EPA
United States
Environmental
Protection Agency
Ozone National Ambient Air Quality
Standards: Scope and Methods Plan
for Health Risk and Exposure
Assessment

April 2011
Office of Air Quality Planning and Standards
U.S. Environmental Protection Agency
Research Triangle Park, NC 27711

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                                                EPA-452/P-11-001
                                                      April 2011
Ozone National Ambient Air Quality Standards:
             Scope and Methods Plan
    for Health Risk and Exposure Assessment
             U.S. Environmental Protection Agency
                 Office of Air and Radiation
           Office of Air Quality Planning and Standards
           Health and Environmental Impacts Division
                  Risk and Benefits Group
           Research Triangle Park, North Carolina 27711

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                                DISCLAIMER
       This planning document has been prepared by staff from the Office of Air Quality
Planning and Standards, U.S. Environmental Protection Agency.  Any opinions, findings,
conclusions, or recommendations are those of the authors and do not necessarily reflect the
views of the EPA. This document is being circulated to facilitate consultation with the Clean Air
Scientific Advisory Committee (CASAC) and to obtain public review. For questions concerning
this document, please contact Mr. John Langstaff (langstaff.john@epa.gov) or Dr. Zach Pekar
(pekar.zachary@epa.gov), U.S. Environmental Protection Agency, Office of Air Quality
Planning and Standards, C504-06, Research Triangle Park, North Carolina 27711.

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                             Table of Contents

1   INTRODUCTION	1-1
    1.1    Background on Last Ozone NAAQS Review	1-3
            1.1.1      Overview of Exposure Assessment for Ozone from Last Review	1-4
            1.1.2      Overview of Health Risk Assessment for Ozone from Last Review .. 1-5
    1.2    Goals for Framing the Assessments in the Current Review	1-7
    1.3    Overview of Current Assessment Plan	1-7
            1.3.1      Air Quality Assessment	1-8
            1.3.2      Exposure Assessment	1-9
            1.3.3      Health Risk Assessment	1-9
2   AIR QUALITY CONSIDERATIONS	2-1
    2.1    Introduction	2-1
    2.2    Air Quality Inputs to Risk and Exposure Assessments	2-1
            2.2.1      Recent Air Quality	2-1
            2.2.2      Air Quality Data Related to Exceptional Events	2-3
    2.3    Development of Estimates of Ozone Air Quality Assuming "Just Meeting" Current
            NAAQS and Potential Alternative NAAQS	2-3
            2.3.1      Background and Conceptual Overview	2-3
            2.3.2      Historical Approach	2-4
    2.4    Policy Relevant Background	2-4
    2.5    Broader Air Quality Characterization	2-7
3   APPROACH FOR POPULATION EXPOSURE ANALYSIS	3-1
    3.1    Introduction	3-1
    3.2    The APEX Population Exposure Model	3-1
    3.3    Populations Modeled	3-7
    3.4    Outcomes to be Generated	3-7
    3.5    Selection of Urban Areas and Time Periods	3-8
    3.6    Development of Model Inputs	3-8
            3.6.1      Population Demographics	3-9
            3.6.2      Commuting	3-9
            3.6.3      Ambient Ozone Concentrations	3-9
            3.6.4      Meteorological Data	3-10
            3.6.5      Specification of Microenvironments	3-10
            3.6.6      Indoor Sources	3-12
            3.6.7      Activity Patterns	3-12
    3.7    Exposure Modeling Issues	3-16
    3.8    Uncertainty and Variability	3-18
4   ASSESSMENT OF HEALTH RISK BASED ON CONTROLLED HUMAN EXPOSURE
    STUDIES	4-1
    4.1    Introduction	4-1
    4.2    Selection of Health Endpoints	4-1
    4.3    Selection of Exposure-Response Functions	4-2
    4.4    Approach to Calculating Risk Estimates	4-2
    4.5    Alternative  Approach Under Consideration For Calculating Risk Estimates	4-4

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    4.6    Uncertainty and Variability	4-6
5   ASSESSMENT OF HEALTH RISK BASED ON EPIDEMIOLOGIC STUDIES	5-1
    5.1    Introduction	5-1
    5.2    Framework for the Ozone Health Risk Assessment	5-5
            5.2.1     Overview of Modeling Approach	5-5
            5.2.2     Air Quality Considerations	5-7
    5.3    Selection of Health Effects Endpoint Categories	5-10
            5.3.1     Selection of Epidemiological Studies and Specification of
                     Concentration-Response Functions	5-12
            5.3.2     Selection of Urban Study Areas	5-16
            5.3.3     Baseline Health Effects Incidence Data and Demographic Data	5-17
            5.3.4     Assessing Risk In Excess of Policy-Relevant Background	5-18
    5.4    Characterization of Uncertainty and Variability in the Context of the Ozone Risk
            Assessment	5-20
            5.4.1     Overview of Approach for Addressing Uncertainty and Variability  5-20
            5.4.2     Addressing Variability	5-23
            5.4.3     Uncertainty Characterization - Qualitative  Assessment	5-25
            5.4.4     Uncertainty Characterization - Quantitative Analysis	5-26
            5.4.5     Representativeness Analysis	5-32
6   PRESENTATION OF RISK ESTIMATES TO INFORM CONSIDERATION OF
    STANDARDS	6-1
7   SCHEDULE AND MILESTONES	7-1
8   REFERENCES	8-3

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                                List of Tables

Table 3-1.  Microenvironments to be Modeled	3-6
Table 3-2.  Studies In CHAD To Be Used For Exposure Modeling	3-14
Table 5-1.  Planned Sensitivity Analyses for the Epidemiologic-Based Risk Assessment	5-28
Table 7-1.  Key Milestones for the Exposure Analysis and Health Risk Assessment for the Ozone
         NAAQS Review	7-1
                               List of Figures

Figure 1-1.  Overview of Risk Assessment Based on Epidemiologic Studies	1-12
Figure 3-1.  Overview of the APEX Model	3-3
Figure 3-2.  The Mass Balance Model	3-11
Figure 4-1.  Major Components of Ozone Health Risk Assessment Based on Controlled Human
         Exposure Studies	4-7
Figure 5-1.  Overview of Risk Assessment Model Based on Epidemiologic Studies	5-8
Figure 5-2.  Overview of Approach For Uncertainty Analysis of Risk Assessment Based on
         Epidemiologic Studies	5-30
                                        in

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                           List of Acronyms/Abbreviations
Act
AHRQ
APEX
AQS
BenMAP
CASAC
CHAD
CONUS
C-R
CSA
CTM
EPA
FEM
FIP
FRM
ISA
NAAQS
NAPS
NCEA
NEI
NERL
NCDC
NCore
NOx
03
OAQPS
OAR
OMB
ORD
PRB
QA
QC
RR
SAB
REA
SEDD
SID
SO2
SOX
TSP
VOC
Clean Air Act
Agency for Healthcare Research and Quality
EPA's Air Pollutants Exposure model, version 4
EPA's Air Quality System
Benefits Mapping Analysis Program
Clean Air Scientific Advisory Committee
EPA's Consolidated Human Activity Database
Continental United States
Concentration-response relationship
Consolidated Statistical Area
Chemical transport models
United States Environmental Protection Agency
Federal Equivalent Method
Federal Implementation Plan
Federal Reference Method
Integrated Science Assessment
National Ambient Air Quality Standards
National Air Pollution Surveillance
National Center for Environmental Assessment
National Emissions Inventory
National Exposure Research Laboratory
National Climatic Data Center
National Core Monitoring Network
Nitrogen oxides
Ozone
Office of Air Quality Planning and Standards
Office of Air and Radiation
Office of Management and Budget
Office of Research and Development
Policy-Relevant Background
Quality assurance
Quality control
Relative risk
Science Advisory Board
Risk and Exposure Assessment
State Emergency Department Databases
State Inpatient Database
Sulfur Dioxide
Sulfur Oxides
Total suspended particulate
Volatile organic compounds
                                         IV

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 1    1   INTRODUCTION
 2           The U.S. Environmental Protection Agency (EPA) is presently conducting a review of
 3    the national ambient air quality standards (NAAQS) for ozone. Sections 108 and 109 of the
 4    Clean Air Act (Act) govern the establishment and periodic review of the NAAQS.  These
 5    standards are established for pollutants that may reasonably be anticipated to endanger public
 6    health and welfare, and whose presence in the ambient air results from numerous or diverse
 7    mobile or stationary sources.  The NAAQS are to be based on air quality criteria, which are to
 8    accurately reflect the latest scientific knowledge useful in indicating the kind and extent of
 9    identifiable effects on public health or welfare that may be expected from the presence of the
10    pollutant in ambient air.  The EPA Administrator is to promulgate and periodically review, at
11    five-year intervals, "primary" (health-based) and "secondary" (welfare-based) NAAQS for such
12    pollutants.1 Based on periodic reviews of the air quality criteria and standards, the Administrator
13    is to make revisions in the criteria and standards, and promulgate any new standards, as may  be
14    appropriate.  The Act also requires that an independent scientific review committee advise the
15    Administrator as part of this NAAQS review process, a function now performed by the Clean Air
16    Scientific Advisory Committee (CASAC).

17           EPA's overall plan and schedule for this ozone NAAQS review are presented in the
18    Integrated Review Plan for the Ozone National Ambient Air Quality Standards Review (U. S.
19    EPA, 201 la). That plan outlines the Clean Air Act (CAA) requirements related to the
20    establishment and reviews of the NAAQS, the process and schedule for conducting the current
21    ozone NAAQS review, and two key components in the NAAQS review process: an Integrated
22    Science Assessment (ISA) and a Risk and Exposure Assessment (REA).  It also lays out the key
23    policy-relevant issues to be addressed in this review as a series of policy-relevant questions that
24    will frame our approach to determining whether the current primary and secondary NAAQS for
25    ozone should be retained or revised.
             Section 109(b)(l) [42 U.S.C. 7409] of the Act 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."

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 1          The ISA prepared by EPA's Office of Research and Development (ORD), National
 2    Center for Environmental Assessment (NCEA), provides a critical assessment of the latest
 3    available policy-relevant scientific information upon which the NAAQS are to be based.  The
 4    ISA will critically evaluate and integrate scientific information on the health and welfare effects
 5    associated with exposure to ozone in the ambient air.  The REA, prepared by EPA's Office of
 6    Air and Radiation (OAR), Office of Air Quality Planning and Standards (OAQPS), will draw
 7    from the information assessed in the ISA.  The REA will include, as appropriate, quantitative
 8    estimates of human and ecological exposures and/or risks associated with recent ambient levels
 9    of ozone, with levels simulated to just meet the current standards, and with levels simulated to
10    just meet possible alternative standards.

11          The REA  will be developed in two parts addressing:  (1) human health risk and exposure
12    assessment and (2) other welfare-related effects assessment. This document describes the scope
13    and methods planned to conduct the human health risk and exposure assessments to support the
14    review of the primary (health-based) ozone NAAQS. A separate document describes the scope
15    and methods planned to conduct quantitative assessments to support the review of the secondary
16    (welfare-based) ozone NAAQS. Preparation of these two planning documents coincides with the
17    development of the first draft ozone ISA (U.S. EPA, 201 Ib) to facilitate the integration of
18    policy-relevant science into all three documents.

19          This planning document is intended to provide enough specificity to facilitate
20    consultation with CAS AC, as well as for public review, in order to obtain advice on the overall
21    scope, approaches, and key issues in advance of the conduct of the risk and exposure analyses
22    and presentation of results in the first draft REA. NCEA has compiled and assessed the latest
23    available policy-relevant science available to produce a first draft of the ISA (U.S. EPA, 201 Ib).
24    The first draft ISA has been reviewed by staff and used in the development of the approaches
25    described below.  This  includes  information on source emissions, atmospheric chemistry, air
26    quality, human exposure, and related health effects. CAS AC consultation on this planning
27    document coincides with its review of the  first draft ISA.  CAS AC and public comments on this
28    document will be taken into consideration in the development of the first draft REA,  the
29    preparation of which will coincide with and draw from the second draft ISA. The second draft
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 1   REA will draw on the final ISA and will reflect consideration of CAS AC and public comments
 2   on the first draft REA.  The final REA will reflect consideration of CAS AC and public
 3   comments on the second draft REA.  The final ISA and final REA will inform the policy
 4   assessment and rulemaking  steps that will lead to a final decision on the ozone NAAQS.

 5          This introductory chapter includes background on the current ozone standards and the
 6   quantitative risk assessment conducted for the last review; the key issues related to designing the
 7   quantitative assessments in this review, building upon the lessons learned in the last review; and
 8   an overview introducing the planned assessments that are described in more detail in later
 9   chapters.  The planned assessments are designed to estimate human exposures and/or health risks
10   that are associated with recent ambient levels, with  ambient levels simulated to just meet the
11   current standards, and with ambient levels simulated to just meet alternative standards that may
12   be considered.  The major components of the assessments (e.g., air quality analyses, quantitative
13   exposure assessment, and quantitative health risk assessments) briefly outlined in the Integrated
14   Review Plan (U.S. EPA, 201 la), are conceptually presented in Figure 1-1, and are described in
15   more detail below in Chapters 2-6.  The schedule  for completing these assessments is presented
16   in Chapter 7.

17   1.1   Background on Last Ozone NAAQS Review
18          As a first step in developing this planning document, we considered the work completed
19   in previous reviews of the primary NAAQS for ozone (U.S. EPA, 201 la, see section 1.3) and in
20   particular the quantitative assessments supporting those reviews.  EPA completed the most
21   recent review of the ozone NAAQS with publication of a decision on March 27, 2008 (73 FR
22   16436). Based on the final CD (U.S. EPA, 2006) published in March of 2006, and on the final
23   Staff Paper (U. S EPA, 2007a) published in July of 2007, the previous EPA Administrator
24   decided to revise the level of the 8-hour primary ozone standard from 0.08 ppm to  0.075 ppm
25   and to revise the secondary to be identical to the primary. As discussed in more detail in the
26   Integrated Review Plan, the current EPA Administrator has decided to reconsider the March 27,
27   2008 decisions on the revisions to the primary and secondary ozone NAAQS.
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 1    1.1.1   Overview of Exposure Assessment for Ozone from Last Review
 2                  The exposure and health risk assessment conducted in the review completed in
 3    March 2008 developed exposure and health risk estimates for  12 urban areas across the U.S.,
 4    which were chosen, based on the location of ozone epidemiological studies and to represent a
 5    range of geographic areas, population demographics, and ozone climatology. That analysis was
 6    in part based upon the exposure and health risk assessments done as part of the review completed
 7    in 1997.l The exposure and risk assessment incorporated air quality data (i.e., 2002 through
 8    2004) and provided annual or ozone season-specific exposure  and risk estimates for these recent
 9    years of air quality and for air quality scenarios simulating just meeting the existing 8-hour
10    ozone standard and several alternative 8-hour ozone standards. Exposure estimates were used as
11    an input to the risk assessment for lung function responses (a health endpoint for which
12    exposure-response functions were available from controlled human exposure studies). Exposure
13    estimates were developed for the general population and population groups including school age
14    children with asthma as well as all school age children. The exposure estimates also provided
15    information on population exposures exceeding potential health effect benchmark levels that
16    were identified based on the observed occurrence of health endpoints not explicitly modeled in
17    the health risk assessment (e.g., lung inflammation, increased airway responsiveness, and
18    decreased resistance to infection) associated with 6-8 hour exposures to ozone in controlled
19    human exposure studies.

20           The exposure analysis took into account several important factors including the
21    magnitude and duration of exposures, frequency of repeated high exposures, and breathing rate
22    of individuals at the time of exposure. Estimates were developed for several indicators of
23    exposure to various levels of ozone air quality, including counts of people exposed one or more
24    times to a given ozone concentration while at a specified breathing rate, and counts of person-
25    occurrences which accumulate occurrences of specific exposure conditions over all people in the
26    population groups of interest over an ozone season.
      1 In the 1994-1997 Ozone NAAQS review, EPA conducted exposure analyses for the general population, children
      who spent more time outdoors, and outdoor workers.  Exposure estimates were generated for 9 urban areas for "as
      is" air quality and for just meeting the existing 1-hour standard and several alternative 8-hour standards. Several
      reports (Johnson et al., 1996a,b,c) that describe these analyses can be found at:
      http://www.epa.gov/ttn/naaqs/standards/ozone/s_o3_pr.html.

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 1          As discussed in the 2007 Staff Paper and in Section Ha of the ozone Final Rule (73 FR
 2    16440 to 16442, March 27, 2008), the most important uncertainties affecting the exposure
 3    estimates were related to modeling human activity patterns over an ozone season, modeling of
 4    variations in ambient concentrations near roadways, and modeling of air exchange rates that
 5    affect the amount of ozone that penetrates indoors.  Another important uncertainty, discussed in
 6    more detail in the Staff Paper (section 4.3.4.7), was the uncertainty in energy expenditure values
 7    which directly affected the modeled breathing rates. These were important since they were used
 8    to classify exposures occurring when children were engaged in moderate or greater exertion and
 9    health effects observed in the controlled human exposure studies generally occurred under these
10    exertion levels for 6 to 8-hour exposures to ozone concentrations at or near 0.08 ppm.  Reports
11    that describe these analyses (U.S. EPA, 2007a,b;  Langstaff, 2007)  can be found at:
12    http ://www. epa.gov/ttn/naaq s/standards/ozone/s_o3_index. html.

13    1.1.2  Overview of Health Risk Assessment for Ozone from Last Review
14                 The human health risk assessment presented in the review completed in March
15    2008 was designed to estimate population risks in a number of urban areas across the U.S.,
16    consistent with the scope of the exposure analysis described above. The risk assessment
17    included risk estimates based on both controlled human exposure studies and epidemiological
18    and field studies. Ozone-related risk estimates for lung function decrements were generated
19    using probabilistic exposure-response relationships based on data from controlled human
20    exposure studies, together with probabilistic exposure estimates from the exposure  analysis. For
21    several other health endpoints, ozone-related risk estimates were generated using concentration-
22    response relationships reported in epidemiological or field studies, together with ambient air
23    quality concentrations, baseline health incidence rates, and population data for the various
24    locations included in the assessment. Health endpoints included in the assessment based on
25    epidemiological or field studies included: hospital admissions for respiratory illness in four urban
26    areas, premature mortality in  12 urban areas, and respiratory symptoms in asthmatic children in 1
27    urban area.

28          In the health risk assessment conducted in the previous review, EPA recognized that there
29    were many sources of uncertainty and variability in the inputs to the assessment and that there
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 1    was a high degree of uncertainty in the resulting risk estimates. The statistical uncertainty
 2    surrounding the estimated ozone coefficients in concentration-response functions as well as the
 3    shape of the exposure-response relationship chosen were addressed quantitatively. Additional
 4    uncertainties were addressed through sensitivity analyses and/or qualitatively.  The risk
 5    assessment conducted for that ozone NAAQS review incorporated some of the variability in key
 6    inputs to the assessment by using location-specific inputs (e.g., location-specific concentration-
 7    response function, baseline incidence rates and population data, and air quality data for
 8    epidemiological-based endpoints, location specific air quality data and exposure estimates  for
 9    the lung function risk assessment). In that review, several urban areas were included in the
10    health risk assessment to provide some sense of the variability in the risk estimates across the
11    U.S.

12           Key observations and insights from the ozone risk assessment, in addition to important
13    caveats and limitations, were addressed in Section II.B of the Final Rule notice (73 FR 16440 to
14    16443, March 27, 2008). In general, estimated risk reductions associated with going from
15    current ozone  levels to just meeting the current and alternative 8-hour standards showed patterns
16    of decreasing estimated risk associated with just meeting the lower alternative 8-hour standards
17    considered.  Furthermore, the estimated percentage reductions in risk were strongly influenced
18    by the baseline air quality year used in  the analysis, which was due to significant year-to-year
19    variability in ozone concentrations. There was also noticeable city-to-city variability in the
20    estimated  ozone-related incidence of morbidity and mortality across the 12 urban areas.
21    Uncertainties associated with estimated policy-relevant background (PRB) concentrations1  were
22    also addressed and revealed differential impacts on the risk estimates depending on the health
23    effect considered as well as the location. EPA also acknowledged that there were considerable
24    uncertainties surrounding estimates of ozone coefficients and the shape for concentration-
25    response relationships and whether or not a population threshold or non-linear relationship  exists
26    within the range of concentrations examined in the epidemiological studies.
      1 For the purposes of the risk and exposure assessments, policy-relevant background (PRB) ozone has been defined
      in previous reviews as the distribution of ozone concentrations that would be observed in the U. S. in the absence of
      anthropogenic (man-made) emissions of ozone precursor emissions (e.g., VOC, CO, NOX) in the U.S., Canada, and
      Mexico.
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 1    1.2   Goals for Framing the Assessments in the Current Review
 2           A critical step in designing the quantitative risk and exposure assessments is to clearly
 3    identify the policy-relevant questions to be addressed by these assessments. As identified above,
 4    the Integrated Review Plan presents a series of key policy questions (U.S. EPA, 201 la, section
 5    3.1). To answer these questions, EPA will integrate information from the ISA and from air
 6    quality, risk, and exposure assessments as we evaluate both evidence-based and risk-based
 7    considerations.

 8           More specifically, to focus the REA, we have identified the following goals for the
 9    exposure and risk assessment: (1) to provide  estimates of the number of people in the general
10    population and in sensitive populations with ozone exposures above benchmark levels; (2) to
11    provide estimates of the number of people in the general population and in sensitive populations
12    with impaired lung function resulting from exposures to ozone;  (3) to provide estimates of the
13    potential magnitude of premature mortality and/or selected morbidity health effects in the
14    population, including sensitive populations, where data are available to assess these subgroups,
15    associated with recent ambient levels of ozone and with just meeting the current suite of ozone
16    standards and any alternative standards that might be considered in selected urban study areas;
17    (4) to develop a better understanding of the influence of various inputs and assumptions on  the
18    risk estimates to more clearly differentiate alternative standards that might be considered
19    including potential  impacts on various sensitive populations; and (5) to gain insights into the
20    distribution of risks and patterns of risk reduction and uncertainties in those risk estimates.  In
21    addition, we are considering conducting an assessment to provide nationwide estimates of the
22    potential magnitude of premature mortality associated with ambient ozone exposures to more
23    broadly characterize this risk on a national scale, to assess  the extent to which we have captured
24    the upper end of the risk distribution, and to support the interpretation of the more detailed risk
25    results  generated for the selected urban study  areas.

26    1.3   Overview of Current Assessment Plan
27           This plan is designed to outline the scope and approaches and highlight key issues in the
28    estimation of population exposures and health risks posed by ozone under existing air quality
29    levels ("as is" exposures and health risks), upon attainment of the current ozone primary
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 1    NAAQS, and upon meeting various alternative standards in selected sample urban areas. This
 2    plan is intended to facilitate consultation with the CAS AC, as well as public review, and to
 3    obtain advice on the overall scope, approaches, and key issues in advance of the completion of
 4    such analyses and presentation of results in the first draft of the ozone Policy Assessment.

 5          The planned ozone exposure analysis and health risk assessment address short-term and
 6    long-term exposures to ozone and associated health effects.  These assessments cover a variety
 7    of health effects for which there is adequate information to develop quantitative risk estimates.
 8    However, there are some health endpoints for which there currently is insufficient information to
 9    develop  quantitative risk estimates.  Staff plans to discuss these additional health endpoints
10    qualitatively in the ozone Policy Assessment.  The risk assessment is intended as a tool that,
11    together with other information on these health endpoints and other health effects evaluated in
12    the ozone ISA and ozone Policy Assessment, can aid the  Administrator in judging whether the
13    current primary standard is requisite to protect public health with an adequate margin of safety,
14    or whether revisions to the standard are appropriate.

15          Staff plans to perform exposure and health risk analyses using the three most recent years
16    of air  quality data available at this time, 2008-2010. The time period to be analyzed will be the
17    ozone season, which in the urban areas to be included in this assessment, varies from April to
18    October to the entire year depending on the region of the  country.

19    1.3.1  Air Quality Assessment
20          Chapter 2  describes assessments planned for the current review of the primary NAAQS
21    for ozone including air quality analyses to be conducted to support quantitative risk and exposure
22    assessments in selected urban study areas as well as to support evidence-based considerations
23    and to place the results of the quantitative assessments into a broader public health perspective.
24    Air quality inputs will include:  (1) recent air quality data for ozone from suitable monitors for
25    each selected urban study area; (2) estimates of background concentrations for each selected
26    urban study area, and (3) simulated air quality that reflects changes in the distribution of ozone
27    air quality estimated to occur when an area just meets the current or alternative ozone standards
28    under consideration.

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 1    1.3.2  Exposure Assessment
 2          Chapter 3 discusses our plan to conduct a quantitative exposure assessment in this
 3    review.  The exposure assessment will build upon the methodology, analyses, and lessons
 4    learned from assessments conducted for other recent NAAQS reviews. EPA plans to model
 5    population exposures to ambient ozone in three or more of the 12 urban areas modeled in the
 6    previous review (Atlanta, Boston, Chicago, Cleveland, Detroit, Houston, Los Angeles, New
 7    York, Philadelphia, Sacramento, St. Louis, and Washington D.C.), as well as a high-elevation
 8    area such as Denver.  The number of areas modeled will depend on the available resources.
 9    These areas were selected to be generally representative of a variety of populations, geographic
10    areas, climates, and different ozone and  co-pollutant levels, and are areas where epidemiologic
11    studies have been conducted that are planned to be used to support the quantitative risk
12    assessment. In addition to providing population exposures for estimation of lung function
13    effects, the exposure modeling will provide a characterization of urban air pollution exposure
14    environments and activities resulting in the highest exposures, differences  in which may partially
15    explain the heterogeneity across urban areas seen in the risks associated with ozone air pollution.

16    1.3.3  Health Risk Assessment
17          The health risk assessment will estimate various health effects associated with ozone
18    exposures for current ozone levels, based on 2008-2010 air quality data, as well as reductions in
19    risk associated with attaining the current 8-hour ozone NAAQS and alternative ozone standards,
20    based on adjusting 2008-2010 air quality data.  Risk estimates will be developed for several
21    urban areas located throughout the U.S., including the areas for which exposure modeling will be
22    performed.  Health endpoints to be examined in the risk assessment include: lung function
23    decrements, respiratory symptoms in asthmatic children,  school absences,  emergency department
24    visits for respiratory causes, respiratory- and cardiac-related hospital admissions, and mortality.

25          At this time, two general types of human studies are particularly relevant for deriving
26    quantitative relationships between ozone levels and human health effects:  (1) controlled human
27    exposure studies and (2) epidemiological and field studies. Controlled human exposure studies
28    involve volunteer subjects who  are exposed while engaged in different exercise regimens to
29    specified levels of ozone under  controlled conditions for  specified amounts of time.  The
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 1    responses measured in such studies have included measures of lung function, such as forced
 2    expiratory volume in one second (FEVi), respiratory symptoms, airway hyper-responsiveness,
 3    and inflammation.  Prior EPA risk assessments for ozone have included risk estimates for lung
 4    function decrements and respiratory symptoms based on analysis of individual data from
 5    controlled human exposure studies.  For the current health risk assessment, staff plans to use the
 6    probabilistic exposure-response relationships which were based on analyses of individual data
 7    that describe the relationship between a measure of personal exposure to ozone and the
 8    measure(s) of lung  function recorded in the study.  The measure of personal exposure to ambient
 9    ozone is typically some function of hourly  exposures - e.g.,  1-hour maximum or 8-hour
10    maximum. Therefore, a risk assessment based on exposure-response relationships derived from
11    controlled human exposure study data requires estimates of personal exposure to ozone, typically
12    on a 1-hour or multi-hour basis.  Because data on personal hourly ozone exposures are not
13    available, estimates of personal exposures to varying ambient concentrations are derived through
14    exposure modeling, as described in Chapter 3.

15          The risk assessment based on controlled human exposure studies is described in
16    Chapter 4. In contrast to the exposure-response relationships derived from controlled human
17    exposure studies, epidemiological and field studies provide estimated concentration-response
18    relationships based on data collected in real world settings. Ambient ozone concentration is
19    typically measured as the average of monitor-specific measurements, using population-oriented
20    monitors. Population health responses for  ozone have included population counts of school
21    absences, emergency room visits, hospital admissions for respiratory and cardiac illness,
22    respiratory symptoms, and premature mortality. As described more fully below in Chapter 5 and
23    outlined in Figure 1-1, a risk assessment based on epidemiological studies typically requires
24    baseline incidence rates and population data for the risk assessment locations.

25          The characteristics that are relevant to the planning and structure of a risk assessment
26    based on controlled human exposure studies versus one based on epidemiology or field studies
27    can be summarized as follows:

28          •   A risk assessment  based on controlled human exposure studies uses exposure-
29              response functions, and thus requires estimates of personal exposures. It therefore
                                                1-10

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 1              involves an exposure modeling step that is not needed in a risk assessment based on
 2              epidemiology or field studies, which uses concentration-response functions.

 3           •   Epidemiological and field studies are carried out in specific real world locations (e.g.,
 4              specific urban areas).  To minimize uncertainty, a risk assessment based on
 5              epidemiological studies can be performed for the locations in which the studies were
 6              carried out.  Controlled human exposure studies, carried out in laboratory settings, are
 7              generally not specific to any particular real world location.  A controlled human
 8              exposure studies-based risk assessment can therefore appropriately be carried out for
 9              any locations for which there are adequate air quality data on which to base the
10              modeling of personal exposures.  There are, therefore, some locations for which a
11              controlled human exposure studies-based risk assessment could appropriately be
12              carried out but an epidemiological studies-based risk assessment could  not, according
13              to our criteria for city selection.

14           •   The adequate modeling of hourly personal exposures associated with ambient
15              concentrations requires more complete ambient monitoring data than are necessary to
16              estimate average ambient concentrations used to calculate risks based on
17              concentration-response relationships. Therefore, there may be some locations in
18              which an epidemiological studies-based risk assessment could appropriately be
19              carried out but a controlled human exposure studies-based risk assessment would
20              have increased uncertainty.

21           •   To derive estimates of risk or risk reduction from concentration-response
22              relationships estimated in epidemiological studies, it is usually necessary to have
23              estimates of the baseline incidences of the health effects involved.  Such baseline
24              incidence estimates are not needed in a controlled human exposure studies-based risk
25              assessment.

26           Overviews of the scope and methods for each type  of risk assessment are discussed  in

27    Chapters 4 and 5 below.
                                                 1-11

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                    Overview of Risk Assessment Model
   Air Quality

        •Recent air quality

        •Air quality simulated to just
        meet current and alternative
        NAAQS

        •Policy relevant background
    Concentration-Response
        •C-R functions derived from
        epidemiological studies for
        various health endpoints
    Baseline Incidence and Demographics
        •Baseline health effects incidence rates
        •Population data
Health
 Risk
Model
Risk Estimates
•Recent air quality
•Current or alternative
NAAQS scenarios
Figure 1-1. Overview of Risk Assessment Based on Epidemiologic Studies
                                        1-12

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 1    2   AIR QUALITY CONSIDERATIONS
 2    2.1   Introduction
 3           A number of air quality analyses are planned to provide inputs for the risk and exposure
 4    assessments that will be conducted for selected urban study areas as well as to provide a broader
 5    understanding of ozone air quality, in order to inform:  (1) evidence-based considerations; (2)
 6    our understanding of the risk and exposure assessment results to better characterize potential
 7    nationwide  public health impacts associated with exposures to ozone; and (3) policy
 8    considerations related to evaluating possible alternative NAAQS. Specific goals for the planned
 9    air quality assessments include:

10       •   Characterizing air quality in various locations across the U.S. in terms of ozone
11           considering differences in ozone ambient concentrations, and spatial and temporal
12           patterns to help inform the selection of specific cities that we plan to include in the risk
13           and  exposure assessments.
14       •   Characterizing background concentrations of ozone based on chemical transport
15           modeling (U.S. EPA, 20lib,  section 3.4).
16       •   Providing air quality distributions for ozone for a number of alternative scenarios in the
17           selected urban study areas including:
18                 o   Recent air quality;
19                 o   Simulation of air quality to just meet the current primary standard;  and
20                 o   Simulation of air quality to just meet potential alternative primary standards
21                     for ozone under consideration.
22       •   Providing a broader characterization of current ozone concentrations nationally (beyond
23           the locations evaluated in the  risk and exposure assessments).
24    2.2   Air Quality Inputs to Risk and Exposure Assessments
25           Important inputs to the ozone risk and exposure assessments are  ambient ozone air
26    quality data. For these assessments,  EPA plans to use 2008-2010 air quality data obtained from
27    EPA's Air Quality System (AQS), as these  are the most recent data available.

28       2.2.1      Recent Air Quality
29           For  ozone, in general, only data collected by Federal reference or equivalent methods
30    (FRMs or FEMs) will be used in the  risk and exposure assessments, consistent with the use of
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 1    such data in most of the health effects studies. However, if an epidemiologic study used non-
 2    FRM/FEM data from ozone monitors in a concentration-response function, consideration will be
 3    given to using the same type of data in the quantitative risk assessment for the same location. In
 4    order to be consistent with the approach generally used in the epidemiological studies that used
 5    estimated ozone concentration-response (C-R) functions for short-term effects, we plan to
 6    average ambient ozone concentrations on each day for which measured data are available for
 7    estimating health effects associated with 24-hour ambient concentrations. If epidemiologic
 8    studies used a composite monitor, then we will consider developing a data set for each
 9    assessment location based on a composite of all monitors according to the method in the
10    epidemiologic study.  As in the last review, some monitoring sites may be omitted, if needed, to
11    best match the set of monitors that were used in the epidemiological studies.

12           In addition to matching our characterization of air quality at each assessment location to
13    the approach from the epidemiological study used in modeling risk, we will also consider
14    alternative approaches for characterizing air quality, if they produce estimates of exposure that
15    are potentially more representative for the populations being assessed (even if they do not match
16    the approach used in the epidemiology studies).  For example,  we may consider the use of
17    monitor data (as described above) fused with photochemical modeling results for ozone. With
18    this approach, we would use the monitor data to characterize absolute ozone  levels across the
19    urban study area (subject to the limitations of the monitoring framework's coverage) with the
20    modeled results used to fill in the spatial pattern or gradient between monitors.  We may also
21    consider alternative approaches for generating composite monitor estimates that do not rely on
22    modeling.  For example, given the potential importance of commuting and workplace exposure in
23    driving overall exposure profiles, we might consider generating composite monitor estimates
24    that weight each monitor by "population exposure" (e.g., the person-hours of exposure
25    associated with the immediate area surrounding a given monitor). With this approach, we would
26    use the results of micro-environmental exposure modeling to estimate the amount of time that a
27    simulated population spends in the vicinity of each monitor (see Section 5.2.2 for additional
28    detail on these alternative approaches being considered for assessing current  exposure).
                                                2-2

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 1          Important factors to consider in deciding how to characterize current ambient ozone
 2    levels include the degree of spatial and temporal heterogeneity in monitored levels seen within a
 3    given assessment location. As part of planning for the analysis, we will consider trends in spatial
 4    and temporal gradients across ozone monitor data in the urban study areas we are considering.

 5    2.2.2  Air Quality Data Related to Exceptional Events
 6          State and local agencies and EPA are in the process of reviewing ozone data for purposes
 7    of making decisions regarding the exclusion of data under the Exceptional Events Rule. We will
 8    include these decisions regarding specific data that should be excluded from consideration when
 9    determining the amount of rollback of air quality to meet the current or alternative ozone
10    standards.

11    2.3   Development of Estimates of Ozone Air Quality Assuming "Just Meeting" Current
12         NAAQS and Potential Alternative NAAQS
13    2.3.1  Background and Conceptual Overview
14          In order to simulate air quality concentrations that "just meet" the current or potential
15    alternative ozone standards in a study area, we consider what mathematical  approach (commonly
16    referred to as rollback) should be used to transform recent air quality into profiles of adjusted air
17    quality that simulate just meeting the current or alternative standards under  consideration.

18          The challenge in developing estimates of ozone air quality for a scenario in which an
19    assessment location is "just meeting" the current standards or alternative standards under
20    consideration is to estimate as realistically as possible how concentrations for all hours at all
21    monitors  will be affected, not just how the design  value from the controlling monitor (or set of
22    monitors  being averaged) will be affected.  The definition of "just meeting" alternative ozone
23    standards uses the same approach as "just meeting" the current standards.

24                 There are many possible ways to create characterizations of air quality to
25    represent scenarios "just meeting" specified ozone standards.  The previous two reviews have
26    used  a method called quadratic rollback, which is  described  below in section 2.3.2. This choice
27    was based on analyses of historical ozone data which found, from comparing the reductions over
28    time  in daily ambient ozone levels in two locations with sufficient ambient air quality data, that
                                                 2-3

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 1    reductions tended to be roughly quadratic (Abt Associates, 2005, Appendix B). We recognize
 2    that the pattern of changes that have occurred in the past may not necessarily reflect the temporal
 3    and spatial patterns of changes that would likely result from future efforts to attain the ozone
 4    standards; therefore, we are considering examining an alternative prospective approach for
 5    rollback, as described in section 2.3.3.

 6    2.3.2   Historical Approach
 7          Prior ozone risk assessments simulated ozone reductions that would result from just
 8    meeting a set of standards using a quadratic adjustment ("quadratic rollback") which decreased
 9    non-background ozone  levels on all hours for all concentrations exceeding the policy-relevant
10    background (PRB).  The portion of the distribution below the estimated PRB concentration was
11    not rolled back, since air quality strategies adopted to meet the  standards would not be expected
12    to reduce the PRB contribution to ozone concentrations. The percentage amount of rollback was
13    just enough so that the standard under consideration was not exceeded.

14          In the risk assessment for this review, we will again evaluate the quadratic rollback
15    approach by comparing it with historical changes in distributions of ozone concentrations in
16    selected locations.  Specifically, EPA plans to evaluate historical ozone air quality changes to
17    assess the implications of using a quadratic rollback approach.  We also plan to consider the
18    premises and outcomes of the quadratic rollback approach against our insights regarding known
19    and likely future emission reductions, e.g., whether it is reasonable to expect that future patterns
20    of changes in ozone air quality would generally be similar to historical patterns of changes in air
21    quality.

22    2.4  Policy Relevant Background
23          A key issue to be addressed in the ozone Policy Assessment is the characterization of
24    policy-relevant background ozone  levels in the U.S. Historically, PRB has been defined as the
25    distribution of ozone concentrations that would be observed in the U.S. in the absence of
26    anthropogenic (man-made) emissions of ozone precursors in the U.S., Canada, and Mexico. This
27    definition allows for analyses that focus on the effects and risks associated with pollutant levels
                                                 2-4

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 1    that have the potential to be controlled by U. S. regulations, through international agreements
 2    with border countries, or by voluntary emissions reductions in the U.S. and elsewhere.

 3          For this assessment, we are planning to estimate concentrations for different background
 4    scenarios using the global-scale chemistry-transport model GEOS-Chem (Bey et al, 2001) to
 5    inform a discussion of how to characterize PRB. The GEOS-Chem model is run on a global
 6    scale and will be used to provide estimates of transported pollutants from emissions of natural
 7    and anthropogenic sources from various geographic areas.  The details of this modeling approach
 8    are briefly summarized below.

 9            The GEOS-Chem modeling system  will be run using emissions and meteorological data
10    for three annual periods (2006, 2007, 2008).  EPA staff is considering how to best use these
11    model results in the exposure and risk analyses, which will be based on 2008 - 2010 air quality.
12    The GEOS-Chem model will be run using two nested grids. The outer grid will be global in
13    extent and utilize a grid resolution of 2.0 by 2.5 degrees. The inner grid will be centered over
14    North America, cover the area from  140-40W / 10-70N,  and use a horizontal resolution of 0.50
15    by 0.67 degrees. Four scenarios will be modeled. First,  a current atmosphere (base case)
16    simulation will be completed using all global  anthropogenic and natural emissions sources. A
17    model performance evaluation will be completed for this scenario using surface air quality
18    measurements and satellite estimates of atmospheric air pollutant concentrations.

19          In addition to the "current atmosphere" or base case run which includes all anthropogenic
20    and biogenic emissions, GEOS-Chem will be run for three additional emissions scenarios to
21    isolate the contributions of internationally transported air pollutants to ozone concentrations in
22    the U.S.:

23          •  A simulation in which U.S. anthropogenic emissions of nitrogen oxides (NOx), non-
24             methane volatile organic  compounds (nMVOC), and carbon monoxide (CO) are set to
25             zero, while anthropogenic emissions outside of the U.S. are maintained at their
26             current levels.
27          •  A simulation in which U.S., Canada,  and Mexico anthropogenic emissions of NOx,
28             nMVOC, and CO are set to zero, while anthropogenic emissions outside of these
29             areas are maintained at their current levels.  This was referred to as policy relevant
30             background (PRB) in the previous review of the ozone NAAQS.
                                                2-5

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 1          •   A simulation in which global anthropogenic emissions of NOx, nMVOC, and CO are
 2              set to zero.
 3          These simulations will allow us to quantify the contribution of U.S. anthropogenic,
 4    Canada and Mexico anthropogenic, international (excluding Canada and Mexico) anthropogenic,
 5    and natural ozone sources to U.S. ozone health risk individually. Since emissions of methane are
 6    at current levels for all of these simulations, we will consider differentiating the contribution of
 7    global methane emissions from natural sources using recently published modeling studies that
 8    examine the effect of perturbations in the methane mixing ratio on global and U.S. air quality
 9    (Fiore et al, 2008, 2009).

10          A growing body of observational and modeling studies suggests that the international
11    anthropogenic contribution to U.S. background ozone levels is substantial and is expected to rise
12    in the future as rapid economic development continues around the world.  Of particular concern
13    is rising Asian emissions of nitrogen oxides (NOx), which can influence U.S. ozone
14    concentrations in the near-term, and methane, which affects background ozone concentrations
15    globally over decadal time scales.  The model simulations of current anthropogenic emissions
16    described above will not allow for projections of future ozone background concentrations nor the
17    contribution from specific global methane sources on  ozone in the present. However, a large
18    multi-model ensemble assessment convened by the Task Force on Hemispheric Transport of Air
19    Pollution (TF HTAP) has produced estimates that may be informative for estimating future
20    global background concentrations and concentrations transported from upwind regions.  In
21    particular, the HTAP 2010 Assessment Report1 estimated that the contribution of NOx, non-
22    methane VOC, and CO emissions in Europe, South Asia, and East Asia to North American
23    ozone concentrations at relatively unpolluted sites is 32% of the contribution of emissions from
24    all four regions (including North America) combined. That contribution is projected to rise to
25    49%  in a conservative emissions growth scenario and  to 52% in a scenario of aggressive global
26    economic development.  The report also concluded that approximately 40% of the mean global
27    ozone increase since the preindustrial era is due to methane, and that rising global methane
28    emissions will have a large influence on future U.S. ozone concentrations.  These results may be
       Available at http://www.htap.org/

                                                2-6

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 1   used to inform estimates of growth of international transport in the future and how those changes
 2   might affect our estimation of future ozone health risks.

 3   2.5   Broader Air Quality Characterization
 4          Information presented in the REA will draw upon air quality data analyzed in the ISA as
 5   well as national and regional trends in air quality as evaluated in EPA's Air Quality Status and
 6   Trends document (U.S. EPA, 2008a), and EPA's Report on the Environment (U.S. EPA, 2008b).
 7   We plan to use this information, and additional analyses, as needed, to develop a broad
 8   characterization of current air quality across the nation. For example, tables of areas and
 9   population in the U.S. exceeding current ozone standards and potential alternative standards will
10   be prepared.  Additional information will be generated on the expected number of days on which
11   the ozone standards are exceeded, adjusting for the number of days monitored. Further, ozone
12   levels in locations and time periods relevant to areas assessed in key short-term epidemiological
13   studies discussed further in Section 5.3.2 will be characterized.  Information on the spatial and
14   temporal characterization of ozone across the national monitoring network will be compiled. To
15   the extent possible, we plan to compare these data to the same parameters in the selected urban
16   study areas considered in the quantitative risk assessment to help place the  results of that
17   assessment into a broader context.
                                                2-7

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 1    3   APPROACH FOR POPULATION EXPOSURE ANALYSIS
 2    3.1   Introduction
 3          Population exposure to ambient ozone levels will be evaluated using the current version
 4    of the Air Pollutants Exposure (APEX) model, a model based on the current state of knowledge
 5    of inhalation exposure modeling. Exposure estimates will be developed for current ozone levels,
 6    based on 2008-2010 air quality data, and for ozone levels associated with just meeting the
 7    current 8-hour ozone NAAQS and alternative ozone standards, based on adjusting 2008-2010 air
 8    quality data.  Exposure estimates will be  modeled for 3 to 12 urban areas located throughout the
 9    U.S. for 1) the general population, 2) school-age children (ages 5 to 18),  3) asthmatic school-age
10    children, 4) outdoor workers,  and 5) the elderly population (aged 70 and older). This choice of
11    population groups includes a strong emphasis on children, which reflects the results of the last
12    review in which children, especially those who are active outdoors, were identified as the most
13    important at-risk group.

14          The exposure estimates will be used as an input to that part of the health risk assessment
15    that is based on exposure-response relationships derived from controlled human exposure
16    studies, discussed in Section 4.3 below. The  exposure analysis will also provide information on
17    population exposure exceeding levels of concern that are identified based on evaluation of health
18    effects that are not included in the quantitative risk assessment. It will also provide a
19    characterization of populations with high exposures in terms of exposure environments and
20    activities.

21    3.2   The APEX Population Exposure Model
22          APEX, also referred to as the Total Risk Integrated Methodology/Exposure (TRIM.Expo)
23    model (U.S. EPA, 2008c,d), has its origins in the NAAQS Exposure Model (NEM), which was
24    developed  in the early 1980's (Biller et al, 1981; McCurdy, 1994, 1995). APEX simulates the
25    movement of individuals through time and space and their exposure to a given pollutant in
26    indoor, outdoor, and in-vehicle microenvironments. Figure 3-1 provides a schematic overview
27    of the APEX model. The model stochastically generates simulated individuals using census-
28    derived probability distributions for demographic characteristics (Figure 3-1, steps 1-3). The
                                                3-1

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 1   population demographics are from the 2000 Census data at the tract or block level, and a national
 2   commuting database based on 2000 Census data provides home-to-work commuting flows
 3   between tracts. A large number of simulated individuals are modeled, and collectively, they
 4   represent a random sample of the study area population.

 5          Diary-derived time activity data are used to construct a sequence of activity events for
 6   each simulated individual  consistent with the individual's demographic characteristics and
 7   accounting for effects of day type (e.g., weekday, weekend) and outdoor temperature on daily
 8   activities (Figure 3-1, step 4). APEX calculates the concentration in the microenvironment
 9   associated with each event in an individual's activity pattern and sums the event-specific
10   exposures within each hour to obtain a continuous time series of hourly exposures spanning the
11   time period of interest (Figure 3-1, steps 5  and 6).  From these exposure estimates, APEX
12   calculates exposures for averaging times greater than one hour.
                                                3-2

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                                               Figure 3-1.  Overview of the APEX Model
       1. Characterize study area
                2. Characterize study population
                                    3. Generate N number of
                                 simulated individuals (profiles)
               2000 Census tract-level data for the entire U.S. (sectors=tracts for the NAAQS ozone exposure application)
         Sector location
              data
       (latitude, longitude)
Sector population data
  (age/gender/race)
  Commuting flow data
(origin/destination sectors)
  Defined study area (sectors
  within a city radius and with air
  quality and meteorological data
  within their radii of influence)


^/



' x
^
j>
r
Population within
the study area


                                                                                   Age/gender/tract-specific
                                                                                   employment probabilities
    Locations of air quality and
   meteorological measurements;
         radii of influence
(        0
- National
 database

- Simulation
 step

Age/gender-specific
physiological
distribution data (body
weight, height, etc)

Distribution functions for
profile variables
(e.g, probability of air
conditioning)





Disl
for
varj
(e.g
spec
- Area-specific
  input data

- Data processor
                                                                   Stochastic
                                                                profile generator
                                                     Distribution functions
                                                     for seasonal and daily
                                                     varying profile variables
                                                     (e.g., window status, car
                                                   - Intermediate step
                                                     or data

                                                   - Output data
                                                   A simulated individual with the
                                                   following profile:
                                                   • Home sector
                                                   • Work sector (if employed)
                                                   •Age
                                                   • Gender
                                                   • Race
                                                   • Employment status
                                                   • Home gas stove
                                                   • Home gas pilot
                                                   • Home air conditioner
                                                   • Car air conditioner
                                                   • Physiological parameters
                                                     (height, weight, etc.)

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                                      Figure 3-1.  Overview of the APEX Model, continued
                                              4. Construct sequence of activity events
                                                   for each simulated individual
                             Diary events/activities
                            and personal information
                              (e.g., from CHAD)
                            Activity diary pools by day
                            type/temperature category
Profile for an
 individual
Stochastic diary
 selector using
age, gender, and
  employment
                                    Selected diary records for each day in the simulation
                                    period, resulting in a sequence of events
                                    (microenvironments visited, minutes spent, and
                                    activity) in the simulation period, for an individual
                          Each day in the simulation
                          period is assigned to an
                          activity pool based on day type
                          and temperature category
                              Maximum/mean daily
                                temperature data
          Stochastic
     calculation of energy
expended per event (adjusted for
 hysiological limits and EPOC)
        and ventilation
            rates
                                         Physiological
                                        parameters from
                                            profile
                                                                        Sequence of events for an
                                                                              individual
                                                                 5-4

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                                         Figure 3-1.  Overview of the APEX Model, concluded
                    5. Calculate concentrations in
                microenvironments for all events for
                      each simulated individual
                                              6. Calculate hourly
                                               exposures for each
                                              simulated individual
                                            7. Calculate
                                       population exposure
                                              statistics
 Microenvironments defined by
grouping of CHAD location codes
                          Select calculation method for
                          each microenvironment:
                          • Factors
                          • Mass balance
Hourly air quality
data for all sectors
           Calculate
      concentrations in all
      microenvironments
                                                                    I
                                            Average exposures
                                            for simulated person,
                                            stratified by
                                            ventilation rate:
                                            • Hourly
                                            •Daily 1-hour max
                                            • Daily 8-hourrjia*	'
                                              Daily.
                                       Population exposure
                                       indicators for:
                                       • Total population
                                       • Children
                                       • Asthmatic children
Hourly concentrations and
minutes spent in each
 microenvironment visited by
the simulated individual
                           Concentrations for all events
                           for each simulated individual
   Sequence of events for
 each simulated individual
                                                 Calculate hourly
                                                 concentrations in
                                                microenvironments
                                                     visited
                                                                   5-5

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 1          APEX employs a flexible approach for simulating microenvironmental concentrations,
 2   where the user can define any number of microenvironments to be modeled and their
 3   characteristics. For this modeling application, we propose modeling the microenvironments
 4   listed in Table 3-1.

 5       	Table 3-1.  Microenvironments to be Modeled	
          Microenvironment                                       Method
          Indoors - residences                                      mass balance
          Indoors - restaurants                                      mass balance
          Indoors - schools                                         mass balance
          Indoors - offices                                          mass balance
          Indoors - shopping                                        mass balance or factors
          Indoors - other                                           mass balance or factors
          Outdoors - public garages and parking lots                  factors
          Outdoors - near road (walking, bicycling)                  factors
          Outdoors - other (e.g., playgrounds, parks)                  factors
          In vehicle - cars and light trucks                           mass balance or factors
          In vehicle - heavy trucks                                  mass balance or factors
          In vehicle - school buses                                  mass balance or factors
          In vehicle - mass  transit vehicles - buses and trolleys        factors
          In vehicle - mass  transit vehicles - underground (subways)   factors

 6          We plan to calculate the concentrations in each microenvironment using either a factors
 7   or mass-balance approach1, depending upon data availability, with probability distributions
 8   representing variability of the parameters that enter into the calculations (e.g., indoor-outdoor air
 9   exchange rates) supplied as inputs to the model.  These distributions represent the variability (not
10   uncertainty) of parameters, and can vary spatially and can be set up to depend on the values of
11   other variables in the model.  For example, the distribution of air exchange rates in a home,
      1 The factors and mass-balance approaches are described in section 3.6.5.

                                                 3-6

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 1    office, or car depends on the ambient temperature and the type of heating and air conditioning
 2    present.  The value of a stochastic parameter can be kept constant for an individual for the entire
 3    simulation (e.g., house volume), or a new value can be drawn hourly, daily, or seasonally from
 4    specified distributions.  APEX also allows the specification of diurnal, weekly, and seasonal
 5    patterns  for microenvironmental parameters.

 6    3.3   Populations Modeled
 7          A detailed consideration of the population residing in each modeled area will be included.
 8    The exposure assessment will include the general population residing in each area modeled as
 9    well as susceptible and vulnerable populations as identified in the ISA. The population groups
10    that we plan to include in the exposure assessment are:
11       •  The general population
12       •  School-age children (ages 5 to 18)
13       •  Asthmatic school-age children
14       •  Outdoor workers
15       •  The elderly population (aged 70 and older)
16          Due to the increased amount of time spent outdoors engaged in relatively high levels of
17    physical activity, school-age children as a group are particularly at risk for experiencing ozone-
18    related health effects as a result of to their increased dose rates.  The proportion of the population
19    of school-age children characterized as being asthmatic will be estimated by statistics on asthma
20    prevalence rates from the National Health Interview Survey (CDC, 2010) and other sources.

21    3.4   Outcomes to be Generated
22          There are several useful indicators of exposure of people to various levels of air
23    pollution. Factors that are important in defining such indicators  include the magnitude and
24    duration of exposures, frequency of repeated high exposures, and ventilation rate (i.e., breathing
25    rate) of the individual at the time of exposure. In this analysis, exposure indicators will include
26    daily maximum 1- and 8-hour average ozone exposures, stratified by equivalent ventilation rates
27    (i.e., ventilation normalized by body surface area).

28          APEX calculates two general types of exposure  estimates:  counts of people and person-
29    occurrences.  The former counts the number of individuals exposed one or more times per ozone
                                                 3-7

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 1    season to the exposure indicator (e.g., exposure level and ventilation rate) of interest. In the case
 2    where the exposure indicator is a benchmark concentration level, the model estimates the number
 3    of people who experience that level of air pollution, or higher, at least once during the modeled
 4    period. The person-occurrences measure counts the number of times per ozone season that an
 5    individual is exposed to the exposure indicator of interest and then accumulates counts over all
 6    individuals. Therefore, the person-occurrences measure conflates people and occurrences: using
 7    this measure, 1 occurrence for 10 people is counted the same as 10 occurrences for 1 person.

 8          Analyses of the APEX results will provide distributions of the numbers of people with 8-
 9    hour average exposure above benchmark levels of 0.06, 0.07, and 0.08 ppm-8 hours,
10    distributions of the numbers  of people with lung function decrements above 10, 15, and 20
11    percent decreases in FEVi, and characterization of the attributes of highly exposed individuals.

12    3.5   Selection of Urban Areas and Time Periods
13          EPA plans to model population exposures to ambient ozone in three or more of the 12
14    urban  areas modeled in the previous review (Atlanta, Boston, Chicago, Cleveland, Detroit,
15    Houston, Los Angeles, New York City, Philadelphia, Sacramento, Seattle, St. Louis,
16    Washington, D.C.) and a high-altitude city, such as Denver. These were selected to be generally
17    representative of a variety of populations, geographic areas, climates, and different ozone and co-
18    pollutant levels, and are areas where epidemiologic studies have been conducted that are planned
19    to be used to support the quantitative risk assessment.

20          The exposure periods to be modeled will be the ozone-monitoring seasons for each urban
21    area.  These encompass the periods when high ambient ozone levels are likely to occur, and are
22    the periods for which routine hourly ozone monitoring data are available.  The ozone seasons for
23    the selected study areas generally range from April through either September or October for most
24    of the  locations in the eastern U.S. to all year in locations in southern California and Texas.

25    3.6   Development of Model Inputs
26          In this section, we describe the plan for developing the inputs to the APEX model.

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 1    3.6.1  Population Demographics
 2          We plan to use tract-level population counts from the 2000 Census of Population and
 3    Housing Summary File  I1. Summary File 1 (SF 1) contains the 100-percent data, which is the
 4    information compiled from the questions asked of all people and about every housing unit.

 5          In the 2000 U.S. Census, estimates of employment were developed by census tract2.  The
 6    file input to APEX will be broken down by gender and age group, so that each gender/age group
 7    combination is given an employment probability fraction (ranging from zero to 1) within each
 8    census tract. The age groupings in this file are: 16-19, 20-21, 22-24, 25-29, 30-34, 35-44, 45-54,
 9    55-59, 60-61, 62-64, 65-69, 70-74, and greater than 75 years of age. Children under 16 years of
10    age will be assumed to be not employed.

11    3.6.2  Commuting
12          As part of the population demographics inputs, it is important to integrate working
13    patterns into the assessment. In addition to using estimates of employment by tract, APEX also
14    incorporates home-to-work commuting data. We plan to use the national commuting database
15    provided with APEX in this analysis. Commuting data were derived from the 2000 Census and
16    were collected  as part of the Census Transportation Planning Package  (CTPP) (U.S. DOT,
17    2000)3. The data used to generate APEX inputs were taken from the "Part 3-The Journey To
18    Work" files. These files contain counts of individuals commuting from home to work locations
19    at a number of geographic scales. These data have been processed to calculate fractions for each
20    tract-to-tract flow to create the national commuting data distributed with APEX. This database
21    contains commuting data for each of the 50 states and Washington, D.C.  This data set does not
22    differentiate people that work at home from those that commute within their home tract.

23    3.6.3  Ambient Ozone Concentrations
24          We plan to conduct exposure modeling based on ozone concentrations measured at
25    ambient air monitors in and near the areas being modeled.  Sources for these data include the
      1 http://www.census.gov/prod/cen2000/doc/sfl .pdf
      2 Employment data from the 2000 Census can be found on the U.S. Census web site:
      http://www.census.gov/population/www/cen2000/phc-t28.html (Employment Status: 2000- Supplemental Tables).
      3 These data are available from the U.S. DOT Bureau of Transportation Statistics (BTS) at the web site:
      http://transtats.bts.gov/.

                                                3-9

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 1    hourly concentration measurements from the monitoring data maintained in EPA's Air Quality
 2    System (AQS).

 3    3.6.4  Meteorological Data
 4          Surface meteorological observations will be obtained from the National Climatic Data
 5    Center1 to provide hourly temperatures for input to APEX. We plan to use all meteorological
 6    stations within and nearby each selected urban study area.

 7    3.6.5  Specification of Microenvironments
 8          Parameters defining each microenvironment will be specified by distributions which
 9    reflect the variability of these parameters.  The parameters needed depend on whether a
10    microenvironment is modeled using the factors model or the mass balance model.

1 1          We plan to use the factors model to model simple environments, like outdoor areas, that
12    do not contain pollutant sources, or microenvironments for which data are not available to use
13    the mass-balance model.  Two parameters  affect the pollutant concentration calculation in the
14    factors method, the proximity and infiltration factors. The proximity factor (FPR) is a unitless
15    parameter that represents the relationship of the ambient concentration outside of the
16    microenvironment (Co) to the concentration at a monitoring station (d) by the equation Co =
17    FPR CA.  The infiltration factor (Finf) is a unitless parameter that represents the equilibrium
18    fraction of pollutant entering a microenvironment from outside the microenvironment.  The
19    concentration inside the microenvironment (C/) is estimated by the equation C/ = FinfCo-  The
20    infiltration factor in the factors model is often expressed as:

71                                           F     Ptl
21
                                              n
                                                  a + k
22    where P is a penetration coefficient, a is an air exchange rate, and k is a loss rate. APEX draws
23    values of these parameters from microenvironment-specific distributions specified by the user, to
24    model the stochastic nature of these factors.

25          The mass balance model is more appropriate for complex environments. The mass
26    balance method assumes that an enclosed microenvironment (e.g., a room in a residence) is a
       See http://www.ncdc.noaa.gov/oa/ncdc.html

                                                3-10

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 1
 2
 3
 4
 5
 6
 7
12
13
14
15
single well-mixed volume in which the air concentration is approximately spatially uniform.
APEX estimates the concentration of an air pollutant in such a microenvironment by using the
following four processes (as illustrated in Figure 3-2):
•  Inflow of air into the microenvironment;
•  Outflow of air from the microenvironment;
•  Removal of a pollutant from the microenvironment due to deposition, filtration, and chemical
   degradation; and
•  Emissions from sources of a pollutant inside the microenvironment.
             Microenvironment
                 Air
                outflow
                                                             Air
                                                            inflow
                   Indoor sources
                                             V
                                          Removal due to:
                                          •Chemical reactions
                                          •Deposition
                                          •Filtration
 9   Figure 3-2. The Mass Balance Model
10          Considering the microenvironment as a distinct, well-mixed volume of air, the mass
11   balance relationship for a pollutant can be described by:
             dC(t} _dC.m(t}  dCout(t}
dt
where:



dt

C(t)
dC.m(f)

dt dt

= Concentration in

rf#

the microenvironment at time



t (ug/m3)

— T? atŁ» r\f* r»VianrrŁ» in f~Yt^ rliiŁ» tr\ Qir ŁmtŁ»rinrr tVt<^ mir»rr\
                                               5-11

-------
 1                  —ouAj_   =  Rate Of change m QJ) due to air leaving the micro
                       dt
 2                  —ioss\_J_   —  j^aje of cnange m C(t) due to all removal processes
                       dt
 3                  —sowce \ >> _  j^e Qf change in C(t) due to all source terms
                        dt
 4           In addition to proximity factors, this method supports parameter distributions for time
 5    varying emissions sources, decay rate,  air exchange rate, volume, and removal rate. We plan to
 6    estimate the distributions of these microenvironment-specific  parameters based on available data
 7    and a review of the literature.

 8    3.6.6   Indoor Sources
 9           We are considering modeling indoor sources of ozone in this analysis, although our focus
10    is on exposure to ozone of ambient origin. Indoor sources of ozone would not be subject to
11    "rollback."

12    3.6.7   Activity Patterns
13           Exposure models use human activity pattern data to predict and estimate exposure to
14    pollutants.  Different human activities,  such as outdoor exercise, indoor reading, or driving, have
15    different pollutant exposure characteristics. In addition, different human activities require
16    different metabolic rates, and higher rates lead to higher doses. To accurately model individuals
17    and their exposure to pollutants, it is critical to have a firm understanding of their daily activities.

18           The Consolidated Human Activity Database (CHAD) provides data on human activities
19    through a database system of collected human diaries, or daily activity logs (McCurdy et al,
20    2000; U.S. EPA, 2002; Graham and McCurdy, 2004). The purpose of CHAD is to provide a
21    basis for conducting multi-route, multi-media exposure assessments (McCurdy et al., 2000). The
22    data contained within CHAD come from multiple surveys with varied structures (Table 3-2).  In
23    general, the surveys have a data foundation based on daily diaries of human activity. Individuals
24    filled out diaries of their daily activities and this information was entered and stored in CHAD.
25    Relevant data for these individuals, such  as age, are included as well. In addition, CHAD
26    contains activity-specific metabolic distributions developed from literature-derived data, which
27    are used to provide an estimate of metabolic rates of respondents through their various activities.

                                                3-12

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1          The locations used in the CHAD diaries must be assigned appropriately to the APEX
2   microenvironments listed in Table 3-1. Each of the microenvironments is designed to simulate
3   an environment in which people spend time during the day. There are many more CHAD
4   locations than microenvironments being modeled (there are over 100 CHAD locations and 14
5   proposed microenvironments modeled in this assessment) thus, most of the microenvironments
6   have multiple CHAD locations mapped to them.
                                             5-13

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1   Table 3-2. Studies In CHAD To Be Used For Exposure Modeling
Study name
Baltimore
California Adults
(CARB)
California Children
(CARB)
California
Adolescents (CARB)
Cincinnati (EPRI)
Denver (EPA)
Los Angeles:
Elementary School
Los Angeles: High
School
NHAPS2-Air
NHAPS-Water
Geographic
coverage
One building
in Baltimore
California
California
California
Cincinnati
metro, area
Denver
metro, area
Los Angeles
Los Angeles
National
National
Study time
period
1/1997-2/1997,
7/1998-8/1998
10/1987-9/1988
04/1989-2/1990
10/1987-9/1988
3/1985, 8/1985
11/1982-2/1983
10/1989
10/1990
9/1992-9/1994
9/1992-9/1994
Subject
ages
72-93
18-94
<1-11
12-17

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Study name
PSID CDS3 1
PSID CDS II
Seattle
RTI Ozone Averting
Behavior
RTP Panel
RTI NSAS
Washington, D.C.
Totals
Geographic
coverage
National
National
Seattle, WA
National
Chapel Hill,
Raleigh, NC
8 cities4
Wash., D.C.
metro, area

Study time
period
3/1997-6/1997,
9/1997-12/1997
10/2002-6/2003
12/2000-5/2001
7/2002-9/2002
6/2000-5/2001
6/2009-9/2009
11/1982-2/1983

Subject
ages
<1-13
5-19
6-91
2-12
55-85
35-92
18-98

Diary-
days
4,989
4,774
1,688
2,882
1,000
4,383
699
35,916
Number of
subjects
2,706
2,505
178
773
37
1,194
699
21,096
Diary type and
study design
Diary; Random
Diary; Random
Diary
Recall; Random
Diary
Recall; Random
Diary; Random

Reference
Hofferthetal. (1999)
Mainieri et al. (2004)
Liu et al. (2003)
Mansfield and Corey
(2003), Mansfield et al.
(2004; 2006)
Williams et al. (2003a,b)
Knowledge Networks
(2009)
Hartwell et al. (1984),
Aklandetal. (1985)

1
2
3
4
NOTE:  The counts in this table refer to subsets of the studies in CHAD for which data are suitable for use in APEX.
2 National Human Activity Pattern Survey. http://www.exposurescience.org/NHAPS
3 The Panel Study of Income Dynamics, Child Development Supplement, http://psidonline.isr.umich.edu/
4 Atlanta, Chicago, Dallas, Houston, Philadelphia, Sacramento/San Joaquin, St. Louis, Washington D.C.
                                                                    3-15

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 1    3.7   Exposure Modeling Issues

 2          In this section, we highlight some aspects of the proposed exposure modeling that have
 3    the potential to significantly contribute to uncertainties in the exposure analysis.  These aspects
 4    of people's exposures are either not modeled or are based on limited information.

 5          •   Representativeness of Personal Activity Patterns
 6          The human activity data will be drawn from the CHAD developed and maintained by the
 7          Office of Research and Development's (ORD) National Exposure Research Laboratory
 8          (NERL). The CHAD includes data from several surveys covering specific time periods
 9          at city, state, and national levels, with varying degrees of representativeness.  The extent
10          to which the human activity database provides a balanced representation of the
11          population being modeled varies across areas. Although the algorithm that constructs
12          activity sequences attempts to account for the effects of population demographics and
13          local climate on activity, this adjustment procedure does not fully account for all intercity
14          differences in people's activities.  Activity patterns are affected by many local factors,
15          including topography, land use, traffic patterns, mass transit systems, and recreational
16          opportunities.  If time and resources permit, to improve the representativeness of the
17          activity patterns, diaries from the American Time Use Survey (Bureau of Labor Statistics,
18          2010; Tudor-Locke et al, 2009, 2010) will be included in the  activity pattern database
19          used by APEX.  The American Time Use Survey (ATUS) provides nationally
20          representative estimates of how and where  Americans spend their time. The ATUS data
21          files include information collected from over 98,000 interviews conducted from 2003 to
22          2009.  The current CHAD database has about 40,000 diaries that APEX can use (Table
23          3-2), so this has the potential to be a significant improvement. The ATUS data are
24          collected through telephone interviews asking about the previous day's activities, and
25          measure the amounts of time people that spend doing various  activities, such as work,
26          childcare, housework, watching television,  exercising, and socializing.

27          •   Longitudinal Personal Activity Patterns
28          In the previous review, it was found that APEX significantly underestimates the
29          frequency of occurrence of individuals experiencing repeated  8-hour average exposures
30          greater than 0.06, 0.07, and 0.08 ppm (Langstaff, 2007). The  assignment of activity
31          diaries to individuals is the primary determinant of the frequency of repeated exposures
32          for individuals.  This is an important consideration, since multiple exposures pose a
33          greater health concern than single exposures. The current methodology for the
34          construction of a year-long activity sequence for each individual does increase the
                                                5-16

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 1           similarity of daily activities for a given simulated individual in terms of the time spent
 2           outdoors, compared to a random assignment of diaries from CHAD to modeled
 3           individuals (Glen et al, 2008). However, repeated routine behavior from one weekday to
 4           the next is not simulated. For example, there are no simulated individuals representing
 5           children in summer camps who spend a large portion of their time outdoors, or adults
 6           with repeating weekday schedules. Improvement of the current approach for creating
 7           year-long activity sequences will be undertaken if sufficient resources are available. We
 8           believe an appropriate approach should adequately account for the day-to-day and week-
 9           to-week repetition of activities common to individuals while maintaining realistic
10           variability between individuals.

11           •   Averting Behavior
12           Behavior changes in response to ozone pollution or in response to air quality index (AQI)
13           notification ("averting behavior") can affect the population distribution of exposures, and
14           was not modeled in the previous review.  Eiswerth et al. (2005) find that increased ozone
15           levels appear to influence the amount of time that asthmatic adults spend in different
16           activities. In a national survey, Mansfield and Corey (2003) find a significant fraction of
17           the people surveyed modifying their activities in response to ozone alerts. Significant
18           research on averting  behavior has been conducted since the last review (Di Novi, 2010;
19           Neidell, 2010, 2005a, 2005b; Neidell et al., 2010; Semenza, 2008; Wen et al.,  2009). A
20           methodology for accounting for averting behavior will be developed for this assessment
21           if sufficient resources are available.

22           •   Modeling Near-Traffic Outdoor Environments and Public Transportation
23           Modeling activities such as walking next to roads, waiting at bus stops, bicycling, and
24           riding motorcycles, buses, subways and trains is difficult due to the limited information
25           available about these activities. It is also difficult to estimate the ambient concentrations
26           in these environments. Ozone concentrations in these environments are typically lower
27           than measurements at centrally located monitors as a result of the titration of ozone by
28           the NO emissions of the vehicles. A number of near-road monitoring studies have been
29           conducted since the last review. An analysis of monitoring data to improve estimates of
30           exposures near-roadways will be undertaken if sufficient resources are available.

31           •   Metabolic equivalent (MET) distributions for activities
32           The distributions of activity-specific MET values are of fundamental importance to the
33           physiological model  in APEX  and therefore to the estimates of lung function decrements.
34           Johnson (2003, section 9.6) states:
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 1               Perhaps the weakest  link in the algorithm is the  step which requires the
 2               analyst to provide a distribution of possible MET  values  for each activity
 3               code.  These distributions are currently based on distributions provided by the
 4               developers of CHAD  (McCurdy et  al, 2000). Because available data were
 5               often insufficient to accurately define a distribution for each activity code, the
 6               developers tended to  follow a conservative approach and over-estimate the
 7               variability of each distribution. Consequently, the Ve values produced by the
 8               ventilation rate algorithm may exhibit an excessive degree of variability.
 9           McCurdy et al. (2000), in a paper describing the development of the MET distributions in
10           CHAD, state:
11               At this stage of development, the METs distribution assignment effort  should
12               be viewed as being preliminary in  nature.  More work is needed to better
13               relate activity codes used in human activity pattern surveys to those long used
14               by exercise physiologists and clinical nutritionists.
15           Staff will review the recent literature related to MET distributions and update the
16           distributions used by APEX, if sufficient resources are available.
17    3.8   Uncertainty and Variability
18           The primary difficulty in performing an exposure modeling uncertainty analysis is the
19    quantitative characterization of the uncertainties  of the  model inputs and model formulation.
20    Information about the variability of model inputs or the variability and uncertainty combined is
21    often available, but it is usually difficult to estimate the uncertainty separately from the
22    variability.  In considering the use of APEX for an ozone exposure assessment, EPA has
23    considered the availability of information to provide plausible distributions or ranges for the
24    uncertainties of all of the  model inputs.  EPA plans to build upon the APEX exposure modeling
25    uncertainty analysis conducted in support of the previous review of the ozone NAAQS
26    (Langstaff,  2007).  We plan to improve on these  distributions of variability and uncertainty,
27    where data  are available to do so, and to extend the analysis of model formulation uncertainty.

28           Once estimates of the uncertainty of the model inputs  have been developed, we plan to
29    propagate these uncertainties through the model to quantify the resultant uncertainty of the
30    model predictions. The APEX uncertainty analysis methodology incorporates a 2-dimensional
31    Monte Carlo sampling approach that explicitly characterizes and models the variability and
32    uncertainty in inputs and outputs.  Essentially, this approach entails performing thousands of
                                                 5-18

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 1    model runs with model inputs randomly sampled from specified distributions reflecting
 2    uncertainty of the model inputs, while each single APEX run simulates distributions of
 3    variability.  This 2-dimensional Monte Carlo method allows for the separate characterization of
 4    the variability and uncertainty in the model results (Morgan and Henrion, 1990; Cullen and Frey,
 5    1999).  This approach allows for great flexibility in specifying uncertainty distributions for any
 6    of the model inputs and parameters that are supplied to APEX by input files.  Furthermore, this
 7    allows us to specify conditional distributions and joint distributions between parameters for
 8    which we have data, which can be critically important in modeling uncertainty (Haas,  1997;
 9    Haas, 1999; Wu and Tsang, 2004).

10          Uncertainties are inherent in modeled representations of physical reality due to
11    simplifying assumptions and other aspects of model formulation. The methods for assessing
12    input parameter uncertainty and model formulation or structure uncertainty are different.  It is
13    difficult to incorporate the uncertainties due to the model formulation into a quantitative
14    assessment of uncertainty in a straightforward manner.  The preferred way to assess model
15    formulation uncertainty is by comparing model predictions with measured values, while having
16    fairly complete knowledge of the uncertainty due to input parameters. EPA plans to ascertain
17    whether sufficient data are available to perform such an evaluation. For example, we will
18    consider using the data collected in the Detroit Study (DEARS *) for this purpose. In the absence
19    of measurements that can be used to estimate model uncertainty, our planned approach to
20    assessing model formulation uncertainty will be to partition this uncertainty into that of the
21    components, or sub-models, of APEX.  For each of the sub-models within APEX, we plan to
22    discuss the simplifying assumptions and those uncertainties associated with the sub-models
23    which are distinct from the input data uncertainties.  Where possible, we plan to evaluate these
24    sub-models by comparing their predictions with measured data.  Alternatively, we may formulate
25    an informed judgment as to a range of plausible uncertainties for the sub-models. We plan to
26    quantitatively assemble the different types of uncertainties and variability to present an
27    integrated analysis of uncertainty and variability.
       See http://www.epa.gov/dears

                                                3-19

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 1    4  ASSESSMENT OF HEALTH RISK BASED ON CONTROLLED HUMAN
 2       EXPOSURE STUDIES
 3    4.1   Introduction
 4          The major components of the portion of the health risk assessment based on data from
 5    controlled human exposure studies are illustrated in Figure 2. The air quality and exposure
 6    analysis components that are integral to this portion of the risk assessment are discussed above in
 7    Sections 2 and 3, respectively. As described in the draft ozone ISA (U.S. EPA, 201 Ib) and
 8    previous ozone Criteria Documents (U.S. EPA, 1996b, 2006), there are numerous controlled
 9    human exposure studies reporting lung function decrements (as measured by changes in FEVi),
10    as well as changes in other measures of lung function, airway responsiveness, respiratory
11    symptoms, and various markers of inflammation. Most of these studies have involved voluntary
12    exposures with healthy adults, although a few studies have been conducted with mild and
13    moderate asthmatics and one study reported lung function decrements for children 8-11 years old
14    (McDonnell et al, 1985).

15          Staff plans to develop lung function decrement risk estimates for the general population,
16    school age children, asthmatic school  age children, outdoor workers, and the elderly population
17    (aged 70 and older) living in 12 urban areas in the U.S.  These areas, identified previously in
18    Section 3.2, represent a range of geographic areas, population demographics, and ozone
19    climatology.  As discussed further in Section 4.4.2, the selection of these areas was also
20    influenced by whether other health endpoints could be examined in the same urban area based on
21    concentration-response relationships developed from epidemiological or field studies.

22    4.2   Selection of Health Endpoints
23          In the last review, the health risk assessment estimated lung function decrements (> 10,  >
24    15, and > 20% changes in FEVi) in children 5-18 years old associated with 8-hour exposures at
25    moderate exertion.  At that time EPA staff and the CAS AC Ozone Panel judged that it was
26    reasonable to estimate the exposure-response relationships for children 5-18 years old based on
27    data from adult subjects (18-35 years old).  As discussed in the 1996 Ozone Staff Paper (EPA,
28    1996a) and 1996 ozone Criteria Document (EPA, 1996b), findings from other chamber studies
29    (McDonnell et al., 1985) for children 8-11 years old and summer camp field studies in at least
                                               4-1

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 1    six different locations in the United States and Canada found lung function changes in healthy
 2    children similar to those observed in healthy adults exposed to ozone under controlled chamber
 3    conditions.  Staff intends to use the same approach in this assessment.

 4    4.3   Selection of Exposure-Response Functions
 5          The health risk assessment  conducted in this new review will build on the approach
 6    developed and applied in the 2008  rulemaking. In that previous assessment, risk estimates for
 7    lung function responses associated with 8-hour exposures while engaged in moderate exertion
 8    were developed.  These estimates were based in part on exposure-response relationships
 9    estimated from the combined data sets from multiple ozone controlled human exposure studies.
10    Data from the studies by Folinsbee et al. (1988), Horstman et al. (1990), and McDonnell et al.
11    (1991) in addition to more recent data from Adams (2002, 2003, 2006) were used to estimate
12    exposure-response relationships for > 10, 15, and 20% decrements in FEVi.

13          The data from these controlled human exposure studies are corrected for effects
14    observed with exposure to clean air to remove any systematic bias that might be present in the
15    data attributable to exercise, diurnal variation, or other effects in addition to those of ozone
16    during the course of an exposure. Generally, this correction for exercise in clean air is small
17    relative to the total effects measures in the ozone-exposed cases. Regression techniques are then
18    used to fit a function to the data. A Bayesian approach is used then to characterize uncertainty
19    attributable to sampling error based on  sample size considerations. Response rates are calculated
20    for 21 fractiles (for cumulative probabilities from 0.05 to 0.95 in steps of 0.05, plus probabilities
21    of 0.01  and 0.99) at a number of ozone  concentrations  (see U.S. EPA, 2007a for details of this
22    approach).

23    4.4   Approach to Calculating Risk Estimates
24          Staff plans to generate several risk measures for this portion of the risk assessment. In
25    addition to the estimates of the number of school age children and other groups experiencing  one
26    or more occurrences of a lung function  decrement > 10, > 15, and > 20% in an ozone season,
27    risk estimates also will be developed for the total number of occurrences of these lung function
28    decrements in school age children and active school age children.
                                                4-2

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 1          A headcount risk estimate for a given lung function decrement (e.g., > 20% change in
 2    FEVi) is an estimate of the expected number of people who will experience that lung function
 3    decrement.  Since EPA is interested in risk estimates associated with ozone concentrations in
 4    excess of policy relevant background concentrations, staff plans to (1) estimate expected risk,
 5    given the personal exposures associated with ambient ozone concentrations, (2) estimate
 6    expected risk, given the personal exposures associated with estimated background ambient ozone
 7    concentrations, and (3) subtract the latter from the former.  As shown in equation 4-1 below, the
 8    headcount risk is then calculated by multiplying the resulting expected risk by the number of
 9    people in the relevant population. Because response rates are calculated for 21 fractiles,
10    estimated headcount risks are similarly fractile-specific.

1 1          The risk (i.e., expected fractional response rate) for the kth fractile, Rk is:
12                  Rk=PjX(RRk e,)  -     Px(Mk  e}                         (4-1)
                         .7=1                    ;=1
13    where:
14          6j = (the midpoint of) the jth category of personal exposure to ozone, under "as is"
15          ambient ozone concentrations;
16           e\= (the midpoint of) the ith category of personal exposure to ozone, under background
17          ambient ozone concentrations;
18          Pj •= the fraction of the population having personal exposures to ozone concentration of Cj
19          ppm, under "as is" ambient ozone concentrations;
20           Pf = the fraction of the population having personal exposures to ozone concentration of
21           efppm, under background ambient ozone concentrations;
22           RRk | ej = k-fractile response rate at ozone concentration BJ;
23           RRk | ef= k-fractile response rate at ozone concentration e\ ;  and
24          N= number of intervals (categories) of ozone personal exposure concentration, under "as
25          is" ambient ozone concentrations; and
26           Nb = number of intervals of ozone personal exposure concentration, under background
27          ambient ozone concentrations.
28          For example, if the median expected response rate under "as is" ambient concentrations is
29    0.065 (i.e., the median expected fraction of the population responding is 6.5%) and the median
30    expected response rate under background ambient concentrations is 0.001 (i.e., the median
                                                4-3

-------
 1    expected fraction of the population responding is 0. 1%), then the median expected response rate
 2    associated with "as is" ambient concentrations above policy relevant background concentrations
 3    is 0.065 - 0.001 = 0.064. If there are 300,000 people in the relevant population, then the
 4    headcount risk is 0.064 x 300,000 =  19,200.

 5    4.5   Alternative Approach Under Consideration For Calculating Risk Estimates
 6          In this new review, if adequate resources are available, staff intends to investigate the
 7    possibility of using an improved model that estimates FEVi responses for individuals associated
 8    with short-term exposures to ozone (McDonnell, Stewart, and Smith, 2010).  This model is based
 9    on the controlled human exposure data included in the prior lung function risk assessment as
10    well as additional data sets for different averaging times and breathing rates.  These data were
1 1    from 15 controlled human ozone exposure studies that included exposure of 541 volunteers (ages
12    18-35 years) on a total of 864 occasions (see McDonnell et al, 2007, for a description of these
13    data).

14          This model calculates the FEVi decrement due to ozone exposure for each diary event as:
                                         t = em (  Pl+foy'j* __ fi+foyij*]
                                       lk    e          .axi](                        (4-2)
16    where Xis given by the solution of the differential equation (4-3):

17                                 f =C(tMt)*6-05*(t)                        (4-3)
                                   at
18          In APEX, because the exposure concentration, exertion level, and ventilation rate are
19    constant over an event, this equation has an analytic solution for each event (events range in
20    duration from 1 to 60 minutes):
21                  X&=X(t0) e--    +K(t(l-e--)          (4-4)
                                                  fis
22    where
23        C(t) is the exposure concentration at time t (ppm),
24        V(t) = VE(t)IBSA is the effective ventilation rate at time t (L min"2 m"2),
25        VE(t) is the expired minute volume at time t (L min"2),
26        BSA is the body surface area (m2),
27        X0 is the value of X(t) at time to,
28        t is the time (minutes),  to is the time at the start of the event,
                                                4-4

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             = ai Age + 0.2, (age in years; ai and 0.2 depend on age range) and
 2        U; = subject-level random effect (zero mean).
 O
 4          The y term is a function of the age of the individual in years.  In the equation from
 5    McDonnell, Stewart, and Smith (2010), y is given as [Age ft - 25], however this term was
 6    developed using only data from individuals aged 18-35.  Using a larger data set that included
 7    individuals with ages ranging from 8 to 76, we performed a piecewise linear fit of the form
 8    y = ai Age + 0,2, for different ranges of ages. The three linear fits (ages 8-16, 16-35, 35-100)
 9    match at each boundary to form a continuous function of age. Exposure data used for the youth
10    fit came from McDonnell et al. 1985, Avol et al. 1987, and Avol et al. 1985. Exposure data for
11    the 36-76 age range were taken from Drechsler-Parks et al. 1987, Drechsler-Parks et al. 1989,
12    Gong et al. 1997, and Hazucha et al. 2003.

13          For youth, we found %AFEVi to be highly correlated with age, with a linear regression
14    giving: y = -3.16 Age + 41.58; but for older adults,  we found it  only varied weakly with age:
15    y = 0.02 Age + 9.3. The middle age range (y = Age  - 25) of 18-35 was extended to younger
16    ages, 16-35, based on discussions with McDonnell.  No data exist for the range age < 8, and due
17    to rapid changes in the physiology of children (as opposed to adults), extension of the fit to lower
18    age ranges is increasingly uncertain and will not be  done. Accordingly, %AFEVi will not be
19    modeled for children under 8  years of age. The parameters ai and 0.2 are age-dependent and are
20    specified in the APEX physiology input file. Staff will conduct  further analyses to inform the
21    choice of these parameters.

22          Here, PI - Pe and the variance of the {U;} are unitless fitted model parameters (see
23    McDonnell, Stewart, and Smith (2010) for details of fit).  Values of U are drawn from a
24    Gaussian distribution with mean zero and variance var(U). They are chosen once for each
25    individual and remain constant throughout the simulation. The best fit values for these
26    parameters given by McDonnell, Stewart, and Smith (2010) are  as follows (to 3 significant
27    figures) :  PI = 9.90, p2 = -0.411, p3 = 0.0164, p4 =  46.9, p5 = 0.00375, p6 = 0.912, Var(U) =
28    0.835.

29          Staff intends to perform an evaluation of this model based on data from clinical studies
30    that were not used in the development of the model.
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1    4.6   Uncertainty and Variability
2          Staff plans to conduct a 2-dimensional Monte Carlo analysis of the uncertainty and
3    variability of the risk estimates based on data from controlled human exposure studies.  This will
4    of necessity be integrated with the exposure modeling uncertainty assessment.
                                               4-6

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                                      "As is" Ambient
                                       Ozone Levels
                          Modeled
                         Background
                        Ozone Levels
       Ambient Population-
       Oriented Monitoring
       for Selected Urban
             Areas
                                   Exposure
                                   Modeling
Controlled Human
Exposure Studies
   (various lung
function endpoints)
                                   Air Quality Adjustment
                                                                      Modeled Hour-by-Hour
                                                                    Exposures Resulting From
                                                                    (1) "As is" Ambient Ozone
                                                                    Levels and (2) Background
                                                                         Ozone Levels
Exposure-Response
   Relationships
  Using Exposure
 Metrics Based on
   Hourly Ozone
    Exposures
                                                                           Modeled Hour-by-Hour
                                                                          Exposures Resulting From
                                                                          (1) "As is" Ambient Ozone
                                                                            Levels and (2) When
                                                                             Standards are Met
                                                      Estimates of Lung
                                                       Function Risk
                                                     Associated with "As
                                                    is" Ozone Levels Over
                                                        Background
                                                                                                              Estimates of Lung
                                                                                                                Function Risk
                                                                                                             Reduction Associated
                                                                                                                with Meeting
                                                                                                                 Standards
1
2
                                    Current or Alternative
                                        Standards
Figure 4-1. Major Components of Ozone Health Risk Assessment Based on Controlled Human Exposure Studies
                                                                              4-7

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 1    5   ASSESSMENT OF HEALTH RISK BASED ON EPIDEMIOLOGIC STUDIES
 2    5.1   Introduction
 3          As discussed in the draft ozone ISA (EPA, 201 Ib), a significant number of
 4    epidemiological and field studies examining a variety of health effects associated with ambient
 5    ozone concentrations in various locations throughout the U.S., Canada, Europe, and other
 6    regions of the world have been published since the last NAAQS review. As a result of the
 7    availability of these epidemiological and field studies and air quality information, staff plans to
 8    expand the ozone risk assessment to include an assessment of selected health risks attributable to
 9    ambient ozone concentrations over policy relevant background concentration and health risk
10    reductions associated with attainment  of current and alternative ozone standards in selected
11    urban locations in the U.S. The major components of the portion of the health risk assessment
12    based on data from epidemiological and field studies are illustrated in Figure 5-1.  The
13    approaches used by staff to select health endpoint categories, urban areas, and epidemiology and
14    field studies to consider for inclusion in the risk assessment are discussed below.

15          This chapter presents an overview of the design of the human health risk assessment to be
16    conducted in the current review of the ozone NAAQS. This design reflects goals laid out in the
17    Integrated Review Plan (U.S. EPA,  201 la, section 5.5) including: (1) to provide estimates of the
18    potential magnitude of premature mortality and/or selected morbidity health effects in the
19    population associated with recent ambient ozone levels and with just meeting the current suite of
20    ozone standards and any alternative standards that might be considered in selected urban study
21    areas; (2) to develop a better understanding of the influence of various inputs and assumptions on
22    the risk estimates; and (3) to gain insights into the distribution of risks and patterns of risk
23    reduction and uncertainties in those risk estimates.

24          Based upon the information assessed in the first draft ISA, we plan to focus the risk
25    assessment on health effect endpoints  for which the weight of the evidence as assessed in the
26    ISA supports the judgment that the overall health effect category is at least likely caused by
27    exposure to ozone either alone and/or  in combination with other pollutants. The planned
28    quantitative risk assessment, is designed to estimate  risks associated with short-term (> 24-hour
                                                 5-1

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 1    average) and long-term (e.g., annual- or seasonal- average) ambient ozone concentrations in
 2    selected urban study areas. We are considering expanding the focus of this risk assessment to
 3    include additional health effect categories beyond those classified as casual or likely causal,
 4    when available evidence presented in the ISA is sufficiently suggestive of a causal association to
 5    support conducting quantitative risk assessment and when inclusion of that endpoint category
 6    will allow us to address potentially important policy issues related to reviewing the ozone
 7    NAAQS.  For example, we are considering including information on birth outcome effects
 8    associated with ambient ozone which would allow us to evaluate additional potentially sensitive
 9    populations (i.e., pregnant women and infants) not previously evaluated in the quantitative risk
10    assessment conducted in the last review. In addition, we are also considering estimating
11    respiratory mortality associated with long-term exposure to ozone.1 EPA recognizes that a
12    decision to include these additional endpoint categories needs to consider the increased
13    uncertainty that their inclusion could introduce into the risk assessment; specifically, the
14    potential for these endpoints not to be causally linked with ozone exposure, despite the statistical
15    associations observed in epidemiological studies.

16           Building upon the assessment completed in the last review, we plan to focus the ozone
17    assessment on modeling risk for a set of selected urban study areas, chosen in order to provide
18    population coverage and to portray the observed heterogeneity in ozone-related risk across
19    selected urban study areas. EPA is considering ways to put the quantitative risk assessment
20    results conducted for a limited number of locations and selected health endpoints into a broader
21    context to better characterize the nature, magnitude, extent, variability, and uncertainty of the
22    public health impacts associated with ozone exposures.

23           In designing the risk assessment, we expect to identify multiple options for specifying
24    specific elements of the risk model (e.g.,  several concentration response functions for a particular
25    health endpoint; several approaches for characterizing ambient ozone levels within urban areas
26    using monitors and/or modeling data).  In these instances, to the extent possible given available
27    information, we will identify those options that we believe has the greatest support in the
      1 As noted in section 5.1, the decision to model long-term exposure-related respiratory mortality is complicated by
      the fact that, while the draft ISA classifies all-cause mortality related to long-term ozone exposure has having a
      suggestive of a casual association, the draft ISA assigns respiratory morbidity as likely to have a causal association.

                                                  5-2

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 1    literature. These modeling elements will then be used, to generate a core (base case) set of risk
 2    estimates. The remaining options identified for specifying elements of the risk model will be
 3    used as part of the sensitivity analysis (see below) to generate an additional set of reasonable risk
 4    estimates that can be used to provide a context, with regard to uncertainty, within which to assess
 5    the set of core (base case) risk results. Note, that in general, those health effects endpoints
 6    falling within health effects endpoint categories assigned a causal or likely causal association
 7    with ozone exposure will be included in the core analysis, while endpoints assigned a suggestive
 8    of a causal association (if modeled quantitatively) would likely be included as part of the
 9    sensitivity analysis. As noted earlier, respiratory mortality associated with long-term exposure
10    represents a special case. Depending on how we ultimately interpret the degree of support for an
11    association with this endpoint and ozone exposure, we may include this endpoint as part of the
12    core estimate, or retain it as a component of the sensitivity analysis.

13           As part of the risk assessment, we will address both uncertainty and variability. In the
14    case of uncertainty, we are planning to use  a four-tiered  approach developed by the World Health
15    Organization (WHO) and used in the risk assessment completed for the last PM NAAQS review.
16    The WHO's four-tiered approach matches the sophistication of the assessment of uncertainty to
17    the overall complexity of the risk assessment, while also considering the potential magnitude of
18    the impact that the risk assessment can have from a regulatory/policy perspective (e.g., risk
19    assessments that are complex and are associated with significant regulatory initiatives would
20    likely be subjected to more sophisticated uncertainty analysis). The WHO framework includes
21    the use of sensitivity analysis both to characterize the potential impact of sources of uncertainty
22    on core risk estimates and (as noted earlier) to generate an alternative set of reasonable risk
23    estimates that supplement the core risk estimates.

24           In the case of variability, we will identify key sources of variability associated with ozone
25    risk (for both short-term and long-term exposure-related endpoints included in the risk
26    assessment) and discuss the degree to which these sources of variability are reflected in the
27    design of the  risk assessment. Note, that in  those cases where a particular source of variability is
28    not sufficiently reflected in core risk estimates, this can introduce uncertainty and potentially bias
29    into the risk estimates since representativeness can be reduced (in certain cases, the sensitivity
                                                  5-3

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 1    analysis may also explore these sources of variability given their potential to introduce
 2    uncertainty into core risk estimates).

 3           As part of the analysis, we will also complete a representativeness analysis designed to
 4    support the interpretation of risk estimates generated for the set of urban study areas included in
 5    the risk assessment. The representativeness analysis will focus on comparing the urban study
 6    areas to national-scale distributions for key ozone-risk related attributes (e.g., demographics
 7    including socioeconomic status, air-conditions use, baseline incidence rates and ambient ozone
 8    levels).  The goal with these comparisons will be to assess the degree to which the urban study
 9    areas provide coverage for different regions of the country  as well as for areas likely to
10    experience elevated ozone-related risk due to their specific mix of attributes related to ozone
11    risk.  As part of the representativeness analysis, we are also considering a  broader national-scale
12    assessment of mortality (both short- and long-term exposure-related). These national-scale
13    mortality estimates would also allow us to assess the degree to which the urban study areas
14    included in the risk assessment provide coverage for areas of the country expected to experience
15    elevated mortality rates due to ozone-exposure.  We note that a national-scale assessment such as
16    this was completed for the risk assessment supporting the latest PM NAAQS review (U.S. EPA,
17    2010) with the results of the analysis being used to support an assessment  of the
18    representativeness of the urban study areas (assessed in the PM NAAQS risk assessment), as
19    described here for ozone. Additional detail on the representativeness analysis is presented in
20    section 5.4.5.

21           The following discussion begins by presenting the framework for the risk  assessment
22    developed to evaluate ozone with more detailed discussions of key components of the risk
23    assessment model including air quality considerations (section 5.2).  Next, we discuss the
24    selection of health effects endpoints to include in the assessment, including the specification of
25    concentration-response (C-R) functions,  baseline incidence data and demographic data (section
26    5.3).  We conclude with the discussion of how uncertainty and variability  will be  addressed in
27    the analysis (section 5.4). This discussion also includes an overview of the representativeness
28    analysis planned for the assessment.
                                                  5-4

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 1    5.2   Framework for the Ozone Health Risk Assessment
 2    5.2.1   Overview of Modeling Approach
 3           Consistent with the last review, we plan to quantify the number of ozone -related adverse
 4    health outcomes by using a health impact function, the components of which are illustrated in
 5    equation 5-1. The health impact function combines information about changes in ambient ozone
 6    air quality concentrations (Ax) with C-R relationships (reflected by P, the ozone coefficient
 7    derived from epidemiological studies) and baseline health incidence data for specific health
 8    endpoints (y) to derive estimates of the change in incidence (Ay) of specific health effects
 9    attributable to ambient ozone concentrations during the period examined among a particular
1 0    population (Pop) . l
11                                   Ay = y e     - 1 Pop                                 (5-1)
12           This type of risk model applies risk coefficients drawn from epidemiological studies that
13    characterize the relationship between ambient ozone levels measured at fixed-site population-
14    oriented monitors and the risk of specific health endpoints in the population. Therefore, it does
15    not require more detailed individual-level exposure modeling described above and relies instead
16    on the use of ambient monitoring data. Specifically, a change in the level of ambient ozone is
17    translated through the risk coefficient (P) to a change in the baseline rate of a particular health
18    effect(s) in the study  population.  This adjustment to the baseline incidence rate can then be
19    combined with population estimates (Pop) to generate a change in the incidence of a specific
20    health endpoint(s) attributable to a change in  ambient ozone.

21           In this review we plan to use the environmental Benefits Mapping and Analysis Program
22    (BenMAP) (Abt, 2008) to perform this calculation across multiple health impact functions and
23    urban areas. This GIS-based computer program draws upon a database of population, baseline
24    incidence and effect coefficients to automate  the calculation of health impacts.  EPA has
25    traditionally relied upon the BenMAP program to estimate the health impacts avoided and
26    economic benefits associated with adopting new air quality rules.  For this analysis, EPA will use
      1 The health risk model given in Equation 5-1 is based on a concentration-response function in which the natural
      logarithm of the incidence of the health effect is a linear function of ozone concentration. We plan to consider other
      mathematical forms where epidemiological studies have reported effects using other model forms.

                                                 5-5

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 4
 5
 6
 9
10
11
12
13
14
15
the model to estimate ozone-related impacts among the health endpoints, and within the urban
areas, discussed below.  BenMAP already contains much of the population and baseline
incidence data, and many of the effect coefficients, needed to perform this analysis; where it
does not, we will specify the model with the appropriate data. The following diagram
summarizes the data inputs (in black text) and outputs (in blue text) for a typical BenMAP
analysis.
              BenMAP data inputs and outputs
                  Census
                Population
                   Data
                Air Quality
                Monitoring

                  Health
                Functions
                                       Population
                                        Estimates
                                  Population
                                   Exposure

                                   Adverse
                                Health Effects
                                                         Population
                                                         Projections
 Air Quality
  Modeling

Incidence and
 Prevalence
    Rates
       BenMAP offers several advantages in terms of modeling population exposure and risk.
First, once we have properly specified the BenMAP software, the program can produce risk
estimates for an array of modeling scenarios across a large number of urban areas. Second, the
program can accommodate a variety of sensitivity analyses. For example, we may consider the
sensitivity of our risk estimates to alternative specifications of concentration-response functions
for the same endpoint. Third, BenMAP would be useful to performing a national assessment of
ozone mortality for the purposes of a representativeness analysis (as discussed earlier in
section 5.1).
16          As described in Figure 5-1, this risk assessment approach requires specifying a number of
17   modeling components related to (a) characterizing air quality, (b) establishing the C-R functions,
                                               5-6

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 1    and (c) specifying the baseline incidence rates and population demographics.  The remainder of
 2    this section discusses each of these modeling components in detail.

 3       5.2.2   Air Quality Considerations

 4           There are several air quality inputs to the risk assessment as illustrated in Figure 5-1.
 5    These have been described in Chapter 2 and include: (a) characterization of recent air quality
 6    (i.e., ambient ozone levels) for each selected urban study area, (b) background concentrations for
 7    each selected urban study area, and (c) projections of ambient air quality for both current and
 8    alternative ozone NAAQS under consideration. Additional detail on these inputs is presented
 9    below:

10       •   Characterizing recent ambient ozone levels for selected urban study areas using
11           monitoring data:  EPA plans to use 3 years (2008-2010) of ambient ozone measurement
12           data to characterize recent air quality conditions (see section 2.2). In aggregating
13           monitoring data (to form composite monitor(s) for each study area) and linking those
14           monitors to study populations within a particular study area, as noted earlier in section
15           2.2, we are considering two approaches. As in the previous ozone NAAQS risk
16           assessment, we plan to match, to the extent possible, the approach for analyzing air
17           quality data used in the epidemiological studies from which the C-R functions are
18           obtained.  For example, in order to be consistent with the approach generally used in the
19           epidemiological studies from which C-R functions have been estimated for effects
20           associated with long-term ozone exposures, we plan to develop and use ambient data for
21           a single composite monitor based on monitored data from all eligible monitors in that
22           study area. Some epidemiological studies have used more sophisticated (and spatially-
23           refined) methods for associating ambient ozone data with a study population. In cases
24           where we include C-R functions from studies using alternative methods to link ambient
25           ozone concentrations with health effects information in our risk assessment, we may
26           consider a more refined approach for linking ozone monitoring data with study
27           populations, to match the approach used in the study. However, in addition to a risk
28           simulation where we attempt to match our use of monitoring data to the approach used in
29           the underlying epidemiological studies, we are also considering an alternative approach
30           where we focus on developing composite monitors that are more representative of
31           exposure profiles experienced by populations currently. As noted in section 2.2, this
32           could involve an alternative weighting scheme for deriving composite monitors where
33           we use the results of micro-environmental exposure  modeling (used in the
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                                 Air Quality
                                    Ambient Monitoring for
                                    Selected Urban Study
                                    Areas (possibly
                                    augmented with CMAQ
                                    modeling data)
                                    Policy Relevant
                                    Background (PRB)
                                    Air Quality Adjustment
                                    Procedures (Rollback) to
                                    Simulate Just Meeting
                                    Current and Alternative
                                    NAAQS
                                                  Recent Ambient
                                                  Ozone Levels
                                                     Changes in
                                                     Distribution of
                                                     Ozone Air
                                                     Quality
                                 Concentration-Response

                                                         Selection of              I       Specification of
                                  Selection of health  I	J Epidemiological Studies to  	„  Concentration -
                                  effects endpoints         Provide Concentration-            Response
                                                         Response Functions            | Functions


                                 Baseline Health Effects Incidence Rates and Demographics

                                 Estimates of City-specific
                                 Baseline Health Effects
                                 Incidence Rates

                               I City-specific Demographic Data
                                                                                     Health
                                                                                      Risk
                                                                                     Model
Risk Estimates:

Recent Air Quality

- Risk above PRB

Simulating Just Meeting
Current  NAAQS

- Risk above PRB

- Incremental difference
in risk compared to
recent air quality

Simulating Just Meeting
Alternative NAAQS
Under Consideration

- Risk above PRB

- Incremental difference
in risk compared to just
meeting current NAAQS
1
2
Figure 5-1.  Overview of Risk Assessment Model Based on Epidemiologic Studies
                                                                                 5-S

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 1           exposure analysis - see Section 3) to generate weights for each monitor in an urban
 2           study area reflecting the fraction of population exposure associated with that monitor
 3           (e.g., the fraction of simulated person-ozone hours associated with the area surrounding
 4           a given monitor). The details of this approach are still being developed.

 5        •  Characterizing recent ambient ozone levels for selected urban study areas using a
 6           combination of monitoring and modeled data:  As discussed in section 2.2, we are
 7           also considering the use of monitor data augmented with modeling in characterizing
 8           recent ambient ozone conditions.  This type of a fused surface has the benefit of
 9           retaining the ambient characterization of absolute ozone levels (at monitors), while using
10           modeled concentrations to characterize the spatial gradient between monitors.  Note,
11           however, that we would still need to specify how this more spatially-refined surface
12           would be related to population in order to generate an exposure surrogate (e.g., would we
13           conduct risk simulation at a more spatially-refined grid cell-level, or would we use the
14           more differentiated ozone surface to generate a  composite measurement value - a single
15           value for the entire study area)? The decision as to whether to pursue this type of fused
16           (model-monitor) surface and if so, how to use it in characterizing exposure, would likely
17           rely heavily on our assessment of the spatial heterogeneity of ozone levels across a
18           subset of our urban study areas - see bullet below.

19        •  Assessment of spatial heterogeneity of ozone  across urban study  areas: As noted in
20           section 2.2., a potentially important component  of designing the risk assessment involves
21           an assessment of the spatial heterogeneity of ambient ozone levels across prospective
22           urban study areas.  This assessment would likely be based on consideration for the
23           pattern of ozone levels (daily time series and seasonal/annual averages)  across monitors
24           within a given urban study area. If spatial heterogeneity across monitors is found to be
25           low (and more specifically, if temporal profiles  for the monitors within a given study
26           area are similar), then there may be little benefit in using more sophisticated approaches
27           for linking ambient ozone levels and populations within a given study area. Conversely,
28           if there is substantial spatial heterogeneity, then the representativeness of an exposure
29           surrogate (e.g., surrogate monitor) could be enhanced by more closely linking ambient
30           ozone levels to demographics.

31        •  Characterizing PRB: As noted in section 2.4, we will rely on characterization of PRB
32           provided by GEOS-Chem modeling to obtain values specific to each urban study area
33           included in the risk assessment. However, we will likely consider several different
34           background definitions (e.g., U.S. background,  a North American background, and
3 5           natural background).
                                                 5-9

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 1        •  Method for adjusting ambient air quality levels to simulate air quality just meeting
 2           current and potential alternative ozone NAAQS: As discussed in section 2.3, EPA is
 3           planning to use the quadratic rollback approach to simulate ozone levels to just meet
 4           current and alternative NAAQS standards. Note, that we may explore the degree of
 5           spatial heterogeneity associated with ambient ozone levels within our urban study areas.
 6           If we find that there is substantial spatial heterogeneity, then this could mean that the
 7           pattern of rollback potentially associated with attainment of an alternative (lower) level
 8           could be more complex from a spatial standpoint (i.e., there is potentially, increased
 9           uncertainty in simulating attainment of either the current or alternative standards).
10           Conversely, if we find that there is limited spatial heterogeneity, then we expect that
11           uncertainty associated with simulating alternative standard levels would be relatively
12           lower.
13    5.3  Selection of Health  Effects Endpoint Categories
14           As noted in section 5.1, based on review of the first draft ISA, we plan to focus the risk
15    assessment on ozone, estimating potential health impacts associated with both short-term and
16    long-term exposures to ozone. In selecting health effects endpoints to include in the risk
17    assessment, we have considered the following factors based upon review of the first draft ISA
18    (U.S. EPA, 201 Ib; Chapters 2, 6, and 7):  (a) the extent to which the health effect endpoints are
19    considered  significant from a  public health standpoint, (b) the overall weight of the evidence
20    from the collective body of epidemiological, clinical, and toxicological studies and the inferences
21    made in the first draft ISA as to whether there is a causal or likely causal relationship between
22    ozone and the health effect endpoint category, (c) whether there is sufficient evidence  to support
23    a causal or  likely causal relationship for the specific health endpoint within the health effect
24    category to warrant inclusion  in the risk assessment, and (d) whether there are well-conducted
25    studies reporting estimated C-R functions for specific health endpoints associated with ambient
26    ozone levels.

27           Based upon review of the first draft ISA, we plan to consider the following health effect
28    endpoint categories in this assessment:

29    Health Effect Categories  Associated with Short-term Ozone Exposure
30           •    respiratory morbidity ( causal association)
31           •    mortality (likely causal association)
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 1    Health Effect Categories Associated with Long-term Ozone Exposure
 2           •  respiratory morbidity (likely casual association)
 3           In addition to the health effect categories presented above, we are considering expanding
 4    the focus of the ozone risk assessment to include additional endpoints from health effect
 5    categories that have been initially judged in the first draft ozone ISA to have a suggestive causal
 6    association with ambient ozone measurements. We plan to consider including these additional
 7    endpoints when they allow us to address potentially important policy issues related to reviewing
 8    the current ozone standards. Risk estimates for endpoints within these additional health effects
 9    categories would likely not be presented as part of the core risk assessment, but rather would be
10    included as part of the sensitivity analysis examining  additional potential health effects
11    endpoints.  Potential health effect endpoint categories being considered for inclusion in the
12    sensitivity analysis include: (a) long-term exposure-related birth outcome effects (allows us to
13    evaluate potentially sensitive populations,  including pregnant women and infants), and (b) long-
14    term exposure-related respiratory mortality (due to the clear public health significance of this
15    endpoint).

16           The respiratory mortality endpoint  deserves some additional discussion. While the
17    general all-cause mortality category was given a suggestive of causal association (for long-term
18    exposure) in the draft ISA, it is important to note that the respiratory morbidity category (again
19    for long-term exposure) was assigned a likely causal association classification in the  draft ISA. If
20    we consider focusing an assessment of long-term exposure related mortality on respiratory
21    mortality (which would be the endpoint most supported by the latest reanalysis of the ACS data),
22    then the appropriate causality association classification for this specific mortality endpoint may
23    be more complicated to determine. While this endpoint falls within a category of mortality
24    (which was assigned a suggestive of casual association), it  is also a type of respiratory health
25    effect (which is given a likely casual association). Therefore, EPA will continue to review
26    information presented in the next draft ISA and look to input provided by both the public and
27    CAS AC to make a determination as to the  appropriate degree of support to assign to  the long-
28    term exposure-related respiratory mortality category.  Ultimately, this determination will result in
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 1    an estimate of respiratory mortality (if it is indeed generated in the first place) to be included as
 2    part of the core estimate, or retained as part of the sensitivity analysis.

 3       5.3.1  Selection of Epidemiological Studies and Specification of Concentration-
 4              Response Functions
 5          As noted above, the risk assessment conducted in this review will build on the approach
 6    developed and applied in the last review.  EPA will rely on a weight-of evidence approach, based
 7    on the ISA's evaluation of new and previously reviewed epidemiologic studies including
 8    identification of relevant C-R functions that characterize the relationships between short- and
 9    long-term ozone exposures and health outcomes, particularly those conducted at or near current
10    ambient concentrations. Quantitative relationships provided in the specific studies (or to be
11    derived by EPA from the data presented in the epidemiologic studies) describe the change in
12    concentration (generally based on ambient fixed-site monitors) associated with a change in
13    health response.  These C-R relationships will be combined with air quality data, baseline
14    incidence data, and population data to develop population health risk estimates.

15          We plan to use specific criteria to  select the epidemiological studies that will be used to
16    provide C-R functions for the quantitative risk assessment including:

17          •  The study addresses one of the health effects endpoint categories identified for
18              inclusion in the risk assessment.
19          •  The study was peer-reviewed,  evaluated in the first draft ISA, and judged adequate by
20              EPA staff for purposes of inclusion in the risk assessment.  Criteria considered by
21              staff include: whether the study provides C-R relationships for locations in the U.S.,
22              whether the study has sufficient sample size to provide effect estimates with a
23              sufficient degree of precision and power, whether the study is a multi-city study, and
24              whether adequate information  is provided to characterize statistical uncertainty.
25          •  The study is not superseded by another study (e.g., if a later study is an extension or
26              replication of a former study, the later study would effectively replace the former
27              study), unless the earlier study has characteristics that are clearly preferable.
28          In addition to the above criteria, other factors, which may be specific to a particular
29    health effect endpoint, or even to a set of studies, may be considered.  For example, several of
30    the studies have improved upon the method of estimating the exposure metric used in most
31    studies which have generally relied upon population-oriented monitoring data. Instead of
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 1    assigning the same ambient ozone concentration to all individuals in a city (based on a central
 2    monitor or the average of several monitors in a city), these studies have assigned "exposures"
 3    according to monitors that better approximate conditions near subjects' residences.  These and
 4    similar studies may provide additional insights into whether reductions in mortality are
 5    attributable to recent, or more historical changes in patterns of long-term ozone exposure.

 6           We also plan to consider the overall study design, including the method used to adjust for
 7    covariates (including confounders and effects modifiers) in identifying candidate studies. For
 8    example, if a given study uses ecological-defined variables (e.g., smoking rates) as the basis for
 9    controlling for confounding, concerns may be raised as to the effectiveness of that control.
10    These factors related to confounding control and consideration of effects modification also will
11    be considered in identifying studies for use as the basis of C-R functions.

12           Once the final  set of epidemiological studies is chosen, the next step will be the selection
13    of C-R functions from those studies.  A number of factors need to be considered in specifying C-
14    R functions related to  short- and long-term exposure studies.  The factors being considered in
15    selecting C-R functions include:

16       •   Single- and multi-pollutant models (pertains to both short-term and long-term
17           exposure studies):  Epidemiological studies often consider health effects associated with
18           ambient ozone independently  as well as together with co-pollutants (e.g., PM, nitrogen
19           dioxide, sulfur dioxide, carbon monoxide). To the extent that any of the co-pollutants
20           present in the ambient air may have contributed to health effects attributed to ozone in
21           single pollutant models, risks  attributed to ozone may be overestimated if C-R functions
22           are based on single pollutant models. This would argue for inclusion of models reflecting
23           consideration of co-pollutants. Conversely, in those instances where co-pollutants are
24           highly correlated with ozone,  inclusion of those pollutants in the health impact model can
25           produce unstable and statistically insignificant effect estimates for both ozone and the co-
26           pollutants.  This situation would argue for inclusion of a model based  exclusively on
27           ozone. Given that single and multi-pollutant models each have potential advantages and
28           disadvantages, we plan to include both types of C-R functions in the risk assessment.

29       •   Single-city versus multi-city studies (typically a factor in short-term exposure studies):
30           All else being equal, we judge C-R functions estimated in the assessment location as
31           preferable to a function estimated in some other location, to avoid uncertainties that may
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 1           exist due to differences associated with geographic location.  There are several
 2           advantages, however, to using estimates from multi-city studies versus studies carried out
 3           in single cities.  Multi-city studies are applicable to a variety of settings, since they
 4           estimate a central tendency across multiple locations. Multi-city studies also tend to have
 5           more statistical power and provide effect estimates with relatively greater precision than
 6           single-city studies due to larger sample sizes, reducing the uncertainty around the
 7           estimated health coefficient. By contrast, single-city studies, while often having lower
 8           statistical power and varying study designs which can make comparison across cities
 9           challenging, do reflect location-specific factors such as differences in underlying health
10           status, and differences in exposure-related factors such as air conditioner use and urban
11           density with larger populations exposed near high-traffic roads. Because single- and
12           multi-city studies have different advantages, we plan to include both types of functions in
13           this analysis, where they are available.  We plan to place greater weight on the use of C-R
14           relationships reflecting adjusted single-city estimates from multi-city studies.  This would
15           include  empirical Bayes adjusted city-specific estimates. These types of effect estimates
16           benefit both from increased statistical power, as well as the potential for specification of
17           city-specific effect estimates.  Conversely, if a multi-city study only provides aggregated
18           effect estimates, but does differentiate those estimates regionally, we plan to use those
19           regional-specific estimates rather than a single national-level estimate by matching
20           selected urban study areas to these regions.

21       •   Multiple lag models (pertinent to short-term exposure time-series studies). If
22           information is available  for a distributed lag model, we plan to use that model. Where
23           there are multiple lags presented, but a  distributed lag model is not included, we plan to
24           consider information presented in the first draft ISA to determine if there is biological
25           support for selecting a specific lag period for a given health effect endpoint.

26       •   Interactions between pollutants and temperature: To the extent that studies explore
27           (a) interactions between pollutant(s) and ozone and (b) interactions between temperature
28           and ozone, we will consider that information in modeling specific endpoints to the extent
29           that relevant concentration-response functions (taking into consideration these factors are
30           available) and/or use this information to help interpreting risk estimates.

31       •   Seasonally-differentiated effects estimates (pertinent to short-term studies). In those
32           instances where  studies presented effect estimates associated with short-term ambient
33           ozone concentrations differentiated by season, we plan to use these seasonal estimates.
34           We plan to link seasonal effect estimates with seasonal ozone air quality data in
35           conducting the risk assessment for selected urban  study areas.
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 1       •   Shape of the functional form of the risk model: In the risk assessment conducted in
 2           the last review, EPA included C-R relationships that reflected linear or log-linear C-R
 3           functions that extended down to estimated background levels for effects related to short-
 4           term exposure and down to lowest measured ambient levels for effects related to long-
 5           term exposure, as well as adjusting these models to reflect various alternative "cutpoint"
 6           models.  The alternative cutpoint models imposed an assumed threshold on the original
 7           C-R function, below which there is little or no population response. The first draft ISA
 8           concludes that there is little support in the literature for a population threshold for short-
 9           term exposure-related effects, although in the case of mortality, the first draft ISA notes
10           that the nature of the mortality effect as well as study design may mean that these studies
11           are not well suited to identify a threshold should it exist (see U.S. EPA, 201 Ib, section
12           2.5.3.2). In the case of long-term exposure related endpoints (specifically for birth
13           outcomes), the first draft ISA notes that study results suggest a clear association with
14           ozone above approximately 30 ppb, with that relationship no longer being statistically
15           significant below that level (i.e., a 95th% confidence interval on the effect  estimate
16           including zero below this ambient ozone level).  Given the  above observation from the
17           first draft ISA regarding the potential for thresholds, we are planning to (a) for all short -
18           term exposure related endpoints, not consider a threshold either in the core analysis, or as
19           part of sensitivity analyses and (b) for long-term exposure-related endpoints, consider a
20           range of threshold levels (e.g., 20, 30 and 40 ppm) along with a no-threshold scenario.
21           Note, that as discussed earlier, all simulations for birth outcomes (if conducted) will be
22           presented as part of the sensitivity analysis.  However, long-term exposure-related
23           mortality (if run) could be included as part of the core risk estimate or as part of
24           sensitivity analyses, depending on how we ultimately interpret the degree  of support for a
25           casual association. In either case, for long-term exposure-related mortality, we would also
26           likely simulate a series of potential thresholds (e.g., 20, 30  and 40 ppb) with risk
27           estimates for these simulations being included as part of the sensitivity analysis.

28           In addition to the factors listed above, there  are additional factors related to the design of
29    individual epidemiological studies which we plan to consider in selecting the C-R functions to be
30    included in the assessment.  For example, studies often include adjustment for covariates with
31    varying degrees of freedom, reflecting the tradeoff between bias and over-adjustment (loss of
32    efficiency).  In these cases, we plan to consider any information provided for specific studies
33    within the first draft ISA and also plan to consider which model form has the strongest statistical
34    fit, while still considering overall biological plausibility.  An additional factor that we will
35    consider in selecting C-R functions to include in the risk assessment is ongoing research into the
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 1    potential mitigating and averting effect that air quality alerts such as the EPA's AirNOW can
 2    have on ozone-related exposure and risk.  If available research is found to provide effect
 3    estimates (or information that can be used to derive effect estimates) that reflect this averting
 4    and/or mitigating activity by the exposed population, then we would consider including risks
 5    based on these adjusted effect estimates as part of our Sensitivity Analysis.

 6       5.3.2  Selection of Urban Study Areas

 7           We plan to build on the risk assessment conducted for the last review and continue to
 8    focus the risk assessment on a set of selected urban study areas. The decision to continue to
 9    focus on modeling a set of selected urban study areas reflects the goal of providing risk estimates
10    that have higher overall confidence due to the  use of location-specific  data when available for
11    these urban locations. In addition, given the greater availability of location-specific data, a more
12    rigorous evaluation of the impact of uncertainty and variability can be conducted for a set of
13    selected urban study areas than would be possible for a broader regional or national-scale
14    analysis. We plan to consider the following factors in the selection of urban study areas:

15       •   Air quality data: The urban area has sufficient recent (2008-2010) air quality data to
16           conduct the risk assessment (See section 2.2.1).

17       •   Location-specific C-R functions: There are C-R functions available from
18           epidemiological studies that we ultimately select to use as the basis for deriving
19           concentration-response functions, for one or more of the selected health endpoints. This
20           primarily applies to short-term epidemiological studies, which more often include city-
21           specific effect estimates.  C-R functions available from long-term epidemiological studies
22           generally combine data from multiple cities.  Specific cities evaluated in the key long-
23           term studies would be considered for inclusion in the risk assessment. We plan to include
24           urban study areas that have been assessed in epidemiological studies that have evaluated
25           health effects associated with both short- and long-term ozone  exposures and, to the
26           extent possible, locations where both morbidity and mortality health endpoints have been
27           evaluated.

28       •   Baseline incidence rates and demographic data: The required urban area-specific
29           baseline incidence rates and population data are available for a recent year for at least one
30           of the health endpoints.
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 1       •   Geographic heterogeneity:  Because ozone distributions and population characteristics
 2           vary geographically across the U.S., we plan to select a set of urban study areas in which
 3           each region of the country is represented.  We plan to define these regions in such a way
 4           as to reflect differences in factors related to ozone distributions, sources, co-pollutants,
 5           exposure, and/or effect estimates.

 6       •   Representing areas with relatively larger vulnerable populations: Baseline incidence
 7           rates (e.g., mortality rates) and ozone exposures are higher in some parts of the country
 8           than others.  We plan to select a set of urban study areas that will include representation
 9           of sensitive populations (e.g., those with higher baseline incidence rates of the health
10           effect endpoints being evaluated, lower air conditioning usage which has been related to
11           higher ambient ozone exposures).

12       •   Consideration of epidemiology studies with more refined exposure metrics: We plan
13           to include urban study areas for which there is a C-R function estimated using a more
14           refined metric of exposure (e.g., smaller geographic units linked to nearest ozone
15           monitors, rather than  constructing a single composite monitor for an entire metropolitan
16           area), where available.

17       5.3.3  Baseline Health Effects Incidence Data and Demographic Data

18           As noted earlier (section 5.2.1), the most common epidemiological-based health risk
19    model expresses the reduction in health risk (Ay) associated with a given reduction in ozone
20    concentrations (Ax) as a percentage of the baseline incidence (y).  To accurately assess the
21    impact of ozone air quality on health risk in the selected urban areas, information on the baseline
22    incidence of health effects (i.e., the incidence under recent air quality conditions) in each
23    location is needed. Where at all possible, we plan to use county-specific incidences  or incidence
24    rates (in combination with county-specific populations). A summary of available baseline
25    incidence data for specific categories of effects is  presented below:

26       •   Availability of baseline incidence data on mortality:  County-specific (and, if desired,
27           age- and race-specific) baseline incidence  data are available for all-cause and cause-
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 1           specific mortality from CDC Wonder.l  The most recent year for which data are available
 2           online is 2005.2

 3       •   Availability of baseline incidence data for hospital admissions and emergency room
 4           (ER) visits:

 5              o   Cause-specific hospital admissions baseline incidence data are available for each
 6                  of 40 states from the State Inpatient Databases (SID).

 7              o   Cause-specific ER visit baseline incidence data are available for 26 states from
 8                  the State Emergency Department Databases (SEDD).

 9              o   SID and SEDD are both developed through the Healthcare Cost and Utilization
10                  Project (HCUP),  sponsored by the Agency for Healthcare Research and Quality
11                  (AHRQ).

12              o   The data generated from HCUPnet (HCUP's  online interactive tool) are state-
13                  level summary statistics, whereas the data from the HCUP distributor are at the
14                  individual discharge level.

15              o   In addition to being able to estimate State-level rates, SID and SEDD can also be
16                  used to obtain county-level hospital admission and ER visit counts by aggregating
17                  the discharge records by county.

18           EPA is in the process of obtaining the county-specific hospital admission and ER visit

19    baseline incidence data for the most recent single year available for most of the States included

20    in the HCUP data.  While we recognize that there is year-to-year variability in baseline incidence

21    data, a single year of data is being obtained due to resource constraints. We plan to examine the

22    potential variability in baseline incidence data and the impact this might have on the risk

23    estimates in sensitivity analyses based on endpoints and locations where we can obtain multi-

24    year baseline incidence data at little or no cost and by examining the variability in baseline

25    incidence rates at the State level.

26    5.3.4   Assessing Risk In Excess of Policy-Relevant Background
27           As noted above, staff plans to assess risks associated with ozone concentrations in excess

28    of policy-relevant background concentrations, and to assess risk reductions associated with just
      1 http://wonder.cdc.gov/mortsql.html
      2 Note:  For years 1999 - 2005, CDC Wonder uses ICD-10 codes; for years prior to 1999, it uses ICD-9 codes.
      Since most of the studies use ICD-9 codes, this means that EPA will have to create or find a mapping from ICD-9
      codes to ICD-10 codes if the most recent data available are to be used.
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 1    meeting current and alternative ozone standards.  Following the methods used in the prior ozone
 2    risk assessment, risks based on a concentration-response function estimated in an
 3    epidemiological or field study will be assessed down to the estimated policy relevant
 4    background.

 5           To assess risks associated with ozone concentrations in excess of policy-relevant
 6    background concentrations, staff will first calculate the difference between "as is" ozone levels
 7    and policy-relevant background.  Staff will then calculate the corresponding change in incidence
 8    of the health effect associated with that change in ambient ozone concentration. If Ax denotes
 9    the change  in ozone level from "as is" concentration to the background concentration, then the
10    corresponding change in incidence of the health effect, Ay, for a log-linear concentration-
11    response function (the most common functional form), is

12                                      Ay  = y[l-e-pAx]                                     (5-1)
13           where y denotes the baseline incidence and P is the coefficient of ozone in the
14    concentration-response function. A similar calculation would be made if the concentration-
15    response function is of a logistic form.

16           To assess the risk reduction associated with just meeting the current standard in those
17    locations that do not currently meet this standard, the procedure will be the same, except that in
18    this part of the risk assessment Ax will be the difference between "as is" ozone levels and the
19    ozone levels that will be estimated to exist if the current standards are just met.

20           To assess the risk reductions associated with just meeting alternative, more stringent
21    standards, above and beyond the risk reductions that would be achieved by just meeting the
22    current standards, Ax will be the difference between ozone levels that will be estimated to exist if
23    the current  standards are just met and ozone levels that will be estimated to exist if the
24    alternative, more stringent, standards are just met.

25           Because the ozone coefficient, P, is estimated rather than known, there is uncertainty
26    surrounding that estimate. This uncertainty is characterized as a normal distribution, with mean
27    equal to the ozone coefficient reported in the study, and standard deviation equal to the standard
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 1    error of the estimate, also reported in the study. From this information, staff plans to construct a
 2    95 percent confidence interval around the reported risk or risk reduction (number of cases of the
 3    health effect avoided), with that confidence interval primarily reflecting sampling error
 4    associated with the underlying effect estimate.l

 5    5.4  Characterization of Uncertainty and Variability in the Context of the Ozone Risk
 6         Assessment
 7    5.4.1   Overview of Approach for Addressing Uncertainty and Variability
 8           An important component of a population health risk assessment is the characterization of
 9    both uncertainty and variability.  Variability refers to the heterogeneity of a variable of interest
10    within a population or across different populations. For example, populations in different regions
11    of the country may have different behavior and activity patterns (e.g., air conditioning  use, time
12    spent indoors) that affect their exposure to ambient ozone and thus the population health
13    response.  The composition of populations in different regions of the country may vary in ways
14    that can affect the population response to exposure to ozone - e.g., two populations exposed to
15    the same levels of ozone might respond differently if one population is older than the other.
16    Variability is inherent and cannot be reduced through further research. Refinements  in the design
17    of a population risk assessment are often focused on more completely characterizing variability
18    in key factors affecting population risk - e.g., factors affecting population exposure or response -
19    in order to produce risk estimates whose distribution adequately characterizes the distribution in
20    the underlying population(s).

21           Uncertainty refers to the lack of knowledge regarding the actual values of inputs to an
22    analysis. Models are typically used in analyses, and  there is uncertainty about the true values of
23    the parameters of the model  (parameter uncertainty) - e.g., the value of the coefficient for ozone
24    in a C-R function. There is also uncertainty about the extent to which the model is an accurate
25    representation of the underlying physical systems or relationships being modeled (model
26    uncertainty) - e.g., the shapes of C-R  functions. In addition, there may be some uncertainty
27    surrounding other inputs to an analysis due to possible measurement error—e.g., the values of
      1 The confidence interval will not reflect the impact of other sources of uncertainty such as alternative model choice
      associated with deriving the effect estimate (although this source of uncertainty may be addressed as part of the
      sensitivity analysis - see Section 5.4.4).

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 1    daily ozone concentrations in a risk assessment location, or the value of the baseline incidence
 2    rate for a health effect in a population.l In any risk assessment, uncertainty is, ideally, reduced to
 3    the maximum extent possible through improved measurement of key variables and ongoing
 4    model refinement. However, significant uncertainty often remains, and emphasis is then placed
 5    on characterizing the nature of that uncertainty and its impact on risk estimates. The
 6    characterization of uncertainty can be both qualitative and, if a sufficient knowledgebase is
 7    available, quantitative.

 8           The characterization of uncertainty associated with risk assessment is often addressed in
 9    the regulatory context using a  tiered approach in which progressively more sophisticated
10    methods are used to evaluate and characterize sources of uncertainty depending on the overall
11    complexity of the risk assessment (WHO, 2008). Guidance documents developed by EPA for
12    assessing air toxics-related risk and Superfund Site risks (U.S. EPA, 2004 and 2001,
13    respectively) as well as recent guidance from the World Health Organization (WHO, 2008)
14    specify multitier approaches for addressing uncertainty.

15           For the ozone risk assessment, as noted above in section  5.1, we are planning to use  a
16    tiered framework developed by WHO to guide the characterization of uncertainty. The WHO
17    guidance presents a four-tiered approach, where the decision to proceed to the next tier is based
18    on the outcome of the previous tier's assessment. The four tiers described in the WHO guidance
19    include:

20       •   Tier 0: recommended  for routine screening assessments, uses default uncertainty actors
21           (rather than developing site-specific uncertainty characterizations);
22       •   Tier 1: the lowest level of site-specific uncertainty characterization, involves qualitative
23           characterization of sources of uncertainty (e.g., a qualitative assessment of the general
24           magnitude and direction of the effect on risk results);
25       •   Tier 2: site-specific deterministic quantitative analysis involving sensitivity analysis,
26           interval-based assessment, and possibly probability bound (high- and low-end)
27           assessment; and
      1 It is also important to point out that failure to characterize variability in an input used in modeling can also
      introduce uncertainty into the analysis. This reflects the important link between uncertainty and variability with the
      effort to accurately characterize variability in key model inputs actually reflecting an effort to reduce uncertainty.
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 1        •  Tier 3: uses probabilistic methods to characterize the effects on risk estimates of sources
 2           of uncertainty, individually and combined.
 3            With this four-tiered approach, the WHO framework provides a means for systematically
 4    linking the characterization of uncertainty to the sophistication of the underlying risk assessment.
 5    Ultimately, the decision as to which tier of uncertainty characterization to include in a risk
 6    assessment will depend both on the overall sophistication of the risk assessment and the
 7    availability of information for characterizing the various sources of uncertainty.

 8           The risk assessment to be completed for the ozone NAAQS review is relatively complex,
 9    thereby warranting consideration of a full probabilistic (WHO Tier 3) uncertainty analysis.
10    However, we  anticipate that limitations in available information will likely prevent this level of
11    analysis from being completed. In particular, the incorporation of uncertainty related to key
12    elements of C-R functions (e.g., competing lag structures, alternative functional forms, etc.) into
13    a full probabilistic WHO Tier 3 analysis would require that probabilities be assigned to each
14    competing specification of a given model element (with each probability reflecting a subjective
15    assessment of the probability that the given specification is the "correct" description of reality).
16    However, for  many model elements we expect that there will be  insufficient information on
17    which to base these probabilities. One approach that has been taken in such cases is expert
18    elicitation; however, this approach is resource- and time-intensive and consequently, it is not
19    feasible to use this technique in support of the ozone risk assessment.l

20           For most elements of this risk assessment, rather than conducting a full probabilistic
21    uncertainty analysis, we do expect to include a qualitative discussion of the potential impact of
22    uncertainty on risk results (WHO Tierl) and/or completed  sensitivity analyses assessing the
23    potential impact of sources of uncertainty on risk results (WHO Tier 2). In conducting sensitivity
24    analyses, we are planning to use both single- and multi-factor approaches (to look at the
25    individual and combined impacts of sources of uncertainty on risk estimates). In addition, in
      1 Note, that while we anticipate that a full probabilistic uncertainty analysis will not completed for this risk
      assessment, we are expecting to use confidence intervals associated with effects estimates (obtained from
      epidemiological studies) to incorporate statistical uncertainty associated with sample size considerations in the
      presentation of risk estimates. Technically, this type of probabilistic simulation represents a Tier 3 uncertainty
      analysis, although as noted here, it will be limited and only address uncertainty related to the fit of the C-R
      functions.
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 1    conducting sensitivity analyses, we expect to use only those alternative specifications for input
 2    parameters or modeling approaches that are deemed to have scientific support in the literature
 3    (and so represent alternative reasonable input parameter values or modeling options). This means
 4    that, as discussed earlier in section 5.1, the alternative risk results generated in the sensitivity
 5    analyses are expected to represent reasonable risk estimates that can be used to provide a context,
 6    with regard to uncertainty, within which to assess the set of core (base case) risk results.
 7    Potential sources of uncertainty included in the sensitivity analysis are presented below in
 8    section 5.3.4.

 9           The remainder of this section discusses how we are planning to address variability and
10    uncertainty within the ozone NAAQS risk assessment. The treatment of variability is discussed
11    first (section 5.4.2) by identifying sources of variability associated with the modeling of ozone-
12    related risk and noting which of those sources are reflected in the risk modeling approach
13    presented here.  Next, the treatment of uncertainty is addressed, which will include both a
14    qualitative  and quantitative component.  The qualitative component is described first (section
15    5.4.3), including plans for identifying and describing key sources of uncertainty, and noting
16    whether those sources of uncertainty  are addressed quantitatively in the risk assessment model.
17    A preliminary list of key sources of uncertainty for the risk assessment is provided as part of this
18    discussion. The quantitative component  of the uncertainty characterization approach, which is
19    structured around single-factor and multi-factor sensitivity analysis methods, is then described
20    (section 5.4.4).  The representativeness analysis planned to support interpretation of the urban
21    study area-level risk estimates is discussed in section 5.4.5.

22        5.4.2   Addressing Variability
23           Key sources of variability associated with the modeling of population-level risk
24    associated with ozone exposure are presented below, including whether, and to what extent, we
25    plan to address each source of variability:

26        •   Spatial gradients in ozone (and related population exposure): This source of
27           variability is likely to be less-well captured in the risk assessment primarily because the
28           majority of epidemiological studies providing effect estimates are themselves limited in
29           reflecting more detailed patterns of ozone exposure among populations. More
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 1           specifically, the epidemiological studies typically use an average ambient concentration
 2           developed across population-oriented monitors as a surrogate for exposure. Note,
 3           however that the exposure assessment described in Chapter 3 may allow this issue to be
 4           investigated to some degree, particularly as it impacts on exposure error misclassification
 5           in the epidemiological studies underpinning the C-R functions used in this risk
 6           assessment. In addition, a few epidemiological studies being considered for inclusion in
 7           this analysis include more refined characterization of population-level exposure (e.g.,
 8           based on more spatially differentiated linkages between population-level monitors and
 9           segments of the study population). We plan to consider the use of those studies with more
10           refined population exposure characterization to examine the issue of spatial gradients in
11           ozone and demographics and the degree to which this source of variability impacts risk
12           estimates.

13       •   Demographics (i.e., greater concentrations of susceptible populations in certain
14           locations): We plan to include multiple urban study areas reflecting differences in
15           demographics in different regions of the country to address this issue. In addition, as
16           noted in the previous bullet, we plan to consider studies with more refined
17           characterization of population-level exposure, to provide insights into the degree to which
18           this source of variability impacts risk estimates.

19       •   Behavior related to ozone  exposure (e.g., outdoor time, air conditioning use):  We
20           plan to  include multiple urban study areas reflecting differences in a variety of factors
21           related  to ozone exposure (e.g., time spent outdoors, air conditioner use,  housing stock,
22           which can affect ozone infiltration, and commuting patterns).

23       •   Susceptibility to specific populations to  ozone exposure (note - this could include a
24           number of factors e.g., magnitude of the effect estimate, underlying health status): We
25           plan to  consider this source  of variability by using effect estimates and lag structures
26           specific to each urban study location.

27       •   Differences in baseline incidence of disease: This  source of variability would
28           potentially be captured through the use of localized  baseline incidence data (e.g., county-
29           level).

30       •   Longer-term temporal variability in ambient  ozone levels (reflecting meteorological
31           trends,  as well  as future changes in the mix of ozone sources and regulations affecting
32           ozone): This is more difficult to incorporate into the analysis and reflects a combination
33           of variability as well as uncertainty.
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 1       5.4.3  Uncertainty Characterization - Qualitative Assessment

 2           As noted in section 5.4.1, we are planning to base the uncertainty analysis carried out for
 3    this risk assessment on the framework outlined in the WHO guidance document (WHO, 2008).
 4    That guidance calls for the completion of a Tier 1 qualitative uncertainty analysis, provided the
 5    initial Tier 0 screening analysis suggests the-re is concern that uncertainty associated with the
 6    analysis is sufficient to significantly affect risk results (i.e., to potentially affect decision making
 7    based on those risk results). Ozone risk assessments completed for previous NAAQS reviews
 8    have clearly identified sources of uncertainty that could have significant impacts on risk
 9    estimates,  thereby allowing us to skip a Tier 0 assessment and proceed directly to a Tier 1
10    analysis (i.e.,  a qualitative discussion of potential sources of uncertainty including an assessment
11    of the nature,  magnitude and potential direction of impact of each source of uncertainty on the
12    core  risk estimates). A preliminary list of potentially important sources  of uncertainty likely to
13    be included in a Tier 1 assessment has been developed for this plan and is  presented below (note,
14    some of these sources  may be addressed in the quantitative uncertainty analysis, when feasible):

15       •   Procedure for characterizing recent air quality for urban study areas: There is
16           uncertainty associated with characterizing recent air quality conditions at individual
17           urban  study areas. This uncertainty is reflected in the number of decisions or options that
18           must be considered in designing an approach for characterizing recent air quality
19           including: (a) whether to rely on monitoring data or to combine it with modeling data, (b)
20           how to match ambient ozone levels to potentially exposed populations from a spatial
21           standpoint (e.g., use a single composite monitor or a more differentiated polygon-based
22           exposure surface, and (c)  if a composite monitor approach is used,  whether to design that
23           composite monitor to most closely match the way monitoring data  were used in the
24           underlying epidemiological study providing the C-R function or  design it to be more
25           representative of potentially current exposures (e.g., weight it by activity profiles
26           simulated for the current population).

27       •   Procedures  for adjusting air quality to simulate alternate standard levels: There is
28           uncertainty in developing the method for adjusting current ambient ozone levels (at
29           individual monitors  used in the risk assessment) to simulate just attaining alternative
30           standard (methods available are likely to include both retrospective empirical monitor-
31           based trend analysis and forward-looking model-based predictions - see section 3.2.1 and
32           section 2.3 for  additional detail).
                                                 5-25

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 1       •   Estimates of policy-relevant background ozone levels in a particular location  There
 2           is uncertainty associated with characterizing background for individual locations (see
 3           Section 5.2.2 for additional detail).

 4       •   The impact of historical air quality on estimates of health risk from long-term ozone
 5           exposures (i.e., the amount of time that a population experiences new lower ambient
 6           ozone levels before there is a noticeable reduction in health effect incidence): Some
 7           studies of long-term mortality provide effect estimates differentiated by consecutive,
 8           multi-year time periods. These studies may provide insights into this issue and the degree
 9           to which it could affect risk estimates (by providing different effect estimates).

10       •   Statistical uncertainty associated with the fit of the C-R function.

11       •   Shape of the C-R function:  Of particular concern is uncertainty related to the shape of
12           the C-R function at lower exposure levels.

13       •   Potential role of co-pollutants and  different lag structures: these are related to the C-
14           R function (and nature of the associated effects estimate).

15       •   Transferability of C-R functions from study locations to urban study area locations:
16           this reflects variation in (a) ozone distributions, (b) the possible role of copollutants in
17           influencing risk, (c) relationship between ambient ozone and actual exposure, and (d)
18           differences in population characteristics. However, it is anticipated that the transferability
19           issue will play less of a role in the upcoming analysis, since studies used to derive C-R
20           functions will often be matched to our urban study area locations. However, there may
21           still be transferability  issues arising from changes in these factors between the time
22           period when the C-R functions were  estimated and the time period of this risk analysis.

23       5.4.4   Uncertainty Characterization - Quantitative Analysis

24           In addition to the Tier 1 qualitative assessment of uncertainty discussed in the previous
25    section, we are also anticipating that we will complete a Tier 2 assessment of uncertainty, which
26    involves application of deterministic methods including sensitivity analysis and bounding
27    analyses.l For this ozone risk assessment, we are planning to focus primarily on single and
28    multi-factor sensitivity analyses which are intended to (a) help identify which uncertainty factors
29    (acting either alone, or in concert with other factors) have a significant impact  on the core risk
      1 As noted earlier, uncertainty related to the statistical fit of the C-R functions will be addressed using probabilistic
      simulation to derive confidence intervals around the core risk estimates (i.e., a Tier 3 probabilistic approach towards
      characterizing the impact of uncertainty on core risk estimates).


                                                  5-26

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 1    estimates and (b) generate an alternative reasonable set of risk estimates that supplement the core
 2    risk estimates and involve overall consideration of uncertainty in the risk estimates.l  This
 3    quantitative uncertainty analysis would likely focus on a subset of the sources of uncertainty
 4    identified above for the Tier 1 assessment with this subset reflecting sources of uncertainty for
 5    which we can clearly identify competing datasets or modeling approaches with some degree of
 6    support in the literature. Table 5-1 identifies those modeling elements that are being considered
 7    for inclusion in the sensitivity analysis and includes identification of the options for each
 8    modeling element that could be considered in the sensitivity analysis. Note, that in each case,
 9    one of these options will likely be identified for the core analysis, with the remaining option(s)
10    either (a) being included as sensitivity analyses, or (b) discussed qualitatively as part of the
11    overall uncertainty analysis.  However, we are not prepared at this stage in the planning process
12    to identify core versus sensitivity analysis options, or to specific which alternative approaches
13    will be included in the quantitative sensitivity analysis versus covered qualitatively.

14            The step-wise procedure for conducting the deterministic uncertainty analysis is
15    illustrated in Figure 5-2. It is  important to point out that we plan to generate a core set of risk
16    estimates prior to conducting the uncertainty analysis. This core set of risk  estimates would be
17    derived by first applying the criteria discussed in preceding sections (sections 5.2 and 5.3) to
18    identify those options for key modeling elements which have the strongest scientific support
19    (with these determinations being based primarily on the evaluation provided in the ISA).2 The
20    core set of risk estimates will be generated for each combination of urban study area  and air
21    quality scenario.
      1 As noted earlier, ideally, we would also include a 2-dimensional probabilistic analysis of uncertainty and
      variability (i.e., a Tier 3 assessment in the WHO framework), since this would allow us to provide a more complete
      and integrated characterization of uncertainty and variability associated with risk estimates. However, limitations in
      our ability to assign rigorous and defensible confidence levels to competing modeling approaches and input datasets
      is expected to prevent us from completing this type of analysis.
      2For example, as noted in section 5.3.1, if a study provides both single-day and distributed lag models, the
      distributed lag model would be used in the core analysis, while the individual day lags, if retained in the risk
      assessment, would be included in the uncertainty analysis. With regard to non-linearity in functions, including the
      potential for thresholds, generally non-threshold models will be used in the core analysis (based on information
      provided in the ISA) and thresholds models, if they are considered at all, would be reserved for the uncertainty
      analysis. It is also important to point out that for some of the modeling elements, multiple options may be included
      as part of the core simulation (e.g., both multi- and single-component models may be used in core simulations for
      specific health endpoints).

                                                    5-27

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1   Table 5-1. Planned Sensitivity Analyses for the Epidemiologic-Based Risk Assessment
       Component of
          the Risk
        Assessment
     Options Potentially Considered for the Sensitivity Analysis
 (Note, these include all options identified for a particular component or
 modeling element - one of the identified options will likely be identified
 for the core analysis, with the remainder being included in the sensitivity
	analysis)	
     Air quality
     Characterization
     of recent air
     quality at urban
     study areas
   use of composite monitors (or other aggregations of monitors) that are
   linked to the method used for representing ambient ozone levels in the
   underlying epidemiological studies providing the C-R functions,
   use of a composite monitor approach that weights monitors by their
   contribution to population exposure (as reflected in
   microenvironmental exposure modeling) rather than matching
   structure to approach used in underlying epidemiological studies
   use of model-monitor fused surfaces. Note, that these alternative
   methods for characterizing current air quality may only be assessed at
   a subset of urban study areas as part of the sensitivity analysis.
     Background
     concentrations
•  use monitor data alone, or in combination with modeling data,
•  whether to use composite monitors or a more spatially-differentiated
                            exposure surface.
     Key design
     element associated
     with air quality
     sensitivity
     analysis
As noted in section 2.2 and 5.1, a key aspect of designing the approach
for characterizing air quality (including core and sensitivity analyses) is
consideration for the degree of spatial heterogeneity in monitored ozone
levels. If the spatial gradient within a study area in ozone levels is not that
substantial, reflecting the dominance of secondary formation for ozone,
than there may be little utility in considering varied approaches for
deriving exposure surrogates and conducting rollbacks (i.e., in a study
area with fairly uniform ozone levels at a given point in time, alternative
approaches for these modeling steps may not produce meaningfully
different results). Conversely , if a study area does have significant spatial
heterogeneity in ozone levels, then the approach used to characterize
current ozone levels and link it to population and the approach used to
conduct rollback could have a notable impact on risk estimates.
                                               5-28

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     Selection of health effect endpoint categories (and endpoints)
     Health effect
     endpoints to
     model
As noted in section 5.3, the core analysis will focus on endpoints
contained within health effect endpoint categories assigned a causal or
likely causal association with ozone exposure. The Sensitivity Analysis
may include endpoints (e.g., developmental) that are assigned a
suggestive of casual association classification. As noted in section 5.3, the
specific category of long-term exposure-related respiratory mortality (if
modeled), may be included in the core analysis, or as part of the
Sensitivity Analysis depending  on how the degree of support for an
association with ozone exposure is ultimately assessed.
     Exposure-response functions
     Extrapolation
     below lowest
     levels of exposure
     used in studies
   Extend model without modification below lowest exposure level
   Extend model to the lowest exposure level reflected in the underlying
   epidemiologic study (with this reflecting a higher-confidence
   calculation)
   Consider alternate model forms for points below lowest exposure
   level (if there is some rationale for this supported by study data,
   including toxicological information).
   Consideration for thresholds (only for long-term exposure-related
   endpoints).
     Consideration for
     alternative C-R
     functions
     reflecting different
     model constructs
     (e.g., single vs.
     multi-pollutant
     functions, single
     vs. multi city
     studies)
As noted in section 5.3.1, if an epidemiological study provides multiple
functions reflecting for example, a single versus multi-pollutant model,
we will include both forms.  Looking more broadly, we will also include
C-R functions (for a  given endpoint) from single and multi-city studies
given the strengths afforded by each (assuming that each study meets
criteria for a sound analysis).
     Bayesian-adjusted
     county-level
     estimates from
     multi-city short-
     term studies
Consideration for impact of using county-level Bayesian-adjusted C-R
functions (extracted form multi-city short-term studies) versus application
of the national-level effect estimates originally provided by these multi-
city studies.
     Baseline Incidence
1
2
     Aggregation scale
Consideration for more aggregate baseline incidence data (national, state,
etc.) versus county-specific information in the county with the best local
baseline incidence data
                                                5-29

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                 Overview of Uncertainty Analysis Approach Developed for the Ozone NAAQS Risk Assessment
              Risk Assessment Modeling Elements
Air
Cot



Quality:
Ambient Monitoring Data for Selected
Urban Areas (specification of composite
monitor)
Modeled Background Concentrations
(PRB)

Air Quality Adjustment (roll-back)
Procedures
icentration-Response:
Selection of Human Epidemiological
Studies to Provide Concentration-
Response Functions

Specification of Concentration -
Response Relationships - consider:
•single-v.s. multi-chemical
• single-v.s. multi-city
• lag
• seasonally-differentiated
• slope of CR function (threshold)
Baseline Health Effects Incidence Rates
and Demographics:



Estimates of City -specific Baseline
Health Effects Incidence Rates

City -specific Demographic Data


                                                               Primary Analysis Generating Core Set of Risk Estimates
                                                                 Apply criteria for
                                                                 selecting subset of
                                                                 modeling element
                                                                 options for use in
                                                                 the core analysis
                                                                                         Generate core set of risk
                                                                                         estimates for each
                                                                                         combination of urban study
                                                                                         area and air quality scenario
                                                                  Apply single-factor
                                                                  sensitivity analysis to
                                                                  identify those
                                                                  modeling elements
                                                                  with a significant
                                                                  impact on risk
                                                                  results
                                                                        •Hi
                                                               Uncertainty Analysis
                                                               Producing Additional Set of
                                                               Plausible Risk Estimates
Identify additional plausible
modeling options (distinct from
those used in the core analysis)
for this set of key modeling
elements
                                                                                    Conduct multi-factor sensitivity
                                                                                    analysis (forthis subset of key
                                                                                    modeling options) to generate
                                                                                    alternative sets of reasonable risk
                                                                                    estimates for a subset of urban
ystudy areas and air quality
                                                                                     scenarios
1

2
Figure 5-2. Overview of Approach For Uncertainty Analysis of Risk Assessment Based on Epidemiologic Studies
                                                                  5-30

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 1           Once the core set of risk estimates has been generated, the uncertainty analysis will begin
 2    with a single-factor sensitivity analysis intended to identify those modeling elements (comprising
 3    the ozone risk assessment framework) that have the potential to significantly impact risk
 4    estimates. This set of key modeling elements would form the basis for the uncertainty analysis.
 5    Next, plausible modeling options (distinct from those used in the core analysis) would be
 6    specified for each of these key modeling elements. In identifying these plausible modeling
 7    options, we plan to place emphasis on identifying input factors or modeling approaches, which,
 8    while representing alternatives to those used in the core simulation, still have some degree of
 9    scientific support in the literature.  Consequently, while we may have less confidence in risk
10    estimates generated using these alternate modeling options relative to the core risk estimates,
11    they could still considered reasonable and consequently may be interpreted as providing
12    additional perspective on overall uncertainty associated with the core set of risk estimates.

13           Once the set of plausible modeling options is specified for the key modeling elements, we
14    plan to use a multi-factor sensitivity analysis to generate a set of reasonable alternative risk
15    estimates. Specifically, various combinations of these alternative modeling options would be
16    used to generate risk estimates, each representing an uncertainty simulation.l We plan to
17    generate this set of alternative risk estimates for a subset of the urban study areas and  air quality
18    scenarios.

19           The combined sets  of core results and alternative risk estimates (for a combination of
20    urban study  area and air quality scenario) could be interpreted as representing an initial
21    characterization of risk for that combination of urban study area and  air quality scenario,
22    reflecting recognized sources of uncertainty in risk modeling. However, this interpretation needs
23    to be tempered by consideration of several factors: (a) this does not represent a characterization
24    of a distribution of uncertainty around the core set of risk estimates, it merely represents several
25    point estimates likely falling within that uncertainty distribution and  (b) the set of modeled risk
26    estimates may not  contain actual upper-bound and lower-bound risk  estimates given
27    scientifically defensible modeling options. Despite these caveats, the risk estimates defined by
      1 Note, that care would be taken in linking these modeling options together to insure that they are compatible and do
      not represent combinations that are scientifically not defensible.

                                                  5-31

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 1    the sets of core and alternative risk estimates should be useful to characterize confidence
 2    associated with the results of the application of the ozone risk assessment model.

 3    5.4.5   Representativeness Analysis
 4           As discussed in section 5.1, we are planning to complete a representativeness analysis
 5    designed to support the interpretation of risk estimates generated for the set of urban study areas
 6    included in the risk assessment. The representativeness analysis will focus on comparing urban
 7    study area-level values to national-scale distributions for key ozone-risk related attributes (e.g.,
 8    demographics including socioeconomic status, air-conditions use, baseline incidence rates and
 9    ambient ozone levels).  The goal, with these comparisons will be to assess the degree to which
10    the urban study areas provide coverage for different regions of the country as well as for areas of
11    the country likely to experience elevated ozone-related risk due to their specific mix of attributes
12    related to ozone risk.

13           The national-scale distributions of ozone risk-related parameters would be specified at
14    the country-level and would be based on generally available data, e.g. from the 2000 Census,
15    CDC, or other sources. The specific values of these parameters for the selected urban study areas
16    would then be plotted on these national-scale distributions, and an evaluation of how
17    representative the selected study areas are of the individual parameters, relative to the national
18    distributions, could be completed. The specific choices of parameters for which we would
19    examine the representativeness of the selected urban study areas would be informed through an
20    assessment of the epidemiology literature. We plan to particularly focus on meta-analyses and
21    multi-city studies which have identified parameters that influence heterogeneity in ozone effect
22    estimates, and exposure studies which have explored determinants of differences in personal
23    exposures to ambient ozone.  While personal exposure is not generally incorporated directly into
24    epidemiology studies evaluating ambient ozone-related effects,  differences in the ozone effect
25    estimates between cities clearly is impacted by differing levels of those exposure determinants.
26    Once we have identified these parameters, we plan to develop city-specific distributions for those
27    parameters (or reasonable surrogates) based on readily available data sources. Formal
28    comparisons of parameter distributions for the set of urban study areas and the city-specific
29    parameter distributions using standard statistical tests (e.g. the Kolmogorov-Smirnov test for
                                                 5-32

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 1    equality of distributions) would not be useful in this context, since we are more interested in
 2    meaningful differences than statistical significance. Therefore, we plan to use graphical
 3    comparisons using probability density functions, cumulative distribution functions, and boxplots.

 4           As noted in section 5.1, as part of the representativeness analysis, we are also considering
 5    generating national-scale ozone mortality estimates (based both on long-term and short-term
 6    exposure).  In the context of both short-term and long-term exposure-related mortality, these
 7    national-level estimates could be used to assess the degree to which our urban study areas
 8    capture urban areas across the U.S. that potentially experience the greatest ozone-related
 9    mortality.  For long-term mortality, we would consider a national-scale assessment conducted at
10    the county-level using the same national-level effect estimate obtained from the cohort study
11    used in modeling each urban study area (with this  assessment likely focusing on respiratory
12    mortality, as discussed above and in section 5.1). For short-term exposure-related mortality,
13    rather than generating a comprehensive national-estimate that has full coverage (i.e., covers all
14    counties in the U.S.), we would likely model the set of urban areas included in the time series
15    study that provided the effect estimates used in the primary estimate of short-term mortality
16    generated for our urban study areas (i.e., we would model mortality for each of the urban study
17    areas included in the underlying time series study). While the mortality estimate for short-term
18    exposure would not be truly national (in that it would not cover all counties in the country), by
19    including most of the larger urban areas in the U.S. it would provide close to a national estimate.
                                                 5-33

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 1    6   PRESENTATION OF RISK ESTIMATES TO INFORM CONSIDERATION OF
 2        STANDARDS
 3           This section discusses the nature of the risk estimates that we plan to generate as part of
 4    the review of the ozone NAAQS. We plan to conduct the risk assessment in two phases. Phase 1
 5    would include analysis of risk associated with recent air quality and simulating air quality to just
 6    meet the current NAAQS.  Phase 2 would focus on evaluating risk associated with simulating air
 7    quality that just meets alternative NAAQS under consideration.

 8           We plan to present risk estimates in two ways: (1) total  (absolute) health effects
 9    incidence (above background) for recent air quality and  simulations of air quality just meeting
10    the current and alternative NAAQS under consideration, and (2) risk reduction estimates,
11    reflecting the difference between (a) risks associated with recent air quality compared to risks
12    associated with just meeting the current NAAQS and (b) reflecting the difference between risks
13    associated with just meeting the current NAAQS compared to risks associated with just meeting
14    alternative NAAQS under consideration.

15           In presenting risk estimates, we plan to emphasize the core (base-case) estimates given
16    that these would include risk estimates with greater overall confidence. We plan to also present
17    additional risk estimates generated as part of the uncertainty analyses in order to provide
18    additional context for understanding the potential impact of uncertainty on the risk estimates and
19    particularly on the core estimates of risk.  The results of the representativeness analysis
20    (discussed in section 5.4.5) would likely be presented using a combination of (a) cumulative
21    probability plots (for the national-level distribution of ozone risk-related parameters) with the
22    locations where the individual urban study areas fell within those distributions noted in the plots
23    and (b) box and whisker plots, again contrasting the national-scale distribution of the ozone risk-
24    related parameters with the values for individual urban study areas.  Similar types of plots would
25    be used to present national-scale mortality estimates (should they be generated); contrasting them
26    with estimates generated for individual urban study areas.
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 1   7   SCHEDULE AND MILESTONES
 2          The Integrated Review Plan provides an overview of ozone review schedule. Table 7-1
 3   below includes the key milestones for the exposure analysis and health risk assessment that will
 4   be conducted as part of the current ozone NAAQS review. A consultation with the CASAC
 5   Ozone Panel is planned for May 19-20, 2011 to obtain input on this draft Scope and Methods
 6   Plan.  Staff will then proceed to develop exposure and health risk estimates associated with
 7   recent ozone levels and levels adjusted to just meet the current 8-hour ozone  standard.  These
 8   estimates and the methodology used to develop them will be discussed in the first draft ozone
 9   exposure analysis and health risk assessment.  This draft report will be released for CASAC and
10   public review in October 2011. EPA will receive comments on these draft documents from the
11   CASAC Ozone Panel and general public at a meeting in November 2011.  The revised exposure
12   analysis and risk assessment reports will include estimates associated with just meeting any
13   alternative standards that may be recommended by staff for consideration. The revised analyses
14   will be released in May 2012 for review by CASAC and the public at a meeting to be held in
15   July 2012.  Staff will consider these review comments and prepare a final exposure analysis and
16   risk assessment report by October 2012.
17
18
Table 7-1. Key Milestones for the Exposure Analysis and Health Risk Assessment for the
Ozone NAAQS Review
Milestone
First Draft Integrated Science Assessment (ISA)
Scope and Methods Plan for the Exposure Analysis and Health Risk
Assessment
CAS AC/public review and meeting on First Draft ISA
CASAC consultation on Scope and Methods Plan
Second Draft ISA
First Draft Exposure Analysis and Risk Assessment
CASAC/public review and meeting on Second Draft ISA and First
Draft Exposure Analysis and Risk Assessment
Final ISA
Second Draft Exposure Analysis and Risk Assessment
First Draft Policy Assessment
Date
March 20 11
April 20 11
May 19-20, 2011
May 19-20, 2011
September 20 11
October 20 11
November 20 11
February 20 12
May 20 12
June 20 12
                                               7-1

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CAS AC/public review and meeting on Second Draft Exposure
Analysis and Risk Assessment and First Draft Policy Assessment
Final Exposure Analysis and Risk Assessment
Second Draft Policy Assessment
CASAC/public review of Second Draft Policy Assessment
Final Policy Assessment
Proposed Rulemaking
Final Rulemaking
July 20 12
October 20 12
November 20 12
January 2013
March 20 13
September 20 13
June 20 14
7-2

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37
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United States                          Office of Air Quality Planning and Standards          Publication No. EPA-452/P-11-001
Environmental Protection               Health and Environmental Impacts Division                                April 2011
Agency                                      Research Triangle Park, NC

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