hourly DV
Figure 2-11. Relationships between annual and hour DVs from 1980 to 2015. Hourly and
annual DVs are plotted for various decades. Linear regression lines are shown. Near-road
monitors are not included in this analysis due to the limited amount of data available.
1991:2000
100-
.NAAQS=53ppb
100-
NAAQS- 53 ppb
NAAQS = 100 ppb
2001:2010
NAAQS = 100 ppb
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2.3.4 Background NO2 Concentrations
In the context of the review of a NAAQS, the EPA generally defines background
concentrations in a way that distinguishes among concentrations that result from precursor
emissions that are relatively less controllable from those that are relatively more controllable
through U.S. policies or through international agreements. One approach to considering
background concentrations is to estimate the pollutant concentrations that would exist in the
absence of anthropogenic emissions from the U.S., Canada, and Mexico. Such background
concentrations are referred to as North American Background (NAB). NAB includes
contributions resulting from emissions by natural sources (e.g., soils, wildfires, and lightning
around the world and by anthropogenic sources outside of the U.S., Canada, and Mexico.50
NO2 background concentrations are much lower than the NO2 concentrations currently
measured in the ambient air (and much lower than current standard levels). In particular, as
discussed in the 2008 ISA, NAB is less than 300 ppt over most of the continental U.S. and less
than 100 ppt in the eastern U.S. (U.S. EPA, 2008, Figure 2.4-18). The distribution of background
concentrations in the 2008 ISA was shown to reflect the distribution of soil NO emissions and
lightning, with some local increases due to biomass burning, mainly in the western U.S. In the
northeastern U.S., where present-day NO2 concentrations are highest, NAB contributes <1% to
the total NO2 concentration (U.S. EPA, 2016, Section 2.5.6).
50 Other approaches to defining background include U.S. background (USB), which includes contributions from
emissions from natural sources and from anthropogenic sources outside the U.S., and natural background, which
includes only contributions from emissions from natural sources.
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2.4 REFERENCES
During, I; Bachlin, W; Ketzel, M; Baum, A; Friedrich, U; Wurzler, S. (2011). A new simplified N0/N02
conversion model under consideration of direct N02-emissions. Meteor Z 20: 67-73.
http://dx.doi.org/10.1.1.27/0941. -2948/201.1/0491.
Hameed, S; Pinto, JP; Stewart, RW. (1979). Sensitivity of the predicted CO-OH-CH4 perturbation to tropospheric
NOx concentrations. J Geophys Res Oceans 84: 763-768. http://dx.doi.org/10.1029/JC084iC02pQ0763
Itano Y et al. (2014). Estimation of Primary N02/NOx Emission Ratio from Road Vehicles Using Ambient
Monitoring Data. Studies in Attn Sci, 1-7
Jimenez J.L. et al. (2000). Remote sensing of NO and N02 emissions from heavy-duty diesel trucks using tunable
diode lasers. Environ Sci Technol, 2380-2387.
Kimbrough S et al. (2016). Near-road NO to N02 Conversion Rate Study using Field Measurements and Dispersion
Modeling Techniques in Las Vegas, NV. Submitted.
Kota S. H. et al. (2013). Simulating near-road reactive dispersion of gaseous air pollutants using a three-dimensional
eulerian model. Sci Total Environ, Simulating near-road reactive dispersion of gaseous air pollutants using
a three-dimensional eulerian model.
Ren, X; vanDuin, D; Cazorla, M; Chen, S; Mao, J; Zhang, L, i; Brune, WH; Flynn, JH; Grossberg, N; Lefer, BL;
Rappenglueck, B; Wong, K; Tsai, C; Stutz, J; Dibb, JE; Jobson, BT; Luke, WT; Kelley, P. (2013).
Atmospheric oxidation chemistry and ozone production: Results from SHARP 2009 in Houston, Texas. J
Geophys Res Atmos 118: 5770-5780. http://dx.doi.org/10.1.002/igrd.50342
Richmond-Bryant, J; Reff, A. (2012). Air pollution retention within a complex of urban street canyons: A two-city
comparison. Atmos Environ 49: 24-32. http://dx.doi.org/10.1.016/i.atmosenv.201.1.1.2.036
Richmond-Bryant J et al. (2016). Estimation of on-road NO2 concentrations, NO2/NOX ratios, and related roadway
gradients from near-road monitoring data. Submitted to Air Quality, Atm and Health .
U.S. EPA. (2008). Integrated Science Assessment for Oxides of Nitrogen - Health Criteria. U.S. EPA, National
Center for Environmental Assessment and Office, Research Triangle Park, NC. EPA/600/R-08/071. July
2008. Available at: http://cfpnb.epa.gov/ncea/cfm/recordisplav.cfm?deid= .1.94645.
U.S. EPA. (2016). Integrated Science Assessment for Oxides of Nitrogen - Health Criteria (2016 Final Report). U.S.
EPA, National Center for Environmental Assessment, Research Triangle Park, NC. EPA/600/R-15/068.
January 2016. Available at: https://cfpnb.epa.gov/ncea/isa/recordisplav.cfm?deid=3.1.0879.
Valin, LC; Russell, AR; Cohen, RC. (2013). Variations of OH radical in an urban plume inferred from NO 2 column
measurements. Geophys Res Lett 40: 1856-1860. http://dx.doi.org/10.1002/grL50267
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3 CONSIDERATION OF EVIDENCE FOR NO2-RELATED HEALTH
EFFECTS
In this chapter, the scientific evidence on health effects attributable to short or long-term
NO2 exposure is discussed, with a focus on the most policy relevant information. Staff has drawn
from the EPA's synthesis and assessment of the scientific evidence presented in the Integrated
Science Assessment for Oxides ofNitrogen - Health Criteria (ISA) (U.S. EPA, 2016). In this
chapter, section 3.1 summarizes the weight of evidence approach used in evaluating and
integrating scientific evidence in the ISA. Section 3.2 discusses the evidence for health effects
attributable to short-term NO2 exposures, and section 3.3 discusses the evidence for health
effects attributable to long-term NO2 exposures. Section 3.4 discusses the potential public health
implications of NCh-attributable effects.
3.1 WEIGHT OF EVIDENCE IN THE ISA
In the current review of the primary NO2 NAAQS, the Agency has used two frameworks:
one for characterizing the strength of the available scientific evidence for health effects
attributable to NO2 exposures and the other a recently developed framework to classify evidence
for factors that may increase risk in some populations or lifestages51 (U.S. EPA, 2015, Preamble,
Section 6). These frameworks provide the basis for robust, consistent, and transparent evaluation
of the scientific evidence, including uncertainties in the evidence, and for drawing conclusions
on air pollution-related health effects and at-risk populations.
With regard to characterization of the health effects, the ISA uses 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, 2015, Preamble Table II). In using the weight-of-evidence approach to inform
judgments about the likelihood that various health effects are caused by exposure to NO2, the
ISA notes that confidence in the relationship increases when the evidence base is large and
consistently supports a relationship with a particular health endpoint. In addition, biological
plausibility, strength, and coherence in the evidence are important aspects considered in making
judgments regarding causality of relationships. Conclusions about biological plausibility,
consistency, and coherence of N02-related health effects are drawn from the integration of
multiple lines of evidence including epidemiologic, controlled human exposure, and animal
51 As defined in Chapter I, the term "population" refers to people having a quality or characteristic in common,
including a specific pre-existing illness or a specific age or lifestage.
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toxicological studies as discussed in the ISA (U.S. EPA, 2015, Preamble, Section 5.c.) and
further described below. In this PA, we consider the full body of health evidence, placing the
greatest emphasis on the effects for which the evidence has been judged in the ISA to
demonstrate a "causal" or a "likely to be a causal" relationship with NO2 exposures.
Controlled human exposure studies can provide direct evidence of relationships between
pollutant exposures and human health effects (U.S. EPA, 2015, Preamble Section 4.c). Because
data on health effects in these studies are collected under closely monitored conditions, this type
of evidence can provide information on exposure concentrations, durations, and ventilation rates
under which effects can occur, as well as information on exposure-response relationships.
Further, as discussed in the ISA, controlled human exposure studies can provide clear and
compelling evidence for an array of human health effects that are directly attributable to acute
exposures to NO2per se (i.e., as opposed to other oxides of nitrogen species, for which NChis an
indicator, or other co-occurring pollutants) (U.S. EPA, 2015, Preamble Section 4.c). In addition,
exposure concentrations used in some controlled human exposure studies are near those found in
the ambient air and results are not subject to uncertainties related to inter-species variation.
Toxicological studies in animals provide another line of experimental evidence that can
inform understanding of effects related to NO2 exposures, particularly the biological action of a
pollutant under controlled and monitored exposure circumstances. Compared to controlled
human exposure studies, animal toxicological studies can examine more severe outcomes,
invasive endpoints (i.e., pathology), and effects of long-term exposures. However, results from
animal studies are subject to uncertainty due to inter-species variation.52 Also, animal studies are
often conducted with NO2 concentrations well above those in ambient air. Although some of
these high concentrations are considered to be ambient-relevant because of dosimetric
considerations (U.S. EPA, 2016, Section 1.2), results from animal studies are subject to
uncertainties regarding the likelihood that such effects could occur with ambient exposures in
humans. Nonetheless, evidence from animal studies can provide support for effects observed in
human studies. Together, evidence from human and animal studies can provide information on
and confidence regarding key events in the proposed mode(s) of action, which informs biological
plausibility for health effects observed in epidemiologic studies.
52 "The differences between humans and other species have to be considered, including metabolism, hormonal
regulation, breathing pattern, and differences in lung structure and anatomy. Given these differences, uncertainties
are associated with quantitative extrapolations of observed pollutant-induced pathophysiological alterations between
laboratory animals and humans, as those alterations are under the control of widely varying biochemical, endocrine,
and neuronal factors." (U.S. EPA, 2016, pp. liii).
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Epidemiologic studies provide information on associations between variability in short-
term and long-term average ambient NO2 concentrations and various health outcomes, including
those related to asthma exacerbation and incidence (i.e., airway responsiveness, lung function
decrements, respiratory symptoms, pulmonary inflammation, hospital admissions, emergency
department visits, and asthma incidence) (U.S. EPA, 2016, Chapters 5 and 6). Epidemiologic
studies can inform our understanding of the effects in the study population of real-world
exposures to the range of NO2 concentrations in ambient air, and can provide evidence of
associations between exposures to ambient NO2 and serious acute and chronic health effects that
cannot be assessed in controlled human exposure studies. Moreover, epidemiologic studies often
include populations or lifestages that may have increased risk for pollutant-related health effects
(e.g., individuals with pre-existing disease, children, and older adults). In evaluating
epidemiologic studies, it is important to consider the degree of uncertainty introduced by
potential confounding variables (e.g., other pollutants, temperature) and other factors (e.g., study
design, exposure assessment, statistical methods) affecting the level of confidence that the
observed health effects are independently related to ambient exposure to NO2.
The ISA also includes an evaluation and synthesis of evidence across scientific
disciplines to inform whether specific populations or lifestages may be at increased risk of a
health effect related to NO2 exposures. The ISA characterizes the evidence for a number of
"factors", including both intrinsic (i.e., biologic, such as pre-existing disease or lifestage) and
extrinsic (i.e., non-biologic, such as diet or socioeconomic status) factors. The categories
considered in classifying the evidence for these potential at-risk factors are "adequate evidence,"
"suggestive evidence," "inadequate evidence," and "evidence of no effect." These categories are
discussed in more detail in the ISA (U.S. EPA, 201, Section 5.c, Table II). In this PA, we focus
our consideration of potential at-risk populations and lifestages on those factors for which the
ISA judges there is "adequate" evidence (U.S. EPA, 2016, Table 7-27). The primary NAAQS are
set requisite to protect the public health with an adequate margin of safety, including the health
of populations at increased risk for pollutant-related health effects, and thus, identifying at-risk
populations and lifestages is a critical part of this review. At-risk populations and potential
public health implications are discussed in more detail in Section 3.4.
3.2 EFFECTS OF SHORT-TERM NOi EXPOSURES
This section discusses the nature of the health effects that have been shown to occur
following short-term NO2 exposures (Section 3.2.1) and the NO2 concentrations at which those
effects have been demonstrated to occur (Section 3.2.2).
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3.2.1
Nature of Effects
Across previous reviews of the primary NO2 NAAQS (U.S. EPA, 1993; U.S. EPA,
2008), evidence has consistently demonstrated respiratory effects attributable to short-term NO2
exposures. In the last review, the 2008 ISA concluded that evidence was "sufficient to infer a
likely causal relationship between short-term NO2 exposure and adverse effects on the
respiratory system" based on the large body of epidemiologic evidence demonstrating positive
associations with respiratory symptoms and hospitalization or ED visits as well as supporting
evidence from controlled human exposure and animal studies (U.S. EPA, 2008, p. 5-6). Evidence
for cardiovascular effects and mortality attributable to short-term NO2 exposures was weaker and
was judged "inadequate to infer the presence or absence of a causal relationship" and "suggestive
of, but not sufficient to infer, a causal relationship," respectively. The 2008 ISA noted an
overarching uncertainty in determining the extent to which NO2 is independently associated with
effects or if NO2 is a marker for the effects of another traffic-related pollutant or mix of
pollutants (U.S. EPA, 2008, Section 5.3.2.2 to 5.3.2.6).
For the current review, there is newly available evidence for both respiratory effects and
other health effects critically evaluated in the ISA as part of the full body of evidence informing
the nature of the relationship between health effects and short-term exposures to NO2 (U.S. EPA,
2016). In characterizing the available evidence and the causal determinations presented in the
ISA, this section poses the following policy-relevant questions:
• To what extent does the currently available scientific evidence strengthen, or otherwise
alter, our conclusions from the last review regarding health effects attributable to
short-term NO2 exposure? Have previously identified uncertainties been reduced?
What important uncertainties remain and have new uncertainties been identified?
As discussed above, causal determinations for health effects related to short-term NO2
exposures are presented in the ISA, which classifies short-term exposures as those that are one
month or less (U.S. EPA, 2016). Table 3-1, below, lists the causal determinations from the ISA
for the current review as well as those from the previous review for respiratory and
cardiovascular health effects, and mortality.53 It is noteworthy that the causal determinations for
respiratory and cardiovascular health effects have been strengthened in the current review due, in
part, to more explicit consideration of the evidence integrated for specific outcomes (e.g., asthma
exacerbation for respiratory) rather than broad outcome categories (e.g., all respiratory effects).
53 Short-term exposure studies on reproductive and birth health effects and cancer are considered below in the
context of long-term exposures.
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The evidence informing these determinations, including uncertainties in that evidence, is
summarized below.
Table 3-1. ISA causal determinations for health effects related to short-term nitrogen
dioxide (NO2) exposures
Health effect
Review completed 2010
Current Review
Respiratory
Sufficient to infer a likely causal
relationship
Causal relationship
Cardiovascular
Inadequate to infer the presence or
absence of a causal relationship
Suggestive of, but not sufficient to
infer, a causal relationship
Total Mortality
Suggestive of, but not sufficient to
infer, a causal relationship
Suggestive of, but not sufficient to
infer, a causal relationship
Respiratory
The ISA concludes that evidence for respiratory effects related to short-term NO2
exposures indicates that there is a causal relationship, primarily based on evidence for asthma
exacerbation. This conclusion is strengthened from the last review "because epidemiologic,
controlled human exposure, and animal toxicological evidence together can be linked in a
coherent and biologically plausible pathway to explain how NO2 exposure can trigger an asthma
exacerbation" (U.S. EPA, 2016, p. 1-17). The 2008 ISA described much of the same evidence
and determined it was "sufficient to infer a likely causal relationship," citing uncertainty as to
whether the epidemiologic results for NO2 primarily reflected the effects of other traffic-related
pollutants. The 2008 ISA did not explicitly evaluate the extent to which various lines of evidence
supported effects on asthma attacks. In contrast, in the current review the ISA states that "the
determination of a causal relationship is not based on new evidence as much as it is on the
integrated findings for asthma attacks with due weight given to experimental studies" (U.S. EPA,
2016, p. lxxxiii).54 When taken together, the epidemiologic evidence for asthma attacks and
controlled human exposure study findings for increased airway responsiveness (AR)55 and
allergic inflammation demonstrate that short-term NO2 exposure has an independent relationship
54 As noted above, experimental studies such as controlled human exposure studies provide support for effects of
exposures to NO2 itself, and generally do not reflect the complex atmospheres to which people are exposed.
55 The ISA states that airway responsiveness is "inherent responsiveness of the airways to challenge by
bronchoconstricting agents" (U.S. EPA, 2016, p. 5-9). More specifically, airway responsiveness refers to increased
sensitivity of the airways to an inhaled bronchoconstricting agent. This is most often quantified as the dose of
challenge agent that results in a 20% reduction in FEVi, but some studies report the change in FEVi for a specified
dose of challenge agent. The change in specific airways resistance (sRaw) is also used to quantify AR.
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with respiratory effects, specifically with asthma exacerbation, and is not just an indicator for
other traffic-related pollutants.
The evaluation of controlled human exposure studies in the ISA focuses on results from a
recently published meta-analysis of NCh-induced increases in AR by Brown (2015). AR has
been the key respiratory outcome from controlled human exposures in the previous and current
reviews of the primary NO2 NAAQS, and the ISA specifically notes that "airway
hyperresponsiveness can lead to poorer control of symptoms and is a hallmark of asthma" (U.S.
EPA, 2016, p. 1-18). Brown (2015) examined the relationship between AR and NO2 exposures in
subjects with asthma across the large body of controlled human exposure studies,56 most of
which were available in the last review (Tables 3-2 and 3-3). More specifically, the meta-
analysis identified the fraction of individuals having an increase in AR following NO2 exposure,
compared to the fraction having a decrease, across studies. The meta-analysis also stratified
results to consider the influence of factors that may affect results including exercise/rest and non-
specific/specific challenge agents.57
The results from the meta-analysis demonstrate that the majority of study volunteers with
asthma experienced increased AR following resting exposure to NO2 concentrations ranging
from 100 to 530 ppb, relative to filtered air. While results from individual studies do not always
demonstrate N02-induced increases in AR, particularly for exposure concentrations between 100
and 200 ppb (U.S. EPA, 2016, Table 5-1; Section 3.2.2.1, below), and important uncertainties
remain due to the lack of an apparent a dose-response relationship (see below), the meta-analysis
indicates that when data are pooled, a statistically significant majority of study volunteers
experienced increases in nonspecific airway responsiveness. Significant majorities experienced
such increases following (1) 20 to 60-minute exposures to 400-530 ppb NO2, (2) 30-minute
exposures to 250 to 300 ppb NO2, and (3) 60-minute exposures to 100 to 200 ppb NO2. When
comparing results across the three exposure categories, the fractions of individuals with
increased AR (out of those who experienced either an increase or a decrease) were 73%, 78%,
56 While these controlled human exposure studies were conducted in people with asthma, a group at increased risk
for N02-related effects, they generally did not evaluate individuals with severe asthma (Brown, 2015). These studies
also did not evaluate people in other potentially at-risk groups.
57 "Bronchial challenge agents can be classified as nonspecific (e.g., histamine; SO2; cold air) or specific (i.e., an
allergen). Nonspecific agents can be differentiated between "direct" stimuli (e.g., histamine, carbachol, and
methacholine) which act on airway smooth muscle receptors and "indirect" stimuli (e.g., exercise, cold air) which
act on smooth muscle through intermediate pathways, especially via inflammatory mediators. Specific allergen
challenges (e.g., house dust mite, cat allergen) also act "indirectly" via inflammatory mediators to initiate smooth
muscle contraction and bronchoconstriction." (U.S. EPA, 2016, p. 5-8)
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and 67%, respectively, and all were statistically significant with /^-values < 0.05 (U.S. EPA,
2016, Table 5-3).
In considering the potential for these increases in AR to be adverse, we note the ISA's
characterization of their clinical relevance. The ISA uses an approach to characterizing clinical
relevance that is based on guidelines from the American Thoracic Society (ATS) and the
European Respiratory Society (ERS) for the assessment of therapeutic agents (Reddel et al.,
2009). Specifically, based on individual-level responses reported in a subset of studies, the ISA
considered a halving of the provocative dose (PD) to indicate responses that may be clinically
relevant.58'59 With regard to this approach, the ISA notes that "in a joint statement of the [ATS]
and [ERS], one doubling dose change in PD is recognized as a potential indicator, although not a
validated estimate, of clinically relevant changes in airway responsiveness (Reddel et al., 2009)"
(U.S. EPA, 2016, p. 5-12). While there is uncertainty in using this approach to characterize
whether a particular response in an individual is "adverse," it can provide insight into the
potential for adversity, particularly when applied to a population of exposed individuals.60
Based on a subset of studies, Brown (2015) shows thatNCh exposures from 100 to 530
ppb resulted in a halving of the dose of a challenge agent required to increase airway
responsiveness (i.e., a halving of the PD) for about a quarter of study volunteers. While these
results support the potential for clinically relevant increases in AR in some individuals with
asthma following NO2 exposures within the range of 100 to 530 ppb, uncertainty remains given
that this analysis is limited to a small subset of the studies included in the broader meta-analysis
and given the lack of an apparent dose-response relationship.61 In addition, compared to
conclusions based on the entire range of NO2 exposure concentrations evaluated (i.e., 100 to 530
ppb), there is greater uncertainty in reaching conclusions about the potential for clinically
relevant effects at any particular NO2 exposure concentration within this range.
58 PD is the dose of challenge agent required to elicit a particular magnitude of change in FEVi or other measure of
lung function.
59 The ISA's characterization of a clinically relevant response is based on evidence from controlled human exposure
studies evaluating the efficacy of inhaled corticosteroids that are used to prevent bronchoconstriction and airway
responsiveness as described by Reddell et al. (2009). Generally, a change of at least one doubling dose is considered
to be an indication of clinical relevance. Based on this, a halving of the PD is taken in the ISA to represent an
increase in AR that indicates a clinically relevant response.
60 Based on recommendations from the ATS, if a population is exposed, NCh-induced increases in AR may be
considered adverse at the population level. This is because the increases in AR could increase the proportion of the
population with clinically important changes that can contribute to the exacerbation of asthma (U.S. EPA, 2016,
section 1.6.5). Chapter 4 (below) presents the results of analyses evaluating the degree to which populations in U.S.
urban areas could experience NO2 exposures shown to increase AR.
61 Section 3.2.2.1 below includes additional discussion of these uncertainties.
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Controlled human exposure studies also evaluated a range of other respiratory effects,
including lung function decrements, respiratory symptoms, and pulmonary inflammation. The
evidence does not consistently demonstrate these effects following exposures to NO2
concentrations at or near those found in the ambient air in the U.S. However, a subset of studies
using exposures to 260 ppb for 15-30 min or 400 ppb for up to 6 hours provide evidence that
study volunteers with asthma and allergy can experience increased inflammatory responses
following allergen challenge. Evidence for pulmonary inflammation was more mixed across
studies that did not use an allergen challenge following NO2 exposures ranging from 300-1,000
ppb (U.S. EPA, 2016, Section 5.2.2.5).
In addition to this evidence for NCh-induced increases in AR and allergic inflammation
in controlled human exposure studies, the ISA also describes consistent evidence from
epidemiologic studies for positive associations between short-term NO2 exposures and an array
of respiratory outcomes related to asthma. Thus, coherence and biological plausibility is
demonstrated in the evidence integrated between controlled human exposure studies and the
various asthma-related outcomes examined in epidemiologic studies. The ISA indicates that
epidemiologic studies consistently demonstrate N02-health effect associations with asthma
hospital admissions and ED visits among subjects of all ages and children, and with asthma
symptoms in children (U.S. EPA, 2016, Sections 5.2.2.4 and 5.2.2.3). The robustness of the
evidence is demonstrated by associations found in studies conducted in diverse locations in the
U.S., Canada, and Asia, including several multicity studies. The evidence for asthma
exacerbation is substantiated by several recent studies with strong exposure assessment
characterized by measuring NO2 concentrations in subjects' location(s). Epidemiologic studies
also demonstrated associations between short-term NO2 exposures and respiratory symptoms,
lung function decrements, and pulmonary inflammation, particularly for measures of personal
total and ambient NO2 exposures and NO2 measured outside schools. This is important because
there is considerable spatial variability in NO2 concentrations, and measurements in subjects'
locations may better represent this variability in ambient NO2 exposures, compared to
measurements at central site monitors (U.S. EPA, 2016, Sections 2.5.3 and 3.4.4). Epidemiologic
studies generally did not find N02-associated changes in inflammatory cell counts in populations
with asthma; however, they did consistently indicate ambient or personal NCh-associated
increases in exhaled nitric oxide (eNO, a marker of airway inflammation), which is coherent with
experimental findings for allergic inflammation (U.S. EPA, 2016, Section 5.2.2.6).
In assessing the evidence from epidemiologic studies, the ISA not only considers the
consistency of effects across studies, but also evaluates other study attributes that affect study
quality, including potential confounding and exposure assignment. Regarding potential
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confounding, the ISA notes that NO2 associations with asthma-related effects persist with
adjustment for temperature; humidity; season; long-term time trends; and PM10, SO2, or O3.
Recent studies also add findings for NO2 associations that generally persist with adjustment for
key copollutants, including PM2.5 and traffic-related copollutants such as elemental carbon (EC)
or black carbon (BC), ultra-fine particles (UFPs), or carbon monoxide (CO) (examined in few
studies). Confounding by organic carbon (OC), PM metal species, or volatile organic compounds
(VOCs) is poorly studied, but NO2 associations with asthma exacerbation tend to persist in the
few available copollutant models. We recognize, however, that copollutant models have inherent
limitations and cannot conclusively rule out confounding (U.S. EPA, 2015, Preamble, Section
4.b). Recent epidemiologic results also suggest asthma exacerbation in relation to indices that
combine NO2 with EC, PM2.5, O3, and/or SO2 concentrations, but neither epidemiologic nor
experimental studies strongly indicate synergistic effects between NO2 and copollutants (U.S.
EPA, 2016, Section 5.2.9).
The ISA also notes that results based on personal exposures or pollutants measured at
people's locations provide support for NO2 associations that are independent of PM2.5, EC/BC,
OC, or UFPs. Compared to ambient NO2 concentrations measured at central-site monitors,
personal NO2 exposure concentrations and indoor NO2 concentrations exhibit lower correlations
with many traffic-related copollutants (e.g., r = -0.37 to 0.31). Thus, these health effect
associations with personal and indoor NO2 may be less prone to confounding by these traffic-
related copollutants (U.S. EPA, 2016, Section 1.4.3).
Overall, in consideration of this evidence in answering the question posed above, we note
that for respiratory effects, the strongest evidence supporting the conclusion of the causal
relationship determined in the ISA comes from controlled human exposure studies demonstrating
N02-induced increases in AR in individuals with asthma, with supporting evidence for a range of
respiratory effects from epidemiologic studies. The conclusion of a causal relationship in the ISA
is based on this evidence, and its explicit integration within the context of effects related to
asthma exacerbation. Most of the controlled human exposure studies assessed in the ISA were
available in the last review, particularly studies of non-specific AR, and thus, do not themselves
provide substantively new information. However, by pooling data from a subset of studies, the
newly available meta-analysis by Brown (2015) has partially addressed an uncertainty from the
last review by demonstrating the potential for clinically relevant increases in AR following
exposures to NO2 concentrations in the range of 100 to 530 ppb. Similarly, the epidemiologic
evidence that is newly available in the current review is consistent with evidence from the last
review and does not alter our understanding of respiratory effects related to ambient NO2
exposures. New epidemiologic evidence does, however, reduce some uncertainty from the last
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review regarding the extent to which effects may be independently related to NO2 as there is
more evidence from studies using measures that better capture personal exposure as well as a
more robust evidence base examining copollutant confounding. Some uncertainty remains in the
epidemiologic evidence regarding confounding by the most relevant copollutants (i.e., those
from traffic).
Cardiovascular
The evidence for cardiovascular health effects and short-term NO2 exposures in the 2016
ISA was judged "suggestive of, but not sufficient to infer, a causal relationship" (U.S. EPA,
2016, Section 5.3.11), which is stronger than the conclusion in the last review that the evidence
was "inadequate to infer the presence or absence of a causal relationship." The more recent
causal determination was primarily supported by consistent epidemiologic evidence from
multiple new studies indicating associations for triggering of a myocardial infarction. However,
further evaluation and integration of evidence points to uncertainty related to exposure
measurement error and potential confounding by traffic-related pollutants. There is consistent
evidence demonstrating NCh-associated hospital admissions and ED visits for ischemic heart
disease, myocardial infarction, and angina as well as all cardiovascular diseases, which is
coherent with evidence from other studies indicating NCh-associated repolarization
abnormalities and cardiovascular mortality. There are experimental studies that provide some
evidence for effects on key events in the proposed mode of action (e.g., systemic inflammation),
but these studies do not provide evidence that is sufficiently coherent with the epidemiologic
studies to help rule out chance, confounding, and other biases. In particular, the ISA concludes
that "[t]here continues to be a lack of experimental evidence that is coherent with the
epidemiologic studies to strengthen the inference of causality for NCh-related cardiovascular
effects, including [myocardial infarction]" (U.S. EPA, 2016, p. 5-335). Beyond evidence for
myocardial infarction, there were studies examining other cardiovascular health effects, but
results across these outcomes are inconsistent. Thus, while the evidence is stronger in the current
review than in the last review, important uncertainties remain regarding the independent effects
of NO2.
Mortality
The ISA concludes that the evidence for short-term NO2 exposures and total mortality is
"suggestive of, but not sufficient to infer, a causal relationship" (U.S. EPA, 2016, Section 5.4.8),
which is the same conclusion reached in the last review (U.S. EPA, 2008). Several recent
multicity studies add to the evidence base for the current review and demonstrate associations
that are robust in copollutant models with PM10, O3, or SO2. However, confounding by traffic-
related copollutants, which is of greatest concern, is not examined in the available copollutant
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models for NCh-associated mortality. Overall, the recent evidence assessed in the ISA builds
upon and supports conclusions in the last review, but key limitations across the evidence include
a lack of biological plausibility as experimental studies and epidemiologic studies on
cardiovascular morbidity, a major cause of mortality, do not clearly provide a mechanism by
which N02-related effects could lead to mortality. In addition, important uncertainties remain
regarding the independent effect of NO2.
3.2.2 Consideration of NO2 Concentrations: Health Effects of Short-Term NO2 Exposures
In evaluating the NO2 exposure concentrations associated with health effects within the
context of the adequacy of the current standard, we consider the following specific question:
• To what extent does the evidence indicate adverse respiratory effects attributable to
short-term exposures to NO2 concentrations lower than previously identified or below the
existing standards?
In addressing this question, we further consider the extent to which NCh-induced adverse effects
have been reported over the ranges of NO2 exposure concentrations evaluated in controlled
human exposure studies and the extent to which N02-associated effects have been reported for
distributions of ambient NO2 concentrations in epidemiologic study locations meeting existing
standards. Each of these is discussed below.
3.2.2.1 NO2 Concentrations in Controlled Human Exposure Studies
As discussed in detail in the ISA (U.S. EPA, 2016) and summarized above in section
3.2.1, controlled human exposure studies, most of which were available and considered in the
last review, have evaluated various respiratory effects following short-term NO2 exposures.
These include AR, inflammation and oxidative stress, respiratory symptoms, and lung function
decrements. Generally, when considering respiratory effects from controlled human exposure
studies in healthy adults without asthma, evidence does not indicate respiratory symptoms or
lung function decrements following NO2 exposures below 4,000 ppb and limited evidence
indicates airway inflammation following exposures below 1,500 ppb (U.S. EPA, 2016, Section
5.2.7).62 There is a substantial body of evidence demonstrating increased AR in healthy adults
with exposures in the range of 1,500-3,000 ppb.
Evidence for respiratory effects following exposures to NO2 concentrations at or near
those found in the ambient air is strongest for AR in individuals with asthma (U.S. EPA, 2016,
Section 5.2.2 p. 5-7). In contrast, controlled human exposure studies evaluated in the ISA do not
62 Exposure durations were from one to three hours in studies evaluating AR and respiratory symptoms, and up to
five hours in studies evaluating lung function decrements.
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provide consistent evidence for respiratory symptoms, lung function decrements, or pulmonary
inflammation in adults with asthma following exposures to NO2 concentrations at or near those
in ambient air (i.e., <1,000 ppb; U.S. EPA, 2016, Section 5.2.2). There is some indication of
allergic inflammation in adults with allergy and asthma following exposures to 260-1,000 ppb.
However, evidence across studies is inconsistent, making it difficult to interpret the likelihood
that these effects could potentially occur following NO2 exposures at or below the level of the
current standard.
Thus, in considering evidence from controlled human exposure studies to address the
above question, we focus on the body of evidence for NCh-induced increases AR in adults with
asthma. In evaluating the NO2 exposure concentrations at which increased AR is observed, we
consider both the group mean results reported in individual studies and the results evaluated
across studies in a recent meta-analysis (Brown, 2015; U.S. EPA, 2016, Section 5.2.2.1). Group
mean responses in individual studies, and the variability in those responses, can provide insight
into the extent to which observed changes in AR are due to NO2 exposures, rather than to chance
alone, and have the advantage of being based on the same exposure conditions. The meta-
analysis by Brown (2015) can aid in identifying trends in individual-level responses across
studies and can have the advantage of increased power to detect effects, even in the absence of
statistically significant effects in individual studies.
Tables 3-2 and 3-3 (adapted from the ISA; U.S. EPA, 2016, Tables 5-1 and 5-2) provide
details for the studies examining AR in individuals with asthma at rest and with exercise,
respectively. These tables note various study details including the exposure concentration,
duration of exposure, type of challenge (nonspecific or specific63), number of study subjects,
number of subjects having an increase or decrease in AR following NO2 exposure, average
provocative dose (PD; dose of challenge agent required to elicit a particular magnitude of change
in FEVi or other measure of lung function) across subjects, and the statistical significance of the
change in AR following NO2 exposures.
63 As previously described, bronchial challenge agents can be classified as nonspecific (e.g., histamine; sulfur
dioxide, SO2; cold air) or specific (i.e., an allergen). Nonspecific agents can be differentiated between "direct"
stimuli (e.g., histamine, carbachol, and methacholine) and "indirect" stimuli (e.g., exercise, cold air) (U.S. EPA,
2016)
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Table 3-2. Resting exposures to nitrogen dioxide and airway responsiveness in
individuals with asthma.3
no2
PPb
Exp.
(min)
Change
in ARb
Average PD ± SEC
Reference
Challenge Type
N
+
-
Air
NO2
p-valued
Ahmed et al., 1983a
100
60
Non-specific,
CARB
20
13
7
6.0 ± 2.4
2.7 ± 0.8
NA
Orehek et al., 1976
100
60
Non-specific,
CARB
20
14
3
0.56 ± 0.08
0.36 ± 0.05
<0.01e
Hazucha et al., 1983
100
60
Non-specific,
METH
15
6
7
1.9 ± 0.4
2.0 ± 1.0
n.s.
Ahmed et al., 1983b
100
60
Specific, RAG
20
10
8
9.0 ± 5.7
11.7 ± 7.6
n.s.
Tunnicliffe et al., 1994
100
60
Specific, HDM
8
3
5
-14.62
AFEV1
-14.41
AFEV1
n.s.
Bylin et al., 1988
140
30
Non-specific, HIST
20
14
6
0.39 ± 0.07
0.28 ± 0.05
0.052f
Orehek et al., 1976
200
60
Non-specific,
CARB
4
3
0
0.60 ±0.10
0.32 ± 0.02
n.s.
Jorres et al., 1990
250
30
Non-specific, SO2
14
11
2
46.5 ± 5.1
37.7 ± 3.5
<0.01
Barck et al., 2002
260
30
Specific, BIR, TIM
13
5
7
-5 ±2
AFEV1
-4 ±2
AFEV1
n.s.
Strand et al., 1997
260
30
Specific, BIR, TIM
18
9
9
860 ± 450
970 ± 450
n.s.
Strand et al, 1998
260
30
Specific, BIR
16
11
4
-0.1 ± 0.8
AFEV1
-2.5 ± 1.0
AFEV1
0.03
Bylin et al., 1988
270
30
Non-specific, HIST
20
14
6
0.39 ± 0.07
0.24 ± 0.04
<0.01
Tunnicliffe et al., 1994
400
60
Specific, HDM
8
8
0
-14.62
AFEV1
-18.64
AFEV1
0.009
Bylin et al, 1985
480
20
Non-specific, HIST
8
5
0
>30
>20
0.04
Mohsenin et al., 1987
500
60
Non-specific,
METH
10
7
2
9.2 ± 4.7
4.6 ± 2.6
0.042
Bylin et al., 1988
530
30
Non-specific, HIST
20
12
7
0.39 ± 0.07
0.34 ± 0.08
n.s.
AR = airway responsiveness; BIR = birch; CARB = carbachol; Exp. = exposure; HDM = house dust mite allergen;
HIST = histamine; METH = methacholine; NA = not available; N02 = nitrogen dioxide; n.s. = less than marginal statistical
significance (i.e., p > 0.10); RAG = ragweed; S02 = sulfur dioxide; TIM = timothy
a Adapted from Table 5-1 in Integrated Science Assessment for Oxides of Nitrogen (Health) - Final (U.S. EPA, 2016, Section
5.2.2.1)
bChange in AR: number of individuals showing increased (+) or decreased (-) airway responsiveness after N02 exposure
compared to air.
°PD ± SE: arithmetic or geometric mean provocative dose (PD) ± standard error (SE). See individual papers for PD
calculation and dosage units. AFE\A| indicates the change in FE\A| response at a constant challenge dose.
Statistical significance of increase in AR to bronchial challenge following N02 exposure compared to filtered air as reported in
the original study unless otherwise specified. Statistical tests varied between studies, e.g., sign test, t-test, and analysis of
variance.
Statistical significance for all individuals with asthma from analysis by Dawson et al. (1979). Orehek et a. (1976) only tested
for differences in sub-sets of individuals classified as "responders" and "non-responders."
This p-value from p. 609 of Bylin et al. (1988) corrects the "n.s." indicated in the 2016 ISA and Brown (2015)
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Table 3-3. Exercising exposures to nitrogen dioxide and airway responsiveness in
individuals with asthma.3
no2
PPb
Exp.
(min)
Change
in ARb
Average PD ± SEC
Reference
Challenge Type
N
+
-
Air
NO2
p-valued
Roger et al., 1990
150
80
Non-specific, METH
19
10d
7d
3.3 ± 0.7
3.1 ± 0.7
n.s.
Kleinman et al.,
1983
200
120
Non-specific, METH
31
20
7
8.6 ±2.9
3.0 ± 1.1
<0.05
Jenkins et al.,
1999
200
360
Specific, HDM
11
6
5
2.94
2.77
n.s.
Jorres et al., 1991
250
30
Non-specific, METH
11
6
5
0.41 ± 1.6
0.41 ± 1.6
n.s.
Strand et al., 1996
260
30
Non-specific, HIST
19
13
5
296 ± 76
229 ± 56
0.08
Avol et al., 1988
300
120
Non-specific, COLD
37
11d
16
d
-8.4 ± 1.8
AFEV1
-10.7 ±2.0
AFEV1
n.s.
Avol et al., 1989
300
180
Non-specific, COLD
34
12d
21
d
-5 ±2
AFEV1
-4 ± 2
AFEV1
n.s.
Bauer et al., 1986
300
30
Non-specific, COLD
15
9
3
0.83 ± 0.12
0.54 ±0.10
<0.05
Morrow et al.,
1989
300
240
Non-specific, CARB
20
7e
2e
3.31 ± 8.64e
AFEV1
-6.98 ± 3.35e
AFEV1
n.s.
Roger et al., 1990
300
80
Non-specific, METH
19
8d
9d
3.3 ± 0.7
3.3 ± 0.8
n.s.
Rubinstein et al.,
1990
300
30
Non-specific, SO2
9
4
5
1.25 ± 0.23
1.31 ± 0.25
n.s.
Riedletal., 2012
350
120
Non-specific, METH
15
6
7
7.5 ±2.6
7.0 ± 3.8
n.s.
Riedl et al., 2012
350
120
Specific, CA
15
4
11
-6.9 ± 1.7
AFEV1
-0.5 ± 1.7
AFEV1
<0.05f
Jenkins et al.,
1999
400
180
Specific, HDM
10
7
3
3.0
2.78
0.018
Witten et al., 2005
400
180
Specific, HDM
15
8
7
550 ± 240
160 ±60
n.s.
Avol et al., 1988
600
120
Non-specific, COLD
37
13e
16
e
-8.4 ± 1.8
AFEV1
-10.4 ±2.2
AFEV1
n.s.
Roger et al., 1990
600
80
Non-specific, METH
19
11d
8d
3.3 ± 0.7
3.7 ± 1.1
n.s.
AR = airway responsiveness; BIR = birch; CARB = carbachol; Exp. = exposure; HDM = house dust mite allergen;
HIST = histamine; METH = methacholine; NA = not available; N02 = nitrogen dioxide; n.s. = less than marginal statistical
significance, p > 0.10; RAG = ragweed; S02 = sulfur dioxide; TIM = timothy
a Adapted from Table 5-2 in Integrated Science Assessment for Oxides of Nitrogen (Health) - Final (U.S. EPA, 2016, Section
5.2.2.1)
bChange in AR: number of individuals showing increased (+) or decreased (-) airway responsiveness after N02 exposure
compared to air.
°PD ± SE: arithmetic or geometric mean provocative dose (PD) ± standard error (SE). See individual papers for PD
calculation and dosage units. AFE\A| indicates the change in FE\A| response at a constant challenge dose.
Statistical significance of increase in AR to bronchial challenge following N02 exposure compared to filtered air as reported in
the original study unless otherwise specified. Statistical tests varied between studies, e.g., sign test, t-test, analysis of
variance.
Statistical significance for all individuals with asthma from analysis by Dawson et al. (1979). Orehek et a. (1976) only tested
for differences in sub-sets of individuals classified as "responders" and "non-responders."
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Consideration of group mean results from individual studies
In first considering studies conducted at rest, we note that the lowest NO2 concentration
to which individuals with asthma have been exposed is 100 ppb, with an exposure duration of 60
minutes in all studies. Of the five studies conducted at 100 ppb, a statistically significant increase
in AR following exposure to N02 was only observed in the study by Orehek et al. (1976) (N =
20). Of the four studies that did not report statistically significant increases in AR following
exposures to 100 ppb NO2, three reported weak trends towards decreased AR (n = 20, Ahmed et
al., 1983b; n = 15, Hazucha et al., 1983; n = 8, Tunnicliffe et al., 1994), and one reported a trend
towards increased AR (n = 20, Ahmed et al., 1983a). Resting exposures to 140 ppb NO2 resulted
in increases in AR that reached marginal statistical significance (n = 20; Bylin et al., 1988). In
addition, the one study conducted at 200 ppb demonstrated a trend towards increased AR, but
this study was small and results were not statistically significant (n = 4; Orehek et al., 1976).
Thus, individual controlled human exposure studies have generally not reported statistically
significant increases in AR following resting exposures to NO2 concentrations from 100 to 200
ppb. Group mean responses in these studies suggest a trend towards increased AR following
exposures to 140 and 200 ppb NO2, while trends in the direction of group mean responses were
inconsistent following exposures to 100 ppb NO2.
In next considering studies in individuals with asthma conducted with exercise, we note
that three studies evaluated NO2 exposure concentrations between 150 and 200 ppb (n = 19,
Roger et al., 1990; n = 31, Kleinman et al., 1983; n = 11, Jenkins et al., 1999). Of these studies,
only Kleinman et al. (1983) reported a statistically significant increase in AR following NO2
exposure (i.e., at 200 ppb). Roger et al. (1990) and Jenkins et al. (1999) did not report
statistically significant increases, but showed weak trends for increases in AR following
exposures to 150 ppb and 200 ppb NO2, respectively. Thus, as with studies of resting exposures,
studies that evaluated exposures to 150 to 200 ppb NO2 with exercise report trends toward
increased AR, though results are generally not statistically significant.
Several studies evaluated exposures of individuals with asthma to NO2 concentrations
above 200 ppb. Of the five studies that evaluated 30-minute resting exposures to NO2
concentrations from 250 to 270 ppb, N02-induced increases in AR were statistically significant
in three (n = 14, Jorres et al., 1990; n = 18, Strand et al., 1988; n = 20, Bylin et al., 1988).
Statistically significant increases in airway responsiveness are also more consistently reported
across studies that evaluated resting exposures to 400-530 ppb NO2, with three of four studies
reporting a statistically significant increase in airway responsiveness following such exposures.
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However, studies conducted with exercise do not indicate consistent increases in AR following
exposures to NO2 concentrations from 300 to 600 ppb (Table 3-3).64
Consideration of results from the meta-analysis
As discussed above in Section 3.2.1, the ISA assessment of the evidence for AR in
individuals with asthma also focuses on a recently published meta-analysis (Brown, 2015)
investigating individual-level data from the studies included in Tables 3-2 and 3-3. While
individual controlled human exposure studies can lack statistical power to identify effects, the
meta-analysis of individual-level data combined from multiple studies (Brown, 2015) has greater
statistical power due to increased sample size. The meta-analysis considered individual-level
responses, specifically whether individual study subjects experienced an increase or decrease in
AR following NO2 exposure compared to air exposure, combining information from the studies
presented in Tables 3-2 and 3-3. Evidence was evaluated together across all studies and also
stratified for exposures conducted with exercise and at rest, and for measures of specific and
non-specific AR. The ISA notes that these methodological differences may have important
implications with regard to results (U.S. EPA, 2016; Brown, 2015; Goodman et al., 2009),
contributing to the ISA's emphasis on studies of resting exposures and non-specific challenge
agents. Overall, the meta-analysis presents the fraction of individuals having an increase in AR
following exposure to various NO2 concentrations (i.e., 100 ppb, 100 ppb to < 200 ppb, 200 ppb
up to and including 300 ppb, and above 300 ppb) (U.S. EPA, 2016, Section 5.2.2.1).65 The
number of participants in each study and the number having an increase or decrease in AR is
indicated in Tables 3-2 and 3-3.
We first consider the meta-analysis results across all exposure conditions (i.e., resting,
exercising, non-specific challenge, and specific challenge). For 100 ppb NO2 exposures, Brown
(2015) reported that, of the study participants who experienced either an increase or decrease in
AR following NO2 exposures, 61% experienced an increase (p = 0.08). For 100 to < 200 ppb
NO2 exposures, 62% of study subjects experienced an increase in AR following NO2 exposures
(p = 0.014). For 200 to 300 ppb NO2 exposures, 58% of study subjects experienced an increase
in AR following NO2 exposures (p = 0.008). For exposures above 300 ppb NO2, 57% of study
64 There are eight additional studies with exercising exposures to 300-350 ppb NO2 as presented in Table 3-3, with
exposure durations ranging from 30-240 minutes. Results across these studies are inconsistent, with only two of
eight reporting significant results. Only one of four studies with exercising exposures of 400 or 600 ppb reported
statistically significant increases in airway responsiveness.
65 Brown et al. (2015) compared the number of study participants who experienced an increase in AR following NO2
exposures to the number who experienced a decrease in AR. Study participants who experienced no change in AR
were not included in comparisons. P-value refers to the significance level of a two-tailed sign test.
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subjects experienced an increase in AR following NO2 exposures, though this fraction was not
statistically different than the fraction experiencing a decrease.
We also consider the results of Brown (2015) for various subsets of the available studies,
based on the exposure conditions evaluated (i.e., resting, exercising) and the type of challenge
agent used (specific, non-specific). For exposures conducted at rest, across all exposure
concentrations (i.e., 100-530 ppb NO2, n = 139; Table 3-2), Brown (2015) reported that a
statistically significant fraction of study participants (71 %,p< 0 .001) experienced an increase in
AR following NO2 exposures, compared to the fraction that experienced a decrease in AR. The
meta-analysis also presented results for various concentrations or ranges of concentrations.
Following resting exposure to 100 ppb NO2, 66% of study participants experienced increased
non-specific AR. For exposures to concentrations of 100 ppb to < 200 ppb, 200 ppb up to and
including 300 ppb, and above 300 ppb, increased non-specific AR was reported in 67%, 78%,
and 73%) of study participants, respectively.66 For non-specific challenge agents, the differences
between the fractions of individuals who experienced increased AR following resting NO2
exposures and the fraction who experienced decreased AR reached statistical significance for all
of the ranges of exposures concentrations evaluated (p < 0.05).
In contrast to the results from studies conducted at rest, the fraction of individuals having
an increase in AR following NO2 exposures with exercise was not consistently greater than 50%,
and none of the results were statistically significant (Brown, 2015). Across all NO2 exposures
with exercise, measures of non-specific AR were available for 241 individuals, 54% of whom
experienced an increase in AR following NO2 exposures relative to air controls. There were no
studies in this group conducted at 100 ppb, and for exercising exposures to 150-200 ppb, 250-
300 ppb, and 350-600 ppb, the fraction of individuals with increased AR was 59%, 55%, and
49%), respectively.
In addition to examining results from studies of non-specific AR, the meta-analysis also
considered results from studies that evaluated changes in specific AR (i.e., AR following an
allergen challenge; n=130; Table 3-3) following NO2 exposures. The results do not indicate
statistically significant fractions of individuals having an increase in specific AR following
exposure to NO2 at concentrations below 400 ppb, even when considering resting and exercising
exposures separately (Brown, 2015). Of the three studies that evaluated specific AR at
concentrations of 400 ppb, one was conducted at rest (Tunnicliffe et al., 1994). This study
66 For the exposure category of "above 300 ppb", exposures included 400, 480, 500, and 530 ppb. No studies used
concentrations between 300 and 400 ppb.
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reported that all individuals experienced increased AR following 400 ppb NO2 exposures
(Brown, 2015, Table 4). In contrast, for exposures during exercise, most study subjects did not
experience NCh-induced increases in specific AR.67 Overall, results across studies are less
consistent for increases in specific AR following NO2 exposures.
Uncertainties in evidence for airway responsiveness
When considering the evidence for NCh-induced increases in AR in individuals with
asthma, there are important uncertainties that should be considered. Both the meta-analysis by
Brown (2015) and an additional meta-analysis and meta-regression by Goodman et al. (2009)
conclude that there is no indication of a dose-response relationship for exposures between 100
and 500 ppb NO2 and increased AR in individuals with asthma. A dose-response relationship
generally increases confidence that observed effects are due to pollutant exposures rather than to
chance; however, the lack of a dose-response relationship does not necessarily indicate that there
is no relationship between the exposure and effect, particularly in these analyses based on
between-subject comparisons (i.e., as opposed to comparisons within the same subject exposed
to multiple concentrations). As discussed in the ISA, there are a number of methodological
differences across studies that could contribute to between-subject differences and that could
obscure a dose-response relationship between NO2 and AR. These include subject activity level
(rest vs. exercise) during NO2 exposure, asthma medication usage, choice of airway challenge
agent (e.g., direct and indirect non-specific stimuli), method of administering the
bronchoconstricting agents, and physiological endpoint used to assess AR. Such methodological
differences across studies likely contribute to the variability and uncertainty in results across
studies and complicate interpretation of the overall body of evidence for N02-induced AR. Thus,
while the lack of an apparent dose-response relationship adds uncertainty to our interpretation of
controlled human exposure studies of AR, it does not necessarily indicate the lack of an NO2
effect.
An additional uncertainty in interpreting these studies within the context of the adequacy
of the protection provided by the NO2 NAAQS is the potential adversity of the reported NO2-
induced increases in AR. As discussed above (section 3.2.1), the meta-analysis by Brown (2015)
used an approach that is consistent with guidelines from the ATS and the ERS for the assessment
of therapeutic agents (Reddel et al., 2009) to assess the potential for clinical relevance of these
responses. Specifically, based on individual-level responses reported in a subset of studies,
67 Forty-eight percent experienced increased AR and 52% experienced decreased AR, based on individual-level data
for study participants exposed to 350 ppb (Riedl et al., 2012) or 400 ppb (Jenkins et al., 1999; Witten et al., 2005)
N02.
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Brown (2015) considered a halving of the provocative dose to indicate responses that may be
clinically relevant.68 With regard to this approach, the ISA notes that "one doubling dose change
in PD is recognized as a potential indicator, although not a validated estimate, of clinically
relevant changes in airway responsiveness (Reddel et al., 2009)" (U.S. EPA, 2016, p. 5-12).
While there is uncertainty in using this approach to characterize whether a particular response in
an individual is "adverse," it can provide insight into the potential for adversity, particularly
when applied to a population of exposed individuals.69
Only five studies provided data for each individual's provocative dose. These five studies
provided individual-level data for a total of 72 study participants (116 AR measurements) and
eight NO2 exposure concentrations, for resting exposures and non-specific bronchial challenge
agents. Across exposures to 100, 140, 200, 250, 270, 480, 500, and 530 ppb NO2, 24% of study
participants experienced a halving of the provocative dose (indicating increased AR) while 8%
showed a doubling of the provocative dose (indicating decreased AR). The relative distributions
of the provocative doses at different concentrations were similar, with no dose-response
relationship indicated (Brown, 2015). While these results support the potential for clinically
relevant increases in AR in some individuals with asthma following NO2 exposures within the
range of 100 to 530 ppb, uncertainty remains given that this analysis is limited to a small subset
of studies and given the lack of an apparent dose-response relationship. In addition, compared to
conclusions based on the entire range of NO2 exposure concentrations evaluated (i.e., 100 to 530
ppb), there is greater uncertainty in reaching conclusions about the potential for clinically
relevant effects at any particular NO2 exposure concentration within this range.
Conclusion
As in the last review, a meta-analysis of individual-level data supports the potential for
increased AR in individuals with asthma following 30 minute to 1 hour exposures to NO2
concentrations from 100 to 530 ppb, particularly for resting exposures and measures of non-
specific AR (N = 33 to 70 for various ranges of NO2 exposure concentrations). Individual studies
most consistently report statistically significant N02-induced increases in AR following
exposures to NO2 concentrations at or above 250 ppb. Individual studies (N = 4 to 20) generally
68 More specifically, clinical relevance in the ISA is based on evidence from controlled human exposure studies
evaluating efficacy of inhaled corticosteroids that are used to prevent bronchoconstriction and airway responsiveness
as described by Reddell et al. (2009). Generally, a change of at least one doubling dose is considered to be an
indication of clinical relevance (this represents a decline in AR as the dose to induce AR is doubled). Based on this,
a halving of the provocative dose is taken in the ISA to represent an increase in AR that is an indication of clinical
relevance.
69 As noted above, the degree to which populations in U.S. urban areas have the potential for such NO2 exposures is
evaluated in Chapter 4.
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do not report statistically significant increases in AR following exposures to NO2 concentrations
at or below 200 ppb, though the evidence suggests a trend toward increased AR following NO2
exposures from 140 to 200 ppb. In contrast, individual studies do not indicate a consistent trend
towards increased AR following 1-hour exposures to 100 ppb NO2. Important limitations in this
evidence include the lack of a dose-response relationship between NO2 and AR and uncertainty
in the adversity of the reported increases in AR. These limitations become increasingly important
at the lower NO2 exposure concentrations (i.e., at or near 100 ppb), where the evidence for NO2-
induced increases in AR is not consistent across studies.
3.2.2.2 Concentrations in Locations of Epidemiologic Studies
We next consider distributions of ambient NO2 concentrations in locations where
epidemiologic studies have examined NO2 associations with asthma-related hospital admissions
or emergency department (ED) visits. These outcomes are clearly adverse and study results
comprise a key line of epidemiologic evidence in the determination of a causal relationship in the
ISA (U.S. EPA, 2016, Section 5.2.9). As in other NAAQS reviews (U.S. EPA, 2014; U.S. EPA,
2011), when considering epidemiologic studies within the context of the adequacy of the current
standard, we emphasize those studies conducted in the U.S. and Canada.70 For short-term
exposures to NO2, we emphasize studies reporting associations with effects judged in the ISA to
be robust to confounding by other factors, including co-occurring air pollutants. In addition, we
consider the statistical significance and precision of study results, and the inclusion of at-risk
populations for which the NCh-health effect associations may be larger. These considerations
help inform the range of ambient NO2 concentrations over which we have the most confidence in
N02-asssociated health effects and the range of concentrations over which our confidence in
such effects is appreciably lower. In our consideration of these issues, we specifically focus on
the following question:
• To what extent have U.S. and Canadian epidemiologic studies reported associations
between asthma-related hospital admissions or emergency department visits and short-
term NO2 concentrations in study areas that would have met the current 1-hour NO2
standard during the study period?
Addressing this question can provide important insights into the extent to which NO2-
health effect associations are present for distributions of ambient NO2 concentrations that would
be allowed by the current standards. The presence of such associations would support the
potential for the current standards to allow the N02-associated effects indicated by epidemiologic
70 Such studies are likely to reflect air quality and exposure patterns that are generally applicable to the U.S. In
addition, air quality data corresponding to study locations and study time periods is often readily available for
studies conducted in the U.S. and Canada. Nonetheless, we recognize the importance of all studies, including other
international studies, in the ISA's assessment of the weight of the evidence that informs causal determinations.
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studies. To the degree studies have not reported associations in locations meeting the current
NO2 standards, there is greater uncertainty regarding the potential for the reported effects to
occur following NO2 exposures associated with air quality meeting those standards.
In addressing the question above and considering the available evidence, we place the
greatest emphasis on studies reporting positive, and relatively precise, health effect associations.
In evaluating whether such associations are likely to reflect NO2 concentrations meeting the
existing 1-hour standard, we consider the 1-hour ambient NO2 concentrations measured at
monitors in study locations during study periods. We also consider what additional information
is available to inform our understanding of the ambient NO2 concentrations that could have been
present in the study locations during the study periods (e.g., around major roads). When
considered together, this information can provide important insights into the extent to which NO2
health effect associations have been reported for NO2 air quality concentrations that likely would
have met the current 1-hour NO2 standard.
We have identified U.S. and Canadian studies of respiratory-related hospital admissions
and emergency department (ED) visits, with a focus on studies of asthma-related effects (studies
identified from ISA Table 5-10).71 For each NO2 monitor in the locations evaluated by these
studies and the ranges of years encompassed by studies, we have identified the 3-year averages
of the 98th percentiles of the annual distributions of daily maximum 1-hour NO2 concentrations.72
These concentrations approximate the design values (DVs) that are used when determining
whether an area meets the primary NO2 NAAQS.73 Thus, these "DVs" can provide perspective
on whether study areas would likely have met or exceeded the primary 1-hour NO2 NAAQS
71 Studies of asthma-related hospital admissions and ED visits were identified in the ISA as comprising an important
line of epidemiologic evidence to support the conclusion that there is a "causal" relationship between short-term
NO2 exposures and respiratory effects (U.S. EPA, 2016). Strong support was also provided by epidemiologic studies
for respiratory symptoms, but the majority of studies on respiratory symptoms were only conducted over part of a
year, complicating the evaluation of a DV based on data from 3 years of monitoring data relative to the respective
health effect estimates. For more information on these studies and the DVs in the study locations, see Appendix A.
72 All study locations had maximum annual design values below 53 ppb (Appendix A).
73 As described in Chapter 2, a design value is a statistic that describes the air quality status of a given area relative
to the NAAQS and that is typically used to classify nonattainment areas, assess progress towards meeting the
NAAQS, and develop control strategies. For the 1-hour NO2 standard, the DV is calculated at individual monitors
and based on 3 consecutive years of data collected from that site. In the case of the 1-hour NO2 standard, the design
value for a monitor is based on the 3-year average of the 98th percentile of the annual distribution of daily maximum
1-hour NO2 concentrations. For more information on these studies and the calculation of study area "DVs," see
Appendix A.
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during the study periods.74 Based on this approach, study locations could have met the current 1-
hour standard over the entire study period if all of the hourly DVs were at or below 100 ppb.
A key limitation in these analyses of NO2 DVs is that currently required near-road NO2
monitors were not in place during study periods. The studies evaluated (see Figure 3-1 below)
were based on air quality from 1980-2006, with most studies spanning the 1990s to early 2000s.
As discussed above in Chapter 2, there were no specific near-road monitoring network
requirements during these years, and most areas did not have monitors sited to measure NO2
concentrations near the most heavily-trafficked roadways. In addition, mobile source NOx
emissions were considerably higher during the time periods of available epidemiologic studies
than in more recent years, suggesting that the NO2 concentration gradients around major roads
were likely more pronounced than indicated by data from recently deployed near-road monitors
(Figure 2-6).75 This information suggests that if the current near-road monitoring network had
been in operation during study periods, NO2 DVs measured at near-road monitors would likely
have been higher than the DVs reflected in Figure 3-1 below. This uncertainty particularly limits
the degree to which we can draw strong conclusions based on study areas with DVs that are at or
just below 100 ppb.
With this key limitation in mind, we consider what the available epidemiologic evidence
can tell us with regard to the adequacy of the public health protection provided by the current 1-
hour standard against short-term NO2 exposures. Figure 3-1, below, highlights the epidemiologic
studies examining associations between asthma hospitalizations or ED visits and short-term
exposures to ambient NO2 that were conducted in the U.S. and Canada. These studies were
identified and evaluated in the ISA and include both the few recently published studies and the
studies that were available in the previous review. Figure 3-1 depicts the range of associations
74 The DVs indicated in Figure 3-1 are different from the NO2 concentrations reported in the studies themselves,
which are often averaged across monitors in the study areas and can reflect averaging periods other than 1 -hour.
Thus, the concentrations reported in the studies are not appropriate for direct comparison to the level of the 1 -hour
standard. We are, however, providing them for additional context. The NO2 concentrations reported in studies are as
follows: Steib etal., 2009: 24-h average: 9.3-22.7ppb; 75th percentile: 12.3-27.6ppb; Linnetal., 2000, 24-h average
3.4ppb; Peel et al., 2005, 1-hmax: 45.9ppb; Ito et al., 2007, 24-h average: 31.1ppb; Villeneuve et al., 2007, 24-h
average: 17.5ppb (Summer) and 28.5ppb (Winter); Burnett et al., 2009, 24-h average: 25.2ppb; Strickland et al.,
2010, 1-h max: 23.3ppb; ATSDR, 2006, 24-h average: 36ppb (Bronx) and 3 lppn (Manhattan); Jaffe et al., 2003, 24-
h average: 50ppb (Cincinnati) and 48ppb (Cleveland); Li et al., 2011: 24-h average: 15.7ppb, 75th percentile:
21.2ppb, Max: 55.2ppb
75 Recent data indicate that, for most near-road monitors, measured 1-hour NO2 concentrations are higher than those
measured at all of the non-near-road monitors in the same CBSA (Section 2.3.2).
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across U.S. and Canadian studies and also indicates maximum and mean hourly DVs for the
study locations and years.76
76 Similar analyses of study area air quality were presented in the 2008 REA. However, because the 1 -hour standard
was set in the 2010 final decision, the methods for calculating 1-hour NO2 DVs had not been established at the time
of the development of the 2008 REA. Therefore, the study area NO2 concentrations identified in the 2008 REA did
not correspond to 1-hour DVs for the current 1-hour NO2 standard. As a result, even when the same study is
evaluated, study areaNCh concentrations are not identical in the 2008 REA and in Figure 3-1 of this PA.
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Study
Location
Hourly DV
(Max, Mean)
Study Years
Li (2011), 2-18 yrs
Detroit
55, 55
2004-2006
Jaffe (2003), 5-34 yrs
Clev/Cinc
92, 83
1991-1996
ATSDR (2006)
Manhattan
86, 86
1999-2000
ATSDR (2006)
Bronx
94, 94
1999-2000
Peel (2005)
Peel (2005), 2-18yrs
Atlanta
Atlanta
100, 90
100, 90
1993-2000
1993-2000
Strickland (2010), 5-17 yrs
Atlanta
100, 87
1993-2004
I to (2007)
New York City
102, 102
1999-2002
Linn (2000)
Los Angeles
171, 170
1992-1995
Burnett (1999)
Toronto
227, 142
1980-1994
Villeneuve (2007), >2 yrs
Edmonton
242, 103
1999-2002
Stieb (2009)
Montreal
Ottawa
Edmonton
St. John
Halifax
Toronto
Vancouver
85.81
198, 94
242, 103
96, 65
67, 64
98,92
86,66
1997-2002
1992-2000
1992-2002
1992-1996
1999-2002
1999-2003
1999-2003
-10
0 5
% Increase
10
15
20
Figure 3-1. U.S. and Canadian epidemiologic studies of short-term NO2 exposures and asthma hospital admissions and
emergency department visits. Study locations and years are reported with hourly DVs for studies asthma hospital admissions and ED
visits with effect estimates and 95% confidence intervals, standardized as described in the ISA (U.S. EPA, 2016). Effect estimates in
blue represent studies that are new in the current review. Clev = Cleveland; Cine = Cincinnati. If ages are not specified after the study,
then hospital admissions and ED visits for all ages were included. Li et al. reports an effect estimate from a time-series analysis and
case-crossover analysis. Because hourly DVs are based on 3 years of data, DVs for the first 2 years of a study period were not considered.
The ATSDR study did not include 3 years, thus DVs reported for these locations include data from a year preceding the study (1998).
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Figure 3-1 includes both multi-city and single city studies. In considering the information
in Figure 3-1, we note that multi-city studies tend to have greater power to detect associations.
The one multi-city study that has become available since the last review (Stieb et al., 2009)
reported a null association with asthma ED visits, based on study locations with maximum DVs
ranging from 67-242 ppb (six of seven study cities had maximum DVs at or above 85 ppb). Of
the single city studies in Figure 3-1, those reporting positive and relatively precise (i.e., relatively
narrow 95% CIs) associations were conducted in locations with maximum, and often mean, DVs
at or above 100 ppb (i.e., Linn et al., 2000; Peel et al., 2005; Ito et al., 2007; Villeneuve et al.,
2007; Burnett et al., 1999; Strickland et al., 2010). For the other single city studies in Figure 3-1,
two reported more mixed results in locations with maximum DVs around 90 ppb (Jaffe et al.,
2003; ATSDR, 2006).77 Associations in these studies were generally not statistically significant,
were less precise (i.e., wider 95% CIs), and included a negative association (Manhattan, NY).
One single city study was conducted in a location with 1-hour DVs well-below 100 ppb (Li et al,
2011), though the reported associations were not statistically significant and were relatively
imprecise. Thus, of the U.S. and Canadian studies that can most clearly inform our consideration
of the adequacy of the current NO2 standards, the lone multicity study did not report a positive
health effect association and the single-city studies reporting positive, and relatively precise,
associations were generally conducted in locations with maximum 1-hour DVs at or above 100
ppb. The evidence for associations in locations with maximum DVs below 100 ppb is more
mixed, and reported associations are generally less precise.
An uncertainty in this body of evidence is the potential for copollutant confounding.
When pollutants are highly correlated, it can be difficult to determine the independent effects of
single pollutants from other pollutants in the mixture. Copollutant (two-pollutant) models can be
used in epidemiologic studies in an effort to disentangle such independent effects, though there
can be limitations in these models due to differential exposure measurement error and high
correlations with traffic-related copollutants. For NO2, the copollutants that are most relevant to
consider are those from traffic sources such as CO, EC/BC, UFP, and VOCs such as benzene as
well as PM2.5 and PM10 (ISA, Section 3.5). Of the studies examining asthma-related hospital
admissions and ED visits in the U.S. and Canada in Figure 3-1, three examined effect estimates
from copollutant models (Ito et al., 2007; Villeneuve et al., 2007; Strickland et al., 2010)). Ito et
al. (2007) found that in copollutant models with PM2.5, SO2, CO, or O3, NO2 consistently had the
77 The study by the U.S. Agency for Toxic Substances and Disease Registry (ATSDR) was not published in a peer-
review journal. Rather, it was a report prepared by New York State Department of Health's Center for
Environmental Health, the New York State Department of Environmental Conservation and Columbia University in
the course of performing work contracted for and sponsored by the New York State Energy Research and
Development Authority and the ATSDR.
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strongest effect estimates that were robust to the inclusion of other pollutants. Villeneuve et al.
(2007) utilized a model including NO2 and CO (r = 0.74) for ED visits in the warm season and
reported that associations for NO2 were robust to CO. Strickland et al. (2010) found that the
relationship between ambient NO2 and asthma ED visits in Atlanta, GA was robust in models
including O3, but copollutant models were not analyzed for other pollutants and the correlations
between NO2 and other pollutants were not reported. Taken together, these studies provide some
evidence for independent effects of N02for asthma ED visits, but some important traffic-related
copollutants (e.g. EC/BC, VOCs) have not been examined in this body of evidence and the
limitations of copollutant models in demonstrating an independent association, particularly for
NO2, are well recognized (U.S. EPA, 2016).
Conclusions
Considering this evidence together, we note the following observations. First, the only
recent multicity study evaluated, which had maximum DVs ranging from 67 to 242 ppb, did not
report a positive association between NO2 and ED visits (Stieb et al., 2009). In addition, of the
single-city studies in Figure 3-1 reporting positive and relatively precise associations between
NO2 and asthma hospital admissions and ED visits, most locations likely had NO2 concentrations
above the current 1-hour NO2 standard over at least part of the study period. Although maximum
DVs for the studies conducted in Atlanta were 100 ppb, it is likely that those DVs would have
been higher than 100 ppb had currently required near-road monitors been in place. For the study
locations with maximum DVs below 100 ppb, mixed results are reported with associations that
are generally statistically non-significant and imprecise, indicating that associations between
NO2 concentrations and asthma-related ED visits are more uncertain in locations that could have
met the current standards. Given that near-road monitors were not in operation during study
periods, it is not clear that even these DVs below 100 ppb indicate study areas that would have
met the current 1-hour standard. Thus, when considering our analyses of study areaN02
concentrations in light of uncertainties related to roadway NO2 concentrations and copollutant
confounding, we reach the conclusion that available U.S. and Canadian epidemiologic studies do
not provide support for N02-associated hospital admissions or ED visits in locations with NO2
concentrations that would have clearly met the current 1-hour NO2 standard.
3.3 EFFECTS OF LONG-TERM NOi EXPOSURES
3.3.1 Nature of Effects
In the last review of the primary NO2 NAAQS, evidence for health effects related to long-
term ambient NO2 exposure was judged "suggestive of, but not sufficient to infer" or
"inadequate to infer the presence or absence of' a causal relationship across health effect
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categories. These included respiratory, cardiovascular, and reproductive and developmental
effects as well as cancer and total mortality. In the current review, new epidemiologic evidence,
in conjunction with explicit integration of evidence across related outcomes, has resulted in
strengthening of some of the causal determinations. Though the evidence of health effects
associated with long-term exposure to NO2 is more robust than in previous reviews, there are still
a number of uncertainties limiting our understanding of the role of long-term NO2 exposures in
causing health effects. We focus our discussion of evidence available in the current review for
health effects related to long-term NO2 exposures, including strengths and limitations, on the
following overarching questions:
• To what extent does the currently available scientific evidence alter or strengthen our
conclusions from the last review regarding health effects attributable to long-term NO2
exposures? Have previously identified uncertainties been reduced? What important
uncertainties remain and have new uncertainties been identified?
Table 3-4 provides an overview of the causal determinations for the previous and current
reviews for long-term NO2 exposures and various health effect categories including respiratory,
cardiovascular, and reproductive and developmental effects, as well as mortality and cancer. In
particular, the causal determination between long-term NO2 exposures and respiratory effects
was strengthened to "likely to be a causal relationship." The evidence on which these causal
judgments are based is summarized below.
Table 3-4. Causal determinations for long-term nitrogen dioxide (NO2) exposure and
health effects evaluated in the ISA for Oxides of Nitrogen in the previous and current
review
Health effect
Review completed 2010
Current Review
Respiratory
Suggestive of, but not sufficient to
infer, a causal relationship
Likely to be a causal relationship
no
Cardiovascular and Diabetes
Inadequate to infer the presence or
absence of a causal relationship
Suggestive of, but not sufficient to
infer, a causal relationship
Total Mortality
Inadequate to infer the presence or
absence of a causal relationship
Suggestive of, but not sufficient to
infer, a causal relationship
Reproductive and Developmental-
Fertility, Reproduction, Pregnancy
Inadequate to infer a causal
relationship
Reproductive and Developmental -
Birth Outcomes
Inadequate to infer the presence or
absence of a causal relationship3
Suggestive of, but not sufficient to
infer, a causal relationship
Reproductive and Developmental-
Postnatal Development
Inadequate to infer a causal
relationship
78 The addition of diabetes to the ISA causal determination is new to this review.
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Cancer
Inadequate to infer the presence or Suggestive of, but not sufficient to
absence of a causal relationship infer, a causal relationship
a Previous review combined evidence for reproductive and developmental effects and made one causal
determination.
Respiratory
The 2016 ISA concluded that there is "likely to be a causal relationship" between long-
term NO2 exposure and respiratory effects, based primarily on evidence integrated across
disciplines for a relationship with asthma development in children.79 Evidence for other
outcomes integrated across epidemiologic and experimental studies, including decrements in
lung function and partially irreversible decrements in lung development, respiratory disease
severity, chronic bronchitis/asthma incidence in adults, COPD hospital admissions, and
respiratory infections, is less consistent and has larger uncertainty in whether there is an
independent effect of long-term NO2 exposure (U.S. EPA, 2016, Section 6.2.9).
The conclusion of a "likely to be a causal relationship" in the current review represents a
change from 2008 ISA conclusion that the evidence was "suggestive of, but not sufficient to
infer, a causal relationship" (U.S. EPA, 2008, Section 5.3.2.4). The epidemiologic evidence base
has expanded since the last review. This expanded evidence includes several recently published
longitudinal studies that indicate positive associations between asthma incidence in children and
long-term NO2 exposures, with improved exposure assessment in some studies based on NO2
modeled estimates for children's homes or NO2 measured near children's homes or schools.
Associations were observed across various periods of exposure, including first year of life, year
prior to asthma diagnosis, and cumulative exposure. In addition, the ISA notes several other
strengths of the evidence base including the general timing of asthma diagnosis and relative
confidence that the NO2 exposure preceded asthma development in longitudinal studies, more
reliable estimates of asthma incidence based on physician-diagnosis in children older than 5
years of age from parental report or clinical assessment, as well as residential NO2
concentrations estimated from land use regression (LUR) models with good NO2 prediction in
some studies.
While the causal determination has been strengthened in this review, important
uncertainties remain. For example, the ISA notes that as in the last review, a "key uncertainty
that remains when examining the epidemiologic evidence alone is the inability to determine
whether NO2 exposure has an independent effect from that of other pollutants in the ambient
79 Elsewhere referred to as "asthma incidence." Asthma development and asthma incidence refer to the onset of the
disease rather than the exacerbation of existing disease.
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mixture" (U.S. EPA, 2016, Section 6.2.2.1, p. 6-21). While a few studies have included
copollutant models for respiratory effects other than asthma development, the ISA states that
"[epidemiologic studies of asthma development in children have not clearly characterized
potential confounding by PM2.5 or traffic-related pollutants [e.g., CO, BC/EC, volatile organic
compounds (VOCs)]" (U.S. EPA, 2016, p. 6-64). The ISA further notes that "[i]n the
longitudinal studies, correlations with PM2.5 and BC were often high (e.g., r = 0.7-0.96), and no
studies of asthma incidence evaluated models to address copollutant confounding, making it
difficult to evaluate the independent effect of NO2" (U.S. EPA, 2016, p. 6-64). High correlations
between NO2 and other traffic-related pollutants were based on modeling, and studies of asthma
incidence that used monitored NO2 concentrations as an exposure surrogate did not report such
correlations (US EPA, 2016, Section 6). This uncertainty is important to consider when
interpreting the epidemiologic evidence regarding the extent to which NO2 is independently
related to asthma development.
For additional context, the ISA also evaluated copollutant confounding in long-term
studies beyond asthma incidence to examine whether studies of other respiratory effects could
provide information on the potential for confounding by traffic-related copollutants. Several
studies examined correlations between NO2 and traffic-related copollutants and found them to be
relatively high in many cases, ranging from 0.54-0.95 for PM2.5, 0.54-0.93 for BC/EC, 0.2-0.95
for PM10, and 0.64-0.86 for OC (U.S. EPA, 2016, Tables 6-1 and 6-3). While these correlations
are often based on model estimates, some are based on monitored pollutant concentrations (i.e.,
McConnell et al. (2003) reported correlations of 0.54 with PM2.5 and EC) (U.S. EPA, 2016,
Table 6-3). Additionally, three studies (McConnell et al., 2003; Maclntyre et al., 2014; Gehring
et al., 2013) evaluated co-pollutant models with NO2 and PM2.5, and some findings suggest that
associations for NO2 with bronchitic symptoms, lung function, and respiratory infection are not
robust because effect estimates decreased in magnitude and became imprecise when a
copollutant was added in the model. Overall, examination of evidence from studies of other
respiratory effects indicates moderate to high correlations between long-term NO2 concentrations
and traffic-related copollutants, with very limited evaluation of the potential for confounding.
Thus, when considering the collective evidence, it is difficult to disentangle the independent
effect of NO2 from other traffic-related pollutants or mixtures in epidemiologic studies (U.S.
EPA, 2016, Sections 3.4.4 and 6.2.9.5).
While this uncertainty continues to apply to the epidemiologic evidence for asthma
incidence in children, the ISA describes that the uncertainty is partly reduced by the coherence of
findings from experimental studies and epidemiologic studies. Experimental studies demonstrate
effects on key events in the mode of action proposed for the development of asthma and provide
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biological plausibility for the epidemiologic evidence. For example, one study demonstrated that
airway hyperresponsiveness was induced in guinea pigs after long-term exposure to NO2
[1,000-4,000 ppb; (Kobayashi and Miura, 1995)]. Other experimental studies examining
oxidative stress report mixed results, but some evidence from short-term studies supports a
relationship between NO2 exposure and increased pulmonary inflammation in healthy humans.
The ISA also points to supporting evidence from studies demonstrating that short-term exposure
repeated over several days (260-1,000 ppb) and long-term NO2 exposure (2,000-4,000 ppb) can
induce Th2 skewing/allergic sensitization in healthy humans and animal models by showing
increased Th2 cytokines, airway eosinophils, and IgE-mediated responses (U.S. EPA, 2016,
Sections 4.3.5 and 6.2.2.3). Epidemiologic studies also provide some supporting evidence for
these key events in the mode of action. Some evidence from epidemiologic studies also
demonstrates associations between short-term ambient NO2 concentrations and increases in
pulmonary inflammation in healthy children and adults (U.S. EPA, 2016, Section 5.2.2.5).
Overall, evidence from experimental and epidemiologic studies provide support for a role of NO2
in asthma development by describing a potential role for repeated exposures to lead to recurrent
inflammation and allergic responses.
In addressing the questions posed at the beginning of this section, we note that there is
new evidence available that strengthens conclusions from the last review regarding respiratory
health effects attributable to long-term ambient N02-exposure. The majority of new evidence is
from epidemiologic studies of asthma incidence in children with improved exposure assessment
(i.e., measured or modeled at or near children's homes or schools), which builds upon previous
evidence for associations of long-term NO2 and asthma incidence and also partly reduces
uncertainties related to measurement error. Explicit integration of evidence for individual
outcome categories (e.g. asthma incidence, respiratory infection) provides improved
characterization of biological plausibility and mode of action, including some new evidence from
studies of short-term exposure supporting an effect on asthma development. Although this partly
reduces the uncertainty regarding independent effects of NO2, because of the high correlation
with other traffic-related copollutants and the general lack of copollutant model results in
epidemiologic studies, the potential for confounding remains a concern when interpreting
epidemiologic studies of NO2 and asthma development. In particular, it remains unclear the
degree to which NO2 itself may be causing the development of asthma versus serving primarily
as a surrogate for the broader traffic-pollutant mix.
Cardiovascular and Diabetes
In the previous review, the 2008 ISA stated that the evidence for cardiovascular effects
attributable to long-term ambient NO2 exposure was "inadequate to infer the presence or absence
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of a causal relationship." The epidemiologic and experimental evidence was limited, with
uncertainties related to traffic-related copollutant confounding (U.S. EPA, 2008). For the current
review, the body of epidemiologic evidence available is substantially larger than that in the last
review and includes evidence for diabetes. The conclusion on causality is stronger in the current
review with regard to the relationship between long-term exposure to NO2 and cardiovascular
effects and diabetes as the ISA judged the evidence to be "suggestive, but not sufficient to infer"
a causal relationship (U.S. EPA, 2016, Section 6.3). The strongest evidence comes from recent
epidemiologic studies reporting positive associations of NO2 with heart disease and diabetes with
improved exposure assessment (i.e., residential estimates from models that well predict NO2
concentrations in the study areas), but the evidence across experimental studies remains limited
and inconsistent and does not provide sufficient biological plausibility for effects observed in
epidemiologic studies. Specifically, the ISA concludes that "[epidemiologic studies have not
adequately accounted for confounding by PM2.5, noise, or traffic-related copollutants, and there
is limited coherence and biological plausibility for N02-related development of heart disease"
(U.S. EPA, 2016, p. 6-98) or "for NCh-related development of diabetes" (U.S. EPA, 2016, p. 6-
99). Thus, substantial uncertainty exists regarding the independent effect of NO2 and the total
evidence is "suggestive of, but not sufficient to infer, a causal relationship" between long-term
NO2 exposure and cardiovascular effects and diabetes (U.S. EPA, 2016, Section 6.3.9).
Reproductive and Developmental Effects
In the previous review, a limited number of epidemiologic and toxicological studies had
assessed the relationship between long-term NO2 exposure and reproductive and developmental
effects. The 2008 ISA concluded that there was not consistent evidence for an association
between NO2 and birth outcomes and that evidence was "inadequate to infer the presence or
absence of a causal relationship" with reproductive and developmental effects overall (U.S. EPA,
2008). In the ISA for the current review, a number of recent studies added to the evidence base,
and reproductive effects were considered as three separate categories: birth outcomes; fertility,
reproduction, and pregnancy; and postnatal development (U.S. EPA, 2016, Section 6.4). Overall,
the evidence is "suggestive of, but not sufficient to infer, a causal relationship" between long-
term exposure to NO2 and birth outcomes and is "inadequate to infer the presence or absence of a
causal relationship" between exposure to NO2 and fertility, reproduction and pregnancy as well
as postnatal development. Evidence for effects on fertility, reproduction, and pregnancy and for
effect on postnatal development is inconsistent across both epidemiologic and toxicological
studies. Additionally, there are few toxicological studies available. The ISA concludes the
change in the causal determination for birth outcomes reflects the large number of studies that
generally observed associations with fetal growth restriction and the improved outcome
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assessment (e.g., measurements throughout pregnancy via ultrasound) and exposure assessment
(e.g., well-validated LUR models) employed by many of these studies (U.S. EPA, 2016, Section
6.4.5). For birth outcomes, there is uncertainty in whether the epidemiologic findings reflect an
independent effect of NO2 exposure.
Total Mortality
In the 2008 ISA, a limited number of epidemiologic studies assessed the relationship
between long-term exposure to NO2 and mortality in adults. The 2008 ISA concluded that the
scarce amount of evidence was "inadequate to infer the presence or absence of a causal
relationship" (U.S. EPA, 2008c). The ISA for the current review concludes that evidence is
"suggestive of, but not sufficient to infer, a causal relationship" between long-term exposure to
NO2 and mortality among adults (U.S. EPA, 2016, Section 6.5.3). This causal determination is
based on evidence from recent studies demonstrating generally positive associations between
long-term exposure to NO2 and total mortality from extended analyses of existing cohorts as well
as original results from new cohorts. In addition, there is evidence for associations between long-
term NO2 exposures and mortality due to respiratory and cardiovascular causes. However, there
were several studies that did not observe an association between long-term exposure to NO2 and
mortality.
Some recent studies examined the potential for copollutant confounding by PM2.5, BC, or
measures of traffic proximity or density in copollutant models with results from these models
generally showing attenuation of the NO2 effect with the adjustment for PM2.5 or BC. It remains
difficult to disentangle the independent effect of NO2 from the potential effect of the traffic-
related pollution mixture or other components of that mixture. Further, as described above, there
is large uncertainty whether long-term NO2 exposure has an independent effect on the
cardiovascular and respiratory morbidity outcomes that are major underlying causes of mortality.
Thus, it is not clear by what biological pathways NO2 exposure could lead to mortality. In
conclusion, the generally positive epidemiologic evidence with uncertainty regarding an
independent NO2 effect is "suggestive of, but not sufficient to infer, a causal relationship"
between long-term exposure to NO2 and total mortality (U.S. EPA, 2016, 6.5.3).
Cancer
The evidence evaluated in the 2008 ISA was judged "inadequate to infer the presence or
absence of a causal relationship" (U.S. EPA, 2008c) based on a few epidemiologic studies
indicating associations between long-term NO2 exposure and lung cancer incidence but lack of
toxicological evidence demonstrating that NO2 induces tumors. In the current review, the
integration of recent and older studies on long-term NO2 exposure and cancer yielded an
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evidence base judged "suggestive of, but not sufficient to infer, a causal relationship" (U.S. EPA,
2016, Section 6.6.9). This conclusion is based primarily on recent epidemiologic evidence, some
of which shows NCh-associated lung cancer incidence and mortality but does not address
confounding by traffic-related copollutants, and is also based on previous toxicological evidence
that implicates NO2 in tumor promotion (U.S. EPA, 2016, Section 6.6.9).
3.3.2 Consideration of NO2 Concentrations: Health Effects of Long-Term NO2 Exposures
In evaluating the adequacy of the current NO2 standards to protect against long-term NO2
exposures, we consider the following question:
• To what extent does the evidence support the occurrence of NCh-attributable asthma
development in children at NO2 concentrations below the existing standards?
To address this question, we consider (1) the extent to which epidemiologic studies
indicate associations between long-term NO2 exposures and asthma development for
distributions of ambient NO2 concentrations that would likely have met the existing standards
and (2) the extent to which effects related to asthma development have been reported following
the range of NO2 exposure concentrations examined in experimental studies. Each of these is
discussed below.
3.3.2.1 Ambient NO 2 Concentrations in Locations of Epidemiologic Studies
As discussed above for short-term exposures (Section 3.2.2.2), when considering
epidemiologic studies within the context of the adequacy of the current NO2 standards, we
emphasize studies conducted in the U.S. and Canada.80 We consider the extent to which these
studies report positive and relatively precise associations with long-term NO2 exposures, and the
extent to which important uncertainties could impact our emphasis on particular studies. For the
studies with potential to inform our conclusions on adequacy, we also evaluate available air
quality information in study locations, focusing on DVs over the course of study periods.
In first considering the availability of studies that could inform our conclusions on
adequacy, we focus the following specific questions:
80 As indicated in Section 3.2.2.2, studies from the U.S. and Canada are likely to reflect air quality and exposure
patterns that are generally applicable to the U.S. In addition, air quality data corresponding to study locations and
study time periods is often readily available for studies conducted in the U.S. and Canada. Nonetheless, we
recognize the importance of all studies, including other international studies, in the ISA's assessment of the weight
of the evidence that informs causal determinations.
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• To what extent do U.S. and Canadian epidemiologic studies report positive, and
relatively precise, associations between long-term NO2 exposures and asthma
development? What are the important uncertainties in these studies?
The epidemiologic studies available in the current review that evaluate associations
between long-term NO2 exposures and asthma incidence are summarized in Table 6-1 of the ISA
(U.S. EPA, 2016, pp. 6-7). There are six longitudinal epidemiologic studies conducted in the
U.S. and Canada that vary in terms of the populations examined and methods used. Of the six
studies, the ISA identifies three as key studies supporting the causal determination (Carlsten et
al., 2011; Clougherty et al., 2007; Jerrett et al., 2008). The other three studies, not identified as
key studies in the ISA causality determination, had a greater degree of uncertainty inherent in
their characterizations of NO2 exposures (Clark et al., 2010; McConnell et al., 2010, Nishimura
et al., 2013). In evaluating the adequacy of the current NO2 standards, we place the greatest
emphasis on the three U.S. and Canadian studies identified in the ISA as providing key
supporting evidence for the causal determination. However, we also consider what the additional
three U.S. and Canadian studies can tell us about the adequacy of the current standards, while
noting the increased uncertainty in these studies.
Effect estimates in U.S. and Canadian studies are generally positive and, in some cases,
statistically significant and relatively precise (U.S. EPA, 2016, Table 6-1; Figure 3-2, below).
However, there are important uncertainties in this body of evidence for asthma incidence,
limiting the extent to which these studies can inform our consideration of the adequacy of the
current NO2 standards to protect against long-term NO2 exposures. For example, there is
uncertainty in the degree to which reported associations are specific to NO2, rather than
reflecting associations with another traffic-related copollutant or the broader mix of pollutants.
Overall, the potential for copollutant confounding has not been well studied in this body of
evidence. As described above (Section 3.3.1), the ISA concludes that "[epidemiologic studies of
asthma development in children have not clearly characterized potential confounding by PM2.5 or
traffic-related pollutants [e.g., CO, BC/EC, volatile organic compounds (VOCs)]" (U.S. EPA,
2016, p. 6-64). The ISA further notes that "[i]n the longitudinal studies, correlations with PM2.5
and BC were often high (e.g., r = 0.7-0.96), and no studies of asthma incidence evaluated
copollutant models to address copollutant confounding, making it difficult to evaluate the
independent effect of NO2" (U.S. EPA, 2016, p. 6-64). Of the U.S. and Canadian studies,
Carlsten et al. (2011) reported correlations between NO2 and traffic-related pollutants (0.7 for
PM2.5, 0.5 for BC based on land use regression). Other U.S. and Canadian studies did not report
quantitative results, but generally reported "moderate" to "high" correlations between NO2 and
other pollutants (U.S. EPA, 2016, Table 6-1). Given the relatively high correlations for NO2 with
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co-occurring pollutants, study authors often interpreted associations with NO2 as reflecting
associations with traffic-related pollution more broadly (e.g., Jerrett et al., 2008; McConnell et
al., 2010).81
Another important uncertainty is the potential for exposure measurement error in these
epidemiologic studies. The ISA states that "a key issue in evaluating the strength of inference
about N02-related asthma development from epidemiologic studies is the extent to which the
NO2 exposure assessment method used in a study captured the variability in exposure among
study subjects" (U.S. EPA, 2016, pp. 6-16). We note that the ISA conclusion of a "likely to be a
causal relationship" is based on the total body of evidence, with the strongest basis for inferring
associations of NO2 with asthma incidence coming from studies that "estimated residential NO2
from LUR models that were demonstrated to predict well the variability in NO2 in study
locations or examined NO2 measured at locations 1-2 km of subjects' school or home" (U.S.
EPA, 2016, pp. 6-21). The studies that meet this criterion were mostly conducted outside of the
U.S. or Canada, with the exception of Carlsten et al. (2011), which used a LUR model with good
predictive capacity. The other U.S. and Canadian studies employed LUR models with unknown
validation or central-site measurements that have well-recognized limitations in reflecting
variability in ambient NO2 concentrations in a community and may not well represent variability
in NO2 exposure among subjects. Thus, the extent to which these U.S. and Canadian studies
provide reliable estimates of asthma incidence for particular NO2 concentrations is unclear.
Overall, in revisiting the question posed above, we note that U.S. and Canadian
epidemiologic studies report positive, and in some cases relatively precise, associations between
long-term NO2 exposure and asthma incidence in children. While it is appropriate to consider
what these studies can tell us with regard to the adequacy of the existing NO2 standards (see
below), the emphasis that we place on these considerations will reflect important uncertainties
related to the potential for confounding by traffic-related copollutants and for exposure
measurement error.
While keeping in mind these uncertainties, we next consider the ambient NO2
concentrations present at monitoring sites in locations and time periods of key U.S. and Canadian
epidemiologic studies. We specifically consider the following question:
81 For example, McConnell et al. (2010) reported that "modeled exposures reflect the mixture of multiple pollutants
from nearby traffic, and the high correlation of pollutants in the mixture precludes identifying the effect of any
specific pollutant in the mixture" (p. 1023 in published article).
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• To what extent do U.S. and Canadian epidemiologic studies report associations with
long-term NO2 in locations likely to have met the current NO2 standards?
As discussed above (Section 3.2.2), addressing this question can provide important
insights into the extent to which NCh-health effect associations are present for distributions of
ambient NO2 concentrations that would be allowed by the current standards. The presence of
such associations would support the potential for the current standards to allow the NO2-
associated asthma development indicated by epidemiologic studies. To the degree studies have
not reported associations in locations meeting the current NO2 standards, there is greater
uncertainty regarding the potential for the development of asthma to result from the NO2
exposures associated with air quality meeting those standards.
To evaluate this issue, we compare NO2 DVs in study areas to the levels of the current
NO2 standards. In additional to comparing annual DVs to the level of the annual standard,
support for consideration of 1-hour DVs comes from the ISA's integrated mode of action
information describing the biological plausibility for development of asthma (Section 3.1,
above). In particular, studies demonstrate the potential for repeated short-term NO2 exposures to
induce pulmonary inflammation and development of allergic responses. The ISA states that
"findings for short-term NO2 exposure support an effect on asthma development by describing a
potential role for repeated exposures to lead to recurrent inflammation and allergic responses,"
which are "identified as key early events in the proposed mode of action for asthma
development" (U.S. EPA, 2016, p. 6-66 and p. 6-64). More specifically, the ISA states the
following (U.S. EPA, 2016, p. 4-64):
The initiating events in the development of respiratory effects due to long-
term NO2 exposure are recurrent and/or chronic respiratory tract
inflammation and oxidative stress. These are the driving factors for
potential downstream key events, allergic sensitization, airway
inflammation, and airway remodeling, that may lead to the endpoint
[airway hyperresponsiveness]. The resulting outcome may be new asthma
onset, which presents as an asthma exacerbation that leads to physician-
diagnosed asthma.
Thus, when considering the protection provided by the current standards against NCh-associated
asthma development, we consider the combined protection afforded by the 1-hour and annual
standards.82
82 It is also the case that broad changes in NO2 concentrations will affect both hourly and annual metrics. This is
discussed in more detail in section 2.3.3 above, and in CASAC's letter to the Administrator (Diez Roux and
Sheppard, 2017). Thus, as in the recent review of the O3 NAAQS (80 FR 65292, October 26, 2015), it is appropriate
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To inform our consideration of whether a study area's air quality could have met the
current NO2 standards during study periods, we have calculated DVs based on the NO2
concentrations measured at existing monitors during the years over which the epidemiologic
studies of long-term NO2 exposures were conducted.83 The DVs for the epidemiologic studies of
asthma incidence conducted in the U.S. and Canada are presented below in Figure 3-2. Mean
DVs represent the average DVs across study periods and maximum DVs represent the year
(annual standard) or 3-year period (1-hour standard) with the highest DV during the study
period. Study locations could have met the current standards for the respective study periods if
all of the annual averages were at or below 53 ppb and all of the 3-year averages of the 98th
percentiles of daily maximum 1-hour NO2 concentrations were at or below 100 ppb.
In interpreting these comparisons of DVs with the NO2 standards, we also consider
uncertainty in the extent to which identified DVs represent the higher NO2 concentrations likely
to have been present near major roads during study periods (see Section 3.2.2). In particular, as
discussed above for short-term exposures, study area DVs are based on NO2 concentrations from
the generally area-wide NO2 monitors that were present during study periods. Calculated DVs
could have been higher if the near-road monitors that are now required in major U.S. urban areas
had been in place. On this issue, we note that the published scientific literature supports the
occurrence of higher NO2 concentrations near roadways and that recent air quality information
from the new near-road NO2 monitoring network generally indicates higher NO2 concentrations
at near-road monitoring sites than at non-near road monitors in the same CBSA (Section 2.3.2).
In addition, mobile source NOx emissions were substantially higher during the majority of study
periods (1986-2006) than they are today (Section 2.1.2), and NO2 concentration gradients around
roadways were generally more pronounced during study periods than indicated by recent air
quality information. Thus, even in cases where DVs during study periods are at or somewhat
below the levels of current standards, it is not clear that study areas would have met the standards
if the currently required near-road monitors had been in place.
to consider the extent to which a short-term standard could provide protection against longer-term pollutant
exposures.
83 As discussed above for short-term exposures, the "DVs" reported here are meant to approximate the values that
are used when determining whether an area meets the primary NO2 NAAQS (see Appendix A).
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Study
Carlsten (2011)
Location
Vancouver
Annual DV Hourly DV
(Max, Mean) (Max, Mean) Study Years Exposure Assessment
Clougherty (2007) Boston
26
34, 29
104
91,75
1995
1987-2004
LUR
LUR
Jerrett (2008)
Clark (2010)
Southern California
1992-2004
Residential
Lompoc (1)
9,7
39, 34
Atascadero (1)
14, 12
55, 52
Santa Maria (1)
15,11
45, 42
Alpine (1)
15, 13
61, 58
Riverside (4)
46,38
142, 124
Los Angeles (3)
60, 42
149, 125
British Columbia
32, 30
67, 67
1999-2000
Central-site-IDW
LUR
Nishimura (2013)
McConnell (2010)
All cities
Puerto Rico 20, 11
Houston 30,25
San Francisco (GALA) 32,22
San Francisco (SAGE)
Chicago 43,33
New York City 49,42
Southern California
Santa Maria (1) 11,10
Alpine (1) 14, 12
Santa Barbara (1) 15,13
Riverside (5) 36, 33
Los Angeles (5) 40, 34
1986-2005
Central site-IDW
83, 62
110, 87
107, 80
180, 94
131, 106
42, 40
54, 50
52, 50
98, 95
121, 105
2002-2006
Central site
0.5 1 1.5 2
Risk or Odds Ratio
2.5
3.5
Figure 3-2. U.S. and Canadian epidemiologic studies of long-term NO2 exposures and asthma incidence. Study locations and
years are reported with annual and hourly DVs for studies of asthma incidence with risk/odds ratios and 95% confidence intervals
standardized to 10 ppb increment in NO2, as presented in the ISA (U.S. EPA, 2016). Effect estimates in red are for studies identified
as key evidence in the ISA (U.S. EPA, 2016). IDW = inverse distance weighted; LUR = land use regression; GALA and SAGE are
different study cohorts. For Carlsten et al. (2011), 1995 was the birth year, for which exposure was estimated. The cohort was
followed to age 7. For Jerrett et al. (2008) and McConnell et al. (2010), parentheses indicate the number of communities represented
by the DVs. For the Riverside and Los Angeles CBSAs, all monitors within the CBSAs were considered given the likelihood that they
represent study communities within those CBSAs due to their close proximity, and DVs for the highest monitor are reported.
Community-specific monitors were selected for the other communities as CBSA-wide monitors had wide spatial distribution and were
not likely to represent the respective study locations (Lompoc, Atascadero, Santa Maria, Alpine, and Santa Barbara). For more details
on DV calculation and study communities, see Appendix A.
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In considering the studies in Figure 3-2, we first note the information from the key
studies as identified in the ISA (Jerrett et al., 2008; Carlsten et al., 2011, Clougherty et al., 2007).
Jerrett et al. (2008) reported positive and relatively precise associations with asthma incidence,
based on analyses across several communities in Southern California.84 Of the 11 study
communities evaluated by Jerrett et al. (2008), most (i.e., seven) had maximum annual DVs that
were near (i.e., 46 ppb for the four communities represented by the Riverside DVs) or above
(i.e., 60 ppb for the three communities represented by the Los Angeles DVs) 53 ppb.85 These
seven communities also had 1-hour DVs (max and mean) that were well-above 100 ppb. The
other key studies (i.e., Carlsten et al., 2011; Clougherty et al., 2007), conducted in single cities,
reported positive but statistically imprecise associations. The annual DVs in locations of these
studies during study years were below 53 ppb, but maximum 1-hour DVs were near
(Clougherty)86 or above (Carlsten) 100 ppb.87
We also consider the information from the other U.S. and Canadian studies available that,
due to additional uncertainties, were not identified as key studies in the ISA (Clark et al., 2010;
McConnell et al., 2010; Nishimura et al., 2013). The multi-city study by Nishimura et al. (2013)
reports a positive and relatively precise association with asthma incidence, based on five U.S.
cities and Puerto Rico (see "combined" estimate in Figure 3-2). Annual DVs in all study cities
were below 53 ppb, while maximum 1-hour DVs were above 100 ppb in four of the five study
cities (mean 1-hour DVs were also near or above 100 ppb in most study cities). Nishimura et al.
(2013) also reported mixed results in city-specific effects estimates. McConnell et al. (2010) also
conducted a multi-community study in Southern California and reported a positive and relatively
precise association between asthma incidence and long-term NO2 exposures based on central-site
measurements. This study encompasses some of the same communities as Jerrett et al. (2008),
84 The multi-community studies by Jerrett et al. (2008) and McConnell et al. (2010) did not include community-
specific analyses.
85 For the studies by Jerrett et al. (2008) and McConnell et al. (2010), the majority of communities were located
within the Los Angeles and Riverside CBS As. Because of this, and because community-specific NO2 monitoring
data were often not available in these areas (Appendix A), DVs for the Los Angeles and Riverside CBS As were
used to represent multiple study communities.
86 As noted above, even in cases where DVs during study periods are at or somewhat below the levels of current
standards, it is not clear that study areas would have met the standards if the currently required near-road monitors
had been in place.
87 As discussed above, DVs are different from the NO2 concentrations reported in the studies themselves, which are
often averaged across study areas and, in some cases, are based on methods other than ambient monitoring. The
annual mean (SD) NO2 concentrations reported in these studies, which are not appropriate for direct comparison to
the current NO2 NAAQS, are as follows: Jerrett et al., 2008: 9.6 (2.5)- 51.3 (4.4) ppb; Carlsten et al., 2011: 17.3
(13.1) ppb; Clougherty: 27.5 (4.3) ppb.
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and while the annual DVs for these study years are more mixed, the 1-hour DVs representing 10
of 13 communities are near or above 100 ppb. Finally, Clark et al. (2010) reported a relatively
precise and statistically significant association in a study conducted over a two-year period in
British Columbia, with annual and hourly DVs of 32 ppb and 67 ppb, respectively. However, this
result, as noted previously, was based on central-site NO2 measurements that have well-
recognized limitations in reflecting variability in ambient NO2 concentrations in a community
and variability in NO2 exposure among subjects.88
Conclusions
Based on the information discussed above, we reach the conclusion that the available
evidence from epidemiologic studies does not provide support for N02-associated asthma
development in locations that would have clearly met the existing annual and 1-hour NO2
standards. This conclusion stems from our consideration of the available evidence from U.S. and
Canadian studies for N02-associated asthma incidence, our consideration of the ambient NO2
concentrations present in study locations during study periods, and the uncertainties and
limitations inherent in the evidence and in our analysis of study area DVs.
With regard to uncertainties in the evidence, we particularly note the potential for
confounding by co-occurring pollutants, as described above, given the following: (1) the
relatively high correlations observed between long-term concentrations of NO2 and long-term
concentrations of other roadway-associated pollutants; (2) the general lack of information from
copollutant models on the potential for NO2 associations that are independent of another traffic-
related pollutant or mix of pollutants. This uncertainty limits what these studies can tell us with
regard to the adequacy of the public health protection provided by the current NO2 standards.
Even if we were to dismiss this fundamental uncertainty in the epidemiologic evidence,
our analysis of study area DVs does not provide support for the occurrence of N02-associated
asthma incidence in locations with ambient NO2 concentrations clearly meeting the current
NAAQS. In particular, for most of the study locations evaluated in the lone key U.S. multi-
community study (Jerrett et al., 2008), 1-hour DVs were above 100 ppb and annual DVs were
near or above 53 ppb. In addition, the two key single-city studies evaluated reported positive, but
relatively imprecise, associations in locations with 1-hour DVs near (Clougherty et al., 2007 in
Boston) or above (Carlston et al., 2011 in Vancouver) 100 ppb. Had currently required near-road
monitors been in operation during study periods, DVs in U.S. study locations would likely have
been higher. Other U.S. and Canadian studies evaluated were subject to greater uncertainties in
88 Annual mean (SD) NO2 concentrations reported in these studies are as follows: Clark et al., 2010: 16.3 (12.3) ppb;
McConnell et al., 2010: 20.4 (8.7-23.6) ppb; Nishimura et al., 2013: 9.9 (2.9) - 32.1 (5.7) ppb.
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the characterization of NO2 exposures. Given these additional uncertainties, the degree to which
these studies can inform our consideration of the adequacy of the current NO2 NAAQS is
limited.
3.3.2.2 NO2 Concentrations in Long-Term Experimental Studies
In addition to the evidence from epidemiologic studies, we also consider evidence from
experimental studies in animals and humans.89 In assessing the evidence for respiratory
morbidity related to long-term NO2 exposures, we consider the following specific question
regarding exposure concentrations in experimental studies:
• To what extent do experimental studies demonstrate effects plausibly related to the
development of asthma following exposures to NO2 lower than previously observed or
at concentrations below the levels of the existing standards?
Experimental studies examining asthma-related effects attributable to long-term NO2
exposures are largely limited to animals exposed to NO2 concentrations well-above those found
in the ambient air (i.e. > 1,000 ppb). As discussed above, the ISA indicates evidence from these
animal studies supports the causal determination by characterizing "a potential mode of action
linking NO2 exposure with asthma development" (U.S. EPA, 2016, p. 1-20). In particular, there
is limited evidence for airway responsiveness in guinea pigs with exposures to 1,000-4,000 ppb
for 6-12 weeks. There is inconsistent evidence for pulmonary inflammation across all studies,
though effects were reported following NO2 exposures of 500-2,000 ppb for 12 weeks. Despite
providing support for the "likely to be a causal" relationship, evidence from these experimental
studies, by themselves, does not provide insight into the occurrence of adverse health effects
following exposures below the levels of the existing NO2 standards.90
Overall Conclusions
Taking all of the evidence and information together, including important uncertainties,
we revisit the question posed at the beginning of this section:
• To what extent does the evidence support the occurrence of NCh-attributable asthma
development in children at NO2 concentrations below the existing standards?
Based on the considerations discussed above, we reach the conclusion that the available
evidence does not provide support for asthma development attributable to long-term exposures to
NO2 concentrations that would meet the existing annual and 1-hour NO2 standards. This
89 While there are not controlled human exposure studies for long-term exposures, we consider the extent to which
evidence from short-term studies can provide support for effects observed in long-term studies.
90 In addition, the ISA draws from short-term experimental evidence to support the biological plausibility of asthma
development. Consideration of the NO2 exposure concentrations evaluated in these studies is discussed in Section
3.3.2.
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conclusion recognizes the NO2 air quality relationships, which indicate that meeting the 1-hour
NO2 standard would be expected to limit annual NO2 concentrations to well-below the level of
the current annual standard (Section 2.3.3, above). This conclusion also recognizes the
uncertainties in interpreting the epidemiologic evidence within the context of evaluating the
existing standards due to the lack of near-road monitors during study periods and due to the
potential for confounding by co-occurring pollutants. Thus, we conclude that epidemiologic
studies of long-term NO2 exposures and asthma development do not provide a clear basis for
concluding that ambient NO2 concentrations allowed by the current standards are independently
(i.e., independent of co-occurring roadway pollutants) associated with the development of
asthma. In addition, while experimental studies provide support for NCh-attributable effects that
are plausibly related to asthma development, the relatively high NO2 exposure concentrations
used in these studies do not provide insight into whether such effects would occur at NO2
exposure concentrations that would be allowed by the current standards.
3.4 POTENTIAL PUBLIC HEALTH IMPLICATIONS
Evaluation of the public health protection provided against ambient NO2 exposures
requires consideration of populations and lifestages that may be at greater risk of experiencing
N02-attributable health effects. In the last review, the 2008 ISA for Oxides of Nitrogen noted
that a considerable fraction of the U.S. population lives, works, or attends school near major
roadways, where ambient NO2 concentrations are often elevated (U.S. EPA, 2008a, Section 4.3).
Of this population, the 2008 ISA concluded that "those with physiological susceptibility will
have even greater risks of health effects related to NO2" (U.S. EPA, 2008, p. 4-12). With regard
to susceptibility, the 2008 ISA concluded that "[pjersons with preexisting respiratory disease,
children, and older adults may be more susceptible to the effects of NO2 exposure" (U.S. EPA,
2008, p. 4-12).
In the current review, the ISA again notes because of the large populations attending
school, living, working, and commuting on or near roads, where ambient NO2 concentrations can
be higher than in many other locations (Section 2.5.3),91 there is widespread potential for
elevated ambient NO2 exposures. For example, Rowangould et al. (2013) found that over 19% of
the U.S. population lives within 100 m of roads with an annual average daily traffic (AADT) of
25,000 vehicles, and 1.3% lives near roads with AADT greater than 200,000. The proportion is
much larger in certain parts of the country, mostly coinciding with urban areas. Among
California residents, 40% live within 100 m of roads with AADT of 25,000 (Rowangould, 2013).
91 The ISA specifically notes that a zone of elevated NO2 concentrations typically extends 200 to 500 m from roads
with heavy traffic (U.S. EPA, 2016, Section 2.5.3).
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In addition, 7% of U.S. schools serving a total of 3,152,000 school children are located within
100 m of a major roadway, and 15% of U.S. schools serving a total of 6,357,000 school children
are located within 250 m of a major roadway (Kingsley et al., 2014). Thus, as in the last review,
the available information indicates that large proportions of the U.S. population potentially have
elevated NO2 exposures as a result of living, working, attending school, or commuting on or near
roadways.
The impacts of exposures to elevated NO2 concentrations, such as those that can occur
around roadways, are of particular concern for populations at increased risk of experiencing
adverse effects. In the current review, our consideration of potential at-risk populations draws
from the 2016 ISA's assessment of the evidence (U.S. EPA, 2016, Chapter 7). The ISA uses a
systematic approach to evaluate factors that may increase risks in a particular population or
during a particular lifestage, noting that increased risk could be due to "intrinsic or extrinsic
factors, differences in internal dose, or differences in exposure" (U.S. EPA, 2016, p. 7-1).
The ISA evaluates the evidence for a number of potential at-risk factors, including pre-
existing diseases like asthma (U.S. EPA, 2016, Section 7.3), genetic factors (U.S. EPA, 2016,
Section 7.4), sociodemographic factors (U.S. EPA, 2016, Section 7.5), and behavioral and other
factors (U.S. EPA, 2016, Section 7.6). The ISA then uses a systematic approach for classifying
the evidence for each potential at-risk factor (U.S. EPA, 2015, Preamble, Section 6.a, Table III).
The categories considered are "adequate evidence," "suggestive evidence," "inadequate
evidence," and "evidence of no effect" (U.S. EPA, 2016, Table 7-1). Consistent with other recent
NAAQS reviews (e.g., 80 FR 65292, October 26, 2015), we focus our consideration of potential
at-risk populations on those factors for which the ISA determines there is "adequate" evidence
(U.S. EPA, 2016, Table 7-27). In the case of NO2, this includes people with asthma, children and
older adults (U.S. EPA, 2016, Table 7-27), based primarily on evidence for asthma exacerbation
or asthma development as evidence for other health effects is more uncertain.
Our consideration of the evidence supporting these at-risk populations specifically
focuses on the following question:
• To what extent does the currently available scientific evidence expand our
understanding of populations and/or lifestages that may be at greater risk for NO2-
related health effects?
In addressing this question, we consider the evidence for effects in people with asthma (Section
3.4.1), children (Section 3.4.2), and older adults (Section 3.4.3) (U.S. EPA, 2016, Chapter 7,
Table 7-27). Section 3.4.4 presents our overall conclusions regarding the populations at
increased risk of N02-related effects.
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3.4.1 People with Asthma
Approximately 8.0% of adults and 9.3% of children (age <18 years) in the U.S. currently
have asthma (Blackwell et al., 2014; Bloom et al., 2013), and it is the leading chronic illness
affecting children (U.S. EPA, 2016, Section 7.3.1). Individuals with pre-existing diseases like
asthma may be at greater risk for some air pollution-related health effects if they are in a
compromised biological state. The 2008 ISA for Oxides of Nitrogen (U.S. EPA, 2008)
concluded that those with pre-existing pulmonary conditions, especially asthma, were likely to
be at greater risk for NCh-related respiratory effects.
As in the last review, controlled human exposure studies demonstrating NCh-induced
increases in AR provide key evidence that people with asthma are more sensitive than people
without asthma to the effects of short-term NO2 exposures. In particular, a meta-analysis
conducted by Folinsbee et al. (1992) demonstrates that NO2 exposures from 100 to 300 ppb
increased AR in the majority of adults with asthma, while AR in adults without asthma was
increased only forNCh exposure concentrations greater than 1,000 ppb (U.S. EPA, 2016, Section
7.3.1). Brown (2015) showed that following resting exposures to NO2 concentrations in the range
of 100 to 530 ppb, about a quarter of individuals with asthma experience clinically relevant
increases in AR to non-specific bronchial challenge. Results of epidemiologic studies are less
clear regarding potential differences between populations with and without asthma (U.S. EPA,
2016, Section 7.3.1). Additionally, studies of activity patterns do not clearly indicate difference
in time spent outdoors to suggest differences in NO2 exposure. However, the meta-analysis of
information from controlled human exposure studies clearly demonstrates increased sensitivity
of adults with asthma compared to healthy adults.92 Thus, consistent with observations made in
the 2008 ISA (U.S. EPA, 2008a), in the current review the ISA determines that the "evidence is
adequate to conclude that people with asthma are at increased risk for N02-related health effects"
(U.S. EPA, 2016, p. 7-7).
3.4.2 Children
According to the 2010 census, 24% of the U.S. population is less than 18 years of age,
with 6.5%) less than age 6 years (Howden and Meyer, 2011). The National Human Activity
Pattern Survey shows that children spend more time than adults outdoors (Klepeis et al., 1996),
and a longitudinal study in California showed a larger proportion of children reported spending
time engaged in moderate or vigorous outdoor physical activity (Wu et al., 201 lb). In addition,
children have a higher propensity than adults for oronasal breathing (U.S. EPA, 2016, Section
4.2.2.3) and the human respiratory system is not fully developed until 18-20 years of age (U.S.
92 Though, as discussed above (Section 3.2), there is uncertainty in the extent to which increases in AR following
exposures to NO2 concentrations near those found in the ambient air (i.e., around 100 ppb) would be adverse.
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EPA, 2016, Section 7.5.1). All of these factors could contribute to children being at higher risk
than adults for effects attributable to ambient NO2 exposures (U.S. EPA, 2016, Section 7.5.1.1).
Epidemiologic evidence across diverse locations (U.S., Canada, Europe, Asia, Australia)
consistently demonstrates adverse effects of both short- and long-term NO2 exposures in
children. In particular, short-term increases in ambient NO2 concentrations are consistently
associated with larger increases in asthma-related hospital admissions, ED visits or outpatient
visits in children than in adults (U.S. EPA, 2016, Section 7.5.1.1, Table 7-13). In general, these
results indicate N02-associated impacts that are 1.8 to 3.4-fold larger in children (Son et al.,
2013; Ko et al., 2007; Atkinson et al., 1999; Anderson et al., 1998). In addition, asthma
development in children has been reported to be associated with long-term NO2 exposures, based
on exposure periods spanning infancy to adolescence (U.S. EPA, 2016, Section 6.2.2.1). Given
the consistent epidemiologic evidence for associations between ambient NO2 and asthma-related
outcomes, including the larger associations with short-term exposures observed in children, the
ISA concludes the evidence "is adequate to conclude that children are at increased risk for NO2-
related health effects" (U.S. EPA, 2016, p. 7-32).
3.4.3 Older adults
According to the 2012 National Population Projections issued by the U.S. Census
Bureau, 13% of the U.S. population was age 65 years or older in 2010, and by 2030, this fraction
is estimated to grow to 20% (Ortman et al., 2014). The 2008 ISA (U.S. EPA, 2008) indicated
that older adults may be at increased risk for N02-related respiratory effects and mortality, and
recent epidemiologic findings add to this body of evidence (US EPA, 2016 Table 7-15). While it
is not clear that older adults experience greater NO2 exposures or doses, epidemiologic evidence
generally indicates greater risk of N02-related health effects in older adults compared with
younger adults. For example, comparisons of older and younger adults with respect to NO2-
related asthma exacerbation generally show larger (one to threefold) effects in adults ages 65
years or older than among individuals ages 15-64 years or 15-65 years (Ko et al., 2007;
Villeneuve et al., 2007; Migliaretti et al., 2005; Anderson et al., 1998). Results for all respiratory
hospital admissions combined also tend to show larger associations with NO2 among older adults
ages 65 years or older (Arbex et al., 2009; Wong et al., 2009; Hinwood et al., 2006; Atkinson et
al., 1999). The ISA determined that, overall, the consistent epidemiologic evidence for asthma-
related hospital admissions and ED visits "is adequate to conclude that older adults are at
increased risk for N02-related health effects" (U.S. EPA, 2016, p. 7-37).
3.4.4 Conclusions
Consistent with the last review, the ISA determined that the available evidence is
adequate to conclude that people with asthma, children, and older adults are at increased risk for
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N02-related health effects. The large proportions of the U.S. population that encompass each of
these groups and lifestages (i.e., 8% adults and 9.3% children with asthma, 24% children, 13%
older adults) underscores the potential for important public health impacts attributable to NO2
exposures. These impacts are of particular concern for members of these populations and
lifestages who live, work, attend school or otherwise spend a large amount of time in locations of
elevated ambient NO2, including near heavily trafficked roadways.
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4 CONSIDERATION OF NOi AIR QUALITY-, EXPOSURE- AND RISK-BASED
INFORMATION
Beyond our consideration of the scientific evidence, discussed above in Chapter 3, we
also consider the extent to which quantitative analyses of NO2 air quality, exposures or health
risks could inform conclusions on the adequacy of the public health protection provided by the
current primary NO2 standards. Such quantitative analyses, if supported, could inform judgments
about the public health impacts of N02-related health effects and could help to place the
evidence for specific effects into a broader public health context. To this end, in the REA
Planning Document (U.S. EPA, 2015) and in this PA, we have evaluated the potential support
for conducting new or updated analyses of NO2 air quality concentrations, exposures and health
risks. In doing so, we have carefully considered the assessments developed as part of the last
review of the primary NO2 NAAQS (U.S. EPA, 2008a) and the newly available scientific and
technical information.
Staff conclusions regarding support for particular quantitative analyses reflect our
assessment of the degree to which updated analyses in the current review are likely to
substantially add to our understanding of NO2 exposures or health risks. These conclusions are
informed by our consideration of the available health evidence and the available technical
information, tools and methods. They build on the preliminary conclusions presented in the REA
Planning Document (U.S. EPA, 2015), and on the CASAC's advice and public input on that
document.
Based on our consideration of the above information, we have conducted updated
analyses examining the occurrence of NO2 air quality concentrations (i.e., as surrogates for
potential NO2 exposures) that may be of public health concern (see below and Appendix B).
Consistent with the anticipated approach discussed in the REA Planning document (U.S. EPA,
2015a, Section 5.2), these updated analyses have been incorporated into this PA, and a separate
REA will not be developed as part of the current review. The analyses discussed below and in
appendix B have been informed by advice from the CASAC and input from the public on the
REA Planning document (Diez Roux and Frey, 2015) and on the draft PA (Diez Roux and
Sheppard, 2017).
Section 4.1 below summarizes our approach to considering potential support for updated
quantitative analyses in this review. Section 4.2, along with the accompanying appendix
(Appendix B), presents updated analyses comparing NO2 air quality with health-based
benchmarks. Sections 4.3 and 4.4 present our consideration of the potential support for updated
exposure and risk assessments, respectively, and our conclusions that such updated assessments
are not supported in the current review.
4-1
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4.1 APPROACH TO CONSIDERING POTENTIAL SUPPORT FOR UPDATED
QUANTITATIVE ANALYSES
In each NAAQS review, selection of the appropriate model(s) for the characterization of
exposures and/or risks is influenced by the nature and strength of the evidence for the subject
pollutant. Depending on the type of evidence available, analyses may include quantitative risk
assessments based on dose-response, exposure-response, or ambient concentration-response
relationships. Analyses may also include comparisons of health-based benchmark
concentrations, drawn from controlled human exposure studies, with modeled exposure estimates
or with ambient air quality concentrations (i.e., as surrogates for potential ambient exposures).
The variety of approaches that have been employed in NAAQS reviews is summarized in Figure
4-1.
Exposure-response
and/or health effect-
based benchmarks
(e g., O3, N02, S02)
Internal
concentration-response
Xje.g., CO, Pb) >
Ambient
concentration-response
(e.g., PM, 03)
Risk Assessment/
Characterization
Dosimetry
Modeling
(Estimates of
internal biomarker
concentration)
Air Quality
Monitoring/
Modeling
(Estimates of
ambient air
concentrations)
Exposure Modeling
(Estimates of inhalation [and, as
relevant, other route] exposure
concentrations)
Figure 4-1. Risk characterization models employed in NAAQS Reviews.
In the last review of the primary NO2 NAAQS, the 2008 ISA concluded that the strongest
evidence supported the occurrence of respiratory effects following short-term NO2 exposures
(U.S. EPA, 2008b). Based on that evidence, the REA employed three approaches to quantify
NO2 exposures and health risks (U.S. EPA, 2008a):
1) Benchmarks were identified based on information from controlled human exposure
studies of N02-induced increases in AR. Ambient NO2 concentrations were compared to
4-2
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these benchmarks. In urban areas across the U.S., such comparisons were made for
ambient NO2 concentrations at locations of NO2 monitoring sites and simulated
concentrations on/near roadways93 (U.S. EPA, 2008a, Chapter 7).
2) Modeled estimates of personal NO2 exposures were compared to benchmarks in a single
urban area (Atlanta, GA), with a focus on children with asthma and people of all ages
with asthma (U.S. EPA, 2008a, Chapter 8).
3) Concentration-response relationships from an epidemiologic study (Tolbert et al., 2007)
were used to estimate N02-associated emergency department visits for respiratory causes
in Atlanta, GA (U.S. EPA, 2008a, Chapter 9).
For this review, conclusions regarding the extent to which the newly available evidence
and information support updated quantitative analyses are based on our consideration of a variety
of factors. As noted above, these include consideration of the available health evidence and the
available technical information, tools and methods. Our consideration of these factors inform
judgments as to the likelihood that particular quantitative analyses will add substantially to our
understanding of NO2 exposures or health risks, beyond the insights gained from the analyses
conducted in the last review. These key considerations and judgments are discussed in the REA
Planning document (U.S. EPA, 2015a) and are summarized in Figure 4-2.
93 Based on the available evidence, there was uncertainty regarding the locations of maximum NO2 concentrations
with respect to roadway emissions and transformation of NO to NO2. Therefore, in the last review the EPA
characterized these simulated concentrations as on- or near-road (75 FR 6474, February 9, 2010).
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Is appropriate
scientific and
technical
information
available to
support
quantitative
assessments?
No
Yes
Is the scientific and/or technical
information that could inform
updated quantitative assessments
substantially different from that
available in previous reviews?
-and-
Would the new information
appreciably reduce the
uncertainties and limitations
identified in previous reviews?
No
Yes
Would updated quantitative
assessments likely inform
decision making in the current
review by adding substantially
to our understanding of
pollutant exposures or
pollutant-attributable health
risks, beyond the insights
gained from previous reviews
and assessments?
No
Yes
I
Updated quantitative assessments are not supported in the current
review
Updated quantitative
assessments are
supported in the current
review
Figure 4-2. Key considerations for updated quantitative analyses.
As indicated in Figure 4-2, an initial consideration is the available health effects evidence
and the foundation it may provide for updated quantitative analyses. As discussed in Chapter 3 of
this PA, our evaluation of the scientific evidence is based on the assessment of the full body of
evidence in the 2016 ISA (U.S. EPA, 2016). Consistent with prior reviews, in considering the
evidence with regard to support for quantitative analyses, we give foremost consideration to
health endpoints for which the ISA concludes the evidence supports a "causal" relationship or
indicates that there is "likely to be a causal" relationship. As discussed in more detail in Chapter
3 of this PA, in the current review, the ISA reaches the following conclusions in this regard:
• "A causal relationship exists between short-term NO2 exposure and respiratory effects based
on evidence for asthma exacerbation" (U.S. EPA, 2016, p. 1-17).
• "There is likely to be a causal relationship between long-term NO2 exposure and respiratory
effects based on evidence for the development of asthma" (U.S. EPA, 2016, p. 1-20).
For all other health endpoints evaluated, the evidence was determined to be either "suggestive of,
but not sufficient to infer, a causal relationship" or "inadequate to infer a causal relationship"
(U.S. EPA, 2016, Table ES-1).
Given these ISA conclusions, our consideration of potential support for updated
quantitative analyses in this review focuses on evidence for health outcomes related to asthma
exacerbation (short-term NO2 exposures) and the development of asthma (long-term NO2
exposures). Our consideration of this evidence is discussed further below as it relates to the
4-4
-------
identification of NO2 benchmarks (Sections 4.2, 4.3) and as to whether updated risk assessments
are supported in the current review (Section 4.4).
4.2 COMPARISON OF NOi AIR QUALITY TO HEALTH-BASED BENCHMARKS
As discussed in Chapter 3, controlled human exposure studies of AR provide the
strongest evidence supporting a causal relationship between short-term NO2 exposures and
respiratory effects.94 A meta-analysis of individual-level data from these studies (Brown, 2015)
supports the occurrence of increased AR in individuals with asthma following resting NO2
exposures from 100 to 530 ppb (U.S. EPA, 2016, Section 5.2.2.1). In the last review, the 2008
REA compared NO2 benchmarks based on information from such controlled human exposure
studies with estimates of ambient NO2 concentrations. These comparisons provided perspective
on the potential for populations to experience NO2 exposures that may be of public health
concern (U.S. EPA, 2008a).
Given that mobile sources were identified in the last review as the largest contributors to
U.S. NOx emissions, and that NO2 monitors were generally not located near heavily trafficked
roads, an important uncertainty identified in the 2008 REA was the characterization of 1-hour
NO2 concentrations around major roadways. Based largely on the fact that information is newly
available in this review from near-road NO2 monitors (Chapter 2), the REA Planning document
reached the preliminary conclusion that updated analyses comparing ambient NO2 concentrations
(i.e., as surrogates for potential exposure concentrations) to health-based benchmarks are
supported (U.S. EPA, 2015a). In particular, the REA Planning document noted that new
information from near-road monitors would be expected to provide important perspective,
beyond what was available in the last review, on the extent to which NO2 exposures around
roads could have potentially important implications for public health (U.S. EPA, 2015a, Section
2.2.1). We have since conducted updated analyses comparing ambient NO2 concentrations to
benchmarks, the details of which are described in Section 4.2.1 and in Appendix B to this PA.
Section 4.2.2 presents our overall conclusions based on these updated analyses.
4.2.1 Updated Analyses Comparing NO2 Air Quality with Health-Based Benchmarks
In this PA, we have conducted updated analyses comparing NO2 air quality to
benchmarks in 23 study areas (Table 4-1). Our selection of study areas focused on CBS As with
94 Increased AR in people with asthma is the only health endpoint that has been shown to occur in controlled human
exposure studies following exposures to NO2 concentrations near those typically found in the ambient air in the U.S.
(Section 3.2).
4-5
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near-road monitors in operation,95 CBSAs with the highest NO2 design values and CBSAs with a
relatively large number of NO2 monitors overall (i.e., providing improved spatial
characterization). Based on these criteria, a total of 23 CBSAs from across the U.S. were selected
as study areas (See Appendix B, Figure B2-1).96 Further evaluation indicates that these 23 study
areas are among the most populated CBSAs in the U.S.; they have among the highest total NOx
emissions and mobile source NOx emissions in the U.S.; and they include a wide range of
stationary source NOx emissions (Appendix B, Figures B2-2 to B2-8).
Air quality-benchmark comparisons were conducted in study areas with unadjusted air
quality and with air quality adjusted upward to just meet the existing 1-hour standard.97 Upward
adjustment was required because all locations in the U.S. meet the current NO2 NAAQS. These
comparisons inform our consideration of the following questions:
• To what extent are the current NO2 standards estimated to allow ambient NO2
concentrations that may be of public health concern? What are the important
uncertainties associated with those estimates?
In addressing these questions, an important focus is on the extent to which ambient NO2
concentrations at or above health-based benchmarks could occur near major roadways. While
data from the recently deployed near-road monitors will inform our consideration of this issue,
the data available from these monitors are limited. Most near-road monitors have been in
operation for only one to two years (Section 2.2.2), and true NO2 DVs could not be calculated at
95 As discussed above (Section 2.2.2), in the last review near-road monitors were required within 50 m of major
roads in large urban areas that met certain criteria for population size or traffic volume. Most near-road monitors are
sited within about 30 m of the road, and in some cases they are sited almost at the roadside (i.e., as close as 2 m from
the road; fattp://www3.epa. gov/tt.n/anitic/nearroad.fatml).
96 Study area CBSAs are Atlanta-Sandy Springs-Roswell, GA; Baltimore-Columbia-Towson, MD; Boston-
Cambridge-Newton, MA-NH; Chicago-Naperville-Elgin, IL-IN-WI; Dallas-Fort Worth-Arlington, TX; Denver-
Aurora-Lakewood, CO; Detroit-Warren-Dearborn, MI; Houston-The Woodlands-Sugar Land, TX; Kansas City,
MO-KS; Los Angeles-Long Beach-Anaheim, CA; Miami-Fort Lauderdale-West Palm Beach, FL; Minneapolis-St.
Paul-Bloomington, MN-WI; New York-Newark-Jersey City, NY-NJ-PA; Philadelphia-Camden-Wilmington, PA-
NJ-DE-MD; Phoenix-Mesa-Scottsdale, AZ; Pittsburgh, PA; Richmond, VA; Riverside-San Bernardino-Ontario,
CA; Sacramento-Roseville-Arden-Arcade, CA; San Diego-Carlsbad, CA; San Francisco-Oakland-Hayward, CA;
St. Louis, MO-IL; Washington-Arlington-Alexandria, DC-VA-MD-WV.
97 In all study areas, ambient NO2 concentrations required smaller upward adjustments to just meet the 1 -hour
standard than to just meet the annual standard. Therefore, when adjusting air quality to just meet the current NO2
NAAQS, we applied the adjustment needed to just meet the 1 -hour standard. Air quality was adjusted such that the
three-year average of the 98th percentiles of the annual distributions of daily maximum 1-hour NO2 concentrations
equals 100 ppb. Information on the air quality adjustment approach can be found in Appendix B, Section B2.4.1.
4-6
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these monitors for the one-hour standard (i.e., because 1-hour DVs are based on three years of
data).98
In this section, we discuss our approach to identifying and interpreting the NO2 health-
based benchmarks (Section 4.2.1.1), summarize the results of the air quality-benchmark
comparisons (Section 4.2.1.2) and discuss uncertainties in these analyses (Section 4.2.1.3). More
detailed descriptions of the approaches used to conduct analyses, and the results of those
analyses, are provided in Appendix B.
4.2.1.1 Health-Based Benchmarks
Based on the evidence from controlled human exposure studies of NCh-induced increases
in AR, the 2008 REA identified NO2 benchmarks from 100 to 300 ppb (U.S. EPA, 2008a). As
discussed further below, in the current review we have again identified benchmarks from 100 to
300 ppb for comparison to ambient NO2 concentrations, based largely on information from the
same controlled human exposure studies of AR that were available in the last review (U.S. EPA,
2016, Tables 5-1 and 5-2). This evidence indicates the potential for increased AR in some people
with asthma following resting exposures to NO2 concentrations from 100 to 530 ppb, though
important uncertainties remain. In its review of the draft PA, CASAC agreed with this range of
health-based benchmarks, stating that "[t]he decision to set the lowest benchmark analyses at 100
ppb NO2 is reasonable as it reflects the lowest level, with sufficient scientific certainty, where
acute NO2 health effects have been shown to occur" (Diez Roux and Sheppard, 2017)."
In identifying the range of NO2 health-based benchmarks to evaluate, and the weight to
place on specific benchmarks within this range, we consider both the group mean responses
reported in individual studies of AR and the results of a meta-analysis that combined individual-
level data from multiple studies (Brown, 2015; U.S. EPA, 2016, Section 5.2.2.1). Group mean
responses in individual studies, and the variability in those responses, can provide insight into the
extent to which observed changes in AR are due to NO2 exposures, rather than to chance alone,
and have the advantage of being based on the same exposure conditions. With regard to
individual studies, we consider both the direction and the statistical significance of group mean
responses in AR following exposures to various NO2 concentrations. Beyond what we can learn
from individual studies, the meta-analysis by Brown (2015) can aid in identifying trends in
individual-level responses across studies and can have the advantage of increased power to
98 One implication of this is that near-road monitors were generally not used as the basis for adjusting air quality to
just meet the current standard. As discussed below (Section 4.2.1.4), this introduces uncertainty into our air quality
adjustments.
99 Though, as noted below, CASAC also suggested consideration of sensitivity analyses based on additional
benchmarks below 100 ppb. These sensitivity analyses are presented in appendix B.
4-7
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detect effects, even in the absence of statistically significant effects in individual studies. With
regard to the meta-analysis, we consider the fraction of people with asthma who experienced
increased AR following exposures to various NO2 concentrations, and the extent to which those
fractions reflect statistically significant majorities of study volunteers.
In first considering studies conducted in resting individuals, where the data are most
consistent (U.S. EPA, 2016, Section 5.2.2.1), we note that the lowest NO2 concentration to which
individuals with asthma have been exposed is 100 ppb. Of the five controlled human exposure
studies conducted at 100 ppb, a statistically significant increase in AR following exposure to
NO2 was only observed in the study by Orehek et al. (1976) (N = 20; Table 3-2). Of the four
studies that did not report statistically significant increases in AR following resting exposures to
100 ppb NO2, three reported non-significant trends toward decreased AR (Ahmed et al., 1983b
(N = 20); Hazucha et al., 1983 (N = 15); Tunnicliffe et al., 1994 (N = 8)), and one reported a
trend towards increased AR (Ahmed et al., 1983a (N = 20)) (Table 3-2). When individual-level
data from these five studies were combined in a meta-analysis, Brown (2015) reported that a
marginally significant100 majority of study participants experienced an increase in AR following
exposure to 100 ppb NO2 (i.e., 61%, p = 0.08; N = 76).101 When the analysis was restricted to
non-specific AR, the percentage who experienced increased AR was larger and statistically
significant (i.e., 66%, p = 0.033; N = 50). In contrast, when the analysis was restricted to specific
AR, study participants exposed to 100 ppb NO2 were evenly divided between experiencing
increases and decreases in AR (i.e., 50% increased and 50% decreased; N = 26) (Brown, 2015,
Table 4).
Compared to 100 ppb, increased AR has been reported more consistently following
exposures to higher NO2 concentrations. In particular, most studies conducted in resting
individuals report statistically significant increases in AR following exposures at or above 250
ppb NO2 (Section 3.2.2). In addition, when resting NO2 exposure concentrations above 100 ppb
are examined in the meta-analysis, results indicate that statistically significant majorities of study
100 In this study, marginal significance is defined as a P-value between 0.10 and 0.05.
101 Brown et al. (2015) compared the number of study participants who experienced an increase in AR following
NO2 exposures to the number who experienced a decrease in AR. Study participants who experienced no change in
AR were not included in comparisons. Thus, of the study participants who experienced either an increase or
decrease in AR following exposure to 100 ppb NO2, 61% experienced an increase and 39% experienced a decrease.
The percentage of total study participants who experienced an increase in AR was slightly smaller than the
percentage reported here and in Brown (2015) (i.e., 55% rather than 61% for 100 ppb NO2 exposure, based on
information in Table 1 of Brown (2015)).
4-8
-------
participants experienced increased AR (Brown, 2015, Tables 3 to 5).102 These results are largely
due to non-specific AR. In contrast, specific AR was not increased in the majority of study
volunteers following exposures to NO2 concentrations at or below 300 ppb. In addition, neither
specific nor non-specific AR was affected following exposures to any of the NO2 concentrations
evaluated during exercise (i.e., including exposure concentrations up to 600 ppb) (Brown, 2015,
Tables 3 to 5).
In further considering these studies within the context of identifying appropriate
benchmarks, we note the discussion of uncertainties in Section 3.2.2.1 above. As discussed in
more detail in that section, there is no indication of a dose-response relationship between NO2
and AR (Goodman, 2009; Brown, 2015). Though the lack of an apparent dose-response
relationship does not necessarily indicate the lack of an NO2 effect, it adds uncertainty to our
interpretation of the controlled human exposure studies of AR. An additional uncertainty is the
clinical relevance of the reported N02-induced increases in AR. While the meta-analysis by
Brown (2015) has partially addressed this uncertainty by evaluating the magnitudes of responses
in a subset of study participants exposed to NO2 concentrations from 100 to 530 ppb,103 this
analysis is limited to a small subset of studies and study participants and, as noted above in
section 3.2.2.1, there is uncertainty in reaching conclusions about the potential for clinically
relevant effects at any particular NO2 exposure concentration within the range evaluated.
When taken together, the results of controlled human exposure studies and of the meta-
analysis by Brown (2015) support consideration of NO2 benchmarks from 100 to 300 ppb, based
largely on studies of non-specific AR in study participants exposed at rest. Benchmarks from the
upper end of this range are supported by the results of individual studies, the majority of which
reported statistically significant increases in AR following NO2 exposures at or above 250 ppb,
and by the results of the meta-analysis by Brown (2015). Benchmarks from the lower end of this
range are supported by the results of the meta-analysis, even though individual studies do not
consistently report statistically significant N02-induced increases in AR following exposures
below 250 ppb. Given uncertainties in the evidence, including the lack of an apparent dose-
response relationship and uncertainty in the potential adversity of reported increases in AR,
102 Specifically, for resting exposures to concentrations of 100 ppb up to < 200 ppb, 200 ppb up to and including 300
ppb, and above 300 ppb, increased AR was reported in 63%, 65%, and 78% of study participants, respectively. The
fractions of individuals who experienced increased AR following resting exposures, compared to the fraction who
experienced decreased AR, reached statistical significance for all of the ranges of exposure concentrations evaluated
(p < 0.05) (Brown, 2015, Table 5).
103 As discussed above (Section 3.2), this analysis indicates the potential for clinically relevant increases in AR in
some people with asthma exposed to NO2 concentrations from 100 to 530 ppb.
4-9
-------
caution is appropriate when interpreting the potential public health implications of 1-hour NO2
concentrations at or above these benchmarks. This is particularly the case for the 100 ppb
benchmark, given the less consistent results at this exposure concentration.
4.2.1.2 Summary of Results
We have evaluated the occurrence of 1-hour ambient NO2 concentrations at or above the
various benchmarks for as-is (i.e., unadjusted) air quality from 2010 to 2015 and for NO2 air
quality adjusted to just meet the existing 1-hour standard.104 In considering these results, our
focus is on the number of days per year that such 1-hour NO2 concentrations could occur at each
monitoring site in each study area. Detailed results of these analyses can be found in Appendix B
(Section B3). In Tables 4-1 and 4-2 below, we present the number of days per year with daily
maximum 1-hour NO2 concentrations calculated to be at or above benchmarks of 100, 150,105
and 200 ppb,106 based on non-near-road monitors (Table 4-1)107 and near-road monitors (Table 4-
2).
104 As noted above, in all study areas, ambient NO2 concentrations required smaller upward adjustments to just meet
the 1-hour standard than to just meet the annual standard. Therefore, when adjusting air quality to just meet the
current NO2 NAAQS, we applied the adjustment needed to just meet the 1-hour standard.
105 Though no studies specifically evaluated the potential for increased AR following exposures to 150 ppb NO2,
results for the 150 ppb benchmark can provide information on the degree to which exceedances of the 100 ppb
benchmark are due to ambient NO2 concentrations closer to 100 ppb or 200 ppb.
106 Because ambient NO2 concentrations never reached or exceeded even the 200 ppb benchmark under the air
quality scenarios that we have evaluated in this draft PA, we do not present results for the 300 ppb benchmark.
107 Most, though not all, of these NO2 monitors are classified as "area-wide." We use the term "area-wide" to refer to
monitors sited at neighborhood, urban, and regional scales, as well as those monitors sited at either micro- or
middle-scale that are representative of many such locations in the same CBSA (Section 2.2.2, above).
4-10
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Table 4-1. Average and maximum number of days per year with non-near road NO2 concentrations at or above
benchmarks.
100 ppb benchmark"-1.
150 ppb benchmark '1.
200 ppb benchmark11.
Study
Area103
As Is Air
Air Quality Adjusted to Just meet the
As Is Air
Air Quality Adjusted to Just meet the
As Is Air
Air Quality Adjusted to Just meet
Quality
Existing 1-hr Standard'
Quality
Existing 1-hr Standard
Quality
the Existing 1-hr Standard
2010-
2010-
2011-
2012-
2013-
2010-
2010-
2011-
2012-
2013-
2010-
2010-
2011-
2012-
2013-
2015
2012
2013
2014
2015
2015
2012
2013
2014
2015
2014
2012
2013
2014
2015
Atlanta, GA
0(0)
3(15)
5(30)
4(16)
2(16)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
Baltimore,
MD
0(0)
4(12)
5(12)
4(12)
4(11)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
Boston, MA
0(0)
5(15)
5(15)
6(18)
3(12)
0(0)
0.5 (1)
0.5(1)
1(1)
0.5(1)
0(0)
0(0)
0(0)
0(0)
0(0)
Chicago, IL
0.5 (1)
7(24)
-
-
4(12)
0(0)
0(0)
-
-
0(0)
0(0)
0(0)
-
-
0(0)
Dallas, TX
0(0)
1(9)
2(11)
2(16)
2(12)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
Denver, CO
0.5 (1)
-
14 (29)
5(10)
4(10)
0(0)
-
0.5(1)
1(1)
0.5(1)
0(0)
-
0(0)
0(0)
0(0)
Detroit, Ml
0(0)
6(12)
9(23)
8(18)
4(9)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
Houston, TX
0.5 (3)
2(12)
2(8)
2(15)
2(14)
0(0)
0.5 (2)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
Kansas City,
MO-KS
0(0)
5(9)
5(9)
7(11)
7(10)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
Los Angeles,
CA
0.5 (1)
5(19)
5(22)
5(23)
4(27)
0(0)
0.5 (1)
0.5(1)
0.5 (1)
0.5(1)
0(0)
0(0)
0(0)
0(0)
0(0)
Miami, FL
0(0)
5(15)
4(12)
5(12)
6(17)
0(0)
0.5 (1)
0.5(1)
1(1)
0.5(1)
0(0)
0(0)
0(0)
0(0)
0(0)
Minneapolis,
MN
0(0)
6(22)
4(11)
4(14)
2(10)
0(0)
0.5 (2)
0.5(3)
0.5 (2)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
New York, NY
0.5 (1)
3(9)
3(12)
3(12)
4(12)
0.5 (1)
0.5 (1)
0.5(1)
0.5 (1)
0.5(1)
0(0)
0(0)
0(0)
0(0)
0(0)
Philadelphia,
PA
0.5 (1)
3(18)
3(23)
-
2(13)
0(0)
0.5 (1)
0.5(1)
-
0.5(1)
0(0)
0(0)
0(0)
-
0(0)
Phoenix, AZ
0.5 (1)
2(9)
4(13)
3(12)
3(9)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
Pittsburgh,
PA
0(0)
2(9)
3(22)
-
3(14)
0(0)
0(0)
0(0)
-
0(0)
0(0)
0(0)
0(0)
-
0(0)
Richmond,
VA
0(0)
6(17)
5(17)
7(15)
2(6)
0(0)
0(0)
0.5(1)
0.5 (1)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
Riverside, CA
0.5 (7)
1(14)
4(25)
-
1(5)
0(0)
0.5 (1)
0.5(8)
-
0(0)
0(0)
0(0)
0(0)
-
0(0)
108Tables 4-1 and 4-2 identify study areas as individual cities. However, actual study areas include the entire CBSAs within which the identified cities are
located. Complete study areas are defined above and in Appendix B.
4-11
-------
Sacramento,
CA
0(0)
2(10)
3(14)
4(21)
3(17)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
San Diego,
CA
0.5 (1)
1(9)
1(12)
1(12)
0.5 (4)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
San
Francisco, CA
0.5 (1)
1(13)
1(23)
1 (11)
2(14)
0(0)
0(0)
0.5(1)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
0(0)
St. Louis, MO
0(0)
-
2(8)
4(14)
1(5)
0(0)
-
0(0)
0(0)
0(0)
0(0)
-
0(0)
0(0)
0(0)
Washington,
DC
0(0)
4(14)
4(20)
5(24)
6(24)
0(0)
0.5 (1)
0.5(1)
0.5 (1)
0.5(1)
0(0)
0(0)
0(0)
0(0)
0(0)
aAll calculated means were rounded to the nearest integer, though for mean values <0.50, rather than round downward to zero, a value of 0.5 was designated to
distinguish it from instances where there were absolutely no (i.e., 0) days at or above benchmark levels during the collective years of interest.
bIn parentheses are the maximum number of days in single year during the collective years of interest.
dBlank cells (indicated by indicate time periods for which sufficient monitoring data were not available to generate estimates
4-12
-------
Table 4-2. Number of days in 2014 and 2015 with near-road NO2 concentrations at or above benchmarks.109
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Komi (111)
2014
2015
2014
2015
2014
2015
2014
2015
2014
2015
2014
2015
Atlanta, OA
130890003
146.000
30
-
0
-
"
-
0
-
0
-
0
-
0
131210056
284.920
2
-
0
-
-
0
-
0
-
0
-
0
Baltimore, MD
240270006
186,750
16
-
0
-
7
-
0
-
0
-
0
-
0
Boston, MA
250250044
198,239
10
0
0
11
6
0
0
0
0
0
0
0
0
Chicago, IL
170313103
1
0
4
4
0
0
0
0
0
0
0
0
Dallas, TX
481131067
235,790
24
-
0
-
5
-
0
-
0
-
0
-
0
Denver, CO
080310027
249,000
9
0
0
5
3
0
0
0
0
0
0
0
0
Detroit, MI
261630093
140,500
9
0
0
9
9
0
0
0
0
0
0
0
0
261630095
172,600
49
-
0
-
5
-
0
-
0
-
0
-
0
Houston, TX
482011066
324,119
24
0
0
4
3
0
0
0
0
0
0
0
0
Kansas City, MO-
RS
290950042
114,495
20
0
0
4
3
0
0
0
0
0
0
0
0
Los Angeles, CA
060590008
272,000
9
0
0
21
1
0
0
0
0
0
0
0
0
Minneapolis, MN
270370480
87,000
30
-
0
-
3
-
0
-
0
-
0
-
0
270530962
277,000
33
0
0
11
10
0
0
0
0
0
0
0
0
New York, NY
340030010
311,234
20
1
-
8
-
1
-
0
-
0
-
0
Philadelphia, PA
421010075
124,610
12
0
0
3
2
0
0
0
0
0
0
0
0
Phoenix, AZ
040134019
320,138
12
0
0
3
2
0
0
0
0
0
0
0
0
Pittsburgh, PA
420031376
87,534
18
-
0
-
11
-
0
-
0
-
0
-
0
Richmond, VA
517600025
151,000
21
-
0
-
9
-
0
-
0
-
0
-
0
Riverside, CA
060710026
245,300
50
-
0
-
8
-
0
-
0
-
0
-
0
San Francisco, CA
060010012
216,000
20
0
0
2
1
0
0
0
0
0
0
0
0
St. Louis, MO
291890016
161,338
27
-
0
-
1
-
0
-
0
-
0
-
0
295100094
159,326
25
0
0
10
2
0
0
0
0
0
0
0
0
109 Where monitor was in operation for 300 or more days in the year. Blank cells (indicated by indicate time periods for which sufficient monitoring data
were not available.
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Based on the results presented in Tables 4-1 and 4-2 above, we make the following key
observations for study areas when air quality was unadjusted ("as-is") and when air quality was
adjusted to just meet the current 1-hour NO2 standard:
1. For unadjusted air quality
a. One-hour ambient NO2 concentrations in study areas, including those near major
roadways, were always below 200 ppb, and were virtually always below 150 ppb.
i. Even in the worst-case years (i.e., the years with the largest number of
days at or above benchmarks), no study areas had any days with 1-hour
NO2 concentrations at or above 200 ppb, and only one area had any days
(i.e., one day) with 1-hour concentrations at or above 150 ppb.
b. One-hour ambient NO2 concentrations in study areas, including those near major
roadways, only rarely reached or exceeded 100 ppb. On average in all study areas,
1-hour NO2 concentrations at or above 100 ppb occurred on less than one day per
year.
i. Even in the worst-case years, most study areas had either zero or one day
with 1-hour NO2 concentrations at or above 100 ppb (7 days in the single
worst-case location and worst-case year).
2. For air quality adjusted to just meet the current primary 1-hour NO2 standard
a. The current standard is estimated to allow no days in study areas with 1-hour
ambient NO2 concentrations at or above 200 ppb. This is true for both area-wide
and near-road monitoring sites, even in the worst-case years.
b. The current standard is estimated to allow almost no days with 1-hour ambient
NO2 concentrations at or above 150 ppb, based on both area-wide and near-road
monitoring sites (i.e., zero to one day per year, on average).
i. In the worst-case years in most study areas, the current standard is
estimated to allow either zero or one day with 1-hour ambient NO2
concentrations at or above 150 ppb. In the single worst-case year and
location, the current standard is estimated to allow eight such days.
c. At area-wide monitoring sites in most of the study areas, the current standard is
estimated to allow from one to seven days per year, on average, with 1-hour
ambient NO2 concentrations at or above 100 ppb. At near-road monitoring sites in
most of the study areas, the current standard is estimated to allow from about one
to 10 days per year with such 1-hour concentrations.
i. In the worst-case years in most of the study areas, the current standard is
estimated to allow from about 5 to 20 days with 1-hour NO2
concentrations at or above 100 ppb (30 days in the single worst-case
location and year).
4.2.1.3 Limitations and uncertainties
There are a variety of limitations and uncertainties in these comparisons of NO2 air
quality with health-based benchmarks. In particular, we note uncertainties in the evidence
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underlying the benchmarks themselves, as discussed in Section 4.2.1.1, uncertainties in the
upward adjustment of NO2 air quality concentrations, and uncertainty in the degree to which
monitored NO2 concentrations reflect the highest potential NO2 exposures. Each of these is
discussed below.
Health-Based benchmarks
The primary goal of this analysis is to inform conclusions regarding the potential for the
existing primary NO2 standards to allow exposures to ambient NO2 concentrations that may be of
concern for public health. As discussed in detail above (Sections 3.2.2, 4.2.1.1), the meta-
analysis by Brown (2015) indicates the potential for increased AR in some people with asthma
following NO2 exposures from 100 to 530 ppb. While it is possible that certain individuals could
be more severely affected by NO2 exposures than indicated by existing studies, which have
generally evaluated adults with mild asthma,110 there remains uncertainty in the degree to which
the effects identified in these studies would be of public health concern. In particular, the lack of
an apparent dose-response relationship between NO2 and AR and uncertainties in the magnitude
and potential adversity of the increase in AR following NO2 exposures complicate our
interpretation of comparisons between ambient NO2 concentrations and health-based
benchmarks. When considered in the context of the less consistent results observed across
individual studies following exposures to 100 ppb NO2, compared to higher exposure
concentrations,111 these uncertainties have the potential to be of particular importance for
interpreting the public health implications of ambient NO2 concentrations at or above the 100
ppb benchmark.112
With regard to the magnitude and clinical relevance of the N02-induced increase in AR
in particular, we note that the meta-analysis by Brown (2015) attempts to address this uncertainty
and inconsistency across individual studies. Specifically, as discussed above (Section 3.2.2), the
meta-analysis evaluates the available individual-level data on the magnitude of the change in AR
following resting NO2 exposures. Brown (2015) reports that the magnitude of the increases in
110 Although Brown (2015) notes that one study included in the meta-analysis (Avol et al., 1989) evaluated children
aged 8 to 16 years and that disease status varied across studies, ranging from "inactive asthma up to severe asthma in
a few studies" (p. 3 in published manuscript).
111 As discussed previously, while the meta-analysis indicates that the majority of study volunteers experienced
increased non-specific AR following exposures to 100 ppb NO2, results were marginally significant when specific
AR was also included in the analysis. In addition, individual studies do not consistently indicate increases in AR
following exposures to 100 ppb NO2.
112 Sensitivity analyses included in Appendix B (Section 3.2, table B3-1) also evaluated l-hourNCh benchmarks
below 100 ppb (i.e., 85, 90, 95 ppb), though the available health evidence does not provide a basis for determining
what exposures to such NO2 concentrations might mean for public health.
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AR observed following resting NO2 exposures from 100 to 530 ppb were large enough to be of
potential clinical relevance in about a quarter of the 72 study volunteers with available data. This
is based on the fraction of exposed individuals who experienced a halving of the provocative
dose of challenge agent following NO2 exposures. This magnitude of change has been
recognized by the American Thoracic Society (ATS) and the European Respiratory Society as a
"potential indicator, although not a validated estimate, of clinically relevant changes in airway
responsiveness" (Reddel et al., 2009) (U.S. EPA, 2016, p. 5-12). While this analysis by Brown
(2015) indicates the potential for some people with asthma to experience effects of clinical
relevance following resting NO2 exposures from 100 to 530 ppb, it is based on a relatively small
subset of volunteers and the interpretation of these results for any specific exposure
concentration within the range of 100 to 530 ppb is uncertain (see section 3.2.2, above).
Approach to adjusting ambient NO? concentrations
These analyses use historical air quality relationships as the basis for adjusting ambient
NO2 concentrations to just meet the current 1-hour standard (Appendix B). The adjusted air
quality is meant to illustrate a hypothetical scenario, and does not represent expectations
regarding future air quality trends. If ambient NO2 concentrations were to increase in some
locations to the point of just meeting the current standards, it is not clear that the spatial and
temporal relationships reflected in the historical data would persist. In particular, as discussed in
Section 2.1.2 of this PA, we expect that ongoing implementation of existing regulations and the
implementation of the recently revised O3 NAAQS113 will result in continued reductions in
ambient NO2 concentrations over much of the U.S. (i.e., reductions beyond the "unadjusted" air
quality used in these analyses). Thus, if ambient NO2 concentrations were to increase to the point
of just meeting the existing 1-hour NO2 standard in some areas, the resulting air quality patterns
may not be similar to those estimated in our air quality adjustments.
There is also uncertainty in the upward adjustment of NO2 air quality because three years
of data are not yet available from most near-road monitors. In most study areas, DVs were not
calculated at near-road monitors and, therefore, near-road monitors were generally not used as
the basis for identifying adjustment factors for just meeting the existing standard.114 In locations
where near-road monitors measure the highest NO2 DVs, reliance on those near-road monitors to
identify air quality adjustment factors would result in smaller adjustments being applied to
113 Based on analyses conducted as part of the 2015 Regulatory Impact Analysis for the O3 NAAQS, available at
https://www3.epa.gov/ttnecasl/docs/ria/naaqs-o3_ria_final_2015-09.pdf.
114 Though in a few study locations, near-road monitors did contribute to the calculation of air quality adjustments,
as described in Appendix B (Table B2-7).
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monitors in the study area. Thus, monitors in such study areas would be adjusted upward by
smaller increments, potentially reducing the number of days on which the current standard is
estimated to allow 1-hour NO2 concentrations at or above benchmarks. Given that near-road
monitors in most areas measure higher 1-hour NO2 concentrations than the area-wide monitors in
the same CBSA (Figures 2-7 to 2-9), this uncertainty has the potential to impact results in many
of our study areas. While the magnitude of the impact is unknown at present, the inclusion of
additional years of near-road monitoring information in the determination of updated air quality
adjustments could result in fewer estimated 1-hour NO2 concentrations at or above benchmarks
in some study areas.
Degree to which monitored NO? concentrations reflect the highest potential NO? exposures
To the extent there are unmonitored locations where ambient NO2 concentrations exceed
those measured by monitors in the current network, the potential for NO2 exposures at or above
benchmarks could be underestimated. In the last review, this uncertainty was determined to be
particularly important for potential exposures around roads. The 2008 REA estimated that the
large majority of modeled exposures to ambient NO2 concentrations at or above benchmarks
occurred on or near roads (U.S. EPA, 2008a, Figures 8-17 and 8-18). When characterizing
ambient NO2 concentrations, the 2008 REA attempted to address this uncertainty by estimating
the elevated NO2 concentrations that can occur on or near the road. These estimates were
generated by applying literature-derived adjustment factors to NO2 concentrations at monitoring
sites located away from the road.115
In the current review, given that the 23 selected study areas have among the highest NOx
emissions in the U.S., and given the siting characteristics of existing NO2 monitors, this
uncertainty likely has only a limited impact on the results of the air quality-benchmark
comparisons. In particular, as described above, mobile sources tend to dominate NOx emissions
within most CBS As, and the 23 study areas evaluated have among the highest mobile source
NOx emissions in the U.S. (Appendix B, Section B2.3.2). Most study areas have near-road NO2
monitors in operation, which are required within 50 m of the most heavily trafficked roadways in
large urban areas. The majority of these near-road monitors are sited within 30 m of the road,
and several are sited within 10 m (see Atlanta, Cincinnati, Denver, Detroit, Los Angeles in
115 Sensitivity analyses included in Appendix B use updated data from the scientific literature (Richmond-Bryant et
al., 2016) to estimate "on-road" NO2 concentrations based on monitored concentrations around a roadway in Las
Vegas (Appendix B, Section B2.4.2). However, there remains considerable uncertainty in the relationship between
on-road and near-road NO2 concentrations, and in the degree to which they may differ. Therefore, in evaluating the
potential for roadway-associated NO2 exposures, we focus on the concentrations at locations of near-road monitors.
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EPA's database of metadata for near-road monitors116). Thus, even though the location of highest
NO2 concentrations around roads can vary (Section 2.1), we anticipate that the near-road NO2
monitoring network, with monitors sited from 2 to 50 m away from heavily trafficked roads,
effectively captures the types of locations around roads where the highest NO2 concentrations
can occur.117
This conclusion is consistent with the ISA's analysis of available data from near-road
NO2 monitors, which indicates that near-road monitors with target roads having the highest
traffic counts also had among the highest 98th percentiles of 1-hour daily maximum NO2
concentrations (U.S. EPA, 2016, Section 2.5.3.2). The ISA concludes that "[ojverall, the very
highest 98th percentile 1-hour maximum concentrations were generally observed at the monitors
adjacent to roads with the highest traffic counts" (U.S. EPA, 2016, p. 2-66).
It is also important to consider the degree to which air quality-benchmark comparisons
appropriately characterize the potential for NO2 exposures near non-roadway sources of NOx
emissions. With regard to this issue, we note that the 23 selected study areas include CBSAs with
large non-roadway sources of NOx emissions. This includes study areas with among the highest
NOx emissions from electric power generation facilities (EGUs) and airports, the two types of
non-roadway sources that emit the most NOx in the U.S. (Appendix B, Section B2.3.2). As
discussed below, several study areas have non-near-road NO2 monitors sited to determine the
impacts of such sources.
Table 2-12 in the ISA (U.S. EPA, 2016) summarizes NO2 concentrations at selected
monitoring sites that are likely to be influenced by non-road sources, including nearby ports,
airports, border crossings, petroleum refining, or oil and gas drilling. For example, the Los
Angeles, CA CBSA includes one of the busiest ports and one of the busiest airports in the U.S.
Out of 18 monitors in the Los Angeles CBSA, three of the five highest 98th percentile 1-hour
maximum concentrations were observed at the near-road site, the site nearest the port, and the
site adjacent to the airport. In the Chicago, IL CBSA, the highest hourly NO2 concentration
measured in 2014 (105 ppb) occurred at the Schiller Park, IL monitoring site, located adjacent to
O'Hare International airport and very close to a major rail yard (i.e., Bedford Park Rail Yard)
(U.S. EPA, 2016, Section 2.5.3.2).118 In addition, one of the highest 1-hour daily maximum NO2
116 This database is found at http://www3.epa.gov/ttn/amtic/nearroad.html
117 Though it remains possible that some areas (e.g., street canyons in urban environments) could have higher
ambient NO2 concentrations than indicated by near-road monitors. This issue is highlighted as a data gap and
research need in section 5.4 below. Sensitivity analyses estimating the potential for on-road NO2 exposures are
described in Appendix B.
118 Sections B5.1 andB5.2 of Appendix B provides data on the large sources of NOx emissions in areas where NO2
monitors are located.
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concentrations recorded in recent years (136 ppb) was observed at a Denver, CO non-near-road
site. This concentration was observed at a monitor located one block from high-rise buildings
that form the edge of the high-density central business district. This monitor is likely influenced
by local traffic, as well as by commercial heating and other activities (U.S. EPA, 2016, Section
2.5.3.2).119 Thus, beyond the NO2 near-road monitors, some NO2 monitors in study areas are also
sited to capture high ambient NO2 concentrations around important non-roadway sources of NOx
emissions.
Conclusions Based on Air Quality-Benchmark Comparisons
As discussed above and in the REA Planning document (U.S. EPA, 2015, Section 2.1.1),
an important uncertainty identified in the 2008 REA was the characterization of 1-hour NO2
concentrations around major roadways. In the current review, data from recently deployed near-
road NO2 monitors improves our understanding of such ambient NO2 concentrations. In this PA
we have conducted updated analyses comparing ambient NO2 concentrations (i.e., as surrogates
of potential exposures) to health-based benchmarks, with a particular focus on study areas where
near-road monitors have been deployed.
As discussed in Chapter 2, recent NO2 concentrations measured in all U.S. locations meet
the existing primary NO2 NAAQS.120 Based on these recent (i.e., unadjusted) ambient
measurements, analyses estimate almost no potential for 1-hour exposures to NO2 concentrations
at or above benchmarks, even at the lowest benchmark examined (i.e., 100 ppb).
Analyses of air quality adjusted upwards to just meet the current 1-hour standard also
indicate virtually no potential for 1-hour exposures to NO2 concentrations at or above the 200
ppb benchmark, and almost none for exposures at or above 150 ppb. This is the case for both
average and worst-case years, including in study areas with near-road monitors sited within a
few meters of heavily trafficked roads. With respect to the lowest benchmark evaluated, analyses
estimate that the current 1-hour standard allows the potential for exposures to 1-hour NO2
119 Recent traffic counts on the nearest streets were 44,850 (in 2014) and 23,389 (in 2013) vehicles per day,
respectively. Traffic counts on other streets within one block of this monitor were 22,000, 13,000, 5,000, and 2,490
vehicles per day. Together, this adds up to more than 100,000 vehicles per day on streets within one block of this
non-near-road monitor (U.S. EPA, 2016, Section 2.5.3.2).
120 As described in section 2.3.1 (above), the maximum NO2 DVs in 2015 for the whole network were well-below
the NAAQS, with the highest values being 30 ppb (annual) and 72 ppb (hourly).
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concentrations at or above 100 ppb on some days (i.e., about one to 10 days per year, on
average).121'122
These results are consistent with our expectations, given that the current 1-hour standard,
with its 98th percentile form, is anticipated to limit, but not eliminate, exposures to 1-hour NO2
concentrations at or above 100 ppb.123 These results are similar to the results presented in the
REA from the last review, based on NO2 concentrations at the locations of area-wide ambient
monitors (Appendix B, Section B5.9, Table B5-66). In contrast, compared to the on/near road
simulations in the last review, these results indicate substantially less potential for 1-hour
exposures to near-road NO2 concentrations at or above benchmarks (Appendix B, Section B5.9,
Table B5-66).124
When these results and associated uncertainties are taken together, we note that the
current 1-hour NO2 standard is expected to allow virtually no potential for exposures to the NO2
concentrations that have been shown most consistently to increase AR in people with asthma
(i.e., above 200 ppb), even under worst-case conditions across a variety of study areas with
among the highest NOx emissions in the U.S. Such NO2 concentrations were not estimated to
occur, even at monitoring sites adjacent to some of the most heavily trafficked roadways.
In addition, the current standard is expected to limit, though not eliminate, exposures to
1-hour concentrations at or above 100 ppb. Though the current standard is estimated to allow 1-
hour NO2 concentrations at or above 100 ppb on some days, the potential public health
implications of exposures to these concentrations are unclear. In particular, while the meta-
analysis by Brown (2015) indicates the potential for increased AR following exposure to 100 ppb
NO2 (i.e., when analyses were restricted to non-specific AR), individual studies generally do not
indicate statistically significant N02-induced increases in AR following exposures to 100 ppb
NO2 and meta-analysis results based on all available data at 100 ppb (i.e., resting exposures,
specific and non-specific AR) were only marginally significant. When combined with
uncertainty due to the lack of an overall dose-response relationship and uncertainty in the degree
121 Results for the 100 ppb benchmark are due primarily to 1 -hour NO2 concentrations that are closer to 100 ppb than
200 ppb, based on the results for the 150 ppb benchmark.
122 Sensitivity analyses included in Appendix B (section 3.2) indicate a greater potential for exposures to NO2
benchmarks below 100 ppb (i.e., 85, 90, 95 ppb). However, as noted above, the uncertainties in the available health
evidence lead to uncertainties in interpreting what such NO2 exposures might mean for public health.
123 The 98th percentile generally corresponds to the 7th or 8th highest 1-hour concentration in a year.
124 On-/near-road simulations in the last review estimated that a 1-hour NO2 standard with a 98th percentile form and
a 100 ppb level could allow about 20 to 70 days per year with 1-hour NO2 concentrations at or above the 200 ppb
benchmark and about 50 to 150 days per year with 1-hour concentrations at or above the 100 ppb benchmark
(Appendix B, Table B5-56).
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to which effects would be adverse should they occur, there is considerable uncertainty regarding
the potential public health implications of exposures to 100 ppb NO2. In limiting exposures to
NO2 concentrations at or above 100 ppb, the current standard provides protection against
exposures for which the evidence of adverse NCh-attributable effects is less certain.
In reaching an overall conclusion based on the results of analyses comparing NO2 air
quality with health-based benchmarks, we consider all of the information discussed above. Given
the results of these analyses, and the uncertainties inherent in their interpretation, we conclude
that there is little potential for exposures to ambient NO2 concentrations that would be of clear
public health concern in locations meeting the current 1-hour standard. Additionally, while a
lower standard level (i.e., lower than 100 ppb) would be expected to further limit the potential
for exposures to 100 ppb NO2, the public health implications of such reductions are unclear,
particularly given that no additional protection would be expected against exposures to NO2
concentrations at or above the higher benchmarks (i.e., 200 ppb and above). Thus, we conclude
that these analyses comparing ambient NO2 concentrations to health-based benchmarks do not
provide support for considering potential alternative standards to increase public health
protection, beyond the protection provided by the current standards.
4.3 MODEL-BASED EXPOSURE ASSESSMENT
In the last review, in addition to analyses comparing NO2 air quality to benchmarks, the
REA used an exposure model to generate estimates of 1-hour personal NO2 exposures in a single
urban study area (i.e., Atlanta, GA). These modeled 1-hour personal exposures were compared to
1-hour benchmarks ranging from 100 to 300 ppb. The exposure assessment in the last review
served to complement the results of the broader, less resource-intensive, analyses comparing NO2
air quality to benchmarks (described above).
In the current review, the REA Planning document (U.S. EPA, 2015, Chapter 3) indicated
that the potential utility of an updated model-based assessment of personal NO2 exposures would
depend on the results of the updated comparison of NO2 air quality with health effect
benchmarks (Section 4.2). To the extent air quality-benchmark comparisons indicate little
potential for exposures to ambient NO2 concentrations that would be of public health concern,
the REA Planning document concluded that the added value of more refined estimates of
personal NO2 exposures would be limited.
As discussed in Section 4.2 above, analyses comparing NO2 air quality with health-based
benchmarks indicate little potential for exposures to ambient NO2 concentrations that would be
of clear public health concern in locations meeting the current 1-hour standard. Given the results
of these analyses, more refined estimates of personal NO2 exposures would be of limited
additional value. Based on these conclusions, we have not conducted an updated assessment of
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modeled NO2 exposures in this review. In its review of the draft PA, CASAC agreed "with the
decision to not conduct any new model-based analyses" (Diez Roux and Sheppard, 2017, p. 5).
4.4 EPIDEMIOLOGY-BASED RISK ASSESSMENT
Risk estimates based on epidemiologic studies have the potential to provide perspective on
the most serious pollutant-associated public health risks (e.g., hospital admissions, emergency
department (ED) visits, premature mortality) in populations that often include at-risk groups.
However, the emphasis given to such quantitative risk estimates depends on the extent to which
the underlying epidemiologic studies address key uncertainties related to NO2 associations with
health effects, including the potential for confounding by co-occurring pollutants. This section
provides an overview of staffs considerations and conclusions regarding potential support for
risk assessment based on information from epidemiologic studies of short short-term (Section
4.3.1) and a long-term (Section 4.3.2) NO2 concentrations.
4.4.1 Short-Term Epidemiologic-Based Risk Assessment
In the last review of the primary NO2 NAAQS, N02-associated respiratory-related ED
visits in the Atlanta Metropolitan Statistical Area (MSA) were estimated for short-term ambient
NO2 concentrations, based on concentration-response functions from an epidemiologic study by
Tolbert et al. (2007) (U.S. EPA, 2008a, Chapter 9). Specifically, the 2008 REA modeled
respiratory-related ED visits (including asthma, chronic obstructive pulmonary disease (COPD),
upper respiratory illness, pneumonia and bronchiolitis) for individuals of all ages based on a 3-
day moving average of the daily maximum 1-hour NO2 concentrations measured at a single
central-site monitor. The REA reported several main findings, including the following:
1. When air quality was adjusted to simulate just meeting the existing annual standard, about 8
to 9% of respiratory-related ED visits in the Atlanta MSA were estimated to be attributable to
short-term NO2 exposures, based on a single-pollutant model. Risk estimates were reduced
by as much as about 60% when based on co-pollutant models.125 Ninety-five percent
confidence intervals, reflecting statistical uncertainty in the NO2 coefficient, included
negative risk estimates when based on co-pollutant models that included O3 or PM10 (U.S.
EPA, 2008a, Section 9.7).
2. When air quality was adjusted to simulate just meeting potential alternative standards with 1-
hour averaging times, standards with levels of 50, 100, and 150 ppb reduced estimated NO2-
associated risks compared to the annual standard alone (U.S. EPA, 2008a, Tables 9-3 and 9-
4).
125 Risk estimates were reduced to about 3% of respiratory-related ED visits based on a co-pollutant model that
included PM10, and to about 4-5% of such visits based on a model that included O3 (U.S. EPA, 2008a, Tables 9-3
and 9-4).
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3. When air quality was adjusted to simulate just meeting a potential alternative standard with a
1-hour averaging time and a level of 200 ppb, estimated risks were similar to those estimated
for the annual standard alone (U.S. EPA, 2008a, Tables 9-3 and 9-4).
The 2008 REA noted that a number of key uncertainties should be considered when
interpreting these results with regard to decisions on the standard. These included the following
(U.S. EPA, 2008a, Section 9.6):
1. Uncertainties in the estimates of NO2 coefficients in concentration-response functions used in
the assessment.
2. Uncertainties concerning the specification of the concentration-response model (including
the shape of the relationships) and whether or not a population threshold exists within the
range of concentrations examined in the studies.
3. Uncertainty concerning potential confounding by co-occurring pollutants.
4. Uncertainty in the adjustment of air quality distributions to simulate just meeting various
standards.
Overall, the 2008 REA concluded that the risks estimated to be associated with just
meeting the annual NO2 standard (i.e., the existing standard at the time of the last review) can be
judged important from a public health perspective (U.S. EPA, 2008a). In the 2010 final decision,
the Agency further noted that a 1-hour standard with a level at or below 100 ppb "could
substantially reduce exposures to ambient NO2 and associated health risks (compared to just
meeting the current standard)" (75 FR 6483, February 9, 2010). Upon further consideration of
these results, and their associated uncertainties, the risk assessment was not used to distinguish
between the support for particular standard levels at or below 100 ppb.
In considering whether to conduct an updated epidemiology-based risk assessment in the
current review, in the REA Planning document we evaluated the newly available information in
the context of that which was previously available. We specifically considered the extent to
which the new information would be likely to reduce uncertainties and/or substantially improve
our understanding of NCh-attributable health risks, beyond the insights gained from the risk
assessment conducted in the last review.
As discussed in more detail in the REA Planning document (U.S. EPA, 2015, Section
4.2.2), the evidence that has become available since the last review has not substantially changed
our understanding of health effects attributable to short-term NO2 exposures or of the
populations potentially at increased risk of such effects. Updated risk estimates based on
information from epidemiology studies in the current review would be subject to the same
uncertainties identified in the 2008 REA.
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In particular, recent studies do not provide an improved basis, compared to the last review,
for quantifying NCh-attributable risks independent of important traffic-related pollutants (e.g.,
CO, EC, UFP, benzene, PM2.5, and PM10) (U.S. EPA, 2016, Section 3.5). The ISA concludes that
an important uncertainty in the current review continues to be the "[sjtrength of inference from
copollutant models about independent associations of NO2, especially with pollutants measured
at central site monitors" (U.S. EPA, 2016, Table 1-1). In particular, of the key studies supporting
the determination of a causal relationship with respiratory effects (U.S. EPA, 2016, Table 5-39),
two U.S. studies evaluating asthma-related hospital admissions or ED visits have become
available since the last review (Strickland et al., 2010; Li et al., 2012). Neither of these studies
reported NO2 health effect associations in co-pollutant models that included other roadway-
related pollutants.
Based on the above considerations, in the REA Planning document we concluded that an
updated epidemiology-based risk assessment estimating respiratory-related endpoints attributable
to short-term NO2 exposures would be subject to uncertainties that are essentially the same as
those identified in the 2008 REA (U.S. EPA, 2008a) (i.e., uncertainties that resulted in the risk
assessment not being used to distinguish between support for particular standard levels at or
below 100 ppb). We reached the preliminary conclusion that such an updated epidemiology-
based risk assessment in the current review would not appreciably reduce uncertainties and
limitations from the assessment conducted in the last review and would be unlikely to
substantially improve our understanding of N02-attributable health risks or increase our
confidence in risk estimates beyond the assessment from the last review. The CASAC agreed
with this conclusion in its review of the REA Planning document, stating that "the CASAC
concurs that an updated epidemiology-based risk assessment in the current review would be
unlikely to substantially improve our understanding of N02-attributable health risks, or to
increase our confidence in risk estimates, beyond the assessment from the last review" (Diez
Roux and Frey, 2015, p. 5). Based on our consideration of the evidence, as summarized above,
and CASAC advice, in this review we have not conducted an updated quantitative risk
assessment of short-term NO2 exposures based on information from epidemiology studies. In
future reviews, potential support for conducting an updated risk assessment will be revisited,
with consideration given to the evidence available at the time of that review and the degree to
which that evidence reduces important uncertainties.
4.4.2 Long-Term Epidemiologic-Based Risk Assessment
This section discusses our consideration of potential support for a quantitative risk
assessment based on information from epidemiology studies of long-term NO2 concentrations,
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and presents our conclusion that such an assessment is not supported by the available evidence
and information. Section 4.4.2.1 summarizes our preliminary considerations and conclusions
presented in the REA Planning document (U.S. EPA, 2015). Section 4.4.2.2 summarizes the
CASAC's advice on those preliminary considerations and conclusions, based on its review of the
NO2 REA Planning document (Diez Roux and Frey, 2015). Section 4.4.2.3 presents our
additional considerations and our conclusion that a quantitative risk assessment is not supported
in the current review.
4.4.2.1 Preliminary Considerations and Conclusions from the REA Planning Document
The REA Planning Document presented our preliminary considerations and conclusions
regarding support for a quantitative risk assessment based on information from epidemiology
studies of long-term NO2 concentrations. In reaching preliminary conclusions, we considered the
evidence assessed in the second draft ISA (U.S. EPA, 2015b), noting the draft ISA determination
that the evidence "indicates there is likely to be a causal relationship between long-term NO2
exposure and respiratory effects" (U.S. EPA, 2015b, section 1.5.1, pp. 1-19 and 1-21) and that
the "strongest evidence is for effects on asthma development" (U.S. EPA, 2015b, Table 1-1). As
discussed above (Section 3.3), key evidence supporting this causal determination comes from
recent epidemiologic cohort studies reporting associations between long-term ambient NO2
exposures (i.e., averaged over 1-10 years) and development of asthma in children, and from
experimental studies for airway responsiveness and allergic responses. The REA Planning
document considered the extent to which epidemiologic studies of long-term NO2 exposures
could support a quantitative risk assessment in the current review.
In reaching preliminary conclusions, the REA Planning document noted that, as for short-
term NO2 exposures, an important uncertainty in the epidemiologic studies of long-term NO2
exposures and health effect associations is the extent to which effects are independently related
primarily to NO2 rather than one or more co-occurring pollutants. Compared to studies of short-
term NO2 exposures, this is an even more important issue for long-term exposures, given the
higher correlations between long-term NO2 concentrations and other pollutants reported in many
epidemiologic studies using LUR to estimate exposures, and the lack of reported correlations in
studies using monitored data (U.S. EPA, 2016, Table 6-1).126
The REA Planning document further noted that, of the key studies evaluating
associations between long-term NO2 exposures and the development of asthma (U.S. EPA, 2016,
126 In studies of NCh-associated asthma development, correlations with co-occurring pollutants were most often
reported for PM. NO2 and PM were often highly correlated in these studies, particularly in studies that used land use
regression or dispersion modeling to estimate long-term NO2 exposures (e.g., r values were greater than 0.9 in
several studies) (U.S. EPA, 2016, Table 6-1).
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Table 6-5), none evaluated associations in co-pollutant models for traffic-related pollutants.
Speaking to this issue, the 2016 ISA notes uncertainty "in identifying an independent effect of
NO2 exposure from traffic-related copollutants because evidence from experimental studies for
effects related to asthma development is limited, and epidemiologic analysis of confounding is
lacking" (U.S. EPA, 2016, Table 1-1). In particular, the ISA states that "correlations with PM2.5
and BC were often high (e.g., r = 0.7-0.96), and no studies of asthma incidence evaluated
copollutant models to address copollutant confounding, making it difficult to evaluate the
independent effect of NO2" (U.S. EPA, 2016, p. 6-64). The REA Planning Document concluded
that a quantitative risk assessment based on information from studies of NCh-associated asthma
development in children would be subject to considerable uncertainty due to the inability to
distinguish the contributions of NO2 from the contributions of other highly correlated pollutants
(U.S. EPA, 2015a). Given this uncertainty, the REA Planning document reached the preliminary
conclusion that such a risk assessment would not substantially add to our understanding of NO2-
attributable health risks and would therefore be of limited value in informing decisions in the
current review.
4.4.2.2 CASA C Advice on the REA Planning Document
In its review of the REA Planning document, the CAS AC generally agreed with staffs
concerns regarding the potential for confounding by co-occurring pollutants, and the potential
implications of such confounding for risk estimates. However, given the stronger evidence for
N02-associated asthma incidence in the current review, the CASAC encouraged EPA staff to
further consider the feasibility of a risk assessment based on information from epidemiology
studies of long-term NO2 exposures. Specifically, the CASAC provided the following advice
(Diez Roux and Frey, 2015, pp. 5-6):
The CASAC concurs with the assessment that a quantitative risk
assessment based on the epidemiologic evidence [of long-term
NO2 exposures] would be challenged by "considerable uncertainty
due to the inability to distinguish the contributions of NO2 from the
contributions of other highly correlated pollutants." Nevertheless
the finding that the evidence for these relationships is likely to be
causal dictates a thoughtful consideration of an updated risk
assessment, even in the face of these uncertainties. The CASAC
encourages the EPA to explore the feasibility of a quantitative risk
assessment based on the long-term epidemiology. The agency may
find that such an REA is not feasible or that it may not
substantially improve the understanding of health risk attributable
to long-term NO2 exposures, in which case the CASAC would
request a clear explanation for this finding.
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4.4.2.3 Staff Conclusions Regarding Potential Support for a Quantitative Risk Assessment
In response to the CASAC's advice, we have further explored potential support for a risk
assessment based on information from epidemiologic studies of long-term NO2 exposures. In
doing so, we note the determination in the final ISA that "[t]here is likely to be a causal
relationship between long-term NO2 exposure and respiratory effects based on evidence for the
development of asthma" (U.S. EPA, 2016, pp. 1-20). While this causal determination provides
the initial motivation for considering a potential quantitative risk assessment of asthma
incidence, we also evaluate other factors important for the conduct and interpretation of such an
assessment. These additional factors generally relate to the availability of required information,
the degree to which that information is subject to important uncertainties and, given those
uncertainties, the degree to which an assessment would be likely to improve our understanding
of N02-associated health risks. Table 4-3 below discusses our consideration of these factors.
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Table 4-3. Factors to Consider in Conducting a Risk Assessment.
Description of Factors
Staffs Consideration of Factors
Conclusions
Availability of U.S. Studies: Our
consideration of epidemiologic studies for
the purpose of quantitative risk
assessment is focused on studies
conducted in the U.S. While studies
conducted outside of the U.S. form an
important part of the overall evidence
base supporting ISA causal
determinations, when quantifying risks in
the U.S. it is important to use
concentration-response relationships from
epidemiologic studies that reflect U.S.
population demographics, air quality and
exposure patterns, monitoring networks
(i.e., when monitors are used as exposure
surrogates), healthcare systems (i.e.,
particularly for outcomes based on
administrative databases), and baseline
incidence rates. While potential
differences in such factors between the
U.S. and other countries do not preclude
the qualitative use of non-U. S. studies in
drawing overall conclusions regarding the
strength of the scientific evidence, these
differences can add considerable
uncertainty into quantitative risk
estimates.
To identify studies that could support quantitative
risk assessment, we focus primarily on the U.S.
studies determined in the ISA to provide "key
evidence" supporting the causal determination for
long-term NO2 exposures and respiratory effects
(U.S. EPA, 2016, Table 6-5). While a number of
the key epidemiologic studies identified in the ISA
were not conducted within the U.S. (U.S. EPA,
2016, Table 6-5), two key U.S. studies are
available (Jerrett et al., 2008; Clougherty et al.,
2007). The multi-community study by Jerrett et al.
(2008) reports associations with asthma
development across several California
communities, while the single-city study by
Clougherty et al. (2007) reports an association
between asthma development and long-term NO2
exposure in Boston.
Although uncertainties and limitations in the other
U.S. studies of long-term NO2 exposures resulted
in the ISA placing less emphasis on them, we also
consider the potential for these studies to provide
an appropriate basis for a quantitative risk
assessment. In particular, the multicity study by
Nishimura et al. (2013) reports associations
between long-term NO2 concentrations and asthma
incidence, based on five U.S. cities, and the multi-
community study by McConnell et al. (2010)
reports associations across several California
communities (study population overlaps with
Jerrett et al., 2008).
Our further consideration of
potential support for a
quantitative risk assessment
focuses on the two key U.S.
epidemiologic studies identified
in the ISA (Jerrett et al., 2008;
Clougherty et al., 2007). While
recognizing the additional
uncertainty, we also consider the
U.S. studies assessed in the ISA
that were not identified as key
studies (McConnell et al., 2010;
Nishimura et al., 2013).
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Exposure Characterization: The choice
of exposure surrogate in an epidemiologic
study can affect the relationship estimated
to exist between ambient pollutant
exposures and the health outcome of
interest. The ISA identifies potential
exposure measurement error as an
important uncertainty in interpreting
epidemiologic studies. Unlike studies of
short-term NO2, in which exposure
measurement error tends to bias
associations toward the null, in studies of
long-term NO2, exposure measurement
error could also bias reported associations
away from the null (U.S. EPA, 2016,
Table A-l).
Beyond consideration of the potential for
exposure measurement error, when
conducting a risk assessment based on a
concentration-response function from an
epidemiologic study, the approach used in
the study to estimate pollutant exposures
is an important consideration. In cases
where it is not feasible to use the same, or
substantially similar, approach to estimate
exposures in the risk assessment,
additional uncertainty is introduced into
risk estimates.
U.S. epidemiologic studies use various approaches
to estimate long-term NO2 exposures. Specifically,
Nishimura et al. (2013) and McConnell et al.
(2010) use air quality data from central-site
ambient monitors in study communities. In
contrast, Jerrett et al. (2008) and Clougherty et al.
(2007) placed passive NO2 samplers in study
areas. Jerrett et al. (2008) placed samplers in the
yards of study volunteers for 2 weeks in the
summer (mid-August) and 2 weeks in the fall-
winter season (mid-November). Four week
averages in each location were used to represent
long-term NO2 exposures. Clougherty et al. (2007)
placed samplers in locations across study
communities for one week per month, and these
data were then used to build a LUR model of
estimated children's residential exposures.
It is unlikely that we could
recreate ambient exposure
surrogates based on passive
samplers at subjects' homes or
other locations, as was done in
the studies by Clougherty et al.
(2007) and Jerrett et al. (2008).
Use of concentration-response
functions from these studies to
estimate risks based on data from
the ambient NO2 monitoring
network would introduce
substantial uncertainty into risk
estimates, limiting the degree to
which such estimates could
inform policy judgments.
Of the U.S. studies, the exposure
surrogates employed by
Nishimura et al. (2013) (Inverse
distance weighting for the four
closest monitors with a 50km
buffer) and McConnell et al.
(2010) (One monitor site per
community) could be applied
most readily to a potential
quantitative risk assessment,
though these surrogates based on
central-site monitors may also be
more prone to exposure
measurement error (U.S. EPA,
2016, p. 6-18). The ambient
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monitoring data used in these
studies is based on central-site
monitors in study communities.
Ambient data from central site
monitors is publically available
through the EPA's Air Quality
System (AQS). Use of such data
would be consistent with
exposure surrogates in previous
NAAQS reviews (e.g., U.S. EPA,
2011; U.S. EPA, 2014).
Baseline Incidence: Epidemiology-based
effect estimates used in modeling risk
typically relate a unit change in the
ambient concentration of a pollutant to a
resulting change in the incidence of the
health outcome being assessed. Thus, a
critical input to a risk assessment is
information on the baseline incidence of
the health outcome in the study population
being assessed and in the study area where
the risk assessment is being conducted.
Jerrett et al. (2008), McConnell et al. (2010) and
Nishimura et al. (2013) all reported associations
with asthma incidence in children, though the age
ranges of the children differed between studies
(and Nishimura reports results up to age 21).
Nishimura et al. (2013) evaluated associations
specifically in Latino and African American
children. National-level age-stratified asthma
prevalence is available within BenMAP-CE (U.S.
EPA, 2015c, Table D-10; CDC, 2008),127 though
age-stratified information on specific racial or
ethnic groups is more limited. Currently, we have
access to this data nationally, but not by state. We
also do not have data on asthma incidence, which
would likely diverge from prevalence rates, and be
the more appropriate baseline metric for asthma
development.
Information on baseline
prevalence, though not incidence,
is likely available for the
populations and outcomes
evaluated by Jerrett et al. (2008),
McConnell et al. (2010), and
Nishimura et al. (2013). In
addition, the use of prevalence
rather than incidence would add
uncertainty to quantitative risk
estimates.
Neither baseline incidence nor
prevalence information is
available for the population
evaluated by Clougherty et al.
(2007), therefore, we cannot
estimate risks based on
information from this study.
127 BenMAP-CE stands for "Environmental Benefits Mapping and Analysis Program - Community Edition." The BenMAP software and associated
documentation are available for download at http://www2.epa.gov/benmap.
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Clougherty et al. (2007) reported an association
with asthma incidence in children in the Boston
area who are exposed to high levels of violence.
Neither baseline incidence nor prevalence rates are
available for this population.
Shape of Concentration-Response
Function: An understanding of the slope
of the concentration-response generated
from the effect estimates is necessary to
accurately understand the relationship
between the outcome and exposure over a
range of ambient concentrations.
Uncertainty in the shape of the
concentration-response function adds
uncertainty to risk estimates.
In the case of NO2 and the development of asthma,
information regarding the shape of the
concentration-response function is very limited.
According to the ISA (U.S. EPA, 2016, p. 6-64),
"[i]n limited analysis of the
concentration-response relationship, results did not
consistently indicate a linear relationship in the
range of ambient NO2 concentrations examined
(Shima et al., 2002; Carlsten et al., 2011)," The
ISA further notes that "these studies did not
conduct analyses to evaluate whether there is a
threshold for effects" (U.S. EPA, 2016, p. 6-64).
Given the few studies that have
evaluated this issue for NO2 and
asthma incidence, and the
ambiguous results reported by
those studies, there is
considerable uncertainty
regarding the shape of the
concentration-response function
for NO2 and asthma incidence.
This includes uncertainty as to
whether a threshold exists below
which effects are not observed.
Control for Potential Confounding:
High correlations between multiple co-
occurring pollutants complicates the
interpretation of health effect associations
with any individual pollutant within the
mixture. The potential for copollutant
confounding is of particular concern for
studies of health effects associated with
long-term ambient concentrations of NO2,
given the relatively high correlations
between NO2 and other traffic-related
pollutants, including PM2.5.
In considering this issue, the ISA concludes that
"[epidemiologic studies of asthma development in
children have not clearly characterized potential
confounding by PM2.5 or traffic-related pollutants
[e.g., CO, BC/EC, volatile organic compounds
(VOCs)]" (U.S. EPA, 2016, p. 6-64). The ISA
further notes that "[i]n the longitudinal studies,
correlations with PM2.5 and BC were often high
(e.g., r = 0.7-0.96), and no studies of asthma
incidence evaluated copollutant models to address
copollutant confounding, making it difficult to
evaluate the independent effect of NO2" (U.S.
EPA, 2016, p. 6-64).
While studies of long-term NO2
and asthma incidence contribute
qualitatively to the ISA's causal
determination, these studies do
not provide a reliable basis for
quantifying the magnitude of the
NO2 contribution to asthma
development. Therefore, any
NO2 risk estimates developed
from these studies would be
subject to considerable
uncertainty. If an NO2 risk
assessment were conducted based
on studies of long-term NO2,
there would be particular
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4-32
uncertainty regarding the extent
to which NO2 risk estimates
reflect the magnitude of NO2
health impacts rather than the
health impacts of traffic-related
pollutants as a whole.
-------
After considering the factors discussed above, we conclude that a quantitative risk
assessment based on epidemiologic studies of long-term NO2 exposures is not supported in this
review. This conclusion is based on our consideration of the available evidence for associations
between long-term NO2 and the development of asthma, including consideration of the
uncertainties that would be inherent in any risk estimates based on that evidence. In particular,
we note most of the available epidemiologic studies of long-term NO2 and asthma incidence are
not appropriate for quantitative risk assessment because they were not conducted in the U.S. Of
the studies that were conducted in the U.S., most were not emphasized in the ISA's
determination of a causal relationship due to important uncertainties. Additionally, most of these
studies used exposure metrics that are not readily transferable to a quantitative risk assessment or
they evaluated populations for which information on baseline incidence is not available (Table 4-
3). The two U.S. studies that are the exception to these limitations (i.e., McConnell et al., 2010;
Nishimura et al., 2013) are still subject to broader uncertainties related to the potential for co-
pollutant confounding of the NO2 association, potential bias due to exposure measurement error,
and the shape of the concentration-response function.
With regard to the potential for copollutant confounding in particular, we note the high
correlations between long-term NO2 concentrations and the long-term concentrations of other
traffic-related pollutants (U.S. EPA, 2016, Section 6.2.2.1, Table 6-1). Given these correlations,
study authors often interpreted associations with NO2 as reflecting associations with a marker of
traffic-related pollution more broadly (e.g., Jerrett et al., 2008; McConnell et al., 2010). Based on
our consideration of all of the above information, we conclude that estimates of the risk of
asthma development associated with long-term NO2 exposures, if developed, would be subject to
considerable uncertainty and would likely not be informative, beyond what can be learned from
our consideration of the studies themselves (Chapter 3). Thus, we have not conducted such an
assessment in this PA. In its review of the draft PA, CASAC agreed "with the decision to not
conduct any new... epidemiologic-based analyses" (Diez Roux and Sheppard, p. 5). In future
reviews, potential support for conducting a quantitative risk assessment will be revisited, with
consideration given to the evidence available at the time of that review and the degree to which
that evidence reduces important uncertainties.
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Center for Environmental Assessment, Research Triangle Park, NC. EPA/600/R-08/071. July
2008. Available at: http://cfpub.epa.gov/ncea/cfm/recordisplav.cfm?deid=l94645.
U.S. EPA. (2014). Integrated Review Plan for the Primary National Ambient Air Quality Standards for Nitrogen
Dioxide. U.S. EPA, National Center for Environmental Assessment and Office of Air Quality Planning and
Standards, Research Triangle Park, NC. EPA-452/R-14-003. June 2014. Available at:
http://www.epa.gov/ttn/naaas/standards/nox/data/201406finalirpprimarvno2.pdf.
U.S. EPA (2015a). Review of the Primary National Ambient Air Quality Standards for Nitrogen Dioxide: Risk and
Exposure Assessment Planning Document. U.S. EPA, Office of Air Quality Planning and Standards,
Research Triangle Park, NC. EPA-452/D-15-001. May 13, 2015. Available at:
https://www3.epa.gov/ttii/naaqs/standards/nox/data/20150504reapiaiining.pdf
U.S. EPA. (2015b). Integrated Science Assessment for Oxides of Nitrogen - Health Criteria (Second External
Review Draft). U.S. EPA, National Center for Environmental Assessment and Office, Research Triangle
Park. EPA/600/R-14/006. January 2015. Available at:
http://clpub.epa.gov/ncea/isa/recordisplay.cfm?deid=288043.
U.S. EPA. (2015c). Benmap-ce User's Manual Appendices. U.S EPA, Office of Air Quality Planning and Standards,
Research Triangle Park, NC. Research Triangle Park, North Carolina. Available at:
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https://www.epa.gov/sites/prodiiction/files/2015-04/documents/benmap-
ce user manual appendices march 2015.pdf.
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, U.S. Environmental Protection Agency, Research
Triangle Park, NC. EPA 452/R-l 1-003. April 2011. Available at:
https://www3.epa.gOv/ttn/naaas/standards/pm/s pm 2007 pa.html
U.S. EPA (2014). Policy Assessment for the Review of the Ozone National Ambient Air Quality Standards. Office
of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park,
NC. EPA 452/R-14-006. August 2014. Available at:
https://www3.epa.gOv/ttn/naaas/standards/ozone/s o3 2008 pa.html
U.S. EPA (2016). Integrated Science Assessment for Oxides of Nitrogen - Health Criteria (2016 Final Report). U.S.
EPA, National Center for Environmental Assessment, Research Triangle Park, NC. EPA/600/R-15/068.
January 2016. Available at: https://cfpub.epa.gov/ncea/isa/recordisplav.cfm?deid=310879.
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5 CONCLUSIONS ON Till ADEQUACY OF THE CURRENT PRIMARY NOi
STANDARDS
This chapter summarizes staffs consideration of the available evidence and information
related to the adequacy of the current primary NO2 standards, as discussed in the preceding
chapters. In addition, this chapter presents staffs conclusions regarding the adequacy of those
standards, including conclusions on each of the elements of the standards (i.e., indicator,
averaging time, level, and form). In reaching conclusions on the adequacy of the current primary
NO2 standards, we revisit the following overarching policy-relevant question for this review:
• Does the currently available scientific evidence and information from quantitative
analyses support or call into question the adequacy of the public health protection
afforded by the current primary NO2 standards?
As discussed in Chapter 1 of this PA, our approach to addressing this overarching
question, and informing the Administrator's judgments on the primary NO2 standards, builds
upon the approach used in the last review of the primary NO2 NAAQS. This approach is
consistent with the requirements of the NAAQS provisions of the CAA and with how the EPA
and the courts have historically interpreted these CAA provisions. In particular, the CAA
requires primary standards that, in the judgment of the Administrator, are requisite to protect
public health with an adequate margin of safety. In setting primary standards that are "requisite"
to protect public health, the EPA's task is to establish standards that are neither more nor less
stringent than necessary for this purpose. One intent of the requirement that primary standards
provide an "adequate margin of safety" is to address uncertainties associated with inconclusive
scientific and technical information. Thus, as discussed in Chapter 1, the CAA does not require
that primary standards be set at a zero-risk level, but rather at a level that limits risk sufficiently
so as to protect public health with an adequate margin of safety.
Section 5.1 below summarizes staffs evidence-based considerations and staffs
conclusions on the extent to which the evidence supports or calls into question the basic elements
of the current primary NO2 standards. Section 5.2 summarizes staffs consideration of
quantitative analyses comparing NO2 air quality with health-based benchmarks, and staffs
conclusions on the extent to which the current standards could allow NO2 exposures of public
health concern. Section 5.3 presents our overall conclusions regarding the adequacy of the public
health protection provided by the current primary NO2 standards. Section 5.4 highlights areas for
additional research and data collection in order to reduce uncertainties in future reviews of the
primary NO2 NAAQS.
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5.1 EVIDENCE-BASED CONSIDERATIONS
As discussed in Chapter 3, in considering the evidence available in the current review
with regard to adequacy of the current 1-hour and annual NO2 standards, we first consider the
nature of the health effects attributable to NO2 exposures (Sections 3.2.1, 3.3.1). In doing so, we
address the following questions:
• To what extent does the currently available scientific evidence alter or strengthen our
conclusions from the last review regarding health effects attributable to ambient NO2
exposures? Are previously identified uncertainties reduced or do important uncertainties
remain? Have new uncertainties been identified?
As described in greater detail in Sections 3.2.1 and 3.3.1, we address these questions for
both short-term and long-term NO2 exposures, with a focus on health endpoints for which the
ISA concludes that the evidence indicates there is a "causal" or "likely to be a causal"
relationship.
In answering the questions above with regard to short-term NO2 exposures, section 3.2.1
notes that, as in the last review, the strongest evidence continues to come from studies examining
respiratory effects. In particular, the ISA concludes that evidence indicates a "causal"
relationship between short-term NO2 exposure and respiratory effects, based on evidence related
to asthma exacerbation. While this conclusion reflects a strengthening of the causal
determination, compared to the last review, this strengthening is based largely on a more specific
integration of the evidence related to asthma exacerbations rather than on the availability of new,
stronger evidence. Though some evidence has become available since the last review, as
summarized below, this evidence has not fundamentally altered our understanding of the
relationship between short-term NO2 exposures and respiratory effects.
The strongest evidence supporting this ISA conclusion comes from controlled human
exposure studies demonstrating N02-induced increases in AR in individuals with asthma. Most
of the controlled human exposure studies assessed in the ISA were available in the last review,
particularly studies of non-specific AR. As in the last review, there remains uncertainty due to
the lack of an apparent dose-response relationship between NO2 exposures and AR and
uncertainty in the potential adversity of NCh-induced increases in AR. The newly available meta-
analysis by Brown (2015) has partially addressed this latter uncertainty by demonstrating the
potential for clinically relevant increases in AR in some asthmatics following exposures to NO2
concentrations from 100 to 530 ppb.128 Supporting evidence for a range of N02-associated
128 As described in Chapter 3, consideration of clinical relevance in the ISA is based on evidence from clinical
studies evaluating efficacy of inhaled corticosteroids that are used to prevent bronchoconstriction and airway
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respiratory effects also comes from epidemiologic studies. While recent epidemiologic studies
provide new evidence based on improved exposure characterizations and co-pollutant modeling,
these studies are consistent with the evidence from the last review and do not fundamentally alter
our understanding of the respiratory effects associated with ambient NO2 exposures. Due to
limitations in the available epidemiologic methods, uncertainty remains in the current review
regarding the potential for confounding by traffic-related copollutants (i.e., PM2.5, EC/BC, CO).
Thus, while some new evidence is available in this review, that new evidence has not
substantially altered our understanding of the respiratory effects that occur following short-term
NO2 exposures.
In answering the questions above with regard to long-term NO2 exposures, Section 3.3.1
notes the ISA conclusion that there is "likely to be a causal relationship" between long-term NO2
exposure and respiratory effects, based largely on the evidence for asthma development in
children. New epidemiologic studies of asthma development have increasingly utilized improved
exposure assessment methods (i.e., measured or modeled at or near children's homes and
followed for many years), which partly reduces uncertainties from the last review related to
exposure measurement error. Explicit integration of evidence for individual outcome categories
(e.g. asthma incidence, respiratory infection) provides an improved characterization of biological
plausibility and mode of action. This improved characterization includes the assessment of new
evidence supporting a role for repeated short-term NO2 exposures in the development of asthma.
High correlations between long-term average ambient concentrations of NO2 and long-term
concentrations of other traffic-related pollutants, together with the general lack of epidemiologic
studies evaluating copollutant models that include traffic-related pollutants, remains a concern in
interpreting associations with asthma development. Specifically, the extent to which NO2 may be
serving primarily as a surrogate for the broader traffic-related pollutant mix remains unclear.
Thus, while the evidence for respiratory effects related to long-term NO2 exposures has become
stronger since the last review, there remain important uncertainties to consider in evaluating this
evidence within the context of the adequacy of the current standards.
Given the evaluation of the evidence in the ISA and the causal determinations (Sections
3.2 and 3.3), our further consideration of the evidence focuses on studies of asthma
exacerbations (short-term exposures) and asthma development (long-term exposures). We next
responsiveness. Generally, a change of at least one doubling dose is considered to be an indication, but not
validation, of clinical relevance (this represents a decline in AR as the dose to induce AR is doubled). Based on this,
a halving of the provocative dose is taken in the ISA to represent an increase in AR that is an indication of clinical
relevance.
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consider what these bodies of evidence indicate with regard to the basic elements of the primary
NO2 standards. In particular, we consider the following question:
• To what extent does the available evidence for respiratory effects attributable to either
short- or long-term NO2 exposures support or call into question the basic elements of
the current primary NO2 standards?
In addressing this question, we evaluate the evidence in the context of the indicator, averaging
times, levels, and forms of the current standards. Each of these elements is discussed below.
Indicator
The indicator for both the current annual and 1-hour NAAQS for oxides of nitrogen is
NO2. While the presence of gaseous species other than NO2 has long been recognized (discussed
in Section 2.1, above), no alternative to NO2 has been advanced as being a more appropriate
surrogate for ambient gaseous oxides of nitrogen. Both previous and recent controlled human
exposure studies and animal toxicology studies provide specific evidence for health effects
following exposure to NO2. Similarly, the large majority of epidemiologic studies report health
effect associations with NO2, as opposed to other gaseous oxides of nitrogen, though the degree
to which monitored NO2 reflects actual NO2 concentrations, as opposed to NO2 plus other
gaseous oxides of nitrogen, can vary (Section 2.2, above). In addition, because emissions that
lead to the formation of NO2 generally also lead to the formation of other NOx oxidation
products, measures leading to reductions in population exposures to NO2 can generally be
expected to lead to reductions in population exposures to other gaseous oxides of nitrogen.
Therefore, an NO2 standard can also be expected to provide some degree of protection against
potential health effects that may be independently associated with other gaseous oxides of
nitrogen even though such effects are not discernable from currently available studies. Given
these considerations, we reach the conclusion that it is appropriate in the current review to
consider retaining the NO2 indicator for standards meant to protect against exposures to gaseous
oxides of nitrogen. In its review of the draft PA, CASAC agreed with this conclusion (Diez Roux
and Sheppard, 2017).
Averaging time
The current primary NO2 standards are based on 1-hour and annual averaging times.
Together, these standards can provide protection against short- and long-term NO2 exposures.
In establishing the 1-hour standard in the last review, the Administrator considered
evidence from both experimental and epidemiologic studies. She noted that controlled human
exposure studies and animal toxicological studies provided evidence that NO2 exposures from
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less than one hour up to three hours can result in respiratory effects such as increased AR and
inflammation. These included five controlled human exposure studies that evaluated the potential
for increased AR following 1-hour exposures to 100 ppb NO2 in people with asthma. In addition,
epidemiologic studies had reported health effect associations with both 1-hour and 24-hour NO2
concentrations, without indicating that either of these averaging periods was more closely linked
with reported effects. Thus, the available experimental evidence provided support for
considering an averaging time of shorter duration than 24 hours while the epidemiologic
evidence provided support for considering both 1-hour and 24-hour averaging times. Given this
evidence, the Administrator concluded that, at a minimum, a primary concern with regard to
averaging time was the level of protection provided against 1-hour NO2 exposures. Based on
available analyses of NO2 air quality, she further concluded that a standard with a 1-hour
averaging time could also be effective at protecting against effects associated with 24-hour NO2
exposures (75 FR 6502, February, 9, 2010).
Based on the considerations summarized above, the Administrator judged that it was
appropriate to set a new NO2 standard with a 1-hour averaging time. She concluded that such a
standard would be expected to effectively limit short-term (e.g., 1- to 24-hours) NO2 exposures
that had been linked to adverse respiratory effects. She also retained the existing annual standard
to continue to provide protection against effects potentially associated with long-term exposures
to oxides of nitrogen (75 FR 6502, February, 9, 2010). These decisions were consistent with
CASAC advice to establish a short-term primary standard for oxides of nitrogen based on using
1-hour maximum NO2 concentrations and to retain the current annual standard (Samet, 2008, p. 2;
Samet, 2009, p. 2).
As in the last review, support for a standard with a 1-hour averaging time comes from
both the experimental and epidemiologic evidence. Controlled human exposure studies evaluated
in the ISA continue to provide evidence that NO2 exposures from less than 1-hour up to three
hours can result in increased AR in individuals with asthma (U.S. EPA, 2016, Tables 5-1 and 5-
2). These controlled human exposure studies provide key evidence supporting the ISA's
determination that "[a] causal relationship exists between short-term NO2 exposure and
respiratory effects based on evidence for asthma exacerbation" (U.S. EPA, 2016, p. 1-17). In
addition, the epidemiologic literature assessed in the ISA provides support for short-term
averaging times ranging from 1-hour up to 24-hours (e.g., U.S. EPA, 2016 Figures 5-3, 5-4 and
Table 5-12). Consistent with the evidence in the last review, the ISA concludes that there is no
indication of a stronger association for any particular short-term duration of NO2 exposure (U.S.
EPA, 2016, section 1.6.1). Thus, a 1-hour averaging time reasonably reflects the exposure
durations used in the controlled human exposure studies that provide the strongest support for the
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ISA's determination of a causal relationship. In addition, a standard with a 1-hour averaging time
is expected to provide protection against the range of short-term exposure durations that have
been associated with respiratory effects in epidemiologic studies (i.e., 1-hour to 24-hours). When
taken together, we reach the conclusion that the combined evidence from experimental and
epidemiologic studies continues to support an NO2 standard with a 1-hour averaging time to
protect against health effects related to short-term NO2 exposures. In its review of the draft PA,
the CAS AC found that there continued to be scientific support for the 1-hour averaging time
(Diez Roux and Sheppard, 2017).
With regard to protecting against long-term exposures, the evidence supports considering
the overall protection provided by the combination of the annual and 1-hour standards. The
current annual standard was originally promulgated in 1971 (36 FR 8186, April 30, 1971), based
on epidemiologic studies reporting associations between respiratory disease and long-term
exposure to NO2. The annual standard was retained in subsequent reviews, in part to provide a
margin of safety against the serious effects reported in animal studies using long-term exposures
to high NO2concentrations (above 8,000 ppb) (U.S. EPA, 1995).
As described above, evidence newly available in the current review demonstrates
associations between long-term NO2 exposures and asthma development in children, based on
NO2 concentrations averaged over year of birth, year of diagnosis, or entire lifetime. Supporting
evidence indicates that repeated short-term NO2 exposures could contribute to this asthma
development. In particular, the ISA states that "findings for short-term NO2 exposure support an
effect on asthma development by describing a potential role for repeated exposures to lead to
recurrent inflammation and allergic responses," which are "identified as key early events in the
proposed mode of action for asthma development" (U.S. EPA, 2016, p. 6-64 and p. 6-65). Taken
together, the evidence supports the potential for recurrent short-term NO2 exposures to contribute
to the asthma development that has been reported in epidemiologic studies to be associated with
long-term exposures. Thus, in establishing standards to protect against adverse health effects
related to long-term NO2 exposures, we reach the conclusion that the evidence supports the
consideration of both 1-hour and annual averaging times. In its review of the draft PA, CAS AC
supported this approach to considering the protection provided against long-term NO2 exposures
by the combination of the annual and 1-hour NO2 standards. CAS AC specifically noted that "it is
the suite of the current 1-hour and annual standards, together, that provide protection against
adverse effects" (Diez Roux and Sheppard, 2017).
Level andform
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In considering the extent to which evidence supports or calls into question the levels or
forms of the current NO2 standards, we revisit the following specific questions addressed in
Chapter 3 of this PA:
• To what extent does the evidence indicate adverse respiratory effects attributable to
short- or long-term NO2 exposures lower than previously identified or below the existing
standards?
In addressing this question in Chapter 3, we consider the range of NO2 exposure concentrations
that have been evaluated in experimental studies (controlled human exposure and animal
toxicology) and the ambient NO2 concentrations in locations where epidemiologic studies have
reported associations with adverse outcomes.
Short-Term
Controlled human exposure studies demonstrate the potential for increased AR in some
people with asthma following 30-minute to 1-hour exposures to NO2 concentrations near those in
the ambient air (Section 3.2.2).129 In evaluating the NO2 exposure concentrations at which
increased AR has been observed, we consider both the group mean results reported in individual
studies and the results from a recent meta-analysis evaluating individual-level data (Brown,
2015; U.S. EPA, 2016, Section 5.2.2.1). Group mean responses in individual studies, and the
variability in those responses, can provide insight into the extent to which observed changes in
AR are due to NO2 exposures, rather than to chance alone, and have the advantage of being
based on the same exposure conditions. The meta-analysis can aid in identifying trends in
individual-level responses across studies and can have the advantage of increased power to
detect effects, even in the absence of statistically significant effects in individual studies.
As discussed in more detail in Section 3.2.2.1, individual studies consistently report
statistically significant N02-induced increases in AR following resting exposures to NO2
concentrations at or above 250 ppb, but have generally not reported statistically significant
increases in AR following resting exposures to NO2 concentrations from 100 to 200 ppb. When
individual-level data from these studies were combined in a meta-analysis, Brown (2015)
reported that significant majorities of study participants experienced increased AR following
resting exposures to NO2 concentrations from 100 to 530 ppb. In some affected individuals, the
129 As discussed in Chapter 3, experimental studies have not reported other respiratory effects following short-term
exposures to NO2 concentrations at or near those found in the ambient air. In addition, experimental studies
examining asthma-related effects attributable to long-term NO2 exposures are limited to exposures to NO2
concentrations well-above those found in the ambient air and well-above those that could occur under the current
standards (i.e. > 1,000 ppb).
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magnitudes of these increases were large enough to have potential clinical relevance. Following
exposures to 100 ppb NO2 specifically, the lowest exposure concentration evaluated, a
marginally significant majority of study participants experienced increased AR.130 Important
limitations in this evidence include the lack of an apparent dose-response relationship between
NO2 and AR and uncertainty in the adversity of the reported increases in AR. These uncertainties
become increasingly important at the lower NO2 exposure concentrations (i.e., at or near 100
ppb), as the evidence for N02-induced increases in AR becomes less consistent across studies.
With regard to the epidemiologic evidence from U.S. and Canadian studies, as described
in Sections 3.2.2.2 and 3.3.2.1, we consider the ambient NO2 concentrations in locations where
such studies have examined associations with asthma-related hospital admissions or emergency
department visits (short-term) or with asthma incidence (long-term). In particular, we consider
the extent to which NCh-health effect associations are consistent, precise, statistically significant,
and present for distributions of ambient NO2 concentrations that likely would have met the
current standards. To the extent N02-health effect associations are reported in study areas that
would likely have met the current standards, the evidence supports the potential for the current
standards to allow the N02-associated effects indicated by those studies. In the absence of
studies reporting associations in locations meeting the current NO2 standards, there is greater
uncertainty regarding the potential for reported effects to be caused by NO2 exposures that occur
with air quality meeting those standards. We also note consideration of important uncertainties in
the evidence, including the potential for copollutant confounding and exposure measurement
error, and the extent to which near-road NO2 concentrations are reflected in the available air
quality data.
With regard to epidemiologic studies of short-term NO2 exposures, as discussed in
Section 3.2.2.2, we note the following. First, the only recent multicity study evaluated (Stieb et
al., 2009), which had maximum 1-hour DVs ranging from 67 to 242 ppb, did not report a
positive association between NO2 and ED visits. In addition, of the single-city studies (see Figure
3-1) that reported positive and relatively precise associations between NO2 and asthma hospital
admissions and ED visits, most locations had NO2 concentrations likely to have violated the
current 1-hour NO2 standard over at least part of the study period. In addition, had near-road NO2
monitors been in place during study periods, DVs would likely have been higher. Thus, it is
130 When the analysis was restricted only to non-specific AR following exposures to 100 ppb NO2, the percentage
who experienced increased AR was larger and statistically significant. In contrast, when the analysis was restricted
only to specific AR following exposures to 100 ppb NO2, the majority of study participants did not experience
increased AR (U.S. EPA, 2016; Brown 2015).
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likely that even the one study location with a maximum DV of 100 ppb (Atlanta) would have
violated the existing 1-hour standard during study periods.131 For the study locations with
maximum DVs below 100 ppb, mixed results have been reported, with associations that are
generally statistically non-significant and imprecise. As with the studies reporting more precise
associations, near-road monitors were not in place during these study periods. If they had been, it
is unclear whether 1-hour DVs would have been below 100 ppb. In drawing conclusions based
on this epidemiologic evidence, we must also consider the potential for copollutant confounding
as ambient NO2 concentrations are often highly correlated with other pollutants. This can
complicate attempts to distinguish between independent effects of NO2 and effects of the broader
pollutant mixture. While this has been addressed to some extent in available studies, uncertainty
remains for the most relevant copollutants (i.e., those related to traffic such as PM2.5, EC/BC, and
CO). Taken together, we reach the conclusion that available U.S. and Canadian epidemiologic
studies of hospital admissions and emergency department visits do not indicate the occurrence of
N02-associated effects in locations and time periods with NO2 concentrations that would clearly
have met the current 1-hour NO2 standard (i.e., with its level of 100 ppb and 98th percentile
form).
In giving further consideration specifically to the form of 1-hour standard, we note that
the available evidence and information is consistent with that informing consideration of form in the
last review. The last review focused on the upper percentiles of the distribution of NO2
concentrations based, in part, on evidence for health effects associated with short-term NO2
exposures from experimental studies which provided information on specific exposure
concentrations that were linked to respiratory effects. In that review, the EPA specified a 98th
percentile form, rather than a 99th percentile, for the new 1-hour standard. In combination with the 1-
hour averaging time and 100 ppb level, a 98th percentile form was judged to provide appropriate
public health protection. In addition, compared to the 99th percentile, a 98th percentile form was
expected to provide greater regulatory stability.132 A 98th percentile form is also consistent with our
consideration of uncertainties in the health effects that have the potential to occur at 100 ppb.
131 Based on recent air quality information for Atlanta, 98th percentiles of daily maximum 1-hour NO2 concentrations
are higher at near-road monitors than non-near-road monitors (Figures 2-9 and 2-10, above). These differences could
have been even more pronounced during study periods, when NOx emissions from traffic sources were higher
(Section 2.1.2, above).
132 As noted in the last review, a less stable form could result in more frequent year-to-year shifts between meeting
and violating the standard, potentially disrupting ongoing air quality planning without achieving public health goals
(75 FR 6493, February 9, 2010).
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Specifically, when combined with the 1-hour averaging time and the level of 100 ppb, the 98th
percentile form limits, but does not eliminate, the potential for exposures to 100 ppb NO2.133
Long-Term
In addressing the question posed above with regard to health effects related to long-term
NO2 exposures, we first consider the basis for the current annual standard. It was originally set to
protect against NCh-associated respiratory disease in children reported in a series of
epidemiologic studies (36 FR 8186, April 30, 1973). In subsequent reviews, the EPA has
retained the annual standard, judging that it provides protection with an adequate margin of
safety against the serious effects that have been reported in animal studies following long-term
exposures to NO2 concentrations above 1,000 ppb. In the 2010 review, the EPA noted that,
though some evidence supported the need to limit long-term exposures to NO2, the evidence for
adverse health effects attributable to long-term NO2 exposures did not support changing the level
of the annual standard.
In the current review, the strengthened "likely to be causal" relationship between long-
term NO2 exposures and respiratory effects is supported by epidemiologic studies of asthma
development. While these studies strengthen the evidence for effects of long-term exposures,
compared to the last review, they are subject to important uncertainties, including the potential
for confounding by traffic-related copollutants. The potential for such confounding is particularly
important to consider when interpreting epidemiologic studies of long-term NO2 exposures given
(1) the relatively high correlations observed, and modeled, between long-term ambient
concentrations of NO2 and long-term concentrations of other roadway-associated pollutants; (2)
the general lack of information from copollutant models on the potential for NO2 associations
that are independent of other traffic-related pollutants or mixtures; and (3) the general lack of
experimental support for effects following long-term exposures to NO2 concentrations near those
in the ambient air. Thus, it is unclear the degree to which the observed effects are independently
related to exposure to ambient concentrations of NO2. The epidemiologic evidence from several
U.S. and Canadian studies is also subject to uncertainty with regard to the extent to which the
studies accurately characterized exposures of the study populations, further limiting what these
studies can tell us regarding the adequacy of the current standards.
While we recognize the above uncertainties, we consider what studies of long-term NO2
and asthma development can tell us with regard to the adequacy of the current NO2 standards. As
discussed above for short-term exposures, we consider the degree to which the evidence
133 The 98th percentile corresponds to about the 7th or 8th highest daily maximum 1-hour NO2 concentration in a year.
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indicates adverse respiratory effects associated with long-term NO2 exposures in locations that
would have met the NAAQS. As summarized in Section 3.3.1, the causal determination for long-
term exposures is supported both by studies of long-term NO2 exposures and studies indicating a
potential role in asthma development for repeated short-term exposures to high NO2
concentrations. As such, when considering the ambient NO2 concentrations present during study
periods, we consider these concentrations within the context of both the 1-hour and annual NO2
standards. As discussed in Section 3.3.2.1, while annual DVs in study locations were often below
53 ppb, maximum 1-hour DVs in most locations were near or above 100 ppb.134' 135 Because
these study-specific DVs are based on the area-wide NO2 monitors in place during study periods,
they likely do not reflect the NO2 concentrations near the largest roadways, which are expected
to be higher in most urban areas. Had near-road monitors been in place during study periods,
NO2 DVs based on near-road concentrations likely would have been higher in many locations,
and would have been more likely to exceed the level of the annual and/or 1-hour standard(s).
Overall, the evidence does not provide support for N02-attributable asthma development
in children in locations with NO2 concentrations that would have clearly met the current annual
and 1-hour standards. The strongest evidence informing the level at which effects may occur
comes from U.S. and Canadian epidemiologic studies that are subject to critical uncertainties
related to copollutant confounding and exposure assessment. Even if these fundamental
uncertainties were to be dismissed, our evaluation indicates that most of the locations included in
epidemiologic studies of long-term NO2 exposure and asthma incidence would likely have
violated either one or both of the current NO2 standards, over at least parts of the study periods.
Based on the information discussed above, we revisit the following question:
• To what extent does the evidence indicate adverse respiratory effects attributable to
short- or long-term NO2 exposures lower than previously identified or below the
existing standards
In addressing this question, we note that (1) experimental studies do not indicate adverse
respiratory effects attributable to either short- or long-term NO2 exposures lower than previously
identified and that (2) epidemiologic studies do not provide support for associations between
adverse effects and ambient NO2 concentrations that would have clearly met the current
standards. Taken together, we reach the conclusion that the available evidence does not support
134 Mean 1-hour DVs from the study periods were also near or above 100 ppb in many study locations.
135 As discussed in Chapter 2, analyses demonstrate that a 1-hour NO2 DV (based on three-year averages of 98th
percentiles of annual distributions of daily maximum l-hourNCh concentrations) at or below 100 ppb generally
corresponds to an annual DV (based on annual average NO2 concentrations) below 35 ppb.
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the need for increased protection against short- or long-term NO2 exposures, beyond that
provided by the existing standards. In its review of the draft PA, the CASAC agreed with this
conclusion, stating that "[t]he CASAC concurs with the EPA that the current scientific literature
does not support a revision to the primary NAAQS for nitrogen dioxide" (Diez Roux and
Sheppard, 2017, p. 9). Therefore, we have not identified potential alternative standard levels or
forms for consideration.
5.2 AIR QUALITY-, EXPOSURE- AND RISK-BASED CONSIDERATIONS
As described in Chapter 4, beyond our consideration of the scientific evidence, we also
consider the extent to which quantitative analyses of NO2 air quality, exposures or health risks
could inform conclusions on the adequacy of the public health protection provided by the current
primary NO2 standards. Conducting such quantitative analyses, if appropriate, could inform
judgments about the public health impacts of N02-related health effects and could help to place
the evidence for specific effects into a broader public health context. To this end, in the REA
Planning Document (U.S. EPA, 2015) and in this PA we have evaluated the extent to which the
available evidence and information provide support for conducting new or updated analyses of
NO2 exposures and/or health risks, beyond the analyses conducted in the 2008 REA (U.S. EPA,
2008). In doing so, we carefully considered the assessments developed as part of the last review
of the primary NO2 NAAQS (U.S. EPA, 2008) and the newly available scientific and technical
information, particularly considering the degree to which updated analyses in the current review
are likely to substantially add to our understanding of NO2 exposures and/or health risks. We
have also considered the CASAC advice and public input received on the REA Planning
Document (see Chapter 4) and on the draft PA (Diez Roux and Sheppard, 2017).
As discussed above and in the REA Planning Document (U.S. EPA, 2015, Section 2.1.1),
an important uncertainty identified in the 2008 REA was the characterization of 1-hour NO2
concentrations around major roadways. The 2008 REA estimated NO2 concentrations on/near
roads by applying literature-derived adjustment factors to NO2 concentrations at area-wide
monitoring sites. A key consideration in the current review is the extent to which newly available
information could reduce uncertainties with regard to NO2 concentrations around major roads.
As discussed in Section 2.3.2, data from recently deployed near-road NO2 monitors provide an
improved understanding of such ambient NO2 concentrations. Therefore, in this PA we have
conducted updated analyses comparing ambient NO2 concentrations (i.e., as surrogates of
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potential exposures) to health-based benchmarks, with a particular focus on study areas where
near-road monitors have been deployed.136
When considering analyses comparing NO2 air quality with health-based benchmarks, we
focus on the following specific questions:
• To what extent are ambient NO2 concentrations that may be of public health
concern estimated to occur in locations meeting the current NO2 standards? What
are the important uncertainties associated with those estimates?
As discussed in Section 4.2, benchmarks are based on information from controlled human
exposure studies of NO2 exposures and AR. In identifying specific NO2 benchmarks, and
considering the weight to place on each, we consider both the group mean results reported in
individual studies and the results of a meta-analysis that combined data from multiple studies
(Brown, 2015; U.S. EPA, 2016, Section 5.2.2.1), as described above.
When taken together, the results of individual controlled human exposure studies and of
the meta-analysis by Brown (2015) support consideration of NO2 benchmarks between 100 and
300 ppb, based largely on studies of non-specific AR in people with asthma exposed at rest. As
discussed in more detail in Section 4.2.1.1, benchmarks from the upper end of this range are
supported by the results of individual studies, the majority of which reported statistically
significant increases in AR following NO2 exposures at or above 250 ppb, and by the results of
the meta-analysis by Brown (2015). Benchmarks from the lower end of this range, including 100
ppb, are supported by the results of the meta-analysis, even though individual studies do not
consistently report statistically significant N02-induced increases in AR at these lower
concentrations. In particular, individual studies have not generally reported significant increases
in AR following resting exposures to 100 ppb NO2, but the meta-analysis indicates that a
marginally significant majority of study participants experienced an increase in AR following
exposures to 100 ppb NO2 (Brown, 20 1 5).137 While there are a variety of factors that likely
underlie the observed variability, they are not fully known and the variability remains an
uncertainty in evaluating these results.
136 We have not conducted more complex NO2 exposure and risk assessments in this review. As discussed above
(Sections 4.3, 4.4) and in the REA Planning document (U.S. EPA, 2015), such updated assessments would be
unlikely to substantially improve our understanding of NO2 exposures and health risks associated with the current
standards, beyond what we know from the air quality-benchmark comparisons described in Chapter 4 (Section 4.2)
and the risk assessment conducted in the last review.
137 Results were statistically significant when analyses were restricted to non-specific AR, but not when analyses
were restricted to specific AR.
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In further considering the potential public health implications of exposures to NO2
concentrations at or above benchmarks, we also note the discussion of uncertainties in Section
3.2.2.1. As discussed in more detail in that section, there is no indication of a dose-response
relationship between NO2 and AR in people with asthma, regardless of the challenge type (i.e.,
specific or non-specific) or exposure conditions (i.e., resting or exercising) (Goodman, 2009;
Brown, 2015). Though the lack of an apparent dose-response relationship does not necessarily
indicate the lack of an NO2 effect, it adds uncertainty to our interpretation of the controlled
human exposure studies of AR. An additional uncertainty is the clinical relevance of the reported
N02-induced increases in AR though, as described above (section 5.1), the meta-analysis by
Brown (2015) has partially addressed this uncertainty.
Thus, while we consider benchmarks from 100 to 300 ppb, uncertainties in the evidence
from controlled human exposure studies suggest that caution is appropriate when interpreting the
potential public health implications of 1-hour NO2 concentrations at these benchmarks. While
this is true even for the higher benchmarks (i.e., given the lack of an apparent dose-response
relationship and remaining uncertainty with regard to adversity), it is particularly the case for the
100 ppb benchmark, where the results of individual studies are inconsistent.
As discussed in Section 4.2, analyses of unadjusted air quality, which meets the current
standards in all locations, indicate almost no potential for 1-hour exposures to NO2
concentrations at or above any of the benchmarks examined, including 100 ppb. Analyses of air
quality adjusted upwards to just meet the current 1-hour standard138 indicate virtually no
potential for 1-hour exposures to NO2 concentrations at or above 200 ppb (or 300 ppb), and
almost none for exposures at or above 150 ppb. This is the case for both averaged estimates and
estimates in worst-case years, including at near-road monitoring sites within a few meters of
heavily trafficked roads. With respect to the lowest benchmark evaluated, analyses estimate that
there is potential for exposures to 1-hour NO2 concentrations at or above 100 ppb on some days
(i.e., about one to 10 days per year, on average). As described above, this result is consistent with
our expectations, given that the current 1-hour standard, with its 98th percentile form, is expected
to limit, but not eliminate, the occurrence of 1-hour NO2 concentrations of 100 ppb.
Thus, the current 1-hour NO2 standard is expected to allow virtually no potential for
exposures to the NO2 concentrations that have been shown most consistently to increase AR in
people with asthma, even under worst-case conditions across a variety of study areas with among
138 In all study areas, ambient NO2 concentrations required smaller upward adjustments to just meet the 1-hour
standard than to just meet the annual standard. Therefore, as noted above (Section 4.2.1), when adjusting air quality
to just meet the current NO2 NAAQS, we applied the adjustment needed to just meet the 1-hour standard.
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the highest NOx emissions in the U.S. Such NO2 concentrations are not estimated to occur, even
at monitoring sites adjacent to some of the most heavily trafficked roadways. In addition, the
current standard provides protection against NO2 exposures that have the potential to exacerbate
asthma symptoms, but for which the evidence indicates greater uncertainty in both the
occurrence of such exacerbations and in their severity, should they occur (i.e., closer to 100 ppb).
Given the results of these analyses, and the uncertainties inherent in their interpretation, we
conclude that there is little potential for exposures to ambient NO2 concentrations that would be
of public health concern in locations meeting the current 1-hour standard.
5.3 CASAC ADVICE
In addition to the evidence and quantitative information discussed above (Chapters 3 and
4), we have also considered the advice and recommendations of the CASAC, based on its review
of the draft PA, and comments from the public on the draft PA (Diez Roux and Sheppard, 2017).
In its comments on the draft PA, the CASAC concurred with staffs overall preliminary
conclusions that it is appropriate to consider retaining the current primary NO2 standards without
revision, stating that, "the CASAC recommends retaining, and not changing the existing suite of
standards" (Diez Roux and Sheppard, 2017). With regard to the individual elements of the
standards, the CASAC stated the following:
• Indicator and averaging time: "[TJhere is strong evidence for the selection of NO2 as the
indicator of oxides of nitrogen" and "for the selection of 1-hour and annual averaging times"
(Diez Roux and Sheppard, 2017 pg. 9).
• Level of the 1-hour standard: "[TJhere are notable adverse effects at levels that exceed the
current standard, but not at the level of the current standard. Thus, the CASAC advises that
the current 1-hour standard is protective of adverse effects and that there is not a scientific
basis for a standard lower than the current 1-hour standard" (Diez Roux, and Sheppard 2017
Pg- 9).
• Form of the 1-hour standard: The CASAC also "recommends retaining the current
form" for the 1-hour standard (Diez Roux and Sheppard, 2017).
• Level of the annual standard: Recognizing that the 1-hour standard can effectively
contribute to limiting long-term NO2 concentrations, the CASAC agreed with the EPA's
decision to focus on the degree of protection provided by the suite of standards against long-
term exposures, rather than the annual standard alone. In providing support for retaining the
level of the existing annual standard, the CASAC specifically noted that "it is the suite of the
current 1-hour and annual standards, together, that provide protection against adverse
effects" (Diez Roux and Sheppard, 2017, p. 9). As noted above, "the CASAC recommends
retaining, and not changing the existing suite of standards" (Diez Roux and Sheppard, 2017).
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5.4 STAFF CONCLUSION ON Till ADEQUACY OF THE CURRENT
STANDARDS
The overarching question guiding our consideration of the available evidence and
information for the current review is:
• Does the available scientific evidence and information support or call into question the
adequacy of the public health protection afforded by the current primary NO2
standards?
Staff has reached the conclusion that the current body of evidence, in combination with
the available information from quantitative analyses, supports the adequacy of the public health
protection provided by the current primary NO2 standards and does not call into question any of
the elements of the current standards. Staff further reaches the conclusion that it is appropriate to
consider retaining the current standards, without revision, in this review. In reaching these
conclusions we particularly note the following:
• The strongest evidence for NCh-related effects comes from controlled human
exposure studies, and a meta-analysis of individual-level data from those studies,
demonstrating the potential for people with asthma to experience NCh-induced
increases in AR following exposures under resting conditions from 100 to 530 ppb.
While increases in AR are considered to be a hallmark of asthma and can lead to
poorer control of symptoms, the potential public health implications of these results
are not clear due to the lack of an apparent dose-response relationship and uncertainty
in the potential adversity of the reported changes in AR. There is additional
uncertainty at the lower end of this range because, while the meta-analysis indicates
that the majority of study volunteers with asthma experienced increased AR
following resting exposures to 100 ppb NO2, individual studies do not consistently
report NCh-induced increases in AR at this exposure concentration.
• While epidemiologic studies provide consistent evidence for associations with
asthma-related effects, studies conducted in the U.S. and Canada do not provide
support for associations of asthma-related hospital admissions or emergency
department visits with exposure to short-term NO2 concentrations in locations that
would have clearly met the current standards. This is particularly the case given that
NO2 concentrations near the most heavily-trafficked roadways are not likely reflected
by the NO2 concentrations measured at monitors in operation during study years. We
additionally note that there is potential for copollutant confounding contributing to
some uncertainty regarding the extent to which the observed effects can be attributed
independently to NO2 exposure.
• While epidemiologic studies report associations between long-term NO2 exposures
and asthma development in children, these studies are subject to important
uncertainties that limit the extent to which they provide insight into the adequacy of
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the public health protection provided by the current standards. These uncertainties
include the potential for copollutant confounding, given the high correlations between
long-term averages of NO2 and other traffic-related pollutants, and the potential for
exposure measurement error. Even if these uncertainties were to be dismissed,
epidemiologic studies conducted in the U.S. and Canada do not indicate associations
of asthma incidence with exposures to long-term NO2 in locations that would have
clearly met the current standards. This is particularly the case given that NO2
concentrations near the most heavily-trafficked roadways are not likely reflected by
monitors in operation during study years.
• The current 1-hour NO2 standard is expected to allow virtually no potential for
exposures to the NO2 concentrations that have been shown most consistently to
increase AR in people with asthma (i.e., above 200 ppb), even under worst-case
conditions across a variety of study areas with among the highest NOx emissions in
the U.S. Such NO2 concentrations were not estimated to occur, even at monitoring
sites adjacent to some of the most heavily trafficked roadways.
• The current 1-hour standard is expected to limit, though not eliminate, the potential
for exposures to 1-hour concentrations at or above 100 ppb. Thus, the current
standard provides protection against NO2 exposures that have the potential to
exacerbate asthma symptoms, but for which the evidence indicates uncertainty in the
occurrence of such exacerbations and in their severity, should they occur.
As noted in Chapter 1 above, in establishing primary standards that, in the
Administrator's judgment, are requisite to protect public health with an adequate margin of
safety, the Administrator seeks to establish standards that are neither more nor less stringent than
necessary for this purpose. The Act does not require that primary standards be set at a zero-risk
level, but rather at a level that reduces risk sufficiently so as to protect public health with an
adequate margin of safety. We additionally note that different public health policy judgments
could lead to different conclusions regarding the extent to which the current standards protect the
public health. Such judgments include those related to the appropriate degree of public health
protection that should be afforded as well as the appropriate weight to be given to various aspects
of the evidence and information, including how to consider uncertainties.
In this context, we recognize that the uncertainties and limitations associated with the
many aspects of the estimated relationships between NO2 exposures and potentially adverse
respiratory effects are amplified with consideration of increasingly lower NO2 concentrations. In
staffs view, there is appreciable uncertainty in the extent to which reductions in asthma
exacerbations or asthma incidence would result from revising the current NO2 standards. The
basis for any consideration of alternative standards would reflect different public health policy
judgments as to the appropriate approach for weighing uncertainties in the evidence.
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Based on all of the above considerations, and on CASAC advice and public input, we
reach the conclusion that it is appropriate to consider retaining the current standards, without
revision, in this review. The available evidence and information do not support the identification
of potential alternative standards that provide a different degree of public health protection. In
light of this conclusion, we have not identified potential alternative standards for consideration.
5.5 AREAS FOR FUTURE RESEARCH AND DATA COLLECTION
The uncertainties and limitations that remain in the review of the primary NO2 standards
are largely related to understanding the range of ambient concentrations over which we have
confidence in the occurrence of NCh-attributable adverse health effects, as indicated by available
epidemiologic, controlled human exposure, and animal toxicological studies. We encourage
continued investigation to further reduce these uncertainties, as described below, including
additional efforts to characterize the adversity and clinical-significance of reported effects.
Looking across the literature, we further encourage the synthesis of evidence into meta-analyses,
such as those recently conducted by Goodman et al. (2009) and Brown (2015). Meta-analyses
can facilitate an integrated assessment of the available evidence, and can provide additional
power to detect effects, beyond that found in many individual studies.
In this section, we highlight areas for future health-related research, model development,
and data collection activities to address the uncertainties and limitations in the current body of
scientific evidence for NO2. These future research areas reflect advice from the CASAC (Diez
Roux and Sheppard, 2017). If undertaken, research focused on the uncertainties highlighted in
this section could provide important evidence for informing future reviews of the primary NO2
NAAQS.139
Epidemiologic Evidence:
In the current review, epidemiologic studies provide the strongest evidence for effects
that can clearly be considered adverse. However, the degree to which these studies provide
clarity regarding the NO2 exposure concentrations at which potentially adverse effects are likely
to occur is limited by inherent uncertainties, including the potential for copollutant confounding
from other traffic-related pollutants, uncaptured NO2 exposure gradients, differential population
exposure, and unexplored effect measure modification. We encourage research to improve our
understanding of these issues, as described below^
139 In some cases, research in these areas can go beyond aiding standard setting to also inform the development of
more efficient and effective control strategies.
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Understanding Potential Confounding: Within the current body of scientific literature,
there are uncertainties related to the extent of potential confounding by traffic-related
copollutants. Given the relatively high correlations between concentrations of NO2 and
several co-occurring pollutants, this issue particularly impacts our consideration of the
epidemiologic evidence for associations between long-term NO2 exposures and asthma
development. As stated in section 3.3.2, the emphasis that is placed on epidemiology
studies reflects, in part, the degree to which the evidence indicates that copollutant
confounding may occur. Thus, additional research into this issue could reduce an
important uncertainty in the existing evidence and could further inform decisions in
future reviews of the primary NO2 standards. In its advice to the Administrator,
CASAC has reiterated the need for additional research on this issue (Diez Roux and
Frey, 2015b).
Seasonal and Indoor/Outdoor exposure gradients: Issues of seasonal differences in NO2
exposures and distinguishing between ambient and indoor exposures need to be better
addressed to improve inferences of health effects (e.g., see Diez Roux and Frey, 2015a).
As noted in the CASAC response to charge questions regarding the second draft of the
ISA, "[t]here can be more interpretation from studies of indoor exposure and for studies
undertaken in different seasons," and "[t]he indoor exposure studies can be informative
because they do not have the same mix of co-pollutants as the outdoor exposure
studies." (Diez Roux and Frey, 2015a).
Differential Population Exposure: There is a need to better address issues of equity and
environmental justice related to the distributions of NO2 exposures among and between
communities of varying socioeconomic status. Such distributions may be related to the
identification of groups at higher risk for adverse effects as a result of combinations of
exposure scenarios, populations, lifestages, and socioeconomic factors. As noted by the
CASAC (Diez Roux and Frey, 2015a), "[tjhere is substantial evidence that groups in
poverty or who are non-white experience higher exposures to NO2, but the
epidemiological evidence is still lacking. It is important to clearly show how the
exposure differences follow socioeconomic status (SES) or racial gradients, because
for those that are considered causal or likely to be causal, there is high potential for
larger health effects even if the epidemiological evidence of a direct effect modification
is lacking." Related to this, it is important to better characterize the locations where
peak NO2 exposures occur (e.g., on-road in vehicles, roadside as pedestrians, in urban
street canyons, near other non-road facilities such as rail yards or industrial facilities)
to potentially identify where higher population exposures may be occurring. Such
improved characterization of peak NO2 exposures may improve our understanding of
N02-related health effects in at-risk populations.
Characterizing At-Risk Populations and Effect Measure Modification: The degree to
which people with asthma are more responsive to NO2 exposures could vary depending
on the disease phenotype (i.e., atopic versus non-atopic). In addition, sensitivity to NO2
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exposures may be enhanced for people who have other conditions, such as diabetes or
cardiovascular disease. These and other factors that could confer sensitivity to NO2
exposures (e.g., psychosocial stress, copollutant exposures) should be further
investigated.
Mode of Action
There is an ongoing scientific need to improve our mechanistic understanding related to
effects of exposure to NO2, including effects on respiratory endpoints (e.g., related to asthma)
and on endpoints related to cardiovascular disease and premature mortality. Research needs
within this category are discussed below.
• Temporal exposure patterns: Research evaluating the latency period for the development
of new asthma, and the NO2 temporal exposure patterns that can contribute to the
disease, would be beneficial to future reviews. For example, an important uncertainty
in the current review is the role of single or repeated short-term NO2 exposures versus
persistent long-term exposures in the development of asthma. This type of work could
inform future consideration of the protection provided by the 1-hour and annual
standards.
• Worsening Asthma Symptoms: In the current review, evidence from controlled human
exposure studies supports the occurrence of increased AR in people with asthma
following resting exposures to NO2 concentrations from 100 to 530 ppb. This increase
in AR suggests the potential for NO2 exposures to worsen asthma symptoms. However,
results of these studies are not always consistent, potentially due to differences in the
endpoints examined, the challenge agents used, or the exposure conditions (exercise
versus rest). In addition, a dose-response relationship is not apparent from the available
data, contributing to uncertainty in our interpretation of these studies. To address these
uncertainties, we encourage future research into the occurrence of increased AR, or
other effects, following exposures to NO2 concentrations found in the ambient air. We
further encourage additional efforts to characterize the potential for exposures to such
ambient NO2 concentrations to result in effects that are adverse or that are clinically
relevant. Such research could help to inform future consideration of the health
protection provided by the NO2 NAAQS.
• Better characterization of non-respiratory endpoints: While the body of epidemiologic
studies reporting associations between NO2 and cardiovascular disease, cardio-
metabolic disease, birth outcomes, and cancer is rapidly growing, there is uncertainty
in the degree to which such effects are specifically associated with NO2. Controlled
human NO2 exposure studies would be most informative in elucidating these potential
relationships, but are unlikely to be feasible for many outcomes. Additionally,
controlled animal exposure studies and other mechanistic studies may be particularly
informative for the next NO2 NAAQS review, as they may foster greater understanding
of mode of action for a wider range of endpoints.
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Spatial and Temporal Gradients in Ambient NO2 Concentrations
An improved understanding of ambient NO2 concentrations around important sources of
NOx emissions would help to inform consideration of potential exposures in future reviews.
• Near-road monitor data: Since the last review, NO2 monitors have been deployed near
major roads in large urban areas across the U.S. Future research should use the data from
these monitors to better understand spatial and temporal gradients in ambient NO2
concentrations. Additionally, characterizations of near road NO2 concentrations should
include measurements of other traffic-related pollutants such as ultra-fine particles
(number concentration), black carbon, PM, and CO.
• Other near-source NO2 concentrations: In addition, we encourage research to better
characterize other near-source environments where NO2 exposures may be of importance.
These environments could include urban street canyons and areas near large sources of
NOx emissions, such as airports or rail yards. Widespread passive NO2 sampling may be
a practical way to identify hotspots, and to characterize NO2 concentration gradients in
such locations.
• Factors that affect ambient NO2 concentrations: Further collection and refinement of
information to better characterize the factors contributing to variability in ambient N02
concentrations is also an ongoing need, including information on air quality monitor site
characteristics, available traffic counts, fleet mix data, and historical emissions
information and trends.
Quantification of Risk and Exposure Estimates
Research into better understanding the uncertainties surrounding risk estimates, as they
relate to risk quantification, as well as research into new methods of risk quantification, may be
helpful to future reviews, as detailed below.
• Quantification of Uncertainties: There is a need for improved methods to quantify key
uncertainties in epidemiology-based risk estimates. In this review, key uncertainties
included those related to copollutant confounding, exposure characterization, baseline
incidence, and the shape of the concentration-response function. Taken together, these
uncertainties contributed to the staff conclusion not to conduct an updated
epidemiology-based risk assessment (as described in section 4.4.2).
• Risk Quantification Methods: Although in this review there was not sufficient new
scientific information to support an updated risk assessment, the development of
exposure quantification methods, models, and data, or new interpretations of existing
information, may inform risk assessments in future reviews.
• Health Benchmarks: There is an ongoing need for scientific information to support the
identification of health-based benchmarks. In the current review, we focus on
benchmarks from 100 to 300 ppb, based on information from controlled human
exposure studies of AR. We encourage additional research into the occurrence of AR,
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or other effects, following exposures to NO2 concentrations at and below 100 ppb.
Such research could provide a more robust body of evidence for reaching conclusions
on the occurrence of increased AR or other effects, and on the potential adversity of
those effects, following exposures to NO2 concentrations near those found in the
ambient air in the U.S. In general, studies are most useful if they characterize the
effects of exposure to NO2 itself, independent of co-occurring pollutants.
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5.6 REFERENCES
Brown, JS. (2015). Nitrogen dioxide exposure and airway responsiveness in individuals with asthma. Inhal Toxicol
27: 1-14. http://dx.doi.org/10.3109/08958378.2014.97996Q
Diez Roux A and Frey H.C. (2015a). Letter from Drs. Ana Diez Roux, Chair and H. Christopher Frey, Immediate
Past Chair, Clean Air Scientific Advisory Committee to EPA Administrator Gina McCarthy. CASAC
Review of the EPA's Integrated Science Assessment for Oxides of Nitrogen- Health Criteria (Second
External Review Draft). EPA-CASAC-15-001. September 9, 2015. Available at:
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CAS AC-.1.5 -00.1. +unsi gned. pdf
Diez Roux A and Frey H.C. (2015b). Letter from Drs. Ana Diez Roux, Chair and H. Christopher Frey, Immediate
Past Chair, Clean Air Scientific Advisory Committee to EPA Administrator Gina McCarthy. CASAC
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Goodman, JE; Chandalia, JK; Thakali, S; Seeley, M. (2009). Meta-analysis of nitrogen dioxide exposure and airway
hyper-responsiveness in asthmatics. Crit Rev Toxicol 39: 719-742.
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Reddel, HK; Taylor, DR; Bateman, ED; Boulet, LP; Boushey, HA; Busse, WW; Casale, TB; Chanez, P; Enright,
PL; Gibson, PG; de Jongste, JC; Kerstjens, HA; Lazarus, SC; Levy, ML; O'Byrne, PM; Partridge, MR;
Pavord, ID; Sears, MR; Sterk, PJ; Stoloff, SW; Sullivan, SD; Szefler, SJ; Thomas, MD; Wenzel, SE.
(2009). An official American Thoracic Society/European Respiratory Society statement: Asthma control
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Sheppard, E (2017). Letter form Dr. Elizabeth A. (Lianne) Sheppard, Chair, Clean Air Scientific Advisory
Committee to EPA Administrator E. Scott Pruitt. CASAC Review of the EPA's Policy Assessment for the
Review of the Primary National Ambient Air Quality Standards for Nitrogen Dioxide (External Review
Draft- September 2016). EPA-CASAC-17-001. March 7th, 2017. Available at:
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Stieb, DM; Szyszkowicz, M; Rowe, BH; Leech, JA. (2009). Air pollution and emergency department visits for
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U.S. EPA (1995). Review of the National Ambient Air Quality Standards for Nitrogen Dioxide: Assessment of
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U.S. EPA (2008). Risk and Exposure Assessment to Support the Review of the NO2 Primary National Ambient Air
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U.S. EPA (2016). Integrated Science Assessment for Oxides of Nitrogen - Health Criteria (2016 Final Report). U.S.
EPA, National Center for Environmental Assessment, Research Triangle Park, NC. EPA/600/R-15/068.
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APPENDIX A
NITROGEN DIOXIDE AIR QUALITY
Table of Contents
LIST OF TABLES i
1. Calculation of Annual and 1-Hour NO2 Design Values 1
2. Annual and Hourly Design Values for Select Epidemiologic Studies 1
2.1 Short-Term Epidemiologic Studies For Asthma-Related Emergency Department Visits
And Hospital Admissions 2
2.2 Design Values For Epidemiologic Studies Of Asthma-Related Respiratory Symptoms 9
2.3 Design Values In Locations For Long-Term Epidemiologic Studies Of Asthma
Incidence In Children 21
3. Distributions of Daily maximum 1-hour NO2 concentrations for locations of short-term
epidemiologic studies 29
4. Trends in 1-Hour and Annual NO2 Design Values 29
5. Evaluation of Roadway Gradients of NO2 Concentrations from 1980-2015 30
6. Comparison of Distribution of NO2 Concentrations from Near-Road Monitors and Non-
Near-Road Monitors for 2013-2015 30
7. Relationship Between 1-Hour and Annual NO2 Design Values 30
8. Relationship Between Annual Averages of the near-road and non-near-road maximum 1-hr
daily NO2 concentrations from 2013-2016 31
A-l
-------
LIST OF TABLES
Table A-l. Hourly and Annual Design Values for Atlanta, GA from 1993-2004 3
Table A-2. Hourly and Annual Design Values for Detroit, MI from 2004-2006 3
Table A-3. Hourly and Annual Design Values for Los Angeles, CA from 1992-1995 4
Table A-4. Hourly and Annual Design Values for Cleveland and Cincinnati, OH from 1991-
1996 4
Table A-5. Hourly and Annual Design Values for New York, NY from 1999-2002 5
Table A-6. Hourly and Annual Design Values for Edmonton, Canada from 1992-2002 6
Table A-7. Hourly and Annual Design Values for Toronto, Canada from 1980-1994 7
Table A-8. Hourly and Annual Design Values for 7 Canadian Cities from 1992-2003 7
Table A-9. Hourly and Annual Design Values for New Haven, CT for 2000-2004 9
Table A-10. Hourly and Annual Design Values for Bronx, NY for 2003-2005 9
Table A-l 1. Hourly and Annual Design Values for Fresno, CA from 2000-2005 10
Table A-12. Hourly and Annual Design Values for 7 U.S. Cities from 1998-2001 10
Table A-13. Hourly and Annual Design Values for Atlanta, GA for 1993-2004 12
Table A-14. Hourly and Annual Design Values for Toronto, Canada from 1980-1994 12
Table A-15. Hourly and Annual Design Values for Toronto, Canada from 1986-1998 13
Table A-16. Hourly and Annual Design Values for Toronto, Canada from 1995-1999 14
Table A-17. Hourly and Annual Design Values for 11 Canadian Cities from 1986-2000 14
Table A-18. Hourly and Annual Design Values for 10 Canadian Cities from 1993-2000 18
Table A-19. Hourly and Annual Design Values for Vancouver, British Columbia in 1995 21
Table A-20. Hourly and Annual Design Values for Vancouver, British Columbia from 1999-
2000 21
Table A-21. Hourly and Annual Design Values for East Boston from 1987-1994 22
Table A-22. Hourly and Annual Design Values for 5 U.S. Cities from 1986-2008 22
Table A-23. Hourly and Annual Design Values for Southern California Communities for 2002-
2006 25
Table A-24. Hourly and Annual Design Values for Southern California Communities from 1993-
2004 27
Table A-25: Distribution of daily maximum 1-hour concentrations of NO2 (ppb) for locations of
U.S. epidemiologic studies of short-term hospital admissions and emergency
department visits 29
A-i
-------
LIST OF FIGURES
Figure A-l. Averages of the near-road and non-near-road maximum 1-hr daily NO2
concentrations from 2013-2016 32
A-ii
-------
1. CALCULATION OF ANNUAL AND 1-HOUR NOi DESIGN VALUES
The following procedures were used to calculate annual and 1-hour design values (DVs) for
N02.
• Raw hourly NO2 data was downloaded from the following sources:
- EPA's AQS database (parameter code 42602)
Canadian NAPs network website (http://maps-cartes.ec.ec.ca)
- US SEARCH network ftp site (ftp://mail.atmospheric-research.com)
• Two types of DVs were calculated for each site in each of the 3 networks.
• Annual D V:
- For each site and year, the annual DV is the mean hourly concentration.
75% of hours in the year must be present for the annual DV to be valid.
• Hourly D V:
- Daily max was identified for each sampling day for each site
Two different methods were used to calculate 98th percentile values:
- Using days with 18 or more hourly samples
- Using days with 1 or more hourly samples
• The final 98th percentile reported is the maximum of the 2 methods for each site and
year.
• DV for each 3-year window was calculated by averaging the annual final 98th percentile
values.
• Hourly DV were considered valid if they meet the following criteria:
- Each day must have samples for >= 75% of hours to be valid.
75%) of days in a quarter must be valid for the quarter to be valid.
A year must have had 4 complete quarters for it to be valid.
A DV must have had 3 valid years to be valid.
2. ANNUAL AND HOURLY DESIGN VALUES FOR SELECT EPIDEMIOLOGIC
STUDIES
Design values (DVs) have been calculated for locations of select epidemiologic studies
examining respiratory effects associated with NO2 exposures. These studies were identified from
the Integrated Science Assessment for Oxides of Nitrogen - Health Criteria. Calculations were
based on methods outlined above.
The DVs reported in the tables below are the highest DVs in the specified location for each year
as calculated according to the methods above. The respective completeness of the hourly and
annual DVs are also reported. For annual DVs, this is reported as the percentage of complete
days in the year. For hourly DVs, this is reported as the number of complete quarters (75% of
A-l
-------
hours in a day, 75% of days in a quarter) for the 3-year period (the specified year and two years
before).
The DVs reported in this technical memo are limited to the locations where key epidemiologic
studies in the current review of the Primary NO2 NAAQS have been conducted. For each
city/CBS A, the respective study(ies) and relevant study details are reported, including the
monitor IDs that were used in determining DVs
2.1 Short-Term Epidemiologic Studies For Asthma-Related Emergency
Department Visits And Hospital Admissions
• Atlanta, GA Strickland et al. - Pediatric asthma ED visits (5-17 yr) 1993-2004
(2010) (entire years)
- Exposure assignment: "Daily concentrations of
ambient 1-hour maximum [NO2].. .were obtained
from several networks of ambient monitors... We
used population-weighting to combine daily pollutant
measurements across monitors."
-Design values: We used 5 specific AQS monitors and
a SEARCH monitor for which we were able to obtain
data to compute design values based on personal
communication with author. (131210001,
131210047, 131210048, 132230003, 162470001,
SEARCH monitor on Jefferson Street)
Pediatric asthma ED visits (5-17 yr) 1993-2004
(May-Oct)
Exposure assignment: NO2 concentrations obtained
from 3 networks of stationary monitors. Three
exposure metrics used: (1) one downtown monitor
was selected as central site, (2) all monitors used to
calculate unweighted average of pollutant
concentrations for all monitors, and (3) population-
weighted average concentration.
Design values: We used the same monitors indicated
in the Strickland et al. (2010) paper. (131210001,
131210047, 131210048, 132230003, 162470001,
SEARCH monitor on Jefferson Street)
Strickland et al.
(2011)
Peel et al. (2005) - Asthma ED visits across all ages and in children (2-
18 yr) (January 1993- August 2000)
- Exposure assignment: Average of NO2
concentrations from monitors for several monitoring
networks
A-2
-------
- Design values: We used the 5 AQS monitors and the
SEARCH monitor that represent air quality in metro
Atlanta. (131210001, 131210047, 131210048,
132230003, 162470001, SEARCH monitor on
Jefferson Street)
Table A-l. Hourly and Annual Design Values for Atlanta, GA from 1993-2004.
Hourly
Annual
Year
Max DV
Number of
Year
Max DV (Monitor
Completeness
(Monitor ID)a
Complete
Quarters3
ID)a
(%)c
1993
-
1993
25 (131210048)
57.3
1994
-
1994
23 (131210048)
83.2
1995
76(131210048)
9
1995
19 (131210048)
96.3
1996
83 (131210048)
11
1996
27 (131210048)
98.4
1997
88 (131210048)
11
1997
25 (131210048)
87.5
1998
99 (JST)
2
1998
26 (JST)
36.5
1999
100 (JST)
5
1999
26 (JST)
80
2000
95 (JST)
9
2000
23 (JST)
95.1
2001
86(131210048)
12
2001
23 (JST)
97.5
2002
86(131210048)
12
2002
19 (JST)
97.9
2003
81 (131210048)
12
2003
20 (JST)
89
2004
75 (131210048)
12
2004
20 (JST)
92
aJST refers to the SEARCH network monitor in Atlanta, GA located on Jefferson Street
bIn the respective 3-year period (12 quarters) for the hourly DV; 9 or more quarters (75%) are required for a DV to be valid
cValid annual DVs require 75% completeness
• Detroit, MI Li et al. (2011) - Pediatric asthma ED visits (2-18 yr), 2004-2006 (entire
years)
- Exposure assignment: Average NO2 concentrations across
two monitors
- Design values: We used the two monitors indicated in the
study. These were the only monitors with valid data during
the study period. (261630016, 26160019)
Table A-2. Hourly and Annual Design Values for Detroit, MI from 2004-2006.
Hourly
Annual
Year
Max DV (Monitor
ID)
Number of Complete
Quarters"
Year
Max DV (Monitor ID)
Completeness
(%)b
2004
12
2004
19(261630016)
77.2
2005
-
12
2005
20 (261630016)
95.4
2006
55 (261630016)
12
2006
16(261630016)
98.9
aIn the respective 3-year period (12 quarters) for the hourly DV; 9 or more quarters {15%) are required for a DV to be valid
bValid annual DVs require 75% completeness
A-3
-------
- Hospital admissions, all ages, 1992-1995 (entire
years)
- Exposure assignment: Average NO2 concentration
over all monitors; Study indicated AQS monitors by
map
- Design values: We identified AQS monitors by
approximation of location by map and operation during
study period: 060370002, 060371002, 060371103,
060590001, 060658001, 060371201, 0603716002,
060370113, 060375001, 060371301, 060372005,
060371601, 060370206, 060591003, 160659001,
060658001, 060711004, 060719004, 060371701
Table A-3. Hourly and Annual Design Values for Los Angeles, CA from 1992-1995.
Hourly
Annual
Year
Max DV
(Monitor ID)
Number of
Complete
Quarters3
Year
Max DV (Monitor
ID)
Completenes
s (%)b
1992
-
12
1992
51 (060371301)
93.6
1993
-
12
1993
50
92.8
1994
171 (060371103)
12
1994
50
95.4
1995
168 (060371103)
12
1995
46
94.1
aIn the respective 3-year period (12 quarters) for the hourly DV; 9 or more quarters (75%) are required for a DV to be valid
bValid annual DVs require 75% completeness
• Cleveland/ Jaffe et al. (2003) -Asthma ED visits, 5-34 yr, July 1991- June 1996
Cincinnati, (summers, June-August)
OH -Exposure assignment: Monitor with highest 24-h avg
concentration
- Design values: We used all operating monitors in
Cleveland and Cincinnati for the study period
(390350033, 390350043, 390350060, 390610035,
390610037)
Table A-4. Hourly and Annual Design Values for Cleveland and Cincinnati, OH from
1991-1996.
Hourly
Annua
Year
Max DV
(Monitor ID)
Number of
Complete
Quarters"
Year
Max DV (Monitor
ID)
Completeness
(%y
Cleveland
1991
-
1991
30(390610035)
83 6
• Los Angeles, Linn et al. (2000)
CA
A-4
-------
1992
-
1992
29 (390350033)
81.3
1993
89 (390350033)
8
1993
28(390350060)
90.1
1994
92 (390350033)
4
1994
28(390350060)
92.6
1995
83 (390350060)
12
1995
27 (390350060)
93.7
1996
83 (390610035)
4
1996
29 (390610037)
98.9
Cincinnati
1991
-
1991
17(210371001)
94.2
1992
-
1992
15 (210371001)
94.4
1993
77 (390610035)
10
1993
18 (210371001)
89.5
1994
76 (390610035)
12
1994
20 (210371001)
92.6
1995
80 (390610037)
4
1995
20 (210371001)
94.2
1996
83 (390610035)
4
1996
19(210371001)
94.2
aIn the respective 3-year period (12 quarters) for the hourly DV; 9 or more quarters (75%) are required for a DV to be valid
bValid annual DVs require 75% completeness
• New York City, Ito et al. (2007) - Asthma ED visits, all ages, 1999-2002 (entire
NY years)
- Exposure assignment: Average NO2 concentrations
from 15 monitors
- Design values: We used the 15 monitors closest to
city center that were in operation during study years
(360050073, 360050080, 360050083, 360050110,
360610010, 360610056, 360810097, 360810098,
360810124, 340130011, 340130016, 340131003,
340170006, 340230011, 340390004)
Asthma ED visits, all ages, Bronx: January 1999-
November 2000, Manhattan: September 1999-
November 2000
Exposure assignment: NO2 concentrations from a
monitor in the Bronx and a monitor in Manhattan
Design values: We used the monitors specified by
location in the study (360050073, 360610010)
Design values for these encompass years before
the study period.
Table A-5. Hourly and Annual Design Values for New York, NY from 1999-2002.
Hourly
Annua
Year
Max DV
Number of
Year
Max DV
Completeness
(Monitor ID)
Complete
Quarters"
(Monitor ID)
(%)b
New York City (including IS
ewark, NJ)
1999
-
1999
42 (340390004)
97.8
2000
-
2000
41 (340390004)
98
2001
102 (340390004)
12
2001
40 (340390004)
97.1
• Bronx/Manhattan, ATSDR 2006
NY
A-5
-------
2002
101 (340390004)
12
2002
40 (340390004)
92.6
Bronx
1999
94 (360050073)
1
1999
32 (360050073)
25.4
2000
94 (360050073)
1
2000
N/A
Manhattan
1999
86(360610010)
11
1999
36 (360610010)
89
2000
86 (360610010)
11
2000
36 (360610010)
92
aIn the respective 3-year period (12 quarters) for the hourly DV; 9 or more quarters (75%) are
required for a DV to be valid
bValid annual DVs require 75% completeness
• Edmonton, Villeneuve et al. -Asthma emergency department visits, all ages > 2 yr,
Canada (2007) 1992-2002 (entire years)
-Exposure assignment: Average NO2 concentration
across three monitoring stations
-Design values: We used the three NAPS monitors in
Edmonton (90121, 90122, 90130)
Table A-6. Hourly and Annual Design Values for Edmonton, Canada from 1992-2002.
Hourly
Annua
Year
Max DV
Number of
Year
Max DV
Completeness
(Monitor ID)
Complete
Quarters3
(Monitor ID)
(%)b
1992
-
1992
36 (90122)
99.5
1993
-
1993
27 (90130)
95.2
1994
242 (90122)
12
1994
27 (90130)
97.6
1995
87 (90122)
12
1995
27 (90130)
97.1
1996
96 (90122)
12
1996
25 (90130)
97.2
1997
96 (90122)
12
1997
26 (90130)
93.3
1998
100 (90122)
12
1998
27 (90130)
98
1999
86 (90122)
12
1999
24 (90130)
99.4
2000
74 (90122)
12
2000
25 (90130)
98.9
2001
70 (90122)
12
2001
25 (90130)
98
2002
76 (90122)
12
2002
25 (90130)
98.3
aIn the respective 3-year period (12 quarters) for the hourly DV; 9 or more quarters {15%) are required for a DV to be valid
bValid annual DVs require 75% completeness
• Toronto, Burnett et al. -Hospital admissions, all ages, 1980-1994 (entire
Canada (1999) years)
-Exposure assignment: Average NO2 concentration
across four monitoring stations that are not likely
influence by a local source (site-specific) (reference
Burnett et al. JAWMA 1998 (48))
-Design values: We identified study monitors using the
map provided in the reference study (60403, 60410,
60413, 60418). We also used other NAPS monitors in
A-6
-------
metro Toronto as they were also representative of
potential population exposures (60401, 60402, 60412,
60414, 60417, 60419, 60420, 60421, 60422, 60423,
60424, 60425)
Table A-7. Hourly and Annual Design Values for Toronto, Canada from 1980-1994.
Hourly
Annual
Year
Max DV
Number of
Year
Max DV
Completeness
(Monitor ID)
Complete
Quarters"
(Monitor ID)
(%)b
1980
-
1980
36 (60403)
95.4
1981
-
1981
36 (60403)
95
1982
127(60412)
8
9182
34 (60412)
89.4
1983
120 (60412)
10
1983
30 (60414)
97.5
1984
123 (60412)
12
1984
36 (60412)
95.6
1985
113 (60412)
10
1985
38 (60412)
38.7
1986
115 (60412)
6
1986
34 (60418)
97
1987
110 (60412)
2
1987
32 (60403)
94.6
1988
130(60412)
3
1988
38 (60422)
56.9
1989
120 (60412)
7
1989
33 (60403)
97.9
1990
117 (60403)
12
1990
30 (60403)
81.2
1991
110 (60403)
12
1991
30 (60422)
91.7
1992
220(60403)
12
1992
45 (60422)
95.6
1993
227 (60403)
12
1993
31 (60420)
28.7
1994
223 (60403)
12
1994
30 (60424)
99.4
aIn the respective 3-year period (12 quarters) for the hourly DV; 9 or more quarters (75%) are required for a DV to be valid
bValid annual DVs require 75% completeness
• Multicity Stieb et al. (2009) - Asthma ED visits, all ages, 1992-2003 (entire years)
(Montreal, - Exposure assignment: Average NO2 concentrations
Ottawa, from all monitors in each city.
Edmonton, St. - Design values: All monitors in the NAPS database
John, Halifax, for each city were used.
Toronto,
Vancouver)
Table A-8. Hourly and Annual Design Values for 7 Canadian cities from 1992-2003.
Hourly
Annual
Year
Max DV
(Monitor ID)
Number
of
Complete
Quarters"
Year
Max DV (Monitor
ID)
Completeness
(%)"
Montreal 1997-2002
A-7
-------
1997
-
1997
31 (50115)
96.8
1998
-
1998
28 (50115)
96.7
1999
85 (50109)
12
1999
29 (50109)
98.9
2000
83 (50109)
12
2000
26 (50109)
92
2001
78 (50109)
10
2001
30(50109)
55
2002
76 (50109)
8
2002
26 (50109)
77.5
Ottawa (60101, 60104) 1992-2000
1992
-
1992
72 (60101)
14.3
1993
-
1993
24 (60101)
98.3
1994
198 (60101)
9
1994
25 (60101)
98.9
1995
78 (60101)
12
1995
25 (60101)
95.8
1996
74 (60101)
10
1996
23 (60101)
66.5
1997
70 (60101)
7
1997
25 (60101)
15.8
1998
73 (60101)
7
1998
25 (60101)
93.9
1999
83 (60101)
7
1999
33 (60101)
59.5
2000
82 (60101)
10
2000
22 (60101)
90.1
Edmonton 1992-2002
1992
-
1992
36 (90122)
99.5
1993
-
1993
27 (90130)
95.2
1994
242 (90122)
12
1994
27 (90130)
97.6
1995
87 (90122)
12
1995
27 (90130)
97.1
1996
96 (90122)
12
1996
25 (90130)
97.2
1997
96 (90122)
11
1997
26 (90122)
93.3
1998
100 (90122)
11
1998
27 (90130)
98
1999
86 (90122)
11
1999
24 (90130)
99.4
2000
74 (90122)
12
2000
25 (90130)
98.9
2001
70 (90122)
12
2001
25 (90130)
98
2002
76 (90122)
12
2002
25 (90122)
95.7
St. John
992-1996
1992
-
1992
14(10102)
88.7
1993
-
1993
15 (10102)
83.9
1994
96 (10102)
7
1994
15 (10102)
21
1995
50 (10102)
4
1995
21 (10102)
1.7
1996
48 (10102)
1
1996
#N/A
Halifax 1999-2002
1999
-
1999
#N/A
2000
-
2000
18 (30118)
83.5
2001
67 (30118)
6
2001
17(30118)
76
2002
61(30118)
9
2002
17(30118)
80
Toronto 1999-2003
1999
-
1999
28 (60430)
79.7
2000
-
2000
30 (60430)
33.1
2001
94 (60423)
7
2001
31 (60403)
11.4
2002
98 (60423)
4
2002
26 (60429)
81.3
2003
84 (60429)
9
2003
27 (60429)
92.3
A-8
-------
Vancouver 1999-2003
1999
-
1999
32(100120)
3.4
2000
-
2000
27(100112)
97.9
2001
86 (100120)
0
2001
26(100112)
97.7
2002
56 (100121)
12
2002
25 (100112)
97.3
2003
56 (100121)
12
2003
25 (100112)
98
aIn the respective 3-year period (12 quarters) for the hourly DV; 9 or more quarters (75%) are
required for a DV to be valid
bValid annual DVs require 75% completeness
2.2 Design Values For Epidemiologic Studies Of Asthma-Related Respiratory
Symptoms
• New Haven, Gent et al. (2009) -Wheeze in asthmatic children (4-12 yr), Aug 2000-
CT Feb 2004
-NO2 central site
-NO2 effect estimated from multipollutant model with
source apportionment factor of EC, zinc, lead,
copper, and selenium
Table A-9. Hourly and Annual Design Values for New Haven, CT for 2000-2004.
Hourly
Annual
Year
Max DV
Number of
Year
Max DV (Monitor
Completenes
(Monitor ID)
Complete
Quarters"
ID)
s (%)b
2000
-
2000
25 (90091123)
95.8
2001
-
2001
27 (90091123)
98.3
2002
75 (090091123)
12
2002
25 (90091123)
98.2
2003
73 (090091123)
12
2003
25 (90091123)
97.6
2004
70(090090027)
4
2004
23 (90091123)
11
aIn the respective 3-year period (12 quarters) for the hourly DV; 9 or more quarters (75%) are required for a DV to be valid
bValid annual DVs require 75% completeness
Bronx, NY Patel et al. (2010) -Wheeze and chest tightness in asthmatic adolescents
(14-20 yr), 2003 - 2005
- N02 central site (PS52, MONITOR ID 360050110)
Table A-10. Hourly and Annual Design Values for Bronx, NY for 2003-2005.
Hourly Annual
A-9
-------
Year
Max DV (Monitor ID)
Number
of
Complete
Quarters"
Year
Max DV (Monitor ID)
Completeness
(%)b
2003
82 (360050110)
9
2003
30 (360050110)
78.4
2004
79 (360050110)
11
2004
30(360050110)
97.4
2005
78 (360050110)
11
2005
29 (360050110)
89.1
aIn the respective 3-year period (12 quarters) for the hourly DV; 9 or more quarters (75%) are required for a DV to be valid
bValid annual DVs require 75% completeness
• Fresno/Clovis, Mann et al. - Wheeze in asthmatic children (6-11 yr), Nov 2000-
CA (2010) April 2005 (subgroup analyses for boys with
intermittent asthma and atopy)
- NO2 central site (CARB monitoring stations used in
study; AQS data used for DVs)
Table A-ll. Hourly and Annual Design Values
'or Fresno, CA from 2000-2005.
Hourly
Annua
Year
Max DV (Monitor ID)
Number
of
Complete
Quarters"
Year
Max DV (Monitor ID)
Completeness
(%)b
2000
-
2000
21 (60190008)
93.8
2001
-
2001
21 (60190008)
94.8
2002
74 (60190008)
12
2002
20 (60190008)
94.4
2003
77 (60190008)
12
2003
19 (60190008)
94.6
2004
72 (60190008)
12
2004
17 (60190008)
94.7
2005
71 (60190008)
12
2005
17 (60190008)
90.2
aIn the respective 3-year period (12 quarters) for the hourly DV; 9 or more quarters (75%) are required for a DV to be valid
bValid annual DVs require 75% completeness
• Multicity O'Connor et al. - Wheeze/cough, slow play, and missed school in
(2008) children with asthma (5-12 yr), Aug 1998 - July 2001
- NO2 central site (monitors near residences)
Table A-12. Hourly and Annual Design Values for 7 U.S. cities from 1998-2001.
Hourly
Annua
Year
Max DV (Monitor ID)
Number
of
Year
Max DV (Monitor ID)
Completeness
(%)b
A-10
-------
Complete
Quarters"
Boston, MA
1998
83 (250250021)
12
1998
31 (250250002)
94.1
1999
81 (250250002)
12
1999
30 (250250002)
93.2
2000
76 (250250002)
11
2000
43 (250250002)
0.1
2001
73 (250250002)
11
2001
30 (250250002)
93.1
Bronx, IS
Y
1998
96(36050080)
11
1998
36(360050080)
97.8
1999
95 (36050080)
11
1999
33 (360050080)
96.7
2000
94 (360050073)
1
2000
33 (360050080)
32.3
2001
94 (360050073)
1
2001
32(360050110)
56.9
Chicago, IL
1998
-
1998
32(170310063)
95.3
1999
-
1999
31.5 (170310063)
98.4
2000
87(170310063)
12
2000
32(170310063)
94.7
2001
86(170310063)
12
2001
32(170310063)
98.7
Dallas, TX
1998
-
12
1998
20 (481130069)
98.3
1999
-
12
1999
21 (481130069)
88.3
2000
75(481130069)
11
2000
19(481130069)
59.5
2001
73 (481130069)
11
2001
19(481130069)
90
New Yor
k, NY
1998
-
1998
42 (340390004)
98.9
1999
-
1999
42 (340390004)
97.8
2000
102 (340390004)
12
2000
41 (340390004)
98
2001
102 (340390004)
12
2001
40 (340390004)
97.1
Seattle, WA
1998
-
1998
20 (530330020)
67
1999
-
1999
22 (530330020)
37.7
2000
65 (530330032)
4
2000
21 (530330032)
24.1
2001
65(530330032)
8
2001
22 (530330032)
32.3
Tucson, AZ
1998
-
1998
17(40191011)
98.3
1999
-
1999
19(40191028)
91.7
2000
58 (40191011)
12
2000
17(40191011)
97.1
2001
58 (40191011)
12
2001
17(40191028)
98.8
aIn the respective 3-year period (12 quarters) for the hourly DV; 9 or more quarters (75%) are required for a DV to be valid
bValid annual DVs require 75% completeness
• Atlanta, GA Darrow et al. - Respiratory ED visits, all ages, 1993-2004
(2010) - Average daily concentration across monitors with
population weighting (5 AQS monitors +SEARCH
monitor, operational 1998)
A-ll
-------
Tolbert et al. - Respiratory ED visits, all ages, March 1994 - Dec
(2007) 2004
- Average of NO2 concentrations across monitors (5
AQS monitors + SEARCH, operational 1998)
Peel et al. (2005) - Respiratory ED visits, all ages, Jan 1993 - Aug 2000)
- Average of NO2 concentrations across monitors (5
AQS monitors + SEARCH, operational 1998))
Table A-13. Hourly and Annual Design Values for Atlanta, GA for 1993-2004
Hourly
Annual
Year
Max DV
(Monitor ID)a
Number of
Complete
Quartersb
Yea
r
Max DV (Monitor
ID)a
Completene
ss (%)c
1993
-
1993
25 (131210048)
57.3
1994
-
1994
23 (131210048)
83.2
1995
76(131210048)
9
1995
19(131210048)
96.3
1996
83 (131210048)
11
1996
27(131210048)
98.4
1997
88 (131210048)
11
1997
25 (131210048)
87.5
1998
99 (JST)
2
1998
26 (JST)
36.5
1999
100 (JST)
5
1999
26 (JST)
80
2000
95 (JST)
9
2000
23 (JST)
95.1
2001
86(131210048)
12
2001
23 (JST)
97.5
2002
86(131210048)
12
2002
19 (JST)
97.9
2003
81 (131210048)
12
2003
20 (JST)
89
2004
75 (131210048)
12
2004
20 (JST)
92
aJST refers to the SEARCH network monitor in Atlanta, GA located on Jefferson Street
bIn the respective 3-year period (12 quarters) for the hourly DV; 9 or more quarters (75%) are required for a DV to be valid
cValid annual DVs require 75% completeness
• Toronto, Burnett et al. - Pediatric respiratory hospital admissions (< 2 yr),
Canada (2001) 1980-1994
- Average NO2 concentrations across 4 monitors with
continuous data and not influence by any local source
(16 monitors from NAPS database; some monitors
with high DV likely influenced by traffic)
Table A-14. Hourly and Annual Design Values
Hourly
Annual
Year
Max DV
(Monitor ID)
Number of
Complete
Quarters"
Yea
r
Max DV (Monitor
ID)
Completene
ss (%)
1980
-
1980
36 (60403)
95.4
or Toronto, Canada from 1980-1994.
A-12
-------
1981
-
1981
36 (60403)
95
1982
127 (60412)
8
1982
34 (60412)
89.4
1983
120 (60412)
10
1983
30 (60403)
93.8
1983
120(60412)
10
1983
30 (60403)
97.5
1984
123 (60412)
12
1984
36 (60412)
95.6
1985
113 (60412)
10
1985
38 (60412)
38.7
1986
115 (60412)
6
1986
34(60418)
97
1987
110(60412)
2
1987
32 (60403)
94.6
1988
130 (60422)
3
1988
38 (60422)
56.9
1989
120 (60422)
7
1989
33 (60403)
97.9
1990
117(60403)
12
1990
30 (60403)
81.2
1991
110(60403)
12
1991
30 (60422)
91.7
1992
220 (60423)
12
1992
45 (60422)
95.6
1993
227 (60423)
12
1993
31 (60420)
28.7
1994
223 (60423)
12
1994
30 (60424)
99.4
aIn the respective 3-year period (12 quarters) for the hourly DV; 9 or more quarters (75%) are required for a DV to be valid
bValid annual DVs require 75% completeness
• Vancouver, Yang et al. (2003) - Respiratory hospital admissions (< 3 yr and > 65 yr),
Canada 1986-1998
- Average NO2 concentrations from 30 monitors from
the British Columbia network (DVs from 7 NAPS
monitors)
Table A-15. Hourly and Annual Design Values
Hourly
Annual
Year
Max DV
Number of
Yea
Max DV (Monitor
Completene
(Monitor ID)
Complete
Quarters"
r
ID)
ss (%)
1986
-
1986
37(100118)
11.3
1987
-
1987
29 (100112)
93.8
1988
104
8 (100120)
1988
33 (100108)
23.7
1989
100
12 (100110)
1989
31 (100112)
94
1990
90
12 (100110)
1990
31 (100112)
92.1
1991
85
12 (100110)
1991
33 (100112)
92.4
1992
230
11(100108)
1992
60 (100112)
89.8
1993
241
8 (100108)
1993
31 (100108)
23.6
1994
322
4(100108)
1994
24 (100112)
97.6
1995
104
1 (100108)
1995
26 (100112)
97.7
1996
66
12 (100121)
1996
27 (100112)
97.8
1997
68
12 (1001120)
1997
29 (100112)
97.5
1998
67
12 (1001120)
1998
29 (100112)
97.4
or Toronto, Canada from 1986-1998.
aIn the respective 3
bValid annual DVs
-year period (12 quarters) for the hourly DV; 9 or more quarters (75%) are required for a DV to be valid
require 75% completeness
A-13
-------
Fung et al. (2006) - Respiratory hospital admissions, 1995-1999
- Average of NO2 concentrations across monitors in
city
-% increase per 30 ppb increment of NO2: 8.6 (4.2,
13.3)
Table A-16. Hourly and Annual Design Values for Toronto, Canada from 1995-1999.
Hourly
Annual
Year
Max DV
(Monitor ID)
Number of
Complete
Quarters"
Yea
r
Max DV (Monitor
ID)
Completene
ss (%)
1995
-
1995
26 (100112)
97.7
1996
-
1996
27 (100112)
97.8
1997
68 (100112)
12
1997
29 (100112)
97.5
1998
67(100112)
12
1998
29 (100112)
97.4
1999
66(100120)
5
1999
32 (100120)
3.4
aIn the respective 3-year period (12 quarters) for the hourly DV; 9 or more quarters (75%) are required for a DV to be valid
bValid annual DVs require 75% completeness
• Multicity Dales et al. (2009) - Pediatric respiratory hospital admissions (0-24 days),
1986-2000
- NO2 concentrations across all monitors in cities
Table A-17. Hourly and Annual Design Values
or 11 Canadian cities from 1986-2000.
Hourly
Annual
Year
Max DV
Number of
Yea
Max DV (Monitor
Completene
(Monitor ID)
Complete
r
ID)
ss (%)
Quarters"
Calgary
1986
-
1986
34 (90227)
97.6
1987
-
1987
34 (90227)
98.9
1988
110(90227)
12
1988
35 (90227)
98.8
1989
105 (90227)
12
1989
35 (90227)
98.9
1990
103 (90227)
12
1990
34 (90227)
94
1991
95 (90227)
12
1991
37 (90227)
98.6
1992
338 (90218)
8
1992
49 (90218)
99
1993
250(90218)
12
1993
31 (90227)
99
1994
254 (90218)
12
1994
29 (90227)
98.9
1995
86 (90227)
12
1995
28 (90227)
98.9
1996
93 (90218)
12
1996
29 (90227)
98.7
1997
98 (90218)
12
1997
30 (90227)
99.4
A-14
-------
1998
106 (90218)
12
1998
31 (90227)
98.5
1999
95 (90218)
12
1999
28 (90227)
97.9
2000
86 (90218)
12
2000
28 (90227)
99.2
Edmonton
1986
-
1986
30 (90130)
95.6
1987
-
1987
31 (90130)
99.5
1988
86 (90130)
12
1988
28 (90130)
98.2
1989
78 (90130)
12
1989
26 (90130)
94.5
1990
80 (90122)
12
1990
27 (90130)
97.9
1991
100(90121)
4
1991
29 (90130)
98.7
1992
243 (90122)
12
1992
36 (90122)
99.5
1993
239 (90122)
12
1993
27 (90130)
95.2
1994
242 (90122)
12
1994
27 (90130)
97.6
1995
87 (90122)
12
1995
27 (90130)
97.1
1996
96 (90122)
12
1996
25 (90130)
97.2
1997
96 (90122)
11
1997
26 (90122)
93.3
1998
100 (90122)
11
1998
27 (90130)
98
1999
86 (90122)
11
1999
24 (90130)
99.4
2000
74 (90122)
12
2000
25 (90130)
98.9
Halifax
1986
-
1986
5 (30115)
97.6
1987
-
1987
19 (30117)
72.6
1988
62 (30117)
6
1988
21 (30117)
65.1
1989
60 (30117)
9
1989
17 (30117)
88
1990
58 (30177)
6
1990
21 (30118)
7.8
1991
62 (30118)
4
1991
22 (30118)
90.8
1992
148 (30118)
8
1992
40 (30118)
93.4
1993
154 (30118)
11
1993
21 (30118)
91.4
1994
148(30118)
10
1994
18 (30118)
86
1995
61 (30118)
10
1995
19 (30118)
82.4
1996
55 (30118)
9
1996
18 (30118)
86.7
1997
58 (30118)
7
1997
#N/A
1998
59 (30118)
7
1998
21 (30118)
96.3
1999
64 (30118)
4
1999
#N/A
2000
59 (30118)
7
2000
18 (30118)
83.5
London
1986
-
1986
26 (60901)
86.1
1987
-
1987
21 (60901)
94.8
1988
73 (60901)
12
1988
20 60901)
97.6
1989
70 (60901)
12
1989
22 (60901)
98.6
1990
70 (60901)
12
1990
21 (60901)
97.9
1991
70 (60901)
12
1991
19(60901)
98.9
1992
113 (60901)
12
1992
13 (60901)
99.2
1993
113 (60901)
12
1993
20(60901)
97.9
1994
113 (60901)
12
1994
23 (60901)
98.7
A-15
-------
1995
70 (60901)
10
1995
22 (60901)
46.5
1996
70 (60901)
6
1996
18 (60903)
98.2
1997
70 (60901)
2
1997
18 (60903)
90.7
1998
59 (60903)
10
1998
18 (60903)
99.4
1999
63 (60903)
10
1999
19 (60903)
96.7
2000
65 (60903)
11
2000
17 (60903)
86.7
Ottawa
1986
-
1986
36 (60101)
96
1987
-
1987
37 (60101)
97.8
1988
93 (60101)
12
1988
34 (60101)
95.4
1989
93 (60101)
12
1989
38 (60101)
97.8
1990
92 (60101)
12
1990
31 (60101)
99
1991
96 (60101)
8
1991
20 (60104)
48.3
1992
263 (60101)
5
1992
72 (60101)
14.3
1993
258 (60101)
5
1993
24 (60101)
98.3
1994
198 (60101)
9
1994
25 (60101)
98.9
1995
78(60101)
12
1995
25 (60101)
95.8
1996
74 (60101)
10
1996
23 (60101)
66.5
1997
70 (60101)
7
1997
25(60101)
15.8
1998
73 (60101)
7
1998
25 (60101)
93.9
1999
83 (60101)
7
1999
33 (60101)
59.5
2000
82 (60101)
10
2000
22 (60101)
90.1
St. John
1986
N/A
1986
N/A
1987
N/A
1987
N/A
1988
N/A
1988
N/A
1989
N/A
1989
N/A
1990
57(10102)
3
1990
15 (10102)
93.1
1991
56(10102)
6
1991
14 (10102)
92.9
1992
99(10102)
9
1992
14 (10102)
88.7
1993
98 (10102)
9
1993
15 (10102)
83.9
1994
96(10102)
7
1994
15 (10102)
21
1995
50(10102)
4
1995
21 (10102)
1.7
1996
48 (10102)
1
1996
N/A
1997
43 (10102)
2
1997
8 (10102)
40.3
1998
32(10102)
6
1998
5 (10102)
93.4
1999
35 (10102)
9
1999
7 (10102)
88.1
2000
36(10102)
10
2000
8(10102)
92.8
Toronto
1986
-
1986
34 (60418)
97
1987
-
1987
32 (60403)
94.6
1988
130 (60422)
3
1988
38 (60422)
56.9
1989
120(60422)
7
1989
33 (60403)
97.9
1990
117(60403)
12
1990
30 (60403)
81.2
1991
110(60403)
12
1991
30 (60422)
91.7
A-16
-------
1992
220 (60423)
12
1992
45 (60422)
95.6
1993
227 (60423)
12
1993
31 (60420)
28.7
1994
223 (60423)
12
1994
30 (60424)
99.4
1995
120 (60420)
1
1995
32 (60425)
13.8
1996
108 (60423)
12
1996
34 (60425)
99.3
1997
103 (60423)
11
1997
35 (60425)
59.3
1998
87 (60423)
11
1998
30 (60403)
97.5
1999
91 (60425)
2
1999
28 (60403)
79.7
2000
89 (60423)
11
2000
30 (60430)
33.1
Vancouver
1986
-
1986
37(100118)
11.3
1987
-
1987
30 (100112)
93.8
1988
104(100120)
8
1988
33 (100108)
23.7
1989
100(100110)
12
1989
31 (100112)
94
1990
90(100110)
12
1990
31 (100112)
92.1
1991
85 (100110)
12
1991
33 (100112)
92.4
1992
230(100108)
11
1992
60 (100112)
89.8
1993
241(100108)
8
1993
31 (100108)
23.6
1994
322(100108)
4
1994
24 (100112)
97.6
1995
104(100108)
1
1995
26 (100112)
97.7
1996
66(100121)
12
1996
27 (100112)
97.8
1997
68 (100112)
12
1997
29 (100112)
97.5
1998
67(100112)
12
1998
29 (100112)
97.4
1999
66(100120)
5
1999
32 (100120)
3.4
2000
67(100120)
1
2000
27 (100112)
97.9
Winnipeg
1986
-
1986
20 (70119)
84.7
1987
-
1987
20 (70119)
87.2
1988
66 (70119)
11
1988
19 (70119)
93.5
1989
60 (70119)
12
1989
19 (70119)
93.9
1990
59(70119)
12
1990
15 (70119)
90.9
1991
57.3 (70118)
12
1991
17 (70119)
93.9
1992
132(70119)
11
1992
14 (70119)
81.7
1993
-
1993
17 (70119)
93
1994
-
1994
17 (70119)
89.4
1995
57(70119)
12
1995
18 (70119)
94.3
1996
55 (70119)
12
1996
18 (70119)
94
1997
60 (70119)
12
1997
18 (70119)
94.3
1998
62 (70119)
12
1998
17 (70119)
93.7
1999
65 (70119)
12
1999
18 (70119)
94
2000
61 (70119)
12
2000
16 (70119)
93.7
Windsor
1986
-
1986
25 (60204)
92.9
1987
-
1987
27 (60204)
88.4
1988
97 (60204)
11
1988
30 (60204)
29.6
A-17
-------
1989
90 (60204)
11
1989
28 (60204)
82.2
1990
87 (60204)
12
1990
25 (60204)
95
1991
83 (60204)
12
1991
25 (60204)
96.4
1992
123 (60204)
12
1992
18 (60204)
98.2
1993
127(60204)
12
1993
26 (60204)
98.7
1994
130 (60204)
12
1994
28 (60204)
99.5
1995
87 (60204)
12
1995
25 (60204)
98.3
1996
79 (60204)
12
1996
26 (60204)
99.3
1997
71 (60204)
12
1997
24 (60204)
94.6
1998
66 (60204)
12
1998
24 (60204)
98.7
1999
67 (60204)
12
1999
23 (60204)
98.2
2000
67 (60204)
12
2000
22 (60204)
97.4
Hamilton
1986
N/A
1986
27 (60501)
91.8
1987
N/A
1987
30 (60501)
14.6
1988
80 (60515)
11
1988
25 (60511)
95.4
1989
77 (60515)
11
1989
26(60512)
98.9
1990
73 (60515)
12
1990
22 (60512)
98.6
1991
70 (60515)
12
1991
22 (60512)
98.8
1992
160(60515)
12
1992
28 (60515)
98.7
1993
163 (60515)
11
1993
23 (60511)
98.2
1994
163 (60515)
11
1994
23 (60511)
94.2
1995
73 (60515)
10
1995
24 (60511)
98.6
1996
67 (60515)
11
1996
22 (60511)
89.8
1997
66 (60515)
11
1997
19 (60511)
77
1998
68 (60515)
12
1998
23 (60515)
79.6
1999
78 (60511)
8
1999
28 (60511)
74.5
2000
80 (60511)
9
2000
23 (60511)
90
aIn the respective 3-year period (12 quarters) for the hourly DV; 9 or more quarters (75%) are required for a DV to be valid
bValid annual DVs require 75% completeness
• Multicity Cakmak et al. - Respiratory hospital admissions, April 1993- March
2011 2000
- NO2 concentrations across all monitors in cities
Table A-18. Hourly and Annual Design Values for 10 Canadian Cities from 1993-2000.
Year
Max DV (Monitor ID)
Year
Max DV
(Monitor ID)
Number of
Complete
Quarters3
Yea
r
Max DV (Monitor
ID)
Completene
ss (%)
Calgary
1993
-
1993
31 (90227)
99
A-18
-------
1994
-
1994
29 (90227)
98.9
1995
86 (90227)
12
1995
28 (90227)
98.9
1996
93 (90218)
12
1996
29 (90227)
98.7
1997
98 (90218)
12
1997
30 (90227)
99.4
1998
106(90218)
12
1998
31 (90227)
98.5
1999
95 (90218)
12
1999
28 (90227)
97.9
2000
86 (90218)
12
2000
28 (90227)
99.2
Edmonton
1993
-
1993
27 (90130)
95.2
1994
-
1994
27 (90130)
97.6
1995
87 (90122)
12
1995
27 (90130)
97.1
1996
96 (90122)
12
1996
25 (90130)
97.2
1997
96 (90122)
11
1997
26 (90130)
93.3
1998
100 (90122)
11
1998
27 (90130)
98
1999
86 (90122)
11
1999
24 (90130)
99.4
2000
74 (90122)
12
2000
25 (90130)
98.9
Halifax
1993
-
1993
21 (30118)
91.4
1994
-
1994
18 (30118)
86
1995
61 (30118)
10
1995
19 (30118)
82.4
1996
55 (30118)
9
1996
18 (30118)
86.7
1997
58 (30118)
7
1997
N/A
#N/A
1998
59 (30118)
7
1998
21 (30118)
96.3
1999
64 (30118)
4
1999
N/A
#N/A
2000
59 (30118)
7
2000
18 (30118)
83.5
London
1993
-
1993
20 (60901)
97.9
1994
-
1994
23 (60901)
98.7
1995
70 (60901)
10
1995
22 (60901)
46.5
1996
70 (60901)
6
1996
18 (60903)
98.2
1997
70 (60901)
2
1997
18 (60903)
90.7
1998
59 (60903)
10
1998
18 (60903)
99.4
1999
63 (60903)
10
1999
20 (60903)
96.7
2000
65 (60903)
11
2000
17 (60903)
86.7
Ottawa
1993
-
1993
24 (60101)
98.3
1994
-
1994
25(60101)
98.9
1995
78 (60101)
12
1995
25 (60101)
95.8
1996
74 (60101)
10
1996
23 (60101)
66.5
1997
70 (60101)
7
1997
25 (60101)
15.8
1998
73 (60101)
7
1998
25 (60101)
93.9
1999
83 (60101)
7
1999
33 (60101)
59.5
2000
82 (60101)
10
2000
22 (60101)
90.1
St. John
1993
-
1993
15.2(10102)
83.9
A-19
-------
1994
-
1994
15.3 (10102)
21
1995
50 (10102)
4
1995
21.3 (10102)
1.7
1996
48 (10102)
1
1996
N/A
N/A
1997
43 (10102)
2
1997
8.2(10102)
40.3
1998
32 (10102)
6
1998
4.8 (10102)
93.4
1999
35 (10102)
9
1999
6.6(10102)
88.1
2000
36 (10102)
10
2000
8.2(10102)
92.8
Toronto
1993
-
1993
31 (60420)
28.7
1994
-
1994
30 (60424)
99.4
1995
120 (60420)
1
1995
32 (60425)
13.8
1996
108 (60423)
12
1996
34 (60425)
99.3
1997
103 (60423)
11
1997
35 (60425)
59.3
1998
87 (60423)
11
1998
30 (60403)
97.5
1999
91 (60425)
2
1999
28 (60403)
79.7
2000
89 (60423)
11
2000
30 (60430)
33.1
Vancouver
1993
-
1993
31(100108)
23.6
1994
-
1994
24 (100112)
97.6
1995
104 (100108)
1
1995
26 (100112)
97.7
1996
66 (100121)
12
1996
27 (100112)
97.8
1997
68 (100112)
12
1997
29 (100112)
97.5
1998
67 (100112)
12
1998
29 (100112)
97.4
1999
66 (100120)
5
1999
32 (100120)
3.4
2000
67(100120)
1
2000
27 (100112)
97.9
Winnipeg
1993
-
1993
17 (70119)
93
1994
-
1994
17 (70119)
89.4
1995
57 (70119)
12
1995
18 (70119)
94.3
1996
55 (70119)
12
1996
18 (70119)
94
1997
60 (70119)
12
1997
187 (70119)
94.3
1998
62 (70119)
12
1998
17 (70119)
93.7
1999
65 (70119)
12
1999
18 (70119)
94
2000
61 (70119)
12
2000
16 (70119)
93.7
Windsor
1993
-
1993
26 (60204)
98.7
1994
-
1994
28 (60204)
99.5
1995
87 (60204)
12
1995
25 (60204)
98.3
1996
79 (60204)
12
1996
26 (60204)
99.3
1997
71 (60204)
12
1997
24 (60204)
96.4
1998
66 (60204)
12
1998
24 (60204)
98.7
199
68 (60204)
12
1999
23 (60204)
98.2
2000
67 (60204)
12
2000
22 (60204)
97.4
A-20
-------
2.3 Design Values In Locations For Long-Term Epidemiologic Studies Of
Asthma Incidence In Children
• Vancouver, Carlsten et al. - LUR used to estimate annual concentrations at birth
British 2011 residential address (birth year exposure) for each
Columbia subject; Air pollution estimates for 1995 generated
from 2003 annual averages; Asthma assessed at 7 yrs
-The 1-hour design value for this study encompasses
years before the study period (i.e. 1993 and 1994)
Table A-19. Hourly and Annual Design Values
'or Vancouver, British Columbia in 1995.
Hourly
Annual
Year
Max DV
(Monitor ID)
Number of
Complete
Quarters"
Yea
r
Max DV (Monitor
ID)a
Completene
ss (%)b
1995
104(100108)
1
1995
26(100112)
97.7
aIn the respective 3-year period (12 quarters) for the hourly DV; 9 or more quarters (75%) are required for a DV to be valid
bValid annual DVs require 75% completeness
• British Clark et al. (2010) - LUR, central site monitors, and IDW used for postal
Columbia code level exposure assignment for duration of
pregnancy and first year of life (1999-2000); Asthma
assessed at 36-59 mos
- Methods from manuscript indicated that exposure
measures were collected from 22 monitors for NO
and NO2, but only 7 monitors from the NAPS
Canadian database had data for use in our approach.
-The 1-hour design values for this study encompass
years before the study period.
Table A-20. Hourly and Annual Design Values for Vancouver, British Columbia from
1999-2000.
Hourly
Annual
Year
Max DV
Number of
Yea
Max DV (Monitor
Completene
(Monitor ID)
Complete
Quarters"
r
ID)
ss (%)b
1999
66(100120)
5
1999
32(100120)
3.7
2000
67(100120)
1
2000
27(100112)
97.9
aIn the respective 3-year period (12 quarters) for the hourly DV; 9 or more quarters (75%) are required for a DV to be valid
bValid annual DVs require 75% completeness
A-21
-------
• Boston Clougherty et al. - LUR model based on 1 week of monitoring per
(2007) month from 1987-2004 at 28 sampling sites was used
to assign residential exposure for exposure in year of
asthma diagnosis; Mean age of asthma diagnosis 6.8
yrs
- Cohort from East Boston; AQS monitor 250250021
was used as representative based on study map
Table A-21. Hourly and Annual Design Values
Hourly
Annual
Year
Max DV
(Monitor ID)
Number of
Complete
Quarters"
Yea
r
Max DV (Monitor
ID)a
Completene
ss (%)b
1987
-
1987
37 (250250021)
92.1
1988
-
1988
33 (250250021)
94.2
1989
91 (250250021)
12
1989
32 (250250021)
98.6
1990
90 (250250021)
12
1990
32 (250250021)
97.8
1991
84 (250250021)
12
1991
32 (250250021)
97.2
1992
81 (250250021)
12
1992
30 (250250021)
96.6
1993
79 (250250021)
12
1993
32 (250250021)
97.2
1994
79 (250250021)
11
1994
30 (250250021)
87.5
1995
76 (250250021)
11
1995
27 (250250021)
96.7
1996
83 (250250021)
11
1996
28 (250250021)
97
1997
82 (250250021)
12
1997
27 (250250021)
97.1
1998
83 (250250021)
12
1998
28 (250250021)
95.6
1999
75 (250250021)
11
1999
27 (250250021)
85.5
2000
72 (250250021)
11
2000
22 (250250021)
86.2
2001
64 (250250021)
10
2001
21 (250250021)
89
2002
60(250250021)
10
2002
23 (250250021)
82.8
2003
54 (250250021)
6
2003
32 (250250021)
0
2004
54 (250250021)
3
2004
N/A
or East Boston from 1987-1994.
aIn the respective 3-year period
bValid annual DVs require 75%
12 quarters) for the hourly DV; 9 or more quarters (75%) are required for a DV to be valid
completeness
• Multicity Nishimura et al. - IDW with 4 closest monitors within 50 km of
(San (2013) residence used for exposure assignment
Francisco, - AQS study monitors identified by visual
Houston, approximation from study maps
Chicago,
Bronx, Puerto
Rico)
Table A-22. Hourly and Annual Design Values for 5 U.S. Cities from 1986-2008.
A-22
-------
Hourly
Annual
Year
Max DV
(Monitor ID)
Number of
Complete
Quarters"
Yea
r
Max DV (Monitor
ID)
Completene
ss (%)b
Chicago, IL
1986
-
1986
42(170310040)
95.6
1987
-
1987
43 (170310040)
76.6
1988
131 (170310053)
0
1988
34(170310063)
38.9
1989
180(170310053)
0
1989
34(170310039)
96.6
1990
94(170310039)
12
1990
31 (170310039)
98.5
1991
88 (170310039)
12
1991
32(170310039)
98.4
1992
84(170310039)
12
1992
30(170310039)
89.3
1993
82(170310039)
12
1993
31 (170310063)
89.2
1994
82(170310063)
11
1994
34(170310039)
62.8
1995
83 (170310063)
12
1995
32(170310063)
98.4
1996
87(170310039)
3
1996
31 (170310063)
95.7
1997
88 (170310063)
12
1997
34(170310063)
97.5
1998
89(170310037)
3
1998
32(170310063)
95.3
1999
88 (170310063)
12
1999
32(170310063)
98.4
2000
87(170310063)
12
2000
32(170310063)
94.7
2001
86.3
(170310063)
12
2001
32(170310063)
98.7
2002
90(170310063)
12
2002
32(170310063)
98.8
2003
87(170310063)
12
2003
31 (170310063)
96.9
2004
86(170310063)
12
2004
29(170310063)
96.6
2005
83 (170310063)
12
2005
30(170310063)
95.7
Houston, TX
1986
-
1986
28 (482011037)
57
1987
-
1987
30(482011037)
93
1988
110(482011037)
10
1988
28 (482011037)
91
1989
110(482011037)
12
1989
28 (482011037)
84.3
1990
107 (482011037)
11
1990
29 (482011037)
71.3
1991
103 (482011037)
11
1991
28 (482011037)
85.6
1992
97 (482011037)
11
1992
28 (482011037)
80.3
1993
-
1993
24 (482011037)
81.6
1994
-
1994
28 (482011037)
82.6
1995
86 (482011037)
10
1995
26 (482011037)
76.4
1996
81 (482011037)
10
1996
23 (482011037)
87.2
1997
79 (482010047)
11
1997
25 (482011037)
81.3
1998
79 (482010047)
11
1998
23 (482011035)
87.4
1999
80(482010047)
10
1999
24 (482010047)
83.2
2000
76 (482010047)
10
2000
21 (482011037)
93.2
2001
79 (482011037)
9
2001
29 (482011037)
22.1
2002
82 (482011037)
5
2002
18 (482010047)
88.2
2003
86 (482011037)
1
2003
18 (482010047)
96
A-23
-------
2004
62 (482010047)
12
2004
19(482011034)
96.7
2005
62 (482010047)
12
2005
18 (482010047)
92.9
New York, NY
1986
-
1986
49 (360610056)
68.6
1987
-
1987
43 (360610056)
40.7
1988
129 (360610056)
6
1988
44 (360610056)
44.5
1989
131 (360610056)
7
1989
49(360610056)
94.3
1990
128 (360610056)
10
1990
46 (360610056)
96
1991
121(360610056)
10
1991
47 (360610056)
96
1992
113 (360610056)
7
1992
36(360610056)
59.4
1993
110(360610056)
6
1993
43 (360610056)
87.1
1994
114(360610056)
7
1994
46 (360610056)
86.6
1995
113 (360610056)
10
1995
42 (360610056)
94.4
1996
110(360610056)
11
1996
42 (360610056)
86.1
1997
99 (360610056)
12
1997
40(360610056)
94.6
1998
96 (360050080)
11
1998
40 (360610056)
91.2
1999
95 (360050080)
11
1999
41 (360610056)
97
2000
94 (360050073)
1
2000
38 (360610056)
95.5
2001
94 (360050073)
1
2001
38 (360610056)
39.1
2002
96 (360610010)
5
2002
38 (360610010)
95.5
2003
105 (360610010)
1
2003
38 (360610056)
58.5
2004
79(360050110)
11
2004
35 (360610056)
62.6
2005
78 (360050110)
11
2005
37(360610056)
97.3
San Francisco, CA
1986
-
1986
24 (060410001)
98.5
1987
-
1987
24 (060750005)
97.3
1988
90 (060750005)
12
1988
26 (060750005)
96.5
1989
93 (060750005)
10
1989
26(060750005)
87.9
1990
87(060750005)
10
1990
21 (060750005)
88.3
1991
87(060750005)
10
1991
24 (060750005)
99.1
1992
83 (060750005)
11
1992
22 (060750005)
97.5
1993
77 (060750005)
12
1993
24 (060750005)
93.1
1994
74 (060750005)
12
1994
22 (060750005)
96.8
1995
70 (060750005)
12
1995
21 (060750005)
97.2
1996
70 (060750005)
12
1996
22 (060750005)
96.8
1997
65 (060750005)
12
1997
20 (060750005)
93.6
1998
63 (060750005)
12
1998
20 (060750005)
95.1
1999
63 (060750005)
12
1999
21 (060750005)
94.6
2000
64 (060750005)
12
2000
20 (060750005)
95
2001
65 (060750005)
12
2001
19 (060750005)
95
2002
61 (060750005)
12
2002
19 (060750005)
93.3
2003
60 (060750005)
12
2003
18 (060750005)
94.3
2004
57 (060750005)
12
2004
17 (060750005)
92.3
2005
55 (060750005)
12
2005
16 (060750005)
94.9
Puerto Rico
A-24
-------
1986
N/A
1986
N/A
1987
N/A
1987
N/A
1988
N/A
1988
N/A
1989
N/A
1989
N/A
1990
N/A
1990
N/A
1991
N/A
1991
N/A
1992
N/A
1992
N/A
1993
N/A
1993
N/A
1994
N/A
1994
N/A
1995
N/A
1995
N/A
1996
N/A
1996
N/A
1997
83(720330006)
0
1997
20 (720330006)
16.4
1998
83 (720330006)
1
1998
12 (720330006)
64.8
1999
67 (720330006)
2
1999
7.3 (720330006)
66.6
2000
80 (720330006)
4
2000
18 (720330006)
75.5
2001
71 (720330006)
6
2001
9 (721270009)
87.5
2002
68 (720330006)
8
2002
7 (721270009)
92.2
2003
41 (720330006)
6
2003
7 (721270009)
60.2
2004
29 (720330008)
0
2004
11 (720330008)
18.1
2005
32 (720330008)
2
2005
9 (721270009)
72.2
bIn the respective 3-year period (12 quarters) for the hourly DV; 9 or more quarters (75%) are required for a DV to be valid
cValid annual DVs require 75% completeness
• Southern CA McConnell et al. - Community central site monitors and line source
(2010) dispersion models used for residential and school
NOx; Kindergarten and 1st grade children (4.8-9 yrs)
enrolled in 2002-2003 and followed for 3 yrs (2002-
2006)
- Models include TRP show independent, significant
association for TRP and attenuation of NO2 effect
-DVs were identified for Santa Maria, Santa Barbara,
Alpine, Los Angeles CBS A, and Riverside CBS A.
Several study communities were located in the Los
Angeles CBS A (Long Beach, Anaheim, Mira Loma,
and Glendora) and several others in the Riverside
CBS A (San Dimas, Upland, Lake Arrowhead, San
Bernardino, Riverside, and Lake Elsinore). The high
DVs for each year were taken for these CBS As rather
than the study communities due to their close
proximity and inability to determine specific
community monitors.
Table A-23. Hourly and Annual Design Values for Southern California Communities for
2002-2006.
A-25
-------
Hourly
Annual
Year
Max DV
Number of
Yea
Max DV (Monitor
Completene
(Monitor ID)
Complete
r
ID)a
ss (%)b
Quarters"
Santa Barbara, CA
2002
-
2002
N/A
2003
-
2003
15 (60830011)
36.1
2004
52 (60830011)
6
2004
13 (60830011)
92.8
2005
50(60830011)
10
2005
12(60830011)
95.1
2006
48 (60830011)
11
2006
11 (60830011)
55.1
Santa Maria, CA
2002
-
2002
11 (60831008)
43.2
2003
-
2003
11 (60831008)
92.7
2004
42(60831008)
10
2004
10 (60831008)
85.2
2005
41 (60831008)
12
2005
10(60831008)
94.5
2006
37(60831008)
10
2006
8(60831008)
55.1
Alpine, CA
2002
-
2002
13(60731006)
93.5
2003
-
2003
14(60731006)
87.1
2004
54 (060731006)
11
2004
12(60731006)
94.1
2005
51 (060731006)
11
2005
11 (60731006)
92.2
2006
46 (060731006)
12
2006
10(60731006)
94
Los Angeles-Long Beach, CA
2002
-
2002
40 (60371002)
93.2
2003
-
2003
35 (60371002)
90.7
2004
121(60370030)
2
2004
34(60371103)
85.4
2005
101 (60371103)
11
2005
31 (60371301)
93.5
2006
92 (60371602)
2
2006
31 (60371301)
94.1
Riverside, CA
2002
-
2002
36(60711004)
94.5
2003
-
2003
34(60711004)
94.8
2004
95 (60711004)
12)
2004
31 (60711004)
94.8
2005
92(60711004)
12
2005
31 (60711004)
94.7
2006
98 (60711004)
12
2006
31 (60711004)
88
aIn the respective 3-year period (12 quarters) for the hourly DV; 9 or more quarters (75%) are required for a DV to be valid
bValid annual DVs require 75% completeness
Jerrett et al. (2008) - LUR model based on Palmes tubes outside homes for 2 weeks in
summer and winter used for exposure assessment; children were
enrolled at age 10 in 1993 or 1996 and followed for 8 yrs or until
high school graduation
-DVs were identified for Santa Maria, Lompoc, Santa Barbara,
Alpine, Atascadero, Los Angeles CBS A, and Riverside CBS A.
Several study communities were located in the Los Angeles CBS A
A-26
-------
(Long Beach, Anaheim, Mira Loma, and Glendora) and several
others in the Riverside CBS A (San Dimas, Upland, Lake
Arrowhead, San Bernardino, Riverside, and Lake Elsinore). The
high DVs for each year were taken for these CBS As rather than the
study communities due to their close proximity and inability to
determine specific community monitors.
Table A-24. Hourly and Annual Design Values for Southern California Communities from
1993-2004.
Hourly
Annual
Year
Max DV
(Monitor ID)a
Number of
Complete
Quartersb
Yea
r
Max DV (Monitor
ID)a
Completene
ss (%)c
Santa Maria
1993
-
1993
7(060831010)
93.3
1994
-
1994
7(060831010)
93.3
1995
45(060831007)
4
1995
12 (060831007)
92.6
1996
45 (060831007)
7
1996
12 (060831007)
88.5
1997
45 (060831007)
11
1997
13 (060831007)
95.1
1998
43 (060831007)
11
1998
12 (060831007)
93.6
1999
44 (060831007)
11
1999
11 (060831007)
89.7
2000
42 (060831007)
8
2000
13 (060831007)
14.6
2001
43 (060831007)
4
2001
15 (060831008)
0.1
2002
37(060831008)
6
2002
11(060831008)
43.2
2003
38 (060831008)
6
2003
11 (060831008)
92.7
2004
42 (060831008)
10
2004
10 (060831008)
85.2
Lompoc, CA
1993
-
12
1993
9 (060832004)
92
1994
-
12
1994
9 (060832004)
92.3
1995
39(060831013)
12
1995
7(060832004)
93.5
1996
36(060831013)
12
1996
7 (060832004)
91.8
1997
36(060831013)
12
1997
7 (060832004)
90.7
1998
35 (060831013)
12
1998
7 (060832004)
92.5
1999
35 (060831013)
12
1999
7 (060832004)
93.4
2000
33 (060831013)
12
2000
6 (060832004)
92.1
2001
33 (060831013)
12
2001
6 (060832004)
93.7
2002
31 (060831013)
11
2002
4 (060832004)
79.8
2003
32(060831013)
11
2003
6 (060832004)
92.9
2004
31 (060831013)
11
2004
6 (060832004)
93.3
Riverside, CA
1993
-
1993
42 (060711004)
94.9
1994
-
1994
41 (060711004)
95
1995
140 (060711004)
12
1995
46 (060711004)
95.1
1996
137(060711004)
11
1996
38 (060711004)
77.1
1997
127 (060711004)
11
1997
36 (060712002)
90.6
A-27
-------
1998
112(060711004)
11
1998
36 (060712002)
95.5
1999
111 (060650012)
11
1999
39 (060711004)
95.8
2000
134 (060650012)
12
2000
38 (060711004)
95.9
2001
142 (060650012)
12
2001
37 (060711004)
95.7
2002
135(060650012)
12
2002
36 (060711004)
94.5
2003
107 (060650012)
12
2003
34 (060711004)
94.8
2004
95 (060711004)
12
2004
31 (060711004)
94.8
Los Angeles-Long Beach, CA
1993
-
1993
50 (060371701)
92.8
1994
-
1994
48 (060371701)
94
1995
149 (060371701)
12
1995
46 (060711004)
95.1
1996
145 (060374002)
12
1996
42 (060371701)
94.2
1997
140(060374002)
12
1997
43 (060371701)
95.4
1998
129 (060374002)
12
1998
43 (060371701)
94.9
1999
127 (060371701)
12
1999
51 (060371701)
95.4
2000
125 (060371701)
12
2000
44 (060371701)
95.8
2001
116(060371701)
12
2001
37 (060371701)
95.7
2002
106 (060374002)
12
2002
36(060371701)
95
2003
104 (060374002)
12
2003
35 (060371701)
95.2
2004
105 (060374002)
12
2004
31 (060371701)
94.2
Alpine, CA (San Diego CBS A)
1993
-
1993
14 (060731006)
97.7
1994
-
1994
13 (060731006)
96.2
1995
61 (060731006)
12
1995
13 (060731006)
97.8
1996
61 (060731006)
12
1996
12 (060731006)
94.5
1997
60 (060731006)
11
1997
11 (060731006)
61.9
1998
54 (060731006)
11
1998
12 (060731006)
93.2
1999
57(060731006)
11
1999
15 (060731006)
91.3
2000
59(060731006)
12
2000
15 (060731006)
83.4
2001
59(060731006)
12
2001
14 (060731006)
94.8
2002
56(060731006)
12
2002
13 (060731006)
93.5
2003
55(060731006)
11
2003
14(060731006)
87.1
2004
54 (060731006)
11
2004
12 (060731006)
94.1
Atascadero, CA (San Luis Obispo CBSA)
1993
-
1993
14 (060798001)
95.1
1994
-
1994
14 (060798001)
93
1995
55 (060798001)
12
1995
12 (060798001)
94.4
1996
52 (060798001)
12
1996
12 (060798001)
94.5
1997
52 (060798001)
12
1997
12 (060798001)
92.3
1998
51 (060798001)
12
1998
11 (060798001)
94.9
1999
56 (060798001)
12
1999
14 (060798001)
93.8
2000
55 (060798001)
12
2000
12 (060798001)
93.6
2001
54 (060798001)
12
2001
11 (060798001)
87.5
2002
51 (060798001)
12
2002
11 (060798001)
92.2
2003
50 (060798001)
12
2003
9 (060798001)
95.2
A-28
-------
2004
48 (060798001)
12
2004
8 (060798001)
95.1
aIn the respective 3-year period (12 quarters) for the hourly DV; 9 or more quarters (75%) are required for a DV to be valid
bValid annual DVs require 75% completeness
3. DISTRIBUTIONS OF DAILY MAXIMUM 1-HOUR NOi CONCENTRATIONS
FOR LOCATIONS OF SHORT-TERM EPIDEMIOLOGIC STUDIES
Table A-25: Distribution of daily maximum 1-hour concentrations of NO2 (ppb) for
locations of U.S. epidemiologic studies of short-term hospital admissions and emergency
U.S. Study Locations (years)
Atlanta (1993-2000)
5-*
o
o
-------
insignificant. If the p-value was less than or equal to 0.05, then the sign of the r-value was used
to determine the direction of the trend: positive for increasing and negative for decreasing.
5. EVALUATION OF ROADWAY GRADIENTS OF NOi CONCENTRATIONS
FROM 1980-2015
Figures 2-6was generated by plotting boxplots of NO2 DVs from urban areas as a function
of bins of distances in meters from the nearest major road. A monitor was considered "Urban" if
it resided inside the boundary of a core-based statistical area (CBSA) as defined by the U.S.
Census Bureau in 2014 (https://www.census.gov/geo/maps-data/data/tiger-line.html). These
distances were determined using 2012 data from the Highway Performance Monitoring System
(HPMS) created by the Federal Highway Administration
(http://www.fhwa.dot.gov/policyinformation/hpms/shapefiles.cfm). The R statistical program
(https://www.r-project.org/) and the gDistance() function in the rgeos package (https://CRAN.R-
project.org/package=rgeos) were used calculate the distances between monitor long/lat points
and the nearest road in the HPMS shapefile. (sentence about the HPMS file is all "major" roads).
The distances and DV datasets were then merged together by monitors and input to graphical
commands to produce the boxplots in Figure 2-6. All DVs, both valid and invalid according the
CFR completeness criteria, were included in the boxplots to more robustly explore the physical
relationship between N02 concentrations and distance from vehicular sources.
6. COMPARISON OF DISTRIBUTION OF NOi CONCENTRATIONS FROM
NEAR-ROAD MONITORS AND NON-NEAR-ROAD MONITORS FOR 2013-2015
Figures 2-7, 2-8, 2-9 and 2-10 were created by calculating the 10th, 25th, 50th, 75th, 90th,
98th, and 99th percentiles of annual NO2 concentrations at each monitor in a CBSA that contained
a monitor in the EPA Near Road Network. No consideration was given to data completeness for
these calculations. If more than one monitor of each given type (i.e. near road or non-near road)
was present in a CBSA, only data from the monitor with the highest 98111 percentile of that type
was included. The above percentiles were then graphed as the boxplots specific to each CBSA,
road type and year as shown in Figures 2-7 through 2-10.
7. RELATIONSHIP BETWEEN 1-HOUR AND ANNUAL NO2 DESIGN VALUES
Figure 2-11 was generated by plotting DVs from monitors and years where both the annual and
hourly DV was valid according to the CFR completeness criteria. Regression statistics (slope,
intercept and R2) were calculated using the lm() function included in the R statistical package.
A-30
-------
8. RELATIONSHIP BETWEEN ANNUAL AVERAGES OF THE NEAR-ROAD
AND NON-NEAR-ROAD MAXIMUM 1-HR DAILY NOi CONCENTRATIONS
FROM 2013-2016
In Figure A-l, we examine how the relationship between hourly and annual NO2 concentrations,
based on 1-hour and annual DVs, has changed since 1980.
A-31
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APPENDIX B
NITROGEN DIOXIDE AIR QUALITY CHARACTERIZATION
Table of Contents
Bl. INTRODUCTION 1-1
B2. APPROACH 2-1
2.1 Air Quality Benchmark Levels 2-1
2.2 Ambient Monitoring Data 2-1
2.3 Study Areas 2-2
2.3.1 Selection approach 2-2
2.3.2 Representativeness evaluation 2-9
2.3.3 Ambient monitor attributes 2-19
2.4 Estimated Ambient NO2 Concentrations 2-19
2.4.1 Adjusting air quality to just meet the existing standards 2-19
2.4.2 Simulating on-road concentrations 2-37
2.4.3 Calculating number of days at or above benchmark levels 2-39
B3, RESULTS 3-1
3.1 Analysis of Historical (1980-2015) Air Quality 3-1
3.2 Analysis of Recent Air Quality (2010-2015) in Selected Study Areas 3-4
3.3 Uncertainty Characterization 3-12
B4. REFERENCES 4-1
B5, SUPPLEMENTAL DATA 5-1
5.1 CBSA Ranked NOx Emission Tables for Top 20 Facility Types 5-1
5.2 Attributes of Ambient NO2 Monitors Used for the 2010-2015 Analysis 5-11
5.3 Attributes of Historical (1990-2009) or Other Ambient NO2 Monitors Not Used for
the 2010-2015 Analysis 5-76
5.4 Upper (>98th) Percentile DM1H Concentration Ratios: Comparison of Area Design
Value Monitor to all Area-wide Monitors by Study Area 5-112
5.5 Number of Days per Year NO2 Concentrations were at or Above 1-Hour
Benchmark Levels: Site-Year Summary Tables (2010 - 2015) 5-115
5.6 Number of Days NO2 Concentrations were at or Above 1-Hour Benchmark Levels:
Simulated On-Road Concentrations (2014-2015 only) 5-119
B-i
-------
5.7 Number (and Percent) of Days per Year NO2 Concentrations were at or Above 1-
Hour Benchmark Levels: CBSA-Wide Summary Tables (2010 - 2015) 5-129
5.8 Data for Individual Near-Road Monitors 2014 and 2015: As Is and Adjusted to Just
Meet the Existing Standard 5-139
5.9 Comparison of Current Results with 2008 REA 5-141
B-ii
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List of Figures
Figure B2-1. The 40 CBSAs identified as potentially useful to inform the air quality
characterization, including the 23 selected study areas for focused analyses 2-9
Figure B2-2. The top 100 CBSAs ranked by 2013 population. Highlighted and named are the 23
selected study areas; also named are those CBSAs ranked in the top 20 2-11
Figure B2-3. CBSAs ranked by total NOx emissions. Highlighted and named are the 23 selected
study areas; also named are those CBSA ranked in the top 20 2-14
Figure B2-4. CBSAs ranked by mobile source NOx emissions. Highlighted and named are the 23
selected study areas; also named are those CBSA ranked in the top 20 2-14
Figure B2-5. CBSAs ranked by percent all mobile source NOx emissions. Highlighted and
named are the 23 selected study areas; also named are those CBSA ranked in the
top 20 2-15
Figure B2-6. CBSAs ranked by NOx emissions from electricity generation via combustion.
Highlighted and named are the 23 selected study areas; also named are CBSA
ranked in the top 20 2-17
Figure B2-7. CBSAs ranked by NOx emissions from airports. Highlighted and named are the 23
selected study areas; also named are CBSA ranked in the top 20 2-18
Figure B2-8. CBSAs ranked by NOx emissions from chemical plants. Highlighted and named
are 11 of the 23 selected study areas (all other study areas had no emissions for this
facility type); also named are CBSA ranked in the top 20 2-18
Figure B2-9. Distribution of DMIHNO2 concentrations (0 - 100th percentile) for a high-
concentration year (1980s) versus a low-concentration year (2000s) adapted from
Rizzo (2008) (left panel) and updated comparison with a recent low-concentration
year (right panel). Atlanta (top panel), New York/New Jersey (middle panel), and
Philadelphia (bottom panel) study areas 2-21
Figure B2-10. Distribution of DMIHNO2 concentrations (0 - 100th percentile) for a high-
concentration year (1980s) versus a low-concentration year (2000s) adapted from
Rizzo (2008) (left panel) and updated comparison with a recent low-concentration
year (right panel). Chicago (top panel), Denver (middle panel), and Los Angeles
(bottom panel) study areas 2-22
Figure B2-11. Distribution of unadjusted (as is) ambient NO2 concentrations, that adjusted using
a proportional factor alone (all proportional), and that adjusted using a combined
proportional factor and ratio approach (proportional to 98th percentile, non-linear
above) in the Philadelphia study area at monitor ID 421010004 across a three-year
averaging period (2011-2013) 2-36
Figure B2-12. Predicted and observed NO2 concentrations for winds from the west using based
on data from a Las Vegas NV near-road measurement study. Predicted median
(solid), predicted 98th and 2nd percentile (dotted), observed median (circles), and
observed 98th and 2nd percentiles (error bars) are shown 2-39
Figure B3-1. The mean (left panel) and maximum (right panel) number of days per year where
DMIHNO2 concentration was > 100 ppb (top panel), > 150 ppb (middle panel),
and > 200 ppb (bottom panel) associated with 3-year average 98th percentile DM1H
NO2 concentrations, using valid-year 1980-2015 ambient monitor data 3-3
Figure B3-2. Mean and maximum number of days per year where DM1H NO2 concentration at
or above 100 ppb: 2010-2015 area-wide monitor site-year summary 3-7
B-iii
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Figure B3-3. Mean and maximum number of days per year where DM1H NO2 concentration at
or above 100 ppb: 2010-2015 near-road monitor site-year summary 3-8
Figure B3-4. Number of days in the year where DM1H NO2 concentration at or above 100 ppb:
simulated on-road concentrations (2014) based on near-road monitor data and using
three simulation factors 3-9
Figure B3-5. Number of days in the year where DM1H NO2 concentration at or above 100 ppb:
simulated on-road concentrations (2015) based on near-road monitor data and using
three simulation factors 3-10
Figure B3-6. Mean and maximum number of days per year where DM1H NO2 concentration at
or above 100 ppb at any site: 2010-2015 CBSA-wide summary using area-wide and
near-road monitors 3-15
Figure B3-7. Mean and maximum number of days per year where DM1H NO2 concentration at
or above 100 ppb at more than one location on the same day: 2010-2015 CBSA-
wide summary using area-wide and near-road monitors 3-16
Figure B3-8. Mean and maximum number of days per year where DM1H NO2 concentration at
or above 100 ppb: CBSA-wide summary using area-wide and near-road monitors
(top panels), using area-wide, near-road, and on-road (bottom panels), at any
location (left panels), at more than one location on the same day (right panels). 3-17
Figure B5-1. Map of all monitors (1990-2015) in the Atlanta study area (top panel) and satellite
views of near-road (middle panels) and area design value monitor (bottom panel)..
5-13
Figure B5-2. Map of all monitors (1990-2015) in the Baltimore study area (top panel) and
satellite views of near-road (middle panel) and area design value monitor (bottom
panel) 5-15
Figure B5-3. Map of all monitors (1990-2015) in the Boston study area (top panel) and satellite
views of near-road (middle panel) and area design value monitor (bottom panels)..
5-17
Figure B5-4. Map of all monitors (1990-2015) in the Chicago study area (top panel) and satellite
views of near-road (middle panel) and area design value monitor (bottom panel)....
5-20
Figure B5-5. Map of all monitors (1990-2015) in the Dallas study area (top panel) and satellite
views of near-road (middle panels) and area design value monitor (bottom panels).
5-22
Figure B5-6. Map of all monitors (1990-2015) in the Denver study area (top panel) and satellite
views of near-road (middle panels) and area design value monitor (bottom panels).
5-26
Figure B5-7. Map of all monitors (1990-2015) in the Detroit study area (top panel) and satellite
views of near-road (middle panels) and area design value monitor (bottom panel)..
5-28
Figure B5-8. Map of all monitors (1990-2015) in the Houston study area (top panel) and satellite
views of near-road (middle panels) and area design value monitor (bottom panel).
5-30
Figure B5-9. Map of all monitors (1990-2015) in the Kansas City study area (top panel) and
satellite views of near-road (middle panel) and area design value monitor (bottom
panel) 5-34
B-iv
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Figure B5-10. Map of all monitors (1990-2015) in the Los Angeles study area (top panel) and
satellite views of near-road (middle panels) and area design value monitor (bottom
panel) 5-36
Figure B5-11. Map of all monitors (1990-2015) in the Miami study area (top panel) and satellite
views of near-road (middle panels) and area design value monitor (bottom panels).
5-40
Figure B5-12. Map of all monitors (1990-2015) in the Minneapolis study area (top panel) and
satellite views of near-road (middle panels) and area design value monitor (bottom
panels) 5-42
Figure B5-13. Map of all monitors (1990-2015) in the New York/Jersey study area (top panel)
and satellite views of near-road (middle panel) and area design value monitor
(bottom panel) 5-44
Figure B5-14. Map of all monitors (1990-2015) in the Philadelphia study area (top panel) and
satellite views of near-road (middle panels) and area design value monitor (bottom
panel) 5-47
Figure B5-15. Map of all monitors (1990-2015) in the Phoenix study area (top panel) and
satellite views of near-road (middle panels) and area design value monitor (bottom
panel) 5-50
Figure B5-16. Map of all monitors (1990-2015) in the Pittsburgh study area (top panel) and
satellite views of near-road (middle panel) and area design value monitor (bottom
panels) 5-53
Figure B5-17. Map of all monitors (1990-2015) in the Richmond study area (top panel) and
satellite views of near-road (middle panel) and area design value monitor (bottom
panel) 5-56
Figure B5-18. Map of all monitors (1990-2015) in the Riverside study area (top panel) and
satellite views of two near-road (middle panels) and area design value monitor
(bottom panel) 5-58
Figure B5-19. Map of all monitors (1990-2015) in the Sacramento study area (top panel) and
satellite views of near-road (middle panels) and area design value monitor (bottom
panel) 5-62
Figure B5-20. Map of all monitors (1990-2015) in the San Diego study area (top panel) and
satellite views of near-road (middle panel) and area design value monitor (bottom
panels) 5-65
Figure B5-21. Map of all monitors (1990-2010) in the San Francisco study area (top panel) and
satellite views of near-road (middle panel) and area design value monitor (bottom
panel) 5-68
Figure B5-22. Map of all monitors (1990-2015) in the St. Louis study area (top panel) and
satellite views of near-road (middle panel) and area design value monitor (bottom
panel) 5-71
Figure B5-23. Map of all monitors (1990-2015) in the Washington DC study area (top panel) and
satellite views of near-road (middle panel) and area design value monitors (bottom
panels) 5-73
B-v
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B-vi
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List of Tables
Table B2-1. CBSAs having NO2 measurement data from the newly designated near-road
monitoring sites (2014-2015) 2-6
Table B2-2. The top twenty near-road CBSAs identified for analysis based on 2010-2015 design
values, number of monitors in operation, and near-road NO2 concentrations 2-7
Table B2-3. The top twenty additional CBSAs identified for analysis based on 2010-2015 design
values and number of monitors in operation 2-8
Table B2-4. The top twenty facility types ranked by total NOx emissions 2-16
Table B2-5. Proportional adjustment factors calculated from ambient monitor design values in
each of the 23 selected study areas 2-25
Table B2-6. Proportional adjustment factors calculated from the single year 98th percentile
DM1H NO2 at the highest near-road (NR) and highest concentration area wide
(AW) monitor in each of the 23 selected study areas 2-26
Table B2-7. Information supporting the selection of proportional factors used to adjust ambient
concentrations (up to and including the 98th percentile DM1H) to just meet the
existing standard 2-27
Table B2-8. Individual monitor-based factors calculated for use in adjusting DM1H ambient NO2
concentrations above the 98th percentile DM1H in the 23 selected study areas... 2-29
Table B2-9. Factors used to simulate on-road NO2 concentrations from near-road monitors sited
at varying distance from a major road 2-39
Table B2-10. Example site-year metric results in the St. Louis study area: number of days per
year 1-hour NO2 concentrations are at or above 100 ppb, by monitor, year, and air
quality scenario along with summary statistics 2-42
Table B2-11. Example CBSA-wide metric results in the St. Louis study area: summary statistics
for the number of days per year where NO2 concentration >100 ppb - anytime in
the study area and for instances when it occurred on the same day at two monitoring
locations 2-44
Table B2-12. Example CBSA-wide metric results in the St. Louis study area: number of days per
year where NO2 concentrations >100 ppb - data stratified by averaging period and
year 2-44
Table B2-13. Example CBSA-wide metric results in the St. Louis study area: NO2 concentrations
stratified by averaging period, year, month, day, and monitor on days where NO2
concentrations >100 ppb 2-45
Table B3-1. The number of days per year where DM1H NO2 concentration was > 85 ppb, > 90
ppb, > 95 ppb, and > 100 ppb associated with 3-year average 98th percentile DM1H
NO2 concentrations at or near the existing NO2 NAAQS, using valid-year 1980-
2015 ambient monitor data 3-4
Table B5-1. CBSA ranked NOx emissions for top 20 facility types: electricity via combustion
and airports 5-1
Table B5-2. CBSA ranked NOx emissions for top 20 facility types: chemical plants and
petroleum refineries 5-2
Table B5-3. CBSA ranked NOx emissions for top 20 facility types: Portland cement
manufacturing and mines/quarries 5-3
Table B5-4. CBSA ranked NOx emissions for top 20 facility types: rail yards and compressor
stations 5-4
B-vii
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Table B5-5. CBSA ranked NOx emissions for top 20 facility types: sources not characterized and
municipal waste combustors 5-5
Table B5-6. CBSA ranked NOx emissions for top 20 facility types: steel mills and gas plants. 5-6
Table B5-7. CBSA ranked NOx emissions for top 20 facility types: mineral processing plants
and pulp and paper plants 5-7
Table B5-8. CBSA ranked NOx emissions for top 20 facility types: institutional (e.g., schools,
hospitals, prisons) and gas plants 5-8
Table B5-9. CBSA ranked NOx emissions for top 20 facility types: chlor-alkali plants and
fertilizer plants 5-9
Table B5-10. CBSA ranked NOx emissions for top 20 facility types: coke battery and plastic,
resin, or rubber product plants 5-10
Table B5-11. Attributes of ambient monitors within the Atlanta study area having recent (2010-
2015) valid-year NO2 concentrations 5-14
Table B5-12. Attributes of ambient monitors within the Baltimore study area having recent
(2010-2015) valid-year NO2 concentrations 5-16
Table B5-13. Attributes of ambient monitors within the Boston study area having recent (2010-
2015) valid-year NO2 concentrations 5-18
Table B5-14. Attributes of ambient monitors within the Chicago study area having recent (2010-
2015) valid-year NO2 concentrations 5-21
Table B5-15. Attributes of ambient monitors within the Dallas study area having recent (2010-
2015) valid-year NO2 concentrations 5-23
Table B5-16. Attributes of ambient monitors within the Denver study area having recent (2010-
2015) valid-year NO2 concentrations 5-27
Table B5-17. Attributes of ambient monitors within the Detroit study area having recent (2010-
2015) valid-year NO2 concentrations 5-29
Table B5-18. Attributes of ambient monitors within the Houston study area having recent (2010-
2015) valid-year NO2 concentrations 5-31
Table B5-19. Attributes of ambient monitors within the Kansas City study area having recent
(2010-2015) valid-year NO2 concentrations 5-35
Table B5-20. Attributes of ambient monitors within the Los Angeles study area having recent
(2010-2015) valid-year NO2 concentrations 5-37
Table B5-21. Attributes of ambient monitors within the Miami study area having recent (2010-
2015) valid-year NO2 concentrations 5-41
Table B5-22. Attributes of ambient monitors within the Minneapolis study area having recent
(2010-2015) valid-year NO2 concentrations 5-43
Table B5-23. Attributes of ambient monitors within the New York/Jersey study area having
recent (2010-2015) valid-year NO2 concentrations 5-45
Table B5-24. Attributes of ambient monitors within the Philadelphia study area having recent
(2010-2015) valid-year NO2 concentrations 5-48
Table B5-25. Attributes of ambient monitors within the Phoenix study area having recent (2010-
2015) valid-year NO2 concentrations 5-51
Table B5-26. Attributes of ambient monitors within the Pittsburgh study area having recent
(2010-2015) valid-year NO2 concentrations 5-54
Table B5-27. Attributes of ambient monitors within the Richmond study area having recent
(2010-2015) valid-year NO2 concentrations 5-57
B-viii
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Table B5-28. Attributes of ambient monitors within the Riverside study area having recent
(2010-2015) valid-year NO2 concentrations 5-59
Table B5-29. Attributes of ambient monitors within the Sacramento study area having recent
(2010-2015) valid-year NO2 concentrations 5-63
Table B5-30. Attributes of ambient monitors within the San Diego study area having recent
(2010-2015) valid-year NO2 concentrations 5-66
Table B5-31. Attributes of ambient monitors within the San Francisco study area having recent
(2010-2015) valid-year NO2 concentrations 5-69
Table B5-32. Attributes of ambient monitors within the St. Louis study area having recent (2010-
2015) valid-year NO2 concentrations 5-72
Table B5-33. Attributes of ambient monitors within the Washington DC study area having recent
(2010-2015) valid-year NO2 concentrations 5-74
Table B5-34. Attributes of ambient monitors within the Atlanta study area not used for the 2010-
2015 analysis 5-76
Table B5-35. Attributes of ambient monitors within the Baltimore study area not used for the
2010-2015 analysis 5-77
Table B5-36. Attributes of ambient monitors within the Boston study area not used for the 2010-
2015 analysis 5-78
Table B5-37. Attributes of ambient monitors within the Chicago study area not used for the
2010-2015 analysis 5-80
Table B5-38. Attributes of ambient monitors within the Dallas study area not used for the 2010-
2015 analysis 5-84
Table B5-39. Attributes of ambient monitors within the Denver study area not used for the 2010-
2015 analysis 5-85
Table B5-40. Attributes of ambient monitors within the Detroit study area not used for the 2010-
2015 analysis 5-86
Table B5-41. Attributes of ambient monitors within the Houston study area not used for the
2010-2015 analysis 5-87
Table B5-42. Attributes of ambient monitors within the Kansas City study area not used for the
2010-2015 analysis 5-88
Table B5-43. Attributes of ambient monitors within the Los Angeles study area not used for the
2010-2015 analysis 5-89
Table B5-44. Attributes of ambient monitors within the Miami study area not used for the 2010-
2015 analysis 5-91
Table B5-45. Attributes of ambient monitors within the Minneapolis study area not used for the
2010-2015 analysis 5-92
Table B5-46. Attributes of ambient monitors within the New York/Jersey study area not used for
the 2010-2015 analysis 5-93
Table B5-47. Attributes of ambient monitors within the Philadelphia study area not used for the
2010-2015 analysis 5-96
Table B5-48. Attributes of ambient monitors within the Phoenix study area not used for the
2010-2015 analysis 5-98
Table B5-49. Attributes of ambient monitors within the Pittsburgh study area not used for the
2010-2014 analysis 5-99
Table B5-50. Attributes of ambient monitors within the Richmond study area not used for the
2010-2015 analysis 5-100
B-ix
-------
Table B5-51. Attributes of ambient monitors within the Riverside study area not used for the
2010-2015 analysis 5-101
Table B5-52. Attributes of ambient monitors within the Sacramento study area not used for the
2010-2015 analysis 5-103
Table B5-53. Attributes of ambient monitors within the San Diego study area not used for the
2010-2015 analysis 5-105
Table B5-54. Attributes of ambient monitors within the San Francisco study area not used for the
2010-2015 analysis 5-106
Table B5-55. Attributes of ambient monitors within the St. Louis study area not used for the
2010-2015 analysis 5-108
Table B5-56. Attributes of ambient monitors within the Washington DC study area not used for
the 2010-2015 analysis 5-110
Table B5-57. Number of days per year NO2 concentrations were at or above benchmark levels:
area-wide and near-road site-year summary table (2010-2015) 5-115
Table B5-58. Number of days NO2 concentrations were at or above benchmark levels: simulated
on-road concentrations for 2014 5-119
Table B5-59. Number of days NO2 concentrations were at or above benchmark levels: simulated
on-road concentrations for 2015 5-123
Table B5-60. Number of monitors used for calculations: area-wide and near-road monitor
CBSA-wide summary table (2010-2015) 5-129
Table B5-61. Mean and maximum number of days per year NO2 concentrations were at or above
1-hour benchmark levels: area-wide and near-road CBSA-wide summary table
(2010-2015) 5-130
Table B5-62. Mean and maximum number of data point locations used for calculations: area-
wide, near-road, and simulated on-road CBSA-wide summary table (2013-2015)...
5-133
Table B5-63. Mean and maximum number of days per year NO2 concentrations were at or above
1-hour benchmark levels: area-wide, near-road, and simulated on-road CBSA-wide
summary table (2013-2015) 5-134
Table B5-64. Percent of days per year NO2 concentrations were at or above 1-hour benchmark
levels: area-wide and near-road CBSA-wide summary table 5-136
Table B5-65. Number per year NO2 concentrations were at or above 1-hour benchmark levels:
individual near-road monitor data where monitor was in operation for at least 300
days in year 2014 and/or 2015 5-139
Table B5-66. Mean and maximum number of days per year NO2 concentrations exceed 1-hour
benchmark levels: comparison of current air quality characterization with 2008
REA 5-143
B-x
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Bl. INTRODUCTION
This document provides the detailed results of an air quality characterization (AQC)
performed as part of the primary NO2 NAAQS review. Ambient concentration measurements
(1980-2015), along with adjusted and simulated ambient concentrations, were evaluated using
approaches described in Chapter 2 of the NO2 REA planning document (REA PD; US EPA,
2015). The approaches and data sets used in this characterization were also informed by review
by the CAS AC (Diez-Roux and Frey, 2015) and public comments, with appropriate
modifications noted here.
As indicated in the REA PD (section 2.1.1), there is a substantially improved body of
information available in the current review to inform an updated characterization of 1-hour
ambient NO2 concentrations. In particular, data from recently deployed NO2 monitors near major
roads, combined with new information from monitoring and modeling studies of NO2
concentration gradients around roads, add to our understanding of ambient NO2 concentrations in
near-road and on-road environments. This new information, combined with recent information
on NOx emissions provides important perspective, beyond what was available from the last
review, on the extent to which NO2 exposures could have potentially important implications for
public health.
We evaluated ambient NO2 concentrations and compared them with health-based
benchmarks, with a particular focus on updating analyses of concentrations occurring near roads.
The following sections describe our technical approach used to conduct these analyses (Section
B2) including a representativeness evaluation of the study areas selected for focused analysis.
Then, the number of days per year having daily maximum 1-hour (DM1H) concentrations at or
above the selected benchmarks was evaluated using historical (1980-2015) unadjusted ambient
monitoring data and several adjusted and simulated air quality scenarios using recent (2010-
2015) ambient monitoring data in selected urban study areas (Section B3). Section B4 follows in
identifying reference material used in developing this document. And finally, detailed
supplemental data are provided in Section B5 to support the analyses presented in Sections B2
and B3.
Bl-1
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B2. APPROACH
This air quality characterization (AQC) requires benchmark concentrations of interest, the
identification of a study area(s) of interest, and the characterization of respective air quality.
Further, the overall representativeness of the characterization is informed by ambient monitoring
physical attributes and local NOx emission source information. Each of these fundamental
components of the AQC, the data and approaches used, and an overview of the air quality
benchmark summary metrics are described in the following sections. SAS version 9.4
(SAS/STAT 13.2; SAS, 2015) was used to process all ambient monitor data files and to perform
all mathematical and statistical analyses of NO2 concentration data.
2.1 AIR QUALITY BENCHMARK LEVELS
The primary goal of this NO2 AQC is to inform policy decisions regarding the likelihood
that the existing or potential alternative standards would allow for exposures to ambient NO2
concentrations that could be of concern for public health. To facilitate such an analysis, we
evaluated the daily maximum 1-hour (DM1H) ambient NO2 concentrations adjusted to just meet
the existing NO2 standards at varying air quality benchmark levels. We identified air quality
benchmark levels based on the range of data from controlled human exposure studies of non-
specific airway responsiveness in people with asthma.1 Because there are few instances where 1-
hour ambient concentrations could go above 200 ppb when considering concentrations that just
meet the existing standards, we focused on the lower end of this range and selected three air
quality benchmark levels of 100, 150 and 200 ppb. Instances when ambient concentrations in
selected study areas are at or above these levels are counted and summarized, as detailed in
section 2.4.3.
2.2 AMBIENT MONITORING DATA
All of the existing ambient NO2 monitoring data from 1980-2015 are considered in this
AQC. Hourly ambient concentration data were obtained from the U.S. EPA's AirData and Air
Quality System (AQS) Data Mart websites.2 Any replicate NO2 measures occurring at the same
1 The majority of study volunteers experienced increased airway responsiveness following exposures to NO2 concentrations
of 100-300 ppb (or higher) for 30 minutes to 2 hours.
2 Data for 1990-2015 obtained from http://aqsdrl .epa.gov/aqsweb/aqstmp/airdata/download files.html#Raw. Data for 1980-
1989 downloaded from https://aqs.epa.gov/aqsweb/documents/data mart welcome.html.
B2-1
-------
monitoring site (though having multiple parameter occurrence codes or POCs) were averaged.
Except for the newly designated near-road monitors, only monitors having a complete year of
data were used in our analyses. Ascertaining a complete year of monitoring data is a multi-step
process. First, valid days are defined as those having at least 18 hours of measurements. Next, a
valid quarter is identified as having at least 75% of valid days within a three-month calendar
period (68-70 days). Finally, where all four quarters in a calendar year are valid, the year of
monitoring data was considered complete. The near-road monitoring data were used as reported
for any year available, regardless of how many hours or days observations were collected.
2.3 STUDY AREAS
We have conducted updated analyses comparing NO2 air quality to benchmarks in several
study areas. Our selection of study areas focused on CBS As having newly designated near-road
monitors, CBSAs having the highest NO2 design values (thus requiring the smallest adjustment
to just meet the existing 1-hour standard) and CBSAs with a relatively large number of NO2
monitors overall (i.e., providing improved spatial characterization). Additional considerations for
evaluating representativeness of selected study areas include CBS A population, ranking of total
NOx emissions and mobile source NOx emissions, and that they include a wide range of other
non-mobile sources having relatively high NOx emissions. The specific steps taken to select the
study areas and how they were evaluated for representativeness are described below.
2.3.1 Selection approach
While all of the existing ambient monitoring data from 1980-2015 are considered in this
AQC, particular adjusted air quality scenarios (i.e., air quality adjusted to just meet the existing
standards) and simulated microenvironmental evaluations (i.e., on-road NO2 concentrations)
warranted the defining of a specific geographic domain, or study area. For added context
regarding the potential exposed population, the core-based statistical area (CBSA) was chosen as
the fundamental geographic area. The following are the approach steps used to identify CBSAs
to consider evaluating in the study area focused portion of this AQC, followed by the application
of this approach to select study areas.
B2-2
-------
1. CBS As were defined at a county level using delineation files available from the U.S.
Census Bureau.3
2. All available ambient NO2 monitor design values4 were linked with CBSA definitions
using the state and county level identifiers contained in the first five digits of the
ambient monitor IDs.5 CBSAs having at least one monitor design value were retained
for further selection steps.
3. The single year 98th percentile DM1H was calculated for all near-road monitors in
any CBSA for 2014 and 2015.6 This near-road monitor summary statistic was then
merged with the CBSA-monitor design value data, again using the appropriate state
and county level identifiers.
4. This combined CBSA-design value-near road data were then grouped into one of two
study area selection pools:
Near-road CBSAs. CBSAs having a newly designated near-road monitor and
reporting NO2 concentrations were given preference for inclusion in an initial
study area selection pool. Because most of the near-road monitors began
operating in 2014, all near-road NO2 concentration data were considered
usable regardless as to whether or not a complete year was available to
maximize the amount of near-road data available for analysis. Fifty CBSAs
were identified as part of this initial selection pool (
3 The counties comprising each CBSA are listed at http://www.census.gov/population/metro/data/def.html. as defined by the
Office of Management and Budget (OMB) fhttp://www. whitehouse. gov/sites/default/files/omb/bulletins/2013/b-l 3-01 .pdf).
Instances where only a portion of the county was identified as part of a given CBSA by the OMB memo, it was assumed in this
analysis that the entire county was part of the CBSA in an effort to be more inclusive in developing the study area selection
pools.
4 A spreadsheet file containing annual average and 98th percentile daily maximum 1 -hour (DM1H) design values was
obtained from http://www.epa.gov/airtrends/values.html. The file obtained contains design values for 2005-2014, though only
years 2010-2014 were used from this file in identifying CBSAs as part of the study area selection pool. Design values for 2015
were calculated using complete year ambient monitor data available as part of this analysis.
5 Each ambient monitor ID has 9 characters (XXYYYZZZZ) as follows: 'XX' indicates the U.S. State code, YYY is used to
identify the county code, and 'ZZZZ' is used for the monitor number.
6 The near-road monitor IDs and attributes were obtained from http://www3.epa.gov/ttnamtil/nearroad.html.
B2-3
-------
a. Table B2-1).
b. Other CBSAs. CBS As not having a newly designated near-road monitor,7 or
CBS As not having reported concentrations at a newly designated near-road
monitor though having at least one design value were included in this group.
One-hundred twenty-seven CBSAs were identified as part of this group (not
shown).
5. The top twenty8 near-road CBSAs were identified from the "near-road CBS A" group
using a rank value, generated using the following quantitative information:
a. For each CBS A, the maximum design value and the total number of monitors
reporting a design value was obtained for all analysis years considered (i.e.,
2010-2015) and the 98th percentile DM1H near-road monitor concentration for
2014 and 2015 were retained. This yielded upwards to twenty-two variables9
describing each CBS A.
b. A global mean value for each of these twenty-two variables was calculated.
Each individual CBS A variable value was then normalized by the global mean
variable value.
c. A rank value for each CBS A was calculated as an equally weighted sum of the
mean normalized variable values, though relative to unity (i.e., the average
CBS A rank value would equal 1). The rank values were then sorted in
7 There are a few CBSAs that have an existing monitor sited in close proximity to a major roadway, though not necessarily
meeting the new near-road monitor designation requirements. For example, a Chicago, IL monitor (ID 170313103) is about 20
meters from a major road (see US EPA, 2008a; 2008 REA Appendix A, Table A-7). It is possible NO2 concentrations from this
monitor (and other monitors in close proximity to a major road) can be used in a manner similar to that of the formally designated
near-road monitors and supplement our characterization of the near-road and on-road microenvironments.
8 This number of "identified CBSA" (i.e., 20) was subjectively chosen for analysis tractability while also retaining a
generally geographically diverse and high NO2 concentration representative collection of CBSAs from the near-road CBSAs
group.
9 Considered here are the six annual average design values, four 3-year average 98th percentile DM1H design values, number
of monitors in each of six years, number of monitors in four 3-year periods, and the 98th percentile DM1H values for the near-
road monitor in 2014 and 2015.
B2-4
-------
descending order with the top twenty near-road CBSA10 presented in Table
B2-2.
6. Twenty additional CBSAs were then identified using a rank value generated using the
same variables and approach listed in step 5, though considering the following
modifications:
a. Not included in the calculation of the rank value was the 98th percentile
DM1H concentration from a newly designated near-road monitor, thus there
were upwards to twenty variables describing each CBSA.
b. CBSAs evaluated in this step were those in the "other CBSA" group (step 4b)
and any "near-road CBSA" not identified in step 5 (i.e., totaling 157 CBSAs).
c. The rank values were sorted in descending order with the top twenty
"additional CBSA" provided in Table B2-3.
We prioritized the order by which the above identified CBSAs could be evaluated for the
benchmark analysis, rather than proceed with performing analyses using all 40 CBSAs. First, all
twenty "near-road CBSAs" in Table B2-2 were selected as study areas, again, based on their
having the newly sited near-road monitors and associated NO2 concentration measurements.
Next, because Detroit, MI and Richmond, VA have new near-road measurement data and add to
the overall U.S. geographic representation, these CBSAs were selected as a study area from the
"additional CBSA" top twenty list. We then selected one CBSA (Chicago, IL) from the list of
additional CBSAs, largely based on it further enhancing overall U.S. geographic representation,
and having the highest population of the remaining CBSAs identified for analysis (see below). In
addition, while Chicago does not have any new near-road measurement data available, we were
interested in evaluating results that could be generated using historically sited monitors that are
situated near a major road. Thus, a total of twenty-three study areas were selected for the focused
analysis in this AQC (Figure B2-1). If other study areas are determined as useful in
characterizing air quality for future assessments as part of this review, the 17 CBSAs remaining
10 The use of unweighted variables in calculating the rank value places emphasis on within-CBSA spatial representation and
areas measuring the highest NO2 concentrations. Justification for this emphasis would include, 1) when properly sited regarding
the most important direct source or precursor emissions, the greater the number of monitors in a CBSA would better represent
ambient concentrations than in CBSAs having fewer monitors, 2) monitors having the highest concentrations would require the
smallest adjustment upwards to just meet the existing standard, possibly limiting uncertainty in generated results, and 3) the risk
associated with highest concentrations (even considering unadjusted concentrations) is by definition of greatest importance when
performing an assessment that uses health effect benchmark concentrations.
B2-5
-------
from the list of "additional CBSAs" above, along with any new near-road CBSAs (e.g., perhaps
those having newly available 2016 near-road NO2 measurements) would likely be considered
first in selecting additional study areas.
B2-6
-------
Table B2-1. CBSAs having NO2 measurement data from the newly designated near-road
monitoring sites (2014-2015).
CBSA Name
Abbr.
CBSA Name
Abbr.
Atlanta-Sandy Springs-Roswell, GA
AT LA
Milwaukee-Waukesha-West Allis, Wl
MILW
Austin-Round Rock, TX
AUST
Minneapolis-St. Paul-Bloomington, MN-WI
MINE
Baltimore-Columbia-Towson, MD
BALT
Nashville-Davidson-Murfreesboro-Franklin, TN
NASH
Birmingham-Hoover, AL
BIRM
New Orleans-Metairie, LA
NORL
Boise City, ID
BOIS
New York-Newark-Jersey City, NY-NJ-PA
NYNY
Boston-Cambridge-Newton, MA-NH
BOST
Oklahoma City, OK
OKLA
Buffalo-Cheektowaga-Niagara Falls, NY
BUFF
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD
PHIL
Charlotte-Concord-Gastonia, NC-SC
CHAR
Phoenix-Mesa-Scottsdale, AZ
PHOE
Cincinnati, OH-KY-IN
CINC
Pittsburgh, PA
PITT
Cleveland-Elyria, OH
CLEV
Portland-Vancouver-Hillsboro, OR-WA
PORT
Columbus, OH
COLO
Providence-Warwick, RI-MA
PROV
Dallas-Fort Worth-Arlington, TX
DALL
Raleigh, NC
RALE
Denver-Aurora-Lakewood, CO
DENV
Richmond, VA
RICH
Des Moines-West Des Moines, IA
DESM
Riverside-San Bernardino-Ontario, CA
RIVR
Detroit-Warren-Dearborn, Ml
DETR
Rochester, NY
RONY
Hartford-West Hartford-East Hartford, CT
HART
Sacramento-Roseville-Arden-Arcade, CA
SACR
Houston-The Woodlands-Sugar Land, TX
HOUS
San Antonio-New Braunfels, TX
SANA
Indianapolis-Carmel-Anderson, IN
INDI
San Diego-Carlsbad, CA
SAND
Jacksonville, FL
JACK
San Francisco-Oakland-Hayward, CA
SANF
Kansas City, MO-KS
KANS
San Jose-Sunnyvale-Santa Clara, CA
SANJ
Las Vegas-Henderson-Paradise, NV
LASV
San Juan-Carolina-Caguas, PR
SJPR
Los Angeles-Long Beach-Anaheim, CA
LOSA
Seattle-Tacoma-Bellevue, WA
SEAT
Louisville/Jefferson County, KY-IN
LOUI
St. Louis, MO-IL
STLO
Memphis, TN-MS-AR
MEMP
Tampa-St. Petersburg-Clearwater, FL
TAMP
Miami-Fort Lauderdale-West Palm Beach, FL
MIAM
Washington-Arlington-Alexandria, DC-VA-MD-WV
WASH
B2-7
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Table B2-2. The top twenty near-road CBSAs identified for analysis based on 2010-2015 design values, number of monitors in operation,
and near-road NO2 concentrations.
CBSA/
Study
Area1
Number of Ambient Monitors in Area with Valid Data
Max Annual Average N02
Max 98th pet 1-hr daily
max N02
2014 near-road
monitor
2015 near-road
monitor
Study
area
rank
value3
2010
2011
2012
2013
2014
2015
2010-
2012
2011-
2013
2012-
2014
2013-
2015
2010
2011
2012
2013
2014
2015
2010-
2012
2011-
2013
2012-
2014
2013-
2015
days
(n)2
98th pet
DM1H
no2
days (n)2
98th pet
DM1H
no2
HOUS
16
16
16
16
16
13
9
9
11
13
15
14
15
13
13
11
60
59
56
52
344
48.8
261
59.2
3.16
LOSA
16
14
9
13
16
15
4
4
5
7
26
25
21
23
22
22
67
64
63
62
361
66
255
74.8
2.74
DALL
11
14
11
11
11
10
9
9
8
8
13
13
12
12
10
10
56
53
49
47
274
40
365
44.9
2.48
SANF
10
10
11
10
9
9
8
8
8
8
16
16
15
17
14
14
74
68
61
57
334
51.7
365
48.9
2.45
NYNY
10
7
8
8
9
8
5
6
7
7
22
25
22
22
22
22
70
67
66
66
184
90
365
67
2.37
RIVR
12
9
11
11
13
11
6
3
2
1
23
21
22
21
20
19
72
62
53
50
0
0
153
77.2
2.15
SAND
8
8
8
8
6
6
6
5
4
4
21
20
20
19
13
14
73
73
57
56
0
0
269
52
1.89
PHOE
6
5
5
5
5
4
4
4
4
4
25
25
26
25
25
22
66
64
64
63
322
59
121
64
1.84
SACR
8
8
8
6
7
7
7
4
4
5
12
13
12
10
11
11
51
50
48
50
0
0
80
52
1.71
BOST
5
5
6
5
5
6
5
4
3
4
19
20
19
18
17
17
51
50
49
51
365
53
362
50
1.64
WASH
7
6
6
7
6
5
5
4
2
3
18
16
17
13
11
13
55
51
48
48
0
0
212
47.1
1.52
PHIL
5
4
5
5
5
4
3
3
1
3
23
20
18
17
18
18
65
61
39
49
358
51.4
357
47.8
1.45
PITT
5
5
5
6
4
3
4
4
1
2
15
16
14
11
10
10
53
49
36
42
122
39.7
365
44.8
1.27
DENV
2
2
2
2
2
3
0
1
2
2
28
24
25
24
23
22
0
62
72
72
361
69.6
92
73.15
1.27
AT LA
3
3
3
3
3
3
3
3
3
3
14
13
12
9
11
10
56
51
49
48
200
49.95
363
54.1
1.16
MINE
3
3
3
3
3
3
2
3
3
3
10
9
11
9
9
8
46
44
45
46
365
48
362
48
1.06
BALT
2
2
2
2
2
1
2
2
2
1
18
18
16
15
16
11
57
52
52
46
275
50.6
352
49.8
1.03
KANS
3
3
3
3
2
2
3
1
1
1
15
15
14
13
13
13
53
52
51
51
358
45.6
352
44.5
1.02
STLO
3
3
2
3
4
2
0
2
2
2
13
13
14
11
12
11
0
53
49
46
365
58.2
364
47.4
0.97
MIAM
5
5
3
4
3
2
2
2
2
2
10
8
8
8
9
5
47
46
46
44
0
0
56
40
0.95
1 abbreviated CBSA name to allow for extended number of columns (see Table B2-1 for complete CBSA title).
2 not used in calculating the rank value, included for information purposes only. Also, where more than one near road monitor was operating in a study area, the number of days corresponds
to the monitor having the greatest 98th pet DM1H (i.e., it is possible that the other near road monitor had a greater number of days where measurements were collected).
3 CBSA's are sorted by descending rank value.
B2-8
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Table B2-3. The top twenty additional CBSAs identified for analysis based on 2010-2015 design values and number of monitors in operation.
CBSA Title
Number of Ambient Monitors in Area with Valid Data
Max Annual Average N02
Max 98th pet 1-hr daily max
no2
Study area
rank value1
2010
2011
2012
2013
2014
2015
2010-
2012
2011-
2013
2012-
2014
2013-
2015
2010
2011
2012
2013
2014
2015
2010-
2012
2011-
2013
2012-
2014
2013-
2015
Santa Maria-Santa Barbara,
CA
11
11
11
11
11
10
11
10
10
9
9
9
10
10
8
8
43
36
36
36
6.57
Baton Rouge, LA
10
8
8
8
8
6
7
7
7
5
13
12
11
10
11
9
54
52
48
41
4.88
Chicago-Naperville-Elgin, IL-
IN-WI
6
5
6
5
6
5
2
0
1
3
25
23
22
21
21
18
62
0
47
63
3.07
Beaumont-Port Arthur, TX
4
4
4
4
4
4
4
4
4
4
8
7
6
6
6
5
37
35
33
33
2.75
Springfield, MA
3
3
3
3
3
3
3
3
3
3
15
16
14
14
13
13
47
46
42
46
2.55
Farmington, NM
4
3
3
3
3
3
2
3
3
3
12
13
13
12
11
11
38
41
37
36
2.39
Bakersfield, CA
4
3
3
4
3
4
3
2
1
1
14
15
15
14
13
12
58
46
48
45
2.32
El Paso, TX
3
2
2
3
3
3
1
1
2
3
17
17
16
14
14
14
61
59
57
58
2.20
Detroit-Warren-Dearborn,
Ml
1
1
3
3
3
4
1
1
2
3
12
12
19
18
16
18
48
44
45
50
2.10
San Luis Obispo-Paso
Robles-Arroyo Grande, CA
3
3
3
3
3
2
3
3
3
2
6
6
7
7
6
3
38
38
39
31
2.05
Richmond, VA
3
3
3
2
2
3
3
2
1
1
12
10
10
8
8
14
52
47
43
41
1.94
Gillette, WY
3
3
4
4
4
3
1
1
1
3
7
6
8
9
10
7
32
32
35
49
1.93
Oklahoma City, OK
2
2
2
2
3
2
2
2
2
1
9
10
9
9
7
7
54
54
51
45
1.75
Oxnard-Thousand Oaks-
Ventura, CA
2
2
2
2
2
2
2
2
2
2
10
9
10
9
9
8
38
37
39
38
1.73
Stockton-Lodi, CA
2
2
2
2
2
2
1
1
1
1
14
15
14
16
13
12
51
53
55
53
1.71
San Jose-Sunnyvale-Santa
Clara, CA
1
2
2
2
1
2
1
2
1
1
14
15
13
15
13
18
50
51
53
50
1.71
El Centra, CA
2
2
2
2
2
2
1
1
1
1
14
14
14
13
12
11
62
64
47
44
1.67
Allentown-Bethlehem-
Easton, PA-NJ
1
2
2
2
2
1
1
2
2
1
11
14
13
13
13
12
40
45
45
48
1.66
Cincinnati, OH-KY-IN
2
2
1
2
3
3
1
1
1
1
15
13
4
12
23
22
32
30
31
32
1.66
Providence-Warwick, RI-MA
1
1
2
2
2
3
1
1
1
2
10
11
10
10
10
22
42
43
42
46
1.61
1 CBSA's are sorted by descending rank value.
B2-9
-------
Study Areas
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Not Selected
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Figure B2-1. The 40 CBSAs identified as potentially useful to inform the air quality
characterization, including the 23 selected study areas for focused analyses.
2.3.2 Representativeness evaluation
The geographical locations of the study areas should adequately represent areas across the
U.S. having seasonal, atmospheric, or other influential factors that could contribute to variability
in NO2 concentrations and potential exposures. Figure B2-1 provides perspective to such an
assessment showing reasonable geographic representation by the 23 selected study areas for the
B2-10
-------
continental U.S., except for the upper northwestern U.S.,11 even when considering the pool of
"other CBS As" not having a newly designated near-road monitor. Obviously, the NO2 design
values and number of ambient monitors operating in any northwestern CBSAs did not rise to a
level necessitating their consideration as a study area of particular interest. Regarding the study
area selection pool of 50 "near-road CBSAs", the three available northwestern CBSAs (Portland-
Vancouver-Hillsboro, OR-WA; Seattle-Tacoma-Bellevue, WA; Boise City, ID) had the 32nd,
45th and 47th highest rank values using the above selection criteria, respectively. When
considering the rank value of available northwestern CBSAs in the "other CBSAs" data set, only
Gillette, WY (12th highest) was ranked within the top 50.
The selected study areas should also capture areas where large portions of the U.S.
population reside, as this would better represent potential risks to populations at a local, urban,
and national scales as well as increasing the likelihood for appropriately representing important
study groups (e.g., children with asthma). To evaluate population representativeness, data were
obtained from the U.S. Census Bureau to characterize the 2013 population in the 23 selected
study areas relative to that of other CBSAs.12 In total, the 23 selected study areas include just
over 124 million people or nearly 40% of total U.S. population. When considering the 17
additional CBSAs identified as potentially useful to inform the AQC, another 14 million people
could be included to the total population considered, thus comprising approximately 44% of the
total U.S. population.
The individual CBS A population values were ranked by descending population, with the
top 100 CBSAs retained and plotted in Figure B2-2. For perspective, all 23 selected study areas
are named and highlighted in the figure. Also named in the figure are CBSAs within the top 20
CBSAs for population (if any other than the 23 selected study areas). This analysis indicates the
selected study areas are representative of the most populated CBSAs in the U.S., having 18 of
the top 20 populated CBSAs in the U.S.
11 Considered here are the states of Washington, Oregon, Idaho, Wyoming, Montana. As an example of the limitations to
number of monitors having complete year data, the Portland-Vancouver-Hillsboro, OR-WA CBSA had 1 monitor available in
each of the years evaluated. Annual average and 3-year 98th percentile DM1H NO2 concentrations were about 9 ppb and 35 ppb,
respectively.
12 Data were downloaded from http://www.census.gov/popest/data/metro/totals/2013/CSA-EST2013-alldata.html. Two files
were used and processed to generate population data that included all study areas: CSA-EST2013-alldata_USCENSUS.txt and
CBSA-EST2013-alldata_USCENSUS.txt. Population data were available for a total of 382 CBSAs.
B2-11
-------
100
2.0E+7
1.8E+7
1.6E+7
2 1.4E+7
O
*~
Seattle-Tacoma-Bell ei
Sacramento-Roseville-Arden-Arcade, CA
f—
Richmond, VA
"ampa-St. Petersburg-Clearwater, FL
Denver-Aurora-Lakewood, CO
Pittsburgh, PA
KansasCity, MO-KS
Figure B2-2. The top 100 CBSAs ranked by 2013 population. Highlighted and named are
the 23 selected study areas; also named are those CBSAs ranked in the top 20.
Information on NOx emissions13 was used to inform our characterization of the important
sources potentially contributing to monitored NO2 concentrations. The recent EPA's National
Emissions Inventory (NEI), the 2011 NEI14 is the most comprehensive source of emissions data
to identify, categorize, and evaluate NOx emissions and emission source types. At a national
level, anthropogenic sources account for more than 90% of NOx emissions (US EPA, 2016,
section 2.3.1, Figure 2-3). Motor vehicles are the largest source, with on-road and off-road
mobile sources contributing nearly 60% of the total NOx emissions nationally. Other important
sources include fuel combustion-utilities (14% of total), fuel combustion-other (11% of total),
and biogenics and wildfires (8% of total). Compared to the national averages, urban areas have
13 Oxidized nitrogen compounds are emitted to the atmosphere primarily as NO, with NO converting to NO2 following its
reaction with O3. Collectively, NO and NO2 are referred to as NOx (U.S. EPA, 2008b, section 2.2).
14 Hie NEI is a national compilation of emissions estimates from all source sectors, collected from state, local, and tribal air
agencies as well as those developed by EPA. The NEI is developed on a tri-annual basis, with 2011 being the most recent base
year currently available and referred to as 2011 NEI. The next NEI base year will be 2014 and will be available in late 2016. For
information on the NEI, see http://www.epa.gov/ttn/chief/eiinfonnation.html.
B2-12
-------
greater contributions to total NOx emissions from both on-road and off-road mobile sources and
smaller contributions from other sources (US EPA, 2016, Figure 2-4, Table 2-1). For example, in
the 21 largest CBSAs in the U.S., more than half of the urban NOx emissions are from on-road
mobile sources, and when combined with off-road mobile sources, account for more than three
quarters of total emissions in these large CBSAs (US EPA, 2016, section 2.3.2).
While this emissions summary at a national level is useful, the most important emission
sources can vary substantially across smaller spatial scales. We evaluated the NOx emissions in
all 177 CBSAs having ambient NO2 concentration measurements (and emissions data were
reported), including the 23 selected study areas to determine the total emissions and emission
sources represented by the CBSAs that were selected as study areas. Two data sets were used to
accomplish this, generated using the 2011 National Emissions Inventory (NEI)15 as follows:
1. County level emissions. This data set contains NOx emissions (in tons per year)
aggregated to the county level. Over 50 sectors are identified, including various
mobile (e.g., "Mobile - Non-Road Equipment - Diesel") and industrial emission
sources (e.g., Industrial Processes - Pulp & Paper). Emissions were summed based on
the counties comprising each CBSA that had at least one ambient monitor in operation
during 2010-2015.16 Using this data set we calculated total NOx emissions, total
mobile source NOx emissions,17 and percent mobile source NOx emissions relative to
total for each CBSA. Results for the 177 CBSAs were ranked for each of these three
emissions variables and plotted in Figure B2-3 to Figure B2-5. In each of these
15 Data downloaded from: http://www.epa.gov/air-emissions-inventories/2011-national-emissions-inventorv-nei-data.
Additional processing was performed here to characterize facilities that did not have entries for the 'facility type' field. Of the
approximately 23,000 NOx facilities, nearly 13,000 were missing a facility type. Information from the available facility name and
the NAICS codes were used to generate the facility type where missing, using the existing list of facility types. The majority of
updates, designated as important primarily based on high total facility emissions, regarded the characterization of
Mines/Quarries, Compressor Stations, Gas Plants, and Chemical Plants. Details regarding these updates to the data file used are
available upon request.
16 We felt that by using emissions from only those counties that had ambient monitors would best represent the emission
levels and source types that could influence available ambient monitoring concentrations. It is possible that ambient monitors
could be proximal to one or more other counties in a CBSA, resulting in potentially mischaracterizing or underestimating
emissions and the influence of particular source types, however, we felt that including emissions from all counties in each CBSA
would more likely result in a greater mischaracterization, and clearly overestimating emission levels and source types that might
influence monitor concentrations. For the purposes of this broad study area evaluation, this assumption was judged as reasonable.
17 All sectors having a description beginning with the word "Mobile" were included in the mobile source NOx emission
sum. Recognizing this includes both on-road and non-road sources, analyses were also performed using only the "Mobile - On-
Road" data. Results for the Mobile - On-Road emissionswere effectively the same as presented here for all mobile sources, i.e.,
the rank order of the study areas/CBSA were the same, only differed in that the total emissions and percent values were less.
B2-13
-------
figures, all 23 of the selected study areas are named and highlighted to indicate where
these areas are ranked relative to the other 154 CBS As. Any CBS A within the top 20
ranked emissions are also named (approximating an upper 90th percentile of CBSAs).
Clearly, the study areas selected for focused analyses in this AQC are among those
CBSAs having the highest total and mobile source NOx emissions (Figure B2-3 and
Figure B2-4). The top 15 CBSAs having the greatest NOx emissions were selected as
study areas and nearly all of the study areas are within the 80th percentile or above for
both total and mobile source emissions. The percent of total NOx contributed by
mobile sources considering the 23 study areas varied (Figure B2-5), and for the most
part, did so in a similar fashion as that exhibited by most CBSAs, ranging from about
65-85% of total NOx emissions. This range in mobile source contribution indicates the
23 selected study areas may contain a variety of important NOx emission source
types, albeit relatively smaller in magnitude when compared with mobile source
emissions alone. Three selected study areas (Atlanta, Phoenix, and San Diego CBSAs)
were ranked within the top 10 CBSAs providing reasonable representation of CBSAs
where nearly all NOx emissions are from mobile sources. About half of the selected
study areas had a mobile source emissions contribution of 65-75%, again, providing
representation of most CBSAs, while representation of lesser mobile source
influenced areas are provided by the Denver (61%), Kansas City (57%), and
Pittsburgh (45%) study areas. While there is a collection of CBSAs having greater
relative contributions from non-mobile sources, their lack of representation is of
limited importance considering the overall low total emissions in these CBSAs.
B2-14
-------
180
2.0E+5
160
CBSA Rank Number forTotal NOx
140 120 100 80 60
40
20
1.8E+5
1.6E+5
2 1.4E+5
>
¦—
C 1.2E+5
O
g 1.0E+5
CO
*/>
•g 8.0E+4
LU
O 6.0E+4
4.0E+4
2.0E+4
0.0E+0
New York-Newark-JerseyCity, NY-NJ-PA
Los Angeles-Long Beach-Anaheim, CA
Chicago-Naperville-Elgin, IL-IN-WI
Da! las-Fort W orth-Arlrigton^.
Miami-Fort Lauderdale-West Palm Beach, FL ^
Houston-The Woodlands-Sugar Land, TX
Riverside-San Bernardino-Ontario, CA
Phoenix-Mesa-Scottsdale, AZ
San Francisco-Oakland-Hayward, CA 4
St Louis. MO-IL
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD
MinneapolB-St. Paul-Bloomington, MN-WI •
Pittsburgh, PA
KansasCity, MO-KS f
Detroit-Warren-Dearborn. Ml 0
SeMt»e-T«oTO-3eBerae,WA «
?e«p»S£. Peteraurg-oesrwater. R. - f
ua wguUBMerrofttaaae. wv - Baton so-se, J*
Washington-Arlington-AJexandri^ DC-VA-MD-WV
Boston-Cambridge-Newton, MA-NH
Denver-Aurora-Lakewood, CO
Atlanta-SandySprines-Roswell.GA ,0— San Diego-Carlsbad,...
BaItimore-Ccrtumbia-Towson, MD
Sacramento—Roseville-Arden-Arcade, CA
Richmond. VA
Figure B2-3. CBS As ranked by total NOx emissions. Highlighted and named are the 23
selected study areas; also named are those CBSA ranked in the top 20.
180
1.6E+5
CBSA Rank Number forTotal Mobile Source NOx
160 140 120 100 80 60 40
20
1.4E+5
Los Angeles-Long Beach-Anaheim, CA
New York-Newark-Jersey City, NY-NJ-PA
~ 1.2E+5
TO
0)
>
£ 1.0E+5
i/i
C 8.0E+4
(/J
V)
£ 6.0E+4
X
O
4.0E+4
2.0E+4
Miami-Fort Lauderdale-West Palm Beach, FL
Da I las-Fort Worth-Arlington, TX
Chicago-Naperville-Elgin, IL-IN-WI
Houston-The Woodlands-Sugar Land, TX
Phoenix-Mesa-Scottsdale, AZ
Riverside-San Bernardino-Ontario, CA
San Francisco-Oak Ian d-Hayward, CA
St. Louis. MO-IL
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD
Minneapois-St. Paul-Bioomington, MN-WI
Atlanta-Sandy Springs-Roswell, GA ^
Sa n D iego-Car Isbad, CA
Washington-Arlington-Alexandri^ DC-VA-MD-WV J*
Boston-Cambridge-Newton, MA-NH . m
Sacramento-Roseville-Arden-Arcade,CA ansas
Baltimore-Columbia-Towson, MD Pittsburgh, PA
Richmond, VA
Denver-Aurora-Lakewood, CO
0.0E+0
Figure B2-4. CBS As ranked by mobile source NOx emissions. Highlighted and named are
the 23 selected study areas; also named are those CBSA ranked in the top 20.
B2-15
-------
180
100
90
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o
1-
80
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o
70
1/1
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40
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i_
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30
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to
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Table B2-4. The top twenty facility types ranked by total NOx emissions.
Facility
Facilities
Total NOx
Maximum
Facility Type
(n)
(tpy)
NOx (tpy)
Electricity Generation via Combustion
670
399292
17104
Airport
4323
96060
5485
Chemical Plant
372
52784
9113
Petroleum Refinery
82
52568
3655
Portland Cement Manufacturing
34
35230
2542
Mines/Quarries
124
34778
11726
Rail Yard
307
33745
1328
Compressor Station
512
33148
1583
not characterized
10804
30897
1688
Municipal Waste Combustor
49
28102
1617
Steel Mill
71
23197
4813
Gas Plant
73
15642
2268
Mineral Processing Plant
29
13929
2942
Pulp and Paper Plant
54
12415
2036
Institutional (school, hospital, prison, etc.)1
843
9705
739
Glass Plant
36
9393
2364
Chlor-alkali Plant
7
6936
6194
Fertilizer Plant
34
6815
2857
Coke Battery
9
6814
3075
Plastic, Resin, or Rubber Products Plant2
195
6215
1221
1 Ranked 26th for facility maximum.
2 Ranked 21st for facility maximum.
By far, EGUs contribute the most NOx emissions compared to any other facility type,
emitting over four times that of the 2nd greatest emission facility type (i.e., airports).
Facilities having emissions originating from a 'chemical plant' contributed to the 3rd
greatest amount of NOx emissions, though constituting approximately half of those by
'airports'. The NOx emissions were evaluated considering each of the 177 CBSAs,
considering each of the top 20 facility types (see supplemental data in Section B5),
with Figures provided here of the top three facility types (Figure B2-6 to Figure
B2-8). Reasonable representation is given by the selected study areas regarding each
of the most important facility types, particularly considering the relative importance of
these facility emissions with respect to mobile source contributions (i.e., being
significantly lower in terms of emissions). For instance, the Houston CBSA has the
greatest NOx emissions contributed by chemical plants (-13,000 tpy, Figure B2-8),
though this amount is small when compared with total mobile source NOx emissions
in the CBSA (-100,000 tpy, Figure B2-4). A few facility types were not well
represented by the selected study areas: 'mines/quarries', 'gas plant' and 'mineral
B2-17
-------
processing', facility types on frequent occasion located in the upper northwestern U.S
(see Table B5-3, Table B5-6, and Table B5-7). Considering the amount of NOx
emissions from these particular facilities relative to the most important emissions
nationwide, lack of representation by the selected study areas was considered as
having limited influence to our overall study objectives. Furthermore, on occasion it
may appear peculiar that a certain emission source is not apparent in selected study
area, but this is a function of the data used for this this analysis. For example, while
the Hartsfield-Jackson Atlanta International Airport NOx emissions are greater than
4,000 tpy, the data are not included in this assessment because the facility is actually
located in Clayton County, a county that does not have an ambient monitor.
180
CBSA Rank Number for EGU NOx
160 140 120 100 80 60
40
2.5E+4
2.0E+4
S 1.5E+4
c/i
C
O
1.0E+4
5.0E+3
0.0E+0
Atl a nta-San dy Springs-Roswell, GA
20
—I—
KansasCity, MO-KS
riBe/JeSeraoi County, CY
Chicago-Naperville-Elgin, IL-IN-WI
Detroit-Warren-Dearborn. Ml
New York-New ark-Jersey City, NY-NJ-PA
Cromati, OtHflT-M
MinneapolB-St. Paul-Bloomington, MN-WI
Washington-Arlington-Alecandri^ DC-VA-MD-WV
Miami-Fort Lauderdale-West Palm Beach, FL
Houston-TheWoodlands-Sugar Land, TX
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD
Richmond, VA ^
Baltimore-Columbia-Towson, MD
Los Angeles-Long Beach-Anaheim, CA Boston-Cambridge-Newton, MA-NH
Phoenw-Mesa-Scottsdale, AZ Dalte-FortWorth-Arlrston,™
Riverside-San Bernard ino-Ontario, CA _ _ _ . , ... ...
San Francisco-Oakland-Hayward, CA
San Diego-Carlsbad,...
Sacramento-Roseville-Arden-Arcade, CA
Figure B2-6. CBSAs ranked by NOx emissions from electricity generation via combustion.
Highlighted and named are the 23 selected study areas; also named are CBSA ranked in
the top 20.
B2-18
-------
180
1.0E+4
9.0E+3
160
CBSA Rank Number for Airport NOx
140 120 100 80 60
40
20
New York-Newark-Jersey City, NY-NJ-PA
8.0E+3
7.0E+3
03
0 6.0E+3
>
C 5.0E+3
O
4.0E+3
3.0E+3
2.0E+3
1.0E+3
0.0E+0
Los Angeles-Long Beach-Anaheim, CA
Chicago-Naperville-Elgin, iL-lN-WI
Miami-Fort Lauderdale-West Palm Beach, FL
San Francisco-Oaklend-Havward. CA
Dall2&FortWorth-Arrngton,TX ^
Washington-Arlington-Alexandri^ DC-VA-MD-WV 9
Houston-The Woodlands-Sugar Land, TX
Denver-Aurora-Lakewood, CO
Phoe nix-Mesa-Scottsdale, AZ
Atlanta-Sandy Springs-Roswell, GA
Baltimore-Columbia-Towson, MD
KansasCity, MO-KS
OrtanasrKesrwneeGertOTa, P. of
Detroit-Warren-Dearborn, Ml
Minneapoiis-St. Paui-Bloomington, MN-Wl
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD «
Boston-Cambridge-Newton, MA-NH
San Diego-Carlsbad,... —
Riverside-San Bernardino-Ontario, CA 9 St. Louis, MO-IL
Sacramento~Roseville-Arden-Arcade,CA
Pittsburgh, PA
Richmond, VA
Figure B2-7. CBS As ranked by NOx emissions from airports. Highlighted and named are
the 23 selected study areas; also named are CBSA ranked in the top 20.
180
1.4E+4
1.2E+4
1.0E+4
TO
¦ 8.0E+3
V)
c
o
X
O
6.0E+3
4.0E+3
2.0E+3
0.0E+0
CBSA Rank Number for Chemical Plant NOx
160 140 120 100 80 60 40
20
Houston-The Woodlands-Sugar Land,TX
•
5o-Se,i^
Cingspon-Snstw-Sfstoi. TN-VA
Mnur.TX —#
Qisries, LA
MsrjftsuW
Mcnprts, TW-VS-6S
Mew t>«esra-V«tsi-«, if- -¦ '
Chicago-Naperville-Elgin, IL-IN-WI
OramBO. omnuN
Phil adelphia-Camd en-Wilmington, PA-NJ-DE-M D
St. Louis, MO-Tl
Baltimore-Columbia-Towson, MD
ft
Los Angeles-Long Beach-Anaheim, CA
San Francisco-Oakland-Hayward, CA
Boston-Cambridge-Newton, MA-NH
Riverside-San Bernardino-Ontario. CA
Detroit-Warren-Dearbom, Ml
\\\\\ \\ 1 !•
\\ ' \ \ ' ' •
\\\\m
Figure B2-8. CBSAs ranked by NOx emissions from chemical plants. Highlighted and
named are 11 of the 23 selected study areas (all other study areas had no emissions for this
facility type); also named are CBSA ranked in the top 20.
B2-19
-------
2.3.3 Ambient monitor attributes
We evaluated the attributes of all individual ambient monitors in each of the 23 study
areas. For added context and historical perspective, data for monitors in operation currently
(2010-2015) and for those operating at any time since 1990 are provided. The attributes for each
set of ambient monitors are summarized in Section 5.2 (monitors used for focused analysis) and
Section 5.3 (monitors not used for focused study area analysis). For monitors used in the focused
analysis, figures are provided showing a satellite view of each the design and near-road monitors.
2.4 ESTIMATED AMBIENT NO2 CONCENTRATIONS
This section describes the approaches used to extend the information provided by the
ambient monitor measurement data alone by developing additional air quality standard scenarios
and addressing selected high-concentration environments that may not necessarily be captured
by the existing monitoring network. The approach describing how air quality would be adjusted
to just meet the existing is provided in section 2.4.1. A discussion of how we plan to use the
ambient monitors along with factors to characterize NO2 concentrations across urban study areas,
including those occurring near-roads and on-roads follows (section 2.4.2). A final section (2.4.3)
discusses the calculation of the air quality benchmarks and summary output metrics of interest.
2.4.1 Adjusting air quality to just meet the existing standards
Unadjusted air quality represents ambient conditions as they are at the time of
measurement. While unadjusted air quality presents perspective regarding existing conditions, it
does not necessarily provide the specific effect that just meeting a specific standard has on
ambient concentrations, exposures, and health risk. To evaluate the ability of a specific air
quality standard to protect public health, ambient NO2 concentrations need to be adjusted such
that they simulate levels of NO2 that would just meet the existing standards (i.e., 100 ppb, 98th
percentile DM1H averaged across 3-years; 53 ppb, annual average) or potential alternative
standards. Such adjustments allow for comparisons of the level of public health protection that
could be associated with just meeting the existing and potential alternative standards.
All areas of the United States currently have ambient NO2 levels below the existing
standards, albeit to varying degrees. Therefore, to simulate just meeting the existing NO2
standards, NO2 air quality levels in all study areas must be adjusted upward. Based on evaluating
changes in the distribution in air quality over time, a two-step adjustment approach was used to
adjust the recent ambient concentrations in each study area to just meet the existing standards.
For this two-step adjustment approach, a proportional approach was used, as described in the
REA Planning document (and used in the 2008 REA), though here the proportional adjustment
was only applied to concentrations up to and including the 98th percentile DM1H (adjustment
step 1). This was based on the observation that concentration changes over time (historical, high
B2-20
-------
concentration years compared with recent, low concentration years) occur in a largely
proportional manner (e.g., Figure B2-9 and Figure B2-10).
An additional modification to address observed deviations from linearity that could occur
at the upper percentile concentrations was used for concentrations above the 98th percentile
(adjustment step 2). In this way, this two-step approach utilizes the simplicity of the proportional
approach used in the 2008 REA but addresses more fully, the observed changes in peak
concentration distributions to better capture the distribution of high NO2 concentrations when
adjusting air quality to meet the existing standards.
To calculate the proportional adjustment factors used for step 1 to estimate concentrations
up to and including the 98th percentile DM1H:
1. Using design values for each monitor having recent (2010-2015) and complete air
quality and considering both the 1-hour and annual standards,18 calculate the
proportional adjustment factor needed to just meet the existing standards by dividing
the standard level by the appropriate monitor design value. Then, identify the monitor
in each study area having lowest adjustment factor for each year/3-year period (i.e.,
indicating the area design value monitor and the controlling standard).19
2. Calculate 98th percentile DM1H concentrations for the near-road monitor (where data
are available in a single year)20 and for the highest concentration monitor in each
study area for the same years available, using the full concentration distribution and
for where simultaneous measurements exist between the two monitor types.
Select the set of proportional adjustment factors based on overall consistency in area
design value monitor identity, consistency and reasonableness in the factor level, and whether
the limited new near road monitor data available indicate the potential for a dramatically
different adjustment factor.
18 See http://www.epa.gov/airtrends/values.html. For this draft AQC, 2014 was the most recent complete year data available.
The period 2010-2014 includes five annual design values and three 3-year averaged hourly design values. Design values for
2015 were calculated here using complete year ambient monitor data available.
19 Often times, only one of the two existing standards (1 -hour or annual) would be the controlling standard in a particular
area, and is identified by the monitor design value that is arithmetically closest to the particular standard. Based on the level and
form of the existing standard DM1H standard, it is expected to be the controlling standard.
20 Only two near-road monitors had three years of continuous monitoring available for this review (one each in Detroit and
St. Louis). Therefore, in most stuidy areas design values DM1H standard cannot be calculated using the near-road monitor data.
It remains informative to calculate similar averaging time metrics for each monitor (near-road and area-wide) using years where
comparable statistics can be calculated.
B2-21
-------
Atlanta (131210048)
High Year: 1985, Low Year: 2007
Atlanta (131210048)
High Year: 1985, Low Year: 2008
~i 1 1 1 r~
20 40 60 80 100
High Year Concentration (ppb)
New York (340273001)
High Year: 1984, Low Year: 2007
0 20 40 60 80 100 120
High Year Concentration (ppb)
New York (340273001)
High Year: 1984, Low Year: 2011
0 50 100 150 200
High Year Concentration (ppb)
Philadelphia (421010004)
High Year: 1984, Low Year: 2007
50 100 150
High Year Concentration (ppb)
Philadelphia (421010004)
High Year: 1984, Low Year: 2013
100 150 200
High Year Concentration (ppb)
50 100 150 200
High Year Concentration (ppb)
Figure B2-9. Distribution of DM1H NO2 concentrations (0 - 100th percentile) for a high-
concentration year (1980s) versus a low-concentration year (2000s) adapted from Rizzo
(2008) (left panel) and updated comparison with a recent low-concentration year (right
panel). Atlanta (top panel), New York/New Jersey (middle panel), and Philadelphia
(bottom panel) study areas.
B2-22
-------
Chicago (170314002)
High Year: 1983, Low Year: 2007
Chicago (170314002)
High Year: 1983, Low Year: 2013
50 100 150
High Year Concentration (ppb)
50 100 150
High Year Concentration (ppb)
Denver (080013001)
High Year: 1980, Low Year: 2007
Denver (080013001)
High Year: 1980, Low Year: 2013
100 150 200
High Year Concentration (ppb)
50 100 150 200
High Year Concentration (ppb)
o o -
Los Angeles (060371701)
High Year: 1985, Low Year: 2007
100 200 300
High Year Concentration (ppb)
o o —
Los Angeles (060371701)
High Year: 1985, Low Year: 2013
100 200 300
High Year Concentration (ppb)
Figure B2-10. Distribution of DM1H NO2 concentrations (0 - 100th percentile) for a high-
concentration year (1980s) versus a low-concentration year (2000s) adapted from Rizzo
(2008) (left panel) and updated comparison with a recent low-concentration year (right
panel). Chicago (top panel), Denver (middle panel), and Los Angeles (bottom panel) study
areas.
B2-23
-------
The collection of all proportional adjustment factors calculated and those used for each
study area are provided in two tables; those using the 3-year average design value data (Table
B2-5) and/or the single year near-road/highest monitor data (Table B2-6) for adjusting individual
years. Table B2-7 indicates the decision process and selection reasoning, where judgements were
made beyond simply choosing any available design value. In all instances where design values
were calculated, the DM1H concentrations resulted in the lowest adjustment factors, thus
indicating the hourly standard is the controlling standard in each of the 23 selected study areas.
To calculate the second set of factors used for adjusting the ambient concentrations above
the 98th percentile DM1H to just meet the existing standard, the following approach was used:
1. For each monitor, using recent, complete year ambient monitor concentrations (2003-
2015)21, divide the DM1H concentrations that are above the 98th percentile DM1H22
by the 98th percentile DM1H, considering each year separately.
2. Generate the mean ratio for each DM1H across all years available23 for each
individual monitor by averaging the values calculated in step 1.
3. Where ratios cannot be reasonably calculated for an individual monitor (i.e., the
monitor is newly sited, such as the near-road monitors), ratios from the area design
value monitor are used.
There is general consistency in the adjustment factor across monitors upwards to about the
4th highest maximum, but beyond this point up to the maximum DM1H adjustment factor there is
increasing variability in the factor across the monitors (Table B2-8). The maximum adjustment
factor by far exhibited the widest range of values within each study area and across study areas.
When comparing the overall distribution of the area design value monitor ratios to the other area-
wide monitor ratios within each study area, the area design value monitor had the highest ratios
in 7 of study areas (Baltimore, Detroit, Denver, Miami, Richmond, St. Louis, and Washington
DC). In 7 study areas the area design value monitor had some of the lowest ratios (Atlanta,
Boston, Houston, Kansas, Los Angeles, Pittsburgh, and Sacramento), while the remaining 8
study areas had their design value monitor ratios falling within the middle of the collection of
21 The collection of years used to calculate the upper percentile concentraiton ratios (2003-2015) was based on the "NOx
SIP CALL" (63 FR 57354) finalized October 27,1998 that required reduction measures to be in place for most of the U.S. by
2003.
22 For a full year of ambient data, there would be 365 or 366 DM1H concentrations. Therefore, upwards to seven unique
ratios could be calculated using these seven days having DM1H concentrations above the 98th percentile DM1H.
23 Thus, an adjustment factor for each monitor and DM1H was calculated by averaging across the, at most, 13 ratio values
(years).
B2-24
-------
area-wide monitors. Given the observed range of these adjustment factors, it is possible that, if
the variability in upper percentile concentrations at the area design value monitors is not
appropriately reflecting that of the near-road monitors, there could be an under-/over-estimation
in the number of days per year at or above benchmark levels when considering the near-road and
on-road results.
B2-25
-------
Table B2-5. Proportional adjustment factors calculated from ambient monitor design values in each of the 23 selected study
areas.
CBSA Title
Adjustment Factors Calculated Using Annual Design Values
Adjustment Factors Calculated Using DM1H Design Values
2010
2011
2012
2013
2014
2015
2010-12
2011-13
2012-14
2013-15
Atlanta-Sandy Springs-Roswell, GA
3.786
4.077
4.417
5.889
4.818
2.650
1.786
1.961
2.041
2.083
Baltimore-Columbia-Towson, MD
2.944
2.944
3.313
3.533
2.944
2.944
1.754
1.923
1.923
2.174
Boston-Cambridge-Newton, MA-NH
2.789
2.650
2.789
2.944
3.118
3.118
1.961
2.000
2.041
1.961
Chicago-Naperville-Elgin, IL-IN-WI
2.120
2.304
2.409
2.524
2.524
2.944
1.613
2.128
1.587
Dallas-Fort Worth-Arlington, TX
4.077
4.077
4.417
4.417
5.300
5.300
1.786
1.887
2.041
2.128
Denver-Aurora-Lakewood, CO
1.893
2.208
2.120
2.208
2.120
1.963
1.613
1.389
1.389
Detroit-Warren-Dearborn, Ml
4.417
4.417
2.789
2.944
3.313
2.944
2.083
2.273
2.222
2.000
Houston-The Woodlands-Sugar Land, TX
3.533
3.786
3.533
4.077
4.077
4.077
1.667
1.695
1.786
1.923
Kansas City, MO-KS
3.533
3.533
3.786
4.077
4.077
4.077
1.887
1.923
1.961
1.961
Los Angeles-Long Beach-Anaheim, CA
2.038
2.120
2.524
2.304
1.963
2.120
1.493
1.563
1.587
1.613
Miami-Fort Lauderdale-West Palm Beach, FL
5.300
6.625
6.625
6.625
5.889
10.600
2.128
2.174
2.174
2.273
Minneapolis-St. Paul-Bloomington, MN-WI
5.300
5.889
4.818
5.889
3.313
3.786
2.174
2.273
2.222
2.174
New York-Newark-Jersey City, NY-NJ-PA
2.409
2.120
2.409
2.409
2.409
2.409
1.429
1.493
1.515
1.515
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD
2.304
2.650
2.944
3.118
2.944
2.944
1.538
1.639
2.564
2.041
Phoenix-Mesa-Scottsdale, AZ
2.120
2.120
2.038
2.120
2.120
2.409
1.515
1.563
1.563
1.587
Pittsburgh, PA
3.533
3.313
3.786
4.818
5.300
4.077
1.887
2.041
2.778
2.381
Richmond, VA
4.417
5.300
5.300
6.625
6.625
3.786
1.923
2.128
2.326
2.439
Riverside-San Bernardino-Ontario, CA
2.304
2.524
2.409
2.524
2.650
1.767
1.389
1.613
1.887
2.000
Sacramento-Roseville-Arden-Arcade, CA
4.417
4.077
4.417
5.300
4.818
4.818
1.961
2.000
2.083
2.000
San Diego-Carlsbad, CA
2.524
2.650
2.650
2.789
4.077
3.786
1.370
1.370
1.754
1.786
San Francisco-Oakland-Hayward, CA
3.313
3.313
3.533
3.118
3.118
2.944
1.351
1.471
1.639
1.754
St. Louis, MO-IL
4.077
4.077
3.786
3.786
3.786
4.077
1.887
2.041
1.923
Washington-Arlington-Alexandria, DC-VA-MD-WV
2.944
3.313
3.118
4.077
4.818
4.077
1.818
1.961
2.083
2.083
Highlighted are the factors used to adjust ambient NO2 concentrations to just meet the existing (controlling) standard, up to and including the 98th percentile DM1H concentrations.
Where additional data were available to inform the calculation of factors missing or not used from this table (not highlighted), those factors can be found highlighted in Table B2-
6.
B2-26
-------
Table B2-6. Proportional adjustment factors calculated from the single year 98th percentile DM1H NO2 at the highest near-
road (NR) and highest concentration area wide (AW) monitor in each of the 23 selected study areas.
CBSA Title
Based on Simultaneous 1-hr Measurements
Based on All Available 1-hr Measurements2
Adjustment Factors
Number of Samples (n)
Adjustment Factors
2013
2014
20151
2013
2014
2015
2013
2014
2015
AW
NR
AW
NR
AW
NR
AW
NR
AW
NR
AW
NR
AW
NR
AW
NR
AW
NR
Atlanta-Sandy Springs-Roswell, GA
2.203
1.988
2.083
2.028
361
358
200
355
363
2.331
1.901
2.002
2.083
1.848
Baltimore-Columbia-Towson, MD
2.070
1.976
1.848
1.965
359
353
275
341
352
2.141
1.942
1.976
1.848
2.008
Boston-Cambridge-Newton, MA-NH
2.041
2.222
1.613
1.786
1.852
2.000
356
202
181
365
365
362
2.000
2.273
1.613
1.887
1.852
2.000
Chicago-Naperville-Elgin, IL-IN-WI
356
359
365
1.563
1.493
1.603
Dallas-Fort Worth-Arlington, TX
2.119
2.500
2.203
2.227
363
364
274
361
365
2.028
2.105
2.500
2.203
2.227
Denver-Aurora-Lakewood, CO
1.479
1.650
1.305
1.445
1.433
1.567
365
213
365
361
365
359
1.479
1.621
1.305
1.437
1.397
1.567
Detroit-Warren-Dearborn, Ml
2.326
2.083
1.961
2.000
2.062
2.381
365
360
364
364
61
361
2.326
2.083
1.961
1.961
2.062
2.000
Houston-The Woodlands-Sugar Land, TX
1.949
2.049
1.852
1.869
359
365
344
349
261
1.739
1.923
2.049
1.852
1.689
Kansas City, MO-KS
2.262
2.519
1.957
2.193
1.912
2.247
354
171
365
358
365
352
2.079
2.457
1.898
2.193
1.912
2.247
Los Angeles-Long Beach-Anaheim, CA
1.179
1.502
1.582
1.629
336
340
361
353
255
1.404
1.179
1.515
1.553
1.337
Miami-Fort Lauderdale-West Palm Beach
2.941
2.500
344
364
341
56
2.273
2.000
2.326
2.500
Minneapolis-St. Paul-Bloomington, MN-WI
2.632
2.222
2.000
2.083
2.294
2.083
363
271
365
365
361
362
2.326
2.222
2.000
2.083
2.294
2.083
New York-Newark-Jersey City, NY-NJ-PA
1.695
1.111
1.479
1.493
365
363
184
365
365
1.613
1.429
1.111
1.479
1.493
Philadelphia-Camden-Wilmington, PA-NJ-
DE-MD
1.695
1.946
1.587
2.053
355
310
358
345
357
1.942
1.695
1.946
1.587
2.092
Phoenix-Mesa-Scottsdale, AZ
1.587
1.695
1.639
1.887
362
365
322
363
365
1.587
1.563
1.695
1.639
1.887
Pittsburgh, PA
2.564
2.519
2.273
2.232
365
42
122
357
365
2.488
2.222
2.519
2.273
2.232
Richmond, VA
2.725
2.174
2.320
2.237
2.538
2.141
350
92
347
269
352
353
2.497
2.174
2.299
2.237
2.625
2.160
Riverside-San Bernardino-Ontario, CA
1.513
1.370
334
330
358
360
1.580
1.572
1.513
1.370
Sacramento-Roseville-Arden-Arcade, CA
2.083
1.923
363
351
365
80
1.916
1.901
2.198
1.923
San Diego-Carlsbad, CA
1.961
1.923
357
220
365
269
1.333
1.429
1.887
1.923
San Francisco-Oakland-Hayward, CA
1.976
1.934
1.880
2.045
365
365
334
365
365
1.678
1.727
1.934
1.880
2.045
St. Louis, MO-IL
2.041
1.951
2.198
1.718
2.203
2.110
365
365
361
365
352
364
1.980
1.951
2.198
1.718
2.203
2.110
Washington-Arlington-Alexandria, DC-VA-
MD-WV
1.961
2.132
365
72
316
212
1.570
1.587
1.730
2.123
1 Used data from the first near road monitor in operation in the area where data were collected from more than one near road monitor in 2015 or where the value was used in
adjustment as an alternative to the factor generated using the design value.
2 Used data from either the near-road monitor having the greatest number of days monitored in 2015 or the monitor having the lowest adjustment factor when the number of days
monitored was similar.
Highlighted are the factors used to adjust ambient NO2 concentrations to just meet the existing (controlling) standard, up to and including the 98th percentile DM1H concentrations.
B2-27
-------
Table B2-7. Information supporting the selection of proportional factors used to adjust
ambient concentrations (up to and including the 98th percentile DM1H) to just meet the
existing standard.
CBS A Title
Adjustment Factor (AF) Selection Reasoning
Comparison of near-ro
(AW) 98"' pet derived
simultaneous
measurement hours
ad (NR) to area wide
adjustment factors
all measurement
hours
Atlanta-Sandy Springs-
Roswell, GA
Same monitor (13089002) had valid design values for
all four periods. NR AFs are lower though generally
similar in value. Preliminary results show on-road
estimation (2015) unusually higher than expected,
likely a function of the DV based AFs. Will use the
2013-15 DV for 2013-14, for 2015 use the lower value
(130890003) generated from the two NR monitors.
NRKAW (2014-15),
NR2
-------
CBS A Title
Adjustment Factor (AF) Selection Reasoning
Comparison of near-road (NR) to area wide
(AW) 98"' pet derived adjustment factors
simultaneous
measurement hours
all measurement
hours
Minneapolis-St. Paul-
Bloomington, MN-WI
Valid, similar design values for all averaging periods.
For 2011-13 (270370020); 2012-14 and 2013-15
(270031002); both had exact same adjustment used
for 2010-12. NR (270530962) full distribution quite
lower in 2015. Averaging all 2013-2015 NR data for
adjustment using both NR monitors (270370480 and
270530962).
NR1
-------
CBS A Title
Adjustment Factor (AF) Selection Reasoning
Comparison of near-ro
(AW) 98"' pet derived
simultaneous
measurement hours
ad (NR) to area wide
adjustment factors
all measurement
hours
San Francisco-Oakland-
Hayward, CA
Same monitor (060750005) had valid design values for
all four periods
NR
-------
Adjustme
nt factor deri\i
ed from ratio o
ac
: DM1H concenl
ross years 2003
rations to 98"'
-2015
nercentile DMlf
H, averaged
CBSA
Site ID
l-1 DM1H
21"1 DM1H
3"' DM1H
4"' DM1H
5"' DM1H
6"' DM1H
7"' DM1H
482311006
1.244
1.146
1.125
1.100
1.071
1.046
1.026
482570005
1.292
1.208
1.151
1.101
1.061
1.050
1.023
483670081
1.718
1.224
1.154
1.143
1.112
1.046
1.027
484390075
1.170
1.163
1.100
1.036
1.019
1.012
1.012
484391002"
1.249
1.158
1.124
1.092
1.049
1.032
1.011
484392003
1.235
1.096
1.090
1.070
1.052
1.034
1.027
484393009
1.215
1.162
1.107
1.058
1.039
1.028
1.012
484393011
1.211
1.127
1.094
1.072
1.037
1.028
1.017
0803100283
1.428
1.163
1.134
1.103
1.069
1.033
1.021
Denver-Au rora-
Lakewood, CO
0803100273
1.428
1.163
1.134
1.103
1.069
1.033
1.021
080013001
1.305
1.188
1.134
1.097
1.063
1.030
1.012
080310002"
1.428
1.163
1.134
1.103
1.069
1.033
1.021
080310026
1.136
1.108
1.072
1.054
1.034
1.030
1.008
2616300953
1.292
1.160
1.109
1.076
1.053
1.030
1.018
Detroit-Warren-
261630019"
1.292
1.160
1.109
1.076
1.053
1.030
1.018
Dearborn, Ml
261630093
1.302
1.119
1.109
1.050
1.020
1.015
1.013
261630094
1.194
1.119
1.087
1.071
1.048
1.027
1.012
4820110523
1.310
1.178
1.129
1.087
1.049
1.031
1.018
4820110663
1.310
1.178
1.129
1.087
1.049
1.031
1.018
480391004
1.292
1.231
1.145
1.096
1.072
1.041
1.026
480391016
1.341
1.182
1.125
1.105
1.071
1.042
1.037
481671034
1.302
1.216
1.149
1.113
1.083
1.069
1.019
482010024
1.300
1.166
1.099
1.067
1.035
1.016
1.009
482010026
1.564
1.370
1.267
1.180
1.109
1.064
1.019
Houston-The
Woodlands-
Sugar Land, TX
482010029
1.545
1.293
1.195
1.137
1.093
1.037
1.013
482010047
1.366
1.217
1.143
1.089
1.051
1.035
1.013
482010055
1.219
1.148
1.114
1.081
1.05
1.028
1.009
482010075"
1.310
1.178
1.129
1.087
1.049
1.031
1.018
482010416
1.260
1.133
1.098
1.082
1.047
1.034
1.018
482011015
1.476
1.173
1.135
1.086
1.056
1.029
1.012
482011034
1.441
1.221
1.151
1.100
1.055
1.043
1.016
482011035
1.343
1.172
1.122
1.081
1.056
1.030
1.008
482011039
1.406
1.211
1.144
1.091
1.049
1.032
1.011
482011050
1.605
1.401
1.220
1.149
1.109
1.045
1.025
483390078
1.478
1.287
1.186
1.140
1.104
1.051
1.032
2909500423
1.417
1.223
1.163
1.135
1.077
1.039
1.021
Kansas City, MO-
201070002
1.682
1.495
1.313
1.233
1.171
1.100
1.023
KS
202090021
1.396
1.181
1.099
1.056
1.036
1.020
1.013
290950034"
1.417
1.223
1.163
1.135
1.077
1.039
1.021
0603740083
1.242
1.146
1.099
1.076
1.042
1.029
1.013
0605900083
1.242
1.146
1.099
1.076
1.042
1.029
1.013
060370002
1.248
1.166
1.141
1.096
1.07
1.047
1.018
060370016
1.272
1.184
1.134
1.097
1.071
1.039
1.020
060370113
1.290
1.159
1.124
1.093
1.074
1.035
1.019
Los Angeles-
Long Beach-
Anaheim, CA
060371103
1.357
1.214
1.160
1.117
1.060
1.032
1.017
060371201
1.343
1.185
1.116
1.060
1.033
1.022
1.009
060371302
1.197
1.151
1.122
1.082
1.066
1.030
1.011
060371602
1.237
1.172
1.104
1.058
1.035
1.023
1.018
060371701"
1.242
1.146
1.099
1.076
1.042
1.029
1.013
060372005
1.415
1.267
1.168
1.130
1.099
1.062
1.036
060374002
1.404
1.256
1.136
1.100
1.062
1.038
1.006
060374006
1.399
1.156
1.128
1.063
1.042
1.027
1.017
060375005
1.354
1.189
1.140
1.105
1.069
1.037
1.017
B2-31
-------
Adjustme
nt factor deri\i
ed from ratio o
ac
: DM1H concenl
ross years 2003
rations to 98"'
-2015
nercentile DMlf
H, averaged
CBSA
Site ID
l-1 DM1H
21"1 DM1H
3"' DM1H
4"' DM1H
5"' DM1H
6"' DM1H
7"' DM1H
060376012
1.339
1.201
1.163
1.109
1.064
1.034
1.018
060379033
1.219
1.146
1.081
1.051
1.027
1.014
1.009
060590007
1.295
1.186
1.143
1.092
1.062
1.033
1.018
060591003
1.297
1.212
1.150
1.105
1.062
1.040
1.02
060595001
1.280
1.151
1.104
1.075
1.049
1.045
1.015
2703704803
1.352
1.231
1.163
1.111
1.080
1.031
1.025
Minneapolis-St.
Paul-
Bloomington,
MN-WI
2705309623
1.352
1.231
1.163
1.111
1.080
1.031
1.025
270031002
1.202
1.151
1.093
1.064
1.053
1.030
1.010
270370020b
1.352
1.231
1.163
1.111
1.080
1.031
1.025
270370423
1.561
1.205
1.121
1.097
1.069
1.044
1.015
1201100353
1.808
1.235
1.100
1.079
1.067
1.034
1.022
Miami-Fort
120110031
1.569
1.375
1.177
1.114
1.072
1.045
1.010
Lauderdale-
120118002
1.566
1.210
1.142
1.103
1.077
1.049
1.028
West Palm
120860027
1.290
1.178
1.134
1.089
1.062
1.046
1.026
Beach, FL
120864002"
1.808
1.235
1.100
1.079
1.067
1.034
1.022
120990020
1.263
1.194
1.169
1.153
1.067
1.025
1.000
3400300103
1.442
1.270
1.154
1.097
1.060
1.038
1.020
340030006
1.218
1.157
1.116
1.066
1.046
1.015
1.015
340130003
1.290
1.225
1.178
1.116
1.077
1.038
1.024
340131003
1.377
1.240
1.172
1.115
1.077
1.038
1.020
New York-
Newark-Jersey
City, NY-NJ-PA
340170006
1.731
1.280
1.225
1.133
1.083
1.048
1.015
340230011
1.326
1.175
1.118
1.088
1.067
1.038
1.014
340273001
1.437
1.255
1.175
1.148
1.101
1.064
1.042
340390004"
1.442
1.270
1.154
1.097
1.060
1.038
1.020
360050110
1.402
1.207
1.158
1.109
1.078
1.051
1.019
360050133
1.207
1.165
1.112
1.069
1.040
1.023
1.013
360590005
1.486
1.325
1.203
1.141
1.094
1.045
1.021
360810124
1.420
1.253
1.149
1.072
1.039
1.023
1.009
4210100763
1.377
1.205
1.142
1.084
1.057
1.035
1.013
4210100753
1.377
1.205
1.142
1.084
1.057
1.035
1.013
Philadelphia-
Camden-
100032004
1.575
1.228
1.147
1.109
1.072
1.023
340070002c
1.238
1.130
1.097
1.059
1.049
1.043
1.013
Wilmington, PA-
420170012
1.309
1.179
1.102
1.075
1.037
1.029
1.011
NJ-DE-MD
420450002
1.485
1.308
1.140
1.090
1.048
1.030
1.023
421010004b
1.377
1.205
1.142
1.084
1.057
1.035
1.013
421010047
1.546
1.354
1.195
1.141
1.091
1.041
1.013
0401340203
1.300
1.194
1.102
1.066
1.053
1.036
1.017
0401340193
1.300
1.194
1.102
1.066
1.053
1.036
1.017
040130019
1.250
1.152
1.112
1.078
1.054
1.028
1.016
Phoenix-Mesa-
040133002
1.147
1.084
1.064
1.050
1.038
1.029
1.014
Scottsdale, AZ
040133003
1.177
1.116
1.071
1.051
1.029
1.024
1.003
040133010b
1.300
1.194
1.102
1.066
1.053
1.036
1.017
040134011
1.384
1.252
1.155
1.119
1.073
1.046
1.021
040139997
1.156
1.095
1.055
1.045
1.028
1.019
1.017
4200313763
1.199
1.144
1.088
1.067
1.043
1.021
1.015
420030008
1.230
1.132
1.097
1.066
1.050
1.037
1.020
420030010b
1.199
1.144
1.088
1.067
1.043
1.021
1.015
Pittsburgh, PA
420031005
1.454
1.243
1.179
1.123
1.088
1.050
1.028
420031008
1.101
1.043
1.043
1.043
1.043
1.014
1.014
420070014
1.265
1.189
1.142
1.106
1.066
1.036
1.022
421250005
1.368
1.170
1.124
1.103
1.061
1.037
1.012
421255200
1.185
1.161
1.161
1.093
1.069
1.047
1.023
Richmond, VA
5176000253
1.349
1.214
1.178
1.125
1.087
1.063
1.033
B2-32
-------
Adjustme
nt factor deri\i
ed from ratio o
ac
: DM1H concenl
ross years 2003
rations to 98"'
-2015
nercentile DMlf
H, averaged
CBSA
Site ID
l-1 DM1H
21"1 DM1H
3"' DM1H
4"' DM1H
5"' DM1H
6"' DM1H
7"' DM1H
5103600021'
1.349
1.214
1.178
1.125
1.087
1.063
1.033
510870014
1.194
1.125
1.087
1.066
1.049
1.035
1.013
517600024
1.357
1.162
1.095
1.062
1.050
1.025
1.015
0607100273
1.334
1.194
1.122
1.079
1.053
1.035
1.017
0607100263
1.334
1.194
1.122
1.079
1.053
1.035
1.017
060650009
1.251
1.161
1.117
1.084
1.058
1.034
1.012
060650012
1.250
1.112
1.066
1.045
1.033
1.025
1.014
060651003
1.212
1.104
1.099
1.072
1.032
1.019
1.007
060651016
1.862
1.451
1.379
1.219
1.210
1.027
1.018
Riverside-San
Bernardino-
Ontario, CA
060655001
1.238
1.156
1.074
1.058
1.037
1.020
1.005
060658001
1.220
1.158
1.104
1.060
1.037
1.019
1.011
060658005
1.286
1.127
1.096
1.056
1.037
1.030
1.022
060659001
1.239
1.169
1.120
1.097
1.060
1.041
1.018
060710001
1.189
1.120
1.080
1.062
1.040
1.029
1.011
060710306
1.245
1.187
1.091
1.055
1.029
1.015
1.008
060711004
1.223
1.156
1.123
1.089
1.053
1.036
1.017
060711234
1.246
1.180
1.137
1.081
1.054
1.033
1.023
060712002"
1.334
1.194
1.122
1.079
1.053
1.035
1.017
060719004
1.349
1.188
1.127
1.093
1.053
1.031
1.009
0606700153
1.207
1.124
1.092
1.072
1.054
1.023
1.010
060610006
1.203
1.143
1.103
1.074
1.067
1.042
1.017
060670002
1.504
1.210
1.125
1.073
1.051
1.030
1.012
Sacramento-
Roseville-
Arden-Arcade,
CA
060670006"
1.207
1.124
1.092
1.072
1.054
1.023
1.010
060670010
1.225
1.131
1.098
1.070
1.049
1.038
1.014
060670011
1.391
1.223
1.158
1.107
1.068
1.039
1.019
060670012
1.288
1.192
1.131
1.098
1.066
1.036
1.025
060670014
1.328
1.182
1.144
1.079
1.051
1.035
1.029
061130004
1.268
1.188
1.126
1.097
1.070
1.053
1.030
0607310173
1.281
1.213
1.129
1.078
1.060
1.043
1.018
060730001
1.197
1.118
1.089
1.066
1.047
1.033
1.012
060730003
1.259
1.106
1.082
1.057
1.035
1.017
1.003
060730006
1.348
1.182
1.154
1.105
1.064
1.046
1.019
San Diego-
Carlsbad, CA
060731002
1.281
1.152
1.092
1.069
1.042
1.021
1.010
060731006
1.411
1.232
1.150
1.117
1.067
1.042
1.009
060731008
1.312
1.224
1.137
1.095
1.055
1.028
1.010
060731010
1.245
1.182
1.130
1.091
1.053
1.019
1.008
060731014
1.196
1.118
1.078
1.039
1.020
1.000
1.000
060731016
1.239
1.153
1.095
1.080
1.058
1.037
1.000
060732007"
1.281
1.213
1.129
1.078
1.06
1.043
1.018
0600100123
1.325
1.150
1.092
1.077
1.056
1.035
1.016
060010007
1.245
1.165
1.103
1.079
1.052
1.028
1.010
060010009
1.274
1.193
1.145
1.083
1.063
1.032
1.025
060010011
1.233
1.160
1.097
1.073
1.051
1.034
1.014
060012004
1.200
1.168
1.136
1.099
1.053
1.030
1.020
San Francisco-
060012005
2.050
1.262
1.204
1.158
1.090
1.086
1.009
Oakland-
060130002
1.250
1.159
1.116
1.082
1.056
1.031
1.019
Hayward, CA
060131002
1.233
1.176
1.133
1.097
1.058
1.043
1.017
060131004
1.311
1.183
1.131
1.107
1.056
1.034
1.012
060132007
1.335
1.107
1.082
1.067
1.061
1.034
1.027
060410001
1.227
1.138
1.101
1.073
1.052
1.035
1.012
060750005"
1.325
1.150
1.092
1.077
1.056
1.035
1.016
060811001
1.299
1.189
1.137
1.082
1.066
1.049
1.023
St. Louis, MO-IL
2918900163
1.332
1.158
1.095
1.061
1.034
1.016
1.011
B2-33
-------
CBSA
Site ID
Adjustme
l-1 DM1H
nt factor deri\i
21"1 DM1H
ed from ratio o
ac
3"' DM1H
: DM1H concenl
ross years 2003
4"' DM1H
rations to 98"'
-2015
5"' DM1H
nercentile DMlf
6"' DM1H
H, averaged
7"' DM1H
295100094''
1.332
1.158
1.095
1.061
1.034
1.016
1.011
171630010
1.317
1.151
1.113
1.065
1.044
1.022
1.017
171630900
1.179
1.143
1.119
1.096
1.068
1.044
1.029
295100085
1.185
1.141
1.125
1.105
1.068
1.064
1.020
295100086"
1.332
1.158
1.095
1.061
1.034
1.016
1.011
Washington-
Arlington-
Alexandria, DC-
VA-MD-WV
1100100513
1.490
1.334
1.209
1.140
1.096
1.060
1.032
110010025
1.269
1.135
1.103
1.057
1.040
1.022
1.007
110010041b
1.490
1.334
1.209
1.140
1.096
1.060
1.032
110010043
1.347
1.219
1.138
1.078
1.059
1.033
1.018
110010050
1.211
1.139
1.052
1.020
1.020
1.010
1.000
240330030
1.236
1.154
1.081
1.059
1.048
1.009
1.002
510130020
1.211
1.132
1.089
1.066
1.050
1.038
1.020
511071005
1.220
1.141
1.090
1.057
1.028
1.019
1.008
511530009
1.333
1.255
1.124
1.078
1.053
1.034
1.012
515100009
1.190
1.136
1.113
1.069
1.038
1.031
1.020
515100021
1.166
1.129
1.086
1.066
1.050
1.031
1.013
a The near-road monitor used the ratios derived from the monitor having the highest design value.
b The area design value monitor ratios used for adjustments made to upper percentile concentrations at the near-road monitor.
c Monitor ID 340070003 (operating during 2003-2008) is sited in close proximity to newly sited monitor ID 340070002
(operating during 2012-2015). The data from both monitors were combined to calculate ratios.
Regarding the application of these factors used to adjust air quality to just meet the
existing 1-hr standard, the following two steps were applied.
1. Adjust all DM1H concentrations proportionally, up to and including the 98th
percentile DM1H, at all monitors and for each single year in each study area using the
appropriate study area and year adjustment factors derived from Table B2-5 and
Table B2-6 multiplied by the DM1H concentrations. Thus, for all of the proposed
study areas selected in this air quality assessment, a single proportional factor,
derived from one monitor in a study area, is used to adjust concentrations (up to and
including the DM1H 98th percentile) measured at all of the other monitors in that
study area. The monitor having the highest design value will have adjusted
concentrations that just meet the existing hourly standard (a 3-year average DM1H
98th percentile of 100 ppb), while all other monitors will have adjusted hourly
B2-34
-------
concentrations/design values less than that value.24 Because there is overlap in the 3-
year averaging periods considered here, there will be instances where multiple values
are generated for a single year of data. For example, for each monitor in operation
during 2012, three separate estimates of adjusted air quality could be calculated for
that year, using the unique adjustment factors derived from each of the three 3-year
periods (2010-2012, 2011-2013, and 2012-2014), where available.
2. For all DM1H concentrations above the 98th percentile DM1H at each individual
monitor, the adjusted 98th percentile DM1H concentration from step 1 is multiplied
by the suite of mean ratios from Table B2-8 to develop the series of days having
DM1H concentrations above the 98th percentile DM1H. As described above, there are
up to seven DM1H ratios, thus upward to seven adjusted concentrations are estimated
above the 98th percentile DM1H for each monitoring year.
As an example, the results of applying these two adjustments are illustrated in Figure
B2-11 for ambient concentrations measured at the monitor (ID 421010004) across a three-year
standard averaging period (2011-2013). Plotted in this figure are the unadjusted DM1H
concentrations, concentrations adjusted to just meet the existing hourly standard using a
proportional factor alone (the approach used in the 2008 REA), and concentrations adjusted to
just meet the existing hourly standard using a proportional factor and additional factors for
estimating concentrations above the 98th percentile DM1H. In this figure, concentrations at or
above the DM1H 80th percentile are plotted to highlight the upper percentiles of the distribution.
We selected a single year of ambient concentrations (2011) to demonstrate the
calculations, though the same approach applies for all three years in that averaging period. Using
the proportional adjustment alone (an increase of 63.9%, Table B2-5, based on the 2011-2013
averaging period) appropriately increases the 80th and 100th DM1H unadjusted concentrations of
44 and 88 ppb (top panel, gray line, Figure B2-11) to 73 and 144 ppb (top panel, red line, Figure
B2-11). When addressing deviations from linearity above the 98th percentile, the suite of ratios
24 Ideally this is how the standard would work but this is not always the case considering the suite of monitors in operation at any
one time in the study areas. The design value for the hourly standard requires three consecutive years of ambient concentraitons
meeting the completeness criteria. For instance, a monitor may not have met completeness criteria for one year, but during two of
the three years it could have concentrations (the full distribution including the 98th percentile) greater than the monitor used to
calculate the highest design value. Effectively when adjusting concentrations to just meet the standard, the area design value
monitor will have a DM1H 98th percentile average concentration of 100 ppb while this other monitor in this hypothetical situation
will have a DM1H 98th percentile average concentration greater than 100 ppb. It is also possible each of the two individual years
within that three year averaging period have a DM1H 98th percentile greater than 100 ppb.
B2-35
-------
used from Table B2-8 extend the upper percentile concentrations to somewhat higher
concentrations (top panel, blue line, Figure B2-11) when compared with that using a proportional
factor alone to adjust all concentrations. For example, the proportionally adjusted 98th percentile
DM1H concentration of 124 ppb is used with the maximum DM1H adjustment factor of 1.377 to
estimate a maximum DM1H concentration of about 172 ppb for this new air quality distribution.
Note also, because of the three-year averaging period and that each monitoring year has a
unique, variable concentration distribution, there will be years where the 98th percentile DM1H is
above 100 ppb, and years where the 98fe percentile DM1H is below 100 ppb (though still the 3-
year average, [124.4+92.4+84.4]/3, is equivalent to 100 ppb). It also follows that there will be
variable numbers of days per year where DM1H concentration are at or above the selected
benchmarks in each year.
While on average, the expected number of days per year would fall somewhere around 8
for the three-year period (i.e., the 98th percentile DM1H value would be the 8th highest value in a
365-day monitor period), because of this year-to-year variation in ambient concentrations, the
actual range benchmark exceedances when considering the individual years will likely extend
above and below this average value. For instance, Figure B2-11 (top panel) indicates that 2011
has the highest adjusted 98th percentile DM1H of 124.4 ppb in the three-year period. Based on
this we know, at a minimum, there would be at least 8 days in that year having DM1H
concentrations > 100 ppb. Further, the figure indicates the DM1H concentration of 100 ppb falls
somewhere between the 93rd and 94th percentile of the 2011 adjusted ambient concentration
distribution, indicating that the number of days per year with DM1H concentrations > 100 ppb is
likely between 21-25 days/year for this year of air quality (i.e., the actual number of benchmark
exceedances for 2011 is calculated as 23 days/year). However, when considering the year having
the lowest adjusted 98th percentile DM1H in that same three-year period (i.e., Figure B2-11,
bottom panel, the 98th pet DM1H for 2013 is 84.4 ppb), there are likely far fewer days/year
having DM1H concentrations > 100 ppb. This is because, for this lowest concentration year
within the standard 3-year averaging period, the DM1H concentration of 100 ppb falls between
the 99th and 100th percentile of the adjusted ambient concentration distribution (i.e., the actual
number of benchmark exceedances for 2013 is calculated as 2 days/year).
B2-36
-------
PHIL421010004-2Q11
| ¦» Asis -w -AII Proportional -* Proportional to 98pct, Non-Linear Above~
124.4
40 50 60 70 80 90 100 110 120 130 140 150 160 1?0
Oaily Maximum 1-hr N02 Concentration (ppb)
PHIL421010004-2012
| -w-As Is
—All Proportional
Proportional to 98ptt, Non-Linear Above
40 50 60 70 80 90 100 110 120 130 140 150 160 170
Daily Maximum 1-hr NOj Concentration (ppb)
P HI L421010004-2013
As Is —Alt Proportional v Proportional to 98pct, Non-Linear Above
40 50 60 70 SO 90 100 110 120 130 140 150 160 170
Dally Maximum 1-hr NOz Concentration (ppb)
Figure B2-11. Distribution of unadjusted (as is) ambient NO2 concentrations, that adjusted
using a proportional factor alone (all proportional), and that adjusted using a combined
proportional factor and ratio approach (proportional to 98th percentile, non-linear above)
in the Philadelphia study area at monitor ID 421010004 across a three-year averaging
period, 2011 (top panel), 2012 (middle panel) and 2013 (bottom panel).
B2-37
-------
2.4.2 Simulating on-road concentrations
The newly designated near-road monitors would best characterize NO2 concentrations
occurring around roadways, when compared to the monitoring information available in the last
review. Based on this newly available data, as well as overall consistency in the information
obtained from the types of monitoring and modeling analyses described in the REA Planning
document, we have concluded that the near-road data would also serve as the basis for estimating
on-road concentrations, generally expected to be similar to or perhaps greater than the near-road
concentrations.
We developed a series of simulation factors to estimate on-road ambient NO2
concentrations from the near-road monitor data, based on measurement data and a statistical
model developed from a near-road study. The simulation factors were derived from a statistical
model similar to that developed in the 2008 REA, though using newly available near-road
monitoring data collected in Las Vegas, NV. Details of the statistical model development and on-
road simulation factor output is fully described in Richmond-Bryant et al. (2017), while
information regarding the measurement data collection are found in Kimbrough et al. (2013).
Fundamentally, based on their forms and the data used to construct these types of statistical
models, the simulation factors derived will generate on-road concentrations that will be greater
than that of the ambient monitor concentrations used to approximate them. That said,
uncertainties remain for where and when the maximum concentrations could occur at fine
temporal and spatial scales based on influential factors such as local meteorological conditions
(e.g., wind speed and direction) and NOx conversion rates.
Briefly, near-road measurements of air quality, traffic, and meteorology were collected at
a study area located adjacent to Interstate-15 (1-15) in Las Vegas NV during Dec. 2008 to Jan.
2010. Downwind sampling sites were located approximately 20 m, 100 m, and 300 m east of the
interstate. Logit-ln functions were developed, considering the influence of local meteorological
conditions (e.g., wind direction and approximate mixing heights).25 The logit-ln functions were
25 We evaluated five model scenarios considering atmospheric conditions and wind direction: 1) all wind and atmospheric
stability conditions combined, 2) winds from the west (210°-330°, where the monitors were downwind of the highway), 3) winds
B2-38
-------
then used to estimate on-road NO2 concentrations and concentrations predicted at varying
distances from the road (i.e., 5, 10, 20, and 30 meters).26 Using each hourly prediction,
statistically modeled on-road NO2 concentrations were compared to the modeled near-road
concentrations to calculate the percent increase in on-road concentrations. As a reminder, while
modeled and observed concentration agreement was reasonable (Figure B2-12), there were no
on-road measurement collected as part of this study, thus an evaluation of the modeled on-road
concentrations could not be performed and remains as an uncertainty.
To a limited extent (and as discussed in the ISA Section 2.5.3.1 that describes near-road
concentration gradients), concentration level can affect the value of the on-road simulation factor
generated using the Las Vegas study data. In general, lower concentration quintiles have greater
percent differences between on-road and away-from-road concentrations than higher
concentration quintiles. Therefore, the distributions of percent increases were stratified by the
near-road concentration distribution quintiles. Because upper percentile concentrations are those
that will most likely lead to the majority of concentrations at or above benchmark levels, here we
selected the factors derived from the upper quantiles (i.e., when near-road concentrations at or
above the 80th percentile). Meteorological conditions also affect the value of the estimated
simulation factor; study conditions where winds were predominantly from the west (downwind
from the road) combined with the presence of atmospheric inversions had a lower percent
difference between on-road and away-from-road concentrations than when winds were
perpendicular to the road combined with conditions associated with greater mixing heights. In
considering these differing meteorological conditions, and seeking a generally conservative
though simple approach using these results to simulate on-road NO2 concentrations here, we
chose three values for estimating on-road concentrations for each distance, based on the two
lowest and highest median values, and the average of these two median values. These on-road
simulation factors are provided in
Table B2-9.
from the east (30°-150°, where the monitors were upwind of the highway), 4) inversion conditions (convective mixing height less
than 300 m), and 5) non-inversion conditions (convective mixing height greater than 300 m).
26 It is possible to generate on-road simulation factors to use for any monitor distance (e.g., 80 m, 200 m from the road),
though the principal objective here was to develop factors to apply to the new near-road monitor concentration data.
B2-39
-------
70
60 i
\
\
N
50 "
Q.
Q.
| 40
1
o 30
u
O
2
20
10
0
0 50 100 150 200 250 300
Distance from road (m)
Figure B2-12. Predicted and observed NO2 concentrations for winds from the west using
based on data from a Las Vegas NV near-road measurement study. Predicted median
(solid), predicted 98th and 2nd percentile (dotted), observed median (circles), and observed
98th and 2nd percentiles (error bars) are shown.
Table B2-9. Factors used to simulate on-road NO2 concentrations from near-road monitors
sited at varying distance from a major road.
Distance
from Road
(meters)
Average upwards adjustment from near-road concentrations given
selected simulation factor used1
Low
Mid
High
5
7%
9%
11%
10
9%
12%
15%
20
12%
16%
19%
30
13%
17%
21%
1 based on Las Vegas, NV near-road measurement study data (see Appendix A of NO2 REA PD).
2.4.3 Calculating number of days at or above benchmark levels
As was discussed above in section 2.4.1, we have identified 1-hour concentrations ranging
from 100, 150, and 200 ppb as the air quality benchmark levels to consider in this air quality
assessment. The complete distribution of DM1H concentrations are used to calculate the number
of days per year concentrations are at or above these benchmark levels at each monitor, for all air
quality scenarios, and within each study area. Specifically, for this draft AQC we generated NO2
air quality benchmark analysis results for 22 study areas having new near-road monitor data and
one study area (Chicago) not yet having a newly designated near-road monitor. The mean and
B2-40
-------
maximum number of days per year having concentrations at or above benchmark levels were
calculated using 2010-2015 air quality for two ambient monitor types (i.e., area wide, near road)
and for where on-road concentrations were simulated.27 The five air quality scenarios include as
is ambient concentrations (unadjusted concentrations, for all years where measured/estimated),
CS1012 (2010-2012 air quality adjusted upwards to just meet the existing standard across the
three-year period), CS1113 (2011-2013, similarly adjusted), CS1214 (2012-2014, similarly
adjusted), and CS1315 (2013-2015, similarly adjusted).
The number of days per year a DM1H exceeds a particular benchmark level are counted
for each monitor in operation. Then these results were summarized using two metrics as follows:
1) Site-Year Metric. Consistent with the NO2 and SO2 REAs (US EPA, 2009; US EPA,
2010, respectively) the first summary result metric is based on monitor site-years of
data, whereas daily exceedances are calculated as such (and considers all individual
site-years in the period of interest, neither weighted by site or year). Because the
means are calculated using data from all monitors in a study area (not just the area
design value monitor), the mean value for this metric would very likely be less than 7
days per year.28 This mean metric can represent, on-average, the number of days per
year at any one site in the study area we could have an exceedance over that
averaging period. The maximum is the single monitor worst air quality year for that
study area. These results are presented using the phrase 'site-year' with calculations
performed for the area wide and near-road monitors separately. Because the on-road
data were mainly limited to measurements during 2014-15 and were mostly from a
single monitor in each study area, these data were presented for those individual years
(means and maximums are not calculated).
For example, the number of days per year having DM1H concentrations above
benchmarks in the St. Louis study area is presented in
27 The number of monitoring days per year can vary, even considering the use of monitors designated as having a valid year of
data. The number of days per year having concentrations at or above benchmark levels was not adjusted considering this fact, that
is to say, if fewer than 365 days were monitored (e.g., only 344 days were available), the number of benchmark exceedances was
not increased by a factor of 1.06. It was assumed that the valid monitoring year reasonably represented what would be considered
a complete year. The same approach was used for the near-road/on-road calculations, that is to say, if only 183 days had valid
measurement data, the number of days per year above benchmarks was not increased by a factor of 1.99 to reflect a full 365-day
period. When presented alone, the near-road/on-road DM1H concentrations at or above benchmarks are generaly presented as
"number of days" rather than "number of days per year".
28 Based on the form of the standard, a value of 7 days per year would expected considering the adjusted area design value
monitor concentrations alone and that all three years of data within the 3-year averaging period meet completeness criteria.
B2-41
-------
Table B2-10. Means were rounded to the nearest integer, though for values <0.50,
rather than round to zero, a value of 0.50 was designated to distinguish it from
instances where there were no (or 0) days where the DM1H was at or above the
benchmarks during the period of interest. Presented in the summary figures that
follow in Section B3 are the mean and maximum values for each of the air quality
scenarios, in each study area, for the area-wide and near-road monitor data. Because
there were three simulation factors (low, mid, high) used in calculating the on-road
concentrations, the results are presented in a summary figure for each study area,
considering 2014 and 2015 individually (the years where ambient monitors had the
greatest number of measurements within the 2013-2015 adjusted air quality scenario).
An important feature to note is that there can be variability in the concentrations
within CBSAs, based on both the year to year variability in concentrations and the
adjustment factor used. For example, when considering monitor 295100086, within
the 3-year averaging period of 2012-14 and using the same series of adjustment
factors for that period, the number of days per year having concentrations at or above
100 ppb is 14, 9, and 2 for years 2012, 2013, and 2014, respectively (
B2-42
-------
Table B2-10), resulting in a coefficient of variation (COV) of about 72%. When
considering benchmark exceedances at the same monitor and within the same year,
though across the three different averaging periods encompassing that year (e.g., 2013
- benchmark exceedances of 4, 9, and 5), the resulting COV is about 44%. In this
latter instance, the three proportional adjustment factors of 1.887, 2.041, and 1.923
(Table B2-5) exhibited much less variability (COV ~ 5%), though when applied to
the same exact ambient concentrations, the slightly higher adjustment factor increased
the estimated concentrations above the benchmark levels by a factor of about 2 when
compared with results from the other averaging periods.
B2-43
-------
Table B2-10. Example site-year metric results in the St. Louis study area: number of days
per year 1-hour NO2 concentrations are at or above 100 ppb, by monitor, year, and air
quality scenario along with summary statistics.
Site ID
year
Number of
Days
Number of days per year 1-hour NO2 concentration > 100 ppb
As Is
CS1113
CS1214
CS1315
Area-wide
171630010
2010
363
0
2011
346
0
0
2013
351
0
1
1
1
2014
365
0
2
1
2015
356
0
0
171630900
2011
361
0
0
2012
365
0
0
0
2013
365
0
0
0
0
2014
359
0
0
0
295100085
2014
361
0
4
1
295100086
2011
349
0
8
2012
350
0
6
14
2013
365
0
4
9
5
2014
365
0
2
1
2015
352
0
2
mean
0
2
4
2
max
0
8
14
5
Near-road
295100094
2013
365
0
5
11
6
2014
365
0
21
10
2015
364
0
2
291890016
2015
357
0
1
mean
0
5
16
5
max
0
5
21
10
On-road
Monitor used
year
scenario
low
mid
high
295100094
2013
As is
0
0
0
2013
CS1315
21
26
37
2014
As is
0
0
0
2014
CS1315
30
37
39
2015
As is
0
0
0
2015
CS1315
13
17
26
291890016
2015
As is
0
0
0
2015
CS1315
4
5
8
Highlighted are the summary statistics provided in the results figures that follow in Section B3.
B2-44
-------
2) CBSA-wide Metric. A new frame of reference was developed for this draft AQC to
account for all days having concentrations at or above benchmarks in each study area.
Rather than consider the monitors singularly (as in the above site-year calculation),
the calendar year for the study area as a whole was considered, that is, there could be
days where more than one monitor has a DM1H at or above a benchmark, though at
times, the exceedances occur on different days of the year. Further, there could be
days where more than one monitor exceeded a given benchmark on the same day. To
analyze the data for each year and study area, the days of the year having a DM1H
concentration at or above a benchmark were retained for each monitor, then
combined to yield a CBSA-wide calendar record for all unique days where a
benchmark was exceeded (i.e., exceedances occurring at any monitor in the CBSA,
including the near-road monitor). The mean value is calculated here as the average of
the 3 years considered in the 3-year averaging period for each study area, the
maximum is the single worst air quality year in the study area. Of course, because it is
a calendar record and includes instances where benchmark exceedances are not
restricted to a single monitoring site (but now the days per year is CBSA-wide), the
number of days per year at or above a benchmark calculated using this CBSA-wide
metric is greater than that generated using the site-year metric, and the means are
typically at or above that would be expected to occur at an area design value monitor
when concentrations are adjusted to just meet the existing standard (i.e., greater than
7 days per year). This mean metric represents 'on-average' the number of days per
year somewhere across the study area the DM1H was at or above a benchmark level
across the 3-year period. Again, the maximum number of days is the worst air quality
year for that study area. These same two statistics (mean and maximum) were
calculated for the number of days in the study area having two monitors having
DM1H concentrations at or above the benchmarks, though occurring on the same day.
And finally, this CBSA-wide metric was expanded to also include all three
concentration locations (area-wide, near-road and on-road). Because most locations
only had the 2014-15 near road data (and hence 2014-15 on-road estimates), the
results are presented for the 2013-2015 period. Also, only the on-road concentrations
simulated using the 'mid' factor were used for the CBSA-wide metric.
An example is provided below, continuing with the St. Louis study area results. The
summary data are first presented in
Table B2-11
B2-45
-------
Table B2-11. These summary data are what would be presented in figures in Section B3,
though would also include results for the other study areas. Results are shown for thelOO
ppb benchmark only, because as in most study areas, exceedances of the higher
benchmarks occurred much less frequently, if at all. In this study area, there were no
simulated on-road concentrations at or above any benchmarks when considering the
unadjusted as is air quality, thus this data is not shown. The data from which the summary means
and maximums were calculated are derived from
Table B2-12, which in turn were generated based on the individual monitor DM1H
concentrations presented in Table B2-13. The CBSA-wide metric calculation including the
simulated on-road concentrations is calculated in a similar manner, though in addition to the
concentrations at all monitors also includes the concentrations on days when the simulated on-
road concentrations are at or above the benchmark levels (not shown). As expected given that the
on-road concentration simulation is based on the near-road monitor data, at a minimum the
benchmark exceedances occur on the same days the near-road monitor had a DM1H
concentration at or above a benchmark level. Though most often, the number of days per year
DM1H on-road concentrations are at or above benchmarks is greater than that estimated for the
near-road monitor.
Table B2-11. Example CBSA-wide metric results in the St. Louis study area: summary
statistics for the number of days per year where NO2 concentration > 100 ppb - anytime in
the study area and for instances when it occurred on the same day at two monitoring
locations.
CBSA-wide metric
Number of days per year where DM1H NO ¦ concentration > 100 ppb
mean
maximum
As is
CS1012
CS1113
CS1214
CS1315
As is
CS1012
CS1113
CS1214
CS1315
Any monitor in
CBS A
0
No DV
7
17
8
0
No DV
8
23
13
At two monitors
(2 x 100 ppb)
0
No DV
1
3
1
0
No DV
3
5
4
Presented are the summary statistics provided in the results figures that follow in Section B3.
Table B2-12. Example CBSA-wide metric results in the St. Louis study area: number of
days per year where NO2 concentrations > 100 ppb - data stratified by averaging period
and year.
Year
Number of days pc
concentr
Any monitor in
study area
>r year where DM1H NO ¦
ation > 100 ppb
At two monitors on
same day
Number of
Monitors
(n)
3-year
averaging
period
2011
8
0
3
1113
2012
6
0
2
1113
2013
6
3
4
1113
B2-46
-------
2012
14
0
2
1214
2013
15
5
4
1214
2014
23
5
5
1214
2013
7
4
4
1315
2014
13
0
5
1315
2015
5
0
4
1315
Table B2-13. Example CBSA-wide metric results in the St. Louis study area: NO2
concentrations stratified by averaging period, year, month, day, and monitor on days
where NO2 concentrations > 100 ppb.
year
month
day
Daily maximum 1-hour N02 on days when concentrations > 100 ppb
monl71630010
monl71630900
mon295100086
mon295100094
mon295100085
mon291890016
2011-2013 (CS1113
averaging period
2011
1
2
45.3
15.9
114.7
2011
1
3
37.7
50.9
139.5
2011
1
5
47.2
56.8
104.7
2011
1
25
80.4
63.0
101.1
2011
2
3
72.9
67.0
111.1
2011
2
4
67.9
61.5
108.3
2011
10
4
69.8
78.8
121.3
2011
10
22
39.6
83.0
106.4
2012
1
6
48.7
100.3
2012
3
28
59.6
114.3
2012
4
1
11.3
108.1
2012
6
13
54.0
102.1
2012
8
23
46.2
104.7
2012
9
4
32.6
131.4
2013
2
23
91.2
63.2
110.4
128.8
2013
2
24
104.4
71.4
126.9
112.0
2013
3
15
73.6
42.5
101.1
105.9
2013
3
22
24.5
14.7
43.8
100.0
2013
3
28
34.0
18.3
57.0
102.6
2013
4
4
88.2
62.3
104.4
98.3
2012-2014 (CS1214
averaging period
2012
1
6
52.7
108.5
2012
2
17
52.0
107.9
2012
3
28
64.5
123.6
2012
4
1
12.2
116.9
2012
4
8
45.1
101.8
2012
5
12
45.1
103.1
2012
5
14
45.3
106.5
2012
5
22
61.6
100.4
2012
6
9
20.2
103.3
2012
6
13
58.4
110.4
B2-47
-------
year
month
day
Daily maximum 1-hour N02 on days when concentrations > 100 ppb
monl71630010
monl71630900
mon295100086
mon295100094
mon295100085
mon291890016
2012
8
23
50.0
113.3
2012
9
4
35.3
142.2
2012
11
15
67.9
100.6
2012
11
16
65.7
101.8
2013
1
15
55.1
42.5
103.1
56.1
2013
2
16
75.5
38.2
88.4
105.7
2013
2
23
98.7
68.4
119.4
139.3
2013
2
24
112.9
77.2
137.3
121.1
2013
3
15
79.6
45.9
109.4
114.6
2013
3
22
26.5
15.9
47.4
108.2
2013
3
27
73.5
74.9
106.6
100.2
2013
3
28
36.7
19.8
61.6
111.0
2013
3
29
91.3
40.4
104.7
69.4
2013
4
2
87.2
37.8
104.2
90.7
2013
4
3
81.6
23.9
100.0
89.1
2013
4
4
95.4
67.4
112.9
106.3
2013
4
5
79.6
55.3
94.1
101.3
2013
9
6
34.9
58.8
100.2
2013
10
28
81.6
58.2
91.6
104.6
2014
1
28
71.4
56.3
58.2
108.1
77.1
2014
2
7
115.6
75.2
58.8
86.0
50.2
2014
2
11
83.7
53.3
59.0
130.1
89.2
2014
2
12
97.7
79.2
75.9
120.7
105.9
2014
2
13
101.0
71.7
82.5
137.6
99.2
2014
2
14
91.6
61.8
54.3
120.1
84.1
2014
3
1
81.6
44.9
68.8
100.9
80.0
2014
3
4
93.4
65.7
66.9
103.0
110.0
2014
3
6
87.8
73.6
75.3
126.0
91.0
2014
3
7
87.8
69.1
79.8
102.8
80.8
2014
3
10
81.6
37.4
48.4
118.8
79.2
2014
3
13
63.3
54.9
58.8
103.9
80.4
2014
3
15
81.6
44.9
64.3
100.2
85.7
2014
3
20
87.8
33.1
70.0
103.0
78.0
2014
4
11
65.3
41.2
70.8
100.9
102.7
2014
4
20
57.1
42.2
88.2
101.9
80.0
2014
4
21
46.9
26.9
85.5
110.3
94.7
2014
7
11
34.7
25.1
32.0
100.0
51.2
2014
9
25
44.9
47.8
70.2
122.9
84.9
2014
9
26
49.0
51.6
117.7
97.2
92.9
2014
9
27
51.0
63.7
102.3
106.1
104.5
2014
11
24
30.6
14.1
25.1
158.2
32.7
B2-48
-------
year
month
day
Daily maximum 1-hour N02 on days when concentrations > 100 ppb
monl71630010
monl71630900
mon295100086
mon295100094
mon295100085
mon291890016
2014
12
12
44.7
34.9
57.6
104.0
57.1
2013-2015 (CS1315
averaging period
2013
2
23
93.0
64.4
112.5
131.2
2013
2
24
106.3
72.8
129.3
114.1
2013
3
15
75.0
43.3
103.0
107.9
2013
3
22
25.0
15.0
44.6
101.9
2013
3
27
69.2
70.5
100.5
94.4
2013
3
28
34.6
18.7
58.1
104.6
2013
4
4
89.9
63.5
106.4
100.2
2014
1
28
67.3
53.1
54.8
101.8
72.7
2014
2
7
108.9
70.8
55.4
81.1
47.3
2014
2
11
78.8
50.2
55.6
122.6
84.0
2014
2
12
92.0
74.6
71.5
113.7
99.8
2014
2
13
95.2
67.6
77.7
129.6
93.5
2014
2
14
86.3
58.3
51.2
113.1
79.2
2014
3
4
88.0
61.9
63.1
97.0
103.6
2014
3
6
82.7
69.4
71.0
118.7
85.8
2014
3
10
76.9
35.2
45.6
111.9
74.6
2014
4
21
44.2
25.4
80.6
103.9
89.2
2014
9
25
42.3
45.0
66.2
115.8
80.0
2014
9
26
46.2
48.7
110.9
91.5
87.5
2014
11
24
28.8
13.3
23.7
149.0
30.8
2015
3
2
88.2
105.6
71.3
2015
3
7
80.3
101.1
89.8
96.9
2015
3
31
72.9
87.1
99.8
111.4
2015
4
1
58.7
79.0
121.4
79.2
2015
9
25
69.2
116.3
55.8
61.5
Highlighted are the DM1H NO2 concentrations that were used in counting the number of days per year at or above
benchmark levels summarized in Table B2-11 and Table B2-12.
B2-49
-------
B3. RESULTS
This section contains the analysis results for the AQC. An overview of the two distinct
analyses is presented in this section followed by sections detailing the analytical results along
with a brief discussion.
For the first analysis, we used the historical (1980-2015) unadjusted (as is) air quality to
provide context for the benchmark exceedance calculations, focusing here on instances where
ambient concentrations have been at or just around the level of the existing 1-hour standard. It is
worth noting that the historical measurement data are representative of real air quality scenarios
that existed at the time the monitoring took place and that changes in emissions control and
atmospheric conditions that have occurred since that time would preclude us from drawing
complete conclusions about the number of exceedances associated with a given 98th percentile
DM1H NO2 concentration, if attempting to use such information as a prediction for future air
quality. Nevertheless, using these unadjusted ambient concentration data remain informative
given the general consistencies in the overall concentration distribution over time at each
monitor and what would be expected regarding the number of exceedances given the form of the
existing 1-hour standard (i.e., for a complete year of data, on average, there would be about 7
days having concentrations at or above the 98th percentile DM1H value of 100 ppb) and the
approach used to adjust concentrations to just meet the existing 1-hour standard.
For the second analysis, i.e., the core results for the AQC in the selected study areas, we
used the most recently available (2010-2015) ambient NO2 monitoring data. Exceedances of
benchmark levels are calculated for the five air quality scenarios (unadjusted as is ambient
concentrations and three sets of concentrations adjusted to just meeting the existing 1-hour
standard) and for the three distinct ambient concentration types (area-wide, near-road, and
simulated on-road).
3.1 ANALYSIS OF HISTORICAL (1980-2015) AIR QUALITY
For this analysis, we first calculated all of the rolling 3-year average 98th percentile
DM1H values for all individual monitors in operation from 1980-2015 that met the completeness
criteria described above (section 2.2). Historical data were used to ensure that concentrations
would be at or around the level of the existing hourly standard. Counted first were the number of
days per year NO2 concentrations were at or above the 1-hour benchmark levels (i.e., 100, 150,
and 200, if any) for the individual years within each 3-year averaging period. Then the mean
number of days per year was calculated (thus, the average of the observed number of days for
each monitor across the 3-year averaging period). Also identified were the maximum number of
B3-1
-------
days per year NO2 concentrations were at or above the 1-hour air quality benchmark levels (thus,
the highest observed number of exceedances at each monitor for a single year in the averaging
period) given the 3-year average 98th percentile DM1H for that monitor. Results of this analysis
are presented in Figure B3-1.
Based on the analysis of all available historical NO2 ambient monitored concentrations
and considering the form, level, and averaging time of the existing 1-hour standard, on average
across a 3-year period, the number of days/year where the DM1H was > 100 ppb ranged from
about 6 to 13 days (Figure B3-1, top left panel). This mean number of days per year, on average,
corresponds well with general expectations described above (i.e., on average there could be
about 8 days/year at or above the 100 ppb benchmark given the form of the standard). The
maximum number of days in a single year that the DM1H was > 100 ppb ranged from about 10
to 20 days (Figure B3-1, top right panel). When considering the 150 ppb benchmark, there are
far fewer days per year exceeding that level when considering ambient concentration at the
existing standard. In most instances, the mean number of days per year where the DM1H was >
150 was less than two (Figure B3-1, middle left panel), while the maximum number of days per
year at or above the same benchmark was less than five (Figure B3-1, middle right panel).
Furthermore, and according to this analysis of all available historical ambient measurement data,
exceeding a 1-hour benchmark level of 200 ppb is a rare occurrence when considering the form
and level of the existing 1-hour standard. For example, of the 23 instances a monitor had a 3-year
average 98th percentile DM1H of 100 ppb, there were no exceedances of the 200 ppb benchmark
on 19 of these occasions (Figure B3-1, bottom right panel). When averaging across the 3-year
period, the mean number of days per year having DM1H NO2 concentrations at or above 200
ppb drops to 1 or less, again with most monitors recording no concentrations at or above the 200
ppb 1-hour benchmark.
It should be noted that for this analysis of historical ambient NO2 concentrations, monitors
in the California CBSAs (Los Angeles, San Francisco, etc.) constitute the bulk of the data where
the 3-year average 98th percentile DM1H concentrations were at or above 100 ppb. However, the
results of this analysis when excluding these areas are similar (data not shown), albeit with a
generally tighter range of values than when including the monitoring data in California (e.g., the
mean number of days per year having DM1H >100 ppb ranged from about 6 to 9). When
considering data only from the 23 selected study areas (not shown), the number of days per year
having DM1H at or above benchmarks and given a particular 3-year average 98th percentile
DM1H is also consistent with that presented in Figure B3-1.
B3-2
-------
Mean Number of Days per Year when DM1H > 100 ppb
Maximum Number of Days per Year when DM1H > 100 ppb
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3-year Avg DM1H 98,h Percentile N02 Concentration (ppb)
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Maximum Number of Days per Year when DM1H > 200 ppb
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3-year Avg DM1H 98th Percentile N02 Concentration (ppb)
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3-year Avg DM1H 98th Percentile N02 Concentration (ppb)
Figure B3-1. The mean (left panel) and maximum (right panel) number of days per year
where DM1H NO2 concentration was > 100 ppb (top panel), > 150 ppb (middle panel), and
> 200 ppb (bottom panel) associated with 3-year average 98th percentile DM1H 1NO2
concentrations, using valid-year 1980-2015 ambient monitor data.
B3-3
-------
To provide additional context for upper percentile daily maximum concentrations below
the lowest benchmark level of 100 ppb, we also used the historical (1980-2015) ambient
monitoring data to evaluate the number of days per year where the DM1H was at or above three
other concentration levels (i.e., 85, 90, and 95 ppb). To do so, we first isolated ambient monitor
data where the 3-year average 98th percentile DM1H NO2 concentrations were at or between 95
and 105 ppb. Then, as was done above for the selected benchmark levels, counted for each of the
years within a 3-year averaging period were the number of days per year each monitor had an
observed concentration at or above 85, 90, and 95 ppb. Table B3-1 provides similar results as
shown in Figure B3-1, though focusing primarily where the 3-year average 98th percentile
DM1H NO2 concentrations were close to the existing NO2 NAAQS. For tracking purposes and
to conceptually link the tabular results to what had been described previously for the 100 ppb
benchmark results depicted in Figure B3-1, the results for the 100 ppb benchmark are provided
in Table B3-1.
Table B3-1. The number of days per year where DM1H NO2 concentration was > 85 ppb, >
90 ppb, > 95 ppb, and > 100 ppb associated with 3-year average 98th percentile DM1H NO2
concentrations at or near the existing NO2 NAAQS, using valid-year 1980-2015 ambient
monitor data.
Selected
DM1H Level
Mean number of days per year where DM1H
NO: concentration at or above selected level
min
mean
max
85
7
20
44
90
7
16
30
95
6
10
22
100
4
8
15
Min: the minimum value of any 3-year average, i.e. the smallest
mean number of days per year occurring at one monitor and
averaged over a 3-year period.
Mean: the mean of all means, i.e., the mean of the mean number
of days per year occurring at each monitor and averaged over a 3-
year period.
Max: the maximum value of any 3-year average, i.e. the greatest
mean number of days per year occurring at one monitor and
averaged over a 3-year period.
3.2 ANALYSIS OF RECENT AIR QUALITY (2010-2015) IN SELECTED
STUDY AREAS
A total of five air quality scenarios were evaluated in each of the 23 selected study areas
(i.e., unadjusted as is ambient NO2 concentrations and NO2 concentrations adjusted to just meet
the existing hourly standard for four 3-year averaging periods) considering the area-wide and
near-road monitoring data available for 2010-2015. We also simulated on-road NO2
B3-4
-------
concentrations based on the available near-road monitoring data.29 Counted were the number of
days per year the DM1H ambient concentrations exceeded the 1-hour benchmark levels of 100,
150, and 200 ppb, considering the area-wide, near-road and on-road concentrations. Because
most study areas had very few days where concentrations were at or above the 150 ppb
benchmark level, the results presented in this section are for the 100 ppb benchmark level only.
Complete results for the 100 ppb benchmark and the other benchmarks are provided in tables in
the Section B5. Presented in each figure in this section are the mean and maximum number of
days per year considering the study area data on a site-year basis, and CBSA-wide basis, as was
described in Section 2.4.3.
Figure B3-2 presents the results for the site-year metric, considering the area-wide
monitors only. Note that where a value does not appear in this Figure for a particular study area,
in all instances, that is because it is equal to zero (no benchmark exceedances). Further, any non-
zero mean values that were less than 1 though not rounding upwards to 1 are presented as equal
to 0.5. When considering the unadjusted (as is) air quality, very few exceedances of the 100 ppb
benchmark were observed during the recent area-wide monitor data analyzed. The maximum
observed values in a single year was seven (Riverside CA) and three (Houston), while most
study areas had a one or no days per year DM1H concentrations > 100 ppb.
When considering air quality adjusted to just meet the existing standard, the results were
generally similar for the four different 3-year averaging periods. In general, the average number
of days per year having 1-hr NO2 concentrations at or above 100 ppb ranged from about 2 to 5 in
the majority of study areas, while the maximum number of days above this same benchmark was
generally about 10 days per year, consistent with the expected number of days per year when
considering the complete suite of monitors in each study area, the elements of the 1-hr standard,
and the procedure used to adjust air quality, as discussed above.
Figure B3-3 presents the results for the site-year metric, considering the near-road
monitors only. Note that where a value does not appear in this figure for a particular study area
29 A few study areas had 2013 data, many more had 2014 data, while all had but one (Chicago) had 2015 data. Where
available, 2013 data were included in the analysis and generation of results though the focus of the near-road and on-road
analysis is on 2014-15.
B3-5
-------
and considering the unadjusted (as is) air quality, in all instances, that is because it is equal to
zero (i.e., no benchmark exceedances) because all study areas had measured concentrations at
some time during the 2010-2015 data analysis period. Where a value does not appear in this
figure for a particular study area and considering the adjusted air quality scenarios, in all but one
instance,30 that is because there were no near-road data available in that 3-year averaging period.
Therefore, in considering these results, compared with the results generated using the area-wide
monitors (Figure B3-2), inferences should be limited to results generated using the unadjusted
concentrations, and the 2013-2015 3-year averaging period.
There were very few simulated on-road concentrations at or above the 1-hour 100 ppb
benchmark considering the unadjusted as is air quality (results not shown). Only the Chicago,
Denver, Los Angeles, Miami, New York/Jersey, and Riverside study areas were estimated to
have at least one day in the monitored year, with the New York/Jersey study area also having one
day per year at or above the 150 and 200 ppb benchmark levels (data shown in Table
B5-58). Figure B3-4 and Figure B3-5 show the number of days where the simulated on-
road DM1H NO2 concentration was at or above the 100 ppb benchmark considering 2014
and 2015 air quality adjusted to just meet the existing 1-hour standard, respectively. In
general, most study areas had between 10 to 20 days in the year >100 ppb and there was
limited variability in the range of values estimated using the three simulation factors. Of
the few study areas having > 20 days in the year, Baltimore, Minneapolis, Pittsburgh, and
Richmond required some of the greatest upward adjustments to simulate just meeting the
existing standard, i.e., adjustment factors used in these study areas were > 2 (Table B2-5),
while most other study area adjustment factors were generally within 1.5 to 1.9. In
addition, the Minneapolis study area used the highest on-road simulation factors because
both monitors were sited about 30 meters from the road (
Table B2-9).
30 The Chicago study area had no (zero) days per year with a DM1H > 100 ppb during the 2011-2013 3-year averaging
period.
B3-6
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B3-9
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B3-10
-------
As expected, there were a greater number of benchmark exceedances considering the
CBS A-wide metric (Figure B3-6) compared to results generated using the site-year metric
(Figure B3-2 and Figure B3-3). This is because the CBSA-wide metric accounts for the total
number of days in the study area when DM1H NO2 concentrations go above benchmark levels,
not just those estimated to occur at a single monitor at a time but considering all monitors
simultaneously operating in the study area. The difference between the two result metrics is an
indicator of how the peak NO2 concentrations vary by day across the study area, and in most
instances, the variability is driven by the highest concentrations occurring at the area design
value monitor and/or the near-road monitor within a study area. On average, most study areas
had about 10 to 15 days per year at or above the 100 ppb benchmark, while the maximum
number of days per year was about 20 when considering the CBSA-wide metric, generally about
a factor or two greater than that estimated using the site-year metric. The number days per year
where DM1H NO2 concentrations were at or above benchmark levels and occurring at more than
one site on the same day was much less; on average 3 days per year DM1H NO2 concentrations
were at or above 100 ppb, while the maximum number of days per year was about 5 (Figure
B3-7), thus indicating simultaneous-site exceedances about a third or less of the time there was at
least one day at or above 100 ppb.
The CBSA-wide metric was also expanded to include the benchmark exceedances
resulting from the simulated on-road concentrations (Figure B3-8) in addition to consideration of
the near-road and area-wide CBSA-wide results discussed above. Because the majority of near-
road data were from 2014-2015, only the results for the 2013-2015 3-year averaging period are
shown. CBSA-wide results considering the area-wide and near-road monitors (Figure B3-8, top
panels) are identical to that shown in Figure B3-6 and Figure B3-7 for that 3-year averaging
period. They were reproduced in this figure for comparison with the CBSA-wide results that also
include exceedances from all three concentration types (i.e., area-wide, near-road, and simulated
on-road) (Figure B3-8, bottom panels). For most study areas, there were about 5 to 10 additional
days per year in the CBS A (on average and for the maximum) having concentrations at or above
the 100 ppb benchmark level, when including the simulated on-road concentrations to the
CBSA-wide metric (visually compare the top left to bottom left panels Figure B3-8). Again,
there fewer days per year where concentrations are at or above the 100 ppb benchmark level and
occurring at two or more locations on the same day (e.g., at both an area-wide and near-road
monitor), though when including the simulated on-road concentrations in the CBSA-wide metric
(Figure B3-8, bottom right panel), the number of occurrences is about twice that of the results
generated using the area-wide and near-road data alone (Figure B3-8, top right panel)
B3-11
-------
3.3 UNCERTAINTY CHARACTERIZATION
Overall, there were a number of assumptions made in this AQC, potentially leading to
uncertainty in the results. Assumptions were well informed and controlled to reduce uncertainties
to the maximum extent possible such as, 1) selection of study areas having the greatest number
of monitors and highest concentrations, thereby reducing the uncertainty in the spatial
representation of ambient concentrations and adjustments made to meet the existing standards, 2)
a systematic selection of the most appropriate factors used to adjust concentrations to just meet
the existing standard considering both the varying degree of completeness of the area-wide
monitors and the limited availability of near-road monitor concentrations, and 3) the use of
newly designated near-road monitor data to best characterize near-road and on-road
concentrations.
The direction of influence these assumptions and modeling approaches could have on the
results of this AQC would vary, and be marked as either under- or over-estimations in
concentrations and number of days at or above benchmark levels, while also potentially having
varied magnitudes. For instance, it is a worthwhile reminder that in some study areas, the near-
road monitor did not have a full monitoring year of data (e.g., in 2014 the NYNJ CBSA near-
road monitor had concentrations for 184 days). Depending on the time of year and hour of day, it
is possible that having a full year of near-road data could lead to a different number of
benchmark exceedances for each of the summary calculations presented here. For example,
maximum concentrations typically occur during the fall/winter early morning weekday commute
hours, thus presence or absence of monitoring during these time could greatly influence the
calculated number of days having benchmark exceedances. In the absence of having a robust
data near-road dataset describing more specifically when or where peak concentrations would
occur (the particular days or periods of the year), we felt at this early stage of the monitor
implementation it was best to characterize the near-road data and simulated on-road
concentrations simply using the actual number of monitored days in the year rather than
including predicted concentrations for the days the near-road monitor was not in operation.
Another important uncertainty regards the design values used to generate factors for
adjusting concentrations to just meet the existing standard. Individual results presented for St.
Louis (section 2.4.3) indicated that small variation in adjustment factors used (COV of-5%)
could have a much greater effect (i.e., a factor of two) in the estimated number of days at or
above the 100 ppb benchmark, even considering the same distribution of ambient concentrations.
While appropriate steps were taken to account for instances where the highest monitor in
operation was included in the calculation of these adjustment factors, having a very limited
number of years (or even days in a year) available for the near-road monitors in most instances
led to the use of area-wide monitor design values to calculate the adjustment factors. It is
B3-12
-------
possible that having complete year data for the near-road monitors in each of the study areas
could result their use as the area design value monitor, such as what has been realized during the
2013-15 averaging period in the Detroit and St. Louis study areas. By not having several
complete years of near-road data available for this analysis, it is possible that both near-road and
on-road concentrations are overestimated, particularly in instances where available comparisons
indicate the area design value monitor concentrations are less than that at the near-road monitor.
Further contributing to the uncertainty in adjusted concentrations were inconsistencies in the area
design value monitor in some study area (e.g., Philadelphia) due to absence of meeting
completeness criteria across several averaging periods. Often times, pseudo single-year design
values were calculated from the highest available monitor (sometime near-road other time near-
road) and used to estimate the adjustment factors to overcome this uncertainty, other times the
adjustment factor was not used and the period was not simulated.
It is also possible that the upper (>98th) percentile DM1H ratios developed from the area
design value monitor and used to adjust upper percentile concentrations for the near-road
monitors (and hence, on-road concentrations) could lead to undesired variability in near-road and
on-road concentrations. However, when comparing the ratio values derived from the area design
value monitor to ratios observed at the other area-wide monitors within each study area, the
majority fell within the range exhibited by the area-wide monitors, suggesting the magnitude of
influence is limited. Even when ratios derived from the area design value monitor were at the
maximum range of values, they are considered reasonable approximations, because in most
areas, the observed variability in the upper percentile concentrations at the near-road monitors is
expected to be similar to that observed at the highest monitors in the study areas.
And finally, as mentioned earlier in describing the approach to simulating on-road
concentrations using factors derived from a statistical model along with the near-road monitored
concentrations, in some instances could lead to overestimations in the estimated number of days
at or above the benchmark levels occurring on-roads. This is in part because the model-derived
factors used assume concentrations on-roads are always greater that those occurring away from
roads. It is possible that for some near-road monitors sited in close proximity to the road (i.e.,
within 10 meters), that measured near-road concentrations reasonably approximate
concentrations occurring on-roads. Further, while a range of on-road simulation factors were
used to represent potentially important influential conditions (e.g., atmospheric stability and
wind direction), the application of the factors was candid, not accounting for the complexities
associated with these conditions at each monitor for every hour measurements were recorded.
This along with not accounting for the variability in the estimated factors (the mean of each the
low, mid and high conditions was used rather than the full distribution of values) would also
B3-13
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contribute to uncertainty in the estimated concentrations, likely resulting in both under and over
estimations.
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B4. REFERENCES
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Exposure Assessment Planning Document. U.S. EPA, Office of Air Quality Planning and Standards.
Research Triangle Park, NC. EPA-452/D-15-001. May 2015. Available at:
https://www3.epa.gov/ttn/naaas/standards/nox/data/20150504reaplanning.pdf
U.S. EPA. (2016). Integrated Science Assessment for Oxides of Nitrogen - Health Criteria (2016 Final Report). U.S.
Environmental Protection Agency, Washington, DC, EPA/600/R-15/068, 2016. Available at:
https://cfpub.epa.gov/ncea/isa/recordisplav.cfm?deid=310879
B4-2
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B5. SUPPLEMENTAL DATA
5.1 CBSA RANKED NOx EMISSION TABLES FOR TOP 20 FACILITY
TYPES
Table B5-1. CBSA ranked NOx emissions for top 20 facility types: electricity via
combustion and airports.
1. Electricity Generation via Combustion
CBSA
NOx (tpy)
Kansas City, MO-KS
22053
Pittsburgh, PA
19492
Farmington, NM
17204
York-Hanover, PA
16965
Urban Honolulu, HI
16330
Paducah, KY-IL
15825
Louisville/Jefferson County, KY-IN
14108
Rock Springs, WY
13179
St. Louis, MO-IL
12118
Bismarck, ND
11366
Denver-Aurora-Lakewood, CO
11310
Birmingham-Hoover, AL
11091
Chicago-Naperville-Elgin, IL-IN-WI
10251
Jacksonville, FL
9757
Detroit-Warren-Dearborn, Ml
9431
New York-Newark-Jersey City, NY-NJ-PA
9007
San Antonio-New Braunfels, TX
8845
Milwaukee-Waukesha-West Allis, Wl
6906
Lake Charles, LA
6505
Cincinnati, OH-KY-IN
6490
2. Airports
CBSA
NOx (tpy)
New York-Newark-Jersey City, NY-NJ-
PA
9396
Los Angeles-Long Beach-Anaheim, CA
6713
Chicago-Naperville-Elgin, IL-IN-WI
6424
Miami-Fort Lauderdale-West Palm
Beach, FL
5518
San Francisco-Oakland-Hayward, CA
4266
Dallas-Fort Worth-Arlington, TX
4171
Washington-Arlington-Alexandria, DC-
VA-MD-WV
3214
Houston-The Woodlands-Sugar Land,
TX
3178
Memphis, TN-MS-AR
3173
Denver-Aurora-Lakewood, CO
3153
Phoenix-Mesa-Scottsdale, AZ
2789
Seattle-Tacoma-Bellevue, WA
2733
Las Vegas-Henderson-Paradise, NV
2541
Charlotte-Concord-Gastonia, NC-SC
2435
Orlando-Kissimmee-Sanford, FL
2352
Detroit-Warren-Dearborn, Ml
2326
Minneapolis-St. Paul-Bloomington,
MN-WI
2287
Philadelphia-Camden-Wilmington,
PA-NJ-DE-MD
2260
Boston-Cambridge-Newton, MA-NH
2216
Urban Honolulu, HI
1758
Highlighted are those selected as study areas.
B5-1
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Table B5-2. CBSA ranked NOx emissions for top 20 facility types: chemical plants and
petroleum refineries.
3. Chemical Plant
CBSA
NOx (tpy)
Houston-The Woodlands-Sugar Land, TX
12719
Baton Rouge, LA
10345
Kingsport-Bristol-Bristol, TN-VA
9113
Beaumont-Port Arthur, TX
6778
Rochester, NY
2610
Lake Charles, LA
2590
Marshall, TX
2031
Memphis, TN-MS-AR
1267
New Orleans-Metairie, LA
1053
Chicago-Naperville-Elgin, IL-IN-WI
695
Cincinnati, OH-KY-IN
688
Philadelphia-Camden-Wilmington, PA-NJ-
DE-MD
570
Paducah, KY-IL
336
Indianapolis-Carmel-Anderson, IN
175
Cleveland-Elyria, OH
165
St. Louis, MO-IL
158
Baltimore-Columbia-Towson, MD
150
Los Angeles-Long Beach-Anaheim, CA
136
Jacksonville, FL
114
San Francisco-Oakland-Hayward, CA
111
4. Petroleum Refineries
CBSA
NOx (tpy)
Houston-The Woodlands-Sugar Land,
TX
7971
Beaumont-Port Arthur, TX
5554
Lake Charles, LA
4726
Philadelphia-Camden-Wilmington,
PA-NJ-DE-MD
4533
Los Angeles-Long Beach-Anaheim, CA
3918
San Francisco-Oakland-Hayward, CA
3204
Mount Vernon-Anacortes, WA
2983
Chicago-Naperville-Elgin, IL-IN-WI
2548
Baton Rouge, LA
2253
Gulfport-Biloxi-Pascagoula, MS
2177
Vallejo-Fairfield, CA
1969
Minneapolis-St. Paul-Bloomington,
MN-WI
1296
Urban Honolulu, HI
1272
New York-Newark-Jersey City, NY-NJ-
PA
1143
Huntington-Ashland, WV-KY-OH
1023
Tulsa, OK
987
Salt Lake City, UT
703
Denver-Aurora-Lakewood, CO
670
El Paso, TX
628
Memphis, TN-MS-AR
532
Highlighted are those selected as study areas.
B5-2
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Table B5-3. CBSA ranked NOx emissions for top 20 facility types: Portland cement
manufacturing and mines/quarries.
5. Portland Cement Manufacturing
CBSA
NOx (tpy)
Riverside-San Bernardino-Ontario, CA
4831
Allentown-Bethlehem-Easton, PA-NJ
3621
San Antonio-New Braunfels, TX
3439
Bakersfield, CA
3390
Dallas-Fort Worth-Arlington, TX
3077
Austin-Round Rock, TX
2245
Birmingham-Hoover, AL
1819
San Jose-Sunnyvale-Santa Clara, CA
1755
Miami-Fort Lauderdale-West Palm
Beach, FL
1663
Tucson, AZ
1634
Davenport-Moline-Rock Island, IA-IL
1605
Reading, PA
1225
Louisville/Jefferson County, KY-IN
1097
Seattle-Tacoma-Bellevue, WA
918
Rapid City, SD
900
Albuquerque, NM
718
Kansas City, MO-KS
647
Waco, TX
465
York-Hanover, PA
158
Bozeman, MT
13
6. Mines/Quarries
CBSA
NOx (tpy)
Gillette, WY
28560
Salt Lake City, UT
4478
Rock Springs, WY
446
Birmingham-Hoover, AL
386
Cheyenne, WY
154
Riverside-San Bernardino-Ontario, CA
125
Las Vegas-Henderson-Paradise, NV
118
Riverton, WY
92
Urban Honolulu, HI
69
Cleveland-Elyria, OH
68
Glenwood Springs, CO
54
Nashville-Davidson-Murfreesboro-
Franklin, TN
25
Carlsbad-Artesia, NM
21
Minneapolis-St. Paul-Bloomington,
MN-WI
20
Casper, WY
19
Worcester, MA-CT
16
Baltimore-Columbia-Towson, MD
14
Philadelphia-Camden-Wilmington,
PA-NJ-DE-MD
11
Kansas City, MO-KS
10
San Diego-Carlsbad, CA
9
Highlighted are those selected as study areas.
B5-3
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Table B5-4. CBSA ranked NOx emissions for top 20 facility types: rail yards and
compressor stations.
7. Rail Yard
CBSA
NOx (tpy)
Chicago-Naperville-Elgin, IL-IN-WI
3005
Memphis, TN-MS-AR
2022
Houston-The Woodlands-Sugar Land, TX
1950
Dallas-Fort Worth-Arlington, TX
1714
Kansas City, MO-KS
1714
Riverside-San Bernardino-Ontario, CA
1264
Austin-Round Rock, TX
1197
Salt Lake City, UT
1180
St. Louis, MO-IL
867
Atlanta-Sandy Springs-Roswell, GA
856
Buffalo-Cheektowaga-Niagara Falls, NY
757
Denver-Aurora-Lakewood, CO
662
Birmingham-Hoover, AL
661
Cincinnati, OH-KY-IN
636
Portland-Vancouver-Hillsboro, OR-WA
595
Stockton-Lodi, CA
569
Sacramento-Roseville-Arden-Arcade,
CA
557
Little Rock-North Little Rock-Conway, AR
550
Pittsburgh, PA
547
Beaumont-Port Arthur, TX
540
8. Compressor Station
CBSA
NOx (tpy)
Hobbs, NM
5208
Glenwood Springs, CO
4516
Farmington, NM
3105
Gillette, WY
2358
Oklahoma City, OK
2135
Rock Springs, WY
1725
Riverside-San Bernardino-Ontario, CA
1648
Indianapolis-Carmel-Anderson, IN
1583
Dallas-Fort Worth-Arlington, TX
1198
Denver-Aurora-Lakewood, CO
659
Carlsbad-Artesia, NM
639
Baton Rouge, LA
610
Birmingham-Hoover, AL
509
Harrisburg-Carlisle, PA
502
Athens, OH
501
Chicago-Naperville-Elgin, IL-IN-WI
482
Marshall, TX
481
Reading, PA
444
York-Hanover, PA
444
Riverton, WY
392
Highlighted are those selected as study areas.
B5-4
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Table B5-5. CBSA ranked NOx emissions for top 20 facility types: sources not
characterized and municipal waste combustors.
9. Not Characterized
CBSA
NOx (tpy)
Chicago-Naperville-Elgin, IL-IN-WI
3827
Riverside-San Bernardino-Ontario, CA
1539
New York-Newark-Jersey City, NY-NJ-PA
1464
Baltimore-Columbia-Towson, MD
1456
Pensacola-Ferry Pass-Brent, FL
1365
Minneapolis-St. Paul-Bloomington, MN-
Wl
1004
Los Angeles-Long Beach-Anaheim, CA
891
Santa Maria-Santa Barbara, CA
838
Bakersfield, CA
693
Austin-Round Rock, TX
667
San Francisco-Oakland-Hayward, CA
614
Denver-Aurora-Lakewood, CO
588
Dallas-Fort Worth-Arlington, TX
567
Miami-Fort Lauderdale-West Palm
Beach, FL
548
Philadelphia-Camden-Wilmington, PA-NJ-
DE-MD
534
Houston-The Woodlands-Sugar Land, TX
529
Las Vegas-Henderson-Paradise, NV
528
Baton Rouge, LA
497
San Diego-Carlsbad, CA
492
Rock Springs, WY
491
10. Municipal Waste Combustor
NOx (tpy)
Miami-Fort Lauderdale-West Palm
Beach, FL
4994
New York-Newark-Jersey City, NY-NJ-
PA
3058
Tampa-St. Petersburg-Clearwater, FL
2552
Boston-Cambridge-Newton, MA-NH
2494
Philadelphia-Camden-Wilmington,
PA-NJ-DE-MD
2308
Detroit-Warren-Dearborn, Ml
1617
Bridgeport-Stamford-Norwalk, CT
1220
Baltimore-Columbia-Towson, MD
1184
Indianapolis-Carmel-Anderson, IN
1076
Hartford-West Hartford-East
Hartford, CT
1064
Urban Honolulu, HI
1043
Worcester, MA-CT
865
Minneapolis-St. Paul-Bloomington,
MN-WI
594
Lancaster, PA
577
Tulsa, OK
564
York-Hanover, PA
498
Washington-Arlington-Alexandria, DC-
VA-MD-WV
471
Los Angeles-Long Beach-Anaheim, CA
424
Modesto, CA
317
Ogden-Clearfield, UT
261
Highlighted are those selected as study areas.
B5-5
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Table B5-6. CBSA ranked NOx emissions for top 20 facility types: steel mills and gas
plants.
11. Steel Mill
CBSA
NOx (tpy)
Chicago-Naperville-Elgin, IL-IN-WI
10939
Detroit-Warren-Dearborn, Ml
2776
Pittsburgh, PA
1886
Cleveland-Elyria, OH
1291
Huntington-Ashland, WV-KY-OH
1243
Baltimore-Columbia-Towson, MD
1166
Birmingham-Hoover, AL
1095
Harrisburg-Carlisle, PA
310
Dallas-Fort Worth-Arlington, TX
298
Philadelphia-Camden-Wilmington, PA-NJ-
DE-MD
261
Reading, PA
239
El Paso, TX
234
Beaumont-Port Arthur, TX
230
Seattle-Tacoma-Bellevue, WA
205
Portland-Vancouver-Hillsboro, OR-WA
193
Fort Wayne, IN
170
Memphis, TN-MS-AR
125
Longview, TX
113
Jacksonville, FL
78
Charlotte-Concord-Gastonia, NC-SC
53
12. Gas Plant
CBSA
NOx (tpy)
Hobbs, NM
5051
Farmington, NM
4053
Rock Springs, WY
1404
Gillette, WY
1041
Longview, TX
571
Carlsbad-Artesia, NM
556
Evanston, WY
392
Bakersfield, CA
372
Riverton, WY
359
Baton Rouge, LA
274
Beaumont-Port Arthur, TX
259
Marshall, TX
212
San Antonio-New Braunfels, TX
198
Kingsport-Bristol-Bristol, TN-VA
162
Dallas-Fort Worth-Arlington, TX
160
Casper, WY
145
Oklahoma City, OK
128
Cheyenne, WY
98
Houston-The Woodlands-Sugar Land,
TX
88
Minneapolis-St. Paul-Bloomington,
MN-WI
63
Highlighted are those selected as study areas.
B5-6
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Table B5-7. CBSA ranked NOx emissions for top 20 facility types: mineral processing
plants and pulp and paper plants.
13. Mineral Processing Plant
CBSA
NOx (tpy)
Rock Springs, WY
7689
Riverside-San Bernardino-Ontario, CA
1872
Las Vegas-Henderson-Paradise, NV
1200
State College, PA
940
Corsicana, TX
705
Detroit-Warren-Dearborn, Ml
547
Manitowoc, Wl
389
Fort Smith, AR-OK
221
Bakersfield, CA
159
Cincinnati, OH-KY-IN
60
Little Rock-North Little Rock-Conway, AR
44
Baltimore-Columbia-Towson, MD
26
Salinas, CA
22
Boston-Cambridge-Newton, MA-NH
18
Chicago-Naperville-Elgin, IL-IN-WI
16
Minneapolis-St. Paul-Bloomington, MN-
Wl
5
Tampa-St. Petersburg-Clearwater, FL
5
Los Angeles-Long Beach-Anaheim, CA
4
Oxnard-Thousand Oaks-Ventura, CA
4
Glenwood Springs, CO
2
14. Pulp and Paper Plant
CBSA
NOx (tpy)
Columbia, SC
2036
York-Hanover, PA
1721
Pensacola-Ferry Pass-Brent, FL
1654
Duluth, MN-WI
1255
Baton Rouge, LA
1132
Charleston-North Charleston, SC
952
Kingsport-Bristol-Bristol, TN-VA
818
Beaumont-Port Arthur, TX
752
Portland-South Portland, ME
463
Philadelphia-Camden-Wilmington,
PA-NJ-DE-MD
459
Riverside-San Bernardino-Ontario, CA
140
Memphis, TN-MS-AR
125
Cincinnati, OH-KY-IN
117
Dallas-Fort Worth-Arlington, TX
74
San Jose-Sunnyvale-Santa Clara, CA
68
Reading, PA
61
New York-Newark-Jersey City, NY-NJ-
PA
59
Owensboro, KY
59
Oxnard-Thousand Oaks-Ventura, CA
55
Tulsa, OK
52
Highlighted are those selected as study areas.
B5-7
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Table B5-8. CBSA ranked NOx emissions for top 20 facility types: institutional (e.g.,
schools, hospitals, prisons) and gas plants.
15. Institutional (school, hospital, prison, etc.)
CBSA
NOx (tpy)
Chicago-Naperville-Elgin, IL-IN-WI
797
New York-Newark-Jersey City, NY-NJ-PA
769
Lansing-East Lansing, Ml
739
Nashville-Davidson-Murfreesboro-
Franklin, TN
602
South Bend-Mishawaka, IN-MI
580
Worcester, MA-CT
347
Fairbanks, AK
339
Los Angeles-Long Beach-Anaheim, CA
294
Philadelphia-Camden-Wilmington, PA-NJ-
DE-MD
287
Boston-Cambridge-Newton, MA-NH
279
Minneapolis-St. Paul-Bloomington, MN-
Wl
269
State College, PA
258
Lexington-Fayette, KY
251
Denver-Aurora-Lakewood, CO
231
Cincinnati, OH-KY-IN
198
Portland-South Portland, ME
197
San Francisco-Oakland-Hayward, CA
175
Washington-Arlington-Alexandria, DC-
VA-MD-WV
175
Baltimore-Columbia-Towson, MD
148
Provo-Orem, UT
131
16. Glass Plant
CBSA
NOx (tpy)
Corsicana, TX
2364
Pittsburgh, PA
1128
Nashville-Davidson-Murfreesboro-
Franklin, TN
850
Waco, TX
781
Fresno, CA
619
Jacksonville, FL
484
Chicago-Naperville-Elgin, IL-IN-WI
464
Portland-Vancouver-Hillsboro, OR-WA
407
Stockton-Lodi, CA
404
Atlanta-Sandy Springs-Roswell, GA
350
San Francisco-Oakland-Hayward, CA
328
Madera, CA
327
Seattle-Tacoma-Bellevue, WA
299
Worcester, MA-CT
230
Modesto, CA
195
Los Angeles-Long Beach-Anaheim, CA
70
Houston-The Woodlands-Sugar Land,
TX
42
Providence-Warwick, RI-MA
36
Tulsa, OK
10
Charleston-North Charleston, SC
4
Highlighted are those selected as study areas.
B5-8
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Table B5-9. CBSA ranked NOx emissions for top 20 facility types: chlor-alkali plants and
fertilizer plants.
17. Chlor-Alkali Plant
CBSA
NOx (tpy)
Lake Charles, LA
6194
Wichita, KS
508
Baton Rouge, LA
170
Cleveland, TN
44
Houston-The Woodlands-Sugar Land, TX
18
Philadelphia-Camden-Wilmington, PA-NJ-
DE-MD
2
18. Fertilizer Plant
CBSA
NOx (tpy)
Baton Rouge, LA
3793
Cheyenne, WY
1718
Gulfport-Biloxi-Pascagoula, MS
322
Tampa-St. Petersburg-Clearwater, FL
319
Pittsburgh, PA
243
Rock Springs, WY
132
Provo-Orem, UT
106
Carlsbad-Artesia, NM
74
Sacramento-Roseville-Arden-
Arcade, CA
36
Cincinnati, OH-KY-IN
19
Salisbury, MD-DE
16
Fresno, CA
15
Houston-The Woodlands-Sugar Land,
TX
12
Riverside-San Bernardino-Ontario, CA
4
Stockton-Lodi, CA
3
Chico, CA
1
Merced, CA
1
Nashville-Davidson-Murfreesboro-
Franklin, TN
1
Vallejo-Fairfield, CA
1
Winston-Salem, NC
1
Highlighted are those selected as study areas.
B5-9
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Table B5-10. CBSA ranked NOx emissions for top 20 facility types: coke battery and
plastic, resin, or rubber product plants.
19. Coke Battery
CBSA
NOx (tpy)
Pittsburgh, PA
3502
Birmingham-Hoover, AL
1839
Chicago-Naperville-Elgin, IL-IN-WI
859
Huntington-Ashland, WV-KY-OH
355
Buffalo-Cheektowaga-Niagara Falls, NY
153
Erie, PA
106
20. Plastic, Resin, or Rubber Products Plant
CBSA
NOx (tpy)
Baton Rouge, LA
2009
Louisville/Jefferson County, KY-IN
778
Stockton-Lodi, CA
633
Marshall, TX
465
Beaumont-Port Arthur, TX
459
Houston-The Woodlands-Sugar Land,
TX
379
Springfield, MA
335
Cincinnati, OH-KY-IN
190
Lake Charles, LA
167
Greenville-Anderson-Mauldin, SC
117
Salt Lake City, UT
115
Pittsburgh, PA
79
Philadelphia-Camden-Wilmington,
PA-NJ-DE-MD
58
Buffalo-Cheektowaga-Niagara Falls,
NY
54
Providence-Warwick, RI-MA
46
Portland-Vancouver-Hillsboro, OR-WA
39
Dallas-Fort Worth-Arlington, TX
37
Denver-Aurora-Lakewood, CO
31
Chicago-Naperville-Elgin, IL-IN-WI
29
New Haven-Milford, CT
29
Highlighted are those selected as study areas.
B5-10
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5.2 ATTRIBUTES OF AMBIENT NO2 MONITORS USED FOR THE 2010-
2015 ANALYSIS
Brief descriptions of the attributes, the sources of data, and any data processing steps
follow provided in section 5.2.
1. Ambient monitor meta-data. Data sets containing general attributes (e.g., geographic
coordinates, local land use, monitor setting, etc.) were obtained from AQS31 and from
querying the AQS database directly.32 The land-use field indicates the prevalent land
use within Vi mile of the monitoring site.33 The measurement scale represents the air
volumes associated with the monitoring area dimensions.34 The monitor objective
describes the monitor in terms of its attempt to generally characterize health effects,
photochemical activity, transport, or welfare effects.35 Monitors most useful for
evaluating public health would be characterized as having a monitor objective of
population exposure and/or highest concentration. In addition, county land areas (mi2)
were estimated from the U.S. Census Bureau.36
2. NOx source emissions data. For characterizing mobile source emissions associated
with each ambient monitor, the same county file described above for evaluating
emissions across all CBSAs was used. Presented separately in this analysis are the
NOx emissions from on-road, non-road, and air-craft/marine/locomotive (AML)
mobile sources as well as the total NOx and percent NOx contributed by all mobile
sources combined. For characterizing facility NOx emissions potentially associated
31 https://www3.epa.gov/airdata/ad_maps.html
32 https://aas.epa.gov/api.
33 Land-use is characterized here as either agricultural (AGRIC), commercial (COMM), desert (DESERT), forest
(FOREST), Industrial (INDUS), military reservation (MILIT), mobile (MOBILE), residential (RESID), or unknown (LINK).
34 Measurement scales for monitors typically are characterized as microscale (in close proximity, up to 100 m from a
source), middle ( 100 m to 0.5 km), neighborhood (500 m to 4 km), or urban (4 to 50 km).
35 Objectives for monitors are characterized here as population exposure (POP_EXP), highest concentration (HI_CONC),
source oriented (SOURCE), general/background (GEN/BKGR), extreme downwind (DOWNWND), maximum ozone
concentration (MAX_03), maximum precursor emissions (MAX_PRE), other (OTH), unknown (UNK), or upwind boackground
(UP_BKGR), regional transport (REG TRANS), quality assuramce (QUAL ASSUR).
36 https://www.census.gOv/support/USACdataDownloads.html#LND. For land area, variable 'LND110210D' was used.
B5-11
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with measured concentrations at each monitor, the 'facility-level by pollutant'
summary file containing emissions for the entire U.S. was downloaded from the 2011
NEI.37 For each ambient monitor, all facilities within a 5 km radius were identified,
then having in their emissions summed based on facility type. Facility types having
aggregated emissions greater than or equal to 10 tpy were retained.
3. Annual average daily traffic data. For characterizing ambient monitor distances to
roads for all monitors (except for the new near-road monitors), we used shapefiles
obtained from the U.S. Department of Transportation (U.S. DOT) Federal Highway
Administration (FHA) 2011 Highway Performance Monitoring System (HPMS).38 Of
relevance here were annual average daily traffic (AADT) data for all roadways39 and
their identities, where available. These data were mapped using ArcMap version
10.3.1 and then joined to NO2 ambient monitors by using the 'Generate near Table'
function. This analysis used a 1000-meter search radius to join a monitor to the 100
segments of road identified by the HPMS closest to it, and all data were projected to
North American Lambert Conformal Conic, GCS North American 1983. From this
output, two metrics were developed. The first was developed using the road closest to
the monitor, while the second was developed using the road having the maximum
AADT. For both of these metrics, the AADT value, the road distance from the
monitor, and the road identity were retained.
Monitor map locations: A plot of all the monitors in each study area is provided using
ArcGIS. Individual satellite views of the near-road and area design value monitors were
generated using google maps and the respective monitor latitudes (lat) and longitudes (Ion).
37 http://aasdrl.epa.gov/aasweb/aastmp/airdata/download files.html#Meta. Note that for historical monitoring data, the
2011 NEI may not necessarily represent actual emissions when the monitor was in operation during earlier years.
38 https://www.fhwa.dot.gov/policvinformation/hpms/shapefiles.cfm.
39 Roads are those characterized as part of the HPMS-defmed Federal-Aid System and include 1) interstate, 2) principal
arterial - other freeways and expressways, 3) principal arterial - other, minor arterial, 4) major collector, 5) urban minor
collector, and 6) other highways that are designated as part of the National Highway System.
B5-12
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Atlanta-Sandy Springs-Roswell, GA
Monitors
Design Monitor
Near Road
Not Used
Study Monitor
Archie Darity Q
131210056
130890003
HBslaateaBr
130890002
Figure B5-1. Map of all monitors (1990-2015) in the Atlanta study area (top panel) and
satellite views of near-road (middle panels) and area design value monitor (bottom panel)
B5-13
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Table B5-11. Attributes of ambient monitors within the Atlanta study area having recent
(2010-2015) valid-year NO2 concentrations.
State abbreviation
GA
GA
GA
GA
GA
County name
DeKalb
DeKalb
Fulton
Paulding
Rockdale
Site ID
130890002
130890003
131210056
132230003
132470001
Lat
33.68797
33.69861
33.77832
33.9285
33.59108
Lon
-84.29048
-84.27261
-84.39142
-85.04534
-84.06529
year_start
1978
2014
2014
1995
1994
year_end
2016
2016
2016
2015
2015
Elevation m
308
238
286
417
219
land use
RESID
COMM
MOBILE
AGRIC
AGRIC
scale
URBAN
MICRO
MICRO
URBAN
URBAN
objectivel
POP EXP
HI CONC
SOURCE
UP BKGR
MAX 03
objective2
MAX PRE
REG TRANS
POP EXP
GEN/BKGR
POP EXP
objective3
HI CONC
QUALASSUR
POP EXP
GEN/BKGR
cnty_landarea_2010
268
268
527
312
130
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
597
30
2
road closest
285
285
85
aadt closest
140820
146000
284920
dist aadtmax
642
road aadtmax
285
aadt aadtmax
141700
County Level NOx Emissions (tpy)
OnRoad mobile nox
11230
11230
16404
1780
1529
NonRoad mobile nox
1962
1962
3939
410
329
AML mobile nox
376
376
1560
297
30
total nox
14719
14719
23989
2688
2112
pct_mobile_nox
92.2
92.2
91.3
92.5
89.4
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Landfill
45
45
Rail Yard
28
Area design value monitor
Near-road monitor
B5-14
-------
Baltimore-Columbia-Towson, MD
Monitors
Design Monitor
Near Road
Not Used
240270006
Rest Area and.Welcome"
CenteiTOaryland
245100040
Figure B5-2. Map of all monitors (1990-2015) in the Baltimore study area (top panel) and
satellite views of near-road (middle panel) and area design value monitor (bottom panel).
B5-15
-------
Table B5-12. Attributes of ambient monitors within the Baltimore study area having recent
(2010-2015) valid-year NO2 concentrations.
State abbreviation
MD
MD
MD
County name
Baltimore
Howard
Bait. (City)
Site ID
240053001
240270006
245100040
Lat
39.31083
39.14309
39.29773
Lon
-76.47444
-76.84608
-76.60460
year_start
1971
2014
1981
year_end
2016
2016
2016
Elevation m
5
10
12
land use
RESID
RESID
RESID
scale
NBHOOD
MICRO
MID
objectivel
POP EXP
HI CONC
HI CONC
objective2
MAX PRE
SOURCE
POP EXP
objective3
HI CONC
GEN/BKGR
cnty_landarea_2010
598
251
81
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
181
16
13
road closest
150
95
1470
aadt closest
29222
186750
9391
dist aadtmax
593
655
road aadtmax
150
83
aadt aadtmax
29932
102860
County Level NOx Emissions (tpy)
OnRoad mobile nox
11198
5298
4573
NonRoad mobile nox
3463
991
646
AML mobile nox
1333
216
1385
total nox
22136
8474
10421
pct_mobile_nox
72.3
76.8
63.4
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Municipal_Waste_Combustor
1134
Steam_H eating_Facil ity
153
SugarJVIill
126
Institutional school hospital_
98
Chemical Plant
70
Rail Yard
74
Electricity_Generation_via_Combu
40
not characterized
36
23
10
Military_Base
Wastewater_T reatment_Facility
31
Mineral_Processing_Plant
Area design value monitor
Near-road monitor
B5-16
-------
Monitors
• Design Monitor
# Near Road
• Study Monitor
Boston-Cambridge-Newton, MA-NH
250250044
250250002
250250042
Figure B5-3. Map of all monitors (1990-2015) in the Boston study area (top panel) and
satellite views of near-road (middle panel) and area design value monitor (bottom panels),
B5-17
-------
Table B5-13. Attributes of ambient monitors within the Boston study area having recent
(2010-2015) valid-year NO2 concentrations.
State abbreviation
MA
MA
MA
MA
MA
MA
County name
Essex
Essex
Essex
Norfolk
Suffolk
Suffolk
Site ID
250092006
250094005
250095005
250213003
250250002
250250040
Lat
42.47464
42.81441
42.77084
42.21177
42.34887
42.34025
Lon
-70.97082
-70.81778
-71.10229
-71.11397
-71.09716
-71.03835
year_start
1993
2010
2003
2002
1975
1992
year_end
2016
2016
2012
2016
2016
2014
Elevation m
52
2
0
192
6
0
land use
COMM
RESID
RESID
FOREST
COMM
INDUS
scale
URBAN
URBAN
NBHOOD
REGION
MICRO
NBHOOD
objectivel
POP EXP
MAX 03
POP EXP
GEN/BKGR
HI CONC
POP EXP
objective2
MAX PRE
UP BKGR
POP EXP
objective3
HI CONC
POP EXP
cnty_landarea_2010
493
493
493
396
58
58
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
486
469
7
216
road closest
363
138
20
121
aadt closest
13142
14059
20942
8618
dist aadtmax
892
117
663
road aadtmax
93
90
90
aadt aadtmax
164568
127957
67804
County Level NOx Emissions (tpy)
OnRoad mobile nox
6568
6568
6568
6685
3201
3201
NonRoad mobile nox
2473
2473
2473
2009
2094
2094
AML mobile nox
860
860
860
619
5446
5446
total nox
15750
15750
15750
12261
14015
14015
pct_mobile_nox
62.9
62.9
62.9
76
76.6
76.6
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Airport
2203
Municipal_Waste_Combustor
705
1021
Steam_H eating_Facil ity
182
182
Institutional school hospital_
209
145
Electricity_Generation_via_Combu
253
41
Rail Yard
20
Wastewater_T reatment_Facility
Aircraft Aerospace or_Related
156
Fabricated Metal Products Plant
42
42
not characterized
27
27
Textile Yarn or_Carpet_Plant
Military_Base
Mineral_Processing_Plant
Area design value monitor
Near-road monitor
B5-18
-------
Table B5-13, continued. Attributes of ambient monitors within the Boston study area
having recent (2010-2015) valid-year NO2 concentrations.
State abbreviation
MA
MA
NH
County name
Suffolk
Suffolk
Rockingham
Site ID
250250042
250250044
330150018
Lat
42.32950
42.32519
42.86254
Lon
-71.08260
-71.05606
-71.38017
year_start
2000
2013
2014
year_end
2016
2016
2016
Elevation m
6
15
123
land use
COMM
COMM
RESID
scale
NBHOOD
MID
REGION
objectivel
POP EXP
POP EXP
POP EXP
objective2
HI CONC
GEN/BKGR
objective3
cnty_landarea_2010
58
58
695
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
56
10
road closest
447
93
aadt closest
9040
198239
dist aadtmax
876
424
road aadtmax
125
93
aadt aadtmax
47853
193777
County Level NOx Emissions (tpy)
OnRoad mobile nox
3201
3201
4691
NonRoad mobile nox
2094
2094
1376
AML mobile nox
5446
5446
533
total nox
14015
14015
8767
pct_mobile_nox
76.6
76.6
75.3
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Airport
Municipal_Waste_Combustor
Steam_H eating_Facil ity
182
182
Institutional school hospital_
222
155
Electricity_Generation_via_Combu
253
161
Rail Yard
20
Wastewater_T reatment_Facility
Aircraft Aerospace or_Related
Fabricated Metal Products Plant
42
42
not characterized
27
27
Textile Yarn or_Carpet_Plant
Military_Base
Mineral_Processing_Plant
Area design value monitor
Near-road monitor
B5-19
-------
170313103
170314002
Chicago-Naperville-Elgin, IL-IN-WI
Jtuusc"-
Monitors
Design Monitor
Near Road
Not Used
Study Monitor
Figure B5-4. Map of all monitors (1990-2015) in the Chicago study area (top panel) and
satellite views of near-road (middle panel) and area design value monitor (bottom panel).
B5-20
-------
Table B5-14. Attributes of ambient monitors within the Chicago study area having recent
(2010-2015) valid-year NO2 concentrations.
State abbreviation
IL
IL
IL
IL
IL
IN
County name
Cook
Cook
Cook
Cook
Cook
Lake
Site ID
170310063
170310076
170313103
170314002
170314201
180890022
Lat
41.87768
41.75140
41.96519
41.85524
42.14000
41.60668
Lon
-87.63503
-87.71349
-87.87626
-87.75247
-87.79923
-87.30473
year_start
1988
2001
1997
1982
1997
1995
year_end
2016
2016
2016
2016
2016
2016
Elevation m
181
186
195
184
198
183
land use
MOBILE
RESID
MOBILE
RESID
RESID
INDUS
scale
MID
NBHOOD
MID
NBHOOD
URBAN
NBHOOD
objectivel
HI CONC
POP EXP
HI CONC
POP EXP
POP EXP
POP EXP
objective2
POP EXP
HI CONC
SOURCE
HI CONC
MAX 03
HI CONC
objective3
SOURCE
GEN/BKGR
POP EXP
SOURCE
cnty_landarea_2010
945
945
945
945
945
499
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
45
188
30
629
211
696
road closest
0
0
12
50
68
90
aadt closest
9518
22435
43900
32400
34600
34754
dist aadtmax
842
663
149
766
970
road aadtmax
90
0
294
50
94
aadt aadtmax
253061
45285
190046
34097
135733
County Level NOx Emissions (tpy)
OnRoad mobile nox
54900
54900
54900
54900
54900
9294
NonRoad mobile nox
19402
19402
19402
19402
19402
2652
AML mobile nox
12900
12900
12900
12900
12900
1450
total nox
113148
113148
113148
113148
113148
38995
pct_mobile_nox
77.1
77.1
77.1
77.1
77.1
34.4
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Steel Mill
4336
Airport
1150
5261
Electricity_Generation_via_Combu
1118
1893
Rail Yard
185
62
440
229
Institutional school hospital_
232
27
31
Chemical Plant
64
115
Landfill
57
lndustrial_Machinery_or_Equipmen
27
Steam_H eating_Facil ity
12
Wastewater_T reatment_Facility
51
Food_Products_Processing_Plant
36
12
Hot_Mix_Asphalt_Plant
not characterized
87
72
18
Glass Plant
Calcined Pet Coke Plant
Automobile Truck or Parts Plant
Area design value monitor
Used as near-road monitor, though not formally designated as such
B5-21
-------
lent on
LewisvWeT
„plar>o
Itorth —
Dallo-
Dallas-Fort Worth-Arlington, TX
Monitors
Design Monitor
Near Road
Not Used
Study Monitor
484391053
481130067
484391002 ]
481130069
Figure B5-5. Map of all monitors (1990-2015) in the Dallas study area (top panel) and
satellite views of near-road (middle panels) and area design value monitor (bottom panels),
B5-22
-------
Table B5-15. Attributes of ambient monitors within the Dallas study area having recent
(2010-2015) valid-year NO2 concentrations.
State abbreviation
TX
TX
TX
TX
TX
TX
County name
Dallas
Dallas
Dallas
Dallas
Denton
Ellis
Site ID
481130069
481130075
481130087
481131067
481210034
481390016
Lat
32.82006
32.91921
32.67645
32.92118
33.21907
32.48208
Lon
-96.86012
-96.80850
-96.87206
-96.75355
-97.19628
-97.02690
year_start
1986
1998
1994
2014
1998
2003
year_end
2016
2016
2016
2016
2016
2016
Elevation m
126.5
190.8
206
177
183
195
land use
COMM
RESID
COMM
COMM
COMM
AGRIC
scale
NBHOOD
NBHOOD
NBHOOD
MICRO
URBAN
NBHOOD
objectivel
MAX PRE
GEN/BKGR
GEN/BKGR
MAX PRE
POP EXP
SOURCE
objective2
POP EXP
POP EXP
POP EXP
DOWNWND
GEN/BKGR
objective3
HI CONC
MAX 03
REG TRANS
cnty_landarea_2010
871
871
871
871
878
935
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
232
435
24
487
road closest
0
289
635
287
aadt closest
24020
23000
235790
27000
dist aadtmax
802
980
959
road aadtmax
35
635
75
aadt aadtmax
210680
209000
251140
County Level NOx Emissions (tpy)
OnRoad mobile nox
36623
36623
36623
36623
7555
5173
NonRoad mobile nox
8462
8462
8462
8462
1685
854
AML mobile nox
1141
1141
1141
1141
1608
272
total nox
51422
51422
51422
51422
13785
11530
pct_mobile_nox
89.9
89.9
89.9
89.9
78.7
54.6
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Portland_Cement_Manufacturing
2431
Airport
384
23
Rail Yard
Steel Mill
298
Compressor_Station
Electricity_Generation_via_Combu
not characterized
15
64
Bakeries
Breweries Distilleries Wineries
Pharmaceutical_Manufacturing
Automobile Truck or Parts Plant
Area design value monitor
Near-road monitor
B5-23
-------
Table B5-15, continued. Attributes of ambient monitors within the Dallas study area
having recent (2010-2015) valid-year NO2 concentrations.
State abbreviation
TX
TX
TX
TX
TX
TX
County name
Ellis
Hunt
Kaufman
Parker
Tarrant
Tarrant
Site ID
481391044
482311006
482570005
483670081
484390075
484391002
Lat
32.17542
33.15309
32.56497
32.86877
32.98789
32.80582
Lon
-96.87019
-96.11557
-96.31769
-97.90593
-97.47718
-97.35657
year_start
2007
2003
2000
2010
2010
1975
year_end
2016
2016
2016
2012
2012
2016
Elevation m
165
161
128
347
241
203
land use
AGRIC
RESID
COMM
RESID
RESID
COMM
scale
URBAN
NBHOOD
NBHOOD
NA
NA
NBHOOD
objectivel
UP BKGR
GEN/BKGR
POP EXP
POP EXP
MAX 03
MAX PRE
objective2
POP EXP
UP BKGR
SOURCE
HI CONC
POP EXP
objective3
UP BKGR
GEN/BKGR
GEN/BKGR
POP EXP
HI CONC
cnty_landarea_2010
935
840
781
903
864
864
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
141
253
450
road closest
69
34
287
aadt closest
3100
13200
21000
dist aadtmax
996
563
road aadtmax
34
34
aadt aadtmax
9100
16200
County Level NOx Emissions (tpy)
OnRoad mobile nox
5173
2894
2984
3270
24824
24824
NonRoad mobile nox
854
627
765
624
4976
4976
AML mobile nox
272
58
194
258
7457
7457
total nox
11530
4450
5774
5893
45082
45082
pct_mobile_nox
54.6
80.4
68.3
70.5
82.6
82.6
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Portland_Cement_Manufacturing
Airport
30
Rail Yard
510
Steel Mill
Compressor_Station
37
70
Electricity_Generation_via_Combu
43
not characterized
37
Bakeries
Breweries Distilleries Wineries
Pharmaceutical_Manufacturing
Automobile Truck or Parts Plant
Area design value monitor
Near-road monitor
B5-24
-------
Table B5-15, continued. Attributes of ambient monitors within the Dallas study area
having recent (2010-2015) valid-year NO2 concentrations.
State abbreviation
TX
TX
TX
TX
County name
Tarrant
Tarrant
Tarrant
Tarrant
Site ID
484391053
484392003
484393009
484393011
Lat
32.66472
32.92247
32.98426
32.65636
Lon
-97.33806
-97.28209
-97.06372
-97.08859
year_start
2015
2010
2000
2002
year_end
2016
2012
2016
2016
Elevation m
214.9
254
165
183
land use
RESID
AGRIC
RESID
COMM
scale
MICRO
NA
NBHOOD
NBHOOD
objectivel
MAX PRE
HI CONC
HI CONC
HI CONC
objective2
POP EXP
MAX 03
POP EXP
objective3
MAX 03
POP EXP
cnty_landarea_2010
864
864
864
864
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
15
653
494
47
road closest
20
0
2499
0
aadt closest
184680
9100
44000
36590
dist aadtmax
832
road aadtmax
0
aadt aadtmax
24180
County Level NOx Emissions (tpy)
OnRoad mobile nox
24824
24824
24824
24824
NonRoad mobile nox
4976
4976
4976
4976
AML mobile nox
7457
7457
7457
7457
total nox
45082
45082
45082
45082
pct_mobile_nox
82.6
82.6
82.6
82.6
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Portland_Cement_Manufacturing
Airport
11
Rail Yard
Steel Mill
Compressor_Station
89
17
Electricity_Generation_via_Combu
not characterized
Bakeries
24
Breweries Distilleries Wineries
25
Pharmaceutical_Manufacturing
35
Automobile Truck or Parts Plant
Area design value monitor
Near-road monitor
B5-25
-------
Denver-Aurora-Lakewood, CO
Monitors
• Design Monitor
• Near Road
• Not Used
• Study Monitor
080310027
080310028
080013001
080310002
Figure B5-6. Map of all monitors (1990-2015) in the Denver study area (top panel) and
satellite views of near-road (middle panels) and area design value monitor (bottom panels).
B5-26
-------
Table B5-16. Attributes of ambient monitors within the Denver study area having recent
(2010-2015) valid-year NO2 concentrations.
State abbreviation
CO
CO
CO
CO
CO
County name
Adams
Denver
Denver
Denver
Denver
Site ID
080013001
080310002
080310026
080310027
080310028
Lat
39.83812
39.75118
39.77949
39.73217
39.78610
Lon
-104.94984
-104.98763
-105.00518
-105.01530
-104.98860
year_start
1975
1972
2014
2013
2015
year_end
2016
2016
2016
2016
2016
Elevation m
1554
1593
1602
1583
1587
land use
AGRIC
COMM
RESID
COMM
COMM
scale
URBAN
NBHOOD
NBHOOD
MICRO
MICRO
objectivel
POP EXP
HI CONC
POP EXP
POP EXP
POP EXP
objective2
GEN/BKGR
POP EXP
objective3
cnty_landarea_2010
1168
153
153
153
153
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
996
19
394
9
6
road closest
76
0
70
25
25
aadt closest
56000
16000
120000
249000
192000
dist aadtmax
338
399
627
road aadtmax
0
70
25
aadt aadtmax
27000
123000
240000
County Level NOx Emissions (tpy)
OnRoad mobile nox
8763
9618
9618
9618
9618
NonRoad mobile nox
1974
2723
2723
2723
2723
AML mobile nox
838
3556
3556
3556
3556
total nox
25245
20042
20042
20042
20042
pct_mobile_nox
45.9
79.3
79.3
79.3
79.3
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Electricity_Generation_via_Combu
8996
249
9245
249
9180
Rail Yard
473
661
188
661
Petroleum_Refinery
670
670
Food_Products_Processing_Plant
11
85
96
96
Dry_Cleaner Perchloroethylene
54
54
54
54
Wastewater_T reatment_Facility
66
66
Hot_Mix_Asphalt_Plant
50
20
42
20
42
Institutional school hospital_
21
11
21
11
Petroleum_Storage_Facility
14
14
Bakeries
12
12
Lumber Sawmill
18
18
not characterized
17
124
124
98
141
Chemical Plant
Compressor_Station
Landfill
Brick Structural_Clay or_Clay
Area design value monitor
Near-road monitor
B5-27
-------
Detroit-Warren-Dearborn, Ml
Monitors
Design Monitor
Near Road
Not Used
Study Monitor
261630093
261630095
261630019
Figure B5-7. Map of all monitors (1990-2015) in the Detroit study area (top panel) and
satellite views of near-road (middle panels) and area design value monitor (bottom panel)
B5-28
-------
Table B5-17. Attributes of ambient monitors within the Detroit study area having recent
(2010-2015) valid-year NO2 concentrations.
State abbreviation
Ml
Ml
Ml
Ml
County name
Wayne
Wayne
Wayne
Wayne
Site ID
261630019
261630093
261630094
261630095
Lat
42.43084
42.38600
42.38681
42.42150
Lon
-83.00014
-83.26619
-83.27051
-83.42504
year_start
1980
2011
2011
2014
year_end
2016
2016
2016
2016
Elevation m
192
0.5
0.5
0.1
land use
RESID
RESID
RESID
COMM
scale
URBAN
MICRO
MID
MICRO
objectivel
POP EXP
POP EXP
DOWNWND
HI CONC
objective2
HI CONC
objective3
MAX 03
cnty_landarea_2010
612
612
612
612
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
337
8
138
49
road closest
97
96
96
275
aadt closest
11522
140500
131100
172600
dist aadtmax
388
767
458
road aadtmax
0
96
96
aadt aadtmax
20000
139400
139400
County Level NOx Emissions (tpy)
OnRoad mobile nox
29767
29767
29767
29767
NonRoad mobile nox
7051
7051
7051
7051
AML mobile nox
3496
3496
3496
3496
total nox
62423
62423
62423
62423
pct_mobile_nox
64.6
64.6
64.6
64.6
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Electricity_Generation_via_Combu
Steel Mill
Municipal_Waste_Combustor
Mineral_Processing_Plant
Petroleum_Refinery
Wastewater_T reatment_Facility
Steam_H eating_Facil ity
Automobile Truck or Parts Plant
95
Institutional school hospital_
Rail Yard
16
not characterized
63
63
Landfill
Area design value monitor
Near-road monitor
B5-29
-------
Houston-The Woodlands-Sugar Land, TX
Beeiumt
Monitors
# Design Monitor
# Near Road
• Not Used
• Study Monitor
482011052
482011066
Krgariizalionj
482010075
Figure B5-8. Map of all monitors (1990-2015) in the Houston study area (top panel) and
satellite views of near-road (middle panels) and area design value monitor (bottom panel).
B5-30
-------
Table B5-18. Attributes of ambient monitors within the Houston study area having recent
(2010-2015) valid-year NO2 concentrations.
State abbreviation
TX
TX
TX
TX
TX
TX
County name
Brazoria
Brazoria
Galveston
Harris
Harris
Harris
Site ID
480391004
480391016
481671034
482010024
482010026
482010029
Lat
29.52044
29.04376
29.25447
29.90104
29.80271
30.03952
Lon
-95.39251
-95.47295
-94.86129
-95.32614
-95.12550
-95.67395
year_start
2001
2003
2007
1973
2001
1997
year_end
2016
2016
2016
2016
2016
2016
Elevation m
19
0
5
24.1
11.9
50.6
land use
RESID
INDUS
COMM
RESID
RESID
RESID
scale
NBHOOD
MID
MID
NBHOOD
MID
URBAN
objectivel
POP EXP
POP EXP
GEN/BKGR
MAX 03
POP EXP
DOWNWND
objective2
SOURCE
UP BKGR
POP EXP
HI CONC
HI CONC
objective3
HI CONC
MAX 03
HI CONC
MAX PRE
UP BKGR
cnty_landarea_2010
1358
1358
378
1703
1703
1703
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
541
112
710
608
road closest
288
2004
3005
0
aadt closest
56070
6900
17400
11020
dist aadtmax
450
road aadtmax
332
aadt aadtmax
10800
County Level NOx Emissions (tpy)
OnRoad mobile nox
3640
3640
2958
49330
49330
49330
NonRoad mobile nox
1176
1176
996
13105
13105
13105
AML mobile nox
1960
1960
4215
14455
14455
14455
total nox
15272
15272
12353
98983
98983
98983
pct_mobile_nox
44.4
44.4
66.1
77.7
77.7
77.7
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Chemical Plant
1092
Petroleum_Refinery
Electricity_Generation_via_Combu
607
Airport
Rail Yard
Plastic Resin or Rubber Produc
Food_Products_Processing_Plant
Wastewater_T reatment_Facility
Petroleum_Storage_Facility
Glass Plant
Chlor alkali Plant
Breweries Distilleries Wineries
Fertilizer Plant
Foundries Iron and Steel
Landfill
not characterized
32
Foundries non ferrous
Area design value monitor
Near-road monitor
B5-31
-------
Table B5-18, continued. Attributes of ambient monitors within the Houston study area
having recent (2010-2015) valid-year NO2 concentrations.
State abbreviation
TX
TX
TX
TX
TX
TX
County name
Harris
Harris
Harris
Harris
Harris
Harris
Site ID
482010047
482010055
482010075
482010416
482011015
482011034
Lat
29.83417
29.69573
29.75278
29.68639
29.76165
29.76800
Lon
-95.48917
-95.49922
-95.35028
-95.29472
-95.08139
-95.22058
year_start
1980
1998
2001
2006
2003
1972
year_end
2016
2016
2014
2016
2016
2016
Elevation m
24
19.5
12
10
5.5
9.1
land use
RESID
RESID
COMM
RESID
COMM
COMM
scale
URBAN
MID
NBHOOD
NBHOOD
MID
NBHOOD
objectivel
POP EXP
GEN/BKGR
HI CONC
POP EXP
SOURCE
POP EXP
objective2
MAX PRE
POP EXP
GEN/BKGR
HI CONC
GEN/BKGR
objective3
POP EXP
HI CONC
cnty_landarea_2010
1703
1703
1703
1703
1703
1703
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
78
582
430
400
58
258
road closest
0
0
59
35
0
10
aadt closest
18330
23070
190000
19000
1830
161200
dist aadtmax
269
962
411
road aadtmax
290
610
0
aadt aadtmax
204930
131820
1870
County Level NOx Emissions (tpy)
OnRoad mobile nox
49330
49330
49330
49330
49330
49330
NonRoad mobile nox
13105
13105
13105
13105
13105
13105
AML mobile nox
14455
14455
14455
14455
14455
14455
total nox
98983
98983
98983
98983
98983
98983
pct_mobile_nox
77.7
77.7
77.7
77.7
77.7
77.7
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Chemical Plant
681
617
120
Petroleum_Refinery
Electricity_Generation_via_Combu
236
Airport
763
Rail Yard
269
271
Plastic Resin or Rubber Produc
13
153
Food_Products_Processing_Plant
97
Wastewater_T reatment_Facility
75
Petroleum_Storage_Facility
37
Glass Plant
42
Chlor alkali Plant
18
Breweries Distilleries Wineries
17
Fertilizer Plant
12
Foundries Iron and Steel
24
Landfill
28
not characterized
71
11
Foundries non ferrous
Area design value monitor
Near-road monitor
B5-32
-------
Table B5-18, continued. Attributes of ambient monitors within the Houston study area
having recent (2010-2015) valid-year NO2 concentrations.
State abbreviation
TX
TX
TX
TX
TX
TX
County name
Harris
Harris
Harris
Harris
Harris
Montgomery
Site ID
482011035
482011039
482011050
482011052
482011066
483390078
Lat
29.73373
29.67003
29.58305
33.85940
29.72167
30.35030
Lon
-95.25759
-95.12851
-95.01554
-118.20070
-95.49265
-95.42513
year_start
1978
1996
2001
2015
2014
2001
year_end
2016
2016
2016
2016
2016
2016
Elevation m
12.5
6
0
13.5
13
77
land use
INDUS
RESID
RESID
RESID
COMM
COMM
scale
NBHOOD
NBHOOD
MID
MICRO
MICRO
URBAN
objectivel
MAX PRE
POP EXP
POP EXP
MAX PRE
MAX PRE
GEN/BKGR
objective2
POP EXP
MAX PRE
SOURCE
POP EXP
objective3
HI CONC
SOURCE
HI CONC
REG TRANS
cnty_landarea_2010
1703
1703
1703
1703
1703
1042
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
56
603
15
24
road closest
0
0
610
59
aadt closest
16170
17980
202120
324119
dist aadtmax
768
682
344
86
road aadtmax
610
0
710
59
aadt aadtmax
125200
31130
187000
293930
County Level NOx Emissions (tpy)
OnRoad mobile nox
49330
49330
49330
49330
49330
5948
NonRoad mobile nox
13105
13105
13105
13105
13105
1208
AML mobile nox
14455
14455
14455
14455
14455
448
total nox
98983
98983
98983
98983
98983
9429
pct_mobile_nox
77.7
77.7
77.7
77.7
77.7
80.6
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Chemical Plant
681
274
24
Petroleum_Refinery
1328
Electricity_Generation_via_Combu
141
210
14
Airport
Rail Yard
21
Plastic Resin or Rubber Produc
13
18
Food_Products_Processing_Plant
Wastewater_T reatment_Facility
Petroleum_Storage_Facility
37
Glass Plant
42
Chlor alkali Plant
Breweries Distilleries Wineries
17
Fertilizer Plant
Foundries Iron and Steel
13
Landfill
not characterized
12
17
Foundries non ferrous
17
Area design value monitor
Near-road monitor
B5-33
-------
Kansas City, KS-MO
Lee's Summit
Monitors
• Design Monitor
# Near Road
Study Monitor
250950042
250950034
Figure B5-9. Map of all monitors (1990-2015) in the Kansas City study area (top panel) and
satellite views of near-road (middle panel) and area design value monitor (bottom panel).
B5-34
-------
Table B5-19. Attributes of ambient monitors within the Kansas City study area having
recent (2010-2015) valid-year NO2 concentrations.
State abbreviation
KS
KS
MO
MO
County name
Linn
Wyandotte
Jackson
Jackson
Site ID
201070002
202090021
290950034
290950042
Lat
38.13588
39.11722
39.10476
39.04791
Lon
-94.73199
-94.63561
-94.57080
-94.45051
year_start
1998
1999
2002
2013
year_end
2013
2016
2016
2016
Elevation m
259
259
296
293
land use
AGRIC
RESID
COMM
MOBILE
scale
REGION
NBHOOD
URBAN
MICRO
objectivel
REG TRANS
POP EXP
POP EXP
SOURCE
objective2
POP EXP
objective3
cnty_landarea_2010
594
152
604
604
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
78
80
20
road closest
2540
29
70
aadt closest
7460
64982
114495
dist aadtmax
801
102
656
road aadtmax
69
70
70
aadt aadtmax
9910
91327
105483
County Level NOx Emissions (tpy)
OnRoad mobile nox
392
4241
13680
13680
NonRoad mobile nox
202
449
3213
3213
AML mobile nox
753
2877
3026
3026
total nox
10650
16058
28515
28515
pct_mobile_nox
12.6
47.1
69.9
69.9
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Electricity_Generation_via_Combu
4392
1230
Rail Yard
1112
522
Mineral Wool Plant
158
Automobile Truck or Parts Plant
43
Food_Products_Processing_Plant
21
21
Chemical Plant
12
Wet Corn Mill
32
Municipal_Waste_Combustor
11
Petroleum_Storage_Facility
11
11
not characterized
Wastewater_T reatment_Facility
Airport
Steam_H eating_Facil ity
Area design value monitor
Near-road monitor
B5-35
-------
Los Angeles-Long Beach-Anaheim, CA
Nalond'
Valley"
Oxnaid
[ rtupneme
f " v Glendale
WeU Hollywood,'
# . Los ArWeU
Arcadia
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FIMont^-SfiVlna
f ^™Moritefc®o ¦ >
HumiOUttW**^. T ' m I t
Jd Park ] V ^h'njer A \
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-p ^akewood A^,1|m -
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/ —V Huntington Irvine
—Beach • "¦—¦
^^ewport Beach,.
Monitors
# Design Monitor
# Near Road
• Not Used
• Study Monitor
060590008
060374008
060371701
Figure B5-10. Map of all monitors (1990-2015) in the Los Angeles study area (top panel)
and satellite views of near-road (middle panels) and area design value monitor (bottom
panel).
B5-36
-------
Table B5-20. Attributes of ambient monitors within the Los Angeles study area having
recent (2010-2015) valid-year NO2 concentrations.
State abbreviation
CA
CA
CA
CA
CA
CA
County name
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Site ID
060370002
060370016
060370113
060371103
060371201
060371302
Lat
34.13650
34.14435
34.05111
34.06659
34.19925
33.90139
Lon
-117.92391
-117.85036
-118.45636
-118.22688
-118.53276
-118.20500
year_start
1980
1979
1983
1979
1980
2008
year_end
2016
2016
2016
2016
2016
2016
Elevation m
183
275
91
87
226
27
land use
RESID
RESID
MOBILE
RESID
COMM
RESID
scale
URBAN
NA
NA
NBHOOD
NA
MID
objectivel
UP BKGR
POP EXP
POP EXP
POP EXP
HI CONC
objective2
POP EXP
HI CONC
POP EXP
objective3
HI CONC
GEN/BKGR
cnty_landarea_2010
4058
4058
4058
4058
4058
4058
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
682
595
819
road closest
210
405
110
aadt closest
250000
295000
135750
dist aadtmax
854
975
889
road aadtmax
210
405
5
aadt aadtmax
266000
313000
227000
County Level NOx Emissions (tpy)
OnRoad mobile nox
80322
80322
80322
80322
80322
80322
NonRoad mobile nox
17796
17796
17796
17796
17796
17796
AML mobile nox
17817
17817
17817
17817
17817
17817
total nox
135857
135857
135857
135857
135857
135857
pct_mobile_nox
85.3
85.3
85.3
85.3
85.3
85.3
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Airport
15
Petroleum_Refinery
68
Rail Yard
200
Calcined Pet Coke Plant
Wastewater_T reatment_Facility
Electricity_Generation_via_Combu
14
Oil or Gas Field On shore
Institutional school hospital_
31
Steam_H eating_Facil ity
17
Chemical Plant
13
Foundries Iron and Steel
35
Foundries non ferrous
17
Breweries Distilleries Wineries
15
Hot_Mix_Asphalt_Plant
12
Petroleum_Storage_Facility
Landfill
10
Food_Products_Processing_Plant
Glass Plant
Secondary_Lead_Smelting_Plant
not characterized
12
15
Municipal_Waste_Combustor
Aircraft Aerospace or_Related
Area design value monitor
Near-road monitor
B5-37
-------
Table B5-20, continued. Attributes of ambient monitors within the Los Angeles study area
having recent (2010-2015) valid-year NO2 concentrations.
State abbreviation
CA
CA
CA
CA
CA
CA
County name
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Site ID
060371602
060371701
060372005
060374002
060374006
060374008
Lat
34.01194
34.06703
34.13260
33.82376
33.80250
33.85944
Lon
-118.06995
-117.75140
-118.12720
-118.18921
-118.22000
-118.20028
year_start
2005
1980
1981
1980
2009
2015
year_end
2016
2016
2016
2013
2016
2016
Elevation m
75
270
250
6
10
12
land use
COMM
COMM
RESID
RESID
INDUS
INDUS
scale
NA
NA
NA
NA
URBAN
MICRO
objectivel
MAX PRE
POP EXP
POP EXP
SOURCE
POP EXP
objective2
POP EXP
HI CONC
POP EXP
SOURCE
objective3
cnty_landarea_2010
4058
4058
4058
4058
4058
4058
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
937
668
490
9
road closest
164
10
405
710
aadt closest
38000
229000
280000
192000
dist aadtmax
969
709
960
230
road aadtmax
605
10
405
710
aadt aadtmax
255000
239000
287000
187000
County Level NOx Emissions (tpy)
OnRoad mobile nox
80322
80322
80322
80322
80322
80322
NonRoad mobile nox
17796
17796
17796
17796
17796
17796
AML mobile nox
17817
17817
17817
17817
17817
17817
total nox
135857
135857
135857
135857
135857
135857
pct_mobile_nox
85.3
85.3
85.3
85.3
85.3
85.3
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Airport
215
Petroleum_Refinery
1775
Rail Yard
22
Calcined Pet Coke Plant
201
Wastewater_T reatment_Facility
Electricity_Generation_via_Combu
14
20
33
14
Oil or Gas Field On shore
Institutional school hospital_
Steam_H eating_Facil ity
Chemical Plant
74
74
24
Foundries Iron and Steel
13
Foundries non ferrous
17
Breweries Distilleries Wineries
Hot_Mix_Asphalt_Plant
Petroleum_Storage_Facility
11
Landfill
Food_Products_Processing_Plant
Glass Plant
Secondary_Lead_Smelting_Plant
not characterized
24
65
Municipal_Waste_Combustor
Aircraft Aerospace or_Related
Area design value monitor
Near-road monitor
B5-38
-------
Table B5-20, continued. Attributes of ambient monitors within the Los Angeles study area
having recent (2010-2015) valid-year NO2 concentrations.
State abbreviation
CA
CA
CA
CA
CA
CA
CA
County name
Los Angeles
Los Angeles
Los Angeles
Orange
Orange
Orange
Orange
Site ID
060375005
060376012
060379033
060590007
060590008
060591003
060595001
Lat
33.95080
34.38344
34.67139
33.83062
33.81931
33.67464
33.92513
Lon
-118.43043
-118.52840
-118.13146
-117.93845
-117.91876
-117.92568
-117.95264
year_start
2004
2001
2001
2001
2013
1989
1976
year_end
2016
2016
2016
2016
2016
2016
2016
Elevation m
21
397
725
10
44
0
82
land use
RESID
COMM
COMM
RESID
MOBILE
RESID
RESID
scale
NBHOOD
NA
MID
URBAN
MICRO
MID
NA
objectivel
UP BKGR
POP EXP
POP EXP
POP EXP
SOURCE
POP EXP
POP EXP
objective2
objective3
cnty_landarea_2010
4058
4058
4058
791
791
791
791
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
432
9
819
road closest
5
5
90
aadt closest
256000
272000
46000
dist aadtmax
735
999
road aadtmax
5
90
aadt aadtmax
267000
47000
County Level NOx Emissions (tpy)
OnRoad mobile nox
80322
80322
80322
19742
19742
19742
19742
NonRoad mobile nox
17796
17796
17796
6422
6422
6422
6422
AML mobile nox
17817
17817
17817
1921
1921
1921
1921
total nox
135857
135857
135857
31763
31763
31763
31763
pct_mobile_nox
85.3
85.3
85.3
88.4
88.4
88.4
88.4
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Airport
5533
Petroleum_Refinery
Rail Yard
Calcined Pet Coke Plant
Wastewater_T reatment_Facility
53
Electricity_Generation_via_Combu
63
Oil or Gas Field On shore
21
66
Institutional school hospital_
Steam_H eating_Facil ity
Chemical Plant
Foundries Iron and Steel
Foundries non ferrous
Breweries Distilleries Wineries
Hot_Mix_Asphalt_Plant
Petroleum_Storage_Facility
Landfill
Food_Products_Processing_Plant
Glass Plant
Secondary_Lead_Smelting_Plant
not characterized
30
24
24
Municipal_Waste_Combustor
Aircraft Aerospace or_Related
Area design value monitor
Near-road monitor
B5-39
-------
Monitors
• Design Monitor
• Near Road
• Not Used
• Study Monitor
Miami-Fort Lauderdale-West Palm Beach, FL
120110035
120864002
120118002
Figure B5-11. Map of all monitors (1990-2015) in the Miami study area (top panel) and
satellite views of near-road (middle panels) and area design value monitor (bottom panels),
B5-40
-------
Table B5-21. Attributes of ambient monitors within the Miami study area having recent
(2010-2015) valid-year NO2 concentrations.
State abbreviation
FL
FL
FL
FL
FL
FL
County name
Broward
Broward
Broward
Miami-Dade
Miami-Dade
Palm Beach
Site ID
120110031
120110035
120118002
120860027
120864002
120990020
Lat
26.27236
26.13268
26.08842
25.73338
25.79871
26.59123
Lon
-80.29477
-80.16982
-80.11119
-80.16181
-80.21005
-80.06087
year_start
1997
2015
1990
1984
1977
2008
year_end
2012
2016
2016
2016
2016
2014
Elevation m
3
3
3
2
5
5
land use
RESID
RESID
RESID
RESID
COMM
RESID
scale
URBAN
URBAN
URBAN
NBHOOD
NBHOOD
NBHOOD
objectivel
HI CONC
POP EXP
POP EXP
POP EXP
HI CONC
HI CONC
objective2
MAX PRE
HI CONC
UP BKGR
POP EXP
POP EXP
objective3
cnty_landarea_2010
1210
1210
1210
1898
1898
1970
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
103
30
19
475
111
road closest
0
95
0
95
95
aadt closest
17600
306000
30500
225000
204500
dist aadtmax
145
52
887
road aadtmax
869
0
95
aadt aadtmax
56000
37500
224500
County Level NOx Emissions (tpy)
OnRoad mobile nox
22197
22197
22197
27024
27024
16775
NonRoad mobile nox
5885
5885
5885
8578
8578
7625
AML mobile nox
8072
8072
8072
14830
14830
4059
total nox
43997
43997
43997
57941
57941
35647
pct_mobile_nox
82.2
82.2
82.2
87
87
79.8
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Airport
1455
Electricity_Generation_via_Combu
837
18
Institutional school hospital_
10
Petroleum_Storage_Facility
13
Wastewater_T reatment_Facility
48
Area design value monitor
Near-road monitor
B5-41
-------
Minneapolis-St Paul-Bloomington, Wl
Monitors
# Design Monitor
# Near Road
• Not Used
• Study Monitor
American Wings(
Air Museum
'.GalifomiafAve'
270530962
270370480
270370020
270031002
Figure B5-12. Map of all monitors (1990-2015) in the Minneapolis study area (top panel)
and satellite views of near-road (middle panels) and area design value monitor (bottom
panels).
B5-42
-------
Table B5-22. Attributes of ambient monitors within the Minneapolis study area having
recent (2010-2015) valid-year NO2 concentrations.
State abbreviation
MN
MN
MN
MN
MN
County name
Anoka
Dakota
Dakota
Dakota
Hennepin
Site ID
270031002
270370020
270370423
270370480
270530962
Lat
45.13768
44.76323
44.77553
44.70612
44.96524
Lon
-93.20761
-93.03255
-93.06299
-93.28580
-93.25476
year_start
1979
1974
1993
2014
2013
year_end
2016
2016
2016
2016
2016
Elevation m
280
288
272
312
259
land use
COMM
INDUS
INDUS
COMM
RESID
scale
URBAN
NBHOOD
NBHOOD
MID
MID
objectivel
GEN/BKGR
POP EXP
SOURCE
SOURCE
SOURCE
objective2
HI CONC
SOURCE
WELF IMP
objective3
POP EXP
WELF IMP
POP EXP
cnty_landarea_2010
423
562
562
562
554
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
81
30
32
road closest
52
35
94
aadt closest
34741
87000
277000
dist aadtmax
231
road aadtmax
52
aadt aadtmax
45818
County Level NOx Emissions (tpy)
OnRoad mobile nox
6662
7604
7604
7604
21967
NonRoad mobile nox
1507
2061
2061
2061
5495
AML mobile nox
1183
295
295
295
3168
total nox
11073
19738
19738
19738
39010
pct_mobile_nox
84.5
50.5
50.5
50.5
78.5
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Petroleum_Refinery
1296
1296
Municipal_Waste_Combustor
594
Electricity_Generation_via_Combu
87
87
332
Institutional school hospital_
174
Rail Yard
111
Pulp_and_Paper_Plant
339
Gas Plant
63
Secondary_Aluminum_Smelting_Refi
29
29
Steam_H eating_Facil ity
15
not characterized
133
49
49
Printing_Publishing_Facility
Food_Products_Processing_Plant
Area design value monitor
Near-road monitor
B5-43
-------
New York-Newark-Jersey City, NY-NJ-PA
Monitors
• Design Monitor
# Near Road
• Study Monitor
340030010
f JO Sfrr.lr.-ot.o-.
i ¦•¦/$?£' • :#Jr
/ Tv»Cl'OpO'lllK..'«V.'|itl
s. S;DfNmy Couxim
- ©= - e .
-»
> /p°°gle
Figure B5-13. Map of all monitors (1990-2015) in the New York/Jersey study area (top
panel) and satellite views of near-road (middle panel) and area design value monitor
(bottom panel).
B5-44
-------
Table B5-23. Attributes of ambient monitors within the New York/Jersey study area
having recent (2010-2015) valid-year NO2 concentrations.
State abbreviation
NJ
NJ
NJ
NJ
NJ
NJ
County name
Bergen
Bergen
Essex
Essex
Hudson
Middlesex
Site ID
340030006
340030010
340130003
340131003
340170006
340230011
Lat
40.87044
40.85358
40.72099
40.75750
40.67025
40.46218
Lon
-73.99199
-73.96621
-74.19289
-74.20050
-74.12608
-74.42944
year_start
2007
2014
2011
1980
1983
1994
year_end
2010
2016
2016
2016
2016
2016
Elevation m
1
87
27
48
3
19
land use
RESID
COMM
RESID
COMM
COMM
AGRIC
scale
NBHOOD
MICRO
NBHOOD
NBHOOD
URBAN
NBHOOD
objectivel
POP EXP
POP EXP
POP EXP
HI CONC
POP EXP
POP EXP
objective2
POP EXP
UP BKGR
objective3
GEN/BKGR
cnty_landarea_2010
233
233
126
126
46
309
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
332
20
613
70
443
805
road closest
93
95
27
1203
501
1
aadt closest
21842
311234
16050
16032
10308
76226
dist aadtmax
432
861
665
road aadtmax
95
21
444
aadt aadtmax
161399
97471
201150
County Level NOx Emissions (tpy)
OnRoad mobile nox
8080
8080
5162
5162
2685
7951
NonRoad mobile nox
3313
3313
2070
2070
1652
2922
AML mobile nox
1070
1070
3355
3355
3040
805
total nox
15763
15763
14172
14172
10059
15574
pct_mobile_nox
79.1
79.1
74.7
74.7
73.3
75
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Airport
2984
2984
Electricity_Generation_via_Combu
450
53
154
Municipal_Waste_Combustor
Petroleum_Refinery
Institutional school hospital_
86
74
74
Wastewater_T reatment_Facility
346
14
Breweries Distilleries Wineries
68
Petroleum_Storage_Facility
14
Pharmaceutical_Manufacturing
22
Fabricated Metal Products Plant
10
Compressor_Station
Steam_H eating_Facil ity
not characterized
36
13
59
17
Area design value monitor
Near-road monitor
B5-45
-------
Table B5-23, continued. Attributes of ambient monitors in the New York/Jersey study area
having recent (2010-2015) valid-year NO2 concentrations.
State abbreviation
NJ
NJ
NY
NY
NY
NY
County name
Morris
Union
Bronx
Bronx
Nassau
Queens
Site ID
340273001
340390004
360050110
360050133
360590005
360810124
Lat
40.78763
40.64144
40.81600
40.86790
40.74316
40.73614
Lon
-74.67630
-74.20837
-73.90200
-73.87809
-73.58549
-73.82153
year_start
1982
1979
1999
2006
1980
2001
year_end
2016
2016
2016
2016
2010
2016
Elevation m
278
5
17
31
27
25
land use
AGRIC
INDUS
RESID
COMM
COMM
COMM
scale
NA
NBHOOD
URBAN
URBAN
NBHOOD
NBHOOD
objectivel
GEN/BKGR
POP EXP
GEN/BKGR
POP EXP
POP EXP
POP EXP
objective2
POP EXP
HI CONC
POP EXP
GEN/BKGR
GEN/BKGR
GEN/BKGR
objective3
QUALASSUR
QUALASSUR
HI CONC
cnty_landarea_2010
460
103
42
42
285
109
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
89
81
74
733
280
road closest
278
0
0
0
0
aadt closest
95512
5300
30500
114700
17100
dist aadtmax
240
464
515
854
453
road aadtmax
95
278
0
0
495
aadt aadtmax
242175
99100
112700
122200
166300
County Level NOx Emissions (tpy)
OnRoad mobile nox
5273
4874
4297
4297
13279
11095
NonRoad mobile nox
1877
1346
1767
1767
3205
4060
AML mobile nox
232
3479
708
708
706
6546
total nox
9111
13636
9912
9912
23967
29220
pct_mobile_nox
81
71.1
68.3
68.3
71.7
74.3
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Airport
Electricity_Generation_via_Combu
525
1048
271
Municipal_Waste_Combustor
981
Petroleum_Refinery
927
Institutional school hospital_
185
249
113
13
Wastewater_T reatment_Facility
36
346
Breweries Distilleries Wineries
Petroleum_Storage_Facility
Pharmaceutical_Manufacturing
Fabricated Metal Products Plant
Compressor_Station
11
Steam_H eating_Facil ity
not characterized
23
266
232
207
Area design value monitor
Near-road monitor
B5-46
-------
421010075
421010076
421010004
Philadelphia-Camden-Willmington, PA-NJ-DE-MD
Monitors
• Design Monitor
• Near Road
• Not Used
• Study Monitor
Figure B5-14. Map of all monitors (1990-2015) in the Philadelphia study area (top panel)
and satellite views of near-road (middle panels) and area design value monitor (bottom
panel).
B5-47
-------
Table B5-24. Attributes of ambient monitors within the Philadelphia study area having
recent (2010-2015) valid-year NO2 concentrations.
State abbreviation
DE
NJ
PA
PA
PA
PA
County name
New Castle
Camden
Bucks
Delaware
Philadelphia
Philadelphia
Site ID
100032004
340070002
420170012
420450002
421010004
421010047
Lat
39.73944
39.93445
40.10722
39.83556
40.00889
39.94465
Lon
-75.55806
-75.12529
-74.88222
-75.37250
-75.09778
-75.16521
year_start
2000
2012
1973
1973
1976
1981
year_end
2016
2016
2015
2016
2016
2015
Elevation m
0
4
12
3
22
21
land use
COMM
INDUS
RESID
INDUS
RESID
RESID
scale
NBHOOD
NBHOOD
NBHOOD
NBHOOD
URBAN
NBHOOD
objectivel
POP EXP
POP EXP
POP EXP
POP EXP
POP EXP
POP EXP
objective2
HI CONC
HI CONC
HI CONC
HI CONC
objective3
QUALASSUR
cnty_landarea_2010
426
221
604
184
134
134
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
67
150
389
378
322
2
road closest
0
1630
413
291
0
291
aadt closest
18588
9932
31120
14988
11437
20495
dist aadtmax
612
994
958
813
998
923
road aadtmax
95
676
95
322
0
611
aadt aadtmax
102035
59788
76480
38843
18485
24562
County Level NOx Emissions (tpy)
OnRoad mobile nox
6459
5353
7680
5643
10201
10201
NonRoad mobile nox
1748
1150
2196
1319
2480
2480
AML mobile nox
1805
1088
220
3600
2160
2160
total nox
13991
9431
12925
17306
21065
21065
pct_mobile_nox
71.6
80.5
78.1
61
70.5
70.5
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Petroleum_Refinery
2146
1315
Municipal_Waste_Combustor
20
297
1260
Electricity_Generation_via_Combu
948
26
107
950
379
Chemical Plant
27
14
275
183
14
Pulp_and_Paper_Plant
240
92
Institutional school hospital_
30
66
Wastewater_T reatment_Facility
14
56
14
Steam_H eating_Facil ity
24
24
Petroleum_Storage_Facility
16
Military_Base
12
Automobile Truck or Parts Plant
11
not characterized
228
13
26
11
Steel Mill
Hot_Mix_Asphalt_Plant
Pharmaceutical_Manufacturing
Area design value monitor
Near-road monitor
B5-48
-------
Table B5-24, continued. Attributes of ambient monitors within the Philadelphia study area
having recent (2010-2015) valid-year NO2 concentrations, continued.
State abbreviation
PA
PA
County name
Philadelphia
Philadelphia
Site ID
421010075
421010076
Lat
40.05386
39.98883
Lon
-74.98584
-75.20721
year_start
2013
2015
year_end
2016
2016
Elevation m
9
4
land use
COMM
COMM
scale
MICRO
NA
objectivel
SOURCE
REG TRANS
objective2
HI CONC
objective3
cnty_landarea_2010
134
134
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
12
18
road closest
95
76
aadt closest
124610
154955
dist aadtmax
road aadtmax
aadt aadtmax
County Level NOx Emissions (tpy)
OnRoad mobile nox
10201
10201
NonRoad mobile nox
2480
2480
AML mobile nox
2160
2160
total nox
21065
21065
pct_mobile_nox
70.5
70.5
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Petroleum_Refinery
Municipal_Waste_Combustor
Electricity_Generation_via_Combu
Chemical Plant
Pulp_and_Paper_Plant
Institutional school hospital_
11
76
Wastewater_T reatment_Facility
Steam_H eating_Facil ity
Petroleum_Storage_Facility
Military_Base
Automobile Truck or Parts Plant
not characterized
Steel Mill
Hot_Mix_Asphalt_Plant
Pharmaceutical_Manufacturing
Area design value monitor
Near-road monitor
B5-49
-------
Phoenix-Mesa-Scottsdale, AZ
Litchfield Park
Tollesor
Mesa
San Tan Valley
Monitors
rtoran mou»m#v
# Design Monitor
)L>'r»enr
# Near Road
• Not Used
• Study Monitor
Tonto National
f ore'st)
«Op
-------
Table B5-25. Attributes of ambient monitors within the Phoenix study area having recent
(2010-2015) valid-year NO2 concentrations.
State abbreviation
AZ
AZ
AZ
AZ
AZ
AZ
County name
Maricopa
Maricopa
Maricopa
Maricopa
Maricopa
Maricopa
Site ID
040130019
040133002
040133003
040133010
040134011
040134019
Lat
33.48385
33.45793
33.47968
33.46093
33.37005
33.39625
Lon
-112.14257
-112.04601
-111.91721
-112.11748
-112.62070
-111.96797
year_start
1990
1967
1975
1993
2004
2014
year_end
2016
2016
2011
2016
2016
2016
Elevation m
333
339
368
325
258
354
land use
RESID
RESID
RESID
RESID
AGRIC
COMM
scale
NBHOOD
NBHOOD
URBAN
MID
URBAN
MICRO
objectivel
POP EXP
POP EXP
POP EXP
POP EXP
POP EXP
SOURCE
objective2
HI CONC
HI CONC
objective3
cnty_landarea_2010
9200
9200
9200
9200
9200
9200
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
356
412
71
87
16
12
road closest
0
10
0
10
0
10
aadt closest
32305
248522
20930
294314
4550
320138
dist aadtmax
806
441
788
880
road aadtmax
0
10
0
85
aadt aadtmax
33670
255082
41860
11562
County Level NOx Emissions (tpy)
OnRoad mobile nox
56748
56748
56748
56748
56748
56748
NonRoad mobile nox
18998
18998
18998
18998
18998
18998
AML mobile nox
3999
3999
3999
3999
3999
3999
total nox
88464
88464
88464
88464
88464
88464
pct_mobile_nox
90.1
90.1
90.1
90.1
90.1
90.1
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Airport
2657
Electricity_Generation_via_Combu
597
597
Rail Yard
58
95
118
Area design value monitor
Near-road monitor
B5-51
-------
Table B5-25, continued. Attributes of ambient monitors within the Phoenix study area
having recent (2010-2015) valid-year NO2 concentrations.
State abbreviation
AZ
AZ
County name
Maricopa
Maricopa
Site ID
040134020
040139997
Lat
33.46155
33.50383
Lon
-112.12816
-112.09577
year_start
2015
1993
year_end
2016
2016
Elevation m
326
346
land use
RESID
RESID
scale
MICRO
NBHOOD
objectivel
SOURCE
POP EXP
objective2
GEN/BKGR
objective3
cnty_landarea_2010
9200
9200
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
20
363
road closest
10
0
aadt closest
260136
20930
dist aadtmax
994
road aadtmax
0
aadt aadtmax
44896
County Level NOx Emissions (tpy)
OnRoad mobile nox
56748
56748
NonRoad mobile nox
18998
18998
AML mobile nox
3999
3999
total nox
88464
88464
pct_mobile_nox
90.1
90.1
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Airport
Electricity_Generation_via_Combu
597
Rail Yard
58
Area design value monitor
Near-road monitor
B5-52
-------
Pittsburgh, PA
Monitors
• Design Monitor
# Near Road
Study Monitor
.Woodlawn Cemetery^
/Fix All Home Repairs
420031376
420030010
420070014
Figure B5-16. Map of all monitors (1990-2015) in the Pittsburgh study area (top panel) and
satellite views of near-road (middle panel) and area design value monitor (bottom panels).
B5-53
-------
Table B5-26. Attributes of ambient monitors within the Pittsburgh study area having
recent (2010-2015) valid-year NO2 concentrations.
State abbreviation
PA
PA
PA
PA
PA
PA
County name
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Beaver
Site ID
420030008
420030010
420031005
420031008
420031376
420070014
Lat
40.46542
40.44558
40.61395
40.61749
40.43743
40.74780
Lon
-79.96076
-80.01615
-79.72941
-79.72766
-79.86357
-80.31644
year_start
1980
1997
2001
2014
2014
1973
year_end
2014
2013
2014
2016
2016
2016
Elevation m
256
0
302
302
355
216
land use
RESID
COMM
RESID
RESID
MOBILE
RESID
scale
NBHOOD
URBAN
NBHOOD
NBHOOD
MICRO
URBAN
objectivel
POP EXP
POP EXP
POP EXP
POP EXP
HI CONC
POP EXP
objective2
QUAL ASSUR
MAX 03
SOURCE
objective3
GEN/BKGR
POP EXP
cnty_landarea_2010
730
730
730
730
730
435
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
73
281
18
250
road closest
0
0
376
18
aadt closest
10385
12007
87534
9424
dist aadtmax
622
797
943
road aadtmax
0
376
18
aadt aadtmax
27906
40356
9949
County Level NOx Emissions (tpy)
OnRoad mobile nox
13259
13259
13259
13259
13259
2106
NonRoad mobile nox
4029
4029
4029
4029
4029
444
AML mobile nox
3322
3322
3322
3322
3322
1957
total nox
35455
35455
35455
35455
35455
21167
pct_mobile_nox
58.1
58.1
58.1
58.1
58.1
21.3
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Steel Mill
255
255
Food_Products_Processing_Plant
212
212
Steam_H eating_Facil ity
166
89
Institutional school hospital_
36
21
Wastewater_T reatment_Facility
75
Hot_Mix_Asphalt_Plant
18
37
Electricity_Generation_via_Combu
33
33
Foundries Iron and Steel
13
Glass Plant
Chemical Plant
Coke_Battery
not characterized
15
15
11
11
12
24
Landfill
Area design value monitor
Near-road monitor
B5-54
-------
Table B5-26, continued. Attributes of ambient monitors within the Pittsburgh study area
having recent (2010-2015) valid-year NO2 concentrations.
State abbreviation
PA
PA
County name
Washington
Washington
Site ID
421250005
421250200
Lat
40.14667
40.17056
Lon
-79.90222
-80.26139
year_start
1973
1983
year_end
2016
2008
Elevation m
232
334
land use
COMM
RESID
scale
NBHOOD
NBHOOD
objectivel
POP EXP
POP EXP
objective2
objective3
cnty_landarea_2010
857
857
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
268
111
road closest
88
40
aadt closest
7716
12273
dist aadtmax
326
799
road aadtmax
88
70
aadt aadtmax
10524
26252
County Level NOx Emissions (tpy)
OnRoad mobile nox
3024
3024
NonRoad mobile nox
856
856
AML mobile nox
686
686
total nox
10067
10067
pct_mobile_nox
45.4
45.4
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Steel Mill
Food_Products_Processing_Plant
Steam_H eating_Facil ity
Institutional school hospital_
Wastewater_T reatment_Facility
Hot_Mix_Asphalt_Plant
Electricity_Generation_via_Combu
Foundries Iron and Steel
Glass Plant
47
Chemical Plant
12
Coke_Battery
12
not characterized
48
Landfill
18
Area design value monitor
Near-road monitor
B5-55
-------
Richmond, VA
Monitors
• Design Monitor
# Near Road
• Study Monitor
Calvin Presijy
Cor
Environ!
Shirley Plantation i
¦ * 1
First Baptist Church©
Figure B5-17. Map of all monitors (1990-2015) in the Richmond study area (top panel) and
satellite views of near-road (middle panel) and area design value monitor (bottom panel).
B5-56
-------
Table B5-27. Attributes of ambient monitors within the Richmond study area having
recent (2010-2015) valid-year NO2 concentrations.
State abbreviation
VA
VA
VA
VA
County name
Charles
Henrico
Richmond City
Richmond City
Site ID
510360002
510870014
517600024
517600025
Lat
37.34438
37.55652
37.56260
37.59082
Lon
-77.25925
-77.40027
-77.46500
-77.46933
year_start
1993
1989
1998
2013
year_end
2016
2016
2012
2016
Elevation m
6
58.5
60
55
land use
RESID
RESID
COMM
RESID
scale
NBHOOD
NBHOOD
NBHOOD
MICRO
objectivel
POP EXP
POP EXP
HI CONC
POP EXP
objective2
HI CONC
POP EXP
SOURCE
objective3
HI CONC
cnty_landarea_2010
183
234
60
60
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
384
271
21
road closest
64
33
95
aadt closest
73062
21792
151000
dist aadtmax
893
537
road aadtmax
95
64
aadt aadtmax
134322
136518
County Level NOx Emissions (tpy)
OnRoad mobile nox
176
4372
2719
2719
NonRoad mobile nox
96
1446
396
396
AML mobile nox
51
695
315
315
total nox
404
7312
6928
6928
pct_mobile_nox
80
89.1
49.5
49.5
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Pulp_and_Paper_Plant
1243
Rail Yard
35
115
115
Institutional school hospital_
10
10
not characterized
438
24
24
Area design value monitor
Near-road monitor
B5-57
-------
Monitors
Riverside-San Bernadino-Ontario, CA
Design Monitor
Near Road
Not Used
Study Monitor
060710027
060710026
¦W- Vi'VJ .
y . "_V» a Mf Oak HI UGt* - m.
060710001 ^!Tf; *.
toaaEi^laEgg psvwcgy^feoeiii ;
060712002
Figure B5-18. Map of all monitors (1990-2015) in the Riverside study area (top panel) and
satellite views of two near-road (middle panels) and area design value monitor (bottom
panel).
B5-58
-------
Table B5-28. Attributes of ambient monitors within the Riverside study area having recent
(2010-2015) valid-year NO2 concentrations.
State abbreviation
CA
CA
CA
CA
CA
CA
County name
Riverside
Riverside
Riverside
Riverside
Riverside
Riverside
Site ID
060650009
060650012
060651003
060651016
060655001
060658001
Lat
33.44787
33.92086
33.94603
33.94471
33.85275
33.99958
Lon
-117.08865
-116.85841
-117.40063
-116.83007
-116.54101
-117.41601
year_start
2008
1995
2007
2014
1978
1975
year_end
2016
2016
2014
2016
2016
2016
Elevation m
366.093
677
249
720
171
250
land use
RESID
COMM
RESID
COMM
RESID
RESID
scale
NBHOOD
NBHOOD
MICRO
REGION
NA
NA
objectivel
GEN/BKGR
POP EXP
POP EXP
GEN/BKGR
POP EXP
HI CONC
objective2
UP BKGR
HI CONC
POP EXP
POP EXP
objective3
QUAL
GEN/BKGR
cnty_landarea_2010
7206
7206
7206
7206
7206
7206
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
387
665
651
road closest
10
91
60
aadt closest
108000
168000
145000
dist aadtmax
925
road aadtmax
10
aadt aadtmax
118000
County Level NOx Emissions (tpy)
OnRoad mobile nox
25868
25868
25868
25868
25868
25868
NonRoad mobile nox
5541
5541
5541
5541
5541
5541
AML mobile nox
2215
2215
2215
2215
2215
2215
total nox
37367
37367
37367
37367
37367
37367
pct_mobile_nox
90
90
90
90
90
90
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Mineral_Processing_Plant
Electricity_Generation_via_Combu
Airport
91
Rail Yard
Pulp_and_Paper_Plant
Fabricated Metal Products Plant
Food_Products_Processing_Plant
Foundries non ferrous
not characterized
11
Municipal_Waste_Combustor
Portland_Cement_Manufacturing
Primary_Aluminum_Plant
Secondary_Aluminum_Smelting_Refi
Steel Mill
Institutional school hospital_
Area design value monitor
Near-road monitor
B5-59
-------
Table B5-28, continued. Attributes of ambient monitors within the Riverside study area
having recent (2010-2015) va
id-year NC
>2 concentrations.
State abbreviation
CA
CA
CA
CA
CA
CA
County name
Riverside
Riverside
San Bernardino
San Bernardino
San Bernardino
San Bernardino
Site ID
060658005
060659001
060710001
060710026
060710027
060710306
Lat
33.99564
33.67649
34.89501
34.06828
34.03090
34.51001
Lon
-117.49330
-117.33098
-117.02448
-117.52531
-117.61714
-117.33143
year_start
2005
1992
1972
2014
2015
1999
year_end
2016
2016
2016
2016
2016
2016
Elevation m
250
1440
690
300
258
913
land use
RESID
RESID
COMM
MOBILE
INDUS
RESID
scale
NA
MID
NA
MICRO
MICRO
NA
objectivel
POP EXP
POP EXP
REG TRANS
HI CONC
POP EXP
REG TRANS
objective2
POP EXP
SOURCE
POP EXP
objective3
cnty_landarea_2010
7206
7206
20057
20057
20057
20057
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
550
956
50
9
324
road closest
15
15
10
60
18
aadt closest
117000
70000
245300
215000
24750
dist aadtmax
667
534
788
road aadtmax
15
60
15
aadt aadtmax
120000
216000
85000
County Level NOx Emissions (tpy)
OnRoad mobile nox
25868
25868
31067
31067
31067
31067
NonRoad mobile nox
5541
5541
5646
5646
5646
5646
AML mobile nox
2215
2215
9344
9344
9344
9344
total nox
37367
37367
68863
68863
68863
68863
pct_mobile_nox
90
90
66.9
66.9
66.9
66.9
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Mineral_Processing_Plant
Electricity_Generation_via_Combu
Airport
550
Rail Yard
223
Pulp_and_Paper_Plant
140
Fabricated Metal Products Plant
20
20
Food_Products_Processing_Plant
Foundries non ferrous
26
not characterized
243
Municipal_Waste_Combustor
11
Portland_Cement_Manufacturing
Primary_Aluminum_Plant
17
Secondary_Aluminum_Smelting_Refi
20
Steel Mill
50
Institutional school hospital_
Area design value monitor
Near-road monitor
B5-60
-------
Table B5-28, continued. Attributes of ambient monitors within the Riverside study area
having recent (2010-2015) va id-year NO2 concentrations.
State abbreviation
CA
CA
CA
CA
County name
San Bernardino
San Bernardino
San Bernardino
San Bernardino
Site ID
060711004
060711234
060712002
060719004
Lat
34.10374
35.76387
34.10002
34.10688
Lon
-117.62914
-117.39700
-117.49201
-117.27411
year_start
1974
1997
1981
1986
year_end
2016
2016
2016
2016
Elevation m
369
545
381
0
land use
RESID
DESERT
INDUS
COMM
scale
NBHOOD
NA
NA
URBAN
objectivel
UP BKGR
POP EXP
POP EXP
objective2
POP EXP
HI CONC
HI CONC
objective3
GEN/BKGR
cnty_landarea_2010
20057
20057
20057
20057
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
road closest
aadt closest
dist aadtmax
road aadtmax
aadt aadtmax
County Level NOx Emissions (tpy)
OnRoad mobile nox
31067
31067
31067
31067
NonRoad mobile nox
5646
5646
5646
5646
AML mobile nox
9344
9344
9344
9344
total nox
68863
68863
68863
68863
pct_mobile_nox
66.9
66.9
66.9
66.9
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Mineral_Processing_Plant
1865
Electricity_Generation_via_Combu
620
105
Airport
Rail Yard
159
Pulp_and_Paper_Plant
Fabricated Metal Products Plant
Food_Products_Processing_Plant
10
Foundries non ferrous
not characterized
213
Municipal_Waste_Combustor
Portland_Cement_Manufacturing
Primary_Aluminum_Plant
Secondary_Aluminum_Smelting_Refi
20
Steel Mill
50
Institutional school hospital_
11
Area design value monitor
Near-road monitor
B5-61
-------
. •
nento
Monitors
Design Monitor
Near Road
Study Monitor
Sacramento-Roseville-Arden-Arcade, CA
Stanislaus
National
Forest
Yolo \
County Pari'
Golden 1 Credit UrPi-"-
060670015
060610006
060670010
Figure B5-19. Map of all monitors (1990-2015) in the Sacramento study area (top panel)
and satellite views of near-road (middle panels) and area design value monitor (bottom
panel).
B5-62
-------
Table B5-29. Attributes of ambient monitors within the Sacramento study area having
recent (2010-2015) valid-year NO2 concentrations.
State abbreviation
CA
CA
CA
CA
CA
CA
County name
Placer
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Site ID
060610006
060670002
060670006
060670010
060670011
060670012
Lat
38.74573
38.71209
38.61378
38.55823
38.30259
38.68330
Lon
-121.26631
-121.38109
-121.36801
-121.49298
-121.42084
-121.16446
year_start
1993
1979
1979
1989
1992
1996
year_end
2016
2016
2016
2016
2016
2016
Elevation m
48
8
8
0
6
98
land use
MOBILE
RESID
RESID
RESID
AGRIC
RESID
scale
NBHOOD
NA
NBHOOD
NBHOOD
NA
NA
objectivel
POP EXP
POP EXP
POP EXP
GEN/BKGR
POP EXP
POP EXP
objective2
HI CONC
HI CONC
HI CONC
HI CONC
HI CONC
objective3
GEN/BKGR
REG TRANS
POP EXP
UP BKGR
MAX 03
cnty_landarea_2010
1407
965
965
965
965
965
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
235
518
road closest
80
50
aadt closest
153000
246000
dist aadtmax
398
road aadtmax
80
aadt aadtmax
158000
County Level NOx Emissions (tpy)
OnRoad mobile nox
4760
12361
12361
12361
12361
12361
NonRoad mobile nox
1492
3122
3122
3122
3122
3122
AML mobile nox
1150
1821
1821
1821
1821
1821
total nox
9158
19897
19897
19897
19897
19897
pct_mobile_nox
80.8
87
87
87
87
87
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Airport
Rail Yard
262
30
Institutional school hospital_
13
not characterized
Area design value monitor
Near-road monitor
B5-63
-------
Table B5-29, continued. Attributes of ambient monitors within the Sacramento study area
having recent (2010-2015) valid-year NO2 concentrations. i
State abbreviation
CA
CA
CA
County name
Sacramento
Sacramento
Yolo
Site ID
060670014
060670015
061130004
Lat
38.65078
38.59332
38.53445
Lon
-121.50677
-121.50380
-121.77340
year_start
2008
2015
1996
year_end
2016
2016
2016
Elevation m
3
12.8
0
land use
COMM
COMM
AGRIC
scale
NBHOOD
MICRO
NBHOOD
objectivel
POP EXP
SOURCE
POP EXP
objective2
objective3
cnty_landarea_2010
965
965
1015
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
20
414
road closest
5
113
aadt closest
186000
38050
dist aadtmax
49
road aadtmax
5
aadt aadtmax
190000
County Level NOx Emissions (tpy)
OnRoad mobile nox
12361
12361
2765
NonRoad mobile nox
3122
3122
1323
AML mobile nox
1821
1821
874
total nox
19897
19897
6713
pct_mobile_nox
87
87
73.9
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Airport
Rail Yard
Institutional school hospital_
20
not characterized
Area design value monitor
Near-road monitor
B5-64
-------
San Diego-Carlsbad, CA
Cnuia Vl
T ijuan<»
Monitors
# Design Monitor
# Near Road
• Not Used
• Study Monitor
060731017
060732007
060731010
Figure B5-20. Map of all monitors (1990-2015) in the San Diego study area (top panel) and
satellite views of near-road (middle panel) and area design value monitor (bottom panels).
B5-65
-------
Table B5-30. Attributes of ambient monitors within the San Diego study area having recent
(2010-2015) valid-year NO2 concentrations.
State abbreviation
CA
CA
CA
CA
CA
CA
County name
San Diego
San Diego
San Diego
San Diego
San Diego
San Diego
Site ID
060730001
060730003
060730006
060731002
060731006
060731008
Lat
32.63123
32.79119
32.83646
33.12771
32.84224
33.21703
Lon
-117.05908
-116.94209
-117.12875
-117.07533
-116.76823
-117.39616
year_start
1973
1971
1975
1973
1981
1997
year_end
2016
2014
2012
2015
2016
2016
Elevation m
55
143
135
204
603
15
land use
RESID
COMM
COMM
COMM
RESID
RESID
scale
NBHOOD
NBHOOD
NBHOOD
NBHOOD
URBAN
NBHOOD
objectivel
POP EXP
MAX PRE
MAX PRE
POP EXP
MAX 03
GEN/BKGR
objective2
GEN/BKGR
POP EXP
POP EXP
GEN/BKGR
GEN/BKGR
UP BKGR
objective3
QUAL
GEN/BKGR
HI CONC
QUAL
HI CONC
POP EXP
cnty_landarea_2010
4207
4207
4207
4207
4207
4207
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
982
514
187
342
492
road closest
805
52
78
8
5
aadt closest
190000
53000
17700
33500
133000
dist aadtmax
933
759
road aadtmax
15
78
aadt aadtmax
174000
32500
County Level NOx Emissions (tpy)
OnRoad mobile nox
27531
27531
27531
27531
27531
27531
NonRoad mobile nox
7428
7428
7428
7428
7428
7428
AML mobile nox
3491
3491
3491
3491
3491
3491
total nox
42700
42700
42700
42700
42700
42700
OnRoad mobile nox
27531
27531
27531
27531
27531
27531
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
pct_mobile_nox
90
90
90
90
90
90
Electricity_Generation_via_Combu
36
57
22
Military_Base
22
19
Pharmaceutical_Manufacturing
Ship_Boat_Manufacturing_or_Repai
not characterized
141
15
Area design value monitor
Near-road monitor
B5-66
-------
Table B5-30, continued. Attributes of ambient monitors within the San Diego study area
having recent (2010-2015) valid-year NO2 concentrations.
State abbreviation
CA
CA
CA
CA
CA
County name
San Diego
San Diego
San Diego
San Diego
San Diego
Site ID
060731010
060731014
060731016
060731017
060732007
Lat
32.70149
32.57936
32.84547
32.98544
32.55216
Lon
-117.14965
-116.92949
-117.12389
-117.08218
-116.93777
year_start
2005
2014
2011
2015
1990
year_end
2016
2016
2016
2016
2014
Elevation m
3
185
132
218
155
land use
COMM
COMM
MILIT
COMM
MOBILE
scale
NBHOOD
NBHOOD
NBHOOD
NBHOOD
MICRO
objectivel
GEN/BKGR
GEN/BKGR
GEN/BKGR
GEN/BKGR
POP EXP
objective2
POP EXP
POP EXP
POP EXP
SOURCE
objective3
MAX PRE
cnty_landarea_2010
4207
4207
4207
4207
4207
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
387
404
37
61
road closest
5
163
15
905
aadt closest
161000
138000
223000
48500
dist aadtmax
964
429
801
244
road aadtmax
5
15
15
905
aadt aadtmax
208000
174000
225000
52000
County Level NOx Emissions (tpy)
OnRoad mobile nox
27531
27531
27531
27531
27531
NonRoad mobile nox
7428
7428
7428
7428
7428
AML mobile nox
3491
3491
3491
3491
3491
total nox
42700
42700
42700
42700
42700
OnRoad mobile nox
90
90
90
90
90
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
pct_mobile_nox
57
Electricity_Generation_via_Combu
11
22
Military_Base
38
Pharmaceutical_Manufacturing
13
Ship_Boat_Manufacturing_or_Repai
88
141
not characterized
57
Area design value monitor
Near-road monitor
B5-67
-------
San Francisco-Oakland-Hayward, CA
Monitors
• Design Monitor
• Near Road
• Not Used
• Study Monitor
060010012
060750005
Figure B5-21. Map of all monitors (1990-2010) in the San Francisco study area (top panel)
and satellite views of near-road (middle panel) and area design value monitor (bottom
panel).
B5-68
-------
Table B5-31. Attributes of ambient monitors within the San Francisco study area having
recent (2010-2015) valid-year NO2 concentrations.
State abbreviation
CA
CA
CA
CA
CA
CA
County name
Alameda
Alameda
Alameda
Alameda
Alameda
Alameda
Site ID
060010007
060010009
060010011
060010012
060012004
060012005
Lat
37.68753
37.74307
37.81478
37.79362
37.87779
37.68962
Lon
-121.78422
-122.16994
-122.28235
-122.26338
-122.30129
-121.63192
year_start
1999
2007
2009
2014
2007
2011
year_end
2016
2016
2016
2016
2010
2016
Elevation m
137
11
0
3.9
6
526
land use
COMM
RESID
RESID
COMM
COMM
AGRIC
scale
NBHOOD
MID
NBHOOD
MICRO
NBHOOD
REGION
objectivel
POP EXP
POP EXP
POP EXP
POP EXP
POP EXP
REG TRANS
objective2
HI CONC
SOURCE
SOURCE
SOURCE
DOWNWND
objective3
REG TRANS
cnty_landarea_2010
739
739
739
739
739
739
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
36
733
20
467
road closest
185
980
880
80
aadt closest
21700
113000
216000
256000
dist aadtmax
426
492
road aadtmax
880
80
aadt aadtmax
225000
270000
County Level NOx Emissions (tpy)
OnRoad mobile nox
19032
19032
19032
19032
19032
19032
NonRoad mobile nox
3158
3158
3158
3158
3158
3158
AML mobile nox
3507
3507
3507
3507
3507
3507
total nox
28381
28381
28381
28381
28381
28381
pct_mobile_nox
90.5
90.5
90.5
90.5
90.5
90.5
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Petroleum_Refinery
Airport
941
Electricity_Generation_via_Combu
48
Glass Plant
327
Rail Yard
56
150
150
Wastewater_T reatment_Facility
53
53
Foundries Iron and Steel
36
Pharmaceutical_Manufacturing
17
17
not characterized
13
13
Landfill
Hot_Mix_Asphalt_Plant
Chemical Plant
Institutional school hospital_
Area design value monitor
Near-road monitor
B5-69
-------
Table B5-31, continued. Attributes of ambient monitors within the San Francisco study
area having recent (2010-2015) valid-year NO2 concentrations.
State abbreviation
CA
CA
CA
CA
CA
CA
CA
County name
Contra Costa
Contra Costa
Contra Costa
Contra Costa
Marin
San Francisco
San Mateo
Site ID
060130002
060131002
060131004
060132007
060410001
060750005
060811001
Lat
37.93601
38.00631
37.96040
37.74365
37.97231
37.76595
37.48293
Lon
-122.02615
-121.64192
-122.35681
-121.93419
-
-122.39904
-
year_start
1980
1981
2002
2011
1967
1985
1967
year_end
2016
2016
2016
2016
2016
2016
2016
Elevation m
26
-2
20
119
3
5
3
land use
RESID
AGRIC
COMM
RESID
COMM
INDUS
INDUS
scale
NBHOOD
REGION
MID
URBAN
MID
NBHOOD
NBHOOD
objectivel
POP EXP
GEN/BKGR
SOURCE
POP EXP
HI CONC
POP EXP
POP EXP
objective2
SOURCE
REG TRANS
POP EXP
UP BKGR
POP EXP
SOURCE
HI CONC
objective3
GEN/BKGR
HI CONC
REG TRANS
HI CONC
QUAL
cnty_landarea_2010
716
716
716
716
520
47
448
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
115
322
453
road closest
101
280
101
aadt closest
136000
103000
202000
dist aadtmax
506
517
road aadtmax
101
101
aadt aadtmax
184000
217000
County Level NOx Emissions (tpy)
OnRoad mobile nox
9307
9307
9307
9307
2282
4623
5623
NonRoad mobile nox
2074
2074
2074
2074
568
1474
1434
AML mobile nox
1973
1973
1973
1973
503
5065
3963
total nox
20714
20714
20714
20714
3967
12406
12208
pct_mobile_nox
64.5
64.5
64.5
64.5
84.5
90
90.3
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Petroleum_Refinery
835
Airport
Electricity_Generation_via_Combu
20
54
Glass Plant
Rail Yard
127
Wastewater_T reatment_Facility
16
74
Foundries Iron and Steel
Pharmaceutical_Manufacturing
not characterized
14
11
14
Landfill
24
Hot_Mix_Asphalt_Plant
Chemical Plant
Institutional school hospital_
10
16
Area design value monitor
Near-road monitor
B5-70
-------
St Louis, MO-IL
Monitors
• Design Monitor
• Near Road
• Not Used
• Study Monitor
295100094
291890016
295100086
Figure B5-22. Map of all monitors (1990-2015) in the St. Louis study area (top panel) and
satellite views of near-road (middle panel) and area design value monitor (bottom panel).
B5-71
-------
Table B5-32. Attributes of ambient monitors within the St. Louis study area having recent
(2010-2015) valid-year NO2 concentrations.
State abbreviation
IL
IL
MO
MO
MO
MO
County name
Saint Clair
Saint Clair
Saint Louis
St. Louis City
St. Louis City
St. Louis City
Site ID
171630010
171630900
291890016
295100085
295100086
295100094
Lat
38.61203
38.52594
38.75264
38.65650
38.67322
38.63106
Lon
-90.16048
-90.03910
-90.44884
-90.19865
-90.23917
-90.28114
year_start
1980
2010
2014
2013
1999
2012
year_end
2016
2015
2016
2016
2016
2016
Elevation m
125
167
148
137
0
128
land use
INDUS
COMM
COMM
RESID
RESID
COMM
scale
NBHOOD
NA
MICRO
NBHOOD
NBHOOD
MICRO
objectivel
POP EXP
GEN/BKGR
SOURCE
HI CONC
POP EXP
SOURCE
objective2
HI CONC
POP EXP
POP EXP
POP EXP
objective3
SOURCE
QUAL ASSUR
cnty_landarea_2010
658
658
508
62
62
62
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
45
139
27
221
205
25
road closest
0
15
70
70
0
64
aadt closest
2900
25000
161338
98966
12972
159326
dist aadtmax
773
830
571
991
438
road aadtmax
55
270
70
70
64
aadt aadtmax
57400
167602
112323
94733
167347
County Level NOx Emissions (tpy)
OnRoad mobile nox
5026
5026
25063
6296
6296
6296
NonRoad mobile nox
1151
1151
4697
663
663
663
AML mobile nox
1003
1003
1902
1590
1590
1590
total nox
8506
8506
39486
10691
10691
10691
pct_mobile_nox
84.4
84.4
80.2
80
80
80
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Breweries Distilleries Wineries
467
Rail Yard
88
271
56
309
Electricity_Generation_via_Combu
252
285
Wastewater_T reatment_Facility
89
81
81
Institutional school hospital_
12
49
70
Ethanol_Biorefineries_Soy_Biodie
49
60
Chemical Plant
27
93
Pharmaceutical_Manufacturing
43
43
Landfill
58
22
Steam_H eating_Facil ity
20
Petroleum_Storage_Facility
14
not characterized
24
13
12
Aircraft Aerospace or_Related
Airport
Area design value monitor
Near-road monitor
B5-72
-------
Monitors
• Design Monitor*!
• Near Road
• Not Used
• Study Monitor
Washington-Arlington-Alexandria, DC-VA-MD-WV
^^——————————
110010051
110010041
110010043
Figure B5-23. Map of all monitors (1990-2015) in the Washington DC study area (top
panel) and satellite views of near-road (middle panel) and area design value monitors
(bottom panels).
B5-73
-------
Table B5-33. Attributes of ambient monitors within the Washington DC study area having
recent (2010-2015) valid-year NO2 concentrations.
State abbreviation
DC
DC
DC
DC
DC
MD
County name
DoColumbia
DoColumbia
DoColumbia
DoColumbia
DoColumbia
Prince
Site ID
110010025
110010041
110010043
110010050
110010051
240330030
Lat
38.58323
38.89557
38.92185
38.97009
38.89477
39.05528
Lon
-77.12190
-76.95807
-77.01318
-77.01672
-76.95343
-76.87833
year_start
1980
1993
1993
2012
2015
2005
year_end
2010
2014
2016
2016
2016
2016
Elevation m
91
8
50
15
25
49
land use
COMM
RESID
COMM
RESID
COMM
RESID
scale
URBAN
NBHOOD
URBAN
NBHOOD
MICRO
URBAN
objectivel
POP EXP
POP EXP
POP EXP
POP EXP
POP EXP
POP EXP
objective2
GEN/BKGR
MAX PRE
MAX 03
HI CONC
GEN/BKGR
objective3
HI CONC
HI CONC
SOURCE
UP BKGR
cnty_landarea_2010
61
61
61
61
61
483
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
107
337
50
15
699
road closest
0
0
0
295
21
aadt closest
25893
37729
10974
115480
15790
dist aadtmax
377
918
789
888
road aadtmax
0
0
0
1
aadt aadtmax
112749
38996
29196
32451
County Level NOx Emissions (tpy)
OnRoad mobile nox
4739
4739
4739
4739
4739
11955
NonRoad mobile nox
2364
2364
2364
2364
2364
2442
AML mobile nox
223
223
223
223
223
309
total nox
9418
9418
9418
9418
9418
21289
pct_mobile_nox
77.8
77.8
77.8
77.8
77.8
69.1
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Airport
Electricity_Generation_via_Combu
212
212
Municipal_Waste_Combustor
Steam_H eating_Facil ity
106
301
106
Military_Base
130
not characterized
11
11
20
Institutional school hospital_
27
26
Rail Yard
16
16
Hot_Mix_Asphalt_Plant
Area design value monitor
Near-road monitor
B5-74
-------
Table B5-33, continued. Attributes of ambient monitors within the Washington DC study
area having recent (2010-2015) valid-year NO2 concentrations.
State abbreviation
VA
VA
VA
VA
VA
County name
Arlington
Loudoun
Prince
Alexandria
Alexandria
Site ID
510130020
511071005
511530009
515100009
515100021
Lat
38.85770
39.02473
38.85287
38.81040
38.80650
Lon
-77.05922
-77.48925
-77.63462
-77.04435
-77.08640
year_start
1977
1998
1994
1975
2012
year_end
2016
2016
2016
2012
2016
Elevation m
16
88
117
9
61
land use
COMM
RESID
RESID
RESID
COMM
scale
NA
NBHOOD
URBAN
NA
NBHOOD
objectivel
POP EXP
POP EXP
POP EXP
POP EXP
POP EXP
objective2
objective3
cnty_landarea_2010
26
516
336
15
15
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
588
113
162
road closest
1
400
236
aadt closest
51469
32550
34262
dist aadtmax
933
507
526
road aadtmax
395
90005
95
aadt aadtmax
199159
49465
153908
County Level NOx Emissions (tpy)
OnRoad mobile nox
1214
2121
3931
682
682
NonRoad mobile nox
1203
2253
1390
127
127
AML mobile nox
1243
1765
297
142
142
total nox
4065
6893
6863
2278
2278
pct_mobile_nox
90
89.1
81.9
41.8
41.8
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Airport
1311
Electricity_Generation_via_Combu
599
558
558
Municipal_Waste_Combustor
471
Steam_H eating_Facil ity
195
Military_Base
25
not characterized
17
15
Institutional school hospital_
14
Rail Yard
Hot_Mix_Asphalt_Plant
11
Area design value monitor
Near-road monitor
B5-75
-------
5.3 ATTRIBUTES OF HISTORICAL (1990-2009) OR OTHER AMBIENT
NO2 MONITORS NOT USED FOR THE 2010-2015 ANALYSIS
Table B5-34. Attributes of ambient monitors within the Atlanta study area not used for the
2010-2015 analysis.
State abbreviation
GA
GA
County name
DeKalb
Fulton
Site ID
130893001
131210048
Lat
33.84574
33.77933
Lon
-84.21340
-84.39576
year_start
1990
1982
year_end
2006
2009
Elevation m
0
290
land use
RESID
COMM
scale
NBHOOD
NBHOOD
objectivel
POP EXP
HI CONC
objective2
GEN/BKGR
POP EXP
objective3
cnty_landarea_2010
268
527
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
408
road closest
0
aadt closest
22130
dist aadtmax
430
road aadtmax
75
aadt aadtmax
348900
County Level NOx Emissions (tpy)
OnRoad mobile nox
11230
16404
NonRoad mobile nox
1962
3939
AML mobile nox
376
1560
total nox
14719
23989
pct_mobile_nox
92.2
91.3
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Landfill
Rail Yard
224
Monitor not used for 2010-2015 analysis (either historical data or were incomplete)
B5-76
-------
Table B5-35. Attributes of ambient monitors within the Baltimore study area not used for
the 2010-2015 analysis.
State abbreviation
MD
MD
MD
County name
Bait. (City)
Bait. (City)
Anne Arundel
Site ID
245100050
245100051
240030019
Lat
39.31861
39.28150
39.10111
Lon
-76.58250
-76.59858
-76.72944
year_start
1995
1997
1980
year_end
2001
1999
2003
Elevation m
49
5
46
land use
RESID
COMM
COMM
scale
REGION
REGION
URBAN
objectivel
POP EXP
POP EXP
UP BKGR
objective2
MAX PRE
HI CONC
POP EXP
objective3
HI CONC
GEN/BKGR
cnty_landarea_2010
81
81
415
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
325
305
road closest
1
1230
aadt closest
21791
12671
dist aadtmax
643
387
road aadtmax
147
1383
aadt aadtmax
29041
35501
County Level NOx Emissions (tpy)
OnRoad mobile nox
4573
4573
7673
NonRoad mobile nox
646
646
1967
AML mobile nox
1385
1385
3129
total nox
10421
10421
20731
pct_mobile_nox
63.4
63.4
61.6
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Municipal_Waste_Combustor
1134
Steam_H eating_Facil ity
50
153
SugarJVIill
126
Institutional school hospital_
113
81
Chemical Plant
70
Rail Yard
74
Electricity_Generation_via_Combu
25
40
not characterized
10
10
Military_Base
52
Wastewater_T reatment_Facility
Mineral_Processing_Plant
19
Monitor not used for 2010-2015 analysis (either historical data or were incomplete)
B5-77
-------
Table B5-36. Attributes of ambient monitors within the Boston study area not used for the
2010-2015 analysis.
State abbreviation
MA
MA
MA
MA
MA
MA
MA
County name
Essex
Essex
Norfolk
Norfolk
Suffolk
Suffolk
Suffolk
Site ID
250090005
250094004
250210008
250210009
250250021
250250035
250250036
Lat
42.70954
42.79027
42.33343
42.31676
42.37783
42.33343
42.33343
Lon
-71.14589
-70.80835
-71.13283
-71.13283
-71.02714
-71.11616
-71.11616
year_start
1981
1994
1982
1982
1980
1982
1983
year_end
2002
2009
1993
1995
2002
1995
1995
Elevation m
46
1
0
0
6
0
0
land use
RESID
RESID
RESID
RESID
RESID
RESID
RESID
scale
NA
URBAN
MID
MICRO
NBHOOD
NA
NA
objectivel
HI CONC
POP EXP
HI CONC
objective2
GEN/BKGR
MAX 03
POP EXP
objective3
POP EXP
SOURCE
cnty_landarea_2010
493
493
396
396
58
58
58
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
654
468
962
19
161
161
road closest
495
9
411
476
129
129
aadt closest
101413
34568
42018
37596
17696
17696
dist aadtmax
810
27
222
222
road aadtmax
495
90
9
9
aadt aadtmax
114788
67804
42578
42578
County Level NOx Emissions (tpy)
OnRoad mobile nox
6568
6568
6685
6685
3201
3201
3201
NonRoad mobile nox
2473
2473
2009
2009
2094
2094
2094
AML mobile nox
860
860
619
619
5446
5446
5446
total nox
15750
15750
12261
12261
14015
14015
14015
pct_mobile_nox
62.9
62.9
76
76
76.6
76.6
76.6
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Airport
2203
Municipal_Waste_Combustor
768
Steam_H eating_Facil ity
182
Institutional school hospital_
258
127
258
258
Electricity_Generation_via_Combu
212
121
353
253
253
Rail Yard
20
20
20
20
Wastewater_T reatment_Facility
Aircraft Aerospace or_Related
Fabricated Metal Products Plant
42
not characterized
48
Textile Yarn or_Carpet_Plant
26
Military_Base
Mineral_Processing_Plant
Monitor not used for 2010-2015 analysis (either historical data or were incomplete)
B5-78
-------
Table B5-36, continued. Attributes of ambient monitors within the Boston study area not
used for the 2010-2015 analysis.
State abbreviation
MA
MA
MA
NH
NH
NH
NH
County name
Suffolk
Suffolk
Suffolk
Rockingham
Rockingham
Rockingham
Rockingham
Site ID
250250037
250250041
250251003
330150009
330150013
330150014
330150015
Lat
42.61676
42.31737
42.40176
43.07814
43.00009
43.07537
43.08259
Lon
-70.99950
-70.96836
-71.03061
-70.76228
-71.19951
-70.74802
-70.76144
year_start
1977
1999
1984
1977
1997
2003
2001
year_end
1990
2014
1999
2001
2003
2008
2003
Elevation m
0
10
59
3
0
10.8
3
land use
COMM
COMM
RESID
COMM
RESID
RESID
COMM
scale
NA
URBAN
URBAN
NA
REGION
NBHOOD
NBHOOD
objectivel
POP EXP
POP EXP
POP EXP
HI CONC
POP EXP
POP EXP
objective2
UP BKGR
HI CONC
POP EXP
UP BKGR
objective3
HI CONC
UP BKGR
cnty_landarea_2010
58
58
58
695
695
695
695
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
212
308
39
road closest
1
0
0
aadt closest
75856
7400
7400
dist aadtmax
967
949
road aadtmax
95
95
aadt aadtmax
69000
70682
County Level NOx Emissions (tpy)
OnRoad mobile nox
3201
3201
3201
4691
4691
4691
4691
NonRoad mobile nox
2094
2094
2094
1376
1376
1376
1376
AML mobile nox
5446
5446
5446
533
533
533
533
total nox
14015
14015
14015
8767
8767
8767
8767
pct_mobile_nox
76.6
76.6
76.6
75.3
75.3
75.3
75.3
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Airport
2203
Municipal_Waste_Combustor
Steam_H eating_Facil ity
Institutional school hospital_
Electricity_Generation_via_Combu
312
842
735
842
Rail Yard
Wastewater_T reatment_Facility
14
Aircraft Aerospace or_Related
Fabricated Metal Products Plant
not characterized
17
22
30
30
Textile Yarn or_Carpet_Plant
Military_Base
29
29
29
Mineral_Processing_Plant
18
18
18
Monitor not used for 2010-2015 analysis (either historical data or were incomplete)
B5-79
-------
Table B5-37. Attributes of ambient monitors within the Chicago study area not used for the
2010-2015 analysis.
State abbreviation
IL
IL
IL
IL
IL
IL
County name
Cook
Cook
Cook
Cook
Cook
Cook
Site ID
170310037
170310039
170310064
170310071
170310072
170310075
Lat
41.97948
41.89448
41.79079
41.86670
41.89581
41.96420
Lon
-87.67006
-87.62033
-87.60165
-87.64977
-87.60768
-87.65867
year_start
1981
1978
1992
1992
1995
1997
year_end
1996
1994
2000
1994
2011
2001
Elevation m
183
180
180
180
181
180
land use
RESID
COMM
RESID
COMM
COMM
RESID
scale
NA
NBHOOD
NBHOOD
NBHOOD
NBHOOD
NBHOOD
objectivel
POP EXP
POP EXP
POP EXP
MAX PRE
POP EXP
objective2
HI CONC
HI CONC
objective3
SOURCE
cnty_landarea_2010
945
945
945
945
945
945
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
829
108
377
52
505
144
road closest
14
0
0
0
0
0
aadt closest
36980
25225
5263
27906
12414
20978
dist aadtmax
987
498
502
750
913
road aadtmax
41
0
90
41
41
aadt aadtmax
151051
29992
266664
127514
104309
County Level NOx Emissions (tpy)
OnRoad mobile nox
54900
54900
54900
54900
54900
54900
NonRoad mobile nox
19402
19402
19402
19402
19402
19402
AML mobile nox
12900
12900
12900
12900
12900
12900
total nox
113148
113148
113148
113148
113148
113148
pct_mobile_nox
77.1
77.1
77.1
77.1
77.1
77.1
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Steel Mill
17
Airport
Electricity_Generation_via_Combu
1118
Rail Yard
21
185
Institutional school hospital_
232
73
232
97
Chemical Plant
Landfill
lndustrial_Machinery_or_Equipmen
Steam_H eating_Facil ity
12
12
12
Wastewater_T reatment_Facility
Food_Products_Processing_Plant
Hot_Mix_Asphalt_Plant
not characterized
114
87
87
Glass Plant
Calcined Pet Coke Plant
Automobile Truck or Parts Plant
Monitor not used for 2010-2015 analysis (either historical data or were incomplete)
B5-80
-------
Table B5-37, continued. Attributes of ambient monitors within the Chicago study area not
used for the 2010-2015 analysis.
State abbreviation
IL
IL
IL
IL
IL
IL
County name
Cook
Cook
Cook
Cook
Cook
Cook
Site ID
170311002
170311601
170313101
170313102
170313601
170314003
Lat
41.61642
41.66812
41.96528
41.97003
41.99670
42.03892
Lon
-87.55782
-87.99057
-87.87633
-87.87645
-87.87951
-87.89896
year_start
1978
1981
1984
1988
1988
1985
year_end
1991
1991
1997
1990
1990
1990
Elevation m
179
226
197
197
197
197
land use
RESID
RESID
MOBILE
COMM
COMM
RESID
scale
NA
NBHOOD
MID
MID
NBHOOD
NA
objectivel
POP EXP
HI CONC
HI CONC
GEN/BKGR
POP EXP
objective2
POP EXP
objective3
cnty_landarea_2010
945
945
945
945
945
945
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
103
29
24
180
468
road closest
83
12
12
90
12
aadt closest
19200
43900
39100
173242
16005
dist aadtmax
669
152
378
827
road aadtmax
83
294
294
12
aadt aadtmax
26600
190046
190046
21000
County Level NOx Emissions (tpy)
OnRoad mobile nox
54900
54900
54900
54900
54900
54900
NonRoad mobile nox
19402
19402
19402
19402
19402
19402
AML mobile nox
12900
12900
12900
12900
12900
12900
total nox
113148
113148
113148
113148
113148
113148
pct_mobile_nox
77.1
77.1
77.1
77.1
77.1
77.1
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Steel Mill
Airport
5261
5261
5261
Electricity_Generation_via_Combu
Rail Yard
268
440
440
48
Institutional school hospital_
19
Chemical Plant
12
Landfill
33
21
lndustrial_Machinery_or_Equipmen
Steam_H eating_Facil ity
Wastewater_T reatment_Facility
Food_Products_Processing_Plant
12
12
Hot_Mix_Asphalt_Plant
11
not characterized
72
83
73
14
Glass Plant
412
Calcined Pet Coke Plant
145
Automobile Truck or Parts Plant
Monitor not used for 2010-2015 analysis (either historical data or were incomplete)
B5-81
-------
Table B5-37, continued. Attributes of ambient monitors within the Chicago study area not
used for the 2010-2015 analysis.
State abbreviation
IL
IL
IL
IL
IL
IL
County name
Cook
Cook
Cook
DuPage
Lake
Lake
Site ID
170314004
170314005
170318003
170431003
170971003
170971007
Lat
41.99947
42.00503
41.63142
41.94753
42.44585
42.46757
Lon
-87.87506
-87.90896
-87.56810
-87.92868
-88.10342
-87.81005
year_start
1988
1988
1991
1987
1990
1994
year_end
1991
1990
2002
1990
1991
2002
Elevation m
197
0
179
201
253
178
land use
MOBILE
COMM
RESID
COMM
AGRIC
FOREST
scale
MID
NBHOOD
NBHOOD
NBHOOD
URBAN
URBAN
objectivel
HI CONC
HI CONC
POP EXP
HI CONC
HI CONC
POP EXP
objective2
POP EXP
DOWNWND
objective3
HI CONC
cnty_landarea_2010
945
945
945
328
444
444
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
254
403
690
644
road closest
90
72
0
59
aadt closest
173242
39700
12612
13500
dist aadtmax
500
806
road aadtmax
90
94
aadt aadtmax
161039
139053
County Level NOx Emissions (tpy)
OnRoad mobile nox
54900
54900
54900
13656
8378
8378
NonRoad mobile nox
19402
19402
19402
4770
5355
5355
AML mobile nox
12900
12900
12900
1293
878
878
total nox
113148
113148
113148
24391
20709
20709
pct_mobile_nox
77.1
77.1
77.1
80.8
70.6
70.6
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Steel Mill
Airport
5261
5261
5261
Electricity_Generation_via_Combu
Rail Yard
48
268
392
Institutional school hospital_
19
Chemical Plant
12
Landfill
33
lndustrial_Machinery_or_Equipmen
Steam_H eating_Facil ity
Wastewater_T reatment_Facility
Food_Products_Processing_Plant
12
Hot_Mix_Asphalt_Plant
11
not characterized
73
79
58
Glass Plant
412
Calcined Pet Coke Plant
Automobile Truck or Parts Plant
18
Monitor not used for 2010-2015 analysis (either historical data or were incomplete)
B5-82
-------
Table B5-37, continued. Attributes of ambient monitors within the Chicago study area not
used for the 2010-2015 analysis.
State abbreviation
IL
IN
IN
Wl
Wl
County name
Will
Jasper
Lake
Kenosha
Kenosha
Site ID
171971011
180730003
180891016
550590016
550590019
Lat
41.22154
41.13585
41.60031
42.58585
42.50472
Lon
-88.19097
-86.98777
-87.33476
-87.87508
-87.80930
year_start
1995
1975
1989
1979
1990
year_end
2007
1990
1997
1992
1990
Elevation m
181
215
183
220
187
land use
AGRIC
AGRIC
RESID
RESID
RESID
scale
REGION
REGION
NBHOOD
NA
NA
objectivel
GEN/BKGR
HI CONC
POP EXP
HI CONC
objective2
UP BKGR
POP EXP
HI CONC
POP EXP
objective3
POP EXP
REG TRANS
cnty_landarea_2010
837
560
499
272
272
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
187
290
road closest
53
158
aadt closest
8793
18674
dist aadtmax
528
623
road aadtmax
90
31
aadt aadtmax
35251
46107
County Level NOx Emissions (tpy)
OnRoad mobile nox
9714
2153
9294
2312
2312
NonRoad mobile nox
4244
449
2652
687
687
AML mobile nox
1315
64
1450
284
284
total nox
29376
10617
38995
6691
6691
pct_mobile_nox
52
25.1
34.4
49.1
49.1
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Steel Mill
4336
Airport
Electricity_Generation_via_Combu
21
Rail Yard
Institutional school hospital_
Chemical Plant
Landfill
lndustrial_Machinery_or_Equipmen
Steam_H eating_Facil ity
Wastewater_T reatment_Facility
Food_Products_Processing_Plant
Hot_Mix_Asphalt_Plant
not characterized
Glass Plant
Calcined Pet Coke Plant
Automobile Truck or Parts Plant
Monitor not used for 2010-2015 analysis (either historical data or were incomplete)
B5-83
-------
Table B5-38. Attributes of ambient monitors within the Dallas study area not used for the
2010-2015 analysis.
State abbreviation
TX
TX
TX
TX
TX
TX
TX
County name
Dallas
Dallas
Denton
Ellis
Ellis
Tarrant
Tarrant
Site ID
481130045
481130055
481210033
481390015
481390017
484390057
484391003
Lat
32.91972
32.61639
33.20623
32.43694
32.47361
32.70694
32.75972
Lon
-96.80806
-96.75694
-97.19557
-97.02500
-97.04250
-97.09361
-97.32806
year_start
1973
1981
1996
1996
2004
1998
1975
year_end
1998
1994
1997
2007
2006
2001
1996
Elevation m
195
132
0
0
0
0
186
land use
RESID
UNK
INDUS
AGRIC
RESID
RESID
COMM
scale
URBAN
NA
URBAN
NBHOOD
NA
NBHOOD
NBHOOD
objectivel
POP EXP
GEN/BKGR
UP BKGR
POP EXP
POP EXP
HI CONC
objective2
HI CONC
HI CONC
SOURCE
HI CONC
POP EXP
objective3
GEN/BKGR
REG
cnty_landarea_2010
871
871
878
935
935
864
864
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
396
794
118
4
road closest
289
342
303
347
aadt closest
23000
13800
39000
35000
dist aadtmax
970
838
road aadtmax
635
35
aadt aadtmax
209000
173390
County Level NOx Emissions (tpy)
OnRoad mobile nox
36623
36623
7555
5173
5173
24824
24824
NonRoad mobile nox
8462
8462
1685
854
854
4976
4976
AML mobile nox
1141
1141
1608
272
272
7457
7457
total nox
51422
51422
13785
11530
11530
45082
45082
pct_mobile_nox
89.9
89.9
78.7
54.6
54.6
82.6
82.6
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Portland_Cement_Manufacturing
1087
1087
Airport
23
24
Rail Yard
151
467
Steel Mill
298
298
Compressor_Station
26
16
Electricity_Generation_via_Combu
164
164
not characterized
Bakeries
Breweries Distilleries Wineries
Pharmaceutical_Manufacturing
Automobile Truck or Parts Plant
55
Monitor not used for 2010-2015 analysis (either historical data or were incomplete)
B5-84
-------
Table B5-39. Attributes of ambient monitors within the Denver study area not used for the
2010-2015 analysis.
State abbreviation
CO
CO
CO
CO
CO
CO
CO
County name
Adams
Arapahoe
Jefferson
Jefferson
Jefferson
Jefferson
Park
Site ID
080017015
080050003
080590006
080590008
080590009
080590010
080930002
Lat
39.84249
39.65721
39.91280
39.87639
39.86193
39.89971
39.24028
Lon
-
-
-
-
-
-
-
year_start
1987
1988
1995
1992
1995
1995
2015
year_end
1994
1996
1999
2001
1999
2001
2016
Elevation m
1605
1654
1802
1718
1848
1877
3027
land use
COMM
COMM
INDUS
INDUS
INDUS
AGRIC
FOREST
scale
NBHOOD
NBHOOD
NA
NBHOOD
NBHOOD
NBHOOD
NA
objectivel
GEN/BKGR
POP EXP
GEN/BKGR
GEN/BKGR
GEN/BKGR
GEN/BKGR
objective2
POP EXP
HI CONC
POP EXP
objective3
HI CONC
cnty_landarea_2010
1168
798
764
764
764
764
2194
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
150
58
67
330
road closest
85
72
93
285
aadt closest
70000
3900
17000
4100
dist aadtmax
341
road aadtmax
85
aadt aadtmax
78000
County Level NOx Emissions (tpy)
OnRoad mobile nox
8763
8397
8825
8825
8825
8825
700
NonRoad mobile nox
1974
2016
2226
2226
2226
2226
99
AML mobile nox
838
285
259
259
259
259
0
total nox
25245
13022
14406
14406
14406
14406
1438
pct_mobile_nox
45.9
82.2
78.5
78.5
78.5
78.5
55.6
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Electricity_Generation_via_Combu
1999
19
19
Rail Yard
Petroleum_Refinery
Food_Products_Processing_Plant
Dry_Cleaner Perchloroethylene
Wastewater_T reatment_Facility
84
Hot_Mix_Asphalt_Plant
19
Institutional school hospital_
16
Petroleum_Storage_Facility
Bakeries
Lumber Sawmill
not characterized
131
118
Chemical Plant
12
Compressor_Station
15
Landfill
27
Brick Structural_Clay or_Clay
16
Monitor not used for 2010-2015 analysis (either historical data or were incomplete)
B5-85
-------
Table B5-40. Attributes of ambient monitors within the Detroit study area not used for the
2010-2015 analysis.
State abbreviation
Ml
Ml
Ml
Ml
Ml
Ml
County name
Macomb
Wayne
Wayne
Wayne
Wayne
Wayne
Site ID
260990009
261630016
261630029
261630062
261631010
261631011
Lat
42.73139
42.35781
42.33809
42.34087
42.29077
42.28010
Lon
-82.79346
-83.09603
-83.02353
-83.06242
-83.12066
-83.12012
year_start
1993
1974
1988
1993
2015
2015
year_end
1998
2007
1990
1993
2016
2016
Elevation m
189
191
183
610
0.1
0.1
land use
COMM
RESID
COMM
RESID
INDUS
INDUS
scale
NA
NBHOOD
MID
NA
MICRO
MICRO
objectivel
HI CONC
POP EXP
HI CONC
POP EXP
MAX 03
MAX 03
objective2
POP EXP
HI CONC
POP EXP
objective3
DOWNWND
cnty_landarea_2010
479
612
612
612
612
612
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
111
21
273
147
498
road closest
0
0
0
0
0
aadt closest
4190
19600
11554
8001
6630
dist aadtmax
653
518
602
608
road aadtmax
94
75
75
0
aadt aadtmax
152200
102200
95200
14446
County Level NOx Emissions (tpy)
OnRoad mobile nox
12634
29767
29767
29767
29767
29767
NonRoad mobile nox
3670
7051
7051
7051
7051
7051
AML mobile nox
108
3496
3496
3496
3496
3496
total nox
20833
62423
62423
62423
62423
62423
pct_mobile_nox
78.8
64.6
64.6
64.6
64.6
64.6
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Electricity_Generation_via_Combu
3865
3865
Steel Mill
2776
2776
Municipal_Waste_Combustor
1617
1617
1617
Mineral_Processing_Plant
547
547
Petroleum_Refinery
412
412
Wastewater_T reatment_Facility
246
246
Steam_H eating_Facil ity
126
126
126
Automobile Truck or Parts Plant
197
197
51
51
Institutional school hospital_
77
51
77
Rail Yard
106
106
not characterized
12
12
Landfill
47
Monitor not used for 2010-2015 analysis (either historical data or were incomplete)
B5-86
-------
Table B5-41. Attributes of ambient monitors within the Houston study area not used for
the 2010-2015 analysis.
State abbreviation
TX
TX
TX
TX
County name
Galveston
Harris
Harris
Montgomery
Site ID
481670014
482011037
482011041
483390089
Lat
29.26332
29.75111
29.75167
30.35389
Lon
-94.85657
-95.36139
-95.08361
-95.42167
year_start
1996
1978
2002
1999
year_end
2007
2001
2003
2001
Elevation m
0
16
0
0
land use
COMM
COMM
COMM
COMM
scale
MID
NBHOOD
MID
MID
objectivel
UP BKGR
HI CONC
GEN/BKGR
GEN/BKGR
objective2
GEN/BKGR
POP EXP
HI CONC
REG TRANS
objective3
REG TRANS
cnty_landarea_2010
378
1703
1703
1042
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
307
454
road closest
59
0
aadt closest
188200
1830
dist aadtmax
663
road aadtmax
45
aadt aadtmax
247520
County Level NOx Emissions (tpy)
OnRoad mobile nox
2958
49330
49330
5948
NonRoad mobile nox
996
13105
13105
1208
AML mobile nox
4215
14455
14455
448
total nox
12353
98983
98983
9429
pct_mobile_nox
66.1
77.7
77.7
80.6
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Chemical Plant
1552
Petroleum_Refinery
Electricity_Generation_via_Combu
236
Airport
Rail Yard
269
Plastic Resin or Rubber Produc
153
Food_Products_Processing_Plant
97
Wastewater_T reatment_Facility
75
Petroleum_Storage_Facility
Glass Plant
Chlor alkali Plant
18
Breweries Distilleries Wineries
Fertilizer Plant
Foundries Iron and Steel
Landfill
28
not characterized
57
Foundries non ferrous
Monitor not used for 2010-2014 analysis (either historical data or were incomplete)
B5-87
-------
Table B5-42. Attributes of ambient monitors within the Kansas City study area not used
for the 2010-2015 analysis.
State abbreviation
KS
KS
MO
MO
MO
MO
MO
County name
Wyandotte
Wyandotte
Clay
Clay
Clay
Jackson
Platte
Site ID
202090001
202090020
290470005
290470006
290470025
290950038
291650023
Lat
39.11306
39.15139
39.30309
39.33191
39.18392
39.10417
39.30003
Lon
-94.62468
-94.61773
-94.37662
-94.58084
-94.49774
-94.49222
-94.70024
year_start
1977
1992
1981
2002
1977
1994
1977
year_end
1999
1998
2010
2004
2002
1996
2004
Elevation m
256
228
314
303
283
0
293
land use
COMM
INDUS
AGRIC
AGRIC
RESID
INDUS
MOBILE
scale
NBHOOD
NA
URBAN
NA
NBHOOD
NA
NA
objectivel
POP EXP
HI CONC
POP EXP
POP EXP
POP EXP
POP EXP
objective2
HI CONC
POP EXP
HI CONC
objective3
cnty_landarea_2010
152
152
397
397
397
604
420
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
193
359
847
290
383
114
road closest
69
69
0
169
435
0
aadt closest
9910
14300
16057
16672
66763
18542
dist aadtmax
717
943
290
465
road aadtmax
70
0
169
435
aadt aadtmax
44600
20342
18823
87490
County Level NOx Emissions (tpy)
OnRoad mobile nox
4241
4241
6048
6048
6048
13680
3523
NonRoad mobile nox
449
449
769
769
769
3213
552
AML mobile nox
2877
2877
797
797
797
3026
1843
total nox
16058
16058
9065
9065
9065
28515
8838
pct_mobile_nox
47.1
47.1
84
84
84
69.9
67
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Electricity_Generation_via_Combu
4392
3175
1425
Rail Yard
1172
221
308
Mineral Wool Plant
158
158
Automobile Truck or Parts Plant
43
43
82
Food_Products_Processing_Plant
21
Chemical Plant
12
48
Wet Corn Mill
32
32
Municipal_Waste_Combustor
11
11
Petroleum_Storage_Facility
11
11
not characterized
16
Wastewater_T reatment_Facility
13
Airport
683
Steam_H eating_Facil ity
18
Monitor not used for 2010-2015 analysis (either historical data or were incomplete)
B5-88
-------
Table B5-43. Attributes of ambient monitors within the Los Angeles study area not used
for the 2010-2015 analysis.
State abbreviation
CA
CA
CA
CA
CA
CA
CA
County name
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Site ID
060370019
060370030
060370031
060370206
060371002
060371301
060371601
Lat
33.34225
34.03528
33.78611
33.95835
34.17605
33.92899
34.01407
Lon
-
-
-
-
-
-
-
year_start
1990
2001
2001
1992
1980
1980
1978
year_end
1990
2002
2002
1996
2014
2008
2005
Elevation m
3
65
0
300
168
27
75
land use
RESID
RESID
RESID
COMM
COMM
COMM
COMM
scale
NA
NA
NA
MID
NA
NA
NBHOOD
objectivel
POP EXP
POP EXP
POP EXP
HI CONC
MAX PRE
objective2
GEN/BKGR
POP EXP
POP EXP
objective3
UP BKGR
HI CONC
cnty_landarea_2010
4058
4058
4058
4058
4058
4058
4058
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
286
513
573
365
378
road closest
5
0
5
105
605
aadt closest
229000
29500
204000
228000
252000
dist aadtmax
406
780
410
825
road aadtmax
10
5
105
605
aadt aadtmax
300000
215000
233000
256000
County Level NOx Emissions (tpy)
OnRoad mobile nox
80322
80322
80322
80322
80322
80322
80322
NonRoad mobile nox
17796
17796
17796
17796
17796
17796
17796
AML mobile nox
17817
17817
17817
17817
17817
17817
17817
total nox
135857
135857
135857
135857
135857
135857
135857
pct_mobile_nox
85.3
85.3
85.3
85.3
85.3
85.3
85.3
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Airport
336
Petroleum_Refinery
2357
21
Rail Yard
240
22
Calcined Pet Coke Plant
201
Wastewater_T reatment_Facility
91
Electricity_Generation_via_Combu
69
19
45
24
36
Oil or Gas Field On shore
Institutional school hospital_
Steam_H eating_Facil ity
Chemical Plant
50
47
Foundries Iron and Steel
Foundries non ferrous
15
Breweries Distilleries Wineries
Hot_Mix_Asphalt_Plant
12
Petroleum_Storage_Facility
11
Landfill
51
108
Food_Products_Processing_Plant
21
Glass Plant
66
Secondary_Lead_Smelting_Plant
31
not characterized
15
65
23
Municipal_Waste_Combustor
304
Aircraft Aerospace or_Related
Monitor not used for 2010-2015 analysis (either historical data or were incomplete)
B5-89
-------
Table B5-43, continued. Attributes of ambient monitors within the Los Angeles study area
not used for the 2010-2015 analysis.
State abbreviation
CA
CA
CA
CA
CA
CA
CA
County name
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Orange
Site ID
060372401
060374101
060375001
060376002
060377001
060379002
060590001
Lat
33.92362
34.42666
33.92288
34.38750
34.71221
34.68999
33.82135
Lon
-
-
-
-
-
-
-
year_start
1980
1989
1979
1989
1980
1990
1971
year_end
1993
1990
2004
2001
1990
2001
2001
Elevation m
58
383
21
375
709
725
128
land use
RESID
RESID
COMM
COMM
COMM
COMM
RESID
scale
NA
NBHOOD
NA
MID
NA
MID
URBAN
objectivel
HI CONC
POP EXP
POP EXP
POP EXP
POP EXP
POP EXP
objective2
MAX 03
GEN/BKGR
objective3
cnty_landarea_2010
CA
CA
CA
CA
CA
CA
CA
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
4058
4058
4058
4058
4058
4058
791
road closest
139
463
aadt closest
405
5
dist aadtmax
239000
267000
road aadtmax
779
aadt aadtmax
405
County Level NOx Emissions (tpy)
OnRoad mobile nox
80322
80322
80322
80322
80322
80322
19742
NonRoad mobile nox
17796
17796
17796
17796
17796
17796
6422
AML mobile nox
17817
17817
17817
17817
17817
17817
1921
total nox
135857
135857
135857
135857
135857
135857
31763
pct_mobile_nox
85.3
85.3
85.3
85.3
85.3
85.3
88.4
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Airport
5533
Petroleum_Refinery
649
Rail Yard
Calcined Pet Coke Plant
Wastewater_T reatment_Facility
Electricity_Generation_via_Combu
45
Oil or Gas Field On shore
66
66
Institutional school hospital_
Steam_H eating_Facil ity
Chemical Plant
Foundries Iron and Steel
Foundries non ferrous
Breweries Distilleries Wineries
Hot_Mix_Asphalt_Plant
Petroleum_Storage_Facility
Landfill
Food_Products_Processing_Plant
Glass Plant
Secondary_Lead_Smelting_Plant
not characterized
42
24
Municipal_Waste_Combustor
Aircraft Aerospace or_Related
11
Monitor not used for 2010-2015 analysis (either historical data or were incomplete)
B5-90
-------
Table B5-44. Attributes of ambient monitors within the Miami study area not used for the
2010-2015 analysis.
State abbreviation
FL
FL
FL
County name
Broward
Palm Beach
Palm Beach
Site ID
120110003
120990021
120991004
Lat
26.28147
26.59381
26.69340
Lon
-80.28255
-80.05849
-80.09921
year_start
1990
2015
1979
year_end
1998
2016
2008
Elevation m
3
9
0
land use
INDUS
INDUS
RESID
scale
NBHOOD
URBAN
MID
objectivel
HI CONC
MAX 03
HI CONC
objective2
POP EXP
HI CONC
POP EXP
objective3
cnty_landarea_2010
1210
1970
1970
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
894
road closest
0
aadt closest
17600
dist aadtmax
895
road aadtmax
0
aadt aadtmax
35000
County Level NOx Emissions (tpy)
OnRoad mobile nox
22197
16775
16775
NonRoad mobile nox
5885
7625
7625
AML mobile nox
8072
4059
4059
total nox
43997
35647
35647
pct_mobile_nox
82.2
79.8
79.8
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Airport
388
Electricity_Generation_via_Combu
18
Institutional school hospital_
Petroleum_Storage_Facility
Wastewater_T reatment_Facility
Monitor not used for 2010-2015 analysis (either historical data or were incomplete)
B5-91
-------
Table B5-45. Attributes of ambient monitors within the Minneapolis study area not used
for the 2010-2015 analysis.
State abbreviation
MN
MN
MN
MN
MN
County name
Dakota
Hennepin
Hennepin
Ramsey
Wright
Site ID
270370428
270530953
270530957
271230864
271710007
Lat
44.79219
45.00247
45.02108
44.99191
45.32913
Lon
-93.08549
-93.24772
-93.28217
-93.18328
-93.83608
year_start
1991
1989
1996
1989
1979
year_end
1992
1996
2002
2002
1997
Elevation m
294
256
0
305
288
land use
AGRIC
COMM
INDUS
RESID
AGRIC
scale
MID
MID
MID
URBAN
NA
objectivel
POP EXP
POP EXP
HI CONC
POP EXP
objective2
SOURCE
HI CONC
objective3
cnty_landarea_2010
562
554
554
152
661
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
781
5
899
road closest
35
0
94
aadt closest
109340
3441
43196
dist aadtmax
107
road aadtmax
94
aadt aadtmax
124250
County Level NOx Emissions (tpy)
OnRoad mobile nox
7604
21967
21967
9912
3496
NonRoad mobile nox
2061
5495
5495
1700
1041
AML mobile nox
295
3168
3168
1435
698
total nox
19738
39010
39010
16428
6143
pct_mobile_nox
50.5
78.5
78.5
79.4
85.2
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Petroleum_Refinery
1296
Municipal_Waste_Combustor
594
594
Electricity_Generation_via_Combu
87
404
72
Institutional school hospital_
145
21
155
Rail Yard
253
358
111
Pulp_and_Paper_Plant
339
Gas Plant
63
Secondary_Aluminum_Smelting_Refi
Steam_H eating_Facil ity
15
not characterized
11
11
Printing_Publishing_Facility
31
Food_Products_Processing_Plant
20
20
Monitor not used for 2010-2015 analysis (either historical data or were incomplete)
B5-92
-------
Table B5-46. Attributes of ambient monitors within the New York/Jersey study area not
used for the 2010-2015 analysis.
State abbreviation
NJ
NJ
NJ
NJ
NJ
NJ
County name
Bergen
Bergen
Essex
Essex
Union
Union
Site ID
340030001
340030005
340130011
340130016
340390008
340395001
Lat
40.80843
40.89858
40.72667
40.72222
40.59803
40.60173
Lon
-73.99236
-74.02990
-74.14374
-74.14694
-74.45381
-74.44107
year_start
1982
2000
1984
2001
1995
1980
year_end
1998
2007
1999
2003
1997
1994
Elevation m
61
6
3
3
18
19
land use
RESID
RESID
INDUS
INDUS
RESID
RESID
scale
NA
NBHOOD
NA
NBHOOD
NBHOOD
NBHOOD
objectivel
POP EXP
POP EXP
POP EXP
POP EXP
POP EXP
objective2
HI CONC
objective3
cnty_landarea_2010
233
233
126
126
103
103
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
965
244
218
16
17
343
road closest
63
4
1
1
28
28
aadt closest
17535
106918
67959
67959
10995
10995
dist aadtmax
965
244
920
984
466
road aadtmax
63
4
95
95
28
aadt aadtmax
22502
113560
221613
115451
18357
County Level NOx Emissions (tpy)
OnRoad mobile nox
8080
8080
5162
5162
4874
4874
NonRoad mobile nox
3313
3313
2070
2070
1346
1346
AML mobile nox
1070
1070
3355
3355
3479
3479
total nox
15763
15763
14172
14172
13636
13636
pct_mobile_nox
79.1
79.1
74.7
74.7
71.1
71.1
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Airport
2984
2984
Electricity_Generation_via_Combu
894
442
442
Municipal_Waste_Combustor
758
758
Petroleum_Refinery
Institutional school hospital_
111
15
74
74
Wastewater_T reatment_Facility
369
14
14
Breweries Distilleries Wineries
68
Petroleum_Storage_Facility
Pharmaceutical_Manufacturing
Fabricated Metal Products Plant
10
10
Compressor_Station
95
Steam_H eating_Facil ity
69
not characterized
68
24
24
Monitor not used for 2010-2015 analysis (either historical data or were incomplete)
B5-93
-------
Table B5-46, continued. Attributes of ambient monitors in the New York/Jersey study area
not used for the 2010-2015 analysis.
State abbreviation
NY
NY
NY
NY
NY
NY
County name
Bronx
Bronx
Bronx
Dutchess
Kings
New York
Site ID
360050073
360050080
360050083
360270007
360470011
360610010
Lat
40.81149
40.83606
40.86585
41.78555
40.73277
40.73955
Lon
-73.90958
-73.92009
-73.88083
-73.74136
-73.94722
-73.98569
year_start
1997
1990
1995
1993
1978
1976
year_end
1999
2000
2007
1993
1996
2001
Elevation m
15
20
24
98
9
38
land use
RESID
RESID
COMM
AGRIC
INDUS
RESID
scale
NA
NBHOOD
NA
NA
NBHOOD
NBHOOD
objectivel
POP EXP
HI CONC
GEN/BKGR
POP EXP
HI CONC
POP EXP
objective2
GEN/BKGR
POP EXP
POP EXP
GEN/BKGR
GEN/BKGR
HI CONC
objective3
QUAL ASSUR
GEN/BKGR
POP EXP
cnty_landarea_2010
42
42
42
796
71
23
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
423
170
82
619
154
57
road closest
0
0
0
44
0
0
aadt closest
5300
20300
11500
10400
22000
18200
dist aadtmax
701
973
700
936
946
road aadtmax
87
95
0
495
0
aadt aadtmax
132100
152100
112700
79400
138500
County Level NOx Emissions (tpy)
OnRoad mobile nox
4297
4297
4297
2626
6463
5664
NonRoad mobile nox
1767
1767
1767
909
4537
7424
AML mobile nox
708
708
708
180
1004
6085
total nox
9912
9912
9912
5090
19011
33400
pct_mobile_nox
68.3
68.3
68.3
73
63.1
57.4
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Airport
12
Electricity_Generation_via_Combu
1048
2462
2906
Municipal_Waste_Combustor
Petroleum_Refinery
Institutional school hospital_
248
108
249
222
201
Wastewater_T reatment_Facility
346
346
14
14
Breweries Distilleries Wineries
Petroleum_Storage_Facility
Pharmaceutical_Manufacturing
Fabricated Metal Products Plant
Compressor_Station
11
11
Steam_H eating_Facil ity
69
69
381
381
not characterized
166
166
232
170
149
Monitor not used for 2010-2015 analysis (either historical data or were incomplete)
B5-94
-------
Table B5-46, continued. Attributes of ambient monitors in the New York/Jersey study area
not used for the 2010-2015 analysis.
State abbreviation
NY
NY
NY
NY
NY
County name
New York
Queens
Queens
Suffolk
Westchester
Site ID
360610056
360810097
360810098
361030009
361195003
Lat
40.75912
40.75527
40.78420
40.82799
41.04500
Lon
-73.96661
-73.75861
-73.84757
-73.05754
-73.70333
year_start
1985
1998
1998
1999
1992
year_end
2008
2001
2006
2010
1993
Elevation m
17
0
6
45
15
land use
COMM
RESID
RESID
RESID
RESID
scale
MID
NA
NA
NA
URBAN
objectivel
HI CONC
GEN/BKGR
GEN/BKGR
POP EXP
POP EXP
objective2
GEN/BKGR
SOURCE
GEN/BKGR
objective3
POP EXP
cnty_landarea_2010
23
109
109
912
431
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
68
189
138
160
road closest
0
0
0
0
aadt closest
23300
5600
5000
81700
dist aadtmax
647
642
550
875
road aadtmax
0
495
0
495
aadt aadtmax
135800
150100
15600
166100
County Level NOx Emissions (tpy)
OnRoad mobile nox
5664
11095
11095
16911
8518
NonRoad mobile nox
7424
4060
4060
6367
2766
AML mobile nox
6085
6546
6546
8122
371
total nox
33400
29220
29220
39142
16263
pct_mobile_nox
57.4
74.3
74.3
80.2
71.7
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Airport
12
1694
131
Electricity_Generation_via_Combu
2835
274
301
Municipal_Waste_Combustor
Petroleum_Refinery
Institutional school hospital_
300
18
41
Wastewater_T reatment_Facility
14
108
Breweries Distilleries Wineries
Petroleum_Storage_Facility
Pharmaceutical_Manufacturing
Fabricated Metal Products Plant
Compressor_Station
11
Steam_H eating_Facil ity
363
not characterized
202
33
14
Monitor not used for 2010-2015 analysis (either historical data or were incomplete)
B5-95
-------
Table B5-47. Attributes of ambient monitors within the Philadelphia study area not used
for the 2010-2015 analysis.
State abbreviation
DE
DE
DE
DE
DE
NJ
County name
New Castle
New Castle
New Castle
New Castle
New Castle
Camden
Site ID
100031003
100031007
100031010
100032002
100033001
340070003
Lat
39.76111
39.55130
39.81722
39.75789
39.81233
39.92304
Lon
-75.49194
-75.73200
-75.56389
-75.54603
-75.45520
-75.097617
year_start
1992
1992
2013
1977
1977
1979
year_end
2000
1999
2014
1992
1992
2008
Elevation m
65
20
0
46
30
7.6
land use
RESID
AGRIC
AGRIC
COMM
RESID
RESID
scale
NA
NA
NA
NBHOOD
NBHOOD
NBHOOD
objectivel
HI CONC
POP EXP
POP EXP
POP EXP
HI CONC
POP EXP
objective2
POP EXP
GEN/BKGR
HI CONC
HI CONC
objective3
UP BKGR
cnty_landarea_2010
426
426
426
426
426
221
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
253
560
374
49
512
road closest
495
301
202
95
605
aadt closest
73574
21601
8387
48906
23085
dist aadtmax
855
578
386
838
road aadtmax
301
95
95
30
aadt aadtmax
38941
100551
118099
57822
County Level NOx Emissions (tpy)
OnRoad mobile nox
6459
6459
6459
6459
6459
5353
NonRoad mobile nox
1748
1748
1748
1748
1748
1150
AML mobile nox
1805
1805
1805
1805
1805
1088
total nox
13991
13991
13991
13991
13991
9431
pct_mobile_nox
71.6
71.6
71.6
71.6
71.6
80.5
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Petroleum_Refinery
2146
Municipal_Waste_Combustor
20
297
Electricity_Generation_via_Combu
948
948
963
26
Chemical Plant
27
27
14
Pulp_and_Paper_Plant
Institutional school hospital_
Wastewater_T reatment_Facility
14
Steam_H eating_Facil ity
Petroleum_Storage_Facility
Military_Base
Automobile Truck or Parts Plant
not characterized
180
180
54
13
Steel Mill
166
Hot_Mix_Asphalt_Plant
Pharmaceutical_Manufacturing
Monitor not used for 2010-2015 analysis (either historical data or were incomplete)
B5-96
-------
Table B5-47, continued. Attributes of ambient monitors within the Philadelphia study area
not used for the 2010-2015 analysis.
State abbreviation
PA
PA
County name
Montgomery
Philadelphia
Site ID
420910013
421010029
Lat
40.11222
39.95733
Lon
-75.30917
-75.17268
year_start
1973
1975
year_end
2008
2005
Elevation m
53
25
land use
RESID
COMM
scale
NBHOOD
NBHOOD
objectivel
POP EXP
POP EXP
objective2
HI CONC
objective3
cnty_landarea_2010
483
134
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
569
198
road closest
276
0
aadt closest
63476
23842
dist aadtmax
586
road aadtmax
76
aadt aadtmax
144789
County Level NOx Emissions (tpy)
OnRoad mobile nox
9533
10201
NonRoad mobile nox
3076
2480
AML mobile nox
219
2160
total nox
16546
21065
pct_mobile_nox
77.5
70.5
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Petroleum_Refinery
Municipal_Waste_Combustor
735
Electricity_Generation_via_Combu
353
Chemical Plant
Pulp_and_Paper_Plant
Institutional school hospital_
66
Wastewater_T reatment_Facility
Steam_H eating_Facil ity
24
Petroleum_Storage_Facility
Military_Base
Automobile Truck or Parts Plant
not characterized
53
Steel Mill
85
Hot_Mix_Asphalt_Plant
16
Pharmaceutical_Manufacturing
26
Monitor not used for 2010-2015 analysis (either historical data or were incomplete)
B5-97
-------
Table B5-48. Attributes of ambient monitors within the Phoenix study area not used for the
2010-2015 analysis.
State abbreviation
AZ
AZ
AZ
AZ
County name
Maricopa
Maricopa
Maricopa
Pinal
Site ID
040134005
040139993
040139994
040218001
Lat
33.41240
33.34885
33.48889
33.29347
Lon
-111.93473
-112.83110
-111.86250
-111.28559
year_start
2000
1996
1991
2001
year_end
2003
2004
1999
2006
Elevation m
352
265
0
634
land use
RESID
INDUS
AGRIC
DESERT
scale
NA
NA
NA
NA
objectivel
POP EXP
GEN/BKGR
UP BKGR
DOWNWND
objective2
HI CONC
objective3
REG TRANS
cnty_landarea_2010
9200
9200
9200
5366
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
522
road closest
0
aadt closest
25583
dist aadtmax
906
road aadtmax
0
aadt aadtmax
36152
County Level NOx Emissions (tpy)
OnRoad mobile nox
56748
56748
56748
9273
NonRoad mobile nox
18998
18998
18998
1575
AML mobile nox
3999
3999
3999
1775
total nox
88464
88464
88464
14883
pct_mobile_nox
90.1
90.1
90.1
84.8
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Airport
Electricity_Generation_via_Combu
83
343
Rail Yard
Monitor not used for 2010-2015 analysis (either historical data or were incomplete)
B5-98
-------
Table B5-49. Attributes of ambient monitors within the Pittsburgh study area not used for
the 2010-2014 analysis.
State abbreviation
PA
PA
PA
PA
PA
County name
Allegheny
Allegheny
Washington
Washington
Westmoreland
Site ID
420030003
420030031
421255001
421255200
421290008
Lat
40.45010
40.44337
40.44528
40.26896
40.30469
Lon
-79.77096
-79.99029
-80.42083
-80.24400
-79.50567
year_start
1987
1980
1998
2012
1997
year_end
1992
2001
2008
2016
2008
Elevation m
11
268
335
327
0
land use
RESID
COMM
AGRIC
AGRIC
COMM
scale
NBHOOD
NBHOOD
REGION
NBHOOD
URBAN
objectivel
POP EXP
POP EXP
REG TRANS
SOURCE
POP EXP
objective2
HI CONC
GEN/BKGR
POP EXP
REG TRANS
objective3
POP EXP
cnty_landarea_2010
730
730
857
857
1028
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
563
66
177
road closest
376
579
30
aadt closest
52875
54441
34536
dist aadtmax
186
road aadtmax
30
aadt aadtmax
44067
County Level NOx Emissions (tpy)
OnRoad mobile nox
13259
13259
3024
3024
4743
NonRoad mobile nox
4029
4029
856
856
1147
AML mobile nox
3322
3322
686
686
1458
total nox
35455
35455
10067
10067
12939
pct_mobile_nox
58.1
58.1
45.4
45.4
56.8
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Steel Mill
Food_Products_Processing_Plant
212
Steam_H eating_Facil ity
166
Institutional school hospital_
36
Wastewater_T reatment_Facility
Hot_Mix_Asphalt_Plant
18
Electricity_Generation_via_Combu
33
Foundries Iron and Steel
13
Glass Plant
Chemical Plant
Coke_Battery
not characterized
15
Landfill
Monitor not used for 2010-2015 analysis (either historical data or were incomplete)
B5-99
-------
Table B5-50. Attributes of ambient monitors within the Richmond study area not used for
the 2010-2015 analysis.
State abbreviation
VA
VA
County name
Caroline
Richmond City
Site ID
510330001
517600021
Lat
38.20087
37.56320
Lon
-77.37742
-77.46721
year_start
1993
1981
year_end
2012
1997
Elevation m
68
58
land use
FOREST
COMM
scale
URBAN
NA
objectivel
HI CONC
HI CONC
objective2
UP BKGR
objective3
GEN/BKGR
cnty_landarea_2010
528
60
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
178
road closest
161
aadt closest
22386
dist aadtmax
342
road aadtmax
33
aadt aadtmax
22984
County Level NOx Emissions (tpy)
OnRoad mobile nox
2472
2719
NonRoad mobile nox
166
396
AML mobile nox
407
315
total nox
3486
6928
pct_mobile_nox
87.3
49.5
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Pulp_and_Paper_Plant
Rail Yard
115
Institutional school hospital_
10
not characterized
65
24
Monitor not used for 2010-2015 analysis (either historical data or were incomplete)
B5-100
-------
Table B5-51. Attributes of ambient monitors within the Riverside study area not used for
the 2010-2015 analysis.
State abbreviation
CA
CA
CA
CA
CA
CA
County name
Riverside
Riverside
Riverside
San Bernardino
San Bernardino
San Bernardino
Site ID
060650004
060650006
060656001
060710006
060710012
060710014
Lat
34.00700
33.49170
33.78942
35.75995
34.42613
34.51250
Lon
-
-
-
-117.37478
-117.56394
-117.33088
year_start
2007
1991
1988
1979
1987
1991
year_end
2011
1993
1990
1994
1998
1999
Elevation m
70
341
439
506
4100
876
land use
RESID
RESID
COMM
INDUS
COMM
RESID
scale
NA
MID
NA
NA
NA
NA
objectivel
POP EXP
POP EXP
REG TRANS
REG TRANS
objective2
objective3
cnty_landarea_2010
7206
7206
7206
20057
20057
20057
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
64
177
580
road closest
15
215
18
aadt closest
152000
82000
40000
dist aadtmax
250
797
road aadtmax
215
15
aadt aadtmax
99000
85000
County Level NOx Emissions (tpy)
OnRoad mobile nox
25868
25868
25868
31067
31067
31067
NonRoad mobile nox
5541
5541
5541
5646
5646
5646
AML mobile nox
2215
2215
2215
9344
9344
9344
total nox
37367
37367
37367
68863
68863
68863
pct_mobile_nox
90
90
90
66.9
66.9
66.9
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Mineral_Processing_Plant
1865
Electricity_Generation_via_Combu
620
Airport
Rail Yard
Pulp_and_Paper_Plant
140
Fabricated Metal Products Plant
20
Food_Products_Processing_Plant
Foundries non ferrous
not characterized
Municipal_Waste_Combustor
11
Portland_Cement_Manufacturing
Primary_Aluminum_Plant
17
Secondary_Aluminum_Smelting_Refi
Steel Mill
Institutional school hospital_
Monitor not used for 2010-2015 analysis (either historical data or were incomplete)
B5-101
-------
Table B5-51, continued. Attributes of ambient monitors within the Riverside study area not
used for the 2010-2015 analysis.
State abbreviation
CA
CA
CA
CA
CA
County name
San Bernardino
San Bernardino
San Bernardino
San Bernardino
San Bernardino
Site ID
060710015
060710017
060711101
060714001
060717002
Lat
35.77495
34.14195
34.14195
34.41807
34.52333
Lon
-117.36756
-116.05584
-116.05917
-117.28560
-117.30449
year_start
1993
1993
1992
1979
1981
year_end
1997
1998
1993
1998
1991
Elevation m
498
607
650
1006
895
land use
INDUS
MOBILE
AGRIC
RESID
COMM
scale
NA
NA
NA
NA
NA
objectivel
REG TRANS
REG TRANS
objective2
POP EXP
POP EXP
objective3
UP BKGR
cnty_landarea_2010
20057
20057
20057
20057
20057
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
665
669
road closest
62
62
aadt closest
7100
13000
dist aadtmax
708
road aadtmax
62
aadt aadtmax
13000
County Level NOx Emissions (tpy)
OnRoad mobile nox
31067
31067
31067
31067
31067
NonRoad mobile nox
5646
5646
5646
5646
5646
AML mobile nox
9344
9344
9344
9344
9344
total nox
68863
68863
68863
68863
68863
pct_mobile_nox
66.9
66.9
66.9
66.9
66.9
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Mineral_Processing_Plant
1865
Electricity_Generation_via_Combu
620
Airport
Rail Yard
Pulp_and_Paper_Plant
Fabricated Metal Products Plant
Food_Products_Processing_Plant
Foundries non ferrous
not characterized
Municipal_Waste_Combustor
Portland_Cement_Manufacturing
33
Primary_Aluminum_Plant
Secondary_Aluminum_Smelting_Refi
Steel Mill
Institutional school hospital_
Monitor not used for 2010-2015 analysis (either historical data or were incomplete)
B5-102
-------
Table B5-52. Attributes of ambient monitors within the Sacramento study area not used for
the 2010-2015 analysis.
State abbreviation
CA
CA
CA
CA
CA
CA
County name
El Dorado
El Dorado
El Dorado
Placer
Placer
Sacramento
Site ID
060170009
060170011
060170012
060610007
060613001
060670001
Lat
38.94574
38.94498
38.81161
39.18407
38.78879
38.66713
Lon
-119.96796
-119.97061
-120.03308
-120.12298
-121.21773
-121.25134
year_start
1981
1992
1999
2002
1991
1979
year_end
1992
2004
2004
2004
1996
1993
Elevation m
1911
1905
2250
1
75
52
land use
COMM
COMM
FOREST
COMM
RESID
RESID
scale
NA
NA
NA
URBAN
NA
NA
objectivel
POP EXP
HI CONC
POP EXP
POP EXP
POP EXP
objective2
GEN/BKGR
objective3
cnty_landarea_2010
1708
1708
1708
1407
1407
965
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
28
86
183
321
road closest
50
50
50
80
aadt closest
30500
30500
8900
89000
dist aadtmax
479
185
486
road aadtmax
50
50
80
aadt aadtmax
33000
33000
113000
County Level NOx Emissions (tpy)
OnRoad mobile nox
1886
1886
1886
4760
4760
12361
NonRoad mobile nox
721
721
721
1492
1492
3122
AML mobile nox
94
94
94
1150
1150
1821
total nox
3637
3637
3637
9158
9158
19897
pct_mobile_nox
74.3
74.3
74.3
80.8
80.8
87
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Airport
Rail Yard
Institutional school hospital_
not characterized
27
27
Monitor not used for 2010-2015 analysis (either historical data or were incomplete)
B5-103
-------
Table B5-52, continued. Attributes of ambient monitors within the Sacramento study area
not used for the 2010-2015 analysis.
State abbreviation
CA
CA
CA
County name
Sacramento
Sacramento
Sacramento
Site ID
060670013
060671001
060675002
Lat
38.63685
38.67490
38.71685
Lon
-121.51440
-121.18689
-121.59301
year_start
1998
1979
1989
year_end
2008
1996
1997
Elevation m
5
57
30
land use
COMM
COMM
AGRIC
scale
NA
NA
NBHOOD
objectivel
HI CONC
HI CONC
POP EXP
objective2
POP EXP
objective3
cnty_landarea_2010
965
965
965
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
704
road closest
5
aadt closest
140000
dist aadtmax
878
road aadtmax
80
aadt aadtmax
144000
County Level NOx Emissions (tpy)
OnRoad mobile nox
12361
12361
12361
NonRoad mobile nox
3122
3122
3122
AML mobile nox
1821
1821
1821
total nox
19897
19897
19897
pct_mobile_nox
87
87
87
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Airport
586
Rail Yard
Institutional school hospital_
not characterized
Monitor not used for 2010-2015 analysis (either historical data or were incomplete)
B5-104
-------
Table B5-53. Attributes of ambient monitors within the San Diego study area not used for
the 2010-2015 analysis.
State abbreviation
CA
CA
CA
CA
County name
San Diego
San Diego
San Diego
San Diego
Site ID
060730005
060731007
060731009
060731018
Lat
33.20269
32.70922
32.69866
32.81798
Lon
-117.36680
-117.15484
-117.13309
-116.96813
year_start
1979
1989
1999
2014
year_end
2001
2005
2001
2016
Elevation m
37
6
0
119
land use
UNK
COMM
RESID
COMM
scale
NBHOOD
NBHOOD
NA
NBHOOD
objectivel
POP EXP
HI CONC
POP EXP
SOURCE
objective2
POP EXP
GEN/BKGR
objective3
POP EXP
cnty_landarea_2010
4207
4207
4207
4207
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
505
575
369
670
road closest
5
5
5
67
aadt closest
192000
162000
159000
83000
dist aadtmax
589
686
road aadtmax
5
5
aadt aadtmax
208000
161000
County Level NOx Emissions (tpy)
OnRoad mobile nox
27531
27531
27531
27531
NonRoad mobile nox
7428
7428
7428
7428
AML mobile nox
3491
3491
3491
3491
total nox
42700
42700
42700
42700
pct_mobile_nox
90
90
90
90
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Electricity_Generation_via_Combu
Military_Base
11
11
Pharmaceutical_Manufacturing
38
38
Ship_Boat_Manufacturing_or_Repai
13
13
not characterized
88
66
Monitor not used for 2010-2015 analysis (either historical data or were incomplete)
B5-105
-------
Table B5-54. Attributes of ambient monitors within the San Francisco study area not used
for the 2010-2015 analysis.
State abbreviation
CA
CA
CA
CA
CA
CA
County name
Alameda
Alameda
Alameda
Contra Costa
Contra Costa
Contra Costa
Site ID
060010003
060010010
060011001
060130003
060130010
060131003
Lat
37.68490
37.76023
37.53583
37.94992
38.03122
37.96420
Lon
-121.76590
-122.19358
-121.96182
-122.35719
-122.13288
-122.34030
year_start
1980
2001
1973
1972
2001
1997
year_end
2000
2003
2010
1997
2003
2002
Elevation m
150
10
17
23
36
15
land use
COMM
RESID
RESID
COMM
COMM
COMM
scale
NA
NBHOOD
URBAN
NA
NBHOOD
NA
objectivel
HI CONC
POP EXP
POP EXP
POP EXP
POP EXP
POP EXP
objective2
POP EXP
HI CONC
WELF IMP
objective3
cnty_landarea_2010
739
739
739
716
716
716
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
137
916
road closest
185
80
aadt closest
24000
186000
dist aadtmax
road aadtmax
aadt aadtmax
County Level NOx Emissions (tpy)
OnRoad mobile nox
19032
19032
19032
9307
9307
9307
NonRoad mobile nox
3158
3158
3158
2074
2074
2074
AML mobile nox
3507
3507
3507
1973
1973
1973
total nox
28381
28381
28381
20714
20714
20714
pct_mobile_nox
90.5
90.5
90.5
64.5
64.5
64.5
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Petroleum_Refinery
941
Airport
Electricity_Generation_via_Combu
327
Glass Plant
127
65
127
Rail Yard
Wastewater_T reatment_Facility
36
Foundries Iron and Steel
Pharmaceutical_Manufacturing
33
39
not characterized
24
24
Landfill
13
Hot_Mix_Asphalt_Plant
Chemical Plant
Institutional school hospital_
941
Monitor not used for 2010-2015 analysis (either historical data or were incomplete)
B5-106
-------
Table B5-54, continued. Attributes of ambient monitors within the San Francisco study
area not used for the 2010-2015 analysis.
State abbreviation
CA
CA
County name
Contra Costa
San Francisco
Site ID
060133001
060750006
Lat
38.02926
37.73380
Lon
-121.89687
-122.38240
year_start
1967
2004
year_end
2008
2005
Elevation m
2
82
land use
RESID
RESID
scale
NBHOOD
NBHOOD
objectivel
HI CONC
POP EXP
objective2
POP EXP
objective3
cnty_landarea_2010
716
47
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
road closest
aadt closest
dist aadtmax
road aadtmax
aadt aadtmax
County Level NOx Emissions (tpy)
OnRoad mobile nox
9307
4623
NonRoad mobile nox
2074
1474
AML mobile nox
1973
5065
total nox
20714
12406
pct_mobile_nox
64.5
90
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Petroleum_Refinery
Airport
Electricity_Generation_via_Combu
350
Glass Plant
Rail Yard
Wastewater_T reatment_Facility
74
Foundries Iron and Steel
Pharmaceutical_Manufacturing
not characterized
17
Landfill
Hot_Mix_Asphalt_Plant
Chemical Plant
56
Institutional school hospital_
10
Monitor not used for 2010-2015 analysis (either historical data or were incomplete)
B5-107
-------
Table B5-55. Attributes of ambient monitors within the St. Louis study area not used for
the 2010-2015 analysis.
State abbreviation
MO
MO
MO
MO
MO
MO
MO
County name
Saint Charles
Saint Charles
Saint Louis
Saint Louis
Saint Louis
Saint Louis
Saint Louis
Site ID
291830010
291831002
291890001
291890004
291890006
291890014
291893001
Lat
38.58192
38.87255
38.52172
38.53278
38.61366
38.71090
38.65026
Lon
-90.83531
-90.22649
-90.34371
-90.38243
-90.49594
-90.47590
-90.35046
min_year
1994
1975
1977
1998
1978
2005
1970
max_year
1998
2010
1998
2010
2005
2010
2010
Elevation m
0
131
183
183
175
193
161
land use
AGRIC
AGRIC
RESID
RESID
RESID
RESID
COMM
scale
NA
URBAN
NA
NA
NA
NBHOOD
NA
objectivel
POP EXP
POP EXP
POP EXP
POP EXP
POP EXP
POP EXP
objective2
MAX 03
QUAL
objective3
HI CONC
cnty_landarea_2010
560
560
508
508
508
508
508
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
905
143
131
539
75
road closest
67
50
50
364
170
aadt closest
26451
30939
19136
27533
114639
aadt aadtmax
927
187
road aadtmax
55
30
dist aadtmax
111855
27183
County Level NOx Emissions (tpy)
OnRoad mobile nox
7867
7867
25063
25063
25063
25063
25063
NonRoad mobile nox
1714
1714
4697
4697
4697
4697
4697
AML mobile nox
580
580
1902
1902
1902
1902
1902
total nox
18373
18373
39486
39486
39486
39486
39486
pct_mobile_nox
55.3
55.3
80.2
80.2
80.2
80.2
80.2
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Breweries Distilleries Wineries
Rail Yard
Electricity_Generation_via_Combu
9891
Wastewater_T reatment_Facility
89
Institutional school hospital_
20
Ethanol_Biorefineries_Soy_Biodie
Chemical Plant
Pharmaceutical_Manufacturing
Landfill
11
Steam_H eating_Facil ity
Petroleum_Storage_Facility
not characterized
22
Aircraft Aerospace or_Related
Airport
Monitor not used for 2010-2015 analysis (either historical data or were incomplete)
B5-108
-------
Table B5-55, continued. Attributes of ambient monitors within the St. Louis study area not
used for the 2010-2015 analysis.
State abbreviation
MO
MO
MO
MO
MO
MO
County name
Saint Louis
Saint Louis
Saint Louis
Saint Louis
St. Louis City
St. Louis City
Site ID
291895001
291897001
291897002
291897003
295100072
295100080
Lat
38.76616
38.72755
38.72727
38.72096
38.62422
38.65864
Lon
-90.28593
-90.37955
-90.37955
-90.36713
-90.19871
-90.24426
min_year
1978
1976
1990
2001
1981
1982
max_year
2005
1990
2001
2004
2005
1999
Elevation m
168
168
168
0
154
152
land use
COMM
RESID
RESID
RESID
COMM
RESID
scale
NA
NA
NA
NBHOOD
NA
NBHOOD
objectivel
POP EXP
POP EXP
POP EXP
POP EXP
POP EXP
POP EXP
objective2
QUAL
SOURCE
HI CONC
HI CONC
objective3
HI CONC
cnty_landarea_2010
508
508
508
508
62
62
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
433
77
53
103
70
483
road closest
0
0
0
0
0
0
aadt closest
39856
32999
32999
32999
16662
12582
aadt aadtmax
536
880
road aadtmax
270
55
dist aadtmax
137862
99609
County Level NOx Emissions (tpy)
OnRoad mobile nox
25063
25063
25063
25063
6296
6296
NonRoad mobile nox
4697
4697
4697
4697
663
663
AML mobile nox
1902
1902
1902
1902
1590
1590
total nox
39486
39486
39486
39486
10691
10691
pct_mobile_nox
80.2
80.2
80.2
80.2
80
80
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Breweries Distilleries Wineries
467
Rail Yard
411
421
Electricity_Generation_via_Combu
285
Wastewater_T reatment_Facility
81
Institutional school hospital_
49
Ethanol_Biorefineries_Soy_Biodie
49
Chemical Plant
27
93
Pharmaceutical_Manufacturing
43
43
Landfill
58
Steam_H eating_Facil ity
20
Petroleum_Storage_Facility
14
not characterized
13
13
13
12
25
Aircraft Aerospace or_Related
24
24
24
Airport
858
858
858
Monitor not used for 2010-2015 analysis (either historical data or were incomplete)
B5-109
-------
Table B5-56. Attributes of ambient monitors within the Washington DC study area not
used for the 2010-2015 analysis.
State abbreviation
DC
VA
VA
VA
VA
VA
County name
DoColumbia
Fairfax
Fairfax
Fairfax
Fairfax
Fairfax
Site ID
110010017
510590005
510590018
510590031
510591004
510591005
Lat
38.90372
38.89410
38.74232
38.76835
38.86817
38.83738
Lon
-77.05137
-77.46520
-77.07743
-77.18347
-77.14276
-77.16338
min_year
1975
1977
1979
2016
1973
2002
max_year
1996
2009
1997
2016
2001
2009
Elevation m
20
76
10
73.5
110
116
land use
COMM
RESID
RESID
COMM
COMM
RESID
scale
NBHOOD
NBHOOD
NA
MICRO
NA
NA
objectivel
POP EXP
POP EXP
POP EXP
HI CONC
POP EXP
POP EXP
objective2
HI CONC
POP EXP
objective3
cnty_landarea_2010
61
391
391
391
391
391
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
52
580
69
94
57
road closest
0
1
7900
50
244
aadt closest
29208
56482
52319
48102
29542
aadt aadtmax
395
411
498
973
road aadtmax
0
95
50
244
dist aadtmax
96163
231651
59759
35060
County Level NOx Emissions (tpy)
OnRoad mobile nox
4739
8221
8221
8221
8221
8221
NonRoad mobile nox
2364
2905
2905
2905
2905
2905
AML mobile nox
223
358
358
358
358
358
total nox
9418
15236
15236
15236
15236
15236
pct_mobile_nox
77.8
75.4
75.4
75.4
75.4
75.4
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Airport
Electricity_Generation_via_Combu
Municipal_Waste_Combustor
Steam_H eating_Facil ity
301
Military_Base
not characterized
59
Institutional school hospital_
27
Rail Yard
Hot_Mix_Asphalt_Plant
11
Monitor not used for 2010-2015 analysis (either historical data or were incomplete)
B5-110
-------
Table B5-56, continued. Attributes of ambient monitors within the Washington DC study
area not used for the 2010-2015 analysis.
State abbreviation
VA
VA
County name
Fairfax
Fairfax City
Site ID
510595001
516000005
Lat
38.93260
38.84483
Lon
-77.19822
-77.31165
min_year
1973
1973
max_year
2009
1992
Elevation m
105
125
land use
RESID
RESID
scale
NBHOOD
NA
objectivel
POP EXP
POP EXP
objective2
objective3
cnty_landarea_2010
391
6
AADT information (distance in meters, road/highway/interstate number, AADT value)
dist closest
74
304
road closest
123
236
aadt closest
41718
43148
aadt aadtmax
853
road aadtmax
495
dist aadtmax
193959
County Level NOx Emissions (tpy)
OnRoad mobile nox
8221
156
NonRoad mobile nox
2905
50
AML mobile nox
358
0
total nox
15236
265
pct_mobile_nox
75.4
77.9
NOx Emissions by Source Type (summed tons per year (tpy), sources within 5 Km emitting at least 10 tpy)
Airport
Electricity_Generation_via_Combu
Municipal_Waste_Combustor
Steam_H eating_Facil ity
Military_Base
not characterized
Institutional school hospital_
Rail Yard
Hot_Mix_Asphalt_Plant
Monitor not used for 2010-2015 analysis (either historical data or were incomplete)
B5-111
-------
5.4 UPPER (>98™) PERCENTILE DM1H CONCENTRATION RATIOS:
COMPARISON OF AREA DESIGN VALUE MONITOR TO ALL
AREA-WIDE MONITORS BY STUDY AREA
The following is a visual representation of the data found in Table B2-8, constructed to
facilitate the comparison of the ratios derived from the area design value monitor relative to
other area-wide monitors. As a reminder, the near-road monitors typically had only 1 full year of
data, the motivation for using the ratios derived from the area design value monitor to adjust the
upper (>98th) percentile DM1H concentrations at each near-road monitor.
AT LA study area
SALT study area
tm
s I I
Upper percentile {>98th percentile) DM1H
s # I
Upper percentile {>98th percentile) DM1H
BOST study area
DALL study area
—»OV/NR monitor <
r
:
1 I I I
Upper percentile <»98th percentile) DM1H
I 1 1 I I I
DENV study area
Sill
Upper percentile |>98th percentile) DM1H
DETR study area
——QV/Mft monitor
I-
if
sis
Upper percentile (>98th percentile) DM1.H
B5-112
-------
Adjustment factor derived from ratio of 0M1H to 98th
percentile DM1H concentrations
Adjustment factor derived from ratio of DM1H to 98th
percentile DM1H concentrations
n 5th DM1H
fl> 5th DM1H
Adjustment factor derived from ratio of DM1H to 98th
percentile DM1H concentrations
Adjustment factor derived from ratio of DM1H to 98th
percentile DM1H concentrations
Adjustment factor derived from ratio of DM1H to 98th
percentile DM1H concentrations
Adjustment factor derived from ratio of DM 1H to 98th
percentile DM1H concentrations
® 5th DM1H
« 3rd DM1H
5th DM1H
O
2
"7 ziiiT/f/rr"
\ mm////
I furf////
|
f
1
if
1
¦
a
1
1
[
s
Adjustment factor derived from ratio of DM1H to 98th
percentile DM1H concentrations
Adjustment factor derived from ratio of DM1H to 98th
percentile DM1H concentrations
"5 3rd DM1H
-------
Adjustment factor derived from ratio of 0M1H to 98th
percentile DM1H concentrations
Adjustment factor derived from ratio of DM1H to 98th
percentile DM1H concentrations
n 5th DM1H
fl> 5th DM1H -
Adjustment factor derived from ratio of DM1H to 98th
percentile DM1H concentrations
Adjustment factor derived from ratio of DM1H to 98th
percentile DM1H concentrations
Adjustment factor derived from ratio of DM1H to 98th
percentile DM1H concentrations
Adjustment factor derived from ratio of DM1H to 98th
percentile DM1H concentrations
Adjustment factor derived from ratio of DM1H to 98th
percentile DM1H concentrations
Adjustment factor derived from ratio of DM1H to 98th
percentile DM1H concentrations
-------
5.5 NUMBER OF DAYS PER YEAR NO2 CONCENTRATIONS WERE AT OR ABOVE 1-HOUR
BENCHMARK LEVELS: SITE-YEAR SUMMARY TABLES (2010 - 2015)
Table B5-57. Number of days per year NO2 concentrations were at or above benchmark levels: area-wide and near-road site-
year summary table (2010-2015).
CBSA
Abbr.
Monitor Type
1-hr
Benchmark
(ppb)
asis
Number
CS1012
5f monitor
CS1113
site-years
CS1214
CS1315
Mea
asis
n number c
be
CS1012
f days per
nchmark le
CS1113
/ear at or a
vel
CS1214
bove
CS1315
Maxim
asis
um numbe
be
CS1012
of days pe
nchmark le
CS1113
r year at or
i/el
CS1214
above
CS1315
ATLA
area wide
100
18
9
9
9
9
0
3
5
4
2
0
15
30
16
16
BALT
area wide
100
11
6
6
6
5
0
4
5
4
4
0
12
12
12
11
BOST
area wide
100
31
15
15
11
16
0
5
5
6
3
0
15
15
18
12
CHIC
area wide
100
23
11
0
0
12
0.5
7
4
1
24
12
DALL
area wide
100
64
34
33
29
30
0
1
2
2
2
0
9
11
16
12
DENV
area wide
100
11
0
5
6
7
0.5
14
5
4
1
29
10
10
DETR
area wide
100
10
4
5
6
6
0
6
9
8
4
0
12
23
18
9
HOUS
area wide
100
85
40
40
44
45
0.5
2
2
2
2
3
12
8
15
14
KANS
area wide
100
14
9
7
6
5
0
5
5
7
7
0
9
9
11
10
LOSA
area wide
100
65
30
26
27
35
0.5
5
5
5
4
1
19
22
23
27
Ml AM
area wide
100
20
12
10
8
8
0
5
4
5
6
0
15
12
12
17
MINE
area wide
100
17
8
9
9
9
0
6
4
4
2
0
22
11
14
10
NYNY
area wide
100
48
23
22
24
25
0.5
3
3
3
4
1
9
12
12
12
PHIL
area wide
100
26
13
14
0
13
0.5
3
3
2
1
18
23
13
PHOE
area wide
100
28
15
14
14
13
0.5
2
4
3
3
1
9
13
12
9
PITT
area wide
100
25
13
15
0
16
0
2
3
3
0
9
22
14
RICH
area wide
100
14
9
8
6
5
0
6
5
7
2
0
17
17
15
6
RIVR
area wide
100
49
24
18
0
25
0.5
1
4
1
7
14
25
5
SACR
area wide
100
42
23
21
20
19
0
2
3
4
3
0
10
14
21
17
SAND
area wide
100
41
22
21
20
19
0.5
1
1
1
0.5
1
9
12
12
4
SANF
area wide
100
55
29
28
28
26
0.5
1
1
1
2
1
13
23
11
14
STLO
area wide
100
15
0
8
9
9
0
2
4
1
0
8
14
5
WASH
area wide
100
33
18
17
15
15
0
4
4
5
6
0
14
20
24
24
ATLA
area wide
150
18
9
9
9
9
0
0
0
0
0
0
0
0
0
0
BALT
area wide
150
11
6
6
6
5
0
0
0
0
0
0
0
0
0
0
BOST
area wide
150
31
15
15
2
16
0
0.5
0.5
1
0.5
0
1
1
1
1
CHIC
area wide
150
23
11
0
0
12
0
0
0
0
0
0
DALL
area wide
150
64
34
33
29
30
0
0
0
0
0
0
0
0
0
0
DENV
area_wide
150
11
0
5
1
7
0
0.5
1
0.5
0
1
1
1
5-115
-------
CBSA
1-hr
Benchmark
Number
5f monitor
site-years
Mea
n number c
be
f days per
nchmark le
/ear at or a
vel
bove
Maxim
um numbe
be
of days pe
nchmark le
r year at or
i/el
above
Abbr.
Monitor Type
(ppb)
asis
CS1012
CS1113
CS1214
CS1315
asis
CS1012
CS1113
CS1214
CS1315
asis
CS1012
CS1113
CS1214
CS1315
DETR
area_wide
150
10
4
5
6
6
0
0
0
0
0
0
0
0
0
0
HOUS
area wide
150
85
40
40
44
45
0
0.5
0
0
0
0
2
0
0
0
KANS
area wide
150
14
9
7
6
5
0
0
0
0
0
0
0
0
0
0
LOSA
area wide
150
65
30
26
27
35
0
0.5
0.5
0.5
0.5
0
1
1
1
1
Ml AM
area wide
150
20
12
10
8
8
0
0.5
0.5
1
0.5
0
1
1
1
1
MINE
area wide
150
17
8
9
9
9
0
0.5
0.5
0.5
0
0
2
3
2
0
NYNY
area wide
150
48
23
22
24
25
0.5
0.5
0.5
0.5
0.5
1
1
1
1
1
PHIL
area wide
150
26
13
14
0
13
0
0.5
0.5
0.5
0
1
1
1
PHOE
area wide
150
28
15
14
14
13
0
0
0
0
0
0
0
0
0
0
PITT
area wide
150
25
13
15
0
16
0
0
0
0
0
0
0
0
RICH
area wide
150
14
9
8
6
5
0
0
0.5
0.5
0
0
0
1
1
0
RIVR
area wide
150
49
24
18
0
25
0
0.5
0.5
0
0
1
8
0
SACR
area wide
150
42
23
21
20
19
0
0
0
0
0
0
0
0
0
0
SAND
area wide
150
41
22
21
20
19
0
0
0
0
0
0
0
0
0
0
SANF
area wide
150
55
29
28
28
26
0
0
0.5
0
0
0
0
1
0
0
STLO
area wide
150
15
0
8
9
9
0
0
0
0
0
0
0
0
WASH
area wide
150
33
18
17
15
15
0
0.5
0.5
0.5
0.5
0
1
1
1
1
ATLA
area wide
200
18
9
9
9
9
0
0
0
0
0
0
0
0
0
0
BALT
area wide
200
11
6
6
6
5
0
0
0
0
0
0
0
0
0
0
BOST
area wide
200
31
15
15
15
16
0
0
0
0
0
0
0
0
0
0
CHIC
area wide
200
23
11
0
0
12
0
0
0
0
0
0
DALL
area wide
200
64
34
33
29
30
0
0
0
0
0
0
0
0
0
0
DENV
area wide
200
11
0
5
6
7
0
0
0
0
0
0
0
0
DETR
area wide
200
10
4
5
6
6
0
0
0
0
0
0
0
0
0
0
HOUS
area wide
200
85
40
40
44
45
0
0
0
0
0
0
0
0
0
0
KANS
area wide
200
14
9
7
6
5
0
0
0
0
0
0
0
0
0
0
LOSA
area wide
200
65
30
26
27
35
0
0
0
0
0
0
0
0
0
0
Ml AM
area wide
200
20
12
10
8
8
0
0
0
0
0
0
0
0
0
0
MINE
area wide
200
17
8
9
9
9
0
0
0
0
0
0
0
0
0
0
NYNY
area wide
200
48
23
22
24
25
0
0
0
0
0
0
0
0
0
0
PHIL
area wide
200
26
13
14
0
13
0
0
0
0
0
0
0
0
PHOE
area wide
200
28
15
14
14
13
0
0
0
0
0
0
0
0
0
0
PITT
area wide
200
25
13
15
0
16
0
0
0
0
0
0
0
0
RICH
area wide
200
14
9
8
6
5
0
0
0
0
0
0
0
0
0
0
RIVR
area wide
200
49
24
18
0
25
0
0
0
0
0
0
0
0
SACR
area wide
200
42
23
21
20
19
0
0
0
0
0
0
0
0
0
0
SAND
area wide
200
41
22
21
20
19
0
0
0
0
0
0
0
0
0
0
SANF
area_wide
200
55
29
28
28
26
0
0
0
0
0
0
0
0
0
0
5-116
-------
CBSA
1-hr
Benchmark
Number
5f monitor
site-years
Mea
n number c
be
f days per
nchmark le
/ear at or a
vel
bove
Maxim
um numbe
be
of days pe
nchmark le
r year at or
i/el
above
Abbr.
Monitor Type
(ppb)
asis
CS1012
CS1113
CS1214
CS1315
asis
CS1012
CS1113
CS1214
CS1315
asis
CS1012
CS1113
CS1214
CS1315
STLO
area_wide
200
15
0
8
9
9
0
0
0
0
0
0
0
0
WASH
area wide
200
33
18
17
15
15
0
0
0
0
0
0
0
0
0
0
ATLA
near road
100
3
0
0
1
3
0
5
6
0
5
8
BALT
near road
100
2
0
0
1
2
0
5
7
0
5
7
BOST
near road
100
3
0
1
2
3
0
2
10
6
0
2
18
11
CHIC
near road
100
5
3
0
0
2
0.5
8
4
1
11
4
DALL
near road
100
3
0
0
1
3
0
1
2
0
1
5
DENV
near road
100
4
0
1
2
4
0
4
3
3
0
4
5
5
DETR
near road
100
6
2
3
3
4
0
11
22
17
7
0
16
31
22
9
HOUS
near road
100
3
0
0
1
3
0
2
4
0
2
5
KANS
near road
100
3
0
1
2
3
0
1
3
3
0
1
4
4
LOSA
near road
100
3
0
0
1
3
0
17
9
0
17
21
Ml AM
near road
100
1
0
0
0
1
0
1
0
1
MINE
near road
100
4
0
1
2
4
0
7
9
7
0
7
13
11
NYNY
near road
100
2
0
0
1
2
2
8
8
2
8
8
PHIL
near road
100
3
0
0
0
3
0
2
0
3
PHOE
near road
100
3
0
0
1
3
0
3
3
0
3
3
PITT
near road
100
2
0
0
0
2
0
7
0
11
RICH
near road
100
3
0
1
2
3
0
1
8
5
0
1
12
9
RIVR
near road
100
2
0
0
0
2
0
7
0
8
SACR
near road
100
1
0
0
0
1
0
2
0
2
SAND
near road
100
1
0
0
0
1
0
4
0
4
SANF
near road
100
2
0
0
1
2
0
1
2
0
1
2
STLO
near road
100
4
0
1
2
4
0
5
16
5
0
5
21
10
WASH
near road
100
1
0
0
0
1
0
4
0
4
ATLA
near road
150
3
0
0
1
3
0
0
0
0
0
0
BALT
near road
150
2
0
0
1
2
0
0
0
0
0
0
BOST
near road
150
3
0
1
0
3
0
0
0
0
0
0
CHIC
near road
150
5
3
0
0
2
0
0
0
0
0
0
DALL
near road
150
3
0
0
1
3
0
0
0
0
0
0
DENV
near road
150
4
0
1
0
4
0
0
0
0
0
0
DETR
near road
150
6
2
3
3
4
0
1
1
0.5
0
0
1
1
1
0
HOUS
near road
150
3
0
0
1
3
0
0
0
0
0
0
KANS
near road
150
3
0
1
2
3
0
0
0
0
0
0
0
0
LOSA
near road
150
3
0
0
1
3
0
0
0
0
0
0
Ml AM
near road
150
1
0
0
0
1
0
1
0
1
MINE
near road
150
4
0
1
2
4
0
0
0
0
0
0
0
0
NYNY
nearroad
150
2
0
0
1
2
1
3
2
1
3
3
5-117
-------
CBSA
1-hr
Benchmark
Number
5f monitor
site-years
Mea
n number c
be
f days per
nchmark le
/ear at or a
vel
bove
Maxim
um numbe
be
of days pe
nchmark le
r year at or
i/el
above
Abbr.
Monitor Type
(ppb)
asis
CS1012
CS1113
CS1214
CS1315
asis
CS1012
CS1113
CS1214
CS1315
asis
CS1012
CS1113
CS1214
CS1315
PHIL
near
road
150
3
0
0
0
3
0
0
0
0
PHOE
near
road
150
3
0
0
1
3
0
0
0
0
0
0
PITT
near
road
150
2
0
0
0
2
0
0
0
0
RICH
near
road
150
3
0
1
2
3
0
0
0
0
0
0
0
0
RIVR
near
road
150
2
0
0
0
2
0
0
0
0
SACR
near
road
150
1
0
0
0
1
0
0
0
0
SAND
near
road
150
1
0
0
0
1
0
0
0
0
SANF
near
road
150
2
0
0
1
2
0
0
0
0
0
0
STLO
near
road
150
4
0
1
2
4
0
0
1
0
0
0
1
0
WASH
near
road
150
1
0
0
0
1
0
0
0
0
ATLA
near
road
200
3
0
0
1
3
0
0
0
0
0
0
BALT
near
road
200
2
0
0
1
2
0
0
0
0
0
0
BOST
near
road
200
3
0
1
2
3
0
0
0
0
0
0
0
0
CHIC
near
road
200
5
3
0
0
2
0
0
0
0
0
0
DALL
near
road
200
3
0
0
1
3
0
0
0
0
0
0
DENV
near
road
200
4
0
1
2
4
0
0
0
0
0
0
0
0
DETR
near
road
200
6
2
3
3
4
0
0
0
0
0
0
0
0
0
0
HOUS
near
road
200
3
0
0
1
3
0
0
0
0
0
0
KANS
near
road
200
3
0
1
2
3
0
0
0
0
0
0
0
0
LOSA
near
road
200
3
0
0
1
3
0
0
0
0
0
0
Ml AM
near
road
200
1
0
0
0
1
0
0
0
0
MINE
near
road
200
4
0
1
2
4
0
0
0
0
0
0
0
0
NYNY
near
road
200
2
0
0
1
2
1
0
0
1
0
0
PHIL
near
road
200
3
0
0
0
3
0
0
0
0
PHOE
near
road
200
3
0
0
1
3
0
0
0
0
0
0
PITT
near
road
200
2
0
0
0
2
0
0
0
0
RICH
near
road
200
3
0
1
2
3
0
0
0
0
0
0
0
0
RIVR
near
road
200
2
0
0
0
2
0
0
0
0
SACR
near
road
200
1
0
0
0
1
0
0
0
0
SAND
near
road
200
1
0
0
0
1
0
0
0
0
SANF
near
road
200
2
0
0
1
2
0
0
0
0
0
0
STLO
near
road
200
4
0
1
2
4
0
0
0
0
0
0
0
0
WASH
near
road
200
1
0
0
0
1
0
0
0
0
5-118
-------
5.6 NUMBER OF DAYS NO2 CONCENTRATIONS WERE AT OR
ABOVE 1-HOUR BENCHMARK LEVELS: SIMULATED ON-ROAD
CONCENTRATIONS (2014-2015 ONLY)
Table B5-58. Number of days NO2 concentrations were at or above benchmark levels:
simulated on-road concentrations for 2014.
CBSA
Abbrev.
Near-road
monitor site ID
Upwards
Adjustment
Year
Number of
monitored
days in year
1-hour
Benchmark
(ppb)
Number of d
benchr
asis
ays at or above
nark level
CS1315
ATLA
131210056
hi -11%
2014
200
100
0
19
BALT
240270006
hi -19%
2014
275
100
0
24
BOST
250250044
hi -15%
2014
365
100
0
34
CHIC
170313103
hi -19%
2014
360
100
1
21
DALL
481131067
hi -21%
2014
274
100
0
9
DENV
080310027
hi -15%
2014
361
100
1
17
DETR
261630093
hi -15%
2014
364
100
0
24
HOUS
482011066
hi -21%
2014
344
100
0
23
KANS
290950042
hi -19%
2014
358
100
0
18
LOSA
060590008
hi -15%
2014
361
100
0
39
MINE
270530962
hi -21%
2014
365
100
0
51
NYNY
340030010
hi -19%
2014
184
100
4
16
PHIL
421010075
hi -15%
2014
358
100
0
10
PHOE
040134019
hi -15%
2014
322
100
0
22
PITT
420031376
hi -19%
2014
122
100
0
8
RICH
517600025
hi -19%
2014
269
100
0
26
SANF
060010012
hi -19%
2014
334
100
0
13
STLO
295100094
hi -21%
2014
365
100
0
39
ATLA
131210056
low- 7%
2014
200
100
0
17
BALT
240270006
low-12%
2014
275
100
0
20
BOST
250250044
low- 9%
2014
365
100
0
26
CHIC
170313103
low-12%
2014
360
100
1
9
DALL
481131067
low-13%
2014
274
100
0
5
DENV
080310027
low- 9%
2014
361
100
1
12
DETR
261630093
low- 9%
2014
364
100
0
16
HOUS
482011066
low-13%
2014
344
100
0
14
KANS
290950042
low-12%
2014
358
100
0
8
LOSA
060590008
low- 9%
2014
361
100
0
32
MINE
270530962
low-13%
2014
365
100
0
31
NYNY
340030010
low-12%
2014
184
100
4
13
PHIL
421010075
low- 9%
2014
358
100
0
6
PHOE
040134019
low- 9%
2014
322
100
0
10
PITT
420031376
low-12%
2014
122
100
0
6
RICH
517600025
low-12%
2014
269
100
0
17
SANF
060010012
low-12%
2014
334
100
0
8
STLO
295100094
low-13%
2014
365
100
0
30
ATLA
131210056
mid- 9%
2014
200
100
0
19
BALT
240270006
mid-16%
2014
275
100
0
20
BOST
250250044
mid-12%
2014
365
100
0
29
CHIC
170313103
mid-16%
2014
360
100
1
14
DALL
481131067
mid-17%
2014
274
100
0
5
DENV
080310027
mid-12%
2014
361
100
1
16
DETR
261630093
mid-12%
2014
364
100
0
19
HOUS
482011066
mid-17%
2014
344
100
0
18
5-119
-------
CBSA
Abbrev.
Near-road
monitor site ID
Upwards
Adjustment
Year
Number of
monitored
days in year
1-hour
Benchmark
(ppb)
Number of d
benchr
asis
ays at or above
nark level
CS1315
KANS
290950042
mid-16%
2014
358
100
0
11
LOSA
060590008
mid-12%
2014
361
100
0
36
MINE
270530962
mid-17%
2014
365
100
0
35
NYNY
340030010
mid-16%
2014
184
100
4
16
PHIL
421010075
mid-12%
2014
358
100
0
8
PHOE
040134019
mid-12%
2014
322
100
0
13
PITT
420031376
mid-16%
2014
122
100
0
7
RICH
517600025
mid-16%
2014
269
100
0
20
SANF
060010012
mid-16%
2014
334
100
0
11
STLO
295100094
mid-17%
2014
365
100
0
37
ATLA
131210056
hi -11%
2014
200
150
0
0
BALT
240270006
hi -19%
2014
275
150
0
0
BOST
250250044
hi -15%
2014
365
150
0
0
CHIC
170313103
hi -19%
2014
360
150
0
0
DALL
481131067
hi -21%
2014
274
150
0
0
DENV
080310027
hi -15%
2014
361
150
0
1
DETR
261630093
hi -15%
2014
364
150
0
1
HOUS
482011066
hi -21%
2014
344
150
0
0
KANS
290950042
hi -19%
2014
358
150
0
1
LOSA
060590008
hi -15%
2014
361
150
0
1
MINE
270530962
hi -21%
2014
365
150
0
2
NYNY
340030010
hi -19%
2014
184
150
2
4
PHIL
421010075
hi -15%
2014
358
150
0
0
PHOE
040134019
hi -15%
2014
322
150
0
0
PITT
420031376
hi -19%
2014
122
150
0
0
RICH
517600025
hi -19%
2014
269
150
0
1
SANF
060010012
hi -19%
2014
334
150
0
0
STLO
295100094
hi -21%
2014
365
150
0
2
ATLA
131210056
low- 7%
2014
200
150
0
0
BALT
240270006
low-12%
2014
275
150
0
0
BOST
250250044
low- 9%
2014
365
150
0
0
CHIC
170313103
low-12%
2014
360
150
0
0
DALL
481131067
low-13%
2014
274
150
0
0
DENV
080310027
low- 9%
2014
361
150
0
1
DETR
261630093
low- 9%
2014
364
150
0
0
HOUS
482011066
low-13%
2014
344
150
0
0
KANS
290950042
low-12%
2014
358
150
0
0
LOSA
060590008
low- 9%
2014
361
150
0
0
MINE
270530962
low-13%
2014
365
150
0
1
NYNY
340030010
low-12%
2014
184
150
1
4
PHIL
421010075
low- 9%
2014
358
150
0
0
PHOE
040134019
low- 9%
2014
322
150
0
0
PITT
420031376
low-12%
2014
122
150
0
0
RICH
517600025
low-12%
2014
269
150
0
0
SANF
060010012
low-12%
2014
334
150
0
0
STLO
295100094
low-13%
2014
365
150
0
1
ATLA
131210056
mid- 9%
2014
200
150
0
0
BALT
240270006
mid-16%
2014
275
150
0
0
BOST
250250044
mid-12%
2014
365
150
0
0
CHIC
170313103
mid-16%
2014
360
150
0
0
DALL
481131067
mid-17%
2014
274
150
0
0
DENV
080310027
mid-12%
2014
361
150
0
1
DETR
261630093
mid-12%
2014
364
150
0
0
HOUS
482011066
mid-17%
2014
344
150
0
0
5-120
-------
CBSA
Abbrev.
Near-road
monitor site ID
Upwards
Adjustment
Year
Number of
monitored
days in year
1-hour
Benchmark
(ppb)
Number of d
benchr
asis
ays at or above
nark level
CS1315
KANS
290950042
mid-16%
2014
358
150
0
0
LOSA
060590008
mid-12%
2014
361
150
0
0
MINE
270530962
mid-17%
2014
365
150
0
1
NYNY
340030010
mid-16%
2014
184
150
2
4
PHIL
421010075
mid-12%
2014
358
150
0
0
PHOE
040134019
mid-12%
2014
322
150
0
0
PITT
420031376
mid-16%
2014
122
150
0
0
RICH
517600025
mid-16%
2014
269
150
0
1
SANF
060010012
mid-16%
2014
334
150
0
0
STLO
295100094
mid-17%
2014
365
150
0
2
ATLA
131210056
hi -11%
2014
200
200
0
0
BALT
240270006
hi -19%
2014
275
200
0
0
BOST
250250044
hi -15%
2014
365
200
0
0
CHIC
170313103
hi -19%
2014
360
200
0
0
DALL
481131067
hi -21%
2014
274
200
0
0
DENV
080310027
hi -15%
2014
361
200
0
0
DETR
261630093
hi -15%
2014
364
200
0
0
HOUS
482011066
hi -21%
2014
344
200
0
0
KANS
290950042
hi -19%
2014
358
200
0
0
LOSA
060590008
hi -15%
2014
361
200
0
0
MINE
270530962
hi -21%
2014
365
200
0
0
NYNY
340030010
hi -19%
2014
184
200
1
2
PHIL
421010075
hi -15%
2014
358
200
0
0
PHOE
040134019
hi -15%
2014
322
200
0
0
PITT
420031376
hi -19%
2014
122
200
0
0
RICH
517600025
hi -19%
2014
269
200
0
0
SANF
060010012
hi -19%
2014
334
200
0
0
STLO
295100094
hi -21%
2014
365
200
0
0
ATLA
131210056
low- 7%
2014
200
200
0
0
BALT
240270006
low-12%
2014
275
200
0
0
BOST
250250044
low- 9%
2014
365
200
0
0
CHIC
170313103
low-12%
2014
360
200
0
0
DALL
481131067
low-13%
2014
274
200
0
0
DENV
080310027
low- 9%
2014
361
200
0
0
DETR
261630093
low- 9%
2014
364
200
0
0
HOUS
482011066
low-13%
2014
344
200
0
0
KANS
290950042
low-12%
2014
358
200
0
0
LOSA
060590008
low- 9%
2014
361
200
0
0
MINE
270530962
low-13%
2014
365
200
0
0
NYNY
340030010
low-12%
2014
184
200
1
1
PHIL
421010075
low- 9%
2014
358
200
0
0
PHOE
040134019
low- 9%
2014
322
200
0
0
PITT
420031376
low-12%
2014
122
200
0
0
RICH
517600025
low-12%
2014
269
200
0
0
SANF
060010012
low-12%
2014
334
200
0
0
STLO
295100094
low-13%
2014
365
200
0
0
ATLA
131210056
mid- 9%
2014
200
200
0
0
BALT
240270006
mid-16%
2014
275
200
0
0
BOST
250250044
mid-12%
2014
365
200
0
0
CHIC
170313103
mid-16%
2014
360
200
0
0
DALL
481131067
mid-17%
2014
274
200
0
0
DENV
080310027
mid-12%
2014
361
200
0
0
DETR
261630093
mid-12%
2014
364
200
0
0
HOUS
482011066
mid-17%
2014
344
200
0
0
5-121
-------
Number of
1-hour
Number of days at or above
CBSA
Near-road
Upwards
monitored
Benchmark
benchmark level
Abbrev.
monitor site ID
Adjustment
Year
days in year
(ppb)
asis
CS1315
KANS
290950042
mid-16%
2014
358
200
0
0
LOSA
060590008
mid-12%
2014
361
200
0
0
MINE
270530962
mid-17%
2014
365
200
0
0
NYNY
340030010
mid-16%
2014
184
200
1
2
PHIL
421010075
mid-12%
2014
358
200
0
0
PHOE
040134019
mid-12%
2014
322
200
0
0
PITT
420031376
mid-16%
2014
122
200
0
0
RICH
517600025
mid-16%
2014
269
200
0
0
SANF
060010012
mid-16%
2014
334
200
0
0
STLO
295100094
mid-17%
2014
365
200
0
0
5-122
-------
Table B5-59. Number of days NO2 concentrations were at or above benchmark levels:
simulated on-road concentrations for 2015.
CBSA
Abbrev.
Near-road
monitor site ID
Upwards
Adjustment
Year
Number of
monitored
days in year
1-hour
Benchmark
(ppb)
Number of c
benchr
asis
ays at or above
nark level
CS1315
ATLA
130890003
hi -21%
2015
363
100
0
22
ATLA
131210056
hi -11%
2015
362
100
0
8
BALT
240270006
hi -19%
2015
352
100
0
42
BOST
250250044
hi -15%
2015
362
100
0
28
CHIC
170313103
hi -19%
2015
358
100
0
18
DALL
481131067
hi -21%
2015
365
100
0
22
DALL
484391053
hi -19%
2015
295
100
0
6
DENV
080310027
hi -15%
2015
359
100
0
11
DENV
080310028
hi -15%
2015
92
100
0
16
DETR
261630093
hi -15%
2015
361
100
0
25
DETR
261630095
hi -21%
2015
362
100
0
19
HOUS
482011052
hi -19%
2015
261
100
0
18
HOUS
482011066
hi -21%
2015
365
100
0
14
KANS
290950042
hi -19%
2015
352
100
0
14
LOSA
060374008
hi -15%
2015
255
100
1
17
LOSA
060590008
hi -15%
2015
364
100
0
4
MIAM
120110035
hi -21%
2015
56
100
1
3
MINE
270370480
hi -21%
2015
363
100
0
10
MINE
270530962
hi -21%
2015
362
100
0
30
NYNY
340030010
hi -19%
2015
365
100
1
31
PHIL
421010075
hi -15%
2015
357
100
0
5
PHIL
421010076
hi -19%
2015
163
100
0
1
PHOE
040134019
hi -15%
2015
365
100
0
6
PHOE
040134020
hi -19%
2015
121
100
0
28
PITT
420031376
hi -19%
2015
365
100
0
47
RICH
517600025
hi -19%
2015
353
100
0
43
RIVR
060710026
hi -21%
2015
360
100
2
44
RIVR
060710027
hi -15%
2015
153
100
0
28
SACR
060670015
hi -19%
2015
80
100
0
6
SAND
060731017
hi -21%
2015
269
100
0
17
SANF
060010012
hi -19%
2015
365
100
0
11
STLO
291890016
hi -21%
2015
357
100
0
8
STLO
295100094
hi -21%
2015
364
100
0
26
WASH
110010051
hi -19%
2015
212
100
0
22
ATLA
130890003
low-13%
2015
363
100
0
18
ATLA
131210056
low- 7%
2015
362
100
0
6
BALT
240270006
low-12%
2015
352
100
0
21
BOST
250250044
low- 9%
2015
362
100
0
23
CHIC
170313103
low-12%
2015
358
100
0
13
DALL
481131067
low-13%
2015
365
100
0
14
DALL
484391053
low-12%
2015
295
100
0
4
DENV
080310027
low- 9%
2015
359
100
0
5
DENV
080310028
low- 9%
2015
92
100
0
9
DETR
261630093
low- 9%
2015
361
100
0
17
DETR
261630095
low-13%
2015
362
100
0
13
HOUS
482011052
low-12%
2015
261
100
0
13
HOUS
482011066
low-13%
2015
365
100
0
8
KANS
290950042
low-12%
2015
352
100
0
6
LOSA
060374008
low- 9%
2015
255
100
1
13
LOSA
060590008
low- 9%
2015
364
100
0
2
5-123
-------
CBSA
Abbrev.
Near-road
monitor site ID
Upwards
Adjustment
Year
Number of
monitored
days in year
1-hour
Benchmark
(ppb)
Number of r
benchr
asis
ays at or above
nark level
CS1315
MIAM
120110035
low-13%
2015
56
100
1
2
MINE
270370480
low-13%
2015
363
100
0
9
MINE
270530962
low-13%
2015
362
100
0
20
NYNY
340030010
low-12%
2015
365
100
1
18
PHIL
421010075
low- 9%
2015
357
100
0
3
PHIL
421010076
low-12%
2015
163
100
0
1
PHOE
040134019
low- 9%
2015
365
100
0
3
PHOE
040134020
low-12%
2015
121
100
0
17
PITT
420031376
low-12%
2015
365
100
0
29
RICH
517600025
low-12%
2015
353
100
0
33
RIVR
060710026
low-13%
2015
360
100
0
21
RIVR
060710027
low- 9%
2015
153
100
0
17
SACR
060670015
low-12%
2015
80
100
0
5
SAND
060731017
low-13%
2015
269
100
0
12
SANF
060010012
low-12%
2015
365
100
0
5
STLO
291890016
low-13%
2015
357
100
0
4
STLO
295100094
low-13%
2015
364
100
0
13
WASH
110010051
low-12%
2015
212
100
0
12
ATLA
130890003
mid-17%
2015
363
100
0
20
ATLA
131210056
mid- 9%
2015
362
100
0
7
BALT
240270006
mid-16%
2015
352
100
0
32
BOST
250250044
mid-12%
2015
362
100
0
24
CHIC
170313103
mid-16%
2015
358
100
0
15
DALL
481131067
mid-17%
2015
365
100
0
16
DALL
484391053
mid-16%
2015
295
100
0
5
DENV
080310027
mid-12%
2015
359
100
0
7
DENV
080310028
mid-12%
2015
92
100
0
13
DETR
261630093
mid-12%
2015
361
100
0
22
DETR
261630095
mid-17%
2015
362
100
0
15
HOUS
482011052
mid-16%
2015
261
100
0
15
HOUS
482011066
mid-17%
2015
365
100
0
9
KANS
290950042
mid-16%
2015
352
100
0
11
LOSA
060374008
mid-12%
2015
255
100
1
17
LOSA
060590008
mid-12%
2015
364
100
0
3
MIAM
120110035
mid-17%
2015
56
100
1
2
MINE
270370480
mid-17%
2015
363
100
0
9
MINE
270530962
mid-17%
2015
362
100
0
25
NYNY
340030010
mid-16%
2015
365
100
1
26
PHIL
421010075
mid-12%
2015
357
100
0
4
PHIL
421010076
mid-16%
2015
163
100
0
1
PHOE
040134019
mid-12%
2015
365
100
0
4
PHOE
040134020
mid-16%
2015
121
100
0
19
PITT
420031376
mid-16%
2015
365
100
0
36
RICH
517600025
mid-16%
2015
353
100
0
39
RIVR
060710026
mid-17%
2015
360
100
1
31
RIVR
060710027
mid-12%
2015
153
100
0
21
SACR
060670015
mid-16%
2015
80
100
0
6
SAND
060731017
mid-17%
2015
269
100
0
15
SANF
060010012
mid-16%
2015
365
100
0
7
STLO
291890016
mid-17%
2015
357
100
0
5
STLO
295100094
mid-17%
2015
364
100
0
17
WASH
110010051
mid-16%
2015
212
100
0
17
ATLA
130890003
hi -21%
2015
363
150
0
1
5-124
-------
CBSA
Abbrev.
Near-road
monitor site ID
Upwards
Adjustment
Year
Number of
monitored
days in year
1-hour
Benchmark
(ppb)
Number of r
benchr
asis
ays at or above
nark level
CS1315
ATLA
131210056
hi -11%
2015
362
150
0
0
BALT
240270006
hi -19%
2015
352
150
0
0
BOST
250250044
hi -15%
2015
362
150
0
0
CHIC
170313103
hi -19%
2015
358
150
0
0
DALL
481131067
hi -21%
2015
365
150
0
0
DALL
484391053
hi -19%
2015
295
150
0
0
DENV
080310027
hi -15%
2015
359
150
0
0
DENV
080310028
hi -15%
2015
92
150
0
1
DETR
261630093
hi -15%
2015
361
150
0
0
DETR
261630095
hi -21%
2015
362
150
0
1
HOUS
482011052
hi -19%
2015
261
150
0
1
HOUS
482011066
hi -21%
2015
365
150
0
0
KANS
290950042
hi -19%
2015
352
150
0
0
LOSA
060374008
hi -15%
2015
255
150
0
0
LOSA
060590008
hi -15%
2015
364
150
0
0
MIAM
120110035
hi -21%
2015
56
150
0
1
MINE
270370480
hi -21%
2015
363
150
0
0
MINE
270530962
hi -21%
2015
362
150
0
2
NYNY
340030010
hi -19%
2015
365
150
1
2
PHIL
421010075
hi -15%
2015
357
150
0
0
PHIL
421010076
hi -19%
2015
163
150
0
0
PHOE
040134019
hi -15%
2015
365
150
0
0
PHOE
040134020
hi -19%
2015
121
150
0
1
PITT
420031376
hi -19%
2015
365
150
0
1
RICH
517600025
hi -19%
2015
353
150
0
1
RIVR
060710026
hi -21%
2015
360
150
0
1
RIVR
060710027
hi -15%
2015
153
150
0
1
SACR
060670015
hi -19%
2015
80
150
0
0
SAND
060731017
hi -21%
2015
269
150
0
0
SANF
060010012
hi -19%
2015
365
150
0
0
STLO
291890016
hi -21%
2015
357
150
0
0
STLO
295100094
hi -21%
2015
364
150
0
0
WASH
110010051
hi -19%
2015
212
150
0
2
ATLA
130890003
low-13%
2015
363
150
0
0
ATLA
131210056
low- 7%
2015
362
150
0
0
BALT
240270006
low-12%
2015
352
150
0
0
BOST
250250044
low- 9%
2015
362
150
0
0
CHIC
170313103
low-12%
2015
358
150
0
0
DALL
481131067
low-13%
2015
365
150
0
0
DALL
484391053
low-12%
2015
295
150
0
0
DENV
080310027
low- 9%
2015
359
150
0
0
DENV
080310028
low- 9%
2015
92
150
0
1
DETR
261630093
low- 9%
2015
361
150
0
0
DETR
261630095
low-13%
2015
362
150
0
0
HOUS
482011052
low-12%
2015
261
150
0
0
HOUS
482011066
low-13%
2015
365
150
0
0
KANS
290950042
low-12%
2015
352
150
0
0
LOSA
060374008
low- 9%
2015
255
150
0
0
LOSA
060590008
low- 9%
2015
364
150
0
0
MIAM
120110035
low-13%
2015
56
150
0
1
MINE
270370480
low-13%
2015
363
150
0
0
MINE
270530962
low-13%
2015
362
150
0
1
NYNY
340030010
low-12%
2015
365
150
1
1
5-125
-------
CBSA
Abbrev.
Near-road
monitor site ID
Upwards
Adjustment
Year
Number of
monitored
days in year
1-hour
Benchmark
(ppb)
Number of r
benchr
asis
ays at or above
nark level
CS1315
PHIL
421010075
low- 9%
2015
357
150
0
0
PHIL
421010076
low-12%
2015
163
150
0
0
PHOE
040134019
low- 9%
2015
365
150
0
0
PHOE
040134020
low-12%
2015
121
150
0
0
PITT
420031376
low-12%
2015
365
150
0
0
RICH
517600025
low-12%
2015
353
150
0
1
RIVR
060710026
low-13%
2015
360
150
0
1
RIVR
060710027
low- 9%
2015
153
150
0
1
SACR
060670015
low-12%
2015
80
150
0
0
SAND
060731017
low-13%
2015
269
150
0
0
SANF
060010012
low-12%
2015
365
150
0
0
STLO
291890016
low-13%
2015
357
150
0
0
STLO
295100094
low-13%
2015
364
150
0
0
WASH
110010051
low-12%
2015
212
150
0
1
ATLA
130890003
mid-17%
2015
363
150
0
0
ATLA
131210056
mid- 9%
2015
362
150
0
0
BALT
240270006
mid-16%
2015
352
150
0
0
BOST
250250044
mid-12%
2015
362
150
0
0
CHIC
170313103
mid-16%
2015
358
150
0
0
DALL
481131067
mid-17%
2015
365
150
0
0
DALL
484391053
mid-16%
2015
295
150
0
0
DENV
080310027
mid-12%
2015
359
150
0
0
DENV
080310028
mid-12%
2015
92
150
0
1
DETR
261630093
mid-12%
2015
361
150
0
0
DETR
261630095
mid-17%
2015
362
150
0
0
HOUS
482011052
mid-16%
2015
261
150
0
1
HOUS
482011066
mid-17%
2015
365
150
0
0
KANS
290950042
mid-16%
2015
352
150
0
0
LOSA
060374008
mid-12%
2015
255
150
0
0
LOSA
060590008
mid-12%
2015
364
150
0
0
MIAM
120110035
mid-17%
2015
56
150
0
1
MINE
270370480
mid-17%
2015
363
150
0
0
MINE
270530962
mid-17%
2015
362
150
0
1
NYNY
340030010
mid-16%
2015
365
150
1
1
PHIL
421010075
mid-12%
2015
357
150
0
0
PHIL
421010076
mid-16%
2015
163
150
0
0
PHOE
040134019
mid-12%
2015
365
150
0
0
PHOE
040134020
mid-16%
2015
121
150
0
1
PITT
420031376
mid-16%
2015
365
150
0
0
RICH
517600025
mid-16%
2015
353
150
0
1
RIVR
060710026
mid-17%
2015
360
150
0
1
RIVR
060710027
mid-12%
2015
153
150
0
1
SACR
060670015
mid-16%
2015
80
150
0
0
SAND
060731017
mid-17%
2015
269
150
0
0
SANF
060010012
mid-16%
2015
365
150
0
0
STLO
291890016
mid-17%
2015
357
150
0
0
STLO
295100094
mid-17%
2015
364
150
0
0
WASH
110010051
mid-16%
2015
212
150
0
2
ATLA
130890003
hi -21%
2015
363
200
0
0
ATLA
131210056
hi -11%
2015
362
200
0
0
BALT
240270006
hi -19%
2015
352
200
0
0
BOST
250250044
hi -15%
2015
362
200
0
0
CHIC
170313103
hi -19%
2015
358
200
0
0
5-126
-------
CBSA
Abbrev.
Near-road
monitor site ID
Upwards
Adjustment
Year
Number of
monitored
days in year
1-hour
Benchmark
(ppb)
Number of r
benchr
asis
ays at or above
nark level
CS1315
DALL
481131067
hi -21%
2015
365
200
0
0
DALL
484391053
hi -19%
2015
295
200
0
0
DENV
080310027
hi -15%
2015
359
200
0
0
DENV
080310028
hi -15%
2015
92
200
0
0
DETR
261630093
hi -15%
2015
361
200
0
0
DETR
261630095
hi -21%
2015
362
200
0
0
HOUS
482011052
hi -19%
2015
261
200
0
0
HOUS
482011066
hi -21%
2015
365
200
0
0
KANS
290950042
hi -19%
2015
352
200
0
0
LOSA
060374008
hi -15%
2015
255
200
0
0
LOSA
060590008
hi -15%
2015
364
200
0
0
MIAM
120110035
hi -21%
2015
56
200
0
0
MINE
270370480
hi -21%
2015
363
200
0
0
MINE
270530962
hi -21%
2015
362
200
0
0
NYNY
340030010
hi -19%
2015
365
200
0
0
PHIL
421010075
hi -15%
2015
357
200
0
0
PHIL
421010076
hi -19%
2015
163
200
0
0
PHOE
040134019
hi -15%
2015
365
200
0
0
PHOE
040134020
hi -19%
2015
121
200
0
0
PITT
420031376
hi -19%
2015
365
200
0
0
RICH
517600025
hi -19%
2015
353
200
0
0
RIVR
060710026
hi -21%
2015
360
200
0
0
RIVR
060710027
hi -15%
2015
153
200
0
0
SACR
060670015
hi -19%
2015
80
200
0
0
SAND
060731017
hi -21%
2015
269
200
0
0
SANF
060010012
hi -19%
2015
365
200
0
0
STLO
291890016
hi -21%
2015
357
200
0
0
STLO
295100094
hi -21%
2015
364
200
0
0
WASH
110010051
hi -19%
2015
212
200
0
0
ATLA
130890003
low-13%
2015
363
200
0
0
ATLA
131210056
low- 7%
2015
362
200
0
0
BALT
240270006
low-12%
2015
352
200
0
0
BOST
250250044
low- 9%
2015
362
200
0
0
CHIC
170313103
low-12%
2015
358
200
0
0
DALL
481131067
low-13%
2015
365
200
0
0
DALL
484391053
low-12%
2015
295
200
0
0
DENV
080310027
low- 9%
2015
359
200
0
0
DENV
080310028
low- 9%
2015
92
200
0
0
DETR
261630093
low- 9%
2015
361
200
0
0
DETR
261630095
low-13%
2015
362
200
0
0
HOUS
482011052
low-12%
2015
261
200
0
0
HOUS
482011066
low-13%
2015
365
200
0
0
KANS
290950042
low-12%
2015
352
200
0
0
LOSA
060374008
low- 9%
2015
255
200
0
0
LOSA
060590008
low- 9%
2015
364
200
0
0
MIAM
120110035
low-13%
2015
56
200
0
0
MINE
270370480
low-13%
2015
363
200
0
0
MINE
270530962
low-13%
2015
362
200
0
0
NYNY
340030010
low-12%
2015
365
200
0
0
PHIL
421010075
low- 9%
2015
357
200
0
0
PHIL
421010076
low-12%
2015
163
200
0
0
PHOE
040134019
low- 9%
2015
365
200
0
0
PHOE
040134020
low-12%
2015
121
200
0
0
5-127
-------
CBSA
Abbrev.
Near-road
monitor site ID
Upwards
Adjustment
Year
Number of
monitored
days in year
1-hour
Benchmark
(ppb)
Number of r
benchr
asis
ays at or above
nark level
CS1315
PITT
420031376
low-12%
2015
365
200
0
0
RICH
517600025
low-12%
2015
353
200
0
0
RIVR
060710026
low-13%
2015
360
200
0
0
RIVR
060710027
low- 9%
2015
153
200
0
0
SACR
060670015
low-12%
2015
80
200
0
0
SAND
060731017
low-13%
2015
269
200
0
0
SANF
060010012
low-12%
2015
365
200
0
0
STLO
291890016
low-13%
2015
357
200
0
0
STLO
295100094
low-13%
2015
364
200
0
0
WASH
110010051
low-12%
2015
212
200
0
0
ATLA
130890003
mid-17%
2015
363
200
0
0
ATLA
131210056
mid- 9%
2015
362
200
0
0
BALT
240270006
mid-16%
2015
352
200
0
0
BOST
250250044
mid-12%
2015
362
200
0
0
CHIC
170313103
mid-16%
2015
358
200
0
0
DALL
481131067
mid-17%
2015
365
200
0
0
DALL
484391053
mid-16%
2015
295
200
0
0
DENV
080310027
mid-12%
2015
359
200
0
0
DENV
080310028
mid-12%
2015
92
200
0
0
DETR
261630093
mid-12%
2015
361
200
0
0
DETR
261630095
mid-17%
2015
362
200
0
0
HOUS
482011052
mid-16%
2015
261
200
0
0
HOUS
482011066
mid-17%
2015
365
200
0
0
KANS
290950042
mid-16%
2015
352
200
0
0
LOSA
060374008
mid-12%
2015
255
200
0
0
LOSA
060590008
mid-12%
2015
364
200
0
0
MIAM
120110035
mid-17%
2015
56
200
0
0
MINE
270370480
mid-17%
2015
363
200
0
0
MINE
270530962
mid-17%
2015
362
200
0
0
NYNY
340030010
mid-16%
2015
365
200
0
0
PHIL
421010075
mid-12%
2015
357
200
0
0
PHIL
421010076
mid-16%
2015
163
200
0
0
PHOE
040134019
mid-12%
2015
365
200
0
0
PHOE
040134020
mid-16%
2015
121
200
0
0
PITT
420031376
mid-16%
2015
365
200
0
0
RICH
517600025
mid-16%
2015
353
200
0
0
RIVR
060710026
mid-17%
2015
360
200
0
0
RIVR
060710027
mid-12%
2015
153
200
0
0
SACR
060670015
mid-16%
2015
80
200
0
0
SAND
060731017
mid-17%
2015
269
200
0
0
SANF
060010012
mid-16%
2015
365
200
0
0
STLO
291890016
mid-17%
2015
357
200
0
0
STLO
295100094
mid-17%
2015
364
200
0
0
WASH
110010051
mid-16%
2015
212
200
0
0
5-128
-------
5.7 NUMBER (AND PERCENT) OF DAYS PER YEAR NO2 CONCENTRATIONS WERE AT OR
ABOVE 1-HOUR BENCHMARK LEVELS: CBSA-WIDE SUMMARY TABLES (2010 - 2015)
Table B5-60. Number of monitors used for calculations: area-wide and near-road monitor CBSA-wide summary table (2010-
2015).
Mean number of monitors per year1
Maximum number of monitors per year1
CBSA Abbrev.
asis
CS1012
CS1113
CS1214
CS1315
asis
CS1012
CS1113
CS1214
CS1315
AT LA
4
3
3
3
4
5
3
3
4
5
BALT
2
2
2
2
2
3
2
2
3
3
BOST
6
5
5
6
6
7
5
6
6
7
CHIC
5
5
5
6
6
5
DALL
11
11
11
10
11
14
14
14
11
12
DENV
3
2
3
4
5
3
3
5
DETR
3
2
3
3
3
4
3
3
3
4
HOUS
15
13
13
15
16
17
16
16
17
17
KANS
3
3
3
3
3
3
3
3
3
3
LOSA
11
10
9
9
13
17
13
10
12
17
MIAM
4
4
3
3
3
5
5
5
3
3
MINE
4
3
3
4
4
5
3
4
4
5
NYNY
8
8
7
8
9
10
9
8
10
10
PHIL
5
4
5
5
6
5
6
6
PHOE
5
5
5
5
5
6
5
5
6
6
PITT
5
4
5
5
6
5
6
6
RICH
3
3
3
3
3
3
3
3
3
3
RIVR
9
8
6
9
13
11
7
13
SACR
7
8
7
7
7
8
8
8
8
8
SAND
7
7
7
4
2
8
8
7
7
7
SANF
10
10
9
10
9
11
11
11
11
10
STLO
3
3
4
4
5
4
5
5
WASH
6
6
6
5
5
7
7
6
6
6
a associated with summary results presented in Table B5-61.
5-129
-------
Table B5-61. Mean and maximum number of days per year NO2 concentrations were at or above 1-hour benchmark levels:
area-wide and near-road CBSA-wide summary table (2010-2015).
CBSA Abbrev.
1-hour
Benchmark
/Metric
Mean nur
asis
nber of days
CS1012
>er year at or
CS1113
above benchr
CS1214
nark level
CS1315
Maximum n
asis
umber of day
CS1012
s per year at
CS1113
)r above bene
CS1214
hmark level
CS1315
ATLA
lOOppb
0
10
14
12
12
0
15
30
19
23
BALT
lOOppb
0
7
9
10
11
0
13
12
14
20
BOST
lOOppb
0
17
15
18
14
0
22
23
27
17
CHIC
lOOppb
0.5
23
14
1
35
16
DALL
lOOppb
0
11
12
14
15
0
15
21
19
18
DENV
lOOppb
0.5
22
9
9
1
33
12
12
DETR
lOOppb
0
14
31
22
11
0
17
35
26
13
HOUS
lOOppb
1
19
14
18
20
3
30
14
24
31
KANS
lOOppb
0
13
11
13
11
0
15
15
17
16
LOSA
lOOppb
1
27
30
30
30
2
43
41
41
43
MIAM
lOOppb
0
14
10
10
12
0
26
13
14
21
MINE
lOOppb
0
15
13
14
12
0
23
14
20
16
NYNY
lOOppb
2
15
14
18
21
3
21
19
30
30
PHIL
lOOppb
0.5
11
13
9
1
22
26
15
PHOE
lOOppb
0.5
9
12
12
11
1
9
15
15
14
PITT
lOOppb
0
9
14
16
0
12
27
23
RICH
lOOppb
0
17
14
19
9
0
30
23
28
11
RIVR
lOOppb
2
9
23
8
8
17
37
10
SACR
lOOppb
0
13
16
20
14
0
15
19
24
19
SAND
lOOppb
0.5
8
9
7
10
1
10
12
12
10
SANF
lOOppb
0.5
8
10
10
12
1
13
23
11
19
STLO
lOOppb
0
7
17
8
0
8
23
13
WASH
lOOppb
0
14
20
20
22
0
24
23
29
30
ATLA
150ppb
0
0
0
0
0
0
0
0
0
0
BALT
150ppb
0
0
0
0
0
0
0
0
0
0
BOST
150ppb
0
1
1
1
0.5
0
1
1
1
1
CHIC
150ppb
0
0
0
0
0
0
DALL
150ppb
0
0
0
0
0
0
0
0
0
0
DENV
150ppb
0
1
0.5
0.5
0
1
1
1
5-130
-------
CBSA Abbrev.
1-hour
Benchmark
/Metric
Mean nur
asis
nber of days
CS1012
>er year at or
CS1113
above benchr
CS1214
nark level
CS1315
Maximum n
asis
umber of day
CS1012
s per year at
CS1113
)r above bene
CS1214
hmark level
CS1315
DETR
150ppb
0
0.5
1
0.5
0
0
1
1
1
0
HOUS
150ppb
0
1
0
0
0
0
2
0
0
0
KANS
150ppb
0
0
0
0
0
0
0
0
0
0
LOSA
150ppb
0
0.5
1
0.5
0.5
0
1
1
1
1
MIAM
150ppb
0
1
1
1
1
0
2
1
2
2
MINE
150ppb
0
1
1
1
0
0
2
3
2
0
NYNY
150ppb
1
1
1
2
2
2
1
2
5
5
PHIL
150ppb
0
1
1
0.5
0
2
2
1
PHOE
150ppb
0
0
0
0
0
0
0
0
0
0
PITT
150ppb
0
0
0
0
0
0
0
0
RICH
150ppb
0
0
0.5
0.5
0
0
0
1
1
0
RIVR
150ppb
0
0.5
3
0
0
1
8
0
SACR
150ppb
0
0
0
0
0
0
0
0
0
0
SAND
150ppb
0
0
0
0
0
0
0
0
0
0
SANF
150ppb
0
0
0.5
0
0
0
0
1
0
0
STLO
150ppb
0
0
0.5
0
0
0
1
0
WASH
150ppb
0
0.5
0.5
1
0.5
0
1
1
1
1
ATLA
200ppb
0
0
0
0
0
0
0
0
0
0
BALT
200ppb
0
0
0
0
0
0
0
0
0
0
BOST
200ppb
0
0
0
0
0
0
0
0
0
0
CHIC
200ppb
0
0
0
0
0
0
DALL
200ppb
0
0
0
0
0
0
0
0
0
0
DENV
200ppb
0
0
0
0
0
0
0
0
DETR
200ppb
0
0
0
0
0
0
0
0
0
0
HOUS
200ppb
0
0
0
0
0
0
0
0
0
0
KANS
200ppb
0
0
0
0
0
0
0
0
0
0
LOSA
200ppb
0
0
0
0
0
0
0
0
0
0
MIAM
200ppb
0
0
0
0
0
0
0
0
0
0
MINE
200ppb
0
0
0
0
0
0
0
0
0
0
NYNY
200ppb
0.5
0
0
0
0
1
0
0
0
0
PHIL
200ppb
0
0
0
0
0
0
0
0
PHOE
200ppb
0
0
0
0
0
0
0
0
0
0
5-131
-------
CBSA Abbrev.
1-hour
Benchmark
/Metric
Mean nur
asis
nber of days
CS1012
>er year at or
CS1113
above benchr
CS1214
nark level
CS1315
Maximum n
asis
umber of day
CS1012
s per year at
CS1113
)r above bene
CS1214
hmark level
CS1315
PITT
200ppb
0
0
0
0
0
0
0
0
RICH
200ppb
0
0
0
0
0
0
0
0
0
0
RIVR
200ppb
0
0
0
0
0
0
0
0
SACR
200ppb
0
0
0
0
0
0
0
0
0
0
SAND
200ppb
0
0
0
0
0
0
0
0
0
0
SANF
200ppb
0
0
0
0
0
0
0
0
0
0
STLO
200ppb
0
0
0
0
0
0
0
0
WASH
200ppb
0
0
0
0
0
0
0
0
0
0
ATLA
2x100
0
0
0
0
1
0
0
0
0
1
BALT
2x100
0
1
0.5
0.5
1
0
3
1
1
1
BOST
2x100
0
6
6
7
6
0
11
11
13
8
CHIC
2x100
0
6
4
0
9
6
DALL
2x100
0
4
3
5
4
0
5
5
10
5
DENV
2x100
0.5
3
2
4
1
4
5
5
DETR
2x100
0
1
4
9
4
0
2
7
16
6
HOUS
2x100
0
5
5
7
9
0
7
7
9
17
KANS
2x100
0
1
1
2
2
0
2
2
5
5
LOSA
2x100
0
10
8
10
13
0
19
15
20
26
MIAM
2x100
0
4
2
3
4
0
10
3
4
7
MINE
2x100
0
2
2
3
3
0
4
3
7
5
NYNY
2x100
0.5
4
5
6
7
1
7
8
10
10
PHIL
2x100
0.5
2
3
2
1
3
6
3
PHOE
2x100
0
2
3
4
4
0
3
5
5
4
PITT
2x100
0
1
2
2
0
2
4
3
RICH
2x100
0
0
0
0.5
0
0
0
0
1
0
RIVR
2x100
0
0.5
3
3
0
1
6
6
SACR
2x100
0
4
4
6
3
0
7
6
8
6
SAND
2x100
0
0.5
0
0
1
0
1
0
0
1
SANF
2x100
0
0
0
1
2
0
0
0
2
5
STLO
2x100
0
1
3
1
0
3
5
4
WASH
2x100
0
5
3
4
6
0
12
5
6
12
5-132
-------
Table B5-62. Mean and maximum number of data point locations used for calculations:
area-wide, near-road, and simulated on-road CBSA-wide summary table (2013-2015).
CBSA
Mean numb
locatio
er of data point
is per year
Maximum numt
locations
>er of data point
per year
Abbrev.
asis
CS1315
asis
CS1315
ATLA
4
5
7
7
BALT
3
3
4
4
BOST
6
7
8
8
CHIC
5
5
6
6
DALL
12
12
14
14
DENV
3
5
7
7
DETR
4
5
6
6
HOUS
15
17
18
18
KANS
3
4
4
4
LOSA
12
14
19
19
MIAM
4
3
5
4
MINE
4
6
7
7
NYNY
9
10
11
11
PHIL
5
6
8
8
PHOE
6
6
8
8
PITT
5
5
6
6
RICH
3
4
4
4
RIVR
9
10
15
15
SACR
7
7
9
9
SAND
7
3
8
8
SANF
10
10
11
11
STLO
4
6
6
6
WASH
6
6
7
7
associated with summary results presented in Tab
e B6-63 and B5-64.
5-133
-------
Table B5-63. Mean and maximum number of days per year NO2 concentrations were at or
above 1-hour benchmark levels: area-wide, near-road, and simulated on-road CBSA-wide
summary table (2013-2015).
CBSA
Abbrev.
1-hr
Benchmark/Metric
Mean number
or above be
asis
of days per year at
snchmark level
CS1315
Maximum num
year at or above
asis
ber of days per
benchmark level
CS1315
ATLA
lOOppb
0
20
0
34
BALT
lOOppb
0
24
0
35
BOST
lOOppb
0
23
0
31
CHIC
lOOppb
0.5
19
1
23
DALL
lOOppb
0
19
0
22
DENV
lOOppb
0.5
15
1
21
DETR
lOOppb
0
22
0
30
HOUS
lOOppb
1
27
3
31
KANS
lOOppb
0
15
0
22
LOSA
lOOppb
1
37
2
53
MIAM
lOOppb
0.5
12
1
21
MINE
lOOppb
0
25
0
37
NYNY
lOOppb
2
28
4
38
PHIL
lOOppb
0.5
11
1
16
PHOE
lOOppb
0.5
19
1
27
PITT
lOOppb
0
24
0
43
RICH
lOOppb
0
25
0
39
RIVR
lOOppb
2
18
8
38
SACR
lOOppb
0
16
0
19
SAND
lOOppb
0.5
20
1
20
SANF
lOOppb
0.5
17
1
20
STLO
lOOppb
0
28
0
38
WASH
lOOppb
0
26
0
41
ATLA
150ppb
0
0
0
0
BALT
150ppb
0
0
0
0
BOST
150ppb
0
0.5
0
1
CHIC
150ppb
0
0
0
0
DALL
150ppb
0
0
0
0
DENV
150ppb
0
1
0
1
DETR
150ppb
0
0
0
0
HOUS
150ppb
0
0.5
0
1
KANS
150ppb
0
0
0
0
LOSA
150ppb
0
0.5
0
1
MIAM
150ppb
0
1
0
2
MINE
150ppb
0
1
0
1
NYNY
150ppb
1
2
2
6
PHIL
150ppb
0
0.5
0
1
PHOE
150ppb
0
0.5
0
1
PITT
150ppb
0
0
0
0
RICH
150ppb
0
1
0
1
RIVR
150ppb
0
1
0
2
SACR
150ppb
0
0
0
0
SAND
150ppb
0
0
0
0
SANF
150ppb
0
0
0
0
STLO
150ppb
0
1
0
2
WASH
150ppb
0
1
0
2
5-134
-------
ATLA
200ppb
0
0
0
0
BALT
200ppb
0
0
0
0
BOST
200ppb
0
0
0
0
CHIC
200ppb
0
0
0
0
DALL
200ppb
0
0
0
0
DENV
200ppb
0
0
0
0
DETR
200ppb
0
0
0
0
HOUS
200ppb
0
0
0
0
KANS
200ppb
0
0
0
0
LOSA
200ppb
0
0
0
0
MIAM
200ppb
0
0
0
0
MINE
200ppb
0
0
0
0
NYNY
200ppb
0.5
1
1
2
PHIL
200ppb
0
0
0
0
PHOE
200ppb
0
0
0
0
PITT
200ppb
0
0
0
0
RICH
200ppb
0
0
0
0
RIVR
200ppb
0
0
0
0
SACR
200ppb
0
0
0
0
SAND
200ppb
0
0
0
0
SANF
200ppb
0
0
0
0
STLO
200ppb
0
0
0
0
WASH
200ppb
0
0
0
0
ATLA
2x100
0
7
0
12
BALT
2x100
0
5
0
9
BOST
2x100
0
11
0
14
CHIC
2x100
0.5
6
1
8
DALL
2x100
0
6
0
9
DENV
2x100
0.5
7
1
10
DETR
2x100
0
10
0
14
HOUS
2x100
0
13
0
17
KANS
2x100
0
5
0
7
LOSA
2x100
0
18
0
32
MIAM
2x100
0
5
0
7
MINE
2x100
0
11
0
14
NYNY
2x100
1
13
2
18
PHIL
2x100
0.5
3
1
4
PHOE
2x100
0
7
0
9
PITT
2x100
0
8
0
17
RICH
2x100
0
6
0
11
RIVR
2x100
0
7
0
19
SACR
2x100
0
4
0
6
SAND
2x100
0
5
0
5
SANF
2x100
0
3
0
7
STLO
2x100
0
8
0
12
WASH
2x100
0
8
0
16
5-135
-------
Table B5-64. Percent of days per year NO2 concentrations were at or above 1-hour benchmark levels: area-wide and near-
road CBSA-wide summary table.
CBSA Abbrev.
1-hour
Benchmark
/Metric
Mean per
asis
cent of days
CS1012
>eryearat or
CS1113
above benchr
CS1214
nark level
CS1315
Maximum p
asis
ercent of day
CS1012
s per year at
CS1113
>r above bene
CS1214
hmark level
CS1315
ATLA
lOOppb
0
2.7
3.8
3.4
3.4
0
4.1
8.2
5.2
6.3
BALT
lOOppb
0
2
2.4
2.7
3.1
0
3.6
3.3
3.8
5.5
BOST
lOOppb
0
4.8
4.1
4.9
3.8
0
6
6.3
7.4
4.7
CHIC
lOOppb
0.1
6.2
3.7
0.3
9.6
4.4
DALL
lOOppb
0
2.9
3.4
3.7
4.1
0
4.1
5.8
5.2
4.9
DENV
lOOppb
0.1
6.1
2.4
2.4
0.3
9
3.3
3.3
DETR
lOOppb
0
3.9
8.6
5.9
2.9
0
4.6
9.6
7.1
3.6
HOUS
lOOppb
0.1
5.1
3.7
4.9
5.4
0.8
8.2
3.8
6.6
8.5
KANS
lOOppb
0
3.7
3
3.6
2.9
0
4.1
4.1
4.6
4.4
LOSA
lOOppb
0.1
7.4
8.1
8.3
8.3
0.6
11.8
11.2
11.2
11.8
MIAM
lOOppb
0
3.9
2.7
2.8
3.2
0
7.1
3.6
3.8
5.8
MINE
lOOppb
0
4
3.7
3.9
3.2
0
6.3
3.8
5.5
4.4
NYNY
lOOppb
0.5
4.1
3.7
4.8
5.8
0.8
5.8
5.2
8.2
8.2
PHIL
lOOppb
0.1
3.1
3.5
2.4
0.3
6
7.1
4.1
PHOE
lOOppb
0.1
2.4
3.4
3.3
3
0.3
2.5
4.1
4.1
3.8
PITT
lOOppb
0
2.4
3.7
4.3
0
3.3
7.4
6.3
RICH
lOOppb
0
4.8
3.9
5.2
2.5
0
8.2
6.3
7.7
3
RIVR
lOOppb
0.5
2.4
6.3
2.3
2.2
4.6
10.1
2.7
SACR
lOOppb
0
3.5
4.5
5.5
3.9
0
4.1
5.2
6.6
5.2
SAND
lOOppb
0.1
2.2
2.4
1.8
2.7
0.3
2.7
3.3
3.3
2.7
SANF
lOOppb
0.1
2.1
2.8
2.7
3.3
0.3
3.6
6.3
3
5.2
STLO
lOOppb
0
1.8
4.7
2.3
0
2.2
6.3
3.6
WASH
lOOppb
0
3.8
5.4
5.4
6
0
6.6
6.3
8
8.2
ATLA
150ppb
0
0
0
0
0
0
0
0
0
0
BALT
150ppb
0
0
0
0
0
0
0
0
0
0
BOST
150ppb
0
0.1
0.3
0.1
0.1
0
0.3
0.3
0.3
0.3
CHIC
150ppb
0
0
0
0
0
0
DALL
150ppb
0
0
0
0
0
0
0
0
0
0
DENV
150ppb
0
0.1
0.1
0.1
0
0.3
0.3
0.3
5-136
-------
CBSA Abbrev.
1-hour
Benchmark
/Metric
Mean per
asis
cent of days
CS1012
>eryearat or
CS1113
above benchr
CS1214
nark level
CS1315
Maximum p
asis
ercent of day
CS1012
s per year at
CS1113
>r above bene
CS1214
hmark level
CS1315
DETR
150ppb
0
0.1
0.1
0.1
0
0
0.3
0.3
0.3
0
HOUS
150ppb
0
0.1
0
0
0
0
0.6
0
0
0
KANS
150ppb
0
0
0
0
0
0
0
0
0
0
LOSA
150ppb
0
0.1
0.1
0.1
0.1
0
0.3
0.3
0.3
0.3
MIAM
150ppb
0
0.4
0.3
0.4
0.4
0
0.6
0.3
0.6
0.6
MINE
150ppb
0
0.1
0.3
0.1
0
0
0.6
0.8
0.6
0
NYNY
150ppb
0.1
0.1
0.1
0.5
0.5
0.6
0.3
0.6
1.4
1.4
PHIL
150ppb
0
0.1
0.1
0.1
0
0.6
0.6
0.3
PHOE
150ppb
0
0
0
0
0
0
0
0
0
0
PITT
150ppb
0
0
0
0
0
0
0
0
RICH
150ppb
0
0
0.1
0.1
0
0
0
0.3
0.3
0
RIVR
150ppb
0
0.1
0.7
0
0
0.3
2.2
0
SACR
150ppb
0
0
0
0
0
0
0
0
0
0
SAND
150ppb
0
0
0
0
0
0
0
0
0
0
SANF
150ppb
0
0
0.1
0
0
0
0
0.3
0
0
STLO
150ppb
0
0
0.1
0
0
0
0.3
0
WASH
150ppb
0
0.1
0.1
0.1
0.1
0
0.3
0.3
0.3
0.3
ATLA
200ppb
0
0
0
0
0
0
0
0
0
0
BALT
200ppb
0
0
0
0
0
0
0
0
0
0
BOST
200ppb
0
0
0
0
0
0
0
0
0
0
CHIC
200ppb
0
0
0
0
0
0
DALL
200ppb
0
0
0
0
0
0
0
0
0
0
DENV
200ppb
0
0
0
0
0
0
0
0
DETR
200ppb
0
0
0
0
0
0
0
0
0
0
HOUS
200ppb
0
0
0
0
0
0
0
0
0
0
KANS
200ppb
0
0
0
0
0
0
0
0
0
0
LOSA
200ppb
0
0
0
0
0
0
0
0
0
0
MIAM
200ppb
0
0
0
0
0
0
0
0
0
0
MINE
200ppb
0
0
0
0
0
0
0
0
0
0
NYNY
200ppb
0.1
0
0
0
0
0.3
0
0
0
0
PHIL
200ppb
0
0
0
0
0
0
0
0
PHOE
200ppb
0
0
0
0
0
0
0
0
0
0
5-137
-------
1-hour
Benchmark
Mean per
cent of days
>eryearat or
above benchr
nark level
Maximum p
ercent of day
s per year at
>r above bene
hmark level
CBSA Abbrev.
/Metric
asis
CS1012
CS1113
CS1214
CS1315
asis
CS1012
CS1113
CS1214
CS1315
PITT
200ppb
0
0
0
0
0
0
0
0
RICH
200ppb
0
0
0
0
0
0
0
0
0
0
RIVR
200ppb
0
0
0
0
0
0
0
0
SACR
200ppb
0
0
0
0
0
0
0
0
0
0
SAND
200ppb
0
0
0
0
0
0
0
0
0
0
SANF
200ppb
0
0
0
0
0
0
0
0
0
0
STLO
200ppb
0
0
0
0
0
0
0
0
WASH
200ppb
0
0
0
0
0
0
0
0
0
0
ATLA
2X100
0
0
0
0
0.1
0
0
0
0
0.3
BALT
2X100
0
0.3
0.1
0.1
0.1
0
0.8
0.3
0.3
0.3
BOST
2X100
0
1.6
1.7
2
1.7
0
3
3
3.6
2.2
CHIC
2X100
0
1.6
1.1
0
2.5
1.6
DALL
2X100
0
1.1
0.8
1.5
1.2
0
1.4
1.4
2.7
1.4
DENV
2X100
0.1
0.7
0.6
1
0.3
1.1
1.4
1.4
DETR
2X100
0
0.3
1.2
2.4
1
0
0.6
1.9
4.4
1.6
HOUS
2X100
0
1.3
1.5
2
2.6
0
1.9
1.9
2.5
4.7
KANS
2X100
0
0.1
0.1
0.5
0.6
0
0.6
0.6
1.4
1.4
LOSA
2X100
0
2.8
2.1
2.7
3.7
0
5.2
4.1
5.5
7.1
MIAM
2X100
0
1
0.6
0.7
1.2
0
2.7
0.8
1.1
1.9
MINE
2X100
0
0.6
0.5
0.7
0.9
0
1.1
0.8
1.9
1.4
NYNY
2X100
0.1
1.2
1.5
1.7
2
0.3
1.9
2.2
2.7
2.7
PHIL
2X100
0.1
0.6
0.7
0.5
0.3
0.8
1.6
0.8
PHOE
2X100
0
0.5
0.9
1
1
0
0.8
1.4
1.4
1.1
PITT
2X100
0
0.4
0.5
0.6
0
0.6
1.1
0.8
RICH
2X100
0
0
0
0.1
0
0
0
0
0.3
0
RIVR
2X100
0
0.1
0.8
0.8
0
0.3
1.6
1.6
SACR
2X100
0
1.2
1.2
1.7
0.9
0
1.9
1.6
2.2
1.6
SAND
2X100
0
0.1
0
0
0.3
0
0.3
0
0
0.3
SANF
2X100
0
0
0
0.3
0.5
0
0
0
0.6
1.4
STLO
2X100
0
0.3
0.9
0.4
0
0.8
1.4
1.1
WASH
2X100
0
1.4
0.8
1
1.7
0
3.3
1.4
1.6
3.3
5-138
-------
5.8 DATA FOR INDIVIDUAL NEAR-ROAD MONITORS 2014 AND 2015:
AS IS AND ADJUSTED TO JUST MEET THE EXISTING STANDARD
Table B5-65. Number per year NO2 concentrations were at or above 1-hour benchmark
levels: individual near-road monitor data where monitor was in operation for at least 300
days in year 2014 and/or 2015.
CBS A Abbrev.
Monitor site id
1-hr
benchmark
Number of days per year DM1H at or above benchmark level
As Is air
quality 2014
As Is air
quality 2015
Adjusted air
quality 2014
Adjusted air
quality 2015
AT LA
130890003
100 ppb
0
7
AT LA
131210056
100 ppb
0
3
BALT
240270006
100 ppb
0
7
BOST
250250044
100 ppb
0
0
11
6
CHIC
170313103
100 ppb
1
0
4
4
DALL
481131067
100 ppb
0
5
DENV
080310027
100 ppb
0
0
5
3
DETR
261630093
100 ppb
0
0
9
9
DETR
261630095
100 ppb
0
5
HOUS
482011066
100 ppb
0
0
4
3
KANS
290950042
100 ppb
0
0
4
3
LOSA
060590008
100 ppb
0
0
21
1
MINE
270370480
100 ppb
0
3
MINE
270530962
100 ppb
0
0
11
10
NYNY
340030010
100 ppb
1
8
PHIL
421010075
100 ppb
0
0
3
2
PHOE
040134019
100 ppb
0
0
3
2
PITT
420031376
100 ppb
0
11
RICH
517600025
100 ppb
0
9
RIVR
060710026
100 ppb
0
8
SANF
060010012
100 ppb
0
0
2
1
STLO
291890016
100 ppb
0
1
STLO
295100094
100 ppb
0
0
10
2
AT LA
130890003
150 ppb
0
0
AT LA
131210056
150 ppb
0
0
BALT
240270006
150 ppb
0
0
BOST
250250044
150 ppb
0
0
0
0
CHIC
170313103
150 ppb
0
0
0
0
DALL
481131067
150 ppb
0
0
DENV
080310027
150 ppb
0
0
0
0
DETR
261630093
150 ppb
0
0
0
0
DETR
261630095
150 ppb
0
0
HOUS
482011066
150 ppb
0
0
0
0
5-139
-------
CBS A Abbrev.
Monitor site id
1-hr
benchmark
Number of days per year DM1H at or above benchmark level
As Is air
quality 2014
As Is air
quality 2015
Adjusted air
quality 2014
Adjusted air
quality 2015
KANS
290950042
150 ppb
0
0
0
0
LOSA
060590008
150 ppb
0
0
0
0
MINE
270370480
150 ppb
0
0
MINE
270530962
150 ppb
0
0
0
0
NYNY
340030010
150 ppb
1
0
PHIL
421010075
150 ppb
0
0
0
0
PHOE
040134019
150 ppb
0
0
0
0
PITT
420031376
150 ppb
0
0
RICH
517600025
150 ppb
0
0
RIVR
060710026
150 ppb
0
0
SANF
060010012
150 ppb
0
0
0
0
STLO
291890016
150 ppb
0
0
STLO
295100094
150 ppb
0
0
0
0
AT LA
130890003
200 ppb
0
0
AT LA
131210056
200 ppb
0
0
BALT
240270006
200 ppb
0
0
BOST
250250044
200 ppb
0
0
0
0
CHIC
170313103
200 ppb
0
0
0
0
DALL
481131067
200 ppb
0
0
DENV
080310027
200 ppb
0
0
0
0
DETR
261630093
200 ppb
0
0
0
0
DETR
261630095
200 ppb
0
0
HOUS
482011066
200 ppb
0
0
0
0
KANS
290950042
200 ppb
0
0
0
0
LOSA
060590008
200 ppb
0
0
0
0
MINE
270370480
200 ppb
0
0
MINE
270530962
200 ppb
0
0
0
0
NYNY
340030010
200 ppb
0
0
PHIL
421010075
200 ppb
0
0
0
0
PHOE
040134019
200 ppb
0
0
0
0
PITT
420031376
200 ppb
0
0
RICH
517600025
200 ppb
0
0
RIVR
060710026
200 ppb
0
0
SANF
060010012
200 ppb
0
0
0
0
STLO
291890016
200 ppb
0
0
STLO
295100094
200 ppb
0
0
0
0
5-140
-------
5.9 COMPARISON OF CURRENT RESULTS WITH 2008 REA
We compared the site-year results generated in this AQC to those generated in the 2008
REA. In general, the estimated number of days per year at or above benchmark levels is similar
when evaluating the area-wide and the near-road monitors, considering both the mean and upper
percentile values (Table B5-66). The mean number of days per year at or above 100 ppb was
about 5, while the maximum number of days per year above that same level ranged from about
10 to 20. There were very few instances where concentrations were at or above the 150 ppb and
200 ppb benchmark levels, regardless of which analytical data set or monitor type was
considered.
There are however large differences in the number of days per year at or above
benchmark levels when considering the simulated on-road concentrations, whereas the 2008
REA results were consistently higher than those generated in the current AQC. For example, the
2008 REA estimated, on average, 30 to 150 days per year at or above the 100 ppb 1-hour
benchmark on-road in the subset of comparable study areas, along with an upper percentile
estimate of ranging from about 100 to 300 days per year. In the current AQC, the mean and
mean number of days where estimated concentrations were at or above the 100 ppb benchmark
ranged from about 5 to 20, with a maximum of just around 40 days per year. In addition, the
number of days having concentrations at or above the higher benchmark levels (i.e., 150 and 200
ppb) were estimated to occur more frequently when considering the 2008 REA results compared
with the current AQC.
This relatively lower range of values estimated in this AQC is to some extent a function of
the fewer number of days monitored for the new near-road monitors, with many having what
would be considered an incomplete year of monitor data (Table B5-58 and Table B5-59). All
data used in the 2008 REA adhered to the standard completeness criteria, while for the current
analysis, this restriction was relaxed for the near-road monitors such that we could include the
maximum number of days/years of near-road data possible. To a greater extent though, the
difference is likely the result of the monitor data used to to simulate the on-road NO2
concentrations. Here we used the near-road monitors, sited in close proximity to major roads
combined with a statistical approach to estimate the on-road concentrations. A similar approach
was used in the 2008 REA, though based on the form of the statistical model used, monitors at
greater distances from roads (i.e., at least 100 m from a road) were used to estimate the on-road
concentrations. The 2008 REA statistical model assumed monitors sited 100 m or greater from a
road was a reasonable distance to not have a direct influence from road emissions, though
recognized as an important uncertainty in that assessment (2008 REA, section 7.4.6).
5-141
-------
In reviewing the current monitor attribute data provided in Table B5-11 through Table
B5-33 (and also Table B5-34 through Table B5-56), while it is possible that at a distance of 100
meters or more from a major road these monitors could have limited direct contribution from
roadway emissions, there remains the potential for other source emissions to substantially
influence concentrations measured at the monitors that were not accounted for before simulating
the on-road concentrations. For example, monitor ID 420450002 located in the Philadelphia
study area was assumed to be not influenced by roadway emissions based on its siting of 322 m
from a major road (Table B5-24) and NO2 concentrations from this monitor were used to
simulate the on-road concentrations. However, in identifying all NOx emission sources within a
5 km radius of that monitor, it is possible that NO2 concentrations measured at that monitor
could be influenced by emissions from one or more facilities, including NOx emissions from
electricity generation (via combustion), petroleum refineries, and municipal waste combustion.
Using this monitor and other monitors40 that could potentially be influenced by significant
facility emissions would tend to overestimate the number of days per year simulated on-road
NO2 concentrations were at or above benchmark levels.
40 For example monitor ID 421010029 also in the Philadelphia study area (Table B5-47) is within 5 km of an
electricity generating unit(s) having summed NOx emissions of 353 tons per year.
5-142
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Table B5-66. Mean and maximum number of days per year NO2 concentrations exceed 1-
hour benchmark levels: comparison of current air quality characterization with 2008 REA.
Study Area
Source of
Results1
Monitor
Distance from
Road or
Category2
AQ Data
Years
Site-
Years3
Number of days per year at or above benchmark level4
>1
Mean
30 ppb
p99/max
>1
Mean
50 ppb
p99/max
>2
Mean
00 ppb
p99/max
Atlanta
Current
Area-wide
2010-12
9
3
15
0
0
0
0
2011-13
9
5
30
0
0
0
0
2012-14
9
4
16
0
0
0
0
2013-15
9
2
16
0
0
0
0
2008 REA
>100 m
2001-03
14
2
15
0
1
0
1
>20m-<100m
2001-03
0
-
-
-
-
-
-
Current
Near-road
2013-15
3
6
8
0
0
0
0
2008 REA
<20m
2001-03
0
-
-
-
-
-
-
Current
On-road
2014
1
19
-
0
-
0
-
2015
2
14
20
0
0
0
0
2008 REA
On-/Near-road
2001-03
1400
46
229
10
104
2
38
Boston
Current
Area-wide
2010-12
15
5
15
0.5
1
0
0
2011-13
15
5
15
0.5
1
0
0
2012-14
11
6
18
1
1
0
0
2013-15
16
3
12
0.5
1
0
0
2008 REA
>100 m
2001-03
6
0
1
0
0
0
0
>20m-<100m
2001-03
14
3
11
0
0
0
0
Current
Near-road
2013-15
3
6
11
0
0
0
0
2008 REA
<20m
2001-03
5
2
6
0
1
0
0
Current
On-road
2014
1
29
-
0
-
0
-
2015
1
24
-
0
-
0
-
2008 REA
On-/Near-road
2001-03
600
26
131
3
25
0
6
Chicago
Current
Area-wide
2010-12
11
7
24
0
0
0
0
2011-13
0
-
-
-
-
-
-
2012-14
0
-
-
-
-
-
-
2013-15
12
4
12
0
0
0
0
2008 REA
>100 m
2001-03
9
1
4
0
1
0
0
>20m-<100m
2001-03
6
7
21
1
3
0
0
Current
Near-road
2013-15
2
4
4
0
0
0
0
2008 REA
<20m
2001-03
4
2
6
0
0
0
0
Current
On-road
2014
1
14
-
0
-
0
-
2015
1
15
-
0
-
0
-
2008 REA
On-/Near-road
2001-03
900
83
257
18
106
4
49
Denver
Current
Area-wide
2010-12
0
-
-
-
-
-
-
5-143
-------
Study Area
Source of
Results1
Monitor
Distance from
Road or
Category2
AQ Data
Years
Site-
Years3
Number of days per year at or above benchmark level4
>1
Mean
30 ppb
p99/max
>1
Mean
50 ppb
p99/max
>2
Mean
00 ppb
p99/max
2011-13
5
14
29
0.5
1
0
0
2012-14
6
5
10
1
1
0
0
2013-15
7
4
10
0.5
1
0
0
2008 REA
>100 m
2001-03
2
2
2
0
0
0
0
>20m-<100m
2001-03
0
-
-
-
-
-
-
Current
Near-road
2013-15
4
3
5
0
0
0
0
2008 REA
<20m
2001-03
2
7
10
2
3
0
0
Current
On-road
2014
1
16
-
1
-
0
-
2015
2
10
13
0.5
1
0
0
2008 REA
On-/Near-road
2001-03
200
99
269
19
103
4
37
Detroit
Current
Area-wide
2010-12
4
6
12
0
0
0
0
2011-13
5
9
23
0
0
0
0
2012-14
6
8
18
0
0
0
0
2013-15
6
4
9
0
0
0
0
2008 REA
>100 m
2001-03
6
3
7
2
7
1
4
>20m-<100m
2001-03
0
-
-
-
-
-
-
Current
Near-road
2013-15
4
7
9
0
0
0
0
2008 REA
<20m
2001-03
0
-
-
-
-
-
-
Current
On-road
2014
1
19
-
0
-
0
-
2015
2
19
22
0
0
0
0
2008 REA
On-/Near-road
2001-03
600
59
186
13
57
5
28
Los Angeles
Current
Area-wide
2010-12
30
5
19
0.5
1
0
0
2011-13
26
5
22
0.5
1
0
0
2012-14
27
5
23
0.5
1
0
0
2013-15
35
4
27
0.5
1
0
0
2008 REA
>100 m
2001-03
51
1
10
0
5
0
0
>20m-<100m
2001-03
35
1
7
0
1
0
1
Current
Near-road
2013-15
3
9
21
0
0
0
0
2008 REA
<20m
2001-03
9
1
3
0
0
0
0
Current
On-road
2014
1
36
-
0
-
0
-
2015
2
10
17
0
0
0
0
2008 REA
On-/Near-road
2001-03
5100
33
160
6
55
1
20
Miami
Current
Area-wide
2010-12
12
5
15
0.5
1
0
0
2011-13
10
4
12
0.5
1
0
0
2012-14
8
5
12
1
1
0
0
2013-15
8
6
17
0.5
1
0
0
5-144
-------
Study Area
Source of
Results1
Monitor
Distance from
Road or
Category2
AQ Data
Years
Site-
Years3
Number of days per year at or above benchmark level4
>1
Mean
30 ppb
p99/max
>1
Mean
50 ppb
p99/max
>2
Mean
00 ppb
p99/max
2008 REA
>100 m
2001-03
6
4
14
0
2
0
0
>20m-<100m
2001-03
3
8
15
0
1
0
0
Current
Near-road
2013-15
1
1
-
1
-
0
-
2008 REA
<20m
2001-03
3
1
1
0
0
0
0
Current
On-road
2015
1
2
-
1
-
0
-
2008 REA
On-/Near-road
2001-03
600
78
189
19
110
5
48
New York
Current
Area-wide
2010-12
23
3
9
0.5
1
0
0
2011-13
22
3
12
0.5
1
0
0
2012-14
24
3
12
0.5
1
0
0
2013-15
25
4
12
0.5
1
0
0
2008 REA
>100 m
2001-03
26
1
5
0
0
0
0
>20m-<100m
2001-03
13
3
13
0
2
0
0
Current
Near-road
2013-15
2
8
8
2
3
0
0
2008 REA
<20m
2001-03
7
1
2
0
0
0
0
Current
On-road
2014
1
16
-
4
-
2
-
2015
1
26
-
1
-
0
-
2008 REA
On-/Near-road
2001-03
2600
57
226
10
73
3
34
Philadelphia
Current
Area-wide
2010-12
13
3
18
0.5
1
0
0
2011-13
14
3
23
0.5
1
0
0
2012-14
0
-
-
-
-
-
-
2013-15
13
2
13
0.5
1
0
0
2008 REA
>100 m
2001-03
14
3
15
0
1
0
1
>20m-<100m
2001-03
7
5
11
0
0
0
0
Current
Near-road
2013-15
3
2
3
0
0
0
0
2008 REA
<20m
2001-03
0
-
-
-
-
-
-
Current
On-road
2014
1
8
-
0
-
0
-
2015
2
3
4
0
0
0
0
2008 REA
On-/Near-road
2001-03
1400
116
294
27
137
7
68
Phoenix
Current
Area-wide
2010-12
15
2
9
0
0
0
0
2011-13
14
4
13
0
0
0
0
2012-14
14
3
12
0
0
0
0
2013-15
13
3
9
0
0
0
0
2008 REA
>100 m
2001-03
5
1
2
0
0
0
0
>20m-<100m
2001-03
2
0
0
0
0
0
0
Current
Near-road
2013-15
3
3
3
0
0
0
0
2008 REA
<20m
2001-03
3
6
11
0
0
0
0
5-145
-------
Study Area
Source of
Results1
Monitor
Distance from
Road or
Category2
AQ Data
Years
Site-
Years3
Number of days per year at or above benchmark level4
>1
Mean
30 ppb
p99/max
>1
Mean
50 ppb
p99/max
>2
Mean
00 ppb
p99/max
Current
On-road
2014
1
13
-
0
-
0
-
2015
2
12
19
0.5
1
0
0
2008 REA
On-/Near-road
2001-03
500
153
337
35
206
6
48
St. Louis
Current
Area-wide
2010-12
0
-
-
-
-
-
-
2011-13
8
2
8
0
0
0
0
2012-14
9
4
14
0
0
0
0
2013-15
9
1
5
0
0
0
0
2008 REA
>100 m
2001-03
9
3
15
0
1
0
0
>20m-<100m
2001-03
11
2
11
0
0
0
0
Current
Near-road
2013-15
4
5
10
0
0
0
0
2008 REA
<20m
2001-03
6
3
6
0
1
0
0
Current
On-road
2014
1
37
-
2
-
0
-
2015
2
11
17
0
0
0
0
2008 REA
On-/Near-road
2001-03
900
125
288
28
153
7
51
Washington
DC
Current
Area-wide
2010-12
18
4
14
0.5
1
0
0
2011-13
17
4
20
0.5
1
0
0
2012-14
15
5
24
0.5
1
0
0
2013-15
15
6
24
0.5
1
0
0
2008 REA
>100 m
2001-03
18
4
11
0
0
0
0
>20m-<100m
2001-03
10
2
5
0
0
0
0
Current
Near-road
2013-15
1
4
4
0
0
0
0
2008 REA
<20m
2001-03
4
6
9
0
1
0
0
Current
On-road
2015
1
17
-
2
-
0
-
2008 REA
On-/Near-road
2001-03
1800
109
310
27
168
7
63
1 Data compared are from the AQC performed for this current NO2 review and the AQC conducted for the 2008 REA. Shading is
added where data can generally be compared for the two sets of results.
2 For the current AQC, the results are separated into three groups: all area-wide monitors regardless of their distance to major
roads, the formally designated near-road monitor, and simulated on-road concentrations (where concentrations from the newly
designated near-road monitors served as the basis for the estimation). For the 2008 NO2 REA AQC, three generally similar
groups of data are presented: area-wide monitors (though having two categories for describing their distance in meters (m)
from a major road, Tables 7-23 (>100 m) and 7-24 (>20 m to <100 m), near-road monitors (based on monitors sited <20 m
from a major road, Table 7-25), and simulated on-road concentrations (where concentrations from monitors sited >100 m from
a major road served as the basis for the estimation, Table 7-28).
3 In general, the average number of monitors operating per year within the three-year group can estimated by dividing the number
of site-years by for the area-wide and near-road data. When using the on-road data, divide by 300.
4 The mean number of exceedances represents the sum of benchmark exceedances occurring at all monitors in a particular
location divided by the total site-years across the three-year monitoring period. The p99 (2008 REA AQC) and maximum
(Current AQC) represent an upper estimate of the number of days/year having a benchmark exceedance at a particular
monitoring site for a single year within the monitoring period.
5-146
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United States Office of Air Quality Planning and Standards Publication No. EPA-452/R-17-003
Environmental Protection Health and Environmental Impacts Division April, 2017
Agency Research Triangle Park, NC
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