Health Risk and Exposure Assessment 

for Ozone

Second External Review Draft
ii

DISCLAIMER

This draft document has been prepared by staff from the Risk and Benefits Group, Health 

and Environmental Impacts Division, Office of Air Quality Planning and Standards, U.S. 

Environmental Protection Agency. Any findings and conclusions are those of the authors and do 

not necessarily reflect the views of the Agency. This draft document is being circulated to 

facilitate discussion with the Clean Air Scientific Advisory Committee to inform the EPA’s 

consideration of the ozone National Ambient Air Quality Standards.

This information is distributed for the purposes of pre-dissemination peer review under 

applicable information quality guidelines. It has not been formally disseminated by EPA. It 

does not represent and should not be construed to represent any Agency determination or policy. 

Questions related to this preliminary draft document should be addressed to Dr. Bryan 

Hubbell, U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, 

C539-07, Research Triangle Park, North Carolina 27711 (email: hubbell.bryan@epa.gov). 
iii

EPA-452/P-14-004a

February 2014

Health Risk and Exposure Assessment for Ozone

Second External Review Draft

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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v

TABLE OF CONTENTS

TABLE OF CONTENTS ...................................................................................................... v

TABLE OF FIGURES ......................................................................................................... xi

TABLE OF TABLES ........................................................................................................ xxv

1 INTRODUCTION .............................................................................................................. 1-1

1.1 HISTORY ........................................................................................................................ 1-3

1.2 CURRENT RISK AND EXPOSURE ASSESSMENT: GOALS AND PLANNED 

APPROACH .................................................................................................................... 1-5

1.3 ORGANIZATION OF DOCUMENT ............................................................................. 1-6

2 OVERVIEW OF EXPOSURE AND RISK ASSESSMENT DESIGN.......................... 2-1

2.1 POLICY-RELEVANT EXPOSURE AND RISK QUESTIONS.................................... 2-2

2.2 AIR QUALITY CHARACTERIZATION ...................................................................... 2-4

2.2.1 O3 Chemistry and Response to Changes in Precursor Emissions.......................... 2-5

2.2.2 Sources of O3 and O3 Precursors ........................................................................... 2-6

2.2.3 Simulation of Meeting Existing and Alternative Standards .................................. 2-7

2.2.4 Consideration of Health Evidence ......................................................................... 2-8

2.2.5 Exposures of Concern............................................................................................ 2-9

2.2.6 Health Endpoints.................................................................................................... 2-9

2.2.7 Exposure and Concentration-response Functions for Health Endpoints ............. 2-13

2.2.8 At-risk Populations.............................................................................................. 2-14

2.3 URBAN-SCALE MODELING OF INDIVIDUAL EXPOSURE................................. 2-15

2.3.1 Microenvironmental O3 Concentrations.............................................................. 2-16

2.3.2 Human Activity Patterns...................................................................................... 2-17

2.3.3 Modeling of Exposures Associated with Simulating Just Meeting O3

Standards........................................................................................................................ 2-19

2.3.4 Considerations in Selecting Urban Case Study Areas for the Exposure 

Analysis.......................................................................................................................... 2-19

2.4 RISK ASSESSMENT.................................................................................................... 2-19

2.4.1 Attributable Risk.................................................................................................. 2-20

2.4.2 Modeling of Risk for Total Exposure to O3......................................................... 2-21

2.4.3 Distributions of Risk Across O3 concentrations .................................................. 2-22

2.5 MODELING OF RISKS ASSOCIATED WITH SIMULATING JUST MEETING O3

STANDARDS................................................................................................................ 2-22

2.6 CONSIDERATIONS IN SELECTING URBAN CASE STUDY AREAS FOR THE 

RISK ANALYSIS.......................................................................................................... 2-23

2.7 RISK CHARACTERIZATION..................................................................................... 2-23

2.8 REFERENCES .............................................................................................................. 2-25
vi

3 SCOPE ................................................................................................................................ 3-1

3.1 OVERVIEW OF EXPOSURE AND RISK ASSESSMENTS FROM LAST REVIEW 3-2

3.1.1 Overview of Exposure Assessment from Last Review.......................................... 3-2

3.1.2 Overview of Risk Assessment from Last Review ................................................. 3-3

3.2 PLAN FOR THE CURRENT EXPOSURE AND RISK ASSESSMENTS.................... 3-5

3.3 CHARACTERIZATION OF UNCERTAINTY AND VARIABILITY IN THE 

CONTEXT OF THE O3 EXPOSURE AND RISK ASSESSMENT............................... 3-7

3.4 AIR QUALITY CHARACTERIZATION .................................................................... 3-10

3.5 EXPOSURE ASSESSMENT ........................................................................................ 3-13

3.6 URBAN-SCALE LUNG FUNCTION RISK ANALYSES BASED ON APPLICATION 

OF RESULTS FROM CONTROLLED HUMAN EXPOSURE STUDIES................. 3-15

3.7 URBAN CASE STUDY AREA EPIDEMIOLOGY-BASED RISK ASSESSMENT.. 3-17

3.8 NATIONAL-SCALE MORTALITY RISK ASSESSMENT........................................ 3-22

3.9 PRESENTATION OF EXPOSURE AND RISK ESTIMATES TO INFORM THE O3

NAAQS POLICY ASSESSMENT................................................................................ 3-24

3.10 REFERENCES .............................................................................................................. 3-26

4 AIR QUALITY CONSIDERATIONS............................................................................. 4-1

4.1 INTRODUCTION ........................................................................................................... 4-1

4.2 OVERVIEW OF O3 MONITORING AND AIR QUALITY DATA.............................. 4-1

4.3 OVERVIEW OF URBAN-SCALE AIR QUALITY INPUTS TO RISK AND 

EXPOSURE ASSESSMENTS........................................................................................ 4-4

4.3.1 Urban Case Study Areas........................................................................................ 4-5

4.3.2 Recent Air Quality ................................................................................................. 4-9

4.3.3 Air Quality Adjustments for “Just Meeting” Existing and Potential Alternative O3

Standards............................................................................................................ 4-14

4.4 OVERVIEW OF NATIONAL-SCALE AIR QUALITY INPUTS............................... 4-30

4.5 UNCERTAINTIES IN MODELING OF RESPONSES TO EMISSION REDUCTIONS 

TO JUST MEET EXISTING AND POTENTIAL ALTERNATIVE STANDARDS .. 4-38

4.6 REFERENCES .............................................................................................................. 4-52

5 CHARACTERIZATION OF HUMAN EXPOSURE TO O3 ........................................ 5-1

5.0 OVERVIEW .................................................................................................................... 5-1

5.1 SYNOPSIS OF O3 EXPOSURE AND EXPOSURE MODELING................................ 5-2

5.1.1 Human Exposure ......................................................................................................... 5-2

5.1.2 Estimating O3 Exposure .............................................................................................. 5-3

5.1.3 Modeling O3 Exposure Using APEX .......................................................................... 5-4

5.2 SCOPE OF THE EXPOSURE ASSESSMENT.............................................................. 5-6

5.2.1 Urban Areas Selected................................................................................................. 5-6

5.2.2 Time Periods Simulated ............................................................................................. 5-7

5.2.3 Ambient Concentrations Used ................................................................................... 5-9
vii

5.2.4 Meteorological Data Used ....................................................................................... 5-10

5.2.5 Populations Simulated.............................................................................................. 5-11

5.2.6 Key Physiological Processes And Personal Attributes Modeled............................. 5-15

5.2.7 Microenvironments Modeled................................................................................... 5-16

5.2.8 Model Output ........................................................................................................... 5-17

5.3 EXPOSURE ASSESSMENT RESULTS...................................................................... 5-23

5.3.1 Overview.................................................................................................................. 5-23

5.3.2 Exposure Modeling Results for Base Air Quality ................................................... 5-24

5.3.3 Exposure Modeling Results for Simulations of Just Meeting Existing and Alternative 

O3 Standards................................................................................................................... 5-25

5.4 TARGETED EVALUATION OF EXPOSURE MODEL INPUT AND OUTPUT DATA

5-35

5.4.1 Analysis of Time-Locaton-Activity Data ................................................................ 5-35

5.4.1.1 General Evaluation of CHAD Study Data: Historical and Recently Acquired Data

5-36

5.4.1.2 Exposure-Relevant Personal Attributes Included in CHAD and APEX Simulated 

Individuals...................................................................................................................... 5-36

5.4.1.3 Evaluation of Afternoon Time Spent Outdoors for CHAD and Survey Participants

5-37

5.4.1.4 Evaluation of Afternoon Time Spent Outdoors for ATUS Survey 

Participants..................................................................................................................... 5-38

5.4.1.5 Evaluation of Outdoor Time and Exertion Level for Asthmatics and NonAsthmatics in CHAD..................................................................................................... 5-39

5.4.2 Characterization of Factors Influencing High Exposures........................................ 5-40

5.4.3 Exposure Results for additional at-risk populations and Lifestages, Exposure 

scenarios, and Air Quality Input Data Used .................................................................. 5-41

5.4.3.1 Exposures Estimated for All School-age Children During Summer Months, 

Neither Attending School or Performing Paid Work..................................................... 5-41

5.4.3.2 Exposures Estimated for Outdoor Workers During Summer Months............... 5-43

5.4.3.3 Exposures Estimated for All School-age Children When Accounting for Averting 

Behavior......................................................................................................................... 5-45

5.4.3.4 Comparison of APEX Estimated Exposures Using Three Different Base Case Air 

Quality Data Sets: AQS, VNA, and EVNA................................................................... 5-46

5.4.3.5 Comparison of APEX Estimated Exposures Using Two Different Adjusted Air 

Quality Data Sets: Quadratic Rollback and HDDM...................................................... 5-48

5.4.4 Limited Performance Evaluations............................................................................ 5-49

5.4.4.1 Personal Exposure Comparisons........................................................................ 5-49

5.4.4.2 Ventilation Rate Comparisons........................................................................... 5-51

5.4.4.3 Evaluation of Longitudinal Profile Methodology.............................................. 5-55

5.5 VARIABILITY AND UNCERTAINTY....................................................................... 5-56
viii

5.5.1 Treatment of Variability........................................................................................... 5-56

5.5.2 Characterization of Uncertainty............................................................................... 5-57

5.6 KEY OBSERVATIONS................................................................................................ 5-65

5.7 REFERENCES .............................................................................................................. 5-71

6 CHARACTERIZATION OF HEALTH RISKS BASED ON CONTROLLED 

HUMAN EXPOSURE STUDIES................................................................................. 6-1

6.1 INTRODUCTION ........................................................................................................... 6-1

6.1.1 Development of Approach for Current Risk Assessment...................................... 6-2

6.1.2 Comparison of Controlled Human Exposure- and Epidemiologic-based Risk 

Assessments......................................................................................................... 6-3

6.2 SCOPE OF LUNG FUNCTION HEALTH RISK ASSESSMENT................................ 6-3

6.2.1 Selection of Health Endpoints ............................................................................... 6-4

6.2.2 Approach for Estimating Health Risk Based on Controlled Human Exposure 

Studies.................................................................................................................. 6-5

6.2.3 Controlled Human Exposure Studies..................................................................... 6-6

6.2.4 The McDonnell-Stewart-Smith (MSS) Model....................................................... 6-8

6.2.5 The Exposure-Response Function Approach Used in Prior Reviews ................. 6-16

6.3 O3 RISK ESTIMATES .................................................................................................. 6-21

6.3.1 Lung Function Risk Estimates Based on the McDonnell-Stewart-Smith Model 6-22

6.3.2 Lung Function Risk Estimates Based on the Exposure-Response Functions 

Approach Used in Prior Reviews....................................................................... 6-28

6.3.3 Comparison of the MSS Model with the Exposure-Response Function 

Approach........................................................................................................................ 6-29

6.4 EVALUATION OF THE MSS MODEL ...................................................................... 6-35

6.4.1 Summary of Published Evaluations..................................................................... 6-35

6.4.2 Children................................................................................................................ 6-35

6.4.3 Threshold vs. Non-Threshold Models................................................................. 6-36

6.5 CHARACTERIZATION OF UNCERTAINTY............................................................ 6-37

6.5.1 Statistical Model Form......................................................................................... 6-38

6.5.2 Convergence of APEX Results............................................................................ 6-41

6.5.3 Application of Model for All Lifestages.............................................................. 6-42

6.5.4 Application of Model for Asthmatic Children..................................................... 6-43

6.5.5 Interaction Between O3 and Other Pollutants...................................................... 6-43

6.5.6 Qualitative Assessment of Uncertainty................................................................ 6-43

6.6 DISCUSSION................................................................................................................ 6-46

6.7 REFERENCES .............................................................................................................. 6-50

7 CHARACTERIZATION OF HEALTH RISK BASED ON EPIDEMIOLOGICAL 

STUDIES ........................................................................................................................ 7-1

7.1 GENERAL APPROACH................................................................................................. 7-1

7.1.1 Basic Structure of the Risk Assessment................................................................. 7-1

7.1.2 Calculating O3-Related Health Effects Incidence................................................ 7-11

7.2 AIR QUALITY CONSIDERATIONS .......................................................................... 7-13
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7.3 SELECTION OF MODEL INPUTS AND ASSUMPTIONS....................................... 7-15

7.3.1 Selection of Urban Study Areas........................................................................... 7-15

7.3.2 Selection of Epidemiological Studies and Specification of 

Concentration-Response Functions ............................................................................... 7-17

7.3.3 Baseline Health Effect Incidence and Prevalence Data....................................... 7-30

7.3.4 Population (demographic) Data ........................................................................... 7-31

7.4 ADDRESSING VARIABILITY AND UNCERTAINTY ............................................ 7-31

7.4.1 Treatment of Key Sources of Variability............................................................. 7-34

7.4.2 Qualitative Assessment of Uncertainty................................................................ 7-38

7.4.3 Description of Core and Sensitivity Analyses..................................................... 7-45

7.5 URBAN STUDY AREA RESULTS............................................................................. 7-47

7.5.1 Assessment of Health Risk After Just Meeting the Existing 75 ppb standard..... 7-65

7.5.2 Assessment of Health Risk Associated with Simulating Meeting Potential 

Alternative Standards of 70, 65, and 60 ppb...................................................... 7-67

7.5.3 Sensitivity Analyses Designed to Enhance Understanding of the Core Risk 

Estimates............................................................................................................ 7-72

7.6 KEY OBSERVATIONS REGARDING OVERALL CONFIDENCE IN THE RISK 

ASSESSMENT AND RISK ESTIMATES ................................................................... 7-83

7.7 REFERENCES .............................................................................................................. 7-86

8 NATIONAL SCALE MORTALITY RISK BURDEN BASED ON APPLICATION OF

RESULTS FROM EPIDEMIOLOGICAL STUDIES ............................................... 8-1

8.1 NATIONAL-SCALE ASSESSMENT OF MORTALITY RELATED TO O3

EXPOSURE..................................................................................................................... 8-1

8.1.1 Methods.................................................................................................................. 8-2

8.1.2 Results.................................................................................................................... 8-6

8.1.3 Sensitivity Analysis ............................................................................................. 8-15

8.1.4 Discussion............................................................................................................ 8-19

8.2 EVALUATING THE REPRESENTATIVENESS OF THE URBAN STUDY AREAS IN

THE NATIONAL CONTEXT ...................................................................................... 8-21

8.2.1 Analysis Based on Consideration of National Distributions of Risk-Related 

Attributes............................................................................................................ 8-22

8.2.2 Analysis Based on Consideration of National Distribution of O3-Related 

Mortality Risk ................................................................................................................ 8-45

8.2.3 Analysis Based on Consideration of National Responsiveness of O3 Concentrations 

to Emissions Changes........................................................................................ 8-49

8.2.5 Discussion............................................................................................................ 8-77

8.3 REFERENCES .............................................................................................................. 8-78

9 SYNTHESIS ........................................................................................................................... 9-1

9.1 INTRODUCTION ........................................................................................................... 9-2

9.2 SUMMARY OF KEY RESULTS ................................................................................... 9-2

9.2.1 Air Quality Considerations (Chapter 4)................................................................. 9-2

9.2.2 Human Exposure Modeling (Chapter 5)................................................................ 9-8
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9.2.3 Health Risks Based on Controlled Human Exposure Studies (Chapter 6).......... 9-13

9.2.4 Health Risks Based on Epidemiological Studies (Chapters 7 and 8) .................. 9-18

9.3 COMPARISON OF RESULTS ACROSS EXPOSURE, LUNG FUNCTION RISK, 

AND EPIDMEIOLOGY-BASED MOTALITY AND MORBIDITY RISK 

ANALYSES................................................................................................................... 9-27

9.3.1 Evaluation of Exposures and Risks After Just Meeting the Existing Standard ... 9-27

9.3.2 Reductions in Exposure and Risk Metrics after Just Meeting Alternative 

Standards........................................................................................................................ 9-31

9.4 OVERALL ASSESSMENT OF REPRESENTATIVENESS OF EXPOSURE AND 

RISK RESULTS ............................................................................................................ 9-36

9.4.1 Representativeness of Selected Urban Case Study Areas in Reflecting Areas 

Across the Nation with Elevated Risk ............................................................... 9-36

9.4.2 Representativeness of Selected Urban Case Study Areas in Reflecting 

Responsiveness of Risk to Just Meeting Existing and Alternative O3

Standards........................................................................................................................ 9-37

9.5 OVERALL ASSESSMENT OF CONFIDENCE IN EXPOSURE AND RISK 

RESULTS ...................................................................................................................... 9-38

9.5.1 Uncertainties in Modeling O3 Responses to Meeting Standards......................... 9-39

9.5.2 Uncertainties in Modeling Exposure and Lung-function Risk ............................ 9-40

9.5.3 Uncertainties in Modeling Epidemiological-based Risk ..................................... 9-40

9.6 OVERALL INTEGRATED CHARACTERIZATION OF RISK IN THE CONTEXT OF 

KEY POLICY RELEVANT QUESTIONS................................................................... 9-42

9.7 REFERENCES .............................................................................................................. 9-47
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TABLE OF FIGURES

Figure 2-1. Overview of Exposure and Risk Assessment Design. ............................................. 2-2

Figure 2-2. Causal Determinations for O3 Health Effects. ....................................................... 2-11

Figure 3-1. Conceptual Diagram for Air Quality Characterization in the Health REA. .......... 3-10

Figure 3-2. Conceptual Diagram for Population Exposure Assessment................................... 3-13

Figure 3-3. Conceptual Diagram of O3 Lung Function Health Risk Assessment Based on 

Controlled Human Exposure Studies................................................................. 3-15

Figure 3-4. Conceptual Diagram of Urban Case Study Area Health Risk Assessment Based on 

Results of Epidemiology Studies....................................................................... 3-18

Figure 3-5. Conceptual Diagram of National O3 Mortality Risk Assessment Based on Results of 

Epidemiology Studies. ....................................................................................... 3-23

Figure 4-1. Map of Monitored 8-hour O3 Design Values for the 2006-2008 Period.................. 4-3

Figure 4-2. Map of Monitored 8-hour O3 Design Values for the 2008-2010 Period.................. 4-4

Figure 4-3. Flowchart of Air Quality Data Processing for Different Parts of the Urban-scale Risk 

and Exposure Assessments.................................................................................. 4-5

Figure 4-4. Trends in Annual 4th Highest 8-hour Daily Maximum O3 Concentrations in ppb for 

the 15 Urban Case Study Areas for 2006-2010. Urban areas are grouped into 3 

regions: Eastern (top), Central (middle), and Western (bottom). ........................ 4-7

Figure 4-5a. Maps of the 5 Eastern U.S. Urban Case Study Areas Including O3 Monitor 

Locations............................................................................................................ 4-11

Figure 4-5b. Maps of the 5 Central U.S. Urban Case Study Areas Including O3 Monitor 

Locations............................................................................................................ 4-12

Figure 4-5c. Maps of the 5 Western U.S. Urban Case Study Areas Including O3 Monitor 

Locations............................................................................................................ 4-13

Figure 4-6. Flowchart of HDDM adjustment methodology to inform risk and exposure 

assessment.......................................................................................................... 4-17

Figure 4-7. Distributions of composite monitor 8-hour daily maximum O3 concentrations from 

ambient measurements (black), quadratic rollback (blue), and the HDDM 

adjustment methodology (red) for meeting the existing standard. Values are based 

on the Zanobetti & Schwartz study areas for April-October of 2006-2008 ...... 4-21
xii

Figure 4-8. Distributions of composite monitor 8-hour daily maximum O3 concentrations from 

ambient measurements (black), quadratic rollback (blue), and the HDDM 

adjustment methodology (red) for meeting the existing standard. Values are based 

on the Zanobetti & Schwartz study areas for June-August of 2006-2008......... 4-22

Figure 4-9. Distributions of composite monitor 8-hour daily maximum values for the 12 urban 

case study areas in the epidemiology-based risk assessment. Plots depict values 

based on ambient measurements (base), and values obtained with the HDDM 

adjustment methodology showing attainment of 75, 70, 65 and 60 ppb standards. 

Values shown are based on CBSAs for April-October of 2007. Note that the 

HDDM adjustment technique was not able to adjust air quality to show attainment 

of a 60 ppb standard in New York, so no boxplot is shown for that case.......... 4-25

Figure 4-10. Distributions of composite monitor 8-hour daily maximum values for the 12 urban 

case study areas in the epidemiology-based risk assessment. Plots depict values 

based on ambient measurements (base), and values obtained with the HDDM 

adjustment methodology showing attainment of 75, 70, 65 and 60 ppb standards. 

Values shown are based on CBSAs for April-October of 2009. Note that Detroit 

air quality was meeting 75 ppb in 2008-2010, and the HDDM adjustment 

technique was not able to adjust air quality to show attainment of a 60 ppb 

standard in New York, so no boxplots are shown for those cases..................... 4-26

Figure 4-11. Maps showing the 4th highest (top) and May-September average (bottom) daily 

maximum 8-hour O3 concentrations in Atlanta based on 2006-2008 ambient 

measurements (left), HDDM adjustment to meet the existing standard (center), 

and HDDM adjustment to meet the alternative standard of 65 ppb (right). Squares 

represent measured values at monitor locations; circles represent VNA estimates 

at census tract centroids..................................................................................... 4-28

Figure 4-12. Maps showing the 4th highest (top) and May-September average (bottom) daily

maximum 8-hour O3 concentrations in New York based on 2006-2008 ambient 

measurements (left), HDDM adjustment to meet the existing standard (center), 

and HDDM adjustment to meet the alternative standard of 65 ppb (right). Squares 

represent measured values at monitor locations; circles represent VNA estimates 

at census tract centroids..................................................................................... 4-29
xiii

Figure 4-13. Maps of 4th highest (top) and May-September average (bottom) daily maximum 8-

hour O3 concentrations in Houston for 2006-2008 ambient measurements (left), 

HDDM adjustment to meet the existing standard (center), and HDDM adjustment 

to meet the alternative standard of 65 ppb (right). Squares represent measured 

values at monitor locations; circles represent VNA estimates at census tract 

centroids............................................................................................................. 4-30

Figure 4-14. May-September average 8-hour daily maximum O3 concentrations in ppb, based on 

a Downscaler fusion of 2006-2008 average monitored values with a 12km 2007 

CMAQ model simulation................................................................................... 4-32

Figure 4-15. June-August average 8-hour daily 10am-6pm mean O3 concentrations in ppb, based 

on a Downscaler fusion of 2006-2008 average monitored values with a 12km 

2007 CMAQ model simulation.......................................................................... 4-33

Figure 4-16. April-September average 1-hour daily maximum O3 concentrations in ppb, based on 

a Downscaler fusion of 2006-2008 average monitored values with a 12km 2007 

CMAQ model simulation................................................................................... 4-34

Figure 4-17. Frequency and Cumulative Distributions of the Three Fused Seasonal Average O3 

Surfaces Based on all CMAQ 12 km Grid Cells ............................................... 4-35

Figure 4-18. 2006-2008 O3 Design Values Versus 2006-2008 Fused Seasonal Average O3 Levels 

for the CMAQ 12km Grid Cells Containing O3 Monitors................................. 4-37

Figure 5-1. Conceptual Framework Used for Estimating Study Area Population O3 Exposure 

Concentrations ..................................................................................................... 5-8

Figure 5-2. Percent of asthmatic school-age children in all study areas with at least one O3

exposure at or above 60 ppb-8hr while at moderate or greater exertion using base 

air quality (2006-2010), stratified by year (top left panel) or by study area (bottom 

left panel). .......................................................................................................... 5-20

Figure 5-3. Percent of asthmatic school-age children in Atlanta with at least one O3 exposure at 

or above 60 ppb-8hr (left top panel), 70 ppb-8hr (middle top panel), and 80 ppb8hr (right top panel while at moderate or greater exertion, years 2006-2010 air 

quality adjusted to just meet the existing and alternative O3 standard levels. The 

multi-panel display (bottom) illustrates the same exposure results expanded to 
xiv

reflect individual data points by year, standard averaging period, and benchmark 

level.................................................................................................................... 5-22

Figure 5-4. Percent of asthmatic school-age children in Atlanta with multiple O3 exposures at or 

above 60 ppb-8hr while at moderate or greater exertion, years 2006-2010 air 

quality adjusted to just meet the existing and alternative O3 standard levels. ... 5-23

Figure 5-5. Percent of all school-age children with at least one daily maximum 8-hr average O3

exposure at or above 60, 70, and 80 ppb while at moderate or greater exertion, 

years 2006-2010, air quality adjusted to just meet the existing and potential 

alternative standards........................................................................................... 5-30

Figure 5-6. Percent of asthmatic school-age children with at least one daily maximum 8-hr 

average O3 exposure at or above 60, 70, and 80 ppb while at moderate or greater 

exertion, years 2006-2010, air quality adjusted to just meet the existing and 

potential alternative standards............................................................................ 5-31

Figure 5-7. Percent of all asthmatic adults with at least one daily maximum 8-hr average O3

exposure at or above 60, 70, and 80 ppb-8hr while at moderate or greater exertion, 

years 2006-2010, air quality adjusted to just meet the existing and potential 

alternative standards........................................................................................... 5-32

Figure 5-8. Percent of all older adults with at least one daily maximum 8-hr average O3 exposure 

at or above 60, 70, and 80 ppb-8hr while at moderate or greater exertion, years 

2006-2010, air quality adjusted to just meet the existing and potential alternative 

standards. ........................................................................................................... 5-33

Figure 5-9. Percent of all school-age children with multiple daily maximum 8-hr average O3

exposures at or above 60 ppb while at moderate or greater exertion, years 2006-

2010, air quality adjusted to just meet the existing and potential alternative 

standards. ........................................................................................................... 5-34

Figure 5-10. Comparison of the percent of all school-age children having daily maximum 8-hr 

average O3 concentration at or above 60 ppb during June, July, and August in 

Detroit 2007: using any available CHAD diary (“All CHAD Diaries”) or using 

CHAD diaries having no time spent in school or performing paid work (“No 

School/Work Diaries”)....................................................................................... 5-42
xv

Figure 5-11. Percent of workers between age 18-35 experiencing exposures at or above selected 

benchmark levels while at moderate or greater exertion using an outdoor worker 

approach (left panel) and a general population-based approach (right panel) 

considering air quality adjusted to just meet the existing standard in Atlanta, GA, 

Jun-Aug, 2006.................................................................................................... 5-45

Figure 5-12. Percent of all school-age children (left panel) and asthmatic school-age children 

(right panel) having daily maximum 8-hr average O3 concentration at or above 

benchmark levels during a 2-day simulation in Detroit, base air quality, August 1-

2, 2007. Red bars indicate exposure results when considering effect of averting. ...

............................................................................................................................ 5-46

Figure 5-13. Comparison of APEX exposure results generated for three study areas (Atlanta, 

Detroit, and Philadelphia) using three different 2005 air quality input data sets: 

AQS, VNA, and eVNA...................................................................................... 5-47

Figure 5-14. Comparison of exposure results generated by APEX using two different air quality 

adjustment approaches to just meet the existing standard in Atlanta: quadratic 

rollback (left panel) and HDDM (right panel)................................................... 5-48

Figure 5-15. Distribution of daily average O3 exposures (top panels) and daily afternoon outdoor 

time (bottom panels) and for DEARS study participants (left panels) and APEX 

simulated individuals (right panels) in Wayne County, MI, July-August 2006 5-50

Figure 5-16. Means (and range) of 6-day average personal O3 exposures, measured and modeled 

(APEX), Upland Ca. Obtained from Figure 8-22 of US EPA (2007b). ............ 5-51

Figure 5-17. Comparison of body mass normalized mean daily ventilation rates estimated by 

APEX (closed symbols) and by Brochu et al., 2006 (open symbols)................ 5-52

Figure 5-18. Comparison of body mass normalized mean daily ventilation rates in male and 

female school-age children (5-18) when correcting Brochu et al. (2006a) results 

with child appropriate VQ estimates.................................................................. 5-53

Figure 5-19. Comparison of body mass normalized daily mean ventilation rates in school-age 

children (5-18) estimated using APEX and literature reported values. ............. 5-55

Figure 5-20. Incremental increases in percent of all school-age children exposed to O3 at or 

above 60 ppb-8hr for each study area, year 2006-2010 air quality. Average 
xvi

percent (left panels), maximum percent (right panels), at least one exposure (top 

panels), at least two exposures (bottom panels) per year................................... 5-69

Figure 6-1. Two-Compartment Model........................................................................................ 6-1

Figure 6-2. Distribution of Responses (Lung Function Decrements in FEV1) Predicted by the 

MSS Model for 20-Year Old Individuals. Exposure to 100 ppb O3 at Moderate 

Exercise (40 L/min, BSA=2 m2) Under the Conditions of a Typical 6.6-hour 

Clinical Study..................................................................................................... 6-13

Figure 6-3. Median Response (Lung Function Decrements in FEV1) Predicted by the MSS 

Model for 20-Year Old Individuals. Exposure to 100 ppb O3 at Moderate Exercise 

(40 L/min, BSA=2 m2) Under the Conditions of a Typical 6.6-hour Clinical 

Study. ................................................................................................................. 6-14

Figure 6-4. Median Response (FEV1 Decrements) Predicted by the MSS Threshold and NonThreshold Models for 20-Year Old Individuals, Constant 100 ppb O3 Exposure, 2 

Hours Heavy Exercise (30 L/min-m2 BSA)...................................................... 6-15

Figure 6-5. Probability of Response ≥ 10% Predicted by the MSS Threshold and Non-Threshold 

Models for 20-Year Old Individuals, Constant 100 ppb O3 Exposure, 2 Hours 

Heavy Exercise (30 L/min-m2 BSA)................................................................. 6-15

Figure 6-6. Probabilistic Exposure-Response Relationships for FEV1 Decrements ≥ 10% for 8-

Hour Exposures At Moderate Exertion, Ages 18-35. Values associated with data 

points are the number of subject-exposures at each exposure concentration .... 6-20

Figure 6-7. Risk results for all school-aged children with ≥ 1 occurrences of FEV1 decrements ≥ 

10, 15, 20% for all cities, year, and scenarios (y-axis is percent of children 

affected). ............................................................................................................ 6-23

Figure 6-8. Risk results for all school-aged children with ≥ 1 occurrences of FEV1 decrements ≥ 

10% under the 0.07 ppm alternative standard showing variability across cities 

(horizontally) and years (vertically)................................................................... 6-24

Figure 6-9. Distribution of Daily FEV1 Decrements ≥ 10% Across Ranges of 8-hour Average 

Ambient O3 Concentrations (Los Angeles, 2006 recent air quality). ................ 6-27

Figure 6-10. Comparison of E-R and MSS Model (restricted to 8-hour average EVR ≥ 13) 

Response Functions (Atlanta 2006 base case, ages 18-35)................................ 6-33
xvii

Figure 6-11. Distribution of Daily Maximum 8-hour Average EVR For Values of EVR ≥ 13 

(L/min-m2) (midpoints on vertical axis) (Atlanta 2006 base case, ages 18-35).......

............................................................................................................................ 6-34

Figure 6-12. Sensitivity (Percent Change) of Population With One or More FEV1 Decrements ≥ 

10% to a 5% Increase in Individual MSS Model Parameter Estimates............. 6-40

Figure 6-13. Lung Function Risk Results, Incremental Increases In Risk For Increasing Standard 

Levels: Percent of All School-aged Children With FEV1 Decrement ≥ 10%, 

Highest Value For Each Study area Over Years................................................ 6-48

Figure 6-14. Lung Function Risk Results, Incremental Increases In Risk For Increasing Standard 

Levels: Percent of All School-aged Children With FEV1 Decrement ≥ 10%, Mean 

Value For Each Study Area Over Years............................................................ 6-49

Figure 7-1. Flow Diagram of Risk Assessment for Short-term Exposure Studies.................... 7-10

Figure 7-2. Heat Maps for Short Term O3-attributable Mortality (Just meeting existing standard 

and risk reductions from just meeting alternative standards) (2007) (Smith et al., 

2009 C-R functions)........................................................................................... 7-55

Figure 7-3. Heat Maps for Short Term O3-attributable Mortality (Just meeting existing standard 

and risk reductions from just meeting alternative standards) (2009) (Smith et al., 

2009 C-R functions) (see Key at bottom of figure)........................................... 7-56

Figure 7-4. Plots of Short-Term O3-attributable All-Cause Mortality for Meeting Existing 

standard and Alternative Standards (Smith et al., 2009) (Simulation year 2007 and 

2009) .................................................................................................................. 7-57

Figure 7-5. Plots of Short-Term O3-attributable Respiratory HA for Meeting Existing standard 

and Alternative Standards (Medina-Ramon, et al., 2006) (Simulation year 2007 

and 2009) ........................................................................................................... 7-61

Figure 7-6. Plots of Long-Term O3-attributable Respiratory Mortality for Meeting Existing 

standard and Alternative Standards (Jerrett et al., 2009) (Simulation year 2007 

and 2009) ........................................................................................................... 7-64

Figure 7-7. Sensitivity Analysis: Short-Term O3-attributable Mortality (air quality-related factors 

including study area size and method used to simulate attainment of existing and 

alternative standard levels) (2009) SA1-smaller (Smith-based) study area, SA2-

alternative method for simulating standards. ..................................................... 7-79
xviii

Figure 7-8. Sensitivity Analysis: Short-Term O3-attributable Mortality (C-R function 

specification) (2009) SA1-regional Bayes-based adjustment; SA2-copollutant 

model (PM10); SA3-Zanobetti and Schwartz-based effect estimates ................ 7-80

Figure 8-1. Conceptual Diagram for National-scale Mortality Risk Assessment ....................... 8-2

Figure 8-2. Estimated annual non-accidental premature deaths (individuals) in 2007 associated 

with average 2006-2008 May-September average 8-hr daily maximum O3 levels 

by county using Smith et al. (2009) effect estimates......................................... 8-10

Figure 8-3. Estimated annual all-cause premature deaths (individuals) in 2007 associated with 

average 2006-2008 June-August average 8-hr daily mean (10am-6pm) O3 levels 

by county using Zanobetti and Schwartz (2008) effect estimates. .................... 8-10

Figure 8-4. Estimated annual adult (age 30+) respiratory premature deaths (individuals) in 2007 

associated with average 2006-2008 April-September average 1-hr daily max O3

levels by county using Jerrett et al. (2009) effect estimates.............................. 8-11

Figure 8-5. Estimated percentage of May-September total non-accidental mortality (all ages) 

attributable to 2006-2008 average O3 levels by county using Smith et al. (2009) 

effect estimates................................................................................................... 8-11

Figure 8-6. Estimated percentage of June-August total all-cause mortality (all ages) attributable 

to 2006-2008 average O3 levels by county using Zanobetti and Schwartz (2008) 

effect estimates................................................................................................... 8-12

Figure 8-7. Estimated percentage of April-September respiratory mortality among adults age 30+ 

attributable to 2006-2008 average O3 levels by county using Jerrett et al. (2009) 

effect estimates................................................................................................... 8-12

Figure 8-8. Cumulative distribution of county-level percentage of all-cause, all-year, and all-age 

mortality attributable to 2006-2008 average O3 for the U.S.............................. 8-13

Figure 8-9. Cumulative percentage of total O3 deaths by baseline O3 concentration. O3

concentrations are reported as May-September average 8-hr daily maximum for 

results based on Smith et al. (2009) effect estimates, June-August average 8-hr 

mean (10am to 6pm) for results based on Zanobetti and Schwartz (2008) effect 

estimates, and April-September average 1-hr daily maximum for results based on 

Jerrett et al. (2009) effect estimates. .................................................................. 8-15
xix

Figure 8-10.Regions used in the sensitivity analysis based on the Smith et al. (2009) regionalprior Bayes-shrunken city-specific and regional average effect estimates (Source: 

Samet et al. 2000).. ............................................................................................ 8-18

Figure 8-11. O3-attributable premature deaths by region as calculated by applying Smith et al. 

(2009) regional prior Bayes-shrunken and regional average effect estimates, as 

compared with the national prior Bayes-shrunken and national average effect 

estimates as in the main results.......................................................................... 8-19

Figure 8-12. Comparison of county-level populations of urban case study area counties to the 

frequency distribution of population in 3,143 U.S. counties. ............................ 8-32

Figure 8-13. Comparison of county-level seasonal mean 8-hr daily maximum O3 concentrations 

in urban case study area counties to the frequency distribution of seasonal mean 

8-hr daily maximum O3 concentrations in 671 U.S. counties with O3 monitors.......

............................................................................................................................ 8-33

Figure 8-14. Comparison of 2007 county-level 4th high 8-hr daily maximum O3 concentrations in 

urban case study area counties to the frequency distribution of 2007 4th high 8-hr 

daily maximum O3 concentrations in 725 U.S. counties with O3 monitors. ...... 8-34

Figure 8-15. Comparison of county-level all-cause mortality in urban case study area counties to 

the frequency distribution of all-cause mortality in 3,137 U.S. counties .......... 8-35

Figure 8-16. Comparison of county-level non-accidental mortality in urban case study area 

counties to the frequency distribution of non-accidental mortality in 3,135 U.S. 

counties.............................................................................................................. 8-36

Figure 8-17. Comparison of city-level all-cause mortality risk coefficients from Zanobetti and 

Schwartz (2008) in urban case study areas to the frequency distribution of allcause mortality risk coefficients from Zanobetti and Schwartz (2008) in 48 U.S. 

cities................................................................................................................... 8-37

Figure 8-18. Comparison of city-level national prior Bayes-shrunken non-accidental mortality 

risk coefficients from Smith et al. (2009) in urban case study areas to the 

frequency distribution of national prior Bayes-shrunken non-accidental mortality 

risk coefficients from Smith et al. (2009) in 98 U.S. cities ............................... 8-38
xx

Figure 8-19. Comparison of county-level percent of population 0 to 14 years old in urban case 

study area counties to the frequency distribution of percent of population 0 to 14 

years old in 3,141 U.S. counties ........................................................................ 8-39

Figure 8-20. Comparison of county-level percent of population 0 to 14 years old in urban case 

study area counties to the frequency distribution of percent of population 0 to 14 

years old in 3,141 U.S. counties ........................................................................ 8-40

Figure 8-21. Comparison of county-level income per capita in urban case study areas to the 

frequency distribution of income per capita in 3,141 U.S. counties.................. 8-41

Figure 8-22. Comparison of county-level July temperature in urban case study area counties to 

the frequency distribution of July temperature in all U.S. counties................... 8-42

Figure 8-23. Comparison of city-level asthma prevalence in urban case study areas to the 

frequency distribution of asthma prevalence in 184 U.S. cities ........................ 8-43

Figure 8-24. Comparison of city-level air conditioning prevalence in urban case study areas to 

the frequency distribution of air conditioning prevalence in 76 U.S. cities ...... 8-44

Figure 8-25. Cumulative distribution of county-level percentage of May-September nonaccidental mortality for all ages attributable to 2006-2008 average O3 for the U.S. 

and the locations of the selected urban study areas along the distribution, using 

Smith et al. (2009) effect estimates.................................................................... 8-47

Figure 8-26. Cumulative distribution of county-level percentage of June-August all-cause 

mortality for all ages attributable to 2006-2008 average O3 for the U.S. and the 

locations of the selected urban study areas along the distribution, using Zanobetti 

and Schwartz (2008) effect estimates................................................................ 8-48

Figure 8-27. Cumulative distribution of county-level percentage of April-September adult (age 

30+) respiratory mortality attributable to 2006-2008 average O3 for the U.S. and 

the locations of the selected urban study areas along the distribution, using Jerrett 

et al. (2009) effect estimates and city definitions from Zanobetti and Schwartz 

(2008)................................................................................................................. 8-49

Figure 8-28. Change in 50th percentile summer season (April-October) daily 8-hr maximum O3

concentrations between 2001-2003 and 2008-2010. ......................................... 8-52

Figure 8-29. Change in 95th percentile summer season (April-October) daily 8-hr maximum O3

concentrations between 2001-2003 and 2008-2010 .......................................... 8-53
xxi

Figure 8-30. Population density at each O3 monitor.................................................................. 8-54

Figure 8-31. Distributions of O3 concentrations for high population density monitors by different 

subsets of months over a 13-year period. From top to bottom in each ribbon plot, 

the blue and white lines indicate the spatial mean of the 95th, 75th, 50th, 25th, and 

5th percentiles for each monitor for every year from 1998-2011....................... 8-56

Figure 8-32. Distributions of O3 concentrations for medium population density monitors by 

different subsets of months over a 13-year period. From top to bottom in each 

ribbon plot, the blue and white lines indicate the spatial mean of the 95th, 75th, 

50th, 25th, and 5th percentiles for each monitor for every year from 1998-2011....

............................................................................................................................ 8-57

Figure 8-33. Distributions of O3 concentrations for low population density monitors by different 

subsets of months over a 13-year period. From top to bottom in each ribbon plot, 

the blue and white lines indicate the spatial mean of the 95th, 75th, 50th, 25th, and 

5th percentiles for each monitor for every year from 1998-2011....................... 8-58

Figure 8-34. Map of O3 trends at specific monitors in the New York area. All upward and 

downward facing triangles represent statistically significant trends from 1998-

2011 (p < 0.05), circles represent locations with no significant trends. Sites used 

in Smith et al (2009) and the Zanobetti and Schwartz (2008) epidemiology studies 

are represented by colored dots. Only monitors with at least seven years of data 

are displayed. The pink star indicates the site with the higher design value in 

2011. The MSA border as defined by the U.S. census bureau is delineated by the 

light blue line. Left panel shows trends in annual 4th highest 8-hr daily maximum 

O3 values, center panel shows trends in annual mean 8-hr daily maximum O3

values, and right panel shows trends in annual median 8-hr daily maximum O3

values.. ............................................................................................................... 8-59

Figure 8-35. Map of O3 trends at specific monitors in the Chicago area. All upward and 

downward facing triangles represent statistically significant trends from 1998-

2011 (p < 0.05), circles represent locations with no significant trends. Sites used 

in Smith et al (2009) and the Zanobetti and Schwartz (2008) epidemiology studies 

are represented by colored dots. Only monitors with at least seven years of data 

are displayed. The pink star indicates the site with the higher design value in 
xxii

2011. The MSA border as defined by the U.S. census bureau is delineated by the 

light blue line. Left panel shows trends in annual 4th highest 8-hr daily maximum 

O3 values, center panel shows trends in annual mean 8-hr daily maximum O3

values, and right panel shows trends in annual median 8-hr daily maximum O3

values.. ............................................................................................................... 8-60

Figure 8-36. Ratio of April-October seasonal average O3 concentrations in the brute force 50% 

NOx emissions reduction CMAQ simulations to April-October seasonal average 

O3 concentrations in the 2007 base CMAQ simulation..................................... 8-64

Figure 8-37. Ratio of April-October seasonal average O3 concentrations in the brute force 90% 

NOx emissions reduction CMAQ simulations to April-October seasonal average 

O3 concentrations in the 2007 base CMAQ simulation..................................... 8-65

Figure 8-38. Ratio of January monthly average O3 concentrations in brute force 50% NOx 

emissions reduction CMAQ simulations to January monthly average O3

concentrations in the 2007 base CMAQ simulation .......................................... 8-66

Figure 8-39. Ratio of January monthly average O3 concentrations in brute force 90% NOx 

emissions reduction CMAQ simulations to January monthly average O3

concentrations in the 2007 base CMAQ simulation .......................................... 8-67

Figure 8-40. Histograms of U.S. population living in locations with increasing and decreasing 

mean O3. Values on the x-axis represent change in mean O3 (ppb) from the 2007 

base CMAQ simulation to the 50% NOx cut CMAQ simulation. The percentages 

of the U.S. population living in areas that have changes less than -1 ppb, from -1 

to +1 ppb, and greater than 1 ppb are shown on the y-axis. Left plots show 

population numbers in locations not included in one of the cases study areas while 

right plots show population numbers in locations included in one of the case study 

areas. Top plots show changes in January monthly mean O3, middle plots show 

changes in seasonal mean June-August O3, and bottom plots show changes in 

seasonal mean April-October O3 ....................................................................... 8-69

Figure 8-41. Histograms of U.S. population living in locations with increasing and decreasing 

mean O3. Values on the x-axis represent change in mean O3 (ppb) from the 2007 

base CMAQ simulation to the 90% NOx cut CMAQ simulation. The percentages 

of the U.S. population living in areas that have changes less than -1 ppb, from -1 
xxiii

to +1 ppb, and greater than 1 ppb are shown on the y-axis. Left plots show 

population numbers in locations not included in one of the cases study areas while 

right plots show population numbers in locations included in one of the case study 

areas. Top plots show changes in January monthly mean O3, middle plots show 

changes in seasonal mean June-August O3, and bottom plots show changes in 

seasonal mean April-October O3 ....................................................................... 8-70

Figure 8-42. Population (as % of total case-study area population) living in locations of 

increasing April-October seasonal mean O3 in the 50% NOx reduction CMAQ 

simulation........................................................................................................... 8-71

Figure 8-43. Population (as % of total case-study area population) living in locations of 

increasing April-October seasonal mean O3 in the 90% NOx reduction CMAQ 

simulation........................................................................................................... 8-72

Figure 8-44. Histograms of U.S. population living in locations with increasing and decreasing 

mean O3. Values on the x-axis represent the change in seasonal mean (AprilOctober) O3 from the 2007 base CMAQ simulation to the 50% NOx cut CMAQ 

simulation. The percentages of the U.S. population living in areas that have 

changes less than -1 ppb, from -1 to +1 ppb, and greater than 1 ppb are shown on 

the y-axis. Left plots show population numbers in locations not included in one of 

the cases study areas while right plots show population numbers in locations 

included in one of the urban case study areas. Bottom plots show histograms for 

low-mid population density areas while top plots show histograms for high 

population density areas..................................................................................... 8-74

Figure 8-45. Histograms of U.S. population living in locations with increasing and decreasing 

mean O3. Values on the x-axis represent the change in seasonal mean (AprilOctober) O3 from the 2007 base CMAQ simulation to the 90% NOx cut CMAQ 

simulation. The percentages of the U.S. population living in areas that have 

changes less than -1 ppb, from -1 to +1 ppb, and greater than 1 ppb are shown on 

the y-axis. Left plots show population numbers in locations not included in one of 

the cases study areas while right plots show population numbers in locations 

included in one of the urban case study areas. Bottom plots show histograms for 
xxiv

low-mid population density areas while top plots show histograms for high 

population density areas..................................................................................... 8-75

Figure 9-1. Distributions of composite monitor 8-hour daily maximum O3 concentrations from 

ambient measurements (black), quadratic rollback (blue), and the HDDM 

adjustment methodology (red) for meeting the existing standard ....................... 9-7

Figure 9-2. Effects of just meeting existing (columns 1 and 2) and alternative (columns 3 through 

8) standards on percent of children (ages 5-18) with at least one O3 exposure at or 

above 60, 70, and 80 ppb-8hr while at moderate or greater exertion, years 2006-

2010.................................................................................................................... 9-11

Figure 9-3. Effects of just meeting existing (75 ppb) and alternative standards on percent of 

children (ages 5-18) exceeding 60 ppb exposure benchmark, highest value across 

years for each urban case study area, 2006-2010 .............................................. 9-13

Figure 9-4. Effects of just meeting existing (column 1) and alternative (columns 2-4) standards 

on percent of children (ages 5-18) with FEV1 decrement > 10, 15, and 20%, years 

2006-2010 .......................................................................................................... 9-16

Figure 9-5. Impact of just meeting existing (75 ppb) and alternative standards on percent of 

children (ages 5-18) with FEV1 decrement > 10%, highest value for each urban 

case study area, 2006-2010................................................................................ 9-18

Figure 9-6. Impacts of just meeting existing (75 ppb) and alternative standard levels on mortality 

risk per 100,000 population for 2007 and 2009................................................. 9-21

Figure 9-7. Impacts of just meeting existing and alternative standard levels on adult (ages 65 and 

older) respiratory hospital admissions risk per 100,000 population for 2007 and 

2009.................................................................................................................... 9-22

Figure 9-8. Comparison of Exposure (Row 1) Lung Function Risk (Row 2) and EpidemiologyBased Risk (Rows 3 and 4) Metrics after Just Meeting the Existing 75 ppb 

Standard ............................................................................................................. 9-30

Figure 9-9. Comparison of Percent Reduction in Key Risk Metrics for Alternative Standard

Levels Relative to Just Meeting the Existing 75 ppb Standard ......................... 9-33
xxv

TABLE OF TABLES

Table 3-1. Short-term O3 Exposure Health Endpoints Evaluated in Urban Case Study Areas........

............................................................................................................................ 3-20

Table 4-1. Monitor and Area Information for the 15 Urban Case Study Areas in the Exposure 

Modeling and Clinical Study Based Risk Assessment ........................................ 4-6

Table 4-2. Monitor and Area Information for the 12 Urban Case Study Areas in the 

Epidemiology Based Risk Assessment................................................................ 4-9

Table 4-3. Summary Statistics Based on the Three Fused Seasonal Average O3 Surfaces Based 

on all CMAQ 12 km Grid Cells......................................................................... 4-36

Table 4-4. Correlation Coefficients Between the Three Fused Seasonal Average O3 Surfaces 

Based on all CMAQ 12 km Grid Cells.............................................................. 4-36

Table 4-5. Correlation Coefficients and Ratios of the 2006-2008 O3 Design Values to the 2006-

2008 Fused Seasonal Average O3 Levels for the CMAQ 12km Grid Cells 

Containing O3 Monitors..................................................................................... 4-38

Table 4-6. Summary of Qualitative Uncertainty Analysis of Key Air Quality Elements in the O3

NAAQS Risk Assessment.................................................................................. 4-42

Table 5-1. General Characteristics of the Population Exposure Modeling Domain Comprising 

Each Study Area .................................................................................................. 5-9

Table 5-2. Asthma Prevalence for Children and Adults Estimated by APEX in Each Simulated 

Study Area ......................................................................................................... 5-12

Table 5-3. Consolidated Human Activity Database (CHAD) Study Information and Diary-days 

Used by APEX................................................................................................... 5-14

Table 5-4. Ventilation equation coefficient estimates (bi) and residuals distributions (ei)....... 5-16

Table 5-5. Microenvironments Modeled, Calculation Method Used, and Variables Included .......

............................................................................................................................ 5-17

Table 5-6. Characterization of Key Uncertainties in Historical and Current APEX Exposure 

Assessments....................................................................................................... 5-58

Table 5-7. Mean and Maximum Percent of all School-age Children Estimated to Experience at 

Least One Daily Maximum 8-hr Average Exposure to O3 at or Above Selected

Health Benchmark Levels.................................................................................. 5-68
xxvi

Table 5-8. Mean and Maximum Percent of All School-age Children Estimated to Experience at 

Least Two Daily Maximum 8-hr Average Exposures to O3 At or Above Selected 

Health Benchmark Levels.................................................................................. 5-70

Table 6-1. Estimated Parameters in the MSS Models .............................................................. 6-11

Table 6-2. Age Term Parameters for Application of the 2012 MSS Threshold Model to All Ages

............................................................................................................................ 6-12

Table 6-3. Study-specific Ozone Exposure-response Data for Lung Function Decrements Based 

on Correcting Individual Responses for the Effect on Lung Function of Exercise 

in Clean Air, Ages 18-35 ................................................................................... 6-16

Table 6-4. Ranges of percents of population experiencing one or more days during the ozone 

season with lung function decrement (∆FEV1) more than 10%. The numbers in 

this table are the minimum and maximum percents estimated over all cities and 

years................................................................................................................... 6-25

Table 6-5. Ranges of percents of population experiencing one or more days during the ozone 

season with lung function decrement (∆FEV1) more than 15%. The numbers in 

this table are the minimum and maximum percents estimated over all cities and 

years. .................................................................................................................. 6-26

Table 6-6. Percents of the General Population and Outdoor Workers (ages 19-35) Experiencing 

1 or More and 6 or More FEV1 Decrements ≥ 15% (based on Atlanta 2006 APEX 

simulations)........................................................................................................ 6-28

Table 6-7. Ranges of percents of school-aged children experiencing one or more days during the 

ozone season with lung function decrement (∆FEV1) more than 10 and 15%. The 

numbers in this table are the minimum and maximum percents estimated over all 

cities and years................................................................................................... 6-29

Table 6-8. Comparison of responses from the MSS model with responses from the population 

exposure-response (E-R) method. 2006 existing standard, ages 5 to 18 ........... 6-30

Table 6-9. Comparison of MSS Model and E-R Model of Previous Reviews for Atlanta, Mar 1-

Oct 30, 2006, ages 18-35 ................................................................................... 6-32

Table 6-10. Comparison of MSS Model and E-R Model of Previous Reviews for Los Angeles, 

Jan 1-Dec 31, 2006, ages 18-35......................................................................... 6-32
xxvii

Table 6-11. Comparison of Responses from the MSS 2010 Model with Responses from 

McDonnell et al. (1985)..................................................................................... 6-36

Table 6-12. Percents of the population by age group with one or more days during the ozone 

season with lung function (FEV1) decrements more than 10, 15, and 20% (Atlanta 

2006 base case). MSS Threshold model, monitors air quality .......................... 6-37

Table 6-13. Percents of the population by age group with one or more days during the ozone 

season with lung function (FEV1) decrements more than 10, 15, and 20% (Atlanta 

2006 base case). MSS No-Threshold model, monitors air quality .................... 6-37

Table 6-14. MSS threshold model estimated parameters with confidence intervals................ 6-39

Table 6-15. Convergence results for the Atlanta 2006 base case with 200,000 simulated 

individuals. Percents of the population by age group with one or more days (and 

six or more days) during the ozone season with lung function (FEV1) decrements 

more than 10, 15, and 20%. Minimum and maximum values and ranges over 40 

APEX runs......................................................................................................... 6-42

Table 6-16. Summary of Qualitative Uncertainties of Key Modeling Elements in the O3 Lung 

Function Risk Assessment ................................................................................. 6-44

Table 7-1. Information on the 12 Urban Case Study Areas in the Risk Assessment ............... 7-14

Table 7-2. Overview of Epidemiological Studies Used in Specifying C-R Functions............. 7-23

Table 7-3. CBSA-based Study Areas with Multiple Effect Estimates from the Smith et al., 2009 

Study .................................................................................................................. 7-27

Table 7-4. Summary of Qualitative Uncertainty Analysis of Key Modeling Elements in the O3

NAAQS Risk Assessment.................................................................................. 7-41

Table 7-5. Specification of the Core and Sensitivity Analyses (air quality simulation)........... 7-46

Table 7-6. Specification of the Core and Sensitivity Analyses (alternative C-R function 

specification)...................................................................................................... 7-47

Table 7-7. Short-Term O3-attributable All Cause Mortality Incidence (2007 and 2009) (Smith et 

al., 2009 C-R Functions).................................................................................... 7-53

Table 7-8. Percent of Total All-Cause Mortality Attributable to O3 and Percent Change in O3-

Attributable Risk (2007 and 2009) (Smith et al., 2009 C-R functions)............. 7-54
xxviii

Table 7-9. Short-Term O3-attributable Morbidity Incidence, Percent of Baseline and Reduction 

in O3-attributable Risk – Respiratory-Related Hospital Admissions (2007 and 

2009) .................................................................................................................. 7-58

Table 7-10. Short-Term O3-attributable Morbidity Incidence, Percent of Baseline and Reduction 

in Ozone-attributable Risk – Emergency Room Visits (2007 and 2009) ......... 7-59

Table 7-11. Short-Term O3-attributable Morbidity Incidence, Percent of Baseline and Reduction 

in Ozone-attributable Risk – Asthma Exacerbations (2007 and 2009) ............. 7-60

Table 7-12. Long-Term O3-attributable Respiratory Mortality Incidence (2007 and 2009) (Jerrett 

et al., 2009 C-R Functions)................................................................................ 7-62

Table 7-13. Long-Term O3-attributable Respiratory Mortality Percent of Baseline Incidence and 

Percent Reduction in O3-attributable Risk (simulation years 2007 and 2009) 

(Jerrett et al., 2009 C-R Functions).................................................................... 7-63

Table 7-14. Sensitivity Analysis for Long-Term O3-attributable Respiratory Mortality –

Alternative C-R Function Specification (regional effect estimates) % of baseline 

all-cause mortality and change in O3-attribuable risk (2009) (Smith et al., 2009, 

O3 season) .......................................................................................................... 7-81

Table 7-15. Sensitivity Analysis for Long-Term O3-attributable Respiratory Mortality –

Alternative C-R Function Specification (national O3-only effect estimates) % of 

baseline all-cause mortality and change in O3-attribuable risk (2009) (Smith et al., 

2009, O3 season) ................................................................................................ 7-82

Table 8-1. Estimated annual O3-related premature mortality in 2007 associated with 2006-2008 

average O3 concentrations (95th percentile confidence interval) ........................ 8-7

Table 8-2. Mean, median, 2.5 percentile, and 97.5 percentile of the estimated percentage of 

mortality attributable to ambient O3 for all 3087 counties in the continental U.S....

.............................................................................................................................. 8-9

Table 8-3. Sensitivity of estimated O3-attributable premature deaths to the application of the 5th 

lowest and 5th highest city-specific risk estimates found by Smith et al. (2009) 

and Zanobetti and Schwartz (2008) to the gridcells between the cities included in

those studies....................................................................................................... 8-16

Table 8-4. Sensitivity of estimated O3-attributable premature deaths to the application of Smith et 

al. (2009) regional prior Bayes-shrunken city-specific and regional average effect 
xxix

estimates, as compared with the national prior Bayes-shrunken city-specific and 

national average effect estimates as in the main results .................................... 8-18

Table 8-5. Data Sources for O3 risk-related Attributes.............................................................. 8-25

Table 8-6. Summary Statistics for Selected O3 Risk-related Attributes.................................... 8-29

Table 8-7. Broad Regional Annual Trends of Concurrent O3 Concentrations and Emissions of 

NOx and VOCs over the 2000-2011 Time Period............................................. 8-61

Table 9-1. Area and Monitoring Information for the 15 Case Study Areas................................ 9-3

Table 9-2. General Patterns in Seasonal (May-Sept) Mean of Daily Maximum 8-hour O3

Concentrations after Adjusting to Meet Existing and Alternative Standards...... 9-5
xxx

LIST OF ACRONYMS/ABBREVIATIONS

AER air exchange rate

AHRQ Agency for Healthcare Research and Quality

APEX Air Pollution Exposure Model

AQI Air Quality Index

AQS Air Quality System

ATUS American Time Use Survey

BenMAP Benefits Mapping and Analysis Program

BRFSS Behavioral Risk Factor Surveillance System

BSA body surface area

CAA Clean Air Act

CASAC Clean Air Science Advisory Committee

CDC Center for Disease Control and Prevention

CDF cumulative distribution functions

CH4 methane

CHAD Consolidated Human Activity Database

CI confidence interval

CMAQ Community Multi-scale Air Quality

CO2 carbon dioxide

C-R Concentration Response (function)

ED emergency department

EGU electric generating unit

EPA U.S. Environmental Protection Agency

ER emergency room

eVNA enhanced Voronoi Neighbor Averaging

EVR equivalent ventilation rate

FEM Federal Equivalent Method

FEV1 one-second forced expiratory volume

FRM Federal Reference Method
xxxi

FVC forced vital capacity

HA hospital admissions

HDDM Higher-order Decoupled Direct Method

HNO3 nitric acid

HO2 hydro-peroxy radical

HUCP Healthcare Cost and Utilization Program

IPCC Intergovernmental Panel on Climate Change

IRP Integrated Review Plan

ISA Integrated Science Assessment

LML lowest measured level

MATS Modeled Attainment Test Software

METs metabolic equivalents of work

MSA Metropolitan Statistical Area

MT metric ton

NAAQS National Ambient Air Quality Standards

NCDC National Climatic Data Center

NEI National Emissions Inventory

NO nitric oxide

NO2 nitrite

NOx nitrogen oxides

O3 Ozone

OAQPS Office of Air Quality Planning and Standards

OH hydroxyl radical

PA Policy Assessment

PDI pain on deep inspiration

PI posterior interval

PM particulate matter

ppb parts per billion

ppm parts per million

PRB Policy Relevant Background

REA Risk and Exposure Assessment
xxxii

RR relative risk

SAB Science Advisory Board

SEDD State Emergency Department Databases

SES socioeconomic status

SID State Inpatient Databases

SO2 sulfur dioxide

STE stratosphere-troposphere exchange

TRIM Expo Total Risk Integrated Methodology Inhalation Exposure

VE ventilation rate

VNA Voronoi Neighbor Averaging

VOC volatile organic carbon

WHO World Health Organization
1-1

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 (O3), and related photochemical 

4 oxidants. The NAAQS review process includes four key phases: planning, science assessment, 

risk/exposure assessment, and policy assessment/rulemaking.1 5 This process and the overall plan 

6 for this review of the O3 NAAQS is presented in the Integrated Review Plan for the Ozone 

7 National Ambient Air Quality Standards (IRP, U.S. EPA, 2011a). The IRP additionally presents 

8 the schedule for the review; identifies key policy-relevant issues; and discusses the key scientific, 

9 technical, and policy documents. These documents include an Integrated Science Assessment 

10 (ISA), Risk and Exposure Assessments (REAs), and a Policy Assessment (PA). This draft Health 

11 REA is one of the two quantitative REAs developed for the review by the EPA’s Office of Air 

12 Quality Planning and Standards (OAQPS); the second is a Welfare REA. This draft Health REA 

13 focuses on assessments to inform consideration of the review of the primary (health-based) 

NAAQS for O3. 14 . 

15 The existing primary (health-based) NAAQS for O3 is set at a level of 75 ppb (0.075 

16 ppm), based on the annual fourth-highest daily maximum 8-hour average concentration, 

17 averaged over three years, and the secondary standard is identical to the primary standard (73 FR 

18 16436). The EPA initiated the current review of the O3 NAAQS on September 29, 2008, with an 

19 announcement of the development of an O3 ISA and a public workshop to discuss policy20 relevant science to inform EPA’s integrated plan for the review of the O3 NAAQS (73 FR 

21 56581). Discussions at the workshop, held on October 29-30, 2008, informed identification of 

22 key policy issues and questions to frame the review of the O3 NAAQS. Drawing from the 

workshop discussions, the EPA developed a draft and then final IRP (U.S. EPA, 2011).2 23 In early 

24 2013, the EPA completed the Integrated Science Assessment for Ozone and Related 

25 Photochemical Oxidants (U.S. EPA, 2013). The ISA provides a concise review, synthesis and

26 evaluation of the most policy-relevant science to serve as a scientific foundation for the review 

27 of the NAAQS. The scientific and technical information in the ISA, including that newly 

 1 For more information on the NAAQS review process see http://www.epa.gov/ttn/naaqs/review.html. 2 On March 30, 2009, EPA held a public consultation with the CASAC Ozone Panel on the draft IRP. The final IRP 

took into consideration comments received from CASAC and the public on the draft plan as well as input 

from senior Agency managers.
1-2

1 available since the previous review on the health effects of O3 includes information on exposure, 

2 physiological mechanisms by which O3 might adversely impact human health, an evaluation of 

3 the toxicological and controlled human exposure study evidence, and an evaluation of the 

4 epidemiological evidence, including information on reported concentration-response (C-R) 

5 relationships for O3-related morbidity and mortality associations, and also includes information 

on potentially at-risk populations and life-stages.3 6

7 This REA is a concise presentation of the conceptual model, scope, methods, key results, 

8 observations, and related uncertainties associated with the quantitative analyses performed. This 

9 REA builds upon the health effects evidence presented and assessed in the ISA, as well as 

10 CASAC advice (Samet, 2011), and public comments on a scope and methods planning document 

11 for the REA (here after, “Scope and Methods Plan,” U.S. EPA, 2011). Preparation of this second 

12 draft REA draws upon the final ISA and reflects consideration of CASAC and public comments 

13 on the first draft REA (Frey and Samet, 2012). This second draft health REA is being released, 

14 concurrently with the second draft welfare REA and second draft PA for review by the CASAC 

15 O3 Panel at a public meeting scheduled for March 25-27, 2014, and for public comment.

16 The second draft PA presents a staff evaluation and preliminary staff conclusions of the 

17 policy implications of the key scientific and technical information in the ISA, and second draft 

18 REAs. When final, the PA is intended to help “bridge the gap” between the Agency’s scientific 

19 assessments presented in the ISA and REAs, and the judgments required of the EPA 

20 Administrator in determining whether it is appropriate to retain or revise the NAAQS. The PA 

21 integrates and interprets the information from the ISA and REAs to frame policy options for 

22 consideration by the Administrator. In so doing, the PA recognizes that the selection of a specific 

23 approach to reaching final decisions on primary and secondary NAAQS will reflect the 

24 judgments of the Administrator. The development of the various scientific, technical and policy 

25 documents and their roles in informing this NAAQS review are described in more detail in the 

26 second draft PA.

 3 The ISA also evaluates scientific evidence for the effects of O3 on public welfare which EPA will consider in its 

review of the secondary O3 NAAQS. Building upon the effects evidence presented in the ISA, OAQPS has 

also developed a second draft of a second REA titled Ozone Welfare Effects Risk and Exposure Assessment 

(U.S. EPA, 2013).
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1 1.1 HISTORY

2 As part of the last O3 NAAQS review completed in 2008, EPA’s OAQPS conducted

3 quantitative risk and exposure assessments to estimate exposures above health benchmarks and 

4 risks of various health effects associated with exposure to ambient O3 in a number of urban study 

5 areas, selected to illustrate the public health impacts of this pollutant (U.S. EPA 2007a, U.S. 

6 EPA, 2007b). The assessment scope and methodology were developed with considerable input 

7 from CASAC and the public, with CASAC generally concluding that the exposure assessment 

8 reflected generally accepted modeling approaches, and that the risk assessments were well done, 

9 balanced and reasonably communicated (Henderson, 2006a). The final quantitative risk and 

10 exposure assessments took into consideration CASAC advice (Henderson, 2006a; Henderson, 

11 2006b), and public comments on two drafts of the risk and exposure assessments.

12 The exposure and health risk assessment conducted in the last review developed exposure 

13 and health risk estimates for 12 urban areas across the U.S., based on 2002 to 2004 air quality 

14 data. That assessment provided annual or O3 season-specific exposure and risk estimates for 

15 these years of air quality and for air quality scenarios, simulating just meeting the then-existing 

16 8-hour O3 standard set in 1997 at a level of 0.08 ppm and several alternative 8-hour standards. 

17 The strengths and limitations in the assessment were characterized, and analyses of key 

18 uncertainties were presented.

19 Exposure estimates from the last assessment were used as an input to the risk assessment 

20 for lung function responses (a health endpoint for which exposure-response functions were 

21 available from controlled human exposure studies). Exposure estimates were developed for the 

22 general population and population groups including school age children with asthma as well as 

23 all school age children. The exposure estimates also provided information on exposures to 

24 ambient O3 concentrations at and above specified benchmark levels (referred to as “exposures of 

25 concern”), to provide some perspective on the public health impacts of health effects associated 

26 with O3 exposures in controlled human exposure studies that could not be evaluated in the 

27 quantitative risk assessment (e.g., lung inflammation, increased airway responsiveness, and 

28 decreased resistance to infection). For several other health endpoints, O3-related risk estimates 

29 were generated using concentration-response relationships reported in epidemiological or field

30 studies, together with ambient air quality concentrations, baseline health incidence rates, and

31 population data for the various locations included in the assessment. Health endpoints included
1-4

1 in the assessment based on epidemiological or field studies included: hospital admissions for 

2 respiratory illness in four urban areas, premature mortality in 12 urban areas, and respiratory 

3 symptoms in asthmatic children in 1 urban area.

4 Based on the 2006 Air Quality Criteria for Ozone (U.S. EPA, 2006), the Staff Paper 

5 (U.S. EPA, 2007), and related technical support documents (including the REAs), the proposed 

6 decision was published in the Federal Register on July 11, 2007 (72 FR 37818). The EPA 

7 proposed to revise the level of the primary standard to a level within the range of 0.075 to 0.070 

8 ppm. Two options were proposed for the secondary standard: (1) replacing the current standard 

9 with a cumulative seasonal standard, expressed as an index of the annual sum of weighted hourly 

10 concentrations cumulated over 12 daylight hours during the consecutive 3-month period within 

11 the O3 season with the maximum index value (W126), set at a level within the range of 7 to 21 

12 ppm-hours, and (2) setting the secondary standard identical to the revised primary standard. The 

13 EPA completed the review with publication of a final decision on March 27, 2008 (73 FR 

14 16436), revising the level of the 8-hour primary O3 standard from 0.08 ppm to 0.075 ppm, as the 

15 3-year average of the fourth highest daily maximum 8-hour average concentration, and revising 

16 the secondary standard to be identical to the revised primary standard.

17 Following promulgation of the revised O3 standard in March 2008, state, public health, 

18 environmental, and industry petitioners filed suit against EPA regarding that final decision. 

19 At EPA’s request, the consolidated cases were held in abeyance pending EPA’s 

20 reconsideration of the 2008 decision. A notice of proposed rulemaking to reconsider the 

21 2008 final decision was issued by the Administrator on January 6, 2010. Three public 

22 hearings were held. The Agency solicited CASAC review of the proposed rule on January 

23 25, 2010, and additional CASAC advice on January 26, 2011. On September 2, 2011, the 

24 Office of Management and Budget returned the draft final rule on reconsideration to EPA for 

25 further consideration. EPA decided to coordinate further proceedings on its voluntary 

26 rulemaking on reconsideration with this ongoing periodic review, by deferring the 

27 completion of its voluntary rulemaking on reconsideration until it completes its statutorily28 required periodic review. In light of that, the litigation on the 2008 final decision proceeded. 

29 On July 23, 2013, the Court ruled on the litigation of the 2008 decision, denying the 

30 petitioners suit except with respect to the secondary standard, which was remanded to the 

31 Agency for reconsideration. The second draft PA provides additional description of the court 
1-5

1 ruling with regard to the secondary standard.

2 1.2 CURRENT RISK AND EXPOSURE ASSESSMENT: GOALS AND PLANNED 

3 APPROACH

4 The goals of the current quantitative exposure and health risk assessments are to provide 

5 information relevant to answering questions regarding the adequacy of the existing O3 standard 

6 and the potential improvements in public health from meeting alternative standards. To meet 

7 these goals, this assessment provides results from several analyses, including (1) estimates of the 

8 number of people in the general population and in at-risk populations and lifestages with O3

9 exposures above benchmark levels, while at moderate or greater exertion levels; (2) estimates of 

10 the number of people in the general population and in at-risk populations and lifestages with 

11 impaired lung function resulting from exposures to O3; and (3) estimates of the potential 

12 magnitude of premature mortality and selected morbidity health effects in the population, 

13 including at-risk populations and lifestages, where data are available to assess these groups. For 

14 each of the analyses, we provide estimates for recent ambient levels of O3 and for air quality 

15 conditions simulated to just meet the existing O3 standard and alternative standards.

16 In presenting these results, we evaluate the influence of various inputs and assumptions 

17 on the exposure and risk estimates to more clearly differentiate alternative standards that might 

18 be considered, including potential impacts on various at-risk populations and lifestages. We also 

19 evaluate the distribution of risks and patterns of risk reduction and uncertainties in those risk 

20 estimates. In addition, we have conducted an assessment to provide nationwide estimates of the 

21 potential magnitude of premature mortality associated with recent ambient O3 concentrations, to 

22 more broadly characterize this risk on a national scale. This assessment includes an evaluation of 

23 the distribution of risk across the U.S., to assess the extent to which we have captured the upper 

24 end of the risk distribution with our urban study area analyses.

25 This current quantitative risk and exposure assessment builds on the approach used and

26 lessons learned in the last O3 risk and exposure assessment, and focuses on improving the 

27 characterization of the overall confidence in the exposure and risk estimates, including related 

28 uncertainties, by incorporating a number of enhancements, in terms of both the methods and data 

29 used in the analyses. This risk assessment considers a variety of health endpoints for which, in 

30 staff’s judgment, there is adequate information to develop quantitative risk estimates that can 

31 meaningfully inform the review of the primary O3 NAAQS.
1-6

1 1.3 ORGANIZATION OF DOCUMENT 

2 The remainder of this document is organized as follows. Chapter 2 provides a conceptual 

3 framework for the risk and exposure assessment, including discussions of O3 chemistry, sources 

4 of O3 precursors, exposure pathways and microenvironments where O3 exposure can be high, at5 risk populations and lifestages, and health endpoints associated with O3. This conceptual 

6 framework sets the stage for the scope of the risk and exposure assessments. Chapter 3 provides 

7 an overview of the scope of the quantitative risk and exposure assessments, including a summary 

8 of the previous risk and exposure assessments, and an overview of the current risk and exposure 

9 assessments. Chapter 4 discusses air quality considerations relevant to the exposure and risk 

10 assessments, including available O3 monitoring data, and important inputs to the risk and 

11 exposure assessments. Chapter 5 describes the inputs, models, and results for the human 

12 exposure assessment, and discusses the literature on exposure to O3, exposure modeling 

13 approaches using the Air Pollution Exposure Model (APEX), the scope of the exposure 

14 assessment, inputs to the exposure modeling, sensitivity and uncertainty evaluations, and 

15 estimation of results. Chapter 6 describes the estimation of health risks based on application of 

16 the results of controlled human exposure studies, including discussions of health endpoint 

17 selection, approaches to calculating risk, and results. Chapter 7 describes the estimation of health 

18 risks in selected urban areas based on application of the results of observational epidemiology 

19 studies, including discussions of air quality characterizations, model inputs, variability and 

20 uncertainty, and results. Chapter 8 describes the national scale risk characterization and urban 

21 area representativeness analysis. Chapter 9 provides an integrative discussion of the exposure 

22 and risk estimates generated in the analyses drawing on the results of the analyses based on both 

23 clinical and epidemiology studies, and incorporating considerations from the national scale risk 

24 characterization.
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1 2 OVERVIEW OF EXPOSURE AND RISK ASSESSMENT DESIGN

2 In this chapter, we summarize our framework for assessing exposures to O3 and the 

3 associated risks to human populations. Figure 2-1 provides an overview of the general design of 

4 this exposure and risk assessment, which includes air quality characterization, review of relevant 

5 scientific evidence on health effects, modeling of exposure, modeling of risk, and risk 

6 characterization. Each element identified in the diagram is described in a specific, identified 

7 chapter of this exposure and risk assessment.

8 In this O3 exposure and risk assessment, modeling of personal exposure and estimation of 

9 risks which rely on personal exposure estimates, are implemented using the Air Pollution 

Exposure model (APEX)1 10 (U.S. EPA, 2012 a, b). Modeling of population level risks for 

11 endpoints based on application of results of epidemiological studies, is implemented using the 

environmental Benefits Mapping and Analysis Program (BenMAP),

2 12 a peer reviewed software 

13 tool for estimating risks and impacts associated with changes in ambient air quality (U.S. EPA, 

14 2013). The overall characterization of risk draws from the results of the exposure assessment and 

15 both types of risk assessment.

16 The remainder of this chapter includes summary discussions of each of the main elements 

17 of Figure 2-1, including policy-relevant exposure and risk questions (Section 2.1), 

18 characterization of ambient O3, including important sources of O3 precursors, and its relation to 

19 population exposures, as well as simulation of just meeting existing and potential alternative O3

20 standards (Section 2.2), review of health evidence identified in the literature describing 

21 associations with ambient O3 (Section 2.3), key components of exposure modeling (Section 2.4), 

22 key components of risk modeling (Section 2.5), and risk characterization (Section 2.6).

23 Specific details related to the scope of the exposure and risk assessments and how each 

24 element will be addressed in the quantitative exposure and risk analysis are provided in Chapter 

25 3.

26

 1 APEX is available for download at http://www.epa.gov/ttn/fera/human_apex.html 2 BenMAP is available for download at http://www.epa.gov/air/benmap/
2-2

1

2 Figure 2-1 Overview of Exposure and Risk Assessment Design

3

4 2.1 POLICY-RELEVANT EXPOSURE AND RISK QUESTIONS

5 The first step in the design is to determine the set of policy-relevant exposure and risk 

6 questions that will be informed by the assessment. Consistent with recommendations from the 

7 recent National Academy of Sciences report “Science and Decisions: Advancing Risk 

8 Assessment” (NAS, 2009), these exposure and risk assessments have been designed to address 

9 the risk questions identified in the Integrated Review Plan for the Ozone National Ambient Air 

10 Quality Standards (U.S. EPA, 2011). We have focused on designing the exposure and risk 

11 assessments to inform consideration of those risk-related policy-relevant questions in the 

12 separately developed O3 NAAQS Policy Assessment. The risk-related policy-relevant questions 

13 identified in the Integrated Review Plan are related to two main activities, evaluation of the 

14 adequacy of the existing standards and, if appropriate, evaluation of potential alternative 

15 standards (U.S. EPA, 2011). With regard to evaluation of the adequacy of the existing standards, 

16 the risk-related policy-relevant questions are: 

Air Quality Characterization

(Chapter 4)

Urban Scale 

Assessment of 

Individual Exposure

(Chapter 5)

Urban Scale Risk 

Analyses Based on 

Application of 

Results from 

Epidemiological 

Studies

(Chapter 7)

National Scale Risk 

Burden Based on 

Application of 

Results from 

Epidemiological 

Studies

Urban Scale Risk (Chapter 8)

Analyses Based on 

Application of 

Results from 

Controlled Human 

Exposure Studies

(Chapter 6)

Risk Characterization

(Chapter 9)

APEX

BenMAP

Review of Health Evidence

(Chapter 2)

Policy Relevant Exposure 

and Risk Questions

(Chapter 2)

Exposure Assessment Risk Assessment
2-3

1 “To what extent do risk and/or exposure analyses suggest that exposures of 

2 concern for O3-related health effects are likely to occur with existing ambient 

3 levels of O3 or with levels that just meet the O3 standard? Are these 

4 risks/exposures of sufficient magnitude such that the health effects might 

5 reasonably be judged to be important from a public health perspective? What are 

6 the important uncertainties associated with these risk/exposure estimates?”

7 With regards to evaluation of potential alternative standards, the risk-related policy-relevant 

8 questions are:

9 “To what extent do alternative standards, taking together levels, averaging times 

10 and forms, reduce estimated exposures and risks of concern attributable to O3

11 and other photochemical oxidants, and what are the uncertainties associated with 

12 the estimated exposure and risk reductions? What conclusions can be drawn 

13 regarding the health protection afforded at-risk populations?”

14

15 This risk and exposure assessment is designed to inform consideration of these questions 

16 through application of exposure and risk modeling for a set of urban case study areas. Exposure 

17 and risk estimates will be generated for recent O3 concentrations, O3 concentrations after 

18 simulating just meeting the existing standards, and O3 concentrations after simulating just 

19 meeting potential alternative standards. Careful consideration will be given to addressing 

20 variability and uncertainty in the estimates, and to the degree to which at-risk populations 

21 experience exposures and risks. Exposure modeling is discussed in Chapter 5 (Urban-Scale 

22 Assessment of Individual Exposure), while risk modeling is discussed in Chapter 6 

23 (Characterization of Health Risks Based on Clinical Studies) and Chapter 7 (Characterization of 

24 Health Risks Based on Epidemiological Studies). Chapter 8 (National-Scale Risk Assessment 

25 and Representativeness Analysis) provides a national-scale assessment of risks under recent O3

26 concentrations to provide context for the urban-scale analyses and to help characterize the 

27 representativeness of the urban-scale analyses.

28 In order to inform consideration of the risk-related policy-relevant questions, the first step 

29 for all of the exposure and risk analyses is simulation of meeting the existing and alternative 

30 standards. To do this, recent air quality measurements of O3 are adjusted such that they mimic a 

31 realistic and general atmospheric response to changes in precursor emissions for the specific 

32 urban area and so that they just meet the existing and alternative standard levels. Conceptually, 

33 there is an almost infinite set of combinations of precursor emissions reductions that will result 

34 in just meeting the existing or alternative standards. The specific combinations of reductions that 

35 might actually be implemented are not relevant for the exposure and risk analyses, as those will 

36 result from the implementation processes which follow the establishment of a standard. 
2-4

1 However, it is appropriate to ask the question of how the patterns of ambient O3 on multiple 

2 temporal scales (hourly, daily, monthly, seasonally) and across each urban area, may respond to 

3 precursor emissions reductions that result in meeting the existing and potential alternative 

4 standards, and how these different patterns of O3 could affect the exposure and risk results. The 

5 answers to these questions are critical inputs to the exposure and risk analyses. Consideration of 

6 the available methods for simulating just meeting existing and alternative standards is discussed 

7 in Chapter 4 (Air Quality Characterization).

8 Analyses presented in this document to inform the policy-relevant risk questions 

9 regarding potential alternative standards, are focused on alternative levels for an 8-hour standard. 

Other elements of the standard (indicator, averaging time, and form),3 10 are addressed in the Policy 

11 Assessment as part of the overall evaluation of the health protection afforded by the primary O3

12 standards.

13 With regard to potential alternative levels for an 8-hour O3 standard, the quantitative risk 

14 assessment evaluates the range of levels in 5 ppb increments from 60 to 70 ppb. These levels 

15 were selected based on the evaluations of the evidence provided in the first draft PA, which 

16 received support from the CASAC in their advisory letter on the first draft PA (Frey and Samet, 

17 2012). For a subset of urban areas, we also evaluated a standard level of 55 ppb, consistent with 

18 recommendations from CASAC to also give consideration to evaluating a level somewhat below 

19 60 ppb. Thus, for most areas, we evaluate exposures and risks for potential alternative standard 

20 levels of 70, 65, and 60 ppb. Some additional analyses were also included for evaluation of 

21 exposures and risks for a potential alternative 8-hour standard level of 55 ppb.

22 2.2 AIR QUALITY CHARACTERIZATION

23 In order to address the policy-relevant questions discussed in Section 2.1, the first step is 

24 characterizing O3 concentrations relevant to estimation of exposure and risk. This requires 

25 characterization of recent O3 concentrations, O3 concentrations after simulating just meeting the 

26 existing standards, and O3 concentrations after simulating just meeting potential alternative 

27 standards. This section provides conceptual information on O3 formation and responsiveness of 

28 O3 to changes in precursor emissions, that inform the simulations of just meeting existing and 

29 alternative standards.

 3 The “form” of a standard defines the air quality statistic that is compared to the level of the standard in determining 

whether an area attains the standard. The existing form of the 8-hour O3 standard is the 4th highest daily 

maximum 8-hour average O3, averaged over 3 years. The “indicator” of a standard defines the chemical species 

or mixture that is to be measured in determining whether an area attains the standard.
2-5

1 2.2.1 O3 chemistry and response to changes in precursor emissions

2 O3 occurs naturally in the stratosphere where it provides protection against harmful solar 

3 ultraviolet radiation, and it is formed closer to the surface in the troposphere from precursor 

4 emissions from both natural and anthropogenic sources. O3 is created when its two primary 

5 precursors, volatile organic compounds (VOC) and oxides of nitrogen (NOx), combine in the 

6 presence of sunlight. VOC and NOx are, for the most part, emitted directly into the atmosphere. 

7 Carbon monoxide (CO) and methane (CH4) can also be important for O3 formation (U.S. EPA, 

8 2013, section 3.2.2).

9 Rather than varying directly with emissions of its precursors, O3 changes in a nonlinear 

10 fashion with the concentrations of its precursors. NOx emissions lead to both the formation and 

11 destruction of O3, depending on the local concentrations of NOx, VOC, and radicals such as the 

12 hydroxyl (OH) and hydroperoxy (HO2) radicals. In areas dominated by fresh emissions of NOx, 

13 these radicals are removed via the production of nitric acid (HNO3), which lowers the O3

14 formation rate. In addition, the depletion of O3 by reaction with NO is called “titration” and is 

15 often found in downtown metropolitan areas, especially near busy streets and roads, and in 

16 power plant plumes. This “titration” results in O3 concentrations that can be much lower than in

17 surrounding areas. Titration is usually confined to areas close to strong NOx sources, and the 

18 NO2 formed can lead to O3 formation later and further downwind. Consequently, O3 response to 

19 reductions in NOx emissions is complex and may include O3 decreases at some times and 

20 locations and increases of O3 in other times and locations. In areas with low NOx concentrations, 

21 such as those found in remote continental areas and rural and suburban areas downwind of urban 

22 centers, the net production of O3 typically varies directly with NOx concentrations, and increases

23 with increasing NOx emissions.

24 In general, the rate of O3 production is limited by either the concentration of VOCs or 

25 NOx, and O3 formation, using these two precursors relies on the relative sources of OH and NOx. 

26 When OH radicals are abundant and are not depleted by reaction with NOx and/or other species,

27 O3 production is referred to as being “NOx-limited” (U.S. EPA, 2013, section 3.2.4). In this 

28 situation, O3 concentrations are most effectively reduced by lowering NOx emissions, rather than 

29 lowering emissions of VOCs. When the abundance of OH and other radicals is limited either 

30 through low production or reactions with NOx and other species, O3 production is sometimes 

31 called “VOC-limited” or “radical limited” or “NOx-saturated” (Jaegle et al., 2001), and O3 is 

32 most effectively reduced by lowering VOCs. However, even in NOx-saturated conditions, very 

33 large decreases in NOx emissions can cause the O3 formation regime to become NOx-limited. 

34 Consequently, reductions in NOx emissions (when large), can make further emissions reductions 

35 more effective at reducing O3. Between the NOx-limited and NOx-saturated extremes there is a 

36 transitional region, where O3 is less sensitive to marginal changes in either NOx or VOCs. In 
2-6

1 rural areas and downwind of urban areas, O3 production is generally NOx-limited. However, 

2 across urban areas with high populations, conditions may vary. For contrast, while data from 

3 monitors in Nashville, TN, suggest NOx-limited conditions exist there, data from monitors in Los 

4 Angeles suggest NOx-saturated conditions (U.S. EPA, 2013, Figure 3-3).

5 2.2.2 Sources of O3 and O3 Precursors

6 O3 precursor emissions can be divided into anthropogenic and natural source categories, 

7 with natural sources further divided into biogenic emissions (from vegetation, microbes, and 

8 animals), and abiotic emissions (from biomass burning, lightning, and geogenic sources). The 

9 anthropogenic precursors of O3 originate from a wide variety of stationary and mobile sources. 

10 In urban areas, both biogenic and anthropogenic VOCs, as well as CO, are important for 

11 O3 formation. Hundreds of VOCs are emitted by evaporation and combustion processes from a 

12 large number of anthropogenic sources. Based on the 2005 national emissions inventory (NEI), 

13 solvent use and highway vehicles are the two main anthropogenic sources of VOCs, with 

14 roughly equal contributions to total emissions (U.S. EPA, 2013, Figure 3-2). The emissions

15 inventory categories of “miscellaneous” (which includes agriculture and forestry, wildfires, 

16 prescribed burns, and structural fires), and off-highway mobile sources are the next two largest 

17 contributing emissions categories with a combined total of over 5.5 million metric tons a year 

18 (MT/year).

19 On the U.S. and global scales, emissions of VOCs from vegetation are much larger than

20 those from anthropogenic sources. Emissions of VOCs from anthropogenic sources in the 2005 

21 NEI were ~17 MT/year (wildfires constitute ~1/6 of that total), compared to emissions from 

22 biogenic sources of 29 MT/year. Vegetation emits substantial quantities of VOCs, such as 

23 isoprene and other terpenoid and sesqui-terpenoid compounds. Most biogenic emissions occur 

24 during the summer because of their dependence on temperature and incident sunlight. Biogenic 

25 emissions are also higher in southern and eastern states than in northern and western states for 

26 these reasons and because of species variations.

27 Anthropogenic NOx emissions are associated with combustion processes. Based on the 

28 2005 NEI, the three largest sources of NOx are on-road and off-road mobile sources (e.g., 

29 construction and agricultural equipment), and electric power generation plants (EGUs) (U.S.

30 EPA, 2013, Figure 3-2). Emissions of NOx therefore are highest in areas having a high density of 

31 power plants and in urban areas having high traffic density. However, it is not possible to make 

32 an overall statement about their relative impacts on O3 in all local areas because EGUs are 

33 sparser than mobile sources, particularly in the west and south and because of the nonlinear 

34 nature of O3 chemistry discussed in Section 2.2.1.
2-7

1 Major natural sources of NOx in the U.S. include lightning, soils, and wildfires. Biogenic 

2 NOx emissions are generally highest during the summer and occur across the entire country,

3 including areas where anthropogenic emissions are low. It should be noted that uncertainties in 

4 estimating natural NOx emissions are much larger than for anthropogenic NOx emissions.

5 O3 concentrations in a region are maintained by a balance between photochemical 

6 production and transport of O3 into the region; and loss of O3 by chemical reactions, deposition 

7 to the surface and transport out of the region. O3 transport occurs on many spatial scales 

8 including local transport between cities, regional transport over large regions of the U.S. and 

9 international/long-range transport. In addition, O3 is also transfered into the troposphere from the 

10 stratosphere, which is rich in O3, through stratosphere-troposphere exchange (STE). STE occurs 

11 in tropopause “foldings” that occur behind cold fronts, bringing stratospheric air with them (U.S. 

12 EPA, 2013, section 3.4.1.1). Contributions to O3 concentrations in an area from STE are defined 

13 as being part of background O3 (U.S. EPA, 2013, section 3.4).

14 2.2.3 Simulation of Meeting Existing and Alternative Standards

15 Conceptually, simulation of meeting existing and alternative standards should reflect the 

16 physical and chemical processes of O3 formation in the atmosphere and estimate how hourly 

17 values of O3 at each monitor in an urban area would change in response to reductions in 

18 precursor emissions, allowing for nonlinearities in response to emissions reductions and allowing 

19 for nonlinear interactions between reductions in NOx and VOC emissions. For this assessment, 

20 we have employed sophisticated air quality models to conduct simulations of hourly O3

21 responses to reductions in precursor emissions. This modeling incorporates all known emissions, 

22 including emissions from both natural and anthropogenic sources within and outside of the U.S. 

23 By using the model-adjustment methodology we are able to more realistically simulate the 

24 temporal and spatial patterns of O3 response to precursor emissions. We chose to simulate just 

25 meeting the existing and alternative standards, by applying equal proportional decreases in U.S. 

26 anthropogenic emissions of NOx and VOC, in order to avoid any suggestion that we are 

27 approximating a specific emissions control strategy that a state or urban area might adopt to meet 

28 a standard. These analyses allow us to apply an adjustment to ambient O3 measurements in the 

29 urban case study areas, to better represent how air quality concentrations at each monitor would 

30 change to meet the existing and alternative standard levels. The details of the specific approach 

31 used to simulating attainment for the existing and alternative standards, are discussed in greater 

32 detail in Chapter 4 and in the Chapter 4 appendices.

33 It is fundamentally a policy decision, as to which sources of precursor emissions are most 

34 appropriate to decrease to simulate just meeting existing and alternative O3 standards. In 

35 addressing the policy-relevant questions regarding the evaluation of alternative standards,
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1 consistent with previous reviews of the O3 standards, this analysis is focused on simulating 

2 reductions in risk associated with precursor emissions originating from anthropogenic sources 

3 within the U.S. In doing so, we recognize that the CAA provides mechanisms primarily for 

4 reducing emissions from U.S. emissions sources. As such, we estimate changes in exposure and 

5 risks likely to result from just meeting alternative standards relative to just meeting the existing 

6 standards, by simulating changes in atmospheric concentrations that represent atmospheric 

7 response to reductions in U.S. anthropogenic emissions. However, we recognize that, in this 

8 approach, we are simulating attainment of existing and alternative standard levels, based on 

9 recent air quality concentrations and the chemical environment and emissions in those years. We 

10 have not mimicked the future-year atmospheric conditions and emissions inventory as would be 

11 done for the implementation process.

12 In addition, while it is possible to decrease O3 concentrations using decreases in either 

13 NOx or VOC or both NOx and VOC, the specific combination of the reductions in those 

14 emissions is a policy decision, with recognition that atmospheric chemistry considerations will 

15 make NOx and VOC decreases more or less effective in specific urban areas, depending on the 

16 degree to which O3 formation is NOx or VOC limited. As discussed above, in most locations, 

17 decreases in NOx are the most effective means to decrease ambient O3 concentrations. However, 

18 in some downtown urban areas, O3 formation is VOC-limited, and therefore smaller decreases in 

19 NOx will not decrease O3.

20 2.2.4 Consideration of Health Evidence

21 A critical input for both the exposure and risk assessments is the health evidence 

22 summarized in the Integrated Science Assessment (ISA) (U.S. EPA, 2013). This health evidence 

23 provides the basis for evaluating the significance of exposures to O3, by informing health 

24 benchmarks for estimating exposures of concern. The evidence also provides the basis for 

25 selecting health endpoints that will be modeled in the risk assessment. This evidence includes 

26 controlled human exposure studies and observational epidemiology studies. The health evidence 

27 is also the source of the specific studies that are used to develop exposure-response (E-R) and 

28 concentration-response (C-R) functions, used in the risk assessment. Finally, the health evidence 

29 provides information on at-risk populations to guide the selections of study populations used in 

30 the exposure and risk assessments. The following subsections summarize key conceptual aspects 

31 regarding exposures of concern, health endpoints, E-R and C-R functions, and at-risk 

32 populations.
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1 2.2.5 Exposures of Concern

2 The O3 ISA identifies health effects associated with exposures to varying concentrations 

3 of O3. However, not all of the evidence is suitable for evaluation in a quantitative risk 

4 assessment. Estimating exposures to ambient O3 concentrations at and above benchmark levels 

5 where health effects have been observed in studies provides a perspective on the public health 

6 impacts of O3-related health effects that have been demonstrated in human clinical and 

7 toxicological studies but cannot currently be evaluated in quantitative risk assessments, such as 

8 lung inflammation, increased airway responsiveness, and decreased resistance to infection.

9 To inform the selection of benchmark levels for O3 exposure, it is appropriate to consider

10 the evidence from clinical studies which have evaluated individual controlled levels of O3

11 exposure. There is substantial clinical evidence demonstrating a range of O3-related effects 

12 including lung inflammation and airway responsiveness in healthy individuals at an exposure 

13 level of 0.080 ppm. There is additional evidence that asthmatics have larger and more serious 

14 effects than healthy people at 0.070 ppm, as well as a substantial body of epidemiological 

15 evidence of associations with O3 levels that extend well below 0.080 ppm. There is a more 

16 limited set of evidence based on clinical studies of healthy individuals exposed at 0.060 ppm in 

17 which O3-related effects have been observed. This is the lowest level at which any O3-related 

18 effects have been observed in clinical studies of healthy individuals (U.S. EPA, 2013, section 

19 6.2.1).

20 Thus, benchmark levels of 0.060 ppm, 0.070 ppm, and 0.080 ppm are used in this 

21 assessment to characterize exposures of concern for a range of potential health effects in healthy 

22 and at-risk populations exposed to O3.

23 2.2.6 Health Endpoints

24 The O3 ISA identifies a wide range of health outcomes associated with short-term 

25 exposure to ambient O3, including an array of morbidity effects as well as 

26 premature mortality. The ISA also identifies several morbidity effects and some 

27 evidence for premature mortality associated with longer-term exposures to O3. In 

28 identifying health endpoints for risk assessment, we have focused on endpoints 

29 that pertain to at-risk populations, have public health significance, and for which 

30 information is sufficient to support a quantitative concentration-response 
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1 relationship, in the case of epidemiological studies, or exposure-response 

relationship, in the case of controlled human exposure studies.

4 2

3 In considering such endpoints for O3, we draw from two types of studies: controlled 

4 human exposure and epidemiological studies. Each study type informs our characterization of 

5 O3 risk and can do so in different ways. Estimates of risk based on results of controlled human 

6 exposure studies are valuable because they provide clear evidence of the detrimental effects of 

7 controlled (and measured) exposures to O3 over multiple hours on lung function at moderate 

8 levels of exertion. Results of these studies can be applied to modeled estimates of population 

9 exposure to provide insights into population exposure characteristics, including types of activity 

10 patterns and microenvironments, which are associated with high levels of risk. Controlled human 

11 exposure studies, however, cannot directly provide relationships for endpoints such as premature 

12 death or hospitalizations, focusing more on intermediate biological endpoints including 

13 inflammatory, blood, neurological, cardiovascular, and respiratory biomarkers or symptoms. 

14 Estimates of risk based on concentration-response functions from observational epidemiology 

15 studies can provide insights on risk for more serious or chronic health endpoints. For example, 

16 epidemiological studies of O3 described in the ISA have evaluated associations between O3 and 

17 various endpoints including respiratory symptoms, respiratory-related hospitalizations and 

18 emergency department (ED) visits, and premature mortality (U.S. EPA, 2013, sections 6.2.9 and 

19 6.3.4). Epidemiological studies also generally focus on a population residing in specific area,

20 which may reflect a broad range of susceptibilities and sensitivities. Controlled human exposure

21 studies typically involve a smaller number of individuals over a more limited range of health 

22 status, in some cases focused on at-risk populations, such as asthmatics and individuals with 

23 COPD. Lastly, while controlled human exposure studies directly measure the exposures eliciting 

24 the recorded effects, epidemiology studies have not traditionally been based on observations of 

25 personal exposure to ambient O3, relying instead on surrogate measures of population exposure. 

26 Such surrogates are often based on simple averages of ambient O3 monitor observations. Thus, 

27 with attention to their differing strengths and limitations, risk analyses based on each type of 

28 study can inform the risk characterization.

29 The O3 ISA makes overall causal determinations based on the full range of 

30 evidence including epidemiological, controlled human exposure, and 

 4 The distinction between concentration-response and exposure-response functions reflects the typical use of 

ambient concentrations as measured at monitor locations as surrogates for population exposures in 

observational epidemiology studies, as compared to the personal exposures to controlled concentrations of O3

that are typically used in controlled human exposure studies. Both types of studies are intended to produce an 

exposure-responserelationship, however, the epidemiology studies are actually providing a concentrationresponse relationship, which captures the exposure-response relationship with errors in exposure measurement.
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1 toxicological studies. Figure 2-1 shows the O3 health effects which have been 

2 categorized by strength of evidence for causality in the O3 ISA (U.S. EPA, 2013, 

3 chapter 2). The ISA determined there to be causal relationships between short4 term exposure to ambient O3 and respiratory effects, including respiratory-related 

5 morbidity and mortality and a likely causal relationship with all-cause total 

6 mortality and with cardiovascular effects; the evidence was concluded to be 

7 suggestive of a causal relationship between short-term exposure to ambient O3

8 and central nervous system effects. The ISA determined to also be a likely causal 

9 relationship between long-term O3 exposures and respiratory effects (including 

10 respiratory symptoms, new-onset asthma, and respiratory mortality), and 

11 determined the evidence to be suggestive of causal relationships between long12 term O3 exposures and total mortality as well as cardiovascular, reproductive and 

13 developmental, and central nervous system effects.

14

15

16 Figure 2-2 Causal Determinations for O3 Health Effects

17

Long-term O3 exposures

Cardiovascular effects

Total Mortality

Respiratory effects

Cardiovascular effects

Total Mortality Respiratory effects

Central nervous 

system effects

Reproductive and 

developmental effects

Central nervous system 

effects

Short-term O3 exposures

Suggestive Likely Causal
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1 The ISA identifies several specific respiratory responses to short-term O3 exposure that 

2 have been evaluated in controlled human exposure studies (U.S. EPA, 2013, section 6.2.1). 

3 These include decreased inspiratory capacity, decreased forced vital capacity (FVC) and forced 

4 expiratory volume in 1 second (FEV1); mild bronchoconstriction; rapid, shallow breathing 

5 patterns during exercise; symptoms of cough and pain on deep inspiration (PDI); and pulmonary 

6 inflammation. While such studies document quantitative relationships between short-term O3

7 exposure and an array of respiratory-related effects, exposure-response data across a range of 

8 concentrations sufficient for developing quantitative risk estimates are only available for O3-

9 related decrements in FEV1 (U.S. EPA, 2013, section 6.2.1).

10 Within the broad category of respiratory morbidity effects, the epidemiology literature 

11 has provided effect estimates for a wide range of health endpoints associated with short-term O3

12 exposures which we have considered for risk assessment. These health endpoints include lung 

13 function, respiratory symptoms and medication use, respiratory-related hospital admissions, and 

14 emergency department visits. In the case of respiratory symptoms, the evidence is most 

15 consistently supportive of the relationship between short-term ambient O3 metrics and 

16 respiratory symptoms and asthma medication use in children with asthma, but not for a 

17 relationship between O3 and respiratory symptoms in children without asthma (U.S. EPA, 2013, 

18 section 6.2.9). In the case of hospital admissions, there is evidence of associations between short19 term ambient O3 metrics and general respiratory-related hospital admissions as well as more 

20 specific asthma-related hospital admissions (U.S. EPA, 2013, section 6.2.7.2).

21 With regard to mortality, studies have evaluated associations between short-term ambient 

22 O3 metrics and all-cause, non-accidental, and cause-specific (usually respiratory or 

23 cardiovascular) mortality. The evidence from respiratory-related morbidity studies provides 

24 strong support for respiratory-related mortality for which a causal determination has been made

25 (U.S. EPA, 2013, Table 2-3). There are also a number of large studies that have found 

26 associations between O3 and all-cause and all non-accidental mortality for which a likely causal 

27 determination has been made (U.S. EPA, 2013, Table 2-3). Thus, it is appropriate to assess risks 

28 for respiratory-related mortality as well as for all-cause total mortality associated with O3

29 exposure. The ISA also reports a likely causal determination for short-term O3 and 

30 cardiovascular effects, including cardiovascular mortality (U.S. EPA, 2013, Table 2-3). This 

31 determination is supported by studies relating total and cardiovascular mortality, coupled with 

32 evidence from animal toxicological studies and controlled human exposure studies which find 

33 effects of O3 on systemic inflammation and oxidative stress. Cardiovascular mortality effects are 

34 covered through the estimation of risks associated with total mortality, which is dominated by 

35 cardiovascular mortality. There are not sufficient epidemiological studies of cardiovascular 
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1 morbidity showing consistent associations to justify inclusion of any cardiovascular morbidity 

2 endpoints in the quantitative risk assessment.

3 With regard to effects associated with long-term O3 exposures, the ISA states that the

4 relationship between O3 and respiratory-related effects, including respiratory symptoms, new5 onset asthma, and respiratory mortality is likely causal (U.S. EPA, 2013, Table 2-3). This 

6 suggests that for long-term exposures, when comparing the evidence for respiratory-related 

7 mortality and total mortality, the evidence is strongest for respiratory-related mortality, which is

8 supported by the strong evidence for respiratory morbidity. As a result, it is appropriate to 

9 include respiratory mortality rather than total mortality in the risk assessment and to give 

10 consideration to inclusion of additional respiratory-related health endpoints.

11 2.2.7 Exposure and Concentration-response Functions for Health Endpoints

12 Estimation of risk requires characterization of the E-R and C-R functions along the full

13 range of potential exposures. For E-R functions, the evidence from individual controlled human 

14 exposure studies provides responses for exposures at and above 60 ppb. McDonnell et al. (2012) 

15 develop an integrated model of FEV1 response that is fit to the results from controlled human 

16 exposure studies and find that a model with a threshold provides the best fit to the data. In 

17 addition, the ISA notes that it is difficult to characterize the E-R relationship at and below 40 ppb 

18 due to the dearth of data at these lower concentrations (U.S. EPA, 2013, section 2.5.4.4). Thus, 

19 for the portion of the risk assessment based on application of results of controlled human 

20 exposure studies, the threshold model is applied.

21 The evidence for a threshold in the C-R functions for mortality and morbidity outcomes 

22 derived from the epidemiological literature is limited. In general, the epidemiological evidence 

23 suggests a generally linear C-R function with no indication of a threshold. However, evaluation 

24 of evidence for a threshold in the C-R function is complicated by the high degree of 

25 heterogeneity between cities in the C-R functions and by the sparse data available at lower 

26 ambient O3 concentrations (U.S. EPA, 2013, sections 2.5.4.4 and 2.5.4.5).

27 The ISA also evaluated whether the magnitude of the relationship between short-term 

28 exposures to O3 and mortality changes at lower concentrations (e.g., whether the C-R function is 

29 non-linear). The ISA concludes that epidemiologic studies that examined the shape of the C-R 

30 curve and the potential presence of a threshold have indicated a generally linear C-R function 

31 with no indication of a threshold in analyses that have examined 8-h max and 24-h avg O3 

32 concentrations, and that the evidence supports less certainty in the shape of the C-R function at 

33 the lower end of the distribution of O3 concentrations, e.g., 24-hour average O3 below 20 ppb,

34 due to the low density of data in this range (U.S. EPA, 2013, section 2.5.4.4). In the absence of 

35 information in the scientific literature on alternative forms of C-R functions at low O3
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1 concentrations, the best estimate of the C-R function is a linear, no-threshold function. The 

2 scientific literature does not provide sufficient information with which to quantitatively 

3 characterize any potential additional uncertainty in the C-R functions at lower O3 concentrations

4 for use in the quantitative risk assessment.

5 Multiple exposures to elevated O3 levels over the course of an O3 season may result in 

6 adaptation within exposed population. Evidence suggests that repeated or chronic exposures to 

7 elevated O3 can result in morphologic and biochemical adaptation which reduces the impacts of 

8 subsequent O3 exposures (U.S. EPA, 2013, section 6.2.1.1). This has implications for exposure 

9 modeling, in that the effects of modeled repeat exposures on risk may be attenuated relative to 

10 the effects of the initial exposures. The ISA notes that “neither tolerance nor attenuation should 

11 be presumed to imply complete protection from the biological effects of inhaled O3, because 

12 continuing injury still occurs despite the desensitization to some responses (U.S. EPA, 2013, 

13 section 6.2.1.1).” The ISA reports that there are limited epidemiological studies evaluating 

14 adaptation to the mortality effects of O3, although the limited evidence does suggest that 

15 mortality effects are decreased in later months during the O3 season relative to earlier months

16 (U.S. EPA, 2013, section 6.3.3). The impact of this phenomenon on risks based on application of 

17 results from epidemiological studies is likely to be small, because the relative risk estimates from 

18 those studies already incorporate any adaptive phenomenon.

19 2.2.8 At-risk Populations

20 The O3 ISA refers to “at-risk” populations as an all-encompassing term used for groups 

21 with specific factors that increase the risk of an air pollutant- (e.g., O3) related health effect in a 

22 population group (U.S. EPA, 2013, chapter 8). Populations or lifestages can experience elevated 

23 risks from O3 exposure for a number of reasons. These include high levels of exposure due to 

24 activity patterns which include a high duration of time in high-O3 locations, e.g., outdoor 

25 recreation or work, high levels of activity which increase the dose of O3, e.g., high levels of 

26 exercise, genetic or other biological factors, e.g., life stage, which predispose an individual to 

27 sensitivity to a given dose of O3, pre-existing diseases, e.g., asthma or COPD, and 

28 socioeconomic factors which may result in more severe health outcomes, e.g., low access to 

29 primary care that can lead to increased emergency department visits or hospital admissions. To 

30 consider risks to these populations, modeling of exposures to O3 needs to incorporate 

31 information on time spent by potentially at-risk populations in high O3 locations. This requires 

32 identification of populations with the identified exposure-related risk factors, e.g. children or 

33 adults engaging in activities involving moderate to high levels of outdoor exertion, especially on 

34 a repeated basis typical of student athletes or outdoor workers, as well as identifying populations 

35 with high sensitivity to O3, e.g. asthmatic children. It also requires that information on O3
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1 concentrations be mapped to locations where at-risk populations are likely to be exposed, e.g. 

2 near roadways where running may occur, or at schools or parks where children are likely to be 

3 engaged in outdoor activities.

4 In addition to consideration of factors that lead to increased exposure to O3, modeling of 

5 risk from O3 exposures should incorporate additional information on factors that can lead to 

6 increased dose of O3 for a given exposure, e.g., increased breathing rates during periods of 

7 exertion. These factors are especially important for risk estimates based on application of the 

8 results of controlled human exposure studies. For risk modeling based on application of 

9 observational epidemiology results, it is also important to understand characteristics of study 

10 populations that can impact observed relationships between ambient O3 and population health 

11 responses.

12 The O3 ISA identifies a number of factors which have been associated with modifications 

13 of the effect of ambient O3 on health outcomes. Building on the causal framework used 

14 throughout the O3 ISA, conclusions are made regarding the strength of evidence for each factor 

15 that may contribute to increased risk of an O3-related health effect based on the evaluation and 

16 synthesis of evidence across scientific disciplines. The O3 ISA categorizes potential risk 

17 modifying factors by the degree of available evidence. These categories include “adequate 

18 evidence,” “suggestive evidence,” “inadequate evidence,” and “evidence of no effect.” See 

19 Table 8-1 of the O3 ISA for a discussion of these categories (U.S. EPA, 2013, chapter 8).

20 Factors categorized as having adequate evidence include asthma, lifestage (children less 

21 than 18 years of age, adults older than 65 years of age), diets with nutritional deficiencies, and 

22 working outdoors. For example, children are the group considered to be at greatest risk because 

23 they breathe more air per unit of body weight, are more likely to be active outdoors when O3

24 levels are high, are more likely than adults to have asthma, and are in a critical time period of 

25 rapid lung growth and organ development. Factors categorized as having suggestive evidence 

26 include genetic markers, sex (some studies have shown that females are at greater risk of 

27 mortality from O3 compared to males), low socioeconomic status, and obesity. Factors 

28 characterized as having inadequate evidence include influenza and other respiratory infections, 

29 COPD, cardiovascular disease, diabetes, hyperthyroidism, race, and smoking (U.S. EPA, 2013, 

30 section 8.5, Table 8-6).

31 2.3 URBAN-SCALE MODELING OF INDIVIDUAL EXPOSURE

32 Estimates of human exposure to O3 provide important information to inform 

33 consideration of policy-relevant questions identified in Section 2.2 regarding the occurrence of 

34 exposures of concern under air quality conditions that meet existing and potential alternative 

35 standards, and also to provide inputs to the portion of the risk assessment based on application of 
2-16

1 results of controlled human exposure studies. Studies that measure human exposure to O3 are 

2 limited. More commonly, human exposure is estimated using sophisticated models which 

3 combine information on ambient O3 concentrations in various microenvironments, e.g. near 

4 roads, in schools, etc., with information on activity patterns for individuals sampled from the 

5 general population or specific subpopulations, e.g. children with asthma.

6 O3 exposure is highly dependent on the ambient O3 concentrations in an urban area. 

7 Given that these concentrations are variable from year to year, it is important to model multiple 

8 years representing the range of variability on O3 concentrations to provide a better 

9 characterization of potential exposures of concern. In addition, other important sources of 

10 variability and uncertainty affecting the exposure estimates should be characterized, including 

11 uncertainty and variability in the data on time-activity patterns, O3 concentrations, and 

12 population inputs. This can be accomplished in part by modeling exposure for multiple urban 

13 areas selected to represent variability in these underlying sources of variability.

14 This section briefly describes the conceptual foundation for key components of exposure 

15 modeling, characterization of microenvironmental O3 concentrations, and characterization of 

16 human activity patterns, including behaviors intended to avert exposures to O3. In addition, a 

17 brief discussion of key factors to consider in selecting urban case study areas for the exposure 

18 analysis is provided. The specific exposure model used in this assessment, APEX, is described 

19 more fully in Chapters 3 and 5. Characterization of ambient O3 concentrations is discussed 

20 earlier in this chapter and in greater detail in Chapter 4.

21 2.3.1 Microenvironmental O3 Concentrations

22 Human exposure to O3 involves the contact (via inhalation) between a person and the 

23 pollutant in the various locations (or microenvironments) in which people spend their time. O3

24 concentrations in some indoor microenvironments, such as within homes or offices, are 

25 considerably lower than O3 concentrations in similarly located outdoor microenvironments, 

26 primarily due to deposition processes and the transformation of O3 into other chemical 

27 compounds within those indoor microenvironments. Concentrations of O3 may also be quite 

28 different in roadway environments, such as might occur while an individual is in a vehicle.

29 Thus, three important classes of microenvironments that should be considered when 

30 evaluating population exposures to ambient O3 are indoors, outdoors, and in-vehicle. Within 

31 each of these broad classes of microenvironments, there are many subcategories, reflecting types 

32 of buildings, types of vehicles, etc. The O3 ISA evaluated the literature on indoor-outdoor O3

33 concentration relationships and found that studies consistently show that indoor concentrations 

34 of O3 are often substantially lower than outdoor concentrations unless indoor sources are present. 

35 This relationship is greatly affected by the air exchange rate, which can be affected by open 
2-17

1 windows, use of air conditioning, and other factors. Ratios of indoor to outdoor O3

2 concentrations generally range from about 0.1 to 0.4 (U.S. EPA, 2013, section 4.3.2). In some 

3 indoor locations, such as schools, there can be large temporal variability in the indoor-outdoor 

4 ratios because of differences in air exchange rates over the day. For example, during the school 

5 day, there is an increase in open doors and windows, so the indoor-outdoor ratio is higher during 

6 the school day compared with an overall average across all hours and days. In-vehicle 

7 concentrations are also likely to be lower than ambient concentrations, although the literature 

8 providing quantitative estimates is smaller. Studies of personal exposure to O3 have identified 

9 that O3 exposures are highest when individuals are in outdoor microenvironments, such as 

10 walking outdoors midday, moderate when in vehicle microenvironments, and lowest in 

11 residential indoor microenvironments (U.S. EPA, 2013, section 4.3.3). Thus the time spent 

12 indoors, outdoors, and in vehicles is likely to be a critical component in estimating O3 exposures.

13 Because of localized chemistry, O3 concentrations on or near roadways can be much 

14 lower than away from roadways. This is due to the high levels of NOX emissions from motor 

15 vehicles, which can lead to NOx titration of O3, reducing O3 levels during times of peak traffic. 

16 The ISA reports evidence that concentrations of NO, NO2, and NOx are negatively correlated 

17 with concentrations of O3 near busy roadways. Because few monitors are located in direct 

18 proximity to roadways, it is important to account for differences between near-road O3

19 concentrations and ambient O3 measurements in modeling exposure. 

20 2.3.2 Human Activity Patterns

21 Human exposure can be measured using several metrics. Exposure to ambient 

22 concentrations is one such metric. It is also possible to model dose, which combines exposure 

23 information with physiological parameters related to activity levels. In order to model exposure 

24 to ambient concentrations, detailed information on the patterns of time spent in different 

25 microenvironments is critical. In order to model O3 dose, additional information on the activities 

26 conducted while in those microenvironments is needed, along with data on physiological 

27 parameters associated with different activities.

28 Several large-scale databases of human time-activity-location patterns have been 

29 compiled. The most comprehensive of these databases in the Consolidated Human Activity 

30 Database (CHAD), which has been the basis of several previous exposure analyses for previous 

31 NAAQS reviews. These databases compile large numbers of diaries of time spent at different 

32 activities in different locations collected as part of smaller studies. The ISA notes the high degree 

33 of variability in activity patterns across the population, as well as the variability in time spent in 

34 different microenvironments. Time-activity-location patterns vary by age group, as well as by 
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1 region of the U.S. Children generally spend more time in outdoor locations and also generally 

2 have higher activity levels in those environments.

3 The dose of O3 received for any given exposure in a microenvironment depends not only 

4 on the activity levels and O3 concentrations in the microenvironment, but also on ventilation 

5 rates, which are related to age, body weight, and other physiological parameters. Children 

6 generally have lower ventilation rates than adults when considering the volume of air breathed 

7 per unit time; however, they tend to have a greater oral breathing contribution than adults, and 

8 due to smaller lung volumes and generally greater breathing frequencies, children breathe at 

9 higher body mass or surface area normalized minute ventilation rates, relative to their lung 

10 volumes. Both of these factors tend to increase their applied or intake dose normalized to lung 

11 surface area. For example, when comparing daily body mass normalized ventilation rates, 

12 children can have up to a factor of two greater ventilation rates when compared to that of adults. 

13 During periods of high activity, ventilation rates for children and young adults can be nearly 

14 double those during moderate activity. Thus, it is important to model levels of activity and 

15 associated ventilation rate as well as time spent in different microenvironments.

16 In addition to modeling daily exposures, it may also be important to understand the 

17 patterns of exposure over an O3 season, including multiple repeated exposures for the same 

18 individuals. Some individuals or subpopulations may exhibit multiple high daily exposures due 

19 to persistent patterns of high activity in microenvironments with high O3 concentrations. For 

20 example, children engaged in numerous outdoor sports over a summer O3 season may have 

21 multiple exposures to elevated O3 levels.

22 Another important issue in characterizing exposure involves consideration of the extent 

23 to which people in relevant population groups modify their behavior for the purpose of 

24 decreasing their personal exposure to O3 based on information about predicted air quality levels 

25 made public through the Air Quality Index (AQI). The AQI is the primary tool EPA has used to 

26 communicate information on predicted occurrences of high levels of O3 and other pollutants. The 

27 AQI provides both the predicted level of air quality in an area along with a set of potential 

28 actions that individuals and communities can take to reduce exposure to air pollution and thus 

29 reduce the risk of health effects associated with breathing ambient air pollution. There are 

30 several studies, discussed in the O3 ISA, that have evaluated the degree to which populations are 

31 aware of the AQI and what actions individuals and communities take in response to AQI values 

32 in the unhealthy range. These studies suggest that at-risk populations, such as children, older 

33 adults, and asthmatics, modify their behavior in response to days with bad air quality, most 

34 commonly by reducing their time spent outdoors or limiting their outdoor activity exertion level. 

35 A challenge remains in how to consider existing averting behaviors within the assessment tools 

36 we use and how best to use improved knowledge of participation rates, the varying types of 
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1 actions performed particularly by potentially at-risk individuals, and the duration of these 

2 averting behaviors to quantify the impact on estimated exposures and health risks.

3 2.3.3 Modeling of Exposures Associated with Simulating Just Meeting O3 Standards

4 In order to address policy-relevant questions regarding changes in exposure associated 

5 with potential alternative standards, the exposure assessment evaluates changes in the O3

6 concentrations, and the resulting changes in exposure, associated with simulating just meeting 

7 alternative standards relative to just meeting the existing standards. The new, model-adjustment 

8 methodology being implemented in this risk and exposure assessment provides for more realistic 

9 responses of hourly O3 concentrations to changes in the precursor emissions that lead to O3

10 formation. Characterization of exposure and changes in exposure when simulating just meeting 

11 the alternative standards are discussed in greater detail in Chapter 5.

12 2.3.4 Considerations in Selecting Urban Case Study Areas for the Exposure Analysis

13 The goal of the urban area exposure analysis is to characterize the variability in exposures 

14 for different locations, taking into account variability in essential factors that affect exposures.

15 Important factors identified earlier that may influence exposure include time activity patterns, 

16 especially activities occurring in outdoor environments; demographics of the exposed 

17 population, e.g., age and income level; and O3 concentrations. In addition to these factors, the 

18 selection of urban areas to include in the exposure analysis takes into consideration the location 

19 of O3 epidemiological studies (for comparability with the risk assessments), the availability of 

20 ambient O3 data and specific exposure information (e.g., air conditioning prevalence), and the 

21 desire to represent a range of geographic areas. To make the exposure analysis most useful in 

22 addressing the key policy-relevant questions, urban case study areas were also chosen such that 

23 most of them exceeded the existing 8-hr O3 standards and potential alternative standards during 

24 the time period of interest.

25 2.4 RISK ASSESSMENT

26 Assessment of risk entails joint consideration of the exposure to a hazard, frequency of 

27 adverse outcomes given exposure, and severity of resulting adverse outcomes. A risk assessment 

28 for O3 requires characterization of exposures to ambient O3 for relevant populations, 

29 identification of appropriate dose-response or concentration-response functions linking O3 with 

30 adverse health outcomes, and characterizing risks for individuals and populations.

31 As discussed above, there are two classes of studies that have provided information to 

32 inform the risk modeling: controlled human exposure studies and observational epidemiology 

33 studies. The conceptual approach to risk assessment varies based on which type of study result is 

34 being applied. This section briefly describes the conceptual foundation for several aspects of risk 
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1 modeling, including the concept of attributable risk, modeling of total risk and incremental risk 

2 reductions, development of risk estimates based on controlled human exposure studies, and 

3 development of risk estimates based on results of observational epidemiology studies.

4 This section briefly describes the conceptual foundation for key elements of risk 

5 modeling, including a discussion of the concept of attributable risk, modeling of risk for total O3

6 exposure and the distribution of risk over O3 concentrations, modeling of risk reductions 

7 associated with alternative standards, and key factors to consider in selecting urban case study 

8 areas for the risk analysis. Characterization of ambient O3 concentrations is discussed earlier in 

9 this chapter and in greater detail in Chapter 4. The specific risk models used in the urban case 

10 study area risk analyses, APEX for analyses based on application of controlled human exposure 

11 studies and BenMAP for analyses based on application of observational epidemiology studies, 

12 are described more fully in Chapters 6 and 7, respectively. Chapter 8 provides an additional 

13 national-scale assessment of mortality risk associated with recent O3 concentrations, to provide 

14 context for evaluating the magnitude of health risks in the urban case study areas and to evaluate 

15 the representativeness of the urban case study areas in estimating O3 risks.

16 2.4.1 Attributable Risk

17 This risk and exposure assessment relies on the concept of attributable risk in evaluating 

18 both total risk and incremental changes in risk associated with just meeting existing and potential 

19 alternative O3 standards. Attributable risk is defined as the difference in incidence of an adverse 

20 effect between an exposed and unexposed population for a specific stressor. Attributable risk is 

21 an important concept when addressing risks that are associated with multiple causes, such as 

22 mortality and respiratory hospital admissions.

23 Estimates of attributable risk require either an exposure-response (E-R) function (for 

24 analyses based on results of controlled human exposure studies) or a concentration-response (C25 R) function (for analyses based on results of epidemiology studies).

26 E-R functions require estimates of exposure, in this case supplied by the APEX modeling 

27 described above. In the case of the lung function endpoint evaluated in this risk analysis, the E-R 

28 function also requires information on age and exertion levels to predict the impact of O3

29 exposure on decrements in lung function. E-R functions may provide estimates of the incidence 

30 of an endpoint or the probability of exceeding benchmark decrement levels.

31 C-R functions derived from relative risk estimates reported in the epidemiological 

32 literature generally require estimates of ambient O3 concentrations, baseline incidence rates, and 

33 estimates of exposed populations. Ambient O3 concentrations should generally be constructed to 

34 match the spatial and temporal averaging used in the underlying epidemiology study; e.g., a 
2-21

1 study may have used a spatial average over a metropolitan statistical area of the 8-hour daily 

2 maximum.

3 As with exposure, attributable risk is highly dependent on the ambient O3 concentrations 

4 in an urban area. Given that these concentrations are variable from year to year, it is important to 

5 model multiple years representing the range of variability of O3 concentrations to provide a 

6 better characterization of risk. In addition, other important sources of variability and uncertainty 

7 affecting the risk estimates should be characterized, including uncertainty and variability in the 

8 C-R and E-R functions, O3 concentrations and O3 exposure, and population inputs. This can be 

9 accomplished in part by modeling risk for multiple urban areas selected to represent variability in 

10 these underlying risk drivers.

11 2.4.2 Modeling of Risk for Total Exposure to O3

12 As discussed earlier in this chapter, ambient O3 is contributed to by emissions from a 

13 variety of sources, including natural, U.S. anthropogenic, and non-U.S. anthropogenic sources. 

14 Once in the atmosphere, O3 molecules created from these different sources of emissions are not 

15 distinguishable. Individuals and populations are exposed to total O3 from all sources, and risks 

16 associated with O3 exposure are due to total O3 exposure and do not vary for O3 exposure 

17 associated with any specific source. Given the absence of a detectable threshold in the available 

18 C-R functions, total risk attributable to O3 will thus be the risk associated with total exposure to 

19 O3, with no threshold or cutpoint applied. To address certain policy-related questions, it is 

20 possible to approximately attribute risk to specific sources through the use of air quality

21 modeling techniques, and this is explored in the Policy Assessment. However, these techniques 

22 are based on applying model results to total O3 risk, rather than on directly modeling risk 

23 attributable to specific sources.

24 As discussed earlier in this chapter, a critical policy-relevant risk question is the O3

25 attributable risk remaining after just meeting the existing O3 standards. This risk includes risks 

26 associated with O3 from all sources after we have simulated just meeting the existing daily 8-

27 hour maximum standard level of 75 ppb. The estimates of total risk remaining after meeting the 

28 existing standard form the reference values for evaluating reductions in risk associated with just 

29 meeting alternative levels of the standard.

30 In addition to providing risk estimates for urban case study areas, it is also useful to 

31 evaluate O3 risks across the entire U.S., both to better understand the total magnitude of the 

32 health burden associated with O3 and to evaluate the representativeness of selected urban case 

33 study areas in characterizing the range and variability in risks across the U.S. The national-scale 

34 risk assessment presented in Chapter 8 is focused on estimating risk associated with recent O3

35 concentrations, rather than on risk after just meeting existing or alternative standards. This is the 
2-22

1 appropriate focus for the national analysis, because the techniques used to simulate just meeting 

2 existing and alternative standards in urban case study areas are less certain in a national context 

3 due to concerns about interdependence between air quality responses in different urban areas; 

4 e.g., just meeting a standard in one urban area would likely have impacts on O3 air quality in 

5 surrounding urban areas. It is beyond the scope of this REA to attempt to simulate control 

6 strategies that would result in national attainment of existing or alternative primary health

7 standards.

8 2.4.3 Distributions of Risk Across O3 concentrations

9 Total O3 risk for the O3 season is calculated by summing daily risks across all days in the 

10 O3 season. Because of the high degree of variability in daily O3 concentrations across an O3

11 season, total O3 risk will include risks calculated for some days with high O3 concentrations as 

12 well as for some days with very low O3 concentrations. Therefore it is appropriate to provide the 

13 distribution of total risk over the range of daily O3 concentrations to allow for an understanding 

14 of how O3 concentrations on different days are contributing to the estimates of total risk. In 

15 addition, as noted in the ISA and discussed above, because of the relatively lower density of data 

16 on days with low concentrations of O3, there is decreased confidence in the shape of the C-R 

17 function at lower O3 concentrations, and therefore lower confidence in risk estimates for days 

18 with lower O3 concentrations, especially in the range below 20 ppb. As a result, it is appropriate 

19 to provide the distribution of total risk over the range of daily O3 concentrations to allow for 

20 better characterization of confidence in the estimates of total risk.

21 2.5 MODELING OF RISKS ASSOCIATED WITH SIMULATING JUST MEETING O3

22 STANDARDS

23 In order to address policy-relevant questions regarding changes in risk associated with 

24 potential alternative standards, the risk assessment evaluates changes in the distribution of O3

25 concentrations, and the resulting changes in risk, associated with simulating just meeting 

26 alternative standards relative to just meeting the existing standards. The new, model-adjustment 

27 methodology being implemented in this risk and exposure assessment provides for more realistic

28 responses of hourly O3 concentrations to changes in the precursor emissions that lead to O3

29 formation. As noted earlier there are multiple combinations of reductions in precursor emissions 

30 that can result in just meeting alternative standards. As a result, there is variability in the 

31 potential changes in the distribution of O3 concentrations and risk that would result from just 

32 meeting existing and alternative standards. Characterization of this variability, as well as 

33 uncertainties in the simulation of just meeting the standards, will be included in Chapters 6 and 

34 7.
2-23

1 2.6 CONSIDERATIONS IN SELECTING URBAN CASE STUDY AREAS FOR THE 

2 RISK ANALYSIS

3 The goal of the urban area risk analysis is to characterize the magnitude of risk and the 

4 impact on risk of meeting existing and potential alternative standards. The selection of specific 

5 urban case study areas is based on a set of factors reflecting both variability in factors that affect 

6 risk and availability of high quality input data, to provide risk estimates that have higher overall 

7 confidence. Important factors identified earlier that may influence risk include O3 concentrations, 

8 demographics, exposure factors, and magnitude of the effect estimate in the C-R function. In 

9 addition to consideration of variability in these factors, urban areas are preferentially selected if 

10 they have O3 concentrations that are above the existing standards and potential alternative 

11 standards, if they have suitable epidemiological studies to provide C-R functions for mortality or 

12 morbidity, if they have adequate monitoring data available to characterize population exposures, 

13 and if they have appropriate baseline health incidence data available.

14 2.7 RISK CHARACTERIZATION

15 Risk characterization is the process of communicating the results of risk (and exposure) 

16 modeling in metrics that have meaning to decision makers. In the specific context of this review, 

17 this translates into providing metrics that are most useful in the Policy Assessment to assess the 

18 adequacy of the existing O3 standards in protecting public health with an adequate margin of 

19 safety and to evaluate the additional protection provided by potential alternative standards.

20 Risk characterization requires careful translation of very complex outputs of exposure 

21 and risk models into simpler metrics, for example, translating hourly O3 exposures in various 

22 microenvironments into estimates of population exposures above alternative exposure 

23 benchmarks. Risk characterization also requires the condensation of a large number of analytical 

24 steps and results to (a) summarize the results of the risk analysis, usually taking detailed results 

25 and condensing them into a more aggregate interpretation while still providing information about 

26 heterogeneity across space and time; (b) communicate the sensitivity of results to different 

27 modeling assumptions; and (c) characterize the qualitative and quantitative uncertainty in results.

28 As described more fully in Chapter 5 and in the Policy Assessment, EPA has selected, 

29 based on providing a reasonable measure of exposures of concern for at-risk populations and 

30 lifestages, aggregate exposure metrics including the number and percent of certain highly 

31 vulnerable populations exposed to levels of O3 above exposure levels that have been identified in 

32 the scientific literature as associated with adverse respiratory responses. As noted in section 

33 2.3.1, these benchmark exposure levels are 0.060 ppm, 0.070 ppm, and 0.080 ppm. Highly 

34 vulnerable populations include active children, older adults, and outdoor workers.
2-24

1 As described more fully in Chapters 6 and 7 and in the Policy Assessment, EPA has 

2 selected, based on providing characterization of risks to the public including at-risk populations 

3 and lifestages, aggregate risk metrics including the number and percent of vulnerable 

4 populations experiencing adverse respiratory responses based on application of results of 

5 controlled human exposure studies and the attributable incidence and percent of baseline 

6 incidence of mortality and morbidity endpoints based on application of results of epidemiology 

7 studies.

8 For all three types of metrics (exposure, risk based on controlled human exposure studies, 

9 and risk based on epidemiology studies) and for the purpose of evaluating the adequacy of the 

10 existing standards, the focus is on the exposure and risk remaining upon just meeting the existing 

11 standards. For the purpose of evaluating alternative standards, the focus in on the changes in 

12 exposure and risk after simulating just meeting the alternative standards, compared to exposures 

13 and risk after simulating just meeting the existing standards.

14 As detailed in Chapter 3, quantitative sensitivity analyses are provided to evaluate the 

15 impacts of critical inputs to the exposure and risk modeling. Limited quantitative uncertainty 

16 analyses are also included, along with a comprehensive qualitative uncertainty assessment. The 

17 overall treatment of uncertainty is guided by the WHO guidelines for uncertainty assessment 

18 (World Health Organization, 2008). These guidelines recommend a tiered approach in which 

19 progressively more sophisticated methods are used to evaluate and characterize sources of 

20 uncertainty depending on the overall complexity of the risk assessment.

21 In order to inform considerations of overall confidence in the risk estimates derived from 

22 application of C-R functions derived from the epidemiological literature, we provide the 

23 distributions of total risk across the entire range of daily 8-hour maximum O3 concentrations. In 

24 addition, we provide an assessment of the representativeness of the urban areas selected for the 

25 risk and exposure analysis in characterizing the overall distribution of risk across the U.S. This 

26 assessment evaluates how well the selected urban areas capture important characteristics that are 

27 associated with risk, including demographics, air quality levels, and factors affecting exposure 

28 such as air conditioning prevalence.
2-25

1 2.8 REFERENCES

2 Frey, C. and J. Samet. 2012. "CASAC Review of the EPA’s Policy Assessment for the Review 

3 of the Ozone National Ambient Air Quality Standards (First External Review Draft –

4 August 2012)." U.S. Environmental Protection Agency Science Advisory Board, EPA5 CASAC-13-003.

6 U.S. Environmental Protection Agency. 2012a. Total Risk Integrated Methodology (TRIM) - Air 

7 Pollutants Exposure Model Documentation (TRIM.Expo / APEX, Version 4.4) Volume I: 

8 User’s Guide. Research Triangle Park, NC: EPA Office of Air Quality Planning and 

9 Standards. (EPA document number EPA-452/B-12-001a). 

10 <http://www.epa.gov/ttn/fera/human_apex.html>.

11 U.S. EPA. 2012 b. "Total Risk Integrated Methodology (TRIM) - Air Pollutants Exposure Model 

12 Documentation (TRIM.Expo / APEX, Version 4.4) Volume II: Technical Support 

13 Document." Research Triangle Park, NC: Office of Air Quality Planning and Standards.

14 (EPA document number EPA-452/B-12-001b). 

15 < http://www.epa.gov/ttn/fera/human_apex.html>.

16 U.S. EPA. 2013. Integrated Science Assessment of Ozone and Related Photochemical Oxidants 

17 (Final Report). Washington, DC: EPA Office of Air and Radiation. (EPA document 

18 number EPA/600/R-10/076F).

19 U.S. EPA. 2013. "Environmental Benefits Mapping Analysis Program Community Edition 

20 (BenMAP-CE v1.0)," posted on December 03, 2013, 

21 <http://www.epa.gov/air/benmap/>.

22 World Health Organization. 2008. "Part 1: Guidance Document on Characterizing and 

23 Communicating Uncertainty in Exposure Assessment, Harmonization Project Document 

24 No. 6." Published under joint sponsorship of the World Health Organization, the 

25 International Labor Organization and the United Nations Environment Programme. WHO 

26 Press, World Health Organization, 20 Avenue Appia, 1211 Geneva 27, Switzerland (tel.: 

27 +41 22 791 2476).

28
3-1

1 3 SCOPE

2 This chapter provides an overview of the scope and key design elements of this 

3 quantitative exposure and health risk assessment. The design of this assessment began with a 

4 review of the exposure and risk assessments completed during the last O3 NAAQS review (U.S. 

5 EPA, 2007a,b), with an emphasis on considering key limitations and sources of uncertainty 

6 recognized in that analysis.

7 As an initial step in the current O3 NAAQS review in October 2009, EPA invited outside 

8 experts, representing a broad range of expertise (e.g., epidemiology, human and animal 

9 toxicology, statistics, risk/exposure analysis, atmospheric science), to participate in a workshop 

10 with EPA staff to help inform EPA’s plan for the review. The participants discussed key policy11 relevant issues that would frame the review and the most relevant new science that would be 

12 available to inform our understanding of these issues. One workshop session focused on planning 

13 for quantitative risk and exposure assessments, taking into consideration what new research 

14 and/or improved methodologies would be available to inform the design of quantitative exposure 

15 and health risk assessment. Based in part on the workshop discussions, EPA developed a draft 

16 IRP (U.S. EPA, 2009) outlining the schedule, process, and key policy-relevant questions that 

17 would frame this review. On November 13, 2009, EPA held a consultation with CASAC on the 

18 draft IRP (74 FR 54562, October 22, 2009), which included opportunity for public comment. 

19 The final IRP incorporated comments from CASAC (Samet, 2009) and the public on the draft 

20 plan, as well as input from senior Agency managers. The final IRP included initial plans for 

21 quantitative risk and exposure assessments for both human health and welfare (U.S. EPA, 2011a, 

22 chapters 5 and 6).

23 As a next step in the design of these quantitative assessments, OAQPS staff developed

24 more detailed planning documents, the O3 National Ambient Air Quality Standards: Scope and 

25 Methods Plan for Health Risk and Exposure Assessment (Health Scope and Methods Plan, U.S.

26 EPA, 2011b) and the O3 National Ambient Air Quality Standards: Scope and Methods Plan for 

27 Welfare Risk and Exposure Assessment (Welfare Scope and Methods Plan, U.S. EPA, 2011c). 

28 These Scope and Methods Plans was the subject of a consultation with CASAC on May 19-20, 

29 2011 (76 FR 23809, April 28, 2011). Based on consideration of CASAC (Samet, 2011) and 

30 public comments on the Scope and Methods Plans, and information in the second draft ISA, we 

31 modified the scope and design of the quantitative risk assessment and provided a memo with 

32 updates to information presented in the Scope and Methods Plans (Wegman, 2012). The Scope 

33 and Methods Plans together with the update memo provide the basis for the discussion of the 

34 scope of this exposure and risk assessment provided in this chapter. This chapter also reflects 
3-2

1 comments received from CASAC based on their review of the first draft Risk and Exposure 

2 Assessment on September 11-12, 2012 (Frey and Samet, 2012).

3 In presenting the scope and key design elements of the current risk assessment, this 

4 chapter first provides a brief overview of the quantitative exposure and risk assessment 

5 completed for the previous O3 NAAQS review in section 3.1, including key limitations and 

6 uncertainties associated with that analysis. The remaining sections describe the current exposure 

7 and risk assessment, following the general conceptual framework described in Chapter 2. Section 

8 3.2 provides a summary of the design of the urban-scale exposure assessment. Section 3.3 

9 provides a summary of the design of the urban-scale risk assessment based on application of 

10 results of human clinical studies. Section 3.4 provides a summary of the design of the urban11 scale risk assessment based on application of results of epidemiology studies. Section 3.5 

12 provides a summary of the design of the national-scale risk burden assessment and 

13 representativeness analysis.

14 3.1 OVERVIEW OF EXPOSURE AND RISK ASSESSMENTS FROM LAST REVIEW

15 The exposure and health risk assessment conducted in the review, completed in March 

16 2008, developed exposure and health risk estimates for 12 urban areas across the U.S. which 

17 were chosen based on the location of O3 epidemiological studies and availability of ambient O3

18 data and to represent a range of geographic areas, population demographics, and O3 climatology. 

19 That analysis was in part based upon the exposure and health risk assessments included in the 

review completed in 1997.1 20 The exposure and risk assessment incorporated air quality data (i.e., 

21 2002 through 2004), and provided annual or O3 season-specific exposure and risk estimates for 

22 these recent years of air quality and for air quality scenarios simulating just meeting the existing 

23 8-hour O3 standard and several alternative 8-hour O3 standards.

24 3.1.1 Overview of exposure assessment from last review

25 Exposure estimates were used as an input to the risk assessment for lung function 

26 responses (a health endpoint for which exposure-response functions were available from 

27 controlled human exposure studies). Exposure estimates were developed for the general 

28 population and population groups including school-age children with asthma as well as all 

29 school-age children. The exposure estimates also provided information on population exposures 

 1 In the 1994-1997 O3 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 that describe these analyses can be found at: 

http://www.epa.gov/ttn/naaqs/standards/O3/s_O3_pr.html.
3-3

1 exceeding potential health effect benchmark levels that were identified based on the observed 

2 occurrence of health endpoints not explicitly modeled in the health risk assessment (e.g., lung 

3 inflammation, increased airway responsiveness, and decreased resistance to infection) associated 

4 with 6-8 hour exposures to O3 in controlled human exposure studies. 

5 The exposure analysis took into account several important factors including the 

6 magnitude and duration of exposures, frequency of repeated high exposures, and breathing rate 

7 of individuals at the time of exposure. Estimates were developed for several indicators of 

8 exposure to various levels of O3 air quality, including counts of people exposed one or more 

9 times to a given O3 concentration while at a specified breathing rate and counts of person10 occurrences (which accumulate occurrences of specific exposure conditions over all people in 

11 the population groups of interest over an O3 season).

12 As discussed in the 2007 Staff Paper (U.S. EPA, 2007c) and in Section II a of the O3

13 Final Rule (73 FR 16440 to 16442, March 27, 2008), the most important uncertainties affecting 

14 the exposure estimates were related to modeling human activity patterns over an O3 season, 

15 modeling of variations in ambient concentrations near roadways, and modeling of air exchange 

16 rates that affect the amount of O3 that penetrates indoors. Another important uncertainty, 

17 discussed in more detail in the Staff Paper (U.S. EPA, 2007c, section 4.3.4.7), was the 

18 uncertainty in energy expenditure values which directly affected the modeled breathing rates. 

19 These were important since they were used to classify exposures occurring when children were 

20 engaged in moderate or greater exertion. Health effects observed in the controlled human 

21 exposure studies generally occurred under these exertion levels for 6 to 8-hour exposures to O3

22 concentrations at or near 0.08 ppm. Reports that describe these analyses (U.S. EPA, 2007a, c; 

23 Langstaff, 2007) can be found at: http://www.epa.gov/ttn/naaqs/standards/O3/s_O3_index.html. 

24 3.1.2 Overview of risk assessment from last review

25 The human health risk assessment presented in the review completed in March 2008 was

26 designed to estimate population risks in a number of urban areas across the U.S., consistent with 

27 the scope of the exposure analysis described above (U.S. EPA, 2007b, c). The risk assessment 

28 included risk estimates based on both controlled human exposure studies and epidemiological 

29 and field studies. O3-related risk estimates for lung function decrements were generated using 

30 probabilistic exposure-response relationships based on data from controlled human exposure 

31 studies, together with probabilistic exposure estimates from the exposure analysis. For several 

32 other health endpoints, O3-related risk estimates were generated using concentration-response 

33 relationships reported in epidemiological or field studies, together with ambient air quality 

34 concentrations, baseline health incidence rates, and population data for the various locations 

35 included in the assessment. Health endpoints included in the assessment based on 
3-4

1 epidemiological or field studies included hospital admissions for respiratory illness in four urban 

2 areas, premature mortality in 12 urban areas, and respiratory symptoms in asthmatic children in 1 

3 urban area. 

4 In the health risk assessment conducted in the previous review, EPA recognized that there 

5 were many sources of uncertainty and variability in the inputs to the assessment and that there 

6 was significant uncertainty in the resulting risk estimates. The statistical uncertainty surrounding 

7 the estimated O3 coefficients in epidemiology-based concentration-response functions as well as 

8 the shape of the exposure-response relationship chosen for the lung function risk assessment 

9 were addressed quantitatively. Additional uncertainties were addressed through sensitivity 

10 analyses and/or qualitatively. The risk assessment conducted for the previous O3 NAAQS review 

11 incorporated some of the variability in key inputs to the assessment by using location-specific 

12 inputs (e.g., location-specific concentration-response functions, baseline incidence rates and 

13 population data, and air quality data for epidemiological-based endpoints, location specific air 

14 quality data and exposure estimates for the lung function risk assessment). In that review, several 

15 urban areas were included in the health risk assessment to provide some sense of the variability 

16 in the risk estimates across the U.S.

17 Key observations and insights from the O3 risk assessment, in addition to important

18 caveats and limitations, were addressed in Section II.B of the Final Rule notice (73 FR 16440 to 

19 14 16443, March 27, 2008). In general, estimated risk reductions associated with going from 

20 then-current O3 levels to just meeting the then-existing and alternative 8-hour standards showed 

21 patterns of decreasing estimated risk associated with just meeting the lower alternative 8-hour 

22 standards considered. Furthermore, the estimated percentage reductions in risk were strongly 

23 influenced by the baseline air quality year used in the analysis, which was due to significant 

24 year-to-year variability in O3 concentrations. There was also noticeable city-to-city variability in 

25 the estimated O3-related incidence of morbidity and mortality across the 12 urban areas. 

Uncertainties associated with estimated policy-relevant background (PRB) concentrations2 26 were 

27 also addressed and revealed differential impacts on the risk estimates depending on the health

28 effect considered as well as the location. EPA also acknowledged that at the time of the previous 

29 review there were considerable uncertainties surrounding estimates of O3 C-R coefficients and 

30 the shape of concentration-response relationships and whether or not a population threshold or 

31 non-linear relationship exists within the range of concentrations examined in the epidemiological 

32 studies.

 2

Policy-relevant background (PRB) O3 has been defined in previous reviews as the distribution of O3 concentrations 

that would be observed in the U.S. in the absence of anthropogenic (man-made) emissions of O3 precursor 

emissions (e.g., VOC, CO, NOx) in the U.S., Canada, and Mexico.
3-5

1 3.2 PLAN FOR THE CURRENT EXPOSURE AND RISK ASSESSMENTS

2 The Scope and Methods Plan, including updates (U.S. EPA, 2011b; Wegman, 2012),

3 outlined a planned approach for conducting the current quantitative O3 exposure and risk 

4 assessments, including broad design issues as well as more detailed aspects of the analyses. A 

5 critical step in designing the quantitative risk and exposure assessments is to clearly identify the 

6 goals for the analysis based on the policy-relevant questions identified in Chapter 2. We have 

7 identified the following goals for the urban area exposure and risk assessments: (1) to provide 

8 estimates of the percent of people in the general population and in sensitive populations with O3

9 exposures above health-based benchmark levels; (2) to provide estimates of the percentage of the 

10 general population and in sensitive populations with impaired lung function (defined based on 

11 decrements in FEV1) resulting from exposures to O3; (3) to provide estimates of the potential

12 magnitude of premature mortality associated with both short-term and long-term O3 exposures, 

13 and selected morbidity health effects associated with short-term O3 exposures; (4) to evaluate the 

14 influence of various inputs and assumptions on risk estimates to the extent possible given 

15 available methods and data; (5) to gain insights into the spatial and temporal distribution of risks 

16 and patterns of risk reduction and uncertainties in those risk estimates. For the exposure and risk 

17 analyses, we will estimate exposures and risks for recent ambient levels of O3 and for O3

18 concentrations after simulating just meeting the existing O3 standard and potential alternative 

19 standards.

20 With regard to selecting alternative levels for the 8-hour O3 standards for evaluation in 

21 the quantitative risk assessment, we base the range of levels on the evaluations of the evidence 

22 provided in the first draft PA, which received support from the CASAC in their advisory letter 

23 on the first draft PA. The first draft PA recommended evaluation of 8-hour maximum 

24 concentrations in the range of 60 to 70 ppb, with possible consideration of levels somewhat 

25 below 60 ppb. The upper end of this range is supported by the clear evidence from both clinical 

26 and epidemiological studies of effects at exposures of 70 ppb reported in the ISA and 

27 summarized in the first draft PA. The lower end of this range is based on considerations of 

28 evidence from clinical studies that have shown lung function decrements in healthy adult 

29 populations at 60 ppb O3 exposures, and that 10 percent of healthy adults exposed to 60 ppb O3

30 experienced lung function decrements that could be adverse to asthmatics. The evidence showing 

31 effects in healthy adults at exposures of 60 ppb supports the consideration of risks to sensitive 

32 populations at exposure levels below 60 ppb, although specific exposure levels below 60 ppb at 

33 which risks may be occurring are not supported by the evidence. An important distinction is that 

34 the evidence from controlled human exposure studies is based on exposures, while the standard 
3-6

1 addresses ambient concentrations. Typically, exposures are lower than ambient concentrations 

because people spend a large fraction of their time indoors where O3 concentrations are lower.

3 2

3 Because of the year-to-year variability in O3 concentrations that results from temporal 

4 variability in meteorology and emissions that drive O3 formation, the exposure and risk 

5 assessments evaluate scenarios for meeting the existing and alternative standards based on 

6 multiple years of O3 data. O3 concentrations from 2006-2010 are used in estimating exposure and 

7 risk. This range of years captures a high degree of variability in meteorological conditions, as 

8 well as reflecting years with higher and lower emissions of O3 precursors.

9 In order to provide greater confidence in the exposure and risk estimates, this REA uses 

10 an urban case study approach for assessing both exposure and risk. This approach provides 

11 greater confidence in estimates by allowing us to make use of air quality data, population

12 information, health data, and epidemiology results that are well matched, and it does not require 

13 extrapolation of results to locations without these data. In addition, the urban case study 

14 approach allows us to simulate just meeting existing and alternative O3 standards for each urban 

area, which is not currently feasible for health risk assessment at the national scale.

4 15 Specific 

16 selection criteria for case study urban areas included in the exposure and risk assessments are 

17 described in the following sections. In order to gain an understanding of how well the urban case 

18 study areas represent risks at a national level and to provide context for the urban case study 

19 results, we also include two national level analyses, 1) estimation of the national mortality 

20 burden associated with recent ambient O3 and 2) characterization of how well the risk estimates 

21 for the set of urban areas modeled reflect the national distribution of mortality risk.

22 Throughout the exposure and risk analyses, we recognize that there are many sources of 

23 variability and uncertainty. Each analysis considers carefully the potential sources and 

24 significance of variability and uncertainties and, where data are available, provides quantitative 

25 assessment of variability and uncertainties, either through probabilistic analyses or through 

26 sensitivity or scenario analyses. In general the analyses follow the WHO guidelines for 

27 uncertainty assessment (World Health Organization, 2008), which recommend a tiered approach 

 3 While almost all people spend a large fraction of their time indoors, there is high variability in this fraction 

between children and adults, and between outdoor workers and indoor workers. The ratio of exposures to 

ambient concentrations will likely be higher for children than adults, and for outdoor workers compared to 

indoor workers. 4 In order to simulate just meeting alternative standards everywhere nationwide using the model-based adjustment 

approach employed in this REA, some areas would see O3 design values decreased below the targeted 

standard level due to O3 transport between locations. We were not able to devise an approach that would 

just meet the standard in every location simultaneously. Using the urban case study approach, we can, 

acknowledging the counterfactual nature of the analysis, assume independence of attainment for each urban 

case study area, which allows us to simulate just meeting the standards in each urban case study area.
3-7

1 in which progressively more sophisticated methods can be used to evaluate and characterize 

2 sources of uncertainty depending on the overall complexity, end use of the assessment, and 

3 resources and data available to conduct particular uncertainty characterizations.

4 The planned approaches for conducting the exposure and risk analyses are briefly 

5 summarized below. We begin with a general discussion of how uncertainty and variability are 

6 addressed in the different elements of the exposure and risk assessment. This is followed by a 

7 discussion of the air quality data that will be used in both the exposure and risk assessments and 

8 then discussions of each component of the exposure and risk assessments.

9 3.3 CHARACTERIZATION OF UNCERTAINTY AND VARIABILITY IN THE 

10 CONTEXT OF THE O3 EXPOSURE AND RISK ASSESSMENT

11 An important component of this population exposure and health risk assessment is the 

12 characterization of both uncertainty and variability. Variability refers to the heterogeneity of a 

13 variable of interest within a population or across different populations. For example, populations 

14 in different regions of the country may have different behavior and activity patterns (e.g., air 

15 conditioning use and time spent indoors) that affect their exposure to ambient O3 and thus the 

16 population health response. The composition of populations in different regions of the country 

17 may vary in ways that can affect the population response to exposure to O3 – e.g., two 

18 populations exposed to the same levels of O3 might respond differently if one population is older 

19 than the other. Variability is inherent and cannot be reduced through further research. 

20 Refinements in the design of a population risk assessment are often focused on more completely 

21 characterizing variability in key factors affecting population risk – e.g., factors affecting 

22 population exposure or response – in order to produce risk estimates whose distribution 

23 adequately characterizes the distribution in the underlying population(s). 

24 Uncertainty refers to the lack of knowledge regarding the actual values of inputs to an 

25 analysis. Models are typically used in analyses, and there is uncertainty about the true values of 

26 the parameters of the model (parameter uncertainty) – e.g., the value of the coefficient for O3 in a 

27 C-R function. There is also uncertainty about the extent to which the model is an accurate 

28 representation of the underlying physical systems or relationships being modeled (model 

29 uncertainty) – e.g., the shapes of C-R functions. In addition, there may be some uncertainty 

30 surrounding other inputs to an analysis due to possible measurement error—e.g., the values of 

31 daily O3 concentrations in a risk assessment location or the value of the baseline incidence rate 

for a health effect in a population.

5 32

 5 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 
3-8

1 In any risk assessment, uncertainty is, ideally, reduced to the maximum extent possible 

2 through improved measurement of key variables and ongoing model refinement. However, 

3 significant uncertainty often remains, and emphasis is then placed on characterizing the nature of 

4 that uncertainty and its impact on risk estimates. The characterization of uncertainty can be both 

5 qualitative and, if a sufficient knowledge base is available, quantitative.

6 The characterization of uncertainty associated with risk assessment is ideally addressed in 

7 the regulatory context using a tiered approach in which progressively more sophisticated 

8 methods are used to evaluate and characterize sources of uncertainty depending on the overall 

9 complexity and intended use of the risk assessment (WHO, 2008). Guidance documents 

10 developed by EPA for assessing air toxics-related risk and Superfund Site risks as well as recent 

11 guidance from the World Health Organization specify multitier approaches for addressing 

12 uncertainty. 

13 Following the approach used for previous NAAQS risk and exposure assessments (U.S. 

14 EPA, 2008c, 2009b, 2010a, b), for the O3 risk assessment, we are using a tiered framework 

15 developed by WHO to guide the characterization of uncertainty. The WHO guidance presents a 

16 four-tiered approach, where the decision to proceed to the next tier is based on the outcome of 

17 the previous tier’s assessment. The four tiers described in the WHO guidance include: 

18 Tier 0: recommended for routine screening assessments, uses default uncertainty factors 

19 (rather than developing site-specific uncertainty characterizations); 

20 Tier 1: the lowest level of site-specific uncertainty characterization, involves qualitative 

21 characterization of sources of uncertainty (e.g., a qualitative assessment of the general magnitude 

22 and direction of the effect on risk results);

23 Tier 2: site-specific deterministic quantitative analysis involving sensitivity analysis, 

24 interval-based assessment, and possibly probability bounded (high-and low-end) assessment; and

25 Tier 3: uses probabilistic methods to characterize the effects on risk estimates of sources 

26 of uncertainty, individually and combined.

27 With this four-tiered approach, the WHO framework provides a means for systematically 

28 linking the characterization of uncertainty to the sophistication of the underlying risk assessment. 

29 Ultimately, the decision as to which tier of uncertainty characterization to include in a risk 

 

with the effort to accurately characterize variability in key model inputs actually reflecting an effort to 

reduce uncertainty.
3-9

1 assessment will depend both on the overall sophistication of the risk assessment and the 

2 availability of information for characterizing the various sources of uncertainty.

3 This risk and exposure assessment for the O3 NAAQS review is relatively complex, 

4 possibly warranting consideration of a full probabilistic (WHO Tier 3) uncertainty analysis. For 

5 the exposure assessment, we include probabilistic representations of important sources of 

6 variability; however, due to lack of information regarding reasonable alternative parameter 

7 settings for model input variable distributions, we are not able to include a complete probabilistic 

8 analysis incorporating both variability and uncertainty. Instead, we provide sensitivity analyses 

9 to explore the impact of specific model assumptions, and we include a comprehensive qualitative 

10 discussion of uncertainty regarding the model inputs and outputs.

11 While a full probabilistic uncertainty analysis is not undertaken for the epidemiology12 based risk assessment due to limits in available information on distributions of model inputs, we 

13 provide a limited assessment using the confidence intervals associated with effects estimates 

14 (obtained from epidemiological studies) to incorporate statistical uncertainty associated with 

15 sample size considerations in the presentation of risk estimates. Technically, this type of 

16 probabilistic simulation represents a Tier 3 uncertainty analysis, although as noted here, it will be 

17 limited and only address uncertainty related to the fit of the C-R functions. Incorporation of 

18 additional sources of uncertainty related to key elements of C-R functions (e.g., competing lag 

19 structures, alternative functional forms, etc.) into a full probabilistic WHO Tier 3 analysis would 

20 require that probabilities be assigned to each competing specification of a given model element 

21 (with each probability reflecting a subjective assessment of the probability that the given 

22 specification is the correct description of reality). However, for most model elements there is 

23 insufficient information on which to base these probabilities. One approach that has been taken 

24 in such cases is expert elicitation; however, this approach is resource- and time-intensive, and,

25 consequently, it is not feasible to use this technique in support of this O3 risk assessment.

26 For most elements of the quantitative risk assessments, rather than conducting a full 

27 probabilistic uncertainty analysis, we include a qualitative discussion of the potential impact of 

28 uncertainty on risk results (WHO Tier 2). For some critical elements of the epidemiology-based 

29 risk assessment, e.g., the effect-estimate in the C-R function, we include sensitivity analyses to 

30 explore the potential impact of our assumptions. This falls under the WHO Tier 2 classification, 

31 although we are not able to assign probabilities to the sensitivity analyses. For these sensitivity 

32 analyses, we will include only those alternative specifications for input parameters or modeling 

33 approaches that are deemed to have scientific support in the literature (and so represent 

34 alternative reasonable input parameter values or modeling options). This means that the array of 

35 risk estimates presented in this assessment is expected to represent reasonable risk estimates that 
3-10

1 can be used to provide some information regarding the potential impacts of uncertainty in the 

2 model elements.

3 3.4 AIR QUALITY CHARACTERIZATION

4 Figure 3-1 diagrams the basic information used in developing the air quality inputs for 

5 the REA. Air quality inputs to the urban area exposure and risk assessments include (1) recent air 

6 quality data developed from O3 ambient monitors in each selected urban study area and (2) 

7 simulated air quality that reflects changes in the distribution of O3 air quality estimated to occur 

8 when the urban area just meets the existing or alternative O3 standards under consideration. In 

9 addition, O3 air quality surfaces for recent years covering the entire continental U.S. were 

10 generated for use in the national-scale assessment. Details of the air quality data used in the REA 

11 are discussed in Chapter 4.

12

13

14 Figure 3-1 Conceptual Diagram for Air Quality Characterization in the Health REA

15

16 The urban case study area exposure and risk analyses are based on five recent years of air 

17 quality data, 2006-2010. We are including 5 years to reflect the considerable variability in 

18 meteorological conditions and the variation in O3 precursor emissions that have occurred in 

19 recent years. The analyses focus on the O3 season, which ranges from April to October in much 

20 of the nation but is longer in some warmer areas such as Los Angeles and Houston. The required 

21 O3 monitoring seasons for the urban case study areas are described in more detail in Chapter 4.

22 In developing the O3 air quality surfaces for the national-scale analysis, a combination of 

23 monitoring data and modeled O3 concentrations are used to provide greater coverage across the 

O3 Precursor Emissions

Ozone Air Quality Data Model-based O3 Sensitivities

O3 Metrics in Urban Case 

Study Areas: recent conditions 

and after just meeting 

existing and alternative standards

National Ambient 

O3 Spatial Fields: 

recent conditions
3-11

1 U.S. The procedure for fusing O3 monitor data with modeling results is described further in 

2 Chapter 4.

3 Several O3 metrics are generated for use in the urban area exposure and risk analyses. 

4 The exposure analyses use hourly O3 concentrations, while the risk analyses use several different 

5 averaging times. The specific metrics used in each analysis are discussed further in following 

6 chapters. For the exposure analysis, hourly O3 concentrations are interpolated to census tracts 

7 using Voronoi neighbor averaging (VNA), a distance weighted interpolation method (Gold, 

8 1997; Chen et al., 2004). For the epidemiology-based risk analysis, we developed a composite of 

9 all monitors in the urban area for application with the epidemiology studies. We also evaluated

10 several different definitions of the spatial boundaries of the urban areas that determined the 

11 monitors included in the spatial average. Some of the epidemiological studies specify a relatively 

12 narrow set of counties within an urban area, while others use a broader definition, such as all 

13 counties in a core based statistical area (CBSA) as defined by the Census Bureau. For those 

14 epidemiological studies that used a relatively narrow set of counties, most were based on 

15 counties in the center of the urban area. In most of these areas, the non-attaining O3 monitors are 

16 not located in the center of the urban area, but instead in the surrounding areas, reflecting the 

17 transport and atmospheric chemistry governing O3 formation. As a result, using a monitor set 

18 that exactly reflects the specific counties used in the epidemiology studies can exclude counties 

19 in an urban area that would realize the most risk reduction resulting from just meeting the O3

20 standard. To better represent the changes in risk that could be experienced in the urban areas, the 

21 core risk estimates for all endpoints will be based on the CBSA definition. Sensitivity analyses 

22 are included to evaluate the effect of using only the counties in each urban area that specifically 

23 match the county set used in the epidemiology studies.

24 Simulation of just meeting the existing and alternative O3 standards is accomplished by 

25 adjusting hourly O3 concentrations measured over the O3 season using a model-based adjustment 

methodology that estimates O3 sensitivities to precursor emissions changes.

6 26 These sensitivities, 

27 which estimate the response of O3 concentrations to reductions in anthropogenic NOx and VOC 

28 emissions, are developed using the Higher-order Decoupled Direct Method (HDDM) capabilities 

29 in the Community Multi-scale Air Quality (CMAQ) model. This modeling approach incorporates 

30 all known emissions, including sources of natural and anthropogenic emissions in and outside of 

31 the U.S. By using the model-based adjustment methodology we are able to more realistically 

32 simulate the temporal and spatial patterns of O3 response to precursor emissions. We chose to 

 6 In the first draft of this REA, we used a statistical quadratic rollback approach to simulate just meeting the existing 

O3 standards. In that draft, we proposed using the model based approach that is being used in this draft, 

and received support for the model based approach from CASAC.
3-12

1 simulate just meeting the existing and alternative standards in the urban cast study areas by 

2 decreasing U.S. anthropogenic emissions of NOx and VOC throughout the U.S using equal 

3 proportional decreases in emissions throughout the U.S., in order to avoid any suggestion that we 

4 are approximating a specific emissions control strategy that a state or urban area might choose to 

5 meet a standard. More details on the HDDM-adjustment approach are presented in Chapter 4 of 

6 this REA and in Simon et al. (2013).

7 In the previous review, background O3 (referred to in that review as policy relevant 

8 background, or PRB) was incorporated into the REA by calculating risk only in excess of PRB. 

9 CASAC members recommended that EPA move away from using PRB in calculating risks

10 (Henderson, 2007). In addition, comments received from CASAC, based on their review of the 

11 first draft Risk and Exposure Assessment on September 11-12, 2012 (Frey and Samet, 2012),

12 agreed with the development of risk estimates with reference to zero O3 concentration. Based on 

13 these recommendations and comments, the second draft REA includes risks associated with O3

14 from all sources after we have simulated just meeting the existing standard and estimates of total 

15 risk remaining after meeting alternative levels of the standards. EPA believes that presenting 

16 total risk is most relevant given that individuals and populations are exposed to total O3 from all 

17 sources, and risks associated with O3 exposure are due to total O3 exposure and do not vary for 

18 O3 exposure associated with any specific source. In addition, background O3 is fully represented 

19 in estimates of total risk given that the measured and adjusted air quality concentrations being 

20 used in the risk and exposure analyses include O3 produced from precursor emissions from both 

21 anthropogenic and background sources. The evidence and information on background O3 that is 

22 assessed in the Integrated Science Assessment (ISA) is considered in the Policy Assessment 

23 (PA) in conjunction with the total risk estimates provided in this second draft REA. With regard 

24 to background O3 concentrations, the PA will consider available information on ambient O3

25 concentrations resulting from natural sources, anthropogenic sources outside the U.S., and 

26 anthropogenic sources outside of North America.

27 In providing a broader national characterization of O3 air quality in the U.S., this REA 

28 draws upon air quality data analyzed in the O3 ISA as well as national O3 databases and 

29 modeling of O3 using the Community Multiscale Air Quality (CMAQ) model. This information,

30 along with additional analyses, is used to develop a broad characterization of recent air quality 

31 across the nation. This characterization includes O3 levels in the urban case study areas for the 

32 time periods relevant to the risk analysis and information on the spatial and temporal 

33 characterization of O3 across the national monitoring network. This information is then used to 

34 place the relative comparative attributes of the selected study areas into a broader national 

35 comparative context to help judge the overall representativeness of the selected study areas in 

36 characterizing O3 risk for the nation. In addition, to better characterize the spatial patterns of 
3-13

1 responses of the distribution of O3 to just meeting existing and alternative O3 standards, we also 

2 provide assessments of the historical patterns of responses of O3 to emissions changes over time 

3 and an assessment of national patterns of responses to emissions changes relative to the spatial 

4 distribution of populations. These analyses are presented in more detail in Chapter 8 and Chapter 

5 8 appendices.

6 3.5 EXPOSURE ASSESSMENT

7 Figure 3-2 diagrams the basic structure of the population exposure assessment. Basic 

8 inputs to the exposure assessment include the following: (1) recent measurements of O3

9 concentrations from monitors in each selected urban study area; (2) O3 concentrations that reflect 

10 changes in the distribution of O3 air quality estimated to occur when an area just meets the 

11 existing or alternative O3 standards under consideration; (3) population and demographic 

12 information, e.g., age, gender, etc.; (4) time-location activity pattern data; and (5) physiological 

13 data, e.g., body mass index, ventilation rates, life-stage development, etc. Basic outputs include 

14 numbers and percent of persons with O3 exposures exceeding health-based benchmark levels and 

15 time-series of O3 exposures and ventilation rates for individuals (for use in the lung function risk 

16 analysis). Details of the exposure modeling are discussed in Chapter 5.

17

18

19 Figure 3-2 Conceptual Diagram for Population Exposure Assessment

O3 Air Quality for Recent Conditions, 

and After Just Meeting Existing 

and Alternative Standards

Estimation of O3

Exposure 

Concentrations

Population and 

Demographic Information

Time Activity 

Pattern Data

Physiological 

Data

Number and % of persons with 8-

hour average exposures at or 

above selected benchmark level 

per year while at moderate or 

greater exertion

Time-series of O3 exposure 

concentrations and ventilation 

rate for individuals
3-14

1

The scope of the exposure assessment includes 15 urban case study areas.

7 2 These areas 

3 were selected to be generally representative of U.S. populations, geographic areas, climates, and 

4 different O3 and co-pollutant levels, and they include all of the urban case study areas used in the 

5 epidemiology-based risk analysis (see Chapter 7). Three additional cities are included in the 

6 exposure modeling beyond those included in the epidemiology-based risk analysis. These cities 

7 are included to provide additional information on heterogeneity in exposure but could not be 

8 included in the epidemiology-based risk analysis because those analyses require additional 

9 information not available in the three additional cities. In addition to providing population 

10 exposures for estimation of lung function effects, the exposure modeling provides a 

11 characterization of urban air pollution exposure environments and activities resulting in the 

12 highest exposures.

13 Population exposure to ambient O3 levels is evaluated using version 4.5 of the APEX 

14 model. The model and updated documentation are available at 

15 http://www.epa.gov/ttn/fera/apex_download.html. Exposures are estimated using recent ambient 

16 O3 concentrations, based on 2006-2010 air quality data, and for O3 concentrations resulting from 

17 simulations of just meeting the existing 8-hour O3 standard and alternative O3 standards, based 

18 on adjusting 2006-2010 air quality data. Because the O3 standard is based on the 3-year average 

of the 4th 19 highest daily 8-hour maximum, we simulate just meeting the standard for two periods, 

20 2006-2008 and 2008-2010. Exposures are estimated for school-age children (ages 5 to 18), 

21 asthmatic school-age children, asthmatic adults (ages 19-95), and older persons (ages 65-95). 

22 This choice of population groups includes a strong emphasis on children, asthmatics, and persons 

23 ≥ 65 years old and reflects the finding of the last O3 NAAQS review (EPA, 2007a) and the ISA 

24 (EPA, 2013, Chapter 8) that these are important at-risk groups.

25 In addition to estimating exposures exceeding health-based exposure benchmarks, the 

26 exposure estimates are used as an input to the portion of the health risk assessment that is based 

27 on exposure-response relationships derived from controlled human exposure studies. The 

28 exposure analysis also provides a characterization of populations with high exposures in terms of 

29 exposure environments and activities. In addition, the exposure analysis offers key observations 

30 based on the results of the APEX modeling, viewed in the context of factors such as averting 

31 behavior and key uncertainties and limitations of the model.

 7 These cities are Atlanta, GA; Baltimore, MD; Boston, MA; Chicago, IL; Cleveland, OH; Dallas, TX; Denver, CO; 

Detroit, MI; Houston, TX; Los Angeles, CA; New York, NY; Philadelphia, PA; Sacramento, CA; St. 

Louis, MO; and Washington, D.C. We also considered included Seattle; however, the available monitoring 

data was not sufficient to accurately characterize O3 exposures for most populations in the Seattle area.
3-15

1 3.6 URBAN-SCALE LUNG FUNCTION RISK ANALYSES BASED ON 

2 APPLICATION OF RESULTS FROM CONTROLLED HUMAN EXPOSURE 

3 STUDIES

4 The major components in the lung function risk assessment are shown in Figure 3-3. 

5 Basic inputs to the analysis include 1) personal exposure to ambient O3 derived from the 

6 exposure modeling described in Section 3.2.3., 2) data from controlled human exposure studies,

7 used to construct exposure-response functions, 3) physiological data, including body mass index, 

8 age, etc., and 4) exercise levels, which determine breathing rates and affect dose. Basic outputs 

9 include the percentage of total population and sub-populations, e.g., children with asthma, with 

10 predicted lung function decrements (measured as decrements in forced expiratory volume in one 

11 second, or FEV1), greater than or equal to 10, 15, and 20 percent, for recent O3 levels and for O3

12 levels after just meeting existing and alternative standards.

13

14

15 Figure 3-3 Conceptual Diagram of O3 Lung Function Health Risk Assessment Based on 

16 Controlled Human Exposure Studies

17

18 Prior EPA risk assessments for O3 have included risk estimates for lung function 

19 decrements and respiratory symptoms based on analysis of individual data from controlled 

20 human exposure studies. The current assessment applies probabilistic exposure-response 

21 relationships which are based on analyses of individual data that describe the relationship 

22 between a measure of personal exposure to O3 and the measure(s) of lung function recorded in 

Exposure Estimates: 

Individual and Population

Lung Function

Exposure-Response 

Models

Data from controlled 

human exposure studies

Physiological 

parameters

Exercise levels 

and duration

% of population with ∆FEV1>10,15,20%

for recent O3 and after just meeting 

existing and alternative standards
3-16

1 the study. The current quantitative lung function risk assessment presents only a partial picture of 

2 the risks to public health associated with short-term O3 exposures, as there are additional 

3 controlled human exposure studies that have evaluated cardiovascular and neurological outcomes 

4 due to O3 exposure. However, these studies do not provide sufficient information with which to 

5 generate exposure-response functions and therefore are not suitable for quantitative risk 

6 assessment.

7 Modeling of risks of lung function decrements is based on application of results from 

8 controlled human exposure studies. These studies involve volunteer subjects who are exposed 

9 while engaged in different exercise regimens to specified levels of O3 under controlled 

10 conditions for specified amounts of time. The responses measured in such studies have included 

11 measures of lung function, such as forced expiratory volume in one second (FEV1), respiratory 

12 symptoms, airway hyper-responsiveness, and inflammation. The lung function risk assessment 

13 includes lung function decrement risk estimates, using FEV1, for the adult population, school-age 

14 children (ages 5-18), and asthmatic school-age children (ages 5-18).

15 In addition to estimating lung function decrements for healthy adults that were the study 

16 groups in the controlled human exposure studies, this lung function risk assessment estimates 

17 lung function decrements (≥ 10, ≥ 15, and ≥ 20% changes in FEV1) in children 5 to <18 years 

18 old. The lung function estimates for children are based on applying data from young adult 

19 subjects (18-35 years old) to children aged 5-18. This is based on findings from other chamber 

20 studies and summer camp field studies documented in the 1996 O3 Staff Paper (U.S. EPA, 

21 1996a) and 1996 O3 Criteria Document (U.S. EPA, 1996b), that lung function changes in healthy 

22 children are similar to those observed in healthy young adults exposed to O3 under controlled 

23 chamber conditions.

24 Risk metrics estimated for lung function risk include the numbers of school-age children 

25 and other population groups experiencing one or more occurrences of a lung function decrement 

26 ≥ 10, ≥ 15, and ≥ 20% in an O3 season and the total number of occurrences of these lung function 

27 decrements in school-age children and active school-age children.

28 The risk assessment includes two different modeling approaches. The first approach 

29 employs a model that estimates FEV1 responses for individuals associated with short-term 

30 exposures to O3 (McDonnell et al., 2012). This model is based on the data from controlled 

31 human exposure studies included in the prior lung function risk assessment as well as additional 

32 data sets for different averaging times and breathing rates. These data were from 23 controlled 

33 human O3 exposure studies that included exposure of 742 volunteers aged 18–35 years (see 

34 McDonnell et al., 2007 and McDonnell et al., 2012, for a description of these data). Outputs from 

35 this model include FEV1 decrements for each simulated individual for each day, which can be 

36 used to calculate the population distribution of FEV1 decrements, and the percent of the 
3-17

1 population with FEV1 decrements ≥ 10, ≥ 15, and ≥ 20% after just meeting existing and 

2 alternative standards.

3 In addition, we are applying the approach used in the last review and in the first draft of 

4 the REA, which employs a probabilistic population-level exposure-response function derived 

5 from the results of a number of controlled human exposure studies.

6 This modeling approach uses a smaller set of controlled human exposure studies and the 

7 population distribution of O3 exposures to directly estimate the percent of the population with 

8 moderate levels of exertion with lung function decrements ≥ 10, ≥ 15, and ≥ 20%.

9 Controlled human exposure studies, carried out in laboratory settings, are generally not 

10 specific to any particular real-world location. A controlled human exposure studies-based risk 

11 assessment can therefore appropriately be carried out for any locations for which there are 

12 adequate air quality data on which to base the modeling of personal exposures. For this

13 assessment, we have selected 15 urban case study areas (matching the areas used in the exposure 

14 analysis), representing a range of geographic areas, population demographics, and O3

15 climatology. These 15 areas also include the 12 urban case study areas evaluated in the risk 

16 analyses based on concentration-response relationships developed from epidemiological or field 

17 studies.

18 In the controlled human exposure study based risk assessment, there are two broad 

19 sources of uncertainty to the risk estimates. One of the important sources of uncertainty is the 

20 estimation of the population distribution of individual time series of O3 exposures and ventilation 

21 rates; these uncertainties are addressed as part of the exposure assessment. The second broad 

22 source of uncertainty in the risk calculation results from uncertainties in the lung function risk 

23 model. Sensitivity analyses are conducted to inform a qualitative discussion of these 

24 uncertainties.

25 3.7 URBAN CASE STUDY AREA EPIDEMIOLOGY-BASED RISK ASSESSMENT

26 The major components of the portion of the urban case study area health risk assessment 

27 based on data from epidemiological studies are illustrated in Figure 3-4. Basic inputs to this 

28 analysis include 1) measured O3 concentrations for recent conditions and adjusted air quality 

29 representing O3 concentrations after just meeting existing and alternative standards, 2) C-R 

30 functions derived from epidemiological studies evaluating associations between O3

31 concentrations and mortality and morbidity endpoints and 3) population counts and baseline 

32 incidence rates for mortality and morbidity endpoints. Basic outputs for each urban area include 

33 estimates of O3-attributable incidence and percent O3-attributable incidence for selected 

34 mortality and morbidity endpoints and changes and percent changes in O3-attributable incidence. 
3-18

1

2 Figure 3-4 Conceptual Diagram of Urban Case Study Area Health Risk Assessment Based 

3 on Results of Epidemiology Studies

4 Epidemiological and field studies provide estimated concentration-response relationships 

5 based on data collected in real--world settings. Ambient O3 concentrations used in these studies 

6 are typically spatial averages of monitor-specific measurements, using population-oriented 

7 monitors. Population health responses for O3 have included population counts of school 

8 absences, emergency room visits, hospital admissions for respiratory and cardiac illness, 

9 respiratory symptoms, and premature mortality. Risk assessment based on epidemiological 

10 studies typically requires baseline incidence rates and population data for the risk assessment 

11 locations. To minimize uncertainties introduced by extrapolation, a risk assessment based on 

12 epidemiological studies can be performed for the locations in which the studies were carried out, 

13 rather than extrapolating results to urban areas where studies for a particular health endpoint 

14 have not been conducted.

15 The set of urban case study areas included in this portion of the risk assessment was

16 chosen in order to provide population coverage and to capture the observed heterogeneity in O3-

17 related risk across selected urban study areas. In addition, locations had to have at least one 

18 epidemiological study conducted in order for the location to be included for a specific endpoint. 

19 This assessment also evaluates the mortality risk results for the selected urban areas within a 

20 broader national context to better characterize the nature, magnitude, extent, variability, and 

21 uncertainty of the public health impacts associated with O3 exposures. This national-scale

22 assessment is discussed in the next section.

O3 Air Quality for Recent Conditions, 

and After Just Meeting Existing 

and Alternative Standards

BenMAP estimation of O3

Attributable Incidence of Mortality

Location-specific Short-term And Morbidity 

Exposure C-R Functions

From Epidemiological Studies

Urban Area Population and 

Baseline Health Data

Urban Area Estimates of O3

attributable incidence of mortality 

and morbidity and change in O3

attributable incidence

Urban Area Estimates of % 

O3 Attributable Incidence and 

Change in % O3 attributable 

Incidence of mortality and morbidity 

National Long-term Exposure 

C-R Functions from 

Epidemiological Studies
3-19

1 We selected 2007 and 2009 as analysis years for the urban case study area risk analysis. 

2 These two years are the midpoint years in the two three-year periods 2006-2008 and 2008-2010. 

3 2007 represents a year with generally higher O3 concentrations, and 2009 represents a year with 

4 generally lower O3 concentrations. Analyses for these two years will provide a good 

5 representation of the effects of baseline O3 concentrations on the risk estimates.

6 This risk assessment is focused on health effect endpoints for which the weight of the 

7 evidence as assessed in the O3 ISA supports the causal determination that a likely causal or 

causal relationship exits between a specific health effect category to be due to exposure to O3. 8 . 

9 The analysis includes estimates of mortality risk associated with short-term 8-hour maximum or 

10 8-hour mean O3 concentrations in all 12 urban case study areas, as well as risk of hospitalization 

11 for chronic obstructive pulmonary disease and pneumonia. In addition, the analysis includes 

12 analysis of hospitalizations for additional respiratory diseases in Los Angeles, New York City, 

13 and Detroit, due to limited availability of epidemiological studies covering these endpoints 

14 across the 12 urban areas. The analysis also evaluates risks of respiratory related emergency 

15 department visits in Atlanta and New York City and risks of respiratory symptoms in Boston, 

16 again based on availability of epidemiological studies in these locations. Table 3-1 summarizes 

17 the endpoints evaluated for each of the 12 urban case study areas.

18
3-20

1 Table 3-1 Short-term O3 Exposure Health Endpoints Evaluated in Urban Case Study 

2 Areas

Urban Case Study 

Area

Mortality COPD and 

Pneumonia 

hospitalizations

Other 

respiratory 

hospitalizations

Respiratory 

Related ED 

visits

Respiratory 

Symptoms

Atlanta, GA X X X

Baltimore, MD X X

Boston, MA X X X

Cleveland, OH X X

Denver, CO X X

Detroit, MI X X X

Houston, TX X X

Los Angeles, CA X X X

New York, NY X X X X

Philadelphia, PA X X

Sacramento, CA X X

St. Louis, MO X X

3

4 This analysis will also estimate the respiratory mortality risks associated with longer-term

5 exposures to O3. This is supported by the O3 ISA, which concluded that the evidence for long6 term exposures to O3 as likely to be causally related to respiratory effects, including respiratory 

7 mortality and morbidity, indicates causal relationship with. There is one national study of long8 term exposures and respiratory mortality which provides a C-R function for use in the risk 

9 assessment. Several other studies have examined long-term exposures and cardiopulmonary 

10 mortality, but consistent with the ISA, we focused on respiratory mortality because of the 

11 additional supporting evidence related to long-term exposure and morbidity. Because the long12 term exposure C-R function is based on comparing O3 and mortality across urban areas, the same 

13 C-R function is applied in each of the 12 urban case study areas. The available epidemiological

14 studies evaluating long term O3 exposures and morbidity endpoints do not provide information 

15 that can be used to develop suitable C-R functions. As a result, we are not including quantitative 

16 risk estimates for morbidity associated with long-term exposures.

17 We have identified multiple options for specifying the concentration-response functions 

18 for particular health endpoints. This risk assessment provides an array of reasonable estimates for
3-21

1 each endpoint based on the available epidemiological evidence. This array of results provides a 

2 limited degree of information on the variability and uncertainty in risk due to differences in study 

3 designs, model specification, and analysis years, amongst other differences.

4 As part of the risk assessment, we address both uncertainty and variability. We provide a 

5 limited probabilistic characterization of uncertainty in the national-scale mortality risk estimates 

6 using the confidence intervals associated with effects estimates (obtained from epidemiological 

7 studies). However, this addresses only one source of uncertainty. For other sources of 

8 uncertainty, we include a number of sensitivity analyses to evaluate the impact of alternative 

9 approaches to simulating just meeting existing and alternative standards, alternative C-R 

10 functions, definitions of O3 seasons to which C-R functions are applied, and definitions of urban 

11 areas to which the C-R functions are applied. In addition, we evaluate the impact in a subset of 

12 locations of using co-pollutant C-R functions. In the case of variability, we identify key sources 

13 of variability associated with O3 risk (for both short-term and long-term exposure-related 

14 endpoints included in the risk assessment) and discuss the degree to which these sources of 

15 variability are reflected in the design of the risk assessment. Finally, we also include a 

16 comprehensive qualitative assessment of uncertainty and variability.

17 We also provide a representativeness analysis (see Chapter 8) designed to support the 

18 interpretation of risk estimates generated for the set of urban study areas included in the risk 

19 assessment. The representativeness analysis focuses on comparing the urban study areas to 

20 national-scale distributions for key O3-risk related attributes (e.g., demographics including 

21 socioeconomic status, air-conditioning use, baseline incidence rates and ambient O3 levels). The 

22 goal of these comparisons is to assess the degree to which the urban study areas provide 

23 coverage for different regions of the country as well as for areas likely to experience elevated O3-

24 related risk due to their specific mix of O3-risk related attributes.

25 The risk assessment based on application of results of epidemiological studies is 

26 implemented using the environmental Benefits Mapping and Analysis Program Community 

27 Edition (BenMAP-CE) (U.S. EPA, 2013), EPA’s GIS-based computer program for the 

28 estimation of health impacts associated with air pollution. BenMAP-CE draws upon a database 

29 of population, baseline incidence and effect estimates (regression coefficients) to automate the 

30 calculation of health impacts. EPA has traditionally relied upon the BenMAP program to 

31 estimate the health impacts avoided and economic benefits associated with adopting new air 

32 quality rules. It is also suitable for estimating risks associated with ambient concentrations of O3

33 and changes in risk resulting from just meeting existing and alternative O3 standards.
3-22

1 3.8 NATIONAL-SCALE MORTALITY RISK ASSESSMENT

2 The major components of the national-scale mortality risk assessment are shown in 

3 Figure 3-5. Basic inputs to this analysis are similar to those for the urban case study area 

4 epidemiology--based assessment and include 1) gridded O3 concentrations over the continental 

5 U.S. for recent conditions, 2) C-R functions relating long-term and short-term exposures to O3 to 

6 mortality, and 3) population and baseline mortality rates. Basic outputs include county and 

7 national estimates of incidence and percent of mortality attributable to O3.

8 The national-scale mortality risk assessment serves two primary purposes. First, it serves 

9 as part of the representativeness analysis discussed above, providing an assessment of the degree 

10 to which the urban study areas included in the risk assessment provide coverage for areas of the 

11 country expected to experience elevated mortality rates due to O3-exposure. Second, it provides a 

12 broader perspective on the distribution of risks associated with recent O3 concentrations 

13 throughout the U.S., and provides a more complete understanding of the overall public health 

burden associated with O3. 14 8 We note that a national-scale assessment such as this was completed 

15 for the risk assessment supporting the latest PM NAAQS review (US EPA, 2010) with the results 

16 of the analysis being used to support an assessment of the representativeness of the urban study 

17 areas assessed in the PM NAAQS risk assessment, as described here for O3.

18

 8 In the previous O3 NAAQS review, CASAC commented that “There is an underestimation of the affected 

population when one considers only twelve urban “Metropolitan Statistical Areas” (MSAs). The CASAC 

acknowledges that EPA may have intended to illustrate a range of impacts rather than be comprehensive in 

their analyses. However, it must be recognized that O3 is a regional pollutant that will affect people living 

outside these 12 MSAs, as well as inside and outside other urban areas.” Inclusion of the national-scale 

mortality risk assessment partially addresses this concern by providing a broader characterization of risk for 

an important O3 health endpoint.
3-23

1

2

3 Figure 3-5 Conceptual Diagram of National O3 Mortality Risk Assessment Based on 

4 Results of Epidemiology Studies

5

6 The national-scale risk assessment focuses on mortality only, due to the availability of 

7 large multi-city epidemiology studies for short-term mortality and the availability of a long-term 

8 mortality study which provides information to develop a suitable C-R function. As noted in the 

9 discussion of the urban case study area analyses, the available epidemiological studies evaluating 

10 long-term O3 exposures and morbidity endpoints do not provide information that can be used to 

11 develop suitable C-R functions. In the case of short-term morbidity endpoints, the available 

12 epidemiological studies are generally located in only a few urban areas and, even in the case of 

13 the multi-city hospitalization studies, cover only a small fraction of the urban areas in the U.S. In 

14 addition, baseline mortality rates are available for every county in the U.S., while baseline 

15 hospitalization rates are available in only a small subset of counties. For these reasons, the 

16 national-scale risk assessment includes only mortality associated with short- and long-term O3

17 exposures.

18 We provide a limited probabilistic characterization of uncertainty in the national-scale 

19 mortality risk estimates using the confidence intervals associated with effects estimates (obtained 

20 from epidemiological studies). However, this addresses only one source of uncertainty. To 

21 address some other key potential sources of uncertainty in the national assessment, we conduct 

22 sensitivity analyses. Risk estimates are provided for two alternative C-R functions for short-term 

23 exposure, reflecting two multi-city epidemiological studies. For short-term exposure-related 

24 mortality, the assessment provides several estimates of national mortality risk, including a full 

25 national-scale estimate including all counties in the continental U.S., and an analysis restricted to

26 the set of urban areas included in the time-series studies that provide the effect estimates. We 

County and national level

estimates of burden of premature 

mortality attributable to O3

BenMAP estimation of O3

Attributable Incidence of Mortality

Nationwide Set of City 

Specific Short-term Exposure 

C-R Functions from 

Epidemiological Studies

Nationwide County Specific 

Population and Baseline 

Health Data

National ambient O3

12 km2 gridded spatial field 

for recent conditions

County and national level 

estimates of % of total mortality 

attributable to O3

National Long-term Exposure

C-R Functions from 

Epidemiological Studies
3-24

1 have greater confidence in the analysis based on the large urban areas included in the 

2 epidemiological studies, but the information from the full analysis of all counties is useful to gain 

3 understanding of the potential magnitude of risk in less urbanized areas. In addition, the national4 scale mortality risk assessment evaluates the sensitivity of the nationwide estimates to 

5 assumptions about the transferability of effect estimates from the cities included in the 

6 underlying epidemiological studies to other cities in the U.S. Finally, the assessment includes a 

7 sensitivity analysis evaluating the use of regional priors city--rather than using a national prior in 

developing the city specific Bayesian adjusted effect estimates.

9 8 These sensitivity analyses are 

9 described in detail in Chapter 8.

10 The national-scale risk assessment is conducted only for recent O3 conditions. We do not 

11 attempt to simulate nationwide O3 concentrations that would result from just meeting the existing 

12 or alternative O3 standards everywhere in the U.S. Such a simulation would require detailed 

13 modeling of attainment strategies in all potential non-attainment areas and would need to take 

14 into account the interdependence of O3 concentrations across urban areas. This type of analysis is 

15 beyond the scope of this risk assessment. Analyses of nationwide attainment are included as part 

16 of the Regulatory Impact Analyses that accompany proposed and final rulemaking packages and 

17 will likely be included in the rulemaking portion of this review.

18 3.9 PRESENTATION OF EXPOSURE AND RISK ESTIMATES TO INFORM THE O3

19 NAAQS POLICY ASSESSMENT

20 We present exposure estimates in three ways: person-occurrences, number, and percent 

21 of persons in different populations (e.g., adults, all school-age children, asthmatic school-age 

22 children, outdoor workers) with at least one 8-hour average exposure at or above benchmark 

23 levels of 60 ppb, 70 ppb, and 80 ppb. In addition, the same types of results are shown for persons 

24 with multiple exposures at or above the benchmark levels. The results are presented in summary 

25 tables and graphics, while detailed tables of results are provided in an appendix. The focus in the 

26 presentation of results is on exposures occurring after simulating just meeting the existing 

27 standard and on the change in number and percent of exposures between meeting the existing 

28 standard and meeting alternative standards. Results are presented for the five modeled years, for 

29 all 15 urban case study areas.

30 Quantitative risk estimates from the analyses based on application of controlled human 

31 exposure studies are presented for the two different risk models. For each model, we provide 

 9 In multi-city Bayesian analyses, it is necessary to specify initial values or “priors” which are then 

“updated” using information from the individual city specific estimates. These priors are generally a mean value 

across all of the cities, in this case, cities in regions or cities across the nation.
3-25

1 estimates of the percent of different populations (adults, all children, children with asthma) with 

2 lung function decrements greater than or equal to 10, 15, and 20 percent. As with exposure, the 

3 focus in the presentation of results is on risk occurring after simulating just meeting the existing 

4 standards and on the change in risk occurring between meeting the existing standard and meeting 

5 alternative standards.

6 Results from the epidemiology-based risk assessment are presented in two ways: (1) total 

7 (absolute) health effects incidence for recent air quality and simulations of air quality just 

8 meeting the existing and alternative standards under consideration and (2) risk reduction 

9 estimates, reflecting the change in the distribution of O3 between scenarios of just meeting the 

10 existing standard and just meeting alternative standards. In addition, risks are presented as the 

11 percent of baseline incidence, and risks per 100,000 population, to allow for comparisons 

12 between urban areas with very different population sizes. We include risk modeled across the 

13 full distribution of O3 concentrations, as well as core risk estimates for O3 concentrations down 

14 to 0 ppb.

15 We present an array of risk estimates in order to provide additional context for 

16 understanding the potential impact of uncertainty on the risk estimates. For core estimates and 

17 sensitivity analyses, we provide the statistical confidence intervals, demonstrating the relative 

18 precision of estimates. The graphical presentation of sensitivity analyses focuses on the 

19 differences from the core estimates in terms of risk per 100,000 population.

20 The results of the representativeness analysis are presented using cumulative probability 

21 plots (for the national-level distribution of O3 risk-related parameters) with the locations where 

22 the individual urban study areas fall within those distributions noted in the plots using vertical 

23 lines. Similar types of plots are used to present the distribution of national-scale mortality 

24 estimates based on the national-scale risk assessment, showing the location of the urban case 

25 study areas within the overall national distribution.

26 Chapter 9 of this risk and exposure assessment provides a synthesis of the results from 

27 the four assessments (urban case study area exposure, urban case study area lung function risk, 

28 urban case study area epidemiology-based risk, and national mortality risk). Chapter 9 focuses 

29 on comparing patterns of results across locations, years, and alternative standards. Chapter 9 also 

30 provides perspective on the overall degree of confidence of the analyses and the 

31 representativeness of the set of results in characterizing patterns of exposure and risk and 

32 patterns of changes in exposure and risk from just meeting alternative standards relative to just 

33 meeting the existing standards.
3-26

1 3.10 REFERENCES

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3 ISPRS J Photogrammetry Remote Sensing, 59(1-2), 60-72.

4 Folinsbee, L.J.; W.F. McDonnell and D.H. Horstman. 1988. Pulmonary function and symptom 

5 responses after 6.6-hour exposure to 0.12 ppm O3 with moderate exercise. JAPCA. 38: 

6 28-35.

7 Gold, C. 1997. Voronoi methods in GIS. In: Algorithmic Foundation of Geographic Information 

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10 Henderson, R. 2007. Clean Air Scientific Advisory Committee’s (CASAC) Review of the 

11 Agency’s Final O3 Staff Paper. EPA-CASAC-07-002. March 26.

12 Horstman, D.H.; L.J. Folinsbee; P.J. Ives; S. Abdul-Salaam and W.F. McDonnell. 1990. O3

13 concentration and pulmonary response relationships for 6.6-hr exposures with five hrs of 

14 moderate exercise to 0.08, 0.10, and 0.12 ppm. Am Rev Respir Dis. 142:1158-1163.

15 Kim, Chong S.; N.E. Alexis; A.G. Rappold; H. Kehrl; J. Milan; J.C. Hazucha; J.C. Lay; M.T.

16 Schmitt; C. Martin; R.B. Devlin; D.B. Peden and D. Diaz-Sanchez. 2011. “Lung 

17 Function and Inflammatory Responses in Healthy Young Adults Exposed to 0.06 ppm O3

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19 (2011): 1215-1221.

20 Langstaff, J.E. 2007. OAQPS Staff Memorandum to O3 NAAQS Review Docket (EPA HQ21 OAR-2005-0172). Subject: Analysis of Uncertainty in O3 Population Exposure 

22 Modeling. [January 31, 2007]. 

23 <http://www.epa.gov/ttn/naaqs/standards/O3/s_O3_cr_td.html>.

24 McDonnell, W.F., et al. 1991. "Respiratory response of humans exposed to low levels of O3 for 

25 6.6 hours." American Review of Respiratory Disease, 147:804-810.

26 McDonnell, W.F.; P.W. Stewart and M.V. Smith. 2007. "The temporal dynamics of O3 –induced

27 FEV1 changes in humans: an exposure-response model." Inhalation Toxicology, 19:483–

28 494.

29 McDonnell W.F.; P.W. Stewart and M.V. Smith. 2010. "Prediction of O3 -induced lung function 

30 responses in humans." Inhalation Toxicology, 22(2):160-8.

31 McDonnell, W.F.; P.W. Stewart; M.V. Smith; C.S. Kim and E.S. Schelegle. 2012. Prediction of

32 lung function response for populations exposed to a wide range of ozone conditions. 

33 Inhalation Toxicology 24: 619-633.

34 Samet, J. 2009. Consultation on EPA’s Draft Integrated Review Plan for the National Ambient 

35 Air Quality Standards for Particulate Matter. (EPA document number EPA-CASAC-10-

36 004. December 3).

37 Samet, J. 2011. Consultation on EPA’s O3 National Ambient Air Quality Standards: Scope and 

38 Methods Plan for Health Risk and Exposure Assessment (April 2011) and O3 National 

39 Ambient Air Quality Standards: Scope and Methods Plan for Welfare Risk and Exposure 

40 Assessment (April 2011). EPA-CASAC-11-008. June 21.
3-27

1 Schelegle, E.S.; C.A. Morales; W.F. Walby; S. Marion and R.P. Allen. 2009. “6.6-Hour 

2 Inhalation of O3 Concentrations from 60 to 87 Parts per Billion in Healthy Humans.” 

3 American Journal of Respiratory and Critical Care Medicine 180 (2009): 265-272.

4 Simon H.; K.R. Baker; F. Akhtar; S.L. Napelenok; N. Possiel; B. Wells and B. Timin. 2013. A 

5 Direct Sensitivity Approach to Predict Hourly Ozone Resulting from Compliance with 

6 the National Ambient Air Quality Standard. Environmental Science and Technology. 

7 2013 Feb. 12. [Epub ahead of print]

8 U.S. Environmental Protection Agency. 1996a. "Review of National Ambient Air Quality 

9 Standards for O3 : Assessment of Scientific and Technical Information - OAQPS Staff 

10 Paper." (EPA document number EPA/452/R-96-007). Research Triangle Park, NC: 

11 Office of Air Quality Planning and Standards. Available from: NTIS, Springfield, VA; 

12 PB96–203435. < http://www.epa.gov/ttn/naaqs/standards/O3 /s_O3_pr.html>.

13 U.S. EPA. 1996b. "Air Quality Criteria for O3 and Related Photochemical Oxidants." (EPA 

14 document number EPA/600/P-93/004aF-cF). Research Triangle Park, NC: Office of 

15 Research and Development, National Center for Environmental Assessment. 

16 < http://cfpub.epa.gov/ ncea/cfm/recordisplay.cfm?deid=2831>.

17 U.S. EPA. 2007a. O3 Population Exposure Analysis for Selected Urban Areas. Research 

18 Triangle Park, NC: Office of Air Quality Planning and Standards. (EPA document 

19 number EPA-452/R-07-010, July).

20 U.S. EPA. 2007b. O3 Health Risk Assessment for Selected Urban Areas. Research Triangle Park, 

21 NC: Office of Air Quality Planning and Standards. (EPA document number EPA 452/R22 07-009, July). 

23 U.S. EPA. 2007c. "Review of the National Ambient Air Quality Standards for O3: Policy 

24 Assessment of Scientific and Technical Information. OAQPS Staff Paper." Research 

25 Triangle Park, NC: Office of Air Quality Planning and Standards. (EPA report number 

26 EPA-452/R-07-007a. < http://www.epa.gov/ttn/naaqs/standards/O3/s_O3_cr_sp.html >.

27 U.S. EPA. 2008a." National Air Quality: Status and Trends Through 2007." Research Triangle 

28 Park, NC: Office of Air Quality Planning and Standards. (EPA document EPA-454/R29 08-006, November). <http://www.epa.gov/airtrends/2008/index.html>.

30 U.S. EPA. 2008b. "EPA’s Report on the Environment." Washington, DC. (EPA document 

31 number EPA/600/R-07/045F, May 2008).

32 <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=190806>.

33 U.S. EPA. 2008c. "Risk and Exposure Assessment to Support the Review of the NO2 Primary 

34 National Ambient Air Quality Standard." (EPA document number EPA-452/R-08-008a, 

35 November 2008). 

36 <http://www.epa.gov/ttn/naaqs/standards/nox/data/20081121_NO2_REA_final.pdf>.

37 U.S. EPA. 2009a. Integrated Review Plan for the O3 National Ambient Air Quality Standards 

38 Review: External Review Draft. Research Triangle Park, NC: Environmental Media 

39 Assessment Group, National Center for Environmental Assessment and Health and 

40 Environmental Impacts Division, Office of Air Quality Planning and Standards. (EPA 

41 document number EPA 452/D-09-001, September).
3-28

1 U.S. EPA. 2009b. "Risk and Exposure Assessment to Support the Review of the SO2 Primary 

2 National Ambient Air Quality Standard." (EPA document number EPA-452/R-09-007, 

3 August 2009). 

4 <http://www.epa.gov/ttn/naaqs/standards/so2/data/200908SO2REAFinalReport.pdf.>

5 U.S. EPA. 2010a. "Quantitative Risk and Exposure Assessment for Carbon Monoxide –

6 Amended." EPA Office of Air Quality Planning and Standards. (EPA document number 

7 EPA-452/R-10-009, July). <http://www.epa.gov/ttn/naaqs/standards/co/data/CO-REA8 Amended-July2010.pdf>.

9 U.S. EPA. 2010b. "Quantitative Health Risk Assessment for Particulate Matter." Research 

10 Triangle Park, NC: Office of Air Quality Planning and Standards. (EPA document 

11 number EPA-452/R-10-005). 

12 <http://www.epa.gov/ttn/naaqs/standards/pm/data/PM_RA_FINAL_June_2010.pdf>.

13 U.S. EPA. 2011a. Integrated Review Plan for the O3 National Ambient Air Quality Standards.

14 Research Triangle Park, NC: National Center for Environmental Assessment, Office of 

15 Research and Development and Office of Air Quality Planning and Standards, Office of 

16 Air and Radiation. (EPA document number EPA 452/R-11-006, April).

17 U.S. EPA. 2011b. O3 National Ambient Air Quality Standards: Scope and Methods Plan for 

18 Health Risk and Exposure Assessment. Research Triangle Park, NC: Office of Air 

19 Quality Planning and Standards. (EPA document number EPA-452/P-11-001, April).

20 U.S. EPA. 2011c. O3 National Ambient Air Quality Standards: Scope and Methods Plan for 

21 Welfare Risk and Exposure Assessment. Research Triangle Park, NC: Office of Air 

22 Quality Planning and Standards. (EPA document number EPA-452/P-11-002, April).

23 U.S. EPA. 2013. "Environmental Benefits Mapping Analysis Program Community Edition 

24 (BenMAP-CE v1.0)." Research Triangle Park, NC: Health and Environmental Impacts 

25 Division, Office of Air Quality Planning and Standards. 

26 <http://www.epa.gov/air/benmap/>.

27 Wegman, L. 2012. Updates to information presented in the Scope and Methods Plans for the O3

28 NAAQS Health and Welfare Risk and Exposure Assessments. Memorandum from Lydia 

29 Wegman, Division Director, Health and Environmental Impacts Division, Office of Air 

30 Quality Planning and Standards, Office of Air and Radiation, US EPA to Holly 

31 Stallworth, Designated Federal Officer, Clean Air Scientific Advisory Committee, US 

32 EPA Science Advisory Board Staff Office. May 2, 2012.

33 World Health Organization. 2008. Harmonization Project Document No. 6. Part 1: Guidance 

34 Document on Characterizing and Communicating Uncertainty in Exposure Assessment.

35 <http://www.who.int/ipcs/methods/harmonization/areas/exposure/en/.>.

36

37
4-1

1 4 AIR QUALITY CONSIDERATIONS

2 4.1 INTRODUCTION

3 Air quality information is used in Chapters 5-8 to assess risk and exposure resulting from 

4 recent O3 concentrations, as well as to estimate the relative change in risk and exposure that 

5 could result from just meeting the existing O3 standard of 75 ppb and the potential alternative 

standard levels of 70 ppb, 65 ppb, and 60 ppb1 6 . The same air quality data are used to examine 

fifteen2 7 urban case study areas in the population exposure analyses discussed in Chapter 5 and 

8 the lung function risk assessment based on application of results from clinical studies discussed 

9 in Chapter 6: Atlanta, GA; Baltimore, MD; Boston, MA; Chicago, IL; Cleveland, OH; Dallas, 

10 TX; Denver, CO; Detroit, MI; Houston, TX; Los Angeles, CA; New York, NY; Philadelphia, 

11 PA; Sacramento, CA; St. Louis, MO; and Washington, DC. The epidemiology-based risk 

assessment discussed in Chapter 7 examines twelve3 12 of the fifteen urban case study areas 

13 evaluated in the population exposure analyses. Finally, Chapter 8 includes an assessment of the 

14 national-scale O3 mortality risk burden associated with recent O3 concentrations, and 

15 characterizes the representativeness of the 15 urban case study areas compared to the rest of the 

16 U.S. This chapter describes the air quality information developed for these analyses, providing 

17 an overview of monitoring data and air quality (section 4.2) and an overview of air quality inputs 

18 to the risk and exposure assessments (section 4.3).

19

20 4.2 OVERVIEW OF O3 MONITORING AND AIR QUALITY DATA

21 To determine whether or not the NAAQS have been met at an ambient O3 monitoring 

22 site, a statistic commonly referred to as a “design value” must be calculated based on 3 

23 consecutive years of data collected from that site. The form of the existing O3 NAAQS design 

value statistic is the 3-year average of the annual 4th 24 highest daily maximum 8-hour O3

25 concentration in parts per billion (ppb), with decimal digits truncated. The existing primary and 

26 secondary O3 NAAQS are met at an ambient monitoring site when the design value is less than 

 1 For a subset of urban areas and analyses, the REA evaluates a standard level of 55 ppb, consistent with 

recommendations from CASAC to also give consideration to evaluating a level somewhat below 60 ppb.

2 In the first draft REA, we proposed to include 16 urban areas in the second draft REA. However, further analysis 

of the air quality information available for Seattle, WA has prompted us to not include that city. This decision 

and supporting analysis are discussed in more detail in Appendix 4-E.

3 These cities are Atlanta, GA; Baltimore, MD; Boston, MA; Cleveland, OH; Denver, CO; Detroit, MI; Houston, 

TX; Los Angeles, CA; New York, NY; Philadelphia, PA; Sacramento, CA; and St. Louis, MO.
4-2

or equal to 75 ppb.4 1 In counties or other geographic areas with multiple monitors, the area-wide 

2 design value is defined as the design value at the highest individual monitoring site, and the area 

3 is said to have met the NAAQS if all monitors in the area are meeting the NAAQS.

4 Air quality monitoring data from 1,468 U.S. ambient O3 monitoring sites were retrieved 

5 by EPA staff for use in the risk and exposure assessments. The initial dataset consisted of hourly 

6 O3 concentrations in ppb collected between 1/1/2006 and 12/31/2010 from these monitors. Data

7 for nearly 1,400 of these monitors were extracted from EPA’s Air Quality System (AQS) 

database5 8 , while the remaining data came from EPA’s Clean Air Status and Trends Network 

9 CASTNET) database which consists of primarily rural monitoring sites. While CASTNET 

10 monitors did not begin reporting regulatory data to AQS until 2011, it is generally agreed that 

11 data collected from these monitors prior to 2011 is of comparable quality to the data reported to 

12 AQS.

13 These data were split into two design value periods, 2006-2008 and 2008-2010, and all 

14 subsequent analyses based on these data were conducted independently for these two periods. 

15 Observations flagged in AQS as having been affected by exceptional events were included the 

16 initial dataset, but were not used in design value calculations in accordance with EPA’s 

17 exceptional events policy. Missing data intervals of 1 or 2 hours in the initial dataset were filled 

18 in using linear interpolation. These short gaps often occur at regular intervals in the ambient data 

19 due to an EPA requirement for monitoring agencies to perform routine quality control checks on 

20 their O3 monitors. Quality control checks are typically performed between midnight and 6:00 

21 AM when O3 concentrations are low. Missing data intervals of 3 hours or more were not 

22 replaced. Interpolated data values were not used in design value calculations.

23 Figures 4-1 and 4-2 show the design values for the existing O3 NAAQS for all regulatory 

24 monitoring sites in the U.S. for the 2006-2008 and 2008-2010 periods, respectively. In general, 

25 O3 design values were lower in 2008-2010 than in 2006-2008, especially in the Eastern U.S. 

26 There were 518 O3 monitors in the U.S. with design values above the existing standard in 2006-

27 2008, compared to only 179 in 2008-2010.

28

 4 For more details on the data handling procedures used to calculate design values for the current O3 NAAQS, see 40 

CFR Part 50, Appendix P.

5 EPA’s Air Quality System (AQS) database is a national repository for many types of air quality and related 

monitoring data. AQS contains monitoring data for the six criteria pollutants dating back to the 1970’s, as well 

as more recent additions such as PM2.5 speciation, air toxics, and meteorology data. At present, AQS receives 

hourly O3 monitoring data collected from nearly 1,400 monitors operated by over 100 state, local, and tribal air 

quality monitoring agencies.
4-3

1

2 Figure 4-1 Map of Monitored 8-hour O3 Design Values for the 2006-2008 Period

3
4-4

1

2 Figure 4-2 Map of Monitored 8-hour O3 Design Values for the 2008-2010 Period

3

4 4.3 OVERVIEW OF URBAN-SCALE AIR QUALITY INPUTS TO RISK AND 

5 EXPOSURE ASSESSMENTS

6 The air quality information input into the urban-scale risk and exposure assessments 

7 includes both recent air quality data from the years 2006-2010, as well as air quality data 

8 adjusted to reflect just meeting the existing and potential alternative standard levels. In this 

9 section, we summarize these air quality inputs and discuss the methodology used to adjust air 

10 quality to meet the existing and potential alterative standards. 

11 Figure 4-3 presents a flowchart of air quality data processing steps for the urban-scale 

12 analyses. The rest of section 4.3.1 will provide more details on each step depicted in the flow 

13 diagram. Additional information is provided in Appendices 4-A, 4-B and 4-D.
4-5

Recent 

Monitored O3:

Hourly 2006-2010 measurements 

at individual monitors

Daily O3 Metrics at

composite monitor8 for 

recent conditions in urban 

case study areas. 

Metrics include:

MDA8

8-hr average

1-hr max

Community Multiscale

Air Quality model

instrumented with the 

Higher Order Decoupled Direct 

Method (CMAQ-HDDM)

Select emissions 

reductions (Nox or 

NOx/VOC) levels applied 

to meet alternate levels 

of the NAAQS

Adjustment of hourly ozone

values for all potential 

emissions reduction levels

Daily O3 metrics at

composite monitor8 in urban case 

study areas after just meeting 

existing and alternative standards

Metrics include:

MDA8

8-hr average

1-hr max

Hourly ozone concentrations after 

just meeting existing and 

alternative standards

Environmental Benefits 

Mapping and Analysis

Program (BenMAP)

Air Pollution Exposure 

Model (APEX)

Epi-based Risk Assessment for

Urban Case Study areas.

Endpoints:

Deaths, hospital admissions etc

Exposure assessment

Endpoints:

Number of individuals exposures to 

A single hourly ozone concentration 

above 60, 70, and 80 ppb 

Benchmark levels

Clinical-based risk assessment

Endpoints:

Number of individuals experiences

FEV1 decrements > 10%

VoronoiNeighbor 

Averaging (VNA)

interpolation

Hourly9 census-tract level 

VNA surfaces for each urban case 

study area for recent conditions

VoronoiNeighbor 

Averaging (VNA)

interpolation

Hourly9 census-tract level 

VNA surfaces for each urban case 

study area after just meeting existing 

And alternative standards

O3 sensitivity

coefficients

12 6 7

3 Figure 4-3 Flowchart of Air Quality Data Processing for Different Parts of the Urban4 scale Risk and Exposure Assessments

5

6 4.3.1 Urban Case Study Areas

7 The 15 urban case study areas in the exposure modeling and lung function risk 

8 assessments covered a large spatial extent, with boundaries generally similar to those covered by 

9 the respective Combined Statistical Areas (CSA) defined by the U.S. Census Bureau. Table 4-1 

10 gives some basic information about the 15 urban case study areas in the exposure assessment, 

11 including the number of ambient monitoring sites, the required O3 monitoring season, and the 

12 2006-2008 and 2008-2010 design values for each study area. All 15 of the urban case study areas 

13 had 8-hour O3 design values above the existing standard in 2006-2008, while 13 urban areas had 

 6 Composite monitors do not always include the highest design value monitor in every urban area.

7 4800 VNA surfaces were created for each urban area/alternative standard level pair: 24 hrs × 365 days × 5 years.

4.3.1.1 Exposure Modeling and Controlled Human Study Based Lung Function Risk 

Assessment
4-6

1 8-hour O3 design values above the existing standard in 2008-2010. Chicago (74 ppb) and Detroit 

2 (75 ppb) had design values meeting the existing standard during the 2008-2010 period. The 

3 design values in the 15 urban areas decreased by an average of 6 ppb between 2006-2008 and 

4 2008-2010, ranging from no change in Sacramento to a decrease of 15 ppb in Atlanta.

5

6Table 4-1 Monitor and Area Information for the 15 Urban Case Study Areas in the Exposure 

7 Modeling and Clinical Study Based Risk Assessment

Area Name

# of 

Counties

# of O3

Monitors

Population

(2010)

Required O3

Monitoring Season

2006-2008 

DV (ppb)

2008-2010 

DV (ppb)

Atlanta 33 13 5,618,431 March - October 95 80

Baltimore 7 7 2,710,489 April - October 91 89

Boston 10 14 5,723,468 April - September 83 77

Chicago 16 26 9,686,021 April - October 78 74

Cleveland 8 13 2,881,937 April - October 82 77

Dallas 11 20 6,366,542 January - December 89 86

Denver 13 26 3,390,504 March - September 86 77

Detroit 9 12 5,218,852 April - September 81 75

Houston 10 22 5,946,800 January - December 91 85

Los Angeles 5 54 17,877,006 January - December 119 112

New York 27 31 21,056,173 April - October 90 84

Philadelphia 15 19 7,070,622 April - October 92 83

Sacramento 7 26 2,755,972 January - December 102 102

St. Louis 17 17 2,837,592 April - October 85 77

Washington 26 22 5,838,518 April - October 87 81

8

9

Since O3 design values are based on the annual 4th 10 highest 8-hour daily maximum O3

11 concentrations from 3-consecutive years, it is useful to look at inter-annual variability. In 

general, the annual 4th 12 highest 8-hour O3 concentrations decreased in 11 of the 15 urban areas 

13 from 2006 to 2010, while remaining relatively constant in the other 4 areas (Figure 4-4). The 

average decrease in the annual 4th 14 highest daily maximum concentration from 2006 to 2010 was 

15 8 ppb. However, there was significant year-to-year variability, and some areas showed increases 

16 in some years relative to 2006, even though the 2010 values were generally lower.

17
4-7

1

2

3

4 Figure 4-4 Trends in Annual 4th Highest 8-hour Daily Maximum O3

5 Concentrations in ppb for the 15 Urban Case Study Areas for 2006-

6 2010. Urban areas are grouped into 3 regions: Eastern (top), Central 

7 (middle), and Western (bottom).

60

70

80

90

100

110

120

130

2006 2007 2008 2009 2010

Baltimore

Boston

NewYork

Philadelphia

Washington

60

70

80

90

100

110

120

130

2006 2007 2008 2009 2010

Atlanta

Chicago

Cleveland

Detroit

SaintLouis

60

70

80

90

100

110

120

130

2006 2007 2008 2009 2010

Dallas

Denver

Houston

LosAngeles

Sacramento
4-8

1 Table 4-2 gives some basic information on the 12 urban case study areas in the 

2 epidemiology-based risk assessment for each set of area boundaries. The spatial extent of each 

urban case study area was based on the respective Core Based Statistical Area (CBSA)8 3 . The 

4 CBSAs were generally smaller than the study areas used in the exposure modeling and clinical 

5 study based risk assessments, except for Baltimore and Houston, where the two study areas were 

6 identical. The rationales for the definitions of the spatial areas used in each type of analysis are 

provided in the corresponding chapters. The final two columns in Table 4-2 show the annual 4th 7

8 highest daily maximum 8-hour O3 concentration in ppb for the monitors within each urban case 

9 study area in 2007 and 2009.

10 It should be noted that the CBSA boundaries used for the urban case study areas in this 

assessment are different than those used in the 1st 11 draft of the REA, where the study areas were 

12 derived from the Zanobetti and Schwartz (2008) study. The change to the CBSA boundaries was 

13 intended to capture a larger portion of the urban area populations by including some surrounding 

14 suburban counties, rather than focusing strictly on the urban population centers. Two sensitivity 

15 analyses were conducted to determine the effect of changing the spatial extent of the urban case 

16 study areas on the epidemiology-based risk estimates. These sensitivity analyses are presented in 

17 Chapter 7, and a summary of the two alternative sets of boundaries for the 12 urban case study 

18 areas are provided in Appendix 4-A.

19 Since O3 is not directly emitted but is formed through photochemical reactions, precursor 

20 emissions may continue to react and form O3 downwind of emissions sources, thus the highest 

21 O3 concentrations are often found downwind of the highest concentrations of precursor 

22 emissions near the urban population center. There were some instances where the highest 

23 monitor occurred outside of the CBSA, but within the exposure area, which was designed to 

24 always include the monitor associated with the area-wide design value. For example, in Los 

25 Angeles, the CBSA includes Los Angeles and Orange counties, but the highest O3 concentrations 

26 are typically measured further downwind in Riverside and San Bernardino counties. Thus, the 

27 values reported in Table 4-2 may not match the values shown in Figure 4-4.

28

29

30

31

 8 Core Based Statistical Areas (CBSAs) are used by the Office of Management and Budget (OMB) to group U.S. 

counties into urbanized areas. These groupings are updated by OMB every 5 years. The CBSAs used in the 

epidemiology based risk assessment are based on the OMB deliniations from 2008. For more information see: 

http://www.whitehouse.gov/sites/default/files/omb/assets/bulletins/b10-02.pdf

4.3.1.2 Epidemiology Based Risk Assessment
4-9

1 Table 4-2 Monitor and Area Information for the 12 Urban Case Study Areas in the 

2 Epidemiology Based Risk Assessment

Area Name # of Counties

# of O3

Monitors

Population 

(2010)

2007 4th high 

(ppb)

2009

4th high 

(ppb)

Atlanta 28 13 5,268,860 102 77

Baltimore 7 7 2,710,489 92 83

Boston 7 11 4,552,402 89 75

Cleveland 5 10 2,077,240 83 72

Denver 10 16 2,543,482 97 79

Detroit 6 8 4,296,250 93 73

Houston 10 22 5,946,800 90 91

Los Angeles 2 21 12,828,837 105 108

New York 23 22 18,897,109 94 81

Philadelphia 11 15 5,965,343 102 74

Sacramento 4 17 2,149,127 93 96

St. Louis 16 17 2,812,896 94 74

3

4 4.3.2 Recent Air Quality

5

6 The sections below summarize the recent air quality data input into the epidemiological 

7 study-based risk assessment, and the exposure and controlled human exposure study-based risk 

8 assessment. Additional details on these inputs are provided in Appendix 4-A.

9

10 As discussed in more detail in Chapter 5, the REA uses the Air Pollutants Exposure 

11 (APEX) model (U.S. EPA, 2012a, b) to simulate exposure and to estimate lung function 

12 decrements based on application of results of controlled human exposure studies to populations 

13 in the 15 urban case study areas. The APEX model uses spatial fields of hourly O3

14 concentrations at each census tract within an urban area to simulate exposure. In the first draft 

15 REA, these hourly spatial fields were generated for four urban areas using the concentrations 

16 from the nearest neighboring O3 monitor. In this draft, we use Voronoi Neighbor Averaging 

17 (VNA) (Gold, 1997; Chen et al, 2004) to estimate hourly O3 concentrations at each census tract 

18 in all 15 urban case study areas, for recent measured air quality, air quality meeting the existing 

19 standard of 75 ppb, and air quality meeting potential alternative standards. The VNA fields were 

4.3.2.1 Exposure Modeling and Controlled Human Exposure Study Based Risk 

Assessment
4-10

1 estimated using ambient hourly O3 concentrations from monitors in each urban area, as well as 

2 monitors within a 50 km buffer region around the boundaries of each area. Additional details on 

3 the procedure used to generate the VNA fields, and a technical justification for the change from 

4 nearest neighbor fields to VNA fields are included in Appendix 4-A.

5

6 Figure 4-5 shows county-level maps of the 15 urban case study areas. Counties colored 

7 pink indicate the study area boundaries used in the Zanobetti & Schwartz (2008) and/or Smith et 

al (2009b) studies9 8 , where applicable. Counties colored gray indicate additional counties within 

9 the CBSA boundaries, and counties colored peach indicate any additional counties included in 

10 the exposure and lung function risk assessments. The X’s indicate locations of the O3 monitors 

11 used in the risk and exposure assessments, including those within the 50 km buffer region used 

12 to create the VNA fields.

 9 The Zanobetti and Schwartz (2008) and Smith et al (2009) study area boundaries were identical for 6 of the 12 

urban case study areas, and had at least one county in common for all 12 urban case study areas. The 

‘Epidemiology Study Area’ labels in figures 4-5 refer to counties included in either of these two studies.
4-11

1

2 Figure 4-5a Maps of the 5 Eastern U.S. Urban Case Study Areas Including O3 Monitor 

3 Locations
4-12

1

2 Figure 4-5b Maps of the 5 Central U.S. Urban Case Study Areas Including O3 Monitor 

3 Locations
4-13

1

2 Figure 4-5c Maps of the 5 Western U.S. Urban Case Study Areas Including O3 Monitor 

3 Locations
4-14

1 We input O3 air quality concentration data for the epidemiology-based risk analyses into 

2 the environmental Benefits Mapping and Analysis Program Community Edition (BenMAP-CE) 

3 (U.S. EPA, 2013) for assessment. We used BenMAP to analyze four different daily O3 metrics in 

4 12 of the 15 urban case study areas, which were the basis for concentration-response 

5 relationships derived in various epidemiology studies:

6 1. Daily maximum 1-hour concentration

7 2. Daily maximum 8-hour concentration

8 3. Daytime 8-hour average concentration (10:00AM to 6:00PM)

9 4. Daily 24-hour average concentration

10 The air quality monitoring data used in BenMAP were daily time-series of “composite 

11 monitor” values for each of the 12 urban areas for years 2007 and 2009, which were chosen to 

12 represent years with high and low O3 concentrations, respectively. The composite monitor values 

13 were calculated by first averaging the hourly O3 concentrations for all monitors within the area14 of-interest (resulting in a single hourly time-series for each urban area), then calculating the four 

15 daily metrics listed above. More details on the composite monitor value calculations and a 

16 presentation of the resulting concentrations can be found in Appendices 4-A and 4-D, 

17 respectively.

18

19 4.3.3 Air Quality Adjustments for “Just Meeting” Existing and Potential Alternative O3

20 Standards

21 The focus of the risk and exposure assessments is the evaluation of risks and exposures 

22 after just meeting existing and alternative standards, and the change in risk between just meeting 

23 existing standards and just meeting alternative standards. These evaluations require estimation of 

24 the change in hourly O3 concentrations that may occur in each urban area when “just meeting” 

25 the existing and potential alternative O3 standards.

26 The first draft REA and the previous O3 NAAQS review used the “quadratic rollback” 

27 method to adjust ambient O3 concentrations to simulate just meeting existing and alternative 

28 standards (U.S. EPA, 2007; Wells et al., 2012).Although the quadratic rollback method replicates 

29 historical patterns of air quality changes better than some alternative methods (e.g. simply 

30 shaving peak concentrations off at the NAAQS level and the proportional rollback technique), its 

31 implementation relies on a statistical relationship instead of on a mechanistic characterization of 

32 physical and chemical processes in the atmosphere. Because of its construct as a statistical fit to 

33 measured O3 values, the quadratic rollback technique cannot capture spatial and temporal 

34 heterogeneity in O3 response and also cannot account for nonlinear atmospheric chemistry that 

4.3.2.2 Epidemiology Based Risk Assessment
4-15

1 causes increases in O3 during some hours and in some locations as a result of emissions 

2 reductions under some circumstances.

3 Photochemical grid models are better able to simulate these phenomena and therefore the 

4 first draft REA proposed to replace quadratic rollback with a model-based O3 adjustment 

5 methodology and presented a test case for Atlanta and Detroit using modeling for July/August 

6 2005 (Simon et al., 2012). The section below summarizes the methodology applied in this 

7 second draft REA to adjust air quality for attainment of existing and alternative standards. This 

8 new methodology applies Higher-Order Decoupled Direct Method (HDDM) capabilities in the 

9 Community Multi-scale Air Quality (CMAQ) model to simulate the response of O3

10 concentrations to reductions in US anthropogenic NOx and VOC emissions. The model 

11 incorporates anthropogenic U.S., Canadian, Mexican and other international emissions, as well 

12 as emissions from non-anthropogenic sources. Since sources of background O3 are incorporated 

13 explicitly in the modeling, specifying U.S. background concentrations is unnecessary. 

14 Application of this approach also addresses the recommendation by the National Research 

15 Council of the National Academies (NRC, 2008) to explore how emissions reductions might 

16 effect temporal and spatial variations in O3 concentrations, and to include information on how 

17 NOx versus VOC control strategies might affect risk and exposure.

18

19 The EPA has developed an HDDM-adjustment methodology to estimate hourly O3

20 concentrations that could occur at each monitor location if urban case study areas were to meet

21 the existing and various alternative levels of the O3 standard. An early version of this 

22 methodology was proposed in the first draft REA (Simon et al., 2012). The methodology was 

23 later improved and published in a peer-reviewed journal (Simon et al., 2013). The methodology 

24 and its application to hourly O3 concentrations in the urban case study areas is summarized 

25 below and described in more detail in Appendix 4-D.

26 The HDDM-adjustment methodology uses the CMAQ photochemical model to determine 

27 monitoring site-specific response of hourly O3 concentrations to reductions in US anthropogenic 

28 NOx and VOC emissions. These responses are then applied to ambient data to create a 5-year 

29 time-series of hourly O3 concentrations at each monitor location which is consistent with 

30 meeting various potential levels of the O3 NAAQS for the two three-year attainment periods 

31 2006-2008 and 2008-2010. The steps are outlined in Figure 4-6 and summarized below:

32 • Step 1: Run CMAQ simulation with HDDM to determine hourly O3 sensitivities to NOx

33 emissions and VOC emissions for the grid cells containing monitoring sites in an urban 

34 area.

4.3.3.1 Methods
4-16

1 • Inputs: Model-ready emissions and meteorology data

2 • Outputs: O3 concentrations and sensitivities at locations of monitoring sites for 

3 each hour in January and April-October, 2007

4 • Step 2: For each monitoring site, season, and hour of the day use linear regression to 

5 relate first order sensitivities of NOx and VOC (SNOx and SVOC) to modeled O3 and

second order sensitivities to NOx and VOC (S2

NOx and S2 6 VOC) to the first order 

7 sensitivities.

8 • Inputs: Step 1 outputs

9 • Outputs: Functions to calculate typical sensitivities based on monitor location, 

10 O3 concentration, season, and hour of the day

11 • Step 3: For each measured hourly O3 value between 2006 and 2010, calculate the first 

12 and second order sensitivities based on monitoring site-, season-, and hour-specific 

13 functions derived in Step 2.

14 • Inputs: Step 2 outputs and hourly ambient data for 2006-2010.

15 • Outputs: Hourly O3 observations paired with modeled sensitivities for all hours 

16 in 2006-2010 at all monitor locations

17 • Step 4: Adjust measured hourly O3 concentrations for incrementally increasing levels of 

18 emissions reductions using assigned sensitivities and then recalculate design values until 

19 an emissions reduction level is reached at which all monitors in an urban area are below 

20 the existing and potential alternative levels of the standard.

21 • Inputs: Step 3 outputs

22 • Outputs: Adjusted hourly O3 values for 2006-2010 at monitor locations to show 

23 compliance with the existing and potential alternative standard levels based on the 

three year average of the 4th 24 highest 8-hour daily max O3 value. For each 

25 standard, two sets of data are created: 2006-2008 and 2008-2010. Because the 

26 emissions reductions used to attain standards in the two time periods might be 

27 different, adjusted 2008 O3 values are different for the two sets of data.

28
4-17

1

2 Figure 4-6 Flowchart of HDDM adjustment methodology to inform risk and exposure 

3 assessments.

4

5 We chose to adjust air quality for just meeting the existing and alternative standards by 

6 decreasing U.S. anthropogenic emissions of NOx and VOC throughout the U.S. For the purpose 

7 of this analysis we used the Community Multiscale Air Quality (CMAQ) model version 4.7.1 

8 equipped with HDDM to simulate 8 months in 2007 (January and April-October). This time 

9 period was chosen to cover the full O3 season and also includes at least one month from each 

10 season of the year. A full description of the model inputs, model set-up, and operational model 

11 evaluation against ambient data is available in Appendix 4-B. Sensitivities derived from the 

12 2007 model simulation were applied to the two 3-year periods of ambient data (2006-2008 and 

13 2008-2010) described in section 4.3.1.1. By applying equal proportional decreases in emissions 

14 throughout the U.S., we were able to estimate how O3 would respond to changes in ambient NOx 

15 and VOC concentrations without simulating a specific control strategy. The model was set up to 

16 track response in hourly O3 concentrations to these across-the-board changes in US 

17 anthropogenic NOx and VOC emissions. In choosing to apply across the board reductions 

18 throughout the modeling domain, we recognize that not all emissions across the domain 

19 contribute equally to nonattainment in each urban area. However, by decreasing emissions 
4-18

1 across the domain, we allow for the possibility of contribution from both regional and local 

2 emissions sources to nonattainment and to the overall distribution of O3 concentrations in urban 

3 areas. The modeling included sources which contribute to background O3 such as biogenic 

4 emissions, wildfire emissions, and transport of O3 and its precursors from international source 

5 regions. In addition, the HDDM tool was set-up to specifically calculate the changes in O3 that 

6 would occur from changes in US anthropogenic emissions alone, yet to account for the effects of 

7 background sources on this response. Consequently, it is not necessary to set a “floor” 

8 background O3 concentration as was done for quadratic rollback because background sources are 

9 explicitly accounted for in the model estimates of O3 response to US anthropogenic emissions.

10 As described in more detail in Appendix 4-D, the HDDM adjustment methodology 

11 estimates hourly O3 concentrations that would be associated with attaining a targeted level of the 

12 standard either though reductions in US anthropogenic NOx emissions alone or through 

13 reductions of both US anthropogenic NOx and VOC emissions in equal percentages. Because 

14 the combined NOx/VOC cuts are constrained to equal percentage cuts of both precursors, this is 

15 not an optimized NOx/VOC control scenario but rather a sensitivity analysis to characterize the 

16 range of results that could be obtained with alternate assumptions. In most of the urban areas, 

17 although the NOx/VOC scenario affected O3 response on some days, it did not affect O3

18 response at the highest design value (or controlling) monitor in such a way to reduce the total 

19 required emissions cuts. However, for the two cities of Chicago and Denver, the NOx/VOC 

20 scenarios allowed for lower percentage emissions cuts (applied to both NOx and VOC) to reach 

21 targeted standard levels than the NOx only scenario. Because of this, the core analyses presented 

22 in Chapters 5, 6, and 7 were based on the NOx only assumption for all cities except for Chicago 

23 and Denver which used the NOx/VOC equal percentage reduction assumption. Sensitivity 

24 analyses were performed to compare the NOx only and the NOx/VOC cases in 9 cities: Denver, 

25 Detroit, Houston, Los Angeles, New York, Philadelphia, and Sacramento. The effects of these 

26 sensitivity analyses on air quality and on the epidemiology-based risk assessment are discussed 

27 in more detail in Appendix 4-D and Chapter 7, respectively.

28 For New York and Los Angeles it should also be noted that a somewhat different 

29 approach was used for the HDDM-adjustment application. The HDDM adjustment methodology 

30 produces estimates of hourly O3 concentrations with standard error bounds for every potential 

31 emission reduction scenario. Uncertainties in the application of the methodology to very large 

32 emissions perturbations along with the fact that the mean estimate does not capture the 

33 variability in modeled responses on similar days resulted in the inability of this methodology to 

34 estimate O3 distributions in these two cities which would meet lower alternative standard levels 

35 (65 ppb for New York, 60 ppb for Los Angeles). This does not indicate that these two areas 

36 would not be able to meet these lower standard levels in reality, but simply reveals the 
4-19

limitations of this adjustment methodology. Consequently for these two cities, we used the 95th 1

2 percent confidence interval lower bound estimate of hourly O3 concentrations to capture a 

3 scenario in which these cities could meet lower standard levels based on the range of responses 

4 in O3 concentrations to emissions reduction predicted by the model for each city (See Appendix 

5 4-D for more details). Estimates of risk for these two cities for these alternative standards will be 

6 significantly more uncertain, reflecting the use of the lower bound O3 predictions.

7

8 The HDDM adjustment technique tended to have several effects on the distribution of air 

9 quality values. First, adjusted hourly O3 concentrations at night and during the morning rush10 hour tended to be higher than the recent observed concentrations (additional details are provided 

11 in Appendix 4-D). The CMAQ model predicts that, in general, these times have NOx titration 

12 conditions meaning that a reduction in NOx causes an increase in O3 concentrations. The NOx 

13 titration effect was most pronounced in urban core areas which have higher volume of mobile 

14 source NOx emissions from vehicles than do the surrounding areas. Response of daytime 

15 concentrations was more varied. In general, O3 tended to increase on low days and decrease on 

16 high days. However, specific monitors that were either always heavily VOC limited or always 

17 heavily NOx limited showed consistent increases and decreases respectively regardless of 

18 whether O3 concentrations were high or low on a particular day. It should be noted that locations 

19 which were heavily VOC limited tended to have much lower observed O3 concentrations than 

20 downwind areas. The tendency of the model to predict O3 increases on lower concentration days 

21 and decreases on higher concentration days also leads to more compressed O3 distributions in the 

22 HDDM adjustment cases. The variability in predicted daily O3 concentrations decreased when 

23 meeting lower standard levels. The following paragraphs summarize a comparison of O3

24 distributions from application of the quadratic rollback and HDDM adjustment approach for a 

25 case where the existing standard is estimated to be met, characterize the distribution of 

26 composite monitor O3 values at different standard levels, and provide a discussion of the spatial 

27 distribution of O3 changes in several cities. More details and figures for other case-study areas 

28 are provided in Appendix 4-D.

29 Figures 4-7 and 4-8 show a comparison of April-October composite monitor O3

30 distributions for recent conditions (2006-2008) and for meeting the existing standard using the 

31 quadratic rollback technique versus the HDDM adjustment methodology. The composite monitor 

32 values in these plots are based on the monitors included in the composite monitor from the 

Zanobetti and Schwartz (2008) study which was used in the 1st 33 draft REA and do not include all 

34 monitors in the CBSA as used in the main Chapter 7 analysis. In general, the O3 distribution in 

4.3.3.2 Resulting Air Quality
4-20

1 the HDDM adjustment case is shifted upward compared to the quadratic rollback case. The 

2 upward shift is more pronounced in the lower parts of the O3 distribution. In all cities displayed 

in Figure 4-7, the 25th 3 percentile, median, and mean of the 8-hour daily maximum O3

4 concentrations are higher in the HDDM adjustment case than the quadratic rollback. In some 

cities (Sacramento and St. Louis) the 75th 5 percentile values appear approximately equivalent in 

the two cases while in other cities the 75th 6 percentile values are slightly higher in the HDDM 

7 adjustment case. In Houston, the very highest portion of the O3 distribution is lower in the 

8 HDDM adjustment case than in the quadratic rollback case but in many cities the upper parts of 

9 the distributions for these two cases are roughly equivalent. Similar results are seen in the 2008-

10 2010 time period; however there are more cases during this time period where HDDM 

11 adjustment and quadratic rollback have similar values in the upper half of the O3 distribution. A 

12 comparison of Figure 4-7 and 4-8 shows that there is some seasonality to this effect. The two 

13 techniques appear to give very similar 8-hour daily maximum O3 composite monitor 

14 distributions during the summer months (June-August) and most of the situations with higher O3

15 levels with the HDDM adjustment come from cooler, lower O3 time periods (April, May, 

16 September, and October). Although here we discuss composite monitor distributions based on 

17 April-October, the risk analyses in Chapter 7 are based on the required O3 monitoring season, 

18 which is longer than April – October for some cities. We expect that the O3 increases shown for 

19 spring and fall months here are also representative of the type of response in other “cool season” 

20 months. The exceptions to this occur in Denver, Houston, New York and Los Angeles which 

21 have higher composite monitor O3 values from the HDDM adjustment compared to quadratic 

22 rollback even in the summer time period.
4-21

1

2 Figure 4-7 Distributions of composite monitor 8-hour daily maximum O3 concentrations from 

3 ambient measurements (black), quadratic rollback (blue), and the HDDM 

4 adjustment methodology (red) for meeting the existing standard. Values are based 

5 on the Zanobetti & Schwartz study areas for April-October of 2006-2008.
4-22

1

2 Figure 4-8 Distributions of composite monitor 8-hour daily maximum O3 concentrations 

3 from ambient measurements (black), quadratic rollback (blue), and the 

4 HDDM adjustment methodology (red) for meeting the existing standard. 

5 Values are based on the Zanobetti & Schwartz study areas for June-August of 

6 2006-2008.
4-23

1

2 Figures 4-9 and 4-10 show “box-and-whisker” plots of the April-October composite 

3 monitor daily maximum 8-hour O3 concentration distributions for the 12 urban case study areas 

4 evaluated in the epidemiology-based risk assessment; for recent air quality, and air quality 

5 adjusted to meet the existing and potential alternative standards. Figure 4-9 shows values from 

6 2007, while figure 4-10 shows values from 2009. Appendix 4-D contains additional plots 

7 comparing the changes in the distribution of composite monitor values in each urban area due to 

8 the air quality adjustments across varying spatial extents, season lengths, and years. In general, 

9 the range of the composite monitor distributions decreased (i.e. the minimum value increased, 

10 while the maximum value decreased) in all 12 urban case study areas as the air quality data were 

11 adjusted to meet lower standard levels. However, the changes within the inter-quartile range of 

12 these distributions (represented by the “boxes”) varied in response to the model-based air quality 

13 adjustments across the 12 urban areas. Three different types of responses are highlighted in the 

14 boxplots for Atlanta, New York, and Houston.

15 The Atlanta boxplots provide an example of an urban area in which all but the lowest 

16 composite monitor values decreased as the air quality data was adjusted to simulate compliance 

17 with progressively lower levels of the standard. The upper tail of the distribution (represented by 

18 the top whisker in each boxplot) decreased more quickly than the remainder of the distribution, 

19 resulting in less total variability in the composite monitor values with each progressively lower 

20 standard level. This type of response was also seen Sacramento and St. Louis, and to a lesser 

21 extent in Baltimore, Denver, and Philadelphia.

In New York, the boxplots showed an initial increase in the 25th 22 percentile and median 

23 composite monitor values when the observed O3 concentrations were adjusted to meet the 

24 existing standard. However, the median composite monitor value decreased relative to the 

25 existing standard as O3 concentrations were adjusted to meet the 70 ppb standard, and both the 

median and 25th 26 percentile values decreased when air quality were further adjusted to meet the 

27 65 ppb standard. When the air quality were adjusted to meet 65 ppb, the median and mean 

28 composite monitor values were lower than under observed conditions. This type of response was 

29 also observed in Cleveland, Detroit, and Los Angeles.

30 In Houston, the median composite monitor value also increased between observed air 

31 quality and air quality adjusted to meet the existing standards. However, the pattern in Houston 

32 differed from New York and other cities as air quality was further adjusted to reflect meeting the 

33 potential alternative standards. The median value remained relatively constant relative to the

existing standard, while the 25th 34 percentile values continued to increase. Thus, in Houston, the 

35 air quality adjustments always resulted in a median composite monitor value higher than what 
4-24

1 was seen in the observed data. The composite monitor distributions in Boston also exhibited this 

2 type of behavior.

3
4-25

1

2

3

4 Figure 4-9 Distributions of composite monitor 8-hour daily maximum values for the 12 urban 

5 case study areas in the epidemiology-based risk assessment. Plots depict values 

6 based on ambient measurements (base), and values obtained with the HDDM 

7 adjustment methodology showing attainment of 75, 70, 65 and 60 ppb standards. 

8 Values shown are based on CBSAs for April-October of 2007. Note that the HDDM 

9 adjustment technique was not able to adjust air quality to show attainment of a 60 

10 ppb standard in New York, so no boxplot is shown for that case.
4-26

1

2 Figure 4-10 Distributions of composite monitor 8-hour daily maximum values for the 12 

3 urban case study areas in the epidemiology-based risk assessment. Plots 

4 depict values based on ambient measurements (base), and values obtained 

5 with the HDDM adjustment methodology showing attainment of 75, 70, 65 

6 and 60 ppb standards. Values shown are based on CBSAs for April-October 

7 of 2009. Note that Detroit air quality was meeting 75 ppb in 2008-2010, and 

8 the HDDM adjustment technique was not able to adjust air quality to show 

9 attainment of a 60 ppb standard in New York, so no boxplots are shown for 

10 those cases.
4-27

1

2 The exposure modeling and the clinical-based risk assessments used spatially varying 

3 surfaces of hourly O3 concentrations estimated at the centroid of each census tract within the 15 

4 urban case study areas. The maps in Figures 4-11, 4-12, and 4-13 depict the spatial distributions 

of the 2006-2008 average 4th 5 highest (top) and May – September mean (bottom) daily maximum 

6 8-hour (MDA8) O3 concentrations for 3 of the 15 urban case study areas; for observed air quality 

7 (left), air quality adjusted to meet the existing standard (center), and air quality adjusted to meet 

8 the 65 ppb alternative standard (right). Appendix 4-A contains additional maps of the observed 

4th 9 highest MDA8 and May – September mean MDA8 concentrations in all 15 urban case study 

10 areas for 2006-2008 and 2008-2010. Appendix 4-D contains maps and related figures showing 

11 the changes in air quality that resulted from the HDDM adjustments for just meeting the existing 

12 standard, and just meeting the potential alternative standard of 65 ppb.

These maps portray the general pattern seen in all 15 urban case study areas for the 4th 13

14 highest concentrations, which decreased when observed air quality were adjusted to meet the 

15 existing standard, and continued to decrease as the air quality were further adjusted to meet the 

16 various alternative standards. The May-September average values also generally decreased in 

17 suburban and rural areas surrounding the urban population center in all 15 areas. However, three 

18 different types of general behavior which were seen in the seasonal average values near the 

19 urban population centers, which are exemplified in Figures 4-11 (Atlanta), 4-12 (New York), and 

20 4-13 (Houston).

21 In Atlanta, the observed May - September average were nearly constant across the entire 

22 study area. The observed values decreased nearly uniformly across the entire study area when 

23 observed air quality was adjusted to meet the existing standard, and continued to do so when air 

24 quality was further adjusted to meet the alternative standard of 65 ppb. The magnitudes of these 

25 decreases were slightly larger in suburban and rural areas than near the urban population center. 

26 This type of behavior was also seen in Sacramento and Washington, D.C.

27 In New York, the observed May – September average values were lower near the urban 

28 population center than in the surrounding suburban areas. When the observed air quality was 

29 adjusted to meet the existing standard, the seasonal average values increased near the urban 

30 population center and decreased in the suburban areas, so that the spatial pattern was reversed. 

31 When air quality was further adjusted to meet the 65 ppb alternative standard, large area-wide 

32 decreases in the seasonal average values were seen relative to the existing standard. While New 

33 York represents one of the most extreme examples, similar behavior was observed in 7 other 

34 urban areas: Baltimore, Cleveland, Dallas, Detroit, Los Angeles, Philadelphia, and St. Louis.

35 Houston started out in a similar fashion as New York. The observed May – September 

36 average concentrations were lower near the urban population center than in the surrounding 
4-28

1 areas, and a similar pattern of increasing and decreasing seasonal average values occurred when 

2 observed air quality was adjusted to meet the existing standard. However, unlike New York, the 

3 seasonal average values near the Houston city center remained nearly constant relative to the 

4 existing standard when air quality were further adjusted to meet the 65 ppb standard. Boston, 

5 Chicago, and Denver exhibited this same type of behavior.

6

4th Highest MDA8 - Base

May - Sep mean MDA8 - Base

4th Highest MDA8 - 75 ppb

May - Sep mean MDA8 - 75 ppb

4th Highest MDA8 - 65 ppb

May - Sep mean MDA8 - 65 ppb

Atlanta, 2006 - 2008

7

8

9 Figure 4-11 Maps showing the 4th highest (top) and May-September average (bottom) 

10 daily maximum 8-hour O3 concentrations in Atlanta based on 2006-2008 

11 ambient measurements (left), HDDM adjustment to meet the existing 

12 standard (center), and HDDM adjustment to meet the alternative standard 

13 of 65 ppb (right). Squares represent measured values at monitor locations; 

14 circles represent VNA estimates at census tract centroids.

15
4-29

4th Highest MDA8 - Base

May - Sep mean MDA8 - Base

4th Highest MDA8 - 75 ppb

May - Sep mean MDA8 - 75 ppb

4th Highest MDA8 - 65 ppb

May - Sep mean MDA8 - 65 ppb

New York, 2006 - 2008

1

2

Figure 4-12 Maps showing the 4th 3 highest (top) and May-September average (bottom) 

4 daily maximum 8-hour O3 concentrations in New York based on 2006-2008 

5 ambient measurements (left), HDDM adjustment to meet the existing 

6 standard (center), and HDDM adjustment to meet the alternative standard 

7 of 65 ppb (right). Squares represent measured values at monitor locations; 

8 circles represent VNA estimates at census tract centroids.

9
4-30

4th Highest MDA8 - Base

May - Sep mean MDA8 - Base

4th Highest MDA8 - 75 ppb

May - Sep mean MDA8 - 75 ppb

4th Highest MDA8 - 65 ppb

May - Sep mean MDA8 - 65 ppb

Houston, 2006 - 2008

1

2

Figure 4-13 Maps of 4th 3 highest (top) and May-September average (bottom) daily 

4 maximum 8-hour O3 concentrations in Houston for 2006-2008 ambient 

5 measurements (left), HDDM adjustment to meet the existing standard 

6 (center), and HDDM adjustment to meet the alternative standard of 65 ppb 

7 (right). Squares represent measured values at monitor locations; circles 

8 represent VNA estimates at census tract centroids.

9

10 4.4 OVERVIEW OF NATIONAL-SCALE AIR QUALITY INPUTS

11

12 The national-scale analysis, presented in Chapter 8, is focused only on evaluating the 

13 total national burden of mortality risk associated with recent O3 conditions. As such it uses a 

14 different approach to characterize air quality conditions throughout the U.S. The national-scale 

15 analysis employs a data fusion approach that takes advantage of the accuracy of monitor 

16 observations and the comprehensive spatial information of the CMAQ modeling system to create 

17 national-scale “fused” spatial surfaces of seasonal average O3 concentrations. Measured O3

18 concentrations from 2006-2008 were fused with modeled concentrations from a 2007 CMAQ 
4-31

1 model simulation, run for a 12 km domain covering the contiguous U.S. In the first draft of the 

2 REA, the spatial surfaces were created using the enhanced Voronoi Neighbor Averaging (eVNA) 

3 technique (Timin et al, 2010), using the EPA’s Model Attainment Test Software (MATS; Abt 

4 Associates, 2010b). In this draft, the spatial surfaces are created using EPA’s Downscaler 

5 software (Berrocal et al, 2012). More details on the ambient measurements, the 2007 CMAQ 

6 model simulation, the Downscaler fusion technique, and a technical justification for changing 

7 from eVNA to Downscaler can be found in Appendix 4-C.

8 Three national “fused” spatial surfaces were created for:

9 1) the May-September average of the 8-hour daily maximum O3 concentrations 

10 (consistent with the metric used by Smith et al. 2009);

11 2) the June-August average of the daily 10am-6pm mean O3 concentrations (consistent 

12 with the metric used by Zanobetti and Schwartz 2008); and 

13 3) the April-September average of the 1-hour daily maximum O3 concentrations 

14 (consistent with the metric used by Jerrett et al 2009).

15 Figures 4-14 to 4-16 show the geographic distributions of these spatial surfaces. The 

16 spatial distributions of these three surfaces are very similar, with the highest levels occurring in 

17 Southern California for all three surfaces.

18
4-32

1

2 Figure 4-14 May-September average 8-hour daily maximum O3 concentrations in ppb, 

3 based on a Downscaler fusion of 2006-2008 average monitored values with a 

4 12km 2007 CMAQ model simulation.

5
4-33

1

2 Figure 4-15 June-August average 8-hour daily 10am-6pm mean O3 concentrations in 

3 ppb, based on a Downscaler fusion of 2006-2008 average monitored values 

4 with a 12km 2007 CMAQ model simulation.

5
4-34

1

2 Figure 4-16 April-September average 1-hour daily maximum O3 concentrations in ppb, 

3 based on a Downscaler fusion of 2006-2008 average monitored values with a 

4 12km 2007 CMAQ model simulation.

5

6 Figure 4-17 shows the frequency and cumulative distributions of these three seasonal 

7 average O3 surfaces based on all grid cells in the 12 km CMAQ modeling domain. The 

minimum, median, mean, 95th 8 percentile, and maximum values for all three surfaces are shown 

9 in Table 4-3, and correlation coefficients between the three metrics are given in Table 4-4.

10 The May-September average 8-hour daily maximum concentrations were most frequently 

11 in the 30-60 ppb range, while the June-August average daily 10am–6pm mean concentrations 

12 were more evenly distributed across a range of 20-60 ppb. The April-September average 1-hour 

13 daily maximum concentrations were about 5 ppb higher on average than the May-September 

14 average 8-hour daily maximum concentrations, and about 8 ppb higher on average than the June15 August average daily 10am-6pm mean concentrations. The correlation coefficients between 

16 these three metrics were all very high (R > 0.97).

17

18
4-35

1

2 Figure 4-17 Frequency and Cumulative Distributions of the Three Fused Seasonal 

3 Average O3 Surfaces Based on all CMAQ 12 km Grid Cells.

4

5

6

7

8
4-36

1 Table 4-3 Summary Statistics Based on the Three Fused Seasonal Average O3 Surfaces 

2 Based on all CMAQ 12 km Grid Cells

Statistic

May-September average 

8-hour daily maximum 

concentration (ppb)

June-August average daily

10am–6pm mean 

concentration (ppb)

April-September average 

1-hour daily maximum 

concentration (ppb)

Minimum 21.8 14.9 26.2

Median 43.6 41.7 48.8

Mean 43.2 40.9 48.2

95th Percentile 54.3 54.8 59.0

Maximum 76.1 80.1 84.2

3

4

5 Table 4-4 Correlation Coefficients Between the Three Fused Seasonal Average O3

6 Surfaces Based on all CMAQ 12 km Grid Cells

Seasonal metrics compared Correlation coefficient

May-September average 8-hour daily maximum vs. 

June-August average daily 10am-6pm mean

0.974

May-September average 8-hour daily maximum vs. 

April-September average 1-hour daily maximum

0.995

June-August average daily 10am-6pm mean vs. 

April-September average 1-hour daily maximum

0.972

7

8 These seasonal average metrics are not equivalent to the form of the existing standard, 

which is based on the 4th 9 highest value rather than on the seasonal mean. Thus, the values shown 

10 in the three fused surfaces should not be directly compared to the existing standard. Figure 4-18

11 shows comparisons between these three metrics and the 2006-2008 O3 design values based on 

12 CMAQ 12 km grid cells containing O3 monitors, and Table 4-5 presents correlation coefficients 

13 and summary statistics based on the ratios between the design values and these three metrics. 

14 The design values were, on average, approximately 50% higher than the seasonal average values, 

15 with substantial spatial heterogeneity, and some variation across the seasonal average metrics. 

16 The April-September average 1-hour daily maximum was the most strongly correlated with the 

17 design values (R = 0.75), followed by the May-September average 8-hour daily maximum (R = 

18 0.71), and then the June-August average daily 10am-6pm mean (R = 0.69).

19
4-37

1

2 Figure 4-18 2006-2008 O3 Design Values Versus 2006-2008 Fused Seasonal Average O3

3 Levels for the CMAQ 12km Grid Cells Containing O3 Monitors.

4

5

6
4-38

1 Table 4-5 Correlation Coefficients and Ratios of the 2006-2008 O3 Design Values to the 

2 2006-2008 Fused Seasonal Average O3 Levels for the CMAQ 12km Grid Cells 

3 Containing O3 Monitors

Statistic

May-September average 

8-hour daily maximum

June-August average 

daily 10am-6pm mean

April-September average 

1-hour daily maximum

Correlation 0.71 0.69 0.75

Ratios

Minimum 1.1 1.1 1.0

2.5th Percentile 1.3 1.3 1.2

Median 1.5 1.5 1.4

Mean 1.6 1.6 1.4

97.5 Percentile 2.0 2.2 1.6

Maximum 2.4 3.0 1.9

4

5

6 4.5 UNCERTAINITIES IN MODELING OF RESPONSES TO EMISSION 

7 REDUCTIONS TO JUST MEET EXISTING AND POTENTIAL ALTERNATIVE 

8 STANDARDS

9 We recognize that there are sources of uncertainty in air quality measurements and the air 

10 quality estimates for each air quality scenario. These sources of uncertainty are described below 

11 and in Table 4-6 which discusses qualitatively the magnitude of uncertainty and potential for 

12 directional bias. 

13 There is inherent uncertainty in all deterministic air quality models, such as CMAQ, the 

14 photochemical grid model which was used to develop the model-based O3 adjustment 

15 methodology. Evaluations of air quality models against observed pollutant concentrations build 

16 confidence that the model performs with reasonable accuracy despite both structural and 

17 parametric uncertainties. A comprehensive model performance evaluation provided in Appendix 

18 4-B shows generally acceptable model performance which is equivalent to or better than typical 

19 state-of-the science regional modeling simulations as summarized in Simon et al (2012). The use 

20 of the Higher Order Decoupled Direct Method (HDDM) within CMAQ to estimate O3 response 

21 to emissions perturbations adds uncertainty to that inherent in the model itself. HDDM allows for 

22 the approximation of O3 concentrations under alternate emission scenarios without re-running 

23 the model simulation with different inputs. This approximation becomes less accurate for larger 

24 emissions perturbations. To accommodate increasing uncertainty at larger emissions 

25 perturbations, the HDDM modeling was performed at three distinct emissions levels to allow for 

26 a better characterization of O3 response over the entire range of emissions levels. The accuracy
4-39

1 of the HDDM estimates can be quantified at distinct emissions levels by re-running the model 

2 with modified emissions inputs and comparing the results. This method was applied to quantify 

3 the accuracy of 3-step HDDM O3 estimates for 50% and 90% NOx cut conditions for each urban 

4 case study areas (as shown in Appendix 4-D). At 50% NOx cut conditions, HDDM using 

5 information from these multiple simulations predicted hourly O3 concentrations with a mean bias 

6 and a mean error less than +/- 1 ppb in all case study areas compared to brute force model 

7 simulations. At 90% NOx cut conditions, HDDM using information from these multiple 

8 simulations predicted hourly O3 concentrations with a mean bias less than +/- 3ppb and a mean 

9 error less than +/- 4 ppb in all case study areas. These small bias and error estimates show that 

10 uncertainty due to the HDDM approximation method is small up to 90% emissions cuts.

11 In order to apply modeled O3 response to ambient measurements, regressions were 

12 developed which relate O3 response to emissions perturbations with ambient O3 concentrations 

13 for every season, hour-of-the-day, and monitor location. Applying O3 responses based on this 

14 relationship adds uncertainty. Preliminary work showed that the relationships developed with 

15 these regressions were generally statistically significant for most season, hour-of-the-day, and 

16 monitor location combinations for 2005 modeling in Detroit and Charlotte (Simon et al, 2012). 

17 Statistical significance was not evaluated for each regression in this analysis since there were 

18 over 460,000 regressions created (322 monitors × 5 sensitivity coefficients × 3 emissions levels 

19 × 4 seasons × 24 hours = 463,680 regressions). Statistics can quantify the goodness of fit for the 

20 modeled relationships and can quantify the uncertainty in response at any given O3 concentration 

21 based on variability in model results at that portion of the distribution for each regression. The 

22 regression model provided both a central tendency and a standard error value for O3 response at 

23 each measured hourly O3 concentration. The base analysis in all case study areas except New 

24 York and Los Angeles used the central tendency which will inherently dampen some of the 

25 variability in O3 response. The standard error of each sensitivity coefficient was propagated 

26 through the calculation of predicted O3 concentrations at various standard levels. These standard 

27 errors reflect the amount of variability that is lost due to the use of a central tendency. Since 

28 emissions reductions increased for lower standard levels the standard errors were larger for 

adjustments to lower standards. Mean (95th 29 percentile) standard errors for the 75 ppb adjustment 

case ranged from 0.13 (0.26) to 1.18 (2.87) ppb in the 15 case study areas. Mean (95th 30 percentile) 

31 standard errors for the 65 ppb adjustment case ranged from 0.54 (1.07) to 1.39 (2.98) ppb. The 

32 largest standard errors occurred in Los Angeles and New York due to the large emissions 

33 reductions applied in these cases. In cases where the use of the central tendency of response 

34 reduced the total estimated emissions reductions required to achieve a given standard level, in 

35 general we expect that the benefits of reducing high O3 concentrations and the disbenefits of 

36 increasing low O3 would both be underestimated. For the exposure assessment which estimates 
4-40

1 health outcomes that occur at O3 concentrations above 60, this would lead to an underestimation 

2 of risks. For the epidemiology-based risk assessment which is effected by the entire range of O3

3 concentrations, the impact is undetermined since changes at both ends of the O3 distribution in 

4 opposite directions would affect the results. The opposite would be true in cases where the use of 

5 the central tendency of response increased the total estimated emissions reductions required to 

6 achieve a given standard. However, given the small standard error values even in the case study 

7 areas with the greatest uncertainty (i.e. less than 1.5 ppb mean standard error), this source of 

8 uncertainty is not expected to substantially impact results.

9 Relationships between O3 response and hourly O3 concentration were developed based on 

10 8 months of modeling: January and April-October 2007. These relationships were applied to 

11 ambient data from 2006-2010. Some locations monitor for months not included in this modeling 

12 (i.e., February, March, November, and December) while others do not. Seasonal relationships 

13 were developed between O3 response to emissions reductions and O3 concentration. Summer was 

14 the only season for which modeling data was created for all months (June, July, August). The 

15 winter relationships were developed based on January modeling, the spring relationships were 

16 developed based on April/May modeling, and the autumn relationships were developed based on 

17 September/October modeling. The reduction in data points (31 or 61 instead of ~90) increases 

18 uncertainty in the statistical fit for these seasons. In addition, the modeling generally showed 

19 more O3 disbenefits to NOx decreases in cooler months. So applying April/May relationships to 

20 March and September/October relationships to November could potentially underestimate O3

21 increases that would happen in those two months in the five case study areas which measure O3

22 during March and/or November: Dallas, Denver, Houston, Los Angeles, and Sacramento. The 

23 eight months that were modeled capture a variety of meteorological conditions. In cases where 

24 other years have more frequent occurances of certain types of meteorological conditions, the 

25 regressions should be able to account for this. For instance, if a monitor only had 2-3 high O3

26 days associated with sunny, high pressure conditions in the 2007 modeling but had 30-40 of 

27 those days in another year, the regression may be more uncertain at those high O3 values but

28 should still be able to capture the central tendency which can be applied to the more frequent 

29 occurances in other years. If, on the other hand, the meteorology/ O3 conditions in another year 

30 were completely outside the range of conditions captured in the model, then the regression based 

31 on modeled conditions might not be able to capture those conditions. Finally, if emissions 

32 change drastically between the modeled period and the time of the ambient data measurements 

33 this could also change the relationship between O3 response and O3 concentrations. The 

34 regressions derived from the 2007 modeling period are only applied to measurements made 

35 within 3 years of the modeled time period. Although some emissions changes did occur over this 
4-41

1 time period, we believe it is still reasonable to apply 2007 modeling to this relatively small 

2 window of measurements which occurs before and after the modeling. 

3 O3 response is modeled for across-the-board reductions in U.S. anthropogenic NOx (and 

4 VOC). These across-the-board cuts do not reflect actual emissions control strategies. The form, 

5 locations, and timing of emissions reductions that would be undertaken to meet various levels of 

6 the O3 standard are unknown. The across-the-board emissions reductions bring levels down 

7 uniformly across time and space to show how O3 would respond to changes in ambient levels of 

8 precursor species but do not reflect spatial and temporal heterogeneity that may occur in local 

9 and regional emissions reductions. In cases where VOC reductions were modeled, equal 

10 percentage NOx and VOC reductions were applied in the adjustment methodology. Regional 

11 NOx reductions are likely to be the primary means used to reduce high O3 concentrations at DV 

12 monitors. In limited cases, VOC emissions reductions may also help lower high O3

13 concentrations at these locations. In actual control strategies, NOx and VOC reductions may be 

14 applied in combination but are unlikely to be applied in equal percentages. The available 

15 modeling constrained the NOx/VOC case to this type of control scenario. The across-the-board 

16 cuts and the equal percentage NOx and VOC reductions scenario does not optimize the lowest 

17 cost or least total emissions combinations as state and local agencies will likely attempt to 

18 achieve.
4-42

1 Table 4-6 Summary of Qualitative Uncertainty Analysis of Key Air Quality Elements in the O3 NAAQS Risk Assessment

Source Description

Potential influence of 

uncertainty on risk 

estimates

KnowledgeBase 

uncertainty*

Comments (KB: knowledge base, INF: influence 

Direction Magnitude of uncertainty on risk estimates)

A. O3

measurements

O3 concentrations 

measured by ambient 

monitoring instruments 

have inherent 

uncertainties associated 

with them. Additional 

uncertainties due to other 

factors may include:

- monitoring 

network locations

- O3 monitoring 

seasons

- monitor 

malfunctions

- wildfire and 

smoke impacts

- interpolation of 

missing data

Both Low Low

KB: O3 measurements are assumed to be 

accurate to within 1⁄2 of the instrument’s Method 

Detection Limit (MDL), which is 2.5 ppb for most 

instruments. EPA requires that routine quality 

assurance checks are performed on all instruments, 

and that all data reported to AQS are certified by 

both the monitoring agency and the corresponding 

EPA regional office. The CASTNET monitoring 

data were subject to their own set of QA 

requirements, and these data are generally 

believed to be of comparable quality to the data 

stored in AQS.

KB: Monitor malfunctions sometimes 

occur causing periods of missing data or poor data 

quality. Monitoring data affected by malfunctions 

are usually flagged by the monitoring agency and 

removed from AQS. In addition, the AQS 

database managers run several routines to identify 

suspicious data for potential removal.

KB: There is a known tendency for smoke 

produced from wildfires to cause interference in 

O3 instruments. Measurements collected by O3

analyzers were reported to be biased high by 5.1–

6.6 ppb per 100 μg/m3 of PM2.5 from wildfire 

smoke ,EPA, 2007). However, smoke 

concentrations high enough to cause significant 

interferences are infrequent and the overall impact 

is believed to be minimal.
4-43

Source Description

Potential influence of 

uncertainty on risk 

estimates

KnowledgeBase 

uncertainty*

Comments (KB: knowledge base, INF: influence 

Direction Magnitude of uncertainty on risk estimates)

KB: Missing intervals of 1 or 2 hours in 

the measurement data were interpolated, which 

may cause some additional uncertainty. However, 

due to the short length of the interpolation periods, 

and the tendency for these periods to occur at 

night when O3 concentrations are low, the overall 

impact is believed to be minimal.

INF: EPA’s current O3 monitoring network 

requirements have an urban focus. Rural areas 

where O3 concentrations are lower tend to be 

under-represented by the current monitoring 

network. The network requirements also state that 

at least one monitor within each urban area must 

be sited to capture the highest O3 concentrations in 

that area, which may cause some bias toward 

higher measured concentrations.

INF: Each state has a required O3

monitoring season which varies in length from 

May – September to year-round. Some states turn 

their O3 monitors off during months outside of the 

required season, while others leave them on. This 

can cause discrepancies in the amount of data 

available, especially in months outside of the 

required monitoring season. The risk estimates 

attempt to minimize these impacts by focusing 

only on months where O3 monitoring is required.
4-44

Source Description

Potential influence of 

uncertainty on risk 

estimates

KnowledgeBase 

uncertainty*

Comments (KB: knowledge base, INF: influence 

Direction Magnitude of uncertainty on risk estimates)

B. Veronoi 

Neighbor 

Averaging (VNA) 

spatial fields

VNA is a spatial 

interpolation technique 

used to estimate O3

concentrations in 

unmonitored areas, which 

has inherent uncertainty

Both LowMedium

LowMedium

KB: VNA interpolates monitored hourly 

O3 concentrations to provide estimates of O3

exposure at each census tract in the 15 urban 

areas. The VNA estimates are weighted based on 

distance from neighboring monitoring sites, thus 

the amount of uncertainty tends to increase with 

distance from the monitoring sites. 

KB: The 15 urban areas each had fairly 

dense monitoring networks which were generally 

sufficient to capture spatial gradients in O3

concentrations. The use of hourly data to create 

the VNA fields instead of daily or other 

aggregates also served to reduce uncertainty by 

better capturing relationships in the diurnal 

patterns between O3 monitors.

C.CMAQ 

modeling

Model predictions from 

CMAQ, like all 

deterministic 

photochemical models, 

have both parametric and 

structural uncertainty 

associated with them

Both LowMedium

LowMedium

KB: Structural uncertainties are 

uncertainties in the representation of physical and 

chemical processes in the model. These include: 

choice of chemical mechanism used to 

characterize reactions in the atmosphere, choice of 

land surface model and choice of planetary 

boundary layer model.

KB: Parametric uncertainties include 

uncertainties in model inputs (hourly 

meteorological fields, hourly 3-D gridded 

emissions, initial conditions, and boundary 

conditions)

KB: Uncertainties due to initial conditions 

are minimized by using a 10 day ramp-up period 
4-45

Source Description

Potential influence of 

uncertainty on risk 

estimates

KnowledgeBase 

uncertainty*

Comments (KB: knowledge base, INF: influence 

Direction Magnitude of uncertainty on risk estimates)

from which model results are not used.

KB: Evaluations of models against 

observed pollutant concentrations build 

confidence that the model performs with 

reasonable accuracy despite the uncertainties listed 

above. A comprehensive model evaluation 

provided in Appendix 4-B shows generally 

acceptable model performance which is equivalent 

or better than typical state-of-the science regional 

modeling simulations as summarized in Simon et 

al (2012). However, both under-estimations and 

over-estimations do occur at some times and 

locations. Generally the largest mean biases occur 

on low O3 days during the summer season. In 

addition, the model did not fully capture rare 

wintertime high O3 events occurring in the 

Western U.S.

D. Higher Order 

Decoupled Direct 

Method (HDDM)

HDDM allows for the 

approximation of O3

concentrations under 

alternate emissions 

scenarios without rerunning the model 

simulation multiple times 

using different emissions 

inputs. This 

approximation becomes 

less accurate for larger 

emissions perturbations 

Both LowMedium

LowMedium

KB: To accommodate increasing 

uncertainty at larger emissions perturbations, the 

HDDM modeling was performed at three distinct 

emissions levels to allow for a better 

characterization of O3 response over the entire 

range of emissions levels. The replication of brute 

force hourly O3 concentration model results by the 

HDDM approximation was quantified for 50% 

and 90% NOx cut conditions for each urban case 

study areas (as shown in Appendix 4-D). At 50% 

NOx cut conditions, HDDM using information 

from these multiple simulations predicted hourly 
4-46

Source Description

Potential influence of 

uncertainty on risk 

estimates

KnowledgeBase 

uncertainty*

Comments (KB: knowledge base, INF: influence 

Direction Magnitude of uncertainty on risk estimates)

especially under nonlinear 

chemistry conditions. 

O3 concentrations with a mean bias and a mean 

error less than +/- 1 ppb in all urban case study 

areas compared to brute force model simulations. 

At 90% NOx cut conditions, HDDM using 

information from these multiple simulations 

predicted hourly O3 concentrations with a mean 

bias less than +/- 3ppb and a mean error less than 

+/- 4 ppb in all urban case study areas.

E. Application of 

HDDM 

sensitivities to 

ambient data

In order to apply 

modeled sensitivities to 

ambient measurements, 

regressions were 

developed which relate O3

response to emissions 

perturbations with 

ambient O3 concentrations 

for every season, hour-ofthe-day and monitor 

location. Applying O3

responses based on this 

relationship adds 

uncertainty.

Both Medium Medium

KB: Preliminary work showed that the 

relationships developed with these regressions 

were generally statistically significant for most 

season, hour-of-the-day, and monitor location 

combinations for 2005 modeling in Detroit and 

Charlotte. Statistical significance was not 

evaluated for each regression in this analysis since 

there were over 460,000 regressions created (322 

monitors × 5 sensitivity coefficients × 3 emissions

levels × 4 seasons × 24 hours = 463,680 

regressions). Statistics can quantify the goodness 

of fit for the modeled relationships and can 

quantify the uncertainty in response at any given 

O3 concentration based on variability in model 

results at that portion of the distribution for each 

regression. However it is not possible to quantify 

the applicability of this modeled relationship to the 

actual atmosphere.

KB: The regression model provided both a 

central tendency and a standard error value for O3

response at each measured hourly O3
4-47

Source Description

Potential influence of 

uncertainty on risk 

estimates

KnowledgeBase 

uncertainty*

Comments (KB: knowledge base, INF: influence 

Direction Magnitude of uncertainty on risk estimates)

concentration. The base analysis used the central 

tendency which will inherently dampen some of 

the variability in O3 response. The standard error 

of each sensitivity coefficient was propagated 

through the calculation of predicted O3

concentrations at various standard levels. These 

standard errors reflect the amount of variability 

that is lost due to the use of a central tendency. 

Since emissions reductions increased for lower 

standard levels the standard errors were larger for 

adjustments to lower standards. Mean (95th

percentile) standard errors for the 75 ppb 

adjustment case ranged from 0.13 (0.26) to 1.18 

(2.87) ppb in the 15 case study areas. Mean (95th

percentile) standard errors for the 65 ppb 

adjustment case ranged from 0.54 (1.07) to 1.39 

(2.98) ppb. The largest standard errors occurred in 

Los Angeles and New York.

INF: The NOx emissions reductions 

resulted in both increases and decreases in O3

depending on the time and location. In cases 

where the use of the central tendency of response 

reduced the total estimated emissions reductions 

required to achieve a given standard level, in 

general we expect that the benefits of reducing 

high O3 concentrations and the disbenefits of 

increasing low O3 would be underestimated. For 

the exposure assessment which estimates health 

outcomes that occur at O3 concentrations above 
4-48

Source Description

Potential influence of 

uncertainty on risk 

estimates

KnowledgeBase 

uncertainty*

Comments (KB: knowledge base, INF: influence 

Direction Magnitude of uncertainty on risk estimates)

60, this would lead to an underestimation of risks. 

For the epidemiology-based risk assessment which 

is effected by the entire range of O3

concentrations, the impact is undetermined since 

changes at both ends of the O3 distribution in 

opposite directions would affect the results. The 

opposite would be true in cases where the use of 

the central tendency of response increased the total 

estimated emissions reductions required to achieve 

a given standard.

F. Applying 

modeled 

sensitivities to unmodeled time 

periods

Relationships 

between O3 response and 

hourly O3 concentration 

were developed based on 

8 months of modeling: 

January and April-October 

2007. These relationships 

were applied to ambient 

data from 2006-2010. 

Some locations monitor 

for months not included in 

this modeling (February, 

March, November, and 

December) while others 

do not.

Both LowMedium

LowMedium

KB: The eight months that were modeled 

capture a variety of meteorological conditions. In 

cases where other years have more frequent 

occurances of certain types of conditions, the 

regressions should be able to account for this. For 

instance, if a monitor only had 2-3 high O3 days 

associated with sunny, high pressure conditions in 

the 2007 modeling but had 30-40 of those days in 

another year, the regression may be more 

uncertain at those high O3 values but should still be 

able to capture the central tendency which can be 

applied to the more frequent occurances in other 

years. If, on the other hand, the meteorology/O3

conditions in another year were completely 

outside the range of conditions captured in the 

model, then the regression based on modeled 

conditions might not be able to capture those 

conditions.

KB: If emissions change drastically 
4-49

Source Description

Potential influence of 

uncertainty on risk 

estimates

KnowledgeBase 

uncertainty*

Comments (KB: knowledge base, INF: influence 

Direction Magnitude of uncertainty on risk estimates)

between the modeled period and the time of the 

ambient data measurements this could also change 

the relationship between O3 response and O3

concentrations. The regressions derived from the 

2007 modeling period are only applied to 

measurements made within 3 years of the modeled 

time period. Although some emissions changes did 

occur over this time period, we believe it is still 

reasonable to apply 2007 modeling to this 

relatively small window of measurements which 

occurs before and after the modeling.

INF: Seasonal relationships were 

developed between O3 response to emissions 

reductions and O3 concentration. Summer was the 

only season for which modeling data was created 

for all months (June, July, August). The winter 

relationships were developed based on January 

modeling, the spring relationships were developed 

based on April/May modeling, and the autumn 

relationships were developed based on 

September/October modeling. The reduction in 

data points (31 or 61 instead of ~90) increases 

uncertainty in the statistical fit for these months. In 

addition, the modeling generally showed more O3

disbenefits to NOx decreases in cooler months. So 

applying April/May relationships to March and 

September/October relationships to November 

could potentially underestimate O3 increases that 

would happen in those two months in the five 
4-50

Source Description

Potential influence of 

uncertainty on risk 

estimates

KnowledgeBase 

uncertainty*

Comments (KB: knowledge base, INF: influence 

Direction Magnitude of uncertainty on risk estimates)

urban case study areas which measure O3 during 

March and/or November: Dallas, Denver, 

Houston, Los Angeles, and Sacramento.

G. Assumptions 

of across-theboard emissions 

reductions 

O3 response is modeled 

for across-the-board 

reductions in U.S. 

anthropogenic NOx (and 

VOC). These across-theboard cuts do not reflect 

actual emissions control 

strategies.

Both LowMedium

LowMedium

KB: The form, locations, and timing of emissions 

reductions that would be undertaken to meet 

various levels of the O3 standard are unknown. 

The across-the-board emissions reductions bring 

levels down uniformly across time and space to 

show how O3 would respond to changes in 

ambient levels of precursor species but do not 

reflect spatial and temporal heterogeneity that may 

occur in local and regional emissions reductions.

H. Assumption of 

equal percentage 

NOx and VOC 

reductions

In cases where VOC 

reductions were modeled, 

equal percentage NOx and 

VOC reductions were 

applied in the adjustment 

methodology.

Both LowMedium Medium

KB: NOx reductions are likely to be the primary 

means used to reduce high O3 concentrations at 

DV monitors. In limited cases, VOC emissions 

reductions may also help lower high O3

concentrations at these locations. NOx and VOC 

reductions may be applied in combination but are 

unlikely to be applied in equal percentages. The 

available modeling constrained the NOx/VOC 

case to this unrealistic scenario. The equal 

percentage NOx and VOC reductions scenario 

does not optimize the lowest cost or least total 

emissions combinations as state and local agencies 

will likely attempt to achieve.

I. Downscaler

Downscaler combines 

monitored and modeled 

concentrations to produce 

a “fused” air quality 

Both LowMedium

LowMedium

KB: Downscaler combines modeled and 

monitored concentrations to provide estimates of 

O3 concentrations in unmonitored areas while 

correcting model biases near monitors. The cross-
4-51

Source Description

Potential influence of 

uncertainty on risk 

estimates

KnowledgeBase 

uncertainty*

Comments (KB: knowledge base, INF: influence 

Direction Magnitude of uncertainty on risk estimates)

surface. Uncertainties may 

occur in sparsely 

monitored regions, or in 

urban areas with dense 

monitoring networks and 

large spatial gradients.

validation analysis in Appendix 4-A shows that 

Downscaler generally gives more accurate 

estimates of air quality in monitored locations than 

either the monitored or modeled values alone. 

However, it is not possible to quantify the 

uncertainty associated with the estimates in 

unmonitored locations.

KB: The air quality surfaces modeled by 

Downscaler for the national-scale risk assessment 

were seasonal average concentrations, which tend 

to have smaller spatial gradients than other metrics 

such as peak concentrations, and thus less 

uncertainty.

INF: The cross-validation analysis in Appendix 4-

A also shows that Downscaler tends to overestimate low concentrations and under-estimate 

high concentrations. The mean bias in the 

estimates in monitored locations is nearly zero, but 

monitor locations are often chosen to capture the 

highest concentrations, thus there might be some 

bias towards higher concentrations in umonitored 

areas.

1 * Refers to the degree of uncertainty associated with our understanding of the phenomenon, in the context of assessing and 

2 characterizing its uncertainty. Sources classified as having a “low” impact would not be expected to impact the interpretation of risk 

3 estimates in the context of the O3 NAAQS review; sources classified as having a “medium” impact have the potential to change the 

4 interpretation; and sources classified as “high” are likely to influence the interpretation of risk in the context of the O3 NAAQS 

5 review.
4-52

1 4.6 REFERENCES

2 Abt Associates, Inc. 2010a. "Environmental Benefits and Mapping Program (Version 4.0)." 

3 Bethesda, MD. Prepared for U.S. Environmental Protection Agency Office of Air Quality 

4 Planning and Standards. Research Triangle Park, NC. Available on the Internet at 

5 <http://www.epa.gov/air/benmap>.

6 Abt Associates, Inc. 2010b. "Model Attainment Test Software (Version 2)." Bethesda, MD. 

7 Prepared for the U.S. Environmental Protection Agency Office of Air Quality Planning 

8 and Standards. Research Triangle Park, NC. Available on the Internet at: 

9 http://www.epa.gov/scram001/modelingapps.mats.htm.

10 Bell, M.L.; A. McDermott; S.L. Zeger; J.M. Samet and F. Dominici. 2004. "Ozone and Short11 term Mortality in 95 U.S. Urban Communities," 1987-2000. JAMA, 292:2372-2378. 

12 Berrocal, V.J.; A. E. Gelfand and D.M. Holland. 2012. "Space-Time Data Fusion Under Error in 

13 Computer Model Output: An Application to Modeling Air Quality." Biometrics, 68(3), 

14 837-848.

15 Chen, J. R.; Zhao; Z. Li. 2004. "Voronoi-based k-order Neighbor Relations for Spatial Analysis." 

16 ISPRS J Photogrammetry Remote Sensing, 59(1-2), 60-72.

17 Duff, M.; R. L. Horst; T.R. Johnson, 1998. "Quadratic Rollback: A Technique to Model 

18 Ambient Concentrations Due to Undefined Emission Controls." San Diego, CA: 

19 Presented at the Air and Waste Management Annual Meeting, June 14-18, 1998. 

20 Fann, N.; A.D. Lamson; S.C. Anenberg; K. Wesson; D. Risley; B.J. Hubbell. 2012. "Estimating 

21 the National Public Health Burden Associated with Exposure to Ambient PM2.5 and 

22 Ozone." Risk Analysis, 32:81-95.

23 Gold, C. 1997. "Voronoi Methods in GIS," Vol. 1340. In Algorithmic Foundation of Geographic 

24 Information Systems (Kereveld M., J. Nievergelt, T. Roos, P. Widmayer eds). Lecture 

25 notes in Computer Science, Berlin: Springer-Verlag, 21-35.

26 Hall, E.; A. Eyth; S. Phillips. 2012. "Hierarchical Bayesian Model (HBM)-Derived Estimates of 

27 Air Quality for 2007: Annual Report." (EPA document number EPA/600/R-12/538). 

28 < http://www.epa.gov/heasd/sources/projects/CDC/AnnualReports/2007_HBM.pdf>.
4-53

1 Jerrett, M.; R.T. Burnett; C.A. Pope, III; K. Ito; G. Thurston; D. Krewski; Y. Shi, E. Calle and 

2 M. Thun. 2009. "Long-term O3 Exposure and Mortality." N. Eng. J. Med., 360:1085-

3 1095.

4 Johnson, T. 2002. "A Guide to Selected Algorithms, Distributions, and Databases Used in 

5 Exposure Models Developed by the Office of Air Quality Planning and Standards," 

6 prepared by TRJ Environmental, Inc. for the U.S. Environmental Protection Agency. 

7 Research Triangle Park, NC: Office of Research and Development.

8 National Research Council of the National Academies. 2008. "Estimating Mortality Risk 

9 Reduction and Economic Benefits from Controlling Ozone Air Pollution." Washington, 

10 DC: The National Academies Press.

11 Rizzo, M. 2005. "A Comparison of Different Rollback Methodologies Applied to Ozone 

12 Concentrations," posted on November 7, 2005, 

13 http://www.epa.gov/ttn/naaqs/standards/ozone/s_O3_cr_td.html.

14 Rizzo, M. 2006. "A Distributional Comparison between Different Rollback Methodologies 

15 Applied to Ambient Ozone Concentrations," posted on May 31, 2006, 

16 http://www.epa.gov/ttn/naaqs/standards/ozone/s_O3_cr_td.html.

17 Simon, H.; K. Baker; N. Possiel; F. Akhtar; S. Napelenok; B. Timin; B. Wells. 2012. "Model18 based Rollback Using the Higher Order Direct Decoupled Method (HDDM)," posted at 

19 < http://www.epa.gov/ttn/naaqs/standards/ozone/s_O3_td.html>.

20 Simon, H.; K. R. Baker; F. Akhtar; S.L. Napelenok; N. Possiel; B. Wells and B. Timin. 2013. "A 

21 Direct Sensitivity Approach to Predict Hourly Ozone Resulting from Compliance with 

22 the National Ambient Air Quality Standard" Environmental Science and Technology, 

23 Vol. 47, 2304-2313.

24 Smith, R.L., B. Xu, P. Switzer. 2009b. "Reassessing the Relationship Between Ozone and Short25 term Mortality in U.S. Urban Communities," Inhale Toxicol, Vol. 21: 37-61. 

26 Timin, B.; K. Wesson and J. Thurman. 2010. "Application of Model and Ambient Data Fusion 

27 Techniques to Predict Current and Future Year PM2.5 Concentrations in Unmonitored 

28 Areas, " in D.G. Steyn and St Rao (eds), Air Pollution Modeling and Its Application XX,

29 Netherlands: Springer, pp. 175-179.
4-54

1 U.S. Environmental Protection Agency. 2007. Review of the National Ambient Air Quality 

2 Standards for Ozone: Policy Assessment of Scientific and Technical Information OAQPS 

3 Staff Paper. Washington, DC: EPA Office of Air and Radiation. (EPA document number 

4 U. S. EPA. 2012a. Integrated Science Assessment for Ozone and Related Photochemical 

5 Oxidants: Third External Review Draft. Research Triangle Park, NC: EPA Office of Air 

6 Quality Planning and Standards. (EPA document number EPA-452/R-07-007; 

7 EPA/600/R-10/076C).

8 U.S. EPA. 2012b. Total Risk Integrated Methodology (TRIM) - Air Pollutants Exposure Model 

9 Documentation (TRIM.Expo / APEX, Version 4.4) Volume I: User’s Guide. Research 

10 Triangle Park, NC: Office of Air Quality Planning and Standards. (EPA document 

11 number EPA-452/B-12-001a). <http://www.epa.gov/ttn/fera/human_apex.html>.

12 U.S. EPA. 2012c. Total Risk Integrated Methodology (TRIM) - Air Pollutants Exposure Model 

13 Documentation (TRIM.Expo / APEX, Version 4.4) Volume II: Technical Support 

14 Document. Research Triangle Park, NC: Office of Air Quality Planning and Standards. 

15 (EPA document number EPA-452/B-12-001b).

16 <http://www.epa.gov/ttn/fera/human_apex.html>.

17 Wells, B.; K. Wesson and S. Jenkins. 2012. "Analysis of Recent U.S. Ozone Air Quality Data to 

18 Support the O3 NAAQS Review and Quadratic Rollback Simulations to Support the First 

19 Draft of the Risk and Exposure Assessment." 

20 <http://www.epa.gov/ttn/naaqs/standards/ozone/s_O3_td.html>.

21 Zhang, L.; D.J. Jacob; N.V. Smith-Downey; D.A. Wood; D. Blewitt; C.C. Carouge; A. van 

22 Donkelaar; D.B. A. Jones; L.T. Murray and Y. Wang. 2011. "Improved Estimate of the 

23 Policy-relevant Background Ozone in the United States Using the GEOS-Chem Global 

24 Model with 1/2°x2/3° Horizontal Resolution Over North America." Atmos Environ, Vol. 

25 45, pp. 6769-6776.

26 Zanobetti, A. and J. Schwartz. 2008. "Mortality Displacement in the Association of Ozone with 

27 Mortality: An Analysis of 48 Cities in the United States." American Journal of 

28 Respiratory and Critical Care Medicine, 177:184-189.

29
5-1

1

2 5 CHARACTERIZATION OF HUMAN EXPOSURE TO OZONE

3 5.0 OVERVIEW

4 As part of the previous 2007 O3 NAAQS review, EPA staff conducted exposure analyses 

for the general population, all school-age children (ages 5-18), all active school-age children,1 5

6 and asthmatic school-age children (U.S. EPA, 2007a,b). Exposure estimates were generated for 

12 urban study areas2 7 for recent years of air quality and for just meeting the existing 8-hr 

8 standard and several alternative 8-hr standards. EPA also conducted a health risk assessment that 

9 produced risk estimates for the number and percent of all school-age children experiencing 

10 impaired lung function and other respiratory symptoms associated with the exposures estimated 

11 for these same 12 study areas.

12 The exposure analysis conducted for this current NAAQS review builds upon the 

13 methodology and lessons learned from the exposure analyses conducted in previous O3 reviews 

14 (U.S. EPA, 1996a, 2007a,b) and information provided in the final ISA (U.S. EPA, 2013). Here, 

we estimate exposures for people residing in 15 urban study areas in the U.S.3 15 The population 

16 exposures to ambient O3 concentrations were modeled using EPA’s Air Pollutants Exposure 

17 (APEX) (US EPA, 2012a,b). Exposures were calculated considering O3 concentrations in recent 

18 years, using 2006 to 2010 spatially interpolated ambient monitoring data. Exposures were also 

19 estimated considering alternative air quality scenarios, that is, where O3 concentrations just meet 

20 the existing 8-hr O3 NAAQS and at several other standard levels considering the same indicator, 

21 form, and averaging time, based on adjusting data as described in Chapter 4. Exposures were 

22 modeled for 1) all school-age children (ages 5-18), 2) asthmatic school-age children (ages 5-18), 

23 3) asthmatic adults (ages 19-95), and 4) all older adults (ages 65-95), each while at moderate or 

greater exertion level at the time of exposure.

4 24 The strong emphasis on children, asthmatics, and 

25 older adults reflects the finding of the last O3 NAAQS review (U.S. EPA, 2007a) and the ISA 

26 (U.S. EPA, 2013, Chapter 8) that these are important at-risk groups. Exposure model output of 

27 interest for this chapter are the percent (and number) of persons exposed at or above 8-hr average 

 1 In the previous 2007 exposure assessment, a study group of active school-age children was identified as children 

having their median daily physical activity index (PAI) over the exposure period ≥ 1.75, an activity level 

characterized by exercise physiologists as being “moderately active” or “active” (McCurdy, 2000). 2 The twelve study areas evaluated in the 2007 exposure assessment were Atlanta, Boston, Chicago, Cleveland, 

Detroit, Houston, Los Angeles, New York, Philadelphia, Sacramento, St. Louis, Washington DC (an area which 

at that time was modeled to include Baltimore as part of the Baltimore-Northern Virginia MSA). 3 In addition to the twelve study areas identified in the 2007 exposure assessment, staff has added Dallas and 

Denver, while also separately modeling Baltimore (from Washington DC) in this current assessment. Inclusion 

of Seattle, WA was considered but not included due to a lack of appropriate monitoring data. 4 The “all school-age children” study group includes both asthmatic and non-asthmatic children ages 5 to 18. The 

“all older adults” includes both asthmatic and non-asthmatic older adults ages 65 to 95. Note also that the 8-hr 

average exposure of interest in both this and the previous assessment was concomitant with moderate or greater 

exertion for all study groups.
5-2

1 O3 concentrations of concern, all while at moderate or greater exertion levels, based on adverse 

2 effects observed in human clinical exposure studies. Further, the complete time series of 

3 individual exposures estimated by APEX serves as input to a module that estimates human health 

4 risk (Chapter 6).

5 This chapter first provides a brief overview of human exposure and exposure modeling 

6 using APEX (section 5.1), the scope of this O3 exposure assessment and key inputs used to 

7 model exposure in the 15 U.S. study areas selected (section 5.2), and followed by the main body 

8 exposure results (section 5.3). Then, section 5.4 presents an assemblage of targeted analyses 

9 designed to provide additional insight to the main body of exposure results by focusing on 

10 important data inputs, additional at-risk populations, lifestages, or scenarios, influential attributes 

11 in estimating exposures, and performance evaluations. The results of these and other exposure 

12 model targeted analyses are integrated in an uncertainty characterization section (section 5.5) 

13 along with a final section summarizing the key observations for this chapter (section 5.6).

14 5.1 SYNOPSIS OF O3 EXPOSURE AND EXPOSURE MODELING

15 5.1.1 Human Exposure

16 Human exposure to a contaminant is defined as “contact at a boundary between a human 

17 and the environment at a specific contaminant concentration for a specific interval of time,” and 

18 has units of concentration times duration (National Research Council, 1991). For air pollutants 

19 the contact boundary is nasal and oral openings in the body, and personal exposure of any

20 individual to a chemical in the air for a discrete time period is fundamentally quantified as (Lioy, 

21 1990; National Research Council, 1991):

22 (5-1)

23

24

where E[t1,t2 25 ] is the personal exposure or exposure concentration during the time period 

26 from t1 to t2, and C(t) is the concentration at time t in the breathing zone. The breathing rate at 

27 the time of exposure will influence the dose received by the individual. While we do not directly 

28 estimate dose in this assessment, intake is the total O3 inhaled (i.e., exposure concentration, 

duration, and ventilation combined).5 29

30

31

 5 In chapter 6, the estimation of risk combines the time series of both the personal exposure concentrations and 

ventilation rate, among other variables in essentially calculating a dose, though not explicitly output from the 

model.

∫ = 2

1

[ , ] ( ) 1 2

t

t

E t t C t dt
5-3

1 5.1.2 Estimating O3 Exposure

2 Exposure to O3 can be directly estimated by monitoring the concentration of O3 in a 

3 person’s breathing zone (close to the nose/mouth) using a personal exposure monitor. Studies 

4 employing this measurement approach have been reviewed in the ISA and EPA O3 Air Quality 

5 Criteria Documents (U.S. EPA, 1986, 1996b, 2006, 2013). Personal exposure measurements 

6 from these studies are useful in describing a general range of exposure concentrations (among 

7 other reported measurement data) and in identifying factors that may influence varying exposure 

8 levels. However, these measurement studies are largely limited by the disparity between sample 

9 measurement duration and exposure concentration averaging-times of interest and in 

10 appropriately capturing variability in population exposure occurring over large geographic areas , 

11 particularly when considering both concentration (e.g., spatial variability) and population (e.g., 

12 age, sex) attributes that influence exposure.

13 O3 exposure for individuals, small groups of individuals or large populations can be 

14 calculated indirectly (or modeled) using Equation 5-1. When employing such an approach in a 

15 population exposure assessment, two basic types of input data are needed; a time-series of O3

16 concentrations that appropriately represents spatial heterogeneity in O3 concentrations and a 

17 corresponding time-series of locations visited by the persons exposed. When considering air 

18 pollutant concentrations, population exposure models are commonly driven by ambient 

19 concentrations. These ambient concentrations may be provided by monitoring data, by air quality 

20 model estimates, or perhaps by a combination of these two data sources. Then, an understanding 

21 of the relationships between ambient pollutants and the locations people occupy is needed. This 

22 is because human exposure, regardless of the pollutant or whether one is interested in individual 

23 or population exposure, depends on where an individual is located, how long they occupy that 

24 location, and what the pollutant concentration at the point of contact is. Furthermore, if interested 

25 in air pollutant intake rate or dose, one needs to know what activity the person is performing 

26 while exposed.

27 Thus, the types of measurement and modeling studies that provide information for more 

28 realistically estimating exposure to O3 can be augmented from the above list to include studies 

29 of: 1) O3 formation, deposition, and decay, 2) people’s locations visited and activities performed, 

30 3) human physiology, and 4) local scale meteorological measurements and/or modeling. Useful 

31 data derived from these varied studies are O3 concentrations (i.e., fixed site, personal exposure, 

32 indoor and outdoor locations), built environment physical factors (i.e., air exchange rates 

33 (AERs), infiltration rates, decay and deposition rates), human time-location-activity patterns 

34 (minute-by-minute, hourly, daily, and longer-term), time-averaged or activity-specific breathing 

35 rates among varying sexes and/or lifestages, and hourly ambient temperatures.
5-4

1 When integrating these varied data (among others such as population demographics and 

2 disease prevalence) and understanding factors affecting exposure, exposure models can extend 

3 beyond the limited information given by measurement data alone. For example, an exposure 

4 model can reasonably estimate exposures for any perceivable at-risk population (e.g., asthmatics 

5 living in a large urban area) and considering any number of hypothetical air quality conditions 

6 (e.g., just meeting a daily maximum 8-hr average concentration of 70 ppb). Exposure models that 

7 account for variability in human physiology can also realistically estimate pollutant intake by 

8 using activity-specific ventilation rates. These types of measurements cannot realistically be 

9 performed for a study group or population of interest, particularly when considering time, cost, 

10 and other constraints. The following section provides an overview of how such exposure 

11 modeling can be done using APEX, the model developed by EPA to perform such calculations 

12 and used to estimate O3 exposures in this REA.

13 5.1.3 Modeling O3 Exposure Using APEX

14 EPA has developed the APEX model for estimating human population exposure to 

15 criteria and air toxic pollutants, used most recently in estimating exposures for the O3 (U.S. EPA, 

16 2007b), nitrogen dioxide (U.S. EPA, 2008), sulfur dioxide (U.S. EPA, 2009a), and carbon 

17 monoxide (U.S. EPA, 2010) NAAQS reviews. APEX is a probabilistic model designed to 

18 account for the numerous sources of variability that affect people’s exposures. An overview of

19 the approaches used by APEX to estimate exposure concentrations is found in Appendix 5A with 

20 details provided in U.S. EPA (2012a,b).

21 Briefly, APEX simulates the movement of individuals through time and space and 

22 estimates their exposure to a given pollutant while occupying indoor, outdoor, and in-vehicle 

23 locations. The model stochastically generates simulated individuals in selected study areas using 

24 census-derived probability distributions for demographic characteristics. Population 

demographics are drawn from the 2000 Census data6 25 at a tract level, and a national commuting 

database based on 2000 Census data provides home-to-work commuting flows between tracts.7 26

27 Any number of individuals can be simulated, and collectively they approximate a random 

28 sampling of people residing in a particular study area.

29 Daily activity patterns for individuals in a study area, an input to APEX, are obtained 

30 from detailed daily time-location-activity pattern survey data that are compiled in the 

31 Consolidated Human Activity Database (CHAD) (McCurdy et al., 2000; U.S. EPA, 2002). These 

32 daily diaries are used to construct a sequence of locations visited and activities performed for 

33 APEX simulated individuals consistent with their demographic characteristics, day-type (e.g., 

 6 Due to resource limitations and data availability, the 2010 Census data have not yet been processed to include in 

this 2nd draft REA. 7 There are approximately 65,400 census tracts in the ~3,200 counties in the U.S.
5-5

1 weekend or weekday), and season of the year, as defined by ambient temperature regimes 

2 (Graham and McCurdy, 2004). The time-location-activity data input to APEX are linked with 

3 personal attributes of the surveyed individuals’ such as age, sex, employment status, day-of4 week surveyed, and daily maximum and daily mean temperature. These specific personal 

5 attribute data are then used by APEX to best match the daily diary with the simulated persons of 

6 interest, using the same variables as first-order diary selection characteristics. The approach is 

7 designed to capture the important attributes contributing to an individuals’ time-location-activity 

8 pattern, and of particular relevance here, time spent outdoors (Graham and McCurdy, 2004). In

9 using a diverse collection of time-location-activity diaries that capture the duration and 

10 frequency of occurrence of visitations/activities performed, APEX can simulate expected 

11 variability in human behavior, both within and between individuals. This, combined with 

12 exposure concentrations, allows for the reasonable estimation of the magnitude, frequency, 

13 pattern, and duration of exposures an individual experiences.

14 A key concept in modeling exposure using APEX is the microenvironment, a term that 

15 refers to the immediate surroundings of an individual at a particular time. APEX has a flexible 

16 approach for modeling micro-environmental concentrations whereas the model user defines the 

17 type, number and characteristics of the microenvironments to be modeled. Typical 

18 microenvironments include indoors at home, indoors at school, near roadways, inside cars, and 

19 outside home. In this exposure assessment, all microenvironmental O3 concentrations are 

20 derived from ambient O3 concentrations input to APEX and are estimated using either a mass21 balance or transfer factors approach, selected by the user. The mass balance approach assumes 

22 that the air in an enclosed microenvironment is well-mixed and that the air concentration is 

23 spatially uniform at a given time within the microenvironment. The approach employs indoor-to24 outdoor AERs (i.e., number of complete air exchanges per hour) and considers removal 

25 mechanisms such as deposition to building surfaces and chemical decay rates. The transfer 

26 factors model is simpler than the mass balance model, and employs two variables, a proximity 

27 factor, used to account for proximity of the microenvironment to sources or sinks of pollution, or 

28 other systematic differences between concentrations just outside the microenvironment and the

29 ambient concentrations, and a penetration factor, which quantifies the degree to which the 

30 outdoor air penetrates into the microenvironment.

31 Activity-specific simulated breathing rates of individuals are used in APEX to 

32 characterize intake received from an exposure. This is done because controlled human exposure

33 studies have shown adverse health outcomes are associated with both elevated concentrations 

34 and study participant exertion levels. The breathing rates calculated by APEX are derived from 

35 the energy expenditure associated with each simulated persons’ activity performed, adjusted for 

36 age- and sex-specific physiological parameters (Graham and McCurdy, 2005). The energy 
5-6

expenditure estimates themselves are derived from distributions of METS8 1 (or metabolic 

2 equivalents of work) associated with every activity performed (McCurdy et al., 2000, using 

3 Ainsworth et al., 1993).

4 An important feature of APEX is the ability to account for variability in exposure by 

5 representing input variables as statistical distributions along with dependent conditional 

6 variables, where appropriate. For example, the distribution of AERs in a home, office, or motor 

7 vehicle can depend on the type of heating and air conditioning present, which are also stochastic 

8 inputs to the model, as well as the ambient temperature on a given day. The user can choose to 

9 keep the value of a stochastic parameter constant for the entire simulation (appropriate for the 

10 volume of a house), or can specify that a new value shall be drawn hourly, daily, or seasonally 

11 from specified distributions.

12 Finally, APEX calculates a unique time-series of exposure concentrations on the order of 

13 minutes or smallest diary event duration that each simulated person may experience during the 

14 modeled time period, based in that individual’s estimated microenvironmental concentrations 

15 and the time spent in each of sequence of microenvironments visited according to the time16 location-activity diary of each individual. Then, hourly average exposures of each simulated 

17 individual are estimated using time-weighted averages of the within-hour exposures. From 

18 hourly exposures, APEX calculates any other time averaged exposure of interest (e.g., 8-hr or 

19 daily average) that a simulated individual experiences during the modeled period.

20 5.2 SCOPE OF THE EXPOSURE ASSESSMENT

21 This section broadly presents the scope of the exposure assessment including descriptions 

22 of the modeling domains, ambient concentrations used, time periods and populations modeled, as 

23 well as identifying key approaches, inputs and outputs used by APEX in estimating population 

24 O3 exposures. Detailed descriptions regarding APEX modeling, model inputs and other 

25 supporting information are provided in Appendix 5A-5E and the APEX user’s guide and 

26 technical support documents (U.S. EPA 2012a,b). Figure 5-1 illustrates the general conceptual 

27 framework for generating our population exposure concentrations, including the time series of 

28 exposure and ventilation rate output generated as input to population risk calculations in Chapter 

29 6.

30 5.2.1 Urban Areas Selected

31 The selection of urban areas to include in the exposure assessment considered the 

32 location of O3 epidemiological studies, the availability of ambient O3 monitoring data, and the 

 8 METS are a dimensionless ratio of the activity-specific energy expenditure rate to the basal or resting energy 

expenditure rate. The metric is used by exercise physiologists and clinical nutritionists to estimate work 

undertaken by individuals as they go through their daily activities (Montoye et al., 1996).
5-7

1 desire to represent a range of geographic areas, encompassing variability in climate and

2 population demographics. Specifically, the criteria included the following:

• The overall set of urban locations should represent a range of geographic areas, urban 

population demographics, and climate, beginning with study areas selected in the 2007 

O3 NAAQS review.

• The locations should be focused on areas that do not meet or are close to not meeting the 

existing 8-hr O3 NAAQS and should include areas having O3 non-attainment 

designations.

• There must be sufficient O3 ambient air quality data for the recent 2006-2010 period.

• The study areas should include the 12 cities modeled in the epidemiologic-based risk 

assessment (Chapter 7).

3 Based on these criteria, we chose the 15 study areas listed in Table 5-1 to develop our 

4 population exposure estimates. We then defined an air quality domain for each study area, 

5 broadly bounding the ambient concentration field where exposures were to be estimated. To do 

6 this, we evaluated 1) counties modeled in the previous 2007 O3 NAAQS review common to 

7 current study areas, 2) political/statistical county aggregations (e.g., whether in a metropolitan 

8 statistical areas or MSAs), and 3) if the study area was designated as a non-attainment area 

(NAA), the counties that were part of the NAA list. We identified a final list of 215 counties9 9 to 

10 comprise the air quality domain for the 15 study areas, the names of which are provided in 

11 Appendix 5B.

12 5.2.2 Time Periods Simulated

13 The exposure periods modeled are the O3 seasons for which routine hourly O3 monitoring 

14 data were available for years 2006 to 2010 (Table 5-1), and defined by 40 CFR part 58, 

15 Appendix D, Table D-3. These periods are designed to reasonably capture year-to-year 

16 variability in ambient concentrations and meteorology and include most of the high 

17 concentration events occurring in each area. Having this wide range of air quality data across 

18 multiple years allows us to more realistically estimate a range of exposures, rather than using a 

19 single year of air quality data. While the number of available O3 monitors may vary slightly from 

20 year to year, we assumed constant representation by the available monitors and associated 

21 statistically interpolated data for each year over the simulation period (see section 5.2.3).

 9 Of the 215 counties defining the air quality domain, 207 remained in the exposure model domain. 
5-8

Figure 5-1 Conceptual Framework Used for Estimating Study Area Population O3 Exposure Concentrations
5-9

1

2 Table 5-1 General Characteristics of the Population Exposure Modeling Domain 

3 Comprising Each Study Area

Study Area 

(Abbreviation) O3 Season1

Study Area Number of:

Counties

Ambient 

Monitors

APEX Air 

Districts

US Census 

Tracts

Persons

All School 

Age 

Children

(age 5-18)

All Ages

(age 5-95)

Atlanta (ATL) Mar 1-Oct 31 32 14 664 678 860,649 3,850,951

Baltimore (BAL) Apr 1-Oct 31 7 12 603 618 505,140 2,209,226

Boston (BOS) Apr 1-Sep 30 7 13 1,005 1,028 905,208 4,449,291

Chicago (CHI) Apr 1-Oct 31 16 28 1,882 2,055 1,899,073 8,345,373

Cleveland (CLE) Apr 1-Oct 31 8 16 802 879 578,733 2,692,846

Dallas (DAL) Mar 1-Oct 31 11 21 1,012 1,036 1,097,004 4,698,392

Denver (DEN) Mar 1-Sep 30 12 25 655 675 560,137 2,626,239

Detroit (DET) Apr 1-Sep 30 9 13 1,419 1,454 1,016,896 4,572,479

Houston (HOU) Jan 1-Dec 31 10 19 779 802 970,528 3,925,054

Los Angeles (LA) Jan 1-Dec 31 5 50 2,000 3,352 3,620,972 14,950,340

New York (NY) Apr 1-Oct 31 27 32 1,900 4,889 3,843,450 18,520,868

Philadelphia (PHI) Apr 1-Oct 31 15 19 1,452 1,555 1,231,052 5,506,954

Sacramento (SAC) Jan 1-Dec 31 7 18 447 461 466,169 1,926,598

St. Louis (STL) Apr 1-Oct 31 15 16 494 518 527,755 2,340,325

Wash., DC (WAS) Apr 1-Oct 31 26 28 1,013 1,037 966,791 4,498,374

All Study Areas - 207 324 16,127 21,037 19,049,557 85,113,310

1 4 Each study area’s O3 monitoring season is defined by 40 CFR part 58, Appendix D, Table D-3.

5 5.2.3 Ambient Concentrations Used

6 We used the available hourly ambient monitor concentration data within and around each 

7 study area along with a statistical interpolation technique (Chapter 4) to estimate hourly census 

8 tract concentrations within the counties comprising each study area. These concentrations served 

9 as the ‘base’ air quality input for each study area year. Ambient concentrations were also 

adjusted to just meet the existing standard (75 ppb, 4th 10 highest 8-hr average, averaged over a 3-

11 year period) and alternative standard levels (70, 65, 60, and 55 ppb) using an air quality model 

12 and the statistical interpolation technique (Chapter 4).

13 These estimated hourly census tract O3 concentrations served as the APEX air districts, 

14 the basic ambient concentrations from which each simulated persons microenvironmental 

15 concentrations are estimated. Having these temporally and spatially resolved air districts in each 
5-10

1 study area allows for better utilization of APEX spatial and temporal capabilities in estimating 

2 exposure. Because APEX simulates where individuals are located at specific times of the day, 

3 more realistic exposure estimates are obtained in simulating the contact of individuals with these 

4 spatially and temporally diverse concentrations.

5 Even though we estimated O3 ambient concentrations at all census tracts in each county6 level study area, the study area exposure modeling domain was defined as a subset of these 

7 census tracts by using the ambient monitoring sites within the urban core of each study area’s air 

8 quality domain and a 30 km radius of influence. This zone of influence is consistent with what 

was done in the 1st 9 draft O3 REA, though in that exposure assessment, only the ambient 

10 monitoring data sites themselves were used to represent the APEX air districts, hence 

11 concentrations measured at a particular monitoring site would be directly extrapolated outwards 

12 to all census tracts within 30 km of that site. In contrast, by incorporating the VNA estimated 

13 concentrations and retaining the same 30 km radius of influence, we are stressing the 

14 significance of the monitor information in defining the urban core air quality while also 

15 reasonably estimating concentration gradients (where such gradients exist) with increasing 

16 distance from monitoring locations.

Thus, all air districts10 17 and census tracts that fall within the 30 km radius of each ambient 

18 monitor were used to estimate the exposures, defining the final exposure modeling domain in 

19 each study area (Table 5-1). The monitor IDs used to select the census tracts to be modeled are 

20 provided in Appendix 5B, while the complete list of census tract IDs where exposures are 

21 modeled are within the APEX control files for each study area (and are the same for each 

22 simulation year).

23 5.2.4 Meteorological Data Used

24 APEX uses study area temperature data to select representative diaries for a particular 

25 day and in selecting an appropriate air exchange rate used to calculate indoor residential 

26 microenvironmental concentrations. APEX uses the data from the closest weather station to each 

27 Census tract. To ensure reasonable coverage for each study area, a few to several meteorological 

28 stations recording hourly surface temperature measurements were identified using data obtained 

from the National Weather Service ISH data files.11 29 Details regarding the meteorological stations 

30 selected and data processing are given in Appendix 5B. Briefly, APEX requires the temperature 

31 input data to be 100% complete. In general, any missing values were filled using a linear 

 10 The original number of air quality districts for New York and Los Angeles needed to be reduced by about half due 

to exceeding personal computer memory capacity when APEX used > 2,000 air districts. See Appendix 5B for 

details. 11 http://www.ncdc.noaa.gov/oa/climate/surfaceinventories.html
5-11

1 interpolation or regression approach that employs information from proximal meteorological 

2 stations.

3 5.2.5 Populations Simulated

4 Exposure was estimated for four at-risk study groups residing in each study area: all 

5 school-age children (ages 5-18), asthmatic school-age children, asthmatic adults (ages 19-95), 

6 and all older adults (ages 65-95). Due to the increased amount of time spent outdoors engaged in 

7 relatively high levels of physical activity (which increases intake), school-age children as a group 

8 are particularly at risk for experiencing O3-related health effects (U.S. EPA, 2013, Chapter 8). 

9 We report results for all school-age children down to age five, recognizing an increasing trend 

10 for younger children to attend school. Some U.S. states allow 4-year-olds to attend kindergarten, 

11 and most states have preschool programs for children younger than five. In 2000, six percent of 

12 U.S. children ages 3 to 19 who attend school were younger than five years old (2000 Census 

13 Summary File 3, Table QT-P19: School Enrollment). Currently we do not estimate exposure for 

14 these younger children due to a lack of information that would let us confidently characterize 

15 these younger aged children. While EPA guidance recommends, for certain instances, an upper 

16 age group of children ages 16 through 21 (U.S. EPA, 2005), we restricted our upper age 

17 classification of children through age 18. In considering the expected variability in activity 

18 patterns over the span of ages 16 through 21 (e.g., time spent outdoors, time in school, each in 

19 contrast to time spent working) and the relatively small difference in respiratory physiology over 

20 that same age span compared with that of adults (e.g., Figure 5-17), factors critical for high O3

21 exposure and dose, we assumed simulated persons age 19 to 21 would be best included in our

22 adult study group. The number of persons represented in each of the 15 study areas is given in 

23 Table 5-1 and, considering all study areas together, captures approximately 32.8 % of all 

24 children ages 5 to18 and 32.0 % of the total U.S. population ages 5 to 95.

25 The number of asthmatic school-age children and asthmatic adults in each study area was 

26 estimated using asthma prevalence from the Center for Disease Control (CDC) and Prevention’s 

National Health Interview Survey (NHIS).12 27 Briefly, years 2006-2010 NHIS survey data were 

28 combined to calculate asthma prevalence, defined as the probability of a “Yes” response to the 

29 question: “do you still have asthma?” among those that responded “Yes” to the question “has a 

30 doctor ever diagnosed you with asthma?”. The asthma prevalence was first stratified by NHIS 

31 defined regions (Midwest, Northeast, South, and West), sex, age (single years for ages 0-17) or 

age groups (ages ≥ 18), and by a family income/poverty ratio.

13 32 These new asthma prevalence 

33 estimates were then linked to U.S. census tract level poverty ratio probabilities (U.S. Census

 12 See http://www.cdc.gov/nchs/nhis.htm (accessed October 4, 2011). 13 The income/poverty ratio threshold used was 1.5, that is the surveyed person’s family income was considered 

either ≤ or > than a factor of 1.5 of the U.S. Census estimate of poverty level for the given year.
5-12

1 Bureau, 2007), also stratified by age and age groups, to generate a final database consisting of 

2 census tract level asthma prevalence for the entire U.S. A detailed description of how the data 

3 base was developed is presented in Appendix 5C, while the estimated asthma prevalence used 

4 for each census tract is provided in the APEX asthma prevalence input file. A summary of the 

5 asthma prevalence calculated for each study area simulation is provided here in Table 5-2. 

6 Table 5-2 Asthma Prevalence for Children and Adults Estimated by APEX in Each 

7 Simulated Study Area

Study Area

Asthma Prevalence (%)

Children (5-18) Adults (18-95) All Persons (5-95)

Atlanta 9.6 6.5 7.2

Baltimore 9.7 6.6 7.3

Boston 11.4 7.9 8.6

Chicago 10.7 7.8 8.4

Cleveland 10.9 7.7 8.4

Dallas 9.9 6.5 7.3

Denver 8.9 7.7 7.9

Detroit 11.1 7.7 8.5

Houston 10.1 6.5 7.4

Los Angeles 9.0 7.7 8.0

New York 12.2 8.1 9.0

Philadelphia 11.3 7.9 8.7

Sacramento 9.0 7.8 8.1

St. Louis 11 7.6 8.4

Washington DC 9.5 6.4 7.1

All Areas 10.5 7.6 8.2

8

9 All simulated persons (either asthmatic or non-asthmatic) used time-location-activity data 

10 from CHAD, the most complete, high quality source of human activity data for use in exposure 

11 modeling. The current CHAD database contains over 53,000 individual daily diaries including

12 time-location-activity patterns for individuals of both sexes across a wide range of ages (<1 to 

13 94). The database is geographically diverse, containing diaries from individuals residing in 

14 several major cities, suburban, and rural areas across the U.S. Time spent performing activities 

15 within particular locations can be on a minute-by minute basis, thus avoiding the smoothing of 

16 potential peak exposures longer event durations would yield.
5-13

1 Table 5-3 summarizes the studies and number of diaries in CHAD used in this 

2 assessment, noting that the total CHAD diaries used by APEX is restricted to just over 41,000 

given our simulation age range (5-95) and additionally selected usability requirements.14 3

4 Additional context regarding the representativeness of the CHAD data in estimating exposure is 

5 provided in section 5.3.1 and Appendix 5G.

6 APEX creates a sequence of daily diaries across the entire O3 season for each simulated 

7 individual using a method designed to capture the tendency of individuals to repeat activities, 

8 based on reproducing realistic variation in a key diary variable (Glen et al., 2008). For this O3

9 analysis, the key variable selected is the amount of time an individual spends outdoors each day, 

10 one of the most important determinants of exposure to high levels of O3 (see section 5.3.2). The 

11 longitudinal method targets two statistics, a population diversity statistic (D) and a within-person 

12 autocorrelation statistic (A). Values of 0.2 for D and 0.2 for A were initially developed based on 

13 analyses by Geyh et al. (2000) and Xue et al. (2004), with both studies evaluating groups of 

14 children ages 7 to 12 in a single study area. We adjusted values for D upwards to 0.5 to reflect a 

broader range of ages and to better estimate repeated activities.15 15 Further details regarding the 

16 development of the longitudinal methodology can be found in U.S. EPA (2012a, b).

17

18

 14 In this assessment, the CHAD diaries must be from persons having a known age, sex, day-of-week, and daily 

temperature. In addition, diaries must have no more than 3 hours total of missing location and/or activity data. 15 A small D means that the overall variability between people in the key diary statistic is smaller than the variability 

observed over days within the same person. A D closer to 1 means that each person shows little variation over 

time relative to the variability between persons.
5-14

1 Table 5-3 Consolidated Human Activity Database (CHAD) Study Information and 

2 Diary-days Used by APEX

Activity Pattern Study (Abbrev.)

General 

Study Area

Study 

Years

Subject

Ages

Diary-days 

(ages 4-94)

Diary-days 

(ages 4-18)

Baltimore Retirement Home (BAL) Baltimore, 

MD

1997-98 72 - 93 304 0

California Youth (CAY) California 1987-88 12 - 17 182 182

California Adults (CAA) California 1987-88 18 - 94 1,555 36

California Children (CAC) California 1989-90 <1 - 11 771 771

Cincinnati (CIN) Cincinnati, 

OH

1985 <1 - 86 2,259 727

Detroit Exposure and Aerosol 

Research (DEA)1,2

Detroit, MI 2005-06 18 - 74 331 5

Denver CO Personal Exposure 

(DEN)

Denver, CO 1982-83 18 - 70 714 7

EPA Longitudinal (EPA)1,2 RTP, NC 1999-2000, 

2002, 06-08

<1 - 60 1,386 0

LA O3 Exposure: Elementary 

School (LAE)

Los Angeles, 

CA

1989 10 - 12 50 50

LA O3 Exposure: High School 

(LAH)

Los Angeles, 

CA

1990 13 - 17 42 42

National Human Activity Pattern 

Study: Air (NHA)

National 1992-94 <1 - 93 4,129 693

National Human Activity Pattern 

Study: Water (NHW)

National 1992-94 <1 - 93 4,099 745

National-Scale Activity Survey 

(NSA)

7 US metro. 

areas

2009 35 - 92 6,825 0

Population Study of Income 

Dynamics I (ISR)1

National 1997 <1 - 13 3,507 3,507

Population Study of Income 

Dynamics II (ISR)1

National 2002-03 5 - 19 4,800 4,793

Population Study of Income 

Dynamics III (ISR)1,2

National 2007-08 10 - 19 2,619 2614

RTI O3 Averting Behavior (OAB)1 35 US metro. 

areas

2002-03 2 - 12 2,187 2,187

RTP Panel (RTP)1 RTP, NC 2000-01 55 - 85 871 0

Seattle (SEA)1 Seattle, WA 1999-2002 6 - 91 1,624 317

Study of Use of Products and 

Exposure Related Behavior (SUP)

1,2

Sac/San

Fran, CA 

Counties

2006-10 1 - 88 2,533 994

Washington, D. C. (WAS) Wash., DC 1982-83 18 - 71 686 10

Totals 1982 - 2010 <1 - 94 41,474 17,680

1 3 Study data added after 2007 O3 NAAQS review.

2 Study data added after 2012 1st 4 Draft O3 REA.
5-15

1

2 5.2.6 Key Physiological Processes And Personal Attributes Modeled

3 The modeling of physiological processes relevant to the O3 exposure and intake is 

4 complex, particularly when representing inter- and intra-personal variability in energy 

5 expenditure (EE) and ventilation rates (VE). APEX has a module capable of estimating several 

6 variables associated with every activity performed by simulated individuals. Briefly, the module 

7 links the diary indicated activities to specific energy expended, the rate of oxygen consumed 

8 (VO2) and the associated ventilation rate, all considering the unique sequence of events 

9 individuals go through each simulated day. The activity-specific time-series of VE estimates

10 ultimately serve as an important variable used in estimating O3 intake as well as in identifying 

11 when simulated individuals performing activities at moderate or greater exertion. In addition, 

12 age, sex, and body mass related physiological differences are specifically taken into account by 

13 the ventilation algorithm, derived using ventilation data obtained from several human studies 

14 (see Graham and McCurdy, 2005):

E O b w V BM =b +b V BM +b + age +b sex+e +e • •

2 3 2 0 1 15 ln( / ) ln( / ) ln(1 ) (5-2)

16 where,

17

18 ln = natural logarithm of variable

V E / BM •

19 = activity specific ventilation rate, body mass normalized (liter air/kg)

20 bi = see Table 5-4

V O2 / BM •

21 = activity specific oxygen consumption rate, body mass normalized 

22 (liter/O2/kg)

23 age = age of the individual (years)

24 sex = sex (-1 for males, +1 for females)

25 eb = randomly sampled error term for between persons N{0, se}, (liter air/kg)

26 ew = randomly sampled error term for within persons N{0, se}, (liter air/kg)

27

28 As indicated by Equation 5-2, the random error (ε) is allocated to two variance 

29 components used to estimate the between-person (inter-individual variability) residuals 

30 distribution (eb) and within-person (intra-individual variability) residuals distribution (ew). The 

31 regression parameters b0, b1, b2, and b3 are assumed constant over time for all simulated persons, 

32 eb is sampled once per person by APEX, while whereas ew varies from event to event. Point 

33 estimates of the regression coefficients and standard errors of the residuals distributions are 

34 given in Table 5-4. See Appendix 5A, Isaacs et al. (2008), and Chapter 7 of the APEX TSD (US 
5-16

1 EPA, 2012b) for further discussion of this module. See also section 5.4.4 for a limited 

2 performance evaluation of this module in estimating ventilation rates. 

3

4 Table 5-4 Ventilation equation coefficient estimates (bi) and residuals distributions (ei)

Age 

group

Ventilation Equation Coefficients1 Random Error1

b0 b1 b2 b3 eb ew

<20 4.3675 1.0751 -0.2714 0.0479 0.0955 0.1117

20-<34 3.7603 1.2491 0.1416 0.0533 0.1217 0.1296

34-<61 3.2440 1.1464 0.1856 0.0380 0.1260 0.1152

61+ 2.5826 1.0840 0.2766 -0.0208 0.1064 0.0676

1 5 These are values of the coefficients and residuals distributions described by Equation (5-2) and described 

6 in Graham and McCurdy (2005).

7

8 Two key personal attributes determined for each simulated individual in this assessment 

9 are body mass (BM) and body surface area (BSA). Each simulated individual’s body mass is 

10 randomly sampled from age- and sex-specific body mass distributions generated from National 

Health and Nutrition Examination Survey (NHANES) data for the years 1999-2004.

16 11 Details in 

12 their development and the parameter values are provided by Isaacs and Smith (2005). Then age13 and sex-specific body surface area can be estimated for each simulated individual based on 

14 logarithmic relationships developed by Burmaster (1998) using body mass as an independent 

15 variable as follows:

2.2781 0.6821 BSA e BM − 16 = (5-3)

17 5.2.7 Microenvironments Modeled

18 APEX is designed to estimate human exposure by using algorithms that attempt to 

19 capture the full range of O3 concentrations expected within several microenvironments. Broadly 

20 aggregated, these can be either indoor, inside a motor vehicle, near road, or outdoor locations. 

21 The two methods available in APEX for calculating pollutant concentrations within 

22 microenvironments are a mass balance model and a transfer factor approach. Table 5-5 lists the 

23 28 microenvironments selected for this analysis and the exposure calculation method used for 

24 each.

25 The importance of modeling indoor microenvironments (e.g., homes, offices, schools) is 

26 underscored by research indicating that personal exposure measurements of O3 may not be well-

 16 Demographic (Demo) and Body Measurement (BMX) datasets for each of the NHANES studies were obtained 

from http://www.cdc.gov/nchs/nhanes/nhanes_questionnaires.htm.
5-17

1 correlated with ambient measurements and indoor concentrations are usually much lower than 

2 ambient concentrations (U.S. EPA, 2013, Section 4.3.3). We used mass balance modeling to 

3 estimate O3 concentrations in all indoor microenvironments, considering probabilistic 

4 distributions of temperature-dependent (where data were available) building air exchange and 

5 chemical decay rates. Parameter settings for each of these variables are provided in Appendix 

6 5B, while additional discussion regarding updates made to air exchange rates using more recent 

7 study data is given in Appendix 5E.

8 The remaining microenvironments were modeled using a transfer factors approach. 

9 Outdoor microenvironmental concentrations were assumed equivalent to ambient concentrations, 

10 near-road concentrations were adjusted considering whether or not O3 concentrations were 

11 reduced by atmospheric reactions (e.g., scavenging by NOX) or other processes, and vehicular 

12 microenvironments considered both the outdoor concentration attenuation and 

13 infiltration/removal in the concentration estimation. Specific parameter settings for each of these 

14 variables are provided in Appendix 5B.

15 Table 5-5 Microenvironments Modeled, Calculation Method Used, and Variables Included

Microenvironment

Calculation 

Method Variables

Indoor: Residence, Community Center or Auditorium, Restaurant, 

Hotel/Motel, Office building/Bank/Post Office, Bar/Night Club/Café, School, 

Shopping Mall/Non-Grocery Store, Grocery Store/Convenience Store, 

Metro-Subway-Train Station, Hospital/Medical/Care Facility, Industrial 

Factory/Warehouse, Other Indoor

Mass 

balance

Building air 

exchange &

chemical 

decay rates

Outdoor: Residential, Park/Golf Course, Restaurant/Café, School Grounds, 

Boat, Other Outdoor Non-Residential

Factors None

Near-road: Metro-Subway-Train Stop, Within 10 Yards of street, Parking 

Garage (covered or below ground), Parking lot (open)/Street parking, 

Service Station

Factors Proximity 

factors

Vehicle: Cars/Light Duty Trucks, Heavy Duty Trucks, Bus, Train/Subway Factors Proximity & 

penetration 

factors

16 5.2.8 Model Output

17 APEX estimates the complete time series of exposure concentrations for every simulated 

18 individual and can summarize data using standardized time metrics (e.g., hourly or daily average, 

19 daily maximum 8-hr average) or can output the minute-by-minute exposure concentrations (as is 

20 needed for the risk estimation in Chapter 6). As an indicator of exposure to O3 air pollution, we

selected the daily maximum 8-hr average O3 exposure17 21 for every simulated individual and 

 17 It is important to stress here that only the maximum 8-hr exposure concentration is retained for each day 

simulated, per person. While every day could contain twenty-four unique 8-hr averages and that it is entirely 
5-18

1 stratified these exposures by exertion level at the time of exposure. This indicator was selected 

2 based on controlled human exposure studies where reported adverse health responses were 

associated with exposure to O3 and while the study subject was exercising.18 3 Factors important in 

4 calculating this indicator includes the magnitude, duration, frequency of exposures, and the 

5 breathing rate of individuals at the time of exposure. As a reminder, the calculated daily 

6 maximum 8-hr average exposure concentrations are distinct from that of daily maximum 8-hr 

7 average ambient concentrations by accounting for simulated individual’s time-location-activity 

8 patterns and O3 concentration decay/variation occurring within the occupied microenvironments.

9 Benchmark levels used in this assessment include 8-hr average O3 exposure 

10 concentrations of 60, 70 and 80 ppb; the same benchmark levels used for the 2007 O3 exposure 

11 assessment (U.S. EPA, 2007b). Estimating exposures to ambient O3 concentrations at and above 

12 these benchmark levels is intended to provide perspective on the public health impacts of O3-

13 related health effects observed in human clinical and toxicological studies, but that cannot 

14 currently be evaluated in quantitative risk assessments (e.g., lung inflammation, increased airway 

15 responsiveness, and decreased resistance to infection). The 80 ppb-8hr benchmark level 

16 represents an exposure level where there is substantial clinical evidence demonstrating a range of 

17 O3-related effects including lung inflammation and airway responsiveness in healthy individuals. 

18 The 70 ppb-8hr benchmark level reflects evidence that asthmatics have larger and more serious 

19 effects than healthy people as well as a substantial epidemiological evidence of adverse effects 

20 associated with O3 levels that extend below 80 ppb-8hr. The 60 ppb-8hr benchmark level 

21 represents the lowest exposure level at which O3-related effects have been observed in clinical 

22 studies of healthy individuals. See ISA section 6.2.1 for further discussions regarding the body of 

23 evidence supporting the selection of these benchmark levels.

24 The level of exertion of individuals engaged in particular activities is approximated by an 

25 equivalent ventilation rate (EVR), that is, ventilation normalized by body surface area (BSA, in 

m

2 26 ) and is calculated as VE/BSA, where VE is the ventilation rate in liters/minute. For 

27 identifying moderate or greater exertion occurring during any 8-hr average exposure period in 

this assessment, we used the lower bound EVR value of 13 (liters/min-m2 28 ) based on a range of 

29 EVRs used by Whitfield et al. (1996) to categorize persons engaged in moderate exertion 

30 activities for an 8-hr period. Whitfield et al. (1996) developed this range from EVR data reported 

31 in a 6.6-hr controlled human exposure study conducted by McDonnell et al. (1991).

 

possible multiple benchmark exceedances could occur for an individual on certain high O3 concentration days, 

staff judge this is not a practical output for the purposes of this assessment. 18 It is worth noting that the adverse health responses in the human clinical studies are generally based on 6.6 hour 

exposure to O3. Therefore, it is possible that the number of benchmark exceedances is underestimated because of 

the lesser likelihood of an 8-hr exposure above the same threshold due to the longer averaging time. 
5-19

1 APEX then calculates two general types of exposure estimates for the population of 

2 interest: the estimated number of people exposed to a specified O3 concentration level and, the 

3 number of days per O3 season that they are so exposed; the latter metric is expressed in terms of 

4 person-days. The former highlights the number of individuals exposed one or more times per O3

5 season at or above a selected benchmark level. The person-days measure estimates the number of 

6 times per season the simulated individuals are exposed at or above a selected benchmark level 

7 and summed across individuals comprising the population. We note that a person-days metric 

8 conflates people and occurrences: one occurrence for each of 10 people would be counted the 

9 same as 10 occurrences for one person (i.e., 10 person-days at or above benchmark level). In this 

10 assessment we are more interested in reporting multiday exposures rather than total person-days, 

11 that is, the number of times an individual experiences multiple exposures at or above a 

12 benchmark level during an O3 season. Given the complexities of the exposure modeling, the four 

13 study groups considered, the 15 study areas, the 5 years of ambient air quality, the multiple air 

14 quality scenarios simulated, and ultimately the output data generated, including both single and 

15 multiday exposures for simulated individuals, the consolidation of the results and the related 

16 graphic depictions used in this assessment requires additional discussion.

17 To begin, a simple example of summary results is the estimated percent of asthmatic 

18 school-age children experiencing exposures at or above a single 8-hr benchmark level when 

19 considering base air quality stratified by year (e.g., Figure 5-2, left panel). This presentation 

20 largely depicts the variability in O3 exposure across the 15 study areas within years, along with 

21 an illustration of broad year-to-year temporal variability. A general finding regarding temporal 

22 variability extracted from this graph would be that fewer asthmatic school-age children exceed 

23 daily maximum 8-hr average exposures of 60 ppb considering 2009 base air quality when 

24 compared with other simulation years. An observation regarding the spatial variability could 

25 include the range of exposures within years (i.e., the study area variability) spans between 15 to 

26 35 percentage points, dependent on the particular simulation year (Figure 5-2, left panel). One 

27 could also stratify the same exposure results by study area (e.g., Figure 5-2, right panel), thus 

28 depicting variability in estimated exposures across years within each study area, along with 

29 having broad study area comparisons. A general finding regarding temporal variability in this 

30 type of presentation would be that the range of exposures within study areas spans about 20 

31 percentage points, though some study areas have a generally small range (<5 percentage points) 

32 for most simulated years. An observation regarding spatial variability could be that Chicago 

33 largely has the fewest asthmatic school-age children at or above benchmark levels, having a 

34 mean about 15%, while Los Angeles consistently has the most asthmatic school-age children, 

35 having a mean about 35%, at or above benchmark levels while at moderate or greater exertion.

36
5-20

1 Figure 5-2 Percent of asthmatic school-age children in all study areas with at least one O3

2 exposure at or above 60 ppb-8hr while at moderate or greater exertion using base air 

3 quality (2006-2010), stratified by year (top left panel) or by study area (bottom left panel).

4

5 While these boxplots are an efficient tool that summarize potentially complex data sets 

6 by illustrating important statistical aspects of data analysis results (e.g., means, ranges, 

7 occasional upper percentile data values), at times important features of the data may be masked 

8 (e.g., trends or patterns within consolidated variables) and the presentation of other aspects of the 

9 exposure results would require the generation of additional graphs (e.g., results for additional 

10 benchmark levels). A tabular format could be one way to present all possible data, though given 

11 the number of APEX simulations performed (i.e., > 1,000) and aforementioned dimensions of 

12 the assessment, linking the trends and patterns across all study areas, years, and benchmark 

13 levels from the numerous output tables would be visually challenging.

14 This discussion regarding properly representing temporal and spatial variability in the 

15 exposure results can be further extended to include the added dimension of the five air quality 

16 scenarios (base, existing standard, and three alternative standard levels). Mindful of these 

17 complexities, we elected to use a multi-panel graphing approach to succinctly summarize the 

18 exposure output data, while also retaining as much information as possible in a single page 

19 format to allow for visual analysis of trends and patterns. As an example, Figure 5-3 (top panels) 
5-21

1 illustrates boxplots for Atlanta similar to those presented above, though the exposure results are 

2 for the three exposure benchmark levels of interest, with each stratified by the particular adjusted 

3 air quality scenario. As expected with increasing stringency of the 8-hr standard level, fewer 

4 asthmatic school-age children are exposed at or above a given benchmark level. Also expected is 

5 the fewer percent of asthmatic school-age children exposed to higher benchmark levels when 

6 compared with lower benchmark levels. While these three graphs can provide a clear depiction 

7 of the exposure results for a single study area, the six years encompassing the two averaging 

8 periods 2006-2008 and 2008-2010 are combined in the graphic and difficulty would remain in 

9 simultaneously exhibiting all 15 areas.

10 To overcome these limitations, Figure 5-3 (lower panel) exhibits all of the dimensions of 

11 the exposure results mentioned above (i.e., year, benchmark level, study area) along with 

12 distinguishing between the two standard averaging periods for each the existing (75 ppb-8hr) and 

13 alternative standard levels (60, 65, 70 ppb-8hr). The nomenclature above each subgraph 

14 indicating the particular air quality scenario requires defining. For example, a panel heading of 

15 “75” contains the exposures estimated when air quality was adjusted to just meet the existing 

standard level of 75 ppb-8hr (4th 16 highest daily maximum 8-hr average O3 concentration averaged 

17 over a three year period) either using years 2006, 2007, and 2008 ambient air quality data or for a 

18 second averaging period that extended from 2008 through 2010 (with results for each given by 

19 two separate lines on the same plot). Exposure results are readily observed for any air quality 

20 scenario, year, or benchmark level of interest. For example, when considering the 75ppb 

21 standard 2006-2008 averaging time scenario, 20% of asthmatic school-age children in Atlanta 

22 experience at least one daily maximum 8-hr average exposure of 60 ppb occurs when 

23 considering year 2006 air quality, while only about 5% experience exposures at or above the 

24 same benchmark level considering 2008 air quality (though when considering the 2008-2010 

25 averaging period, approximately 20% of asthmatic children are estimated experience at least one 

26 exposure at or 60 ppb-8hr). Fewer than 5% of asthmatic school-age children in Atlanta 

27 experience at least one benchmark exposure of 70 ppb-8hr considering any year and any air 

28 quality scenario, including just meeting the existing O3 standard.

29 Because APEX simulates the complete time series of exposure for every simulated 

30 individual, also output is the number of times an individual experiences a benchmark exceedance 

31 over the duration of the simulation (i.e., the entire O3 season simulated in each study area). These 

32 data can also be summarized in a similar multi-panel format, though differ slightly in 

33 composition from that of Figure 5-3. Instead of displaying the percent of persons with at least 

34 one exceedance of each of the three benchmarks, presented are the percent of persons with 

35 multiple exposures at or above a single benchmark within an O3 season. For example, Figure 5-4

36 illustrates the percent of asthmatic school-age children in Atlanta having multiple days where 
5-22

1 exposures (≥2, ≥4, and ≥6 per O3 season) were at or above 60 ppb-8hr considering the 2006-

2 2010 air quality adjusted to just meet the existing and alternative standards levels. When 

3 considering 2006 air quality adjusted to just meet the existing standard, approximately 10% of 

4 asthmatic school-age children experienced at least two days where their daily maximum 8-hr 

5 average exposure was at or above 60 ppb, though fewer than 5% experienced such exposures in 

6 2009. When collectively considering all simulated air quality scenarios and years, fewer than 3% 

7 of asthmatic school-age children experienced at least four exposures at or above 60 ppb and 

8 virtually no asthmatic school-age children experienced six or more such exposures over the O3

9 season.

10

11

12 Figure 5-3 Percent of asthmatic school-age children in Atlanta with at least one O3

13 exposure at or above 60 ppb-8hr (left top panel), 70 ppb-8hr (middle top panel), and 80 

14 ppb-8hr (right top panel while at moderate or greater exertion, years 2006-2010 air quality 

15 adjusted to just meet the existing and alternative O3 standard levels. The multi-panel 

16 display (bottom) illustrates the same exposure results expanded to reflect individual data 

17 points by year, standard averaging period, and benchmark level.

18

19 Also worth discussing is the appearance of a similar pattern between the benchmark level 

20 results (Figure 5-3) and the number of exceedances of a single benchmark (Figure 5-4). Because 

21 the ambient concentration is an important determinant in exposure concentrations, it is not 

22 surprising to see that the trend over years for persons having at least one exposure at or above a 

23 particular benchmark level (e.g., 60 ppb-8hr) is similar to those experiencing at least two 

24 exposures above 60 ppb-8hr (though a smaller percentage of persons). This is because years 

25 having the highest peak concentrations will yield the greatest percent of persons above 

26 benchmark levels, and when one year has a day with the highest concentration, it is likely that 

Exposure 

Benchmark 

(ppb)

% of Asthmatic Children (5-18)

60 ppb benchmark 70 ppb benchmark 80 ppb benchmark
5-23

1 year also has a second day with a similarly and relatively high concentration, and so on. Using 

2 the same logic, one might also conclude that there could be a pattern between the percent of 

3 persons experiencing a single exceedance of 70 ppb-8hr and multiple exceedances (e.g., four) of 

4 60 ppb-8hr also driven by the overall ambient concentration distribution. However, given that 

5 very few persons experience these types of benchmark exceedances, determining the relationship 

6 between the two (if present) may not be of practical significance. For brevity, the complete 

7 multiday exposure results for all APEX simulations are presented in Appendix 5F, with results 

8 presented for one study group (e.g., all school-age children) in the main body of the REA.

9

10

11 Figure 5-4 Percent of asthmatic school-age children in Atlanta with multiple O3 exposures

12 at or above 60 ppb-8hr while at moderate or greater exertion, years 2006-2010 air quality 

13 adjusted to just meet the existing and alternative O3 standard levels.

14

15 5.3 EXPOSURE ASSESSMENT RESULTS

16 5.3.1 Overview 

17 The results of the exposure analysis are presented as a series of figures focusing on the 

18 defined range of benchmark levels (i.e., persons experiencing daily maximum 8-hr average O3

19 exposure concentrations at or above 60 ppb, 70 ppb, and 80 ppb), noted as being of particular 

20 health concern (Section 5.2.8). A range of concentrations in the air quality data over the five year 

21 period (2006-2010) were used in the exposure model, providing a range of estimated exposures 

22 output by APEX. The adjusted air quality was developed using two distinct 3-year period design 

values (2006-2008 and 2008-2010), as described in Chapter 4.19 23 Exposures were estimated for 

24 four study groups of interest (i.e., all school-age children (5-18), asthmatic school-age children, 

25 asthmatic adults (19-95), and older adults (65-95)) in each of the 15 study areas. 

26 In this exposure assessment, we are primarily interested in O3 exposures associated with 

27 the ambient air quality adjusted to just meet the existing and potential alternative O3 standards.

 19 Thus, the year 2008 will have two sets of estimated exposures, one from each of the two sets of design values. In 

Figure 5-2, the greater temporal variability observed for 2008 is driven in part by differences in some study areas 

resulting from the air quality adjustment period. Exposure results for both 2008 averaging periods are provided 

when presenting data by year. Where mean results are presented in subsequent results sections, the two values 

given for year 2008 were first averaged to give a single exposure value for 2008 before averaging across all 

years.

Number of 

Exceedances 

Per O3 Season

≥ 2

≥ 4

≥ 6

% of Asthmatic Children (5-18)
5-24

1 Thus, most of the exposure results presented and discussed are for where ambient air quality was 

2 adjusted to just meet these particular scenarios. While understanding exposures and health risks 

3 associated with historical and existing air quality is important, the primary goal of this and any 

4 REA is to evaluate to what extent the existing NAAQS, and its associated air quality, protect 

5 health and to what extent alternative NAAQS protect health. Exposure results associated with 

6 recent (base) air quality are briefly discussed here first, though largely reported in Appendix 5F.

7 5.3.2 Exposure Modeling Results for Base Air Quality

8 The exposure results for the base air quality are distinguished from the other air quality 

9 scenario results primarily due to the wide ranging variability in estimated exposures across the 

10 study areas and years. The variability in exposures are the result of the wide ranging variability 

11 in ambient concentration levels, with perhaps some years in some study areas exhibiting air 

12 quality at or near that just meeting the current 8-hr standard, while other study areas and years 

13 exhibiting air quality levels much higher than the existing 8-hr standard. These exposures are 

14 informative in describing the existing or recent health risks associated with a unique air quality 

15 scenario, but because they variably diverge from a set concentration level of interest (such as the 

16 existing 8-hr standard), they are of limited relevance in evaluating the adequacy of either the 

17 existing NAAQS as well as potential alternative air quality standards. That said, detailed tabular 

18 and graphic presentations of exposure results associated with the base air quality (years 2006-

19 2010) are provided in Appendix 5F, with only key findings summarized in the following

20 discussion.

21 Consistent with the previously discussed observations regarding year-to-year variability 

22 in ambient concentrations (Chapter 4), most study areas have the greatest percent of all school23 age children experiencing concentrations at or above the three benchmark levels during 2006 or 

24 2007 along with having the lowest percent of all school-age children exposed during 2009. In 

25 general, between 20-40% of all school-age children experience at least one O3 exposure at or 

26 above 60 ppb-8hr, 10-20% experience at least one O3 exposure at or above 70 ppb-8hr, and 0-

27 10% experience at least one O3 exposure at or above 80 ppb-8hr, all while at moderate or greater 

exertion (i.e., an 8-hr EVR ≥ 13 L/min-m

2 28 ) and considering the base air quality (2006-2010).

29 Year-to-year variability observed for asthmatic school-age children and the percent of asthmatic 

30 school-age children were similar to exposure results for all school-age children, largely a 

31 function of having both simulated study groups using an identical time-location-activity diary 

32 pool to construct each simulated individual’s time series of activities performed and locations 

33 visited.

34 The overall year-to-year pattern of exposure for asthmatic adults is similar to that 

35 observed for all school-age children, though the percent of the asthmatic adult study group
5-25

1 exposed is lower by a factor of about three or more. Having a lower percent of asthmatic adults 

2 exposed is expected given that outdoor time expenditure is an important determinant of O3

3 exposure (section 5.4.2) and that adults spend less time outdoors than children (section 5.4.1), as 

4 well as adults having a lower outdoor participation rate. The percent of all older adults

5 experiencing exposures at or above the selected benchmark levels is lower by a fewer percentage 

6 points when compared with the results for asthmatic adults. Again, older adults, on average,

7 would tend to spend less time outdoors and do so with less frequency when compared with both 

8 adults and children (section 5.4.1), in addition to fewer older adults performing activities at 

9 moderate or greater exertion for extended periods of time, thus leading to fewer persons exposed 

10 to O3 concentrations of concern.

11 The year-to-year patterns of the single and multiple exposure occurrences considering 

12 base air quality (2006-2010) were similar among the four exposure study groups, therefore only 

13 results for all school-age children will be summarized here. Depending on the year and study 

14 area, about 10-25% of all school-age children could experience at least two exposures above the 

15 60 ppb-8hr benchmark during the O3 season, while about 5-10% school-age children could 

16 experience at least four. Most study areas and years are estimated to have fewer than 5% of all 

17 school-age children experience six or more exposures above 60 ppb-8hr considering the base air 

18 quality. When considering the multi-day exposures for all school-age children at or above the 70 

19 ppb-8hr benchmark, about 2-10% of all school-age children could experience at least two 

20 exposures during the O3 season, while four or more exposures were generally limited to fewer 

21 than 4% of all school-age children. Almost half of the study area-year combinations had no 

22 school-age children experiencing two or more exposures at or above the 80 ppb-8hr benchmark, 

23 with the other half estimated to have about 1% of all school-age children experiencing two or 

24 more exposures at or above the 80 ppb-8hr benchmark. School-age children having four or more 

25 80 ppb-8hr benchmark exceedances were limited to only a few study area years and, where a 

26 non-zero value was estimated, were limited to ≤ 0.5% of the study group.

27 5.3.3 Exposure Modeling Results for Simulations of Just Meeting Existing and 

28 Alternative O3 Standards

29 In this section, we present the exposures estimated when considering the air quality 

30 adjusted to just meeting the existing O3 NAAQS standard, as well as when considering potential 

31 alternative standard levels (55, 60, 65, 70 ppb 8-hr) of the existing standard. Comprehensive 

32 multi-panel displays of exposure results are presented for each of the study groups of interest, 

33 i.e., all school-age children (5-18), asthmatic school-age children, asthmatic adults (19-95), and 

34 all older adults (ages 65-95; Figure 5-5 to Figure 5-8, respectively). Included in each display are 

35 the three benchmark levels (60, 70, and 80 ppb-8hr), the five years of air quality (2006-2010), for 

36 the 15 study areas. A single multi-panel display is used to present the results for each of the four 
5-26

1 study groups, beginning with the estimated percent of persons exposed at least one time at or 

2 above the selected benchmark levels. Modeled exposures in the 15 study areas and considering 

3 each benchmark level are presented on the same scale to allow for direct comparisons across the 

4 multi-panel display. The most notable patterns in the exposure results are described here using 

5 one study group (i.e., all school-age children), as there is a general consistency in the year-to6 year variability within each study area across all four study groups. Any deviation from the 

7 observed pattern will be discussed for the subsequent study group.

8 We note that after adjusting to just meet a potential 8-hr ambient standard level of 55 

9 ppb, there were nearly no persons exposed at or above any of the selected benchmark levels, thus 

10 these data, while modeled, are not presented in detail here. In addition, in one study area 

11 (Chicago), O3 ambient monitor design values were below that of the existing standard during the 

12 2008-2010, therefore APEX simulations could not be performed for meeting the existing 

13 standard for that 3-year period. And finally, we were not able to simulate just meeting a standard 

14 level of 60 ppb-8hr or below in the New York study area (see Chapter 4 for details), thus APEX 

15 simulations for these air quality scenarios could not be performed in New York.

16 Figure 5-5 illustrates the exposures estimated for all school-age children in each study 

17 area with general observations as follows. After adjusting air quality to just meet the existing and 

18 alternative standards, there are virtually no school-age children exposed at or above 80 ppb-8hr, 

19 with very few school-age children exposed at or above the 70 ppb-8hr benchmark. For example, 

20 out of 87 possible study area and year combinations considering air quality adjusted to just meet 

21 the existing standard (the least stringent standard level considered here), only 29 resulted in >

22 0.1% estimated percent of all school-age children exposed at least once at or above the 80 ppb23 8hr benchmark with the maximum percent of all school-age children exposed estimated for St. 

24 Louis (1.1%). Ninety-four percent of study area and year combinations had fewer than 5% of all 

25 school-age children experiencing at least one daily maximum 8-hr average exposure ≥ 70 ppb

26 considering ambient air quality adjusted to just meeting the existing standard, again with a 

27 maximum of 8.1% occurring in St. Louis. When considering air quality adjusted to just meet an 

28 8-hr ambient standard level of 70 ppb, ≤ 0.2% of all school-age children experience at least one

29 80 ppb-8hr exposure benchmark exceedance for all study area and year combinations, while for 

30 76 or 90 study area and year combinations, ≤ 1% of all school-age children experience a 70 ppb31 8-hr exposure benchmark exceedance. This pattern of having very few school-age children 

32 experiencing exposures at or above 70 and 80 ppb-8hr is as expected given the nature of the air 

33 quality adjustment procedure that limits 8-hr ambient concentrations at or above the selected 

34 potential alternative standard level.

35 In contrast, approximately 10-20% percent of all school-age children are estimated to be

36 exposed to at least one 60 ppb-8hr concentration when considering air quality just meeting the 
5-27

1 existing standard (Figure 5-5). And similar to that mentioned above regarding exposures 

2 associated with the base air quality, a general year-to-year exposure pattern emerges with respect 

3 to study area and year. For the Northeastern (Boston, New York), Mid-Atlantic (Philadelphia, 

4 Washington DC, Cleveland) and Mid-Western (Chicago, Detroit, and St. Louis) study areas, the 

5 maximum percent of all school-age children exposed generally occurs during year 2007. For the 

6 Southern (Atlanta, Dallas, Houston) and Western (Denver, Los Angeles, Sacramento) study 

7 areas, the maximum exposure occurs during year 2006. Deviations from this temporal exposure 

8 pattern appear mostly as a result of the standard averaging period, with the 2008-2010 period

9 producing equal or greater maximum exposures during either 2008, 2010, or both years and most 

10 prevalent in the Northeastern and Mid-Atlantic study areas (Baltimore, Boston, New York, 

11 Philadelphia, Washington DC; note also a trend in Atlanta, Denver, St. Louis).

12 These 60 ppb-8hr exposure patterns remain consistent when considering air quality 

13 adjusted to just meet a 70 ppb-8hr ambient standard, though the percent of all school-age 

14 children exposed is less than that observed when considering the air quality adjusted to just meet 

15 existing standard. Further, 75 of 90 study area and year combinations are estimated to have ≤

16 10% of all school-age children experience a 60 ppb-8hr or greater exposure, though between 10-

17 20% of all school-age children were estimated to be exposed for a few study area and year 

18 combinations (e.g., Atlanta-2006, Chicago-2007 and -2010, and Houston-2009). When 

19 considering air quality adjusted to just meet a 65 ppb standard level, the percent of all school-age 

20 children experiencing an exposure at or above 60 ppb-8hr diminishes to 5% or less for most 

21 study areas and years (i.e., 81 of 90 study area year combinations).

22 All of what has been described regarding the estimated exposures to school-age children 

23 (i.e., the year-to-year and benchmark level patterns, and the percent of the study group exposed) 

24 also applies to the exposures estimated for asthmatic school-age children (Figure 5-6). Different 

25 however would be the relative number of asthmatic school-age children exposed in each study 

26 area if compared with all school-age children, as the asthma prevalence rates vary by study area

27 (Table 5-2), though on average are about 10% of the population of children.

28 The percent of asthmatic adults (Figure 5-7) experiencing daily maximum 8-hr average 

29 exposures above the selected benchmark levels is sharply lower than that estimated for all 

30 school-age children. For example, only three of a possible 84 study area and year combinations 

31 (Chicago-2007, Houston-2009, and St. Louis-2007) were estimated have > 0.1% of asthmatic 

32 adults experience a daily maximum 8-hr average exposure ≥ 80 ppb, and only six of a possible 

33 84 study area and year combinations were estimated have >1% of asthmatic adults experience an 

34 daily maximum 8-hr average exposure ≥ 70 ppb, all occurring when considering air quality just 

35 meeting the existing standard. No study area or year combination has more than 10% of 

36 asthmatic adults estimated to experience an exposure at or above 60 ppb-8hr when considering 
5-28

1 air quality just meeting the existing standard, with 67 of 84 study area and year combinations 

2 estimated to have 5% or less asthmatic adults experiencing such exposures.

3 When considering air quality adjusted to just meeting a standard level of 70 ppb-8hr, no 

4 asthmatic adults experience an exposure at or above 80 ppb-8hr and ≤ 0.6% experience a daily 

5 maximum 8-hr average exposure ≥ 70 ppb for any study area or year combination. Less than 5% 

6 of asthmatic adults could experience an exposure at or above 60 ppb-8hr when considering air 

7 quality adjusted to just meet a standard level of 70 ppb-8hr for 88 or 90 possible study area year 

8 combinations, with the maximum percent of adult asthmatics exposed outside this range

9 occurring in Denver (6.8%-2008) and St. Louis (5.5%-2007).

10 Older adults are estimated to have the fewest exposures above the two highest benchmark 

11 levels when considering the adjusted air quality. For example, only two of a possible 84 study 

12 area and year combinations (St. Louis-2007 and Washington DC-2008) were estimated have >

13 0.1% of asthmatic adults experience a daily maximum 8-hr average exposure ≥ 80 ppb, and only 

14 six of a possible 84 study area and year combinations were estimated have > 1% of asthmatic 

15 adults experience a daily maximum 8-hr average exposure ≥ 70 ppb, all occurring when 

16 considering air quality just meeting the existing standard (Figure 5-8). Also, exceeding the 60 

17 ppb-8hr exposure benchmark appears to be limited to fewer than 5% of all older adults when 

18 considering air quality adjusted to just meet the existing standard and a standard level of 70 ppb19 8hr, and occurs in < 2% of all older adults when considering a standard level of 65 ppb-8hr.

20 An example of multi-day exposure results associated with adjusted air quality is provided 

21 in Figure 5-9. The percent of all school-age children estimated to experience multi-day exposures 

22 above benchmark levels during each study area’s O3 season is largely limited to two air quality 

23 scenarios: the existing standard and air quality adjusted to just meeting a standard level of 70 

24 ppb-8hr. This is because of the small percent of school-age children experiencing even a single 

25 exposure above the lowest benchmark level when considering standard levels at or below 65 

26 ppb-8hr. In addition, when experiencing multiple exposures, most school-age children appear to 

27 have at most two days above benchmark levels per O3 season, even when considering the lowest 

28 benchmark level of 60 ppb-8hr. For example, 81 of 87 possible study area and year combinations 

29 have < 10% of all school-age children experiencing two or more exposures ≥ 60 ppb-8hr when 

30 considering an ambient standard level of 75 ppb-8hr, while 83 of 90 possible study area and year 

31 combinations have < 5% of all school-age children experiencing two or more exposures ≥ 60 

32 ppb-8hr when considering an ambient standard level of 70 ppb-8hr. With increasing stringency 

33 in the standard level to 65 ppb-8hr, 81 of 90 possible study area and year combinations have <

34 1% of all school-age children experiencing two or more exposures ≥ 60 ppb-8hr.

35 Multi-day exposure to the higher exposure benchmarks (either the 70 or 80 ppb-8hr) is a 

36 rare occurrence, even when considering the air quality adjusted to the existing O3 standard. For 
5-29

1 example, there were no school-age children experiencing two or more exposures above 80 ppb2 8hr in all but one study area year combination and, and when considering that one study year 

3 having a non-zero value (St. Louis-2007), the estimated percent of all school-age children at or 

4 above the exposure benchmark was only 0.1%. Further, 83 of 87 possible study area and year 

5 combinations have < 1% of all school-age children experiencing two or more exposures ≥ 70 

6 ppb-8hr, also when considering an ambient standard level of 75 ppb-8hr.

7

8
5-30

1

2 Figure 5-5 Percent of all school-age children with at least one daily maximum 8-hr 

3 average O3 exposure at or above 60, 70, and 80 ppb while at moderate or greater 

4 exertion, years 2006-2010, air quality adjusted to just meet the existing and potential 

5 alternative standards.
5-31

1

2 Figure 5-6 Percent of asthmatic school-age children with at least one daily 

3 maximum 8-hr average O3 exposure at or above 60, 70, and 80 ppb while at 

4 moderate or greater exertion, years 2006-2010, air quality adjusted to just meet the 

5 existing and potential alternative standards.
5-32

1

2 Figure 5-7 Percent of all asthmatic adults with at least one daily maximum 8-hr 

3 average O3 exposure at or above 60, 70, and 80 ppb-8hr while at moderate or 

4 greater exertion, years 2006-2010, air quality adjusted to just meet the existing and 

5 potential alternative standards.
5-33

1

2 Figure 5-8 Percent of all older adults with at least one daily maximum 8-hr average 

3 O3 exposure at or above 60, 70, and 80 ppb-8hr while at moderate or greater 

4 exertion, years 2006-2010, air quality adjusted to just meet the existing and potential 

5 alternative standards.
5-34

1

2 Figure 5-9 Percent of all school-age children with multiple daily maximum 8-hr 

3 average O3 exposures at or above 60 ppb while at moderate or greater exertion, 

4 years 2006-2010, air quality adjusted to just meet the existing and potential 

5 alternative standards.

6

Number of 

Exceedances 

Per O3 Season

≥ 2

≥ 4

≥ 6
5-35

1 5.4 TARGETED EVALUATION OF EXPOSURE MODEL INPUT AND OUTPUT 

2 DATA 

3 This section summarizes the results of several targeted evaluations intended to provide 

4 additional insights to APEX input data or approaches used to estimate exposures (CHAD data 

5 attributes and activity pattern evaluations, comparison of CHAD outdoor time data with ATUS, 

6 comparisons of asthmatic outdoor time expenditure and exertion levels to that of non7 asthmatics), exposure results for additional exposure populations of interest (outdoor workers, 

8 school-age children during summers, impact of averting), and model performance evaluations 

9 (personal exposure measurements and independent ventilation rate estimates compared with 

10 APEX estimates). Detailed analysis results are provided in Appendix 5G.

11 5.4.1 ANALYSIS OF TIME-LOCATON-ACTIVITY DATA

12 While CHAD is the most comprehensive and relevant source of time-location-activity 

13 data available for use in our exposure modeling, there are a few limitations to the survey data

14 contained therein, many of which are founded in the individual studies from which activity 

15 patterns were derived (Graham and McCurdy, 2004). CHAD is a collection of related survey 

16 data, though individual study attributes can range widely (e.g., survey participant ages, region or 

17 city of residence, time-of-year data collected). We note that many of the assumptions about use 

18 of these activity patterns in exposure modeling are strengthened by the manner in which they are 

19 used by APEX. This is done by focusing on selecting the most important individual attributes 

20 that contribute to variability in human behavior (e.g., age, sex, day-of-week, ambient 

21 temperature) and linking these attributes of simulated individuals to the population demographics 

22 of each census tract (see section 5.2.5) and the study area temperatures (section 5.2.4). Further, 

23 one key lifestyle attribute is also accounted for in generating longitudinal diary profiles by 

24 simulating both the intra- and interpersonal variability in time spent outdoors (section 5.2.5; Glen 

25 et al., 2008).

26 A few questions may arise as to the representativeness of the CHAD diaries to the 

27 simulated population. For example, the year of a particular survey study may differ from our 

28 simulated exposure population by as much as 30 years (i.e., some activity pattern data were 

29 generated in the 1980s). In addition, there are other personal attributes (e.g., ethnicity, income 

level, lifestyle factors20 30 ), health conditions (e.g., asthma, cardiovascular disease), and situational 

31 factors (e.g., availability of parks and recreation areas) that are not used in creating the simulated 

32 persons that could be influential in estimating exposures. Considering this, a number of 

 20 Examples of such factors for adults could include married/unmarried, having infants or young children/no 

children. Lifestyle factors for children could include whether the child is active/non-active or whether or not 

there is time spent outdoors. 
5-36

1 evaluations were performed to answer questions regarding important personal attributes used in 

2 generating simulated individuals and the general representativeness of the CHAD time-location3 activity data. First though, we summarize the newly acquired activity pattern data now included 

in CHAD compared with data available and used in the 1st 4 draft O3 REA.

5 5.4.1.1 General Evaluation of CHAD Study Data: Historical and Recently Acquired Data

The number of diary days having complete information and used by APEX in the 2nd 6

7 draft O3 REA is 41,474 (Table 5-3). This is an increase of about 8,700 diaries currently used by 

APEX compared with what was used by APEX in the 1st 8 Draft O3 REA. Further, there have been 

9 eight new study data sets incorporated into CHAD and used in our current exposure assessment 

10 since the previous O3 NAAQS review conducted in 2007, most of which were from recently 

11 conducted activity pattern studies (see Appendix 5B, Section 5B-4 for more information 

12 regarding these studies). The diary data included from these new studies have more than doubled 

13 the total activity pattern data used for 2007 O3 exposure modeling and has increased the number 

14 of children’s diaries by about a factor of five. Currently, the majority of diaries (54%) from 

15 CHAD are taken from surveys conducted in the past decade, while the pre-1990s diaries 

16 represent less than 15% of the total diaries available by APEX.

17 5.4.1.2 Exposure-Relevant Personal Attributes Included in CHAD and APEX Simulated 

18 Individuals

19 The survey participants whose diary data are within CHAD were asked a number of 

20 questions regarding their personal attributes. The number and type of attributes present for 

21 diaries in CHAD is driven largely by the original intent of the individual study. In our exposure 

22 assessment, we have strict requirements to simulate individuals using several personal attributes, 

23 namely age, sex, temperature (as a surrogate for seasonal variation in activity patterns), and day24 of-week. These attributes are considered as important drivers influencing daily activity patterns 

25 (Graham and McCurdy, 2004) and when diaries do not have these particular attributes for a 

26 particular day, the diary day will not be used by APEX. We compared the representation of these 

and other attributes in the current CHAD used by APEX with that in the 1st 27 draft O3 REA and 

28 found strong similarities in the attribute distributions between both databases, suggesting little 

29 change in the overall composition of the database regarding these influential attributes.

30 While there may be other personal or situational attributes that affect daily time 

31 expenditure (e.g., socioeconomic status, occupation of an employed person), these attributes are 

32 typically not included in our assessment to generate simulated individuals simply because the 

33 response to the attribute is missing for most of the study participants/CHAD diary days. For 

34 example, income level is missing for about two-thirds of the CHAD diaries because either the 

35 original study did not have an income/occupation related survey question or perhaps the 

36 participant refused to answer the question if it were posed. If one were to select this personal 
5-37

1 attribute in developing a simulated individual’s activity pattern (among using any other attribute 

2 having missing responses), the pool of diaries available to simulate individuals may be extremely 

3 limited, likely leading to repetition of diaries used for individuals or groups of similar individuals 

4 and artificially reducing both intra- and inter-personal variability in time expenditure, or perhaps 

5 resulting in model simulation failure altogether. This is why personal attributes are carefully 

6 selected and prioritized according to both their prevalence in CHAD and whether the attribute 

7 has a known significant influence on activity patterns.

8 5.4.1.3 Evaluation of Afternoon Time Spent Outdoors for CHAD and Survey Participants

9 There have been questions raised regarding the representativeness of the diaries from 

10 studies conducted in the 1980s and whether there are any recognizable patterns in time 

11 expenditure in the CHAD diaries across the time period when data were collected. Because time 

12 spent outdoors is a significant factor influencing daily maximum 8-hr average O3 exposures, we

13 evaluated the current collection of CHAD diaries used by APEX for two metrics and considering 

14 two dimensions: outdoor participation rate (i.e., the percent of people who spent some time 

15 outdoors during their survey day) and the mean time spent outdoors for where the persons spent

16 at least one minute outdoors or at least 2 hours outdoors. Because time spent outdoors is an 

17 important determinant for highly exposed individuals, we summarize the results here for the 

18 diaries having at least 2 hours of outdoor time here, while all other results are provided in 

19 Appendix 5G. CHAD diaries were stratified by five age groups (4-18, 19-34, 35-50, 51-64, 65+) 

20 and three decades (1980s, 1990s, and 2000s) using the year the particular activity pattern study 

21 was conducted. We note that CHAD is composed of primarily cross-sectional data (single diary 

22 days per person), thus the trend evaluated over the three decades is changes (if any) in 

23 participation rate and the time spent outdoors by the composite study population, not within 

24 individuals.

25 Regardless of decade and duration of time spent outdoors, children tended to have the 

26 highest outdoor participation rate when compared with the other age groups, while the oldest 

27 adults (aged 65 or greater) tend to have the lowest participation rate. The CHAD diaries from the 

28 1980’s studies for children ages 4-18 have the highest outdoor participation rate (50%) compared 

29 to other decades (35-40%) and all other age groups and decade of collection. When considering 

30 the pool of diaries available for this age group, these 1980’s studies contribute to approximately

31 19% of diaries having two or more hours of time spent outdoors during the afternoon. This 

32 translates to a small effect on the overall outdoor participation rate for diary pools that would 

33 include these earlier studies (39% participation rate) compared to the participation rate excluding 

34 these studies (36% participation rate). In general, these outdoor participation rates are similar to 

35 the finding reported recently by Marino et al. (2012) of 37.5%, though estimated for pre-school 

36 age children. Thus, when considering participation in outdoor activities and the 
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1 representativeness of the CHAD study data from the 1980s, it is unlikely that use of these oldest

2 diaries would strongly influence exposure model estimates.

3 There is variability in the amount of outdoor time evaluated over the three decades, with 

4 diaries from the 2000’s studies exhibiting perhaps the lowest range of mean outdoor time (190-

5 220 min/day) compared with the 1980’s (210-240 min/day) and 1990’s (212-258 min/day)

6 studies, a trend perhaps most notable when considering the children’s diaries (a decrease in time 

7 spent outdoors of about 30 minutes over the three decades). However, the coefficient of variation 

8 (COV) for each of the age groups and across all decades for the cross-sectional data was 

9 consistently about 40%, supporting a general conclusion of no appreciable differences in the 

10 mean time spent outdoors over the three decades of data collection. Thus, when considering all 

11 diaries having at least 2 hours of afternoon outdoors time and the representativeness of the 

12 CHAD study data from the 1980s, inclusion of these earlier diaries is also unlikely to have a

13 strong adverse influence on exposure modeling outcomes. Though combined with the higher 

14 participation rate for these earlier diaries, exposures estimated using these diaries may be higher 

15 than when estimated when excluding these diaries from CHAD.

16 5.4.1.4 Evaluation of Afternoon Time Spent Outdoors for ATUS Survey Participants

17 We evaluated recent year (2002-2011) time expenditure data from the American Time 

18 Use Survey (ATUS) (US BLS, 2012). As was done with the CHAD data set, the purpose was to 

19 evaluate trends (if any) in outdoor time over the period of time data were collected. A few 

20 strengths of the ATUS data are (1) its recent and ongoing data collection efforts, (2) large sample 

21 size (totaling over 120,000 diary days), (3) national representativeness, and (4) that varying diary 

22 approaches would not be an influential or confounding factor in evaluating trends over time.

23 ATUS does however have a few noteworthy limitations when compared with the CHAD 

24 data: (1) there are no survey participants under 15 years of age, (2) time spent at home locations 

25 is neither distinguished as indoors or outdoors, and (3) missing or unknown location data can 

26 comprise a significant portion of a persons’ day (on average, about 40% (George and McCurdy, 

27 2009)). To overcome the limitation afforded by the ambiguous home location, we identified 

28 particular activity codes most likely to occur outdoors (e.g., participation in a sport) to better 

29 approximate each ATUS individual’s outdoor time expenditure. Missing time was circumvented 

30 by our focused analysis: about 85% of missing time information occurs outside of the hours of 

31 interest here (i.e., before 12:00 PM and after 8:00 PM). Data were stratified by the same five age 

32 groups as was done for the CHAD data, though here the time trends were assessed over 

33 individual survey years.

34 When considering person-days having at least 2 hours of time spent outdoors, there were 

35 no clear trends over the nine year ATUS study period regarding either the participation rate or 

36 the mean time spent outdoors for any of the age groups. Consistent with CHAD, the participation 
5-39

1 rate of children was greater than that of the other age groups. The range in ATUS diary outdoor 

2 participation rate (10-20%) for all age groups is lower than that observed for the CHAD data 

3 (generally between 20-40%), while the range in mean time spent outdoors (190-240 minutes per 

4 day) was similar to that of the CHAD data. The lower participation rate for ATUS participants is 

5 not surprising given the lack of distinction regarding time indoors and outdoors while at home 

6 for ATUS participants and possibly influenced in part by not having any activity patterns for 

7 children under 15 years old. Overall, results of the ATUS data analysis generally support the 

8 representativeness of the CHAD data, and while participation in outdoor activities calculated 

9 using ATUS diaries was less than CHAD diaries, ATUS survey methods obfuscate the strength 

10 of this finding.

11 5.4.1.5 Evaluation of Outdoor Time and Exertion Level for Asthmatics and Non-Asthmatics in 

12 CHAD

13 Due to limited number of CHAD diaries with survey requested health information, all 

14 CHAD diaries are assumed appropriate for any APEX simulated individual (i.e., whether 

15 asthmatic, non-asthmatic, or no compromising health condition was indicated), provided they 

16 concur with age, sex, temperature, and day-of-week selection criteria. In general, the assumption 

17 of modeling asthmatics similarly to healthy individuals (i.e., using the same time-location18 activity profiles) is supported by the activity analyses reported by van Gent et al. (2007) and

19 Santuz et al. (1997), though other researchers, for example, Ford et al., (2003), have shown 

20 significantly lower leisure time activity levels in asthmatics when compared with persons who 

21 have never had asthma. To provide additional support to the assumption that any CHAD diary 

22 day can be used to represent the asthmatic population regardless of the study participants’ 

23 characterization of having asthma or not, we first compared participation in afternoon outdoor 

24 activities at elevated exertion levels among asthmatic, non-asthmatic, and unknown health status 

25 using the CHAD diaries. We then compared compatible CHAD diary days with literature 

26 reported outdoor time participation at varying activity levels.

27 In the first comparison, participation in afternoon outdoor activities for non-asthmatic

28 children and adults in CHAD were found similar when compared with their respective asthmatic 

29 cohorts (both about 40-50%). Outdoor participation rate for persons having unknown asthma

30 status, a smaller fraction of the total diaries, varied ±10% from that having known asthma status

31 (children were higher, adults were lower). The amount of time spent outdoors by the persons that 

32 did so varied little across the two populations and three asthma categories. On average, CHAD 

33 diaries from children indicate approximately 21⁄4 hours of afternoon time is spent outdoors, 80% 

34 of which is at a moderate or greater exertion level, again regardless of their asthma status, known 

35 or unknown. Slightly less afternoon time is spent outdoors by adults when compared with 

36 children, and while their participation in moderate or greater exertion level activities is much less 
5-40

1 (about 63%), there was little difference between asthmatic adults and non-asthmatic adults

2 considering outdoor time or percent at moderate or greater exertion. 

3 For the second comparison, the percentage of waking hours outdoors at varying activity 

4 levels for asthmatics reported in three independent asthma activity pattern studies (Shamoo et al.,

5 1994; EPRI, 1988; EPRI 1992) were compared to CHAD diary days having similar personal 

6 attributes and stratified by asthma status. The range in the percent of waking hours outside at 

7 moderate activity level for CHAD diaries was similar to that estimated using the three 

8 independent literature sources (2-10%), however the range in percent of outdoor time associated 

9 with strenuous activities using the CHAD asthmatic diaries extends beyond that of asthmatic 

10 persons from the three independent studies by about a factor of two higher. At this time, the 

11 reason for this difference is unknown. Overall, given the above mentioned similarities in outdoor 

12 time, participation, and activity levels, use of a CHAD diary regardless of a persons’ asthma 

13 condition is reasonably justified based on the available data analyzed.

14 5.4.2 Characterization of Factors Influencing High Exposures

15 We investigated the factors that influence persons experiencing the highest daily 

16 maximum 8-hr average exposures. These exposure results in six selected study areas, Atlanta, 

17 Boston, Denver, Houston, Philadelphia, and Sacramento, considering base air quality and air 

18 quality just meeting the existing standard were combined with each simulated individual’s 

19 microenvironmental time expenditure during the afternoon hours (12:00 PM through 8:00 PM), 

20 times of day commonly when daily peak high O3 concentrations occur. We first evaluated the 

relative contribution seven variables21 21 had on the total explained variability in daily maximum 8-

22 hr average exposures. We then evaluated the distribution of identified influential variables for 

23 simulated individuals with the highest exposures. And finally, we identified the 

24 microenvironmental locations highly exposed persons occupied and the activities performed 

25 within them, given that within an 8-hr time frame most persons would likely visit multiple 

26 locations and perform different activities.

27 When considering only person days having the highest daily maximum 8-hr average O3

28 exposures at any of the six study areas and either air quality scenario and age groupings, 

29 collectively the main effects of ambient concentrations and outdoor time combined with their 

30 interaction similarly contribute to approximately 80% of the total explained variance results, 

31 suggesting that for highly exposed persons, the most important influential factors are time spent 

32 outdoors corresponding with high daily maximum 8-hr average ambient O3 concentrations.

 21 The seven variables include the main effects of (1) daily maximum 8-hr ambient O3, (2-4) afternoon time spent 

outdoors, near-roads, and inside vehicles, and (5) physical activity index (PAI), while also including interaction 

effects from (6) afternoon time outdoors by daily maximum 8-hr ambient concentration and (7) PAI by afternoon 

time outdoors.
5-41

1 The distributions of afternoon outdoor time and ambient concentration for highly exposed 

2 individuals were evaluated considering base air quality and air quality adjusted to just meeting 

3 the existing standard. As an example, exposure results in Boston indicated that for about half of 

4 the days, simulated school-age children experiencing high exposures spend about 240 minutes 

5 outdoors during the afternoon hours along with experiencing daily maximum 8-hr average 

6 ambient O3 concentrations ≥ 75 ppb. In contrast when adjusting ambient concentrations to just 

7 meeting the existing standard, for about half of the days, simulated school-age children

8 experiencing similar high exposures need to spend about 280 minutes outdoors during the 

9 afternoon hours along with experiencing daily maximum 8-hr average ambient O3 concentrations 

10 ≥ 60 ppb. Simply put, under conditions of lower ambient concentrations, persons need to spend a 

11 significantly greater amount of time outdoors to experience similar exposures observed at higher 

12 ambient concentration conditions.

13 When considering these highly exposed children, on average about half of children’s total 

14 afternoon time is spent outdoors on high exposure days, 40% is spent indoors, while only 10% of 

15 time is spent near-roads or inside motor vehicles. In general, greater than half of the time highly 

16 exposed children spent outdoors specifically involves performing a moderate or greater exertion 

17 level activity, such as a sporting activity. While apportionment of afternoon microenvironmental 

18 time was similar for highly exposed adults in other age groups considered (e.g., 19-35), 

19 important high exertion activities performed outdoors also included those associated with paid 

20 work and performing chores.

21 5.4.3 Exposure Results for Additional At-Risk Populations and Lifestages, Exposure 

22 Scenarios, and Air Quality Input Data Used

23 5.4.3.1 Exposures Estimated for All School-age Children During Summer Months, Neither 

24 Attending School or Performing Paid Work

25 As mentioned earlier in describing the longitudinal approach used in the main body of the

26 exposure assessment, the sequence of activity diaries for all simulated individuals is determined 

27 by a user-selected profile variable of interest. In this assessment our longitudinal diary approach 

28 uses time spent outdoors to link together CHAD diary days, an attempt to appropriately balance 

29 intra- and inter-personal variability in that variable. For the primary exposure results, all 

30 available diaries were used in developing any one sample pool without restriction outside of the 

31 particular characteristics on interest in developing the pool (i.e., age, sex, day-of-week, 

32 temperature, time spent outdoors). In this targeted simulation in Detroit during three summer 

33 months of 2007 (June, July, and August), we restricted the diary pool of all school-age children 

34 to include only those diary days that did not have any time spent inside a school nor had time 

35 spent performing paid work during any day of the week. The results of this targeted simulation 

36 were compared to an identical simulation, only differing in that all CHAD diary days were used 
5-42

1 i.e., including any diary day for persons having school time or paid work, and as was done for 

2 the main body of this exposure assessment.

3 Figure 5-10 indicates that when restricting the CHAD diary pool to include only those 

4 diaries having no time spent at school or performing paid work activities, there is about 1/3 or 

5 33% increase in the number of all school-age children at or above the 60 ppb-8hr benchmark, a 

6 relationship also consistent across the alternative standards and when considering multiple 

7 exposures. A similar relationship was found for the other benchmarks (not shown, see Appendix 

8 5-G). Clearly, based on the analysis results reported in section 5.4.2 regarding factors influencing 

9 those highly exposed, using only activity pattern data that do not include school or work-related 

10 events (which would likely occur more so indoors than outdoors) and sampling from a pool of 

11 diaries consistent with summer temperatures would increase the likelihood simulated individuals 

12 spend time outdoors and be exposed to concentrations at or above the selected benchmarks.

13

14 Figure 5-10 Comparison of the percent of all school-age children having daily maximum 8-

15 hr average O3 concentration at or above 60 ppb during June, July, and August in Detroit 

16 2007: using any available CHAD diary (“All CHAD Diaries”) or using CHAD diaries 

17 having no time spent in school or performing paid work (“No School/Work Diaries”).

standard level (ppb) 60 65 70 75

>= 3 Exposures-NoSchool/Work Diaries

>= 3 Exposures-All CHAD Diaries

>= 2 Exposures-NoSchool/Work Diaries

>= 2 Exposures-All CHAD Diaries

>= 1 Exposure-NoSchool/Work Diaries

>= 1 Exposure-All CHAD Diaries

Percent of Children with 8-hr Daily Max Exposure > 60 ppb

0% 2% 4% 6% 8% 10% 12% 14% 16% 18% 20%
5-43

1 5.4.3.2 Exposures Estimated for Outdoor Workers During Summer Months

2 A targeted APEX simulation was performed for the Atlanta study area to simulate 

3 summertime exposures for two hypothetical outdoor worker study groups, persons between the 

4 age 19-35 and 36-55, using 2006 air quality just meeting the existing standard. To do this, both 

5 the daily and longitudinal activity patterns used by APEX were adjusted to best reflect patterns 

6 expected for outdoor workers (e.g., a standardized work schedule during weekdays) while also 

7 maintaining variability in those patterns across various occupation types. Briefly, the distribution 

8 of all employed persons’ occupations was estimated using data provided by the U.S. Bureau of 

Labor and Statistics (US BLS, 2012b)22 9 and linked with 144 occupation titles from the 

Occupational Information Network (O*NET)23 10 identified as having one or more days per week 

11 where paid work was performed outdoors. These data were then aggregated to twelve broadly 

12 defined BLS occupation groups, generating a data set containing the number of days per week 

13 work time would be performed outdoors by that occupation group and properly weighted to 

14 reflect the population distribution of persons employed in each outdoor work group. Then, 

15 existing CHAD diary days reflecting outdoor paid work were identified, isolated and replicated 

16 to reflect this BLS/O*NET outdoor participation rate and occupation group frequencies. A 

17 10,000 person simulation was performed by APEX using this adjusted CHAD activity pattern 

18 database designed to simulate outdoor workers and compared with exposure results generated 

19 from an identical APEX simulation of all employed persons, though differing by using the 

20 standard CHAD database and population-based modeling approach used in the main body REA. 

21 Details regarding the development of CHAD activity patterns used as input to simulate outdoor 

22 workers, as well as other settings and conditions for APEX is described in Appendix 5G.

23 Estimated exposures are presented in Figure 5-11 for one of two age study groups 

24 investigated (results for both age groups were similar) and considering either a longitudinal 

25 approach designed specifically to reflect an outdoor worker weekday schedule (left panel) or 

26 when using our general population-based modeling approach (right panel). The results indicate 

27 that when accounting for a structured schedule that includes repeated occurrences of time spent 

28 outdoors for a specified study group, all while simulated individuals are likely to be more 

29 consistently performing work tasks that may be at or above moderate or greater exertion levels, 

30 there are a greater percent of the study group experiences exposures at or above the selected 

31 health effect benchmark levels than that estimated using our general population-based modeling 

32 approach. Keep in mind outdoor workers are expected to experience more exposures at or above 

33 benchmark levels, though represent a fraction of the total employed population. It is possible 

 22 U.S. employment data by SOC codes were obtained from: http://www.bls.gov/emp/#tables: Table 1.2 

Employment by occupation, 2010 and projected 2020. 23 Additional information is available at http://www.onetonline.org.
5-44

1 that, in using the general population-based approach along with the longitudinal algorithm that 

2 accounts for within and between variability in outdoor time, a number of outdoor workers are 

3 incidentally simulated and represent a significant portion of those who experienced exposures at 

or above benchmark levels.

24 4 However, the differences between exposures estimated for the two 

5 longitudinal approaches become much greater when considering the percent of persons 

6 experiencing multiple exposure days at or above benchmark levels, primarily when considering 

7 the 60 ppb-8hr benchmark level. For example, ≤ 2% of the general population-based exposure 

8 group was estimated to have two or more exposures at or above 60 ppb-8hr, while >17% of 

9 specifically simulated outdoor workers were estimated to experience exposures at or above that 

10 same level.

 24 In this outdoor worker exposure scenario, approximately 30% of our outdoor worker study group ages 19-55 were 

estimated to experience at least one exposure at or above 60 ppb-8hr while at moderate or greater exertion. 

Assuming outdoor workers constitute approximately 12% of the workforce (Appendix G, Table 5G-8), outdoor 

workers experiencing at least one exposure at or above 60 ppb-8hr could contribute 3.6% to a total exposed 

population (i.e., outdoor and non-outdoor workers). For the same air quality scenario and using the general 

population-based approach, we estimated 5-8% of a total employed study group (incidentally comprised of 

outdoor and non-outdoor workers) would experience exposures at or above the same benchmark, suggesting 

between 48-75% of persons experiencing exposures above the 60 ppb benchmark have similar activity pattern 

characteristics as outdoor workers.
5-45

1 Figure 5-11 Percent of workers between ages 19-35 experiencing exposures at or above 

2 selected benchmark levels while at moderate or greater exertion using an outdoor worker 

3 approach (left panel) and a general population-based approach (right panel) considering 

4 air quality adjusted to just meet the existing standard in Atlanta, GA, Jun-Aug, 2006.

5

6 5.4.3.3 Exposures Estimated for All School-age Children When Accounting for Averting 

7 Behavior

8 A growing area of air pollution research involves evaluating the actions persons might 

9 perform in response to high O3 concentration days (ISA, section 4.1.1). Most commonly termed 

10 averting behaviors, they can be broadly characterized as personal activities that either reduce 

11 pollutant emissions or limit personal exposure levels. The latter topic is of particular interest in 

12 this REA due to the potential negative impact it could have on O3 concentration-response (C-R) 

13 functions used to estimate health risk and on time expenditure and activity exertion levels 

14 recorded in the CHAD diaries used by APEX to estimate O3 exposures. To this end, we have 

15 performed an additional review of the available literature here beyond that summarized in the 

16 ISA to include several recent technical reports that collected and/or evaluated averting behavior 

17 data (Graham, 2012). The purpose was to generate a few reasonable quantitative approximations 

18 that allow us to better understand how averting behavior might affect time-location-activity 

19 patterns, and then simulate how such personal adjustments might affect our population exposure 

20 estimates.

21 Based on the elements evaluated in our literature review (i.e., air pollution awareness,

22 prevalence and duration of averting response), we conclude that most people are aware of alert 

23 notification systems (in particular those persons having compromised health and reside in an 

24 urban area). We approximate that 30% of all asthmatics (or 15% of the general population) may 

25 reduce their outdoor activity level on alert days (e.g., KS DOH, 2006; McDermott et al., 2006; 

26 Wen et al., 2009; Zivin and Neidell, 2009) and that outdoor time/exertion during afternoon hours 

27 may be reduced by about 20-40 minutes in response to an air quality alert notification

0%

5%

10%

15%

20%

25%

30%

1 2 3 4 5 6

Percent of Population Experiencing Exposures At or 

Above Selected Benchmark Level While at 

Moderate or Greater Exertion

Number of Times Benchmark Exceeded (June-August 2006)

Outdoor Worker Scenario-based Approach (ages 19-35)

Atlanta, 2006, Just Meet Existing 75 ppb Standard

80 PPB

70 PPB

60 PPB

0%

2%

4%

6%

8%

10%

1 2 3 4 5 6

Percent of Population Experiencing Exposures At or 

Above Selected Benchmark Level While at 

Moderate or Greater Exertion

Number of Times Benchmark Exceeded (June-August 2007)

General Population-based Approach (ages 19-35)

Atlanta, 2006, Just Meet Existing 75 ppb Standard

80 PPB

70 PPB

60 PPB
5-46

1 (Bresnahan et al., 1997; Mansfield et.al, 2006, Neidell, 2010; Sexton, 2011). We used these 

2 literature derived estimates to generate an adjusted activity diary pool used by APEX to simulate

3 a 2-day exposure period (August 1-August 2, 2007) in Detroit to approximate the effect averting 

4 may have on exceedances of exposure benchmarks.

5 When considering base air quality and our designed target to represent averting 

6 performed by the general population – 15.3 % of all simulated school-age children spent on 

7 average 44 minutes less time outdoors – resulting in approximately one percentage point or 

8 fewer children experienced exposures at or above any of the selected benchmark levels (Figure 

9 5-12, left panel). When considering base air quality and our designed target to represent an 

10 averting response by the population of asthmatics – 30.3% of simulated asthmatic school-age 

11 children spent on average 44 minutes less time outdoors – resulting in approximately two 

12 percentage points or fewer experienced exposures at or above any of the selected benchmark

13 levels (Figure 5-12, right panel).

1 Figure 5-12 Percent of all school-age children (left panel) and asthmatic school-age 

2 children (right panel) having daily maximum 8-hr average O3 concentration at or above 

3 benchmark levels during a 2-day simulation in Detroit, base air quality, August 1-2, 2007. 

4 Red bars indicate exposure results when considering effect of averting.

5

6 5.4.3.4 Comparison of APEX Estimated Exposures Using Three Different Base Case Air 

7 Quality Data Sets: AQS, VNA, and EVNA

8 For this exposure assessment, we elected to use a modeling approach to estimate the 

9 ambient input concentration field and better account for spatial gradients that may exist (Chapter 

10 4). To support the selection of VNA, we compared exposure results separately generated using 

11 ambient monitor (AQS), eVNA, and VNA as input to APEX for three study areas: Atlanta, 

12 Detroit, and Philadelphia. All APEX settings were generally consistent with the simulations 

13 discussed previously, though the air quality data differed in that the year selected was 2005 

14 (based on the available CMAQ data) and that a 4 Km grid was used to define the spatial area for 

15 this evaluation rather than census tracts. Daily maximum 8-hr average exposures were estimated 

0%

2%

4%

6%

8%

10%

12%

14%

60 70 80

Percent of All Schol-age Children at or 

Above Selected Benchmark While at 

Moderate or Greater Exertion

8-hour Exposure Benchmark (ppb)

No Averting

15.3% avert a mean of 44 minutes

0%

2%

4%

6%

8%

10%

12%

14%

60 70 80

Percent of Asthmatic School-age Children at or Above Selected Benchmark While at 

Moderate or Greater Exertion

8-hour Exposure Benchmark (ppb)

No Averting

30.4% avert a mean of 44 minutes
5-47

1 for asthmatic school-age children residing in the same census tracts comprising each air quality 

2 domain and summarized in Figure 5-13.

3 Exposure results for all three air quality input data sets were very comparable, with a few 

4 notable differences. Using AQS monitor concentration data tended to result in a 1-3% greater 

5 percent of asthmatic school-age children at or above each of the selected benchmark levels when 

6 compared with exposures estimated using VNA concentrations. While the VNA concentrations 

7 are based on the AQS monitor data, the approach generates a concentration gradient with 

8 distance from areas of known concentration that are typically less than the observed values, thus 

9 yielding fewer persons exposed to the highest concentrations. Using the eVNA approach to 

10 generate ambient concentrations tended to result in 2-5% greater percent of asthmatic school-age 

11 children at or above each of the selected benchmark levels when compared with exposures 

12 estimated using either the AQS or VNA approaches. This is because at times, the eVNA 

13 approach estimated high concentrations in areas where no observations were present, based on 

14 modeling which captures gradients in O3 that may result from nearby sources (see Chapter 4).

15

16 Figure 5-13 Comparison of APEX exposure results generated for three study areas 

17 (Atlanta, Detroit, and Philadelphia) using three different 2005 air quality input data 

18 sets: AQS, VNA, and eVNA.

19

20

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

60 70 80 60 70 80 60 70 80

Percent of Asthmatic School-Age Children with One or more Exposures ≥ Benchmark while at Moderate 

or Greater Exertion, 2005 Base Air Quality

Daily Maximum 8-hr Average Ozone Exposure Benchmark (ppb)

aqs

evna

vna

Atlanta Detroit Philadelphia
5-48

1 5.4.3.5 Comparison of APEX Estimated Exposures Using Two Different Adjusted Air Quality 

2 Data Sets: Quadratic Rollback and HDDM

3 We elected to use an air quality modeling based approach rather than the previously used 

4 statistical approach to adjust air quality to just meet the current and alternative standard levels

5 (Chapter 4). To support the selection of the HDDM approach, we compared exposure results for 

6 the scenario of just meeting the existing standard, separately generated using air quality inputs 

7 obtained using the quadratic rollback and HDDM method to adjust air quality for the Atlanta 

8 study area. All APEX settings were generally consistent with the simulations discussed 

9 previously, though both the air quality data sets used in this comparison differed from that done 

10 in the main exposure results above in that only the ambient monitor locations were used to define 

11 the air districts and assumed a 30 km radius of influence, as was done for the first draft REA. 

12 Daily maximum 8-hr average exposures were estimated for asthmatic school-age children in 

13 census tracts within 30 km of each air district and summarized in Figure 5-14.

14

15

16 Figure 5-14 Comparison of exposure results generated by APEX using two different 

17 air quality adjustment approaches to just meet the existing standard in Atlanta: 

18 quadratic rollback (left panel) and HDDM (right panel).

19

20 The quadratic adjusted air quality resulted in slightly fewer percent of asthmatic school21 age children exposed at or above the highest benchmark (80 ppb-8hr) when compared with 

22 exposures estimated using the HDDM model simulation approach, though a significantly greater 

23 percent of asthmatic school-age children were exposed to the lowest benchmark (60 ppb-8hr) 

24 using the quadratic approach. This is because the quadratic approach generally targets the highest 2006 2007 2008

0%

3%

6%

9%

12%

15%

60 70 80 60 70 80

% of Asthmatic School-Age Children with 1 or more 

Exposures ≥ Benchmark while at Moderate or 

Greater Exertion and Just Meeting Existing Standard

Daily Maximum 8-hr Average Ozone Exposure Benchmark (ppb)

Quadratic Rollback Approach HDDM-Model Simulation
5-49

1 concentrations for adjustment, while the HDDM approach accounts for changes across the full 

2 concentration distribution to meet the adjusted concentration level of interest.

3 5.4.4 Limited Performance Evaluations

4 5.4.4.1 Personal Exposure Comparisons

5 A new evaluation of APEX was performed using a subset of personal O3 exposure 

6 measurements obtained from the Detroit Exposure and Aerosol Research Study (DEARS) (Meng 

7 et. al, 2012). For five consecutive days, personal O3 outdoor concentrations along with daily 

8 time-location activity diaries were collected from 36 adult study participants in Wayne County 

9 Michigan during July and August 2006. An APEX simulation was performed considering these 

10 same geographic and temporal features, followed with the sub-setting of APEX output data

11 according to important personal attributes of the DEARS study participants (5-day collection 

12 study periods, age/sex distributions, outdoor time, ambient concentrations, and air exchange 

13 rate). A comparison sample was generated randomly from the complete simulation, selecting for 

14 50 APEX simulated individuals.

15 For both data sets and considering the two output variables separately (outdoor time and 

16 daily exposure), the median daily values for each study participant were ranked, then plotted 

17 along with each individual’s corresponding minimum and maximum value using each 

18 individual’s 5 person-days of data (Figure 5-15). In spite of the distinct matching of influential 

19 personal attributes, over 50% of APEX simulated individuals had median daily O3 exposure 

20 concentrations above 10 ppb, while only 3% of DEARS participants’ median values exceeded 10 

21 ppb. The reason(s) for this difference is being investigated.

22
5-50

1 Figure 5-15 Distribution of daily average O3 exposures (top panels) and daily afternoon 

2 outdoor time (bottom panels) and for DEARS study participants (left panels) and APEX 

3 simulated individuals (right panels) in Wayne County, MI, July-August 2006.

4

5 APEX modeled exposures have previously been compared with personal exposure 

6 measurements for O3 (US EPA, 2007b). Briefly, APEX O3 simulation results were compared 

7 with 6-day personal O3 concentration measurements for children ages 7-12 (Xue et al., 2004; 

8 Geyh et al., 2000). Two separate areas of San Bernardino County were surveyed: urban Upland 

9 CA, and the combined small mountain towns of Lake Arrowhead, Crestline, and Running 

10 Springs, CA. Available ambient monitoring data for these locations during the same study years 

11 (1995-1996) were used as the air quality input to APEX. APEX predicted personal exposures, 

12 averaged similarly across a 6-day period, matched reasonably well for much of the concentration 

13 distribution considering both locations, but tended to underestimate exposures at the upper 

14 percentiles of the distribution. The average difference between the 6-day means was less than 1 

15 ppb, with a range of -11 ppb to +8 ppb, though predicted upper bounds for a few averaged 

16 exposures having higher exposure concentrations were under-predicted by up to 24 ppb (e.g., 

17 Figure 5-16). In addition, modeled exposure concentration variability was less than that observed 

18 in the personal exposure measurements. At the time of analysis, these differences were proposed 

0

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0 60 120 180 240 300 360 420 480 540 600

Cumulative Percent (%)

Daily Total Outdoor Time (minutes)

p50

min

max

DEARS APEX
5-51

1 to be largely driven by under-estimation of the spatial variability of the outdoor concentrations 

2 used by APEX (US EPA, 2007b).

3

4

5 Figure 5-16 Means (and range) of 6-day average personal O3 exposures, measured 

6 and modeled (APEX), Upland Ca. Obtained from Figure 8-22 of US EPA (2007b).

7 5.4.4.2 Ventilation Rate Comparisons

8 The algorithm used by APEX to estimate minute-by-minute ventilation rate serves as the 

9 basis for recent updates to the ventilation rate distributions provided in EPAs Exposure Factors 

10 Handbook (U.S. EPA, 2009b; US EPA, 2011). During the development of the ventilation 

11 distributions for EPA at that time, two peer-reviewed studies were identified as providing

12 somewhat relevant measurement data to evaluate the APEX energy expenditure and ventilation 

13 algorithm (see Graham, 2009 for additional comparison details). The results of this evaluation 

14 are summarized below.

15 Briefly, Brochu et al. (2006a,b) presents data for ventilation rates derived from tracking 

16 doubly-labeled water (DLW) consumption/elimination to estimate energy expenditure in healthy 

17 normal-weight males and females, ages from 1 month to 96 years (n=1,252). Estimates of energy 

18 expended were combined with a fixed oxygen uptake factor (H=0.21) and using a fixed 

ventilatory equivalent (VQ)

25 19 of 27. The DLW measurement period ranged from 7-21 days, 

20 resulting in time-averaged metrics that may in some instances provide reasonable estimates for a

21 mean daily ventilation rate, but not useful for estimating variability in an individual’s ventilation 

22 rate over shorter time periods (as is needed by APEX). Further, while DLW is considered by 

23 some as a ‘gold standard’ for measuring energy expenditure, this characterization would not 

24 necessarily be directly transferable to approximations that use this measured value (i.e., 

25 ventilation rate in Brochu et al. (2006a,b) is a calculated value, not measured). Reported 

 25 The ventilatory equivalent (VQ) is the ventilation rate (VE) divided by the oxygen consumption rate (VO2) 

 

 

 

0

20

40

60

80

1

Concentration (ppb)

Measured APEX range

measured APEX
5-52

1 ventilation rates are daily averages for several age groupings (e.g. ages 1 to < 2, 2 to < 5, 5 to <

2 7, etc.) along with derived percentiles, each assuming the existence of normally distributed data.

3 A 14-day APEX simulation was performed (i.e., the median of 7-21 days for the DLW 

4 measurement study) to estimate daily ventilation rates for comparison with the time-averaged 

5 Brochu et al (2006a) data. Twenty-five thousand persons were simulated by APEX to generate a 

6 reasonable number of persons within each year of age and other potential categorical variables 

7 (e.g., 100-200, although a few older age groups resulted in having fewer persons). It is important 

8 when comparing the two types of data for them to be similar as possible, particularly since age 

9 and body mass are important influential variables in both estimation methods. A total of 9,613 

10 normal-weight individuals were simulated by APEX and used for the following analysis. Multi11 day ventilation rates were averaged across the 14-day simulation period, yielding a mean daily 

12 ventilation rate for each person to best represent the DLW time averaging done by Brochu et al. 

13 (2006a).

14 Figure 5-17 compares the APEX simulated individuals body mass normalized mean daily 

15 ventilation rates with those reported by Brochu et al. (2006a; Table 2, page 684) for several age 

16 groupings of normal-weight individuals. The two largest differences appear for children of both 

17 sexes less than age 10 (i.e., Brochu et. al (2006a) estimates are systematically lower than APEX 

18 estimates) and for ages 16-33 (i.e., APEX estimates are lower than Brochu et al (2006a). Body 

19 mass normalized ventilation rates also appear to be slightly higher using APEX when 

20 considering persons above age 64 and for both sexes.

21

22 Figure 5-17 Comparison of body mass normalized mean daily ventilation rates 

23 estimated by APEX (closed symbols) and by Brochu et al., 2006 (open symbols).

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

0 10 20 30 40 50 60 70 80 90 100

Body Mass Normalized Mean Daily 

Ventilation (m3/day-kg)

Age (years)

APEX Females

Brochu et al. (2006) Females

APEX Males

Brochu et al. (2006) Males
5-53

1

2 One principal issue identified by us as potentially responsible for some of the above 

3 differences in ventilation estimates is in the VQ used by Brochu et al. (2006a). A single value of 

4 27 was used in estimating ventilation rates for both children and adults, however it is widely 

5 recognized that while a VQ of 27 may be a reasonable approximation for estimating mean 

6 ventilation rates of adults, it is not appropriate for use in estimating mean ventilation rates in 

7 children. With this in mind, the Brochu et al. (2006a) ventilation estimates were modified here 

8 using the VQ estimates offered by Arcus-Arth and Blaisdell (2007). Figure 5-18 illustrates the 

9 comparison of APEX body mass normalized mean daily ventilation rates with that of Brochu et 

10 al. (2006a) corrected ventilation estimates. The body mass normalized ventilation estimates for 

11 school-age children are more similar to those generated by APEX when correcting the Brochu et 

12 al (2006a) VQ parameter. Thus, mean ventilation rates generated by APEX are reasonably 

13 correlated with independent measures from the Brochu et al. (2006a, b) estimates, particularly 

14 when correcting the Brochu et al (2006a) ventilation estimates for children using a more 

15 appropriate estimate of VQ for children.

16

17 Figure 5-18 Comparison of body mass normalized mean daily ventilation rates in 

18 male and female school-age children (5-18) when correcting Brochu et al. (2006a) 

19 results with child appropriate VQ estimates.

20

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

Body Mass Normalized Mean Daily 

Ventilation (m3/day-kg)

Age (years)

APEX Females

Brochu et al. (2006) Females - Corrected VQ

APEX Males

Brochu et al. (2006) Males - Corrected VQ
5-54

1 In a second study identified for comparison with APEX estimates, Arcus-Arth and 

2 Blaisdell (2007) provide ventilation estimates for children <19 years of age using energy intake 

3 (EI, or calories consumed) and body mass data provided by the USDA’s Continuing Survey of 

4 Food Intake for Individuals (CSFII; USDA, 2000). Two-day daily average EIs were combined 

5 with a values of H (i.e., 0.22 for infants, 0.21 for non infants) and VQ (i.e., 33.5 for children 0-8, 

6 30.6 for boys 9-18, 31.5 for girls 9-18 years old). Again, time-averaging of the data may provide 

7 reasonable estimates of a daily mean, but offer no variability in ventilation estimates for shorter 

8 durations. Furthermore, data for both sexes are combined and reported by age, with stratified 

9 results by sex reported only for aggregated age groups (males and females, 9-18 years old).

10 A 2-day model simulation was performed by APEX to generate ventilation estimates for 

children to compare with results of Arcus-Arth and Blaisdell (2007).26 11 APEX ventilation 

12 estimates were time-averaged to generate mean daily values, and since the data reported in 

13 Arcus-Arth and Blaisdell (2007) were not separated by sex (outside of broad age categories), the 

14 APEX estimates were also combined by sex to provide a comparable mean estimate for each 

15 year of age (5-18). Body mass was also not used as a categorical variable in Arcus-Arth and 

16 Blaisdell (2007), therefore all APEX simulated individuals were used, regardless of whether they 

17 could be classified as overweight or of normal weight. In addition, daily ventilation rates for a 

18 few age groups of children were obtained from Tables 3 and 4 of Brochu et al. (2006a), though

19 considering both estimates for normal and overweight individuals (there were no combined data 

20 available). The Brochu et al. (2006a) results have been corrected for VQ as noted above using 

21 VQ estimates of Arcus-Arth and Blaisdell (2007) and added for comparison.

22 Figure 5-19 illustrates ventilation rate estimates from the APEX simulation, along with 

23 associated data for school-age children (ages 5-18) obtained from the two publications. Daily 

24 mean ventilation estimates are quite similar at each year of age, with slightly higher estimates by 

25 Arcus-Arth and Blaisdell (2007) at ages 9 and above, particularly when compared with APEX 

26 ventilation estimates. Ventilation estimates are remarkably similar for school-age children for all 

27 three sources of data, particularly when considering the differences in the type of input data used 

28 and the varied approaches of APEX, Brochu et al. (2006a), and Arcus-Arth and Blaisdell (2007).

29 This overall agreement suggests reasonable confidence can be conferred to the algorithm used by 

30 APEX to estimate at a minimum, daily mean ventilation rates.

 26 Table III, page 103 of Arcus-Arth and Blaisdell (2007) provided body mass normalized ventilation rates.
5-55

1

2 Figure 5-19 Comparison of body mass normalized daily mean ventilation rates in 

3 school-age children (5-18) estimated using APEX and literature reported values.

4

5 5.4.4.3 Evaluation of Longitudinal Profile Methodology

6 We evaluated the APEX approach used for linking together cross-sectional activity 

7 pattern diaries to generate longitudinal profiles for our simulated individuals (Appendix 5G, 

8 Section 5G-3). Of particular interest were how well variability in outdoor participation rate and 

9 the amount of time expended were represented in our population-based exposure simulations. 

10 Our goal in developing the most reasonable longitudinal profiles is to capture expected, 

11 important features of population activity patterns, i.e., there is correlation within an individual’s 

12 day-to-day activity patterns (though neither exactly repeated nor entirely random for individuals) 

13 and variability across the modeled study group in day-to-day activity patterns (i.e., not every 

14 simulated individual in the study group does the same activity on the same day).

15 The simulated longitudinal profiles indicate the method for linking together cross16 sectional diaries generates a diverse mixture of persons having variable, though expected, 

17 activity patterns: A small fraction of the simulated population spend a limited amount of 

18 afternoon time outdoors and occurring at a low frequency across an O3 season, a small fraction 

19 consistently spends a greater amount (> 2 hours) of time outdoors and occurring at greater 

20 frequency (e.g., 4/5 days per week), while the remaining simulated individuals fall somewhere in 

21 between regarding participation and total time. While we are not aware of a population database 

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

Body Mass Normalized Ventilation 

(m3/day-kg) 

Age (years)

APEX Mean

Brochu et al. (2006) Normals Mean - Corrected VQ

Arcus-Arth and Blaiedell (2007) Mean

Brochu et al. (2006) OW/OB Mean - Corrected VQ
5-56

1 available to compare with these simulated results, we are comfortable with the method 

2 performance in representing the intended variability in longitudinal activity patterns (see section 

3 5G-3 for details). 

4 5.5 VARIABILITY AND UNCERTAINTY

5 An important issue associated with any population exposure or risk assessment is the 

6 characterization of variability and uncertainty. Variability refers to the inherent heterogeneity in 

7 a population or variable of interest (e.g., residential air exchange rates). The degree of variability 

8 cannot be reduced through further research, only better characterized with additional 

9 measurement. Uncertainty refers to the lack of knowledge regarding the values of model input 

10 variables (i.e., parameter uncertainty), the physical systems or relationships used (i.e., use of 

11 input variables to estimate exposure or risk or model uncertainty), and in specifying the scenario 

12 that is consistent with purpose of the assessment (i.e., scenario uncertainty). Uncertainty is, 

13 ideally, reduced to the maximum extent possible through improved measurement of key 

14 parameters and iterative model refinement. The approaches used to assess variability and to 

15 characterize uncertainty in this REA are discussed in the following two sections. Each section 

16 also contains a concise summary of the identified components contributing to uncertainty and 

17 how each source may affect the estimated exposures.

18 5.5.1 TREATMENT OF VARIABILITY

19 The purpose for addressing variability in this REA is to ensure that the estimates of 

20 exposure and risk reflect the variability of ambient O3 concentrations, population and lifestage 

21 characteristics, associated O3 exposure and dose, and potential health risk across the study area 

22 and for the simulated at-risk study groups. In this REA, there are several algorithms that account 

23 for variability of input data when generating the number of estimated benchmark exceedances or 

24 health risk outputs. For example, variability may arise from differences in the population 

25 residing within census tracts (e.g., age distribution) and the activities that may affect population 

26 and lifestage exposure to O3 (e.g., time spent inside vehicles, time performing moderate or 

27 greater exertion level activities outdoors). A complete range of potential exposure levels and 

28 associated risk estimates can be generated when appropriately addressing variability in exposure 

29 and risk assessments; note however that the range of values obtained would be within the 

30 constraints of the input parameters, algorithms, or modeling system used, not necessarily the 

31 complete range of the true exposure or risk values.

32 Where possible, we identified and incorporated the observed variability in input data sets 

33 to estimate model parameters within the exposure assessment rather than employing standard 

34 default assumptions and/or using point estimates to describe model inputs. The details regarding 
5-57

1 variability distributions used in data inputs are described in Appendix 5B. To the extent possible 

2 given the data available for the assessment, we accounted for variability within the exposure 

3 modeling. APEX has been designed to account for variability in some of the input data, 

4 including the physiological variables that are important inputs to determining ventilation rates. 

5 As a result, APEX addresses much of the variability in factors that affect human exposure. 

6 Important sources of the variability accounted for in this analysis are summarized in Appendix 

7 5D.

8 5.5.2 CHARACTERIZATION OF UNCERTAINTY

9 While it may be possible to capture a range of exposure or risk values by accounting for 

10 variability inherent to influential factors, the true exposure or risk for any given individual within 

11 a study area is unknown, though can be estimated. To characterize health risks, exposure and risk 

12 assessors commonly use an iterative process of gathering data, developing models, and 

13 estimating exposures and risks, given the goals of the assessment, scale of the assessment 

14 performed, and limitations of the input data available. However, significant uncertainty often 

15 remains and emphasis is then placed on characterizing the nature of that uncertainty and its 

16 impact on exposure and risk estimates.

17 The REA’s for the previous O3, NO2, SO2, and CO NAAQS reviews each presented a 

18 characterization of uncertainty of exposure modeling (Langstaff, 2007; US EPA 2008, 2009a, 

19 2010). The qualitative approach used in this and other REAs is described by WHO (2008). 

20 Briefly, we identified the key aspects of the assessment approach that may contribute to 

21 uncertainty in the exposure and risk estimates and provided the rationale for their inclusion. 

22 Then, we characterized the magnitude and direction of the influence on the assessment results 

23 for each of these identified sources of uncertainty. Consistent with the WHO (2008) guidance, 

24 staff scaled the overall impact of the uncertainty by considering the degree of uncertainty as 

25 implied by the relationship between the source of uncertainty and the exposure concentrations. A 

26 qualitative characterization of low, moderate, and high was assigned to the magnitude of 

27 influence and knowledge base uncertainty descriptors, using quantitative observations relating to 

28 understanding the uncertainty, where possible. A summary of the key findings of those prior 

29 characterizations that are most relevant to the current O3 exposure assessment are provided in 

30 Table 5-6.
5-58

Table 5-6 Characterization of Key Uncertainties in Historical and Current APEX Exposure Assessments

Sources of Uncertainty

Historical Uncertainty Characterization Is rating 

appropriate for 

current APEX O3

exposure 

assessment?

Influence of Uncertainty 

on Exposure/Intake 

Dose Estimates Knowledgebase 

Category Uncertainty Comments Element Direction Magnitude

Ambient Monitoring 

Concentrations

Database Quality Both Low Low

All ambient pollutant measurements available 

from AQS are both comprehensive and 

subject to quality control.

Yes. No further 

characterization 

needed.

Instrument Measurement 

Error Over Low Low Mean bias estimated as 1.2% (CV of 4.4%). 

See Table 2 and Figure 6 of Langstaff (2007).

Yes. No further 

characterization 

needed.

Missing Data Substitution 

Method Both Low Low

Overall completeness of data yield negligible 

mean bias (~0) along with an estimated 

standard deviation of 4 ppb when replacing 

missing values. See Table 3 of Langstaff 

(2007).

Yes. No further 

characterization 

needed.

Temporal Representation Both Low Low

Appropriately uses 1-hr time-series of O3

concentrations for 5 years. No missing data 

for any hour input to APEX.

Yes. No further 

characterization 

needed.

Spatial Representation: 

Large Scale Both Low Low Tens of monitors used in each study area.

Yes. No further 

characterization 

needed.

Spatial Representation: 

Neighborhood Scale (1) Both Low Low

Spatial interpolation using jackknife method 

(removal of a single monitor) yielded 

generally unbiased observed/predicted ratios 

(mean 1.06), having an estimated standard 

deviation of 0.2. Langstaff (2007). 

Yes. For the 

uncertainties 

characterized, the 

historical rating is 

appropriate if and 

when using ambient 

monitor data alone 

to represent air 

quality surface. 

However in this 2nd

draft REA, localscale air quality was 

estimated using 

VNA (see below).

Spatial Representation: 

Neighborhood Scale (2) Over Low Low

When reducing the APEX radius setting from 

an unlimited value (actual value used) to 10 

km (i.e., the tendency would be to more 

accurately represent exposure), a smaller 

fraction (1-3 percentage points) of population 

exceeds benchmark levels. See Figures 7 – 9 

of Langstaff (2007).
5-59

Sources of Uncertainty

Historical Uncertainty Characterization Is rating 

appropriate for 

current APEX O3

exposure 

assessment?

Influence of Uncertainty 

on Exposure/Intake 

Dose Estimates Knowledgebase 

Category Uncertainty Comments Element Direction Magnitude

Spatial Representation:

Local Scale VNA estimates Both Low Low -

Moderate

Scenario-based evaluation in three study 

areas indicated small differences in exposure 

results when comparing ambient monitor data 

or statistically interpolated concentrations to 4 

Km grid as an input to APEX (Figure 5-13). 

General dependencies of the approaches 

used could lead to observed lack of 

distinction in exposure results. 

Yes. Newly 

evaluated.

Spatial Representation: 

Vertical Profile Both Moderate Moderate

Differences between ground-level (0-3 

meters) and building rooftop sited (25 meters) 

monitor concentrations can be significant. 

Most importantly, use of higher elevation 

monitors would tend to overestimate groundlevel exposures (i.e., persons outdoors). 

Yes. Given judged 

impact to exposure, 

additional 

characterization is 

possibly warranted.

Adjustment of Air 

Quality to Simulate 

Just Meeting the 

Current Standard

Quadratic Approach Both Low -

Moderate Moderate

Variable differences (e.g., none to a factor of 

two or three) in the estimated number of 

persons exposed across study areas when 

using differing 3-year roll-back periods for a 

single year of air quality (Langstaff, 2007).

Yes. Uncertainty in 

the approach has 

resulted in use of

HDDM approach.

HDDM Simulation 

Approach Both Low -

Moderate

Low -

Moderate

Expected patterns in both air quality and 

exposure result from HDDM/emissions 

reduction approach (full distribution affected

rather than only upper percentiles, Figure 5-

14). Variable differences remain (e.g., none to 

a factor of two or three) in the estimated 

percent of persons exposed across study 

areas when using differing 3-year roll-back 

periods for 2008 air quality (Figures 5-5 to 5-

9). New York study area could not be 

simulated to just meet 60 and 55 ppb 

alternative standards.

Yes. Newly 

evaluated.

APEX: General Input 

Databases

Population Demographics 

and Commuting (US 

Census)

Under Low Low

Comprehensive and subject to quality control. 

Differences in 2000 data versus modeled 

years (2006-2010) are likely small when 

estimating percent of population exposed.

Yes. No further 

characterization 

needed.
5-60

Sources of Uncertainty

Historical Uncertainty Characterization Is rating 

appropriate for 

current APEX O3

exposure 

assessment?

Influence of Uncertainty 

on Exposure/Intake 

Dose Estimates Knowledgebase 

Category Uncertainty Comments Element Direction Magnitude

Activity Patterns (CHAD) Both Low -

Moderate

LowModerate

Comprehensive and subject to quality control. 

Significantly increased number of diaries 

used to estimate exposure from prior review 

and 1st draft REA for this review (Table 5-3).

Thoroughly evaluated trends and patterns in 

historical data – no major issues noted with 

use of historical data to represent current 

patterns (Figures 5G-1 and 5G-2). Compared 

outdoor participation and time with ATUS 

data base – CHAD participation is higher than 

ATUS, likely due to ATUS survey methods. 

Activity data for asthmatics generally similar 

to non-asthmatics (Tables 5G2-to 5G-5). 

Remaining uncertainty with other influential 

factors that cannot be accounted for (e.g., 

SES, region/local outdoor participation rates)

Yes. Newly 

evaluated.

Meteorological (NWS) Both Low Low

Comprehensive and subject to quality control, 

few missing values. Limited application in 

selecting CHAD diaries and AERs.

Yes. No further 

characterization 

needed.

Poverty Status (US 

Census) Weighted Asthma 

Prevalence (CDC) 

Both Low Low

Data used are from a peer-reviewed quality 

controlled source. Application accounts for 

variability in most important influential 

variables (age, sex, region, poverty) though 

possible that variability in microscale 

prevalence not entirely represented.

New. Could possibly 

use further 

characterization, 

though typically 

available local 

prevalence rates 

are not well 

stratified by 

influential variables. 
5-61

Sources of Uncertainty

Historical Uncertainty Characterization Is rating 

appropriate for 

current APEX O3

exposure 

assessment?

Influence of Uncertainty 

on Exposure/Intake 

Dose Estimates Knowledgebase 

Category Uncertainty Comments Element Direction Magnitude

APEX: 

Microenvironmental 

Concentrations

Outdoor Near-Road and 

Vehicular: Proximity 

Factors

Both Low LowModerate

Uncertainty in mean value used approximated 

as 15 percentage points. See Figure 10 and 

Table 7 of Langstaff (2007). May be of 

greater importance in certain study areas or 

under varying conditions, though even with 

this mean difference, in-vehicle 

penetration/decay decreases exposures and 

hence importance of in-vehicle 

microenvironments.

Yes. No further 

characterization 

needed.

Indoor: Near-Road Over Low Low

Expected reduction in O3 for persons 

residing near roads not modeled here, but 

when included, there is a small reduction 

(~3%) in the number of persons experiencing 

exposure above benchmark levels (Langstaff, 

2007). 

Yes. No further 

characterization 

needed.

Indoor: Air Exchange

Rates Both Low Moderate

Uncertainty due to random sampling variation 

via bootstrap distribution analysis indicated 

the AER GM and GSD uncertainty for a given 

study area tends range to at most from fitted 

±1.0 GM and ± 0.5 GSD hr-1

. Nonrepresentativeness remains an important 

issue as city-to-city variability can be wide 

ranging (GM/GSD pairs can vary by factors of 

2-3) and data available for city-specific 

evaluation are limited (Langstaff, 2007). Also, 

indoor exposures are estimated as not 

important to daily maximum 8-hr average O3

exposure.

Yes. No further 

characterization 

needed.
5-62

Sources of Uncertainty

Historical Uncertainty Characterization Is rating 

appropriate for 

current APEX O3

exposure 

assessment?

Influence of Uncertainty 

on Exposure/Intake 

Dose Estimates Knowledgebase 

Category Uncertainty Comments Element Direction Magnitude

Indoor: A/C Prevalence 

(AHS) Both Low Low

Comprehensive and subject to quality control, 

estimated 95th percentile confidence bounds 

range from a few to just over ten percentage 

points, though some cities use older year 

data (Table 9 of Langstaff, 2007). Note, 

variable indicates presence/absence not 

actual use. Also, indoor exposures are 

estimated here as limited in importance to 

daily maximum 8-hr average exposures and 

sensitivity analyses in NO2 REA (in-vehicle 

was most influential exposure ME) concluded 

indoor prevalence variable was of limited 

importance. 

Yes. No further 

characterization 

needed.

Indoor: Removal Rate Both Low Low

Greatest uncertainty in the input distribution 

regarded representativeness, though 

estimated as unbiased but correct to within 

10% (Langstaff, 2007).

Yes. No further 

characterization 

needed.

Vehicular: Penetration 

Factors Both Low Moderate

Input distribution is from an older 

measurement study though consistent with 

recent, albeit limited data.

Yes. No further 

characterization 

needed.
5-63

Sources of Uncertainty

Historical Uncertainty Characterization Is rating 

appropriate for 

current APEX O3

exposure 

assessment?

Influence of Uncertainty 

on Exposure/Intake 

Dose Estimates Knowledgebase 

Category Uncertainty Comments Element Direction Magnitude

APEX: Simulated 

Activity Profiles 

Longitudinal Profiles Under Low -

Moderate Moderate

Depending on the longitudinal profile method 

selected, the number of persons experiencing 

multiple exposure events at or above a 

selected level could differ by about 15 to 50% 

(see Appendix B, Attachment 4 of NO2 REA).

Long-term diary profiles (i.e., monthly, 

annual) do not exist for a population, limiting 

the evaluation.

The general population-based modeling 

approach used for main body REA results 

does not assign rigid schedules, for example 

explicitly representing a 5-day work week for 

employed persons. However, when 

considering such scheduling (e.g., outdoor 

workers or all children spending entire 

summer season not in-school), estimated 

exposures are greater than when not 

considering rigid weekly/seasonal schedules. 

For our hypothetical outdoor worker scenario, 

the number of multiday exposures at or above 

benchmark levels was primarily affected 

(though mainly the 60 ppb level, Figure 5-11), 

while both percent of children experiencing 

single and multiday exposures were 

increased by about 30% when simulating a 

rigid schedule (Figure 5-10).

Yes. Newly 

evaluated.

Commuting Both Low Moderate

New method used in this assessment is

designed to link Census commute distances 

with CHAD vehicle drive times. Considered 

an improvement over the former approach 

that did not match distance and time. While 

vehicle time accounted for through diary 

selection, not rigidly scheduled. However, Invehicle exposures are not important drivers 

for persons exceeding benchmark levels 

(section 5.3.2). 

Yes. Newly 

evaluated.
5-64

Sources of Uncertainty

Historical Uncertainty Characterization Is rating 

appropriate for 

current APEX O3

exposure 

assessment?

Influence of Uncertainty 

on Exposure/Intake 

Dose Estimates Knowledgebase 

Category Uncertainty Comments Element Direction Magnitude

At-Risk Population and 

Lifestages Both Low Low –

Moderate

An updated evaluation shows activity patterns 

of asthmatics are similar to that of nonasthmatics (section 5.3.1, Tables 5G-2 to 5G5).Yes. Newly 

evaluated.

APEX: Physiological 

Processes

Body Mass (NHANES) Unknown Low Low

Comprehensive and subject to quality control, 

though older (1999-2004) than current 

simulated population, possible small regional 

variation is not represented by national data. 

Yes. No further 

characterization 

needed.

NVO2max Unknown Low Low

Upper bound control for unrealistic activity 

levels rarely used by model, thus likely not 

very influential.

Yes. No further 

characterization 

needed.

RMR Unknown Low Low

Approach from older literature (Schofield, 

1985), used in ventilation equation. Note 

ventilation rate estimates are reasonable.

Newly identified. 

May need additional

characterization.

METS distributions Over Low -

Moderate

Low -

Moderate

APEX estimated daily mean METs range 

from about 0.1 to 0.2 units (between about 5-

10%) higher than independent literature 

reported values (Table 15 of Langstaff, 2007). 

Shorter-term values are of greater importance 

in this assessment.

Yes. Given judged 

impact to exposure, 

additional 

characterization is 

needed.

Ventilation rates Over Low -

Moderate

Low -

Moderate

APEX estimated daily ventilation rates can be 

greater (2-3 m3/day) than literature reported 

measurement values (Table 25 of Langstaff, 

2007), though if accounting for measurement 

bias this minimizes the discrepancy (Graham 

and McCurdy, 2005; see Figures 5-18 and 5-

19). Also, a shorter-term comparison (hours 

rather than daily), while more informative, 

cannot be performed due to lack of data.

Yes. Additional

characterization 

would be warranted 

if minute or hourly 

ventilation rate data 

were available.

Exposure Benchmark 

Level

EVR characterization of 

moderate or greater 

exertion

Over Moderate Low -

Moderate

Given that the EVR serves as a cut point for 

selecting persons performing at moderate or 

greater exertion and is a lower bound value 

(~5th percentile), the simulated number of 

persons achieving this level of exercise is 

possibly overestimated.

Newly identified. 

May need additional 

characterization.
5-65

1 5.6 KEY OBSERVATIONS

2 Two additional tables are provided to additionally summarize the exposure results across 

3 all study areas and years of air quality data: Table 5-7 contains the percent of all school-age 

4 children experiencing at least one exposure at or above the three exposure benchmark levels, 

5 while Table 5-8 contains the percent of all school-age children experiencing at least two 

6 exposures at or above the three exposure benchmark levels, with both tables considering results 

associated with each of the adjusted air quality scenarios.27 7 Two descriptive statistics are 

8 provided from the exposure results for each study area: the mean percent of persons exposed in 

9 each study area averaged across the 5 years simulated and the maximum percent of persons 

10 exposed in each study area, representing the worst year of air quality simulated. Figure 5-20

11 illustrates the estimated mean and maximum percent of all school-age children exposed for each 

12 study area when considering the 60 ppb-8hr benchmark and adjusted air quality scenarios, and 

13 using the data provided in Table 5-7 and Table 5-8. 

14 Presented below are key observations resulting from the O3 exposure analysis:

15 • General: The estimated percent of any study group exposed at least once at or above the 

16 selected benchmark levels were highest considering the base air quality though percent 

17 exposed varied by study area, year, and benchmark level (Appendix 5F). Very few 

18 persons within any study group (all are estimated to be < 0.3%) experienced any 

19 benchmark exceedances when considering an alternative standard level of 55 ppb-8hr 

20 (data not shown).

21 • Study Group: The percent of all school-age children exposed at or above the selected 

22 benchmark levels across all study areas, years, and air quality scenarios were similar to 

23 exposures for asthmatic school-age children (e.g., Figure 5-5 and Figure 5-6, 

24 respectively) with both of these study groups having consistently higher percent of 

25 persons exposed than that estimated for asthmatic adults and all older adults (Figure 5-7

26 and Figure 5-8, respectively), generally by about a factor of three or more. The percent of 

27 all older adults at or above any benchmark level tended to be only a few percentage 

28 points or less when compared with corresponding benchmark exceedances for asthmatic 

29 adults. 

30 • 80 ppb-8hr Exposure Benchmark: In general, less than 1% of any study group, 

31 including all school-age children and any study area, was exposed at least once at or 

32 above the highest exposure benchmark, 80 ppb-8hr, when considering the existing

 27 The maximum sample size is 6 years based on years simulated, and for a few instances varied based on available 

air quality (e.g., Chicago does not have 3 years simulated for just meeting the current standard during 2008-2010 

period because air quality was below the current standard, thus the total sample size for this study area is 3.
5-66

1 standard air quality scenario (Table 5-7). When considering a standard level of 70 ppb2 8hr, ≤ 0.2% of any study group and any study area was exposed at least once at or above 

3 that same benchmark.

4 • 70 ppb-8hr Exposure Benchmark: Less than 10% of any study group, including all 

5 school-age children and any study area, was exposed at least once at or above an 

6 exposure benchmark of 70 ppb-8hr, when considering the existing standard air quality 

7 scenario (Table 5-7). When considering a standard level of 70 ppb-8hr, ≤ 3.5% of any 

8 study group and in any study area was exposed at least once at or above that same 

9 benchmark. A standard level of 65 ppb-8hr is estimated to reduce the percent of persons 

10 at or above an exposure benchmark of 70 ppb-8hr to ≤0.5% of any study group and in 

11 any study area.

12 • 60 ppb-8hr Exposure Benchmark: In general, no more than 26% of any study group in 

13 any study area was exposed at least once at or above the lowest exposure benchmark, 60 

14 ppb-8hr, when considering the existing standard air quality scenario (Table 5-7, Figure 

15 5-20). When considering a standard level of 70 ppb-8hr, < 20% of any study group in 

16 any study area was exposed at least once at or above that same benchmark. A standard 

17 level of 65 ppb-8hr is estimated to reduce the percent of persons at or above an exposure 

18 benchmark of 60 ppb-8hr to ≤ 10% of any study group and study area.

19 • Multi-day Benchmark Exceedances: When considering air quality adjusted to just meet 

20 the existing standard, multi-day exposure benchmark exceedances are largely limited to 

21 two or more exceedances at the 60 ppb-8hr benchmark, all occurring for < 15% of any 

22 study group in any study area (e.g., Table 5-8, Figure 5-9). There were no persons 

23 estimated to experience any multi-day exposures at or above 80 ppb-8hr for any study

24 group in any study area, while ≤ 2.2% of persons were estimated to experience two or 

25 more exposures at or above 70 ppb-8hr, each considering any adjusted air quality 

26 scenario.

27 • Targeted Data Evaluations: Afternoon time spent outdoors, along with ambient O3

28 concentrations are the most influential factors when considering those persons highest 

29 exposed. There is no apparent temporal trend in the amount of outdoor time or 

30 participation rate when comparing historical CHAD diaries (1980s studies) to recently 

31 collected diary data (2000s studies); regardless, majority of CHAD data are from studies 

32 conducted since 2000. Use of activity pattern data from non-asthmatics to represent 

33 asthmatics appears reasonably justified based on an evaluation indicating their having 

34 similar outdoor time expenditure and attaining similar activity levels. APEX estimated 

35 daily exposures are somewhat comparable to personal exposure measurements; however, 

36 both over- and under-estimations occurred to varying degrees (Figure 5-15; Figure 5-16). 
5-67

1 APEX estimated ventilation rates were comparable to literature provided estimates, 

2 particularly those of school-age children (Figure 5-19).

3 • Targeted Exposure Scenarios: When considering a modeling approach that more 

4 rigidly schedules longitudinal time location activity patterns compared with the standard 

5 longitudinal approach used by APEX, a greater percent of persons experience at least one 

6 or more exposures at or above benchmark levels. For example, an APEX model 

7 simulation using only summer time (no school) CHAD diary days for non-working 

8 school-age children generated approximately 30% more persons at or above exposure 

9 benchmark levels compared with exposures estimated using our population-based 

10 modeling approach (Figure 5-10). When accounting for a fraction of the population to 

11 avert in response to a bad air quality day, approximately 1-2 percentage point fewer 

12 persons experienced exposures at or above benchmark levels compared with exposures 

13 estimated using our population based modeling approach (Figure 5-12).

14
5-68

Table 5-7 Mean and Maximum Percent of all School-age Children Estimated to 

Experience at Least One Daily Maximum 8-hr Average Exposure to O3 at or Above 

Selected Health Benchmark Levels

Study Area

Adjusted 

Air 

Quality 

Scenario

Percent of All School-Age Children Experiencing At Least 

One Exposure At or Above Selected Benchmark Level1

60 ppb-8hr 70 ppb-8hr 80 ppb-8hr

mean max mean max mean max

Atlanta

75 14.8 19.3 2.8 4.4 0.3 0.7

70 7.5 10.8 0.7 1.4 0.1 0.2

65 2.9 4.8 0.2 0.5 0 0

Baltimore

75 12.2 19.0 2.0 4.0 0.2 0.4

70 7.1 11.8 0.7 1.2 0.1 0.1

65 3.0 5.4 0.2 0.3 0 0

Boston

75 13.8 21.9 2.8 6.6 0.3 1.0

70 9.0 15.7 1.2 3.2 0.1 0.2

65 3.4 6.7 0.2 0.5 0 0

Chicago

75 13.7 24.7 3.2 7.5 0.2 0.7

70 9.2 16.0 1.0 2.7 0 0.1

65 4.2 8.1 0.2 0.4 0 0

Cleveland

75 10.2 18 1.4 3.7 0.1 0.2

70 4.2 9.3 0.3 0.9 0 0

65 1.1 3.0 0.1 0.2 0 0

Dallas

75 12.9 22.9 1.9 4.5 0.1 0.3

70 7.5 16.0 0.6 1.5 0 0.1

65 3.0 7.6 0.1 0.3 0 0

Denver

75 17.0 25.6 1.7 4.1 0.1 0.5

70 10.2 18.9 0.5 1.7 0 0.1

65 3.8 9.5 0.1 0.4 0 0

Detroit

75 14.1 19.1 2.4 4.2 0.1 0.2

70 7.3 10.3 0.5 0.9 0 0

65 2.9 4.6 0.1 0.2 0 0

Houston

75 11.4 17.8 2.3 5.5 0.3 0.7

70 6.6 11.9 0.8 2.1 0 0.1

65 2.7 5.7 0.1 0.4 0 0

Los Angeles

75 9.5 10.2 0.6 1.0 0 0.1

70 4.4 5.0 0.1 0.2 0 0

65 1.1 1.5 0 0 0 0

New York

75 10.9 19.0 1.6 3.7 0.1 0.3

70 3.3 6.6 0.2 0.5 0 0

65 0 0.1 0 0 0 0

Philadelphia

75 13.8 20.5 2.1 4.2 0.2 0.4

70 7.1 11.8 0.6 1.5 0 0.1

65 2.4 4.6 0.1 0.3 0 0

Sacramento

75 10.3 16.5 1.6 2.7 0.1 0.2

70 5.8 10.0 0.4 0.9 0 0

65 2.7 4.7 0.1 0.2 0 0

St. Louis

75 16.3 25.8 3.3 8.1 0.3 1.1

70 10.2 16.9 1.0 2.7 0.1 0.2

65 3.9 7.3 0.1 0.4 0 0

Washington

75 13.2 23.4 2.4 6.0 0.2 0.8

70 6.6 12.5 0.6 1.4 0 0.1

65 2.3 5.0 0.1 0.2 0 0

1 The mean is the arithmetic average of the estimated percent of all school-age children exposed across 2006-2010 

year air quality; max is the highest estimated percent of all school-age children exposed in a year.
5-69

Figure 5-20 Incremental increases in percent of all school-age children exposed to O3 at or above 60 ppb-8hr for each study 

area, year 2006-2010 air quality. Average percent (left panels), maximum percent (right panels), at least one exposure (top 

panels), at least two exposures (bottom panels) per year.

standard level (ppb) 60 65 70 75

Washington

St. Louis

Sacramento

Philadelphia

New York

Los Angeles

Houston

Detroit

Denver

Dallas

Cleveland

Chicago

Boston

Baltimore

Atlanta

Percent of All School-Age Children with at Least One 8-hr Daily Max Exposure >= 60 ppb

0% 2% 4% 6% 8% 10% 12% 14% 16% 18%

standard level (ppb) 60 65 70 75

Washington

St. Louis

Sacramento

Philadelphia

New York

Los Angeles

Houston

Detroit

Denver

Dallas

Cleveland

Chicago

Boston

Baltimore

Atlanta

Percent of All School-Age Children with at Least Two 8-hr Daily Max Exposure >= 60 ppb

0% 1% 2% 3% 4% 5% 6% 7% 8% 9%

standard level (ppb) 60 65 70 75

Washington

St. Louis

Sacramento

Philadelphia

New York

Los Angeles

Houston

Detroit

Denver

Dallas

Cleveland

Chicago

Boston

Baltimore

Atlanta

Percent of All School-Age Children with at Least One 8-hr Daily Max Exposure >= 60 ppb

0% 2% 4% 6% 8% 10% 12% 14% 16% 18% 20% 22% 24% 26%

standard level (ppb) 60 65 70 75

Washington

St. Louis

Sacramento

Philadelphia

New York

Los Angeles

Houston

Detroit

Denver

Dallas

Cleveland

Chicago

Boston

Baltimore

Atlanta

Percent of All School-Age Children with at Least Two 8-hr Daily Max Exposure >= 60 ppb

0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10% 11% 12% 13% 14% 15%
5-70

1 Table 5-8 Mean and Maximum Percent of All School-age Children Estimated to 

2 Experience at Least Two Daily Maximum 8-hr Average Exposures to O3 At or 

3 Above Selected Health Benchmark Levels

Study Area

Adjusted 

Air 

Quality 

Scenario

Percent of All School-Age Children Experiencing At Least 

Two Exposures At or Above Selected Benchmark Level1

60 ppb-8hr 70 ppb-8hr 80 ppb-8hr

mean max mean max mean max

Atlanta

75 6.0 8.9 0.4 0.7 0 0

70 2.1 3.3 0 0.1 0 0

65 0.4 0.8 0 0 0 0

Baltimore

75 4.6 8.4 0.2 0.5 0 0

70 1.8 3.7 0 0.1 0 0

65 0.4 0.9 0 0 0 0

Boston

75 4.5 9.7 0.3 1.1 0 0

70 2.2 5.5 0.1 0.4 0 0

65 0.4 1.1 0 0 0 0

Chicago

75 5.3 11.6 0.5 1.3 0 0

70 2.5 5.7 0.1 0.2 0 0

65 0.8 1.8 0 0 0 0

Cleveland

75 3.1 7.5 0.1 0.5 0 0

70 0.9 2.6 0 0 0 0

65 0.1 0.5 0 0 0 0

Dallas

75 4.8 12.2 0.2 0.8 0 0

70 2.2 7.1 0 0.1 0 0

65 0.5 2.0 0 0 0 0

Denver

75 7.6 14.4 0.2 0.4 0 0

70 3.5 9.2 0 0.1 0 0

65 0.7 2.8 0 0 0 0

Detroit

75 5.0 8.6 0.3 0.8 0 0

70 1.9 3.6 0 0.1 0 0

65 0.4 1.1 0 0 0 0

Houston

75 3.8 6.3 0.2 0.6 0 0

70 1.5 2.9 0 0.1 0 0

65 0.3 0.7 0 0 0 0

Los Angeles

75 4.1 4.5 0.1 0.1 0 0

70 1.6 1.8 0 0 0 0

65 0.3 0.3 0 0 0 0

New York

75 3.4 8.0 0.1 0.4 0 0

70 0.5 1.4 0 0 0 0

65 0 0 0 0 0 0

Philadelphia

75 5.0 8.7 0.2 0.5 0 0

70 1.7 3.3 0 0.1 0 0

65 0.3 0.6 0 0 0 0

Sacramento

75 3.7 7.4 0.2 0.5 0 0

70 1.5 3.4 0 0.1 0 0

65 0.4 0.9 0 0 0 0

St. Louis

75 7.0 13.8 0.6 2.2 0 0.1

70 3.2 7.0 0.1 0.3 0 0

65 0.7 2.0 0 0 0 0

Washington

75 5.5 12.5 0.4 1.4 0 0

70 2.0 5.0 0 0.1 0 0

65 0.4 1.2 0 0 0 0

1 4 The mean is the arithmetic average of the estimated percent of all school-age children exposed across 2006-2010 

5 year air quality; max is the highest estimated percent of all school-age children exposed in a year.

6
5-71

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19 <http://www.epa.gov/ttn/naaqs/standards/co/data/CO-REA-Amended-July2010.pdf>.

20 U.S. EPA. 2011. Exposure Factors Handbook, 2011 edition. Washington, DC: EPA National 

21 Center for Environmental Assessment. (EPA document number EPA/600/R-09/052F).

22 Available at: <http://www.epa.gov/ncea/efh/pdfs/efh-complete.pdf>.

23 U.S. EPA. 2012a. Total Risk Integrated Methodology (TRIM) - Air Pollutants Exposure Model 

24 Documentation (TRIM.Expo / APEX, Version 4.4) Volume I: User’s Guide. Research 

25 Triangle Park, NC: EPA Office of Air Quality Planning and Standards. (EPA document 

26 number EPA-452/B-12-001a). Available at: 

27 <http://www.epa.gov/ttn/fera/human_apex.html>.

28 U.S. EPA. (2012b. Total Risk Integrated Methodology (TRIM) - Air Pollutants Exposure Model 

29 Documentation (TRIM.Expo / APEX, Version 4.4) Volume II: Technical Support 

30 Document. Research Triangle Park, NC: EPA Office of Air Quality Planning and 

31 Standards. (EPA document number EPA-452/B-12-001b). Available at: 

32 <http://www.epa.gov/ttn/fera/human_apex.html>.

33 U.S. EPA. 2013. Integrated Science Assessment of Ozone and Related Photochemical Oxidants. 

34 Research Triangle Park, NC: EPA National Center for Environmental Assessment. (EPA 

35 document number EPA 600/R-10/076F). Available at:

36 <http://cfpub.epa.gov/ncea/isa/recordisplay.cfm?deid=247492#Download>.

37 van Gent, R.; K. van der Ent; L. E. M. van Essen-Zandvliet; M. M. Rovers; J. L. L. Kimpen; G. 

38 de Meer and P. H. C. Klijn. 2007. "No Difference in Physical Activity in (Un)diagnosed 

39 Asthma and Healthy Controls." Pediatric Pulmonology, 42:1018-1023.
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2 Index and Change of Outdoor Activity Among Adult Asthma in Six States," BRFSS, 2005. 

3 Journal of Community Health, 34:40-46.

4 Whitfield, R.; W. Biller; M. Jusko and J. Keisler. 1996. A Probabilistic Assessment of Health 

5 Risks Associated with Short- and Long-Term Exposure to Tropospheric Ozone. Argonne, 

6 IL: Argonne National Laboratory.

7 World Health Organization. 2008. Uncertainty and Data Quality in Exposure Assessment."Part 

8 1: Guidance Document on Characterizing and Communicating Uncertainty in Exposure 

9 Assessment." Available at: 

10 <http://www.who.int/ipcs/publications/methods/harmonization/exposure_assessment.pdf.

11 Xue, J.; T. McCurdy; J. Spengler and H. Özkaynak. 2004. "Understanding Variability in Time 

12 Spent in Selected Locations for 7-12 Year Old Children." Journal of Exposure Analysis 

13 and Environmental Epidemiology,14(3):222-33.

14 Zivin, J. G. and M. Neidell. 2009. "Days of Haze: Environmental Information Disclosure and 

15 Intertemporal Avoidance Behavior." Journal of Environmental Economic Management,

16 58(2):119-128.
6-1

1 6 CHARACTERIZATION OF HEALTH RISKS BASED ON 

2 CONTROLLED HUMAN EXPOSURE STUDIES

3 6.1 INTRODUCTION

4 This chapter presents information regarding the methods and results for a controlled 

5 human exposure-based O3 (O3) health risk assessment that builds upon the methodology used in 

6 the assessment conducted as part of the O3 NAAQS review completed in 2008 and also 

7 introduces a new method for estimating risk. In the previous review, EPA conducted a health risk 

8 assessment that produced risk estimates for the number and percent of school-aged children, 

9 asthmatic school-aged children, and the general population experiencing lung function

10 decrements associated with O3 exposures for 12 urban areas, where lung function is measured as 

11 forced expiratory volume in one second (FEV1). That portion of the risk assessment was based 

12 on exposure-response relationships developed from analysis of data from several controlled 

13 human exposure studies which were combined with population-level exposure distributions

14 developed for children and adults. Risk estimates for lung function decrements were developed 

15 for recent air quality levels and for just meeting the existing 8-hour standard and several 

16 alternative 8-hour standards. The methodological approach followed in the last risk assessment 

17 and risk estimates resulting from that assessment are described in the 2007 Staff Paper (U.S. 

18 EPA, 2007a).

19 The goals of the current O3 risk assessment are to provide estimates of the number and 

20 percentage of persons that would experience adverse respiratory effects associated with recent O3

21 levels and with meeting the existing and potential alternative O3 standards in specific urban 

22 areas; and to develop a better understanding of the influence of various inputs and assumptions 

23 on the risk estimates. The current assessment includes estimates of risks of lung function 

24 decrements in school-aged children (ages 5 to 18), asthmatic school-aged children, and the adult

25 population (19 and above). We recognize that there are many sources of uncertainty in the inputs

26 and approach used in this portion of the health risk assessment which make the specific estimates 

27 uncertain, however, we have sufficient confidence in the magnitude and direction of the 

28 estimates provided by the assessment for it to serve as a useful input to decisions on the 

29 adequacy of the O3 standard and risk reductions associated with alternative standards.

30 We are estimating lung function risk using two methodologies in this review. The 

31 primary results are based on a new model that estimates FEV1 responses for individuals

32 associated with short-term exposures to O3 (McDonnell, Stewart, and Smith, 2012). We refer to 

33 this model as the McDonnell-Stewart-Smith (MSS) model. We also provide estimates following 

34 the methodology used in previous reviews which provides population level estimates of the 
6-2

1 percent and number of people at risk. We refer to this model as the Exposure-Response (E-R) 

2 model used in previous reviews. Both of these models are implemented in the air pollution 

3 exposure model APEX (EPA, 2012b,c). Following this introductory section, this chapter 

4 discusses the scope of the controlled human exposure study based risk assessment, describes the 

5 risk models, and provides key results from the assessment. The results of sensitivity analyses are 

6 reported and key uncertainties are identified and summarized. More detailed descriptions of 

7 several parts of the analyses are included in appendices that accompany the REA.

8 6.1.1 Development of Approach for Current Risk Assessment

9 The lung function risk assessment described in this chapter builds upon the methodology 

10 and lessons learned from the risk assessment work conducted for previous reviews (EPA, 1996, 

11 2007a). The current risk assessment also is based on the information evaluated in the ISA (EPA, 

12 2013a). The general approach used in the current risk assessment was described in the Scope and 

13 Methods Plan for Health Risk and Exposure (EPA, 2011), that was released to the CASAC and 

14 general public in April 2011 for review and comment and which was the subject of a 

15 consultation with the CASAC O3 Panel in May 2011. The first draft REA was reviewed by 

16 CASAC in September 2012. The approach used in the current risk assessment reflects 

17 consideration of the comments offered by CASAC members and the public on the Scope and 

18 Methods Plan and the first draft REA.

19 Controlled human exposure studies involve volunteers who are exposed while engaged in 

20 different exercise regimens to specified levels of O3 under controlled conditions for specified 

21 amounts of time. For the current health risk assessment, we are using probabilistic exposure22 response relationships based on analysis of individual data that describe the relationship between 

23 measures of personal exposure to O3 and measures of lung function recorded in the studies.

24 Therefore, a risk assessment based on exposure-response relationships derived from controlled 

25 human exposure study data requires estimates of personal exposure to ambient O3. Because data 

26 on personal hourly exposures to O3 of ambient origin are not available, estimates of personal 

27 exposures to varying ambient concentrations are derived through exposure modeling, as 

28 described in Chapter 5. While the quantitative risk assessment based on controlled human 

29 exposure studies addresses only lung function responses, it is important to note that other 

30 respiratory responses have been found to be related to O3 exposures in these types of studies, 

31 including increased lung inflammation, increased respiratory symptoms, increased airway 

32 responsiveness, and impaired host defenses. Sufficient information is not available to 

33 quantitatively model these other endpoints. Section 6.2 of the ISA provides a discussion of these 

34 additional health endpoints which are an important part of the overall characterization of risks 

35 associated with ambient O3 exposures.
6-3

1 6.1.2 Comparison of Controlled Human Exposure- and Epidemiologic-based Risk 

2 Assessments

3 In contrast to the exposure-response relationships derived from controlled human 

4 exposure studies, epidemiological studies provide estimated concentration-response

5 relationships based on data collected in real world community settings. The assessment of health

6 risk based on epidemiological studies is the subject of Chapter 7. The characteristics that are 

7 relevant to carrying out a risk assessment based on controlled human exposure studies versus one 

8 based on epidemiology studies can be summarized as follows:

9 • The relevant controlled human exposure studies in the ISA provide data that can be used 

10 to estimate exposure-response functions, and therefore a risk assessment based on these 

11 studies requires as input (modeled) personal exposures to ambient O3. The relevant 

12 epidemiological studies in the ISA provide concentration-response functions, and,

13 therefore, a risk assessment based on these studies requires as input (actual monitored or 

14 adjusted based on monitored) ambient O3 concentrations, and personal exposures are not 

15 required as inputs to the assessment.

16 • Epidemiological studies are carried out in specific real world locations (e.g., specific 

17 urban areas). To minimize extrapolation uncertainty, a risk assessment based on 

18 epidemiological studies is best performed in locations where the studies took place. 

19 Controlled human exposure studies, carried out in laboratory settings, are generally not 

20 specific to any particular real world location. A risk assessment based on controlled 

21 human exposure studies can therefore appropriately be carried out for any location for 

22 which there are adequate air quality and other data on which to base the modeling of 

23 personal exposures.

24 • To derive estimates of risk from concentration-response relationships estimated in 

25 epidemiological studies, it is usually necessary to have estimates of the baseline 

26 incidences of the health effects involved. Such baseline incidence estimates are not 

27 needed in a controlled human exposure studies-based risk assessment.

28 6.2 SCOPE OF LUNG FUNCTION HEALTH RISK ASSESSMENT

29 The current controlled human exposure-based O3 health risk assessment is one approach 

30 used to estimate risks associated with exposure to ambient O3 in a number of urban areas 

31 selected to illustrate the public health impacts of this pollutant. The short-term exposure related 

32 health endpoints selected for this portion of the O3 health risk assessment include those for which 

33 the ISA concludes that the evidence as a whole supports the general conclusion that O3, acting 

34 alone and/or in combination with other components in the ambient air pollution mix is causal or

35 likely to be causally related to the endpoint.

36 In the 2007 O3 NAAQS review, the controlled human exposure-based health risk 

37 assessment involved developing risk estimates for lung function decrements (≥ 10, ≥ 15, and 

38 ≥ 20% changes in FEV1) in school-aged children (ages 5 to 18 years old). The strong emphasis 
6-4

1 on children reflects the finding of previous O3 NAAQS reviews that children are an important at2 risk group. Due to the increased amount of time spent outdoors engaged in relatively high levels 

3 of physical activity (which increases intake), school-aged children as a group are particularly at 

4 risk for experiencing O3-related health effects.

5 Outdoor workers and other adults who engage in moderate exertion for prolonged 

6 periods or heavy exertion for shorter periods during the day also are clearly at risk for 

7 experiencing similar lung function responses when exposed to elevated ambient O3

8 concentrations. In this second draft REA, we focus the quantitative risk assessment for lung 

9 function decrements on all and asthmatic school-aged children (ages 5-18), and the adult 

10 population (ages 19 and above).

11 For the second draft assessment, lung function risks are estimated for 15 cities, Atlanta, 

12 Baltimore, Boston, Chicago, Cleveland, Dallas, Denver, Detroit, Houston, Los Angeles, New 

13 York, Philadelphia, Sacramento, St. Louis, and Washington, DC.

14 6.2.1 Selection of Health Endpoints

15 The ISA identifies several responses to short-term O3 exposure that have been evaluated 

16 in controlled human exposure studies (US EPA, 2013, sections 6.2.1.1, 6.2.2.1, 6.2.3.1, and 

17 6.3.1). These include decreased inspiratory capacity; decreased forced vital capacity (FVC) and 

18 forced expiratory volume in one second (FEV1); mild bronchoconstriction; rapid, shallow 

19 breathing patterns during exercise; symptoms of cough and pain on deep inspiration (PDI);

20 increased airway responsiveness; and pulmonary inflammation. Such studies provide direct 

21 evidence of relationships between short-term O3 exposure and an array of respiratory-related 

22 effects, however, there are only sufficient exposure-response data at different concentrations to 

23 develop quantitative risk estimates for O3-related decrements in FEV1. Other responses to O3

24 which may be equally or more important then FEV1 decrements (e.g., inflammation) do not 

25 necessarily correlate with FEV1 responses (ISA, section 6.2.3.1) and this risk assessment is not 

26 able to address these other responses.

27 As stated in the 2006 Criteria Document (Table 8-3, p.8-68) for adults with lung disease, 

28 even moderate functional responses (e.g., FEV1 decrements ≥ 10% but < 20%) would likely 

29 interfere with normal activities for many individuals, and would likely result in more frequent 

30 medication use. In a recent letter to the Administrator, the CASAC O3 Panel stated that 

31 “'Clinically relevant' effects are decrements > 10%, a decrease in lung function considered 

32 clinically relevant by the American Thoracic Society” (Samet, 2011, p.2). The CASAC O3 Panel 

33 also stated that:

34 a 10% decrement in FEV1 can lead to respiratory symptoms, especially in 

35 individuals with pre-existing pulmonary or cardiac disease. For example, 

36 people with chronic obstructive pulmonary disease have decreased ventilatory 
6-5

1 reserve (i.e., decreased baseline FEV1) such that a ≥ 10% decrement could lead 

2 to moderate to severe respiratory symptoms (Samet, 2011, p.7).

3

4 This is consistent with the most recent official statement of the American Thoracic Society on 

5 what constitutes an adverse lung function health effect of air pollution:

6 The committee recommends that a small, transient loss of lung function, by 

7 itself, should not automatically be designated as adverse. In drawing the 

8 distinction between adverse and nonadverse reversible effects, this committee 

9 recommended that reversible loss of lung function in combination with the 

10 presence of symptoms should be considered adverse (ATS, 2000, p.672).

11

12 For this lung function risk assessment, a focus on the mid- to upper-end of the range of 

13 moderate levels of functional responses and higher (FEV1 decrements ≥ 15%) is appropriate for 

14 estimating potentially adverse lung function decrements in active healthy adults, while for people 

15 with asthma or lung disease, a focus on moderate functional responses (FEV1 decrements down 

16 to 10%) may be appropriate.

17 6.2.2 Approach for Estimating Health Risk Based on Controlled Human Exposure 

18 Studies

19 The major components of the health risk assessment based on data from controlled 

20 human exposure studies are illustrated in Figure 3-3 in Chapter 3. As shown in this figure, under 

21 this portion of the risk assessment, exposure estimates for a number of different air quality 

22 scenarios (i.e., recent year of air quality, just meeting the existing 8-hour and alternative 

23 standards) are combined with probabilistic exposure-response relationships derived from the 

24 controlled human exposure studies to develop risk estimates associated with recent air quality 

25 and after simulating just meeting the existing and alternative standards. The health effect 

26 included in this portion of the risk assessment is lung function decrement, as measured by

27 changes in FEV1. The population risk estimates for a given lung function decrement (e.g., ≥ 15% 

28 reduction in FEV1) are estimates of the expected number of people who will experience that lung 

29 function decrement, the number of times that people experience repeated occurrences of given 

30 lung function decrements, and the number of occurrences (person-days) of the given lung 

31 function decrement. The air quality and exposure analysis components that are integral to this 

32 portion of the risk assessment are discussed in Chapters 4 and 5.

33 We used two approaches to estimate health risk. As done for the risk assessment 

34 conducted during the previous O3 NAAQS review, a Bayesian Markov Chain Monte Carlo 

35 approach was used to develop probabilistic exposure-response functions. These functions were 

36 then applied to the APEX estimated population distribution of 8-hour maximum exposures for 

persons at or above moderate exertion (≥ 13 L/min-m

2 37 body surface area) to estimate the number 
6-6

1 of persons expected to experience lung function decrements. The primary approach, based on the 

2 McDonnell-Stewart-Smith FEV1 model, uses the time-series of O3 exposure and corresponding 

3 ventilation rates for each APEX simulated individual to estimate their personal time-series of 

4 FEV1 reductions, selecting the daily maximum reduction for each person. A key difference 

5 between these approaches is that the previous method estimates a population distribution of 

6 FEV1 reductions, where the MSS model estimates FEV1 reductions at the individual level. Each 

7 of these approaches is discussed in detail below.

8 6.2.3 Controlled Human Exposure Studies

9 Modeling of risks of lung function decrements as a function of exposures to O3 is based 

10 on application of results from controlled human exposure studies. As discussed in Chapter 6 of 

11 the ISA (EPA, 2013a), there is a significant body of controlled human exposure studies reporting 

12 lung function decrements and respiratory symptoms in adults associated with 1- to 8-hour

13 exposures to O3. In the ISA sections on controlled human exposure (Sections 6.2.1.1, 6.2.2.1, 

14 6.2.3.1, and 6.3.1) over 140 references to human clinical studies are reported.

15 6.2.3.1 Life Stages

16 Consistent with the approach used in the previous O3 NAAQS review and lacking a 

17 significant body of controlled human exposure studies on children, we judge that it is reasonable 

18 to estimate exposure-response relationships for lung function decrements associated with O3

19 exposures in children 5-18 years old based on data from young adult subjects (18-35 years old). 

20 As discussed in the ISA (EPA, 2013a), findings from clinical studies for children and summer 

21 camp field studies of children 7-17 years old in at least six different locations in the U.S. and 

22 Canada found lung function decrements in healthy children similar to those observed in healthy 

23 young adults exposed to O3 under controlled chamber conditions. There are fewer studies of 

24 young children than adolescents to draw upon, which may add to uncertainties in the modeling. 

25 Additional uncertainties are likely introduced since the lungs and airways of children are 

26 developing, while development is complete in adults (Dietert et al., 2000). The primary period of 

27 alveolar development is from birth to around eight years of age, but there is evidence for 

28 continued development through adolescence. The adult number of alveoli is reached by 2–3 

29 years of age and the size and surface area of the alveoli increase until after adolescence (Hislop, 

30 2002; Narayanan et al., 2012).

31 Lung function responses to O3 exposure for adults older than 18 decrease with age until 

32 around age 55, when responses are minimal. “Children, adolescents, and young adults appear, on 

33 average, to have nearly equivalent spirometric responses to O3, but have greater responses than 

34 middle-aged and older adults when similarly exposed to O3” (ISA p. 6-21). “In healthy 

35 individuals, the fastest rate of decline in O3 responsiveness appears between the ages of 18 and 
6-7

1 35 years (Passannante et al., 1998; Seal et al., 1996), more so for females then males (Hazucha et 

2 al., 2003). During the middle age period (35-55 years), O3 sensitivity continues to decline, but at 

3 a much lower rate. Beyond this age (>55 years), acute O3 exposure elicits minimal spirometric 

4 changes” (ISA p. 6-23).

5 6.2.3.2 Asthma

6 There have been several controlled human exposure studies of the effects of O3 on

7 asthmatic subjects, going back to 1978 (Linn et al., 1978). In reference to these studies, the ISA 

8 states that “[b]ased on studies reviewed in the 1996 and 2006 O3 AQCDs, asthmatic subjects 

9 appear to be at least as sensitive to acute effects of O3 as healthy nonasthmatic subjects” (ISA p. 

10 6-20). Studies published since the 2006 O3 AQCD do not alter this conclusion (ISA, p. 6-20 to 6-

11 21). In the 2010 O3 NAAQS proposal (75 FR 2969-2972), EPA describes the evidence that 

12 people with asthma are as sensitive as, if not more sensitive than, normal subjects in manifesting 

13 O3-induced pulmonary function decrements.

14 In reference to epidemiologic studies, the ISA states that “[t]he evidence supporting 

15 associations between short-term increases in ambient O3 concentration and increases in 

16 respiratory symptoms in children with asthma is derived mostly from examination of 1-h max, 

17 8-h max, or 8-h avg O3 concentrations and a large body of single-region or single-city studies. 

18 The few available U.S. multicity studies produced less consistent associations.” (ISA, p. 6-101 to 

19 6-102). “Although recent studies contributed mixed evidence, the collective body of evidence 

20 supports associations between increases in ambient O3 concentration and increased asthma 

21 medication use in children” (ISA, p. 6-109).

22 6.2.3.3 Ethnicity

23 There are two controlled human exposure studies that have assessed differences in lung 

24 function responses comparing ethnic groups (ISA, p. 6-23 to 6-24). Both of these studies show 

25 greater FEV1 decrements in blacks than whites, however, epidemiologic studies were less 

26 supportive of this difference in response. The data available are insufficient to quantify any 

27 differences that might exist due to the limited number of studies and a lack of consistency 

28 between disciplines.

29 6.2.3.4 Body Mass Index

30 Some studies have found greater FEV1 decrements to be associated with increasing BMI. 

31 BMI was included in some of the models of McDonnell et al. (2012); however, the BMI terms 

32 were found to be statistically insignificant, indicating that the effect of BMI on FEV1 in the 

33 presence of O3 is likely to be small, within the range of BMIs of the subjects studied.
6-8

1 6.2.3.5 Outdoor Workers

2 Although there are no controlled human exposure studies that have had specifically 

3 outdoor workers as subjects, the studies are applicable to outdoor workers: the 6.6-hour 

4 experimental protocol was intended to simulate the performance of heavy physical labor for a 

5 full workday (ISA, p. 6-9).

6 6.2.3.6 Variability of Responses

7 Responses to O3 exposure are variable within the population, even within cohorts of 

8 similar people (e.g., healthy young adult white males) (ISA, p. 6-16 to 6-20). Factors which 

9 contribute to interindividual variability include health status, body mass index, age, sex, 

10 race/ethnicity, and the intrinsic responsiveness of individuals. Other factors which contribute to 

11 the variability of responses include the duration and concentration of O3 exposure, the level of 

12 exercise and breathing rate, attenuation due to repeated exposures, and co-exposures with other 

13 pollutants. For specific individuals, lung function responses tend to be reproducible over a period 

14 of several months.

15 6.2.4 The McDonnell-Stewart-Smith (MSS) Model

16 In this review, EPA is investigating the use of a new model that estimates FEV1

17 responses for individuals associated with short-term exposures to O3 (McDonnell, Stewart, and 

18 Smith, 2007; McDonnell, Stewart, and Smith, 2010). This is a fundamentally different approach 

19 than the previous approach, for which the exposure-response function is at a population level, not 

20 an individual level. This model was developed using the controlled human exposure data

21 described in Section 6.2.5 as well as incorporating several additional data sets from studies using 

22 shorter exposure durations and different exertion levels and breathing rates. These data were 

23 from 15 controlled human O3 exposure studies that included exposure of 541 volunteers (ages 18

to 351 24 ) on a total of 864 occasions. These data are described in McDonnell et al. (1997).

25 Schelegle et al. (2009) found that there appears to be a delay in response when modeling FEV1

26 decrements as a function of cumulative dose and estimated a threshold associated with the delay.

27 McDonnell et al. (2012) refit their 2010 model using data from eight additional studies with 201

28 subjects and incorporating a threshold parameter into the model. Their threshold parameter 

29 allows for modeling a delay in response until cumulative dose rate (taking into account decreases 

30 over time according to first order reaction kinetics) reaches a threshold value and is found by 

31 McDonnell et al. (2012) to slightly improve model fit. That latest model is the model described 

 

1 The ages in these studies range from 18 years 1 month to 35 years 1 month.
6-9

1 here and is the model used in this risk assessment. The threshold is not a concentration threshold 

2 and does not preclude responses at low concentration exposures.

3 Schelegle et al. (2012) have also developed a 2-compartment model for predicting FEV1

4 decrements (ISA, p. 6-15,16). Their model is similar to the MSS model in that it accounts for the 

5 effects of cumulative dose coupled with an exponential decay and also has a threshold, below 

6 which response is delayed. The primary difference between this model and the MSS model is 

7 that in the Schelegle et al. model the net cumulative dose is multiplied by an individual's 

8 responsiveness coefficient to obtain a predicted FEV1 decrement, whereas in the MSS model the 

9 FEV1 decrement increases as a sigmoid-shaped function of the net cumulative dose rate. Also, 

10 the Schelegle et al. model’s threshold is based on cumulative intake dose (the integral of 

11 concentration x volume inhaled), where the MSS model’s threshold is based on net cumulative 

12 dose rate (taking into account the first order decay). A direct comparison of the results of these 

13 two models has not been performed.

14 The MSS model is conceptually a two-compartment model. The cumulative amount of

15 exposure to O3 (exposure concentration times ventilation rate, loosely speaking a measure of 

16 dose rate) is modeled in the first compartment and modified by an exponential decay factor to 

17 yield an intermediate quantity X. The response (lung function decrement) of the individual to X

18 is modeled in the second compartment (Figure 6-1). The threshold parameter imposes the 

19 constraint that there is no response while the value of X is below the threshold value.

20

21 Figure 6-1. Two-Compartment Model

22

23 C is exposure concentration, V is ventilation rate, t is time, X is an intermediate quantity, 

24 a is a decay constant. Adapted from Figure 1 in McDonnell et al. (1999).

25

C(t)V(t)

dX/dt=C(t)V(t)-aX

X(t)

aX (metabolism)

(dose)

logistic

response ΔFEV1

compartment 1 compartment 2
6-10

1 X is given by the solution of the differential equation (6-1):

2 (Equation 6-1)

3

X(t) increases with “dose” (C·Vβ6 4 ) over time for an individual and allows for removal of 

O3 with a half-life of 1/β5 through the 2nd 5 term in equation (6-1). In APEX, because the exposure 

6 concentration, exertion level, and ventilation rate are constant over an event, this equation has an 

7 analytic solution for each event (“events” in APEX are intervals of constant activity and 

8 concentration, where an individual is in one microenvironment, and range in duration from one

9 to 60 minutes):

10 (Equation 6-2)

11 This model calculates the FEV1 decrement due to O3 exposure (compartment 2) as:

12 (Equation 6-3)

13 where Tijk = max{0, Xijk – β9}. β9 is a threshold parameter which allows X to increase up to the 

14 threshold before the median response is allowed to exceed zero.

15 The variables in the above equations are defined as:

16 The indices i,j,k refer to the ith subject at the jth time for the kth experiment for that subject,

17 C(t) is the O3 exposure concentration at time t (ppm) during the event,

18 V(t) = VE(t)/BSA is the ventilation rate normalized by body surface area at time t

 (L/min-m

2 19 ),

VE(t) is the expired minute volume at time t (L min-1 20 ), 

BSA is the body surface area (m2 21 ),

22 t is the time (minutes), t0 is the time at the start of the event,

23 Ageik is age in years of the ith subject in the kth study,

24 Ā is an age parameter (taken to be the approximate mean age of the clinical study subjects in 

25 the McDonnell, Stewart, and Smith 2007 (Ā=25), 2010 (Ā=25), and 2012 (Ā=23.8) papers),

26 Ui is a subject-level random effect (between-individual variability not otherwise captured by 

27 the model), and

εijk 28 is a variability term, which includes measurement error and intra-individual variability 

29 not otherwise captured by the model.
6-11

1 The βs and the variances of the {Ui} and {εijk } are fitted model parameters (see 

2 McDonnell, et al. (2007, 2010, and 2012) for details). In APEX, values of Ui and εijk are drawn 

3 from Gaussian distributions with mean zero and variances var(U) and var(ε), constrained to be 

4 within ±2 standard deviations from the means. The values of Ui are chosen once for each 

5 individual and remain constant for individuals throughout the simulation. The εijk are sampled 

6 daily for each individual. The best fit values (based on maximum likelihood) for these 

7 parameters are listed in Table 6-1. The values in parentheses are standard errors of the estimates

8 (given here to two significant digits; the values in the papers are given to up to five significant 

9 digits). Although some of the parameters are quite different in the three models in Table 6-1, the 

10 predictions of these three models are similar. The relative influences of the parameters are 

11 discussed in Section 6.5.1.

12 Table 6-1. Estimated Parameters in the MSS Models

Model β1 β2 β3 β4 β5 β6 β9 var(U) var(E)

20071,

20102

9.9047

(0.61)

–0.4106

(0.11)

0.0164

(0.0030)

46.9397

(7.3)

0.003748

(0.00027)

0.9123

(0.054)

0.835

(0.080)

13.8279

(0.36)

20123 9.8057

(0.74)

–0.1907

(0.28)

0.01839

(0.0051)

65.826

(12)

0.003191

(0.00021)

0.8753

(0.086) 0 0.9449(0.083)

17.120

(1.2)

2012T4 10.916

(0.84)

–0.2104

(0.31)

0.01506

(0.0033)

13.497

(4.7)

0.003221

(0.00021)

0.8839

(0.065)

59.284

(10)

0.9373

(0.082)

17.0816

(1.2)

13 The values in parentheses are standard errors of the estimates.

1 14 McDonnell, Stewart, and Smith (2007). Ā = 25.

2 15 McDonnell, Stewart, and Smith (2010). Ā = 25. 3 16 McDonnell, et al. (2012). Ā = 23.8. No-threshold.

4 17 McDonnell, et al. (2012). Ā = 23.8. Threshold.

18 We are using this model to estimate lung function decrements for people ages 5 and 

19 older. However this model was developed using only data from individuals aged 18 to 35 and the 

20 age adjustment term [β1 + β2 (Ageijk – Ā)] in the model is not appropriate for all ages. In addition 

21 to this age term, the effects of age are also taken into account through the dependence of 

22 ventilation rate and body surface area on age. The APEX estimates of lung function risk for 

23 different age groups are also influenced by the time spent outdoors and the activities engaged in 

24 by those groups, which vary by age (see Appendix 6-E).

25 Clinical studies data for children which could be used to fit the model for children are not 

26 available at this time. In the absence of data, we are extending the model to ages 5 to 18 by 

27 holding the age term constant at the age 18 level. Since the response increases as age decreases 

28 in the range 18 to 35, this trend may extend into ages of children, in which case the responses of 
6-12

1 children could be underestimated. However, the slope of the age term in the MSS model is 

2 estimated based on data for ages 18 to 35 and does not capture differences in age trend within 

3 this range; in particular, we don’t know at what age the response peaks, which could be above or 

4 below 18. The evidence from clinical studies indicates that the responsiveness of children to O3

5 is about the same as for young adults (ISA, 2012, p. 6-21). This suggests that the age term for 

6 children should not be higher than the age term for young adults.

7 Because the responses to O3 decline from age 18 until around age 55 and for ages older 

8 than 55 the response are minimal, we let the age term for ages 35 to 55 linearly decrease to zero 

9 and set it to zero for ages > 55.

10 “In healthy individuals, the fastest rate of decline in O3 responsiveness 

11 appears between the ages of 18 and 35 years .... During the middle age period 

12 (35-55 years), O3 sensitivity continues to decline, but at a much lower rate. 

13 Beyond this age (>55 years), acute O3 exposure elicits minimal spirometric 

14 changes.” (ISA, 2012, p. 6-23)

15 In order to extend the age term to ages outside the range of ages the MSS model is based 

16 on (ages 18-35), we parameterize the age term by [β1 + β2(α1 Age + α2)], for different ranges of 

17 ages (α1 and α2 depend on age), requiring that these terms match at each boundary to form a

18 piecewise linear continuous function of age. The foregoing assumptions result in the following 

19 values of α1 and α2 for four age ranges (Table 6-2).

20 Table 6-2. Age Term Parameters for Application of the 2012 MSS Threshold 

21 Model to All Ages

Age Range β1 β2 α1 α2

5 – 17 10.916 –0.2104 0 –5.8

18 – 35 10.916 –0.2104 1 –23.8

36 – 55 10.916 –0.2104 2.0341 –59.994

> 55 0 0 0 0

22

23 The lung function decrements estimated by the MSS (2010) model for a particular case 

24 are illustrated in Figure 6-2 and Figure 6-3. Figure 6-2 shows the predictions of the MSS model 

for 20-year old individuals with a (typical) body surface area (BSA) of 2 m2 25 and a target 

26 ventilation rate of 40 L/min (moderate exertion) and an O3 exposure level of 100 ppb, under the 

27 conditions of a typical 6.6-hour clinical study. Subjects alternated 50 minutes of moderate 

28 exercise with 10 minutes of rest for the first three hours, with the exercise occurring first. For the 

29 next 35 minutes (lunch), subjects continued exposure at rest. For the remaining three hours of the 

30 exposure period, subjects again alternated 50 minutes of exercise with 10 minutes of rest. The
6-13

1 inter-individual variability predicted by this model is depicted by the boxplots in this figure. The 

2 predictions for the median individual over time are given by the line. Minute-by-minute 

3 predictions for the median individual for an exposure level of 100 ppb are shown in Figure 6-3.

4 The stairstep response results from the pattern of exercise and rest during the experiment.

5

6

7 Figure 6-2. Distribution of Responses (Lung Function Decrements in FEV1) 

8 Predicted by the MSS Model for 20-Year Old Individuals. Exposure to 100 ppb O3 at 

Moderate Exercise (40 L/min, BSA=2 m2 9 ) Under the Conditions of a Typical 6.6-hour 

10 Clinical Study.

11

12 The bottom and top edges of the boxes are at the 25th and 75th percentiles. The center horizontal line is 

13 drawn at the 50th percentile (median). The whiskers are at the 1st and 99th percentiles. FEV1 decrement (%)

-5

0

5

10

15

20

25

30

35

40

45

hour

0 1 2 3 4 5 6 7
6-14

1

2 Figure 6-3. Median Response (Lung Function Decrements in FEV1) Predicted by 

3 the MSS Model for 20-Year Old Individuals. Exposure to 100 ppb O3 at Moderate Exercise 

(40 L/min, BSA=2 m2 4 ) Under the Conditions of a Typical 6.6-hour Clinical Study.

5

6 Figure 6-4 and Figure 6-5 illustrate the threshold effect based on McDonnell et al. 

7 (2012). Figure 6-4 is a graph of the median response for a population of 20-year old individuals 

8 over a 6.6-hour time period. The exposure concentration is a constant 100 ppb over this time 

9 period, while the individuals are exercising from hour 1 to hour 3 and at rest otherwise. There is 

10 a 30-minute delay in response due to the threshold; without the threshold, the response starts 

11 increasing when exercise starts. Figure 6-5 shows the corresponding probability of a response 

12 (FEV1 decrement) ≥ 10% over the time period for the two models. There is very little difference 

13 in response between the threshold and non-threshold models. FEV1 decrement (%)

0

1

2

3

4

5

6

7

hour

0 1 2 3 4 5 6 7
6-15

∆FEV1%

0

1

2

3

4

hour

0 1 2 3 4 5 6 7

Model no-threshold threshold

rest exercise rest

delay due to threshold

Figure 6-4. Median Response (FEV1 Decrements) Predicted by the MSS Threshold and 

Non-Threshold Models for 20-Year Old Individuals, Constant 100 ppb O3 Exposure, 2 

Hours Heavy Exercise (30 L/min-m2 BSA).

Figure 6-5. Probability of Response ≥ 10% Predicted by the MSS Threshold and 

Non-Threshold Models for 20-Year Old Individuals, Constant 100 ppb O3 Exposure, 2 

Hours Heavy Exercise (30 L/min-m2 BSA). Probability of Response (%)

 0%

 2%

 4%

 6%

 8%

 10%

 12%

 14%

 16%

 18%

 20%

 22%

hour

0 1 2 3 4 5 6 7

Model no-threshold threshold
6-16

1 6.2.5 The Exposure-Response Function Approach Used in Prior Reviews

2 As described in section 3.1.2 of the 2007 Risk Assessment Technical Support Document

3 (EPA, 2007b), a Bayesian Markov Chain Monte Carlo approach (Lunn et al., 2012) was used to 

4 estimate probabilistic exposure-response relationships for lung function decrements associated 

5 with 8-hour O3 exposures occurring at moderate exertion. In the previous review, summary data 

6 from the Folinsbee et al. (1988), Horstman et al. (1990), McDonnell et al. (1991), and Adams 

7 (2002, 2003, 2006) studies were combined to estimate exposure-response relationships for 8-

8 hour exposures at moderate exertion for each of the three measures of lung function decrement 

9 (≥ 10, ≥ 15, ≥ 20% decrements in FEV1). In this second draft REA we have updated this 

10 exposure-response function with the results from two additional studies (Kim et al., 2011; 

11 Schelegle et al., 2009). The controlled human exposure study data were corrected for the effect 

12 of exercise in clean filtered air on an individual basis to remove any systematic bias that might 

13 be present in the data attributable to an exercise effect (ISA, Section 6.2.1.1). This is done by 

14 subtracting the FEV1 decrement in filtered air from the FEV1 decrement (at the same time point) 

15 during exposure to O3. An example of this calculation is given in Appendix 6-D.

16 Table 6-3 presents a summary of the study-specific results based on correcting all 

17 individual responses for the effect on lung function decrements of exercise in clean air.

18

Table 6-3. Study-specific O3 Exposure-response Data for Lung Function Decrements

Based on Correcting Individual Responses for the Effect on Lung Function of Exercise in 

Clean Air, Ages 18-35

Study, Grouped by 

Average O3 Exposure Protocol Number 

Exposed

Number of Responsesa

ΔFEV1 ≥ 

10%

ΔFEV1 ≥ 

15%

ΔFEV1 ≥ 

20%

0.04 ppm O3

Adams et al. (2002) Square-wave, face mask 30 2 (2) 0 (0) 0 (0)

Adams et al. (2006) Triangular 30 0 (0) 0 (0) 0 (0)

0.06 ppm O3

Adams et al. (2006)

Square-wave 30 2 (2) 0 (0) 0 (0)

Triangular 30 2 (2) 2 (2) 0 (0)

Kim et al. (2011) Square-wave 59 3 (6) 1 (3) 0 (0)

Schelegle et al. (2009) Variable levels (0.06 ppm avg) 31 4 (8) 2 (3) 1 (1)

0.07 ppm O3

Schelegle et al. (2009) Variable levels (0.07 ppm avg) 31 6 (12) 3 (7) 2 (3)

0.08 ppm O3

Adams et al. (2002) Square-wave, face mask 30 6 (6) 5 (5) 2 (2)
6-17

Table 6-3. Study-specific O3 Exposure-response Data for Lung Function Decrements

Based on Correcting Individual Responses for the Effect on Lung Function of Exercise in 

Clean Air, Ages 18-35

Study, Grouped by 

Average O3 Exposure Protocol Number 

Exposed

Number of Responsesa

ΔFEV1 ≥ 

10%

ΔFEV1 ≥ 

15%

ΔFEV1 ≥ 

20%

Adams et al. (2003)

Square-wave, chamber 30 6 (6) 2 (2) 1 (1)

Square-wave, face mask 30 5 (5) 2 (2) 2 (2)

Variable levels (0.08 ppm 

avg), chamber 30 6 (6) 1 (1) 1 (1)

Variable levels (0.08 ppm 

avg), face mask 30 5 (5) 1 (1) 1 (1)

Adams et al. (2006) Square-wave 30 7 (7) 2 (2) 1 (1)

Triangular 30 9 (9) 3 (3) 1 (1)

F-H-M b Square-wave 60 17 (19) 11 (14) 8 (8)

Kim et al. (2011) Square-wave 30 4 (6) 1 (1) 0 (0)

Schelegle et al. (2009) Variable levels (0.08 ppm avg) 31 10 (15) 5 (8) 4 (6)

0.087 ppm O3

Schelegle et al. (2009) Variable levels (0.087 ppm 

avg) 31 14 (17) 10 (12) 7 (9)

0.1 ppm O3

F-H-M b Square-wave 32 13 (13) 11 (12) 6 (9)

0.12 ppm O3

Adams et al. (2002) Square-wave, chamber 30 17 (17) 12 (12) 10 (10)

Square-wave, face mask 30 21 (21) 13 (13) 7 (7)

F-H-M b Square-wave 30 18 (19) 15 (15) 10 (10)

a. The first number in each cell is the number of responses based on post-exposure decrements in FEV1

(i.e., we used only the last FEV1 measurement and the pre-exposure FEV1 to obtain a single percentage change in 

FEV1 for each subject in each experiment). The numbers in parentheses are the numbers of responses based on 

maximum FEV1 decrements. Specifically, when there were multiple FEV1 measurements after the beginning of the 

exposure, we calculated multiple FEV1 percentage changes for each subject in each experiment and used the 

maximum change when calculating the numbers of responses greater than 10%, 15%, and 20%.

b. F-H-M combines data from Folinsbee et al. (1988), Horstman et al. (1990), and McDonnell et al. (1991).

1

2 For the risk assessment conducted during the 2007 O3 NAAQS review (EPA, 2007b), 

3 EPA considered both linear and logistic functional forms in estimating the exposure-response 

4 relationship and chose a 90 percent logistic/10 percent piecewise-linear split using a Bayesian 

5 Markov Chain Monte Carlo approach. This Bayesian estimation approach incorporates both 

6 model uncertainty and uncertainty due to sampling variability.
6-18

1 For each of the three measures of lung function decrement, EPA assumed a 90 percent

probability that the exposure-response function has the following 3-parameter logistic form:2 2 

(1 )(1 )

* (1 ) ( ; , , ) γ β γ

γ β α

α β γ + + +

− = x

x

e e

e e

y x 3 , (Equation 6-4)

4 where x denotes the O3 concentration (in ppm) to which the individual is exposed, y 

5 denotes the corresponding response (decrement in FEV1 > 10%, > 15% or > 20%), and α, β, and 

6 γ are the three parameters whose values are estimated.

7 We assumed a 10 percent probability that the exposure-response function has the 

8 following linear with threshold (hockey stick) form:







+ <

+ + > = 0, 0

, 0

( ; , ) for x

x for x

y x

α β

α β α β 9 α β (Equation 6-5)

10 We assumed that the number of responses, S, out of N subjects exposed to a given 

11 concentration, x, has a binomial distribution with response probability given by Eq (6-4) with 90 

12 percent probability and response probability given by Eq (6-5) with 10 percent probability. In the 

13 2007 review, we also considered 80/20 and 50/50 probabilities for the logistic and hockey stick

14 forms, and ran those as sensitivity analyses. We performed those analyses with the updated data 

15 and found that for each of the three exposure-response curves, the 90/10 mix has smaller error in 

16 fit (weighted RMSE) than the other two combinations of probabilities, and we are using only that 

17 function in this review.

18 In some of the controlled human exposure studies, subjects were exposed to a given O3

19 concentration more than once – for example, using a constant (square-wave) exposure pattern in 

20 one protocol and a variable (triangular) exposure pattern in another protocol. However, because 

21 there were insufficient data to estimate subject-specific response probabilities, we assumed a 

22 single response probability (for a given definition of response) for all individuals and treated the 

23 repeated exposures for a single subject as independent exposures in the binomial distribution. 

24 For each of the two functional forms (logistic and linear), we derived a Bayesian 

25 posterior distribution using this binomial likelihood function in combination with prior 

26 distributions for each of the unknown parameters (Box and Tiao, 1973). We assumed lognormal 

27 priors with maximum likelihood estimates of the means and variances for the parameters of the 

28 logistic function, and normal priors, similarly with maximum likelihood estimates for the means 

29 and variances, for the parameters of the linear function. For each of the two functional forms 

 

2 The 3-parameter logistic function is a special case of the 4-parameter logistic, in which the function is forced to go 

through the origin, so that the probability of response to 0.0 ppm is 0.
6-19

considered, we used 1,000 iterations as the “burn-in” period3 1 followed by 9,000 iterations for the 

2 estimation. Each iteration corresponds to a set of values for the parameters of the (logistic or 

3 linear) exposure-response function. We combined the 9,000 sets of values from the logistic 

4 model runs with the last 1,000 sets of values from the linear model runs to get a single combined 

5 distribution of 10,000 sets of values reflecting the 90 percent/10 percent assumption stated 

6 above. WinBUGS version 1.4.3 was used for these analyses (WinBUGS; Lunn et al., 2012).

For any O3 concentration, x, we can derive the nth 7 percentile response value, for any n, by 

8 evaluating the exposure-response function at x using each of the 10,000 sets of parameter values 

9 (9,000 of which were for a logistic model and 1,000 of which were for a linear model). The 

resulting 2.5th percentile, median (50th percentile), and 97.5th 10 percentile exposure-response 

11 functions for changes in FEV1 > 10% are shown in Figure 6-6, along with the response data to 

12 which they were fit. The corresponding exposure-response functions for changes in FEV1 >

13 15% and > 20% are shown in Appendix 6-A. The values of the functions are also provided in

14 Appendix 6-A.

15

 

3 Markov chain Monte Carlo (MCMC) simulations require an initial adaptive “burn-in” set of iterations, which are 

not used. This allows the MCMC sampling to stabilize.
6-20

1

2 Figure 6-6. Probabilistic Exposure-Response Relationships for FEV1 Decrements ≥ 

3 10% for 8-Hour Exposures At Moderate Exertion, Ages 18-35. Values associated with data 

4 points are the number of subject-exposures at each exposure concentration.

5

6

7 The population risk is estimated by multiplying the expected risk by the number of 

8 people in the relevant population, as shown in Equation 6-6 below. The risk (i.e., expected 

fractional response rate) for the kth 9 fractile, Rk is estimated as:

1

( |)

N

k j kj

j

R P x RR e =

10 = ∑ (Equation 6-6)

11 where: 

12 ej = (the midpoint of) the jth category of personal exposure to O3;

13 Pj = the fraction of the population having personal exposures to O3 concentration

14 of ej ppm;

k j 15 RR | e = k-fractile response rate at O3 exposure concentration ej;

16 N = number of intervals (categories) of O3 personal exposure concentration.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16

Response Rate (%)

Ozone Exposure (ppm)

97.5th Pctl (FEV1≥10%)

Median (FEV1≥10%)

2.5th Pctl (FEV1≥10%)

Data for FEV1≥10%

60 150

31 331

32

31

90

Functional form mix:

90% logistic &

10% linear
6-21

1 Exposure estimates used in this portion of the risk assessment were obtained from APEX 

2 for each of the fifteen urban areas and the five air quality scenarios. Chapter 5 provides 

3 additional details about the inputs and methodology used to estimate population exposure in the 

4 urban areas. Exposure estimates for all and asthmatic school-aged children (ages 5 to 18) were 

5 combined with probabilistic exposure-response relationships for lung function decrements 

6 associated with 8-hour exposure while engaged in moderate exertion. Individuals engaged in 

7 activities that resulted in an average equivalent (BSA-normalized) ventilation rate (EVR) for the 

8-hour period at or above 13 L/min-m

2 8 BSA were included in the exposure estimates for 8-hour

9 moderate or greater exertion. This range was selected based on the EVRs for the group of 

10 subjects in the controlled human exposure studies that were the basis for the exposure-response 

11 relationships used in this portion of the risk assessment.

12 6.3 O3 RISK ESTIMATES

13 This section provides lung function risk estimates associated with several air quality 

14 scenarios: five recent years of air quality as represented by 2006 to 2010 monitoring data, and air 

15 quality in those years after simulating just meeting the existing O3 standard and alternative 

16 standard levels of 0.070, 0.065, and 0.060 ppm. The risk measures presented here are the 

17 percents of the population estimated to experience lung function responses greater then 10, 15, 

18 and 20%, one or more times or six or more times during an O3 season, for three age groups: 

19 school-aged children (ages 5-18), young adults (ages 19-35) and adults ages 36-55. Results for 

20 adults older than 55 are not presented since the responses for this age group are estimated to be 

21 minimal. People with multiple events with large lung function decreases are more at risk than 

22 those with only one such event during the O3 season. Although six events is less than once per 

23 month, we see dramatic decreases in population risk in going from one or more to six or more 

24 events during a season, which is why we report on six or more events rather than a higher 

25 number.

26 In the figures and tables that follow, “base” indicates the base case scenario of recent air 

27 quality for the indicated year. “75,” “70,” “65,” and “60” respectively indicate the existing O3

28 standard and alternative standard levels of 0.070, 0.065, and 0.060 ppm. “75 6-8” indicates the 

29 0.075 ppm existing 8-hour standard based on rollback for the 2006-2008 period, while “75 8-10” 

30 indicates the existing standard scenario based on rollback for the 2008-2010 period. There are 

31 two estimates of results for the 2008 existing and alternative standard scenarios (because 2008 

32 overlaps the two rollback periods) and one for each of the other four years. These two estimates 

33 for 2008 can be quite different because of the relationship between the design value over the 

34 three-year period and the amount of adjustment to the air quality distribution in 2008 that can 

35 result.
6-22

1 6.3.1 Lung Function Risk Estimates Based on the McDonnell-Stewart-Smith Model

2 Results based on the McDonnell-Stewart-Smith (2012) threshold model are summarized

3 in this section; detailed results can be found in Appendix 6-B.

4 Figure 6-7 shows the results for school-aged children in the same format used in exposure 

5 results, explained in Section 5.3.1. Figure 6-7 depicts results for all cities, year, and scenarios for 

6 ages 5 to 18 with ≥ 1 occurrences of FEV1 decrements ≥ 10, 15, 20% and illustrates the variation 

7 of results across cities, year, and scenarios.

8 Figure 6-8 shows the variation across cities (horizontally) and years (vertically) for the 

9 percent of school-aged children with ≥ 1 occurrences of FEV1 decrements ≥ 10% with air quality 

10 just meeting the potential alternative standard of 0.07 ppm. The points above each study area on 

11 this graph represent the risk for the six years for the study area (2008 has two points, 

12 corresponding to the different 2006-2008 and 2008-2010 design values used to adjust the air 

13 quality to meet 0.07 ppm). There is substantial variability both across years and across cities. 

14 Denver has the highest overall risks, while Cleveland and New York have the lowest. Los 

15 Angeles has the smallest variation across years, with a range of 2.3% (from 14.3% to 16.6%). 

16 The other cities have a range of around 4% to 7.5% across years.

17 Table 6-4 and Table 6-5 present summary results (ranges over cities and years) of FEV1

18 decrements ≥ 10 and 15% estimated by the MSS model for the different age groups. The results 

19 for asthmatic school-aged children are very similar to the results for all school-aged children and 

20 are not presented here.

21 Figure 6-9 illustrates the distribution of responses (FEV1 decrements > 10%) across 

22 ranges of ambient concentrations of O3 for school-aged children for one city and scenario (Los 

23 Angeles, 2006 recent air quality). The concentrations are daily 8-hour average ambient 

24 concentrations during the 8-hour period with maximum 8-hour average exposure for that day.

25
6-23

Figure 6-7. Risk results for all school-aged children with ≥ 1 occurrences of FEV1

decrements ≥ 10, 15, 20% for all cities, year, and scenarios (y-axis is percent of children affected). Percent of school-aged children with 

≥ 1 occurrence of FEV1 decrements 

≥ 10%
6-24

1

2

3 Figure 6-8. Risk results for all school-aged children with ≥ 1 occurrences of FEV1

4 decrements ≥ 10% under the 0.07 ppm alternative standard showing variability across 

5 cities (horizontally) and years (vertically).

6

Percent of population

 8%

 9%

 10%

 11%

 12%

 13%

 14%

 15%

 16%

 17%

 18%

 19%

 20%

 21%

Urban area

A

t

l

a

n

t

a

B

a

l

t

i

m

o

r

e

B

o

s

t

o

n

C

h

i

c

a

g

o

C

l

e

v

e

l

a

n

d

D

a

l

l

a

s

D

e

n

v

e

r

D

e

t

r

o

i

t

H

o

u

s

t

o

n

L

o

s

A

n

g

e

l

e

s

N

e

w

Y

o

r

k

P

h

i

l

a

d

e

l

p

h

i

a

S

a

c

r

a

m

e

n

t

o

S

t

L

o

u

i

s

W

a

s

h

i

n

g

t

o

n
6-25

1

2 Table 6-4. Ranges of percents of population experiencing one or more days during 

3 the O3 season with lung function decrement (∆FEV1) more than 10%. The numbers in this 

4 table are the minimum and maximum percents estimated over all cities and years.

5

percent experiencing ≥ 1 day with 

ΔFEV1 ≥ 10%

percent experiencing ≥ 6 days with 

ΔFEV1 ≥ 10%

Age group Scenario minimum maximum minimum maximum

5 to 18 base 11% 31% 1% 9%

5 to 18 75 11% 22% 1% 6%

5 to 18 70 8% 20% 1% 5%

5 to 18 65 2% 18% 0% 4%

5 to 18 60 4% 13% 0% 3%

19 to 35 base 3% 13% 0% 1%

19 to 35 75 3% 9% 0% 1%

19 to 35 70 2% 8% 0% 1%

19 to 35 65 1% 6% 0% 1%

19 to 35 60 1% 5% 0% 0%

36 to 55 base 1% 4% 0% 0%

36 to 55 75 1% 2% 0% 0%

36 to 55 70 0% 2% 0% 0%

36 to 55 65 0% 2% 0% 0%

36 to 55 60 0% 1% 0% 0%

> 55 All 0% 0% 0% 0%

6

7
6-26

1 Table 6-5. Ranges of percents of population experiencing one or more days during 

2 the O3 season with lung function decrement (∆FEV1) more than 15%. The numbers in this 

3 table are the minimum and maximum percents estimated over all cities and years.

4

percent experiencing ≥ 1 day with 

ΔFEV1 ≥ 15%

percent experiencing ≥ 6 days with 

ΔFEV1 ≥ 15%

Age group Scenario minimum maximum minimum maximum

5 to 18 base 2% 12% 0% 3%

5 to 18 75 2% 6% 0% 1%

5 to 18 70 2% 5% 0% 1%

5 to 18 65 0% 4% 0% 1%

5 to 18 60 0% 3% 0% 0%

19 to 35 base 0% 3% 0% 0%

19 to 35 75 0% 2% 0% 0%

19 to 35 70 0% 1% 0% 0%

19 to 35 65 0% 1% 0% 0%

19 to 35 60 0% 1% 0% 0%

36 to 55 base 0% 1% 0% 0%

36 to 55 75 0% 0% 0% 0%

36 to 55 70 0% 0% 0% 0%

36 to 55 65 0% 0% 0% 0%

36 to 55 60 0% 0% 0% 0%

> 55 All 0% 0% 0% 0%

5

6

7 These concentrations are less than but close to daily maximum 8-hour average ambient 

8 concentrations and are greater than daily maximum 8-hour average exposure concentrations. The 

9 percents in this chart reflect the frequencies of person-days with FEV1 decrements ≥ 10% within 

10 a concentration bin as percents of all person-days with FEV1 decrements ≥ 10%. Figure 6-9

11 shows that more than 90% of daily instances of FEV1 decrements ≥ 10% occur when 8-hour 

12 average ambient concentrations are above 40 ppb for this modeled scenario. This distribution 

13 will be different for different cities, years, and air quality scenarios.
6-27

1

2 Figure 6-9. Distribution of Daily FEV1 Decrements ≥ 10% Across Ranges of 8-hour 

3 Average Ambient O3 Concentrations (Los Angeles, 2006 recent air quality).

4

5 Outdoor workers spend more time outdoors than the general population and therefore are 

6 at higher risk for health effects due to O3. We conducted simulations of outdoor workers ages 19-

7 35 for Atlanta (2006) for the current and alternative standards to estimate the risk of this group 

8 for experiencing FEV1 decrements ≥ 15%. The methodology for simulating outdoor workers

9 involves modifying activity diaries to represent outdoor workers and is described in Section 

10 5.3.3 in Chapter 5. Table 6-6 shows the results of these simulations and compares them with the 

11 results for the general population, ages 19-35. The percents of people experiencing one or more 

12 FEV1 decrements ≥ 15% during the 2006 O3 season in Atlanta are 3.6 times higher for outdoor 

13 workers than for the general population (ages 19-35) under the current standard, and range up to 

14 5.3 times higher for the alternative standards. The percents of people experiencing six or more 

15 FEV1 decrements ≥ 15% during the 2006 O3 season in Atlanta are 20 times higher for outdoor 

16 workers than for the general population under the current standard, and range up to 150 times 

17 higher for the alternative standards. As expected, we see that the risk of repeated occurrences of 

18 FEV1 decrements ≥ 15% is much greater for outdoor workers than for the general population. Frequency of responses (percent)

0

2

4

6

8

10

12

14

16

18

20

8-hour average concentration (ppb)

0-5

5-10

10-20

20-3030-40

40-5050-60

60-70

70-8080-90

90-100

100-110110-120

> 120
6-28

1 Part of the reason for this is that APEX tends to underestimate the number of individuals who 

2 have very repetitive activity patterns (e.g., 9 to 5 weekdays office workers) when using the 

3 CHAD activity database and the method selected for generating longitudinal diary profiles (see 

4 Section 5.3.1).

5

6 Table 6-6. Percents of the General Population and Outdoor 

7 Workers (ages 19-35) Experiencing 1 or More and 6 or More FEV1

8 Decrements ≥ 15% (based on Atlanta 2006 APEX simulations)

General 

population

ages 19-35

Outdoor workers 

ages 19-35

1 or more occurrences

Current standard 1.2% 4.3%

70 ppb alt. std. 0.84% 3.2%

65 ppb alt. std. 0.55% 2.5%

60 ppb alt. std. 0.32% 1.7%

6 or more occurrences

Current standard 0.06% 1.2%

70 ppb alt. std. 0.018% 0.93%

65 ppb alt. std. 0.005% 0.74%

60 ppb alt. std. 0.005% 0.55%

9

10 6.3.2 Lung Function Risk Estimates Based on the Exposure-Response Functions 

11 Approach Used in Prior Reviews

12 In this section we present lung function risk estimates for all school-aged children 

13 following the methodology used in previous reviews, based on updated exposure-response (E-R)

14 functions. In Appendix 6-C we compare these estimates with those from the previous review.

15

16 Table 6-7 provides an overall summary of results for each air quality scenario by 

17 tabulating the minimum and maximum estimates over all cities and years of percents of all 

18 school-aged children (ages 5 to 18) experiencing one or more days (during the O3 season) with 

19 FEV1 decrement more than 10 and 15%. This table can be compared with Table 6-4 and Table 6-

20 5, which have analogous results for the MSS model. These results are much lower than the MSS 

21 model results. The reasons for this are described in Section 6.3.3 below.
6-29

1

2 Table 6-7. Ranges of percents of school-aged children experiencing one or more 

3 days during the O3 season with lung function decrement (∆FEV1) more than 10 and 15%. 

4 The numbers in this table are the minimum and maximum percents estimated over all 

5 cities and years.

Scenario

minimum

percent

experiencing

≥ 1 day with

Δ FEV1 ≥ 10%

maximum

percent

experiencing

≥ 1 day with

Δ FEV1 ≥ 10%

minimum

percent

experiencing

≥ 1 day with

Δ FEV1 ≥ 15%

maximum

percent

experiencing

≥ 1 day with

Δ FEV1 ≥ 15%

base 2% 11% 0% 5%

75 2% 6% 1% 2%

70 2% 6% 0% 2%

65 1% 5% 0% 1%

60 2% 3% 0% 1%

6

7

8 6.3.3 Comparison of the MSS Model with the Exposure-Response Function Approach

9 There are two key differences between the MSS and E-R models. The E-R model 

10 estimates the distribution of FEV1 decrements across the population or study group, whereas the 

11 MSS model estimates FEV1 decrements at the individual level and then these are aggregated to 

12 obtain the population distribution. Thus the MSS model allows for detailed analyses of 

13 conditions that influence risk. Second, the E-R model estimates FEV1 decrements only for 8-

14 hour average exposures when the 8-hour average exertion level is moderate or greater. The MSS 

15 model estimates FEV1 decrements for any averaging time and therefore accounts for a wider 

16 range of activities that might result in FEV1 decrements.

17 A comparison of the MSS model with the exposure-response function approach for the 

18 2006 existing standard scenarios is summarized in Table 6-8, which lists estimates of the 

19 percents of school-aged children estimated to experience lung function responses greater then 10, 

20 15, and 20%. The MSS model estimates are significantly higher than the exposure-response 

21 function approach estimates. In most cases, the MSS model gives results about a factor of three

22 higher than the exposure-response function model for school-aged children. This is expected, 

23 since, as discussed above, the MSS model includes responses for a wider range of exposure 

24 protocols (under different levels of exertion, lengths of exposures, and patterns of exposure 

25 concentrations) than the exposure-response model of previous reviews.

26
6-30

1 Table 6-8. Comparison of responses from the MSS model with responses from the 

2 population exposure-response (E-R) method. 2006 existing standard, ages 5 to 18

Urban area ≥ 10% FEV1 decrement ≥ 15% FEV1 decrement ≥ 20% FEV1 decrement

MSS model E-R method MSS model E-R method MSS model E-R method

Atlanta 19.2% 5.6% 5.3% 1.7% 2.1% 0.7%

Baltimore 18.6% 5.4% 5.2% 1.6% 2.1% 0.7%

Boston 13.6% 4.5% 3.7% 1.2% 1.4% 0.5%

Chicago 14.4% 4.7% 3.9% 1.3% 1.6% 0.5%

Cleveland 13.5% 4.2% 3.3% 1.1% 1.2% 0.4%

Dallas 21.6% 6.0% 6.4% 1.8% 2.7% 0.8%

Denver 20.2% 5.8% 5.6% 1.7% 2.2% 0.7%

Detroit 13.6% 4.4% 3.5% 1.2% 1.3% 0.4%

Houston 16.2% 4.7% 4.2% 1.3% 1.6% 0.5%

Los Angeles 18.2% 4.8% 4.2% 1.2% 1.5% 0.5%

New York 12.7% 4.2% 3.2% 1.1% 1.2% 0.4%

Philadelphia 16.4% 4.8% 4.2% 1.3% 1.6% 0.5%

Sacramento 17.9% 5.1% 4.8% 1.4% 2.8% 0.6%

St. Louis 18.6% 5.4% 5.1% 1.6% 2.0% 0.6%

Washington 15.9% 4.6% 4.0% 1.3% 1.5% 0.5%

3

4 Since the E-R method of the previous reviews only looks at 8-hour exposures

concomitant with EVR ≥ 13 L/min-m

2 5 BSA (hereafter, EVR ≥ 13), it is of interest to compare 

6 the E-R method results with the corresponding MSS model results (instances of ∆FEV1 ≥ 10, 15, 

7 20% concomitant with EVR ≥ 13).

8 We performed this comparison for four APEX simulations: the Atlanta March 1-October 

9 30, 2006 base case, ages 18-35; the Los Angeles May 29-July 28, 2006 base case, age 25; the 

10 Los Angeles May 29-July 28, 2006 base case, ages 18-35; and the Los Angeles Jan 1-Dec 31, 

11 2006 base case, ages 18-35.

12 For the Atlanta simulation, the E-R function approach gives 5.0, 1.8, and 0.9% 

13 responding for ∆FEV1 ≥ 10, 15, 20%. The MSS model approach gives 11.54, 3.26, and 1.28%

14 responding for ∆FEV1 ≥ 10, 15, 20%. The percents of the population for ∆FEV1 ≥ 10, 15, 20% at 

15 the end of the daily max 8-hour average exposure period where the concomitant 8-hour average 

16 EVR is ≥ 13 are 6.67%, 2.09%, and 0.84%. 15.17% of the population never have any instances 

17 of EVR ≥ 13 and 0.41% have at least one occurrence of ∆FEV1 ≥ 10% while never having EVR 

18 ≥ 13 for any 8-hour period. 4.46% (not among the 15.17%) have instances of ∆FEV1 ≥ 10% but 
6-31

1 none of those instances with concomitant EVR ≥ 13. The 11.54% responding is made up of 

2 6.67% of the population with instances of ∆FEV1 ≥ 10% concomitant with EVR ≥ 13 and 4.87% 

3 with instances of ∆FEV1 ≥ 10% not concomitant with EVR ≥ 13.

4 Table 6-9 and Table 6-10 summarize the pertinent results for the Atlanta and three Los 

5 Angeles simulations. Looking at the first rows of these tables shows that these models have 

6 similar corresponding results. The broader scope of activity/exposure patterns encompassed by 

7 the MSS model, beyond the 8-hour average EVR ≥ 13 restriction of the E-R model, contributes 

8 from a third to a half to the total MSS model risk and to a large part explains the differences 

9 between the models for ages 18-35. The difference between the MSS and E-R models is larger 

10 for school-aged children than for adults ages 18-35 due to the increased EVR and time spent 

11 outdoors in children compared to adults.

12
6-32

Table 6-9. Comparison of MSS Model and E-R Model of Previous Reviews for Atlanta, Mar 1-Oct 30, 2006, ages 18-35

Component of results MSS model

∆FEV1 ≥ 10%

E-R model

∆FEV1 ≥ 10%

MSS model

∆FEV1 ≥ 15%

E-R model

∆FEV1 ≥ 15%

MSS model

∆FEV1 ≥ 20%

E-R model

∆FEV1 ≥ 20%

profiles with instances of ΔFEV1 ≥ cutoff 

concomitant with 8-hour EVR ≥ 13 6.7% 5.0% 2.1% 1.8% 0.8% 0.9%

profiles with instances of ∆FEV1 ≥ cutoff never 

concomitant with 8-hour EVR ≥ 13 4.8% 1.2% 0.5%

Final result of each model 11.5% 5.0% 3.3% 1.8% 1.3% 0.9%

Table 6-10. Comparison of MSS Model and E-R Model of Previous Reviews for Los Angeles, Jan 1-Dec 31, 2006, ages 18-35

Component of results MSS model

∆FEV1 ≥ 10%

E-R model

∆FEV1 ≥ 10%

MSS model

∆FEV1 ≥ 15%

E-R model

∆FEV1 ≥ 15%

MSS model

∆FEV1 ≥ 20%

E-R model

∆FEV1 ≥ 20%

profiles with instances of ΔFEV1 ≥ cutoff 

concomitant with 8-hour EVR ≥ 13 7.9% 6.2% 2.6% 2.6% 1.2% 1.4%

profiles with instances of ∆FEV1 ≥ cutoff never 

concomitant with 8-hour EVR ≥ 13 6.5% 1.8% 0.8%

Final result of each model 14.4% 6.2% 4.4% 2.6% 2.0% 1.4%
6-33

1 Figure 6-10 compares the E-R function to the response curve of the MSS model restricted 

2 to 8-hour average EVR ≥ 13 and shows that these curves are very close. The MSS model has a 

3 higher response for the low and high ranges of exposure concentrations, while the E-R model is 

4 higher in the mid-range of exposures.

5 Figure 6-10. Comparison of E-R and MSS Model (restricted to 8-hour average EVR 

6 ≥ 13) Response Functions (Atlanta 2006 base case, ages 18-35).

7 Another element of the difference between the models derives from the distribution of 

8 EVR in the clinical studies the E-R approach is based on and how this compares to the 

9 distribution of EVR in the APEX simulations. Most of the clinical studies are conducted with a 

target EVR of 20 L/min-m2 10 BSA and the actual EVRs vary somewhat around this value. The 

rationale for the cutpoint of 13 L/min-m2 11 BSA is described in EPA’s responses to comments on 

12 the 1996 proposed rule on the NAAQS for O3 (Federal Register, 1996) as “for the 8-hr health 

13 risk assessment the range (based on being within 2 standard deviations of the mean) of EVRs 

14 observed in the subjects who participated in the study [McDonnell et al., 1991] was 13-27 liters 

per minute per meter squared [BSA] (L/min-m

2 15 ).” Figure 6-11 shows the distribution of EVRs ≥ Response probability

 0%

 1%

 2%

 3%

 4%

 5%

 6%

 7%

 8%

 9%

 10%

 11%

 12%

 13%

 14%

 15%

 16%

 17%

 18%

 19%

 20%

 21%

 22%

 23%

 24%

Ozone exposure (ppb)

0 10 20 30 40 50 60 70 80

∆FEV1>=10% (MSS) ∆FEV1>=15% (MSS) ∆FEV1>=20% (MSS)

∆FEV1>=10% (E-R) ∆FEV1>=15% (E-R) ∆FEV1>=20% (E-R)
6-34

1 13 for the Atlanta simulation and is clearly shifted much lower than the distribution of EVR in 

2 the clinical studies. This could lead to an overestimation of the percent of responders by the E-R 

3 method, since higher EVRs lead to higher lung function decrements and it is applying an E-R 

4 function based on EVRs around 20 to a population with median EVRs around 14.5.

5

6

7 Figure 6-11. Distribution of Daily Maximum 8-hour Average EVR For Values of 

EVR ≥ 13 (L/min-m2 8 ) (midpoints on vertical axis) (Atlanta 2006 base case, ages 18-35).

9

PCT.

CUM.

PCT.

34.78 34.78

22.39 57.17

14.63 71.80

9.64 81.44

6.36 87.80

4.20 92.00

2.77 94.77

1.80 96.56

1.17 97.74

0.77 98.51

0.51 99.02

0.34 99.37

0.22 99.59

0.15 99.74

0.10 99.83

0.06 99.90

0.04 99.94

0.03 99.97

0.01 99.98

0.01 99.99

0.00 99.99

0.00 100.00

0.00 100.00

EVR

 35.5

 34.5

 33.5

 32.5

 31.5

 30.5

 29.5

 28.5

 27.5

 26.5

 25.5

 24.5

 23.5

 22.5

 21.5

 20.5

 19.5

 18.5

 17.5

 16.5

 15.5

 14.5

 13.5

FREQUENCY

0 200000 400000 600000 800000 1000000 1200000
6-35

1 6.4 EVALUATION OF THE MSS MODEL

2 6.4.1 Summary of Published Evaluations

3 McDonnell et al. (2010) performed a detailed evaluation of their model using two 

4 methods: (1) cross-validation and (2) comparison of an independent data set against the 

5 predictions of the model.

6 The cross-validation was based on the data set of 15 EPA studies from which their 

7 original model was developed (McDonnell et al., 2007). This data set has 541 subjects, each with 

8 multiple measurements during single experiments. Subjects were omitted from the data set, one 

9 at a time, the model refit to the reduced data set, and the resulting parameters used to predict the 

10 FEV1 decrements for the omitted subject. The authors then compare the mean predictions and 

11 mean observed values for each subject and presented these results in a scatter plot (Figure 1b, 

12 McDonnell et al., 2010). The observations exhibit much more variability than the predictions; for 

13 observed values of 20%, predicted values range from around 2 to 19%; and all observed values 

14 above 20% are underpredicted (the observed values range from -20 to 60%, while the predicted 

15 values range from 0 to 20%). These features result from the omission of the inter- and intra16 individual variability terms (Ui and εijk) in the MSS model (equation 6-3), which are accounted 

17 for in the risk estimates in this chapter.

18 Model predictions were compared against an independent data set of seven clinical 

19 studies with a total of 204 subjects (McDonnell et al., 2010). Graphs of predicted and observed 

20 study means vs. time show fair to good model fit. The authors do not present overall fit statistics 

21 that are directly commensurate with the statistics of interest in this risk assessment: the 

22 proportions of people with FEV1 decrements greater than 10, 15, and 20%.

23 McDonnell et al. (2012) do compare observed and predicted proportions of people with 

24 FEV1 decrements greater than 10, 15, and 20% and provide the corresponding scatter plots

25 (Figure 4). They find the model to be unbiased, with the slopes of the observed vs. predicted 

lines for 10, 15, and 20% to be around 1.0 and the R2 26 respectively 0.78, 0.73, and 0.67. The 

27 higher observed proportions of people with FEV1 decrements greater than 10, 15, and 20% 

28 tended to be substantially underpredicted.

29 6.4.2 Children

30 A clinical study with children (ages 8-11; mean, 10 years; n=22), exposed to 120 ppb O3

31 over 2.5 hours at heavy exertion levels was done by McDonnell et al. (1985). This study could be 

32 used to fit the model for children if all of the measurements of FEV1 and ventilation rates were 

33 available. The paper lists the end-of exposure FEV1 responses for each individual (but not 

34 ventilation rates), which we use to compare with the MSS model with the age term extension

35 described in Section 6.2.4. The numbers of subjects with clean-air adjusted responses greater 
6-36

1 than 10%, 15%, and 20% are respectively 4, 2, and 1, corresponding to 18.2%, 9.1%, and 4.5% 

2 of the number of subjects. We ran the MSS 2010 model using the mean and standard deviation of 

3 the ventilation rates reported in the paper. Resting ventilation rates were assumed to be 10.4 

L/min (Avol et al., 1985) and BSA to be 1.08 m2 4 (EPA, 2011). Details of this comparison can be 

5 found in Appendix 6-D.

6 Table 6-11 compares the results of this simulation with the results of McDonnell et al. 

7 (1985). The agreement is fairly good. Due to the limited sample size of 22 subjects from only 

8 one study and the assumptions made in running the MSS model, this does not provide 

9 confirmation that the age term extension is correct; on the other hand, this comparison does not 

10 indicate that there is a problem with the age term extension. Information is not available that 

11 would allow us to provide respectable confidence intervals for these estimates.

12

13 Table 6-11. Comparison of Responses from the MSS 2010 Model with Responses 

14 from McDonnell et al. (1985)

≥ 10% FEV1 decrement ≥ 15% FEV1 decrement ≥ 20% FEV1 decrement

MSS model McDonnell 

et al. (1985) MSS model McDonnell et al. (1985) MSS model McDonnell et al. (1985)

Percent 

responding

18.4% 18.2%

(4 subjects)

6.8% 9.1%

(2 subjects)

2.3% 4.5%

(1 subject)

15

16 6.4.3 Threshold vs. Non-Threshold Models

17 The difference between the results of the MSS threshold and non-threshold models is 

18 minor, with the threshold version estimates of lung function decrements almost identical to the 

19 no-threshold version for the Atlanta 2006 recent air quality base case, as can be seen by 

20 comparing Table 6-12 with Table 6-13. This is consistent with the logistic form of the model, 

21 where the impact of exposures to low concentrations on risk is small.

22

23
6-37

1 Table 6-12. Percents of the population by age group with one or more days during 

2 the O3 season with lung function (FEV1) decrements more than 10, 15, and 20% (Atlanta 

2006 base case). MSS Threshold model, monitors air quality.

4 3

4

Age 

Group

ΔFEV1 ≥ 

10%

ΔFEV1 ≥ 

15%

ΔFEV1 ≥ 

20%

5 to 18 31% 13% 6.4%

19 to 35 11% 3.1% 1.3%

36 to 55 3.7% 0.60% 0.14%

5

6 Table 6-13. Percents of the population by age group with one or more days during 

7 the O3 season with lung function (FEV1) decrements more than 10, 15, and 20% (Atlanta 

8 2006 base case). MSS No-Threshold model, monitors air quality.

9

Age 

Group

ΔFEV1 ≥ 

10%

ΔFEV1 ≥ 

15%

ΔFEV1 ≥ 

20%

5 to 18 31% 13% 6.6%

19 to 35 11% 3.1% 1.3%

36 to 55 3.8% 0.60% 0.15%

10

11

12 6.5 CHARACTERIZATION OF UNCERTAINTY

13 In the controlled human exposure study based risk assessment, there are two broad 

14 sources of uncertainty to the risk estimates. One of the most important sources of uncertainty is 

15 the estimation by APEX of the population distribution of individual time series of O3 exposures 

16 and ventilation rates. The uncertainty regarding these estimated exposures is discussed in 

17 Chapter 5; they are not discussed further here.

18

 

4 In the first draft REA, monitor-level air quality was provided as input to the APEX model. As discussed in 

Chapter 5, tract-level air quality was used in APEX for this second draft REA. Monitor-level air quality is used for 

the APEX simulations here, since these simulations take less time to run. This does not affect the analyses here, 

since the two air quality formats yield very similar results (see Appendix 6-F).
6-38

1 In this section, uncertainties associated with the second broad source of uncertainty in the 

2 risk calculation are discussed, namely, uncertainties in the lung function risk model. The specific 

3 sources of uncertainty covered are:

4 • Statistical model form

5 • Convergence of APEX results

6 • Application of model for all lifestages

7 • Application of model for asthmatic children

8 • Interaction between O3 and other pollutants

9 6.5.1 Statistical Model Form

10 The MSS model is a 2-compartment model, the form of which is based on physical 

11 considerations. It accomodates these key features of human exposure studies: (1) FEV1

12 responses increase with increasing O3 concentration, ventilation rate, and duration of exposure, 

13 (2) the effect of each of these three variables depends on the levels of the other two variables, (3) 

14 FEV1 responses depend on age, (4) certain individuals are consistently more responsive to O3

15 exposure, and (5) O3 –induced FEV1 decrements improve within a few hours of cessation of 

16 exposure (McDonnell et al, 2007). These considerations support the form of the model, as do 

17 model evaluation that have been performed (Section 6.4.1). Although the model does not have 

good predictive ability for individuals (psuedo-R2 18 0.28), it does better at predicting the 

proportion of individuals with FEV1 decrements ≥ 10, 15, and 20% (psuedo-R2 19 s of 0.78, 0.74, 

20 0.68) (McDonnell et al, 2012).

21 The clinical studies that these models’ estimates are based on were conducted with young 

22 adult volunteers rather than randomly selected individuals, so it may be that selection bias has 

23 influenced the model parameter estimates.

24 The parameter estimates are not very precise, as the result of the likelihood surface being 

25 somewhat flat in the neighborhood of the maximum likelihood estimates. Table 6-14 gives 95 

26 percent confidence intervals for each of the parameter estimates as percents of the estimates, 

27 based on the standard errors reported by McDonnell et al. (2012). Figure 6-10 shows how much 

28 the modeled number of children with one or more FEV1 decrements ≥ 10% changes when each 

29 parameter is increased by five percent (keeping the other parameters fixed at their estimates).

30 The scenario modeled is the Los Angeles 2006 recent conditions base case. The physiological 

31 parameter MET, a measure of the level of exertion for a given activity (see Appendix 6-E), is

32 also included here for comparison. MET is a key variable in calculating ventilation rates and is 

33 specified by a distribution for each activity. Here we have shifted all MET distributions by +5% 

34 of their means.
6-39

1 Table 6-14. MSS threshold model estimated parameters with confidence intervals

β1 β2 β3 β4 β5 β6 β9 var(U) var(E)

parameter 

estimate

10.916 -0.2104 0.01506 13.497 0.003221 0.8839 59.284 0.9373 17.0816

standard 

error 0.8446 0.31 0.00333 4.734 0.000207 0.0647 10.192 0.0824 1.1506

95% conf. 

interval ±15% ±289% ±43% ±69% ±13% ±14% ±34% ±17% ±13%

2 from McDonnell et al. (2012). 

3 The most influential parameter in Figure 6-12 is β6, the power to which ventilation rate is 

4 raised in the MSS model. An increase of five percent in β6 leads to 27, 40, and 47 percent 

5 increases respectively in the modeled number of children with FEV1 decrements ≥ 10, 15 and 

6 20%. The next most influential parameter is the variance of E, the intra-individual variability 

7 term. The least influential parameter is β2, the slope of the age term. These changes of five 

8 percent are much less than the 95 percent confidence intervals of the parameter estimates, so the 

9 uncertainty in the risk estimates resulting from parameter uncertainty is likely to be more than is 

10 indicated in Figure 6-12.

11 Age Term Significance 

12 As discussed in Section 6.5.3 below, there are uncertainties in extrapolating the MSS 

13 model down to age 5 from the age range of 18 to 35 to which the model was fit. Further 

14 considerations indicating that the uncertainty of the extension to children of the MSS model 

15 could be substantial are that the age coefficient β2 = −0.21 (s.e. 0.31) in the MSS model is not 

16 statistically significantly different from zero; and when the MSS model is fit to the U.C. Davis 

17 clinical data the age term is positive, β2 = +0.19 (0.60), although also not statistically 

18 significantly different from zero (McDonnell et al., 2012). Note that, in the previous section, β2

19 was found to be the least influential model parameter.
6-40

Figure 6-12. Sensitivity (Percent Change) of Population With One or More FEV1

Decrements ≥ 10% to a 5% Increase in Individual MSS Model Parameter Estimates.
6-41

1 The Variability Term ε

2 The variability term ε in equation 6-3 is assumed by the MSS model to have a Gaussian 

3 distribution with mean zero and estimated standard deviation 4.135 (in the threshold model). 

4 Since the actual values are bounded, we truncate the variability term distribution at ±2 standard 

5 deviations (±8.27), a convention we use for the distributions of several physiological variables 

6 input to APEX in the physiology input file. To look at the effect of truncating the variability 

7 distribution, we conducted simulations with the variability term truncated at ±20, the range of the 

8 actual values of the variability term. We find that this constraint has a very large effect on 

9 estimates of percents of the population with FEV1 decrements ≥ 10 and 15% and less of an effect 

10 for 20%. The percent of children with FEV1 decrements ≥ 10% increases from 31% to 92% 

11 when increasing the truncation point from 8.27 to 20. Details of this comparison and additional 

12 results are presented in Appendix 6-F. The assumption that the distribution of the variability term 

13 ε is Gaussian is convenient for fitting the model, but is not accurate. The extent to which this 

14 mis-specification affects the estimates of the parameters of the MSS model is not clear.

15 6.5.2 Convergence of APEX Results

16 APEX accounts for several sources of variability by drawing random variables from 

17 specified distributions. Some variables are drawn once for each simulated individual (e.g., age, 

18 location of residence), some are drawn every day or every hour for each simulated individual, 

19 and others are drawn more frequently, at the event level (e.g., activity). Increasing the number of 

20 individuals simulated in an APEX run increases the accuracy of the modeled variability and the 

21 results of the APEX runs are more reproducible. In order to assess the number of individuals to 

22 simulate to achieve convergence of APEX results, we perform multiple APEX runs with 

23 identical inputs except for the random number seed, and look at the variability of the results of 

24 these model runs. Table 6-15 summarizes the results of 40 APEX simulations of the Atlanta 

25 2006 base case with 200,000 simulated individuals. For each of these measures, the range of 

26 results over the 40 APEX runs is less than one percent. This analysis of the convergence of 

27 APEX results shows that modeling 200,000 simulated individuals is adequate for reasonable 

28 convergence of the FEV1 risk measures.

29
6-42

1 Table 6-15. Convergence results for the Atlanta 2006 base case with 200,000 

2 simulated individuals. Percents of the population by age group with one or more days (and 

3 six or more days) during the O3 season with lung function (FEV1) decrements more than 

4 10, 15, and 20%. Minimum and maximum values and ranges over 40 APEX runs.

ΔFEV1 ≥ 10% ΔFEV1 ≥ 15% ΔFEV1 ≥ 20%

Age group min max range min max range min max range

1 or more days in the season

5 to 18 31.3% 32.1% 0.88% 12.4% 12.9% 0.49% 6.21% 6.71% 0.50%

19 to 35 11.1% 11.5% 0.39% 3.00% 3.26% 0.26% 1.11% 1.32% 0.22%

36 to 55 3.54% 3.79% 0.25% 0.55% 0.68% 0.13% 0.13% 0.20% 0.07%

6 or more days in the season

5 to 18 9.28% 9.73% 0.45% 2.80% 3.18% 0.38% 1.11% 1.37% 0.27%

19 to 35 1.09% 1.25% 0.16% 0.15% 0.21% 0.06% 0.03% 0.06% 0.03%

36 to 55 0.22% 0.30% 0.08% 0.01% 0.03% 0.02% 0.00% 0.01% 0.01%

5

6 6.5.3 Application of Model for All Lifestages

7 The exposure-response functions derived from controlled human exposure studies 

8 involving 18-35 year old subjects were used to estimate responses for school-aged children (ages 

9 5-18). This was in part justified by the findings of McDonnell et al. (1985) who reported that 

10 children 8-11 years old experienced FEV1 responses similar to those observed in adults 18-35 

years old when both groups were exposed to 120 ppb O3 at an EVR of 32-35 L/min/m2 11 . In 

12 addition, a number of summer camp studies of school-aged children exposed in outdoor 

13 environments in the Northeast also showed O3-induced lung function changes similar in 

14 magnitude to those observed in controlled human exposure studies using adults, although the 

15 studies may not directly comparable. The MSS model predicts increasing responsiveness with 

16 younger participants in the age range of 18-35 years, as shown in Figure 6E-4 (Appendix 6-E), 

17 which might indicate that responsiveness would continue to increase as age decreases from 18.

18 In extending the MSS model to children, we fixed the age term in the model at its highest value, 

19 the value for age 18. If continuing the MSS model trend were to accurately describe continued

20 increased response in children, then the fixed age term for children may have underestimated the 

21 effects on children, and particularly younger children. On the other hand, if FEV1 responses for 

22 children are similar to those observed in adults 18-35 years old, as the evidence suggests, then 

23 our approach to extending the age term would overestimate the response to children (see Table 

24 6E-3 in Appendix 6-E).
6-43

1 In considering extending the MSS model to ages older than 36, we note that, in general, 

2 O3 responsiveness steadily declines for persons aged 35-55, with persons >55 eliciting minimal 

3 responsiveness (ISA, section 6.2.1.1). As described in Section 6.2.4, we extended the age term

4 from the value at 36 linearly to zero at age 55, and set it to zero for ages above 55 (see Error! 

5 Reference source not found.). The uncertainty of this extrapolation may be substantial, but 

6 these age groups are not the primary focus in the clinical risk assessment.

7 6.5.4 Application of Model for Asthmatic Children

8 The risk assessment used the same exposure-response relationship, developed from data 

9 collected from healthy study subjects, and applied it to all persons, children, and asthmatic 

10 children. Based on limited evidence from a few human exposure studies, it is likely that subjects

11 having asthma are at least as sensitive to acute effects of O3 as other subjects not having this 

12 health condition (ISA, page 6-20 to 6-21). An analysis by Romieu et al. (2002) indicated a larger

13 O3-associated decrement in FEV1 among children with moderate to severe asthma than 

14 among all children with asthma (ISA, page 6-54). This suggests that the lung function 

15 decrements presented in this assessment for asthmatic children may be underestimated. The 

16 magnitude of influence this element might have on our risk estimates remains unknown at this 

17 time. In addition, asthmatic children may have less reserve lung capacity to draw upon when 

18 faced with decrements, and therefore a ≥10% decrement in lung function may be a more adverse 

19 event in an asthmatic child than a healthy child.

20 6.5.5 Interaction Between O3 and Other Pollutants

21 Because the controlled human exposure studies used in the risk assessment involved only 

22 O3 exposures, it was assumed that estimates of O3-induced health responses would not be 

23 affected by the presence of other pollutants (e.g., SO2, PM2.5, etc). The magnitude of influence

24 that potential interactions might have on our risk estimates remains unknown at this time.

25 6.5.6 Qualitative Assessment of Uncertainty

26 EPA staff have identified key sources of uncertainty with respect to the lung function risk 

27 estimates. These are: the physiological model in APEX for ventilation rates, the O3 exposures 

28 estimated by APEX, the MSS model applied to ages 18 to 35, and extrapolation of the MSS 

29 model to children ages 5 to 18. The first two of these are discussed in Chapter 5. At this time we 

30 do not have quantitative estimates of uncertainty for any of these. Table 6-16 provides a 

31 qualitative assessment of the uncertainty resulting from each of these key sources. The primary 

32 source of uncertainty is the MSS model, applied to ages 18 to 35.

33
6-44

Table 6-16. Summary of Qualitative Uncertainties of Key Modeling Elements in the O3 Lung Function Risk 

Assessment

Source Description

Potential influence of 

uncertainty on risk 

estimates

KnowledgeBase 

Direction Magnitude uncertainty* Comments 

The physiological 

model in APEX for 

ventilation rates

The physiological model in APEX 

takes into account the population 

distribution of individual physiological 

characteristics and activities and 

models minute-by-minute ventilation 

rates for each simulated individual 

using a series of physiological 

relationships known with varying 

degrees of certainty.

Over LowMedium

LowMedium

Ventilation rates are a key input to the MSS model.

Figure 6E-3 in Appendix 6-E gives an overview of the 

physiological model in APEX for ventilation rates.

Comparisons with ventilation rates reported in the literature 

show fairly good agreement with APEX ventilation rates 

(Section 5.4.4).

O3 exposures

The O3 exposures estimated by APEX 

and their uncertainties are discussed in 

Chapter 5.

Both LowMedium Low

O3 exposures are a key input to the MSS model.

The McDonnellStewart-Smith (MSS) 

FEV1 model for ages 

18 to 35

The MSS model is integrated into 

APEX and predicts FEV1 decrements 

for each simulated individual.

Both MediumHigh Low

There is a good conceptual foundation for the structure of 

this model, but the variability in measurements of FEV1 and 

estimated parameters of the model introduce uncertainty 

into the model predictions of large FEV1 decrements. The 

estimated parameters have fairly wide confidence intervals 

(Table 6-1) and the risk results are sensitive to varying the 

parameters (Figure 6-12).

The most influential parameter is β6, the power to which 

ventilation rate is raised in the MSS model. An increase of 

five percent in β6 leads to a 27 percent increase in the 

modeled number of children with FEV1 decrements ≥ 10%.

(The 95 percent confidence interval of this parameter 

estimate is ±14%.)

The variability term ε [in equation 6-3] is assumed by the 

MSS model to have a Gaussian distribution with mean zero 

and estimated standard deviation 4.135. Since the actual 

values are bounded, we truncate the variability term 

distribution at ±2 standard deviations (±8.27), a convention 

we use for the distributions of several physiological 
6-45

Source Description

Potential influence of 

uncertainty on risk 

estimates

KnowledgeBase 

Direction Magnitude uncertainty* Comments 

variables input to APEX in the physiology input file. To 

look at the effect of truncating the variability distribution, 

we conducted simulations with the variability term 

truncated at ±20, the range of the actual values of the 

variability term. We find that this constraint has a large 

effect on estimates of percents of the population with FEV1

decrements ≥ 10 and 15% and less of an effect for 20%. 

The percent of children with FEV1 decrements ≥ 10% 

increases from 31% to 92% when increasing the truncation 

point from 8.27 to 20.

Extrapolation of the 

MSS model to 

children

The MSS model is based on studies 

with subjects ranging in age from 18 to 

35 years; therefore prediction for 

individuals outside this age range 

involves assumptions for extrapolation 

of the MSS model for individuals <18 

and >35 years of age..

Both Medium Low

Summer camp studies and one clinical study of children 

indicate that FEV1 responses for children are similar to 

those observed in adults 18-35 years old. See discussion in 

Section 6.5.3.

* Refers to the degree of uncertainty associated with our understanding of the phenomenon, in the context of assessing and characterizing its uncertainty 
6-46

1 6.6 DISCUSSION

2 The second draft lung function risk assessment evaluated risks of lung function 

3 decrements due to O3 exposure for all three groups: school-age children ages 5 to 18, young 

4 adults ages 19 to 35, and adults ages 36 to 55. Adults older than 55 have minimal O3-induced 

5 lung function risk. Two models were used, one based on application of an individual level 

6 exposure-response function, the MSS model introduced in this review, and one based on 

7 application of a population level E-R function consistent with the model used in the previous O3

8 review which applies probabilistic population-level exposure-response relationships for lung 

9 function decrements (measured as percent reductions in FEV1) associated with 8-hour moderate 

10 exertion exposures. The MSS model is preferred, due to its ability to model individual exposures 

11 for a wide range of exposure times and levels of exercise (Section 6.2.4; ISA pages 6-15 to 6-

12 16). Both models provide estimates of the percent of the groups experiencing a reduction in lung 

13 function for three different levels of impact, 10, 15, and 20% decrements in FEV1. These levels 

14 of impact were selected based on the literature discussing the adversity associated with these 

15 types of lung function decrements (US EPA, 2012, Section 6.2.1.1; Henderson, 2006). For the 

16 second draft assessment, lung function risks were estimated for 15 cities: Atlanta, Baltimore, 

17 Boston, Chicago, Cleveland, Dallas, Denver, Detroit, Houston, Los Angeles, New York, 

18 Philadelphia, Sacramento, St. Louis, and Washington, DC.

19 Based on the MSS model, the percents of population estimated to experience lung 

20 function responses greater then 10, 15, and 20%, associated with O3 exposure while engaged in 

21 various levels of exertion, vary considerably for different years and cities under the recent air 

22 quality scenarios and also for the existing and alternative standard scenarios (Figure 6-7 and 

23 Figure 6-8, Table 6-4 and Table 6-5). The estimates for ≥ 10% FEV1 decrement for school-age 

24 children for recent O3 concentrations range across cities and years from 11 to 31 percent, and 

25 range from 11 to 22 percent after simulating just meeting the existing standard. The estimates for 

26 ≥ 15% FEV1 decrement for school-age children for recent O3 concentrations range across cities 

27 and years from 2 to 12 percent, and range from 2 to 6 percent after simulating just meeting the 

28 existing standards. The estimates for ≥ 20% FEV1 decrement for recent O3 concentrations range 

29 across cities and years from 1 to 6 percent, and range from 1 to 3 percent after simulating just 

30 meeting the existing standards.

31 Figure 6-13 displays the risks and the incremental increases in risk for increasing 

32 standard levels, where risk is taken to be the highest value for each study area (over years) of the 

33 percent of school-aged children with FEV1 decrement ≥ 10%. The risks in this figure for 

34 Washington, DC, for example, are about 9.6% for the alternative standard level of 60 ppb and 

35 13.4% for the alternative standard level of 65 ppb. The length of the orange bar is the 
6-47

1 incremental risk (3.8%) in going from the 60 ppb to the 65 ppb alternative standards. This figure 

2 shows that there are significant increases in incremental risk for all 15 cities in the progression of 

3 alternative standard levels from 60 ppb to the level of the existing standard, 75 ppb. The pattern 

4 of reductions for lung function decrements larger than 15 and 20% are similar. As discussed in 

5 Section 4.3.1, the New York 60 ppb alternative standard was not modeled and the risk for NY for 

6 that scenario would not necessarily be zero. Figure 6-14 displays the risks and the incremental 

7 increases in risk for increasing standard levels, where risk is taken to be the mean value for each 

8 study area (over years) of the percent of school-aged children with FEV1 decrement ≥ 10%.

9 Similar to the MSS model results, the percents of school-age children estimated to 

10 experience lung function responses greater then 10, 15, and 20% based on the population level 

11 E-R function exhibit variation across years and cities. However, the MSS model estimates are 

12 significantly higher than the E-R approach estimates. For lung function responses greater than 

13 10, 15, and 20% the MSS model gives results typically a factor of three higher than the E-R 

14 model for school-aged children. Both models give higher responses for higher concentrations, 

15 compared to lower concentrations, as can be seen in Figures 6-6, 6-9, and 6-10.

16 The MSS model was applied to estimate lung function risk for outdoor workers (ages 19-

17 35) in Atlanta for one year (2006). The proportion of outdoor workers with FEV1 decrements ≥ 

18 15% ranges from 3.6 to 5.3 times the proportion of the general population (ages 19-35) with 

19 FEV1 decrements ≥ 15% across the different standards simulated. The proportion of outdoor 

20 workers with multiple occurrences of FEV1 decrements ≥ 15% is much greater than for the 

21 general population.

22
6-48

Figure 6-13. Lung Function Risk Results, Incremental Increases In Risk For Increasing Standard Levels: Percent of

All School-aged Children With FEV1 Decrement ≥ 10%, Highest Value For Each Study area Over Years5

 

5 New York level 60 was not modeled . We do not know what the percent risk would be for NY under the 60 ppb alternative standard, but it would not 

necessarily be zero.

standard level (ppb) 60 65 70 75

Washington

St Louis

Sacramento

Philadelphia

New York

Los Angeles

Houston

Detroit

Denver

Dallas

Cleveland

Chicago

Boston

Baltimore

Atlanta

percent of school-aged children with FEV1 decrement > 10%

0% 2% 4% 6% 8% 10% 12% 14% 16% 18% 20% 22% 24%
6-49

Figure 6-14. Lung Function Risk Results, Incremental Increases In Risk For Increasing Standard Levels: Percent of

All School-aged Children With FEV1 Decrement ≥ 10%, Mean Value For Each Study Area Over Years6

 

6 New York level 60 was not modeled . We do not know what the percent risk would be for NY under the 60 ppb alternative standard, but it would not 

necessarily be zero.

standard level (ppb) 60 65 70 75

Washington

St Louis

Sacramento

Philadelphia

New York

Los Angeles

Houston

Detroit

Denver

Dallas

Cleveland

Chicago

Boston

Baltimore

Atlanta

percent of school-aged children with FEV1 decrement > 10%

0% 2% 4% 6% 8% 10% 12% 14% 16% 18% 20%
6-50

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7-1

1

2 7 CHARACTERIZATION OF HEALTH RISK BASED ON

3 EPIDEMIOLOGICAL STUDIES

4 This chapter provides an overview of the methods used to estimate health risks in 

5 selected urban areas based on application of results of epidemiology studies. Section 7.1.1

6 discusses the basic structure of the risk assessment, identifying the modeling elements and 

7 related sources of input data needed for the analysis and presenting an overview of the approach 

8 used in calculating health effect incidence using concentration-response (C-R) functions based 

9 on epidemiological studies. Section 7.2 discusses air quality considerations. Section 7.3

10 discusses the selection of model inputs including: (a) selection of urban study areas, (b) selection 

11 of epidemiological studies and specification of C-R functions, (c) specification of baseline health 

12 effect incidence and prevalence rates, and (d) estimation of population (demographic) counts. 

13 Section 7.4 describes how uncertainty and variability are addressed in the risk assessment, 

14 including specification of the sensitivity analyses completed for the risk assessment and how 

15 these differ from the core risk estimates. Section 7.5 summarizes the risk estimates that are 

16 generated, including both the core estimates and sensitivity analyses. Finally, Section 7.6

17 provides an assessment of overall confidence in the risk assessment together with a set of key 

18 observations regarding the risk estimates generated.

19 7.1 GENERAL APPROACH

20 7.1.1 Basic Structure of the Risk Assessment

21 This risk assessment involves the estimation of the incidence of specific health effect 

22 endpoints associated with exposure to ambient O3 for defined populations located within a set of 

23 urban study areas. Because the risk assessment focuses on health effect incidence experienced by 

24 defined populations, it represents a form of population-level risk assessment and does not 

25 estimate risks to individuals within the population. Furthermore, because it models risk for 

26 residents in a set of urban study areas, it is not intended to provide an estimate of national-level 

risk1 27 .

28 The general approach used in both the prior and current O3 risk assessments relies on C29 R functions based on effect estimates and model specifications obtained from epidemiological 

30 studies. Since these studies derive effect estimates and model specifications using averages of

31 ambient air quality data from fixed-site, population-oriented monitors, uncertainty arising from 

 1 Chapter 8 provides a limited assessment of national risk focused on the mortality burden associated with recent O3

levels. This risk and exposure assessment does not provide an analysis of the risk reductions that would be 

expected for the entire U.S. after meeting either the existing or alternative standards.
7-2

1 the application of these functions in an O3 risk assessment is decreased if, in modeling risk, we 

2 also use ambient air quality data at fixed-site, population-oriented monitors to characterize 

3 exposure. Therefore, we developed a composite monitor for each urban study area to represent a 

4 surrogate population exposure by averaging O3 concentrations across the monitors in that study 

5 area to produce a single composite hourly time series of values. The O3 metrics used in 

6 evaluating risk are derived from the composite monitor hourly time series distribution (see 

sections 7.2 and Chapter 4 for additional detail on the characterization of ambient O3 levels).2 7

8 The general O3 health risk model, illustrated in Figure 7-1, combines O3 air quality data, 

9 C-R functions, baseline health incidence and prevalence data, and population data (all specific to 

10 a given urban study area) to derive estimates of the annual incidence of specified health effects 

11 for that urban study area attributable to O3 exposure. This risk assessment models risk for 12 

12 urban study areas we selected to provide coverage for the types of urban O3 scenarios likely to 

13 exist across the U.S. (see section 7.3.1). Chapter 8 provides an assessment of the degree to which 

14 the 12 selected urban areas are representative of other urban areas in the U.S. that are likely to 

15 experience elevated risks from exposure to ambient O3 under recent conditions.

16 This risk assessment provides an updated set of estimates for risk under recent O3

17 conditions and just meeting the existing standard, and additional estimates of risk if alternative 

18 standards are just met, with an emphasis on reductions in risk between just meeting the existing 

19 standard and just meeting alternative standards (the full set of risk estimates, including 

20 simulation of risk under current conditions is presented in Appendix 7-B). The alternative 

21 standard levels evaluated are 70, 65 and 60 ppb (expressed using the current form of the O3

22 standard).

23 We simulated just meeting the existing and alternative O3 standards by adjusting hourly 

24 O3 concentrations measured over the O3 season using a model-based adjustment methodology 

that estimates O3 sensitivities to precursor emissions changes.

3 25 These sensitivities, which 

26 estimate the response of O3 concentrations to reductions in anthropogenic NOx and VOC 

27 emissions, are developed using the Higher-order Decoupled Direct Method (HDDM) capabilities 

28 in the Community Multi-scale Air Quality (CMAQ) model. More details on the HDDM29 adjustment approach is presented in Chapter 4 of this REA and in Simon et al. (2013). 

30 As discussed in Chapters 2 and 3, in modeling risk we employ continuous non-threshold 

31 C-R functions relating O3 exposure to health effect incidence. The use of non-threshold 

 2 This holds for all air quality metrics used in modeling short-term mortality and morbidity endpoints. However, the 

air metric used in modeling long-term mortality is based on a seasonal average of maximum hourly values 

derived for each O3 monitor within an urban study area with those individual averages then combined to generate 

a single seasonal average composite monitor value for each study area (see section 7.2 for more detail). 3 In the first draft of this REA, we used a statistical quadratic rollback approach to simulate just meeting the existing 

O3 standards. In that first draft, we proposed using the model based approach used in this draft, and received 

support for the model based approach from CASAC (Frey, H.D., 2012). 
7-3

1 functions reflects the discussion of the relevant studies in the O3 ISA (see O3 ISA, section 

2 2.5.4.4, U.S. EPA 2013a). However, also consistent with the conclusions of the O3 ISA, we 

3 recognize that the evidence from the studies indicates less confidence in specifying the shape of 

4 the C-R function at O3 concentrations towards the lower end of the distribution of data used in 

5 fitting the curve due to the reduction in the number of data points available. The O3 ISA noted 

6 that the studies indicate reduced certainty in specifying the shape of the C-R function 

7 specifically for short-term O3-attributable respiratory morbidity and mortality, in the range 

8 generally below 20 ppb (for both 8hr-maximum and 24hr metrics) (O3 ISA, section 2.5.4.4). 

9 However, care needs to be taken in interpreting this range of reduced confidence indicated in the 

10 studies and applying it to the interpretation of risk estimates generated for a specific urban study 

11 area. This is because there is considerable heterogeneity in the effect of O3 on mortality across 

12 urban study areas (O3 ISA section 6.6.2.3). Additionally, it is likely that levels of confidence 

13 associated with C-R functions (including ranges of reduced confidence in specifying the 

14 function) also vary across urban study areas reflecting underlying differences in factors 

15 impacting the exposure-response relationship for O3, such as demographic differences and 

16 exposure measurement error. For these reasons, the ≤20 ppb range discussed in the O3 ISA

17 should be viewed as a more generalized range to be considered qualitatively or semi18 quantitatively, along with many other factors, when interpreting the risk estimates rather than as 

a fixed, bright-line.4 19

Based on comments we received from CASAC on the 1st 20 draft REA, we are no longer 

including estimates of risk down to the lowest measured level (LML).5 21 Instead, through the use 

22 of heat map tables, we focus on providing estimates of total risk, and the distribution of risk over 

concentrations of O3.

6 23 Coupled with information about what the studies indicate about the C-R 

24 function at lower O3 concentrations, this provides for a more complete understanding of 

25 confidence in estimated risk than simply truncating risk at the LML.

26 In modeling risk for all health endpoints included in the analysis, for recent O3 conditions 

27 and just meeting the existing standard, we estimated total risk (down to zero). For meeting the 

 4 This range of reduced confidence in the shape of the C-R function is most appropriately applied to area-wide 

averages (i.e., composite monitor values) of the type often used in epidemiological studies rather than to the 

range of O3 associated with a particular monitor. This reflects the fact that the observations presented in the O3

ISA are themselves based on consideration for epidemiological studies which use composite monitor values. 5 Based on their November 19, 2012 letter commenting on the 1st draft REA, CASACrecommended against 

inclusion of risk estimates based on the LML in the core analysis due to the fact that there is little difference 

between these estimates and risk estimates based on total O3 exposure and that LML information is not available 

for many of the epidemiological studies used in the REA (Frey and Samet, 2012). However, they recommend a 

more limited exploration of the LML and its implications for risk for one or more areas. In response, we have 

included coverage for LML as part of our discussion of the heat maps results presented in section 7.5.1 . 6 Heat map tables illustrate the distribution of estimated O3-related deaths across daily O3 levels for each urban study 

and allow a quick visual comparison of trends (in the distribution of total O3 risk as we as risk reductions) across 

ambient O3 ranges both within and across study areas (see section 7.5).
7-4

1 existing and alternative standards, we estimated both total risk as well as the difference in risk, 

2 representing the degree of risk reduction associated with just meeting the existing and alternative

3 standard levels. When calculating risk differences, we focus on comparing total risk after just 

4 meeting each alternative standard with total risk after just meeting the existing standards. We 

5 also evaluate the incremental change in risk from meeting increasingly lower alternative standard 

6 levels. Risk results are presented in terms of absolute numbers and changes in the O3 attributable 

7 incidence of mortality and morbidity, and in terms of the percent of baseline mortality and 

8 morbidity attributable to O3. We also provide risks per 100,000 population (to normalize risks 

9 across urban areas with different size populations to facilitate comparisons).

10 As with previous NAAQS-related risk assessments, for this analysis we have generated 

11 two categories of risk estimates, including a set of core (or primary) estimates and an additional 

12 set of sensitivity analyses. The core risk estimates utilize C-R functions based on 

13 epidemiological studies for which we have relatively greater overall confidence and which 

14 provide the best coverage for the broader O3 monitoring period (rather than focusing only on the 

15 summer season). Although it is not strictly possible to assign quantitative levels of confidence to 

16 these core risk estimates due to data limitations, they are generally based on inputs having higher 

17 overall levels of confidence relative to risk estimates that are generated using other C-R 

18 functions. Therefore, emphasis is placed on the core risk estimates in making observations 

19 regarding total risk and risk reductions associated with recent conditions and after just meeting 

20 the existing and alternative standard levels. By contrast, the sensitivity analysis results typically 

21 reflect application of C-R functions covering a wider array of design elements which can impact 

22 risk (e.g., length of season, copollutants models, lag structures, statistical modeling methods etc). 

23 The sensitivity analysis results provide insights into the potential impact of these design elements 

24 on the core risk estimates, thereby informing our characterization of overall confidence in the 

core risk estimates. 25 7 We have significantly expanded our sensitivity analysis relative to that 

completed for the 1st 26 draft REA to address a wider range of modeling elements which can impact 

27 the core risk estimates. Details of the design of the core and sensitivity analyses (including 

28 modeling element composition) for each of the health effect endpoints categories covered in this 

29 risk assessment are presented in section 7.4.3 and briefly summarized below.

30 For short-term exposure related mortality, our core analysis is based on application of C31 R functions obtained from the Smith et al., 2009 epidemiological study (see section 7.3.2). In 

 7 In presenting both the core and sensitivity analysis, we include both point estimates and 95th percentile confidence 

intervals (CIs). The 95th percentile CIs reflect the statistical fit of the underlying effect estimates and therefore 

reflect the statistical power of the epidemiological studies supplying the effect estimates. Often in comparing 

sensitivity analysis with core risk estimates, we focus not only on the point estimates, but also on the confidence 

intervals since these inform our understanding of confidence in the respective risk estimates. 
7-5

1 addition, we have completed an expanded array of sensitivity analyses which provide coverage 

2 for a number of modeling elements including: (a) time period reflected in risk modeling (summer 

3 season versus full monitoring period), (b) peak O3 metric (8hr maximum versus 8hr mean) (c) 

use of regional versus national-based Bayesian adjustment in deriving effect estimates,8 4 (d) use 

of single (O3-only) versus copollutant (O3 and PM10) models, 9 5 (e) application of alternative C-R 

6 functions based on Zanobetti and Schwartz, 2008 (see section 7.3.2) and (f) size of the urban 

study area (CBSA versus smaller multi-county study area)10 7 (see sections 7.4.3 and 7.5.3 for 

8 additional detail on the sensitivity analyses completed). In addition to these sensitivity analyses, 

9 we have considered alternative methods for adjusting air quality to attain existing and alternative

10 standards (NOx-only versus combination of VOC and NOx reductions). Additional sensitivity 

11 analyses exploring lag structure may also provide useful information, but are not possible due to 

12 the lack of availability of Bayes adjusted estimates for alternative lag structures.

13 For short-term exposure morbidity, we have effect estimates covering a wide range of

14 design elements including co-/single-pollutant models and lag structure. However, we were not 

15 in a position to differentiate between these alternative model forms in terms of overall 

16 confidence and have therefore included all of these estimates in the core analysis. This range of 

17 risk estimates can also be viewed as a sensitivity analysis where there is no clear “core” estimate 

18 and instead, the full range of risk estimates is considered to provide the best overall picture of 

19 risk for a specific endpoint (see section 7.3.2 and 7.4.3).

20 Our analysis also includes estimates of long-term exposure related respiratory mortality, 

21 including a core estimate based on a co-pollutant model (with PM2.5) together with sensitivity 

22 analyses exploring regional heterogeneity in the effect estimate and application of a national-

 8 Short-term O3-attributable mortality in this analysis is modeled using Bayesian-adjusted effect estimates. This

approach involves adjustment of each city’s effect estimate using a prior distribution reflecting the O3-mortality 

relationship seen across the broader set of cities considered in the epidemiological study. For the sensitivity 

analysis, we compare the use of a national prior distribution (the core approach) for the Bayesian adjustment 

with use of a regional prior. 9 The copollutants model results are limited by the reduced number of days with copollutants sampling (either 1 in 3 

or 1 in 6) which makes it difficult to evaluate the statistical significance of these results in view of the large 

posterior standard deviations (Smith et al., 2009). This increased uncertainty associated with the estimates 

prevents these results from being treated as part of the core analysis. Never the less, they provide perspective on 

the potential magnitude of risk associated with copollutants modeling and as such make an important 

contribution as a sensitivity analysis for short-term O3-attributable mortality. 10 Core based statistical areas (CBSAs) are U.S. geographic areas defined by the Office of Management and Budget 

(OMB). They include an urban center of at least 10,000 people combined with adjacent urban and surburban 

areas that are socioeconomically tied to the urban center by commuting. CBSAs tend to be significantly larger 

than the study areas used in the epidemiological studies providing effect estimates. We have used risk estimates 

based on CBSAs in the core analysis in order to better represent the changes in risk that could be experienced in 

the broader urban areas and to avoid the introduction of known bias into the risk assessment. We have included 

risk estimates based on the smaller study areas from the original epidemiological studies as sensitivity analyses 

(see discussion later in this section for additional detail).
7-6

level estimates focusing only on O3 (see section 7.5.3).

11 1 The decision to model this endpoint is 

2 based on our evaluation of the evidence as summarized in the O3 ISA and comments received 

from CASAC based on the 1st 3 draft risk assessment (Frey H.D., 2012 p. ).

4 As noted earlier, for this draft, we have modeled all core risk estimates using study areas 

5 based on the core-based statistical area (CBSA) regardless of whether the epidemiological 

6 studies providing the effect estimates used the CBSA spatial definition or a different spatial 

7 study area definition. The decision to use CBSA-based study areas in all core simulations for this 

8 draft reflects our desire to better represent the changes in risk that could be experienced in the urban 

9 areas and avoid introducing substantial known bias into the risk estimates. As discussed in 

10 Chapter 4 (section 4.3.1.2), most nonattaining O3 monitors are not located in the center of the urban 

11 area, but instead in the surrounding areas, reflecting the transport and atmospheric chemistry 

12 governing O3 formation. The monitors in the urban core areas are usually most affected by local 

13 sources of NOx and experience lower concentrations of O3 since the NO is titrating the O3 in these 

14 areas. For these monitors, simulating attainment of the existing and alternative standard levels can 

15 result in an increase in O3 concentrations, while areas further out from the core experience the 

16 expected reduction in O3 level . Had we focused risk estimates on the smaller urban core areas 

17 used in some of the epidemiological studies, we would not have fully captured the changes in 

18 risk estimated to be experienced by the broader urban area since we would have been focusing 

19 only on those areas experiencing net increases in O3 (when simulating attainment of the existing 

20 and alternative standard levels). By modeling risk for the core analysis using the more inclusive 

21 CBSA study areas, we insure that risk estimates will include consideration both for the relatively 

22 smaller core urban areas experiencing increases in O3 as well as the broader urban and suburban 

23 area experiencing risk reductions. We will also insure that, to a greater extent, the analysis 

24 includes the county with the design value monitor in the assessment of risk (see section 7.2).

25 There is a degree of uncertainty introduced through application of effect estimates to 

26 study areas (i.e., CBSAs) that do not match those used in the underlying epidemiological studies.

27 This uncertainty should be viewed within the context of the overall larger uncertainty associated 

28 with transferring effect estimates from the context of the epidemiological studies to the context 

29 of the risk assessment. The epidemiological studies used in modeling short-term exposure-related 

30 endpoints generate effect estimates based on day to day variation in O3 and health effects, using 

31 the area wide average O3 concentrations. Area wide O3 averaging masks the specific population 

32 distribution of O3 exposures which reflects the times and durations of exposures to O3 measured 

33 at individual monitors in an urban area. We apply those effect estimates to the air quality 

 11 The seasonal average metrics used in the long-term mortality estimate are not very sensitive to the reduced 

number of days with co-pollutant monitoring, and as such it is appropriate to use the co-pollutant model in 

generating the core risk estimates.
7-7

1 scenarios of just meeting existing and alternative standards, where we are shifting the entire 

2 distribution of daily O3 concentrations, and altering the relationships between O3 concentrations 

3 at different monitors, and thus likely altering the relationship between area wide average O3 and 

4 the population distribution of O3 exposures. By doing so, we introduce an additional source of 

5 exposure measurement error, which goes beyond the impact that measurement error has on the 

6 effect estimate, and introduces additional uncertainty into the estimates of risk associated with 

7 simulating meeting existing and alternative standards.

8 Our decision to use the CBSA to define the spatial extent of each urban study area 

9 reflects the greater weight we place on minimizing biases relative to minimizing uncertainty, 

10 although we strive to minimize both where possible. The sensitivity analysis related to using 

11 study-based spatial definitions for urban areas shows clearly that using the smaller urban areas 

12 biases downward the risk reductions across an urban area. Thus, to avoid this bias in risk 

13 estimates we accept a measure of increased uncertainty associated with the application of effect 

14 estimates to study areas that are larger than those used in some of the original epidemiological 

15 studies providing those effect estimates.

16 Using the CBSA definitions of urban areas can partially address the bias caused by 

17 focusing only on urban core areas. However, it does not address this bias fully in some areas 

18 because of the unevenness in monitoring throughout urban areas. In some urban areas the 

19 monitors are more evenly distributed across the CBSA, while in other areas they are not. For 

20 example, in some urban areas, there is a high density of monitors in the urban core counties, with 

21 less density of monitors in surrounding counties also in the CBSA. Because we use a simple 

22 average (to match the averaging used in the epidemiology studies) of monitors across the CBSA,

23 this means that O3 concentrations in areas where there are more monitors (e.g. in urban core 

24 counties) will get a higher weight in the average O3 concentrations relative to O3 concentrations 

25 in other parts of the CBSA. To the extent that the area with the higher density of monitors 

26 experiences increases in O3 while the remaining area experiences decreases in O3, the overall 

27 average O3 concentrations applied to populations in the entire CBSA will be weighted more 

28 towards O3 increases, which will attenuate the overall risk reduction that may be associated with 

29 meeting alternative O3 standards. We are not able to determine the magnitude of this remaining 

30 bias; however, it is expected to be higher in locations with a high percentage of total CBSA 

31 monitors concentrated in urban core counties.

32 The risk assessment reflects consideration for five years of recent air quality data from 

33 2006 through 2010, with these five years reflecting two three-year attainment simulation periods 

34 that share a common overlapping year (i.e., 2006-2008 and 2008-2010 - see section 7.2). We

35 selected these two attainment simulation periods to provide coverage for a more recent time 

36 period with relatively elevated O3 levels (2006-2008) and recent time period with relatively 
7-8

1 lower O3 levels (2008-2010). For the REA, we model risk for the middle year of each three-year 

2 attainment simulation period in order to provide estimates of risk for a year with generally higher 

3 O3 levels (2007) and a year with generally lower O3 levels (2009). In modeling risk, we matched 

4 the population data used in the risk assessment to the year of the air quality data. For example, 

5 when we used 2007 air quality data, we used 2007 population estimates. For baseline incidence 

6 and prevalence, rather than interpolating rates for the two specific years modeled in the risk 

7 assessment, we selected the closest year for which we had existing incidence/prevalence data 

8 (i.e., for simulation year 2007, we used available data for 2005 and for simulation year 2009, we 

9 used data from 2010). The calculation of baseline incidence and prevalence rates is described in 

10 section 7.3.4.

11 The risk assessment procedures described in more detail below are diagramed in Figure 

12 7-1. To estimate the change in incidence of a given health effect resulting from a given change in 

13 ambient O3 concentrations in an assessment location, the following analysis inputs are necessary:

14 • Air quality information including: (1) O3 air quality data from each of the 

15 simulation years included in the analysis (2007 and 2009) from population-oriented 

16 monitors in the assessment location (these are aggregated to form composite monitor 

17 values used to represent population exposure), and (2) a method for adjusting the air 

18 quality data to simulate just meeting the current or alternative suite of O3 standards. 

19 (These air quality inputs are discussed in more detail in Chapter 4).

20 • C-R function(s): which provide an estimate of the relationship between the health 

21 endpoint of interest and O3 concentrations (for this analysis, C-R functions used were 

22 applied to urban study areas matching the assessment locations from the 

23 epidemiological studies used in deriving the functions, in order to increase overall 

24 confidence in the risk estimates generated - see section 7.3.2). For O3, 

25 epidemiological studies providing information necessary to specify C-R functions are

26 readily available for O3-related health effects associated with short-term exposures 

27 (Section 7.1.2 describes the role of C-R functions in estimating health risks associated 

28 with O3). In addition, the Jerrett et al. (2009) study provided a C-R function for 

29 modeling mortality risks associated with longer-term exposures to O3.

30 • Population information (baseline health affects incidence and prevalence rates 

31 and population): The baseline incidence provides an estimate of the incidence rate 

32 (number of cases of the health effect per year or day, depending on endpoint, usually 

33 per 10,000 or 100,000 general population) in the assessment location corresponding 

34 to recent ambient O3 levels in that location. The baseline prevalence rate describes the 

35 prevalence of a given disease state or conditions (e.g., asthma) within the population 
7-9

1 (number of individuals with the disease state/condition, usually per 10,000 or 100,000 

2 general population). To derive the total baseline incidence or prevalence per year, this 

3 rate must be multiplied by the corresponding population number (e.g., if the baseline 

4 incidence rate is number of cases per year per 100,000 population, it must be 

5 multiplied by the number of 100,000s in the population) (Section 7.3.4 summarizes 

6 considerations related to the baseline incidence and prevalence rates and population 

7 data inputs to the risk assessment).

8

9 In addition to the inputs described above, it is also necessary to specify the spatial extent 

10 of the study areas that will be modeled. These study areas definitions determine the composition 

11 of (a) the composite monitor values (which specific set of monitors are used in constructing the 

12 composite monitor, reflecting the area-wide average across monitors for each study area), (b) the 

13 specific set of effect estimates that will be used (matching the study areas to the specific set of 

14 effect estimates in the epidemiological studies being used to support modeling of endpoints), (c) 

15 the baseline incidence data and (d) the population demographic (count) data for each study area. 

16 As mentioned earlier, for this REA we have modeled 12 urban study areas and have used the 

17 CBSA spatial definition to specify the extent of each of these urban areas (see section 7.3.1 for 

18 additional details on study area selection).
7-10

Figure 7-1 Flow Diagram of Risk Assessment for Short-term Exposure Studies
7-11

1 This risk assessment was implemented using the EPA’s Environmental Benefits Mapping 

2 and Analysis Program—Community Edition, Version 0.63 (BenMAP-CE) (U.S. EPA, 2013b). 

3 This GIS-based computer program draws upon a database of population, baseline 

4 incidence/prevalence rates and effect coefficients to automate the calculation of health impacts. 

5 For this analysis, the standard set of effect coefficients and health effect incidence data available 

6 in BenMAP has been augmented to reflect the latest studies and data available for modeling O3

7 risk. EPA has traditionally relied upon the BenMAP program to estimate the health impacts 

8 avoided and economic benefits associated with adopting new air quality rules. For this analysis, 

9 EPA used the model to estimate O3-related risk for the suite of health effects endpoints described 

10 in section 3.2. There are three primary advantages to using BenMAP for this analysis, as 

11 compared to the procedure for estimating population risk followed in the last review. First, once 

12 we have configured the BenMAP software for this particular O3 analysis, the program can 

13 produce risk estimates for an array of modeling scenarios across a large number of urban areas. 

14 Second, the program can more easily accommodate a variety of sensitivity analyses. Third, 

15 BenMAP allowed us to complete the national assessment of O3 mortality described in Chapter 8, 

16 which plays in important role in assessing the representativeness of the urban study area analysis. 

17 7.1.2 Calculating O3-Related Health Effects Incidence

18 The C-R functions used in the risk assessment are empirically estimated associations

19 between average ambient concentrations of O3 and the health endpoints of interest (e.g., 

20 mortality, hospital admissions, emergency department visits). This section describes the basic 

21 method used to estimate changes in the incidence of a health endpoint associated with changes in 

22 O3, using a “generic” C-R function of the most common functional form.

23 Although some epidemiological studies have estimated linear C-R functions and some 

24 have estimated logistic functions, most of the studies used a method referred to as “Poisson 

25 regression” to estimate exponential (or log-linear) C-R functions in which the natural logarithm 

26 of the health endpoint is a linear function of O3:

27

x y Be β 28 = (1)

29 where x is the ambient O3 level, y is the incidence of the health endpoint of interest at O3

30 level x, β is the coefficient relating ambient O3 concentration to the health endpoint, and B is the 

31 incidence at x=0, i.e., when there is no ambient O3. The relationship between a specified ambient 

32 O3 level, x0, for example, and the incidence of a given health endpoint associated with that level 

33 (denoted as y0) is then

34

0

0

x y Be β 35 = (2)
7-12

1

2 Because the log-linear form of a C-R function (equation (1) is by far the most common 

3 form, we use this form to illustrate the “health impact function” used in the O3 risk assessment.

4 If we let x0 denote the baseline (upper) O3 level, and x1 denote the lower O3 level, and y0

5 and y1 denote the corresponding incidences of the health effect, we can derive the following 

6 relationship between the change in x, Δx= (x0- x1), and the corresponding change in y, Δy, from 

equation (1).12 7

∆ ∆

y y y y e x = − = − − ( ) [ ]. 0 1 0 1 β 8 (3)

9

10 Alternatively, the difference in health effects incidence can be calculated indirectly using 

11 relative risk. Relative risk (RR) is a measure commonly used by epidemiologists to characterize 

12 the comparative health effects associated with a particular air quality comparison. The risk of 

13 mortality at ambient O3 level x0 relative to the risk of mortality at ambient O3 level x1, for 

14 example, may be characterized by the ratio of the two mortality rates: the mortality rate among 

15 individuals when the ambient O3 level is x0 and the mortality rate among (otherwise identical) 

16 individuals when the ambient O3 level is x1. This is the RR for mortality associated with the 

17 difference between the two ambient O3 levels, x0 and x1. Given a C-R function of the form 

18 shown in equation (1) and a particular difference in ambient O3 levels, Δx, the RR associated 

with that difference in ambient O3, denoted as RRΔx, is equal to eβΔx 19 . The difference in health 

20 effects incidence, Δy, corresponding to a given difference in ambient O3 levels, Δx, can then be 

21 calculated based on this RRΔx as:

22

( ) [1 (1/ )] 0 1 0 RR x y y y y ∆ = − = − ∆ 23 . (4)

24

25 Equations (3) and (4) are simply alternative ways of expressing the relationship between 

26 a given difference in ambient O3 levels, Δx > 0, and the corresponding difference in health 

effects incidence, Δy.13 27 These health impact equations are the key equations that combine air 

28 quality information, C-R function information, and baseline health effects incidence information 

29 to estimate ambient O3 health risk.

 12 If Δx < 0 – i.e., if Δx = (x1- x0) – then the relationship between Δx and Δy can be shown to be 

( ) [ 1] ∆ = 1 − 0 = 0 − ∆x y y y y e β . If Δx < 0, Δy will similarly be negative. However, the magnitude of Δy will be 

the same whether Δx > 0 or Δx < 0 – i.e., the absolute value of Δy does not depend on which equation is used. 13 When calculating total risk associated with a specific air quality scenario, Δx is the total O3 concentration 

associated with a given study area (as noted earlier in section 7.1.1, we are not incorporating thresholds, such as 

LMLs into this analysis).
7-13

1 7.2 AIR QUALITY CONSIDERATIONS

2 Air quality data are discussed in detail in Chapter 4 of this report. Here we describe those 

3 air quality considerations that are directly relevant to the estimation of health risks in the 

4 epidemiology based portion of the risk assessment. As described in section 7.1.1, the risk 

5 assessment uses composite (area-wide average) monitor values derived for each urban study area 

6 as the basis for characterizing population exposure in modeling risk. The use of composite 

7 monitors reflects consideration for the way ambient O3 data are used in the epidemiological 

8 studies providing the C-R functions (see section 7.1.1). For the short-term exposure related 

9 health endpoints, the composite monitor values derived for this analysis include hourly time 

10 series for each study area (where the O3 value for each hour is the average of measurements 

11 across the monitors in that study area reporting values for that hour). Once these composite 

12 monitor hourly time series are constructed, we can then extract short-term peak O3 metrics 

13 needed to model specific health effects endpoints. For short-term O3-attributable endpoints, 

14 reflecting consideration for available evidence in the published literature (see section 7.3.2), we 

15 have focused the analysis on short-term peak O3 metrics including 1hr maximum, 8hr mean and 

16 8hr maximum. The 24 hour average has been deemphasized for this analysis, although it is still 

17 used in risk modeling when use of C-R functions based on this metric allow us to cover a 

specific health effect endpoint/location of particular interest14 18 (see section 7.3.2).

19 For modeling mortality risk associated with long-term O3-attributable we construct 

20 seasonally-averaged maximum hourly O3 values (see section 7.3.2). The derivation of composite 

21 monitor distributions used in modeling this health effect endpoint is different than that used for 

22 short-term O3-attributable endpoints. Specifically, for the long-term O3-attributable endpoint we 

23 first construct the seasonally-averaged peak O3 metric for each monitor within a given study area 

24 and then average those monitor-specific metric values together to generate a single composite 

25 value to use in generating risk estimates for that study area.

26 In applying effect estimates obtained from epidemiological studies we attempted to 

27 match the modeling period (e.g. O3 monitoring season) associated with each epidemiology study. 

28 This increases overall confidence in the risk compared with using a single more generalized 

29 specification of the modeling period. As discussed earlier, we modeled all health effect endpoints 

30 for the core analysis using a CBSA-based study area. The use of the CBSA-based study areas 

31 addresses potential bias that would have occurred had we focused the risk assessment on the 

32 smaller core urban study areas. (see section 7.1.1). Table 7-1 identifies (a) the counties 

33 associated with the CBSA definition for each of the 12 urban study areas, (b) the number of O3

34 monitors associated with each CBSA (and a flag for whether the design value monitor is 

 14 In order to provide estimates of respiratory-related HA for LA, we did include a C-R function based on Linn et al., 

2000, which utilizes a 24 hour average exposure metric.
7-14

1 contained within the CBSA), (c) the number of monitors associated with the smaller Smith et al., 

2 2009-based study areas, and (d) the specific O3 modeling period for each study area. A map 

3 showing the counties and monitors for these 12 urban areas can be found in Chapter 4 (figure 4-

4 5, Section 4.3.2.1).

5

6 Table 7-1 Information on the 12 Urban Case Study Areas in the Risk Assessment

Study Area

Counties associated with the CBSA 

definition

# of O3

Monitors 

within the 

CBSAa Required O3 Monitoring Season

Atlanta

Barrow, Bartow, Butts, Carroll, Cherokee

Clayton, Cobb, Coweta, Dawson, DeKalb, 

Douglas, Fayette, Forsyth, Fulton, Gwinnett, 

Haralson, Heard, Henry, Jasper, Lamar, 

Meriwether, Newton, Paulding, Pickens, Pike, 

Rockdale, Spalding, Walton

13 (3) March - October

Baltimore Anne Arundel, Baltimore, Carroll, Harford, 

Howard, Queen Anne's, Baltimore 7 (1) April - October

Boston Essex, Middlesex, Norfolk, Plymouth, 

Suffolk, Rockingham, Strafford 11* (2) April - September

Cleveland Cuyahoga, Geauga, Lake, Lorain, Medina 10* (4) April - October

Denver

Adams, Arapahoe, Broomfield, Clear Creek, 

Denver, Douglas, Elbert, Gilpin, Jefferson, 

Park

16 (6) March - September

Detroit Lapeer, Livingston, Macomb, Oakland, St. 

Clair, Wayne 8 (4) April - September

Houston

Austin, Brazoria, Chambers, Fort Bend, 

Galveston, Harris, Liberty, Montgomery, 

San Jacinto, Waller

22 (17) January - December

Los Angeles Los Angeles, Orange 21* (17) January - December

New York

Bergen, Essex, Hudson, Hunterdon, 

Middlesex, Monmouth, Morris, Ocean, 

Passaic, Somerset, Sussex, Union, Bronx, 

Kings, Nassau, New York, Putnam, Queens, 

Richmond, Rockland, Suffolk, Westchester, 

Pike

22 (7) April - October

Philadelphia

New Castle, Cecil, Burlington, Camden, 

Gloucester, Salem, Bucks, Chester, Delaware, 

Montgomery, Philadelphia

15 (4) April - October

Sacramento El Dorado, Placer, Sacramento, Yolo 17 (8) January - December

St. Louis

Bond, Calhoun, Clinton, Jersey, Macoupin, 

Madison, Monroe, St. Clair, Franklin, 

Jefferson, Lincoln, St. Charles, St. Louis, 

Warren, Washington, St. Louis

17 (2) April - October

7 a – This column presents the number of monitors within each CBSA, whether the design value falls outside 

8 of the CBSA (denoted with an “*”) and the number of monitors within the smaller Smith et al., 2009-based study 

9 area (in parenthesis).

10

11 We estimate risk associated with recent O3 conditions as well as risk associated with 

12 simulating just meeting the existing and alternative standards. While the derivation of composite 
7-15

1 monitor hourly O3 distributions (and associated peak exposure metrics) for recent conditions is 

2 relatively straightforward, the generation of these estimates for the scenarios of just meeting the 

3 existing and alternative standards is more complex. The procedures for simulating attainment of 

4 both existing and alternative O3 standards are presented in Chapter 4 and Chapter 4 appendices.

5 Summary statistics for the air metrics used in modeling risk for each of the 12 urban 

6 study areas under recent conditions and simulated attainment of the existing and alternative

7 standard levels are presented in Chapter 4 (see section 4.3.3.2, Figures 4-10 (2007) and 4-11 

8 (2009)).

9 7.3 SELECTION OF MODEL INPUTS AND ASSUMPTIONS

10 7.3.1 Selection of Urban Study Areas

11 This analysis focuses on modeling risk for a set of urban study areas, reflecting the goal 

12 of providing risk estimates that have greater overall confidence due to the use of location13 specific data when available for these urban locations. In addition, given the greater availability 

14 of location-specific data, a more rigorous evaluation of the impact of uncertainty and variability 

15 can be conducted for a set of selected urban study areas than would be possible for a broader 

16 regional or national-scale analysis. We considered the following factors in selecting the 12 urban 

17 study areas included in this analysis:

18 • Air Quality Data: An urban area has reasonably comprehensive monitoring data for the 

19 period of interest (2006-2010) to support the risk assessment. This criterion was 

20 evaluated qualitatively by considering the number of monitors within the CBSA of the

21 prospective urban areas. Locations with one or two monitors would be excluded since 

22 they had relatively limited spatial coverage in characterizing O3 levels.

23 • Elevated Ambient O3 Levels: Because we are interested in evaluating the potential 

24 magnitude of risk reductions associated with just meeting the existing and alternative O3

25 standard levels, we focus on study areas with elevated ambient O3 levels at or above the 

26 existing standard, such that just meeting alternative O3 standard levels would result in 

27 some degree of risk reduction. 

28 • Location-specific C-R Functions: Given the health endpoints selected for inclusion in 

29 the analysis (see section 7.3.2), there are epidemiological studies of sufficient quality 

30 available for these urban study areas to provide the C-R functions necessary for modeling 

31 risk. This criterion primarily applies to short-term epidemiological studies since the 

32 associated health effect endpoints are the primary focus of the REA. Short-term O3-
7-16

1 attributable epidemiological studies often include city-specific effect estimates, and in 

2 some cases are multi-city studies that provide estimates for multiple cities.

3 • Baseline Incidence Rates and Demographic Data: The required urban area-specific 

4 baseline incidence rates and population data are available for a recent year for at least one 

5 of the health endpoints.

6 • Geographic Heterogeneity: Because O3 distributions and population characteristics vary 

7 geographically across the U.S., we selected urban study areas to provide coverage for 

8 regional variability in factors related to O3 risk including variability in the spatial pattern 

9 of O3 in the urban area, population exposure (differences in residential housing density, 

10 air conditioning use and commuting patterns), demographic characteristics (baseline 

11 incidence rates, SES) and variability in effect estimates. The degree to which the set of 

12 urban study areas provided coverage for regional differences across the U.S. in many of 

13 these O3 risk-related factors was evaluated as part of the representativeness analysis 

14 presented in Chapter 8.

15 Application of the above criteria resulted in the selection of 12 urban study areas for 

16 inclusion in the risk assessment including: 

17 • Atlanta, GA

18 • Baltimore, MD

19 • Boston, MA

20 • Cleveland, OH

21 • Denver, CO

22 • Detroit, MI

23 • Houston, TX

24 • Los Angeles, CA

25 • New York, NY

26 • Philadelphia, PA

27 • Sacramento, CA

28 • St. Louis, MO

29

30 The specific set of counties used in defining each of the 12 urban study areas based on 

31 the CBSA is presented in Table 7-1. 
7-17

1 7.3.2 Selection of Epidemiological Studies and Specification of Concentration-Response 

2 Functions

3 Once the set of health effect endpoints to be included in the risk assessment has been 

4 specified, the next step was to select the set of epidemiological studies that will provide the 

5 effect estimates and model specifications used in the C-R functions. This section describes the 

6 approach used in completing these tasks and presents a summary of the epidemiological studies 

7 and associated C-R functions specified for use in the risk assessment.

8 In Chapter 2, section 2.5 we identified the set of health effect categories and associated 

9 endpoints to be included in this assessment, based on review of the evidence provided in the O3

10 ISA (U.S. EPA, 2013a). The selection of specific health effect endpoints to model within a given 

11 health effect endpoint category is an iterative process involving review of both the strength of 

12 evidence (for a given endpoint) as summarized in the O3 ISA together with consideration for the 

13 available epidemiological studies supporting a given endpoint and the ability to specific key 

14 inputs needed for risk modeling, including effect estimates and model forms. Ultimately, 

15 endpoints are only selected if (a) they are associated with an overarching effect endpoint 

16 category selected for inclusion in the risk assessment and (b) they have sufficient 

17 epidemiological study support to allow their modeling in the risk assessment. Health effect 

18 endpoints selected for inclusion in the second draft REA include:

19

20 Short-term O3-attributable:

21 • Mortality (likely to be a casual relationship)

22 o All-cause (non-accidental)

23 o Cardiovascular

24 o Respiratory 

25 • Respiratory effects (causal relationship)

26 o ED (asthma, wheeze, all respiratory symptoms)

o HA (COPD, asthma, all respiratory) 15 27

28 o Respiratory symptoms

29

30 Long-term O3-attributable:

 15 Regarding COPD-related HA, the O3 ISA states that “Although limited in number, both single- and multi-city 

studies consistently found positive associations between short-term O3 exposures and asthma and COPD hospital 

admissions.” (U.S. EPA 2013a, p. 6-128). It is also important to point out that when modeling of COPD-related 

HA is limited to the summer months (as was done for the REA), available effect estimates have tighter 

confidence intervals and are generally positive, which increases overall confidence in the resulting risk estimate 

(see U.S. EPA 2013a, Figure 6-19).
7-18

1 • Respiratory effects, focusing on respiratory-related mortality (likely causal 

relationship).

16 2

3

4 We selected epidemiological studies to support modeling of the health effect endpoints 

listed above by applying a number of criteria including17 5 :

6 • The study was peer-reviewed, evaluated in the O3 ISA, and judged adequate by EPA 

7 staff for purposes of inclusion in the risk assessment. We considered the following 

8 criteria: whether the study provides C-R relationships for locations in the U.S., 

9 whether the study has sufficient sample size to provide effect estimates with a 

10 sufficient degree of precision and power, and whether adequate information is 

11 provided to characterize statistical uncertainty.

12 • Preference for multicity studies given that they typically have greater power and 

13 reflect patterns of O3 related health effects over a range of urban areas (and regions) 

14 which can display variability in key risk-related factors such as exposure 

15 measurement error. In the case of short-term O3-attributable mortality, we also 

16 favored those multi-city studies for which we could obtain Bayesian-adjusted city17 specific estimates from the study authors, since these incorporate both city-specific 

18 effect information with information from the broader array of cities included in the 

19 study. In those instances where we did not have multi-city studies (e.g., with many of 

20 the short-term respiratory-related morbidity endpoints) we use single-city studies.

21 • The study design is considered robust and scientifically defensible, particularly in 

22 relation to methods for covariate adjustment, including treatment of confounders, as 

23 well as treatment of effect modifiers. For example, if a given study used ecological24 defined variables (e.g., smoking rates) as the basis for controlling for confounding, 

25 concerns may be raised as to the effectiveness of that control.

26 • The study is not superseded by another study (e.g., if a later study is an extension or 

27 replication of a former study, the later study would effectively replace the former 

28 study), unless the earlier study has characteristics that are clearly preferable (e.g., 

29 inclusion of copollutants models, or use of a peak exposure metric of interest).

 16 The O3 ISA classifies long-term O3-attributable respiratory health effects, including respiratory-related mortality, 

as having a likely causal classification. By contrast, it classifies long-term O3-attributable total mortality as 

having a suggestive of a causal relationship classification (O3 ISA, 2012, Chapter 1). We have focused on 

modeling long-term O3-attributable respiratory-related mortality given the greater support for this health 

endpoint relative to total mortality. 17 In addition to the criteria listed here, we also attempted to include studies that provide coverage for populations 

considered particularly at-risk for a particular health (e.g., children, individuals with preexisting disease). 

However, a study would have to meet the criteria listed here (in addition to providing coverage for an at-risk 

population) in order for that study to be used to derive C-R functions.
7-19

1 We applied the above criteria and selected the set of epidemiological studies presented in 

2 Table 7-2 for use in specifying C-R functions (Table 7-2 also describes elements of the C-R 

3 functions specified using each epidemiological study, as discussed below).

4 As part of methods refinement for this risk assessment, we considered studies that 

5 utilized more sophisticated and potentially representative exposure surrogates in characterizing 

6 population exposure (e.g., using population-weighted O3 monitor values instead of equally7 weighted monitors, linking exposures in individual counties or U.S. Census tracts to the nearest 

8 monitor, rather than using a composite monitor value to represent the entire study area).

9 However, analysis conducted by EPA demonstrated that use of the simpler composite monitor 

10 approach (as used for other short-term O3-attributable morbidity endpoints) generated risk 

11 estimates that were very close to those generated using the population-weighted O3 metric (see 

12 REFERENCE- Karen Wesson???). Therefore, in order to conserve time and resources, we 

13 modeled this endpoint using the more generalized composite monitor-based metric. And finally, 

14 a number of the long-term O3-attributable morbidity studies originally considered for modeling 

15 this endpoint category did involve more complex O3 metrics (e.g., Atkinbami et al., 2010, Meng 

16 et al., 2010, and Moore et al., 2008). However, limitations in the study-level data required to 

17 support risk assessment prevents us at this point from completing a quantitative risk assessment 

for this category of health endpoints with a reasonable degree of confidence.18 18

19 Based on additional evaluation of the literature, we have substituted Smith et al., 2009 for 

20 Bell et al., 2004 as a source of Bayes-adjusted city-specific effect estimates to support modeling 

21 short-term O3-attributable mortality. This decision reflects a number of factors. The Smith et al., 

22 2009 study includes a wider range of simulations exploring sensitivity of the mortality effect to 

23 different model specifications including (a) regional versus national Bayes-based adjustment, (b) 

24 copollutants models considering PM10, and (c) all - year versus O3-season based estimates. This 

25 is contrasted with the Bell et al., 2004 study which does not provide this degree of model 

26 exploration. In obtaining the city-specific Bayes-adjusted effect estimates for the Smith et al., 

27 2009 study from the study authors, we were provided with estimates reflecting this range of 

28 alternative model specifications which allowed us to incorporate them into both the core and 

29 sensitivity analysis portions of the REA (see section 7.4.3). In addition, the Smith et al., 2009 

30 study does not use the trimmed mean approach employed in the Bell et al., 2004 study in 

31 preparing O3 monitor data. We have a number of concerns regarding the trimmed mean approach 

32 including (1) the potential loss of temporal variation in the data when the approach is used (this 

33 could impact the size of the effect estimate) and (2) a lack of complete documentation for the 

 18 However, as noted in section 7.7.3 of the first draft REA, these limitations do not prevent the use of this evidence 

from informing consideration of the levels of exposure at which specific types of health effects may occur (i.e., 

the evidence analysis, which is an important aspect of the O3 NAAQS review). Rather, these limitations only 

prevent the quantitative estimation of risk with a reasonable degree of confidence.
7-20

1 approach which prevents us from fully reviewing the technique and using it in preparing O3

2 metrics for the REA. Given these concerns, we view it as advantageous that the Smith et al., 

3 2009 study does not use the trimmed mean approach.

4 With the exception of the trimmed mean approach, the Smith et al., 2009 study was 

5 intended to reproduce the results of the Bell et al., 2004 analysis. Thus, the core risk results 

based on Smith et al 2009 are comparable to the 1st 6 draft REA estimates based on Bell et al 2004, 

7 while the alternative models provided in Smith et al 2009 allow for an expanded set of sensitivity 

8 analyses. The comparability of the Smith et al 2009 and Bell et al 2004 estimates is confirmed by

9 the graphical comparison in Smith et al 2009 of mortality effect estimates (for the 24hr O3

10 metric) with matching effect estimates from Bell et al., 2004. This comparison demonstrates the 

11 close match of the two studies (for this particular scenario).

12 Reflecting the points made above, in modeling short-term O3-attributable mortality, we 

13 have included a core analysis based on the national-Bayesian adjusted city-specific effect 

14 estimates (reflecting the full O3 monitoring period in each city) obtained from Smith et al., 2009. 

15 As sensitivity analyses, we have included effect estimates obtained from Smith et al., 2009 

16 which reflect application of copollutants models (including PM10), Bayes adjustment using a 

regional prior,19 and a shorter fixed O3 measurement period (April-October). In the 1st 17 draft 

18 REA, we had also included national Bayes-adjusted effect estimates (reflecting a fixed June19 August period) obtained from Zanobetti and Schwartz, 2008 as part of the core analysis. 

However, we have decided to instead include these as part of the sensitivity analysis in this 2nd 20

21 draft of the REA since these effect estimates cover a more limited warm-weather period and 

22 consequently will generate only partial characterizations of mortality risk (since they exclude 

23 risk occurring during the non-summer months).

24 We have also included estimates of respiratory-related mortality associated with long25 term O3 exposures based on effect estimates obtained from Jerrett et al., 2009. The decision to 

26 model long-term O3-attributable mortality reflects consideration for evidence supporting a likely 

27 to be a causal relationship for long-term O3-attributable respiratory effects, including mortality 

28 (O3 ISA, section 2.5.2, U.S. EPA, 2013a). After considering its strengths and weaknesses, we 

29 consider the Jerrett et al. (2009) study to be an appropriate basis for estimating long-term O3-

30 related respiratory mortality risk. Key strengths of this study are that it (a) included 1.2 million 

31 participants in the American Cancer Society cohort from all 50 states, DC, and Puerto Rico; 

32 included O3 data from 1977 (5 years before enrollment in the cohort began) to 2000; (b) 

33 considered co-pollutant models that controlled for PM2.5; and (c) explored the potential for a 

34 threshold concentration associated with the long-term mortality endpoint. Importantly, this study 

 19 With application of a regional prior within Bayesian adjustment, city-specific effect estimates are adjusted 

towards the regional value rather than a national value as is the case with the application of a national prior.
7-21

1 was also the first to explore the relationship between long-term O3 exposure and respiratory 

2 mortality (rather than focusing on cardiopulmonary mortality). Key limitations are possible 

3 exposure misclassification and uncontrolled confounding by temperature, which are endemic to 

4 most long-term epidemiological studies. While Jerrett et al. (2009) found negative associations 

5 between O3 exposure and cardiovascular mortality when controlling for PM2.5, null or negative 

6 associations for O3 are consistent with the evidence that PM2.5 is the pollutant most strongly 

7 associated with cardiovascular disease (EPA 2009 PM ISA).

8 Our analysis includes a core estimate based on a co-pollutant model (with PM2.5). The 

9 seasonal average metrics used in the long-term exposure mortality estimate are not very sensitive 

10 to the reduced number of days with co-pollutant monitoring, and as such it is appropriate to 

11 include the copollutant model as the core estimate. We also include two sensitivity analyses for 

12 long-term O3-attributable respiratory mortality including: (a) application of regionally13 differentiated effect estimates (although these do not include a copollutants model specification)

14 and (b) application of a single pollutant (O3-only) national-based effect estimate.

15 The effect estimates used in modeling long-term O3-attributable mortality (see Table 7-2) 

16 utilize a seasonal average of peak (1hr maximum) measurements. These long-term exposure 

17 metrics can be viewed as long-term exposures to daily peak O3 over the warmer months, as 

18 compared with annual average levels such as are used in long-term PM exposure calculations. 

19 This increases the need for care in interpreting these long-term O3-attributable mortality 

20 estimates together with the short-term O3-attributable mortality estimates, in order to avoid 

21 double counting. It is also important to keep in mind that our estimates of short-term O3-

22 attributable mortality are for all-causes, while estimates of long-term O3-attributable mortality 

23 are focused on respiratory-related mortality. This further limits the ability to compare estimates 

24 of long-term and short-term exposure related mortality.

25 Once the set of epidemiology studies described above was selected, the next step was to 

26 specify C-R functions for use in the risk assessment. Several factors were considered in 

27 identifying the effect estimates and model forms used in specifying C-R functions for each 

28 endpoint. These factors are described below:

29

30 • O3 Exposure Metric: In the risk assessment supporting the previous O3 NAAQS review, 

31 for short-term exposure, we included C-R functions based on 24hr averages as well as a 

32 number of peak O3 measurements. However, given that the the current O3 NAAQS 

33 standard uses an 8hr form and given that many of the clinical studies involving O3 also 

34 utilize shorter exposures (on the order of 2 to 8 hrs – see O3 ISA, section 6.2.1.1), we 

35 wanted to see if the latest epidemiological studies for O3 also supported use of an 8hr 

36 averaging time in modeling risk. Several epidemiological studies completed since the last 
7-22

1 review provide limited support for stronger associations between health endpoints and 

2 peak O3 metrics (i.e., 1hr maximum, 8hr maximum and 8hr means) relative to 24hr 

3 averages. Specifically, a study of respiratory ED visits in Atlanta (Darrow et al., 2011) 

4 found stronger associations with peak metrics (including 1hr and 8hr maximum 

5 measurements) compared with 24hr averages (see O3 ISA section 6.2.7.3 and Figure 6-

6 17, U.S. EPA, 2013a). Similarly, for short-term exposure-related mortality, there are also 

7 a limited number of epidemiologic studies that have compared mortality associations 

8 with peak O3 metrics and the 24hr average metric. Although the O3 ISA recognizes that 

9 24hr exposure metrics when used in time series studies may result in smaller risk 

10 estimates, ultimately it concludes that “Overall, the evidence from time-series and panel 

11 epidemiologic studies does not indicate that one exposure metric is more consistently or 

12 strongly associated with mortality or respiratory-related health effects” (U.S. EPA, 

13 2013a, section 2.5.4.2). Based on consideration for the evidence summarized in the O3

14 ISA, we have decided to focus on peak exposure metrics because of the limited evidence 

15 that these metrics may be associated with higher risk estimates relative to the 24 hr 

16 exposure metric. However, we recognize that, as summarized in the O3 ISA, there is only 

17 weak support for differentiating between these two categories of short-term exposure 

18 metric.
7-23

Table 7-2 Overview of Epidemiological Studies Used in Specifying C-R Functions

Epidemiological 

study

(stratified by O3-

attributable health 

endpoints)

Health 

endpoints

Location 

(urban study 

area(s) 

covered)

Exposure metric 

(and modeling 

period) Additional study design details Notes regarding application in the analysis

Short-term O3-attributable mortality

Smith et al., 2009 Nonaccidental, 

respiratory, 

cardiovascul

ar

95 large urban 

communities 

(provides 

coverage for all 

12 urban study 

areas)

24hr avg, 8hr max, 

1hr max. April 

through October 

and all year

Adjusting for time-varying 

confounders (PM, weather, 

seasonality). Lag structure included 

0, 1, 2 and day 3 lag as well as 0-6 

day distributed lag. Age range: all 

ages. 

Focused on the 8hr max-based metric C-R 

functions for the REA (see text discussion later in 

this section). Obtained Bayes-adjusted city-specific 

effect estimates for non-accidental all-cause 

mortality from Dr. Smith (personal communication, 

Dr. Richard L. Smith, January 15, 2013) reflecting 

consideration for the following modeling elements: 

(a) regional- versus national-prior Bayes model 

adjustment, (b) single pollutant versus copollutants 

(PM10) models, and (c) full monitoring period 

versus summer only (April-October). For the core 

analysis, we focused on the single pollutant (O3-

only) model covering the full monitoring period.

The copollutants model (with PM10) was included 

as a sensitivity analysis (see section 7.4.3).

Zanobetti and 

Schwartz (2008) 

Nonaccidental, 

respiratory, 

cardiovascul

ar

48 U.S. cities 

(provides 

coverage for the 

12 urban study 

areas)

8hr max. JuneAugustEffect controlled for season, day of 

week, and temperature. Lag 

structure included 0-3d, 0-20 and 

4-20 day). Age range: all ages

Obtained Bayes-adjusted city-specific effect 

estimates for non-accidental, respiratory and 

cardiovascular from Dr. Zanobetti (personal 

communication, Dr. Antonella Zanobetti, January 

5, 2012). These effect estimates reflect a 0-3 day 

distributed lag and are based on 8hr mean O3

levels measured between June and August. 

Estimates were generated for each study area using 

this constrained warm-season period.

Short-term O3-attributable morbidity - HA for respiratory effect)

Medina-Ramon et 

al., 2006.

HA: COPD, 

pneumonia

36 cities 

(provides 

coverage for all 

12 urban study 

areas)

8hr mean. warm 

(May-September), 

cool (OctoberApril), all year

Distributed lag (0-1 day). Age 

range: ≥ 65yrs. Controlled for day 

of the week and weather (including 

temperature). 

Generated risk estimates based on warm season for 

COPD only (May-September). 

Linn et al., 2000 HA: 

unscheduled 

LA only 24hr mean, LA O3

season (all year), 

Lag 0. Age range: all ages. Used 

subgroup analysis to explore the 

Included effect estimate based on 24hr avg metric 

(for summer) since this provided additional 
7-24

Epidemiological 

study

(stratified by O3-

attributable health 

endpoints)

Health 

endpoints

Location 

(urban study 

area(s) 

covered)

Exposure metric 

(and modeling 

period) Additional study design details Notes regarding application in the analysis

for 

pulmonary 

illness

winter, spring, 

summer and 

autumn

effect of temporal variation, 

weather and autocorrelation on O3

effect.

coverage for HA in L.A. Modeled using air quality 

for June-August.

Lin et al., 2008 HA: 

respiratory 

disease

NY State (used 

to cover NYC)

1hr max (for 

10am-6pm 

interval), warm 

season (AprilOctober)

Lag 0, 1, 2, 3. Age range: <18yrs. 

Models adjusted for the 

confounding effects of 

demographic characteristics,

particulate matter(PM10), 

meteorological conditions,

day of the week, seasonality, longterm trends, and different

lag periods of exposure.

Used 1hr max metric applied to the city-specific 

O3 season for NYC (April-October).

Katsouyanni et al 

2009

HA: 

cardiovascul

ar disease, 

chronic 

obstructive 

pulmonary 

disease, 

pneumonia, 

all 

respiratory 

14 cities 

(provides 

coverage for 

Detroit only)

1hr max. Summer 

only and all year

Lag 0-1day. Age range: ≥ 65yrs.

Models accounted for seasonal 

patterns, but also, for weekend and 

vacation effects, and for epidemics 

of respiratory disease. The data 

were also analyzed to detect 

potential thresholds in the 

concentration–response 

relationships.

C-R function applied only for all respiratory 

endpoint. Used June-August-based composite 

monitor. 

Silverman et al., 

2010

HA: asthma 

(ICU and 

non-ICU) 

NYC 8hr max. Warm 

season (AprilAugust)

Includes control for PM2.5. Lag 0-1 

day. Age range: children 6-18yrs. 

The model adjusted for temporal

trends, weather, and day of the 

week.

Applied C-R function (for O3 and O3 with control 

for PM2.5) to the city-specific O3 season for NYC 

(slightly longer than the modeling period used in 

the study).

Short-term O3-attributable morbidity– ED and ER visits (respiratory)

Ito et al., 2007 ED: asthma NYC 8hr max. Warm 

season (AprilSeptember)

Includes models controlling for 

SO2, NO2, CO and PM2.5. Lag: 0, 

1, and distributed lag (0-1 day). 

Age range: all ages. Model adjusts 

for temporal trends, weather terms, 

day-of-week and other pollutants.

Applied C-R functions (for O3 alone and O3 with 

control for listed pollutants) to the city-specific O3

season for NYC (slightly longer than the modeling 

period used in the study).

Tolbert et al., 2007 ED: all 

respiratory

Atlanta 8hr max. Summer 

(March-October)

Includes models controlling for 

NO2, CO, PM10.and NO2/ NO2. 

Age range: all ages. Model controls 

for temporal trends, temperature, 

Applied C-R functions (for O3 alone and O3 with 

control for listed pollutants) to the city-specific O3

season for Atlanta.
7-25

Epidemiological 

study

(stratified by O3-

attributable health 

endpoints)

Health 

endpoints

Location 

(urban study 

area(s) 

covered)

Exposure metric 

(and modeling 

period) Additional study design details Notes regarding application in the analysis

other pollutants. 

Strickland et al., 

2010

ER: 

respiratory 

Atlanta 8hr max (based on 

population 

weighted average 

across monitors). 

Warm season 

(May to October) 

and cool 

(November to 

April)

Lag: average of 0-2 day, 

distributed lag 0-7 day. Age range: 

5-17yrs. Model controls for 

seasonal trends and meteorology.

Included effect estimates based on both lag 

structures and used composite monitor values for 

city-specific O3 season. 

Darrow etl al., 2011 ED: all 

respiratory

Atlanta 8hr max, 1hr max, 

24hr avg for 

summer (MarchOctober).

Lag: 1day. Age range: all ages. The 

study used a time series analysis 

similar to case-crossover with 

crossover matching based on daily 

temperature (rather than day of the 

week) to provide control for this 

key risk-related factor.

Used city-specific O3 season-based composite 

monitor values.

Short-term O3-attributable morbidity – respiratory symptoms 

Gent et al., 2003 Respiratory 

symptoms: 

wheeze, 

persistent 

cough, chest 

tightness, 

shortness of 

breath

Springfield MA 

(study used to 

cover Boston)

1hr max, 8hr max Lag: 0 and 1 day. Age range: 

asthmatic children <12 yrs. Model 

adjusted for temperature.

Included effect estimates for different symptoms 

based on both 8hr max and 1hr max metrics (for 

city-specific O3 season composite monitor values 

for Boston). The study area (which focuses on 

Springfield and the northern portion of 

Connecticut) does not encompass Boston. 

However, we are willing to accept uncertainty 

associated with using effect estimates from this 

study to provide coverage for Boston given the goal 

of providing coverage for this morbidity endpoint. 

However, there is increased uncertainty associated 

with modeling for this endpoint.

Long-term O3-attributable respiratory mortality

Jerrett et al., 2009 Respiratory, 

cardiovascul

ar, 

cardiopulmo

nary

96 metropolitan 

statistical areas 

(provides 

coverage for all 

12 study areas)

Seasonal average 

(i.e. Apr-Sep) of 

the peak daily 1hr 

max values.

>30 yrs of age, includes nationallevel and regional effect estimates 

(only national-level estimate has 

copollutants modeling considering 

PM2.5 along with O3). Modeling 

included consideration for a range 

of potential confounders evaluated

Included national copollutants model-based effect 

estimates in core analysis and single-pollutant 

model regional effect estimates and national effect 

estimates as sensitivity analyses.
7-26

Epidemiological 

study

(stratified by O3-

attributable health 

endpoints)

Health 

endpoints

Location 

(urban study 

area(s) 

covered)

Exposure metric 

(and modeling 

period) Additional study design details Notes regarding application in the analysis

at both the ecological level and 

personal level.
7-27

1 • Single-and Multi-pollutant Models (pertains to both short-term and long-term 

2 exposure studies): Epidemiological studies often consider health effects associated with 

3 ambient O3 using both single-pollutant and co-pollutant models. To the extent that any of 

4 the co-pollutants present in the ambient air may have contributed to health effects 

5 attributed to O3 in single pollutant models, risks attributed to O3 may be overestimated or 

6 underestimated if C-R functions are based on single pollutant models. This would argue 

7 for inclusion of models reflecting consideration of co-pollutants. Conversely, in those 

8 instances where co-pollutants are highly correlated with O3, inclusion of those pollutants 

9 in the health impact model can produce unstable and statistically insignificant effect 

10 estimates for both O3 and the co-pollutants. Furthermore, there are often significant 

11 differences in sampling frequencies for each pollutant included in copollutants models, 

12 which can lead to a loss of statistical power in copollutants models (relative to single 

13 pollutant models). These last points could argue for inclusion of a model based 

14 exclusively on O3. Given that single and multi-pollutant models each have potential 

15 advantages and disadvantages, to the extent possible, given available information we 

16 have included both types of C-R functions in the risk assessment.

17 • Multiple Effect Estimates within a Given CBSA-based Study Area: As noted earlier 

18 in section 7.1.1, for this analysis, all health endpoints, including short-term O3-

19 attributable mortality are modeled using CBSA-based study areas. In the case of both 

20 Smith et al., 2009 and Zanobetti and Schwartz 2008, these CBSA-based study areas are 

21 larger than the study areas used in these epidemiological studies to derive effect 

22 estimates. Furthermore, for some of the CBSA-based urban study areas, several of the 

23 smaller study areas evaluated in the epidemiological study fall within a single larger 

24 CBSA-based study area. For example, with the Smith et al., 2009 study, multiple effect 

25 estimates are available for the CBSA-defined study areas of Los Angeles and New York 

26 City. Specifically, the Smith et al., 2009 study provides separate effect estimates for (a) 

27 Santa Anna/Anaheim and Los Angeles study areas, both of which fall within the larger 

28 CBSA-based Los Angeles study area and (b) New York, Jersey City and Newark study 

29 areas, all of which fall within the larger CBSA-defined New York study area (see Table 

30 7-3). This raises the question of how to specify the effect estimate for these larger CBSA31 based study areas when there are multiple effect estimates available from the 

32 epidemiological study. For this analysis, in those instances where there are multiple effect 

33 estimates, we have decided to use the effect estimate that represents the largest number of 

34 residents within each CBSA-based study area. There is uncertainty associated with this 

35 decision which is discussed both in section 7.4.2 and section 7.5.3 (as part of the air 

36 quality-related sensitivity analysis discussion).
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1 Table 7-3 CBSA-based Study Areas with Multiple Effect Estimates from the Smith et 

2 al., 2009 Study*

CBSA 

Study Area

Smith et al., 

2009 (smaller) 

study areas with 

CBSA-based 

study area

Population 

totals

Mortality 

effect 

estimate Comments

New York 

City

New York, NY 9,100,000 0.0009 New York study area dominates from 

a population standpoint, so that effect 

estimate was chosen to represent the 

entire CBSA. An additional 8.3 

million people live in portions of the 

New York CBSA not covered by the 

Smith et al., 2009 study areas.

Jersey City, NJ 630,000 0.0001

Newark, NJ 780,000 0.0005

Los Angeles

Santa 

Ana/Anaheim, 

CA

3,000,000 0.0002

Los Angeles dominates from a 

population standpoint, so that effect 

estimate was chosen to represent the 

entire CBSA. In this case, the full 

CBSA-based study area is covered by 

the Smith et al., 2009-based subareas.

Los Angeles, CA 9,800,000 0.0001

3 * Source: obtained from Dr. Smith (personal communication, Dr. Richard L. Smith, January 15, 2013)

4 • Single-city Versus Multi-city Studies: All else being equal, we judge C-R functions 

5 estimated in the assessment location as preferable to a function estimated in some other 

6 location, to avoid uncertainties that may exist due to differences associated with 

7 geographic location. There are several advantages, however, to using estimates from 

8 multi-city studies versus studies carried out in single cities. Multi-city studies are 

9 applicable to a variety of settings, since they estimate a central tendency across multiple 

10 locations. Multi-city studies also tend to have more statistical power and provide effect 

11 estimates with relatively greater precision than single-city studies due to larger sample 

12 sizes, reducing the uncertainty around the estimated health coefficient. By contrast, 

13 single-city studies, while often having lower statistical power and varying study designs 

14 which can make comparison across cities challenging, reflect location-specific factors 

15 such as differences in underlying health status, and differences in O3 exposure-related 

16 factors such as air conditioner use and patterns of urban residential density. There is a 

17 third type of study design that generates Bayes-adjusted city-specific effect estimates, 

18 thereby combining the advantages of both city-specific and multi-city studies. Bayes19 adjusted city-specific estimates begin with a city-specific effect estimate and shrink that 

20 towards a multi-city mean effect estimate based on consideration for the degree of 

21 variance in both estimates. We have elected to place greater confidence on these types of 

22 Bayesian-adjusted effect estimates when they are available. Otherwise, given the 

23 advantages for both city-specific and multi-city effect estimates, we have used both types 

24 when available.
7-29

1 • Multiple Lag Models: Based on our review of evidence provided in the O3 ISA, we 

2 believe there is increased confidence in modeling both short-term O3-attributable

3 mortality and respiratory morbidity risk based on exposures occurring up to a few days 

4 prior to the health effect, with less support for associations over longer exposure periods 

5 or effects lagged more than a few days from the exposure (see O3 ISA section 2.5.4.3,

6 U.S. EPA, 2013a). Consequently, we have favored C-R functions reflecting shorter lag 

7 periods (e.g., 0, 1 or 1-2 days). With regard to the specific lag structure (e.g, single day 

8 versus distributed lags), the O3 ISA notes that epidemiological studies involving 

9 respiratory morbidity have suggested that both single day and multi-day average 

10 exposures are associated with adverse health effects (see O3 ISA section 2.5.4.3). 

11 Therefore, when available both types of lag structures where considered in specifying C12 R functions for short-term O3-attributable mortality and morbidity.

13 • Seasonally-differentiated Effects Estimates: The previous O3 Air Quality Criteria 

14 Document (AQCD) (published in 2006) concluded that aggregate population time-series 

15 studies demonstrate a positive and robust association between ambient O3 concentrations 

16 and respiratory-related hospitalizations and asthma ED visits during the warm season (see 

17 O3 ISA section 2.5.3.1 U.S. EPA, 2013a). The current O3 ISA notes that recent studies of 

18 short-term O3-attributable respiratory mortality in the U.S. suggest that the effect is 

19 strengthened in the summer season (O3 ISA section 2.5.3.1, U.S. EPA, 2013a). In 

20 addition, many of the key epidemiological studies discussed in the current O3 ISA 

21 exploring both short-term exposure related mortality and morbidity have larger (and more 

22 statistically significant) effect estimates when evaluated for the summer (O3) season, 

23 relative to the full year (see O3 ISA Figures 6-20 and 6-27, U.S. EPA, 2013a). However, 

24 if we focus the assessment of risk on the warm season, we bias our estimate by excluding 

25 potential effects associated with cooler (non-summer) months. Given our desire to 

26 provide a more complete picture of overall risk in each of the study areas, we have 

27 favored (for the core analysis) effect estimates that cover the full O3 monitoring period 

28 specific to each study area, rather than the more limited warm (summer) period.

29 • Shape of the Functional Form of the Risk Model (including threshold): The current 

30 O3 ISA concludes that there is little support in the literature for a population threshold for 

31 short-term O3-attributable effects. However, specifically in relation to mortality, the O3

32 ISA concludes that a national or combined analysis may not be appropriate to identify 

whether a threshold exists (see O3 ISA, section 2.5.4.4, U.S. EPA, 2013a).20 33 Given the 

 20 Specifically, given the multi-city nature of these mortality studies combined with the variability in O3 and other 

factors related to exposure and risk, the O3 ISA concludes that these studies are not well positioned to evaluate 

the potential for a threshold in the mortality effect.
7-30

1 above general observation from the O3 ISA regarding the low potential for thresholds, we 

2 did not include C-R functions for any of the short-term O3-attributable health endpoints 

modeled that incorporated a threshold.21 3

4 Application of the above criteria resulted in an array of C-R functions specified for the 

5 risk assessment (see Table 7-2), including functions covering short-term O3-attributable

6 mortality and morbidity and long-term O3-attributable mortality. In presenting the C-R functions 

7 in Table 7-2, we have focused on describing key attributes of each C-R function (and associated 

8 source epidemiological study) relevant to a review of their use in the risk assessment. More 

9 detailed technical information including effect estimates and model specification is provided in 

10 Appendix 7A. Specific summary information provided in Table 7-2 includes:

11 • Health endpoints: identifies the specific endpoints evaluated in the study. Generally 

12 we included all of these in our risk modeling, however, when a subset was modeled, 

13 we reference that in the “Notes” column (last column in the table).

14 • Location: identifies the specific urban areas included in the study and maps those to 

15 the set of 12 urban study areas included in the risk assessment.

16 • Exposure metric: describes the exposure metric used in the study, including the 

17 specific modeling period (e.g., O3 season, warm season, full year). We developed two 

18 categories of composite monitor values to match the modeling periods used in the two 

19 short-term O3-attributable mortality studies providing C-R functions for the analysis. 

20 For the remaining morbidity endpoints, we mapped specific C-R functions to 

21 whichever of these two composite monitor categories most closely matched the 

22 modeling period used in the underlying epidemiological study. This mapping (for 

23 morbidity endpoint C-R functions) is described in the “Notes” column (the seasons 

24 reflecting in modeling for each C-R function are also presented in Appendix 7A).

25 • Additional study design details: this column provides additional information primarily 

26 covering the lag structure and age ranges used in the study. 

27 • Notes regarding application in second draft analysis: as the name implies, this 

28 column provides notes particular to the application of a particular epidemiological 

29 study and associated C-R functions in the risk assessment.

 21 While clinical studies have suggested the presence of a threshold for respiratory effects, these should not be used 

to support specification of population-level thresholds for use in the epidemiological-based risk assessment. The 

clinical studies focus on relatively small and clearly defined populations of healthy adults which are not 

representative of the broader residential populations typically associated with epidemiological studies, including 

older individuals and individuals with existing health conditions which place them at greater risk for O3-related 

effects. Therefore, the clinical studies are unlikely to have the power to capture population thresholds in a 

broader and more diverse urban residential population, should those thresholds exist.
7-31

1 7.3.3 Baseline Health Effect Incidence and Prevalence Data 

2 As discussed earlier (section 7.1.2), the most common epidemiological-based health risk 

3 model expresses the change in health risk (∆y) associated with a given change in O3

4 concentrations (∆x) as a percentage of the baseline incidence (y). To accurately assess the impact 

5 of O3 air quality on health risk in the selected urban areas, information on the baseline incidence 

6 of health effects (i.e., the incidence under recent air quality conditions) in each location is 

7 needed. In some instances, health endpoints are modeled for a population with an existing health 

8 condition, necessitating the use of a prevalence rate. Where at all possible, we use county9 specific incidences or incidence rates (in combination with county-specific populations). In some 

10 instances, when county-level incidence rates were not available, BenMAP can employ more 

11 generalized regional rates (see BenMAP Guidance Manual for additional detail, Abt Associates, 

12 Inc. 2010). For prevalence rates (which were only necessary for modeling respiratory symptoms 

13 among asthmatic children using Gent et al., (2008) - see Table 7-2), we utilized a national-level 

14 prevalence rate appropriate for the age group being modeled. A summary of available baseline 

15 incidence data for specific categories of effects (and prevalence rates for asthma) is presented 

16 below:

17 • Baseline incidence data on mortality: County-specific (and, if desired, age- and race18 specific) baseline incidence data are available for all-cause and cause-specific 

mortality from CDC Wonder.22 19 The most recent year for which data are available 

online is 2005 and this was the source of incidence data for the risk assessment.23 20

21 • Baseline incidence data for hospital admissions and emergency room (ER) visits: 

22 Cause-specific hospital admissions baseline incidence data are available for each of 

23 40 states from the State Inpatient Databases (SID). Cause-specific ER visit baseline 

24 incidence data are available for 26 states from the State Emergency Department 

25 Databases (SEDD). SID and SEDD are both developed through the Healthcare Cost 

26 and Utilization Project (HCUP), sponsored by the Agency for Healthcare Research 

27 and Quality (AHRQ). In addition to being able to estimate State-level rates, SID and 

28 SEDD can also be used to obtain county-level hospital admission and ER visit counts 

29 by aggregating the discharge records by county.

30 • Asthma prevalence rates: state-level prevalence rates that are age group stratified are 

31 available from the Centers for Disease Control and Prevention (CDC) Behavioral 

32 Risk Factor Surveillance System (BRFSS) (U.S. CDC, 2010).

 22 http://wonder.cdc.gov/mortsql.html 23 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 ICD9 codes to ICD-10 codes if the most recent data available are to be used.
7-32

1 Incidence and prevalence rates are presented as part of the full set of model inputs 

2 documented in Appendix 7A. The incidence rates and prevalence rates provided in Table 7A-1 

3 are weighted average values for the age group associated with each of the C-R functions. These 

4 weighted averages are calculated within BenMAP using more refined age-differentiated 

5 incidence and prevalence rates originally obtained from the data sources listed in the bullets 

6 above.

7 7.3.4 Population (demographic) Data 

8 To calculate baseline incidence rates, in addition to the health baseline incidence data we 

9 also need the corresponding population. We obtained population data from the 2010 U.S. Census

10 (http://www.census.gov/popest/counties/asrh/). These data are then used as the basis for back11 casting estimates for simulation years (in this case, 2007 and 2009) (see Appendix J of the 

12 BenMAP User’s Manual for additional detail, U.S. EPA, 2012b). Total population counts used in 

13 modeling each of the health endpoints evaluated in the analysis (differentiated by urban study 

14 area and simulation year) are provided as part model inputs presented in Appendix 7A.

15 7.4 ADDRESSING VARIABILITY AND UNCERTAINTY

16 An important component of a population risk assessment is the characterization of both 

17 uncertainty and variability. Variability refers to the heterogeneity of a variable of interest within 

18 a population or across different populations. For example, populations in different regions of the 

19 country may have different behavior and activity patterns (e.g., air conditioning use, time spent 

20 indoors) that affect their exposure to ambient O3 and thus the population health response. The 

21 composition of populations in different regions of the country may vary in ways that can affect 

22 the population response to exposure to O3 – e.g., two populations exposed to the same levels of 

23 O3 might respond differently if one population is older than the other. Variability is inherent and 

24 cannot be reduced through further research. Refinements in the design of a population risk 

25 assessment are often focused on more completely characterizing variability in key factors 

26 affecting population risk – e.g., factors affecting population exposure or response – in order to 

27 produce risk estimates whose distribution adequately characterizes the distribution in the 

28 underlying population(s).

29 Uncertainty refers to the lack of knowledge regarding the actual values of inputs to an 

30 analysis. Models are typically used in analyses, and there is uncertainty about the true values of 

31 the parameters of the model (parameter uncertainty) – e.g., the value of the coefficient for O3 in a 

32 C-R function. There is also uncertainty about the extent to which the model is an accurate 

33 representation of the underlying physical systems or relationships being modeled (model 

34 uncertainty) – e.g., the shapes of C-R functions. In addition, there may be some uncertainty 
7-33

1 surrounding other inputs to an analysis due to possible measurement error—e.g., the values of 

2 daily O3 concentrations in a risk assessment location, or the value of the baseline incidence rate 

for a health effect in a population.24 3 In any risk assessment, uncertainty is, ideally, reduced to the 

4 maximum extent possible through improved measurement of key variables and ongoing model 

5 refinement. However, significant uncertainty often remains, and emphasis is then placed on 

6 characterizing the nature of that uncertainty and its impact on risk estimates. The characterization 

7 of uncertainty can be both qualitative and, if a sufficient knowledgebase is available, 

8 quantitative.

9 The selection of urban study areas for the O3 risk assessment was designed to cover the 

10 range of O3-related risk experienced by the U.S. population and, in general, to adequately reflect 

11 the inherent variability in those factors affecting the public health impact of O3 exposure.

12 Sources of variability reflected in the risk assessment design are discussed in section 7.4.1, along 

13 with a discussion of those sources of variability which are not fully reflected in the risk 

14 assessment and consequently introduce uncertainty into the analysis.

15 The characterization of uncertainty associated with risk assessment is often addressed in 

16 the regulatory context using a tiered approach in which progressively more sophisticated 

17 methods are used to evaluate and characterize sources of uncertainty depending on the overall 

18 complexity of the risk assessment (WHO, 2008). Guidance documents developed by EPA for 

19 assessing air toxics-related risk and Superfund Site risks (U.S.EPA, 2004 and 2001, respectively) 

20 as well as recent guidance from the World Health Organization (WHO, 2008) specify multi21 tiered approaches for addressing uncertainty.

22 The WHO guidance, in particular, presents a four-tiered approach for characterizing 

23 uncertainty (see Chapter 3, section 3.2.6 for additional detail on the four tiers included in the 

24 WHO’s guidance document). With this four-tiered approach, the WHO framework provides a 

25 means for systematically linking the characterization of uncertainty to the sophistication of the 

26 underlying risk assessment. Ultimately, the decision as to which tier of uncertainty 

27 characterization to include in a risk assessment will depend both on the overall sophistication of 

28 the risk assessment and the availability of information for characterizing the various sources of 

29 uncertainty. We used the WHO guidance as a framework for developing the approach used for 

30 characterizing uncertainty in this risk assessment.

31 The overall analysis in the O3 NAAQS risk assessment is relatively complex, thereby 

32 warranting consideration of a full probabilistic (WHO Tier 3) uncertainty analysis. However, 

33 limitations in available information prevent this level of analysis from being completed at this 

 24 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 about population means and population variability.
7-34

1 time. In particular, the incorporation of uncertainty related to key elements of C-R functions 

2 (e.g., competing lag structures, alternative functional forms, etc.) into a full probabilistic WHO 

3 Tier 3 analysis would require that probabilities be assigned to each competing specification of a 

4 given model element (with each probability reflecting a subjective assessment of the probability 

5 that the given specification is the “correct” description of reality). However, for many model 

6 elements there is insufficient information on which to base these probabilities. One approach that 

7 has been taken in such cases is expert elicitation; however, this approach is resource- and time8 intensive and consequently, it was not feasible to use this technique in the current O3 NAAQS 

review to support a WHO Tier 3 analysis.

25 9

10 For most elements of this risk assessment, rather than conducting a full probabilistic 

11 uncertainty analysis, we have included qualitative discussions of the potential impact of 

12 uncertainty on risk results (WHO Tier1). As discussed in section 7.1.1, for this draft of the risk 

13 assessment, we have also expanded the sensitivity analysis considerably to cover a range of 

14 model elements (this represents a WHO Tier 2 analysis). The specific modeling elements 

15 covered in the sensitivity analysis for each health effects endpoint together with the specification 

16 of the core analysis is presented in section 7.4.3. As part of the sensitivity analysis, we have also 

completed an influence analysis using estimated elasticities of response26 17 designed to determine 

18 which of the input factors used in calculating risk are primarily responsible for inter-city 

19 variability in risk. This influence analysis focuses on the response of core short-term exposure20 related mortality risk to inputs since this is one of the key risk metrics completed for the REA 

21 (see section 7.4.3).

22 In addition to the qualitative and quantitative treatment of uncertainty and variability 

23 which are described here, we have also completed an analysis to evaluate the representativeness 

24 of the selected urban study areas against national distributions for key O3 risk-related attributes 

25 to determine whether they are nationally representative or more focused on a particular portion 

26 of the distribution for a given attribute (see Chapter 8, section 8.2.1). In addition, we have 

27 completed a second analysis addressing the representativeness issue, which identified where the 

28 12 urban study areas included in this risk assessment fall along a distribution of national-level 

29 short-term and long-term exposure-related mortality risk (see Chapter 8, section 8.2.2). This 

30 analysis allowed us to assess the degree of which the 12 urban study areas capture locations 

31 within the U.S. likely to experience elevated levels of risk related to O3 exposure (for both short32 term and long-term O3-attributable mortality).

 25 While a full probabilistic uncertainty analysis was not completed for this risk assessment, we were able 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. 26 Elasticities are a measure of sensitivity calculated as the percent change in the response variable for a one percent 

change in the input variable.
7-35

1 The remainder of this section is organized as follows. Key sources of variability which 

2 are reflected in the design of the risk assessment, along with sources excluded from the design, 

3 are discussed in section 7.4.1. A qualitative discussion of key sources of uncertainty associated 

4 with the risk assessment (including the potential direction, magnitude and degree of confidence 

5 associated with our understanding of the source of uncertainty – the knowledge base) is 

6 presented in section 7.4.2. The design of the core analysis and sensitivity analysis completed for 

7 each of the health effect endpoint categories modeled in the risk assessment is discussed in 

8 section 7.4.3.

9 7.4.1 Treatment of Key Sources of Variability

10 The risk assessment was designed to cover the key sources of variability related to 

11 population exposure and exposure response, to the extent supported by available data. Here, the 

12 term key sources of variability refers to those sources that we believe have the potential to play 

13 an important role in impacting population incidence estimates generated for this risk assessment.

14 Specifically, hawse have concluded that these sources of variability, if fully addressed and 

15 integrated into the analysis, could result in adjustments to the core risk estimates which might be 

16 relevant from the standpoint of interpreting the risk estimates in the context of the O3 NAAQS 

17 review. The identification of sources of variability as “key” reflects consideration for sensitivity 

18 analyses conducted for previous O3 NAAQS risk assessments, which have provided insights into 

19 which sources of variability can influence risk estimates, as well as information presented in the 

20 O3 ISA.

21 As with all risk assessments, there are sources of variability which have not been fully 

22 reflected in the design of the risk assessment and consequently introduce a degree of uncertainty 

23 into the risk estimates. While different sources of variability were captured in the risk 

24 assessment, it was generally not possible to separate out the impact of each factor on population 

25 risk estimates, since many of the sources of variability are reflected collectively in a specific 

26 aspect of the risk model. For example, inclusion of urban study areas from different regions of 

27 the country likely provides some degree of coverage for a variety of factors associated with O3

28 risk (e.g., air conditioner use, differences in population commuting and exercise patterns, 

29 weather). However, the model is not sufficiently precise or disaggregated to allow the individual 

30 impacts of any one of these sources of variability on the risk estimates to be characterized.

31 Key sources of potential variability that are likely to affect population risks are discussed 

32 below, including the degree to which they are captured in the design of the risk assessment:

33 • Heterogeneity in the Effect of O3 on Health Across Different Urban Areas: A 

34 number of studies cited in the O3 ISA have found evidence for regional heterogeneity 

35 in the short-term O3-attributable mortality effect (Smith et al., 2009 and Bell and 
7-36

1 Dominici, 2008, Bell et al., 2004, Zanobetti an Schwartz 2008 – see O3 ISA section 

2 6.6.2.2, U.S. EPA, 2013a). These studies have demonstrated that differences in effect 

3 estimates between cities can be quite substantial (see O3 ISA Figures 6-32 and 6-33). 

4 Therefore, for the short-term O3-attributable mortality endpoint modeled using Smith 

5 et al., 2009-based effect estimates, we have included Bayes-adjusted city-specific 

6 effect estimates reflecting application of both a regional- and national-prior, both of 

7 which are intended to capture cross-city differences in effect estimates for the 

8 mortality endpoint, while still reflecting input from the more stable regional, or 

9 national-level signal. The national-prior based estimates are included in the core 

10 analysis since they have greater overall power, while the regional-prior based 

11 estimates are included as sensitivity analyses to explore the impact of using regional 

prior in developing the Bayes-adjusted estimates (see section 7.4.3).

27 12 For short-term 

13 morbidity endpoints, typically we have used city-specific effect estimates; however, 

14 for most endpoints, we only have estimates for a subset of the urban study areas 

15 (typically NYC, Atlanta and/or LA). Therefore, although our risk estimates do reflect 

16 the application of city-specific effect estimates, because we do not have estimates for 

17 all 12 urban study areas, we do not provide comprehensive coverage for 

18 heterogeneity in modeling the respiratory morbidity endpoint category. Long-term 

19 O3-attributable mortality has been shown to demonstrate regional heterogeneity. 

20 Specifically, Jerrett et al., 2009 presented regional effect estimates that demonstrated 

21 considerable heterogeneity ranging from essentially a no-effect (for the Northeast and 

22 Industrial Midwest) to effects substantially larger than the national effect (Southeast, 

23 Southwest and Upper Midwest) (see Table 4 in Jerrett et al., 2009). There are many 

24 potential explanations for regional heterogeneity including differences in O3-

25 attributable factors and potential confounding, potential for the presence of (and 

26 regional differences in) averting behavior, and variation in sample sizes which can 

27 impact stability of effect estimates. For the core analysis, we use a national effect 

28 estimate in modeling long-term exposure related mortality. Consideration of regional 

29 effect estimates are included as a sensitivity analysis (see section 7.4.3 and 7.5.3).

30 • Exposure Measurement Error Associated with O3 Effect Estimates: Exposure 

31 measurement error refers to uncertainty associated with using ambient monitor based 

32 exposure surrogate metrics to represent the actual exposure of an individual or 

33 population. As such, this factor can be an important contributor to variability in 

34 epidemiological study results across locations, and uncertainty in results for any 

 27 Note, that in some instances, there may be insufficient variance between cities to generate city-specific estimates 

using a regional prior, which compromises their use in the core analysis.
7-37

1 specific city (O3 ISA, p. 1xii). Exposure measurement error can result from a number 

2 of factors (e.g., central site monitors not representing actual patterns of personal 

3 exposure including activity patterns, presence of non-ambient sources of exposure for 

4 the pollutant of interest) (O3 ISA, 1xii). These factors can vary across urban study 

5 areas (and even within urban study areas), thereby contributing to differences in the 

6 nature and magnitude of exposure measurement error across locations and ultimately 

7 to differences in effect estimates and associated confidence levels. Exposure 

8 measurement error is related to heterogeneity in effect estimates, since regional 

9 differences in effect estimates can result in part, from differences in exposure 

10 measurement error as noted here.

11 • Intra-urban Variability in Ambient O3 Levels: The picture with regard to within 

12 city variability in ambient O3 levels and the potential impact on epidemiologic-based 

13 effect estimates is somewhat more complicated. The O3 ISA notes that spatial 

14 variability in O3 levels is dependent on spatial scale with O3 levels being more 

15 homogeneous over a few kilometers due to the secondary formation nature of O3, 

16 while levels can vary substantially over tens of kilometers. Community exposure may 

17 not be well represented when monitors cover large areas with several subcommunities 

18 having different sources and topographies as exemplified by Los Angeles which 

19 displays significantly greater variation in inter-monitor correlations than does, for 

20 example, Atlanta or Boston (see O3 ISA section 4.6.2.1 U.S. EPA 2013a). Despite the 

21 potential for substantial variability across monitors the O3 ISA notes that studies have 

22 tended to demonstrate that monitor selection has only a limited effect on the 

23 association of short-term O3 exposure with health effects. The likely explanation for 

24 this is that, while absolute values for a fixed point in time can vary across monitors in 

25 an urban area, the temporal patterns of O3 variability across those same monitors 

26 tends to be well correlated. Given that most of the short-term O3-attributable O3

27 epidemiological studies are time series in nature, the O3 ISA notes that the stability of 

28 temporal profiles across monitors within most urban areas means that monitor 

29 selection will have little effect on the outcomes of an epidemiological study 

30 examining short-term O3-attributable mortality or morbidity (see O3 ISA section 

31 4.6.2.1 U.S. EPA 2013a). For this reason, we conclude that generally intra-city 

32 heterogeneity in O3 levels is not a significant factor likely to impact estimates of 

33 short-term O3-attributable risk. One exception is LA which, due to its size and 

34 variation in O3 sources and other factors impacting O3 patterns such as topography, 

35 displays significant variation in ambient O3 levels with a subsequent impact on risk. 

36 However, in the case of LA (as with the other urban study areas), we model risk using 
7-38

1 composite monitors which do not provide spatially-differentiated representations of 

2 exposure and consequently, we do not address this source of variability in the risk 

3 assessment. As discussed in the uncertainty section, short-term exposure mortality 

4 effect estimates for the New York CBSA (Smith et al., 2009) display significant 

5 variability. However, it is not clear which factors are primarily responsible for this 

6 heterogeneity (e.g., differences in the urban structure, residential behavior, or ambient 

7 O3 levels within the CBSA). The potential for intra-city heterogeneity in O3 levels to 

8 affect risk is more pronounced with long-term O3-attributable mortality where the 

9 relationship between annual trends in ambient O3 (as represented using composite 

10 monitor values) and annual mortality is compared between urban study areas in order 

11 to derive effect estimates. Here, pronounced heterogeneity in O3 levels within a given 

12 city can result in exposure misclassification, if that heterogeneity is not well 

13 represented by the composite monitor for that city. Different degrees of exposure 

14 misclassification across urban study areas can introduce uncertainty into the overall 

15 national-level effect estimate for long-term exposure-related mortality. Furthermore, 

16 if that exposure measurement error has a regional trend, then measurement error can 

17 potentially result in apparent regional heterogeneity in the effect estimates. The 

18 degree to which there is true regional heterogeneity is made uncertain by the presence 

19 of differential measurement error across regions.

20 • Variability in the Patterns of Ambient O3 Reduction Across Urban Areas: The 

21 simulated patterns of ambient O3 concentrations across an urban area can vary based 

22 on the methodology used to adjust ambient O3 concentrations to represent just 

23 meeting the current or alternative suites of standards. For the 1st draft REA, we used 

24 a statistical approach called the “quadratic rollback” method for simulating just 

25 meeting the current O3 standard. Although the quadratic rollback method replicates 

26 historical patterns of air quality changes better than some alternative methods, its 

27 implementation relies on a statistical relationship instead of on a mechanistic 

28 characterization of physical and chemical processes in the atmosphere. Because of its 

29 construct as a statistical fit to measured O3 values, the quadratic rollback technique 

30 cannot capture spatial and temporal heterogeneity in O3 response and also cannot 

31 account for nonlinear atmospheric chemistry that causes increases in O3 as a result of 

32 emissions reductions of certain O3 precursors under some circumstances. As noted in 

33 section 7.1.1, for this draft of the REA, we have employed a model-based O3

34 adjustment methodology in the risk assessment for simulating O3 concentrations 

35 under current and alternate standard levels. Use of this model-based approach allows 

36 the risk assessment results to more fully account for non-linearities in O3 formation 
7-39

1 and to reflect spatial and temporal heterogeneity in O3 response, including NOx 

2 titration conditions under which a reduction in NOx causes an increase in O3

3 concentrations, in some core urban locations.

4 • Demographics and Socioeconomic-status (SES)-related Factors: Variability in 

5 population density, particularly in relation to elevated levels of O3 has the potential to 

6 influence population risk, although the significance of this factor also depends on the 

7 degree of intra-urban variation in O3 levels (as discussed above). In addition, 

8 community characteristics such as pre-existing health status, ethnic composition, SES 

9 and the age of housing stock (which can influence rates of air conditioner use thereby 

10 impacting rates of infiltration of O3 indoors) can contribute to observed differences in 

11 O3-related risk (discussed in O3 ISA – section 2.5.4.5, U.S. EPA, 2013a). Some of the 

12 heterogeneity observed in effect estimates between cities in the multicity studies may 

13 be due to these community characteristics, and while we cannot determine how much 

14 of that heterogeneity is attributable to these factors, the degree of variability in effect 

15 estimates between cities in our analysis should help to capture some of the latent 

16 variability in these community characteristics.

17 • Baseline Incidence of Disease: We collected baseline health effects incidence data 

18 (for mortality and morbidity endpoints) from a number of different sources (see 

19 section 7.3.4). Often the data were available at the county-level, providing a relatively 

20 high degree of spatial refinement in characterizing baseline incidence given the 

21 overall level of spatial refinement reflected in the risk assessment as a whole. 

22 Otherwise, for urban study areas without county-level data, either (a) a surrogate 

23 urban study area (with its baseline incidence rates) was used, or (b) less refined state24 level or national incidence rate data were used.

25 7.4.2 Qualitative Assessment of Uncertainty

26 As noted in section 7.4, we have based the design of the uncertainty analysis carried out 

27 for this risk assessment on the framework outlined in the WHO guidance document (WHO, 

28 2008). That guidance calls for the completion of a Tier 1 qualitative uncertainty analysis, 

29 provided the initial Tier 0 screening analysis suggests there is concern that uncertainty associated 

30 with the analysis is sufficient to significantly impact risk results (i.e., to potentially affect 

31 decision making based on those risk results). Given previous sensitivity analyses completed for 

32 prior O3 NAAQS reviews, which have shown various sources of uncertainty to have a potentially 

33 significant impact on risk results, we believe that there is justification for conducting a Tier 1 

34 analysis.
7-40

1 For the qualitative uncertainty analysis, we have described each key source of uncertainty 

2 and qualitatively assessed its potential impact (including both the magnitude and direction of the 

3 impact) on risk results, as specified in the WHO guidance. Similar to our discussion of 

4 variability in the last section, the term key sources of uncertainty refers to those sources that the 

5 we believe have the potential to play an important role in impacting population incidence 

6 estimates generated for this risk assessment (i.e., these sources of uncertainty, if fully addressed 

7 could result in adjustments to the core risk estimates which might impact the interpretation of 

8 those risk estimates in the context of the O3 NAAQS review). These key sources of uncertainty 

9 have been identified through consideration for sensitivity analyses conducted for previous O3

10 NAAQS risk assessments, together with information provided in the final O3 ISA and comments 

11 provided by CASAC on the analytical plan for the risk assessment.

12 Table 7-4 includes the key sources of uncertainty identified for the O3 REA. For each 

13 source of uncertainty, we have (a) provided a description, (b) estimated the direction of influence 

14 (over, under, both, or unknown) and magnitude (low, medium, high) of the potential impact of 

15 each source of uncertainty on the risk estimates, (c) assessed the degree of uncertainty (low, 

16 medium, or high) associated with the knowledge-base (i.e., assessed how well we understand 

17 each source of uncertainty), and (d) provided comments further clarifying the qualitative 

18 assessment presented.

19 The categories used in describing the potential magnitude of impact for specific sources 

20 of uncertainty on risk estimates (i.e., low, medium, or high) reflect our consensus on the degree 

21 to which a particular source could produce a sufficient impact on risk estimates to influence the 

interpretation of those estimates in the context of the O3 NAAQS review.28 22 Sources classified as 

23 having a “low” impact would not be expected to impact the interpretation of risk estimates in the 

24 context of the O3 NAAQS review; sources classified as having a “medium” impact have the 

25 potential to change the interpretation; and sources classified as “high” are likely to influence the 

26 interpretation of risk in the context of the O3 NAAQS review. Because this classification of the 

27 potential magnitude of impact of sources of uncertainty is not based on our direct quantitative 

28 assessments, we use qualitative judgments, in some cases informed by other relevant quantitative 

29 analyses. Therefore, the results of the qualitative analysis of uncertainty are not useful for 

30 making quantitative estimates of confidence, e.g. probabilistic statements about risk. However, 

31 they can be used to support the interpretation of the risk estimates, including the assessment of 

32 overall confidence in the risk estimates. In addition, they can also be used in guiding future 

33 research to reduce uncertainty related to O3 risk assessment. As with the qualitative discussion of 

 28 For example, if a particular source of uncertainty were more fully characterized (or if that source was resolved, 

potentially reducing bias in a core risk estimate), could the estimate of incremental risk reduction in going from 

the current to an alternative standard level change sufficiently to produce a different conclusion regarding the 

magnitude of that risk reduction in the context of the O3 NAAQS review?
7-41

1 sources of variability included in the last section, the characterization and relative ranking of 

2 sources of uncertainty addressed here is based on our consideration of information provided in 

3 previous O3 NAAQS risk assessments (particularly past sensitivity analyses), the results of risk 

4 modeling completed for the current O3 NAAQS risk assessment and information provided in the 

5 third draft O3 ISA as well as earlier O3 Criteria Documents. Where appropriate, in Table 7-4, we 

6 have included references to specific sources of information considered in arriving at a ranking 

7 and classification for a particular source of uncertainty.
7-42

Table 7-4 Summary of Qualitative Uncertainty Analysis of Key Modeling Elements in the O3 NAAQS Risk Assessment

Source Description

Potential influence of 

uncertainty on risk 

estimates

KnowledgeBase 

uncertainty*

Comments 

(KB: knowledge base, INF: influence of uncertainty on risk 

Direction Magnitude estimates)

A. Adjustment of recent 

air quality measurements 

of O3 to simulate 

attainment of both 

existing and alternative 

standard levels

See Chapter 4 for details Both LowMedium Low-medium See Chapter 4 for more details (specific call-outs to be added)

B. Use of CBSA-based 

study areas in modeling 

risk (i.e., potential 

mismatch between study 

areas used in the REA 

and study areas used in 

the epidemiological 

studies providing the 

effect estimates used in 

modeling health effect 

endpoints)

If the set of monitors used in a 

particular urban study area to 

characterize population exposure as 

part of an ongoing risk assessment 

do not match the ambient monitoring 

data used in the original 

epidemiological study, then 

uncertainty can be introduced into 

the risk estimates. This uncertainty 

is balanced in part by the reduction 

in bias that results from using the 

expanded CBSA definition. (See 

section 7.1.1 for more details.)

However, it should be noted that 

because these epidemiological 

studies occurred in the past, 

sometimes it can be impossible to 

exactly match the monitors used in 

the study using recent air quality 

data given that monitors may have 

moved to a different location or there 

may not be measurements available

at specific monitors in the more

recent time period.

Both Lowmedium Low-medium

KB and INF: In modeling risk for the current draft of the REA, we used 

CBSA-based study areas for all health effect endpoints. As discussed in 

section 7.1.1, the use of the larger CBSA study areas allows us to better 

reflect how the change in air quality affects risk across the entire urban 

area and to avoid introducing known bias into the REA by focusing 

risk estimates on that subpopulation living in areas likely to experience 

potential increases in O3 (and excluding the larger population of urban 

and suburban areas likely to experience reductions in ambient O3

levels). While the use of the larger CBSA-based study areas addresses 

this source of known bias, it also introduces uncertainty into the REA 

since we are no longer matching the REA study areas to the study areas 

in the epidemiological studies providing the effect estimates used in 

modeling health effects endpoints. Given available data, it is not 

possible at this point to reliably characterize the degree of uncertainty 

introduced into the REA by having this mismatch in study areas.

However, the potential bias avoided through the use of the larger 

CBSA study areas (with its acknowledged uncertainty) is substantial, 

as illustrated in the sensitivity analyses exploring spatial study area (see 

section 7.5.3).

C. Application of C-R 

functions based on a 

specific temporal and 

spatial pattern of 

correlations between O3

monitors in an urban 

area (as reflected in the 

The effect estimates used in this risk 

assessment reflect a specific spatial 

and temporal pattern of ambient O3

(as represented by the particular 

monitoring network providing data 

for the underlying epidemiological 

study). However, if the spatial and 

Both Lowmedium Low-medium

KB and INF: With application of the HDDM adjustment approach, we 

simulate potential changes in the spatial and temporal pattern of O3 for 

a study areas when just meeting the existing and alternative standards 

relative to patterns under recent conditions. This introduces uncertainty 

into the application of the original effect estimates, since the exposure 

surrogate represented by the composite monitor values may no longer 

match that of the underlying epidemiological study. However, it is not 
7-43

Source Description

Potential influence of 

uncertainty on risk 

estimates

KnowledgeBase 

uncertainty*

Comments 

(KB: knowledge base, INF: influence of uncertainty on risk 

Direction Magnitude estimates)

epidemiological study 

providing the effect 

estimates) to a simulated 

change in the patterns of 

those correlations when 

we estimate risk in the 

REA.

temporal pattern of O3 levels in the 

study areas being modeled differ 

significantly from the patterns in the 

original epidemiological study (for 

those same study areas), then 

uncertainty can be introduced into 

the risk estimates. 

possible, given available data, to characterize quantitatively the 

magnitude of this uncertainty. This is probably most true in the urban 

areas of New York and Los Angeles where simulation meeting the 

existing and alternative standards using the HDDM-adjustment 

approach relied on large NOx reductions and there is very little day-today variability in the resulting O3 concentrations.

D. Characterizing intraurban population 

exposure in the context 

of epidemiology studies 

linking O3 to specific 

health effects

Exposure misclassification within 

communities that is associated with 

the use of generalized population 

monitors (which may miss important 

patterns of exposure within urban 

study areas) introduces uncertainty 

into the effect estimates obtained 

from epidemiology studies.

Under 

(generally)

Lowmedium Medium

KB and INF: Despite the potential for substantial variability in O3

levels across monitors (particularly in larger urban areas with greater 

variation in sources and topography such as L.A.), the O3 ISA notes 

that studies have tended to demonstrate that monitor selection has only 

a limited effect on the association of short-term O3 exposure with 

health effects (see O3 ISA section 4.6.2.1, US EPA, 2013a). However, 

this issue could be more of a concern in larger urban areas which may 

exhibit greater variation in O3 levels due to diverse sources, 

topography and patterns of commuting.

E. Statistical fit of the CR functions 

Exposure measurement error 

combined with other factors (e.g., 

size of the effect itself, sample size, 

control for confounders) can effect 

the overall level of confidence 

associated with the fitting of 

statistical effect-response models in 

epidemiological studies. 

Both

Medium 

(short-term 

health 

endpoints)

Medium

INF: For short-term mortality and morbidity health endpoints, there is 

greater uncertainty associated with the fit of models given the smaller 

sample sizes often involved, difficulty in identifying the etiologically 

relevant time period for short-term O3 exposure, and the fact that 

models tend to be fitted to individual counties or urban areas (which 

introduces the potential for varying degrees of confounding and effects 

modification across the locations). These studies can also have effects 

estimates that are not statistically significant. For this risk assessment, 

in modeling short-term mortality, we are not relying on locationspecific models. Instead, we are using city-specific effects estimates 

derived using Bayesian techniques (these combine national-scale 

models with local-scale models). Exposure measurement error 

(uncertainty associated with the exposure metrics used to represent 

exposure of an individual or population) can also be an important 

contributor to uncertainty in effect estimates associated both with shortterm and long-term O3-attributable studies (O3 ISA, p. 1xii). Together 

with other factors (e.g., low data density), exposure measurement error 

can result in the smoothing of epidemiologically-derived response 

functions and the obscuring of thresholds should they exist (O3 ISA, p. 

Ixix). In addition, exposure measurement error can vary across 

different populations even within the same urban study area. For 
7-44

Source Description

Potential influence of 

uncertainty on risk 

estimates

KnowledgeBase 

uncertainty*

Comments 

(KB: knowledge base, INF: influence of uncertainty on risk 

Direction Magnitude estimates)

example a particular group could have an activity pattern that results in 

central site monitors (in that urban study area) being particularly poor 

at representing that group’s exposure to ambient O3. In this example, 

an effect estimate derived for that specific population based on O3

exposure characterized using central site monitors would have 

increased uncertainty relative to effect estimates generated for other 

population with different activity patterns and lower levels of exposure 

measurement error.

F. Shape of the C-R 

functions

Uncertainty in predicting the shape 

of the C-R function, particularly in 

the lower exposure regions which 

are often the focus in O3 NAAQS 

regulatory reviews. 

Both Medium Low-medium

KB and INF: Studies reviewed in the O3 ISA that attempt to 

characterize the shape of the O3 C-R curve along with possible 

“thresholds” (i.e., O3 concentrations which must be exceeded in order 

to elicit an observable health response) have indicated a generally 

linear C-R function with no indication of a threshold (for analyses that 

have examined 8-h max and 24-h avg O3 concentrations). However, 

the ISA notes that the studies from which the C-R functions are derived 

indicate there is less certainty in the shape of the C-R curve at the 

lower end of the distribution of O3 concentrations (in the range below 

20 ppb) due to the low density of data in the studies in this range.

G. Addressing copollutants 

The inclusion or exclusion of copollutants which may confound, or 

in other ways, affect the O3 effect, 

introduces uncertainty into the 

analysis.

Both Lowmedium Medium

KB and INF: The O3 ISA notes that across studies, the potential 

impact of PM indices on O3-mortality risk estimates tended to be 

much smaller than the variation in O3-mortality risk estimates across 

cities. This suggests that O3 effects are independent of the relationship 

between O3 and mortality. However, interpretation of the potential 

confounding effects of PM on O3-mortality risk estimates requires 

caution. This is because the PM-O3 correlation varies across regions, 

due to the difference in PM components, complicating the 

interpretation of the combined effect of PM on the relationship between 

O3 and mortality. Additionally, the limited PM or PM component 

datasets used as a result of the every-3rd- and 6th-day PM sampling 

schedule instituted in most cities limits (in most cases) the overall 

sample size employed to examine whether PM or one of its 

components confounds the O3-mortality relationship (O3ISA section 

2.5.4.5, US EPA, 2013a).

H. Specifying lag 

structure (short-term 

exposure studies)

There is uncertainty associated with 

specifying the exact lag structure to 

use in modeling short-term O3-

Both LowMedium Low

KB and INF: The majority of studies examining different lag models 

suggest that O3 effects on mortality occur within a few days of 
7-45

Source Description

Potential influence of 

uncertainty on risk 

estimates

KnowledgeBase 

uncertainty*

Comments 

(KB: knowledge base, INF: influence of uncertainty on risk 

Direction Magnitude estimates)

attributable mortality and 

respiratory-related morbidity. exposure. Similar, studies examining the impact of O3 exposure on 

respiratory-related morbidity endpoints suggests a rather immediate 

response, within the first few days of O3 exposure (see O3ISA section 

2.5.4.3, US EPA, 2013a). Consequently, while the exact nature of the 

ideal lag models remains uncertain, generally, we are fairly confident 

that they would be on the order of a day to a few days following 

exposure.

I. Using studies from 

one geographic area to 

cover urban areas 

outside of the study area

In the case of Gent et al., 2003 (used 

in modeling asthma exacerbations in 

Boston), we are using C-R functions 

based on an epidemiological study of 

a region (northern Connecticut and 

Springfield) that does not encompass 

the actual urban study area assessed 

for risk (Boston).

Both Medium Low

INF: Factors related to O3 exposure including commuting patterns, 

exercise levels etc may differ between the region reflected in the 

epidemiological study and Boston. If these differences are great, then 

applying the effect estimate from the epidemiological study to Boston 

could be subject to considerable uncertainty and potential bias. 

J. Characterizing 

baseline incidence rates

Uncertainty can be introduced into 

the characterization of baseline 

incidence in a number of different 

ways (e.g., error in reporting 

incidence for specific endpoints, 

mismatch between the spatial scale 

in which the baseline data were 

captured and the level of the risk 

assessment). 

Both Lowmedium Low

INF: The degree of influence of this source of uncertainty on the risk 

estimates likely varies with the health endpoint category under 

consideration. There is no reason to believe that there are any 

systematic biases in estimates of the baseline incidence data. The 

influence on risk estimates that are expressed as incremental risk 

reductions between alternative standards should be relatively 

unaffected by this source of uncertainty.

KB: The county level baseline incidence and population estimates at 

the county level were obtained from data bases where the relative 

degree of uncertainty is low.

* Refers to the degree of uncertainty associated with our understanding of the phenomenon, in the context of assessing and characterizing its uncertainty 

(specifically in the context of modeling PM risk) 
7-46

1 7.4.3 Description of Core and Sensitivity Analyses

2 As discussed in section 7.1.1, this risk assessment includes a set of core (higher 

3 confidence) risk estimates which are supplemented by sensitivity analyses. The sensitivity 

4 analyses explore the potential impact that variation in specific model design elements can have 

5 on the core risk estimates. This section specifies which design elements are included in both the 

6 core and sensitivity analyses completed for each of the health effect endpoint categories included 

7 in the risk assessment. We divided the sensitivity analyses into two categories: (a) those 

8 involving air quality characterization and (b) those associated directly with the specification of 

9 the C-R functions used in estimating risk. We recognize that there can be overlap between these 

10 categories with some modeling elements (e.g., modeling period) affecting both the composite 

11 monitor distribution as well as representing an element of C-R function specification. However, 

12 we have retained these two categories to aid in the presentation and discussion of sensitivity 

analysis results.

29 13 The sensitivity analyses also included an initial influence analysis designed to 

14 evaluate which of the model inputs are primarily responsible for inter-city variability 

15 (heterogeneity) in risk. The influence analysis uses estimatedelasticities of risk with respect to 

16 the risk function input variables, focusing on the short-term exposure-related mortality endpoint 

17 and associated input parameters since this is one of the key risk estimates generated for the REA

18 (additional detail on how the influence analysis was conducted is presented in section 7.5.3).

19 Table 7-5 presents the alternative approaches for adjusting the O3 distributions used in 

20 the sensitivity analysis and also identifies the approaches used in the core analysis for each of the 

21 study areas. The alternative air quality adjustment approaches examine the differences in 

22 changes in air quality and risk when applying NOx-only versus NOx and VOC reductions in the 

23 HDDM-adjustment approach. It should be noted that when NOx and VOC reductions were used 

24 in the HDDM-adjustment approach in this sensitivity analysis, the same percent reduction for 

25 both pollutants was used in the air quality adjustment for meeting the existing and alternative 

26 standard in each urban area. More details on these alternative air quality adjustment approaches 

27 are discussed in Chapter 4 and appendices.

28 Besides the approach used to adjust the distributions of O3, another fact which has a 

29 direct impact on composite monitor composition is the specification of the study area (since this 

30 determines the mix of monitors that will be included in constructing the composite monitor). As 

31 discussed in section 7.1.1, for the core analysis, we modeled all endpoints (for all study areas) 

 29 As noted in 7.1.1, in presenting both the core and sensitivity analyses, we include both point estimates and 95th

CIs, with the latter reflecting the statistical fit of the effect estimates (and hence the power of the underlying 

epidemiological study). In comparing core and sensitivity analyses, we not only focus on point estimates, but 

also on the CIs since they provide insights into differences in the degree of statistical support for the effect 

estimates underlying the risk estimates and therefore, overall confidence in those estimates. 
7-47

1 using CBSA-based study areas. For the sensitivity analysis (for the short-term O3-attributable

2 mortality endpoint), we included a smaller study area based on the original study area definition 

used in the Smith et al., 2009 study.30 3

4 Table 7-6 presents the model elements included in sensitivity analyses exploring 

5 alternative C-R function specifications. These sensitivity analyses were applied both to short6 term O3-attributable mortality and long-term O3-attributable mortality. As discussed in section 

7 7.1.1, we were not able to differentiate between alternative C-R function specifications for short8 term O3-attributable morbidity endpoints and therefore included the full set of alternative C-R 

9 function specifications in the core analysis. This results in a distribution of core risk estimates for 

10 each endpoint which can be used to gain insights into the impact of different C-R function 

11 specifications on risk. Because separate sensitivity analyses were not completed for short-term 

12 O3-attributable morbidity endpoints, this category is not included in Table 7-6.

13

14 Table 7-5 Specification of the Core and Sensitivity Analyses (air quality simulation)

Study Area

Core simulation

(type of precursor reduced to 

adjust O3 distribution) Sensitivity analysis

Atlanta, GA NOx-only 

Alternative modeling 

approach not evaluated

Baltimore, MD NOx-only 

Boston, MA NOx-only 

Cleveland, OH NOx-only 

Denver, CO NOx & VOC NOx-only 

Detroit, MI NOx-only NOx & VOC 

Houston, TX NOx-only NOx & VOC 

Los Angeles, CA NOx-lower bound* NOx & VOC-lower bound* 

New York, NY NOx-lower bound* (exclude 60) NOx& VOC-lower bound* 

Philadelphia, PA NOx-only NOx & VOC 

Sacramento, CA NOx-only NOx & VOC 

 30 We did not include an alternative study area simulation as a sensitivity analysis for long-term exposure related 

mortality, since we are using a single (national) effect estimate in modeling this endpoint, and consequently, the 

use of an effect estimate from a smaller study area to represent a somewhat larger area (as is the case with shortterm O3-attributable mortality) is likely to introduce less uncertainty.
7-48

Study Area

Core simulation

(type of precursor reduced to 

adjust O3 distribution) Sensitivity analysis

St. Louis, MO NOx-only Alternative modeling 

approach not evaluated

1 A lower-bound fit of the HDDM-based O3 sensitivities (reflecting a greater increment of O3 reduction per 

2 unit of VOC and/or NOx reduction) was required in simulation of the alternative standard levels.

3

4 Table 7-6 Specification of the Core and Sensitivity Analyses (alternative C-R 

5 function specification)

Health effect 

endpoint 

category

Modeling elements included

Core analysis Sensitivity analysis

Short-term O3-

attributable

mortality

- Full monitoring period 

(specific to each study 

area), 8hr max metric, 

national-Bayes adjusted, 

single pollutant model.

- effect estimates obtained 

from: Smith et al., 2009 

study 

- summer (warm month), 

8hr mean, regional-bayes 

adjusted, multi-pollutant 

(with PM10). 

- effect estimates obtained 

from Zanobetti and 

Schwartz, 2008 and Smith 

et al., 2009

Long-term O3-

attributable

mortality

- Single national estimate, 

two-pollutant model 

(PM2.5), long-term peak 

trend metric (based on 

daily 1hr max values), 

CBSA-based study area.

- effect obtained from 

Jerrett et al., 2009 study

- Regional-differentiated 

effect estimates, single 

pollutant model.

- National-level effect 

estimate, single pollutant 

model.

- effect estimates also 

obtained from Jerrett et al., 

2009 study

6

7 7.5 URBAN STUDY AREA RESULTS 

8 This section discusses risk estimates generated for the set of 12 urban study areas, 

9 including both the core risk estimates and accompanying sensitivity analyses. In summarizing 

10 risk estimates, this discussion focuses on results most relevant to two policy-related questions: 

11 (a) to what extent is the existing O3 standard protective of public health , and, (b) what is the 

12 nature and magnitude of additional public health protection provided by the suite of alternative 

13 standards under consideration? Consequently, we focus on two types of risk estimates including 

14 the magnitude of O3-attributable risk after simulation of just meeting the existing standard and
7-49

1 the degree of risk reduction potentially provided by each of the alternative standards relative to 

just meeting the existing standard.

31 2

3 This section is organized as follows. We begin by presenting the core risk estimates in 

4 both tabular and graphical format at the end of this section. We then present key observations 

5 about the risk estimates for just meeting the existing standard (for core risk) in section 7.5.1. Key 

6 observations related to risk estimates for just meeting alternative standard levels, and for 

7 estimates of risk changes comparing alternative standards to just meeting the existing standard

8 (again, for core risk) are presented in section 7.5.2. After presenting key observations related to 

9 the core risk estimates, we then present key observations resulting from the sensitivity analyses 

10 (section 7.5.3).

11 A number of details regarding the design of the core risk assessment should be kept in 

12 mind when reviewing the core risk estimates presented in this section (see section 7.1.1 for 

13 additional detail on these design elements):

14 • All risk estimates reflect application of a CBSA-based study area.

15 • Estimates are presented for two simulation years (2007 and 2009):

16 • Short-term O3-attributable mortality estimates are generated for all 12 urban study 

17 areas, while most short-term O3-attributable morbidity estimates (depending on the 

18 specific health endpoint) are generated for only a subset of urban study areas. Long19 term O3-attributable mortality is modeled for all 12 urban study areas. 

20 • For all health effect endpoints, we model risk down to zero O3 and do not include 

21 either consideration for LML or alternative threshold levels.

22 There are several categories of risk metrics generated for the core mortality and 

23 morbidity endpoints modeled in this analysis. Below we describe both the types of risk metrics 

24 generated for the core analysis and the specific types of tables and figures used in presenting 

25 those metrics.

26

27 I. Core short-term O3-attributable mortality estimates

28 • Table presenting estimates of O3-attributable mortality incidence after just 

29 meeting the existing standard and the estimated change in incidence associated 

30 with meeting each of the alternative standard levels relative to the existing 

standard (Table 7-7): These estimates include point estimates and 95th 31 percentile 

32 confidence intervals representing uncertainty associated with the statistical fit of the 

33 effect estimates.

 31 As part of this draft of the risk assessment, we have also generated estimates of risk under recent conditions as 

well as estimates of the degree of risk reduction (relative to risk under recent conditions) associated with the 

simulated attainment of the existing standard. See Appendix 7B.
7-50

1 • Table presenting estimates of the percent of total mortality attributable to O3

2 after just meeting the existing standard and the percent reduction in O3-

3 attributable risk associated with each alternative standard (Table 7-8).

4 • Heat maps for mortality illustrating distribution across daily O3 levels of total 

5 O3-attributable risk after just meeting the existing standard and risk reductions 

6 after meeting alternative standards (Figures 7-2 and 7-3): Heat maps are provided 

7 for each of the 12 urban areas. The color gradient in each figure reflects the 

8 distribution of mortality (or the change in mortality) across the range of daily 8-hour 

9 O3 levels and provides a visual tool to explore trends in mortality counts across daily 

10 O3 levels and between cities. Visual patterns in the figures presenting total risk and 

11 risk reduction are interpreted differently:

12 o For figures depicting total O3-attributable risk, colors range from blue (lower

13 mortality count) to red (higher mortality count). As an example, with Figure 7-2, 

14 top heat map (which presents total O3-attributable risk for the existing standard in 

15 2007, based on Smith et al., 2009 C-R functions), if we focus on the first row 

16 (Atlanta, GA), we see a value of 38 under the column 55-60 ppb. This value 

17 reflects the fact that 38 of the 270 O3-attributable deaths estimated for Atlanta 

18 after just meeting the existing 75 ppb standard in 2007 occurred on days with 

19 composite monitor O3 levels between 55 and 60 ppb. Similarly, in the same row, 

20 we see that only 3 O3 attributable deaths occurred on days when the composite 

21 monitor value was between 20 and 25 ppb. We also include the total O3-

22 attributable incidence (for each study area) in the final column marked “Total”.

23 o For figures depicting changes in risk associated with simulation of existing and 

24 alternative standard levels, we see that the pattern is more complex since we can 

25 have a combination of increases and decreases in risk in the heat maps, with 

26 increases in risk identified as red to yellow and decreases in risk identified as 

27 yellow to blue. Increases in risk are negative numbers, decreases are positive. In 

28 addition, in the final three columns of each map, we provide estimates of the total 

29 O3-attributable incidence, as well as the total broken down into the subtotals 

30 across days with increases (negative) and days with decreases (positive) in that 

31 incidence. The increase and decrease for a given study area should sum 

32 (accounting for rounding in these subtotals) to the overall total for O3-attributable 

33 deaths for that study area. Several factors can contribute to the patterns of changes 

34 in O3-attributable risk reflected in these maps. For example, non-linearities in O3
7-51

1 formation can result in increases in O3 on some days, even when simulating 

2 attainment of a lower alternative standard (see section 7.1.1). In addition, 

3 simulation of alternative standard levels can result in a change in the overall 

4 distribution of the composite monitor ambient O3 distribution. Often, this change 

5 will take the form of a shift in the upper tail of the distribution towards the mean,

6 given that simulated attainment of alternative standard levels targets higher O3

7 days. If we look at figure 7-2 at the second map (Decrease 75 to 70) and 

8 specifically at the row for Houston, we see that there is a -4 increase in deaths 

9 distributed across 20-35 ppb days and a decrease in deaths of 9, primarily 

10 distributed across 40-60 ppb days.

11 • Graphic plots of O3-attributable deaths per 100,000 population for just meeting 

12 the existing and alternative standards (Figures 7-4): O3This plot provides 

13 estimates that are adjusted for the size of the underlying urban population, thereby 

14 allowing the mortality estimates and associated trends to be more readily compared 

15 across urban study areas (consideration of absolute O3 mortality is complicated by the 

16 role that underlying urban population plays in driving total O3-attributable mortality –

17 larger study areas like Los Angeles and New York having substantially larger 

18 mortality estimates primarily due to their higher underlying populations). These 

19 figures allow us to evaluate the overall magnitude of risk reductions across standard 

20 levels and determine the degree to which those trends differ for different study areas.

21 Tables summarizing incidence, percent of baseline incidence and percent reduction in O3-

22 attributable risk for short-term O3-attributable morbidity (Tables 7-9 through 7-11): Three 

23 categories of short-term O3-attributable mortality effects were modeled for the analysis

24 (respiratory related HA, respiratory-related ER visits and asthma exacerbations). As discussed in 

25 section 7.1.1, these morbidity effects were modeled for a combination of all 12 urban study areas 

26 and a subset of those study areas depending on the endpoint (see below). The C-R functions 

27 available for modeling many of these morbidity endpoints included consideration for a number 

28 of design elements (e.g., copollutants and lag structure). However, as noted earlier in section 

29 7.1.1, for short-term exposure morbidity endpoints with multiple C-R functions, we were not 

30 able to differentiate between C-R functions in terms of overall confidence and consequently we 

31 could not identify a single core model. Therefore, when we have multiple C-R functions 

32 reflecting different treatments of key design elements such as lag structure, we consider the risk 

33 estimates that result from the full set of C-R functions to represent a core range of risk. Each of 

34 the tables summarizing short-term O3-attributable morbidity risk present several risk metrics 
7-52

1 including: (a) total O3-attributable incidence (after just meeting the existing standard), (b) 

2 reductions in O3-attributable incidence (for each of the alternative standard levels relative to just 

3 meeting the existing standard), (c) percent of baseline incidence attributable to O3 (after just 

4 meeting the existing standard) and (d) percent reductions in O3-attributable risk (for each of the 

alternative standard levels). In presenting these morbidity risk estimates, we do not include 95th 5

6 percentile confidence intervals in order to conserve space. Specific tables summarizing these 

7 morbidity incidence estimates include:

8 o HA visits (for respiratory symptoms including asthma): Table 7-9 presents 

9 estimates of the incidence of HA (for respiratory symptoms, chronic lung disease 

10 and asthma). Risk estimates are generated for a subset of the urban study areas for 

11 some of the health endpoints (e.g., New York City for HA [chronic lung disease 

12 and asthma]), while HA (respiratory-related) estimates cover all 12 urban study

13 areas.

14 o ER visits (for respiratory symptoms including asthma): Table 7-10 presents

15 estimates of the incidence of ER visits for respiratory symptoms and asthma) 

16 specifically for New York City and Atlanta based on C-R functions obtained from 

17 several epidemiological studies.

18 o Asthma exacerbations: Table 7-11 presents estimates of the incidence of asthma 

19 exacerbations (including estimates for a range of symptoms) for Boston, the only 

20 urban study area with C-R functions supporting modeling for this endpoint.

21 • Graphic plots of O3-attributable respiratory-related HA per 100,000 residents 

22 for the existing and alternative standard levels (Figures 7-5): This figure is 

23 intended to complement Figure 7-4 which presents the same type of risk information 

24 for short-term O3-attributable mortality. By plotting respiratory HA per 100,000, we 

25 adjust for the underlying population which makes trends in risk more comparable 

26 across urban study areas. We have only created this graphic for respiratory HA (based 

27 on application of Medina-Ramon et al., 2006) since that is the only morbidity 

28 endpoint modeled for all 12 urban study areas. As with the mortality figure, this

29 figure allows us to evaluate the overall magnitude of risk reductions across standard 

30 levels and determine the degree to which those trends differ for different study areas.

31 III. Core long-term O3-attributable mortality estimates

32
7-53

1 • Table presenting estimates of long-term O3-attributable mortality incidence 

2 including total risk after just meeting the existing standard and risk reductions 

3 based on comparing risks after meeting alternative standards to risks after 

4 meeting the existing standard (Table 7-12): Estimates presented in Table 7-12

reflect respiratory mortality and include 95th 5 percentile confidence intervals 

6 representing uncertainty associated with the statistical fit of the effect estimates used. 

7 Estimates presented in these tables allow for consideration for the magnitude of risk 

8 associated with just meeting the existing standard and the pattern of risk reduction (in 

9 incidence) in meeting alternative standards relative to the existing standard.

10 • Table presenting estimates of the percent of respiratory mortality attributable to 

11 O3 and percent reductions in O3-attributable risk for long-term O3-attributable

12 mortality (Table 7-13).

13 • Graphic plots of O3-attributable deaths per 100,000 population for just meeting 

14 the existing and alternative standards (Figures 7-6): This plot provides estimates 

15 that are adjusted for the size of the underlying urban population, thereby allowing the 

16 mortality estimates and associated trends to be more readily compared across urban 

17 study areas (consideration of absolute O3 mortality is complicated by the role that 

18 underlying urban population plays in driving total O3-attributable mortality – larger 

19 study areas like Los Angeles and New York having substantially larger mortality 

20 estimates primarily due to their higher underlying populations). These figures allow 

21 us to evaluate the overall magnitude of risk reductions across standard levels and 

22 determine the degree to which those trends differ for different study areas.
7-54

1 Table 7-7 Short-Term O3-attributable All Cause Mortality Incidence (2007 and 2009) 

2 (Smith et al., 2009 C-R Functions) (O3 season, CBSA-based study area, no threshold)

3

4 NA: for NYC, the model-based adjustment methodology was unable to adjust O3 distributions such that they would meet the 

5 lower alternative standard level of 60 ppb.

Absolute Incidence

75ppb 75-70 75-65 75-60

270 12 21 34

(-370 - 890) (-16 - 39) (-30 - 72) (-47 - 110)

440 13 27 45

(-250 - 1100) (-7 - 33) (-15 - 68) (-25 - 110)

350 7 20 32

(-500 - 1200) (-10 - 24) (-28 - 67) (-45 - 110)

430 14 32 64

(-41 - 890) (-1 - 28) (-3 - 67) (-6 - 130)

86 2 4 8

(-280 - 440) (-6 - 10) (-14 - 23) (-25 - 40)

660 23 42 69

(32 - 1300) (1 - 44) (2 - 81) (3 - 130)

680 5 11 24

(130 - 1200) (1 - 9) (2 - 20) (4 - 43)

1300 43 87 160

(-530 - 3000) (-18 - 100) (-36 - 210) (-66 - 380)

2800 130 640 NA

(1700 - 3900) (80 - 190) (380 - 890) NA

1200 35 76 120

(270 - 2200) (8 - 62) (17 - 140) (25 - 210)

370 7 13 23

(-390 - 1100) (-7 - 20) (-13 - 39) (-24 - 70)

430 18 39 60

(-110 - 950) (-5 - 41) (-10 - 86) (-15 - 130)

240 9 16 23

(-340 - 800) (-12 - 28) (-22 - 54) (-32 - 77)

400 7 17 28

(-220 - 1000) (-4 - 19) (-10 - 44) (-15 - 71)

320 -1 5 14

(-450 - 1100) (2 - -4) (-7 - 17) (-19 - 47)

400 12 29 49

(-38 - 830) (-1 - 24) (-3 - 60) (-5 - 100)

83 0 2 7

(-270 - 420) (-1 - 2) (-6 - 10) (-23 - 37)

580 -21 -6 15

(28 - 1100) (-1 - -41) (0 - -12) (1 - 30)

700 -1 4 14

(130 - 1200) (0 - -1) (1 - 7) (3 - 26)

1300 41 89 160

(-540 - 3100) (-17 - 100) (-37 - 210) (-68 - 390)

2600 84 440 NA

(1600 - 3700) (50 - 120) (260 - 610) NA

1100 19 44 69

(240 - 2000) (4 - 34) (10 - 78) (15 - 120)

370 6 12 21

(-390 - 1100) (-7 - 19) (-13 - 38) (-22 - 64)

380 8 21 37

(-96 - 840) (-2 - 18) (-5 - 46) (-9 - 83)

Study Area

Air Qualtiy Scenario

Atlanta, GA

Baltimore, MD

Change in Incidence 

2007 Simulation Year

New York, NY

Philadelphia, PA

Sacramento, CA

St. Louis, MO

Boston, MA

Cleveland, OH

Denver, CO

Detroit, MI

Houston, TX

Los Angeles, CA

Sacramento, CA

St. Louis, MO

Atlanta, GA

Baltimore, MD

Boston, MA

Cleveland, OH

Denver, CO

Detroit, MI

2009 Simulation Year

Houston, TX

Los Angeles, CA

New York, NY

Philadelphia, PA
7-55

1 .

2

3

4

5

6 Table 7-8 Percent of Total All-Cause Mortality Attributable to O3 and Percent Change in 

7 O3-Attributable Risk (2007 and 2009) (Smith et al., 2009 C-R functions) (O3 season, 

8 CBSA-based study area, no threshold) 

9

10 NA: for NYC, the model-based adjustment methodology was unable to adjust O3 distributions such that they would meet the 

11 lower alternative standard level of 60 ppb.

12

13

% of Baseline 

Incidence

75ppb 75-70 75-65 75-60

Atlanta, GA 1.1 4 8 12

Baltimore, MD 1.9 3 6 10

Boston, MA 1.2 2 5 9

Cleveland, OH 2.4 3 7 14

Denver, CO 0.8 2 5 9

Detroit, MI 3.0 3 6 10

Houston, TX 1.9 1 2 3

Los Angeles, CA 1.0 3 7 13

New York, NY 4.1 5 22 NA

Philadelphia, PA 3.2 3 6 9

Sacramento, CA 1.2 2 3 6

St. Louis, MO 2.5 4 9 14

Atlanta, GA 1.0 3 7 9

Baltimore, MD 1.8 2 4 7

Boston, MA 1.1 -0.3 2 4

Cleveland, OH 2.3 3 7 12

Denver, CO 0.8 0.3 2 8

Detroit, MI 2.7 -4 -1 3

Houston, TX 1.9 -0.1 0.5 2

Los Angeles, CA 1.1 3 7 13

New York, NY 3.9 3 16 NA

Philadelphia, PA 3.0 2 4 6

Sacramento, CA 1.2 2 3 6

St. Louis, MO 2.3 2 5 9

Study Area

Air Quality Scenario

% Change in O3-Attributable 

Risk

2007 Simulation Year

2009 Simulation Year
7-56

1 Figure 7-2 Heat Maps for Short Term O3-attributable Mortality (Just meeting existing 

2 standard and risk reductions from just meeting alternative standards) (2007) (Smith 

3 et al., 2009 C-R functions) (see Key at bottom of figure) 

4

5

6 NA: for NYC, the model-based adjustment methodology was unable to adjust O3 distributions such that they would meet the 

7 lower alternative standard level of 60 ppb.

8

9 Key: For current standard (75) which is an absolute risk metric expressed as incidence of mortality, color gradient ranges from 

10 blue (smallest O3-related mortality count) to red (highest O3-related mortality count). For estimates of decreases in risk, color 

11 gradient ranges from red (increase in risk – negative cell values) to blue (reduction in risk – positive cell values).

12

Current Standard (75)

Study area Daily 8hr Max Ozone Level (ppb) Total 

 0-5 5-10 10-15 15-20 20-25 25-30 30-35 35-40 40-45 45-50 50-55 55-60 60-65 65-70 70-75 >75

Atlanta, GA 0 0 0 0 3 5 18 24 41 52 63 38 15 6 3 0 267

Baltimore, MD 0 0 0 1 1 11 22 43 84 71 69 73 44 12 9 3 443

Boston, MA 0 0 0 0 4 20 45 50 58 57 35 20 30 9 13 11 353

Cleveland, OH 0 0 0 1 5 14 40 65 89 81 43 40 31 12 10 0 431

Denver, CO 0 0 0 0 0 0 1 5 6 13 17 23 15 4 2 0 86

Detroit, MI 0 0 0 0 2 7 42 72 123 147 75 52 56 20 43 17 655

Houston, TX 0 0 0 0 17 49 126 146 148 95 50 49 3 0 0 0 683

Los Angeles, CA 0 0 0 0 0 0 0 17 340 445 388 44 13 5 0 0 1,253

New York, NY 0 0 0 0 21 98 297 544 741 475 364 233 39 0 0 0 2,812

Philadelphia, PA 0 0 0 2 0 34 62 156 213 236 209 165 101 42 9 10 1,238

Sacramento, CA 0 0 0 0 1 18 53 98 67 65 40 20 5 2 0 0 367

St. Louis, MO 0 0 0 1 3 7 18 65 66 76 74 47 29 29 12 3 430

Decrease 75 to 70

Study area Daily 8hr Max Ozone Level (ppb) Total 

 0-5 5-10 10-15 15-20 20-25 25-30 30-35 35-40 40-45 45-50 50-55 55-60 60-65 65-70 70-75 >75 Inc. Dec.

Atlanta, GA 0 0 0 0 0 0 0 1 1 2 3 2 1 0 0 0 12 0 12

Baltimore, MD 0 0 0 0 0 0 0 0 1 2 3 3 2 1 1 0 13 -1 14

Boston, MA 0 0 0 0 0 0 0 0 1 1 1 1 1 0 1 1 7 -1 8

Cleveland, OH 0 0 0 0 0 0 0 0 2 3 2 2 2 1 1 0 14 -2 15

Denver, CO 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 2 0 2

Detroit, MI 0 0 0 0 0 0 -1 0 2 5 3 3 4 2 3 1 23 -2 24

Houston, TX 0 0 0 0 -1 -1 -2 0 2 2 2 2 0 0 0 0 5 -4 9

Los Angeles, CA 0 0 0 0 0 0 0 0 6 16 17 2 1 0 0 0 43 0 43

New York, NY 0 0 0 0 -1 -2 0 12 27 32 35 25 5 0 0 0 134 -11 146

Philadelphia, PA 0 0 0 0 0 -1 0 0 3 7 8 9 6 3 1 1 35 -3 38

Sacramento, CA 0 0 0 0 0 -1 -1 1 2 2 2 1 0 0 0 0 7 -1 8

St. Louis, MO 0 0 0 0 0 0 0 1 2 3 4 3 2 2 1 0 18 0 19

Decrease 75 to 65

Study area Daily 8hr Max Ozone Level (ppb) Total 

 0-5 5-10 10-15 15-20 20-25 25-30 30-35 35-40 40-45 45-50 50-55 55-60 60-65 65-70 70-75 >75 Inc. Dec.

Atlanta, GA 0 0 0 0 0 0 1 1 3 4 6 4 2 1 1 0 21 0 22

Baltimore, MD 0 0 0 0 0 0 0 0 3 4 5 7 5 1 1 0 27 -2 28

Boston, MA 0 0 0 0 0 -1 0 1 2 3 3 2 3 1 2 2 20 -2 22

Cleveland, OH 0 0 0 0 0 0 0 2 6 7 5 5 4 2 2 0 32 -3 34

Denver, CO 0 0 0 0 0 0 0 0 0 0 1 2 1 0 0 0 4 -1 5

Detroit, MI 0 0 0 0 0 0 -1 1 4 8 6 6 7 3 6 3 42 -3 45

Houston, TX 0 0 0 0 -2 -3 -3 0 4 5 4 5 0 0 0 0 11 -9 20

Los Angeles, CA 0 0 0 0 0 0 0 0 13 33 35 4 1 1 0 0 87 0 87

New York, NY 0 0 0 0 -1 2 24 85 149 136 136 90 19 0 0 0 640 -6 646

Philadelphia, PA 0 0 0 0 0 -1 -1 0 7 15 17 18 12 6 1 2 76 -5 81

Sacramento, CA 0 0 0 0 0 -1 -1 2 3 4 3 2 0 0 0 0 13 -3 15

St. Louis, MO 0 0 0 0 0 0 0 3 4 7 8 6 4 4 2 1 39 -1 39

Decrease 75 to 60

Study area Daily 8hr Max Ozone Level (ppb) Total 

 0-5 5-10 10-15 15-20 20-25 25-30 30-35 35-40 40-45 45-50 50-55 55-60 60-65 65-70 70-75 >75 Inc. Dec.

Atlanta, GA 0 0 0 0 0 0 1 2 4 7 9 6 3 1 1 0 34 0 34

Baltimore, MD 0 0 0 0 0 0 0 1 5 7 9 11 8 2 2 1 45 -2 47

Boston, MA 0 0 0 0 0 -1 1 2 4 5 5 3 5 2 3 2 32 -2 34

Cleveland, OH 0 0 0 0 -1 0 0 5 12 14 10 10 8 4 3 0 64 -3 67

Denver, CO 0 0 0 0 0 0 0 0 0 1 2 3 2 1 0 0 8 -1 8

Detroit, MI 0 0 0 0 0 0 -1 2 8 14 10 9 11 5 9 4 69 -4 73

Houston, TX 0 0 0 0 -3 -5 -4 2 8 10 7 8 1 0 0 0 24 -13 37

Los Angeles, CA 0 0 0 0 0 0 0 2 41 59 48 6 2 1 0 0 159 0 159

New York, NY

Philadelphia, PA 0 0 0 0 0 -2 -1 1 11 23 26 27 17 8 2 2 116 -7 123

Sacramento, CA 0 0 0 0 0 -2 -2 4 6 7 5 3 1 0 0 0 23 -4 27

St. Louis, MO 0 0 0 0 0 0 0 5 7 11 12 9 6 7 3 1 60 -1 61

NA

Change in risk

Change in risk

Change in risk
7-57

1 Figure 7-3 Heat Maps for Short Term O3-attributable Mortality (Just meeting existing 

2 standard and risk reductions from just meeting alternative standards) (2009) (Smith 

3 et al., 2009 C-R functions) (see Key at bottom of figure)

4

5 NA: for NYC, the model-based adjustment methodology was unable to adjust O3 distributions such that they would meet the 

6 lower alternative standard level of 60 ppb.

7

8 Key: For current standard (75) which is an absolute risk metric, color gradient ranges from blue (smallest O3-related mortality 

9 count) to red (highest O3-related mortality count). For estimates of decreases in risk, color gradient ranges from red (increase in 

10 risk – negative cell values) to blue (reduction in risk – positive cell values).

11

12

Current Standard (75)

Study area Daily 8hr Max Ozone Level (ppb) Total 

 0-5 5-10 10-15 15-20 20-25 25-30 30-35 35-40 40-45 45-50 50-55 55-60 60-65 65-70 70-75 >75

Atlanta, GA 0 0 1 2 8 16 18 33 49 44 29 30 9 2 0 0 241

Baltimore, MD 0 0 0 1 3 13 41 71 64 92 64 45 11 0 0 0 404

Boston, MA 0 0 1 0 11 25 45 57 50 53 48 7 3 6 9 3 319

Cleveland, OH 0 0 0 0 5 25 46 68 75 81 57 28 11 6 0 0 401

Denver, CO 0 0 0 0 0 1 2 4 9 17 22 20 6 2 1 0 83

Detroit, MI 0 0 1 9 7 26 46 67 114 148 38 51 46 0 21 6 579

Houston, TX 0 0 0 6 28 51 122 123 114 90 84 36 27 7 4 4 695

Los Angeles, CA 0 0 0 0 0 0 2 17 281 328 496 152 9 0 0 0 1,285

New York, NY 0 0 0 6 36 215 427 356 632 469 274 175 56 0 0 0 2,645

Philadelphia, PA 0 0 0 2 16 51 159 126 219 175 198 97 68 0 0 0 1,112

Sacramento, CA 0 0 0 0 2 23 64 69 73 56 42 33 6 0 0 0 367

St. Louis, MO 0 0 1 6 7 17 29 54 52 77 66 53 13 8 0 0 383

Decrease 75 to 70

Study area Daily 8hr Max Ozone Level (ppb) Total 

 0-5 5-10 10-15 15-20 20-25 25-30 30-35 35-40 40-45 45-50 50-55 55-60 60-65 65-70 70-75 >75 Inc. Dec.

Atlanta, GA 0 0 0 0 0 0 0 1 2 3 2 2 1 0 0 0 8 -2 10

Baltimore, MD 0 0 0 0 0 0 -1 0 1 3 2 2 1 0 0 0 7 -2 9

Boston, MA 0 0 0 0 -1 -1 -1 0 0 1 1 0 0 0 0 0 -1 -6 5

Cleveland, OH 0 0 0 0 0 -1 0 1 2 3 3 2 1 0 0 0 12 -2 14

Denver, CO 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 -1 1

Detroit, MI 0 0 -1 -3 -2 -6 -5 -5 -5 -2 0 2 2 0 2 1 -21 -29 8

Houston, TX 0 0 0 -1 -2 -3 -3 -1 1 2 3 2 2 0 0 0 -1 -10 10

Los Angeles, CA 0 0 0 0 0 0 0 0 4 11 20 6 0 0 0 0 41 0 41

New York, NY 0 0 0 -1 -3 -14 -8 8 22 31 22 19 6 0 0 0 84 -38 121

Philadelphia, PA 0 0 0 0 -1 -2 -2 -1 5 5 8 5 4 0 0 0 19 -9 28

Sacramento, CA 0 0 0 0 0 -1 -1 1 2 2 2 2 0 0 0 0 6 -2 8

St. Louis, MO 0 0 0 -1 -1 -1 0 1 1 3 3 3 1 1 0 0 8 -4 12

Decrease 75 to 65

Study area Daily 8hr Max Ozone Level (ppb) Total 

 0-5 5-10 10-15 15-20 20-25 25-30 30-35 35-40 40-45 45-50 50-55 55-60 60-65 65-70 70-75 >75 Inc. Dec.

Atlanta, GA 0 0 0 0 -1 -1 0 1 3 5 3 4 2 0 0 0 16 -3 19

Baltimore, MD 0 0 0 0 0 -1 -1 0 3 6 5 4 1 0 0 0 17 -3 21

Boston, MA 0 0 0 0 -1 -1 -1 0 1 2 3 1 0 0 1 0 5 -6 11

Cleveland, OH 0 0 0 0 -1 -1 0 4 5 8 7 4 2 1 0 0 29 -3 32

Denver, CO 0 0 0 0 0 0 0 0 0 0 1 2 1 0 0 0 2 -1 3

Detroit, MI 0 0 -1 -4 -2 -7 -5 -4 -2 3 2 4 5 0 3 1 -6 -27 21

Houston, TX 0 0 0 -2 -4 -5 -5 -1 2 4 6 3 3 1 1 1 4 -18 21

Los Angeles, CA 0 0 0 0 0 0 0 0 10 23 42 14 1 0 0 0 89 0 89

New York, NY 0 0 0 -1 -5 -17 16 52 107 120 81 63 21 0 0 0 437 -43 479

Philadelphia, PA 0 0 0 -1 -2 -4 -4 -1 10 11 17 10 8 0 0 0 44 -15 59

Sacramento, CA 0 0 0 0 0 -2 -1 2 3 4 3 3 1 0 0 0 12 -4 16

St. Louis, MO 0 0 -1 -2 -1 -1 0 2 3 6 6 6 2 1 0 0 21 -5 26

Decrease 75 to 60

Study area Daily 8hr Max Ozone Level (ppb) Total 

 0-5 5-10 10-15 15-20 20-25 25-30 30-35 35-40 40-45 45-50 50-55 55-60 60-65 65-70 70-75 >75 Inc. Dec.

Atlanta, GA 0 0 -1 0 -1 -1 0 2 5 6 5 5 2 0 0 0 23 -3 26

Baltimore, MD 0 0 0 0 -1 -1 -1 1 5 10 8 6 2 0 0 0 28 -4 32

Boston, MA 0 0 0 0 -2 -1 -1 2 2 5 5 1 1 1 2 1 14 -6 20

Cleveland, OH 0 0 0 0 -1 -1 1 7 9 13 11 6 2 1 0 0 49 -3 53

Denver, CO 0 0 0 0 0 0 0 0 0 1 2 3 1 0 0 0 7 -1 8

Detroit, MI 0 0 -1 -4 -2 -7 -5 -2 2 10 4 8 8 0 4 2 15 -26 41

Houston, TX 0 0 0 -2 -7 -7 -7 0 5 8 11 6 5 1 1 1 14 -25 40

Los Angeles, CA 0 0 0 0 0 0 0 2 32 44 63 22 1 0 0 0 164 0 164

New York, NY

Philadelphia, PA 0 0 0 -1 -3 -6 -4 0 16 17 25 14 11 0 0 0 69 -20 88

Sacramento, CA 0 0 0 0 -1 -3 -1 3 6 6 5 5 1 0 0 0 21 -6 27

St. Louis, MO 0 0 -1 -2 -1 -2 0 3 5 10 9 9 3 2 0 0 37 -6 43

NA

Change in risk

Change in risk

Change in risk
7-58

Figure 7-4 Plots of Short-Term O3-attributable All-Cause Mortality for Meeting Existing 

standard and Alternative Standards (Smith et al., 2009) (Simulation year 2007 and 

2009)

2007 Simulation year

2009 Simulation year
7-59

Table 7-9 Short-Term O3-attributable Morbidity Incidence, Percent of Baseline and 

Reduction in O3-attributable Risk – Respiratory-Related Hospital Admissions (2007 

and 2009)

NA: for NYC, the model-based adjustment methodology was unable to adjust O3 distributions such that they would meet the 

lower alternative standard level of 60 ppb.

Absolute 

Incidence

Percent of 

Baseline

75ppb 75-70 75-65 75-60 75ppb 75-70 75-65 75-60

HA (respiratory); Detroit (Katsouyanni et al., 2009)

1hr max, penalized splines 230 13 23 37 2.8 5 10 15

1hr max, natural splines 230 12 22 36 2.7 5 10 15

HA (respiratory); NYC (Silverman and Ito, 2010; Lin et al., 2008)

HA Chronic Lung Disease (Lin) 120 6.7 29 3.3 5 23

HA Asthma (Silverman) 420 28 120 27.6 5 21

HA Asthma, PM2.5 (Silverman) 310 20 84 20.1 5 22

1hr max penalized splines 790 19 38 60 2.4 2 5 7

HA (COPD less asthma); all 12 study areas (Medina-Ramon, et al., 2006)

Atlanta, GA 67 4 6 10 2.5 5 9 15

Baltimore, MD 77 3 6 9 2.6 4 7 12

Boston, MA 100 2 6 10 2.2 2 6 9

Cleveland, OH 61 2 5 10 2.4 4 8 17

Denver, CO 27 1 2 3 2.9 3 6 11

Detroit, MI 90 3 6 9 2.5 3 6 10

Houston, TX 68 1 2 4 2.1 1 3 6

Los Angeles, CA 180 8 16 25 2.7 4 9 13

New York, NY 180 11 50 NA 2.2 6 28 NA

Philadelphia, PA 130 4 10 15 2.5 3 7 11

Sacramento, CA 34 1 2 4 2.5 3 7 11

St. Louis, MO 53 3 5 8 2.6 5 10 15

HA (respiratory); Detroit (Katsouyanni et al., 2009)

1hr max, penalized splines 220 3.6 13 25 2.5 2 6 11

1hr max, natural splines 210 3.4 12 24 2.4 2 6 11

HA (respiratory); NYC (Silverman and Ito, 2010; Lin et al., 2008)

HA Chronic Lung Disease (Lin) 120 5.1 21 3.2 4 17

HA Asthma (Silverman) 410 24 96 27.2 4 17

HA Asthma, PM2.5 (Silverman) 310 17 68 19.8 4 18

HA (respiratory); LA (Linn et al., 2000)

1hr max penalized splines 640 18 39 62 2.4 2 4 7

HA (COPD less asthma); all 12 study areas (Medina-Ramon, et al., 2006)

Atlanta, GA 65 3 5 8 2.2 5 8 12

Baltimore, MD 74 2 4 6 2.3 2 5 8

Boston, MA 92 0 1 4 2.0 0 1 4

Cleveland, OH 58 2 5 8 2.2 3 8 14

Denver, CO 27 0 1 3 2.7 1 4 11

Detroit, MI 81 -3 -2 1 2.2 -4 -2 1

Houston, TX 71 1 2 4 2.2 1 2 5

Los Angeles, CA 200 8 16 26 2.7 4 8 13

New York, NY 170 7 35 NA 2.1 4 20 NA

Philadelphia, PA 120 2 6 9 2.3 2 4 7

Sacramento, CA 41 1 3 4 2.4 3 7 11

St. Louis, MO 51 2 4 6 2.4 3 8 12

NA NA

Air Quality Scenario

% Change in Ozone-Related 

Risk

Endpoint/Study Area/Descriptor

2009 Simulation Year

2007 Simulation Year

Change in Incidence

HA (respiratory); LA (Linn et al., 2000)

NA NA
7-60

Table 7-10 Short-Term O3-attributable Morbidity Incidence, Percent of Baseline and 

Reduction in O3-attributable Risk – Emergency Room Visits (2007 and 2009) 

NA: for NYC, the model-based adjustment methodology was unable to adjust O3 distributions such that they would meet the 

lower alternative standard level of 60 ppb.

Absolute 

Incidence

Percent of 

Baseline

75ppb 75-70 75-65 75-60 75ppb 75-70 75-65 75-60

ER Visits (repiratory); Atlanta (Strickland et al., 2007)

Distributed lag 0-7 days 7,500 410 740 1,200 19.6 4 8 13

Average day lag 0-2 4,500 230 420 670 11.6 5 8 13

ER-visits (respiratory); Atlanta (Tolbert et al., 2007, Darrow et al., 2011)

Tolbert 8,100 360 670 1,100 5.8 4 8 12

Tolbert-CO 7,200 320 590 940 5.1 4 8 12

Tolbert-NO2 6,500 290 530 840 4.6 4 8 12

Tolbert-PM10 5,100 230 420 660 3.6 4 8 12

Tolbert-PM10, NO2 4,900 220 400 640 3.5 4 8 12

Darrow 4,400 190 360 560 3.1 4 8 12

ER-visits (asthma); NYC (Ito et al, 2007)

single pollutant model 9,000 530 2,300 19.9 5 22

PM2.5 7,100 410 1,800 15.5 5 22

NO2 5,800 330 1,500 12.8 5 23

CO 9,500 570 2,500 21.0 5 22

SO2 7,300 420 1,900 16.0 5 22

ER Visits (repiratory); Atlanta (Strickland et al., 2007)

Distributed lag 0-7 days 6,800 310 570 800 17.2 4 7 10

Average day lag 0-2 4,000 170 320 460 10.1 4 7 10

ER-visits (respiratory); Atlanta (Tolbert et al., 2007, Darrow et al., 2011)

Tolbert (single pollutant 7,400 270 500 720 5.1 3 6 9

Tolbert-CO 6,600 240 450 640 4.5 3 6 9

Tolbert-NO2 6,000 210 400 580 4.1 3 6 9

Tolbert-PM10 4,700 170 310 450 3.2 3 6 9

Tolbert-PM10, NO2 4,500 160 300 430 3.1 3 6 9

Darrow (single pollutant 4,000 140 270 380 2.8 3 6 9

ER-visits (asthma); NYC (Ito et al, 2007)

single pollutant model 8,800 400 1,800 19.3 4 17

PM2.5 6,900 310 1,400 15.0 4 17

NO2 5,700 250 1,100 12.4 4 18

CO 9,300 430 1,900 20.4 4 17

SO2 7,100 320 1,400 15.5 4 17

2009 Simulation Year

NA NA

NA NA

Endpoint/Study Area/Descriptor

Air Quality Scenario

% Change in Ozone-Related 

Risk

2007 Simulation Year

Change in Incidence
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Table 7-11 Short-Term O3-attributable Morbidity Incidence, Percent of Baseline and 

Reduction in O3-attributable Risk – Asthma Exacerbations (2007 and 2009) 

Absolute 

Incidence

Percent of 

Baseline

75ppb 75-70 75-65 75-60 75ppb 75-70 75-65 75-60

Asthma exacerbation (wheeze); Boston (Gent et al., 2003, 2004)

 Chest Tightness 69,000 2,100 5,600 8,600 28.8 2 5 9

 Shortness of Breath 49,000 1,400 3,700 5,700 16.2 2 6 10

 Chest Tightness (1hr max) 51,000 1,200 3,200 5,000 21.2 2 5 8

 Shortness of Breath (1hr max) 59,000 1,300 3,600 5,800 19.6 2 5 8

 Chest Tightness (PM2.5) 69,000 2,100 5,600 8,700 29.1 2 5 9

 Chest Tightness (PM2.5) 64,000 1,900 5,100 8,000 26.8 2 5 9

 Wheeze (PM2.5) 130,000 3,800 10,000 16,000 23.2 2 6 9

Asthma exacerbation (wheeze); Boston (Gent et al., 2003, 2004)

 Chest Tightness 63,000 490 2,400 4,800 27.0 0.4 2 5

 Shortness of Breath 45,000 330 1,600 3,200 15.1 1 3 6

 Chest Tightness (1hr max) 47,000 -180 790 2,200 19.8 -0.4 1 3

 Shortness of Breath (1hr max) 54,000 -210 910 2,500 18.3 -0.4 1 4

 Chest Tightness (PM2.5) 64,000 500 2,400 4,800 27.2 0.4 2 5

 Chest Tightness (PM2.5) 59,000 450 2,200 4,400 25.1 0.4 3 5

 Wheeze (PM2.5) 120,000 900 4,300 8,700 21.7 0.5 3 6

2009 Simulation Year

Endpoint/Study Area/Descriptor

Air Quality Scenario

% Change in Ozone-Related 

Risk

2007 Simulation Year

Change in Incidence
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Figure 7-5 Plots of Short-Term O3-attributable Respiratory HA for Meeting Existing 

standard and Alternative Standards (Medina-Ramon, et al., 2006) (Simulation year 

2007 and 2009) 

2007 Simulation year

2009 Simulation year
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Table 7-12 Long-Term O3-attributable Respiratory Mortality Incidence (2007 and 2009) 

(Jerrett et al., 2009 C-R Functions) (CBSA-based study area, no threshold)

NA: for NYC, the model-based adjustment methodology was unable to adjust O3 distributions such that they would meet the 

lower alternative standard level of 60 ppb.

Absolute Incidence

75ppb 75-70 75-65 75-60

710 43 78 120

(260 - 1100) (15 - 71) (26 - 130) (41 - 200)

750 33 67 110

(270 - 1200) (11 - 55) (23 - 110) (37 - 180)

1,100 35 93 140

(400 - 1800) (12 - 58) (32 - 150) (49 - 240)

530 26 56 100

(190 - 820) (9 - 43) (19 - 93) (35 - 170)

480 19 39 64

(170 - 740) (6 - 31) (13 - 64) (22 - 100)

760 35 63 99

(270 - 1200) (12 - 59) (21 - 100) (33 - 160)

550 9.5 19 32

(190 - 860) (3 - 16) (6 - 31) (11 - 53)

2,600 140 260 410

(930 - 4000) (46 - 230) (89 - 430) (140 - 670)

1,800 120 480

(660 - 2900) (41 - 200) (160 - 790)

1,300 56 120 170

(450 - 1900) (19 - 93) (40 - 190) (59 - 290)

680 31 60 100

(250 - 1100) (10 - 51) (20 - 98) (34 - 170)

600 34 69 100

(210 - 930) (230 - 1000) (23 - 110) (35 - 170)

700 41 76 100

(250 - 1100) (14 - 68) (26 - 120) (36 - 170)

730 25 54 84

(260 - 1100) (8 - 41) (18 - 90) (28 - 140)

1,100 6.8 42 85

(380 - 1700) (2 - 11) (14 - 69) (29 - 140)

510 24 54 84

(180 - 800) (8 - 41) (18 - 89) (29 - 140)

490 9.0 28 70

(180 - 770) (3 - 15) (10 - 47) (24 - 120)

720 -8.9 18 51

(260 - 1100) (-3 - -15) (6 - 30) (17 - 85)

610 14 30 49

(220 - 950) (5 - 23) (10 - 49) (17 - 82)

2,800 130 280 430

(1000 - 4300) (45 - 220) (94 - 460) (150 - 710)

1,900 110 390

(670 - 2900) (37 - 180) (130 - 630)

1,200 44 94 140

(430 - 1900) (15 - 73) (32 - 160) (47 - 230)

730 34 66 110

(260 - 1100) (12 - 57) (22 - 110) (36 - 170)

580 24 53 86

(210 - 900) (210 - 910) (18 - 87) (29 - 140)

NA

Study Area

Air Qualtiy Scenario

Atlanta, GA

Baltimore, MD

Change in Incidence 

2007 Simulation Year

New York, NY

Philadelphia, PA

Sacramento, CA

St. Louis, MO

Boston, MA

Cleveland, OH

Denver, CO

Detroit, MI

Houston, TX

Los Angeles, CA

Sacramento, CA

St. Louis, MO

Atlanta, GA

Baltimore, MD

Boston, MA

Cleveland, OH

Denver, CO

Detroit, MI

2009 Simulation Year

Houston, TX

Los Angeles, CA

New York, NY

Philadelphia, PA

NA
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Table 7-13 Long-Term O3-attributable Respiratory Mortality Percent of Baseline 

Incidence and Percent Reduction in O3-attributable Risk (simulation years 2007 and 

2009) (Jerrett et al., 2009 C-R Functions) (CBSA-based study area, no threshold)

NA: for NYC, the model-based adjustment methodology was unable to adjust O3 distributions such that they would meet the 

lower alternative standard level of 60 ppb.

% of Baseline 

Attributable to 

Ozone

70ppb 75-70 75-65 75-60

Atlanta, GA 17.7 5 9 15

Baltimore, MD 18.1 4 8 12

Boston, MA 16.7 3 7 11

Cleveland, OH 16.9 4 9 17

Denver, CO 20.1 3 7 11

Detroit, MI 17.7 4 7 11

Houston, TX 16.1 1 3 5

Los Angeles, CA 19.6 4 9 13

New York, NY 15.9 6 24 NA

Philadelphia, PA 17.7 4 8 12

Sacramento, CA 17.1 4 7 13

St. Louis, MO 17.9 5 10 15

Atlanta, GA 16.1 5 9 13

Baltimore, MD 16.9 3 6 10

Boston, MA 15.9 1 3 7

Cleveland, OH 16.1 4 9 15

Denver, CO 19.7 1 5 12

Detroit, MI 17.1 -1 2 6

Houston, TX 16.6 2 4 7

Los Angeles, CA 19.9 4 8 13

New York, NY 15.9 5 18 NA

Philadelphia, PA 16.7 3 7 10

Sacramento, CA 17.3 4 8 12

St. Louis, MO 17.1 4 8 13

2009 Simulation Year

Study Area

Air Quality Scenario

Change in O3-Attributable Risk

2007 Simulation Year
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Figure 7-6 Plots of Long-Term O3-attributable Respiratory Mortality for Meeting Existing 

standard and Alternative Standards (Jerrett et al., 2009) (Simulation year 2007 and 

2009) 

2007 Simulation Year

2009 Simulation Year
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1 The presentation of key observations drawn from review of the core risk estimates is 

2 divided into two sections: 1) the assessment of health risks associated with just meeting the

3 existing standard (section 7.5.1) and 2) the assessment of risk changes from meeting alternative 

4 standards relative to meeting the existing standard(section 7.5.2). The presentation of key 

5 observations in each of these two sections is further separated into those associated with (a) 

6 short-term O3-attributable mortality, (b) short-term O3-attributable morbidity and (c) long-term 

7 O3-attributable mortality. Unless otherwise noted, all risk estimates discussed in these three 

8 sections are core risk estimates. In some cases we refer to the confidence intervals around risk 

9 estimates. When an effect estimate is drawn from a study with low statistical power, confidence 

10 intervals can be wide, and can include negative values because of the assumptions of normality 

11 in the distribution of the effect estimate. Negative lower-confidence bounds do not imply that 

12 additional exposure to O3 has a beneficial effect, but rather that the estimated O3 effect estimate 

13 in the C-R function was not statistically significantly different from zero, and thus has a higher 

14 degree of uncertainty as to the magnitude of the estimated risk. As noted earlier, presentation of 

15 sensitivity analysis results and their use in interpreting the core risk estimates is covered in 

16 section 7.5.3.

17 7.5.1 Assessment of Health Risk After Just Meeting the Existing 75 ppb standard

18 The analysis of risk after simulating just meeting the existing standard focuses on 

19 absolute risk, since this is of greatest relevance in evaluating the adequacy of the existing 

20 standard.

21

22 Short-term O3-attributable mortality

23

24 • After meeting the existing standard, estimates of O3-related all-cause mortality range 

25 across urban areas from 86 to 2,800 deaths (for simulation year 2007) and from 83 to 

26 2,600 deaths (for simulation year 2009) (see Table 7-7). This translates into from 0.8 

27 to 4.1% of baseline all-cause mortality (for simulation year 2007) and from 0.8 to 

28 3.9% (for simulation year 2009) (see Table 7-8) in these study areas. Generally, O3-

29 attributable all-cause mortality risks continue to be lower for the 2009 simulation year 

30 as compared with the 2007 simulation year (with the exception of Houston), 

31 reflecting the generally lower ambient O3 levels associated with 2009 for most of the 

32 study areas (see Tables 7-7 and 7-8).

33 • Confidence intervals (CIs) reflecting the statistical fit of the effect estimates used in 

34 modeling risk demonstrate substantial variability across the 12 urban study areas. In 
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general, the upper 95th 1 percentile CI tends to be from 2-3 times larger than the point 

2 estimate for the 12 urban study areas (see Table 7-7). However, some cities have 

3 markedly wider confidence intervals (e.g., Denver where the upper CI is ~5 times the 

4 point estimate), while others have tighter relative CIs (e.g., New York, where the 

5 upper CI is ~1.4 times larger than the point estimate). This variation in the CIs 

6 associated with risk estimates can reflect a number of factors including the statistical 

7 power of the underlying epidemiological study, which is based on the population size, 

8 and differences in the magnitude of such factors as exposure measurement error and 

9 correlations between O3 and other pollutants.

10 After just meeting the existing O3 standard, all-cause mortality estimates based on C11 R functions from Smith et al., 2009 (for simulation year 2007) continue to be driven 

12 largely by days with total O3 levels falling in the range of 30 to 70 ppb, with 87 to 

13 99% of the mortality estimate across the 12 urban study areas associated with days in 

14 this range. A smaller, but still significant fraction (9 to 24%) of the mortality risk is 

associated with days above 60 ppb (see Figure 7-2, “Existing standard (75)” plot).32 15

16 For 2009, this trend continues although risk distributions are shifted down somewhat 

17 (reflecting the lower ambient O3 levels generally seen in this simulation year 

18 compared with 2007) (see Figure 7-3, “current standard (75)” plot). For 2009, a 

19 substantial portion (2% to 24%) of O3-attributable mortality risk is now associated 

20 with days having O3 measurements 55-60 ppb or higher. A relatively smaller fraction 

21 (~0% to 2%) of total mortality estimates for the existing standard are associated with 

days having ambient O3 levels of 20 ppb or less.

33 22

23 • Estimates of O3- attributable respiratory-related HA range from 10’s to 100’s of cases 

24 (after just meeting the existing standard) depending on the type of respiratory HA 

25 endpoint modeled and the specific urban study areas evaluated (see Table 7-9). All 12 

26 urban study areas were modeled for one of more respiratory-related HA endpoints.

 32 Houston has a significantly smaller percentage (<1) of its mortality signal associated with days above 60ppb. 33 In the first draft O3 REA, we included consideration for surrogate LMLs (based on the lowest composite monitor 

values used in modeling short-term exposure-related mortality for each urban study area – see Table 7-5, First 

Draft REA, U.S. EPA, 2012). For the 8hr max monitoring season LML (applicable to the Smith et al.., 2009-

based core risk estimate generated for this second draft REA), we have values ranging from 4 to 17 ppb and from 

5 to 16 ppb (across the 12 urban study areas for 2007 and 2009, respectively). If we look at heat maps 

characterizing the distribution of short-term exposure-related mortality for recent conditions (see “recent 

conditions” heat maps in Figures 7B-1 and 7B-2 in Appendix B) we see that the vast majority of ozone-related 

mortality falls above these surrogate LML ranges. Consequently we see, that had we integrated consideration for 

the surrogate LMLs into modeling of short-term exposure-related mortality, there would have been very little 

change in estimates of risk.
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1 • O3- attributable ER (for respiratory symptoms) ranged into the thousands for both 

2 New York and Atlanta under simulated attainment of the existing standard (these 

3 were the only two study areas modeled for this health endpoint) (see Table 7-10).

4 • Estimates of O3-attributable asthma exacerbation (wheeze) in Boston are in the tens 

5 of thousands to over 100,000 (see Table 7-11). The percent of baseline for this 

6 endpoint (after just meeting the existing standard) is generally in the 20-30% range 

7 which is markedly higher than other short-term morbidity endpoints modeled for this 

8 analysis (see Table 7-11 and compare to values in 7-9 and 7-10).

9

10 Long-term O3-attributable mortality

11 • After simulating just meeting the existing standard, estimates of O3-related respiratory 

12 mortality range across urban areas from 480 to 2,600 deaths (for 2007) and from 490 

13 to 2,800 deaths (for 2009) (see Table 7-12). This translates into from 16.3 to 20.8% of 

14 baseline across the 12 urban study areas (for 2007) and from 15.9 to 20.7% (for 2009) 

15 using the single Jerrett et al., 2009 C-R national-scale function applied to each urban 

16 area (see Table 7-13). As discussed in section 7.3.2, because of the long-term 

17 exposure metric (seasonal mean of daily 8-hour maximum) employed in risk 

18 modeling, there is the potential for some degree of overlap between short-term and 

19 long-term exposure-related mortality estimates. For that reason, these two categories 

20 of mortality estimates cannot be considered distinct and should not be added to 

21 estimate total mortality.

• 95th 22 percentile CIs for long-term O3-attributable respiratory mortality suggest greater 

23 power (and potential less heterogeneity) associated with modeling this health 

24 endpoint, compared with short-term O3-attributable mortality. None of the CIs for 

25 long-term O3-attributable mortality include negative estimates as lower bounds (see 

26 Table 7-12).

27 7.5.2 Assessment of Health Risk Associated with Simulating Meeting Potential 

28 Alternative Standards of 70, 65, and 60 ppb

29 As discussed earlier, we have considered three alternative standard levels (70, 65 and 60 

30 ppb), each evaluated using the form and averaging time of the existing standard. In presenting 

31 risk estimates associated with the simulated attainment of each of these alternative standard 
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1 levels, we focus on the change in risk associated with a comparison of O3 levels after simulation 

2 of the existing standard with levels after simulation of each of the alternative standard levels. 

3 This is of greatest relevance in comparing the potential public health benefit associated with each 

4 of the alternative standards relative to the level of protection afforded by just meeting the 

5 existing standard.

6 In reviewing these risk estimates, it is important to keep in mind that simulation of 

7 alternative standard levels is based on a reaching a peak-based attainment metric. Based on the 

8 simulated air quality information for the 12 urban study areas, there is a tendency for O3 to 

increase on lower concentration days and decrease on higher concentration days.34 9 Therefore, it 

10 is not immediately clear that we would expect risk reductions when applying C-R functions that 

11 are based on the full distribution of daily 8-hour max values. Specifically, risk reductions are 

12 only expected to the extent that the composite monitor daily 8-hour max values decrease as 

13 lower alternative standards are simulated. As discussed in Chapter 4 (section ???), after 

14 adjustment to alternative standard levels, decreases in O3 typically occur on higher O3 days 

15 which tend to occur during warmer (summer) months and are concentrated in suburban areas. 

16 Conversely, increases in O3, ttypically occur lower O3 days which tend to occur in the cooler 

17 portions of the year and are focused in core urban areas. In general, variability in predicted daily 

18 O3 concentrations decreases when meeting lower standard levels.

19

20 Short-term O3-attributable mortality

21

22 • In our analysis, the mortality risk metric is generally not responsive to meeting the 

23 existing and alternative standard levels. This reflects a number of factors all related to 

24 1) how O3 concentrations respond to reductions in NOx emissions used to meet the 

25 standards, and 2) how the risk metrics are associated with temporal and spatial 

26 patterns of O3. As discussed in section 7.1.1, mortality risk is modeled using 

27 composite monitor values (i.e., averages of O3 measurements across monitors in an 

28 urban study area) which removes spatial variability in measured O3 within an urban 

29 study area (also removing variability in changes in O3 across an urban area resulting 

30 from NOx reductions). Furthermore, in modeling total mortality risk for the core 

31 analysis, we add the risk changes occurring across all days within the monitored O3

32 season, including days with low values of O3 as well as days with high values of O3. 

33 This means that we include both decreases in risk on those days when O3 is estimated

34 to decrease (generally occurring on days with higher values of O3) and increases in

 34 This relationship is also observed in ambient air quality measurements as discussed in Chapter 4 and appendices.
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1 risk when O3 is simulated to increase (generally associated with lower values of O3). 

2 The dampened response of short-term mortality risk can be contrasted with clinical 

3 study-based risk estimates. The clinical study-based estimates primarily reflect 

4 changes in the upper end of the O3 distribution where we tend to see more uniform 

5 reductions under simulation of alternative standard levels. In addition, clinical-based 

6 estimates of risk are based on detailed micro-environmental exposure modeling which 

7 uses individual monitor values instead of composite monitor values, thereby resulting 

8 is less dampening of spatial variability in O3 within a given urban study area.

9 • Generally, the magnitude of risk reduction increases as lower alternative standard 

10 levels are simulated. For example, for the lowest alternative standard we evaluated, 

11 60 ppb, across the 12 urban study areas, we predict from 8 to 160 fewer O3-

12 attributable deaths for simulation year 2007 (relative to risk after just meeting the 

13 existing standard) (see Table 7-7). This range is from 7 to 160 deaths for simulation 

14 year 2009. These ranges (for the 60 ppb standard level) translate into a 3 to 14% 

15 reduction in O3-attributable risk relative to risk after just meeting the existing 

16 standard (see Table 7-8).

17 • As noted in section 7.1.1, some of the urban study areas are projected to experience 

18 increases in O3 (and hence risk) when attainment with the existing standard and some 

19 of the alternative standard levels is simulated. Focusing specifically on the alternative 

20 standard levels, we see that, for the core analysis, this potential increase in risk only 

21 occurs for the 2009 simulation year and specifically for three of the urban study areas 

22 (Boston, Detroit and Houston – see Table 7-7). For example, Detroit is predicted to 

23 have an increase of 21 O3-attributable deaths after meeting the 70 ppb standard (when 

24 compared to risk remaining after meeting the existing standard). However, we 

25 estimate a net reduction of 15 O3-attributable deaths after meeting the 60 ppb level 

26 (again based on comparison to risk after meeting the existing standard). Furthermore, 

27 for all three urban study areas with initial risk increases (based on comparing meeting

28 the existing standard to meeting alternative standards), we see that these increases are 

29 offset after meeting the lowest alternative standard simulated (60 ppb) (see Table 7-

30 7). The potential for risk increases is increased somewhat for several of the urban 

31 study areas when we simulate how the O3 distribution shifts from recent conditions to 

32 just meeting the existing standard (see Appendix 7B, Tables 7B-1 and 7B-2). 

33 Specifically, in simulating estimated risk from moving from recent conditions to 

34 attaining the existing standard, we see that for the 2007 simulation year, two of the 
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1 study areas (Houston and Los Angeles) have risk increases after meeting the existing 

2 standard compared to recent conditions while half of the twelve urban study areas 

3 have risk increases for the 2009 simulation year in adjusting air quality to meetthe 

4 existing standard relative to recent conditions. It is also important to keep in mind 

5 that, for the urban areas of New York and Los Angeles, there are additional 

6 uncertainties in the simulation of existing and alternative standards given the 

7 limitations in the application of the adjustment methodology to very large emissions 

8 perturbations and the fact that the 95th percent confidence interval lower bound 

9 estimate of hourly O3 concentrations was used to capture a scenario in which these 

10 cities could meet lower standard levels (65 ppb for New York and 60 ppb for Los 

11 Angeles). In five of these eight cases, the initial risk increases (including the increase

12 in going from recent conditions to the existing standard) is fully offset after meeting 

the lowest alternative standard level (60 ppb).35 13

14 • Figure 7-4 provides plots of short-term mortality risk for the existing and alternative

15 standards adjusting for total exposed population (i.e., O3-attributable deaths per 

16 100,000 exposed). From this figure it can be seen that total O3-attributable risk, even 

17 when adjusted for population, varies substantially across the 12 urban study areas, 

18 with New York and Philadelphia having the highest risk and Boston and Denver the 

19 lowest. This spread in risk (adjusted for population) reflects, to a great extent, 

20 differences in the effect estimates used in modeling this endpoint for each study area, 

21 which can in turn reflect a number of factors (e.g., differences in behavior such as 

22 outdoor activity across cities and differences in exposure measurement error). 

23 However, despite considerable variability in absolute O3-attributable risk, Figure 7-4 

24 also suggests that most of the study areas display relatively limited reduction in O3-

25 attributable risk across the three alternative standards (with the exception of New 

York, which has a notable decrease in risk for the 70 to 65 ppb standard level).36 26 This 

27 suggests that a substantial fraction of O3-attributable risk would still remain, even 

28 after simulated attainment of the lowest alternative standard considered.

29 • Heat map plots of risk reductions for 2007 suggest that most of the risk reductions 

30 associated with simulation of all three alternative standards occur on days with 

 35 For both LA and Houston (in 2007) and Houston (2009) a modest net risk increase still persists (compared to risk 

under recent conditions), even when we have simulated the lowest alternative standard considered (60 ppb) (see 

Tables 7B-1 and 7B-2). 36 With the New York City study area, we recognize however that there is significant uncertainty associated with the 

use of the CBSA-based study area due to significant heterogeneity in short-term O3-attributable mortality effect 

estimates (from Smith et al., 2009) falling within that larger urban study area (see discussion in section 7.6.1).
7-72

1 composite O3 level between 35 and 60 ppb (see Figure 7-4). By contrast, most of the 

2 risk increases occur on days with composite O3 levels between 20 and 35ppb (see 

3 Figure 7-4). This is expected given that most of the increases in urban core O3 are 

4 associated with lower O3 days where NOx titration is prevalent (see Appendix D, 

5 section 4.6 Figures 40-54). Very little of the projected change in risk (increases, or 

6 decreases) for any of the alternative standards considered occurred on days with O3

7 levels below 20 ppb O3Similar observations hold for risk results generated for 

8 simulation year 2009.

9 Short-term O3-attributable morbidity

10 • Generally, because the short-term O3 exposure-related morbidity endpoints use the 

11 same air metrics as used in modeling short-term O3-attributable mortality (i.e., 8hr 

12 maximum and 8hr mean) the pattern of risk reduction seen for these morbidity 

13 endpoints are similar to those seen with short-term mortality (see Tables 7-9 though 

14 7-11 and Figure 7-5). However, New York, as mentioned with regard to short-term 

15 O3-attributable mortality, has substantially higher percent reductions (for O3-

16 attributable risk) compared with the other study areas. For example, with ER visits 

17 (asthma), under the lowest alternative standard in simulation year 2007, New York is 

18 estimated to have a 22 to 23% reduction in the number of ER-visits associated with 

19 O3 exposure (see Table 7-10).

20 Long-term O3-attributable mortality

21 • Although long-term O3-attributable mortality is modeled using a different O3 metric 

22 (essentially a long-term trend in the 1hr maximum for the hottest two seasons – see 

23 section 7.3.2) the overall magnitude and pattern of reduction in O3-related risk is 

24 similar to that seen with short-term exposure related mortality. Specifically, for the 

25 2007 simulation year, for most urban study areas risk reductions range from 11 to 

26 15% (for the 60 ppb standard) (see Table 7-13). Risk reductions are generally slightly 

27 smaller across alternative standard levels for simulation year 2009. For the 2009 

28 simulation year, for Detroit, we see a relatively small risk increase for the 70 ppb 

29 alternative standard (compared to risk under the existing standard). However that 

30 initial increase is offset by risk reductions for the other (lower) alternative standard 

31 levels simulated (see Table 7-13).
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1 7.5.3 Sensitivity Analyses Designed to Enhance Understanding of the Core Risk Estimates

2 We have completed a number of sensitivity analyses intended to support interpretation of 

3 the core risk estimates. These sensitivity analyses, which are described in section 7.4.3, can be 

4 divided into two categories: (a) sensitivity analyses exploring factors impacting air quality 

5 characterization (specifically composite monitor composition) and (b) sensitivity analyses 

6 exploring the impact of alternative C-R function specifications. As noted in section 7.4.3, we 

7 also completed an initial influence analysis designed to identify which of the input factors to the 

8 risk model (for short-term exposure-related mortality) are primarily responsible for inter-city 

9 variability in that risk metric. This section summarizes the results of these sensitivity analyses 

10 and presents key observations related to those analyses, beginning with the influence analysis 

11 and then proceeding to sensitivity analyses focused on air quality characterization and alternative 

12 C-R function specification.

13

14 Influence analysis

15 The influence analysis considered three factors involved in modeling risk for the short16 term exposure-related mortality endpoint including: baseline incidence, composite monitor O3

17 levels and Bayes-adjusted city-specific effect estimates (recall that the core risk estimate is based 

18 on effect estimates derived as part of analyses published in Smith et al., 2009). Each of these 

19 input factors displays inter-study area variation and are responsible, collectively, for 

heterogeneity in risk estimates.37 20 In completing the analysis, we first calculated a central 

21 tendency estimate of risk based on the mean of each input factor across the 12 urban study areas 

22 for the 2009 simulation year (i.e., using the average of the city-specific values for each of the 

23 input factors). We then systematically varied each of the three heterogeneity-related factors 

24 (effect estimate, composite monitor-based O3 level and baseline incidence) to one standard 

25 deviation (SD) above its mean value (reflecting variance across the 12 urban study area values) 

26 and noted the percent increase produced by that perturbation over the initial mean risk estimate. 

27 This influence analysis allowed us to explore the impact of both model form – specifically, 

28 potential non-linearities in the model – as well as the relative magnitude of variability in each of 

29 the three heterogeneity-related input factors on risk. The influence analysis generated the 

30 following results: baseline incidence (23%), composite monitor-based O3 level (8%), and effect 

31 estimate (58%). In other words, the 58% result for effect estimate means that use of a value 1 SD 

32 over the mean (for the effect estimate) in generating risk, resulted in a risk estimate that was 58% 

 37 Note, that the demographic count input factor also varies across the study areas and is an important factor in 

determining total incidence. However, for the influence analysis, we used deaths per 100,000 as the risk metric 

which standardizes on demographic count and therefore allowed us to exclude this input parameter in conducting 

the influence analysis.
7-74

1 larger than the risk estimate based on the mean of all input factors. These results clearly show 

2 that, of the three input factors considered, the effect estimate is primarily responsible for inter3 city variability in short-term exposure-related risk.

4 Interestingly, when we look at the coefficient of variation (CV) for these three 

5 heterogeneity-related input factors we see values almost identical to the influence analysis results

6 in terms of relative magnitude to each other (i.e., 0.232, 0.084, and 0.527 for baseline incidence, 

7 composite monitor-based O3 levels and effect estimate, respectively). Given that the CV values 

8 only reflect variability in each input factor and not model form (i.e., do not reflect potential non9 linearities in the model), the fact that the CV values almost exactly match the influence analysis

10 results in terms of relative magnitude suggest that there is very little if any non-linearity in the 

11 model calculations involving these three input factors. Had non-linearity existed to a significant 

12 extent, then the influence anlaysis results would have differed substantially from the CV results. 

13 The fact that both analyses suggest a primary role for the effect estimate in driving inter-city 

14 variability in risk emphasizes the importance of the sensitivity analyses exploring alternative C-R 

15 functions specifications that were completed for the REA (see below).

16

17 Air quality-related sensitivity analyses

18

19 This category of sensitivity analysis covers (a) the use of a smaller study area (the Smith 

20 et al., 2009 study areas) as contrasted with the CBSA-based study areas used in the core analysis, 

21 and (b) the use of alternative approaches to simulate attainment of the existing and alternative

22 standards (for a subset of the study areas) (see section 7.4.3 for additional detail). This category 

23 of sensitivity analysis was applied to short-term O3-attributable mortality given the importance of 

the endpoint in the policy-context.38 24

25 To allow for easier visual comparisons, we have presented the results of this sensitivity 

26 analysis category in graphical form (see Figure 7-7, numerical results are presented in Appendix 

7C). This figure presents point estimates and 95th 27 percentile confidence ranges for the core model 

28 and for two sensitivity analyses: (a) SA1 (use of the smaller Smith et al., 2009 based study area) 

29 and (b) SA2 (use of the alternative approach to simulating attainment). SA2 is not presented for 

30 all of the study areas, only for the subset included in these alternative simulations (see section 

31 7.4.3). The sensitivity analyses results presented in Figure 7-7 are the changes in O3-related risk 

32 that result from meeting the three alternative standards relative to meeting the existing standard. 

33 Furthermore, these changes reflect deaths per 100,000, which standardizes the estimates on 

 38 Observations regarding the sensitivity of core short-term O3-attributable mortality risk to these sensitivity analyses 

can be applied with care to the core short-term O3-attributable morbidity endpoints, since many of these used 

similar air quality metrics in modeling risk.
7-75

1 population. This removes variation in the size of the underlying exposed population as a factor to 

2 consider in interpreting these results. 

3 For the sensitivity analysis examining use of the smaller Smith et al., 2009 study area, we 

4 have also included heat maps similar to those used in conveying core estimates for short-term 

5 exposure related mortality (see section 7.5 for a description of the heat maps used in the core 

6 analysis). These heat maps (included in Appendix C – see Figure 7C-1) allow us to consider how 

7 changes in risk, including both reductions in risk and increases in risk are distributed across the 

8 O3 air quality distributions for each study area.

9 Key observations related to the air quality-related sensitivity analyses include:

10

11 • Use of smaller study area reduces magnitude of risk reduction: For most of the 

12 study areas, use of the smaller Smith et al., 2009-based study area resulted in smaller 

13 risk reductions (again expressed in terms of changes in deaths per 100,000). For 

14 example, in Figure 7-7 (Baltimore plot), we see that estimated change in risk for SA1 

15 (the smaller study area) are lower than estimated change in risk for the core scenario. 

16 This likely reflects the mix of monitors in the smaller study areas which results in a 

17 smaller change in the composite monitor value (for the existing standard versus 

18 alternative standard levels) as compared with composite monitor values based on the 

19 larger CBSA study area. However, it is important to keep the relative small 

20 magnitude of these risk reductions in mind when considering these sensitivity 

21 analysis results. Most of these differences in risk reductions are less than 1 individual 

22 per 100,000 which reflects the fact that total risk reduction (for short-term O3-

23 attributable mortality) across the urban study areas is relatively small (see Table 7-7).

24 o Reductions in risk are focused on higher O3 days while increases 

25 are focused on lower O3 days: Figure 7C-1 allows us to consider patterns 

26 in risk reductions and increases when using the smaller Smith et al., 2009-

27 based study areas in modeling risk. Figure 7C-1 (particularly the plots of 

28 risk decreases) suggests that decreases in risk tend to occur on days with 

29 composite monitor O3 concentrations ranging from 40-70ppb, while 

30 increases in risk tend to occur on days with composite monitor values in 

31 the range at or below 30-40 ppb (with most risk increases falling in the 

32 range of 15ppb to 40ppb). As noted in 7.1.1, there is less confidence in 

33 specifying the nature of the C-R function (and therefore less confidence in 

34 specifying risk) in the range below 20 ppb.
7-76

1 • Application of effect estimates derived for smaller study areas to larger CBSA2 based study areas: As noted in section 7.3.2, in those instances where an 

3 epidemiological study provides effect estimates for multiple subareas within a larger 

4 CBSA-based study area, we are selecting the effect estimates that represent the 

5 largest number of individuals to model that CBSA-based study areas. There is 

6 uncertainty associated with this approach. Specifically, as illustrated in Table 7-3, 

7 effect estimates within some of the CBSA-based study areas can display considerable 

8 heterogeneity. For example, consider the Smith et al., 2009-based effect estimates 

9 that fall within the CBSA-based New York study areas (these vary from 0.0001 to 

10 0.0009 – almost a 10 fold factor, see Table 7-3). Furthermore, with the CBSA-based 

11 New York study area, Smith et al., 2009-based effect estimates only cover about half 

12 of the total population, with 8.3 million residents living within portions of the CBSA 

13 not covered by Smith et al., 2009-based effect estimates. As noted in section 7.3.2, in 

14 these types of situations, we have decided to use the single effect estimates 

15 representing the largest number of residents in modeling the larger CBSA-study area. 

16 This reflects the observation that, in the case of the New York CBSA, one of the 

17 available effect estimates (for the New York study area), represents ~7 times the 

18 population of the other effect estimates (see Table 7-3). In the case of the Los 

19 Angeles CBSA, there is significantly less difference between the available effect 

20 estimates, making the issue of heterogeneity (and the specification of a single effect 

21 estimate for this study area) less important. Never the less, we recognize that the issue 

22 of heterogeneity does complicate extrapolation of effect estimates for smaller study 

23 areas to the larger CBSA study areas modeled in this analysis and does introduce a 

24 degree of uncertainty that is difficult to characterize.

25 • Use of alternative approach for simulating attainment of existing and alternative

26 standard levels: Use of an alternative approach to simulate attainment of the existing 

27 and alternative standard levels did not produce a consistent trend in terms of changes 

28 in risk between existing and alternative standards relative to the core analysis. For 

29 example, if we look at Figure 7-7 (plot for Houston), we see that SA2 (reflecting 

30 application of the alternative simulation approach) has a larger risk reduction than the 

31 core estimate. By contrast, if we look at the plot for Los Angeles, we see that the SA2 

32 risk change is lower than the core estimate. Again, as with the sensitivity analysis 

33 results looking at study area size, it is important to keep in mind that the magnitude of 

34 these differences is relatively small, reflecting the small magnitude of mortality risk 

35 associated with these analyses in general (see Table 7-7). It is also important to note

36 that in the alternative simulation approach, the HDDM-adjustment approach assumed 
7-77

1 the same percent reductions of NOx and VOC and did not examine if a different air 

2 quality distribution could have been obtained with a different combination of NOx 

3 versus VOC reductions. For most of the urban areas, the percent NOx and VOC

4 reductions were very similar to the NOx-only percent reductions. The similarity in the 

5 NOx reductions between the two approaches could be the reason for there being little 

6 difference in the risk estimates between the core and the alternative approach.

7

8 Sensitivity analyses related to specification of C-R functions

9

10 This category of sensitivity analysis covers a number of factors related to the 

11 specification of C-R functions for both short-term O3-attributable and long-term O3-attributable

12 mortality. In the case of short-term O3-attributable mortality, we consider (a) the use of Bayes 

13 adjusted effect estimates using regional priors (as contrasted with the Bayes adjusted values 

14 using a national prior applied in the core analysis), (b) the use of a copollutants model 

15 considering PM10 (as contrasted with the single pollutant model used in the core analysis) and (c) 

16 application of effect estimates from Zanobetti and Schwartz 2008 reflecting a summer focused 

17 analysis (as contrasted with the Smith et al., 2009-based analysis reflecting the entire monitoring 

18 period in each study area, which is used in the core analysis). For long-term O3-attributable

19 mortality, we consider the use of regionally-differentiated single pollutant effect estimates 

20 obtained from Jerrett et al., 2009, as contrasted with the single national copollutants model used 

21 in the core analysis (see section 7.1.1). We also present estimates for long-term O3 attributable 

22 mortality based on application of results from a national level single pollutant model.

23 For sensitivity analyses examining alternative specification of the C-R function for short24 term O3-attributable mortality, we have used the same graphical approach as used in presenting 

25 results of the sensitivity analyses examining air quality characterization (i.e., plots of point 

estimates with 95th 26 percentile C.I.s for the core and sensitivity analyses for each of the study 

27 areas – see Figure 7-8). Here we also plot estimates of risk changes using deaths per 100,000 to 

28 standardize in terms of total exposed population. For the sensitivity analysis considering 

29 alternative C-R functions for long-term O3-attributable mortality, we present results in tabular 

30 form (Table 7-14). Specifically, for both the core and sensitivity analysis, we present (a) the 

31 percent of baseline mortality attributable to O3 (under simulated attainment of the existing 

32 standard) and (b) the percent reduction in O3-attributablerisk for each of the alternative standard 

33 levels. Key observations related to sensitivity analyses examining alternative C-R functions 

34 specifications include:

35 • Use of regional Bayes-adjusted effect estimates in modeling short term O3-

36 attributable mortality: The use of Bayes-adjusted effect estimates with regional 
7-78

1 priors in modeling short-term O3-attributable mortality, had a mixed impact across the 

2 urban study areas, with some study areas having increased changes in risk and others 

3 having smaller changes, relative to the core analysis. For example, in Figure 7-8 (plot

4 for Baltimore), SA1 had a larger change in risk compared with the core analysis. 

5 However, as with the sensitivity analyses examining air quality-related factors 

6 (discussed above), it is important to keep in mind that the overall magnitude of the 

7 O3-attributable mortality risk is relatively small and that these differences in changes 

8 in risk (comparing SA1 to the core analysis) are generally in the fraction of a person 

9 per 100,000 exposed population.

10 • Use of a copollutants model (with PM10) in modeling short-term O3-attributable

11 mortality: The use of the PM10 copollutant model in modeling short-term O3-

12 attributable mortality (as contrasted with the single pollutant model used in the core 

13 analysis) tended to have a relatively small effect on estimates of risk changes for the 

14 alternative standards considered. For example, in Figure 7-8 (plot for Boston), we see 

15 that the estimates of risk changes for SA2 (reflecting application of the PM10

16 copollutant model) is essentially the same as the core risk estimate. It is important to 

17 keep in mind that the PM10 copollutant model suffers from significantly reduced 

18 power due to the 1/3 to 1/6 day sampling frequency used in measuring PM10 (this 

19 reduces the number of observations available to support epidemiological analysis). 

20 This has the impact of greatly increasing the confidence intervals on the SA2 risk 

21 estimates relative to the core estimates.

22 • Use of Zanobetti and Schwartz 2008 effect estimates in modeling short-term O3-

23 attributable mortality: The use of Zanobetti and Schwartz, 2008 effect estimates 

24 (reflecting a focus on the warmer summer months) produces a mixed set of results 

25 when compared to the core risk estimates. If we look at Figure 7-8 we see that, for 

26 Boston, estimates of risk changes for SA3 (reflecting application of the Zanobetti and 

27 Schwartz 2008 effect estimates) are significantly larger than core estimates. By 

28 contrast, SA3 estimates of risk changes for Houston are significantly smaller than the 

29 core estimates. It is important, however to keep in mind that the Zanobetti and 

30 Schwartz 2008 effect estimates will tend to under-estimate total risk since they only 

31 model impacts during the summer months (while the Smith et al., 2009 effect 

32 estimates allow us to model impacts for the entire O3 monitoring season in each study 

33 area). Note that if the O3 effect were only occurring during the summer months, then 

34 the total risk estimated using effect estimates from the two studies would be similar. 

35 However, because the risks in many locations are smaller (using the Zanobetti and 
7-79

1 Schwartz, 2008 based effect estimates), this suggests that the O3 effect occurs outside 

2 of the summer months evaluated in this study.

3 • Use of regional-differentiated effect estimates in modeling long-term O3-

4 attributable mortality: Risk estimates generated using regional-specific effect 

5 estimates for long-term O3-attributable mortality differ substantially from the core 

6 estimates based on a single national-level effect estimate (see Table 7-14). 

7 Furthermore, the risk estimates generated using the regional effect estimates display 

8 considerable variability (see Table 7-14) reflecting the significant variability in the 

9 underlying effect estimates (see Jerrett et al., 2009, Table 4). The regional effect 

10 estimates range from 0.99 (for the Northeast) to 1.21 (for the Southwest) and include 

11 1.00 (no O3 effect for the Industrial Midwest). As noted earlier in section 7.5, 

12 negative risk estimates should not be interpreted as suggesting that O3 exposure is 

13 beneficial. Rather, these suggest that there may be instability in the underlying 

14 estimates or that potential confounding has not been fully addressed. Regional effect 

15 estimates used in this analysis have considerably larger confidence intervals than the 

16 national estimate (compare values in Jerrett et al., 2009 Table 3 with values in Table 

17 4). This suggests that the regional estimates are less stable than the national estimates 

18 and are subject to considerably greater uncertainty. For this reason, while the results 

19 of this sensitivity analysis point to the potential for regional heterogeneity in the long20 term O3-attributable mortality effect estimate, we do not have significant confidence 

21 in the regionally-based risk estimates themselves given the relatively large confidence 

22 intervals associated with those estimates.

23 • Use of national-based single pollutant model in modeling long-term O3-

24 attributable mortality: Risk estimates generated using the national-level O3-only 

25 effect estimate were significantly lower (~30%) than the core risk estimates which 

26 utilize a copollutants model (which includes PM2.5) (see Table 7-15). In this case, 

27 control for another pollutant results in a stronger O3 signal, possibly due to an 

28 association between PM2.5 and a confounder or effect modifier associated with the 

29 O3-related effect.
7-80

Figure 7-7 Sensitivity Analysis: Short-Term O3-attributable Mortality (air quality-related factors including study area size 

and method used to simulate attainment of existing and alternative standard levels) (2009) SA1-smaller (Smith-based) 

study area, SA2-alternative method for simulating standards.

Standard levels (delta)

75-70 75-65 75-60 
7-81

Figure 7-8 Sensitivity Analysis: Short-Term O3-attributable Mortality (C-R function specification) (2009) SA1-regional Bayesbased adjustment; SA2-copollutant model (PM10); SA3-Zanobetti and Schwartz-based effect estimates.

Standard levels (delta)

75-70 75-65 75-60 
7-82

1 Table 7-14 Sensitivity Analysis for Long-Term O3-attributable Respiratory Mortality –

2 Alternative C-R Function Specification (regional effect estimates) % of baseline all3 cause mortality and change in O3-attribuable risk (2009) (Smith et al., 2009, O3 season))

4

5 NA: for NYC, the model-based adjustment methodology was unable to adjust O3 distributions such that they would meet the 

6 lower alternative standard level of 60 ppb.

7

75ppb 75-70 75-65 75-60

Atlanta, GA 16.6 5 10 14

Baltimore, MD 17.4 3 6 10

Boston, MA 15.9 1 3 7

Cleveland, OH 16.8 4 9 15

Denver, CO 20.0 1 5 12

Detroit, MI 17.0 -1 2 6

Houston, TX 16.9 2 4 7

Los Angeles, CA 20.7 4 8 13

New York, NY 16.7 5 18 18

Philadelphia, PA 17.2 3 7 10

Sacramento, CA 18.0 4 8 12

St. Louis, MO 17.7 4 8 13

Atlanta, GA 41.21 4 8 11

Baltimore, MD -7.01 4 9 13

Boston, MA -6.19 1 5 9

Cleveland, OH 0.00 0 0 0

Denver, CO 27.38 1 4 11

Detroit, MI 0.00 0 0 0

Houston, TX 41.15 2 3 6

Los Angeles, CA 4.46 3 6 10

New York, NY -6.61 7 24 NA

Philadelphia, PA -6.89 4 9 13

Sacramento, CA 24.79 4 7 11

St. Louis, MO 0.00 0 0 0

Sensitivity analysis

Study Area

Air Quality Scenario

Change in O3-Attributable Risk

Core Simulation

 

Baseline 

Incidence 

Attributable to 

Ozone
7-83

1 Table 7-15 Sensitivity Analysis for Long-Term O3-attributable Respiratory Mortality –

2 Alternative C-R Function Specification (national O3-only effect estimates) % of 

3 baseline all-cause mortality and change in O3-attribuable risk (2009) (Smith et al., 

4 2009, O3 season))

5

6 NA: for NYC, the model-based adjustment methodology was unable to adjust O3 distributions such that they would meet the 

7 lower alternative standard level of 60 ppb.

8

Percent of 

Baseline 

Incidence

75ppb 75-70 75-65 75-60

Atlanta, GA 16.6 5 10 14

Baltimore, MD 17.4 3 6 10

Boston, MA 15.9 1 3 7

Cleveland, OH 16.8 4 9 15

Denver, CO 20.0 1 5 12

Detroit, MI 17.0 -1 2 6

Houston, TX 16.9 2 4 7

Los Angeles, CA 20.7 4 8 13

New York, NY 16.7 5 18 18

Philadelphia, PA 17.2 3 7 10

Sacramento, CA 18.0 4 8 12

St. Louis, MO 17.7 4 8 13

Atlanta, GA 11.9 5 10 14

Baltimore, MD 12.2 3 7 10

Boston, MA 11.1 1 3 7

Cleveland, OH 11.8 4 10 15

Denver, CO 14.1 2 5 12

Detroit, MI 11.9 -1 2 6

Houston, TX 11.9 2 4 7

Los Angeles, CA 14.6 4 9 14

New York, NY 11.7 5 19 19

Philadelphia, PA 12.0 3 7 10

Sacramento, CA 12.6 4 8 13

St. Louis, MO 12.4 4 8 13

Sensitivity analysis

Study Area

Air Quality Scenario

Change in O3-Attributable Risk

Core Simulation
7-84

1 7.6 KEY OBSERVATIONS REGARDING OVERALL CONFIDENCE IN THE RISK

2 ASSESSMENT AND RISK ESTIMATES

3 This section discusses our overall confidence associated with risk estimates presented in 

4 this draft of the REA. We begin by presenting a set of key observations related to overall 

5 confidence in the risk assessment. These observations are drawn largely from (a) consideration 

6 for the systematic approach used in designing the risk assessment, (b) our assessment of the 

7 degree to which we have captured key sources of variability in the analysis (section 7.4.1) (c) our 

8 qualitative assessment of uncertainty in the risk assessment (section 7.4.2), and (d) the results of 

9 the sensitivity analyses completed (section 7.5.3). Once we present these observations, we 

10 provide a synthesis statement reflecting our overall degree of confidence in the risk estimates (at 

11 the end of this section). Key observations addressing overall confidence in the analysis include:

12 • A deliberative process was used in specifying each of the analytical elements 

13 comprising the risk model. This is in line with recommendations made by the 

14 National Research Council in Science and Decisions, Advancing Risk Assessment

15 (NRC, 2009. P. 89-90) for improving risk assessment as applied in the regulatory 

16 context. This deliberative process included first identifying specific goals for the 

17 analysis, and then designing the analysis to meet those goals, given available 

18 information and methods. Specific analytical elements reflected in the design include: 

19 selection of urban study areas, characterization of ambient air O3 levels, selection of 

20 health endpoints to model and selection of epidemiological studies (and specification 

21 of C-R functions) (see sections 7.1.1 and 7.3). In addition, the design of this draft of 

22 the REA reflects consideration for comments provided by the public and by CASAC 

in their review of the 1st 23 draft REA in letter form (Frey, H. C. 2012.).

24 • Review of available literature (as specified in the O3 ISA, U.S. EPA. 2013a), resulted 

25 in a decision not to incorporate a true (no effect) threshold into our risk modeling. 

26 Conversely, the studies used to develop the C-R functions indicate a range of ambient 

27 O3 (area-wide daily levels, based on averaging across monitors in locations with 

28 multiple monitors, of ≤ 20ppb) below which there is reduced confidence in specifying 

29 the nature of the concentration-response relationship, based on less data in the studies, 

30 specifically for short-term O3-attributable respiratory mortality and morbidity (see 

31 section 7.1.1). In any case, only a relatively small fraction of short-term O3-

32 attributable mortality reflected in the risk estimates is associated with days in this 

33 range with the vast majority of the risk estimates reflecting days with peak O3

34 measurements well above this level (see section 7.5.1 and 7.5.2). O3

35 • Modeling of short-term O3-attributable mortality utilized Bayes-adjusted city-specific 

36 effect estimates (see section 7.1.1 and section 7.3.2). These effect estimates are 
7-85

1 considered to have increased overall confidence since they combine elements of the 

2 local city-specific signal with a broader scale (national) signal.

3 • Use of CBSA-based study areas in modeling all health endpoints in order to address 

4 known bias associated with using smaller study areas. As discussed in 7.1.1, we have 

5 used larger CBSA-based study areas to avoid focusing the risk assessment only on 

6 core urban areas (often used in the epidemiological studies providing effect estimates) 

7 which can experiences increases in O3 based on simulated attainment of both existing 

8 and alternative standard levels. There is uncertainty in using effect estimates based on 

9 smaller study areas to represent larger CBSA-based study areas (see section 7.4.2 and 

10 7.5.3). A key concern is heterogeneity in the effect estimates which may suggest 

11 increased uncertainty in applying effect estimates to larger study areas (since larger 

12 study areas may display heterogeneity in the nature of the relationship between O3

13 exposure and risk). It is possible also that this heterogeneity varies across urban areas, 

14 or regionally. For both categories of mortality endpoints (short-term and long-term

15 O3-attributable), potential heterogeneity in the mortality effect even within larger 

16 urban areas remains a potentially important source of uncertainty.

17 • Specifically in relation to short-term exposure-related mortality and morbidity which 

18 depend on time-series studies, there is uncertainty in applying effect estimates derived 

19 based on evaluating the longitudinal (in terms of time) relationship between ambient 

20 O3 and a particular health effect to the modeling of a discrete shift in the entire 

21 distribution that occurs when you simulate an alternative standard. Specifically, the 

22 time-series studies relate unit changes in day to day O3 with a degree of impact on 

23 baseline health effect rates. In the risk assessment, we use this effect estimate to 

24 predict risk for a unit shift in daily composite monitor value. There is uncertainty in 

25 this application of the effect estimates, although it is not possible at this time to 

26 characterize either qualitatively, or quantitatively the magnitude of this uncertainty 

27 and the degree of any potential bias that could be introduced into the simulation of 

28 risk.

29 • Use of HDDM-adjustment approach to simulate attainment of both the existing and 

30 alternative standard levels provides more refined estimates of ambient O3

31 distributions given its ability to characterize the physical and chemical processes of 

32 O3 formation in the atmosphere However, in the case of both the New York and Los 

33 Angeles study areas, given the limitations in the application of the adjustment 

34 methodology to very large emissions perturbations and the need to use the 95th 

35 percent confidence interval lower bound estimate to simulate attainment of these 

36 standard levels, we have reduced overall confidence in the simulation of the O3
7-86

1 concentrations for these study areas and consequently all health endpoints modeled 

2 for risk for these two study areas (see section 7.4.2 and 7.5.3).

3 • Sensitivity analyses exploring alternative C-R functions for modeling short-term O3-

4 attributable mortality (e.g., Bayes regional prior based estimates, copollutants 

5 models) suggested that alternative models can have a moderate impact on risk (see 

6 section 7.5.3). This modest impact reflects primarily the relatively small magnitude of 

7 short-term O3-attributable mortality reductions simulated for the alternative standard 

8 levels.

9 • The use of alternative C-R functions for modeling long-term O3-attributable mortality

10 (specifically the regional-based estimates referenced earlier) was shown to have a 

11 significant impact on risk (see section 7.5.3). However, concerns over the power and 

12 hence stability of the regional effect estimates used in this simulation limit our ability 

13 to draw firm conclusions regarding the potential magnitude of that regional 

14 heterogeneity.

15

16 Based on the key observations regarding confidence presented above, we draw the 

17 following conclusions regarding overall confidence in the risk estimates generated for this draft 

18 of the REA. We have a reasonable degree of confidence in short-term O3-attributable mortality 

19 and morbidity estimates for ten of the twelve study areas. This confidence is tempered somewhat 

20 by concerns over potential heterogeneity in effect estimates for mortality which can impact the 

21 risk assessment given our use of larger CBSA-based study areas. Our confidence in risk 

22 estimates generated for both New York and Los Angeles is considerably lower than for the 

23 remaining ten study areas due to (a) concerns over air quality modeling (specifically the use of 

24 lower-bound fits to the DDM model) and (b) specifically in the case of New York, evidence for 

25 significant heterogeneity in the mortality effect estimates for subareas within the CBSA. For 

26 long-term O3-attributable mortality, we also have a reasonable degree of confidence in our risk 

27 estimates. However, as with short-term O3-attributable mortality, this confidence is also 

28 tempered by concerns over regional heterogeneity in the O3 effect. If we had regionally29 differentiated effect estimates for this endpoint that had sufficient power and stability, we would 

30 consider using these as the basis for generating core risk estimates (rather than the national-level 

31 effect estimate used in the current analysis).

32
7-87

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19 Strickland, M. J.; L. A. Darrow; M. Klein; W. D. Flanders; J. A. Sarnat; L. A. Waller, et al. 2010. "Short20 term Associations between Ambient Air Pollutants and Pediatric Asthma Emergency Department 

21 Visits." American Journal of Respiratory Critical Care Medicine, 182, 307-316.

22 Tolbert, P. E.; M. Klein; J. L. Peel; S. E. Sarnat and J. A. Sarnat. 2007. "Multipollutant Modeling Issues 

23 in a Study of Ambient Air Quality and Emergency Department Visits in Atlanta." Journal of 

24 Exposure Science and Environmental Epidemiology, 17 Suppl 2, S29-35.

25 U.S. Center for Disease Control. 2010. "Centers for Disease Control and Prevention, Behavioral Risk 

26 Factor Surveillance System (BRFSS), 2010, Table “Table C1 Adult Self-Reported Current 

27 Asthma Prevalence Rate (Percent) and Prevalence (Number) by State or Territory.” Available at: 

28 <http://www.cdc.gov/asthma/brfss/2010/current/tableC1.htm>.

29 U.S. Environmental Protection Agency. 2001. Risk Assessment Guidance for Superfund. Vol. III, Part A. 

30 Process for Conducting Probabilistic Risk Assessment (RAGS 3A). Washington, DC: EPA. (EPA 

31 document number EPA 540-R-02-002; OSWER 9285.7-45; PB2002 963302). Available at:

32 <http://www.epa.gov/oswer/riskassessment/rags3adt/index.htm)>.

33 U.S. EPA. 2004. EPA’s Risk Assessment Process for Air Toxics: History and Overview. In: Air Toxics 

34 Risk Assessment Reference Library, Technical Resource Manual, Vol. 1., pp. 3-1 – 3-30. (EPA 

35 document number EPA-453-K-04-001A). Washington, DC: EPA. Available at: 

36 <http://www.epa.gov/ttn/fera/data/risk/vol_1/chapter_03.pdf)>.

37 U.S. EPA. 2007. O3 Health Risk Assessment for Selected Urban Areas. Research Triangle Park, NC: EPA 

38 Office of Air Quality Planning and Standards. (EPA document number EPA 452/R-07-009). 

39 Available at: <http://www.epa.gov/ttn/naaqs/standards/O3/s_O3_cr.html>.

40 U.S. EPA. 2011. O3 National Ambient Air Quality Standards: Scope and Methods Plan for Health Risk 

41 and Exposure Assessment. Research Triangle Park, NC: EPA. (EPA document number EPA42 452/P-11-001).
7-89

1 U.S. EPA. 2012. Health Risk and Exposure Assessment for O3 First External Review Draft. Research 

2 Triangle Park, NC: U.S. Environmental Protection Agency, Research Triangle Park, NC. (EPA 

3 document number EPA 452/P-12-001).

4 U.S. EPA. 2013a. Integrated Science Assessment for O3 and Related Photochemical Oxidants: Final.

5 Research Triangle Park, NC: U.S. Environmental Protection Agency. (EPA document number 

6 EPA/600/R-10/076F).

7 U.S. EPA. 2013b. Environmental Benefits Mapping and Analysis Program—Community Edition (Version 

8 0.63), 2013a. Research Triangle Park, NC: U. S. Environmental Protection Agency. Available at: 

9 <hhtp://www.epa.gov/air/benmap/beta.html>.

10 World Health Organization. 2008. Part 1: Guidance Document on Characterizing and Communicating 

11 Uncertainty in Exposure Assessment, Harmonization Project Document No. 6. Published under 

12 joint sponsorship of the World Health Organization, the International Labour Organization and 

13 the United Nations Environment Programme. WHO Press, World Health Organization, 20 

14 Avenue Appia, 1211 Geneva 27, Switzerland (tel.: +41 22 791 2476).

15 Zanobetti, A; J. Schwartz. 2008. "Mortality Displacement in the Association of O3 with Mortality: An 

16 Analysis of 48 Cities in the United States." American Journal of Respiratory and Critical Care 

17 Medicine, 177: 184-189.
8-1

1 8 NATIONAL SCALE MORTALITY RISK BURDEN BASED ON

2 APPLICATION OF RESULTS FROM EPIDEMIOLOGICAL STUDIES

3 As described in Chapter 2, the O3 ISA (U.S. EPA 2013) concluded that there is likely to 

4 be a causal relationship between short-term O3 exposure and all-cause mortality and that there is 

5 likely to be a causal relationship between long-term O3 exposure and respiratory effects, 

6 including respiratory mortality. Chapter 7 estimated health risks associated with recent O3

7 concentrations and meeting the current and alternative O3 standards in 12 selected urban study 

8 areas. In this chapter we estimate nationwide premature mortality attributable to recent short9 term and long-term exposures to ambient O3 (Section 8.1); and assess the degree to which the 

10 selected urban case study areas represent the full national distribution of risk-related attributes 

11 and air quality dynamics (Section 8.2). Compared with the urban scale analysis in Chapter 7, this 

12 analysis includes full spatial coverage across the U.S. but has less geographic specificity in the 

13 concentration-response functions that are used to calculate O3-attributable mortality. The national 

14 scale analysis is therefore intended as a complement to the urban scale analysis, providing both a 

15 broader assessment of O3-related health risks across the U.S. as well as an evaluation of how 

16 well the urban study areas examined in Chapter 7 represent the full distribution of O3-related 

17 health risks and air quality dynamics in the U.S.

18

19 8.1 NATIONAL-SCALE ASSESSMENT OF MORTALITY RELATED TO O3

20 EXPOSURE

21 This section estimates the total annual deaths for 2007 populations associated with 

22 average 2006-2008 O3 levels across the continental U.S. We first describe the methods and 

23 inputs used to estimate O3-attributable risk across the continental U.S., including O3 exposure 

24 estimates, population and baseline mortality rate estimates, and epidemiologically derived O3-

25 mortality effect estimates. Results for the estimation of O3-attributable risk are then discussed in 

26 terms of the magnitude and percent of total mortality attributable to O3 exposure. We provide 

27 two analyses to give perspective on the confidence in the estimates of O3-related mortality: (1) 

28 risk estimated only within the urban areas for which O3 mortality effect estimates are available;

29 and (2) the distribution of O3-related deaths across the range of observed 2006-2008 average O3

30 concentrations fused with modeled 2007 concentrations. These results are then synthesized and 

31 compared with previous estimates of the burden of O3 exposure on mortality in the U.S. from the 

32 literature in a discussion section.

33
8-2

1

2 Figure 8.1 Conceptual Diagram for National-scale Mortality Risk Assessment

3

4 8.1.1 Methods

5 This section describes the inputs and datasets used to conduct the national-scale 

6 assessment of O3-attributable risk. As shown in the conceptual diagram in Figure 8-1, we 

7 conduct this analysis using the BenMAP software, which uses projections of the size and 

8 geographic distribution of the potentially exposed population along with estimates of the ambient 

9 O3 concentrations to estimate O3-attributable health risks. In general, this analysis uses the same 

10 analytical structure and many of the same inputs as are used in the epidemiology-based

11 assessment of O3-attributable risk in the selected urban case study areas in Chapter 7. We refer 

12 back to Chapter 7 for details on these shared inputs, and describe where the urban-scale and 

13 national-scale analyses use divergent methods.

14

15

16
8-3

1 Air quality inputs to this analysis are described in detail in Chapter 4. In contrast to the 

2 urban study areas analysis in Chapter 7, the national-scale analysis employs a data fusion 

3 approach that takes advantage of the accuracy of monitor observations and the comprehensive 

4 spatial information of the CMAQ modeling system to create national-scale “fused” spatial 

5 surfaces of seasonal average O3. Measured O3 concentrations from 2006-2008 were fused with 

6 modeled concentrations from a 2007 CMAQ model simulation, run for a 12 km domain covering 

7 the contiguous U.S. In the first draft of the REA, the spatial surfaces were created using the 

8 enhanced Voronoi Neighbor Averaging (eVNA) technique (Timin et al, 2010), using the EPA’s 

9 Model Attainment Test Software (MATS; Abt Associates, 2010b). In this draft, the spatial 

10 surfaces are created using EPA’s Downscaler software (Berrocal et al, 2012). More details on 

11 the ambient measurements, the 2007 CMAQ model simulation, the Downscaler fusion technique, 

12 and a technical justification for changing from eVNA to Downscaler can be found in Chapter 4.

13 Three “fused” spatial surfaces were created for: (1) the May-September mean of the 8-hr 

14 daily maximum (consistent with the metric used by Smith et al. 2009); (2) the June-August mean 

15 of the 8-hr daily mean from 10am to 6pm (consistent with the metric used by Zanobetti and 

16 Schwartz 2008); and (3) the April-September mean of the 1-hr daily maximum (consistent with 

17 the metric used by Jerrett et al. 2009) O3 concentrations across the continental U.S. These fused 

18 spatial surfaces each represent one seasonal average across 2006-2008, rather than three separate 

19 years of concentrations. Section 4.3.2 presents maps, distributions, and statistical 

20 characterizations of these O3 concentrations metrics across the U.S., including how they compare 

21 to 2006-2008 design values.

22

23 While Chapter 7 assessed both mortality and morbidity risks associated with O3

24 concentrations, due to limitations in baseline morbidity incidence rates, the national scale 

25 assessment focuses on mortality risks only. To quantify the impact of O3 concentrations on 

26 mortality, we apply risk estimates drawn from two major short-term epidemiological studies and 

27 one long-term epidemiological study. These studies are consistent with those used in the analysis 

28 of O3-related risk in selected urban areas (Section 7.2) and those mortality endpoints concluded 

29 to have a causal or suggestive causal relationship with O3 exposure by the 2013 Integrated 

30 Science Assessment for O3 and Related Photochemical Oxidants (U.S. EPA 2013).

31 For short-term mortality, we use city-specific and national average risk estimates drawn 

32 from the Smith et al. (2009) study of O3 and mortality in 98 U.S. urban communities between 

33 1987 and 2000 as our main results, and the Zanobetti and Schwartz (2008) study of O3 and 

8.1.1.1 Ambient O3 Concentrations

8.1.1.2 Concentration-Response Functions
8-4

1 mortality in 48 U.S. cities between 1989 and 2000 as a sensitivity analysis, consistent with the 

2 urban case study analysis in Chapter 7. City-specific effect estimates for both studies are 

3 provided in Appendix 4-A.

4 Smith et al. (2009) found that the average non-accidental mortality increase across all 98

5 urban areas was 0.32% ± 0.08 (95% posterior interval [PI], 0.41%-0.86%) for a 10 ppb increase 

6 in the 8-hr daily maximum O3 concentration, based on April to October O3 observations. Since

7 the national-scale analysis requires a single modeling period definition but some monitors only 

8 collect data from May to September, the corresponding city-specific effect estimates are applied 

9 to each day from May to September in BenMAP using May to September average 8-hr daily 

10 maximum O3 concentration based on 2006-2008 observed concentrations fused with 2007 

11 modeled concentrations. The length of the O3 season can affect the magnitude of mortality effect 

12 estimates – a longer season may yield higher effect estimates per unit O3 concentration since O3 

13 concentrations over the longer season may be lower than the O3 concentrations over the warmest 

14 months only. Conversely, if the longer period captures periods of lower O3-related mortality 

15 incidence, the effect estimates may be lower than effect estimates for the warmest months only.

16 Our application of the Smith et al. (2009) April to October effect estimates to May to September 

17 O3 concentrations likely introduces some bias in the results, but it is unclear in which direction.

18 Zanobetti and Schwartz (2008) found that the average total mortality increase across all 

19 48 cities was 0.53% (95% confidence interval, 0.28%-0.77%) for a 10 ppb increase in June20 August 8-hr daily mean O3 concentration from 10 am to 6 pm, using a 0-3 day lag. We apply the 

21 city-specific effect estimates that correspond to this national average effect estimate each day 

22 from June to August in BenMAP using the June to August, mean 8-hr daily mean O3

23 concentration based on 2006-2008 observed concentrations fused with 2007 modeled 

24 concentrations. Consistent with Chapter 7, these results are presented as a sensitivity analysis.

25 As in Chapter 7, we use city-specific risk estimates from the short-term epidemiology 

26 studies, but apply them here only to the counties that were included in the epidemiology studies 

27 rather than to the entire core-based statistical area (CBSA). Chapter 7 estimated risk across entire 

28 CBSAs to more completely capture expected O3 changes across broader areas and avoid bias 

29 resulting from including only those areas where O3 is expected to increase under alternative 

30 standards. The inclusion of the entire CBSA in that analysis required the application of a single 

31 effect estimate to the entire CBSA. However, the national-scale assessment is a gridded analysis, 

32 which allows greater spatial resolution in the application of effect estimates. In addition, eight 

33 CBSAs nationwide included multiple cities defined separately by Smith et al. (2009), some of 

34 which showed considerable heterogeneity in effect estimates within the same CBSA.

35 Heterogeneity among effect estimates within a single CBSA implies that effect estimates from 

36 one county may not be accurate representations of effect estimates in nearby 
8-5

1 counties. However, since city-specific effect estimates often have low power due to small 

2 population size, we are unable to draw a strong conclusion regarding how well one county's 

3 effect estimates represents those in nearby counties. For this national-scale assessment, we 

4 apply effect estimates from each city as defined in the epidemiology studies to retain the full set 

5 of information available from those studies. In addition, for counties not included by the 

6 epidemiology studies, we apply the average effect estimate derived from all the urban areas 

7 included in each of the studies (“national average”) as it takes advantage of a wider and more 

8 diverse population.

9 Since both national average estimates from these studies are based on urban areas only, 

10 we have higher confidence in their application to other U.S. urban areas than to rural areas. To 

11 demonstrate the magnitude of the results for which we have the highest confidence, we present 

12 the percentage of estimated deaths occurring within the urban areas included in the 

13 epidemiological studies and within all urban areas across the U.S. Lower confidence in the 

14 results for rural areas does not indicate that the mortality risk among populations living in such 

15 areas is unaffected by O3 pollution. Rather, the level of understanding for the O3-mortality 

16 relationship in these areas is simply lower due to a lack of available epidemiological data at these 

17 levels. We also examine the effect of varying the effect estimate applied between the cities 

18 included by the epidemiology studies in a sensitivity analysis.

19 We quantify long-term O3-related respiratory mortality in this REA since the Integrated 

20 Science Assessment for O3 and Related Photochemical Oxidants (O3 ISA) concluded that the 

21 evidence supports a likely to be causal relationship between long-term O3 exposure and 

22 respiratory effects, including respiratory morbidity and respiratory-related mortality (U.S. EPA, 

23 2013). As detailed in Chapter 7, we quantify long-term O3-related mortality using the respiratory 

24 mortality effect estimates from the Jerrett et al. (2009) two-pollutant model that controlled for 

25 PM2.5 concentrations, applied to each gridcell across the entire United States. This model found 

26 that a 10 ppb increase in the April-September average of the 1-hr daily maximum O3

27 concentration was associated with a 4% (95% confidence interval, 1.0%-6.7%) increase in 

28 respiratory mortality.

29 This analysis uses the same baseline mortality rates and population estimates as were 

30 used in the urban case study area analysis in Chapter 7. We derive baseline incidence rates for 

31 mortality by age, cause, and county from the CDC Wonder database (CDC, 2004-2006). As this 

32 database only provides baseline incidence rates in 5-year increments, we use data for the year 

33 2005, the closest year to the analysis year 2007 used for the population and air quality modeling.

8.1.1.3 Demographic Inputs
8-6

1 We use 2007 population because it matches both the year of the emissions inventory and 

2 meteorology used for the air quality modeling.

3 The starting point for estimating the size and demographics of the potentially exposed 

4 population is the 2010 census-block level population, which BenMAP aggregates up to the same 

5 grid resolution as the air quality model. BenMAP back-casts this 2010 population to the analysis 

6 year of 2007 using county-level growth factors based on economic projections (Woods and 

7 Poole Inc., 2012).

8

9 8.1.2 Results

10 Table 8.1 summarizes the estimated O3-related premature mortality associated with 2006-

11 2008 average O3 concentrations under various assumptions for the health impact function.

12 Applying Smith et al. (2009) effect estimates for May-September, we estimate 15,000 (95% CI, 

13 1,400-28,000) premature O3-related non-accidental deaths annually for 2007. As a sensitivity 

14 analysis, we apply Zanobetti and Schwartz (2008) effect estimates for June-August, finding

15 16,000 (95% CI, 6,000-25,000) premature O3-related all-cause deaths annually for 2007. Figure 

16 8.2 Figure 8.4 show that estimated O3-related mortality is most concentrated in highly populated 

17 counties or those counties with urban areas found to have high effect estimates by Smith et al. 

18 (2009) or Zanobetti and Schwartz (2008). For the application of Jerrett et al. (2009) national 

19 average effect estimate for April-September, we estimate 45,000 (95% CI, 17,000-70,000) 

20 premature O3-related respiratory deaths among adults age 30 and older.

21 Because the epidemiological studies included only selected urban areas, we are more 

22 confident in the magnitude of the estimated O3-related deaths occurring within those urban areas. 

23 As shown in Table 8.1, approximately 43% of the O3-related deaths estimated using Smith et al. 

24 (2009) effect estimates occur in the 98 urban locations included in that study, and 30% of the O3-

25 related deaths estimated using Zanobetti and Schwartz (2008) effect estimates occur in the 48 

26 urban areas included in that study. We are also more confident in extrapolating the national 

27 average effect estimates to other urban areas than we are to rural areas, as the national average 

28 estimates are based on all urban areas included by the study. To estimate the percentage of total 

29 O3-attributable deaths occurring within all urban areas across the continental U.S., we sum the 

30 results for the 12km gridcells that have a total population greater than 12,000 (approximately 

equal to the 95th 31 percentile of gridcell populations across the continental U.S.). The percentage of 

32 O3-attributable deaths occurring within urban areas defined in this way is 65% for results based 

33 on Smith et al. (2009) effect estimates and 64% for results based on Zanobetti and Schwartz 

34 (2008) effect estimates. While our confidence is lower when the national average effect estimates 
8-7

1 are extrapolated to rural areas, less certainty in the magnitude of O3-related deaths in rural areas 

2 does not imply that O3 has no effect on health in these areas.

3

4 Table 8-1 Estimated annual O3-related premature mortality in 2007 associated with 2006-

5 2008 average O3 concentrations (95th percentile confidence interval)

1 6 City-specific effect estimates are applied to the gridcells lying within the cities defined in the epidemiological 

7 studies. Average effect estimates across all cities included in the epidemiological studies (national average) are 

8 applied to all other gridcells. For the application of Smith et al. (2009) effect estimates, city-specific effect 

9 estimates were applied to 2,227 gridcells and the national average to 44,064 gridcells. For the application of 

10 Zanobetti and Schwartz (2008) effect estimates, city-specific effect estimates were applied to 925 gridcells and 

11 the national average to 45,366 gridcells.

2 12 National average effect estimates are based on the average of all cities included in the epidemiological studies

13 applied to all 12km gridcells nationally.

14 Table 8.1 also shows O3-related deaths estimated by applying the national average risk 

15 estimate from the epidemiological studies to all gridcells in the U.S. Compared with applying 

16 city-specific effect estimates to the gridcells corresponding to each urban area, using the national 

17 average effect estimate for all gridcells yields equivalent central estimates. However, applying 

18 the national average also results in tighter confidence intervals since the national average effect 

19 estimates had higher statistical power and thus tighter confidence bounds compared with the 

20 effect estimates for individual cities.

21 Table 8.2 shows the mean, median, 2.5 percentile and 97.5 percentile of the estimated 

22 percentage of mortality attributable to ambient O3 across all counties in the U.S. Using Smith et 

23 al. (2009) effect estimates, O3-attributable mortality contributes an average of 1.5% (95% 

24 confidence interval, 1.1%-1.8%) to county-level May-September non-accidental mortality (all 

25 ages) and 0.6% (0.4%-0.7%) to all year all-cause mortality (all ages). For results using Zanobetti 

Source of risk estimate and modeling period

Exposure 

duration Age

City-specific 

effect 

estimates1

National 

average effect 

estimate2

Smith et al. (2009), May-September Short-term >0 15,000 16,000

95% confidence interval (1,400-28,000) (7,200-22,000)

% occurring within the 98 cities 43%

Zanobetti and Schwartz (2008), June-August Short-term >0 16,000 15,000

95% confidence interval (6,000-25,000) (8,300-22,000)

% occurring within the 48 cities 30%

Jerrett et al. (2009), April-September Long-term ≥30 

years

- 45,000

95% confidence interval (17,000-70,000)
8-8

1 and Schwartz (2008) effect estimates, O3-attributable mortality contributes an average of 2.5% 

2 (95% confidence interval, 1.7%-3.0%) to county-level June-August all-cause mortality (all ages) 

3 and 0.6% (0.4%-0.8%) to all year all-cause mortality (all ages). For the results using Jerrett et al. 

4 (2009) effect estimates, O3-attributable mortality contributes an average of 18.5% (95% 

5 confidence interval, 15.2%-21.5%) to county-level April-September adult (age 30+) respiratory 

6 mortality and 1.9% (1.3%-2.6%) to all year all-cause mortality (all ages). Figure 8.5 through

7 Figure 8.7 show that the counties with the highest percentage of mortality attributable to O3 are 

8 typically those with the highest O3 levels.

9 Figure 8.8 displays the cumulative distribution of the percent of county-level all-cause, 

10 all-age, and all-year mortality attributable to ambient O3 using effect estimates from all three

11 epidemiological studies. For the results based on Smith et al. (2009) and Zanobetti and Schwartz 

12 (2008) effect estimates, 0.8% of all-cause, all-age, and all-year mortality is attributable to O3 for 

13 approximately 99% of U.S. counties. For the results based on Jerrett et al. (2009) effect 

14 estimates, 2.8% of all-cause, all-age, and all-year mortality is attributable to O3 for 

15 approximately 99% of U.S. counties.

16
8-9

1 Table 8-2 Mean, median, 2.5 percentile, and 97.5 percentile of the estimated percentage 

2 of mortality attributable to ambient O3 for all 3087 counties in the continental 

U.S.1 3

Source of risk estimate, modeling period,

and mortality endpoint used to generate 

percentage

Total 

incidence 

(2005)

Percentage of total incidence attributable 

to O3

Mean 

(%)

Median 

(%)

2.5

Percentile 

(%)

97.5 

Percentile 

(%)

Smith et al. (2009), May-September

Non-accidental mortality, all ages 964,837 1.5 1.5 1.1 1.8

All-cause mortality, all ages 1,028,334 1.4 1.4 1.0 1.7

All-cause mortality, all ages, all year 2,454,896 0.6 0.6 0.4 0.7

Zanobetti and Schwartz (2008), June-August

All-cause mortality, all ages 618,345 2.5 2.5 1.7 3.0

All-cause mortality, all ages, all year 2,454,896 0.6 0.6 0.4 0.8

Jerrett et al. (2009), April-September

Respiratory mortality, ages 30+ 236,756 18.5 18.7 15.2 21.5

All-cause mortality, all ages 1,229,968 3.8 3.7 2.6 5.2

All-cause mortality, all ages, all year 2,454,896 1.9 1.9 1.3 2.6

 1 For the mortality endpoints matching the epidemiology studies as a percentage of incidence of the same 

endpoint for the same seasonal definition, and as a percentage of all-cause mortality for all age groups (both 

seasonal and all year).
8-10

1

2 Figure 8.2 Estimated annual non-accidental premature deaths (individuals) in 

3 2007 associated with average 2006-2008 May-September average 8-hr daily 

4 maximum O3 levels by county using Smith et al. (2009) effect estimates

5

6 Figure 8.3 Estimated annual all-cause premature deaths (individuals) in 2007 

7 associated with average 2006-2008 June-August average 8-hr daily mean (10am8 6pm) O3 levels by county using Zanobetti and Schwartz (2008) effect estimates
8-11

1

2 Figure 8.4 Estimated annual adult (age 30+) respiratory premature deaths 

3 (individuals) in 2007 associated with average 2006-2008 April-September 

4 average 1-hr daily max O3 levels by county using Jerrett et al. (2009) effect 

5 estimates

6

7 Figure 8.5 Estimated percentage of May-September total non-accidental 

8 mortality (all ages) attributable to 2006-2008 average O3 levels by county using 

9 Smith et al. (2009) effect estimates
8-12

1

2 Figure 8.6 Estimated percentage of June-August total all-cause mortality (all ages) 

3 attributable to 2006-2008 average O3 levels by county using Zanobetti and Schwartz 

4 (2008) effect estimates

5

6

7 Figure 8.7 Estimated percentage of April-September respiratory mortality among 

8 adults age 30+ attributable to 2006-2008 average O3 levels by county using Jerrett et 

9 al. (2009) effect estimates

10

11
8-13

1

2 Figure 8.8 Cumulative distribution of county-level percentage of all-cause, all-year, and 

all-age mortality attributable to 2006-2008 average O3 for the U.S.

2 3

4

 2 Estimated O3-attributable deaths are based on the mortality cause, age group, and season inherent to the 

epidemiological study upon which it is based (May-September non-accidental mortality for all ages for results based 

on Smith et al. (2009) effect estimates, June-August all-cause mortality for all ages for results based on Zanobetti 

and Schwartz (2008) effect estimates, and April-September respiratory mortality for ages 30+ for results based on 

Jerrett et al. (2009) effect estimates).
8-14

1 Figure 8.9 shows the cumulative distribution of the county-level percent of total O3-

2 related deaths by O3 concentration. The mortality results based on Smith et al. (2009) 

3 concentration-response functions are compared with the May-September average of the 8-hr 

4 daily maximum O3 concentration, those based on Zanobetti and Schwartz (2008) concentration5 response functions are compared with the June-August average of the 8-hr mean O3

6 concentration from 10am to 6pm, and those based on Jerrett et al. (2009) concentration-response 

7 functions are compared with the April-September average of the 1-hr daily maximum O3

8 concentration, consistent with the O3 concentration metrics used in each study. The mortality 

9 results based on Zanobetti and Schwartz (2008) effect estimates are shifted to the right of the 

10 mortality results based on the Smith et al. (2009) concentration response functions because the 

11 seasonal averaging time for the results based on Zanobetti and Schwartz (2008) is limited to the 

12 summer months when O3 tends to be highest. Similarly, the mortality results based on Jerrett et 

13 al. (2009) effect estimates are shifted to the right of the mortality results based on Zanobetti and 

14 Schwartz (2008) and Smith et al. (2009) because Jerrett et al. (2009) results use the seasonal 

15 average of the 1-hr daily maximum, which tends to be higher than the seasonal average of 8-hr 

16 daily maximum and seasonal average of 8-hr daily mean metrics (see Figure 4-18). For all three 

17 epidemiology studies, we find that 90-95% of O3-related deaths occur in locations where the 

18 May to September average 8-hr daily maximum, June to August average 8-hr daily mean (10am19 6pm), or April to September average 1-hr daily maximum O3 concentrations are greater than 40 

20 ppb. A seasonal average concentration of 40 ppb corresponds to 2006-2008 design values 

21 ranging from approximately 50 to 90 ppb, depending on the seasonal average concentration 

22 metric (see Figure 4-19).

23

24
8-15

1

2 Figure 8.9 Cumulative percentage of total O3 deaths by baseline O3 concentration. O3

3 concentrations are reported as May-September average 8-hr daily maximum 

4 for results based on Smith et al. (2009) effect estimates, June-August average 

5 8-hr mean (10am to 6pm) for results based on Zanobetti and Schwartz (2008) 

6 effect estimates, and April-September average 1-hr daily maximum for results 

7 based on Jerrett et al. (2009) effect estimates.

8

9 8.1.3 Sensitivity analysis

10 For the results presented above, the national average effect estimate for results based on 

11 Smith et al. (2009) and Zanobetti and Schwartz (2008) was applied to all gridcells between the 

12 cities included in the studies. However, O3-mortality effect estimates have been shown to exhibit 

13 significant regional variability across the U.S. (e.g. Smith et al. 2009). Smith et al. (2009) found 

14 that using the national average effect estimate may overestimate risk in cities that have low effect 

15 estimates, including Los Angeles and Denver, but may underestimate risk in cities that have high 

16 effect estimates, including New York City and Chicago. We conduct two sensitivity analyses 

17 aimed at characterizing the sensitivity of estimated O3-attributable premature deaths to the use of 

18 national average effect estimates between the cities that were included by Smith et al. (2009) and 

19 Zanobetti and Schwartz (2008).

20 First, we examine the sensitivity of estimated O3-attributable premature deaths to the 

application of the 5th highest and 5th 21 lowest effect estimates of all the cities included in the Smith 

22 et al. (2009) and Zanobetti and Schwartz (2008) studies to the gridcells between the cities 
8-16

1 included in these studies (Table 8.3). As in the main results, city-specific effect estimates are 

applied to the gridcells in which the cities lie. Applying the 5th 2 highest effect estimate from Las 

3 Vegas to the gridcells between the cities included by Smith et al. (2009) yields a 36% lower 

estimate of O3-attributable deaths as compared with the main results. Applying the 5th 4 lowest 

5 effect estimate from Dallas/Ft. Worth yields a 42% higher estimate of O3-attributable deaths as 

compared with the main results. Applying the 5th 6 lowest effect estimate from Los Angeles to the 

7 gridcells between the cities included by Zanobetti and Schwartz (2008) yields a 37% lower 

estimate of O3-attributable deaths as compared with the main results. Applying the 5th 8 highest 

9 effect estimate from Columbus, OH, yields a 30% higher estimate of O3-attributable deaths as 

10 compared with the main results.

11

12 Table - 8.3 Sensitivity of estimated O3-attributable premature deaths to the application of 

13 the 5th lowest and 5th highest city-specific risk estimates found by Smith et al. 

14 (2009) and Zanobetti and Schwartz (2008) to the gridcells between the cities 

15 included in those studies.

Source of risk estimate and sensitivity study Beta and city

O3-attributable 

mortality

Percent 

change 

from main 

results

Smith et al. (2009), May-September

5th lowest city beta 0.00014 

Las Vegas, NV

9,600

(-20,000 – 38,000)

-36%

5th highest city beta 0.000538 

Dallas/Ft. Worth, TX

21,000

(-1000 – 43,000)

+42%

Zanobetti and Schwartz (2008), June-August

5th lowest city beta 0.000274 

Los Angeles, CA

9,800

(-3,700 – 23,000)

-37%

5th highest city beta 0.000739 

Columbus, OH

20,000

(100 – 40,000)

+30%

16

17

18 Second, we examine the sensitivity of estimated O3-attributable premature deaths to the 

19 application of Smith et al. (2009) Bayesian-shrunken city-specific estimates using regional 

20 average priors rather than the national average prior (Table 8.4). For gridcells between the cities 

21 included by Smith et al. (2009), we apply the regional average effect estimate, rather than the 

22 national average effect estimate as in the main results. Regional definitions are shown in Figure 
8-17

1 8.10. Estimated O3-attributable deaths using the regional prior city-specific effect estimates and 

2 the regional average effect estimates between the 98 cities included by Smith et al. (2009) are

3 approximately 20% larger than the main results, with 38% of estimated deaths occurring in the 

4 98 cities rather than 43%. The 95% confidence interval for the results using the regional prior 

5 spans zero, whereas the 95% confidence interval for the results using the national prior does not. 

6 Since the regional average effect estimates are all based on fewer data points (in some regions, 

7 the regional average is based on only seven cities; see Appendix 8-A) than is the national 

8 average, the confidence interval for each regional average effect estimate is large and sometimes 

9 spans zero. The large confidence intervals for the regional average effect estimates drive the 

10 confidence interval that spans zero for O3-attributable mortality estimated using regional prior 

11 effect estimates. Confidence intervals that span zero do not imply that higher O3 is associated 

12 with decreased mortality, as there is no biologically plausible mechanism for such an effect, and 

13 in no case do we see a significant negative central estimate. Rather, confidence intervals 

14 spanning zero indicate a lack of statistical power to precisely determine the magnitude of an 

15 effect.

16 Figure 8.11 shows estimated O3-attributable deaths by region using the national average 

17 prior compared with using the regional average priors from Smith et al. (2009). Results generally 

18 follow conclusions made by Smith et al. (2009) based on the magnitude of the regional effect 

19 estimates. For example, using the national average effect estimate may substantially 

20 underestimate O3-attributable deaths in the North East and Industrial Midwest where regional 

21 effect estimates are large. Using the national average effect estimate may also overestimate O3-

22 attributable deaths in the Upper Midwest, Southern California, and South West, which were 

23 found to have small effect estimates. However, these three regions have very large confidence 

24 intervals which all span zero, since these regional averages are based on few cities (7, 7, and 9, 

25 respectively, compared with 26 in the South East, 19 in Industrial Midwest, 16 in North East, and 

26 12 in North West; see Appendix 8-A).

27
8-18

1 Table - 8.4 Sensitivity of estimated O3-attributable premature deaths to the application of 

2 Smith et al. (2009) regional prior Bayes-shrunken city-specific and regional 

3 average effect estimates, as compared with the national prior Bayes-shrunken 

4 city-specific and national average effect estimates as in the main results.

Risk estimate

O3-attributable premature 

deaths

Percent O3-

attributable deaths in 

98 cities

City-specific, national prior 

with national average 

15,000

(1,400 – 28,300)

43%

City-specific, regional prior 

with regional averages 

18,000

(-2,000 – 24,000)

38%

5

6

7

8

9

10 Figure 8.10 Regions used in the sensitivity analysis based on the Smith et al. (2009) 

11 regional-prior Bayes-shrunken city-specific and regional average effect 

12 estimates (Source: Samet et al. 2000).

13
8-19

1

2 Figure 8.11 O3-attributable premature deaths by region as calculated by applying Smith 

3 et al. (2009) regional prior Bayes-shrunken and regional average effect 

4 estimates, as compared with the national prior Bayes-shrunken and national 

5 average effect estimates as in the main results

6

7 8.1.4 Discussion

8 We estimated the total all-cause deaths associated with short-term exposure to recent O3

9 levels across the continental U.S., using average 2006-2008 observations from the O3 monitoring 

10 network fused with a 2007 CMAQ simulation and city-specific O3-mortality effect estimates 

11 from two short-term epidemiology studies. Applying Smith et al. (2009) effect estimates for 

12 May-September, we estimate 15,000 (95% CI, 1,400-28,000) premature O3-related non13 accidental deaths (all ages) annually for 2007. Using Smith et al. (2009) effect estimates, O3-

14 attributable mortality contributes an average of 1.5% (95% confidence interval, 1.1%-1.8%) to

15 county-level May-September non-accidental mortality (all ages) and 0.6% (0.4%-0.7%) to all 

16 year all-cause mortality (all ages). As a sensitivity, we apply Zanobetti and Schwartz (2008) 

17 effect estimates for June-August, finding 16,000 (95% CI, 6,000-25,000) premature O3-related 

18 all-cause deaths (all ages) annually for 2007. For results using Zanobetti and Schwartz (2008) 

19 effect estimates, O3-attributable mortality contributes an average of 2.5% (95% confidence 

20 interval, 1.7%-3.0%) to county-level June-August all-cause mortality (all ages) and 0.6% (0.4%-

21 0.8%) to all year all-cause mortality (all ages). For the application of Jerrett et al. (2009) effect 

22 estimates for April-September, we estimate 45,000 (95% CI, 17,000-70,000) premature O3-

23 related adult (age 30 and older) respiratory deaths. For the results using Jerrett et al. (2009) effect 

-5000 0 5000 10000 15000

Industrial Midwest

North East

North West

South East

South West

Southern California

Upper Midwest

Ozone-attributable premature deaths

national prior regional prior
8-20

1 estimates, O3-attributable mortality contributes an average of 18.5% (95% confidence interval, 

2 15.2%-21.5%) to county-level April-September adult (age 30+) respiratory mortality and 1.9% 

3 (1.3%-2.6%) to all year all-cause mortality (all ages). For all three epidemiology studies, we find 

4 that 90-95% of O3-related deaths occur in locations where the May to September average 8-hr 

5 daily maximum, June to August average 8-hr daily mean (10am-6pm), or April to September 

6 average 1-hr daily maximum O3 concentrations are greater than 40 ppb. A seasonal average 

7 concentration of 40 ppb corresponds to 2006-2008 design values ranging from approximately 50 

8 to 90 ppb, depending on the seasonal average concentration metric.

9 A previous analysis estimated that short-term O3 exposure was associated with 4,700 

10 (95% CI, 1,800-7,500) premature deaths nationwide annually, based on 2005 O3 concentrations

11 and Bell et al. (2004) national average effect estimates (Fann et al., 2012). The results estimated 

12 here are higher, resulting mainly from two important differences. First, Fann et al. (2012) 

13 estimated risk only above North American background, simulated O3 concentrations in the 

14 absence of North American anthropogenic emissions, which was set to 22 ppb in the east and 30 

15 ppb in the west. Fann et al. (2012) also used a national average mortality effect estimate for 8-hr 

16 daily maximum O3 during the warm season only, calculated using ratios of 24-hr mean 

17 concentrations to 8-hr daily maximum concentrations (see Abt Associates 2010). The Smith et 

18 al. (2009) national average beta used here, 0.000322, is based on April-October O3 data and is 

19 approximately 23% larger than that used by Fann et al. (2012), 0.000261. Since the risk 

20 modeling period (and the seasonal definition for the seasonal average 8-hr daily maximum 

21 concentration) was May to September for both studies, the higher beta used here yields a larger 

22 O3 mortality estimate. These two differences in methods explain the larger O3 mortality estimates

23 of this analysis compared with the previous estimate by Fann et al. (2012).

24 Estimated O3-attributable premature deaths based on Jerrett et al. (2009) effect estimates 

25 are approximately three times larger than results based on Smith et al. (2009) and Zanobetti and 

26 Schwartz (2008) effect estimates. The mean estimated county-level percent of all-cause, all-year, 

27 and all-age mortality is also three times larger for results based on Jerrett et al. (2009) effect 

28 estimates, indicating that the larger estimate does not simply result from a longer modeling 

29 period or different population subset (e.g. adult respiratory disease for Jerrett et al. (2009) effect 

30 estimates versus all-age non-accidental or all-cause mortality for Smith et al. (2009) and 

31 Zanobetti and Schwartz (2008) effect estimates). Recent studies using long-term O3-mortality 

32 relationships found by Jerrett et al. (2009) to quantify the burden of mortality due to 

33 anthropogenic O3 globally (Anenberg et al. 2010, 2011) and for the U.S. specifically (Fann et al. 

34 2012) have also found that using Jerrett et al. (2009) long-term effect estimates yields O3-related 

35 mortality burden estimates that are approximately two to four times larger than estimates based 

36 on short-term effect estimates. Since long-term mortality relationships include both acute and 
8-21

1 chronic exposure effects, the significantly larger mortality estimates calculated using long-term 

2 concentration-mortality relationships suggest that considering only short-term mortality may 

3 exclude a substantial portion of O3-related risk. However, since the short-term mortality 

4 relationships include a larger population (all ages versus adults ages 30 and older only) and all 

5 mortality causes, the short-term mortality relationships may capture some O3 effects that are not 

6 captured by Jerrett et al. (2009). It is likely that some portion of the estimated premature deaths 

7 attributable to short-term O3 exposure is captured by estimated premature deaths attributable to 

8 long-term O3 exposure, but the extent of the overlap between these estimates is unknown.

9

10 8.2 EVALUATING THE REPRESENTATIVENESS OF THE URBAN STUDY AREAS 

11 IN THE NATIONAL CONTEXT

12 To further support interpretation of risk estimates generated in Section 7.2, this section 

13 presents three analyses that assess the representativeness of the 12 urban study areas in the 

14 national context. First, we assess the degree to which the urban study areas represent the range of 

15 air quality levels and key O3 risk-related attributes that vary spatially across the nation. We have 

16 partially addressed this issue by selecting urban study areas in different geographical regions of 

17 the country (see Section 7.2). In this section, we evaluate how well the selected urban areas 

18 represent the overall U.S. for a set of spatially-distributed O3 risk related variables (e.g. weather, 

19 demographics including socioeconomic status, baseline health incidence rates; Section 8.2.1). 

20 Section 8.2.2 identifies where our 12 urban study areas fall along the distribution of O3-

21 attributable mortality risk across the U.S. This analysis allows us to assess the degree to which 

22 the 12 urban study areas capture locations within the U.S. likely to experience elevated levels of 

23 risk related to ambient O3. Finally, we give a national context to the estimated O3 responses to 

24 emission changes in the urban study areas by assessing how well these 12 areas and the 3 

25 additional exposure areas represent air quality trends and responses to emissions across the entire 

26 U.S. (Section 8.2.3).

27 We do not attempt to assess the representativeness of the 15 urban study areas considered 

28 in the exposure assessment for O3 related risk because data limitations preclude us from being 

29 able to characterize individual-level exposure across the U.S. However, the urban study areas 

30 considered in both the exposure and risk assessments shared common selection criteria, 

31 including consideration of O3 concentrations, availability of adequate monitoring data, 

32 demographics, and exposure factors. Therefore, conclusions from this analysis of the 

33 representativeness of the 12 urban study areas for risk would also apply to those areas for 

34 exposure.
8-22

1 8.2.1 Analysis Based on Consideration of National Distributions of Risk-Related

2 Attributes

3 This section evaluates how well the urban study areas reflect national-level variability in 

4 a series of O3 risk-related variables. For this analysis, we first generate distributions for risk5 related variables across the U.S. and for the specific urban study areas considered in Section 7.2

6 from generally available data (e.g. from the 2000 Census, Centers for Disease Control (CDC), or 

7 other sources). We then plot the specific values of these variables for the selected urban study 

8 areas on these distributions, and evaluate how representative the selected study areas are of the 

9 national distributions for these individual variables.

10 Estimates of risk (either relative or absolute, e.g. number of cases) within our risk 

11 assessment framework are based on four elements: population, baseline incidence rates, air 

12 quality, and the coefficient relating air quality and the health outcome (i.e. the O3 effect 

13 estimates). Each of these elements can contribute to heterogeneity in risk across urban locations, 

14 and each is variable across locations. In addition, there may be other identifiable factors that 

15 contribute to the variability of the four elements across locations. In this assessment, we examine 

16 the representativeness of the selected urban area locations for the four main elements, as well as

17 factors that have been identified as influential in determining the magnitude of the C-R function 

18 across locations.

19 While personal exposure is not incorporated directly into O3 epidemiology studies, city20 specific O3 effect estimates are affected by differing levels of exposure which in turn are related 

21 to variability in exposure determinants. The correlation between monitored O3 and personal O3

22 exposure also varies between cities. The O3 ISA has comprehensively reviewed epidemiological 

23 and toxicological studies to identify variables which may affect the O3 effect estimates used in 

24 the city-specific risk analysis in Section 7.2 and the national-scale risk analysis in Section 8.1

25 (U.S. EPA 2013). Determinants of the O3 effect estimates used in risk assessment can be grouped 

26 into four broad areas:

27 • Demographics: education, income, age, unemployment rates, race, body mass index and 

28 physical conditioning, public transportation use, and time spent outdoors.

29 • Baseline health conditions: asthma, chronic obstructive pulmonary disease,

30 cardiovascular disease (atherosclerosis, congestive heart disease, atrial fibrillation, 

31 stroke), diabetes, inflammatory diseases, and smoking prevalence.

32 • Climate and air quality: O3 levels, co-pollutant levels (annual mean PM2.5), temperatures

(days above 90 degrees, mean summer temp, 98th 33 percentile temp).

34 • Exposure determinants: air conditioning prevalence.

35 Although data limitations preclude our ability to conduct a national-scale exposure assessment as 

36 we have done for O3-attributable risk in Section 8.1, we assess the representativeness of the 
8-23

1 urban study areas across the national distribution of climate, air quality, and air conditioning 

2 prevalence, factors which influence individual exposure. As discussed in detail in Chapter 5, no 

3 available data base is sufficient to assess the national representativeness of time spent outdoors, 

4 another important personal exposure determinant, among persons residing in each of the urban 

5 case study areas. However, previous analyses suggest that children’s time spent outdoors varies 

6 little across U.S. regions (section 8.10.2 of U.S. EPA, 2009). In addition, as discussed in Section 

7 5.1.1, time spent outdoors and the percent of person-days having at least one minute outdoors 

8 (participation rate) does not appear to vary much over the past few decades based on analyses 

9 using the CHAD database, nor does there appear to be a temporal trend over the past decade 

10 based on analyses using the American Time Use Survey (ATUS). In considering that many of 

11 the activity pattern studies in CHAD were from national surveys conducted in metropolitan areas

12 and that the evaluation results indicate little difference in time expenditure over broad 

13 geographic areas and survey collection years, it is likely that the distribution of time spent 

14 outdoors generated for the simulated persons in the 15 urban study areas (Chapter 5) reasonably

15 reflects the most important elements of a national distribution of time spent outdoors.

16 Based on these identified potential risk determinants, we identify datasets that could be 

17 used to generate nationally representative distributions for each parameter. We are not able to 

18 identify readily available national datasets for all variables. In these cases, if we are able to 

19 identify a broad enough dataset covering a large enough portion of the U.S., we use that dataset 

20 to generate the parameter distribution. In addition, we are not able to find exact matches for all of 

21 the variables identified through our review of the literature. In cases where an exact match is not 

22 available, we identify proxy variables to serve as surrogates. For each parameter, we report the 

23 source of the dataset, its degree of coverage, and whether it is a direct measure of the parameter 

24 or a proxy measure (Table 8.5). Summary statistics for the most relevant variables are provided 

25 in Table 8.6.

26 Figure 8.12 through Figure 8.18 show the cumulative distribution functions (CDF) 

27 plotted for the nation for the four critical risk function elements (population, air quality, baseline 

28 incidence, and the O3 effect estimate), as well as where the urban study areas fall on the 

29 distribution. While the urban-scale analysis in Chapter 7 includes the full core-based statistical 

30 area for the selected cities, we consider here only the counties included in each city as defined by 

31 the epidemiological studies, since we only have information on O3 effect estimates for these 

32 counties. This approach is consistent with the national-scale assessment of O3-attributable risk in 

33 Section 8.1, from which we draw county-level O3-attributable risk estimates for the 

34 representativeness analysis in Section 8.2.3. These figures focus on critical variables representing 

35 each type of risk determinant, e.g. we focus on all-cause and non-accidental mortality rates, but 

36 we also have conducted analyses for cardiovascular and respiratory mortality separately. The 
8-24

1 vertical black lines in each graph show the values of the variables for the individual urban study 

2 areas. The city-specific values that comprise the national CDF for mortality risks found by

3 Zanobetti and Schwartz (2008) are also displayed on the graphs of those attributes, as the number 

4 of cities included in that study is smaller (48 cities). The complete set of analyses is provided in 

5 Appendix 4-A.

6 These figures show that the selected urban study areas represent the upper percentiles of 

7 the distributions of population and do not represent the locations with lower populations (urban 

study areas are all above the 90th 8 percentile of U.S. county populations). This is consistent with 

9 the objectives of our case study selection process, e.g. we are characterizing risk in areas that are 

10 likely to be experiencing excess risk due to O3 levels above alternative standards. The urban 

11 study areas span the full range of seasonal average 8-hr daily maximum O3 concentrations in 

12 monitored U.S. counties and the full distribution of O3 risk coefficients across the cities included 

13 by Smith et al. (2009) and Zanobetti and Schwartz (2008). The urban study area analysis 

14 includes the two cities with the highest risk coefficients found by Smith et al. (2009) – New York 

15 City and Philadelphia – as well as the two highest found by Zanobetti and Schwartz (2008) –

16 New York City and Detroit. In Table 8.6, respiratory and cardiovascular mortality have higher 

17 concentration-response relationships than non-accidental and all-cause mortality because they 

18 are based on a smaller baseline population and are the diseases most affected by O3 exposure. 

19 The urban study areas do not capture the upper end of the distribution of baseline mortality, 

20 including all-cause (Figure 8.15) and non-accidental mortality (Figure 8.16), as well as 

21 cardiovascular and respiratory mortality (see Appendix 8-B). The interpretation of this is that the 

22 case study risk estimates may not capture the additional risk that may exist in locations that have 

23 the highest baseline mortality rates.
8-25

Table - 8.5 Data Sources for O3 risk-related Attributes

Potential risk 

determinant Metric Year Source

Degree of 

national 

coverage

Demographics

Age Percent age 85 years and 

older

2005 County Characteristics, 2000-2007 Interuniversity Consortium for Political and 

Social Research

All counties

Age Percent age 65 years and

older

2005 County Characteristics, 2000-2007 Interuniversity Consortium for Political and 

Social Research

All counties

Age Percent age 14 years and 

younger

2005 County Characteristics, 2000-2007 Interuniversity Consortium for Political and 

Social Research

All counties

Education Population with less than 

high school diploma

2000 USDA/ERS,

http://www.ers.usda.gov/Data/Education/

All counties

Unemployment Percent unemployed 2005 County Characteristics, 2000-2007 Interuniversity Consortium for Political and 

Social Research

All counties

Income Per capita personal income 2005 County Characteristics, 2000-2007 Interuniversity Consortium for Political and 

Social Research

All counties

Race Percent nonwhite 2006 County Characteristics, 2000-2007 Interuniversity Consortium for Political and 

Social Research

All counties
8-26

Potential risk 

determinant Metric Year Source

Degree of 

national 

coverage

Population Total population 2008 Cumulative Estimates of Resident 

Population Change for the United States, 

States, Counties, Puerto Rico, and Puerto 

Rico Municipios: April 1, 2000 to July 1, 

2008, Source: Population Division, U.S. 

Census Bureau

All counties

Population density Population/square mile 2008 Cumulative Estimates of Resident 

Population Change for the United States, 

States, Counties, Puerto Rico, and Puerto 

Rico Municipios: April 1, 2000 to July 1, 

2008, Source: Population Division, U.S. 

Census Bureau (calculated “as the crow 

flies”)

All counties

Urbanicity ERS Classification Code 2003 County Characteristics, 2000-2007 Interuniversity Consortium for Political and 

Social Research

All counties

Climate and Air Quality

O3 levels Monitored 4th high 8-hr

daily maximum

2007 EPA Air Quality System (AQS) 725 Monitored 

counties

O3 levels Seasonal mean 8-hr daily 

maximum

Avg. 2006-2008 AQS 671 Monitored 

counties

O3 levels Seasonal mean 1-hr daily Avg. 2006-2008 AQS 671 Monitored 
8-27

Potential risk 

determinant Metric Year Source

Degree of 

national 

coverage

maximum counties

O3 levels Seasonal mean Avg. 2006-2008 AQS 671 Monitored 

counties

PM2.5 levels Monitored annual mean 2007 AQS 617 Monitored 

counties

Temperature Mean July temp 1941-1970 County Characteristics, 2000-2007 Interuniversity Consortium for Political and 

Social Research

All counties

Relative Humidity Mean July RH 1941-1970 County Characteristics, 2000-2007 Interuniversity Consortium for Political and 

Social Research

All counties

Exposure Determinants

Ventilation Percent residences with no 

air conditioning

2004 American Housing Survey 76 cities

Baseline Health Conditions

Baseline mortality All Cause CDC Wonder 1999-2005 All counties

Baseline mortality Non Accidental CDC Wonder 1999-2006 All counties

Baseline mortality Cardiovascular CDC Wonder 1999-2007 All counties

Baseline mortality Respiratory CDC Wonder 1999-2008 All counties

Baseline morbidity Acute myocardial 

infarction prevalence

2007 Behavioral Risk Factor Surveillance System 

(BRFSS) 

184 metropolitan 

statistical areas 
8-28

Potential risk 

determinant Metric Year Source

Degree of 

national 

coverage

(MSA)

Baseline morbidity Diabetes prevalence 2007 BRFSS 184 MSA

Baseline morbidity Stroke prevalence 2007 BRFSS 184 MSA

Baseline morbidity Congestive heart disease

prevalence

2007 BRFSS 184 MSA

Obesity Body Mass Index 2007 BRFSS 184 MSA

Level of exercise Vigorous activity 20 

minutes

2007 BRFSS 184 MSA

Level of exercise Moderate activity 30 

minutes or vigorous 

activity 20 minutes

2007 BRFSS 184 MSA

Respiratory risk 

factors

Current asthma 2007 BRFSS 184 MSA

Smoking Ever smoked 2007 BRFSS 184 MSA

C-R Estimates

Mortality risk Non Accidental 2009 Smith et al. (2009) 98 cities

Mortality risk All Cause 2008 Zanobetti and Schwartz (2008) 48 cities

Mortality risk Cardiovascular 2008 Zanobetti and Schwartz (2008) 48 cities

Mortality risk Respiratory 2008 Zanobetti and Schwartz (2008) 48 cities
8-29

Table - 8.6 Summary Statistics for Selected O3 Risk-related Attributes

Average Standard Deviation Maximum Minimum

Sample Size 

(# of counties or 

cities)

Risk Attribute

Urban 

Study 

Areas

U.S.

Dataset

Urban 

Study 

Areas

U.S. 

Dataset

Urban 

Study 

Areas

U.S. 

Dataset

Urban 

Study 

Areas

U.S. 

Dataset

Urban 

Study 

Areas 

U.S. 

Dataset

Demographics

Population per county

 

1,642,198 

 

97,020 

 

1,972,403 

 

312,348 

 

9,862,049 

 

9,862,049

 

354,361 

 

42 23 3143

Population density (Pop/sq mile) 10,378 

 

258 

 

16,550 

 

1,757 

 

71,758 

 

71,758 

 

1,313 

 

0 23 3143

Median age (Years) 35.7 38.6 2.3 4.4 40.0 55.3 32.1 20.1 23 3141

% Age 0 to 14 years 20.7 19.0 2.4 2.9 24.6 36.8 14.7 0.0 23 3141

% Age 65+ years 11.3 14.9 2.5 4.1 15.2 34.7 5.8 2.3 23 3141

% Age 85+ years 1.7 2.1 0.6 0.9 2.5 7.7 0.5 0.1 23 3141

Unemployment rate (%) 5.7 5.4 1.2 1.8 8.6 20.9 4.1 1.9 23 3133

% with less than high school diploma 20.9 22.6 7.9 8.8 37.7 65.3 8.7 3.0 23 3141

Income per capita ($) 40305 27367 14238 6604 93377 93377 23513 5148 23 3086

% Non-white 36.4 13.0 15.3 16.2 86.7 95.3 31.7 0.0 23 3141

% Commute by public transportation* 7.1 1.6 8.1 2.5 30.7 30.7 1.5 0.0 12 366

Health Conditions

Prevalence of CHD (%) * 3.6 4.3 0.8 1.3 4.6 8.7 2.6 1.8 11 184

Prevalence of asthma (%) * 8.5 8.1 1.3 1.9 11.2 13.2 6.0 3.6 11 184

Prevalence of diabetes (%) * 8.1 8.5 1.2 2.1 10.6 16.5 5.4 2.2 11 184

Prevalence of AMI (%) * 3.6 4.1 0.6 1.3 4.8 10.2 2.8 1.7 11 184

Prevalence of obesity (%) * 24.7 26.0 4.0 4.1 32.7 35.7 18.7 14.0 11 182

Prevalence of stroke (%) * 2.6 2.7 0.7 1.0 3.7 6.5 1.5 0.7 11 184

Prevalence of ever smoked (%)* 18.3 19.6 3.1 4.0 23.1 34.4 14.2 6.5 11 184

Prevalence of exercise (20 minutes, 

%)* 29.5 28.0 2.7 4.8 33.8 44.1 23.7 15.4 11 183

Prevalence of exercise (30 

minutes,%)* 50.2 49.7 2.3 5.4 55.3 67.1 47.4 37.3 11 182

Non-accidental mortality (deaths per 

100,000 people) 756.2 950.6 204.1 249.6 1139.5 1958.4 361.6 117.7 23 3142
8-30

Average Standard Deviation Maximum Minimum

Sample Size 

(# of counties or 

cities)

Risk Attribute

Urban 

Study 

Areas

U.S.

Dataset

Urban 

Study 

Areas

U.S. 

Dataset

Urban 

Study 

Areas

U.S. 

Dataset

Urban 

Study 

Areas

U.S. 

Dataset

Urban 

Study 

Areas 

U.S. 

Dataset

All cause mortality (deaths per 

100,000 people) 810.1 1022.3 217.4 258.6 1257.8 2064.2 402.5 176.8 23 3142

Cardiovascular mortality (deaths per 

100,000 people) 310.5 392.1 93.9 121.0 459.6 970.4 122.4 37.5 23 3142

Respiratory mortality (deaths per 

100,000 people) 66.2 97.3 17.0 32.3 90.1 351.0 34.8 13.3 23 3136

Air Quality and Climate

O3 4th high maximum 8-hr average

(ppm) 0.087 0.077 0.009 0.010 0.105 0.126 0.072 0.033 23 725

O3 seasonal mean (ppb) 33.9 34.5 5.4 6.6 51.0 64.8 25.8 8.6 22 671

O3 seasonal mean of maximum 8-hr

average (ppb) 50.7 48.6 7.5 7.2 70.2 79.7 40.8 13.3 22 671

O3 seasonal mean of 1-hr daily 

maximum (ppb) 58.8 54.7 7.5 8.0 85.1 92.4 46.5 17.6 22 671

PM2.5 annual mean (μg/m3) 14.1 11.7 2.6 3.1 16.9 22.5 8.4 3.4 23 617

PM2.5 98th %ile daily average (μg/m3) 35.8 30.7 8.1 9.3 59.0 81.1 21.2 9.1 23 617

Average temperature (°F) 57.2 57.2 5.0 7.9 70.3 76.2 50.1 39.0 23 202

July temperature long term average

(°F) 76.0 75.9 3.4 5.4 83.3 93.7 68.5 55.5 23 3104

July Relative Humidity long term 

average (%) 61.5 56.2 10.2 14.6 70.0 80.0 28.0 14.0 23 3104

Exposure Determinants

% No air conditioning* 15.5 16.6 85.7 79.1 42.9 86.7 0.4 0.0 12 76

C-R Estimates

Non-accidental mortality O3 risk* 0.000388 0.000322 0.000217 0.000131 0.000917 0.000917 0.000148 -0.000033 12 98

All Cause mortality O3 risk* 0.000627 0.000527 0.000314 0.000205 0.001092 0.001092 0.000163 0.000096 12 48

Respiratory mortality O3 risk* 0.000877 0.000800 0.000282 0.000186 0.001424 0.001424 0.000307 0.000307 12 48

Cardiovascular mortality O3 risk* 0.000898 0.000825 0.000173 0.000124 0.001064 0.001064 0.000418 0.000418 12 48
8-31

*Attribute for which only city-specific data were available.
8-32

1

2

3

4 Figure 8.12 Comparison of county-level populations of urban case study area counties to 

5 the frequency distribution of population in 3,143 U.S. counties.

0%

10%

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40%

50%

60%

70%

80%

90%

100%

100 1000 10000 100000 1000000 10000000

% of U.S. Counties

Population, 2008

All Counties CDF Case Study Counties

Urban case study areas 

are all above the 90th 

percentile of county 

populations
8-33

1

2 Figure 8.13 Comparison of county-level seasonal mean 8-hr daily maximum O3 

3 concentrations in urban case study area counties to the frequency distribution 

4 of seasonal mean 8-hr daily maximum O3 concentrations in 671 U.S. counties 

5 with O3 monitors.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

30 40 50 60 70

% of Monitored Counties

Seasonal Mean 8-hr Daily Max Ozone Concentration, Average 2006-2008 

(ppb)

All Counties CDF Case Study Counties
8-34

1

Figure 8.14 Comparison of 2007 county-level 4th 2 high 8-hr daily maximum O3 

3 concentrations in urban case study area counties to the frequency 

distribution of 2007 4th 4 high 8-hr daily maximum O3 concentrations in 725 

5 U.S. counties with O3 monitors.

6

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

40 50 60 70 80 90 100 110

% of Monitored Counties

4th High 8-hr Daily Maximum Ozone, 2007 (ppb)

All Counties CDF Case Study Counties
8-35

1

2 Figure 8.15 Comparison of county-level all-cause mortality in urban case study area 

3 counties to the frequency distribution of all-cause mortality in 3,137 U.S. 

4 counties.

5

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

500 600 700 800 900 1000 1100 1200 1300 1400 1500

% of U.S. Counties

All Cause Mortality per 100,000 Population, 1999-2005

All Counties CDF Case Study Counties

Urban case 

study areas are 

all below the 

85th percentile 

of county all 

cause mortality
8-36

1

2 Figure 8.16 Comparison of county-level non-accidental mortality in urban case study area 

3 counties to the frequency distribution of non-accidental mortality in 3,135 

4 U.S. counties.

5

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

300 500 700 900 1100 1300 1500

% of U.S. Counties

Non Accidental Mortality per 100,000 Population, 1999-2005

All Counties CDF Case Study Counties

Urban case study 

counties are all 

below the 80th 

percentile of non 

accidental 

mortality
8-37

1

2 Figure 8.17 Comparison of city-level all-cause mortality risk coefficients from Zanobetti 

3 and Schwartz (2008) in urban case study areas to the frequency distribution 

4 of all-cause mortality risk coefficients from Zanobetti and Schwartz (2008) in 

5 48 U.S. cities. 

6

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0.0002 0.0004 0.0006 0.0008 0.001 0.0012

% of Z&S Cities

All Cause Mortality Risk Coefficient (β)

All cities All Cities CDF Case Study Cities
8-38

1

2 Figure 8.18 Comparison of city-level national prior Bayes-shrunken non-accidental 

3 mortality risk coefficients from Smith et al. (2009) in urban case study areas 

4 to the frequency distribution of national prior Bayes-shrunken non-accidental 

5 mortality risk coefficients from Smith et al. (2009) in 98 U.S. cities. 

6

7 Figure 8.19 through Figure 8.24 show national CDFs and the urban study area values for 

8 several selected potential risk attributes. These potential risk attributes do not directly enter the 

9 risk equations, but have been identified in the literature as potentially affecting the magnitude of 

10 the O3 C-R functions reported in the epidemiological literature. Comparison graphs for other risk 

11 attributes are provided in Appendix 4-A. The selected urban study areas do not capture the 

12 higher end percentiles of several risk characteristics, including populations 65 years and older, 

13 baseline cardiovascular disease prevalence, baseline respiratory disease prevalence, and smoking 

14 prevalence. Summarizing the analyses of the other risk attributes, we conclude that the urban 

15 study areas provide adequate coverage across population, population density, O3 levels (seasonal 

16 mean, seasonal mean 8-hr daily maximum, and seasonal mean 1-hr daily maximum), PM2.5 co17 pollutant levels, temperature and relative humidity, unemployment rates, percent non-white 

18 population, asthma prevalence obesity prevalence, income, and less than high school education. 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 0.0002 0.0004 0.0006 0.0008 0.001

% of NMMAPS Cities

Non Accidental Mortality Risk Coefficient (β)

All Cities CDF Case Study Cities
8-39

1 We also conclude that while the urban study areas cover a wide portion of the distributions, they 

2 do not provide coverage for the upper end of the distributions of percent of population 65 and

older (below 60th percentile), percent of population 85 years and older (below 75th 3 percentile), 

4 prevalence of angina/coronary heart disease (below 70th percentile), prevalence of diabetes 

(below 85th percentile), stroke prevalence (below 90th 5 percentile), prevalence of heart attack 

6 (below 80th percentile), prevalence of smoking (below 85th percentile), all-cause mortality rates 

(below 85th percentile), non-accidental mortality rates (below 80th 7 percentile), cardiovascular 

mortality rates (below 75th percentile) and respiratory mortality rates (below 50th 8 percentile), and 

percent of residences without air conditioning (below 90th 9 percentile). In addition, the urban 

10 study areas do not capture the highest or lowest ends of the distribution of exercise prevalence

and do not capture the low end of the distribution of public transportation use (above the 65th 11

12 percentile).

13

14 Figure 8.19 Comparison of county-level percent of population 0 to 14 years old in urban 

15 case study area counties to the frequency distribution of percent of population 

16 0 to 14 years old in 3,141 U.S. counties. 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

14 16 18 20 22 24 26 28 30

% of U.S. Counties

% Younger than 15 Years Old, 2005

All Counties CDF Case Study Counties
8-40

1

2 Figure 8.20 Comparison of county-level percent of population age 65 years old and older 

3 in urban case study area counties to the frequency distribution of percent of 

4 population age 65 and older in 3,141 U.S. counties. 

5

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

5 7 9 11 13 15 17 19 21 23 25 27

% of U.S. Counties

% 65 Years and Older, 2005

All Counties CDF Case Study Counties

Urban case study

counties are all 

below the 60th 

percentile of county 

% of population 65 

years and older
8-41

1

2 Figure 8.21 Comparison of county-level income per capita in urban case study areas to 

3 the frequency distribution of income per capita in 3,141 U.S. counties.

4

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

10000 20000 30000 40000 50000 60000 70000 80000 90000 100000

% of U.S. Counties

Income per capita, 2005 ($)

All Counties CDF Case Study Counties
8-42

1

2 Figure 8.22 Comparison of county-level July temperature in urban case study area 

3 counties to the frequency distribution of July temperature in all U.S. counties.

4

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

66 68 70 72 74 76 78 80 82 84 86

% of U.S. Counties

Mean Temperature for July, 1941-1970 (F)

All Counties CDF Case Study Counties
8-43

1

2 Figure 8.23 Comparison of city-level asthma prevalence in urban case study areas to the 

3 frequency distribution of asthma prevalence in 184 U.S. cities.

4

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

4 6 8 10 12 14

% of Cities

Asthma Prevalence , 2007 (%)

Comparison of Urban Case Study Area with U.S. Distribution (184 BFRSS 

Cities) - Asthma Prevalence

All Cities CDF Case Study Cities
8-44

1

2 Figure 8.24 Comparison of city-level air conditioning prevalence in urban case study 

3 areas to the frequency distribution of air conditioning prevalence in 76 U.S. 

4 cities.

5

6 Based on the above analyses, we can draw several inferences regarding the 

7 representativeness of the urban case studies. First, the case studies represent urban areas that are 

8 among the most populated in the U.S. Second, they represent areas with relatively high levels of 

O3 (4th 9 high 8-hr daily maximum, seasonal mean 8-hr daily maximum, seasonal mean 1-hr daily 

10 maximum, and seasonal mean). Third, they capture well the range of city-specific effect 

11 estimates found by Smith et al. (2009) and Zanobetti and Schwartz (2008) studies. These three 

12 factors would suggest that the urban study areas should capture well overall risk for the nation, 

13 with a potential for better characterization of the high end of the risk distribution.

14 However, there are several other factors that suggest that the urban study areas may not 

15 be representing areas that may have a high risk per ppb of O3. Several of the factors with 

16 underrepresented tails, including age and baseline mortality are spatially correlated (R=0.81), so 

17 that certain counties which have high proportions of older adults also have high baseline 

18 mortality and high prevalence of underlying chronic health conditions. Because of this, omission 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 10 20 30 40 50 60 70 80 90 100

% of Cities

No air conditioning, 2004 (%)

All Cities CDF Case Study Cities

Urban study areas 

are all below the 

90th percentile of 

percent of 

residences with no 

air conditioning
8-45

1 of certain urban areas with higher percentages of older populations, for example, cities in 

2 Florida, may lead to underrepresentation of high risk populations. However, with the exception 

3 of areas in Florida, most locations with high percentages of older populations have low overall 

4 populations, less than 50,000 people in a county. And even in Florida, the counties with the 

5 highest O3 levels do not have a high percent of older populations. This suggests that while the 

6 risk per exposed person per ppb of O3 may be higher in these locations, the overall risk to the 

7 population is likely to be within the range of risks represented by the urban case study locations.

8 Due to data limitations, we were only able to assess the representativeness of the urban 

9 study areas in terms of one exposure-related attribute, air conditioning prevalence. Assessing the 

10 representativeness of the urban study areas in terms of air conditioning prevalence, we found that 

11 the urban study areas do not capture the highest end of percent of residences without air 

12 conditioning. If the cities with the lowest air conditioning prevalence also have high O3 levels, 

13 we could be missing a high risk portion of the population that is exposed to O3 indoors as air 

infiltrates indoors from outdoors. However, 4th 14 highest 8-hr daily maximum O3 levels in the 

cities in the top 10th 15 percentile of percentage of residences without air conditioning (mainly in 

16 northern California and Washington) are approximately average (0.08 ppm) or lower than 

17 average. Since these concentrations are not the highest found across the U.S., we are likely not 

18 excluding a high risk population that has both low air conditioning prevalence and high O3

19 concentrations, and the overall risk to the population is likely to be within the range of risks 

20 represented by the urban case study locations.

21

22 8.2.2 Analysis Based on Consideration of National Distribution of O3-Related Mortality 

23 Risk

24 In this section we discuss the second representativeness analysis which identifies where 

25 the 12 urban study areas examined in Chapter 7 fall along the distribution of estimated national26 scale mortality risk. This assessment reveals whether the baseline O3 mortality risks in the 12 

27 urban case study areas represent more typical or higher end risk relative to the national risk 

28 distribution presented in Section 8.1. For consistency, we compare the national O3 mortality risk 

29 distribution to the O3 mortality risk results for the urban study areas that were generated from the 

30 national-scale assessment in Section 8.1, rather than the results from the urban study area 

31 analysis in Chapter 7 which uses different methods. To be consistent with the national-scale 

32 assessment, we define the urban study areas here as they were defined in the epidemiology 

33 studies, rather than including full core-based statistical areas as in Chapter 7. The results of this 

34 representativeness analysis are presented graphically in Figure 8.25 through Figure 8.27, which 

35 display the cumulative distribution of total mortality attributable to ambient O3 at the county 

36 level developed as part of the national-scale analysis. Values for the 23 counties included in the 
8-46

1 urban case study areas as defined in the epidemiology studies are then superimposed on top of 

2 the cumulative distribution to assess the representativeness of the urban case study areas.

3 For the results based on Smith et al. (2009) effect estimates, New York City and 

4 Philadelphia have the highest percentage of May-September non-accidental mortality attributable 

5 to ambient O3 of the 12 urban study areas and are located at the highest end of the distribution of 

6 U.S. O3-related mortality risk (Figure 8.25). Of the 12 urban study areas, these two cities had the 

7 highest effect estimates found by Smith et al. (2009; See Appendix 4-A). Boston and Los 

8 Angeles had the lowest O3-related mortality risk of the 12 urban study areas and are located at 

9 the lowest end of the U.S. distribution. Overall, O3 mortality risk in the 12 urban study areas are 

10 representative of the full distribution of U.S. O3-related mortality risk, with the mean percentage 

11 of May-September non-accidental mortality for all ages of 1.5% (95% confidence interval, 1.1-

12 1.8%).

13 For the results based on Zanobetti and Schwartz (2008) effect estimates, Detroit and New 

14 York City are at the very highest end of the U.S. distribution of county-level risk of June-August 

15 all-cause mortality due to ambient O3 (Figure 8.26). These two cities had the highest effect 

16 estimates of the 48 cities included in the study (see Appendix 4-A). The high effect estimates in 

17 Detroit and New York City could be due to high rates of public transportation use (for New York 

18 City), low air conditioning prevalence, high smoking prevalence (in Detroit), high incidence of 

19 mortality and other adverse health outcomes (e.g. diabetes, stroke, acute myocardial infarction, 

20 etc.), and high unemployment rates. Houston and Los Angeles had the lowest risk and were 

21 located at the very lowest end of the U.S. distribution of county-level risk of mortality due to

22 ambient O3. These two cities had the lowest effect estimates found by Zanobetti and Schwartz 

23 (2008), possibly because they cover a large spatial extent and have high rates of time spent 

24 driving, which could lead to exposure misclassification in the underlying epidemiologic study. 

25 Houston also has a very high rate of air conditioning use (nearly 100% of residences) and Los 

26 Angeles has been shown to have high rates of adaptive behavior on high ambient O3 days (i.e. 

27 more time spent indoors as a result of high ambient O3 concentrations; Neidell 2009, 2010), both 

28 of which would lead to lower personal O3 exposure relative to other cities. Overall, O3 mortality 

29 risk in the 12 urban study areas are representative of the full distribution of U.S. O3-related 

30 mortality risk, with the mean percentage of June-August all-cause mortality for all ages of 2.5% 

31 (95% confidence interval, 1.7-3.0%).

32 For the results based on Jerrett et al. (2009) effect estimates, the 12 urban study areas are 

33 centered more in the middle of the distribution of U.S. county-level risk of adult (ages 30 and 

34 older) respiratory mortality due to ambient O3 exposure. These results are based on the 

35 application of a single national average effect estimate to all gridcells across the U.S., rather than 

36 city-specific effect estimates as were applied for the results based on Smith et al. (2009) and 
8-47

1 Zanobetti and Schwartz (2008) effect estimates. Therefore, the location of the urban study areas 

2 on the distribution of county-level risk is driven mainly by O3 concentration and not by the effect 

3 estimate. While Denver, Atlanta, Sacramento, and Los Angeles are at the highest end of the U.S. 

4 distribution, Figure 8.27 shows that some counties have a higher percentage of mortality 

5 attributable to O3 than these four cities. Overall, O3 mortality risk in the 12 urban study areas are 

6 representative of the full distribution of U.S. O3-related mortality risk, with the mean percentage 

7 of April-September respiratory mortality for adults ages 30 and older of 18.7% (95% confidence 

8 interval, 15.2-21.5%). However, we are not capturing the very highest end of O3-related risk 

9 based on Jerrett et al. (2009) effect estimates in the 12 urban study areas.

10

11

12 Figure 8.25 Cumulative distribution of county-level percentage of May-September non13 accidental mortality for all ages attributable to 2006-2008 average O3 for the 

14 U.S. and the locations of the selected urban study areas along the 

15 distribution, using Smith et al. (2009) effect estimates.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0.5 1 1.5 2 2.5 3 3.5 4

Cumulative percentage of counties

Percentage of baseline mortality attributable to ozone

Results based on Smith et al. (2009) effect estimates

Selected urban study area
8-48

1

2 Figure 8.26 Cumulative distribution of county-level percentage of June-August all-cause 

3 mortality for all ages attributable to 2006-2008 average O3 for the U.S. and 

4 the locations of the selected urban study areas along the distribution, using 

5 Zanobetti and Schwartz (2008) effect estimates.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

Cumulative percentage of counties

Percentage of baseline mortality attributable to ozone

Results based on Zanobetti and Schwartz (2008) effect estimates

Selected urban study areas
8-49

1

2 Figure 8.27 Cumulative distribution of county-level percentage of April-September adult 

3 (age 30+) respiratory mortality attributable to 2006-2008 average O3 for the 

4 U.S. and the locations of the selected urban study areas along the 

5 distribution, using Jerrett et al. (2009) effect estimates and city definitions 

6 from Zanobetti and Schwartz (2008).

7

8 8.2.3 Analysis Based on Consideration of National Responsiveness of O3 Concentrations 

9 to Emissions Changes

10 Estimates of O3 response to precursor emissions reductions (NOx and VOC) are 

11 important inputs to estimation of risk for scenarios of just meeting existing and alternative O3

12 standards. To evaluate the national representativeness of O3 responses to decreases in precursor 

13 emissions in the 15 urban study areas, we examine two different types of air quality data. In 

14 section 8.2.3.1 we examine ambient O3 trends that have been measured at monitor locations 

15 across the country over a recent period of decreasing NOx emissions. This analysis provides 

16 real-world observations but does not isolate the effects of emissions changes alone and can only 

17 characterize past phenomena. In section 8.2.3.2, we look at air quality model predictions of 

18 temporal and spatial patterns of O3 changes in response to further NOx reductions from 2007 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

12 14 16 18 20 22 24 26 28

Cumulative percentage of counties

Percentage of baseline mortality attributable to ozone

Results based on Jerrett et al. (2009) effect estimate

Selected urban study areas
8-50

1 levels. This analysis is subject to typical model limitations but has the advantage of isolating the 

2 effects of precursor emissions changes and has the ability to simulate how O3 would change in 

3 response to NOx (and VOC) emissions reductions (relative to recent 2007 levels) similar to those 

4 used in the HDDM adjustment scenarios for just meeting existing and alternative standards. 

5 These two complimentary analyses give qualitatively similar results, building confidence that the 

6 overarching conclusions are robust across the US as a whole.

7 This section describes how annual distributions of O3 measurements collected by EPA's 

8 national monitoring network have changed between 1998 and 2011. These years were chosen 

9 because large reductions in anthropogenic NOx emissions have occurred over this time period 

10 especially in the Eastern half of the United States. From 2000 to 2011 nationwide NOx emissions 

were cut almost in half (from 22.6 to 12.9 million tons per year )

3 11 . However, it should be noted

12 that these reductions did not occur uniformly across the country. Improvements in vehicle 

13 emissions standards helped reduce NOx emissions in many locations throughout the country. In 

14 contrast, EPA rules like the NOx SIP call were focused on controlling emissions from power 

15 plants in the Eastern US and consequently there have been relatively larger reductions in NOx 

16 emissions in the East. In addition, some urban areas which have traditionally had high O3 levels, 

17 like Los Angeles and Houston, have substantially cut local NOx and VOC emissions to improve 

18 their air quality. Also, there may be some localized areas in which NOx emissions have 

19 increased due to population growth, new sources such as oil and gas development, or increased 

20 wildfire activity. Appendix 8-C provides plots of emissions trends by region of the U.S. These 

21 plots show that each of nine regions of the U.S. have experienced decreasing NOx emissions 

22 ranging from approximately a 20% decrease to a 45% decrease from 2002 to 2011 depending on 

23 the region. Conversely, VOC emissions have increased in some regions since 2002 (the South, 

24 the Southwest, and the West-North-Central) and decreased in others. Due to non-linear O3

25 formation chemistry and the potential for changes in local chemical regimes resulting from these 

26 emissions reductions, past trends may not reflect the ambient changes which will occur from 

27 future emissions reductions. Nonetheless, these ambient data provide information on actual O3

28 changes in response to emissions reductions and can give insight into the types of changes in O3

29 that have occurred both within and outside the urban study areas.

First, we look at national maps which show changes in 50th percentile and 95th 30 percentile 

summer season (April-October)4 31 8-hour daily maximum O3 values (Figure 8.28,Figure 8.29).

 3 Data were accessed from EPA’s emission trend website on August 15, 2013: 

http://www.epa.gov/ttn/chief/trends/trends06/national_tier1_caps.xlsx

4 The April-October time period corresponds to the required monitoring season for most of the 12 urban areas. 

Therefore, in selecting a consistent time period that could be analyzed for the urban case study areas, we chose to 

8.2.3.1 Ambient patterns in trends of measured O3 concentrations
8-51

1 These maps reflect the absolute (ppb) difference between O3 percentiles from two three-year 

periods (2001-2003 and 2008-2010)5 2 . Figure 8.28 shows that increases in median O3

3 concentrations occurred in many large urban areas including both study area locations in Chapter 

76 and non-study area locations7 4 . Only a few monitors with increasing median O3 appear outside 

5 of cities, most notably in southwestern Colorado and central Kansas. The increases in urban 

6 areas are likely explained by O3 “disbenefits” to NOx reductions which were described in 

7 Chapter 4, Appendix 4-C and in the following section of this chapter. Widespread decreases of 

8 median O3 in suburban and rural locations suggest the efficacy of large NOx emissions 

9 reductions on reducing O3 over large regions of the country. Finally, the less frequently observed 

10 cases of median O3 increases in rural areas are likely caused by different phenomena. Cooper et 

11 al. (2012) suggested that increasing rural O3 in the Western US may be due to increasing oil and 

gas development, wildfires and O3 transport from Asia. Conversely, Figure 8.29 shows that 95th 12

13 percentile O3 values for these two sets of years decrease in almost all urban as well as rural areas 

of the country. Only a few sites in Colorado, Nevada, and California show any increases in 95th 14

15 percentile O3 between 2001-2003 and 2008-2010. The consistent decreases across most of the 

16 United States indicate that the large NOx reductions from power plants and mobile sources have 

17 been quite successful in reducing O3 on the highest O3 days. These results suggest that many of 

18 the urban case study areas may show O3 responses that are typical of other large urban areas in 

19 the U.S. However, decreasing O3 in large non-urban portions of the country may not be fully 

20 captured in the urban case studies.

 

use the April-October time period in Chapter 4 for composite monitor distributions to summarize ozone values 

relevant to the epidemiology-based risk assessment. 

5 These two three-year periods were chosen to represent years before and after most NOx emission reductions were 

in place. In addition the 2001-2003 period was used to designate areas for the 1997 8-hour ozone standard and 

the 2008-2010 period was used to designate areas for the 2008 8-hour ozone standard. Data from these two time 

periods have undergone extensive quality checks.

6 Los Angeles, Denver, Houston, Atlanta, Chicago, Detroit, Cleveland, New York, Philadelphia, and Washington 

D.C.

7 San Francisco, Reno, Phoenix, New Orleans, Birmingham, Miami, and Cincinnati 
8-52

1

Figure 8.28 Change in 50th 2 percentile summer season (April-October) daily 8-hr maximum 

3 O3 concentrations between 2001-2003 and 2008-2010.
8-53

1

Figure 8.29 Change in 95th 2 percentile summer season (April-October) daily 8-hr 

3 maximum O3 concentrations between 2001-2003 and 2008-2010.

4 To examine these trends further, we evaluate the 1998-2011 data from the 15 case-study 

areas. Only monitors within the 15 study areas8 5 were analyzed, and within each study area, 

6 monitors were put into three groups based on the degree of urbanization. The degrees of 

7 urbanization were determined by the population density of the census tract containing the 

monitor (plotted in Figure 8.30). Population data were obtained from the U.S. Census Bureau9 8 , 

9 and the classes were determined by breaks in the population density calculated from those data: 

“high population density" (> 1000 people/km2 10 ), “medium population density" (between 400 and 

1000 people/km2

), and “low population density" (< 400 people/km2 11 ). Data were additionally split 

12 out into three different time periods (all months, warm months: May through September, and 

13 cool months: October through April). These warm and cool season categorizations were chosen 

14 to isolate effects that are observed at different times of year. The April-October time period

 8 These 15 areas are the 12 urban case study areas in the epidemiological-based risk assessment and the 3 additional 

exposure urban case study areas.

9 Obtained from: http://www2.census.gov/geo/tiger/TIGER2010DP1/Tract_2010Census_DP1.zip
8-54

1 which was examined in Figure 8.28 and Figure 8.29 include all warm season and two cold 

2 season months and thus show behavior that has influences from both. Summaries were thus 

3 calculated for groups of monitors specific to 1) Study Area, 2) Month subset, and 3) Urban class.

4

5

6 Figure 8.30 Population density at each O3 monitor.

7

8 Figure 8.31, Figure 8.32, and Figure 8.33 display the data described above in ribbon plots 

9 for high, medium, and low population density monitor locations in each case study area. The 

lines bordering the dark and light red ribbons in this plot are (from top to bottom) the 95th, 75th 10 , 

25th, and 5th percentiles of the annual data indicated by each panel, and the median (i.e. 50th 11

12 percentile) is shown by the line in the middle of the central lighter ribbon. The colors of the lines 

13 separating the ribbons depict significant trends (dark blue for decreasing and light blue for 

14 increasing) or no significant trend (white). Statistical significance for multi-year O3 trends was 

15 determined using the Spearman rank order correlation coefficient (p-value < 0.05). Plots showing 

a characterization of the entire O3 distribution (not just discrete cut points of 5th, 25th, 50th, 75th 16 , 

and 95th 17 percentiles) are provided in Appendix 8-C.

18 These plots show consistent trends over the past 13 years for O3, with high O3 values 

19 decreasing fairly uniformly across different regions and areas of different degrees of 

20 urbanization. Conversely, mean and median trends appear quite different in high, medium, and 
8-55

1 low population density areas. Mid-range O3 concentrations at low population density locations 

2 within the case study areas (so still relatively close to a major city) have generally decreased over 

a period of substantial NOx emissions reductions10 3 . This decrease is most pronounced in the 

4 summer months and in the Eastern half of the U.S. (low population density monitors in 3 out of 

the 5 Western11 case-study areas and in only 4 out of the 10 Eastern12 5 case study areas do not 

6 have significant decreases in summertime median O3 concentrations). Mid-range O3

7 concentrations in many, but not all, high population density areas have significantly increased in 

8 winter months. Wintertime increases were significant in 11 of the 15 areas (only Atlanta, Boston, 

9 Houston and Sacramento did not increase significantly). Thirteen out of 15 summertime high 

population density area trends in median O3 were not significant13 10 , but combining winter and 

11 summer measurements to determine annual trends showed that Denver, Los Angeles, New York 

12 and Philadelphia high population density sites had significantly increasing annual median O3

while Boston, Chicago, Dallas and St. Louis had significantly increasing 25th 13 percentile O3 but 

14 no significant median trend. These results reflect increasing mid-range O3 concentrations mainly 

15 confined to urban centers during periods of NOx reductions. One important point to note is that 

the design value monitor (the monitor with the highest average (over three years) of 4th 16 highest 

17 daily maximum value) in most of the case-study locations is located outside of the high 

18 population density area (as defined here). Downward trends in medium and low population 

19 density areas are therefore generally representative of the behavior at the highest O3 monitor in 

20 an area, whereas trends in urban centers may be important from an exposure perspective.

21 In summary, any increasing O3 trends occur more in highly populated areas, during cool 

22 months, and at the lower end of the O3 distribution. Conversely, any decreasing O3 trends occur 

23 more during warm months, in lower population areas, and at the upper end of the O3 distribution. 

24 One result of these two phenomena is a narrowing of the range of O3 concentrations over this 

25 period of decreasing NOx emissions. For instance, there are many cases where the top and 

26 bottom of a single distribution exhibit different trends. For example, the low population density 

27 monitors of Dallas, Los Angeles, Philadelphia and Saint Louis and the high population density 

28 monitors for Baltimore, Dallas, and Philadelphia for all months show a significant increase in the 

5th percentile and a simultaneous significant decrease in the 95th 29 percentile. More common is a 

 10 Denver is an outlier among the case study areas with consistently increasing mid-range ozone trends across 

seasons and urban classifications. Denver may be subject to increasing emissions from large wildfires and oil 

and gas development which are not typical of other urban case study areas. In addition, Denver is particularly 

susceptible to influences from stratospheric intrusions and international transport due to its high altitude 

11 Western case study areas for this purpose include: Dallas, Denver, Houston, Los Angeles, and Sacramento 

12 Eastern case study areas for this purpose include: Atlanta, Baltimore, Boston, Chicago, Cleveland, Detroit, New 

York, Philadelphia, St. Louis, and Washington D.C.

13 Only Houston and Dallas had statistically significant trends in median summertime urban ozone 
8-56

1 significant change in one end of the distribution, but no significant change in the other (e.g., the 

2 summer months at high population density monitors in all case study areas except Baltimore, 

3 Chicago, and Detroit). It is important to note that there are also cases where both ends of the 

4 distribution change in the same manner and there is therefore no narrowing of the range of O3

5 concentrations in these areas.

6

7

8 Figure 8.31 Distributions of O3 concentrations for high population density monitors by 

9 different subsets of months over a 13-year period. From top to bottom in each 

ribbon plot, the blue and white lines indicate the spatial mean of the 95th, 75th 10 , 

50th, 25th, and 5th 11 percentiles for each monitor for every year from 1998-2011.
8-57

1

2 Figure 8.32 Distributions of O3 concentrations for medium population density monitors 

3 by different subsets of months over a 13-year period. From top to bottom in 

each ribbon plot, the blue and white lines indicate the spatial mean of the 95th 4 , 

75th, 50th, 25th, and 5th 5 percentiles for each monitor for every year from 1998-

6 2011.

7
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1

2 Figure 8.33 Distributions of O3 concentrations for low population density monitors by 

3 different subsets of months over a 13-year period. From top to bottom in each 

ribbon plot, the blue and white lines indicate the spatial mean of the 95th, 75th 4 , 

50th, 25th, and 5th 5 percentiles for each monitor for every year from 1998-2011. 

6

7 Maps of ambient trends in both New York City and Chicago most clearly show these 

8 trends and further illustrate this behavior. Figures 8.34 and 8.35 show trends in daily maximum 

9 8-hour O3 values these two cities for May-September. Plots for other case-study areas are 

10 provided in Appendix 8-C. For both cities, the fourth highest 8-hr daily maximum O3 value 

11 either has a downward trend or no trend at all monitors. In New York (Figure 8.34), mean and 

12 median O3 values significantly decrease at downwind locations in New York and Connecticut. 

13 Conversely, median O3 values significantly increase from 1998 to 2011 at two core urban sites 

14 (one at City College of NY in upper Manhattan and one near Queen’s college) and at a nearby 

15 site on Long Island. Similarly, in Chicago (Figure 8.35), mean and median trends in O3 are 

16 downward or insignificant in Indiana and in suburban Illinois locations and show increases near 

17 the highly populated urban core.

18
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Figure 8.34 Map of O3 trends at specific monitors in the New York area. All upward and downward facing triangles 

represent statistically significant trends from 1998-2011 (p < 0.05), circles represent locations with no 

significant trends. Sites used in Smith et al (2009) and the Zanobetti and Schwartz (2008) epidemiology 

studies are represented by colored dots. Only monitors with at least seven years of data are displayed. The 

pink star indicates the site with the higher design value in 2011. The MSA border as defined by the U.S. 

census bureau is delineated by the light blue line. Left panel shows trends in annual 4th highest 8-hr daily 

maximum O3 values, center panel shows trends in annual mean 8-hr daily maximum O3 values, and right 

panel shows trends in annual median 8-hr daily maximum O3 values.
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Figure 8.35 Map of O3 trends at specific monitors in the Chicago area. All upward and downward facing triangles 

represent statistically significant trends from 1998-2011 (p < 0.05), circles represent locations with no 

significant trends. Sites used in Smith et al (2009) and the Zanobetti and Schwartz (2008) epidemiology 

studies are represented by colored dots. Only monitors with at least seven years of data are displayed. The 

pink star indicates the site with the higher design value in 2011. The MSA border as defined by the U.S. 

census bureau is delineated by the light blue line. Left panel shows trends in annual 4th highest 8-hr daily 

maximum O3 values, center panel shows trends in annual mean 8-hr daily maximum O3 values, and right 

panel shows trends in annual median 8-hr daily maximum O3 values.
8-61

1 To demonstrate how changes in emissions of NOx and anthropogenic VOCs might be 

2 driving these trends, Table 8-7 shows trends of O3 in high and low population areas and annual 

National Emissions Inventories (NEI) for 2002, 2005, 2008 and 201114 3 aggregated to the level of 

the NOAA Climate Regions15 4 . There is moderate correspondence between the decreases in NOx 

5 emissions across the regions with the observed decreases in warm season O3 concentrations in 

6 low population areas. VOCs show little correspondence to any of the O3 trends, which is likely 

7 due to complications from 1) the mix of chemicals with a large range of reactivities; 2) complex 

8 non-linear chemistry; and 3) the potential impact of the much larger magnitude of biogenic vs. 

9 anthropogenic emissions on a regional scale. Details of these calculations can be found in 

10 Appendix 8-C.

11

12 Table - 8.7 Broad Regional Annual Trends of Concurrent O3 Concentrations and 

13 Emissions of NOx and VOCs over the 2000-2011 Time Period

14

15

16 This section presents an analysis of the CMAQ modeling of O3 responses to “across-the17 board” U.S. anthropogenic precursor emissions reductions described in Appendix 4b. In this 

18 analysis, we compare the modeled responses of O3 concentrations in the case study areas to the 

19 modeled O3 responses in the rest of the U.S. For this purpose, we used five CMAQ model 

20 simulations: 1) a base simulation which included 2007 emissions of all O3 precursors, 2) a 50% 

21 NOx cut simulation in which U.S. anthropogenic NOx emissions were reduced by 50% from 

22 2007 levels, 3) a 90% NOx cut simulation in which U.S. anthropogenic NOx emission were 

23 reduced by 90% from 2007 levels, 4) a 50% NOx/VOC cut simulation in which both U.S. 

24 anthropogenic NOx and VOC emission were reduced by 50% from 2007 levels, and 5) a 90% 

25 NOx/VOC cut simulation. These simulations are analyzed to characterize responses in O3 to 

 14 http://www.epa.gov/ttnchie1/trends/

15 http://www.ncdc.noaa.gov/monitoring-references/maps/us-climate-regions.php

8.2.3.2 Modeled O3 response to emissions reductions across the United States
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1 “across the board” emissions cuts at four distinct levels and do not represent the exact adjustment 

2 cases that were used to estimate O3 concentrations consistent with individual case study areas 

3 just meeting various potential levels of the NAAQS standard. However, these four cases 

4 generally span the range of emissions perturbations that were applied in the HDDM adjustment 

5 methodology described in Chapter 4 and in Appendix 4d.

6 In this analysis we focus on seasonal mean O3 and population as proxies for 

7 epidemiology based risk estimates in Chapter 7. Since the epidemiology studies used in Chapter 

8 7 show relatively linear response of health outcomes to O3 concentrations throughout the entire 

9 range of measured O3 values, examining seasonal mean values should provide some 

10 understanding of locations where O3 health effects are expected to increase and decrease as a 

11 result of precursor emission reductions. By combining population information with these spatial 

12 distributions of seasonal O3 responses, we can better understand expected O3 behavior in 

13 locations where people live. This is not a detailed risk assessment but can provide information on 

14 the representativeness of the case-study areas to the nation as a whole in terms of expected O3-

15 related health outcomes.

16 To begin, we examine maps displaying ratios of mean O3 concentrations in the emissions 

17 cut simulations to mean O3 concentrations in the 2007 base simulation. Figure 8.36 and Figure 

18 8.37 show the ratio of seasonal (April-October) mean O3 in the two NOx emissions reduction 

19 simulations to that in the base simulation for the entire model domain. Figure 8.38 and Figure 

20 8.39 depict the ratio of January mean O3 for the two NOx cut simulations. Figures showing the 

21 ratios based on the May-September seasonal average and figures for the NOx/VOC emissions 

22 reductions scenarios are provided in Appendix 8-C. The maps show widespread decreases (i.e., 

23 ratios less than 1) in seasonal mean O3 across the country. These decreases are especially 

24 pronounced in the Eastern U.S. and in California. O3 increases (i.e., ratios greater than 1) are 

25 confined to urban core areas except in January. The spatial extent of these O3 increases are 

26 generally less for the 90% NOx cut simulation than for the 50% NOx cut simulation although the 

27 magnitude is greater over very limited areas in Chicago, Seattle, and San Francisco. The O3

28 increases are most widespread in the cooler months (January, April, and October). For the April29 October seasonal average O3 concentrations, VOC in addition to NOx cuts did not substantially 

30 change the ratios of O3 in the emissions reduction scenarios to O3 in the base scenario. In the 

31 Northeast and Midwest, increases in seasonal mean O3 concentrations were mainly confined to 

32 urban case study areas of New York, Detroit, Chicago, and St. Louis. In the Southeastern U.S., 

33 the urban areas which show up as having increased seasonal mean O3 in the 50% NOx cut 

34 simulations include Miami, Orlando, Tampa, and New Orleans (only Miami has O3 increase in 

35 the 90% NOx cut simulation). The only case-study area in the southeast, Atlanta, does not 

36 experience increases in seasonal mean O3 in the model simulation (this is consistent with the 
8-63

1 changes in risk estimated for Atlanta, which show no increases in total risk as alternative 

2 standards are simulated). In the central U.S., seasonal mean O3 in the case study areas of Denver,

3 Houston, and Dallas and non-case-study areas of San Antonio, Duluth, and Minneapolis 

4 increased with 50% NOx reductions. Seasonal mean O3 increases were seen only in Houston, 

5 Minneapolis, and Duluth with 90% reductions in simulated NOx emissions. The Northwestern 

6 U.S. showed some of the most widespread increases in seasonal mean O3 in the 50% and 90% 

7 NOx cut simulations covering the Seattle and Portland metro areas as well the San Francisco Bay 

8 area and in a single model grid cell for Sacramento (50% NOx reduction case only). Sacramento 

9 is the only city in the Northwest that was included as a case study area. Finally, Los Angeles (a 

10 case study area), San Diego, Phoenix, and Bakersfield were the areas for which CMAQ predicted 

11 seasonal mean O3 increases with the 50% NOx cut simulation. These O3 increases disappeared 

12 (or were largely diminished in the case of LA) in the 90% NOx cut case. Based on these maps, it 

13 appears that in the Northeast and the Central U.S., the case-study area selection likely 

14 oversampled these O3 increases on a geographic basis since all locations outside of city centers 

15 experienced decreasing seasonal mean O3 with the NOx reduction model simulations. However 

16 in two regions, the Southeast and the Northwest, the urban case study area did not experience 

17 increases in seasonal mean O3 concentrations while other urban areas in the region did. In these 

18 two regions, the urban case-study selection likely under-sampled the locations which 

19 experienced increases in seasonal mean O3.
8-64

1

2 Figure 8-36 Ratio of April-October seasonal average O3 concentrations in the brute force 

3 50% NOx emissions reduction CMAQ simulations to April-October seasonal average O3

4 concentrations in the 2007 base CMAQ simulation.

5
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1

2

3 Figure 8.37 Ratio of April-October seasonal average O3 concentrations in the brute force 

4 90% NOx emissions reduction CMAQ simulations to April-October seasonal 

5 average O3 concentrations in the 2007 base CMAQ simulation.

6

7
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1

2 Figure 8.38 Ratio of January monthly average O3 concentrations in brute force 50% NOx

3 emissions reduction CMAQ simulations to January monthly average O3

4 concentrations in the 2007 base CMAQ simulation.
8-67

1

2 Figure 8.39 Ratio of January monthly average O3 concentrations in brute force 90% NOx

3 emissions reduction CMAQ simulations to January monthly average O3

4 concentrations in the 2007 base CMAQ simulation.

5

6 In order to characterize the representativeness of case study areas in a more quantitative 

manner, paired O3 concentrations and population data16 7 were extracted from each model grid cell 

8 and categorized in various manners. Figure 8.40 and Figure 8.41 depict the percent of U.S. 

9 population living in areas with increases or decreases in monthly or seasonal mean O3 under the 

10 emissions reductions scenarios (50% NOx cut and 90% NOx cut respectively) compared to O3 in 

11 the base modeling scenario. The top panels show data for January monthly mean O3, the center 

12 panels show data for seasonal mean O3 (June-August), and the bottom panels show data for 

13 seasonal mean O3 (April-October). Tabulated results and equivalent plots for the combined 

 16 Block level population data from the 2010 Census was aggregated to the 12km CMAQ grid cell level. The 2007 

population was then calculated using population growth factors developed by Woods and Poole Economics, Inc.
8-68

1 NOx/VOC cut simulations are provided in Appendix 8-C. Month by month break-outs for each 

2 case study area are also available in Appendix 8-C.

3 The vast majority of the U.S. population lives in areas where the CMAQ simulations 

4 predict mean O3 decreases for the June-August and April-October time periods. The majority of 

5 population living in case-study areas also lives in locations with decreasing seasonal mean O3

6 concentration under NOx reduction scenarios. As discussed previously, more locations have 

7 increasing mean O3 in the cooler months as demonstrated by the fact that almost all of the U.S. 

8 population lives in locations where the model predicts increases in mean O3 in January. The case 

9 study areas represent 29% of the total U.S. population. These areas account for 20-30% of the 

10 U.S. population that experience decreasing seasonal mean O3 for April-October in the NOx cut 

11 simulations and 50-60% of the U.S. population that experience increasing seasonal O3 for April12 October. Consequently, the urban-case study areas over-sample populations living in locations 

13 with increasing seasonal mean O3 in response to NOx cuts compared to populations living in 

14 locations with decreases in seasonal mean O3. In all panels displayed in Figures 8.28 and 8.29, 

15 most of the population lives in locations where increases or decreases in mean O3 were greater 

16 than 1 ppb.

17
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1

2 Figure 8.40 Histograms of U.S. population living in locations with increasing and 

3 decreasing mean O3. Values on the x-axis represent change in mean O3 (ppb) 

4 from the 2007 base CMAQ simulation to the 50% NOx cut CMAQ simulation. 

5 The percentages of the U.S. population living in areas that have changes less 

6 than -1 ppb, from -1 to +1 ppb, and greater than 1 ppb are shown on the y7 axis. Left plots show population numbers in locations not included in one of the 

8 cases study areas while right plots show population numbers in locations 

9 included in one of the case study areas. Top plots show changes in January 

10 monthly mean O3, middle plots show changes in seasonal mean June-August 

11 O3, and bottom plots show changes in seasonal mean April-October O3.
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1

2 Figure 8.41 Histograms of U.S. population living in locations with increasing and 

3 decreasing mean O3. Values on the x-axis represent change in mean O3 (ppb) 

4 from the 2007 base CMAQ simulation to the 90% NOx cut CMAQ 

5 simulation. The percentages of the U.S. population living in areas that have 

6 changes less than -1 ppb, from -1 to +1 ppb, and greater than 1 ppb are shown 

7 on the y-axis. Left plots show population numbers in locations not included in 

8 one of the cases study areas while right plots show population numbers in 

9 locations included in one of the case study areas. Top plots show changes in 

10 January monthly mean O3, middle plots show changes in seasonal mean June11 August O3, and bottom plots show changes in seasonal mean April-October 

12 O3.

13
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1 The proportion of the population living in locations of increasing seasonal mean O3 in 

2 response to NOx emissions reductions varies considerably between case study areas. Figure 8.42

3 and Figure 8.43 show these proportions by city for the 50% and 90% NOx reduction scenarios. 

4 For the 50% NOx reduction scenario, the CMAQ results predict that four out of fifteen study 

5 ares (Chicago, Detroit, Los Angeles, and New York) have more than 50% of their populations 

6 living in locations with increasing mean O3 for April-October. Most other urban case study areas 

7 have between 5% and 30% of their populations living in these areas with increasing mean O3

8 levels. For the 90% NOx reduction scenario, the percent of population living in such locations 

9 decreases substantially for all cities, leaving four out of fifteen study areas (Detroit, Houston, 

10 Los Angeles, and New York) with more than 5% of their populations living in areas with 

11 increasing mean O3 levels.

12

13

14 Figure 8.42 Population (as % of total case-study area population) living in locations of 

15 increasing April-October seasonal mean O3 in the 50% NOx reduction 

16 CMAQ simulation.

17
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1

2 Figure 8.43 Population (as % of total case-study area population) living in locations of 

3 increasing April-October seasonal mean O3 in the 90% NOx reduction 

4 CMAQ simulation.

5

6 We can further understand these results by looking at them in terms of population density 

7 in the case-study areas versus across the U.S. as a whole. As in Section 8.2.3.1, we define census 

tracts with population density greater than 1000 people/km2 8 as high population density, but the 

9 low-mid population density classification used here is a combination of the low and medium 

10 classifications in that section. Figure 8.44 and Figure 8.45 split out the April-October results 

11 from Figure 8.40 and Figure 8.41 into high and low-mid sub-categories. Appendix 8-C provides 

12 similar breakouts for the other panels in Figure 8.40 and Figure 8.41. First, based on these 

13 definitions, we see that 57% of the population in case-study areas lives in high population 

14 density locations while only 27% of the U.S. population does. As discussed above, the high 

15 population areas are more likely to experience increases in mean O3 as a result of NOx emission 

16 reductions compared to lower population areas. Therefore, the fact that the case-study areas used 

17 in the risk and exposure assessments are more densely populated than the country as a whole 

18 means that these analyses may estimate higher risks under emissions reduction scenarios than 

19 would be experienced, on average, across the country. Figure 8.44 and Figure 8.45 show 

20 generally similar shapes for the high population density histograms in the study-area and non21 study area locations. In the 50% NOx cut simulation, 69% of the population living in high 

22 density case-study areas would experience increases in mean seasonal O3 compared to 63% of 

23 the population the population living in high density areas of the country as a whole. Similarly in 

24 the 90% NOx cut simulation, 28% of the population in high density locations both within the 

25 study areas and across the U.S. as a whole lives in locations of increasing seasonal mean O3. This 
8-73

1 suggests that the selected study areas adequately represent population-weighted changes in mean 

2 O3 for people living in high density areas. Similarly, less densely populated locations within the 

3 case-study areas show O3 increases equivalent to those seen in less densely populated areas in 

4 the U.S. as a whole. In the 50% NOx cut simulation, 7% of people in low-mid density study area 

5 locations live where mean seasonal O3 is increasing, while 5% of people in all low-mid density 

6 U.S. locations live where mean seasonal O3 is increasing. Similarly, in the 90% NOx cut 

7 simulation, the numbers are 2% for both low-mid density study area populations and for low-mid 

8 density populations in the U.S. as a whole. Thus the oversampling of populations living in 

9 locations of increasing mean seasonal O3 in response to NOx cuts, as shown in Figures 8.28 and 

10 8.30, appears to be entirely due to the fact that the study areas oversample populations living in 

11 high density areas compared to the U.S. population as a whole.

12

13

14

15
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1

2 Figure 8.44 Histograms of U.S. population living in locations with increasing and 

3 decreasing mean O3. Values on the x-axis represent the change in seasonal 

4 mean (April-October) O3 from the 2007 base CMAQ simulation to the 50% 

5 NOx cut CMAQ simulation. The percentages of the U.S. population living in 

6 areas that have changes less than -1 ppb, from -1 to +1 ppb, and greater than 

7 1 ppb are shown on the y-axis. Left plots show population numbers in 

8 locations not included in one of the cases study areas while right plots show 

9 population numbers in locations included in one of the urban case study 

10 areas. Bottom plots show histograms for low-mid population density areas 

11 while top plots show histograms for high population density areas.
8-75

1

2 Figure 8.45 Histograms of U.S. population living in locations with increasing and 

3 decreasing mean O3. Values on the x-axis represent the change in seasonal 

4 mean (April-October) O3 from the 2007 base CMAQ simulation to the 90% 

5 NOx cut CMAQ simulation. The percentages of the U.S. population living in 

6 areas that have changes less than -1 ppb, from -1 to +1 ppb, and greater than 

7 1 ppb are shown on the y-axis. Left plots show population numbers in 

8 locations not included in one of the cases study areas while right plots show 

9 population numbers in locations included in one of the urban case study 

10 areas. Bottom plots show histograms for low-mid population density areas 

11 while top plots show histograms for high population density areas.

12
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1 8.2.5 Discussion

2

3 We evaluated two different questions, 1) to what degree are the 15 cities evaluated in the 

4 exposure and risk analyses representative of the overall U.S. population with regards to total O3

5 risk?, and 2) to what degree are they representative of the overall U.S. population with regards to 

6 the degree of risk reduction that might be observed in response to just meeting the existing and 

7 alternative standards.

8 Regarding the first question, we observe that the 12 urban study areas considered in the 

9 urban-scale risk assessment presented in Section 7.2 capture urban areas that are among the most 

10 populated in the U.S., have relatively high O3 levels, and represent the range of city-specific 

11 effect estimates found by Smith et al. (2009) and Zanobetti and Schwartz (2008). These three 

12 factors suggest that the urban study areas capture overall risk for the nation well, with a potential 

13 for better characterization of the high end of the risk distribution. We find that the urban study 

14 areas are not capturing areas with the highest baseline mortality rates, those with the oldest 

15 populations, and those with the lowest air conditioning prevalence. These areas tend to have 

16 relatively low O3 concentrations and low total population, suggesting that the urban study areas 

17 are not missing high risk populations that have high O3 concentrations in addition to greater 

18 susceptibility per unit O3. We also find that the 12 urban study areas represent the full range of 

19 county-level O3-related risk across the entire U.S. We conclude from these analyses that the 12 

20 urban study areas adequately represent O3-related risk across the U.S.

21 Concerning the second question, we observe that the 15 urban areas considered in the 

22 exposure and risk assessment case study areas over-sample populations living in locations with 

23 increasing seasonal mean O3 in response to NOx cuts. This suggests that the selected study areas 

24 adequately represent population-weighted changes in mean O3 concentrations for urban 

25 populations, but may be under-representing decreasing median O3 concentrations in suburban 

26 and rural areas. As a result, the risk estimates for populations in the selected urban study areas 

27 may understate the risk reductions that might be achieved across the broader U.S. population.

28

29
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1

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17 Mortality: An Analysis of 48 Cities in the United States." American Journal of 

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19 Zanobetti, A. and J. Schwartz. 2011. "Ozone and Survival in Four Cohorts with Potentially 

20 Predisposing Diseases." American Journal of Respiratory and Critical Care Medicine, 

21 194:836-841.

22 Zhang, L.; D. J. Jacob; N.V. Smith-Downey; D. A. Wood; D. Blewitt; C. C. Carouge; A. van 

23 Donkelaar; D. B. A. Jones; L.T. Murray and Y. Wang. 2011. "Improved Estimate of the 

24 Policy-relevant Background Ozone in the United States Using the GEOS-Chem Global 

25 Model with 1/2°x2/3° Horizontal Resolution Over North America." Atmospheric

26 Environment, 45:6769-6776.

27
9-1

1 9 SYNTHESIS

2 9.1 INTRODUCTION

3 This assessment estimates exposures to O3 and resulting mortality and morbidity health 

4 risks based on the findings of the O3 ISA (U.S. EPA, 2013) that short-term O3 exposures are 

5 causally related to respiratory effects, and likely causally related to cardiovascular effects, and 

6 that long term O3 exposures are likely causally related to respiratory effects. The assessment 

7 evaluates total exposures and risks associated with the full range of observed O3 concentrations, 

8 as well as the incremental changes in exposures and risks between just meeting the existing 

9 standard of 75 ppb and just meeting alternative standard levels of 70, 65, and 60 ppb using the 

form and averaging time of the existing standard: the annual 4th 10 highest daily maximum 8-hour 

11 O3 concentration, averaged over three consecutive years. We evaluated alternative standard 

12 levels of 70, 65, and 60 consistent with recommendations from CASAC to consider alternative 

13 standard levels between 60 and 70 ppb (Frey and Samet, 2012).

14 Following the conceptual framework described in Chapter 2, the assessment evaluates

15 exposures and lung function risk in 15 urban case study areas, and mortality and morbidity risks 

16 based on concentration-response functions derived from epidemiology studies in 12 of these 

urban case study areas1 17 . The results from these assessments will help inform consideration of the 

18 adequacy of the existing primary O3 standards, and potential risk reductions associated with 

19 several alternative levels of the standard (for the current form and averaging time). In addition, to 

20 place the urban case study area analyses in a broader context, Chapter 8 of this assessment 

21 estimates the national burden of mortality associated with recent O3 levels, and evaluates the 

22 representativeness of the 15 urban case study areas in characterizing O3 exposures and risks 

23 across the U.S. This synthesis focuses on the urban case study area assessments of exposure and 

24 risk for the scenarios of just meeting the existing and alternative standards. For this synthesis, we 

25 discuss the results of the national-scale assessment as they relate to understanding the breadth of 

26 O3 risks across the U.S. and to the national representativeness of the urban case study area risk 

27 results.

28 To facilitate interpretation of the results of the exposure and risk assessment, this chapter 

29 provides a synthesis of the various results, focusing on comparing and contrasting those results 

30 to identify common patterns, or important differences. These comparisons will focus on patterns 

 1 Three additional urban case study areas were evaluated for the human exposure assessment and lung function risk 

assessment to provide greater geographic representation. There was insufficient information available to 

conduct the epidemiology-based risk assessment in these 3 additional areas. Also, we originally planned to 

include Seattle, WA as a 16th urban case study area, but due to limitations in the available air quality 

monitoring data, we determined that it would not be appropriate to model exposure and risks for Seattle (see 

appendix 4-E).
9-2

1 across urban case study areas, across years of analysis, and across alternative standards. In 

2 addition, factors related to each specific type of analysis that may influence comparisons 

3 between the analyses are identified and discussed. The degree to which the integrated results are 

4 representative of national patterns of exposure and risk is evaluated. Overall confidence in the 

5 results, as well as relative confidence between the different analyses is also assessed. The chapter 

6 concludes with an overall integrated characterization of exposure and risk in the context of key 

7 policy-relevant questions raised in Chapter 2.

8 9.2 SUMMARY OF KEY RESULTS

9 9.2.1 Air Quality Considerations (Chapter 4)

10 Table 9-1 below gives information on the monitoring network, population, and observed 

11 peak O3 concentrations for the 15 case study areas, for the years included in the exposure, lung 

12 function risk, and epidemiology based risk assessments. The number of counties, number of O3

13 monitors, population, and design values (DV) are based on the area definitions used in the 

14 exposure modeling and clinical-based lung function risk assessments, while the 2007 and 2009 

annual 4th 15 highest values are based on the Core Based Statistical Areas (CBSAs) used in the 

epidemiology-based risk assessment. The “N/A” values in the 2007 and 2009 4th 16 high columns 

17 are for the three urban areas not included in the epidemiology-based risk assessment. The data 

show a trend of lower peak O3 concentrations (i.e., the 2008-2010 design values and 2009 4th 18

high values are generally lower than the 2006-2008 design values and 2007 4th 19 high values, 

20 respectively).
9-3

1

2 Table 9-1 Area and Monitoring Information for the 15 Case Study Areas

Area Name

# of 

Counties

# of O3

Monitors

Population 

(2010)

2006-2008 

DV (ppb)

2007 4th

high (ppb)

2008-2010 

DV (ppb)

2009 4th

high 

(ppb)

Atlanta 33 13 5,618,431 95 102 80 77

Baltimore 7 7 2,710,489 91 92 89 83

Boston 10 14 5,723,468 83 89 77 75

Chicago 16 26 9,686,021 78 N/A 74 N/A

Cleveland 8 13 2,881,937 82 83 77 72

Dallas 11 20 6,366,542 89 N/A 86 N/A

Denver 13 26 3,390,504 86 97 77 79

Detroit 9 12 5,218,852 81 93 75 73

Houston 10 22 5,946,800 91 90 85 91

Los Angeles 5 54 17,877,006 119 105 112 108

New York 27 31 21,056,173 90 94 84 81

Philadelphia 15 19 7,070,622 92 102 83 74

Sacramento 7 26 2,755,972 102 93 102 96

St. Louis 17 17 2,837,592 85 94 77 74

Washington 26 22 5,838,518 87 N/A 81 N/A

3

4 In this analysis, we employed a photochemical model-based adjustment methodology 

5 Simon et al. (2012) using the Higher-Order Decoupled Direct Method (HDDM) capabilities in 

6 the Community Multi-scale Air Quality Model (CMAQ) (hereafter referred to as HDDM air 

7 quality adjustment). The HDDM air quality adjustment methodology replaced the quadratic 

8 rollback technique used in the first draft REA to estimate O3 concentrations consistent with just 

9 meeting existing and alternative O3 standards. The HDDM air quality adjustment procedure 

10 estimates the change in observed hourly O3 concentrations at a given set of monitoring sites 

11 resulting from national across-the-board reductions in U.S. anthropogenic NOx and/or VOC 

12 emissions. In this analysis, we adjusted O3 concentrations to just meet the existing standard of 75 

ppb2 13 and potential alternative standards of 70, 65, and 60 ppb at ambient monitoring sites in the 

14 15 case study areas for the 2006-2008 and 2008-2010 periods. In most locations, only NOx 

15 reductions were used to adjust the distribution of O3 concentrations, because of the 

16 ineffectiveness of VOC reductions in reducing peak O3 concentrations needed to meet the 

 2 Attainment with the existing standard level of 75ppb is determined by the 4th highest maximum 8-hour O3

concentration, averaged over 3 years (hereafter referred to as the existing standard).
9-4

1 existing and alternative standard levels. Sensitivity analyses were also conducted in some 

2 locations to evaluate the impact of decreasing both NOx and VOC emissions.

3 The HDDM air quality adjustment methodology represents a substantial improvement 

4 over the quadratic rollback method used to adjust O3 concentrations in previous reviews. First, 

5 quadratic rollback was a purely mathematical technique which attempted to reproduce the 

6 distribution of observed O3 concentrations just meeting various standards, while the new 

7 methodology uses photochemical modeling to simulate the response in O3 concentrations due to 

8 changes in precursor emissions based on current understanding of atmospheric chemistry and 

9 transport. Second, quadratic rollback used the same mathematical formula to adjust 

10 concentrations at all monitors within each case study area for all hours, while HDDM allows the 

11 adjustments to vary both spatially across each case study area and temporally across hours of the 

12 day and across seasons. Finally, quadratic rollback was designed to only allow decreases in O3

13 concentrations, while the HDDM air quality adjustment allows both increases and decreases in 

14 O3 concentrations in response to reductions in NOx or VOC emissions. For example, in response 

15 to reductions in NOx emissions, the HDDM methodology is able to capture increases in O3

16 concentrations that can occur in urban cores characterized by titration of O3 by fresh NO 

17 emissions and decreases in O3 concentrations downwind.

18 Following HDDM adjustment of O3 concentrations, several general trends are evident in 

19 the changes in O3 patterns across the case study areas and across the alternative standard levels. 

20 In all 15 case study areas, peak O3 concentrations tended to decrease while the O3 concentrations

21 in the lower part of the distribution of O3 tended to increase as the concentrations were adjusted 

22 to meet the existing and alternative standards. In addition, O3 concentrations in the high and mid23 range portions of the O3 distribution generally decreased in the outer, more rural and suburban 

24 portions of the urban case study areas, while the O3 response to NOx reductions was more varied 

within the urban cores. In particular, while the peak (annual 4th 25 highest daily maximum 8-hour)

26 concentrations upon which the existing and alternative standards are defined generally decreased 

27 in the urban core of the case study areas in response to modeled reductions in primarily NOx

28 emissions, the O3 responses near the center of the O3 distribution at these locations followed one 

29 of three patterns when focusing on the mean of the daily maximum 8-hour O3 concentrations

30 from May to September, as shown in Table 9-2.

31
9-5

1 Table 9-2 General Patterns in Seasonal (May-Sept) Mean of Daily Maximum 8-hour O3

2 Concentrations after Adjusting to Meet Existing and Alternative Standards*

After Adjusting to Meet 

Existing Standard

After Further Adjusting to Meet 

Lower Alternative Standards

Case Study Areas 

Showing Pattern

Decreased Continued to decrease

Atlanta

Sacramento

Washington, D.C

Increased Decreased

Baltimore

Cleveland,

Dallas

Detroit

Los Angeles

New York

Philadelphia

St. Louis

Increased Continued to increase or remained 

constant

Boston

Chicago

Denver

Houston

3 * These patterns refer to O3 responses in the urban core of each urban case study area based on analysis of the 

4 interpolated monitor values used as inputs to the exposure and lung function risk analyses.

5

6 The air quality inputs to the exposure modeling and clinical-based lung function risk 

7 assessments were estimated hourly O3 concentrations at each census tract in the 15 case study 

8 areas. These values were interpolated from the observed and HDDM-adjusted monitoring data 

9 using the Voronoi Neighbor Averaging (VNA) technique. This technique was shown to be an 

10 improvement upon the nearest neighbor technique used in the first draft REA and previous O3

11 NAAQS reviews (see Appendix 4-A for details). Consequently, the spatial variability of

12 observed and HDDM-adjusted O3 is better accounted for in these analyses compared to those in 

13 the first draft REA.

14 The air quality inputs to the epidemiology based risk assessment were “composite 

15 monitor” values, a time-series of the spatially averaged monitoring data, in 12 of the 15 case 

16 study areas. Consequently, in cases of urban case study areas within which O3 was predicted to 

17 increase in some locations and decrease in others, the air quality inputs to this analysis represent 

18 a “net” effect for each case study area. The spatial extent of the case study areas used in the 

19 composite monitor averages were CBSAs. These CBSA areas are larger than the Zanobetti & 

20 Schwartz, 2008 (Z & S) study areas (used in the first draft REA) which include only a subset of 

21 the CBSA focused on urban cores. Figure 9-1 (reproduced from Figure 4-7) shows box plots of 

22 the composite monitor values for 2006-2008 based on the observed data (black), data adjusted to 

23 meet the existing standard using quadratic rollback (blue), and the HDDM adjustment procedure 
9-6

1 (red) for the Z & S areas. The two adjustment methods were generally comparable in terms of 

2 the changes in the upper quartile of the distribution. However, by design, quadratic rollback 

always estimated decreases in the 75th percentile, median, and 25th 3 percentile of the composite 

4 monitor values, while HDDM estimated decreases in these values in some urban case study 

5 areas, and increases in other areas consistent with atmospheric chemistry. HDDM-based 

6 adjustments always produced increases in the lower tail of the distribution, while the lower tail 

7 values generally remained unchanged with quadratic rollback. The differences between the two 

8 adjustment procedures were the most pronounced in Los Angeles and New York, where the 

9 largest reductions in NOx were required in order to meet the existing and alternative standards. 

10 These large reductions in NOx caused a relatively large increase in lower O3 concentrations 

11 because of the reduction in NOx titration of O3. As was noted in Chapter 4, the HDDM-based O3

12 estimates become more uncertain for larger changes in NOx and VOC emissions, and thus there 

13 was less overall confidence in those results. Even in these cases, the HDDM approach is still 

14 preferable because it captures better the overall shift in the distribution of O3 concentrations.

15
9-7

1

2 Figure 9-1 Distributions of composite monitor 8-hour daily maximum O3 concentrations 

3 from ambient measurements (black), quadratic rollback (blue), and the HDDM 

4 adjustment methodology (red) for meeting the existing standard.

5
9-8

1 9.2.2 Human Exposure Modeling (Chapter 5)

2 The population exposure assessment evaluates exposures to O3 using the Air Pollution 

3 Exposure (APEX) model for the general population, all school-aged children (ages 5-18),

4 asthmatic school-aged children (ages 5-18), asthmatic adults (ages > 18), and older persons (ages 

5 65 and older), with a focus on populations engaged in moderate or greater exertion (e.g. children 

6 engaged in outdoor recreational activities). The strong emphasis on children, asthmatics, and 

7 older adults reflected the findings of the last O3 NAAQS review (U.S. EPA, 2007) and the ISA 

8 (U.S. EPA, 2013, Chapter 8) that these are important at-risk groups. 

9 We assessed exposure in 15 urban case study areas – Atlanta, Baltimore, Boston, 

10 Chicago, Cleveland, Dallas, Denver, Detroit, Houston, Los Angeles, New York, Philadelphia, 

11 Sacramento, St. Louis, and Washington, D.C. – for recent O3 concentrations (2006-2010) and for 

12 O3 concentrations adjusted to just meet existing and alternative standards for two time periods 

(2006-2008 and 2008-2010)3 13 . The analysis provided estimates of the percent of several 

14 populations of interest exposed to concentrations above three health-relevant 8-hour average O3

15 exposure benchmarks: 60, 70, and 80 ppb. The ISA includes studies showing statistically 

16 significant effects at each of these benchmark levels (U.S. EPA, 2013). These benchmarks were 

17 selected to provide some perspective on the public health impacts from exposures to various 

18 concentrations that have been associated with O3-related health effects (e.g., lung inflammation 

19 and increased airway responsiveness) in controlled human exposure and toxicological studies, 

20 but cannot currently be evaluated in quantitative risk assessments. In addition, the exposure 

21 assessment also identified the specific microenvironments and activities that contribute most to 

22 exposure and evaluated at what times and how long individuals were in key microenvironments 

23 and were engaged in key activities. This assessment focused on persons experiencing the highest 

24 daily maximum 8-hour exposure within each study area. The assessment found that:

25 • Childhood is an important lifestage where higher exposures and risks can occur, 

26 due to the higher time spent outdoors by children, the higher exposure 

27 concentration experienced by children while outdoors (i.e. when they are 

28 dismissed from school in the afternoon and during the summer, when they may be 

29 at an outdoor camp all day) and engagement in moderate or high exertion level 

30 activities.

 3 Attainment with the O3 standard is based on the 4th highest maximum 8-hour O3 concentration, averaged over 3 

years. We evaluated two different 3-year periods in determining how air quality in each of the analytical years 

would respond to just meeting the existing and alterative levels of the standard. This was done to evaluate the 

effect of variability in meteorology and emissions on exposures and risks associated with just meeting the 

existing and alternative standards. For the exposure and lung function risk analyses, which provide estimates 

for each of the five analytical years, this results in two estimates for 2008, because 2008 is included in each of 

the 3-year averaging periods and there are separate analytical results for 2008 for the adjusted air quality 

resulting from simulating attainment in each of the two 3-year periods.
9-9

1 • Persons spending a large portion of their time outdoors during afternoon hours 

2 experienced the highest 8-hour O3 exposure concentrations given that O3

3 concentrations in other microenvironments were simulated to be lower than 

4 ambient concentrations.

5 • Highly exposed children spend half of their outdoor time (on average) engaged in 

6 moderate or greater exertion levels, such as in sporting activities. Highly exposed 

7 adults also spent their outdoor time engaged in moderate or greater exertion levels 

8 though on average, not as frequently as children.

9 Across the 15 urban case study areas, we find that children are of greatest concern for O3

10 exposures compared to other lifestages due to the greater amount of time they spend outdoors 

11 engaged in moderate or higher exertion activities. The exposure analysis estimates that children 

12 have the highest percent of exposures of concern of any of the at-risk populations or lifestages. 

13 As a result, we focus on the results for children (ages 5-18) in the remainder of this discussion. 

14 Figure 9-2 (reproduced from Figure 5-11) shows the results of the exposure assessment for all 15

15 urban case study areas, showing trends across the analytical years for the percent of children with 

16 at least one 8-hour exposure greater than the 60, 70, and 80 ppb benchmarks.

17 The limited availability of longitudinal activity diary data and the general population 

18 modeling approach used may underestimate the correlation in activity patterns for certain 

19 susceptible populations (e.g., outdoor workers), and underestimate how often there are repeated 

20 exposures to O3 concentrations above the exposure benchmarks. As a result, although we are 

21 able to report the percent of the population with at least one exposure greater than the alternative 

22 exposure benchmarks, we are less confident in the estimated percent of the population 

23 experiencing more than one exposure. Individuals with repeated exposures may be at greater risk 

24 of significant health effects (U.S. EPA, 2013, Section 6.2.1.1). In addition, the limited data on 

25 responses to air quality alerts (e.g., averting behavior) indicates that a small percentage of the 

26 population may engage in averting behavior in response to air pollution, which may overstate 

27 actual exposures if individuals reduce their exposure during periods of high O3.

28 The benchmark exposures of concern are not equivalent to ambient standard levels, as 

29 exposures reflect the full pattern of O3 concentrations throughout a season, coupled with time 

30 spent outdoors and indoors engaged in different activities. Thus, just meeting the existing 

31 standard will result in shifts in the entire distribution of O3 over a three year period, and will 

32 change the percent of populations experiencing each of the exposure benchmarks of concern. 

33 Figure 9-2 shows that the percent of children above the 60 ppb benchmark declines consistently 

34 across the 15 urban case study areas when just meeting potential alternative standards of 70, 65, 

35 and 60. For most urban case study areas and years, the percent of children above the 60 ppb 

36 benchmark is reduced by over half when O3 is adjusted to meet the 65 ppb alternative standard 
9-10

1 relative to the 75 ppb standard. In many urban case study areas and years, just meeting the 65 

2 ppb alternative standard results in close to zero percent of children above the 60 ppb benchmark. 

3 For the 70 and 80 ppb benchmarks, meeting an alternative standard of 70 ppb results in a small 

4 percentage of children exceeding the benchmarks.

5 Year-to-year variability is relatively pronounced for exceedances of the 60 ppb 

6 benchmark. In addition, we observe a geographic pattern to the years with the maximum percent 

7 of exceedances of the exposure benchmarks reflecting the regional O3 patterns across years. In 

8 general, northeastern urban case study areas saw the highest percentage of exceedances during 

9 2007, while southern and western urban case study areas saw a higher percentage of exceedances 

10 during 2006. However, these patterns are somewhat dependent on the 3-year averaging period 

11 used to determine whether the standards are met. In general variability in the percent of children 

12 exceeding the 60 ppb exposure benchmark across urban case study areas is similar to the 

13 variability across years.

14 The percent of children with multiple exposures above the exposure benchmarks is 

15 generally much lower compared to the percent of children with single exposures above the 

16 benchmarks. However, as noted above, we have lower confidence in these estimates. Even for 

17 the lowest benchmark level of 60 ppb, most locations and years have less than 10 percent of 

18 children experiencing 2 or more exposures when just meeting the existing standard of 75 ppb, 

19 less than 5 percent when just meeting an alternative standard of 70 ppb, and less than 1 percent 

20 when just meeting an alternative standard of 65 ppb. For most urban case study areas and years, 

21 less than 1 percent of children experience 2 or more exposures above the 70 ppb exposure 

22 benchmark when just meeting the existing standard.
9-11

1

2 Figure 9-2 Effects of just meeting existing (column 1) and alternative (columns 2 through 

3 4) standards on percent of children (ages 5-18) with at least one O3 exposure at or 

4 above 60, 70, and 80 ppb-8hr while at moderate or greater exertion, years 2006-

2010.

4 5

 4 We were not able to adjust air quality to just meet the 60 ppb alternative standard in the New York City by 

reducing U.S. NOx and VOC emissions (see chapter3 and appendix 4-D for details). Detroit was already 

meeting the existing standard for 2008-2010.
9-12

1 Table 5-6 summarized the percent of the population of children (ages 5-18) with at least 

2 one daily 8-hour exposure above the 60, 70, and 80 ppb benchmarks, providing both the mean 

3 and maximum percentage across the five analytical years for each urban case study area. For O3

4 adjusted to just meet the existing standard of 75 ppb, the highest maximum percentage of 

5 children exceeding the 60 ppb benchmark across years, 26 percent, occurs in Denver, which also 

6 has the highest mean percentage across years. After just meeting the existing standard, Los 

7 Angeles has the lowest maximum (10 percent) and mean (9.5 percent) percentage of children 

8 exceeding the 60 ppb benchmark across years, likely reflecting the highly skewed nature of O3

9 concentrations in that urban case study area. For example, just meeting the existing standards in 

10 Los Angeles moves the majority of O3 concentrations (sites and days) well below 60 ppb (See 

11 Appendix 4-D). Patterns across urban case study areas are generally similar after just meeting 

12 alternative standards of 70, 65, and 60 ppb, with the exception that the lowest maximum and 

13 mean percentage of children for the alternative standard level of 65 ppb occurs in the New York 

14 City urban case study area, which had very large (greater than 90 percent) reductions in NOx 

15 emissions that were used to adjust air quality to just meet the 65 ppb standard level in that urban 

16 case study area. This resulted in the distribution of O3 concentrations covering most days of the

17 year and most monitoring sites shifting dramatically downward, with most concentrations well 

18 below 60 ppb across the New York City urban case study area. The level of confidence in the 

19 results for the New York City and Los Angeles study areas for just meeting the alternative 

20 standards is lower than that for some of the other urban case study areas due to the HDDM-based 

21 O3 estimates becoming more uncertain for very large changes in precursor emissions.

22 Figure 9-3 (reproduced from Figure 5-19) shows the results of the exposure assessment 

23 for all 15 urban case study areas, showing the effect on the percent of children with one or more 

24 exposures above the 60 ppb benchmark of just meeting the existing and alternative standards. 

25 For each alternative standard, Figure 9-2 shows the maximum percent of children exceeding the 

26 benchmark across the modeled years 2006-2010. Patterns of results are similar for the 70 ppb 

27 and 80 ppb benchmarks, however, the maximum percents of children exceeding those higher 

28 benchmarks are much smaller for all alternative standards. The percent of children exceeding the 

29 80 ppb benchmark is close to zero once the existing standard is met. The percent of children with 

30 two or more exposures exceeding the 60 ppb benchmark level is substantially lower when just 

31 meeting the existing standard, and is close to zero for the 70 ppb and 80 ppb benchmarks. This 

32 percentage drops substantially when meeting the 70 ppb standard, and is close to zero in most 

33 urban case study areas when meeting the 65 ppb and 60 ppb alternative standards. Patterns for 

34 asthmatic children are very similar to patterns for all children.
9-13

1

2 Figure 9-3 Effects of just meeting existing (75 ppb) and alternative standards on percent of 

3 children (ages 5-18) exceeding 60 ppb exposure benchmark, highest value across 

years for each urban case study area, 2006-2010.

5 4

5

6 9.2.3 Health Risks Based on Controlled Human Exposure Studies (Chapter 6)

7 Using the estimates of exposure from APEX combined with results from controlled 

8 human exposure studies, we estimated the number and percent of at-risk populations or lifestages

9 (all children aged 5-18, children with asthma aged 5-18, adults aged 18-35, adults aged 36-55, 

10 and outdoor workers) experiencing selected decrements in lung function. The analysis focuses on

11 estimates of the percent of each at-risk population or lifestage experiencing a reduction in lung 

12 function (mostly for durations of one to five hours) for three different levels of impact, 10, 15, 

 5 We were not able to adjust air quality to just meet the 60 ppb alternative standard in New York City urban case 

study area by reducing U.S. NOx and VOC emissions (see chapter3 and appendix 4-D for details). Detroit was 

already meeting the existing standard for 2008-2010.
9-14

1 and 20 percent decrements in FEV1. These levels of impact were selected based on the literature 

2 discussing the adversity associated with increasing lung function decrements (US EPA, 2013, 

3 Section 6.2.1.1). Consistent with the exposure assessment, we focus this summary on lung 

4 function decrements in children as they are the lifestage likely to have the greatest percentage at5 risk due to higher levels of exposure and exertion. Within the overall population of children, 

6 asthmatic children may have less reserve lung capacity to draw upon when faced with 

7 decrements, and therefore a ≥10% decrement in lung function may be a more adverse event in an 

8 asthmatic child than a healthy child.

9 Lung function risks (based on experiencing an estimated 10, 15, or 20 percent decrement 

10 in lung function) were estimated for each of the 15 urban case study areas in which human 

11 exposures were modeled. Two models were used to estimate lung function risks: one based on 

12 application of a population level exposure-response (E-R) function consistent with the approach

13 used in the previous O3 NAAQS review, and one based on application of an individual level E-R

14 function (the McDonnell-Stewart-Smith (MSS) model), introduced in this review, which 

15 incorporates individual differences in physiology, age, and activity patterns (McDonnell et al., 

16 2012). Because the individual level E-R function approach allows for a more complete estimate 

17 of risk (incorporating risk responses at varying activity levels, not just moderate or greater 

18 exertion), we focus on the results of that approach for this discussion.

19 The MSS model as implemented in APEX has a term that adjusts the lung function 

20 response according to an individual's age. The MSS model was fit using data from subjects who 

21 ranged in age from 18 to 35. Thus, the MSS model is not able to account for differences in lung 

22 function at different age groups between the ages of 5 and 18. However, age does have a 

23 pronounced effect on lung function response in the APEX model. APEX models differences in 

24 physiological parameters due to age, and these result in age-dependent predictions of ventilation 

25 rates, which are used in the MSS model. Ventilation rates also depend on the activities being 

26 performed, which are also age-dependent. As a result of differences in physiology and activities, 

27 the lung function responses vary by age (see Appendix 6-E).

28 Figure 9-4 (reproduced from Figure 6-6) shows the results of the lung function risk 

29 assessment for all 15 urban case study areas, showing trends across the analytical years for the 

percent of children with predicted lung function decrements greater than or equal to 10 percent6 30 . 

 6 We have introduced a new method (relative to the O3 NAAQS review completed in 2008) for calculating the 

percent of the at-risk populations (all children and asthmatic children) experiencing lung function decrements, 

based on modeling of individual level responses to O3 exposures. This model yields significantly higher 

estimates of the percent of children experiencing lung function decrements greater than 10, 15, and 20 percent.

This may be partly due to the specific data inputs from clinical studies used to derive the function, but is also to 

be expected because the MSS model can reflect greater sensitivity of children to O3 exposures because it 

allows for age variability in the relationship between O3 and FEV1 decrements, and younger populations are 

more responsive to O3 exposures than older populations.
9-15

1 Specifically, Figure 9-4 shows that the percent of children (age 5-18) with greater than or equal 

2 to 10 percent lung function decrement declines consistently across the 15 urban case study areas

3 when just meeting the existing 75 ppb standard, as well as the alternative standards of 70, 65, and 

4 60. The percent of children at-risk at the 10 percent decrement level remains at or above 10 

5 percent in many locations after just meeting the 60 ppb alternative standard. The percentage of 

6 children with greater than or equal to a 15 or 20 percent lung function decrement is much lower

7 for all alternative standards, with close to zero percent of children at-risk when just meeting the 

8 alternative standard of 60 ppb. In general variability in percent of children at-risk across urban 

9 case study areas is similar to variability across years.

10

 
9-16

1

2 Figure 9-4 Effects of just meeting existing (column 1) and alternative (columns 2-4) 

3 standards on percent of children (ages 5-18) with FEV1 decrement > 10, 15, and 20%, years 

2006-2010.

7 4

 7 We were not able to adjust air quality to just meet the 60 ppb alternative standard in New York City urban case 

study area by reducing U.S. NOx and VOC emissions (see chapter3 and appendix 4-D for details). Detroit was 

already meeting the existing standard for 2008-2010. Percent of All School-Age Children
9-17

1 Figure 9-5 (reproduced from Figure 6-11) shows the results of the lung function risk

2 assessment for all 15 urban case study areas, showing the effect on the risk of a 10 percent or 

3 greater lung function decrement in children (ages 5-18) of just meeting the existing and 

4 alternative O3 standards. For each alternative standard, Figure 9-5 shows the maximum percent 

5 risk over all of the modeled years 2006-2010.

6 There is no consistent pattern in the percent of children with 10 percent or greater lung 

7 function decrement across urban case study areas just meeting the existing standard of 75 ppb. 

8 The 5-year maximum estimated percent of children at-risk ranges from 17 to 22 percent across 

9 urban case study areas. The percent reduction in 5-year maximum risk when just meeting the 70 

10 ppb alternative standard is more consistent across urban case study areas, ranging from 8 to 23 

11 percent (excluding New York City, which had a reduction of 29 percent). Reductions in risk 

12 when just meeting the 65 ppb alternative standard are also generally consistent across urban case 

13 study areas, with the exception of New York City. Incremental reductions in risk when just 

14 meeting the alternative 65 ppb standard compared with just meeting the 70 ppb alternative 

15 standard range from 17 to 31 percent excluding New York City, which has a reduction in risk of 

16 more than twice as much as the next largest reduction. Incremental reductions in risk from just 

17 meeting the alternative 60 ppb standard compared with just meeting the 65 ppb standard are 

18 generally consistent, ranging from 16 to 46 percent, with somewhat larger reductions in risk 

19 occurring in Cleveland and Denver. Overall, the 5-year maximum percent of children at-risk for 

20 lung function decrements of 10 percent or more exceeds 13 percent, 10 percent, and 5 percent in 

21 all urban case study areas except New York after just meeting alternative standards of 70, 65, 

22 and 60, respectively. Patterns of risk reductions are also similar for the alternative lung function 

23 decrement levels of 15 percent and 20 percent. However, the initial percent of the population 

24 experiencing these decrements when just meeting the existing standard are substantially lower.

25 Patterns of risk responses using the population level exposure-response model are similar

26 to the MSS individual risk model. However, the starting values for the percent of the population 

27 at risk are lower, reflecting the limits of the model in reflecting individual level responses, and 

28 the limited coverage of the model for exposures at lower exertion levels. For children, the MSS 

29 model gives results typically a factor of three higher than the population level E-R model used in 

30 the previous O3 NAAQS review.

31

 
9-18

1 Figure 9-5 Impact of just meeting existing (75 ppb) and alternative standards on percent 

2 of children (ages 5-18) with FEV1 decrement > 10%, highest value for each urban 

case study area, 2006-2010.

8 3

4

5 9.2.4 Health Risks Based on Epidemiological Studies (Chapters 7 and 8)

6 The epidemiology-based risk assessment evaluated mortality and morbidity risks from 

7 short-term O3 exposures and mortality risks from long-term exposures to O3 by applying

8 concentration-response (C-R) functions derived from selected epidemiology studies. The 

9 analysis included both a set of urban case study area case studies and a national-scale 

10 assessment. The urban case study analyses evaluated mortality and emergency department (ED) 

11 visits, hospitalizations, and respiratory symptoms associated with recent O3 concentrations 

12 (2006-2010) and with O3 concentrations adjusted to just meet the existing and alternative O3

 8 We were not able to adjust air quality to just meet the 60 ppb alternative standard in New York City by reducing 

U.S. NOx and VOC emissions (see chapter3 and appendix 4-D for details). Detroit was already meeting the 

existing standard for 2008-2010.
9-19

1 standards (see section 9.2.1 and Chapter 4). Mortality and hospital admissions (HA) were 

2 evaluated in 12 urban case study areas, while ED visits and respiratory symptoms were evaluated 

3 in a subset of areas with supporting epidemiology studies. The 12 urban case study areas were: 

4 Atlanta, GA; Baltimore, MD; Boston, MA; Cleveland, OH; Denver, CO; Detroit, MI; Houston, 

5 TX; Los Angeles, CA; New York, NY; Philadelphia, PA; Sacramento, CA; and St. Louis, MO. 

6 The urban case study analyses focus on risk estimates for the middle year of each three-year 

7 design value period (2006-2008 and 2008-2010) in order to provide estimates of risk for a year 

8 with generally higher O3 concentrations (2007) and a year with generally lower O3

9 concentrations (2009).

10 Most of the endpoints evaluated in epidemiology studies cover the entire study 

11 population including children and adults. Because most mortality and hospitalizations occur in 

12 older persons, these epidemiology-based risk estimates are better indicators of effects in adults 

13 than in children. This is an important distinction from the human exposure and lung function risk 

14 assessments, which focus on children. The only endpoints specific to children are asthma and all 

15 respiratory hospital admissions using the New York specific epidemiology study, respiratory ER 

16 visits in Atlanta, and respiratory symptoms in asthmatic children in Boston.

17 Both the urban case study area and national-scale assessments provide the absolute 

18 incidence and percent of incidence attributable to O3. In addition, risks are presented in terms of 

19 incidence per 100,000 population to control for the differences in the sizes of the populations 

20 across urban case study areas, and to allow for comparison of risks using different definitions of 

21 urban extent. In previous reviews, O3 risks have only been estimated for the portion of total O3

22 attributable to North American anthropogenic sources (above what was referred to in previous 

23 reviews as “policy-relevant background O3”). In contrast, this assessment estimates risk for O3

24 concentrations down to zero, reflecting the lack of evidence for a detectable threshold in the C-R 

25 functions (U.S. EPA, 2013, Chapter 2), and the understanding that U.S. populations may 

26 experience health risks associated with O3 resulting from emissions from all sources, both natural 

27 and anthropogenic, within and outside the U.S. In order to better reflect how O3 distributions are 

28 likely to respond to just meeting existing and potential alternative standard levels, we adjusted 

29 O3 concentrations to just meet existing and potential alternative standard levels using reductions 

30 in only U.S. anthropogenic emissions of O3 precursors. Thus, the estimated changes in risk 

31 between just meeting the existing standards and just meeting potential alternative standard levels 

32 only reflect reductions in U.S. anthropogenic emissions.

33 However, consistent with the conclusions in the O3 ISA (U.S. EPA, 2013), we have 

34 relatively lower certainty about the shape of the C-R function towards the lower end of the 

35 distribution of O3 concentrations used in fitting the function due to the reduction in the number 

36 of O3 measurements in this portion of the distribution. We discuss this source of uncertainty 
9-20

1 below. In addition, we provide the distribution of mortality incidence across the range of O3

2 concentrations in Chapter 7 to inform discussions of uncertainty in the results.

3 9.2.4.1 Urban Case Study Results

4 Figures 9-6 and 9-7 (reproduced from Figures 7-4 and 7-5) show the results of the 

5 mortality and adult (ages 65 and older) respiratory hospital admissions risk assessments for all 12 

6 urban case study areas, showing the effect on the incidence per 100,000 population just meeting

7 the existing 75 ppb standard and alternative O3 standards of 70, 65, and 60 ppb in 2007 and 

8 2009.
9-21

1

2

3 Figure 9-6 Impacts of just meeting existing (75 ppb) and alternative standard levels on 

mortality risk per 100,000 population for 2007 and 2009.

9 4

5

 9 As noted earlier, we were not able to adjust air quality to just meet the 60 ppb alternative standard in New York 

City by reducing U.S. NOx and VOC emissions (see chapter3 and appendix 4-D for details). Detroit was 

already meeting the existing standard for 2008-2010.

2007 Simulation year

2009 Simulation year
9-22

1

2

3 Figure 9-7 Impacts of just meeting existing and alternative standard levels on adult (ages 

4 65 and older) respiratory hospital admissions risk per 100,000 population for 2007 

and 2009.

10 5

6

 10 We were not able to adjust air quality to just meet the 60 ppb alternative standard in New York City urban case 

study area by reducing U.S. NOx and VOC emissions (see chapter3 and appendix 4-D for details). Detroit was 

already meeting the existing standard for 2008-2010.

2007 Simulation year

2009 Simulation year
9-23

1 In some urban case study areas which have large NOx emissions (e.g. from heavy 

2 downtown traffic), O3 levels are artificially low because the NOx emissions remove O3 through a 

3 chemical reaction (see section 9.2.1 and Chapter 4). In these places, when NOx emissions are 

4 decreased to reduce peak O3 concentrations across the entire CBSA, which often includes 

5 locations outside of the urban core areas, lower concentrations of O3 can go up. This can also 

6 happen in other areas on the lowest O3 days. This phenomenon occurs in some locations when 

7 meeting lower alternative standards as well.

8 The overall trend across urban case study areas is small decreases in mortality and 

9 morbidity risk as O3 concentrations are adjusted to just meet incrementally lower alternative 

10 standard levels. In New York, there are somewhat greater decreases in these risks, reflecting the 

11 relatively large emission reductions used to adjust air quality to just meet the 65 ppb alternative 

12 standard, and the substantial change in the distribution of O3 concentrations that resulted. We 

13 were not able to adjust O3 concentrations to just meet the 60 ppb alternative standard in the New 

14 York City urban case study area. Risks vary substantially across urban case study areas;

15 however, the general pattern of reductions across the alternative standards is similar between 

16 urban case study areas. Because of the generally lower baseline O3 concentrations in 2009, risks 

17 are generally slightly lower in 2009 relative to 2007; however, the patterns of reductions in risk

18 are very similar between the two years.

19 Mortality and morbidity risks generally do not show large responses to meeting existing 

20 or alternative levels of the standard for several reasons. First, these risks are based on C-R 

21 functions that are approximately linear along the full range of concentrations, and therefore

22 reflect the impact of changes in O3 along the complete range of 8-hour average O3

concentrations. This includes days with low baseline11 23 O3 concentrations that are predicted to

24 have increases in O3 concentrations, as well as days with higher starting O3 concentrations that 

25 are predicted to have decreases in O3 concentrations as a result of just meeting existing and 

26 alternative standards. Second, these risks reflect changes in the urban-area wide monitor average, 

27 which will not be as responsive to air quality adjustments as the design value monitor, and which 

28 includes monitors with both decreases and increases in 8-hour concentrations. Third, the days 

29 and locations with predicted increases in O3 concentrations (generally those with low to 

30 midrange starting O3 concentrations) resulting from just meeting the existing or alternative 

31 standard levels generally are frequent enough to offset days and locations with predicted 

32 decreases in O3. The heat maps presented in Figures 7-2 and 7-3 demonstrate that just meeting

33 progressively lower alternative standard levels narrows the distribution of risk across the range 

 11 By low baseline concentrations, we mean area-wide average O3 concentrations between approximately 10 and 40 

ppb prior to adjustments to just meet the existing and alternative standards.
9-24

1 of O3 concentrations. In addition, the distribution of risk tends to be more centered on area-wide 

2 average concentrations in the range of 25 to 55 ppb after just meeting an alternative standard of 

3 60 ppb. The focus of the epidemiological studies on urban case study area-wide average O3

4 concentrations, and the lack of thresholds coupled with the linear nature of the C-R functions 

5 mean that in this analysis, the impact of a peak-based standard (which seeks to reduce peak 

6 concentrations regardless of effects on low or mean concentrations) on estimates of mortality and 

7 morbidity risks based on results of those studies is relatively small. For example, for mortality 

8 and hospital admissions, we find a less than 10 percent reduction in risk for most urban case 

9 study areas when just meeting the 70 ppb and 65 ppb alternative standards compared to just 

10 meeting the existing standard, and a less than 25 percent reduction in risk for all urban case study 

11 areas when just meeting the 60 ppb standard compared to just meeting the existing standard. The 

12 general pattern for other morbidity risks is similar to hospital admissions. However, we are not 

13 able to draw strong conclusions about the results across urban case study areas, because of the 

14 limited number of urban case study areas represented for most of the endpoints.

15 We have applied city-specific mortality effect estimates to each urban case study area 

16 based on the largest multi-city epidemiological study. However, for many of the urban case study 

17 areas, the risk estimates have wide confidence intervals that can include zero, due to the lower 

18 statistical power of some of the city-specific effect estimates relative to the national combined 

19 effect estimate across cities. Furthermore, there is significant variability in these effect estimates 

20 across the 12 urban case study areas, with some urban case study areas having effect estimates 

from 5 to 7 times greater than other cites (see Chapter 7, section 7.4.1).

12 21 The variability in effect 

22 estimates, along with differences in O3 concentrations, is a driver for the overall variability in the 

23 risk results across cities. Smith et al (2009) reports an overall significant national mortality effect 

24 estimate with confidence intervals that do not include zero, reflecting the much greater statistical 

25 power available when pooling information across urban case study areas.

26 We also evaluated mortality risks in the 12 urban case study areas associated with long27 term O3 exposures (based on the seasonal average (April to September) of the peak daily one28 hour maximum concentrations). Risks from long-term exposures after just meeting the existing 

29 standard are substantially greater than risks from short-term exposures, ranging from 16 to 20 

30 percent of respiratory mortality across urban case study areas. However, the percent reductions 

31 in long-term mortality risks are similar to those for mortality from short-term exposures. For 

32 example, we find a less than 10 percent reduction in risk relative to just meeting the existing 

 12 This substantial heterogeneity in effect estimates can reflect a number of factors including differences in 

population susceptibility and behavior related to O3 exposure and risk (e.g., proximity to roadways, use of air 

conditioning, commuting patterns, time spent outdoors) and differences in the degree to which the O3

monitoring network used in the epidemiological study reflects patterns of population exposure. 
9-25

1 standard in most areas when just meeting the 70 ppb and 65 ppb alternative standards, and a less 

2 than 20 percent reduction when just meeting the 60 ppb alternative standard level. Risk 

3 reductions for the New York City urban case study area are much greater when just meeting the 

4 65 ppb alternative standard compared to just meeting the existing standard, with a 24 percent 

5 reduction in risk in 2007.

6 New York and Los Angeles have characteristics that make epidemiological risk estimates 

7 particularly uncertain. In the case of New York, the expansion of the urban case study area

8 definition to the CBSA adds uncertainty due to the large and diverse nature of the CBSA. The 

9 New York CBSA includes two urban case study areas which have separate effect estimates 

10 available from the Smith et al. (2009) study. These separate effect estimates (for Newark, NJ and 

11 Jersey City, NJ) are smaller than the effect estimate for New York, however, they are also based 

12 on much smaller populations, and have relatively wider confidence bounds, reflecting low 

13 statistical power. For consistency with other urban case study areas and to allow for comparison 

14 between the CBSA-based risk estimates and the smaller study area based estimates (see the 

15 sensitivity analyses in Chapter 7), we elected to apply the New York city effect estimate, which 

16 is based on a very large population and has high precision, to all of the counties in the New York 

17 CBSA. While this adds substantial uncertainty to the absolute incidence of mortality for the New 

18 York CBSA, it does not affect the pattern of risk reductions when just meeting alternative 

19 standards. In addition, as noted earlier, the O3 adjustments to meet existing and alternative 

20 standards in New York and Los Angeles also have additional uncertainties relative to the other 

21 10 urban case study areas.

22 We conducted a number of sensitivity analyses based on a population normalized 

23 mortality risk metric, e.g. mortality risk per 100,000 population. Maintaining the general linear, 

24 no-threshold functional form, mortality risks per 100,000 population are generally robust to 

25 alternative specifications of the C-R functions, although in several urban case study areas, using 

26 effect estimates from Smith et al. (2009) which were derived using regional priors rather than 

national priors results in higher risk estimates13 27 . Using the effect estimates from Zanobetti and 

28 Schwartz (2008) has no consistent effect on risk results across the urban case study areas. Using 

29 effect estimates based on a copollutant model with PM10, mortality risks are higher in some 

30 locations and lower in others. However, in all locations the confidence intervals are substantially 

 13 In Bayesian modeling, effect estimates are “updated” from an assumed prior value using observational data. In the 

Smith et al (2009) approach, the prior values are either a regional or national mean of the individual effect 

estimates obtained for each individual city. The Bayesian adjusted city specific effect estimates are then 

calculated by updating the selected prior value based on the relative precision of each city-specific estimate 

and the variation observed across all city-specific individual effect estimates. City-specific estimates are pulled 

towards the prior value if they have low precision and/or there is low overall variation across estimates. Cityspecific estimates are given less adjustment if they are precisely estimated and/or there is greater overall 

variation across estimates.
9-26

1 wider using the copollutant model with PM10 (due to fewer days with both pollutants measured), 

2 which makes it difficult to determine whether the increases and decreases in estimates relative to 

3 the core estimates are real or the result of statistical error.

4 We selected the CBSA as the spatial definition for the urban case study areas. We made 

5 this selection to address a downward bias that we identified resulting from a mismatch between 

6 the smaller urban core areas used in the epidemiology studies and the larger areas where O3

7 concentrations are expected to change as a result of meeting the existing and alternative standard 

8 levels (see Chapter 7). We included a sensitivity analysis evaluating the result of using a smaller 

9 geographic area including only the counties used in the epidemiology study. As expected, using a 

10 smaller geographic extent for the urban case study areas results in smaller, and in some cases 

11 negative risk reductions when compared to using the CBSA definitions. This reflects the fact that 

the controlling14 12 monitor in many of the 12 urban case study areas is located outside of the small 

13 set of counties included in the Smith et al. (2009) urban case study area definitions, and some of 

14 the monitors that are within that more limited spatial extent are more prone to O3 titration due to 

15 local NOx emission sources. As a result, those monitors are more likely to see increases in O3

16 which will, if other monitors with higher concentrations in the broader regions are not included, 

17 lead to estimated increases in risk due to the application of a linear, no threshold C-R function. 

18 This bias can be substantial, especially in St. Louis and several urban case study areas in the 

19 Northeast, including Boston, New York, and Philadelphia, where the highest concentration 

20 monitors are outside the Smith et al. (2009) urban case study area definitions.

21 Sensitivity analyses were conducted for scenarios of just meeting existing and alternative 

22 standards using combinations of NOx and VOC emissions reductions (as compared to NOx 

23 reductions alone). The addition of VOC emissions reductions had little impact with the exception 

24 of New York and Los Angeles, where risk was decreased relative to the NOx-only reduction 

25 scenario.

26 9.2.4.2 National-scale Assessment Results

27 The national-scale assessment evaluated only mortality associated with recent O3

28 concentrations across the entire U.S for 2006-2008. The national-scale assessment is a 

29 complement to the urban scale analysis, providing both a broader geographic assessment of O3-

30 related health risks across the U.S., as well as an evaluation of how well the 12 urban study areas 

31 represented the full distribution of O3-related health risks in the U.S. The national-scale 

32 assessment demonstrates that there are O3 risks across the U.S, not just in urban case study areas, 

33 even though the O3 concentrations in many areas were lower than the existing standard level. 

 14 The controlling monitor is the monitor with the highest design value within a defined non-attainment area.
9-27

1 While we did not assess the changes in risk at a national level associated with just meeting 

2 existing and alternative standards, just meeting existing and alternative standards would likely 

3 reduce O3 concentrations both in areas that are not meeting those standards and in locations 

4 surrounding those areas, leading to risk reductions that are not included in the urban-scale 

5 analysis.

6 9.3 COMPARISON OF RESULTS ACROSS EXPOSURE, LUNG FUNCTION RISK, 

7 AND EPIDEMIOLOGY-BASED MORTALITY AND MORBIDITY RISK 

8 ANALYSES

9 In considering the overall results across the human exposure, lung function risk, and 

10 epidemiology-based risk assessments, we focus on the key policy-relevant metrics and levels for

11 each type of assessment. For the human exposure assessment, we selected exposures above the 

12 60 ppb exposure benchmark for all children (ages 5-18). We select this exposure metric because 

13 children represent a key at-risk lifestage, and the 60 ppb exposure benchmark is the lowest 

14 exposure level associated with significant findings in controlled human exposure studies. For the 

15 lung function risk assessment, we selected the results for lung function decrements greater than 

16 or equal to 10 percent for all children (ages 5-18). We select this lung function risk metric 

17 because children represent a key at-risk lifestage, and a 10 percent lung function decrement 

18 represents a potentially more adverse event in asthmatic children. For the epidemiology-based 

19 risk assessment we selected the core short-term exposure mortality results and the respiratory 

20 hospital admission results, because these endpoints were estimated for all of the 12 urban case 

21 study areas. Generally speaking, these metrics provide the most differentiation between the 

22 alternative standards, helping to inform policy-relevant questions regarding adequacy of the 

23 existing standard, and public health impacts of meeting alternative standards. The other metrics 

24 analyzed in this REA (e.g. other exposure benchmarks and other lung function decrements) show 

25 less response to just meeting the existing standard or potential alternative standard levels.

26 As discussed in Chapter 2, we designed the exposure and risk assessment to help inform 

27 two fundamental questions related to the adequacy of the existing standard in protecting public 

28 health and the degree of exposure and risk reductions associated with alternative standards 

29 compared with the existing standard. The following discussion evaluates the three types of 

30 analyses we conducted in terms of the consistency of the information provided to inform these 

31 questions.

32 9.3.1 Evaluation of Exposures and Risks After Just Meeting the Existing Standard

33 To compare the results of the three assessments in urban case study areas, we plot the key 

34 metrics from each analysis across urban case study areas for the two common years of analysis 

35 (i.e., 2007 and 2009). For three urban case study areas (i.e., Chicago, Dallas, and Washington 
9-28

1 D.C.) we have only the exposure and lung function risk assessments, as these urban case study 

2 areas did not have sufficient information to estimate epidemiology-based risks. The 

3 epidemiology-based metrics are the percent of baseline short-term exposure mortality, based on 

4 the core estimates using the C-R functions from Smith et al. (2009), and respiratory hospital 

5 admissions based on the core estimates using the C-R functions from Medina-Ramon (2006),

6 attributable to O3. Figure 9-8 presents the exposures and risks after just meeting the existing 

7 standard of 75 ppb. Each row represents one of the key analytical results; each column gives the 

8 results for 2007 and 2009 for each urban case study area. The scale of each analytical metric for 

9 each analysis differs, and thus the comparisons across analyses should focus on overall patterns 

10 rather than on direct comparisons of numeric estimates.

11 All of the metrics show substantial variability among urban case study areas, although 

12 there appears to be less variability in lung function risk and hospital admission risk compared 

13 with the exposure metric and mortality risk. The differences between estimates for 2007 and 

14 2009 are much higher for some urban case study areas (e.g. Baltimore and Philadelphia) for the 

15 exposure metric than any of the risk metrics. This may reflect the explicit threshold nature of the 

16 exposure metric, which focused on exposures above a benchmark level of 60 ppb. Differences 

17 between years in exposures above the 60 ppb benchmark after just meeting the existing standard 

18 are dependent on the number of days during each year with decreases in higher O3

19 concentrations, as well as the magnitude of the decreases in O3 on those higher O3 concentration 

20 days. These in turn are sensitive to the shape of the O3 distribution in the analytical year prior to

21 just meeting the existing standard (which determines the starting number of days above 60 ppb)

22 and the response to emissions reductions applied in meeting the existing standard for 2007 or 

23 2009. There is some consistency between metrics in the urban case study areas with highest 

24 values for the exposure and lung function risk metrics. However, there were still differences, 

25 especially for Los Angeles, which had one of the higher values for lung function risk in 2009, 

26 but had one of the lower percentages of children exposed above the 60 ppb benchmark. This 

27 again points to the importance of the threshold nature of the exposure metric, combined with the 

28 tendency for more substantial decreases in peak O3 concentrations relative to mid-range and low 

29 concentrations when just meeting the existing standard.

30 There is little consistency within urban case study areas between the epidemiology risk 

31 metrics and the exposure and lung function risk metrics, and there is also little consistency 
9-29

1 between the mortality and hospital admission risks. Houston has the lowest metric values in 2007 

2 (except for mortality risk), but in 2009 has some of the higher risk metrics (except for hospital 

3 admission risk). New York has the highest mortality risk in 2007 and 2009 but has among the 

4 lowest hospital admission risks in both years.
9-30

Figure 9-8 Comparison of Exposure (Row 1) Lung Function Risk (Row 2) and Epidemiology-Based Risk (Rows 3 and 4)

Metrics after Just Meeting the Existing 75 ppb Standard.

Atlanta Baltimo Boston Chicag Clevela Dallas Denve Detroit Housto Los An New Yo Philade Sacram St. Lou Washin

0

10

20

0

5

10

15

20

0

1

2

3

4

0

1

2

3

60 ppb Exposure % of Children Exc 

>=10% FEV1 Dec% of Children with 

Attributable to O3% of Baseline Mo 

HA Attributable to % of Baseline Res 

Year

2007

2009
9-31

1 9.3.2 Reductions in Exposure and Risk Metrics after Just Meeting Alternative Standards

2 To compare the results of the three assessments for urban case study areas after just 

3 meeting alternative standards relative to the existing standard, we express each result as a percent 

4 of the metric value when just meeting the existing standard. Figure 9-9 presents the percent 

5 reduction in exposures and risks after just meeting alternative standards relative to just meeting 

6 the existing standard of 75 ppb. In this plot, each row represents one of the key analytical results

7 and each column gives the results for 2007 and 2009 for each urban case study area. The scales 

8 are the same between analyses, and as such, it is informative to examine both the overall patterns 

9 of change between alternative standards, and also the absolute value of the percent reductions in 

10 risk metrics between analyses. In interpreting this chart, higher values mean greater reductions in 

11 risk or exposure relative to just meeting the existing standards. Because these are percent 

12 reductions, the maximum value is one hundred percent, which if reached would indicate that 

13 risks or benchmark exposures are completely eliminated when the alternative standard is met in 

14 the urban case study area as was seen for the 60 ppb exposure benchmark.

15 Many of the differences in results across the metrics are driven by how each metric is 

16 affected by the O3 data input to the analysis. In general, the impact of the HDDM adjustments to 

17 O3 vary based on three main considerations: 1) the degree to which the exposure or risk metric is 

18 sensitive to changes across the various ranges of O3 concentrations (e.g. high, mid-range, low); 

19 2) whether the exposure or risk metric uses individual census tract concentrations or area-wide 

20 average concentrations; and 3) changes in the distribution of O3 concentrations in the year of 

21 analysis between recent O3 concentrations and adjusted (meeting the existing or alternative 

22 standards) O3 scenarios. With respect to 1), the exposure benchmark metric, which focuses only 

23 on exposures above 60 ppb, will not be sensitive at all to changes in O3 concentrations in the 

24 range below 60 ppb. The lung function risk metric, which depends on the dose rate and 

25 individuals’ characteristics, does not have a concentration threshold. However, because of the 

26 logistic form of the response function, it is less sensitive to lower O3 concentrations and has very 

27 few FEV1 responses greater than 10 percent when exposure concentrations are below 20 ppb and 

28 very few FEV1 responses greater than 15 percent when exposure concentrations are below 40 

29 ppb. On the other hand, the mortality and hospital admission risk metrics are based on non30 threshold, approximately linear C-R functions, and therefore will be sensitive to changes in O3

31 along the full range of O3. As discussed in Chapter 4, because O3 at lower concentrations may 

32 increase following HDDM adjustment in some locations and on some days to just meet

alternative standards15 33 , this can lead to increases in risk on some days, which can lead to a net 

 15 The frequency and magnitude of increases in spatially averaged mean concentrations in an urban case study area

occur during a season when adjusting air quality to just meet a standard vary considerably between the existing 
9-32

1 increase or decrease in risk over the entire year, depending on whether the days with increased 

2 risk exceed days with decreased risk (generally due to a preponderance of days with lower O3

3 concentrations). With respect to 2), the exposure and lung-function risk metrics are based on 

4 concentrations at individual census tracts since they depend on O3 exposure modeled by moving 

5 each individual through their environment. Because of this, the exposure and lung-function risk 

6 metrics are most affected by the spatial and temporal variability of O3 concentrations across the 

7 urban case study area. The mortality and hospital admission risk metrics are calculated applying 

8 C-R functions to area-wide, daily maximum 8-hr average O3 concentrations. As a result, the 

9 spatial variability in O3 concentrations between the monitors will only influence the 

10 epidemiology-based risk estimates in how they influence the area-wide average. With respect to 

11 3), all three metrics are influenced by how the distribution of O3 concentrations changes between 

12 recent O3 conditions and after adjustment to just meet existing and alternative O3 standard levels.

 and alternative standards. The highest frequency of occurrence of days with increasing O3 happens when 

adjusting air quality to just meet the existing standards, and decreases as air quality is further adjusted to just 

meet lower alternative standard levels.
9-33

Figure 9-9 Comparison of Percent Reduction in Key Risk Metrics for Alternative Standard Levels Relative to Just Meeting 

the Existing 75 ppb Standard.

Atlanta Baltimo Boston Chicag Clevela Dallas Denver Detroit Housto Los An New Yo Philade Sacram St Loui Washin

0

25

50

75

100

0

25

50

75

100

0

25

50

75

100

0

25

50

75

100

Exposures>60ppb Lung Function Ris Mortality Hospital Admissio

2007 2009 2007 2009 2007 2009 2007 2009 2007 2009 2007 2009 2007 2009 2007 2009 2007 2009 2007 2009 2007 2009 2007 2009 2007 2009 2007 2009 2007 2009

Analytical Year

% Reduction from Just Meeting Existing Standard

Alternative Standard

70 ppb

65 ppb

60 ppb
9-34

1 The exposure and lung function risk metrics are most affected by the reductions in the 

2 individual monitors’ peak O3 concentrations, including the magnitude of these reductions and the 

3 number of days that experience these reductions. In contrast, the mortality and hospital 

4 admission risk metrics are affected by changes in the mean of the seasonal, area-wide average O3

5 concentrations, where the mean is determined by the frequency and magnitude of increases 

versus decreases in area-wide, maximum daily 8-hr O3 concentrations16 6 . In addition to O3

7 concentrations, there are other factors that affect the variability across urban case study areas for 

8 these three metrics, such as activity data and exposure factors for the exposure and lung function 

9 risk metrics and the study-specific C-R functions for the mortality and hospital admission risk 

10 metrics.

11 One clear observation is that the percent reductions in risk from meeting alternative 

12 standard levels relative to meeting the existing standard for the two epidemiology-based 

13 endpoints are much smaller than for the exposure benchmark and lung function risk endpoints. 

14 The maximum percent reduction in the mortality and hospital admissions risk relative to just 

15 meeting the existing standard across years, locations, and alternative standards is less than 25 

16 percent, and for many years/locations, the reductions in these risks when just meeting the lowest 

17 alternative standard, 60 ppb, are less than 10 percent. The exposure benchmark results show the 

18 most reductions when comparing just meeting the existing standard to just meeting alternative 

19 standards. Just meeting the 65 ppb standard results in reductions in the percent of children 

20 exceeding the 60 ppb exposure benchmark by over 50 percent in all urban case study areas, and 

21 by over 75 percent in 12 of the 15 urban case study areas evaluated. For most locations and 

22 years, just meeting the 60 ppb alternative standard reduced the percent of children exceeding the 

23 60 ppb exposure benchmark by over 90 percent compared to just meeting the existing standard. 

24 Reductions in lung function risk were also much higher than reductions in mortality and hospital 

25 admissions risk. Just meeting the 65 ppb standard results in reductions in lung function risk by 

26 over 25 percent in most locations and years, and just meeting the 60 ppb standard results in 

27 reductions by over 40 percent in most locations and years.

28 There is general consistency in the city-to-city patterns of reductions in the exposure and 

29 lung function risk metrics, although the decreases in lung function risk are less than half as large 

30 as the reductions in the percent of children exceeding the 60 ppb exposure benchmark (with the 

31 clear exception of New York city, which we will discuss further below). The patterns of 

32 reductions in mortality and hospital admission risk are generally consistent with the patterns for 

 16 As noted previously, changes in the spatial extent of the urban case study areas over which monitors are averaged 

can change the magnitude and sign of the change in the spatial average O3 concentration for an urban case 

study area. For example, we found that we bias the risk estimates low when using urban case study area 

definitions that include only urban core counties and not the counties with monitors experiencing the most 

reductions in O3.
9-35

1 exposure and lung function risk for 2007, with the exception of Houston and Philadelphia. 

2 However, for 2009, the patterns for mortality and hospital admission risk are quite different, both 

3 from the 2007 results, and from the exposure and lung function risk results. This is due to the 

4 generally lower O3 concentrations in 2009, which results in a greater number of days with 

5 predicted increases in O3 concentrations at low concentrations, fewer days with very high 

6 concentrations where predicted reductions in O3 occur, and a smaller predicted decrease in O3

7 concentrations on those high days. This affects the mortality and hospital admissions risk more 

8 than the exposure and lung function risk metrics because those metrics incorporate thresholds, 

9 and therefore are not responsive to changes in O3 concentrations below those thresholds. 

10 Additional considerations are important in interpreting the reduction in exposure and risk 

11 between the existing standard and alternative standards. The REA analyses focus on reducing 

peak O3 concentrations, in particular the 4th 12 high O3 concentration averaged over 3-years so as to 

13 simulate meeting the existing standard or various alternative standards. In addition, the air 

14 quality adjustments are based on applying reductions in U.S. anthropogenic emissions. In this 

15 way, the adjusted air quality reflects day-to-day O3 concentrations that could occur when 

16 focusing on reducing high O3 concentrations rather than on reducing mean O3 concentrations. In 

17 addition, because the analyses do not include reductions of O3 precursor emissions from sources 

18 other than U.S. anthropogenic emissions (e.g. international emissions, biogenics, etc), the O3

19 concentrations in the adjusted air quality account for O3 created from natural and international 

20 sources, even if 100 percent emissions reductions are applied to U.S. anthropogenic sources in 

21 adjusting air quality scenarios.

22 Finally, with respect to the epidemiology based analyses, we note that 2007, which had 

23 generally higher O3 concentrations than 2009, had more days where O3 concentrations decreased 

24 as a result of adjusting peak O3 concentrations to just meet alternative standards. Thus just 

25 meeting alternative standards resulted in net decreases in risk in all locations, with the exception 

26 of Houston for just meeting the 70 ppb alternative standard. In contrast, 2009, which had 

27 generally lower concentrations than 2007, had more days in the range where O3 concentrations 

28 were increased as a result of adjusting peak O3 concentrations to just meet alternative standards, 

29 and thus the patterns reflect some locations where mortality and hospital admissions risk 

30 increases. However, for 2009, in all locations, when just meeting the lowest alternative standard 

31 of 60 ppb, mortality and hospital admission risks are decreased relative to just meeting the 

32 existing standard.

33
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1 9.4 OVERALL ASSESSMENT OF REPRESENTATIVENESS OF EXPOSURE AND 

2 RISK RESULTS 

3 9.4.1 Representativeness of Selected Urban Case Study Areas in Reflecting Areas Across 

4 the Nation with Elevated Risk

5 We selected urban case study areas for the exposure and risk analyses based on several 

6 criteria (e.g. recent elevated O3 concentrations and presence of at-risk populations and lifestages)

7 we identified as likely indicators of areas and populations likely to experience high O3 exposures 

8 and risks (see Section 7.3.1). We then conducted several analyses to determine the extent to 

9 which our selected urban case study areas actually represent the highest mortality and morbidity 

risk areas. We compared the distributions of risk characteristics17 10 and mortality risk (based on 

11 recent O3 concentrations) for the 12 urban case study areas used in the epidemiology-based risk 

12 assessment with the corresponding national distributions. We also evaluated the degree to which 

13 our selected urban case study areas represent the patterns of O3 concentration changes 

14 experienced by the overall U.S. population.

15 Based on the comparisons of distributions of risk characteristics, the selected urban case 

16 study areas represent urban case study areas that are among the most populated in the U.S., have 

17 relatively high peak O3 concentrations, and capture well the range of city-specific mortality risk 

18 effect estimates. These three factors alone would suggest that the case study urban case study 

19 areas should capture well the overall risk for other heavily populated urban case study areas in 

20 the nation, with a potential for better characterization of the high end of the risk distribution. The 

21 selected urban case study areas do not include those with the highest numbers of some at-risk 

22 populations or lifestages, specifically older people with high baseline mortality rates. However, 

23 most locations in the U.S. (except Florida) with high percentages of older people have low 

24 overall populations, less than 50,000 people in a county, or low O3 concentrations. This suggests 

25 that while the risk per exposed person per ppb of O3 may be higher in these locations, the overall 

26 risk to the population is likely to be within the range of risks represented by the urban case study 

27 locations.

28 Based on the comparisons of distributions of short-term O3 exposure mortality risk (using 

29 the percent of mortality metric) for recent O3 concentrations, the 12 selected urban case study 

30 areas are representative of the full distribution of U.S. O3-related mortality risk in urban case 

31 study areas. Two of the selected areas, New York and Philadelphia are representative of the 

32 highest end of the distribution of short-term O3 mortality risk. Overall, O3 mortality risk for 

33 short-term O3 exposures in the 12 urban study areas are representative of the full distribution of 

 17 In this context, risk characteristics are the elements of populations, air quality, and inputs to the C-R functions that 

are expected to be correlated with estimated mortality risks (see Chapter 8).
9-37

1 U.S. urban O3-related mortality, representing both high end and low end risk counties. For the 

2 long-term O3 exposure mortality risk metric (again using the percent of mortality), the 12 urban 

3 study areas are representative of the central portion of the distribution of risks across all U.S. 

4 counties; however, the selected 12 urban case study areas do not capture the very highest (greater 

than 98th percentile) or lowest (less than 25th 5 percentile) ends of the national distribution of long6 term exposure-related O3-related risk.

7 9.4.2 Representativeness of Selected Urban Case Study Areas in Reflecting 

8 Responsiveness of Risk to Just Meeting Existing and Alternative O3 Standards

9 While we selected urban case study areas to represent those populations likely to 

10 experience elevated risks from O3 exposure, we did not include among the selection criteria the 

11 responsiveness of O3 in the urban case study area to decreases in O3 precursor emissions that 

12 would be needed to just meet existing or alternative standards.

13 In our preliminary evaluations of risk modeling results, we observed a consistent 

14 presence of days with low to midrange starting O3 concentrations for which O3 concentrations 

15 (using the 8-hour maximum metric) increased after adjustments to just meet the existing and 

16 alternative standards across the selected urban case study areas. As noted above, this led to 

17 estimates of increased risk on those days, and in some cases, estimates of increased risk over the 

18 course of the O3 season, reflecting both the magnitude and frequency of the predicted increases 

19 relative to the predicted decreases in O3 concentrations. As explained above, this pattern was

20 more pronounced when using a more spatially limited definition of the urban case study areas, 

21 but even when using the CBSA definitions, there were still days when the area-wide average O3

22 increased, primarily due to predicted increases in O3 in the core counties of the urban case study

23 areas.

24 In order to better understand how prevalent this type of air quality response was across 

25 the U.S., we conducted several additional analyses of O3 concentrations. These included

26 evaluations of trends at O3 monitors during a period of time with significant O3 precursor 

27 emission reductions, and evaluations of temporal and spatial patterns of O3 changes across the 

28 U.S., based on air quality modeling results, to simulate how O3 would change across the U.S. in 

29 response to NOx (and VOC) emissions reductions (relative to recent 2007 levels) similar to those 

30 used in the HDDM adjustments (see section 9.2.1 above). The latter analysis includes an 

31 assessment of the association of different types of O3 responses with population counts to help 

32 characterize the degree to which populations in the U.S. experience O3 conditions like those in 

33 the selected 15 urban case study areas (see Chapter 8).

34 Overall, both types of analyses showed that decreases in O3 precursor emissions lead to 

35 decreases in O3 concentrations in areas with higher starting O3 concentrations, which tend to be 

36 rural or suburban case study areas, and on days with higher O3 concentrations. The analyses also 
9-38

1 indicate that in urban core areas (those with high levels of fresh NOx emissions), decreases in 

2 NOx emissions can lead to increases in O3, primarily for days when initial O3 concentrations are 

3 suppressed due to NOx titration. The observed widespread decreases of median O3 in suburban 

4 and rural locations when NOx emissions are decreased suggest the efficacy of large NOx 

5 emissions reductions on reducing O3 over large regions of the country.

6 These results suggest that many of the urban case study areas may show O3 responses 

7 that are typical of other large urban case study areas in the U.S., but may not represent the 

8 response of O3 in other populated areas of the U.S., including suburban case study areas, smaller 

9 urban case study areas, and rural areas. These smaller urban case study areas would be more 

10 likely than our urban case study areas to experience area-wide average decreases in mean O3

11 concentrations as O3 standards are met. Even though large urban case study areas throughout the 

12 U.S. have high population density, 73 percent of the U.S. population lives outside of these high 

population density areas18 13 , and thus, a large proportion of the population is likely to experience 

14 greater mortality and morbidity risk reductions in response to reductions in 8-hour O3

15 concentrations than are predicted by our modeling in the selected urban case study areas. The 

16 analyses presented in Section 8.2.3.2 show that populations in the case study areas we selected 

17 are approximately twice as likely to experience increasing mean O3 concentrations as

18 populations in the U.S. as a whole. Because our selection strategy for risk modeling was focused 

19 on identifying areas with high risk, we tended to select large urban population centers. As 

20 discussed in the previous section, this strategy was largely successful in including those urban 

21 case study areas in the upper end of the O3 risk distribution. However, this also has led to an 

22 overrepresentation of the populations living in locations where we estimate increasing mean 

23 seasonal O3 in response to adjusting air quality to just meet the existing and alternative standards 

24 using NOx emissions reductions. The implication of this is that our estimates of mortality and 

25 morbidity risk reductions for the selected urban case study areas are likely to understate the 

26 average risk reduction that would be experienced across the population and should not be seen as 

27 representative of potential risk reductions for most of the U.S. population.

28 9.5 OVERALL ASSESSMENT OF CONFIDENCE IN EXPOSURE AND RISK 

29 RESULTS

30 As with any complex analysis using estimated parameters and inputs from numerous data 

31 sources and models, there are many sources of uncertainty that may affect our exposure and risk 

32 estimates. These sources of uncertainty are discussed in each of the chapters related to air 

33 quality, exposure, lung function risk, and epidemiology based mortality and morbidity risk. The 

 18 High population density areas are defined here as locations with population densities greater than 1000 

people/km2
9-39

1 overall effect of the combined set of uncertainties on confidence in the interpretation of the 

2 results of the analyses is difficult to quantify. However, we provide our judgment of our overall 

3 confidence here, with an understanding that alternative judgments may also be supported.

4 The degree to which each analysis was able to incorporate quantitative assessments of 

5 uncertainty differed, due to differences in available information on uncertain parameters and 

6 complexities in propagating uncertainties through the models. In general, we followed the World 

7 Health Organization tiered approach to uncertainty characterization (WHO, 2008), which 

8 includes both quantitative and qualitative assessments. Each chapter includes a table identifying 

9 and characterizing the potential impact of key uncertainties on risk estimates, including the 

10 degree to which we were able to quantitatively address those uncertainties.

11 In considering our overall confidence in the results, there are several key considerations 

12 discussed below related to sources of uncertainty which we were not able to fully quantify, but 

13 which may have a large impact on both overall confidence and confidence in individual analyses.

14 9.5.1 Uncertainties in Modeling O3 Responses to Meeting Standards

15 There is inherent uncertainty in all deterministic air quality models, such as CMAQ, the 

16 photochemical grid model used to develop the model-based O3 adjustment methodology. 

17 Evaluations of air quality models against observed pollutant concentrations build confidence that 

18 the model performs with reasonable accuracy despite both structural and parametric 

19 uncertainties. A comprehensive model performance evaluation provided in Appendix 4-B shows 

20 generally acceptable model performance that is equivalent to or better than typical state-of-the 

21 science regional modeling simulations described in Simon et al. (2012). Two additional sources 

22 of uncertainties in the HDDM adjustment methodology are the applicability of HDDM 

23 sensitivities over large emissions perturbations and the variability in data used to create 

24 regressions which allowed the application of these sensitivities to ambient data. Both sources of 

25 uncertainty are shown to be reasonably small in chapter 4 with the first having a mean error of 

26 less than 1ppb for 50% NOx cuts and less than 4 ppb for 90% NOx cuts. The uncertainty 

27 introduced from the application of regressions to determine sensitivities were quantified by 

28 propagating uncertainties in the sensitivities through to uncertainties in the final predicted O3

29 concentrations which had standard errors less than 1.4 ppb for all adjustment scenarios. New 

30 York and Los Angeles had the largest uncertainties in these two areas due to the fact that they 

31 required the largest reductions in NOx emissions. Uncertainties stemming from the application 

32 of 8-months of model data to 5-years of ambient data and the across-the-board emissions cut 

33 assumptions are further discussed in chapter 4 but are not expected to substantially degrade 

34 confidence in the air quality results.
9-40

1 9.5.2 Uncertainties in Modeling Exposure and Lung-function Risk

2 With regard to the exposure and lung-function risk estimates, the modeling explicitly 

3 incorporates population variability in many of the modeling inputs. We did not attempt to 

4 probabilistically incorporate the many sources of uncertainty in model parameters or input data 

5 due to limitations in the ability to specify distributions characterizing our confidence in those 

6 variables. To explore the impacts of some of the more important sources of uncertainty, we 

7 conducted a limited set of sensitivity analyses. For the exposure assessment, the estimate of 

8 repeated exposures above exposure benchmarks is based on the limited set of diaries of activity 

9 data available in the Consolidated Human Activity Database (CHAD) database (see Chapter 5). 

10 The method for constructing activity patterns over the course of an O3 season may not fully 

11 capture the behavior of children who have systematically high outdoor activity levels. As a 

12 result, while we are able to report the percent of children with two or more exposures, modeling 

13 of the distribution of multiple exposures is limited, and the ability to identify the percent of the 

14 population with unusually high numbers of multiple exposures is not possible.

15 For the lung function risk assessment, sensitivity analyses indicate that the MSS model 

16 parameter related to the impact of the ventilation rate was most influential in determining the 

17 estimated number of children with FEV1 decrements greater than 10 percent. Estimates of lung 

18 function decrements are also influenced by how much variability in individual response is 

19 assumed in the MSS model. Sensitivity analyses indicate that when a greater amount of 

20 variability is allowed in the MSS model, the percent of children ages 5-18 with FEV1 decrements 

21 greater than 10 percent can increase substantially. In addition, we performed analyses to 

22 understand the age-related factors in APEX that could influence the estimated FEV1 decrements. 

23 It was found that the four most influential factors influencing the relationship between the 

24 predicted FEV1 decrement and age are the decreasing level of exertion, the decreasing equivalent 

25 ventilation rate (with increasing age), the higher time spent outdoors by children, and the higher

26 exposure concentration experienced by children while outdoors. These all lead to children having 

27 higher FEV1 decrements than adults, and are more influential than the MSS model age term.

28 9.5.3 Uncertainties in Modeling Epidemiological-based Risk

29 A major issue in using the results of the epidemiology studies in estimating risk is the 

30 narrow geographic definition used for urban case study areas in the epidemiology studies. In 

31 many of the urban case study areas, we observe two distinct patterns of O3 response to the 

32 reductions in precursor emissions we evaluated to just meet the existing and alternative standard

33 levels. The first pattern generally occurs in areas outside the urban core (e.g. suburban and rural 

34 areas), and on days when O3 concentrations are on the higher end of the distribution of O3

35 concentrations, and is characterized by predicted decreases in 8-hour O3 concentrations. These 
9-41

1 tend to be the locations where the highest 8-hour design values occur. The second pattern 

2 generally occurs in the urban core, and on days when O3 concentrations are on the lower end of 

3 the distribution of O3 concentrations, and is characterized by predicted increases in 8-hour O3

4 concentrations. The narrow definitions of urban case study areas used in the epidemiological 

5 studies generally included the urban core areas, but did not include all of the suburban or rural 

6 areas. The narrow geographic definitions led to a clear downward bias in the estimates of risk 

7 changes that would be associated with just meeting the standards in the urban case study areas, 

8 because the risk changes would reflect the locations with a tendency towards increases in 8-hour 

9 O3, but would not include locations outside the urban core with decreases in O3. In many cases, 

10 the narrowly defined geographic definitions used in the epidemiology studies did not even 

11 include the location with the monitor that was violating the standard. We addressed this bias by 

12 expanding the urban case study area to the CBSA. However, this adds additional uncertainty to 

13 the risk estimates, and reduces our confidence that we have a good match between the basis of 

14 the C-R function (just urban core locations) and the risk analysis context (including both urban 

15 core counties and other counties in the CBSA). A clear implication of this decision is that the 

16 absolute incidence estimates will be larger than if the analysis was limited to a smaller number of 

17 counties. For this reason, we have placed more emphasis on risk metrics that have been

18 normalized for population size (e.g. risks per 100,000 population and percent risk), so as to 

19 facilitate comparisons between cities of different population sizes and to reduce the influence of 

20 population size on the risk metrics.

21 The epidemiology studies used as the source for C-R functions for short-term exposure 

22 mortality and morbidity endpoints all use time-series approaches to estimate the effect of daily 

23 variations in O3 concentrations on daily mortality or morbidity incidence. The effect estimates 

24 developed in these epidemiology studies were based on air quality and health information 

25 observed over periods of time in the past (1987-2000). These effect estimates were based on day26 to-day variations in area-wide O3 concentrations estimated from observed concentrations at 

27 monitors that reflect a specific set of emissions and atmospheric conditions. In our REA 

28 analyses, we apply these effect estimates to adjusted air quality scenarios that are reflective of 

29 substantial changes in O3 concentrations across an area due to, in some cases, large decreases in 

30 NOx and VOC emissions reductions. The resulting spatial and temporal patterns of O3 may not 

31 be the same as the spatial and temporal patterns of O3 that existed at the time of the 

32 epidemiology study. The potential for different spatial and temporal patterns in O3

33 concentrations between the adjusted air quality scenarios and the air quality observed during the 

34 epidemiology study period potentially adds uncertainty to the estimates of risk, as it is not clear 

35 the degree to which the exposure surrogate used in the epidemiology study correlates with the 

36 exposure surrogate used in the risk analysis. The degree of this potential uncertainty increases 
9-42

1 with the amount of emissions reductions applied in the adjusted air quality scenario. This is

2 because as the amount of emissions reductions applied increases, the spatial and temporal 

3 patterns of O3 concentrations become increasingly different from those patterns observed for 

4 recent O3 concentrations (2006-2010) that are more similar, although not identical (due to 

5 reductions in NOx between 2000 and 2006), to the patterns for the time period covered by the 

6 epidemiology studies (1987-2000) . We are not able to quantify the effect or magnitude of this 

7 uncertainty, because we do not know the relationship between O3 variability and the C-R 

8 functions. However, to the extent that the uncertainty is shown to be important, it seems 

9 reasonable to conclude that the larger the adjustment to the O3 distributions, the more likely there 

10 could be a mismatch in the exposure surrogates.

11 Overall, these sources of uncertainty cause us to have reduced confidence in estimates of 

12 short-term risk based on modeling the larger (CBSA-based) study areas using the multi-city time 

13 series-based effect estimates. This reduces the utility of the risk assessment in directly informing 

14 the decision regarding the level of the standard since we have lower confidence in estimates of 

15 absolute risk associated with a given standard level. However, the risk assessment can still be 

16 useful in providing estimates of the general magnitude and direction of changes in risk associated 

17 with an alternative standard level.

18 9.6 OVERALL INTEGRATED CHARACTERIZATION OF RISK IN THE 

19 CONTEXT OF KEY POLICY RELEVANT QUESTIONS

20 Our analyses set out to inform two questions: 1) what are the magnitudes of exposures of 

21 concern and risks for O3-related health effects that are estimated to occur with O3 concentrations

22 that just meet the existing O3 standard?; and 2) to what extent do alternative standards reduce 

23 estimated exposures and risks of concern attributable to O3, focusing on at-risk populations and 

24 lifestages? In evaluating risk, we did not limit the assessment to just the absolute risk that is 

25 attributable to U.S. or North American emissions, as this is not relevant to answer the two 

26 questions. Instead, we estimated total risk from all O3 concentrations and the distribution of risk 

27 over the range of O3 concentrations. Our estimates of changes in risk from meeting alternative O3

28 standard levels relative to meeting the existing standard reflect only the impact of reductions in 

29 U.S. precursor emissions on O3 distributions, recognizing that these emissions are most likely to 

30 be affected by implementation of the standards.

31 To inform these questions, we conducted air quality, exposure, and risk analyses for 

32 selected urban case study areas. We evaluated changes in the distribution of O3 concentrations 

33 along the full range of O3 concentrations down to zero. We have utilized a new method

34 (compared to the O3 NAAQS review completed in 2008) for estimating O3 concentrations 

35 consistent with attaining existing and alternative standards, based on modeling the response of 
9-43

1 O3 concentrations to reductions in U.S. anthropogenic NOx and VOC emissions, using the

2 HDDM capabilities in CMAQ. This modeling incorporates all known emissions, including 

3 emissions from non-anthropogenic sources and anthropogenic emissions from sources in and 

4 outside of the U.S. As a result, background O3 concentrations are directly modeled and, therefore,

5 do not need to be separately specified. Application of this approach also addresses the 

6 recommendation by the National Research Council of the National Academies (NRC, 2008) to 

7 explore how emissions reductions might affect temporal and spatial variations in O3

8 concentrations, and to include information on how NOx versus VOC control strategies might 

9 affect exposure to O3 and potential risks.

10 We estimated exposures and risks using several different metrics. Consistent with the 

11 available evidence, we estimated the percentages of different study populations and lifestages 

12 with exposures exceeding several health-based exposure benchmarks. We estimated lung 

13 function risks based on a model of individual risk of lung function decrements that incorporates a 

14 dose-equivalent threshold and individual exposures, activity levels, and physiology. We 

15 estimated mortality and morbidity risks based on non-threshold C-R functions derived from 

16 epidemiology studies. These three different analyses result in differing sensitivities of results to 

17 changes in the O3 concentration distribution. Because the three metrics are affected differently in 

18 the analyses by changes in O3 at low concentration levels, it is important to understand these 

19 changes in O3 at low concentrations in interpreting differences in the results across metrics.

20 We also evaluated the degree to which exposures of concern and lung function risk were 

21 reduced in the portions of urban case study areas (urban core areas) that were more likely to 

22 experience an increase in low concentrations of O3, and in some cases an overall net increase in 

23 epidemiology based mortality and morbidity risk (results for this assessment are presented in 

24 Appendix 9A). We compared these estimates of changes in exposures and lung function risk to 

25 estimates of changes in exposures and lung function risk in the areas outside of the urban core 

26 areas to judge whether for exposures of concern and lung function risk we see the same pattern 

27 of risk reduction between those areas.

28 Both exposures of concern and lung-function risk estimates in the core urban case study 

29 areas showed similar patterns compared with the areas outside the urban cores when just meeting 

30 the existing and potential alternative standards. Thus, we observe that in urban core areas which 

31 in some cases showed overall increases in epidemiology based mortality and morbidity risk 

32 when looking across these same air quality scenarios (see section 9.5.3), we generally see

33 reductions in exposures of concern and lung function risk. These findings illustrate that 

34 populations within core urban case study areas are likely to experience risk reductions for health 

35 endpoints reflected in the exposure and lung-function analyses. 
9-44

1 The mortality and morbidity risk assessment is the analysis that is most sensitive to the 

2 increases in O3 in the lower part of the distribution of initial O3 concentrations at some monitors 

3 and on some days after meeting the existing and alternative standards in some urban case study 

4 areas. As demonstrated in the heat maps (Figures 7-2 and 7-3), the increases in O3 (and resulting

5 estimated increases in risk) occur largely on days with initial O3 concentrations in the range of 10 

6 to 40 ppb. In addition, mean O3 concentrations for the urban case study areas change little 

7 between air quality scenarios for meeting the existing and alternative standards, because mean 

8 concentrations reflect both the increases in O3 at lower concentrations and the decreases in O3

9 occurring on days with high O3 concentrations. This leads to small net changes in mortality and 

10 morbidity risk estimates for many of the urban case study areas. For New York, we find there is 

11 a larger decrease relative to other urban case study areas (nearly five times as large as the next 

12 largest result for Los Angeles), in mortality and respiratory hospital admissions when just 

13 meeting the 65 ppb alternative standard compared to just meeting the existing standard, 

14 reflecting the large degree of air quality adjustment needed to meet the standard at all monitors in 

15 New York. Both the net change in risk and the distribution of risk across the range of O3

16 concentrations in the urban case study areas may be relevant in considering the degree of 

17 additional protection provided by just meeting existing and alternative standards.

18 The dampened response of short-term mortality risk can be contrasted with lung function 

19 risk estimates based on application of results from controlled human exposure studies. The lung 

20 function risk estimates primarily reflect changes in the upper end of the O3 distribution and 

21 reflect counts of exceedances of lung function decrement benchmarks, rather than summing risks 

22 across all days in the season. In addition, lung function risks are based on detailed micro23 environmental exposure modeling which uses individual monitor values instead of composite 

24 monitor values, thereby resulting in less dampening of spatial variability in O3 within a given 

25 urban study area.

26 The exposure benchmark analysis is the least sensitive to changes in O3 in the lower part 

27 of the distribution of initial O3 concentrations, because the lowest of the exposure benchmarks is 

28 at 60 ppb, well above the portion of the distribution of initial O3 concentrations that increased.

29 Since the modeled exposures will always be less than or equal to the monitor concentrations, a 

30 benchmark of exposure at 60 ppb is above the range of O3 concentrations where the HDDM 

31 approach estimates increases in concentrations. Thus, this metric is most reflective of the 

32 decreases in O3 at high concentrations that are expected to result from just meeting the existing 

33 and alternative standards.

34 The lung function risk analysis is less sensitive than the mortality and morbidity risk 

35 assessments to increases at very low concentrations of O3, because the risk function is logistic 

36 and shows little response at lower O3 dose rates that tend to occur when ambient concentrations 
9-45

1 are lower (generally less than 20 ppb for the 10 percent FEV1 decrement and generally less than 

2 40 ppb for the 15 percent FEV1 decrement). However, because there are still some increases in 

3 O3 concentrations that occur in the 50 to 60 ppb range where the estimated risk is more 

4 responsive, there may be some reduction in the magnitude of the risk decrease (this is evident 

5 when comparing the lung function risk metric with the exposure benchmark metric in figure 9-

6 8).

7 The exposure-based lung function risk assessment is based on controlled human exposure 

8 studies which studied responses in healthy adults. Although the lung function model based on 

9 this population shows less responsiveness at lower ambient concentrations, the applicability of 

10 this model to the responses of more sensitive populations and lifestages, including children and 

11 asthmatics, is uncertain. In addition, although the most complete information for generating an 

12 exposure-response function is available for FEV1 as a measure of lung function, there are other, 

13 potentially more public health relevant effects, such as lung inflammation, which have also been 

14 shown to respond to O3. As such, the lung function risk analysis should be seen as providing 

15 useful but not complete information on risks of health responses to O3.

16 Exposures above health benchmarks and risks remain after adjusting O3 to just meet the 

17 existing standard. The percentage of children with at least one 8-hour O3 exposure exceeding 60 

18 ppb is greater than 10 percent in at least one of the five analytical years for all of the 15 urban 

19 case study areas. The percent of children with a predicted decrement in lung function greater 

20 than or equal to 10 percent is greater than 16 percent in at least one of the five analytical years 

21 for all of the 15 urban case study areas, and for a 15 percent decrement is less than 7 percent for 

22 all years and areas. O3-attributable mortality is slightly less than one percent up to four percent of 

23 total mortality across the 12 urban case study areas, with little variation between 2007 and 2009. 

24 O3-attributable respiratory hospital admissions are between 2 and 3 percent across the 12 urban 

25 case study areas, with little variation between 2007 and 2009. The percent attributable risk for 

26 other morbidity endpoints is somewhat higher than for respiratory hospital admissions, but we 

27 only estimated these endpoints for a more limited set of urban case study areas due to data 

28 limitations.

29 The degree of reduction in exposures and risks when adjusting O3 from just meeting the 

30 existing standard to just meeting lower alternative standard levels varies considerably between 

31 metrics. The greatest degree of reduction occurs in exposures above the 60 ppb exposure 

32 benchmark, followed by reductions in lung function decrements greater than or equal to 10 

33 percent, with the smallest changes in mortality and respiratory hospital admissions. Although the 

34 magnitude of reduction differs between the different exposure and risk metrics, there are 

35 generally the same patterns of reductions for the exposure benchmark and lung function risk 

36 metrics, showing consistent reductions across all 15 urban case study areas. Risk reductions also 
9-46

1 occur in most of the urban case study areas for mortality and respiratory hospital admissions. 

2 However, these reductions are small, and reflect net changes in risk that include days with risk 

3 increases as well as risk decreases. For most urban case study areas, the greatest incremental 

4 reductions in exposures above the 60 ppb benchmark occurred when just meeting 70 ppb

5 compared to just meeting the existing standard. Just meeting lower standards of 65 ppb and 60 

6 ppb had incrementally smaller reductions in the percent of children exposed above 60 ppb. 

7 Incremental lung function risk reductions are more even between alternative standards, with 

8 similar or greater incremental reductions for the 65 ppb and 60 ppb alternatives compared with 

9 the incremental reductions for just meeting 70 ppb. Incremental reductions in mortality and 

10 respiratory hospital admissions risk are small between alternative standards, but more urban case 

11 study areas have somewhat larger risk reductions when comparing just meeting the 60 ppb 

12 alternative to just meeting the 65 ppb standard, than when comparing 65 ppb to 70 ppb or 70 ppb 

13 to 75 ppb. Long-term exposure mortality risk results show larger absolute estimates of mortality 

14 risk and more consistent reductions across urban case study areas. However, percent changes in 

15 long-term exposure mortality are similar to those for short-term exposure mortality.

16 In conclusion, we have estimated that exposures and risks remain after just meeting the 

17 existing standards and that in many cases, just meeting alternative standard levels results in

18 reductions in those exposures and risks. Meeting alternative standards has larger impacts on 

19 metrics that are not sensitive to changes in lower O3 concentrations. When meeting the 70, 65, 

20 and 60 ppb alternative standards, the percent of children experiencing exposures above the 60 

21 ppb health benchmark falls to less than 20 percent, less than 10 percent, and less than 3 percent 

22 in the worst O3 year for all 15 case study urban case study areas, respectively. Lung function risk 

23 also drops considerably as lower standards are met. When meeting the 70, 65, and 60 ppb 

24 alternative standards, the percent of children with lung function decrements greater than or equal 

25 to 10 percent falls to less than 21 percent, less than 18 percent, and less than 14 percent in the 

26 worst O3 year for all 15 case study urban case study areas, respectively. Mortality from short27 and long-term O3 exposures and respiratory hospitalization risk is not greatly affected by 

28 meeting lower standards, reflecting the impact of increasing O3 on low concentration days, and 

29 the non-threshold nature of the C-R function.
9-47

1 9.7 REFERENCES

2 Frey, C. and J. Samet. 2012. CASAC Review of the EPA’s Policy Assessment for the Review of 

3 the O3 National Ambient Air Quality Standards (First External Review Draft – August 

4 2012). U.S. Environmental Protection Agency Science Advisory Board, (Document 

5 number EPA-CASAC-13-003).

6 Medina-Ramon, M.; A. Zanobetti and J. Schwartz. 2006. "The Effect of O3 and PM10 on Hospital 

7 Admissions for Pneumonia and Chronic Obstructive Pulmonary Disease: A National 

8 Multicity Study."American Journal of Epidemiology, 163(6), 579-588.

9 McDonnell, W.F.; P. W. Stewart; M. V. Smith; C. S. Kim C.S. and E. S. Schelegle. 2012. 

10 "Prediction of lung function response for populations exposed to a wide range of O3

11 conditions." Inhalation Toxicology, 24:619-633.

12 Simon, H.; K. R. Baker; F. Akhtar; S. L. Napelenok; N. Possiel; B. Wells and B. Timin. 2013. 

13 "A Direct Sensitivity Approach to Predict Hourly O3 Resulting from Compliance with the 

14 National Ambient Air Quality Standard." Environmental Science and Technology, 47, 

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16 Smith, RL; B. Xu and P. Switzer. 2009. "Reassessing the Relationship Between O3 and Short17 term Mortality in U.S. Urban Communities." Inhalation Toxicology, 21: 37-61. 

18 U.S. Environmental Protection Agency. 2007. O3 Health Risk Assessment for Selected Urban 

19 case study areas. Research Triangle Park, NC: Office of Air Quality Planning and 

20 Standards. (EPA document number EPA 452/R-07-009). Available at:

21 <http://www.epa.gov/ttn/naaqs/standards/O3/s_O3_cr.html>.

22 U.S. EPA. 2013. Integrated Science Assessment for O3 and Related Photochemical Oxidants: 

23 Final. Research Triangle Park, NC: U.S. Environmental Protection Agency. (EPA 

24 document number EPA/600/R-10/076F).

25 World Health Organization. 2008. Part 1: Guidance Document on Characterizing and 

26 Communicating Uncertainty in Exposure Assessment, Harmonization Project Document 

27 No. 6. Published under joint sponsorship of the World Health Organization, the 

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32 Mortality: An analysis of 48 Cities in the United States." American Journal of 

33 Respiratory and Critical Care Medicine, 177:184-189.

34
United States

Environmental Protection

Agency

Office of Air Quality Planning and Standards

Air Quality Strategies and Standards Division

Research Triangle Park, NC

Publication No. EPA-452/P-14-004a

February 2014