# Risk and Exposure Assessment to Support the Review of the NO 2 

# Primary National Ambient Air Quality Standard

### EPA document # EPA-452/R-08-008a 

### November 2008



# Risk and Exposure Assessment to Support the Review of the NO 2 

# Primary National Ambient Air Quality Standard

U.S. Environmental Protection Agency Office of Air Quality Planning and Standards Research Triangle Park, North Carolina

## Disclaimer

This document has been prepared by staff fr om the Ambient Standards Group, Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency. Any opinions, findings, conclusions, or recommendations are those of the authors and do not necessarily reflect the views of the EPA. For questions concerni ng this document, please contact Dr. Stephen Graham (919-541-4344; graham.stephen@epa.gov ), Mr. Harvey Richmond (919-541-5271; richmond.harvey@epa.gov ), or Dr. Scott Jenkins (919-541-11 67; jenkins.scott@epa.gov).

## Table of Contents

List of Tables ............................................................................................................................... v

List of Figures ............................................................................................................... ................ ix

List of Acronyms/Abbreviation............................................................ ................... xiii

1. INTRODUCTION ..............................................................................................................................................1

1.1 O VERVIEW

............................................................................................................................... ............................1 1.2 H ISTORY

............................................................................................................................... ...............................5 1.2.1 History of the NO 2 NAAQS......................................................................................................................... .5 1.2.2 Health Evidence from Previous Review ..................................................................................... .................6 1.2.3 Assessment from Previous Review .......................................................................................... ....................7 1.3 S COPE OF THE R ISK AND E XPOSURE A SSESSMENT FOR THE C URRENT R EVIEW ...................................................7 2. SOURCES, AMBIENT LE VELS, AND EXPOSURES ..................................................................................9

2.1 S OURCES OF NO 2 ............................................................................................................................... ..................9 2.2 A MBIENT L EVELS OF NO 2 ............................................................................................................................... .....9 2.2.1 Background on NO 2 monitoring network....................................................................................................9 2.2.2 Trends in ambient concentrations of NO 2 .................................................................................................13 2.2.3 Uncertainty Associated with the Ambient NO 2 Monitoring Method .........................................................14 2.3 E XPOSURE TO NO 2 ............................................................................................................................... ..............15 2.3.1 Overview ................................................................................................................. ..................................15 2.3.2 Uncertainty Asso ciated with Ambient Levels as a Surrogate fo r Exposure ..............................................16

3. AT RISK POPULATIONS ......................................................................................................... .....................17

3.1 O VERVIEW

............................................................................................................................... ..........................17 3.2 SUSCEPTIBILITY : P RE

- EXISTING D ISEASE ...........................................................................................................18 3.3 SUSCEPTIBILITY : A GE

............................................................................................................................... .........19 3.4 SUSCEPTIBILITY : G ENETICS

............................................................................................................................... .19 3.5 SUSCEPTIBILITY : G ENDER

............................................................................................................................... ...20 3.6 VULNERABILITY : P ROXIMITY ( ON OR NEAR ) TO R OADWAYS ..............................................................................20 3.7 VULNERABILITY : S OCIOECONOMIC STATUS .......................................................................................................21 3.8 C ONCLUSIONS

............................................................................................................................... .....................21

4. HEALTH EFFECTS .............................................................................................................. ..........................23

4.1 I NTRODUCTION

............................................................................................................................... ...................23 4.2A DVERSE R ESPIRATORY E FFECTS F OLLOWING S HORT -T ERM E XPOSURES .........................................................26 4.2.1 Overview ................................................................................................................. ..................................26 4.2.2 Respiratory Emergency Departme nt Visits and Ho spitalizations ............................................................. 27 4.2.3 Respirat ory Symptoms..................................................................................................... ..........................28 4.2.4 Lung Host Defe nses and Immunity.......................................................................................... ..................31 4.2.5 Airway Response.......................................................................................................................................32 4.2.6 Airway Inflammation...................................................................................................... ...........................35 4.2.7 Lung Function...........................................................................................................................................36 4.2.8 Conclusions and Coherence of Eviden ce for Short-Term Resp iratory Effects..........................................36 4.3 O THER A DVERSE E FFECTS F OLLOWING S HORT -T ERM E XPOSURES ...................................................................37 4.4 A DVERSE E FFECTS F OLLOWING L ONG -T ERM E XPOSURES ................................................................................384.4.1 Respiratory Morbidity.................................................................................................... ...........................38 4.4.2 Mo rta lity ................................................................................................................ ...................................40 4.4.3 Other Long-Term Effects.................................................................................................. .........................40 4.5 R ELEVANCE OF SPECIFIC H EALTH E FFECTS TO THE NO 2 R ISK C HARACTERIZATION .........................................41 4.5.1 Overview ................................................................................................................. ..................................41 4.5.2 Epidemiology ............................................................................................................................................42 4.5.3 Controlled Human Exposure Studies ........................................................................................ ................44

5. IDENTIFICATION OF POTENTIAL ALTER NATIVE STANDARDS FOR ANALYSIS......................46

5.1 INTRODUCTION

............................................................................................................................... ...................46 5.2 INDICATOR.....................................................................................................................................................46 5.3 AVERAGING TIME ........................................................................................................................... .............46 5.4 FORM ........................................................................................................................... ....................................48 5.5 LEVEL.......................................................................................................................... ....................................49

6. OVERVIEW OF APPROACHES TO ASSESSING EXPOSURES AND RISKS ......................................55

6.1 I NTRODUCTION

............................................................................................................................... ...................55 6.2 P OTENTIAL HEALTH BENCHMARK LEVELS .........................................................................................................57 6.3 SIMULATING THE CURRENT AND ALTERNATIVE STANDARDS ..............................................................................59 6.3.1 Adjustment of Ambient Ai r Quality ........................................................................................ ...................59 6.3.2 Adjustment of Potential He alth Effect Be nchmark Levels................................................................... ......64 7. AMBIENT AIR QUALITY ASSESSMENT AND HEALTH RISK CHARACTERIZATION ................67

7.1 O VERVIEW

............................................................................................................................... ..........................67 7.2 A PPROACH

............................................................................................................................... ..........................69 7.2.1 Air Quality Data Screen.................................................................................................. ..........................70 7.2.2 Selection of Locations for Air Qua lity Anal ysis .......................................................................... ..............71 7.2.3 Site Characteristics of Ambient NO 2 Monitors .........................................................................................73 7.2.4 Estimation of On-Road Concentra tions using Ambient Concentrations...................................................80 7.2.5 Air Quality C oncentratio n Metri cs............................................................................................................83 7.3 A IR Q UALITY AND H EALTH R ISK C HARACTERIZATION R ESULTS ..............................................................85 7.3.1 Ambient Air Quality (As Is).............................................................................................. .........................85 7.3.2 On-Road Concentrations Derived From Ambient Air Qu ality (As Is) ......................................................97 7.3.3 Ambient Air Quality Adjust ed to Just Meet the Current and Alternative St andards...............................102 7.3.4 On-Road Concentr ations Derived From Ambient Air Quality Adjusted to Just Meet the Current and Alternative Standards.......................................................................................................................................114 7.4 U NCERTAINTY A NALYSIS

............................................................................................................................... .122 7.4.1 Air Quality Database ..................................................................................................... .........................124 7.4.2 Measurement Technique for Ambient NO 2 ..............................................................................................125 7.4.3 Temporal Representation .................................................................................................. ......................125 7.4.4 Spatial Representation ................................................................................................... .........................127 7.4.5 Air Quality Adjustment Procedure......................................................................................... .................130 7.4.6 On-Road Concen tration Si mulation........................................................................................................133 7.4.7 Health Benchmark......................................................................................................... ..........................143 7.5 K EY O BSERVATIONS

............................................................................................................................... .........144

8. EXPOSURE ASSESSMENT AND HEALTH RISK CHARACTERIZATION .......................................146

8.1 O VERVIEW

............................................................................................................................... ........................146 8.2 O VERVIEW OF H UMAN E XPOSURE M ODELING USING APEX...........................................................................149 8.3 C HARACTERIZATION OF S TUDY A REA .............................................................................................................151 8.3.1 Study Area Selection ..................................................................................................... ..........................151 8.3.2 Study Area Description ................................................................................................... ........................152 8.3.3 Time Period of Analysis .................................................................................................. ........................152 8.3.4 Populations Analyzed..................................................................................................... .........................152 8.4 C HARACTERIZATION OF AMBIENT A IR Q UALITY USING AERMOD.................................................................154 8.4.1 Overview ................................................................................................................. ................................154



### ii8.4.2 General Model In puts .............................................................................................................................154 8.4.2.1 Meteorol ogical Inputs .................................................................................................. ..................................... 154 8.4.2.2 Surface Characteristic s and Land Use Analysis .......................................................................... ...................... 155 8.4.2.4 Other AERMOD Input Speci fications...................................................................................... ......................... 155 8.4.3 Major Link On-Roa d Emission Estimat es ...............................................................................................156 8.4.3.1 Emission S ources and Locations......................................................................................... .............................. 156 8.4.3.2 Emission Source Strength ............................................................................................... .................................. 158 8.4.3.3 Other Emi ssion Parameters .............................................................................................. ................................. 160 8.4.4 Minor Link On-roa d Emission Estimat es................................................................................................161 8.4.5 Adjustment of On-road Mobile Source Strengths to 2002 NEI Vehicle Emissions .................................163 8.4.5 Stationary Sources Emissions Preparation................................................................................. ............164 8.4.6 Airport Emissi ons Prepar ation ...............................................................................................................165 8.4.7 Receptor Locations....................................................................................................... ...........................167 8.4.8 Modeled Air Q uality Evaluation ........................................................................................... ..................168 8.4.8.1 Comparison of Hourly Cumulative Dens ity Functions ...................................................................... ............... 168 8.4.8.2 Comparison of annual averag e diurnal concentr ation profiles ............................................................ .............. 170 8.4.8.3 Comparison of estimated on-road NO 2 concentr ations ..................................................................................... 173 8.4.8. Using unadjusted AERMOD predicted NO 2 concentr ations ............................................................................... 177 8.5 S IMULATED P OPULATION

............................................................................................................................... .177 8.6 C ONSTRUCTION OF L ONGITUDINAL A CTIVITY S EQUENCES .............................................................................179 8.7 C ALCULATING M ICROENVIRONMENTAL C ONCENTRATIONS ............................................................................179 8.7.1 Microenvironments Modeled................................................................................................ ...................180 8.7.2 Microenvironme nt Descriptions............................................................................................ ..................181 8.7.2.1 Microenvironment 1: Indoor-R esidence................................................................................... ......................... 181 8.7.2.2 Microenvironments 2-7: A ll other indoor mi croenvir onments.............................................................. ............ 184 8.7.2.3 Microenvironments 8 and 9: Outdoor Micr oenvironm ents ................................................................... ............ 184 8.7.2.4 Microenvironment 10: Outdoors- General .................................................................................. ....................... 185 8.7.2.5 Microenvironments 11 and 12: In Vehi cle- Cars and Trucks , and Mass Transit............................................. .. 185 8.8 E XPOSURE MEASURES AND H EALTH R ISK CHARACTERIZATION ..........................................................185 8.8.1 Adjustment for Just Meeting the Current and Alternat ive Standards......................................................18 7 8.9 E XPOSURE M ODELING AND H EALTH R ISK C HARACTERIZATION R ESULTS ......................................................189 8.9.1 Overview ................................................................................................................. ................................189 8.9.2 Annual Average Exposure Concentrati ons (as is)........................................................................... ........190 8.9.3 Daily Average Exposures (as is) .......................................................................................... ...................192 8.9.4 One-Hour Exposures....................................................................................................... ........................197 8.9.4.1 Overview............................................................................................................... ............................................ 197 8.9.4.2 Estimated Number of 1-hour Expos ures Above Selected Levels (as is) ..................................................... ...... 197 8.9.4.3 Estimated Number of 1-hour Exposures Above Selected Levels (current st andard)......................................... 2 07 8.9.4.4 Estimated Number of 1-hour Exposures A bove Selected Levels (alternative st andard s).................................. 209 8.10 K EY O BSERVATIONS

............................................................................................................................... ......213 8.11 R EPRESENTATIVENESS OF E XPOSURE R ESULTS .............................................................................................214 8.11.1 In troduc tion..........................................................................................................................................214 8.11.2 Description of Da ta Compiled an d Summarized............................................................................ ......214 8.11.2.1 HAPEM6 Near-Roa d Population Data Base ................................................................................ .................. 215 8.11.2.2 American Housi ng Survey ( AHS) Data .................................................................................... ...................... 218 8.11.2.3 Federal Highway Ad ministration (F HWA) Data ............................................................................ ................ 220 8.11.4 Discussion .............................................................................................................. ...............................221 8.12 U NCERTAINTY A NALYSIS .............................................................................................................................22 3 8.12.1 Dispersion M odeling Uncer tainties ......................................................................................................224 8.12.1.1 AERM OD Algorithms .................................................................................................... ............................... 224 8.12.1.2 Meteor ological Inputs ................................................................................................ .................................... 226 8.12.1.3 Mobile Sour ce Characterization....................................................................................... .............................. 228 8.12.1.4 On-Road Em issions Estimates .......................................................................................... ............................. 230 8.12.1.5 O 3 Monitoring Data for OLM and PVMRM Options..................................................................................... 234 8.12.1.6 Use of Unadjusted AERMOD NO 2 Concentr ations....................................................................................... 235 8.12.2 Exposure Mo deling Uncer tainties........................................................................................................237 8.12.2.1 Populati on Data Base .................................................................................................. .................................... 237 8.12.2.2 Commuti ng Data Base ................................................................................................... ................................. 238 8.12.2.2 Human Time-Locati on-Activity Pattern Data ............................................................................. .................... 239



### iii8.12.2.3 Longitu dinal Profile .................................................................................................. ...................................... 240 8.12.2.4 Meteor ological Data................................................................................................... ..................................... 242 8.12.2.5 Air Excha nge Rates (AER) .............................................................................................. ............................... 243 8.12.2.6 Air Conditi oning Prevalence........................................................................................... ................................ 246 8.12.2.7 Indoor S ource Estimation.............................................................................................. .................................. 248

9. CHARACTERIZATION OF HEALTH RISK S USING DATA FROM EPIDEMIOLOGICAL STUDIES .............................................................................................................................................................252

9.1 I NTRODUCTION

............................................................................................................................... .................252 9.2 G ENERAL A PPROACH

............................................................................................................................... ........253 9.3 A IR Q UALITY INFORMATION ............................................................................................................................258 9.4 C ONCENTRATION -R ESPONSE F UNCTIONS ........................................................................................................259 9.5 B ASELINE H EALTH E FFECTS INCIDENCE D ATA ................................................................................................261 9.6 A DDRESSING U NCERTAINTY AND V ARIABILITY ..............................................................................................262 9.7 R ISK ESTIMATES FOR EMERGENCY DEPARTMENT VISITS ..................................................................................269

10. EVIDENCE- AND EXPOSURE/RISK-BASED CONSIDERATIONS RELATED TO THE PRIMARY NO 2 NAAQS ......................................................................................................................... ...............................275

10.1 IN TRODUCTION

............................................................................................................................... ...............275 10.2 GENERAL A PPROACH

............................................................................................................................... ......276 10.3 A DEQUACY OF THE CURRENT ANNUAL STANDARD ........................................................................................278 10.3.1 Evidence-ba sed considerations.......................................................................................... ..................279 10.3.2 Exposure- and risk -based considerations ................................................................................. ............282 10.3.2.1 Key uncertainties.................................................................................................... ........................................ 284 10.3.2.2 Assessm ent results .................................................................................................... ...................................... 287 10.3.3 Conclusions regarding the adequacy of the cu rrent standard .............................................................. 290 10.4 POTENTIAL ALTERNATIVE STANDARDS...........................................................................................291 10.4.1 In dicator................................................................................................................................................291 10.4.2 Aver aging Time.......................................................................................................... ...........................291 10.4.3 Form.................................................................................................................... ..................................296 10.4.4 Level......................................................................................................................................................299 10.4.4.1 Evidence-bas ed consid erations......................................................................................... ............................... 299 10.4.4.2 Exposure- and risk -based cons iderations ............................................................................... ......................... 304 10.4.4.3 Conclusions regarding level ........................................................................................... ................................. 309

11. REFERENCES................................................................................................................. .............................310

APPENDICES APPENDIX A - Supplement to the NO2 Air Quality Characterization APPENDIX B - Supplement to the NO2 Exposure Assessment APPENDIX C - Nitrogen Dioxide Health Risk Assessment for Atlanta, GA



### iv

## List of Tables

Number Page

Table 2-1. NOx Network Distri bution of Monitor Objectives ..................................................... 12 Table 2-2. NOx Network Distribution across Measurement Scales. ............................................ 13 Table 3-1. Overview of Susceptibi lity and Vulnerability Factors ................................................ 18 Table 4-1. Weight of Evidence for Causal Determination ........................................................... 24 Table 4-2. Causality judgments made in the IS A for endpoints associated with short-term NO 2 

### exposures ...................................................................................................................... 25 Table 4-3. Causality judgments made in the IS A for endpoints associat ed with long-term NO 2 

### exposures ...................................................................................................................... 26 Table 4-4. Proposed Mechanisms Whereby NO2 and Respiratory Virus Infections May Exacerbate Upper and Lower Airway Symptoms ........................................................ 32 Table 4-5. Fraction of nitrogen dioxide-e xposed asthmatics with increased nonspecific ........... 34 airway hyperresponsiveness .......................................................................................................... 34 Table 7-1. Counts of complete and incomplete site-years of NO2 ambient monitoring data. .... 71 Table 7-2. Locations selected for NO 2 

### Air Quality Characterizati on, associated abbreviations, and values of selection criteria. .................................................................................... 73 Table 7-3. Percent of ambient NO 2 

### monitors with selected monito ring objectives, using all valid site-years of historical air quality (1995-2000). ........................................................... 75 Table 7-4. Percent of ambient NO 2 

### monitors with selected monito ring objectives, using all valid site-years of recent air quality (2001-2006). ................................................................ 75 Table 7-5. Percent of ambient NO 2 

### monitors with selected measurement scales, using all valid site-years of historical air quality (1995-2000). ........................................................... 76 Table 7-6. Percent of ambient NO 2 

### monitors with selected measurement scales, using all valid site-years of recent air quality (2001-2006). ................................................................ 77 Table 7-7. Percent of ambient NO 2 

### monitors with selected land use, using all valid site-years of historical air quality (1995-2000)................................................................................. 77 Table 7-8. Percent of ambient NO 2 

### monitors with selected land use, using all valid site-years of recent air quality (2001-2006). ..................................................................................... 78 Table 7-9. Distance of ambient monitors to the nearest major sources in selected locations. ...... 79 Table 7-10. Derived C v 

### / C b 

### ratios ( m ) for two season groups used for adjusting ambient NO 2 

### concentrations to simulate on-road NO 2 

### concentrations. ............................................. 82 Table 7-11. Monitoring site-y ears and annual average NO 2 

### concentrations, using recent air quality data ( as is ) and monitors sited ≥ 100 m of a major road. .................................. 86 Table 7-12. Monitoring site-y ears and annual average NO 2 

### concentrations, using recent air quality data ( as is ) and monitors sited >20 m and <100 m of a major road. ............... 87 Table 7-13. Monitoring site-y ears and annual average NO 2 

### concentrations, using recent air quality data ( as is ) and monitors sited ≤ 20 m of a major road. .................................... 88 Table 7-14. Number of daily maximum exceedan ces of short-term (1-hour) potential health effect benchmarks in a year, using 2001-2003 recent NO 2 

### air quality ( as is ) and monitors sited ≥ 100 m of a major road. ....................................................................... 91 Table 7-15. Number of daily maximum exceedan ces of short-term (1-hour) potential health effect benchmarks in a year, using 2004-2006 recent NO 2 

### air quality ( as is ) and monitors sited ≥ 100 m of a major road. ....................................................................... 92

v

### Table 7-16. Number of daily maximum exceedan ces of short-term (1-hour) potential health effect benchmarks in a year, using 2001-2003 recent NO 2 

### air quality ( as is ) and monitors sited >20 m and <100 m of a major road. ..................................................... 93 Table 7-17. Number of daily maximum exceedan ces of short-term (1-hour) potential health effect benchmarks in a year, using 2004-2006 recent NO 2 

### air quality ( as is ) and monitors sited >20 m and <100 m of a major road. ..................................................... 94 Table 7-18. Number of daily maximum exceedan ces of short-term (1-hour) potential health effect benchmarks in a year, using 2001-2003 recent NO 2 

### air quality ( as is ) and monitors sited ≤ 20 m from a major road. ..................................................................... 95 Table 7-19. Number of daily maximum exceedan ces of short-term (1-hour) potential health effect benchmarks in a year, using 2004-2006 recent NO 2 

### air quality ( as is ) and monitors sited ≤ 20 m from a major road. ..................................................................... 96 Table 7-20. Estimated annual average NO 2 

### concentrations on-roads, us ing recent air quality data ( as is ) and an on-road adjustment factor. ..................................................................... 99 Table 7-21. Estimated number of daily maximu m exceedances of short-term (1-hour) potential health effect benchmarks on-road s in a year, using 2001-2003 recent NO 2 

### air quality ( as is ) and an on-road adjustment factor. ................................................................... 100 Table 7-22. Estimated number of daily maximum exceedances of short-term (1-hour) potential health effect benchmarks on-road s in a year, using 2004-2006 recent NO 2 

### air quality ( as is ) and an on-road adjustment factor. ................................................................... 101 Table 7-23. Estimated annual mean NO2 con centration and the number of daily maximum exceedances of short-term (1-hour) potential he alth effect benchmarks in a year, using 2001-2003 air quality adjusted to just meeting a 1-hour 100 ppb 98th percentile alternative standard and monitors sited ≥ 100 m of a major road .............................. 109 Table 7-24. Estimated annual mean NO2 con centration and the number of daily maximum exceedances of short-term (1-hour) potential he alth effect benchmarks in a year, using 2001-2003 air quality adjusted to just meeting a 1-hour 100 ppb 98 th 

### percentile alternative standard and monitors site d >20 m and <100 m from a major road. ....... 110 Table 7-25. Estimated annual mean NO2 con centration and the number of daily maximum exceedances of short-term (1-hour) potential he alth effect benchmarks in a year, using 2001-2003 air quality adjusted to just meeting a 1-hour 100 ppb 98 th 

### percentile alternative standard and monitors sited ≤ 20 m from a major road. ........................... 111 Table 7-26. Estimated mean number of daily maximum exceedances of 100 ppb 1-hour NO 2 

### concentrations in a year, using 2001-2003 air quality as is and that adjusted to just meeting the current and alternative standards (98 th 

### percentile) for monitors sited ≥ 100 m, >20 m and <100 m, and ≤ 20 m of a major road. .................................................. 112 Table 7-27. Estimated mean number of daily maximum exceedances of 150 ppb 1-hour NO 2 

### concentrations in a year, using 2001-2003 air quality as is and air quality adjusted to just meeting the current and alternative standards (98 th 

### percentile) for monitors sited ≥ 100 m, >20 m and <100 m, and ≤ 20 m of a major road. ......................................... 113 Table 7-28. Estimated annual mean NO 2 

### concentration and the number of daily maximum exceedances of short-term (1-hour) potential health effect benchmarks on-roads in a year, using recent air quality (2001-2003) ad justed to just meeting a 1-hour 100 ppb 98 th 

### percentile alternative standard and an on-road adjustment factor. ...................... 119 Table 7-29. Estimated mean number of daily maximum exceedances of 100 ppb 1-hour NO 2 

### concentrations on-roads in a year, using air quality as is and air quality adjusted to just

vi

### meeting the current and alternative standards (98 th 

### percentile) and an on-road adjustment factor. ....................................................................................................... 120 Table 7-30. Estimated mean number of daily maximum exceedances of 150 ppb 1-hour NO 2 

### concentrations on-roads in a year, using air quality as is and air quality adjusted to just meeting the current and alternative standards (98 th 

### percentile) and an on-road adjustment factor. ....................................................................................................... 121 Table 7-31. Summary of qualitati ve uncertainty analysis for th e air quality and health risk characterization. ......................................................................................................... 123 Table 7-32. Percent di fference in 1-hour NO 2 

### concentrations for three modeled receptors in Atlanta at different vertical heights, using AERMOD predicted 2002 air quality. .... 129 Table 7-33. Number of 1-hour NO 2 

### concentrations above 100 ppb for three modeled receptors in Atlanta at different vertical heights, using AERMOD predicted 2002 air quality. .... 130 Table 7-34. Comparison of empirical distribution of on-road adjust ment factors used in on-road concentration estimation with a fitted lognormal distribution. .................................. 140 Table 7-35. Absolute difference in the estima ted number of exceedance s of potential health effect benchmarks on-roads using either a fitted lognormal distribution or empirical distribution of the on-road adjust ment factors and 2004-2006 air quality as is and air quality adjusted to just meet the current annual standard. ......................................... 142 Table 8-1. Statistical summary of average annual daily traffic (AADT) volumes (one direction) for Atlanta AERMOD simulations. ............................................................................ 157 Table 8-2. Average heavy duty vehicle (HDV) fraction for Atlanta AERMOD simulations. .. 158 Table 8-3. Average calculated speed by link type in Atlanta modeling domain. ...................... 160 Table 8-4. On-road area source sizes

. ........................................................................................ 160 Table 8-5. On-road emissions from ma jor and minor links in Atlanta, 2002. ........................... 162 Table 8-6. On-road vehicle emi ssion strengths by county for Atlanta modeling domain: modeled vs NEI 2002. .............................................................................................................. 163 Table 8-7. Summary statistics of estimated on-road hourly NO 2 

### concentrations (ppb) and the numbers of hourly concentrations above 100, 150, and 200 ppb in a year using both the AERMOD and the on-road ambient mon itor simulation approaches in Atlanta. 176 Table 8-8. Asthma prevalence rates by age and gender used for Atlanta. ................................. 178 Table 8-9. List of microenvironments modeled and calculation methods used. ....................... 181 Table 8-10. Geometric means (GM) and standard deviations (GSD) for air exchange rates by A/C type and temperature range us ed for Atlanta exposure assessment. .................. 182 Table 8-11. Data used to estimate removal rate constant for indoor microenvironments. ........ 183 Table 8-12. Probability of gas stove cooking by hour of the day. ............................................. 183 Table 8-13. Adjusted potential health effect be nchmark levels used by APEX to simulate just meeting the current standard and vari ous alternative sta ndards considered. ............. 187 Table 8-14. Percent of population within selected distances of a major road in several locations.

............................................................................................................................... ..... 217 Table 8-15. Residential A/C pr evalence and roadway distance statistics for housing units in several locations (AHS, 2008). .................................................................................. 219 Table 8-16. Population and roadway statis tics for several locations (FHWA, 2002). ............... 220 Table 8-17. Summary of quali tative uncertainty analysis for the exposure assessment. ........... 223 Table 8-18. National vehicle miles travel ed by roadway category and vehicle type. ............... 232 Table 8-19. Observed peak hour truck percentages on Interstate 75 (I-75) using 2002 traffic count data. .................................................................................................................. 232

vii

### Table 8-20. Comparison of exposure results using a 0.55 versus 0.97 A/C prevelance for 2002 air quality without indoor sources. ............................................................................. 248 Table 8-21. Comparison of exposure result s using a uniform versus lognormal NO 2 

### indoor decay distribution for 2002 air quality without indoor sources. ................................ 249 Table 9-1. Characterization of Key Uncertainties in the Emergency Department Visits Health Risk Assessment for the Atlanta Region. ................................................................... 264 Table 9-2. Estimated Percent of Total Annual Inci dence of Respiratory ED Visits Associated with "As Is" NO2 Concentrations and NO2 C oncentrations that Just Meet Alternative Standards in Atlanta, GA, Based on Adjusting 2005 NO2 Concentrations.* ........... 270 Table 9-3. Estimated Percent of Total Annual Inci dence of Respiratory ED Visits Associated with "As Is" NO 2 

### Concentrations and NO 2 

### Concentrations that Just Meet Alternative Standards in Atlanta, GA, Based on Adjusting 2006 NO 2 

### Concentrations.* ........... 271 Table 9-4. Estimated Percent of Total Annual Inci dence of Respiratory ED Visits Associated with "As Is" NO 2 

### Concentrations and NO 2 

### Concentrations that Just Meet Alternative Standards in Atlanta, GA, Based on Adjusting 2007 NO 2 

### Concentrations.* ........... 272 Table 10-1. Ratios of short-term to annual average NO 2 

### concentrations .................................. 293 Table 10-2. Ratios of 1-h daily maximum NO 2 

### concentrations to 24-h average concentrations (ppm) .......................................................................................................................... 295 Table 10-3. Mean annual NO 2 

### concentrations for 2004-2006 given just meeting alternative 1-h standards (98 th 

### percentile) .......................................................................................... 296 Table 10-4. NO 2 

### concentrations (ppm) corresponding to 2 nd 

### -9 th 

### daily maximum and 98 th 

### /99 th 

### percentile forms (2004-2006) ..................................................................................... 298 Table 10-5. Mean number of days per year (a veraged over the 2004-2006 time period) estimated to have ambient (central site monitor) 1-h daily maximum NO 2 

### concentrations ≥ 0.10 ppm assuming 98 th 

### and 99 th 

### percentile forms of a 0.20 ppm standard ....................... 299 Table 10-6. Percent of counties that may be above the level of the standard, given different levels (based on years 2004-2006) ....................................................................................... 305

viii

## List of Figures

Number Page

Figure 1-1. Overview of the an alyses described in this doc ument and their interconnections ....... 4 Figure 5-1. NO 2 

### effect estimates (95% CI) for ED vis its/HA and associated 1-h daily maximum NO 2 

### levels (98 th 

### and 99 th 

### percentile values in boxes) .................................................. 52 Figure 5-2. NO 2 

### effect estimates for respiratory sympto ms and associated 1-h daily maximum NO 2 

### levels (98 th 

### and 99 th 

### percentile values in boxes) .................................................. 53 Figure 6-1. Comparison of measured daily maximum NO 2 

### concentration percentiles in Atlanta for three high concentration years (1985, 1986, 1988) versus three low concentration years (2005-2007) at one ambient monitor. ................................................................. 61 Figure 6-2. Distributions of hourly NO2 concentr ations at twelve ambient monitors in the Boston CMSA, as is (top) and air quality adju sted to just meet the current standard (bottom), Year 1995. .................................................................................................... 62 Figure 6-3. Comparison of adju sted ambient monitoring concentrations (CS) or adjusted benchmark level (dashed line) to simulate just meeting the current annual average standard in Atlanta for year 2001. ................................................................................ 65 Figure 6-4. Comparison of the upper per centiles for where ambient monitoring NO 2 

### concentrations (CS) and the benchmark level (dashed line) were adjusted to simulate just meeting the current annual standard in Atlanta for year 2001. The hourly NO 2 

### concentration distributions are provided in Figure 6-3. ............................................... 66 Figure 7-1. Illustration of thr ee roadway distance categories used to characterize ambient monitors in the Air Quality Characterization. .............................................................. 80 Figure 7-2. Estimated mean number of daily maximum exceedances of short-term (1-hour) potential health effect benchmarks in a y ear, using recent NO2 air quality (2001-2003) adjusted to just meeting the current a nnual standard (0.053 ppm). Left graph: monitors ≥ 100m from a major road; Middle graph: monitors >20 m and <100 m from a major road; Right graph: monitors ≤ 20 m from a major road. ................................ 105 Figure 7-3. Estimated mean number of daily maximum exceedances of short-term (1-hour) potential health effect benchmar ks in a year, using recent NO 2 

### air quality (2004-2006) adjusted to just meeting the current a nnual standard (0.053 ppm). Left graph: monitors ≥ 100m from a major road; Middle graph: monitors >20 m and <100 m from a major road; Right graph: monitors ≤ 20 m from a major road). .............................. 106 Figure 7-4. Estimated number of daily maximu m exceedances of short-term (1-hour) potential health effect benchmarks (100 ppb, top; 200 ppb, bottom) in Chicago in a year, using recent NO 2 

### air quality data (2001-2006) adjusted to just meeting alternative 1-hour standard levels (98 th 

### percentile, left; and 99 th 

### percentile, right) and monitors sited ≥ 100 m, > 20 m and < 100 m, ≤ 20 m of major roads. ....................................................... 107 Figure 7-5. Estimated mean number of daily ma ximum exceedances of 200 ppb in four locations (Phoenix, Los Angeles, Washington DC, and St. Louis) in a y ear, using recent NO 2 

### air quality data (2001-2006) adjusted to just meeting alternative 1-hour 98 th 

### percentile standard levels and monitors sited ≥ 100 m, > 20 m and < 100 m, ≤ 20 m of major roads. .......................................................................................................................... 108 Figure 7-6. Estimated mean number of daily maximum exceedances of short-term (1-hour) potential health effect benchmarks on-roads in a year, using recent NO 2 

### air quality

ix

### adjusted to just meeting the current a nnual standard (0.053 ppm) and an on-road adjustment factor. Left graph: 2001- 2003 air quality; Right graph: 2004-2006 air quality. ........................................................................................................................ 116 Figure 7-7. Estimated number of daily maximum exceedances of short-term (1-hour) potential health effect benchmarks (100 ppb, top; 200 ppb, bottom) on-roads in Chicago in a year, using recent NO 2 

### air quality (2001-2006) adjusted to just meeting alternative 1- hour standard levels (98 th 

### percentile, left; and 99 th 

### percentile) and an on-road adjustment factor. ....................................................................................................... 117 Figure 7-8. Estimated mean number of daily maximum exceedances of 200 ppb on-roads in four locations (Phoenix, Los Angeles, Washingt on DC, and St. Louis) in a year, using recent NO 2 

### air quality (2001-2006) adjusted to just meeting alternative 1-hour 98 th 

### percentile standard levels and an on-road adjustment factor. .................................... 118 Figre 7-9. Distri bution of 1-hour NO 2 

### concentrations for three mode led receptors in Atlanta at different vertical heights, usi ng AERMOD predicted 2002 air quality. .................... 129 Figure 7-10. Comparison of measured daily maximum NO2 concentration percentiles in Philadelphia for one high concentration year s (1984) versus a low concentration years (2007) at four ambient monitors. ............................................................................... 133 Figure 7-11. Comparison of th e distribution of estimated C v 

### /C b 

### ratios or m for the not summer category with fitted distributions. .............................................................................. 141 Figure 7-12. Comparison of th e distribution of estimated C v 

### /C b 

### ratios or m for the summer category with fitted distributions. .............................................................................. 141 Figure 8-1. General flow used for NO 2 

### exposure assessment. .................................................. 148 Figure 8-2. Four county modeling domain used for Atlanta exposure assessment. .................. 153 Figure 8-3. The 478 U.S. Census tracts represen ting area sources for on-road mobile emissions that do not occur on major roadway links. ................................................................. 162 Figure 8-4. Location of major ro adway links and major stationary emission sources in Atlanta modeling domain........................................................................................................ 166 Figure 8-5. Location of modeled receptors in Atlanta modeling domain. ................................. 168 Figure 8-6. Comparison of m easured ambient monitor NO 2 

### concentration distribution with the modeled monitor receptor and receptors within 4 km of the monitors at three locations in Atlanta for Year 2002. ........................................................................................... 171 Figure 8-7. Comparison of m easured ambient monitor NO 2 

### concentration diurnal profile with the modeled monitor receptor and receptors within 4 km of the monitors at three locations in Atlanta for Year 2002. ............................................................................ 172 Figure 8-8. Comparison of on-road/non-road ra tios developed from AERMOD concentration estimates for year 2002 and those derive d from data reported in published NO 2 

### measurement studies. ................................................................................................. 175 Figure 8-9. Comparison of annua l average AERMOD predicted NO 2 

### concentrations (on-road and non-road receptors) and APEX modeled NO 2 

### exposures (with and without modeled indoor sources) in Atla nta modeling domain for year 2002. ....................... 191 Figure 8-10. Comparison of estimated annual average NO 2 

### exposures for Years 2001-2003 in Atlanta modeling domain without modeled indoor sources. ..................................... 192 Figure 8-11. Distribu tion of measured daily average personal NO 2 

### exposures for individuals in Atlanta, stratified by two seasons (fall or spring) and cooking fuel (gas or electric). Minimum (min), median (p50), and maximum (max) were obtained from each

x

### individual’s multi-day exposure measurements . The figure generated here was based on personal exposure measurements obtained from Suh (2008). .............................. 195 Figure 8-12. Distribution of estimated daily average NO 2 

### exposures for individuals in Atlanta, stratified by two seasons (fall or spring) and with and wit hout indoor sources, for Year 2002 APEX simulation. Lower bound (2.5th pe rcentile, p2.5), median (p50), and upper bound (97.5th percentile, p97.5) were calculated from each simulated persons 365 days of exposure. A random sample of 5% of persons (about 2,500 individuals) is presented in each figure to limit the density of the graphs. ....................................... 196 Figure 8-13. Estimated number of all simulated asthmatics in the Atlant a model domain with at least one NO 2 

### exposure at or above the potential h ealth effect benchmark levels, using modeled 2001-2003 air quality (as is), without indoor sources. ................................ 199 Figure 8-14. Estimated number of simulated as thmatic children in the Atlanta model domain with at least one NO 2 

### exposure at or above the pote ntial health effect benchmark levels, using modeled 2001-2003 air quality (a s is), without mode led indoor sources.

............................................................................................................................... ..... 199 Figure 8-15. Estimated number of all simulated asthmatics in the Atlant a model domain with at least one NO 2 

### exposure at or above potential hea lth effect benchmark levels, using modeled 2002 air quality (as is), both w ith and without modeled indoor sources. .... 201 Figure 8-16. Estimated number asthmatic person -days in the Atlanta model domain with an NO 2 

### exposure at or above potential health eff ect benchmark levels, using modeled 2002 air quality (as is), both with and without modeled indoor sources. ................................. 201 Figure 8-17. Fraction of time all simulated pers ons in the Atlanta model domain spend in the twelve microenvironments that corresponds with exceedances of the potential NO 2 

### health effect benchmark levels, a) ≥ 100 ppb, b) ≥ 200 ppb, and c) ≥ 300 ppb, year 2002 air quality (as is) without indoor sources. ......................................................... 204 Figure 8-18. Fraction of time all simulated pers ons in the Atlanta model domain spend in the twelve microenvironments that corresponds with exceedances of the potential NO 2 

### health effect benchmark levels, a) ≥ 100 ppb, b) ≥ 200 ppb, and c) ≥ 300 ppb, year 2002 air quality (as is) with indoor sources. .............................................................. 205 Figure 8-19. Estimated percent of all asthmatics in the Atlanta modeling domain with repeated NO2 exposures above potential health e ffect benchmark levels, using modeled 2002 air quality (as is), without indoor sources. ................................................................. 206 Figure 8-20. Estimated percent of all asthmatics in the Atlanta modeling domain with repeated NO 2 

### exposures above potential health eff ect benchmark levels, using modeled 2002 air quality (as is), with indoor sources. ...................................................................... 207 Figure 8-21. Estimated number of all asthmatics in the Atlanta modeling domain with at least one NO 2 

### exposure at or above the potential he alth effect benchmark level, using modeled 2002 air quality just meeting the curr ent standard (cur std), with and without modeled indoor sources. ............................................................................................. 208 Figure 8-22. Estimated percent of asthmatics in the Atlanta modeling do main with repeated NO 2 

### exposures above health effect benchmark levels, using modeled 2002 air quality just meeting the current standard, without modeled indoor sources. ................................ 209 Figure 8-23. Estimated percent of asthmatic s in the Atlanta mode ling domain with NO 2 

### exposures at or above potential health effect benchmark levels, using modeled 2002 air quality adjusted to just meeting poten tial alternative standards, without indoor

sources. ....................................................................................................................... 211

xi

### Figure 8-24. Estimated percent of asthmatics in the Atlanta modeling domain with multiple NO 2 

### exposures at or above potential health effect benchmark levels, using modeled 2002 air quality adjusted to ju st meeting a 50 ppb level 99 th 

### percentile form alternative standard, without indoor sources. .............................................................................. 211 Figure 8-25. Estimated percent of asthmatics in the Atlanta modeling domain with multiple NO 2 

### exposures at or above potential health effect benchmark levels, using modeled 2002 air quality adjusted to ju st meeting a 50 ppb level 99 th 

### percentile form alternative standard, with indoor sources. .................................................................................... 212 Figure 8-26. Estimated percent of asthmatics in the Atlanta modeling domain with multiple NO 2 

### exposures at or above potential health effect benchmark levels, using modeled 2002 air quality adjusted to ju st meeting a 100 ppb level 99 th 

### percentile form alternative standard, without indoor sources. .............................................................................. 212 Figure 8-27. Comparison of estimated population a nd total roadway miles in 18 locations (data from FHWA (2002) provided in Table 8-16). ........................................................... 221 Figure 8-28. Comparison of high ranked AERMOD 1-hour NO 2 

### concentrations ( μ g/m 3 

### ) from mobile sources across four NO 2 

### monitoring locations based on JST vs. ATL meteorological inputs for 2002. ................................................................................. 228 Figure 8-29. Comparison of the average ratios of predicted/observed concentrations of NO 2 

### across four ambient monitors based on weekday vs. weekend only. ......................... 234 Figure 8-30. Example comparison of estimated geometric mean and geometric standard deviations of AER (h-1) for homes with air conditioning in several cities. .............. 244 Figure 8-31. Example of boot strap simulati on results used in evaluating random sampling variation of AER (h-1) di stributions (RTP data). ....................................................... 245 Figure 8-32. Example of boot strap simulati on results used in evaluating random sampling variation of AER (h-1) di stributions (outside CA). ................................................... 246 Figure 9-1. Major components of nitrogen diox ide health risk assessment for emergency department visits. ....................................................................................................... 256

xii

## List of Acronyms/Abbreviations

AADT Annual average daily traffic A/C Air conditioning AER Air exchange rate AERMOD American Meteorological So ciety (AMS)/EPA Regulatory Model AHS American Housing Survey APEX EPA’s Air Pollutants Exposure model, version 4 ANOVA One-way analysis of variance AQS EPA’s Air Quality System AS Asthma symptoms BAL bronchoalveolar lavage BRFSS Behavioral Risk Fact or Surveillance System C Cough CAA Clean Air Act CAMD EPA’s Clean Air Markets Division CAMP Childhood Asthma Management Program CASAC Clean Air Scientific Advisory Committee CDC Centers for Disease Control CHAD EPA’s Consolidated Human Activity Database CHF Congestive Heart Failure Clev/Cinn Cleveland and Cincinnati, Ohio CMSA Consolidated me tropolitan statistical area CO Carbon monoxide COPD Chonic Obstructive Pulmonary Disease COV Coefficient of Variation C-R Concentration-Response CTPP Census Transportation Planning Package DVRPC Delaware Valley Re gional Planning Council ECP Eosinophil cationic protein EDR Emergency de partment visits for respiratory disease EDA Emergency department visits for asthma EDAC Emergency department visits for asthma – children FHWA Federal Highway Administration HAAC Hospital admissions for asthma - children ER Emergency room EPA United States Environmental Protection Agency EOC Exposure of Concern GM Geometric mean GSD Geometric standard deviation GST Glutathione S -transferase (e.g., GSTM1, GSTP1, GSTT1) h Hour HNO3 Nitric acid HONO Nitrous acid ID Identification ISA Integrated Science Assessment

xiii

### ISH Integrated Surface Hourly Database km Kilometer L95 Lower limit of the 95 th 

### confidence interval LA Los Angeles, California m Meter max Maximum ME Microenvironment med Median MI Myocardial Infarction min Minimum MS Morning symptoms MSA Metropolitan statistical area NAAQS National Ambient Air Quality Standards NAICS North American Industr ial Classification System NCEA National Center for Environmental Assessment NEI National Emissions Inventory NEM NAAQS Exposure Model NCDC National Climatic Data Center NHAPS National Human Activity Pattern Study NHIS National Health Interview Survey NO 2 

### Nitrogen dioxide NO x 

### Oxides of nitrogen NO 3 - 

### Nitrate ion NWS National Weather Service NYC New York City NYDOH New York Department of Health O 3 

### Ozone OAQPS Office of Air Qualit y Planning and Standards OR Odds ratio ORD Office of Research and Development ORIS Office of Regulatory Inform ation Systems identification code POC Parameter occurrence code ppb Parts per billion PEN Penetration factor PM Particulate matter PMN Polymorphonuclear ppm Parts per million PRB Policy-Relevant Background PROX Proximity factor PVMRM Plume Volume Molar Ratio Method RECS Residential Energy Consumption Survey RIU Rescue inhaler use RR Relative risk SAS Statistical Analysis Software SB Shortness of breath SEP Social-economic position

xiv

### xv SIC Standard Industrial Code SD Standard deviation se Standard error TDM Travel Demand Modeling tpy Tons per year TRIM EPA’s Total Risk Integrated Methodology U95 Upper limit of the 95 th 

### confidence interval US DOT United States Department of Transportation US EPA United States Environmental Protection Agency USGS United States Geological Survey VMT Vehicle miles traveled W Wheeze

# 1. INTRODUCTION

1.1 OVERVIEW

The U.S. Environmental Protection Agency (E PA) is conducting a review of the national ambient air quality standards (NAAQS) for nitrogen dioxide (NO 2 

### ). Sections 108 and 109 of the Clean Air Act (The Act) govern the establishment a nd periodic review of the air quality criteria and the NAAQS. These standards are establis hed for pollutants that may reasonably be anticipated to endanger public heal th or welfare, and whose presence in the ambient air results

from numerous or diverse mobile or stationa ry sources. The NAAQS are based on air quality criteria, which reflect the latest scientific knowledge useful in indicating the kind and extent of identifiable effects on public health or welfare th at may be expected from the presence of the pollutant in ambient air. The EPA Administrator promulgates and periodically reviews primary (health-based) and secondary (welfare-based) NAAQS for such pollutants. Based on periodic reviews of the air quality criteria and standards, the Administrator makes revisions in the criteria and standards and promulgates any new standards as may be appropriate. The Act also requires that an independent scientific review committee advise the Admini strator as part of this NAAQS review process, a function now performed by the Clean Air Scientific Advisory Committee (CASAC). The Agency has recently made a number of changes to the process for reviewing the NAAQS (described at http://www.epa.gov/ttn/naaqs/ ). In making these changes, the Agency consulted with CASAC. This new process, which is being applied to the current review of the

NO 2 

### NAAQS, contains four major components. Each of these components, as they relate to the

review of the NO 2 

### primary NAAQS, is described below. The first of these components is an integr ated review plan. This plan presents the schedule for the review, the process for conducti ng the review, and the key policy-relevant science issues that will guide the review. The in tegrated review plan for this review of the NO 2 

### primary NAAQS is presented in the Integrated Review Plan for the Primary National Ambient Air Quality Standard for Nitrogen Dioxide (EPA, 2007a). The polic y-relevant questions identified in this document to guide the review are:

1

### • Has new information altered the scientific s upport for the occurrence of health effects following short- and/or long-term exposur e to levels of nitrogen oxides (NO x 

### ) found in

the ambient air?

• What do recent studies focused on the near -roadway environment tell us about health

effects of NO x 

### ?

• At what levels of NO x 

### exposure do health effects of concern occur?

• Has new information altered conclusions from previous reviews regard ing the plausibility of adverse health effects caused by NO x 

### exposure?

• To what extent have important uncertainties identified in the 1996 review been reduced and/or have new uncertainties emerged?

• What are the air quality relationships be tween short-term and long-term exposures

to NO x 

### ? Additional questions will become relevant if th e evidence suggests that revision of the current standard might be appropriat e. These questions are:

• Is there evidence for the occurrence of a dverse health effects at levels of NO x 

### lower than those observed previously? If so, at what levels and what are the important uncertainties

associated with that evidence?

• Do exposure estimates suggest th at exposures of concern for NO x 

### -induced health effects

will occur with current ambient levels of NO 2 

### or with levels that just meet current, or potential alternative, standards? If so, ar e these exposures of sufficient magnitude such that the health effects might reasonably be judged to be important from a public health perspective? What are the important uncer tainties associated with these exposure

estimates?

• Do the evidence, the air quality assessment , and the risk/exposure assessment provide support for considering different standard indicators or averaging times?

• What range of levels is supported by the ev idence, the air quality assessment, and the risk/exposure assessments? What are the uncer tainties and limitations in the evidence

and the assessments?

2

### • What is the range of forms supported by the ev idence, the air quality assessment, and the exposure/risk assessments? What are the unc ertainties and limitati ons in the evidence

and the assessments? The second component of the review proce ss is a science assessment. A concise synthesis of the most policy-relevant science has been compiled into the Integrated Science Assessment (ISA). The ISA is supported by a se ries of annexes that contain more detailed

information about the scientific literature. The ISA to support this review of the NO 2 

### primary NAAQS is presented in the Integrated Science Assessment for Oxides of Nitrogen - Health

Criteria , henceforth referred to as the ISA (EPA, 2008a). The third component of the re view process is a risk and exposure assessment, which is described in this document. The purpose of th is document is to communicate EPA’s assessment of exposures and risks associated with ambient NO 2 

### . In this assessment, we have developed estimates of human exposures and risks asso ciated with current ambient levels of NO 2 

### , with levels that just meet the current standard, and with le vels that just meet potential alternative standards. Figure 1-1 (below) pr esents a schematic overview of the analyses described in this document and how those analyses fit together. E ach of the steps highlighted in Figure 1-1 is

described in more detail in subsequent sections.

3

### Figure 1-1. Overview of the analyses described in this document and their interconnections

The results of the risk and exposure assessment is considered alongside the health evidence, as evaluated in the final ISA, to inform the policy assessment and ru lemaking process, as discussed below in chapter 10. The draft plan for conducting the risk and exposure assessment to support

the NO 2 

### primary NAAQS is presented in the Nitrogen Dioxide Health Assessment Plan: Scope and Methods for Exposure and Risk Assessment , henceforth referred to as the Health Assessment Plan (EPA, 2007b). The first draft of the ri sk and exposure assessment is presented in Risk and Exposure Assessment to Suppor t the Review of the NO 2 

### Primary National Ambient Air Quality Standard: First Draft (EPA, 2008b). The second draft is presented in Risk and Exposure

4

### Assessment to Support the Review of the NO 2 

### Primary National Ambient Air Quality Standard: Second Draft (EPA, 2008c). The fourth component of the process is the policy assessment and rulemaking. The Agency’s views on policy options will be published in the Federal Register as an advance notice of proposed rulemaking (ANPR). This policy assessment will address the adequacy of the current standard and of potential alternative standards, which will be defined in terms of indicator, averaging time, form, 1 

### and level. To accomplish this, the policy assessment will

consider the results of the fina l risk and exposure assessment as well as the scientific evidence (including evidence from the epidemiologic, controlled human exposure, and animal toxicological literatures) evalua ted in the ISA, drawing from the discussion in chapter 10. Taking into consideration CASAC advice and recommendations, as well as public comment on the ANPR, the Agency will publish a proposed rule, to be followed by a public comment period. Taking into account comments received on the proposed rule, the Agency will issue a final rule to complete the rulemaking process.



## 1.2 HISTORY

1.2.1 History of the NO 2 

### NAAQS On April 30, 1971, EPA promulgated identi cal primary and secondary NAAQS for NO 2 

### under section 109 of the Act. The standards we re set at 0.053 parts per million (ppm), annual average (36 FR 8186). In 1982, EPA published Air Quality Criteria for Oxides of Nitrogen (EPA, 1982), which updated the scientific criteria upon which the initial NO 2 

### standards were based. On February 23, 1984, EPA proposed to re tain these standards (49 FR 6866). After taking into account public comments, EPA published the final decision to retain these standards on June 19, 1985 (50 FR 25532). On July 22, 1987, EPA announced that it was undertaking plans to revise the 1982 air quality criteria (52 FR 27580). In November 1991, EPA released an updated draft air quality criteria document for CASAC and public re view and comment (56 FR 59285). The draft document provided a comprehensive assessment of the available scientific and technical

information on health and welfar e effects associated with NO 2 

### and other oxides of nitrogen. The

1 The “form” of a standard defines the air quality statistic that is to be compared to the level of the standard in determining whether an area attains the standard.



### 5

### CASAC reviewed the draft document at a mee ting held on July 1, 1993 and concluded in a closure letter to the Administrator that the document “provides a scientifically balanced and defensible summary of current knowledge of th e effects of this pollutant and provides an adequate basis for EPA to make a deci sion as to the appropriate NAAQS for NO 2 

### ” (Wolff, 1993). The Air Quality Criteria Do cument for the Oxides of Nitrogen was then finalized (EPA, 1993). The EPA also prepared a Staff Paper that su mmarized an air quality assessment for NO 2 

### conducted by the Agency (McCurdy, 1994), summar ized and integrated the key studies and

scientific evidence contained in the revised ai r quality criteria document, and identified the

critical elements to be consid ered in the review of the NO 2 

### NAAQS. The CASAC reviewed two drafts of the Staff Paper and concluded in a clos ure letter to the Administ rator (Wolff, 1995) that the document provided a “scientifically adequa te basis for regulatory decisions on nitrogen dioxide.” In September of 1995, EPA finalized the Staff Paper en titled, “Review of the National Ambient Air Quality Standards for Nitrogen Diox ide: Assessment of Scientific and Technical Information” (EPA, 1995). In October 1995, the Administrator announced he r proposed decision not to revise either the primary or secondary NAAQS for NO 2 

### (60 FR 52874; October 11, 1995) . A year later, the

Administrator made a final determina tion not to revise the NAAQS for NO 2 

### after careful evaluation of the comments received on the propo sal (61 FR 52852, October 8, 1996). The level for both the existing primary and secondary NAAQS for NO 2 

### is 0.053 parts per million (ppm) (100 micrograms per cubic meter of air [ μ g/m 3 

### ]), annual arithmetic average, calculated as the

arithmetic mean of the 1-hour NO 2 

### concentrations.

1.2.2 Health Evidence from Previous Review The prior Air Quality Criteria Document (AQCD) for Oxides of Nitrogen (EPA, 1993)

concluded that there were two ke y health effects of greatest con cern at ambient or near-ambient

levels of NO 2 

### , increased airway responsiveness in as thmatic individuals after short-term exposures and increased occurrence of respiratory illness in children with longer-term exposures.

Evidence also was found for increased risk of emphysema, but this was of major concern only with exposures to levels of NO 2 

### much higher than then-current ambient levels. The evidence regarding airway responsiveness was drawn largely from controlled human exposure studies.

6

### The evidence for respiratory illness was draw n from epidemiologic studies that reported associations between respiratory sy mptoms and indoor exposures to NO 2 

### in people living in homes with gas stoves. The biol ogical plausibility of the epid emiologic results was supported by toxicological studies that detected changes in lung host defenses following NO 2 

### exposure. Subpopulations considered potentially mo re susceptible to the effects of NO 2 

### included individuals with pr eexisting respiratory disease, children, and the elderly.

1.2.3 Assessment from Previous Review In the previous review of the NO 2 

### NAAQS, risks were characterized by comparing ambient monitoring data, which were used as a surrogate for exposure, with potential health

benchmark levels identified from controlled hum an exposure studies. At the time of the review, a few studies indicated the possibi lity for adverse health effect s due to short-term (e.g., 1-hour) exposures between 0.20 ppm and 0.30 ppm NO 2 

### . Therefore, the focus of the assessment was on the potential for short-term (i.e., 1-hour) exposures to NO 2 

### levels above potential health benchmarks in this range. The assessment used monitoring data from the years 1988-1992 and screened for sites with one or more hourly exce edances of potential short-term health effect

benchmarks. Predictive models were then c onstructed to relate th e frequency of hourly

concentrations above short-term health eff ect benchmarks to a range of annual average concentrations, including the current standard. Based on the results of this analysis, both CASAC (Wolff, 1995) and the Administrator (60 FR 52874) concluded that the minimal occurrence of short-term peak con centrations at or above a potenti al health effect benchmark of 0.20 ppm (1-h average) indicated that the existing annual sta ndard would provide adequate health protection against short-term exposures . This conclusion was a key element in the

decision in the 1996 review to retain the existing annual standard.



## 1.3 SCOPE OF THE RISK AND EXPOSURE ASSESSMENT FOR THE

CURRENT REVIEW

NO x 

### , for purposes of this document, include multiple gaseous (e.g., NO 2 

### , NO, HONO) and particulate (e.g., nitrate) speci es. As discussed in the integrated review plan (2007a), the

current review of the NO 2 

### NAAQS will focus on the gaseous species of NO x 

### and will not consider health effects directly asso ciated with particulate species of NO x 

### . Of the gaseous

7

### species, EPA has historically determined it appropri ate to specify the indicat or of the standard in

terms of NO 2 

### because the majority of the information regarding health effects and exposures is

for NO 2 

### . In the current review, staff notes that no alternative to NO 2 

### has been advanced as being a more appropriate surrogate for ambient gaseous NO x 

### . Controlled human exposure studies and animal toxicology studies provi de specific evidence for health effects following exposure to

NO 2 

### . Epidemiologic studies also typically report levels of NO 2 

### , as opposed to other gaseous

NO x 

### , though the degree to which monitored NO 2 

### reflects actual NO 2 

### levels, as opposed to NO 2 

### plus other gaseous NO x 

### , can vary (e.g.,. see section 2.2.3 of this document). Therefore, NO 2 

### will be used as the indicator for the gaseous NO x 

### in the risk and exposure assessments described in

this document.

8

# 2. SOURCES, AMBIENT LEVELS, AND EXPOSURES

2.1 SOURCES OF NO 2



### Ambient levels of NO 2 

### are the product of both direct NO 2 

### emissions and emissions of

other NO x 

### (e.g, NO), which can then be converted to NO 2 

### (for a more detailed discussion see the ISA, section 2.2). Nationally, anthropogenic sour ces account for approximately 87% of total

NO x 

### emissions. Mobile sources (both on-ro ad and off-road) account for about 60% of total anthropogenic emissions of NO x 

### , while s tationary sources (e.g., electrical utilities and industry) account for the remainder (annex table 2.6-1). Highway vehicles represent the major mobile source component. In the United States, approximately half the mobile source emissions are contributed by diesel engines and half are emitted by gasoline-fueled vehicles and other sources ( 

### annex section 2.6.2 and Table 2.6-1)

. 

### Apart from these anthropogenic sources, there are also natural sources of

NO x 

### including microbial activity in soils, lightni ng, and wildfires (ISA, section 2.2.1 and annex section 2.6.2).



## 2.2 AMBIENT LEVELS OF NO 2



### 2.2.1 Background on NO 2 

### monitoring network From the inception of the NO 2 

### monitoring network in the la te 1970’s through the present day, the number of monitoring si tes has remained relatively stable (Watkins, 2008). As of October 2008, there were 409 NO x 

### monitors within the United States actively reporting NO 2 

### data into EPA’s Air Quality System (AQS). The NO 2 

### network was original ly established for implementation of the NO 2 

### NAAQS promulgated in 1971. The first requirements for NO 2 

### monitoring to implement the 1971 NO2 NAAQS were established in May of 1979. At that time,

two NO 2 

### national ambient monitoring stations (NAMS) were required in areas of the country with populations greater th an 1,000,000. 40 CFR Part 58, Appendix D, section 3.5. The regulations noted that within urban areas, two permanent monitors are sufficient, and with respect to those two monitors provided: The first station (category (a), middle scale or neighborhood scale) would be to measure the photochemical production of NO 2 

### and would best be located in that part of the urban area wher e the emission density of NO x 

### is the highest. The second station (category (b) urban sc ale), would be to measure the NO 2 

### produced

9

### from the reaction of NO with O 3 

### and should be downwi nd of the area peak NO x 

### emission areas.

40 CFR Part 58, Appendix D, section 3.5.

In October 2006, EPA revised the monitoring requirements for NO 2 

### in light of the fact

that there are no NO 2 

### non-attainment areas under the cu rrent standards. The 2006 rule eliminated the minimum requirements for the number of NO 2 

### monitoring sites. 40 CFR Part 58, Appendix D, section 4.3. However, the rule re quires continued operatio n of existing State and local monitoring stations (SLAMS) until disc ontinuation is approved by the EPA Regional

Administrator. The revised rule fu rther requires that where SLAMS NO 2 

### monitoring is ongoing,

“at least one NO 2 

### site in the area must be located to measure the maximum concentration of

NO 2 

### .” As noted above, the size of the NO 2 

### network has remained fairly stable through time, even though no minimum monitoring sites were required under the 2006 rule. The maintenance

of the NO 2 

### monitoring network has been driven by several factors, in cluding the need to support ozone (O 3 

### ) modeling and forecasting, the need to track PM precursors, and a general desire on the part of states to continue to understand trends in ambient NO 2 

### . To characterize the current NO 2 

### network, staff has reviewed the NO 2 

### network meta-data.

The data reviewed are those avai lable from AQS in October 2008, fo r monitors reporting data in 2008. The meta-data fields are typically created by state and local agencies when a monitor site is opened, moved, or re-characteri zed. While these files are usef ul for characterizing specific monitors, there is some uncertainty surrounding this meta-data given that there is no routine or enforced process for updating or correcting meta-dat a fields. With this un certainty in mind, staff has compiled information on the monitoring objectiv es and measurement scales for monitors in

the NO 2 

### network. The monitor objective meta-data field describes the purpose of the monitor. For example the purpose of a particular monitor could be to characterize health effects, photochemical activity, transport, and/or welf are effects. As of October 2008, there were 489 records of NO 2 

### monitor objective values (some monitors have mu ltiple monitor objectives). Table 2-1 lists the distribution of monitoring objectives across th e network. There are 12 categories of monitor objectives for NO 2 

### monitors within AQS. The “other” categ ory is for sites likely addressing a

10

### state or local need outside of the routine objectives, and the “unknown” category represents missing meta-data. The remaining categories stem directly from categorizations of site types within CFR. In 40 CFR Part 58 Appe ndix D, there are six examples of NO 2 

### site types:

1. Sites located to determine the highest con centration expected to occur in the area covered by the network (Highest Concentration).

2. Sites located to measure typical concentrations in areas of high population (Population Exposure).

3. Sites located to determine the impact of significant sources or source categories on air quality (Source Oriented).

4. Sites located to de termine general background con centration levels (General Background).

5. Sites located to de termine the extent of regi onal pollutant transport among populated areas; and in suppor t of secondary standard s (Regional Transport).

6. Sites located to measure air pollution im pacts on visibility, vegetation damage, or other welfare-based impacts (Welfare Related Impacts).

The remaining four categories av ailable are a result of updating the AQS database. In the more recent upgrade to AQS, the data handlers inserted the available site types for Photochemical Assessment Monitoring Stations (PAMS) network. These PAMS site types are spelled out in 40 CFR Part 58 Appendix D:

1. Type 1 sites are established to charact erize upwind backgroun d and transported

O 3 

### and its precursor concentrations entering the area and will identify those areas which are subjected to tr ansport (Upwind Background).

2. Type 2 sites are established to monito r the magnitude and type of precursor emissions in the area where maximum precursor emissions are expected to impact and are suited for the monitoring of urba n air toxic pollutant s (Max. Precursor Impact).

3. Type 3 sites are intended to monitor maximum O 3 

### concentrations occurring downwind from the area of maximum precursor emissions (Max. O 3 

### Concentration).

11

### 4. Type 4 sites are established to ch aracterize the downwind transported O 3 

### and its precursor concentrations exiting the area and will identify those areas which are potentially contributing to overwhelmi ng transport in other areas (Extreme Downwind).

Table 2-1. NOx Network Distribution of Monitor Objectives 

### NOx Monitor Objective Number of Monitor Objective Records Percent Distribution

Population Exposure 177 36.20 Highest Concentration 58 11.86 General Background 51 10.43 Max. Precursor Impact (PAMS Type 2 Site) 21 4.29

Source Oriented 19 3.89 Upwind Background (PAMS Type 1 Site) 18 3.68

Regional Transport 12 2.45 Other 9 1.84 Max. O 3 

### Concentration (PAMS Type 3 Site) 8 1.64

Extreme Downwind (PAMS Type 4 Site) 3 0.61

Welfare Related Impacts 1 0.20 Unknown 112 22.90 Totals: 489 100%

The spatial measurement scales are laid out in 40 CFR Part 58, Appendix D, Section 1 “Monitoring Objectives and Spatial Scales.” This part of the regulation spells out what data from a monitor can represent in terms of ai r volumes associated with area dimensions:

Microscale - 0 to 100 meters Middle Scale - 100 to 500 meters Neighborhood Scale - 500 meters to 4 kilometers Urban Scale - 4 to 50 kilometers Regional Scale - 50 kilometers up to 1000km

There are meta-data records for the NO 2 

### network to indicate what the measurement scale of a particular monitor repr esents. There are 386 NO 2 

### monitor records in AQS with available

measurement scale information. Table 2-2 show s the measurement scale distribution across all

NO 2 

### sites form the available data in AQS of monitors reporti ng data in 2008.

12

### Table 2-2. NOx Network Distribution across Measurement Scales. 

### Measurement Scale Number of Measurement Scale Records Percent Distribution

Microscale 3 0.78 Middle Scale 23 5.96 Neighborhood 212 54.92 Urban Scale 119 30.83 Regional Scale 29 7.51 Totals: 386 100%

In summary, upon review of the known 409 mo nitors reporting data to AQS in 2008, and

the distribution of the available data from the categories of monitor objective and measurement scale, we see the NO 2 

### network is primarily targeting public health and photochemical process monitoring objectives. We note that nearly half of the monitor objectiv e records are directly targeting public health thr ough the population exposure (36.2 %) and highest concentration (11.8%) categories alone. The other categories serv e to inform public health concerns, but also address photochemistry issues where NO x 

### serves as a precursor to oz one. Further, it appears that approximately 10% of NO 2 

### monitors are in place to serve the PAMS network. In reality, a large majority of sites likely could se rve both public health and photo chemistry related objectives due to their proximity to urban areas. The excep tions would likely be categories such as upwind background, extreme downwind, regional transport, and possibly maximum O 3 

### concentration. These four categories only repres ent approximately 7% of the NO 2 

### network, and have a higher likelihood of being rural and likely regional in scale.

2.2.2 Trends in ambient concentrations of NO 2 

### As noted above, NO 2 

### is monitored largely in urban areas and, therefore, data from the

NO 2 

### monitoring network is generally more repres entative of urban areas than rural areas. According to monitoring data, nationwide levels of ambient NO 2 

### (annual average) decreased 41% between 1980 and 2006 (ISA, Figure 2.4-15). Between 2003 and 2005, national mean

concentrations of NO 2 

### were about 15 ppb for averaging periods ranging from a day to a year. The average daily maximum hourly NO 2 

### concentrations were approximately 30 ppb. These values are about twice as high as the 24-h aver ages. The highest maximu m hourly concentrations (~200 ppb) between 2003 and 2005 are more than a f actor of ten higher than the mean hourly or

13

### 24-h concentrations (ISA, Figure 2.4- 13). The highest levels of NO 2 

### in the United States can be found in and around Los Angeles, in the Midwest, and in the Northeast. Policy-relevant background concentrations, which are those concentra tions that would occur in the United States in the absence of anthopogenic emissions in continental North America (defined here as the United States, Canada, and Mexico), are estim ated to range from only 0.1 ppb to 0.3 ppb (ISA, section 2.4.6).

Ambient levels of NO 2 

### exhibit both seasonal and diurnal variation. In southern cities, such as Atlanta, higher concentr ations are found during winter, cons istent with the lowest mixing layer heights being found during that time of th e year. Lower concentrations are found during summer, consistent with higher mixing layer heights and increased rates of photochemical

oxidation of NO 2 

### . For cities in the Midwest and Northeas t, such as Chicago and New York City, higher levels tend to be found from late winter to early spring with lowe r levels occurring from summer though the fall. In Los Angeles the high est levels tend to occur from autumn though early winter and the lowest levels from spring though early summer. Mean and peak

concentrations in winter can be up to a factor of two larger than in the summer at sites in Los Angeles. In terms of daily variability, NO 2 

### levels typically peak duri ng the morning rush hours. Monitor siting plays a key role in evaluating diurnal variability as monitors located further away from traffic will show cycles that are less pr onounced over the course of a day than monitors

located closer to traffic.

2.2.3 Uncertainty Associated with the Ambient NO 2 

### Monitoring Method The method for estimating ambient NO 2 

### levels (i.e., subtraction of NO from a measure of

total NO x 

### ) is subject to interference by NO x 

### oxidation products (e.g., PAN, HNO 3 

### ) (ISA, section 2.3). Limited evidence suggests that these compounds result in an overestimate of NO 2 

### levels by roughly 20 to 25% at typical ambien t levels. Smaller relative erro rs are estimated to occur in measurements taken near strong NO x 

### sources since most of the mass emitted as NO or NO 2 

### would not yet have been further oxidized. Relativ ely larger errors appe ar in locations more distant from strong local NO x 

### sources. Additionally, many NO 2 

### monitors are elevated above ground level in the cores of large ci ties. Because most sources of NO 2 

### are near ground level (i.e.,

combustion emissions from traffi c), there is a gradient of NO 2 

### with higher levels near ground level and lower levels being detected at the elevated monitor. One comparison has found an

14

### average of a 2.5-fold higher NO 2 

### concentration measured at 4 meters above the ground compared to 15 meters above the ground. The ISA notes that levels are likely even higher at elevations below 4 meters (ISA, section 2.5.3.3). Another s ource of uncertainty in exposure estimates can

result from monitor location. NO 2 

### monitors are sited for compliance with air quality standards rather than for capturing small-scale variability in NO 2 

### concentrations near sources such as roadway traffic. Significant gradients in NO 2 

### concentrations near roadways have been observed in several studies, and NO 2 

### concentrations have been found to be correlated with distance from roadway and traffic volume (ISA, section 2.5.3.2).



## 2.3 EXPOSURE TO NO 2



### 2.3.1 Overview Human exposure to an airborne pollutant can be characterized by contact between a person and the pollutant at a specific concentrat ion for a specified period of time (ISA, section 2.5.1). The integrated exposure of a person to a gi ven pollutant is the time-weighted average of the exposures over all time intervals for all micr oenvironments in which the individual spends time. Microenvironments in which people are exposed to air pollutants such as NO 2 

### typically

include residential indoor environments a nd other indoor locations , near-traffic outdoor environments and other outdoor locations, and in vehicles (ISA, Figure 2.5-1). There is a large amount of va riability in the time that i ndividuals spend in different microenvironments, but on average people spend the majority of their time (about 87%) indoors. Most of this time is spent at home with less ti me spent in an office/workplace or other indoor locations (ISA, Figure 2.5-1). On average in the U.S., people spend about 8% of their time outdoors and 6% of their time in vehicles. Signi ficant variability surrounds each of these broad estimates, particularly when considering influentia l personal attributes such as age or gender; when accounting for daily, weekly, or seasonal fact ors influencing personal behavior; or when characterizing individual variability in time sp ent in various locations (McCurdy and Graham, 2003; Graham and McCurdy, 2004). Typically, the tim e spent outdoors or in vehicles could vary by 100% or more depending on which of these in fluential factors are considered. Exposure misclassification can result when the time spent in different microenvironments is not taken into consideration and may obscure the true relations hip between ambient air pollutant exposures and

health outcomes.

15

### 16 2.3.2 Uncertainty Associated with Ambient Levels as a Surrogate for Exposure Many epidemiologic studies rely on measures of ambient NO 2 

### concentrations as surrogates for personal exposure to ambient NO 2 

### . Results have been mixed regarding the appropriateness of using ambient levels of NO 2 

### as a surrogate for personal exposures to ambient

NO 2 

### . Studies examining the association between ambient NO 2 

### and personal exposure to NO 2 

### have generated mixed results due to 1) the prevalence of indoor sources of NO 2 

### ; 2) the spatial heterogeneity of NO 2 

### in study areas; 3) the seasonal and ge ographic variability in the infiltration

of ambient NO 2 

### ; 4) differences in the time spent in different microenvironments; and 5) differences in study design. As a result, some researchers have concluded that ambient NO 2 

### may be a reasonable proxy for personal exposure, while others have noted that caution must be exercised (ISA, section 2.5.9). However, the possi ble consequences of th is exposure error do not bias conclusions in a positive direction (see chapter 4 of this document) since it generally tends to reduce, rather than increase, e ffect estimates (ISA, section 5.2.2).

# 3. AT RISK POPULATIONS

3.1 OVERVIEW

Specific groups within the ge neral population are at increased risk for suffering adverse

effects from NO 2 

### exposure. This could occur because they are affected by lower levels of NO 2 

### than the general population (susceptibility), because they experience a larger health impact than the general population to a given level of expos ure (susceptibility), and/or because they are exposed to higher levels of NO 2 

### than the general population ( vulnerability). The term susceptibility generally encompasses innate (e.g. , genetic or developmen tal) and/or acquired (e.g., age or disease) factors th at make individuals more likel y to experience effects with exposure to pollutants. Given the likely heterogeneity of indi vidual responses to air pollution, the severity of health effects experienced by a susceptible subgroup may be much greater than that experienced by the population at large. Fact ors that may influence susceptibility to the effects of air pollution include age (e.g., infants, children, elde rly); gender; race/ethnicity; genetic factors; and pre-existi ng disease/condition (e.g., obesity, diabetes, re spiratory disease, asthma, chonic obstructive pulmonary disease (COPD), cardiovascular disease, airway hyperresponsiveness, respiratory infection, ad verse birth outcome) (ISA, sections 4.3.1, 4.3.5, and 5.3.2.8). In addition, some population groups are vulnerable to pollu tion-related effects because their air pollution exposures are higher than those of the general population. Factors that may influence vulnerability to the effects of air pollution include socioeconomic status, education level, air conditioning use, proximity to roadways, geographic location, level of physical activity, and work environment (e.g., ind oor versus outdoor) (ISA, section 4.3.5). The

ISA discusses factors that can conf er susceptibility and/or vulnerability to air pollution with most

of the discussion devoted to factors for which NO 2 

### -specific evidence exists (ISA, section 4.3). These factors are presented in table 3-1 below ( from section 4.3.5 of the ISA) and are discussed in subsequent sections of this chapter (see ISA, chapter 4 for more detail).

17

### Table 3-1. Overview of Susceptibility and Vulnerability Factors



## 3.2 SUSCEPTIBILITY: PRE-EXISTING DISEASE

A number of health conditions have been found to put individuals at greater risk for adverse events following exposure to air polluti on. In general, these include asthma, COPD, respiratory infection, conduction disorders, cong estive heart failure (CHF), diabetes, past myocardial infarction (MI), obesit y, coronary artery disease, lo w birth weight/prematurity, and hypertension (ISA, sections 4.3.1, 4.3.5, and 5.3.2.9). In addition to these conditions, epidemiologic evidence indicates that individuals with bronchial or airway hyperresponsiveness, as determined by methacholine provocation, ma y be at increased risk for experiencing respiratory symptoms (ISA, s ection 4.3.1). In considering NO 2 

### specifically, the ISA evaluated studies on asthmatics, individuals with cardiopulmonary disease, and diabetics (ISA, sections 4.3.1.1 and 4.3.1.2). These groups are discussed in more detail below. Epidemiologic and controlled human exposur e studies, supported by animal toxicology studies, have provided evidence for associations between NO 2 

### exposure and respiratory effects in asthmatics (ISA, section 4.3.1.1). The ISA found evidence from epidemiologic studies for an

association between ambient NO 2 

### and children’s hosp ital admissions, emergency department (ED) visits, and calls to doctors for asthma. NO 2 

### levels were associated with aggravation of asthma effects that include symptoms, medicatio n use, and lung function. Time-series studies also demonstrated a relationship in children betw een hospital admissions or ED visits for asthma

and ambient NO 2 

### levels, even after adjusting for co-pollu tants such as particulate matter (PM) and carbon monoxide (CO) (ISA, section 4.3.1.1). Important evidence was also available from

18

### epidemiologic studies of indoor NO 2 

### exposures. Recent studies have shown associations with asthma attacks and severity of virus-induced asthma (ISA, section 4.3.1.1). In addition, in controlled human exposure studies, airway hyperresponsiveness in asthmatics occurred following exposure to lower NO 2 

### concentrations than the concen trations that caused effects on other endpoints (ISA, sections 5.3.2.1-5.3.2.6). Compared to asthma, less evidence is availa ble to support cardiova scular disease as a mediator of susceptibility to NO 2 

### . However, recent epidemiologic studies report that individuals with preexisting conditions (e.g., including diabetes, CHF, prior MI) may be at increased risk for

adverse cardiac health events associated with ambient NO 2 

### concentrations (IS A, section 4.3.1.2). The small number of controlled human exposure and animal toxicological studies that have evaluated cardiovascular endpoints provide only limited supporting evidence for susceptibility to

NO 2 

### in persons with cardiovascular disease (ISA, section 4.3.1.2).



## 3.3 SUSCEPTIBILITY: AGE

The ISA identifies infants, ch ildren (i.e., <18 years of age), and older adults (i.e., >65 years of age) as groups that are potentially more susceptible than the general population to the

health effects associated with ambient NO 2 

### concentrations (ISA, section 4.3.2). The ISA found

evidence that associations of NO 2 

### with respiratory ED visits a nd hospitalizations were stronger among children and older adults, though not all st udies had comparable findings on this issue (ISA, section 4.3.2). In addition, long-term expos ure studies suggest eff ects in children that include impaired lung function gr owth, increased respiratory symp toms and infections, and onset of asthma (ISA, section 3.4 and 4.3.2). In some studies, associations between NO 2 

### and hospitalizations or ED visits for CVD have been observed in elderly popu lations. Among studies that observed positive associations between NO 2 

### and mortality, a comparison indicated that, in general, the elderly population was more sus ceptible than the non-el derly population to NO 2 

### effects (ISA, section 4.3.2).



## 3.4 SUSCEPTIBILITY: GENETICS

As noted in the ISA (section 4.3.4), genetic factors related to health outcomes and ambient pollutant exposures merit consideration. Several criteria mu st be satisfied in selecting and establishing useful links be tween polymorphisms in candidate genes and adverse respiratory

19

### effects. First, the candidate gene must be significantly involved in the pathogenesis of the adverse effect of interest. Second, polymorphism s in the gene must produce a functional change in either the protein product or in the level of expression of the protein. Third, in epidemiologic studies, the issue of confounding by other environmental exposures mu st be carefully considered (ISA, section 4.3.4). Investigation of genetic susceptibility to NO 2 

### effects has focused on the glutathione Stranferase (GST) gene. Several GST genes ha ve common, functionally-im portant alleles that affect host defense in the lung (ISA, section 4.3.4 ). GST genes are inducible by electrophilic species (e.g., reactive oxygen species) and individual s with genotypes that result in enzymes with reduced or absent peroxidase activ ity are likely to have reduced de fenses against oxidative insult. This could potentially result in increased sus ceptibility to inhaled oxidants and radicals. However, data on genetic susceptibility to NO 2 

### are only beginning to emerge and, while it remains plausible that there ar e genetic factors that can infl uence health responses to NO 2 

### , the few available studies do not pr ovide specific support for genetic susceptibility to NO 2 

### exposure (ISA, section 4.3.4).



## 3.5 SUSCEPTIBILITY: GENDER

As reported in the ISA, a limited number of NO 2 

### studies have stratifie d results by gender. The results of these studies were mixed, and th e ISA does not draw conclusions regarding the potential for gender to confer su sceptibility to the effects of NO 2 

### (ISA, section 4.3.3).



## 3.6 VULNERABILITY: PROXIMIT Y (ON OR NEAR) TO ROADWAYS

The ISA includes discussion of vulnerable populations that expe rience increased NO 2 

### exposures on or near roadways (ISA, section 4.3.6) . Large gradients in NOx concentrations near roadways lead to increased exposures for indi viduals residing, working, or attending school in the vicinity of roadways. Many studies find that indoor, pers onal, and outdoor NO 2 

### levels are strongly associated with proximity to traffic or to traffic dens ity (ISA, section 4.3.6). Due to high air exchange rates, NO 2 

### levels inside a vehicle could ra pidly approach levels outside the vehicle during commuting (ISA, sec tion 4.3.6). Mean in-vehicle NO 2 

### levels are between 2 and 3

times ambient levels measured at fixed sites ne arby (ISA, section 4.3.6). Therefore, individuals with occupations that requi re them to be in traffic or close to traffic (e.g., bus and taxi drivers,

20

### highway patrol officers, toll coll ectors) and individuals with l ong commutes could be exposed to relatively high levels of NO 2 

### compared to ambient levels. Due to the high peak exposures while driving, total personal exposure could be underestimated if exposures while commuting are not

considered.



## 3.7 VULNERABILITY: SOCIOECONOMIC STATUS

The ISA discusses evidence that socioeconomic status (SES) modifies the effects of air pollution (section 4.3.6). Many recent studies ex amined modification by SES indicators on the association between mortality and PM or other indices such as traffic density, distance to roadway, or a general air pollution index (IS A, section 4.3.6). SES modification of NO 2 

### associations has been examined in fewer studi es. For example, in a study conducted in Seoul, South Korea, community-level SES indicators modifi ed the association of air pollution with ED

visits for asthma. Of the five criteria air pollutants evaluated, NO 2 

### showed the strongest association in lower SES districts compared to high SES districts (Kim et al., 2007). In addition, Clougherty et al. (2007) evaluated exposure to violence (a potent ial surrogate for SES) as a

modifier of the effect of traffic -related air pollutants, including NO 2 

### , on childhood asthma. The authors reported an elevated risk of asthma with an increase in NO 2 

### exposure solely among children with above-median exposure to violen ce in their neighborhoods (ISA, section 4.3.6). Although these recent studies have evaluated the impact of SES on vulnerability to NO 2 

### , they are

too few in number to draw definitiv e conclusions (ISA, section 5.3.2.8).



## 3.8 CONCLUSIONS

The population potentially affected by NO 2 

### is large. A considerable fraction of the population resides, works, or attends school ne ar major roadways, and these individuals are likely to have increased exposure to NO 2 

### (ISA, section 4.4). Based on data from the American Housing Survey, approximately 36 million indivi duals live within 300 feet (~90 meters) of a four-lane highway, railroad, or airport (ISA, sect ion 4.4). Furthermore, in California, 2.3% of

schools with a total enrollment of more than 150,000 students were located within ~500 feet of high-traffic roads, with a hi gher proportion of non-white an d economically disadvantaged students attending those schools (I SA, section 4.4). Of this population, asthmatics and members of other susceptible groups discussed above will ha ve even greater risks of health effects related

21

### 22 to NO 2 

### . In the United States, approximately 10% of adults and 13% of children have been diagnosed with asthma, and 6% of adults have been diagnosed with COPD (ISA, section 4.4). The prevalence and severity of asthma is higher among certain ethnic or racial groups such as Puerto Ricans, American Indians, Alaskan Natives, and African Americans (ISA, section 4.4). Furthermore, a higher prevalence of asthma am ong persons of lower SES and an excess burden of asthma hospitalizations and mortality in minority and inner-city communities have been observed (ISA, section 4.4). In addition, population groups based on age also comprise substantial segments of the population th at may be potentially at risk for NO 2 

### -related health impacts. Based on U.S. census data from 2000, about 72.3 million (26%) of the U.S. population are under 18 years of age, 18.3 million (7.4%) are under 5 years of age, and 35 million (12%) are 65 years of age or older. Hence, large proporti ons of the U.S. population are in age groups that are likely to have increased susceptibility and vulnerability for health effects from ambient NO 2 

### exposure. The considerable size of the populatio n groups at risk indicat es that exposure to

ambient NO 2 

### could have a significant impact on public health in the United States.

# 4. HEALTH EFFECTS

4.1 INTRODUCTION

The ISA, along with its associated annexe s, provides a comprehensive review and

assessment of the scientific evidence relate d to the health effects associated with NO 2 

### exposures. For these health effects, the ISA characterizes judgments about causality with a hierarchy (for discussion see ISA, section 1.3) that contains the following five levels.



### • Sufficient to infer a causal relationship

• Sufficient to infer a likely causal rela tionship (i.e., more likely than not)

• Suggestive but not sufficient to infer a causal relationship

• Inadequate to infer the presence or absence of a causal relationship

• Suggestive of no causal relationship Judgments about causality are informed by a series of criteria that are based on those set forth by Sir Austin Bradford Hill in 1965 (ISA, table 1.3- 1). These criteria include strength of the observed association, availability of experime ntal evidence, consistency of the observed association, biological plausibili ty, coherence of the evidence, temporal relationship of the observed association, and the presence of an e xposure-response relationship. A summary of each of the five levels of the hierarchy is provided in table 1.3-2 of the ISA, which has been included below (table 4-1).

23

### Table 4-1. Weight of Evidence for Causal Determination



### The judgments of the ISA, along with the rationa le supporting those judgments, are summarized in tables 4-2 and 4-3 below (ISA, table 5.3-1) and are presented in more detail in subsequent sections of this chapter.

24

### Table 4-2. Causality judgments made in the IS A for endpoints associated with short-term NO 2 

### exposures



### 25

### Table 4-3. Causality judgments made in the IS A for endpoints associated with long-term NO 2 

### exposures



## 4.2 ADVERSE RESPIR ATORY EFFECTS FOLLOWING SHORT-TERM

EXPOSURES

4.2.1 Overview

The ISA concludes that, taken t ogether, recent studies provide scientific evidence that is sufficient to infer a likely causal relationship between short-term NO 2 

### exposure and adverse effects on the respiratory system (ISA, secti on 5.3.2.1). This finding is supported by the large body of recent epidemiologic evid ence as well as findings from human and animal experimental studies. These epidemiologic and experiment al studies encompass a number of endpoints including ED visits and hosp italizations, respiratory sympto ms, airway hyperresponsiveness, airway inflammation, and lung function. Effect estimates from epidemiologic studies conducted

in the United States and Cana da generally indicate a 2-20% 2 

### increase in risks for ED visits and hospital admissions and higher risks for respirator y symptoms (ISA, section 5.4). The findings

2 Effect estimates in the ISA were standardized to a 30 ppb increase in NO 2 for studies that evaluated 1-h daily maximum NO 2 concentrations and to a 20 ppb increase for studies that evaluated 24-h average concentrations.



### 26

### relevant to these endpoints, whic h provide the rationale to support the judgment of a likely causal relationship, are described in more detail below.

4.2.2 Respiratory Emergency Department Visits and Hospitalizations Epidemiologic evidence exists for positive associations of short-term ambient NO 2 

### concentrations below the current NAAQS with in creased numbers of ED visits and hospital admissions for respiratory causes, especially as thma (ISA, section 5.3.2.1). Total respiratory causes for ED visits and hospitalizations typi cally include asthma, bronchitis and emphysema (collectively referred to as COPD), pneumonia, upper and lower respirator y infections, and other minor categories. Temporal associations between ED visits or hospital ad missions for respiratory

diseases and ambient levels of NO 2 

### have been the subject of over 50 peer-reviewed research publications since th e review of the NO 2 

### NAAQS that was complete d in 1996. These studies have examined morbidity in different age groups and have often utilized multi-pollutant models to evaluate potential confounding effects of co- pollutants. Associations are particularly consistent among children (< 14 years) and older adults (> 65 years) when all respiratory outcomes are analyzed together (ISA, figures 3.1-8 and 3.1-9) and among children and subjects of all ages for asthma admissions (ISA, figures 3.1-12 and 3.1-13). When examined with copollutant models, associations of NO 2 

### with respiratory ED visits and hospital admissions were generally robust and independent of the effects of co-pollutant s (ISA, figures 3.1-10 and 3.1-11). The plausibility and coherence of these effects are supported by experimental (i.e., toxicologic and controlled human exposure) studies that eval uate host defense and immune system changes, airway inflammation, and airway responsiveness (s ee subsequent sections of this document and ISA, section 5.3.2.1). Of the ED visit and hospital admission studies reviewed in the ISA, 6 key studies were conducted in the United States (ISA, table 5.4-1). Of these 6 studies, 4 evaluated associations

with NO 2 

### using multi-pollutant models (Peel et al., 2005 and Tolbert et al., 2007 in Atlanta; New York Department of Health (NYDOH), 2006 and It o et al., 2007 in New York City) while 2 studies used only single pollutant models (Linn et al., 2000; Jaffe et al., 2003). In the study by Peel and colleagues, investigators evaluated ED visits among all ages in Atlanta, GA during the period of 1993 to 2000. Using single pollutant m odels, the authors reported a 2.4% (95% CI: 0.9, 4.1) increase in respiratory ED visits asso ciated with a 30-ppb increase in 1-h max NO 2



### 27

### concentrations. For asthma visits, a 4.1% (95% CI: 0.8%, 7.6%) increase was estimated in individuals 2 to 18 years of age. Tolbert and colleagues reanaly zed these data with 4 additional years of information and found essentially simila r results in single pollutant models (2.0% increase, 95% CI: 0.5, 3.3). This same study found that the associations were positive, but not statistically-significant, in multi-pollutant models that included PM 10 

### or O 3 

### . In the study conducted by the New York Department of Health, investigators evaluated asthma ED visits in Bronx and Manhattan, New York over the peri od of January, 1999 to November, 2000. In Bronx, the authors estimated a 6% (95% CI: 1%- 10%) increase in visits per 20 ppb increase in 24-h average concentrations of NO 2 

### and a 7% increase in visits pe r 30 ppb increase in daily 1-h

maximum concentrations. These effects were not statistically-significant in 2-pollutant models

that included PM 2.5 

### or SO 2 

### . In Manhattan, the authors found non-significant decreases (3% for 24-h and a 2% for daily 1-h maximum) in ED visits associated with increasing NO 2 

### . In the study by Ito and colleagues, investigators evaluated ED visits for asthma in New York City during the years 1999 to 2002. The authors estimated a 12% (95% CI: 7%, 15%) incr ease in risk per 20 ppb increase in 24-h ambient NO 2 

### . Risk estimates were robust and remained statistically significant in multi-pollutant models that included PM 2.5 

### , O 3 

### , CO, and SO 2 

### . With regard to the studies that evaluated only singl e pollutant models, Linn et al. (2000) detected a statisticallysignificant increase in hospital admissions and Ja ffee et al. (2003) detected a positive, but statistically-nonsignificant, increase in ED visits associated with 24-h NO 2 

### concentrations.

4.2.3 Respiratory Symptoms

Evidence for associations between NO 2 

### and respiratory symptoms is derived primarily from the epidemiologic literature, although the experimental evidence for airway inflammation and immune system effects (described in the ISA, section 3.1 and summarized in subsequent sections of this document) does provide some pl ausibility and coherence for the epidemiologic results (ISA, section 5.3.2.1). Consistent evidence has been ob served for an association of respiratory effects with indoor and personal NO 2 

### exposures in children (ISA, sections 3.1.5.1 and 5.3.2.1) and with ambient levels of NO 2 

### as measured by community monitors (ISA, sections 3.1.4.2 and 5.3.2.1, see Figure 3.1-6). In the results of multi-pollutant models, NO 2 

### associations in multicity studies are generally robust to adjustment for co-pollutants including O 3 

### , CO, and

28

### PM 10 

### (ISA, sections 3.1.4.3, 5.3.2.1 and Figure 3.1-7). Specific studies of respiratory symptoms

are discussed in more detail below. Studies of Ambient NO 2 

### Epidemiologic studies using community am bient monitors have found associations

between ambient NO 2 

### concentrations and respiratory symptoms (ISA, sections 3.1.4.2 and 5.3.2.1, Figure 3.1-6) in cities where NO 2 

### concentrations were within the range of 24-h average concentrations observed in recent years. Se veral studies have been published since the 1996

review of the NO 2 

### NAAQS including single-city studies (e .g., Ostro et al., 2001; Delfino et al., 2002) and multi-city studies in ur ban areas covering the continental United States and southern Ontario (Schwartz et al., 1994; Mortimer et al., 2002; Schildcrout et al., 2006). The multi-city

studies are discussed in more detail below. Schwartz el at (1994) studie d 1,844 schoolchildren, followed for 1 year, as part of the Six Cities Study that included the cities of Wa tertown, MA, Baltimore, MD, Kingston-Harriman, TN, Steubenville, OH, Topeka, KS, and Portage, WI. Respiratory symptoms were recorded daily. The authors reported a significan t association between 4-day mean NO 2 

### levels and incidence of cough among all child ren in single-pollutant models , with an odds ratio (OR) of 1.61 (95% CI: 1.08, 2.43) standardized to a 20-ppb increase in NO 2 

### . The incidence of cough increased up to approximately mean NO 2 

### levels (~13 ppb) (p = 0.01), after which no further

increase was observed. The significant association betw een cough and 4-day mean NO 2 

### level remained unchanged in models that included O 3 

### but lost statistical si gnificance in two-pollutant

models that included PM 10 

### (OR = 1.37 [95% CI: 0.88, 2.13]) or SO 2 

### (OR = 1.42 [95% CI: 0.90, 2.28]). Mortimer et al. (2002) studi ed the risk of asthma symptoms among 864 asthmatic children in New York City, NY, Baltimore, MD, Washington, DC, Cleveland, OH, Detroit, MI, St Louis, MO, and Chicago, IL. Subjects were followed daily for four 2-week periods over the course of nine months with morning and eveni ng asthma symptoms and peak flow recorded. The greatest effect was observed for morning sy mptoms using a 6-day moving average, with a reported OR of 1.48 (95% CI: 1.02, 2.16) per 20 ppb increase in NO 2 

### . Although the magnitudes of effect estimates were generally robust in multi-pollutant m odels that included O 3 

### (OR for 20- ppb increase in NO 2 

### = 1.40 [95% CI: 0.93, 2.09]), O 3 

### and SO 2 

### (OR for NO 2 

### = 1.31 [95% CI:

29

### 0.87, 2.09]), or O 3 

### , SO 2 

### , and PM 10 

### (OR for NO 2 

### = 1.45 [95% CI: 0.63, 3.34]), they were not statistically-significant. Schildcrout et al. (2006) investigated the association between ambient NO 2 

### and respiratory symptoms and resc ue inhaler use as part of the Childhood Asthma Management Program (CAMP) study. The st udy reported on 990 asthmatic childr en living within 50 miles of

an NO 2 

### monitor in Boston, MA, Baltimore, MD, Toronto, ON, St. Louis, MO, Denver, CO, Albuquerque, NM, or San Diego, CA. Symptoms a nd use of rescue medication were recorded daily, resulting in each subject having an averag e of approximately two months of data. The authors reported the stronge st association between NO 2 

### and increased risk of cough for a 2-day lag, with an OR of 1.09 (95% CI: 1.03, 1.15) for each 20-ppb increase in NO 2 

### occurring 2 days before measurement. Multi-pollutant models that included CO, PM 10 

### , or SO 2 

### produced similar results (ISA, Figure 3.1-5, panel A). Additionally, increased NO 2 

### exposure was associated with increased use of rescue medication, with the st rongest association for a 2-day lag, both for single- and multi-pollutant models (e.g., for an increase of 20-ppb NO 2 

### in the single-pollutant model, the RR for increased inhale r usage was 1.05 (95% CI: 1.01, 1.09). Studies of Indoor NO 2 

### Evidence supporting increased resp iratory morbidity following NO 2 

### exposures is also

found in studies of indoor NO 2 

### (ISA, section 3.1.4.1). For example, in a randomized intervention study in Australia (P ilotto et al., 2004), students atte nding schools that switched out unvented gas heaters, a major source of indoor NO 2 

### , experienced a decrease in both levels of

NO 2 

### and in respiratory symptoms (e.g., difficulty breathing, chest ti ghtness, and asthma attacks) compared to students in school s that did not switch out unvent ed gas heaters (ISA, section 3.1.4.1). An earlier indoor study by Pilotto and colleagues (1997) also found that students in classrooms with higher levels of NO 2 

### had higher rates of respirator y symptoms (e.g., sore throat, cold) and absenteeism than students in classrooms with lower levels of NO 2 

### . This study detected a significant concentration-response relations hip, strengthening the argument that NO 2 

### is causally related to respiratory morbidity. A num ber of other indoor studies conducted in homes have also detected significan t associations between indoor NO 2 

### and respiratory symptoms (ISA, section 3.1.4.1).

30

### 4.2.4 Lung Host Defenses and Immunity Impaired host-defense systems and increased ri sk of susceptibility to both viral and

bacterial infections after NO 2 

### exposures have been observed in epidemiologic, controlled human exposure, and animal toxicological studies (ISA, section 3.1.1 and 5.3.2.1). A recent epidemiologic study (Chauhan et al., 2003) provide s evidence that increased personal exposure

to NO 2 

### worsened virus-associated symptoms and decreased lung function in children with

asthma. The limited evidence from controlle d human exposure studies indicates that NO 2 

### may increase susceptibility to injury by subsequent viral challenge at exposures of as low as 0.6 ppm for 3 hours in healthy adults (Frampton et al., 2002) . Toxicological studies have shown that lung host defenses, including mucociliary clearance and immune cell function, are sensitive to NO 2 

### exposure, with effects observed at concentrations of less than 1 ppm (ISA, section 3.1.7). When taken together, epidemiologic and experimental studies linking NO 2 

### exposure with viral illnesses provide coherent and cons istent evidence that NO 2 

### exposure can result in lung host defense or immune system effects (ISA, sections 3.1.7 a nd 5.3.2.1). This group of outcomes also provides some plausibility for other respiratory system effects. For example, effects on ciliary action (clearance) or immune cell function (i.e. macr ophage phagocytosis) could be the basis for the effects observed in epidemiologic studies, includi ng increased respiratory illness or respiratory symptoms (ISA, section 5.3.2.1). Proposed mechanisms by which NO 2 

### , in conjunction with viral infections, may exacerbate airway symptoms are summarized in table 4-4 below (ISA, table 3.1- 1).

31

### Table 4-4. Proposed Mechanisms Whereby NO2 and Respiratory Virus Infections May Exacerbate Upper and Lower Airway Symptoms



### 4.2.5 Airway Response

In acute exacerbations of asthma, bronchial sm ooth muscle contraction occurs quickly to narrow the airway in response to exposure to va rious stimuli including allergens or irritants. Bronchoconstriction is the dominant physiological event leading to clinical symptoms and

interference with ai rflow (National Heart, Lung, and Blood Institute, 2007). Inhaled pollutants

such as NO 2 

### may enhance the inherent responsiveness of the airway to a chal lenge by allergens and nonspecific agents (ISA, section 3.1.3). In the laboratory, airway resp onses can be measured by assessing changes in pulmonary function (e.g., decline in FEV 1 

### ) or changes in the inflammatory response (e.g., using markers in br onchoalveolar lavage (BAL) fluid or induced sputum) (ISA, section 3.1.3). The ISA (section 5.3.2.1) draws two broad conc lusions regarding the airway response following NO 2 

### exposure. First, the ISA concludes that NO 2 

### exposure may enhance the

32

### sensitivity to allergen-induced decrements in lung function and increase the allergen-induced airway inflammatory response at exposures as low as 0.26 ppm NO 2 

### for 30 minutes (ISA, section 5.3.2.1 and Figure 3.1-2). Second, exposure to NO 2 

### has been found to enhance the inherent responsiveness of the airway to subsequent nonspecific challenges in controlled human exposure studies (section 3.1.4.2). In gene ral, small but significant in creases in nonspecific airway responsiveness were observed in the range of 0.2 to 0.3 ppm NO 2 

### for 30-minute exposures and at 0.1 ppm NO 2 

### for 60-minute exposures in asthmatics. These conclusions are consistent with results from animal toxicological studies whic h have detected 1) in creased immune-mediated pulmonary inflammation in rats exposed to hous e dust mite allergen following exposure to 5 ppm NO 2 

### for 3-h and 2) increased responsiveness to non-specific challenges following subchronic (6-12 weeks) ex posure to 1 to 4 ppm NO 2 

### (ISA, section 5.3.2.1). Enhanced airway responsiveness could have important clinical implications for asth matics since transient increases in airway responsiveness following NO 2 

### exposure have the potential to increase symptoms and worsen asthma control (ISA, section 5.4). In addition, the ISA cites the controlled human exposure literature on the NO 2 

### airway response as being supportive of the epidemiologic evidence on respiratory morbidity (ISA, section 5.4) . Because studies on airway responsiveness have been used to identify potential health effect benchmark values and to inform the identification of potential altern ative standards for ev aluation (see sections 4.5 and 5 of this document), more detail is provided below on the specific studies that form the basis for the

conclusions in the ISA re garding this endpoint. Folinsbee (1992) conducted a meta-analysis usin g individual level da ta from 19 clinical

NO 2 

### exposure studies measuring ai rway responsiveness in asth matics (ISA, section 3.1.3.2).

These studies included NO 2 

### exposure levels between 0.1 ppm and 1.0 ppm and most of them used nonspecific bronchoconstricting agents such as methacholine, carbachol, histamine, or cold air. The largest effects were observed for subjects at rest. Among subjects exposed at rest, 76% experienced increased airway res ponsiveness following exposure to NO 2 

### levels between 0.2 and 0.3 ppm. Results from an update of this meta -analysis (results combin ed only from nonspecific responsiveness studies) are presented in the ISA (Table 3.1 -3) and in Table 4-5 below.

33

### Table 4-5. Fraction of nitrogen dioxide-e xposed asthmatics with increased nonspecific airway hyperresponsiveness 3



### As noted in Table 4-5, when exposed at rest 66% of subjects experien ced an increase in airway responsiveness following exposure to 0.1 ppm NO 2 

### , 67% of subjects experienced an increase in airway responsiveness following exposure to NO 2 

### concentrations between 0.1 and 0.15 ppm (inclusively), 75% of subjects experi enced an increase in airway responsiveness following exposure to NO 2 

### concentrations between 0.2 and 0.3 ppm (inclusively), and 73% of subjects experienced an increase in airway responsiveness following exposure to NO 2 

### concentrations above 0.3 ppm. Effects of NO 2 

### exposure on the direction of airway responsiveness are statistically-sig nificant at all of these levels . Because this meta-analysis evaluates only the direction of the change in airw ay responsiveness, it is not possible to discern the magnitude of the change from these data. However, the results do su ggest that short-term exposures to NO 2 

### at near-ambient levels (<0.3 ppm) can alter airway responsiveness in people with mild asthma (ISA, section 3.1.3.2). Several studies published sin ce the 1996 review address th e question of whether lowlevel exposures to NO 2 

### enhance the response to specific alle rgen challenge in mild asthmatics (ISA, section 3.1.3.1). These rece nt studies suggest that NO 2 

### may enhance the sensitivity to allergen-induced decrements in lung function and increase the allergen-induced airway inflammatory response. Strand et al. (1997) demonstrated that single 30-minute exposures to 0.26-ppm NO 2 

### increased the late phase res ponse to allergen challenge 4 hours after exposure, as measured by changes in lung function. In a sepa rate study (Strand et al., 1998), 4 daily repeated exposures to 0.26-ppm NO 2 

### for 30 minutes increased both the ear ly and late-phase responses to allergen, as measured by changes in lung function. Barck et al. (2002) used the same exposure and challenge protocol in the earlier Strand st udy (0.26 ppm for 30 min, with allergen challenge

3 

### Values are the fraction of asthmatics (out of the total number of individuals in parenthesis) having an increase in airway responsiveness following NO 2 

### versus air exposure. See table 3.1-3 in the ISA for more detail. B 

### indicates p ≤ 0.05 and C 

### indicates p ≤ 0.01.

34

### 4 hours after exposure), and perf ormed BAL 19 hours after the alle rgen challenge to determine

NO 2 

### effects on the allergen-induced inflammatory response. Compared with air followed by allergen, NO 2 

### followed by allergen caused an in crease in the BAL recovery of polymorphonuclear (PMN) cells a nd eosinophil cationic protein (ECP ) as well as a reduction in total BAL fluid volume and cell viability. ECP is released by degranulating eosinophils, is toxic to respiratory epithe lial cells, and is thought to play a role in the pathogenesis of airway injury in asthma. Subsequently, Barck et al. (2005) exposed 18 mild as thmatics to air or 0.26 ppm NO 2 

### for 15 minutes on day 1, followed by two 15 minut e exposures separated by 1 hour on day 2, with allergen challenge after exposures on both days 1 and 2. Sputum was induced before exposure on day 1 and after exposures (morning of day 3). Compared to air plus allergen, NO 2 

### plus allergen resulted in increased levels of ECP in both sputum and blood and increased myeloperoxidase levels in blood. All exposures in these studies (Barck et al., 2002, 2005; Strand et al., 1997, 1998) used subjects at re st. They used an adequate number of subjects, included air control exposures, randomized e xposure order, and separated expos ures by at least 2 weeks. Together, they indicate the possi bility for effects on allergen responsiveness in some asthmatics following brief exposures to 0.26 ppm NO 2 

### . However, other recent studies have failed to find effects using similar, but not identical, appro aches (ISA, section 3.1.3.1). The differing findings may relate in part to differences in timing of the allergen challenge, the use of multiple versus single-dose allergen challenge, th e use of BAL versus sputum i nduction, exercise versus rest during exposure, and differences in subject susceptibility (ISA, section 3.1.3.1).

4.2.6 Airway Inflammation

Effects of NO 2 

### on airway inflammation have be en observed in controlled human exposure and animal toxicological studies at higher than ambien t levels (0.4-5 ppm). The few available epidemiologic studies were sugges tive of an associati on between ambient NO 2 

### concentrations and inflammatory response in th e airway in children, t hough the associations

were inconsistent in the adu lt populations examined (ISA, section 3.1.2 and 5.3.2.1). Controlled human exposure studies provi de evidence for increased airway inflammation at NO 2 

### concentrations of <2.0 ppm. The onset of inflam matory responses in healthy subjects appears to be between 100 and 200 ppm-minutes, i.e., 1 ppm fo r 2 to 3 hours (ISA, Figure 3.1-1). Increases in biological markers of inflamma tion were not observed consistently in healthy animals at levels

35

### of less than 5 ppm; however, in creased susceptibility to NO 2 

### concentrations of as low as 0.4 ppm was observed when lung vitamin C was reduced (by di et) to levels that we re <50% of normal. These data provide some evidence for biological plausibility and one potential mechanism for other respiratory effects, such as exacerbation of asthma symptoms and increased ED visits for asthma (ISA, section 5.3.2.1).

4.2.7 Lung Function Recent epidemiologic studies that examin ed the association between ambient NO 2 

### concentrations and lung function in children and adults generally produced inconsistent results (ISA, sections 3.1.5.1 and 5.3.2.1). Controlled hu man exposure studies generally did not find

direct effects of NO 2 

### on lung function in healthy adults at levels as high as 4.0 ppm (ISA, section 5.3.2.1). For asthmatics, the direct effects of NO 2 

### on lung function also have been inconsistent at exposure concentrations of less than 1 ppm NO 2 

### .

4.2.8 Conclusions and Coherence of Evidence for Short-Term Respiratory Effects As noted previously, the ISA concludes that the findings of epidemiologic, controlled human exposure, and animal toxico logical studies provide evidence that is sufficient to infer a likely causal relationship for respiratory effects following short-term NO 2 

### exposure (ISA, sections 3.1.7 and 5.3.2.1). The ISA (section 5.4) c oncludes that the strongest evidence for an

association between NO 2 

### exposure and adverse human health effects comes from epidemiologic studies of respiratory symptoms, ED visits, and hospital admissions. These studies include panel and field studies, studies that control for the effects of co-occurring pollutants, and studies

conducted in areas where the whole di stribution of ambient 24-h average NO 2 

### concentrations was below the current NAAQS level of 0.053 pp m (53 ppb) (annual average). The effect estimates from the U.S. and Canadian studies ge nerally indicate a 2-20% (see footnote 2 above) increase in risks for ED visits and hospital admissions. Risks associated with respiratory symptoms are generally higher (ISA, section 5.4). Overall, the epidemiologic evidence for resp iratory effects can be characterized as consistent, in that associations are reported in studies conducted in numerous locations with a variety of methodological approaches. Consid ering this large body of epidemiologic studies alone, the findings are also coherent in the se nse that the studies re port associations with respiratory health outcomes that are logically linked together. In addi tion, a number of these

36

### associations are statistically-significant, partic ularly the more precise effect estimates (ISA, section 5.3.2.1). These epidemiologic studies ar e supported by evidence from toxicological and controlled human exposure studie s, particularly by controlled human exposure studies that evaluate airway hyperresponsivenes s in asthmatic individuals (ISA, section 5.4). Together, the epidemiologic and experimental data sets form a plausible, consistent, an d coherent description of a relationship between NO 2 

### exposures and an array of advers e respiratory health effects that range from the onset of respirator y symptoms to hospital admission. However, as noted in the ISA (section 5.4), it is difficult to determine “the extent to

which NO 2 

### is independently associated w ith respiratory effects or if NO 2 

### is a marker for the

effects of another traffic-rela ted pollutant or mix of polluta nts.” On-road vehicle exhaust emissions are a nearly ubiquitous source of combustion pollutant mixtures that include NO x 

### and can be an important contributor to NO 2 

### levels in near-road locatio ns. Although this complicates the efforts to quantify specific NO 2 

### -related health effects, the evidence summarized in the ISA

indicates that NO 2 

### associations generally remain robust in multi-pollutant models and supports a

direct effect of short-term NO 2 

### exposure on respiratory morbidity at ambient concentrations below the current NAAQS level. The robustness ofepidemiologic findings to adjustment for copollutants, coupled with data from animal and human experimental studies, support the determination that the relationship between NO 2 

### and respiratory morbidity is likely causal, while still recognizing the relationship between NO 2 

### and other traffic related pollutants and the potential for confounding.



## 4.3 OTHER ADVERSE EFFECT S FOLLOWING SHORT-TERM

EXPOSURES

The ISA concludes that the epidemiologic ev idence is suggestive but not sufficient to infer a causal relationship between short-term exposure to NO 2 

### and all-cause and cardiopulmonary-related mortality (ISA, section 5.3.2.3). Results from several large U.S. and European multi-city studies and a meta-analysis study indicate positive associations between

ambient NO 2 

### concentrations and the risk of all-caus e (nonaccidental) mortality, with effect estimates ranging from 0.5 to 3.6% excess risk in mortality per standardized increment (20 ppb for 24-h averaging time, 30 ppb for 1-h averaging time) (ISA, section 3.3.1, Figure 3.3-2, section 5.3.2.3). In general, the NO 2 

### effect estimates were robust to adjustment for co-pollutants. Both

37

### cardiovascular and respiratory mortality ha ve been associated with increased NO 2 

### concentrations in epidemiologic studies (ISA, Figure 3.3-3); how ever, similar associations were observed for other pollutants, including PM and SO 2 

### . The range of risk estimates for excess mortality is generally smaller than that for other pollu tants such as PM. In addition, while NO 2 

### exposure, alone or in conjunction with ot her pollutants, may contribute to increased mortality, evaluation of the specificity of this effect is difficult. Clinical studies showing hematologic effects and animal toxicological studies showing bioc hemical, lung host defense, permeability, and inflammation changes with short-term exposures to NO 2 

### provide limited evidence of plausible pathways by which risks of mortality may be increas ed, but no coherent picture is evident at this time (ISA, section 5.3.2.3).

The ISA concludes that the available eviden ce on cardiovascular health effects following short-term exposure to NO 2 

### is inadequate to infer the presence or absence of a causal relationship at this time (ISA, section 5.3.2.2). Evidence from epidemiologic studies of heart rate variability, repolarization changes, and cardi ac rhythm disorders among heart pa tients with ischemic cardiac disease are inconsistent (ISA, s ection 5.3.2.2). In most studies, a ssociations with PM were found to be similar or stronger than associations with NO 2 

### . Generally positive associations between

ambient NO 2 

### concentrations and hospital admissions or ED visits for cardiovascular disease have been reported in single-pollutant models (ISA , section 5.3.2.2); however, mo st of these effect estimate values were diminished in multi-pollutant models that also contained CO and PM indices (ISA, section 5.3.2.2). Mech anistic evidence of a role for NO 2 

### in the development of

cardiovascular diseases from studies of biomar kers of inflammation, cell adhesion, coagulation, and thrombosis is lacking (ISA, secti on 5.3.2.2). Furthermore, the effects of NO 2 

### on various hematological parameters in animals are inc onsistent and, thus, pr ovide little biological plausibility for effects of NO 2 

### on the cardiovascular system (ISA, section 5.3.2.2).



## 4.4 ADVERSE EFFECTS FOLLOW ING LONG-TERM EXPOSURES

4.4.1 Respiratory Morbidity The ISA concludes that overall, the epid emiologic and experimental evidence is suggestive but not sufficient to infer a causal relationship between long-term NO 2 

### exposure and respiratory morbidity (ISA, section 5.3.2.4). The available database evaluating the relationship between respiratory illn ess in children and long-term exposures to NO 2 

### has increased since the

38

### 1996 review of the NO 2 

### NAAQS. A number of epidemiologic studies have examined the effects of long-term exposure to NO 2 

### and reported positive associations with decrements in lung function and partially irreversible decrements in lung function growth (IS A, section 3.4.1, figures 3.4-1 and 3.4-2). Specifically, results from the California-based Children’s Health Study, which

evaluated NO 2 

### exposures in children over an 8-year period, demonstrated deficits in lung function growth (Gauderman et al., 2004). This effect has also been observed in Mexico City, Mexico (Rojas-Martinez et al., 2007a,b) and in Oslo, Norway (Oftedal et al., 2008), with decrements ranging from 1 to 17.5 ml per 20- ppb increase in annual NO 2 

### concentration. Similar associations have been found for PM, O 3 

### , and proximity to traffic (<500 m), though these studies did not report the results of co-pollutant models. The high correlation among traffic-related pollutants makes it difficult to accurately estim ate independent effects in these long-term exposure studies (ISA, section 5.3.2.4). With re gard to asthma incidence and long-term NO 2 

### , two major cohort studies, the Children’s Health Study (Gaude rman et al., 2005) and a birth cohort study in the Netherlands (Brauer et al., 2007), observe d significant associations. However, several other studies failed to find consistent asso ciations between long-term NO 2 

### exposure and asthma outcomes (ISA, section 5.3.2.4). Similarly, epidemiologic studies

conducted in the United States a nd Europe have produced incons istent results regarding an association between long-term exposure to NO 2 

### and respiratory symptoms (ISA, sections 3.4.3 and 5.3.2.4). While some positive associations were noted, a large number of symptom

outcomes were examined and the results acro ss specific outcomes were inconsistent (ISA, section 5.3.2.4). Animal toxicological studies may provide biolog ical plausibility for the chronic effects of

NO 2 

### that have been observed in epidemiologi c studies (ISA, sections 3.4.5 and 5.3.2.4). The main biochemical targets of NO 2 

### exposure appear to be antioxida nts, membrane polyunsaturated fatty acids, and thiol groups. NO 2 

### effects include changes in oxi dant/antioxidant homeostasis and chemical alterations of lipids and proteins . Lipid peroxidation has been observed at NO 2 

### exposures as low as 0.04 ppm for 9 months and at exposures of 1.2 ppm for 1 week, suggesting

lower effect thresholds with l onger durations of exposure. Ot her studies showed decreases in formation of key arachidonic acid metabolites in AMs following NO 2 

### exposures of 0.5 ppm.

NO 2 

### has been shown to increase collagen synthesis ra tes at concentrations as low as 0.5 ppm.

This could indicate increased tota l lung collagen, which is associated with pulmonary fibrosis, or

39

### increased collagen turnover, which is associated with remodeling of lung connective tissue. Morphological effects following chronic NO 2 

### exposures have been identified in animal studies

that link to these increases in co llagen synthesis and may provide pl ausibility for the deficits in lung function growth descri bed in epidemiologic studi es (ISA, section 3.4.5).

4.4.2 Mortality The ISA concludes that the epidemiologic eviden ce is inadequate to infer the presence or absence of a causal relationship between long-term exposure to NO 2 

### and mortality (ISA, section 5.3.2.6). In the United States and European coho rt studies examining th e relationship between long-term exposure to NO 2 

### and mortality, results have been inconsistent (ISA, section 5.3.2.6). Further, when associations were sugg ested, they were not specific to NO 2 

### but also implicated PM

and other traffic indicators. The relati vely high correlations reported between NO 2 

### and PM indices make it difficult to interpret these obser ved associations at this time (ISA, section 5.3.2.6).

4.4.3 Other Long-Term Effects The ISA concludes that the available epid emiologic and toxicological evidence is inadequate to infer the presence or absence of a causal relationship for carcinogenic, cardiovascular, and reproductive and devel opmental effects related to long-term NO 2 

### exposure (ISA, section 5.3.2.5). Epidemiologic studies conduc ted in Europe have shown an association between long-term NO 2 

### exposure and increased incidence of cancer (ISA, section 5.3.2.5). However, the animal toxicological studies have provided no clear evidence that NO 2 

### acts as a carcinogen (ISA, section 5.3.2.5). The very limite d epidemiologic and toxicological evidence does not suggest that long-term exposure to NO 2 

### has cardiovascular effects (ISA, section 5.3.2.5). The epidemiologic evidence is not consistent for associations between NO 2 

### exposure and fetal growth retardation; however, some evidence is accumulating for effects on preterm delivery (ISA, section 5.3.2.5). Scant animal ev idence supports a weak association between NO 2 

### exposure and adverse birth outcomes and provides little mechanistic information or biological plausibility for the epidemiologic findings.

40

## 4.5 RELEVANCE OF SPECIFIC HEALTH EFFECTS TO THE NO 2 

## RISK

CHARACTERIZATION

4.5.1 Overview As described previously, the ISA characterizes judgments about causality with a hierarchy (for discussion see ISA, section 1.3) that contains the following five levels.



### • Sufficient to infer a causal relationship

• Sufficient to infer a likely causal rela tionship (i.e., more likely than not)

• Suggestive but not sufficient to infer a causal relationship

• Inadequate to infer the presence or absence of a causal relationship

• Suggestive of no causal relationship In order to be judged sufficient to infer a causa l relationship, an association must have been observed between the pollutant and the outcome in studies where chan ce, bias, and confounding

can be ruled out with reasonable confidence. Human clinical studies provide the strongest evidence for causality while other lines of ev idence (e.g., epidemiologic studies) provide support

for this determination. An inference of a cau sal relationship is generally based on multiple studies from more than one research group. In order to be judged sufficient to infer a likely causal relationship, an association must have been observed between the pollutant and the outcome in studies where chance, bias, and confounding are minimized even though

uncertainties remain. These uncertainties coul d be due to the difficulty associated with addressing chance, bias, and conf ounding and/or due to the fact th at other lines of evidence are

limited or inconsistent. An inference of a likely causal relationship is generally based on multiple studies from more than one research gr oup. In order to be judged suggestive, but not sufficient to infer a causal relationship, existing evidence must suggest an association between the pollutant and the outcome but that evid ence is weakened because chance, bias, and confounding cannot be ruled out (see table 4-1). For example, this determination might apply if at least one high-quality study shows an associ ation, but the results of other studies are inconsistent (ISA, Table 1.3-2) . For purposes of the quantita tive characterization of NO 2 

### health risks, staff has judged it appropr iate to focus on endpoints for wh ich the ISA concludes that the

available evidence is sufficient to infer either a causal or a likely causal relationship. This is consistent with judgments that have been ma de in other recent NAAQS reviews (e.g., see EPA,

41

### 2005) and it will help ensure that our risk ch aracterization is based on endpoints for which a causal relationship with NO 2 

### is judged to be more likely than not (see table 4-1 above and ISA, table 1.3-2). The only endpoint evidence for which the evid ence is judged to be sufficient to infer either a causal or a likely causal relationship is respiratory morbidity following short-term NO 2 

### exposure. Therefore, for purposes of char acterizing health risks associated with NO 2 

### , we have focused on respiratory morbidity endpoints that have been associated with short-term NO 2 

### exposures. Other endpoints (e.g., long-term effects) will be considered as part of the evidencebased evaluation of potential al ternative standards during the rulemaking stage of the NAAQS review. In evaluating the appropriateness of specific endpoints for use in the NO 2 

### risk characterization, we have consider ed both epidemiologic and contro lled human exposure studies.

4.5.2 Epidemiology The ISA characterizes the epidemiologic evidence for respiratory effects as consistent, in that associations are reported in studies conducted in numerous lo cations and with a variety of methodological approaches (ISA, section 5.3.2.1). The findings are also coherent in the sense that the studies report associations with resp iratory health outcomes that are logically linked together (ISA, section 5.3.2.1). When the epidem iologic literature is considered as a whole, there are generally positive associations between NO 2 

### and respiratory symptoms, hospitalization,

and ED visits. A number of these associations ar e statistically significant, particularly the more precise effect estimates (ISA, section 5.3.2.1). However, the ISA (section 5.4) offers the following caveat to consider when interpreting th e epidemiologic results: “It is difficult to

determine from these new studies the extent to which NO 2 

### is independently associated with respiratory effects or if NO 2 

### is a marker for the effects of anot her traffic-related pollutant or mix of pollutants (see Section 5.2.2 for more details on exposure issues). A factor contributing to uncertainty in estimating the NO 2 

### -related effect from epidemiologic studies is that NO 2 

### is a component of a complex air pollu tion mixture from traffic relate d sources that include CO and

various forms of PM.” These caveats should be considered when inte rpreting a quantitative NO 2 

### risk estimate based on the epidemiology literature. Despite these uncertainties, the ISA (section 5.4) concludes that, “Although this complicates the efforts to disentangle specific NO 2 

### -related health effects, the evidence summarized in this assessment indicates that NO 2 

### associations

42

### generally remain robust in multi-pollutant models and supports a direct effect of short-term NO 2 

### exposure on respiratory morbidity at ambient concentrations below the current NAAQS. The robustness of epidemiologic findings to adjustme nt for copollutants, coupled with data from animal and human experimental studies, support a determination that th e relationship between

NO 2 

### and respiratory morbidity is likely causal, while still recognizing th e relationship between

NO 2 

### and other traffic related pollutants.” When evaluating epidemiologic studies as to their appropriateness for use as the basis for a quantitative risk assessment, staff has considered several factors. First, we have judged that

studies conducted in the United St ates are preferable to those c onducted outside the United States given the potential for effect estimates to be im pacted by factors such as the ambient pollutant mix, the placement of monitors, activity patterns of the population, and ch aracteristics of the healthcare system. Second, we j udged that studies of ambient NO 2 

### are preferable to those of

indoor NO 2 

### . This does not suggest that indoor studie s are uninformative in the review of an ambient standard. In fact, indoor studies provide a large part of the evidence base used in the ISA to reach conclusions regarding caus ality. However, studies of indoor NO 2 

### focus on individuals exposed to NO 2 

### from indoor sources. These ind oor sources can re sult in exposure patterns, NO 2 

### levels, and co-pollutants that are differe nt from those typically associated with

ambient NO 2 

### . Because the purpose of a quantitative risk assessment based on the epidemiological literature would be to inform decisions regarding an ambient NO 2 

### standard, the preferred approach would be to consider studies of ambient NO 2 

### . Third, we judged it appropriate to focus on studies of ED visits and hospital admissions. When compared to studies of respiratory symptoms, the publ ic health significance of ED vi sits and hospital admissions are less ambiguous (e.g., because of the potential disconnect between health outcomes and subjective symptom ratings). In addition, baseline incidence data are more readily available for these endpoints. Finally, we judged it appr opriate to focus on studi es that evaluated NO 2 

### health effect associations using both single- and multi- pollutant models. Taking these factors into consideration, we have chosen to focus on th e studies by Peel and colleagues (2005) and by Tolbert and colleagues (2007) in Atlanta, Georgia. The epidem iology-based risk assessment is described in more detail in subseque nt sections of this document.

43

### 4.5.3 Controlled Human Exposure Studies Controlled human exposure studies have addr essed the consequences of short-term (e.g., 30-minutes to several hours) NO 2 

### exposures for a number of hea lth endpoints in cluding airway responsiveness, host defense and immunity, infl ammation, and lung function (ISA, section 3.1). In identifying health endpoint s from controlled human exposure studies on which to focus the

characterization of NO 2 

### health risks, staff judges it appropr iate to focus on endpoints that occur

at or near ambient levels of NO 2 

### and endpoints that are of clinical significance. With regard to

the NO 2 

### levels at which different e ffects have been documented, th e ISA concludes that 1) in

asthmatics NO 2 

### may increase the allergen-i nduced airway inflammatory response at exposures as low as 0.26-ppm for 30 min (ISA, Figure 3.1-2) and NO 2 

### exposures between 0.2 and 0.3 ppm for 30 minutes or 0.1 ppm for 60-minutes can result in small but significant increases in nonspecific airway responsiveness (ISA, section 5.3.2.1) ; 2) limited evidence indicates that NO 2 

### may increase susceptibility to injury by subsequent viral ch allenge following exposures of 0.6-1.5 ppm for 3 hours; 3) evidence exists fo r increased airway inflammation at NO 2 

### concentrations less than 2.0 ppm; and 4) the direct effects of NO 2 

### on lung function in asthmatics have been inconsistent at exposure concentrations below 1 ppm (ISA, section 5.3.2.1). The ISA notes that epidemiologic studies have repor ted health effects associatio ns in areas reporting maximum ambient concentrations from 100 to 300 ppb (ISA, Tables 5.3-2 and 5.3-3). Therefore, of the health effects caused by NO 2 

### in controlled human exposure studi es, the only effect identified by

the ISA to occur at or near ambient levels is increased airway responsiveness in asthmatics. Staff judges that airway responsiveness in the asthmatic population is an appropriate

focus for the risk characterization for several re asons. First, the ISA c oncludes that “persons with preexisting pulmonary c onditions are likely at grea ter risk from ambient NO 2 

### exposures than the general public, with the most extensive evidence available for asthmatics as a potentially susceptible group” (ISA, section 5.3.2.8). Second, when discussing the clinical significance of

NO 2 

### -related airway hyperresponsiven ess in asthmatics, the ISA concludes that “transient increases in airway responsiveness following NO 2 

### exposure have the potential to increase symptoms and worsen asthma control” (ISA, sec tions 3.1.3 and 5.4). That th is effect could have public health implications is suggested by the la rge size of the asthmatic population in the United States (see above and ISA, Table 4.4-1). Third, NO 2 

### effects on airway responsiveness in asthmatics are part of the body of experimental ev idence that provides plau sibility and coherence

44

### for the effects observed on hospital admissions a nd ED visits in epidemiologic studies (ISA, section 5.3.2.1). As a result of these consid erations, although studi es on other endpoints evaluated in controlled human exposure studies provide qualitative support for the ability of NO 2 

### to cause adverse effects on respiratory hea lth, the focus for purpose of quantifying risks

associated with ambient NO 2 

### is airway responsiveness in asthmatics (see below). Because many of the studies of airway respons iveness evaluate only a single level of NO 2 

### and because of methodological differences between the studies, staff has judged that the data are not sufficient to derive an exposure-response relati onship in the range of interest. Therefore, the most appropriate approach to characterizi ng risks based on the controlled human exposure evidence for airway responsiveness is to compare estimated NO 2 

### air quality and exposure levels with potential health effect benchmark le vels. Estimates of hourly peak air quality concentrations and personal exposures to ambient NO 2 

### concentrations at and above specified potential health effect benchmark levels provide some perspective on the potential health impacts

of NO 2 

### exposure. Staff recognizes that there is high inter-individual variability in NO 2 

### -induced effects on airway responsiveness su ch that only a subset of asth matic individuals exposed at and above a given benchmark level may actually be expected to experience an adverse effect. Potential health benchmark levels and the approa ch to using these benchmarks to characterize

health risks are described in more detail in chapter 6.

45

# 5. IDENTIFICATION OF POTENTIAL ALTERNATIVE STANDARDS FOR ANALYSIS

5.1 INTRODUCTION

The primary goals of the NO 2 

### risk and exposure assessment described in this document are to estimate short-term exposures and potential human health risks associated with 1) recent

levels of ambient NO 2 

### ; 2) NO 2 

### levels associated with just m eeting the current standard; and 3)

NO 2 

### levels associated with just meeting potential alternative standards. This section identifies potential alternative standards in terms of indi cator, averaging time, form, and level and provides

the rationale that was used to select them.



## 5.2 INDICATOR

The NO x 

### , for purposes of this document, include multiple gaseous (e.g., NO 2 

### , NO) and particulate (e.g., nitrate) species. In considering the appropriaten ess of different indicators, we

note that the health effects associat ed with particulate species of NO x 

### have been considered

within the context of the health effects of am bient particles in the Agency’s review of the NAAQS for PM. Thus, as discussed in the integrat ed review plan (2007a), the current review of

the NO 2 

### NAAQS is focused on the gaseous species of NO x 

### and will not consider health effects directly associated with particulate species of NO x 

### . Of the gaseous species, EPA has historically determined it appropriate to specify the indicator of th e standard in terms of NO 2 

### because the majority of the information regarding health effects and exposures is for NO 2 

### . The final ISA has

found that this continues to be the case and, therefore, staff believes that NO 2 

### remains the most appropriate indicator.



## 5.3 AVERAGING TIME

The current annual standard for NO 2 

### was originally set in 1971 based on epidemiologic studies that supported a link betw een adverse respiratory effects and long-term exposure to lowlevels of NO 2 

### . Although the quantitative basis for the a nnual averaging time was later called into question (60 FR 52876), the annual standard was re tained in the most recent review (60 FR 52876) for two key reasons. First, the evidence showing the most serious health effects associated with long-term exposures (e.g., emph ysematous-like alterations in the lung and

46

### increased susceptibility to inf ection) came from animal studies conducted at concentrations well above those permitted in the ambient air by th e annual standard. Second, an air quality assessment conducted by EPA concluded that areas that meet the annual standard would be unlikely to experience short-term peaks above leve ls that had been show n in controlled human exposure studies to impact endpoints of potentia l concern (i.e., airway responsiveness). The issue of averaging time will be reconsid ered in the current review. As described above, the ISA concludes that, when taken togeth er, “recent studies provide scientific evidence

that NO 2 

### is associated with a range of respiratory effects and is sufficient to infer a likely causal relationship between short-term NO 2 

### exposure and adverse effect s on the respiratory system” (ISA, section 5.3.2.1). This conclusion is based, in part, on the observation that a number of epidemiologic studies have detected positive as sociations between short-term (e.g., 1-h, 24-h)

NO 2 

### concentrations and health effects. Many of these studies have been conducted in locations where long-term ambient levels of NO 2 

### are well below the current a nnual standard. As a result, staff has concluded that it is ap propriate to consider alternative averaging times for their ability to protect against health effect s associated with short-term NO 2 

### levels and/or exposures.

In contrast to the conclusion in the ISA con cerning respiratory morbidity associated with short-term exposures to NO 2 

### , the ISA concludes that the “evid ence examining the effect of longterm exposure to NO 2 

### on respiratory morbidity is suggestiv e but not sufficient to infer a causal relationship” (ISA, section 5.3.2.4). In addition, the ISA concludes that the available evidence for the effect of long-term exposure to NO 2 

### on other health outcomes (i.e., mortality, cancer, cardiovascular effects, reproductive and developm ental effects) is “inadequate to infer the presence or absence of a causal relationship” (ISA, sections 5.3.2.5 and 5.3.2.6). As a result,

staff has not considered alte rnative long-term standards in the current assessment. In considering appropriate short-term averagin g times, staff has considered evidence from both experimental and epidemiologic studies. New evidence from controlled human exposure studies generally evaluates exposures between 30 minutes and 3 hours while epidemiologic studies have used different short-term averaging periods, most commonly 1-h and 24-h (ISA, section 3.1). A few epidemiologic studies have considered both 1-h and 24-h averaging times, allowing comparisons to be made. The ISA repor ts that such comparisons failed to reveal

differences between effect estimates based on a 1-h averaging time versus those based on a 24-h averaging time (ISA, section 5.3.2.7). Therefore, the ISA concludes that it is not possible to

47

### discern whether effects observed in epidemiologi c studies are attributable to average daily (or multiday) concentrations (24-h avg) or high, peak exposures (1-h max) (ISA, section 5.3.2.7). In addition, the ISA concludes that experimental studies in both animals and humans provide

evidence that NO 2 

### exposures from less than 1 hour up to 3 hours can result in respiratory effects (section 5.3.2.7). Given that th e epidemiologic evidence does not provide clear guidance in choosing between 1-h and 24-h averaging times, and given that the experimental literature provides support for the occurren ce of effects following exposure s of shorter duration than 24 hours (e.g., 1-h), staff has chosen to evaluate standards with a 1-h averaging time.



## 5.4 FORM

In evaluating alternative forms for the primary st andard, staff recognizes that it is important to have a form that 1) reflects the health risks posed by elevated NO 2 

### concentrations and 2) achieves a balance between limiting the occurrence of peak concentrations and providing a stable and robust regulatory target. Cons istent with judgments made in recent reviews of the PM (71 FR 61144) and O 3 

### (73 FR 16436) NAAQS, staff judges th at a concentration-based form averaged over 3 years for the NO 2 

### standard would better reflect health risks and would provide greater stability than a form based on expected exceedances. A concentration-based form would give proportionally greater weight to hours when NO 2 

### concentrations are well above the level of

the standard than to hours when concentrations are just above the standard, while an expected exceedance form would give the sa me weight to hours that just exceed the standard as to hours that greatly exceed the standard. Therefore, a concentration-based form averaged over 3 years

better reflects the health risks posed by elevated NO 2 

### concentrations and, in developing potential

alternative standards for consid eration, we have focused on standards with this concentrationbased form. The most recent review of the PM NAAQS (completed in 20 06) judged that using a

98 th 

### percentile form averaged ove r 3 years provides an appropriate balance between limiting the occurrence of peak concentrati ons and providing a stable regul atory target (71 FR 61144). In consideration of this balance, we have determined it appropriate in the current review to evaluate

both the 98 th 

### and 99 th 

### percentile NO 2 

### concentrations averaged over 3 years. 4 

### We have judged that these percentiles, when combin ed with the range of alternatives identified for the level of the

4 98 th or 99 th percentiles of the 1-h daily maximum NO 2 concentrations would be calculated for each of 3 consecutive years. The 98 th or 99 th percentile concentrations for each of these 3 years would then be averaged together.



### 48

### standard (see below), offer a sufficient range of options to balance the objective of providing a stable regulatory target against the objective of limiting the occurr ence of peak concentrations.



## 5 .5 LEVEL

In developing an approach to formulating an appropriate range of NO 2 

### levels for analysis,

staff has taken into account several consideratio ns including the following. First, since the

review of the NO 2 

### NAAQS that was completed in 1996, a large number of published epidemiologic studies have evaluated associations between respiratory morbidity and short-term

levels of ambient NO 2 

### . In general, these studies report pos itive associations and a number of these associations are statistically-significant. The ISA notes that many of these studies have

been conducted in locations where ambient levels of NO 2 

### are well below the level of the current NAAQS (ISA, section 5.3.2.1). Second, controlle d human exposure studies have detected

effects of NO 2 

### exposure on several health endpoints. Of these, only airway hyperresponsiveness is associated with exposures to NO 2 

### concentrations at or near am bient levels. In fact, the NO 2 

### exposure levels associated with increased airw ay responsiveness overlap the maximum ambient

NO 2 

### concentrations in some locations where asso ciations with respiratory effects have been detected. Third, limitations in both epidemio logic studies (e.g., conf ounding by co-pollutants) and controlled human exposure studies (e.g., most sensitive populations likely not evaluated) suggest that an appropriate appro ach to identifying levels for poten tial alternative standards is to consider both types of studies. In considering both types of studies, we note that NO 2 

### concentrations represent different metrics when reported in epidemiologic studie s versus controlled human exposure studies.

Concentrations of NO 2 

### reported in epidemiologic studies are typically based on ambient monitoring data while NO 2 

### levels reported in controlled hu man exposure studies represent the

concentration of NO 2 

### in the breathing zone of the indivi dual. Therefore, consideration of NO 2 

### levels from controlled human expos ure studies when identifying alte rnative levels for an ambient

standard introduces some uncertain ty. For example, elevated NO 2 

### monitors, particularly in inner cities, likely underestimate persona l exposures that occur at lower elevations closer to traffic (ISA, section 5.2.2). In situations where personal exposure to ambient NO 2 

### is higher than ambient levels measured at a monitor, ambien t standard levels based on controlled exposure studies could be less health-p rotective than levels based on concentrations reported in

49

### epidemiologic studies at ambient monitors. Howe ver, in studies where it has been determined, the ratio of personal exposure to NO 2 

### of ambient origin to the ambient NO 2 

### concentration ranges from approximately 0.3 to 0.6 (ISA, section 5.2.2). This suggests that in some cases personal exposure concentrations for ambient NO 2 

### are lower than the levels of NO 2 

### measured at ambient monitors. In situations where personal exposure concentrations for ambient NO 2 

### are lower than the levels measured at ambient monitors, an am bient standard level based on controlled exposure studies could be more health-p rotective than a level based on concentrations reported in epidemiologic studies at ambient monitors. Overa ll, because some individuals are likely exposed

to NO 2 

### levels higher than those measured at ambi ent monitors (ISA, section 5.2.2) while others are likely exposed to NO 2 

### levels lower than those measured at ambient monitors (see ISA, tables 2.5-4 and 2.5-5), we have chosen to use NO 2 

### concentrations associated with both epidemiologic studies and controlled human expos ure studies for purposes of sel ecting alternative levels for analysis. As a result of the above considerations, to de termine the levels that should be evaluated staff has relied on both key epidemiologic studies conducted in the United St ates that evaluate

associations between short-term levels of NO 2 

### and respiratory morbidity (symptoms, hospital admissions, ED visits) and on controlled human exposure studies that evaluate airway hyperresponsiveness following NO 2 

### exposure. Figures 5-1 and 5-2 below show standardized

effect estimates 5 

### and the 98 th 

### and 99 th 

### percentile concentrations of daily 1-h maximum NO 2 

### for locations and time periods that correspond to key U.S. epidemiologic studies identified in the ISA (see table 5.4-1 in ISA for a list of key studies; Thompson and Jenkins, 2008). Of the key U.S. epidemiologic studies incl uded in figures 5-1 a nd 5-2, the highest 1-h

NO 2 

### concentrations were detected in the two studies conducted in Los Angeles (Linn et al., 2000; Ostro et al., 2001). For these studies, the 98 th 

### and 99 th 

### percentile 1-h daily maximum

concentrations of NO 2 

### overlap levels that the ISA concludes are associated with increased airway responsiveness in controlled human exposure studies (ISA, section 5.3.2.1). Therefore, staff judges that the combination of the epidemiologic studies by Linn et al. (2000) and Ostro et al. (2001), as well as the meta-analysis (Folinsbee, 1992; ISA, table 3.1-3; table 4-2 of this document) of controlled human exposure stud ies on airway responsiveness, provide an

5 The effect estimates presented in figures 5-1 and 5-2 are for those endpoints included in figure 5.3-1 and table 5.4- 1 of the ISA.



### 50

### 51 appropriate basis for identifying the upper end of the range of standard levels to be considered.

Given that the ISA concludes that significant increases in airway responsiveness are associated with short-term exposures to NO 2 

### at 0.2 to 0.3 ppm and given that the epidemiologic studies by Linn et al. (2000) and Ostro et al . (2001) are asso ciated with 98 th 

### and 99 th 

### percentile 1-h daily

maximum NO 2 

### levels that are just below (Li nn et al., 2000) and just above (99 th 

### percentile level for Ostro et al., 2001) 0.2 ppm (s ee figures 1 and 2 below), staff judges that an appropriate upper end of the range of potential standard levels is a daily maximum 1-h NO 2 

### concentration of 0.20

ppm.

### Figure 5-1. NO 2 

### effect estimates 6 

### (95% CI) for ED visits/HA and associated 1-h daily maximum NO 2 

### levels (98 th 

### and 99 th 

### percentile values in boxes 7 

### )

6 Effect estimates presented in figures 5-1 and 5-2 are from single pollutant models only. The studies by Tolbert et al., (2007); Peel et al., (2005); NYDOH (2006); Ito et al., (2007); and Delfino et al. (2002) also evaluated multipollutant models. NO 2 effect estimates retained statistical-significa nce in the study by Ito, but not in the other studies.

7 Authors of relevant U.S. and Canadian studies were c ontacted and air quality statistics from the study monitor that recorded the highest NO 2 levels were requested. In cases where authors provided 1-hour daily maximum air quality statistics, this information is presented in figures 1 and 2 (studies by Tolbert, Peel, NYDOH, Delfino). In one case (study by Ito) authors provided 24-hour air quality data, but identified a specific monitor in AQS. We used AQS to reconstruct the 1-hour daily maximum air quality for that monitor during the time period of the study. In three cases (studies by Jaffe, Linn, Ostro), we were not able to identify appropriate statistics from the information provided by the authors and the authors did not provide monitor identifi cation information. In these cases, we attempted to reconstruct the air quality data set for the location and tim e of the study using EPA’s Air Quality System (AQS). Prior to identifying potential alternativ e standards, we did not receive air quality information from any of the Canadian authors contacted and we were unable to reconstr uct the air quality data sets for the Canadian studies. Therefore, for purposes of identifying levels of potentia l alternative standards, our analysis was based on these key U.S. studies. Note that the NO 2 concentrations reported in table 1 of the study by Jaffe are labeled as 24-hour concentrations, but the author indicated in a personal co mmunication (Jaffe, 2008) that they actually represent 1- hour daily maximum concentrations.



### 52

### Figure 5-2. NO 2 

### effect estimates for respiratory sy mptoms and associated 1-h daily maximum NO 2 

### levels (98 th 

### and 99 th 

### percentile values in boxes)

In identifying additional standa rd levels that should be anal yzed, staff has considered that 1) health effect associations in epidemiologic studies are observed in lo cations with 1-h daily

maximum levels of NO 2 

### below 0.2 ppm (i.e., 99 th 

### percentile levels in seve ral studies are close to 0.1 ppm); 2) controlled human exposure stud ies that evaluate the ability of NO 2 

### to elicit airway hyperresponsiveness have assessed mild asthmatics and more severely affected asthmatics could experience increased airway respons iveness at lower levels of NO 2 

### than observed in these

53

### studies; and 3) a meta-analysis presented in the ISA (see Table 4-2) detects statisticallysignificant effects on the direction of airway responsiveness following short-term NO 2 

### exposures as low as 0.1 ppm. As a result of these consid erations, staff judges that it would be appropriate to consider additional standard levels that pr ovide a margin of safety relative to 0.20 ppm. Therefore, we will also c onsider daily maximum 1-h NO 2 

### standard levels of 0.10 ppm and 0.15

ppm. In identifying the lower end of the range of standards that will be analyzed, staff has considered the fact that the study by Delfino et al., (2002) provides evidence for associations

between short-term ambient NO 2 

### concentrations and respiratory morbidity in a location where

the 98 th 

### and 99 th 

### percentile concentra tions of the 1-h daily maximum levels of NO 2 

### were well below 0.1 ppm (Delfino et al., 2002). This study detects associations between 1-h and 8-h (only

8-h associations were statisti cally-significant) levels of NO 2 

### and asthma symptoms in a location

where the 98 th 

### and 99 th 

### percentile 1-h daily maximum NO 2 

### concentrations were 0.050 and 0.053 ppm, respectively. The 8-h effect estimate in this study remained positive, but became statistically non-significant, in a two- pollutant model that also included PM 10 

### . Staff judges that it is appropriate to base the lower end of the range of alternative standard levels on this study by Delfino et al. (2002). Therefore, we will also consider a 1-h daily maximum standard level of 0.050 ppm.

54

# 6. OVERVIEW OF APPROACHES TO ASSESSING EXPOSURES AND RISKS

6.1 INTRODUCTION

The purpose of the assessments described in this document is to characterize exposures

and risks associated with recent ambient levels of NO 2 

### , with levels associated with just meeting

the current NO 2 

### NAAQS, and with levels associated w ith just meeting potential alternative standards (see chapter 5 of this document for disc ussion of potential alternative standards). To characterize health risks, we have employed th ree approaches. With each approach, we have

characterized health risks associat ed with the air quality scenario s of interest (i.e., recent air quality unadjusted, air quality adjusted to simula te just meeting the current standard, and air quality adjusted to simulate just meeting potential alternative standards). In the first approach,

NO 2 

### air quality levels have been compared to po tential health effect benchmark values derived from the controlled human exposure literature (see section 6.2 below for discussion of benchmark levels). In the second approach, mo deled estimates of actual exposures have been compared to potential health effect benchmar ks. In the third appr oach, exposure-response relationships from epidemiologic studies have been used to estimate health impacts. An overview of the approaches to characterizing hea lth risks is provided below and each approach is described in more detail in subsequent sections of this document and the associated appendices. In the first approach, we have compared NO 2 

### air quality with potential health effect

benchmark levels for NO 2 

### . Scenario-driven air quality anal yses have been performed using

ambient NO 2 

### concentrations for the years 1995 though 2006. With this approach, NO 2 

### air quality serves as a surrogate for exposu re. All U.S. monitoring sites where NO 2 

### data have been collected are represented by this analysis and, as such, the results generated are considered a broad characterization of national air quality and human exposures that might be associated with these concentrations. An advantage of this appr oach is its relative simplicity; however, there is uncertainty associated with the assumption that NO 2 

### air quality can serve as an adequate surrogate for exposure to ambient NO 2 

### . Actual exposures might be influenced by factors not considered by this approach, such as the spatial and temporal vari ability in human activities.

55

### In the second approach, we have used an inhalation exposure model to generate more realistic estimates of personal exposures. Estimate s of personal exposure have been compared to potential NO 2 

### health benchmark levels. For this expos ure analysis, a probabilistic approach was used to model individual exposures consid ering the time people spend in different

microenvironments and the variable NO 2 

### concentrations that occur within these microenvironments across time, space, and microe nvironment type. This approach to assessing exposures was more resource intensive than usin g ambient levels as a surrogate for exposure; therefore, staff has included the analysis of only one specific location in the U.S. (Atlanta MSA) 8 

### . Although the geographic scope of this analys is is restricted, the approach provides

realistic estimates of NO 2 

### exposures, particularly those expos ures associated with important

emission sources of NO x 

### and NO 2 

### , and serves to complement the broad air quality

characterization.

For the characterization of risks in both the air quality analysis and the exposure modeling analysis described above, staff has used a range of short- term potential health effect benchmarks. The levels of potential benchmarks are based on NO 2 

### exposure levels that have been associated with increased airway res ponsiveness in asthmatics in controlled human exposure studies (ISA, section 5.3.2.1; see abov e for discussion). Benchmark values of 100, 150, 200, 250, and 300 ppb have been compared to both NO 2 

### air quality levels and to estimates

of NO 2 

### exposure. When NO 2 

### air quality is used as a surrogate for exposure, the output of the analysis is an estimate of the number of times per year specific locations experience 1-h levels of

NO 2 

### that exceed a particular benchmark. When personal exposures are simulated, the output of the analysis is an estimate of the number of individuals at risk for experiencing daily maximum

1-h levels of NO 2 

### of ambient origin that ex ceed a particular benchmark. An advantage of using potential health effect benchmark levels to characterize health risks is that the effects observed in controlled human exposure stud ies clearly result from NO 2 

### exposure. This is in contrast to

health effects associated with NO 2 

### in epidemiologic studies, whic h may also be associated with pollutants that co-occur with NO 2 

### in the ambient air. Thus, when using epidemiologic studies as

the basis for risk characteriza tion, the unique contribution of NO 2 

### to a particular health effect

8 In the document titled Risk and Exposure Assessment to Support the Review of the NO 2 Primary National Ambient Air Quality Standard: First Draft , we have presented the results of an expo sure analysis for Philadelphia. Based on CASAC comments received on that exposure analysis, we ha ve refined our approach and applied those refinements to the Atlanta analysis presented in this document. The original Philadelphia analysis is presented in the appendix to this document, but has not been modified since the first draft.



### 56

### may be difficult to quantify. A disadvantage of the potential benchmark approach is that the magnitude of the NO 2 

### effect on airway responsiveness can va ry considerably from individual to individual and not all asthmatics would be expe cted to respond to the same levels of NO 2 

### exposure. Therefore, the public health impacts of NO 2 

### -induced airway hyperresponsiveness are difficult to quantify. In the third approach, we have estimated resp iratory ED visits as a function of ambient

levels of NO 2 

### measured at a fixed-site monitor represen ting ambient air quality for an urban area. In this approach, concentration-resp onse functions are derived from NO 2 

### epidemiologic studies

and are used to estimate the im pact of ambient levels of NO 2 

### , as measured at a fixed-site monitor, on ED visits. By focusing on a different health endpoint from the first two approaches described above, this epidemiology-based appr oach provides additional perspective on the potential public health impacts of NO 2 

### . Relative to the approaches that use controlled human exposure studies, this approach to characterizing health risk s has several advantages. For example, the public health significance of the e ffect in question (i.e., ED visits) is less ambiguous in terms of its impact on individuals than is an increase in the airway response measured in a controlled human exposure study. In addition, the con centration-response relationship reflects real -world levels of NO 2 

### and co-pollutants present inambient air. However, a disadvantage of this approach is the ambiguity and complexity associated with quantifying the

contribution of NO 2 

### to the reported health impacts relativ e to the contributions of co-occurring pollutants.



## 6.2 POTENTIAL HEALTH BENCHMARK LEVELS

As noted above (section 4.5.3 and 6.1), staff ha s developed potential health benchmark levels that are based on results from controlled human exposure studies of airway responsiveness. The purpose of these potential health benchmark le vels is to provide a basis for comparing NO 2 

### concentrations shown to increase airw ay responsiveness in asthmatics (from controlled human exposure stud ies) to estimates of NO 2 

### exposures derived from our air quality and exposure analyses. Because the purpose of the benchmarks is to provide a way to link

estimates of NO 2 

### exposure with levels known to produ ce respiratory effects in individual asthmatics, benchmark levels have not been developed from the epidemiologic literature.

57

### To identify potential health effect benchmar ks, staff has relied on the ISA’s evaluation of

the NO 2 

### human exposures studies. Controlled human exposure studie s involving allergen challenge in asthmatics suggest that NO 2 

### exposure may enhance the sensitivity to allergeninduced decrements in lung function and increase the allergen-induced airway inflammatory response at exposures as low as 0.26-ppm NO 2 

### for 30 min (ISA, Figure 3.1-2 and section 5.3.2.1). Exposure to NO 2 

### also has been found to enhance th e inherent responsiveness of the airway to subsequent nonspecific challenges (ISA, section 5. 3.2.1). In asthmatics, small but significant increases in nonspecific airway responsiveness have been observed in the range of 0.2 to 0.3 ppm NO 2 

### for 30 minute exposures and at 0.1 ppm NO 2 

### for 1-h exposures (ISA, section 5.3.2.1). Therefore, for the risk char acterization, staff judges that 1-h NO 2 

### levels in this range are appropriate to consider as potential health benchmarks for comparison to air quality levels and exposure estimates. To characterize health risks with respect to this range, potential health effect benchmark values of 0.10 ppm, 0.20 ppm, 0.25 ppm, and 0.30 ppm ha ve been employed to reflect the lower- middle- and upper-end of the range identified in the ISA as levels at which controlled human exposure studies have pr ovided evidence for the occurrence of NO 2 

### -induced airway hyperresponsiveness. In choosing this range, we recognize that uncer tainties exist regardi ng the percentage of asthmatics expected to e xperience an increase in responsiveness following NO 2 

### exposure and in the clinical implications of such an increase. A meta-analysis presented in the ISA (see Table 4- 2 above) suggests that between 66% and 75% of asthmatics may experience an increase in airway responsiveness following short-term NO 2 

### exposures in the range of 0.1 to 0.3 ppm. However, this meta-analysis provides in formation only on the direction of the NO 2 

### effect and not on its magnitude. In addition, the NO 2 

### controlled human exposure studies of airway responsiveness have focused primarily on mild asth matics. It is possible that more severely affected asthmatics could experience a more severe response following NO 2 

### exposures in this range. It is also possible th at they could experience a res ponse at lower levels of NO 2 

### than the mild asthmatics included in these studies. However, even considering these uncertainties, staff judges that the identified range of concentrations is sufficient to provide some perspective on the potential public health impacts of NO 2 

### exposures, especially when the results of the risk characterization based on airway responsiveness are considered in conjunction with the risk assessment based on the epidemiology literature.

58

## 6.3 SIMULATING THE CURRENT AND ALTERNATIVE STANDARDS

A primary goal of these risk and exposure as sessments is to evaluate the ability of the

current NO 2 

### standard (0.053 ppm annual average) a nd potential alternat ive standards (0.05, 0.10, 0.15, and 0.20 ppm; see chapter 5 of this document) to protect public health. In order to evaluate the ability of a specific standard to protect public health, ambient NO 2 

### concentrations need to be adjusted such that they simulate levels of NO 2 

### that just meet that standard. Such adjustments allow comparisons of the level of public health protection that c ould be associated with just meeting the current and potential alternative standa rds. All areas of the United States currently

have ambient NO 2 

### levels below the current annual standard. Therefore, to simulate just meeting the current annual standard, NO 2 

### air quality levels must be adjusted upward. Similarly, to simulate a potential standard that is below curren t air quality levels, those current levels must be adjusted downward. This process of adjusting ai r quality to simulate just meeting a specific

standard is described in mo re detail below.

6.3.1 Adjustment of Ambient Air Quality Based on the level of U.S. policy-relevant background (PRB) and observed trends in ambient monitoring, ambient NO 2 

### concentrations were proportiona lly adjusted at each location using the maximum monitored concentration that occurred in each ye ar. Policy-relevant background is defined as the distribution of NO 2 

### concentrations that would be observed in the U.S. in the absence of anthropogenic (man-made) emissions of NO 2 

### precursors in the U.S., Canada, and Mexico. Policy-releva nt background for most of the c ontinental U.S. is estimated to be less than 300 parts per trilli on (ppt) (ISA, Section 2.4.6). In the Northeastern U.S. where present-day NO 2 

### concentrations are highest, this amount s to a contribution of less than 1% percent of the total observed ambient NO 2 

### concentration. This low contribution of PRB to NO 2 

### concentrations provides support fo r a proportional method to adjust air quality, i.e., an equal adjustment of air quality values across the entire air quality distribution to just meet a target

value. While annual average concentra tions have declined signifi cantly over the time period of analysis, the variability in the concentrations, both the annual av erage and 1-hour concentrations, have remained relatively constant. This trend is apparent when considering the air quality data collectively (Appendix A, section 7) and when considering individual locations (Rizzo, 2008).

59

### As an example, Figure 6-1 compares the trends in daily maximum NO 2 

### 1-hour concentration percentiles at the one ambient monito r in Atlanta that was in operation as far back as 1985 and is currently part of the monitoring network. Three recent years of data (2005-2007) were selected to constitute a series of low concentration year data along with three historical years of data (1985, 1986, and 1988) constituting a se ries of high concentration year data. As shown in the figure, the relations hips between the low and high c oncentration years at each of the daily maximum concentration percentiles are mostly linear, with R 2 

### values ranging from 0.88 to 0.99. Where deviation from linearity did occur, it occurred primarily at a single point, either at the maximum daily maximum or the minimu m daily maximum 1-hour concentration. This

indicates that the rate of decrea se in ambient air quality concen trations at the mean value is consistent with the rate of change at the lower and upper daily maxi mum 1-hour concentration percentiles. This trend provide s support for the use of a proportional approach to adjust current ambient concentrations to represent air quality under both the current and alternative standard

scenarios.

60

### Figure 6-1. Comparison of measured daily maximum NO 2 

### concentration percentiles in Atlanta for three high concentration years (1985, 1986, 1988) versus three low concentration years (2005-2007) at one ambient monitor.

To adjust concentrations that simulate just meeting the current annual average standard of 0.053 ppm, proportional adjustment factors F for each location ( i ) and year ( j ) were derived by the following:

ij ij 

### CSF max, 

### / = equation (6-1)

where,

F

ij 

### = NO 2 

### concentration adjustment f actor (unitless) in location i given the annual average standard and for each year j

S

= Current standard level (i.e., 53 ppb, annual average NO 2 

### concentration)

C

max,ij 

### = The maximum annual average NO 2 

### concentration at a monitor in each

location i and for each year j (ppb)

In these cases where staff simulated a proportional adjustment in ambient NO 2 

### concentrations using equation (6-2), it was a ssumed that the current temporal and spatial

distribution of air concentrations (as characterized by the current air quality data) is maintained

and increased NO x 

### emissions contribute to increased NO 2 

### concentrations, with the highest monitor (in terms of annual averages) being adjusted so that it just meets the current 0.053 ppm annual average standard. Values for each air qua lity adjustment factor used for each location evaluated in the air quality and risk characteri zation are given in Appendix A (section 7.2). For each location and calendar year, all the hourly NO 2 

### concentrations in a location were multiplied by the same constant value F to make the highest annual mean equal to 53 ppb for that location and year. For example, of twelve monitors measuring NO 2 

### in Boston for year 1995 (Figure 6-2, top), the maximum annual average concentration was 30.5 ppb, giving an adjustment factor of F = 53/30.5 = 1.74 for that year. All hourly concentr ations measured at all monitoring sites in that location are then multiplied by 1.74, resul ting in an upward scaling of hourly NO 2 

### concentrations for that year. Therefore, one monitoring site in Boston for year 1995 would have an annual average concentration of 0.053 pp m, while all other monitoring sites would have an annual

61

### average concentration below that value, although stil l proportionally scaled up by 1.74 (Figure 6- 2, bottom). Then, using the adju sted hourly concentrations to si mulate just meeting the current standard, the metrics of interest (e.g., annual mean NO 2 

### concentration, the number of potential health effect benchmark exceedances) were estimated for each site-year.

Figure 6-2. Distributions of ho urly NO2 concentrations at tw elve ambient monitors in the Boston CMSA, as is (top) and air quality adju sted to just meet the current standard (bottom), Year 1995.



### 62

### Proportional adjustment factors were also de rived considering the form, averaging time, and levels of the potential alternative standards under consideration. Discussion regarding the staff selection of each of these components is pr ovided in chapter 5 of this document. The 98 th



### and 99 th 

### percentile 1-hour NO 2 

### daily maximum concentrations averaged across three years of monitoring were used in calculating the adjustment factors at each of four standard levels as

follows:

i j ijk

likl 

### C

SF

max, 3

1 

### 3 / ⎟ ⎟ ⎟ ⎟ ⎟

⎠ ⎞

⎜ ⎜ ⎜ ⎜ ⎜

⎝ ⎛

= 

# ∑

= 

### equation (6-2)

where,

F

ikl



### = NO 2 

### concentration adjustment f actor (unitless) in location i given alternative standard percentile form k and standard level l across a 3-year period

S l



### = Standard level l (i.e., 50, 100, 150, 200 ppb 1-hour NO 2 

### concentration (ppb))

C ijk 

### = Selected percentile k (i.e., 98 th 

### or 99 th 

### ) 1-hour daily maximum NO 2 

### concentration at a monitor in location i (ppb) for each year j

As described above for adjustments made in simulating just meeting the current standard, it was assumed that the current temporal and sp atial distribution of air concentrations (as characterized by the current NO 2 

### air quality data) is maintained and increased NO x 

### emissions

contribute to increased NO 2 

### concentrations, with the highest monitor (in terms of the 3-year average at the 98 th 

### or 99 th 

### percentile) being adjusted so th at it just meets the level of the particular 1-hour alternative standard. Since th e alternative standard levels range from 50 ppb through 200 ppb, both proportional upward and downw ard adjustments were made to the 1-hour

ambient NO 2 

### concentrations. The values for each air quality adjustment factor used for each location evaluated in the air quality and risk ch aracterization are given in Appendix A, section 7.2. Due to the form of the alternative standards, the expected utility of such an analysis, and the limited time available to conduct the analysis, onl y the more recent air quality data were used (i.e., years 2001-2006) and separated into tw o 3-year periods, 2001-2003 and 2004-2006. The 1-

hour ambient NO 2 

### concentrations were adjusted in a similar manner described above for just meeting the current standard, however, due to the form of these standards, only one factor was

63

### derived for each 3-year period, rather than one factor for each calendar year as was done with just meeting the current standard.

6.3.2 Adjustment of Potential Health Effect Benchmark Levels 

### Rather than proportionally modi fy the air quality concentrat ions used for input to the exposure model, a proportional ad justment of the potential hea lth effect benchmark level was performed. This was done to reduce the proc essing time associated with the exposure modeling

simulations since there were several thousands of receptors modeled in the Atlanta exposure assessment. In addition, because the adjustment procedure is proportional, the application of a downward adjustment of the sel ected benchmark level is mathematically equivalent to a proportional upward adjustment of the air quality c oncentrations. The same approach used in the air quality adjustment described above was us ed in the exposure modeling to scale the benchmark levels to simulate just meeting the cu rrent and potential alternative standards. For example, an adjustment factor of 2.27 was de termined for Atlanta for year 2001 to simulate ambient concentrations just meeting the current standard, based on the maximum annual average

NO 2 

### concentration of 23.3 ppb observed at an ambi ent monitor for that year (see Appendix A, section 7.2). Therefore, the 1-hour potential he alth effect benchmark levels of 100, 200, and 300 ppb were proportionally adjusted to 44, 88, and 132 ppb, respectively for year 2001. A comparison of the two procedures is pres ented in Figure 6-3 where air quality is adjusted to simulate just meeting the current annual standard and where the benchmark is adjusted to simulate air quality that just meets the current standard with using the as is air quality. This example uses the distribution of hourly NO 2 

### concentrations measured at the maximum ambient monitor (ID 1312100481) within th e Atlanta modeling domain for year 2001.

If we were interested in the number of exceed ances of 200 ppb 1-hour under the current standard scenario for example, this would be equivalent to counting the number of exceedances of 88 ppb using the as is air quality. For additional clarity, the same ambient air qua lity data are presented in Figure 6-4, only with expansion of the highest pe rcentiles on the graph to allow fo r the counting of the number of exceedances. In using the air quality adjusted to just meet the current standard, i.e., the as is air quality was adjusted upwards by a factor 2.27, there are twelve exceedances of 200 ppb 1-hour. When considering the as is air quality without adjustment but with a downward adjustment of

64

### the benchmark by the same factor of 2.27, ther e are the same number of exceedances of 200 ppb 1-hour. This benchmark adjustment procedure was applied in Atlanta where exposure modeling was performed to simulate just meeting the curren t and alternative standards. Additional details regarding derivation of the ad justed benchmark levels used in the exposure modeling are provided in chapter 8 of this document.

Figure 6-3. Comparison of adjusted ambient mo nitoring concentrations (CS) or adjusted benchmark level (dashed line) to simulate just meeting the cu rrent annual average standard in Atlanta for year 2001.



### 6599.7 99.8 99.9 100

50 100 150 200 250 300 350 400 1-hour NO 2 (ppb)Cumulative Percentile

ATL1312100481 ATL1312100481_CS Adjust AQ up Adjust Benchmark Down



### Figure 6-4. Comparison of the upper percen tiles for where ambient monitoring NO 2 

### concentrations (CS) and the benchmark level (d ashed line) were adjusted to simulate just meeting the current annual standard in Atlanta for year 2001. The hourly NO 2 

### concentration distributions are provided in Figure 6-3.



### 66

# 7. AMBIENT AIR QUALITY ASSESSMENT AND HEALTH RISK

CHARACTERIZATION

7.1 OVERVIEW

Ambient monitoring data for each of the years 1995 through 2006 were used in this analysis to characterize NO 2 

### air quality across the U.S. This air quality data, as well as other

NO 2 

### concentrations derived from am bient levels, were used as a su rrogate to estimate potential human exposure. While an ambient monitor measures NO 2 

### concentrations at a stationary location, the monitor may well represent the con centrations that persons residing nearby are exposed to. The quality of th e extrapolation of ambient moni tor concentration to personal exposure will be dependent upon the spatial distribu tion of important emission sources, the siting of the ambient monitors, and consideration of places that persons visit. It is within this context that the approach for characterizing the ambient NO 2 

### air quality was designed.

Based on the health effects information from the human clinical and epidemiological studies, the averaging time of interest for the air quality ch aracterization was 1-hour, with concentration levels ranging from between 100 a nd 300 ppb. Since the current standard is based on annual average levels of NO 2 

### while the most definitive health effects evidence is associated with short-term exposures (i.e., 30-minute to 1-hour, or one to severa l day), the air quality analysis required the development of a model that relates annual average and short-term levels of

NO 2 

### . To characterize this relationship and to estimate the number of exceedances of the potential health effect benchmarks in specifi c locations, an empirical model, employing the annual average and hourly concentrations, was ch osen to avoid some of the difficulties in extrapolating outside the range of the observed air quality.

The available NO 2 

### air quality were first divided into two six-year groups; one contained data from years 1995-2000, representing an historical data set; the ot her contained the monitoring years 2001-2006, representing recent ambient monitoring. Each of these monitoring year-groups were evaluated considering the NO 2 

### concentrations as they were reported and representing the conditions at that time (termed in this assessment “ as is ”). This served as the first air quality scenario, with the results with in each year-group sepa rated by monitor distance

67

### from a major road. 1 

### The ambient monitor data were cate gorized in this manner to account for the potential influence of vehicle emissions on concentrations meas ured at the monitors within close proximity to roadways. There is potential for different concentration levels measured at each of these locations (i.e., near-road versus away from road) and thus potentially different exposure concentrations experienced by those persons spending time in these locations. A

second scenario used the as is ambient monitoring data obtained from monitors sited ≥ 100 m from a major road and a simplified on-road simu lation approach (described below in section 7.2.4) to estimate on-road NO 2 

### concentrations for each of the year-groups. This scenari developed by recognizing that vehicles are important emission sources of NO x 

### and NO 2 

### and that people spend time inside vehicl o was

es on roads.

Two additional scenarios followed in similar fashion to the as is air quality analysis,

however these scenarios considered the ambient NO 2 

### concentrations simulated to just meeting the current standard of 0.053 ppm annual average and each of the alternative 1-hour standards of 50, 100, 150, and 200 ppb. 2 

### Due to the form of the alterna tive standards considered here (98 th



### and 99 th 

### percentiles of the daily maximum concentr ations averaged over 3 years), the recent ambient monitoring data set was divided into two three-year groups, 2001-2003 and 2004-2006. Thus, the air quality characterization results are separated into tw o broad analyses, one using air quality as is and the ot her where air quality was adjusted to just meeting the current and

alternative standards. Within both of these an alyses, an additional simulation was performed to

estimate NO 2 

### concentrations on roads. The first scenar io described above is the only scenario that uses purely measurement data. Each of the other scenarios either uses a simulation procedure to estimate on-road concentrations (s cenario 2), concentrations that just meet a particular standard level (scen ario 3), or both (scenario 4). Because many of the NO 2 

### ambient monitoring sites used in this analysis are primarily targeting public health monitori ng objectives, the results are consid ered a broad characterization of national air quality and potential human expos ures that might be associated with these

scenario-driven concentrations. Th e output of this air quality char acterization is an estimate the

1 As part of our earlier analysis reported in the 2 nd draft REA, the historical data were separated into two-road distance categories, <100 m and ≥ 100 m from a major road. The recent data were separated into both the two- and three-road distance categories.

2 As part of our earlier analysis reported in the 1 st draft REA, the historical data we re evaluated using concentrations as is and for air quality adjusted to just m eet the current standard. Only the r ecent data (2001-2006) were evaluated using air quality adjusted to just meet potential alternative standards and divided into two three-year groups.



### 68

### number of times per year specific lo cations experience daily maximum levels 3 

### of NO 2 

### that may cause adverse health effects in susceptible i ndividuals. Each location that was evaluated contained one to several monitors operating for a few to several years, generating a number of site-years of data. The number of site-years in a location were us ed to generate a distribution of two exposure and risk characte rization metrics; the annual av erage concentrations and the number of daily maximum exceedances that did ( observed data) or could occur (simulated data) in a year for that location. The mean and medi an values were reported to represent the central tendency of each metric for the f our scenarios in each air quality year-group. For example, the mean annual average concentration for a location is the arithmetic average of all site-year annual average concentrations in that location given the particular year-group. The minimum annual average concentration served to represent a lower bound and could be a single or multiple siteyear(s) of data in the location given the particul ar year-group, dependent on the distribution of annual average concentrations for each site-year. Since there were either multiple site-years of monitoring or numerous simulations performed for each location using all available site-years of data, results for the upper percen tiles generally included the 95 th 

### , 98 th 

### , and 99 th 

### percentiles of the distribution. As described for the minimum value, these upper percentile estimates could alsorepresent either a single or multiple site-year(s) of data at a location given the particular yeargroup.



## 7.2 APPROACH

There were five broad steps to allow for the characterization of the air quality. The first step involved collecting, compili ng, and screening the ambient air qua lity data colle cted since the prior review in 1995 to ensu re consistency with the NO 2 

### NAAQS requirements. Then, criteria based on the current standard and the potential he alth effect benchmark levels were used to identify specific locations for analysis using descri ptive statistical analysis of the screened data set. All remaining monitoring da ta not identified by th e selection criteria we re grouped into one of two non-specific categories.

3 The historical data (1995-2000) were only evaluate d for total number of exceedances, not for daily maximum exceedances as done for the recent air qu ality data (2001-200 6). Because of the differences in benchmarks levels, differences in the number of road-to-monitor categories used, and different exceedance levels, the historical data analyses are presented in Appendix A section A-9.1 to A- 9.3, along with the comparable metrics using the recent (2001-2006) air quality.



### 69

### The locations (both the specific and non-specific) served as the geographic centers of the analysis, where application of the empirical model was done to estimate concentrations and the number of exceedances of potentia l health effect benchmark leve ls. Next, due to expected

variable influence of road emissions on ambien t monitor concentrations , the monitors within each of the named locations were categorized acco rding to particular attributes, including land use characteristics, location type, monitoring objective, monitoring height above ground, and distance to major roadways. In addition to the use of the ambi ent concentrations ( as is ) and ambient concentrations just meeting the curren t and alternative standa rd levels, on-road NO 2 

### concentrations were estimated in each location to approximate the potential exposure and risk

metrics associated with these concentrations. An d finally, air quality metrics of interest were calculated using air quality data from each scenario.

7.2.1 Air Quality Data Screen

NO 2 

### air quality data and associated do cumentation from the years 1995 through 2006 were downloaded from EPA’s Air Quality System (AQS) for this purpose (EPA, 2007c, d). A

site was defined by the state, county, site code , and parameter occurren ce code (POC), which gives a 10-digit monitor ID code. As required by the NO 2 

### NAAQS, a valid year of monitoring data is needed to calculate the annual average co ncentration. A valid year at a monitoring site was comprised of 75% of valid days in a year, wi th at least 18 hourly measurements for a valid day (thus at least 274 or 275 valid days depending on presence of a leap year and a minimum of 4,932 or 4,950 hours). This served as the screening criterion for ambient m onitoring data used in the air quality characterization. Site-years of data are the tota l numbers of years the collectiv e monitors in a location were in operation. Of a total of 5,243 site-years of data in the entire NO 2 

### 1-hour concentration database, 1,039 site-years did not meet the abov e completeness criterion and were excluded from any further analyses. In additi on, since shorter term average con centrations are of interest, the remaining site-years of data were further scr eened for 75% completeness on hourly measures in a year (i.e., containing a minimum of 6,570 or 6,588, depending on presence of a leap year). Twenty-seven additional site-years were exclud ed, resulting in 4,177 complete site-years in the analytical database. Table 7-1 provides a summar y of the site-years incl uded in the analysis,

70

### relative to those excl uded, by location and by two site-year groups. 4 

### The air quality data from AQS were separated into thes e two groups, one representing historical data (1995-2000) and the other representing recent ambient monitoring data (2001-2006) to account for anticipated longterm temporal variability in NO 2 

### concentrations within each lo cation. The selection of specific locations was a companion analysis to this da ta screening, and is di scussed in section 7.2.2.

Table 7-1. Counts of complete and incomplete site-years of NO2 ambient monitoring data. Number of Site-Years 1 Site-Years Complete Incomplete % Complete Location 1995-2000 2001-2006 1995-2000 2001-2006 1995-2000 2001-2006 Atlanta 24 29 5 1 83% 97% Boston 58 47 16 34 78% 58% Chicago 47 36 20 22 70% 62% Cleveland 11 11 2 2 85% 85% Colorado Springs 26 ND 4 4 87% ND Denver 26 10 10 4 72% 71% Detroit 12 12 4 1 75% 92% El Paso 14 30 11 0 56% 100% Jacksonville 6 4 0 2 100% 67% Las Vegas 16 35 4 9 80% 80% Los Angeles 193 177 16 19 92% 90% Miami 24 20 1 4 96% 83% New York 93 81 12 24 89% 77% Philadelphia 46 39 6 8 88% 83% Phoenix 22 27 8 25 73% 52% Provo 6 6 0 0 100% 100% St. Louis 56 43 3 9 95% 83% Washington DC 69 66 21 18 77% 79% Other MSA 1135 1177 249 235 82% 83% Other Not MSA 200 243 112 141 64% 63% Total 4177 1066 80% Notes: 1 The average number of monitors operating per ye ar within the six-year group can be estimated by dividing the number of site-years by 6 (the total would be divided by 12). The actual number of monitors operating in any specific year is va riable. See Appendix A-4, Table A-1 as an example. ND no available monitoring data.



### 7.2.2 Selection of Locations for Air Quality Analysis Criteria were established for selecting mon itoring sites with high annual means and/or frequent exceedances of potential health effect benchmarks. Sel ected locations were those that

4 At the time data were downloaded from AQS, 14 of 18 named locations and the 2 grouped locations contained enough data to be considered valid for year 2006.



### 71

### had a maximum annual mean NO 2 

### level at a particular monitor greater than or equal to 25.7 ppb, which represents the 90 th 

### percentile across all locations and site-years, and/or had at least one reported 1-hour NO 2 

### level greater than or equal to 200 ppb, the lowest level of the potential

health effect benchmarks. 5 

### A location in this context would incl ude a geographic area that encompasses more than a single air quality moni tor (e.g., particular cit y, metropolitan statistical area (MSA), or consolidated metr opolitan statistical area or CMSA). First, all ambient monitors were identified as either belongi ng to a CMSA, a MSA, or neither. Then, locations of interest were identified through statistical analysis of the ambient NO 2 

### air quality data for each site

within a location.

Fourteen locations met both selection criteria and an additional four met at least one of the criteria (see Table 7-2). 6 

### In addition to these 18 specific locations, the remaining sites were grouped into two broad location groupings. The Other CMSA location contains all the other sites

that are in MSAs or CMSAs but are not in any of the 18 specified locations. The Other Not MSA

location contains all the sites that are not in an MSA or CMSA. The final database for analysis included air quality data from a total of 204 m onitors within the named locations, 331 monitors

in the Other CMSA group, and 92 monitors in the Other Not MSA group.

5 At the time the locations were selected for analysis, the ISA identified 200 ppb as a lower level where health effects were observed in human clinical studies. 6 New Haven, CT, while meeting both criteria, did not have any recent exceedances of 20 0 ppb and contained one of the lowest maximum concentration-to-mean ratios. Theref ore this location was not separated out as a specific location for analysis and was grouped within a non-specfic category.



### 72

### Table 7-2. Locations selected for NO 2 

### Air Quality Characterization, associated abbreviations, and values of selection criteria.

Location

Type 1 Code Description Abbreviation Maximum # of Exceedances

of 200 ppb Maximum Annual Mean (ppb)

MSA 0520 Atlanta, GA Atlanta* 1 26.6

CMSA 1122 Boston-Worcester -Lawrence, MANH-ME-CT Boston* 1 31.1

CMSA 1602 Chicago-Gary-Kenosha, IL-IN-WI Chicago 0 33.6

CMSA 1692 Cleveland-Akron, OH Cleveland* 1 28.1

MSA 1720 Colorado Springs, CO Colorado Springs* 69 34.8

CMSA 2082 Denver-Boulder-Greeley, CO Denver* 2 36.8

CMSA 2162 Detroit-Ann Arbor-Flint, MI Detroit* 12 25.9

MSA 2320 El Paso, TX El Paso* 2 35.1

MSA 3600 Jacksonville, FL Jacksonville 2 15.9

MSA 4120 Las Vegas, NV-AZ Las Vegas* 11 27.1

CMSA 4472 Los Angeles-Riverside-Orange County, CA Los Angeles* 5 50.6

CMSA 4992 Miami-Fort Lauderdale, FL Miami 3 16.8

CMSA 5602 New York-Northern New JerseyLong Island, NY-NJ-CT-PA New York* 3 42.2

CMSA 6162 Philadelphia-Wilmington-Atlantic City, PA-NJ-DE-MD Philadelphia* 3 34.0

MSA*\ 6200 Phoenix-Mesa, AZ Phoenix* 37 40.5

MSA 6520 Provo-Orem, UT Provo 0 28.9

MSA 7040 St. Louis, MO-IL St. Louis* 8 27.2

CMSA 8872 Washington-Baltimore, DC-MDVA-WV Washington DC* 2 27.2

MSA/CMSA - Other MSA/CMSA Other MSA 10 31.9

- - Other Not MSA Other Not MSA 2 19.7 Notes: 1 CMSA is consolidated metropolitan statistical area; M SA is metropolitan statistical area according to the

1999 Office of Management and Budget definit ions (January 28, 2002 revision). * Indicates locations that satisfied both the annual average and exceedance criteria.



### 7.2.3 Site Characteristics of Ambient NO 2 

### Monitors The siting of the ambient monitors is of pa rticular importance, recognizing that the purpose of the monitoring could have an influence on the measured NO 2 

### concentrations and subsequent interpretation in the air qualit y characterization. Specific monitoring site characteristics provided in AQS were obtained, including the m onitoring objective, measurement

73

### scale, and predominant land-use. Additional features such as proximity to NO x 

### emission sources, including mobile and stationary sources, were estimated using each monitoring site and emission source geographic coordinates. Each of th ese attributes is summarized here to provide perspective on the representation the ambient NO 2 

### monitoring network with in each location. A more thorough discussion of the ambient mon itoring network is provided in Chapter 2.

Individual monitor site characte ristics are given in Appendix A-4. The monitor objective meta-data field describes the nature of the network in terms of its attempt to generally characterize health effects, photochemical activity, transport, or welfare effects. In recognizing that there were variable numbers of ambient monitors in operation or with valid data in a given year, and that th e air quality characteri zation was performed for particular year-groups of data, the monitoring objectives were we ighted by the number of siteyears. In addition, the monitors can have more than one objective. To evaluate the representation of the monitors used for the pur poses of the REA, the four objective categories were used in the following order (i.e ., population exposure, high concentration, general/background, unknown) to char acterize the monitor with one objective. All other objectives were grouped into an “Other” categor y. Tables 7-3 and 7- 4 summarize the monitoring objectives for each location using the historical and recent air qua lity data, respectively. Most locations contained monitoring site-years of data that would target pub lic health through the population exposure and highest conc entration categories. Where these categories were not the predominant objective, the monitoring objective was mainly unknown.

74

### Table 7-3. Percent of ambient NO 2 

### monitors with selected monitoring objectives, using all valid site-years of historical air quality (1995-2000).

Location Population Exposure High Concentration General /Background Unknown Other Atlanta 88 13 Boston 43 21 33 3 Chicago 55 32 11 2 Cleveland 100 Colorado Springs 100 Denver 23 19 27 31 Detroit 42 42 17 El Paso 50 14 29 7 Jacksonville 100 Las Vegas 50 13 13 13 13 Los Angeles 23 9 3 64 2 Miami 50 50 New York 26 40 2 32 Philadelphia 85 13 2 Phoenix 73 27 Provo 100 St. Louis 21 11 68 Washington DC 38 19 39 4 Other MSA 45 14 4 32 5 Other Not MSA 22 7 17 35 19



### Table 7-4. Percent of ambient NO 2 

### monitors with selected monitoring objectives, using all valid site-years of recent air quality (2001-2006).

Location Population Exposure High Concentration General /Background Unknown Other Atlanta 79 21 Boston 66 15 15 4 Chicago 53 47 Cleveland 100 Denver 50 50 Detroit 50 50 El Paso 80 20 Jacksonville 100 Las Vegas 54 14 14 17 Los Angeles 20 10 2 64 5 Miami 50 50 New York 40 23 1 36 Philadelphia 79 10 10 Phoenix 67 22 4 7 Provo 100 St. Louis 28 21 51 Washington DC 48 17 27 8 Other MSA 45 16 9 23 6 Other Not MSA 22 5 25 21 27



### 75

### Similarly, the overall measurement scale of the monitors used for the air quality

characterization in each location was evaluated based on the valid s ite-years of air quality data. The measurement scale represents the air vo lumes associated with the monitoring area

dimensions. While a monitor can have mu ltiple objectives, each monitor has only one

measurement scale. Tables 7-5 and 7-6 summarize the measurement scales of the monitors in each location using the historical and recent air quality data, respectively. Most locations contained monitoring site-years of data with measurement scales of urban (4 to 50 km) or neighborhood (500 m to 4 km). Wh ere these categories were not the predominant objective, the

measurement scale was commonly not indicated. The land use meta-data indicate the prevalent land use within 1⁄4 mile of the monitoring

site. Tables 7-7 and 7-8 summarize the land us e surrounding the monitors in each location using the historical and recent air quali ty data, respectively. Most lo cations contained monitoring siteyears of data from areas within residential a nd commercial areas, but were generally dominated by residential land use. Two locations however were characterized with site-years of data associated entirely with commercial la nd use (i.e., Jacksonville and Provo).

Table 7-5. Percent of ambient NO 2 

### monitors with selected measurement scales, using all valid siteyears of historical air quality (1995-2000). Location Regional Urban Neighborhood Middle Micro None Atlanta 63 38 Boston 3 26 28 12 31 Chicago 11 6 55 26 2 Cleveland 45 55 Colorado Springs 100 Denver 23 65 12 Detroit 42 42 17 El Paso 29 50 7 14 Jacksonville 100 Las Vegas 13 75 13 Los Angeles 17 14 14 55 Miami 38 63 New York 6 53 6 34 Philadelphia 13 74 13 Phoenix 23 55 14 9 Provo 100 St. Louis 11 21 68 Washington DC 1 26 42 30 Other MSA 4 21 42 2 1 30 Other Not MSA 8 22 26 45



### 76

### Table 7-6. Percent of ambient NO 2 

### monitors with selected measurement scales, using all valid siteyears of recent air quality (2001-2006). Location Regional Urban Neighborhood Middle Micro None Atlanta 59 41 Boston 4 23 57 11 4 Chicago 14 53 33 Cleveland 27 73 Denver 50 50 Detroit 50 50 El Paso 60 40 Jacksonville 100 Las Vegas 3 14 83 Los Angeles 18 15 11 56 Miami 50 50 New York 6 46 5 43 Philadelphia 13 77 10 Phoenix 26 37 22 15 Provo 100 St. Louis 14 35 51 Washington DC 27 48 24 Other MSA 4 22 44 4 1 25 Other Not MSA 19 20 27 1 33



### Table 7-7. Percent of ambient NO 2 

### monitors with selected land use, using all valid site-years of historical air quality (1995-2000). Location Residential Commercial Industrial Mobile Agricultural Other Atlanta 33 25 42 Boston 41 45 10 3 Chicago 57 6 26 11 Cleveland 45 55 Colorado Springs 54 15 27 4 Denver 19 38 42 Detroit 83 17 El Paso 14 86 Jacksonville 100 Las Vegas 27 33 13 20 7 Los Angeles 55 32 4 5 3 1 Miami 63 25 13 New York 36 36 13 13 1 Philadelphia 72 13 13 2 Phoenix 100 Provo 100 St. Louis 41 32 11 16 Washington DC 57 36 7 Other MSA 42 33 7 4 14 Other Not MSA 21 20 15 7 35 2



### 77

### Table 7-8. Percent of ambient NO 2 

### monitors with selected land use, using all valid site-years of recent air quality (2001-2006). Location Residential Commercial Industrial Mobile Agricultural Other Atlanta 38 21 41 Boston 28 62 11 Chicago 53 14 33 Cleveland 45 55 Denver 50 50 Detroit 100 El Paso 40 60 Jacksonville 100 Las Vegas 40 23 17 17 3 Los Angeles 58 34 3 3 1 1 Miami 75 25 New York 43 35 8 13 1 Philadelphia 64 21 15 Phoenix 93 7 Provo 100 St. Louis 42 30 14 14 Washington DC 64 27 9 Other MSA 42 34 8 2 13 Other Not MSA 23 13 14 9 39 1



### Mobile and stationary sources (i.e., primarily power generating utilities using fossil fuels) are the most significant contri butors to nitrogen oxides (NO x 

### ) emissions in the U.S. (ISA, section 2.2.1). Therefore, the distances of each ambient monitor in the named locations to major roads and stationary sources were calculated. 7 

### The estimated distances of the monitors to major roads ranged from a few meters to several hundred meters (Table 7-9). On average, most of the ambient monitors are placed at a distance of 100 me ters or greater from a major road, however in locations with a large monitori ng network such as Boston, Chicago, or New York, there were a few monitors sited within close proximity (<20 me ters) of a road. Most of the monitors were sited at much greater distances to NO x 

### stationary sources than major roads. In general, monitors

were located at least 1 km from NO x 

### stationary sources, with over ha lf of the monitors located at

distances > 5 km. Because there is potential for roadway emissions to affect concentrations at monitors sited close to major roads, each of the ambient monitors were further categorized based on the monitor distance from major roads. Three catego ries were identified, the first containing those

7 Major road types were defined as: primary limited access or interstate, primary US and State highways, secondary State and County, freeway ramp, or other ramps. Distance s were estimated for stationary sources within 10 km having emissions greater than 5 tons per year (tpy). See Appendix A-4 for details for the approach used.



### 78

### monitors sited at or within 20 meters, ( ≤ 20 m), those between 20 mete r and 100 meters (>20 m, <100 m), and those located at leas t 100 meters from a major road ( ≥ 100 m) (Figure 7-1).

Table 7-9. Distance of ambient monitors to th e nearest major sources in selected locations.

Distance (m) of monitors to nearest major road Distance (m) of monitor to nearest 5 ton per year (tpy) NO x stationary source Location n 1 min med max min med max Atlanta 4 134 505 809 656 7327 9847 Boston 21 7 70 337 142 5363 9988 Chicago 12 2 93 738 411 7277 9994 Cleveland 4 2 134 187 956 7278 9884 Colorado Springs 6 79 180 386 782 6340 9933 Denver 7 18 65 748 910 5904 9979 Detroit 3 339 393 415 321 7549 9997 El Paso 7 33 128 718 119 6085 9991 Jacksonville 1 144 708 5720 9558 Las Vegas 10 1 181 914 3837 7237 9950 Los Angeles 43 1 89 570 140 6165 9991 Miami 4 15 55 103 1323 7611 9117 New York 26 6 119 508 103 6467 9983 Philadelphia 10 45 167 630 231 5689 9982 Phoenix 7 7 141 433 833 6355 9890 Provo 1 353 1214 8178 9433 St. Louis 13 5 97 421 396 7120 9990 Washington DC 16 14 83 338 288 6254 9973 Notes: 1 n is the number of monitors operating in a particu lar location between 1995 and 2006. The min, med, and max represent the minimum, median, and maximum percent iles of the distribution for the distance in meters (m) of the monitor to the nearby sources. Moni tors >1km from a major road and monitors having no stationary sources within 10 km are not included in this distribution. Individual monitor distances and stationary source emissions within 10 km is provided in Appendix A-4.



### 79< 20 m to < 100 m ≥ 100 m ≤ 20 m



## + +

+

+ + ambient monitor location major road 

## +

+

+ + +

< 20 m to < 100 m ≥ 100 m ≤ 20 m



## + +

+

+ + ambient monitor location major road 

## +

+

+ + +

Figure 7-1. Illustration of three roadway dist ance categories used to characterize ambient monitors in the Air Quality Characterization.

7.2.4 Estimation of On-Road Concentrat ions using Ambient Concentrations Since mobile sources can account for a large part of personal expos ures to ambient NO 2 

### in some individuals, the potential impact of roadway levels of NO 2 

### was evaluated. A strong relationship has been reported between NO 2 

### levels measured on roadways and NO 2 

### measured at increasing distance from the road. This relationshi p has been described previously (e.g., Cape et al., 2004) using an exponential decay equation of the form:

equation (7-1) kx vbx 

### eCCC − 

### += where,

C

x 

### = NO 2 

### concentration at a given distance ( x ) from a roadway (ppb)

C

b 

### = NO 2 

### concentration (ppb) at a distance fr om a roadway, not directly influenced by road or non-road source emissions

C

v 

### = NO 2 

### concentration contribution from vehicles on a roadway (ppb)

k

= Combined formation/decay constant describing NO 2 

### with perpendicular distance from roadway (meters -1 

### )

x

= Distance from roadway (meters)

80

### Based on the findings of several resear chers, much of the decline in NO 2 

### concentrations

with distance from the road has been shown to occur within the first few meters (approximately 90% within a 10 meter distance), returning to ne ar ambient levels between 200 to 500 meters (Rodes and Holland, 1981; Bell and Ashenden, 1997; Gilbert et al., 2003; Pl eijel et al., 2004). At a distance of 0 meter, referred to here as on-road , the equation reduces to the sum of the nonsource influenced NO 2 

### concentration and the concentration contribution expected from vehicle emissions on the roadway using

equation (7-2) 

### )1( mCC

ar



### += where,

C r 

### = 1-hour on-road NO 2 

### concentration (ppb)

C

a 

### = 1-hour ambient monitoring NO 2 

### concentration (ppb) either as is or adjusted to just meet the current or alternative standards

m = Ratio derived from estimates of C v 

### / C b 

### (from equation (7-1))

and assuming that C a 

### = C b 

### . 8



### To estimate on-road NO 2 

### levels as a function of the level recorded at ambient monitors at a distance from a roadway, empirical data from published scientific literature were used. A

literature review was conducted to id entify published studies containing NO 2 

### concentrations on roadways and at varying distances from roadways. Relevant data identified from this literature review were used to estimate the ratio ( m ) of the on-road vehicle concentrations ( C v 

### ) to NO 2 

### concentration at a distance from a roadway ( C b 

### ) (equation 7-1), generating a distribution of values for use in estimating on-road concentra tions from the ambient monitor concentrations (Table 7-10). The distribution of derived m ratios were evaluated for possible stratification using potential influential factors re ported in the collec tion of studies, the number of values of m available for potential groupings, and how the data were to be applied to the ambient monitoring

8 Note that C a may differ from C b since C a could include the influence of on-road as well as non-road sources. However, it is expected that for mo st monitors sited at a distance of ≥ 100 from a major road, the influence of onroad emissions would be neglibible so that C a ≅ C b .



### 81

### data. In general, categ orizing the data based on summer and not summer seasons were determined appropriate, containing 21 and 19 samples, respectively. Then, on-road NO 2 

### concentrations are estimated by probabilistically applying the distribution of adjustment factors (1+ m ) to the ambient monitor concentrations used in this study that were sited at distances ≥ 100 m of a major road. See Appendix A, sect ion 8 for a detailed explanation of the m ratio derivation

and the literature sources used.

Table 7-10. Derived C v 

### / C b 

### ratios ( m ) for two season groups used for adjusting ambient NO 2 

### concentrations to simulate on-road NO 2 

### concentrations. C v

/ C b ratios ( m )

Summer Not Summer 0.49 0.22 0.51 0.25 0.52 0.36 0.67 0.36 0.70 0.42 0.74 0.47 0.75 0.58 0.78 0.59 0.78 0.64 0.79 0.75 0.90 0.78 0.92 0.79 0.93 0.79 0.94 0.82 1.13 0.86 1.19 1.08 1.21 1.14 1.32 1.50 1.95 1.54 2.43 2.45 2.70



### Theoretically, NO 2 

### concentrations can increase at a distance from the road due to

chemical interaction of NO x 

### with O 3 

### , the magnitude of which can be driven by certain meteorological conditions (e.g., wind direction). As such, the maximum NO 2 

### concentration may

not occur on the road but at a distance from the road. However, there are two important components of this estimation procedure that need consideration. First, the relationship developed from the peer-reviewed NO 2 

### roadway and near-road measurement studies was used to

82

### estimate NO 2 

### concentrations that occur on the road and not used to estimate NO 2 

### concentrations

that could occur at a distance from the road. If there are peak concentrations at a given distance from a roadway that occur frequently in a location, the ambient monitors located within 20 m or 100 m of a road would capture th ese concentrations, where such monitors are available in a location. Second, since there is potential for monitors that ar e sited near roadways to be influenced by vehicle emissions and equation (7-2) assumes the ambient concentration is approximating NO 2 

### concentrations not directly influenced by the roadway, the ambient monitors <100 m were not used for estimating the on-road NO 2 

### concentrations in this analysis. The uncertainty regarding any additi onal issues or assumptions and the potential effect on exposure

estimates are discussed in section 7.4.

To estimate on-road NO 2 

### concentrations, each monitoring site was randomly assigned one on-road adjustment factor (1+ m ) for summer months and one for non-summer months from the derived empirical distribution. On-road adju stment factors were assigned randomly because we expect the empirical relationship between C v 

### and C b 

### to vary from place to place and we do not have sufficient information to match specific ra tios with any of the locations simulated in this assessment. Hourly NO 2 

### levels were estimated for each site -year of data in a location using equation (7-2) and the randomly assigned on-road adjustment factors. The process was simulated 100 times for each site-year of hourly data. For example, the Boston CMSA location

had 210 random selections from the on-road distri butions applied independen tly to the total siteyears of data (105). Following 100 simulations , a total of 10,500 site-y ears of data were generated using this procedure (along with 21,000 randomly assigned on-road values selected from the appropriate empirical distribution).

7.2.5 Air Quality Concentration Metrics For each of the four air quality characteriza tion scenarios considered, two concentration metrics were calculated, in cluding the annual average NO 2 

### concentrations for each site-year and the number of exceedances of the potential health e ffect benchmark levels. To characterize this relationship and to estimat e the number of exceedances of the potential hea lth effect benchmarks in specific locations, several possible models we re explored (i.e., exponential regression, logistic regression, a regression assuming a Poisson distribu tion, and an empirical model). An empirical model, employing the annual average and hourly NO 2 

### concentrations, was chosen to avoid some

83

### of the difficulties in extrapolat ing outside the range of the measurement data. In addition, an empirical model could be used for any averag ing time of interest. A detailed discussion justifying the selection of the em pirical approach rather than using a regression approach is provided in Appendix A, section 6. Using Figure 7-1 as an illustrative example of a location, assume that for a recent air quality monitoring year-group (2001-2003) there ar e an equal number of valid monitoring years (i.e., 3 years in this example) at each monitoring site indicated in the figure. In total there would be 27 site-years of data (9 monitors by 3 y ears) and, when separated into the major road categories, there would be 6, 15, and 6 site-y ears of ambient concentration data at ≤ 20 m, < 20 and < 100 m, ≥ 100 m from a major road, respectively. In the first scenario, the air quality is analyzed without adjustment, giving mean annual average concentrations based on averaging the 6, 15, and 6 annual average NO 2 

### concentrations within each resp ective monitor-to-road distance category. Median annual average concentrations are also provided to represent the central tendency and are the median concentration values of the 6, 15, and 6 site-years of data. Due to the limited number of site-years of data fo r each of the road dist ance categories, the 98 th 

### and 99 th 

### percentile values are essentially the same, i.e., the maximum site-year annual average concentration. The same approach was used fo r the counts of exceedances in a year, reporting each the central tendency values and the lo wer and upper percentiles for each location. In the second scenario, air quality metrics for the estimated on-road NO 2 

### concentrations were generated in a similar manner. The con centration distributions for the annual average

concentrations and the distributions for the numbe r of exceedances of shor t-term potential health

effect benchmark levels were calculated for each location and year-group. In using the hypothetical location described a bove (Figure 7-1) for years 2001-2003, 600 site-years of hourly

on-road concentrations were simu lated (6 site-years by 100 simulations each). Mean (average of the 600 site-year values) and me dian (the average of the 300 th 

### and 301 st 

### site-year values) are reported to represent the central tendency of each air quality metric. Since there were multiple site-years and numerous simulations performed at each location using all valid site-years of data, results for the upper percen tiles also included the 98 th 

### and 99 th 

### percentiles of the distribution.

The 98 th 

### and 99 th 

### percentiles were the 588 th 

### and the 594 th 

### highest site-year values, respectively, of the 600 calculated and ranked values. Roadways with high vehicle densities are likely better represented by on-road concentration estimates at these upper tails of the distribution.

84

## 7.3 AIR QUALITY AND HEALTH RISK CHARACTERIZATION

RESULTS

7.3.1 Ambient Air Quality (As Is) As described earlier, this first scenario analyzing the as is air quality is based purely on the measurement data. The air quality data obtain ed from AQS were first separated into two sixyear groups, one representing historical data (1 995-2000) and the other re presenting more recent data (2001-2006). The two broad six-year groups of data were compared using each location’s distribution of annual averag e concentrations and the total number of 1-hour exceedances of the potential health effect benchmark levels. Briefly, annual average concentrations were about 15% higher for the historical data when compared with the more recent data using corresponding locations. In general, concentrations were about 20-25% higher at monitors sited <100 m from a major road considering either six-year group of data. While the exceedances of potential health

effect benchmark levels were limited to a fe w locations, in general, a greater number of exceedances were observed using the historical data set and at locations sited <100 m from a major road when compared with the recent air quality. Detailed descriptive statistics regarding

concentration distributions for all locations, monitoring sites, and all monitoring years are provided in the Appendix A, section 5. Detailed analyses were performed using the recent as is ambient monitoring data. As described in section 6.2.1, to remain consistent wi th the planned analysis of alternative standards (sections 7.3.3 and 7.3.4), the recent ambient monitoring data were se parated into two three-year groups, 2001-2003 and 2004-2006. A summary of th e descriptive statistics for the annual average ambient NO 2 

### concentrations at each selected location for years 2001-2003 and 2004- 2006 are provided in Tables 7-11, 7-12 and 7-13 for monitors sited ≥ 100 m, 20 m to <100 m, and

≤ 20 m from a major road, respectiv ely. None of the locations contained a measured exceedance of the current annual average standard of 0.053 ppm at any monitor, although Los Angeles and New York had at least one annual average concentration ≥ 40 ppb during 2001-2003. There were

a fewer number of locations with monitors sited <100 m of a major road, however in most of the locations where comparative monitoring data were available, the annual average concentrations were greater at the monitors within 100 m of a major road (in 33 of 42 possible location/yeargroup combinations) when compared with monitors ≥ 100 m of a major road. Annual average

85

### concentrations measured at monitors located ≤ 20 m from a major road were also frequently greater than those measured at monitors si ted <20 m and >100 m fo r most location/year combinations (68%). Where concentrations were greater at the monitors <100 m from a major road, the concentrations were on average about 40-65% higher when compared with the more distant monitors in each corresponding location. A comparison of the three-year groups of data

within each monitor si te-group indicates that the more r ecent monitoring concentrations (i.e., 2004-2006) were frequently lower, on average by about 13-19%. These average trends in concentration across year-group and monitor site group were ge nerally observed across all percentiles of the distribution.

Table 7-11. Monitoring site-years and annual average NO 2 

### concentrations, using recent air quality data ( as is ) and monitors sited ≥ 100 m of a major road. 2001-2003 2004-2006 Annual Mean NO 2 (ppb) 2 Annual Mean NO 2 (ppb) 2 Location SiteYears 1 mean min med p95 p98 p99 SiteYears 1 mean min med p95 p98 p99 Atlanta 14 12 4 15 23 23 23 15 11 3 14 18 18 18 Boston 6 10 5 11 12 12 12 8 9 7 9 10 10 10 Chicago 9 22 17 20 28 28 28 8 19 16 18 24 24 24 Cleveland 3 18 17 17 19 19 19 ND Denver 2 24 21 24 26 26 26 3 20 18 20 21 21 21 Detroit 6 21 19 20 23 23 23 6 17 14 17 20 20 20 El Paso 12 15 10 16 18 18 18 12 14 8 15 18 18 18 Jacksonville 2 14 14 14 15 15 15 2 14 13 14 14 14 14 Las Vegas 16 10 2 7 22 22 22 11 9 1 6 20 20 20 Los Angeles 51 22 5 24 34 36 37 54 18 5 18 27 31 31 Miami 6 9 7 9 10 10 10 4 8 7 8 8 8 8 New York 26 20 11 18 28 31 31 22 19 10 20 25 27 27 Philadelphia 14 20 15 18 28 28 28 12 17 14 16 25 25 25 Phoenix 5 27 22 29 29 29 29 9 24 21 24 26 26 26 Provo 3 24 22 24 25 25 25 3 24 21 22 29 29 29 St. Louis 9 17 14 17 21 21 21 4 15 12 14 18 18 18 Washington DC 18 18 9 21 25 25 25 17 15 7 16 22 22 22 Other MSA 612 13 1 13 21 22 24 565 11 1 11 19 21 23 Other Not MSA 127 7 1 6 14 15 16 116 7 1 6 15 16 16 Notes: 1 The average number of monitors operating per year with in the three-year group can be estimated by dividing the number of site-years by 3. 2 Annual means for each monitor were first calculated bas ed on all hourly values in a year. Then the mean of the annual means was estimated as the sum of all the annual me ans in a particular location divided by the number of site-years across the monitoring period. The min, med, p95, p98, p99 represent the minimum, median, 95 th , 98 th

, and 99 th percentiles of the distribution for the annual means in the selected three-year group. ND no available monitoring data.



### 86

### Table 7-12. Monitoring site-years and annual average NO 2 

### concentrations, using recent air quality data ( as is ) and monitors sited >20 m and <100 m of a major road. 2001-2003 2004-2006 Annual Mean NO 2 (ppb) 2 Annual Mean NO 2 (ppb) 2 Location SiteYears 1 mean min med p95 p98 p99 SiteYears 1 mean min med p95 p98 p99 Atlanta ND ND Boston 14 17 9 19 25 25 25 11 15 10 16 19 19 19 Chicago 6 31 28 31 32 32 32 6 29 28 29 31 31 31 Cleveland ND 2 15 14 15 17 17 17 Denver ND ND Detroit ND ND El Paso 3 21 20 21 22 22 22 3 15 13 13 18 18 18 Jacksonville ND ND Las Vegas 3 6 3 6 9 9 9 ND Los Angeles 35 24 4 24 40 41 41 22 25 9 27 33 34 34 Miami 3 14 13 14 16 16 16 2 13 13 13 14 14 14 New York 13 31 21 30 40 40 40 11 28 18 29 36 36 36 Philadelphia 7 24 19 24 30 30 30 6 22 18 22 26 26 26 Phoenix 2 23 22 23 24 24 24 3 19 19 19 20 20 20 Provo ND ND St. Louis 11 14 9 12 25 25 25 8 12 8 10 22 22 22 Washington DC 10 20 14 22 26 26 26 12 18 13 18 24 24 24 Other MSA ND ND Other Not MSA ND ND Notes: 1 The average number of monitors operating per year with in the three-year group can be estimated by dividing the number of site-years by 3. 2 Annual means for each monitor were first calculated bas ed on all hourly values in a year. Then the mean of the annual means was estimated as the sum of all the annual me ans in a particular location divided by the number of site-years across the monitoring period. The min, med, p95, p98, p99 represent the minimum, median, 95 th , 98 th

, and 99 th percentiles of the distribution for the annual means. ND no available monitoring data.



### 87

### Table 7-13. Monitoring site-years and annual average NO 2 

### concentrations, using recent air quality data ( as is ) and monitors sited ≤ 20 m of a major road. 2001-2003 2004-2006 Annual Mean NO 2 (ppb) 2 Annual Mean NO 2 (ppb) 2 Location SiteYears 1 mean min med p95 p98 p99 SiteYears 1 mean min med p95 p98 p99 Atlanta ND ND Boston 5 21 7 23 30 30 30 3 24 23 23 25 25 25 Chicago 4 22 22 22 24 24 24 3 19 18 20 20 20 20 Cleveland 3 23 22 22 24 24 24 3 21 18 22 22 22 22 Denver 2 36 35 36 37 37 37 3 28 27 28 29 29 29 Detroit ND ND El Paso ND ND Jacksonville ND ND Las Vegas 3 22 21 22 23 23 23 2 19 19 19 20 20 20 Los Angeles 9 30 23 29 37 37 37 6 27 20 29 31 31 31 Miami 3 6 6 6 7 7 7 2 6 6 6 6 6 6 New York 7 28 25 28 30 30 30 2 28 27 28 28 28 28 Philadelphia ND ND Phoenix 3 35 34 35 37 37 37 5 23 11 31 32 32 32 Provo ND ND St. Louis 6 18 16 19 20 20 20 5 16 15 16 17 17 17 Washington DC 4 23 20 24 26 26 26 5 19 14 18 23 23 23 Other MSA ND ND Other Not MSA ND ND Notes: 1 The average number of monitors operating per year with in the three-year group can be estimated by dividing the number of site-years by 3. 2 Annual means for each monitor were first calculated bas ed on all hourly values in a year. Then the mean of the annual means was estimated as the sum of all the annual me ans in a particular location divided by the number of site-years across the monitoring period. The min, med, p95, p98, p99 represent the minimum, median, 95 th , 98 th

, and 99 th percentiles of the distribution for the annual means. ND no available monitoring data.



### The number of daily maximum exceedances of the potential health effect benchmark levels (100, 150, 200, 250, and 300 ppb NO 2 

### for 1-hour) is shown in Tables 7-14 through 7-19 using recent as is ambient monitoring data and consideri ng the three road distance categories. As a reminder, these exceedance data are based on whether the daily maximum concentration at a monitor was above the benchmark level; a single monitor value of 10 would represent ten days in the year where there was at least one 1-hour exceedance of the benchmark level. Since there are multiple monitors and monitoring years in a location, a distribution of these values can be generated. The mean represents the central tend ency value for each location; it is the average number of days per year that there were daily maximum 1-hour NO 2 

### concentration exceedances observed over the three y ear period in a location. The upper percentiles (98 th 

### and 99 th 

### ) represent

88

### an upper-level estimate of the number of days in one year there were daily maximum 1-hour concentration exceedances at a particular monito r (or possibly more than one) in ae location. In general, the number of daily maximum 1-hour benchmark exceedances was low across all locations and considering both three-year groups of the as is air quality and monitors located

≥ 100 m from a major road (Tables 7-14 and 7-15). The average number of exceedances of the 100 ppb 1-hour NO 2 

### concentration across each location was typically zero to a few, with one location (Provo) containing an average of fourt een exceedances. Considering that there are 365 days in a year, this many exceedances amounts to a small fraction of the year (at most 4%) with an exceedance of the lowest potential health eff ect benchmark level at a few locations. For the locations with greater than one annual averag e exceedance, the numbers were primarily driven by a single site-year of data. Fo r example, Detroit contained a la rgest number of exceedances of 200 ppb (a maximum of 4 days in the year) for as is air quality data from years 2001-2003 (Table 7-14). All of these exceedances occu rred at one monitor (ID 2616300192) during one year (2002). Provo contained that greatest num ber of daily maximum exceedances of both the 100 and 150 ppb benchmark level (Table 7-15), asso ciated with measurements from one monitor (ID 4904900021) in 2006. However, when consideri ng the collective locations (both the named locations and aggregated), daily maximum exceedan ces of 150 and above were rarely occurred. When considering monitors sited <20 m and >100 m of a major road (Table 7-16 and 7- 17), only a few locations contained exceedances of the potential health effect benchmark levels, driven mainly by observations from one or two monitors. For example, in Los Angeles a single year (2001) for two monitors (IDs 060370030 and 060371103) were re sponsible for many of the observed exceedances of 100 ppb in the 2001-2003 year-group (each had 31 exceedances in a year). Each of these monito rs are located about 50 m from a major road along with around 40 stationary sources located within 10 km of this monitor, over half of which contained estimated emissions of less than 15 tons per year (tpy) (Appendix A, Table A-8). When considering the higher benchmark levels, nearly all lo cations had no daily maximum 1-hour NO 2 

### concentrations

≥ 150 ppb. At monitoring locations ≤ 20 m to major roads, about half of the locations contained a non-zero average number of daily maximum exceed ances of 100 ppb (Tables 7-18 and 7-19). Of those, Denver, Los Angeles, and Phoenix contai ned the highest average number of exceedances given either the 2001-2003 or 2004-2006 year-groups. On average, the percent of days in a year

89

### 90 with a daily maximum exceedance at these locati ons ranged from about 1-2%. As far as the higher benchmark levels, nearly all lo cations had no daily maximum 1-hour NO 2 

### concentrations

≥ 150 ppb.Table 7-14. Number of daily maximum exceedances of short-term (1-hour) potential health effect benchmarks in a year, using 2001-2003recent NO2air quality (as is) and monitors sited≥100 m of a major road.Exceedances of 100 ppb1Exceedances of 150 ppb1Exceedances of 200 ppb1Exceedances of 250 ppb1Exceedances of 300 ppb1Locationmeanminmedp98p99meanminmedp98p99meanminmedp98p99meanminmedp98p99meanminmedp98p99Atlanta0003300011000000000000000Boston0000000000000000000000000Chicago0002200000000000000000000Cleveland0001100000000000000000000Denver2122200000000000000000000Detroit3027710155100441004400022El Paso0001100011000000000000000Jacksonville1011110111101111011100000Las Vegas0001100000000000000000000Los Angeles401171800018000040000000000Miami0000000000000000000000000New York0003300000000000000000000Philadelphia0001100011000110001100000Phoenix0000000000000000000000000Provo0000000000000000000000000St. Louis0001100000000000000000000WashingtonDC0001100000000000000000000Other MSA0001400000000000000000000Other NotMSA0005500011000010000000000Notes:1The mean number of exceedances represents the sum of the daily maximum exceedances occurring at all monitors in a particular location divided bythe number of site-years across the monitoring period. The min, med, p98, and p99 represent the minimum, median, 98th, and 99thpercentiles of thedistribution for the number of daily maximum exceedances in any one year within the monitoring period.August 2008 - Draft91Table 7-15. Number of daily maximum exceedances of short-term (1-hour) potential health effect benchmarks in a year, using 2004-2006recent NO2air quality (as is) and monitors sited≥100 m of a major road.Exceedances of 100 ppb1Exceedances of 150 ppb1Exceedances of 200 ppb1Exceedances of 250 ppb1Exceedances of 300 ppb1Locationmeanminmedp98p99meanminmedp98p99meanminmedp98p99meanminmedp98p99meanminmedp98p99Atlanta0001100000000000000000000Boston0000000000000000000000000Chicago0000000000000000000000000ClevelandNDDenver1003300000000000000000000Detroit0000000000000000000000000El Paso0000000000000000000000000Jacksonville2023310122101110000000000Las Vegas0000000000000000000000000Los Angeles0002200000000000000000000Miami0000000000000000000000000New York0002200000000000000000000Philadelphia0000000000000000000000000Phoenix0001100000000000000000000Provo140043437002020000000000000000St. Louis0000000000000000000000000WashingtonDC0002200000000000000000000Other MSA0001100000000000000000000Other NotMSA0002300002000010000100000Notes:1The mean number of exceedances represents the sum of the daily maximum exceedances occurring at all monitors in a particular location divided bythe number of site-years across the monitoring period. The min, med, p98, and p99 represent the minimum, median, 98th, and 99thpercentiles of thedistribution for the number of daily maximum exceedances in any one year within the monitoring period.ND no available monitoring data.August 2008 - Draft92Table 7-16. Number of daily maximum exceedances of short-term (1-hour) potential health effect benchmarks in a year, using 2001-2003recent NO2air quality (as is) and monitors sited >20 m and <100 m of a major road.Exceedances of 100 ppb1Exceedances of 150 ppb1Exceedances of 200 ppb1Exceedances of 250 ppb1Exceedances of 300 ppb1Locationmeanminmedp98p99meanminmedp98p99meanminmedp98p99meanminmedp98p99meanminmedp98p99AtlantaNDBoston0000000000000000000000000Chicago2016600000000000000000000ClevelandNDDenverNDDetroitNDEl Paso2023300000000000000000000JacksonvilleNDLas Vegas0000000000000000000000000Los Angeles601313100022000110001100000Miami0000000000000000000000000New York1008800000000000000000000Philadelphia0001100000000000000000000Phoenix0000000000000000000000000ProvoNDSt. Louis0000000000000000000000000WashingtonDC0000000000000000000000000Other MSANDOther NotMSANDNotes:1The mean number of daily maximum exceedances represents the sum ofthe daily maximum exceedances occurring at all monitors ina particularlocation divided by the number of site-years across the monitoring period. The min, med, p98, and p99 represent the minimum, median, 98th, and 99thpercentiles of the distribution for the number of daily maximum exceedances in anyone year within the monitoring period.ND no available monitoring data.August 2008 - Draft93Table 7-17. Number of daily maximum exceedances of short-term (1-hour) potential health effect benchmarks in a year, using 2004-2006recent NO2air quality (as is) and monitors sited >20 m and <100 m of a major road.Exceedances of 100 ppb1Exceedances of 150 ppb1Exceedances of 200 ppb1Exceedances of 250 ppb1Exceedances of 300 ppb1Locationmeanminmedp98p99meanminmedp98p99meanminmedp98p99meanminmedp98p99meanminmedp98p99AtlantaNDBoston0001100000000000000000000Chicago1015500000000000000000000Cleveland1011110111000000000000000DenverNDDetroitNDEl Paso0000000000000000000000000JacksonvilleNDLas VegasNDLos Angeles1019900011000000000000000Miami1012210111101111011110111New York1013300011000000000000000Philadelphia1011100000000000000000000Phoenix0000000000000000000000000ProvoNDSt. Louis0001100000000000000000000WashingtonDC0001100000000000000000000Other MSANDOther NotMSANDNotes:1The mean number of exceedances represents the sum of the daily maximum exceedances occurring at all monitors in a particular location divided bythe number of site-years across the monitoring period. The min, med, p98, and p99 represent the minimum, median, 98th, and 99thpercentiles of thedistribution for the number of daily maximum exceedances in any one year within the monitoring period.ND no available monitoring data.August 2008 - Draft94Table 7-18. Number of daily maximum exceedances of short-term (1-hour) potential health effect benchmarks in a year, using 2001-2003recent NO2air quality (as is) and monitors sited≤20 m from a major road.Exceedances of 100 ppb1Exceedances of 150 ppb1Exceedances of 200 ppb1Exceedances of 250 ppb1Exceedances of 300 ppb1Locationmeanminmedp98p99meanminmedp98p99meanminmedp98p99meanminmedp98p99meanminmedp98p99AtlantaNDBoston0001100000000000000000000Chicago0000000000000000000000000Cleveland0000000000000000000000000Denver737101011111000000000000000DetroitNDEl PasoNDJacksonvilleNDLas Vegas1002200000000000000000000Los Angeles6069900000000000000000000Miami0000000000000000000000000New York1011100000000000000000000PhiladelphiaNDPhoenix2114400000000000000000000ProvoNDSt. Louis0001100000000000000000000WashingtonDC0001100000000000000000000Other MSANDOther NotMSANDNotes:1The mean number of exceedances represents the sum of the daily maximum exceedances occurring at all monitors in a particular location divided bythe number of site-years across the monitoring period. The min, med, p98, and p99 represent the minimum, median, 98th, and 99thpercentiles of thedistribution for the number of daily maximum exceedances in any one year within the monitoring period.ND no available monitoring data.August 2008 - Draft95August 2008 - Draft96Table 7-19. Number of daily maximum exceedances of short-term (1-hour) potential health effect benchmarks in a year, using 2004-2006recent NO2air quality (as is) and monitors sited≤20 m from a major road.Exceedances of 100 ppb1Exceedances of 150 ppb1Exceedances of 200 ppb1Exceedances of 250 ppb1Exceedances of 300 ppb1Locationmeanminmedp98p99meanminmedp98p99meanminmedp98p99meanminmedp98p99meanminmedp98p99AtlantaNDBoston0000000000000000000000000Chicago0000000000000000000000000Cleveland0000000000000000000000000Denver2113300011000000000000000DetroitNDEl PasoNDJacksonvilleNDLas Vegas0000000000000000000000000Los Angeles3028800000000000000000000Miami0000000000000000000000000New York1011100000000000000000000PhiladelphiaNDPhoenix2023300000000000000000000ProvoNDSt. Louis0000000000000000000000000WashingtonDC0000000000000000000000000OtherMSA/CMSANDOther NotMSANDNotes:1The mean number of exceedances represents the sum of the daily maximum exceedances occurring at all monitors in a particular location divided bythe number of site-years across the monitoring period. The min, med, p98, and p99 represent the minimum, median, 98th, and 99thpercentiles of thedistribution for the number of daily maximum exceedances in any one year within the monitoring period.ND no available monitoring data.

### 7.3.2 On-Road Concentrations Derived Fr om Ambient Air Quality (As Is) Descriptive statistics for estimated on-road NO 2 

### concentrations for each recent three-year group are presented in Table 7-20. These estimate d on-road concentrations were generated using the simulation procedure described above (sec tion 7.2.3) and represent the second air quality scenario. For the 17 named locations, the calcul ation only used monitors sited at a distance ≥ 100 m of a major road. The tw o aggregate locations (i.e., Other CMSA and Not MSA ) did not have estimated monitor distances to major roads theref ore all monitoring data available were used to

estimate the distribution of on-road NO 2 

### concentrations. The simulated on-road annual average NO 2 

### concentrations are, on average, 80% higher than the respective ambient levels at distances ≥ 100 m from a road. This falls within the range

of on-road to distant monitor concentration ratios reported in the ISA (about 2-fold higher concentrations on-roads) (ISA, section 2.5.4). De nver, Los Angeles, and Phoenix were predicted to have the highest on-road annual average NO 2 

### levels. This is a direct result of these locations already containing some of the highest as-is NO 2 

### concentrations prior to the on-road simulation (see Table 7-11). Estimated on-road annual aver age concentrations were greater by about 10% when using the 2001-2003 monitoring data co mpared with the 2004-2006 monitoring data.

The median of the simulated concentration estimates for Los Angeles were compared

with NO 2 

### measurements provided by West erdahl et al. (2005) for arte rial roads and freeways in the same general location during spring 2003. A lthough the averaging time is not exactly the same, comparison of the medians is judged to be appropriate. 9 

### The estimated median on-road concentration for 2001-2003 is 41 ppb which falls w ithin the range of 31 ppb to 55 ppb identified by Westerdahl et al. (2005). On average, most locations are predicte d to have fewer than 3 daily maximum

exceedances of 1-hour NO 2 

### concentrations per year given the 200 ppb potential health effect benchmark, while the median frequency of exceedances in most locations is estimated to be 1 or less per year (Tables 7-21 and 7-22). When considering the lowest 1-hour benchmark of 100 ppb, most locations (30 out of 38 location/year-g roups) were estimated to have fewer than 50 daily maximum exceedances per year, on average. There are generally fewer predicted mean

9 Table 7-12 considers annual average of hourly measurements, while Westerdahl et al. (2005) reported timeaveraged concentrations of 2-4 hours. Over time, the mean of 2-4 hour aver ages will be similar to the mean of hourly concentrations, with the main difference being in the variability (and hence the various percentiles of the distribution outside the central tendency).



### August 2008 - Draft 97

### exceedances of the potential health effect benchmark levels when considering the 2004-2006 recent air quality compared with the 2001-2003 air qua lity. Areas with a relatively high number of estimated exceedances (e.g., Provo, as described in section 7.3.1) are likely influenced by the presence of a small number of monitors and one or a few exceptional site -years where there were unusually high concentrations at the upper percentil es of the concentratio n distribution. When considering higher benchmark levels (i.e., 250 ppb), the mean number of estimated daily

maximum exceedances was none or one for most locations. The upper percentiles (98 th 

### and 99 th 

### ) for estimated number of daily maximum exceedances of 100 ppb in most locations were under 200 per year, outside of Denver, Los Angeles, Phoenix, and Provo. In general, ther e were fewer estimated exceedances using the 2001-2003 air quality compared with the 2004-2006 data. Most locations had >100 estimated daily maximum 1-hour exceedances of 150 ppb per year at the 98 th 

### and 99 th 

### percentiles and as expected, the frequency of benchmark exceedances at all locations was lower when considering any of the higher benchmark levels (i.e., 200, 250, 300 ppb, 1-hour average) compared with the lower benchmarks. For example, it is estimat ed that between about 10 and 30 daily maximum exceedances of 200 ppb could occur in a year in mo st locations as an upper bound estimate. The estimated upper percentile number of exceed ances of 250 and 300 ppb 1-hour were generally

less than 10 in most locations.

August 2008 - Draft 98

### August 2008 - Draft 99 Table 7-20. Estimated annual average NO 2 

### concentrations on-roads, using recent air quality data ( as is ) and an on-road adjustment factor. 2001-2003 2004-2006 Annual Mean NO 2 (ppb) 2 Annual Mean NO 2 (ppb) 2 Location SiteYears 1 mean min med p95 p98 p99 SiteYears 1 mean min med p95 p98 p99 Atlanta 1400 22 5 24 41 47 53 1500 20 4 22 36 40 42 Boston 600 17 7 18 27 29 30 800 16 9 15 22 24 24 Chicago 900 39 21 37 60 65 68 800 35 20 33 51 57 60 Cleveland 300 32 22 32 42 43 45 ND Denver 200 42 27 40 61 63 64 300 36 23 36 50 51 53 Detroit 600 37 24 36 51 54 57 600 31 18 30 43 45 47 El Paso 1200 27 13 27 40 43 44 1200 25 10 25 38 42 43 Jacksonville 200 26 18 26 34 36 37 200 24 17 23 33 36 37 Las Vegas 1600 19 3 14 44 48 51 1100 16 2 11 40 44 46 Los Angeles 5100 41 6 40 69 77 82 5400 33 6 32 55 60 65 Miami 600 16 9 15 23 24 25 400 14 9 13 19 19 20 New York 2600 36 14 34 58 65 73 2200 35 12 35 54 58 61 Philadelphia 1400 36 18 33 56 64 66 1200 31 18 30 45 51 59 Phoenix 500 49 28 47 70 72 77 900 43 26 42 59 64 65 Provo 300 43 28 41 58 61 64 300 43 26 41 68 70 71 St. Louis 900 31 18 30 43 48 50 400 27 16 26 38 41 42 Washington DC 1800 33 11 34 54 58 63 1700 28 9 28 46 51 52 Other MSA 61200 23 1 22 41 47 50 56500 20 1 20 36 41 45 Other Not MSA 12700 12 1 11 27 31 33 11600 12 1 10 27 31 33 Notes: 1 The average number of monitors operating per year with in the three-year group can be estimated by dividing the number of site-years by 300. 2 Annual means for each monitor were first calculated bas ed on all simulated hourly values in a year. Then the mean of the annual means was estimated as the sum of all the annual means in a particular location divided by the number of simulated site-years across the monitori ng period. The min, med, p95, p98, p99 represent the minimum, median, 95 th , 98 th , and 99 th percentiles of the distribution for the annual means. ND no available monitoring data.Table 7-21. Estimated number of daily maximum exceedances of short-term (1-hour) potential health effect benchmarks on-roads in ayear, using 2001-2003 recent NO2air quality (as is) and an on-road adjustment factor.Exceedances of 100 ppb1Exceedances of 150 ppb1Exceedances of 200 ppb1Exceedances of 250 ppb1Exceedances of 300 ppb1Locationmeanminmedp98p99meanminmedp98p99meanminmedp98p99meanminmedp98p99meanminmedp98p99Atlanta230513016940036511008130003400011Boston501364000035000110000000000Chicago520351801919026268200242900051200013Cleveland3102183102501303010010100003300011Denver898742422591705809430125261005500011Detroit41130130141905444630216182017710177El Paso320191361454012426000560001100001Jacksonville1307555610167101111011110111Las Vegas23041711944005462000890001100000Los Angeles7105723125117069410850041481001623000710Miami7025064100610000120000000000New York42028177201701556320024240006900012Philadelphia37019149172601496210011240003500011Phoenix101183280315160211312420017200004500000Provo61138248289900626410011110002200000St. Louis250121281393002837000560001100011WashingtonDC3601716920560046541009140001100000Other MSA16031051292002232000470001200000Other NotMSA4004362100915000250001200011Notes:1The mean number of exceedances represents the sum of the daily maximum exceedances occurring at all monitors in a particular location divided bythe number of simulated site-years across the monitoring period.The min, med, p98, and p99 represent the minimum, median, 98th, and 99thpercentilesof the distribution for the number of daily maximum exceedances in any one year within the monitoring period.August 2008 - Draft100August 2008 - Draft101Table 7-22. Estimated number of daily maximum exceedances of short-term (1-hour) potential health effect benchmarks on-roads inayear, using 2004-2006 recent NO2air quality (as is) and an on-road adjustment factor.Exceedances of 100 ppb1Exceedances of 150 ppb1Exceedances of 200 ppb1Exceedances of 250 ppb1Exceedances of 300 ppb1Locationmeanminmedp98p99meanminmedp98p99meanminmedp98p99meanminmedp98p99meanminmedp98p99Atlanta17021141202002627000670001100000Boston200182100011000000000000000Chicago3602014816150041531007180001200000ClevelandNDDenver632491901951004475220011140003300000Detroit2009901032002024000360000000000El Paso240121141433002023000440001100000Jacksonville1105485920256101441003310033Las Vegas15001331482005464000680000100000Los Angeles38025150169601435510011140002300011Miami601464600066000110000000000New York35023149171501404510013150003400011Philadelphia220101011302002022000330000000000Phoenix7705327529310015771100780001100000Provo51044160160170268701200444470043433004040St. Louis150576831002024000250000000000WashingtonDC21071191432002022000460001200000Other MSA1001791001001218000230000100000Other NotMSA4004365100713000250002200002Notes:1The mean number of exceedances represents the sum of the daily maximum exceedances occurring at all monitors in a particular location divided bythe number of simulated site-years across the monitoring period.The min, med, p98, and p99 represent the minimum, median, 98th, and 99thpercentilesof the distribution for the number of daily maximum exceedances in any one year within the monitoring period.ND no available monitoring data.

### 7.3.3 Ambient Air Quality Adjusted to Just Meet the Curren t and Alternative

Standards

As described in section 6.2, each of the curren t and alternative sta ndards were evaluated using the more recent air quality data set (i .e., 2001-2006) serving as the third air quality scenario. Analysis results are presented for a few selected locations, potential health effect benchmarks, and alternative standard levels, since there were a tota l of 10 air quality analyses (8 alternative standards, the current standard, and as is air quality), for each year-group of data (2001-2003 and 2004-2006), for each of the three monitor-to-road categories ( ≤ 20 m; <20 and <100m; and ≥ 100 m from a major road), and evaluated at five potential health effect benchmark levels (100, 150, 200, 250, 300 ppb 1-hour). All of the results for each location are provided in Appendix A, section 9, much of which is summ arized here in a series of key figures. Figures 7-2 and 7-3 illustrate the estimated mean number of daily maximum exceedances

of the three lowest benchmark concentrations (i.e., 100, 150, and 200 ppb) us ing air quality data adjusted to just meeting the current annual average standard for year-groups 2001-2003 and 2004-2006, respectively. The number of estimated daily maximum exceedances of 100 ppb generally ranges from ten to fifty, with pattern of exceedances based on monitor siting consistent

with that noted above for the as is data. In general, there we re a greater number of daily

maximum exceedances at the near road monitors compared with those sited ≥ 100 m from a major road, although a few monitors sited at ≥ 100 m from a major road contained more

estimated exceedances than the monitors sited within 20 m of a major road (e.g., Denver).

There were also differences in the estimates fo r each three-year group from what was expected. For example, a few of the locations had an esti mated daily maximum number of exceedances of 100 ppb that were slightly higher for the 2004- 2006 year-group when co mpared with the 2001- 2003 year-group (e.g., Detroit in Figures 7-2 and 7- 3). The estimated number of daily maximum exceedances of 150 and 200 ppb were much lower than that of 100 ppb, the mean number of exceedances was fewer than 20 for most location years and roadway-monitor groupings. Note that fifty-one of the 81 possible year-group and monitoring site data combinations at the 19 locations did not have any exceedances of the 20 0 ppb level when using air quality adjusted to just meeting the current standard.

August 2008 Draft 102

### Figure 7-4 presents the mean estimated number of daily maximum exceedances when considering the air quality adjusted to just meetin g the potential alternativ e standard levels, using Chicago as an example to illustrate the patterns in the estimated exceedances for two forms of the standard. These patterns presented for Ch icago apply to the othe r locations, with a few exceptions. As expected, the estimated number of daily maximum exceedances is lower for a

99 th 

### percentile form compared with each corresponding level using the 98 th 

### percentile form of alternative standard. In genera l, the number of estimated daily maximum exceedances of the potential health effect benchmark levels at moni toring sites < 100 m from a major road is greater

than the numbers estimated for monitors sited ≥ 100 m from a major road. This is what one would expect given the great er potential for vehicle em issions influencing ambient

concentrations at near road monitors. There we re also a slightly grea ter number of estimated daily maximum exceedances at the monitors sited ≤ 20 m compared with those sited between 20- 100 m. As expected, the number of exceedances of the potential he alth effect benchmark levels

decreases with decreasin g alternative standard level. Regardless of three-year group or monitoring group, an alternative standard leve l of 100 ppb tended to reduce the number of estimated exceedances of 100 ppb to either a few to none. Figure 7-5 presents mean estimated number of daily maximum exceedances of the 200 ppb concentration level for a few additional lo cations, Phoenix, Los Angeles, Washington DC, and St. Louis. Again, there are trends in these re sults that are consistent with that reported for the Chicago results, with few exceptions. For example, in St. Louis the estimated number of daily maximum exceedances at monitors located ≥ 100 m from a major road were greater than those estimated using the monitoring sites < 100 m fr om a major road. Also note that there were variable results when comparing year-groups acr oss the different locations within the monitor site-group; sometimes the year 2001-2003 cont ained greater numbers of exceedances when compared with 2004-2006 (e.g., St. Louis), and ot her times it did not (e.g. Los Angeles). However, the alternative standard levels of eith er 100 or 150 ppb at either percentile consistently reduced the mean number of daily maxi mum exceedances of 200 ppb to about zero. Tables 7-23, 7-24, and 7-25 summarize the annu al mean concentrations and estimated number of exceedances in a year given 2001-2003 ai r quality adjusted that just meets the 1-hour 100 ppb 98 th 

### percentile standard at monitors sited ≥ 100 m, >20 m and < 100 m, and ≤ 20 m from

August 2008 Draft 103

### August 2008 Draft 104 a major road, respectively. The tables provi de a more comprehensive comparison of the numbers of daily maximum exceedances for the lowe st potential health effect benchmarks (i.e., 100, 150, and 200 ppb), as well as providing upper percentile estimates for each of the parameters. The results for this particular year-g roup are provided to describe patterns within a given standard level, results were similar for the 2004-2006 air quality data. The complete results for all of the standard levels and y ear-groups of air qualit y, including the observed number of daily maximum exceedances ( as is air quality) are provided in Appendix A, section 9.

Most locations contained a mean of fewer th an 5 daily maximum exceedances of the 100 ppb concentration level, with upper percentile es timates ranging from the 5 to about 15. These results are comparably less than those estimated using air quality adjusted to just meeting the current standard (Figure 7-2). At potential he alth effect benchmark levels above 100 ppb, there were few estimated exceedances, particularly at and above the 200 ppb level, considering both the mean and the upper percentiles.

Tables 7-26 summarizes the obs erved and estimated mean numbers of exceedances of 100 ppb using the 2001-2003 as is air quality and air quality adju sted to just meet the current standard and the potential alternative 98 th 

### percentile standards at each location. The number of daily maximum exceedances for the as is air quality generally fe ll within the number of exceedances estimated using alternative 1-hour 98 th 

### percentile standards of 50 ppb and 100 ppb at each location. When the air quality was adjust ed to just meeting the current annual average standard, the estimated number of daily maximu m exceedances was generally near that estimated using the alternative 1-hour 98 th 

### percentile standard of 150 ppb at each location. In a similar manner, Table 7-27 summarizes the observed and estimated mean numbers of exceedances of 150 ppb 1-hour at each location. The numbe r of daily maximum exceedances using as is air quality in each location was most similar to that estimated using the alternative 1-hour 98 th 

### percentile standard of 50 ppb, while estimates using the air quality adjusted to just meeting the current standard again approached the estimate d numbers of exceedance using the alternative 1-

hour 98 th 

### percentile standard of 150 ppb at each location.Monitors Sited >=100 m from a Major Road0 25 50 75 100 125 150 175 200 225AtlantaBostonChicagoClevelandDenverDetroitEl PasoJacksonvilleLas VegasLos AngelesMiamiNew YorkPhiladelphiaPhoenixProvoSt. LouisWashington DCOther MSAOther Not MSA≥100 ppb≥150 ppb≥200 ppbMonitors Sited >20 m, <100 m from a Major Road0 25 50 75 100 125 150 175 200 225≥100 ppb≥150 ppb≥200 ppbNo dataNo dataNo dataNo dataNo dataNo dataNo dataNo dataMonitors Sited >=20 m from a Major Road0 25 50 75 100 125 150 175 200 225≥100 ppb≥150 ppb≥200 ppbNo dataNo dataNo dataNo dataNo dataNo dataNo dataNo dataEstimated mean number of daily maximum exceedances of 1-hour NO2concentrations of 100, 150, 200 ppbFigure 7-2. Estimated mean number of daily maximum exceedances of short-term (1-hour) potential health effect benchmarksin a year, using recent NO2 air quality (2001-2003) adjusted to just meeting the current annual standard (0.053 ppm). Leftgraph: monitors≥100m from a major road; Middle graph: monitors >20 m and <100 m from a major road; Right graph:monitors≤20 m from a major road.105Figure 7-3. Estimated mean number of daily maximum exceedances of short-term (1-hour) potential health effect benchmarksin a year, using recent NO2air quality (2004-2006) adjusted to just meeting the current annual standard (0.053 ppm). Leftgraph: monitors≥100m from a major road; Middle graph: monitors >20 m and <100 m from a major road; Right graph:monitors≤20 m from a major road).Monitors Sited >=100 m from a Major Road0 25 50 75 100 125 150 175 200 225AtlantaBostonChicagoClevelandDenverDetroitEl PasoJacksonvilleLas VegasLos AngelesMiamiNew YorkPhiladelphiaPhoenixProvoSt. LouisWashington DCOther MSAOther Not MSA≥100 ppb≥150 ppb≥200 ppbNo dataMonitors Sited >20 m, <100 m from a Major Road0 25 50 75 100 125 150 175 200 225≥100 ppb≥150 ppb≥200 ppbNo dataNo dataNo dataNo dataNo dataNo dataNo dataNo dataMonitors Sited <=20 m from a Major Road0 25 50 75 100 125 150 175 200 225≥100 ppb≥150 ppb≥200 ppbNo dataNo dataNo dataNo dataNo dataNo dataNo dataNo dataEstimated mean number of daily maximum exceedances of 1-hour NO2concentrations of 100, 150, 200 ppb in a Year106Figure 7-4. Estimated number of daily maximum exceedances ofshort-term (1-hour) potential health effect benchmarks (100ppb, top; 200 ppb, bottom) in Chicago in a year, using recent NO2air quality data (2001-2006) adjusted to just meetingalternative 1-hour standard levels (98thpercentile, left; and 99thpercentile, right) and monitors sited≥100 m, > 20 m and < 100m,≤20 m of major roads.2001-2003,<=20m2001-2003,>20m <100m2001-2003,>=100m2004-2006,<=20m2004-2006,>20m <100m2004-2006,>=100m5010015020099th Percentile Alternative 1-Hour NO2Standard Level2001-2003,<=20m2001-2003,>20m <100m2001-2003,>=100m2004-2006,<=20m2004-2006,>20m <100m2004-2006,>=100m0255075100125

Number of Daily Maximum Excee ppb NO 2 1-hour in a Ye150175200225

dances of 100 ar2001-2003,<=20m2001-2003,>20m <100m2001-2003,>=100m2004-2006,<=20m2004-2006,>20m <100m2004-2006,>=100m0123456785010015020098th Percentile Alternative 1-Hour NO2Standard Level

Number of Daily Maximum Exceedances of 200 ppb NO 2 1-hour in a Year2001-2003,<=20m2001-2003,>20m <100m2001-2003,>=100m2004-2006,<=20m2004-2006,>20m <100m2004-2006,>=100m107Figure 7-5. Estimated mean number ofdaily maximum exceedances of 200 ppb in four locations (Phoenix, Los Angeles,Washington DC, and St. Louis)in a year, using recent NO2air quality data (2001-2006) adjustedto just meeting alternative 1-hour 98thpercentile standard levels and monitors sited≥100 m, > 20 m and < 100 m,≤20 m of major roads.- Phoenix -70123456

Number of Daily Maximum Exceedances of 200 ppb NO 2 1-hour in a Year2001-2003,<=20m2001-2003,>20m <100m2001-2003,>=100m2004-2006,<=20m2004-2006,>20m <100m2004-2006,>=100m- Los Angeles -2001-2003,<=20m2001-2003,>20m <100m2001-2003,>=100m2004-2006,<=20m2004-2006,>20m <100m2004-2006,>=100m- Washington DC -012345675010015020098th Percentile Alternative 1-Hour NO2Standard Level

Number of Daily Maximum Exceedances of 200 ppb NO 2 1-hour in a Year- St. Louis -5010015020098th Percentile Alternative 1-Hour NO2Standard Level2001-2003,<=20m2001-2003,<=20m2001-2003,>20m <100m2001-2003,>20m <100m2001-2003,>=100m2001-2003,>=100m2004-2006,<=20m2004-2006,<=20m2004-2006,>20m <100m2004-2006,>20m <100m2004-2006,>=100m2004-2006,>=100m108Table 7-23. Estimated annual mean NO2 concentration and the number of daily maximum exceedances of short-term (1-hour) potentialhealth effect benchmarks in a year, using 2001-2003 air quality adjusted to just meeting a 1-hour 100 ppb 98th percentile alternativestandard and monitors sited≥100 m of a major road.Number of Daily Maximum Exceedances of 1-Hour Level3Annual Mean NO2(ppb)2≥100 ppb≥150 ppb≥200 ppbLocationSiteYears1MeanMinMedp98p99MeanMinMedp98p99MeanMinMedp98p99MeanMinMedp98p99Atlanta1415519292920015150001100011Boston6137151616000110000000000Chicago92519233232100440001100000Cleveland32525252626213330000000000Denver22422242727212220000000000Detroit62321232626313772017710144El Paso122014212424302880001100011Jacksonville2262626262673710101011110111Las Vegas16153113232100770000000000Los Angeles511841929291005100000500000Miami6161316191940314140002200000New York262212203434100550000000000Philadelphia14272025393930215150001100011Phoenix53126333434101220000000000Provo33734383939646990000000000St. Louis9272226323230115150001100000Washington DC18261229353540311110000000000Other MSA612131132325000140000000000Other Not MSA1277171718000660001100011Notes:1The average number of monitors operating per year within the three-year group is estimated by dividing the number of site-yearsby 3.2Annual means for each monitor were first calculated based on allsimulated hourly values in a year. Then the mean of the annualmeans was estimated as the sum of all the annual means in a particular location divided by the number of simulated site-years across themonitoring period. The min, med, p98, and p99 represent the minimum, median, 98th, and 99thpercentiles of the distribution for theannual average concentration in any oneyear within the monitoring period.3The mean number of exceedances represents the sum of daily maximum exceedances occurring at all monitors in a particular locationdivided by the number of site-years across the monitoring period. The min, med, p98, and p99 represent the minimum, median, 98th, and99thpercentiles of the distribution for the number of daily maximum exceedances in any one year within the monitoring period.109Table 7-24. Estimated annual mean NO2 concentration and the number of daily maximum exceedances of short-term (1-hour) potentialhealth effect benchmarks in a year, using 2001-2003 air quality adjusted to just meeting a 1-hour 100 ppb 98thpercentilealternative standard and monitors sited >20 m and <100 m from a major road.Number of Daily Maximum Exceedances of 1-Hour Level3Annual Mean NO2(ppb)2≥100 ppb≥150 ppb≥200 ppbLocationSiteYears1MeanMinMedp98p99MeanMinMedp98p99MeanMinMedp98p99MeanMinMedp98p99AtlantaNDBoston14231226353530211110000000000Chicago6353335373771421211003300000ClevelandNDDenverNDDetroitNDEl Paso32726282828627990000000000JacksonvilleNDLas Vegas38481212100440000000000Los Angeles35193193232100770001100011Miami3272427292984615150001100000New York13332333444430113130002200000Philadelphia7342633424250411110000000000Phoenix22625262727000000000000000ProvoNDSt. Louis11221318383820011110000000000Washington DC102920313636202550000000000Other MSANDOther Not MSANDNotes:1The average number of monitors operating per year within the three-year group is estimated by dividing the number of site-yearsby 3.2Annual means for each monitor were first calculated based on allsimulated hourly values in a year. Then the mean of the annualmeans was estimated as the sum of all the annual means in a particular location divided by the number of simulated site-years across themonitoring period. The min, med, p98, and p99 represent the minimum, median, 98th, and 99thpercentiles of the distribution for theannual average concentration in any oneyear within the monitoring period.3The mean number of exceedances represents the sum of daily maximum exceedances occurring at all monitors in a particular locationdivided by the number of site-years across the monitoring period. The min, med, p98, and p99 represent the minimum, median, 98th, and99thpercentiles of the distribution for the number of daily maximum exceedances in any one year within the monitoring period.ND no available monitoring data.110Table 7-25. Estimated annual mean NO2 concentration and the number of daily maximum exceedances of short-term (1-hour) potentialhealth effect benchmarks in a year, using 2001-2003 air quality adjusted to just meeting a 1-hour 100 ppb 98thpercentilealternative standard and monitors sited≤20 m from a major road.Number of Daily Maximum Exceedances of 1-Hour Level3Annual Mean NO2(ppb)2≥100 ppb≥150 ppb≥200 ppbLocationSiteYears1MeanMinMedp98p99MeanMinMedp98p99MeanMinMedp98p99MeanMinMedp98p99AtlantaNDBoston52910324141200660001100000Chicago42625262727201660000000000Cleveland3323132343473710100000000000Denver2373737383873710102123300000DetroitNDEl PasoNDJacksonvilleNDLas Vegas33231323232711011110000000000Los Angeles92318232929100330000000000Miami31211121212101110000000000New York73027303333101220000000000PhiladelphiaNDPhoenix3414040434364411110000000000ProvoNDSt. Louis62825293030313660001100000Washington DC43328343636636990001100000Other MSANDOther Not MSANDNotes:1The average number of monitors operating per year within the three-year group is estimated by dividing the number of site-yearsby 3.2Annual means for each monitor were first calculated based on allsimulated hourly values in a year. Then the mean of the annualmeans was estimated as the sum of all the annual means in a particular location divided by the number of simulated site-years across themonitoring period. The min, med, p98, and p99 represent the minimum, median, 98th, and 99thpercentiles of the distribution for theannual average concentration in any oneyear within the monitoring period.3The mean number of exceedances represents the sum of daily maximum exceedances occurring at all monitors in a particular locationdivided by the number of site-years across the monitoring period. The min, med, p98, and p99 represent the minimum, median, 98th, and99thpercentiles of the distribution for the number of daily maximum exceedances in any one year within the monitoring period.ND no available monitoring data.111Table 7-26. Estimated mean number ofdaily maximum exceedances of 100 ppb 1-hour NO2concentrations in a year, using 2001-2003 airqualityas isand that adjusted to just meeting the current and alternative standards (98thpercentile) for monitors sited≥100m, >20 m and <100 m, and≤20 m of a major road.Sites≥100 m of a major roadSites >20 m and <100 m of a major roadSites≤20 m of a major roadAlternative 1-hour 98thpercentile standardAlternative 1-hour 98thpercentile standardAlternative 1-hour 98thpercentile standardLocationAsIsCurStd50100150200AsIsCurStd50100150200AsIsCurStd50100150200Atlanta080022065NDNDBoston030033403803351160190229104Chicago023013110627407902120210228119Cleveland0700249133ND01110779187Denver2990237149ND7480768224Detroit399131773NDNDEl Paso0115033512521700677178NDJacksonville11521774152NDNDLas Vegas0690141114024011146121107141280Los Angeles42101937631011553642012172Miami0106044110701520873155078012876New York0801167212603501541180138143Philadelphia02903531710440584216NDPhoenix032018921304004921027206160293Provo016206200327NDNDSt. Louis065036017504102371190560346175WashingtonDC061045815305702571680780673209Other MSA01600318NDNDOther NotMSA0370016NDNDNotes:The mean number of exceedances represents the sum of daily maximum exceedances occurring at all monitors in a particular location dividedby the number of site-years across the monitoring period.ND No available monitoring data.112113Table 7-27. Estimated mean number ofdaily maximum exceedances of 150 ppb 1-hour NO2concentrations in a year, using 2001-2003 airqualityas isand air quality adjusted to just meeting the current and alternative standards (98thpercentile) for monitors sited≥100 m, >20 m and <100 m, and≤20 m of a major road.Sites≥100 m of a major roadSites >20 m and <100 m of a major roadSites≤20 m of a major roadAlternative 1-hour 98thpercentile standardAlternative 1-hour 98thpercentile standardAlternative 1-hour 98thpercentile standardLocationAsIsCurStd50100150200AsIsCurStd50100150200AsIsCurStd50100150200Atlanta01800210NDBoston00000103003190100215Chicago010011405017530100215Cleveland0500227ND01400750Denver02100217ND1502733Detroit1141239NDNDEl Paso0130031603200640NDJacksonville14411741NDNDLas Vegas060011902001502200776Los Angeles020015030018040017Miami023004230420084201700116New York00001701003250100118Philadelphia01003270200542NDPhoenix000014100000110200688Provo04006112NDNDSt. Louis050033103002210300323WashingtonDC040043002002280700638Other MSA010001NDNDOther NotMSA060001NDNDNotes:The mean number of exceedances represents the sum of daily maximum exceedances occurring at all monitors in a particular location dividedby the number of site-years across the monitoring period.ND no available monitoring data.

### 7.3.4 On-Road Concentrations Derived From Ambient Air Quality Adjusted to Just

Meet the Current and Alternative Standards

Just as was done with the as is air quality data, on-road NO 2 

### concentrations were estimated using the air quality adjusted to just meeting the current and alternative standard and the approach described in section 7.2.3. The analysis was performed using the more recent air quality separated into two three-year groups (2001-2003 and 2004-2006) ba sed on the form of the potential alternative standards (i.e., a 3-year average) and repr esents the fourth air quality scenario. Results are presented in a manner cons istent with section 7.3.3, whereby the number of daily maximum exceedances of the potential benchmark levels was estimated. However, for the sake of brevity only key figures and tables are provided here. The complete results for the estimated on-road concentrations and numbers of benchmark exceedances are provided in Appendix A, section 9. Figures 7-6 illustrates the estimated mean number of daily maximum exceedances of the 100, 150, and 200 ppb levels on-roads, given air quali ty adjusted to just meeting the current annual average standard for two three-year groups. Most locations contained an average of twohundred or more estimated daily maximum exceed ances of 100 ppb, much greater than those estimated using either th e ambient monitors sited ≥ 100 m, >20 and <100 m, an ≤ 20 m of a major road (Figures 7-2 and 7-3). The estimated numbers of daily maximum exceedances of the 150 and 200 ppb levels were also higher on-roads. Most locations were estimated to contain at least one-hundred exceedances of 150 ppb and between 50 and 100 exceedances of 200 ppb on-roads when using air quality concentrations adjust ed to just meeting the current standard. The effect of the potential alternativ e standards on the estimated on-road NO 2 

### concentrations was also analyzed at each of th e locations. Figure 7-7 i llustrates each of the standard levels (50, 100, 150, and 200 ppb 1-hour) and the two forms (98 th 

### and 99 th 

### percentiles) evaluated, again using Chicago as an example to de scribe patterns in the number of exceedances. The patterns observed in Figure 7-2 and describe d in section 7.3.3 for the ambient monitors are similar to that observed here, albeit with greater numbers of exceedances estimated on-roads compared with those estimated for monitors near-r oads or sited at a dist ance from major roads. Estimated numbers of daily maximum concen trations above 100 ppb ar e between 50 and one

114

### hundred considering a standard level of 100 ppb (either percentile), however daily maximum exceedances of 200 ppb are estimated to be between one and four.

Similar numbers of exceedances on-roads we re estimated at other locations using air quality adjusted to just meeting the potential al ternative standards. Fi gure 7-8 illustrates the estimated number of exceedances of 200 ppb at four selected locations as an example, Phoenix, Los Angeles, Washington DC, and St. Louis, using a 98 th 

### percentile form of a 1-hour standard. The number of concentrations above 200 ppb is similar at each of the locations (including Chicago), particularly when comparing the 100 ppb standard level, ranging from two to seven. Table 7-28 presents a more comprehensive compar ison at this particular standard level (98 th 

### percentile at 100 ppb) using 2001-2003 adjusted air quality at each of the locations. For most locations, the estimated mean number of da ily maximum exceedances of 200 ppb on-roads was seven or less, with upper percentil es estimated to number about 30 to 70 of exceedances. The mean number of exceedances of 250 and 300 ppb we re less, ranging from a few to tens of occurrences in a year.

Tables 7-29 and 7-30 summarizes the obser ved and estimated mean numbers of exceedances of 100 and 150 ppb on-roads, respectively, using all the recent air quality as is and that adjusted to just meet the curren t standard and the potential alternative 98 th 

### percentile

standards at each location. Patterns for th e estimated on-road concentrations using as is air quality and air quality adjusted to just meet the current annual st andard followed similar patterns

observed for the monitors sited ≥ 100 m, > 20 and <100 m, and ≤ 20 m of a major road (see Tables 7-25 and 7-26, for the daily maximu m exceedances of 100 and 150 ppb using 2001-2003 air quality). The estimated number of da ily maximum exceedances on-roads using the as is air quality was within the range of estimates provided by the alternative 1-hour 98 th 

### percentile standards of 50 and 100 ppb, while the estimated on-road exceedances of 150 ppb was within the range of estimated exceedances using the 150 and 200 ppb alternative standard levels.

115

### 116 Figure 7-6. Estimated mean number of daily maximum exceedances of short-term (1-hour) potential health effect benchmarks on-roads in a year, using recent NO 2 

### air quality adjusted to just meeting the current annual standard (0.053 ppm) and an on-road adjustment factor. Left graph: 2001-2003 air quality; Right graph: 2004-2006 air quality. 0 50 100 150 200 250 300 350 Atlanta

Boston

Chicago

Cleveland

Denver

Detroit

El Paso

Jacksonville

Las Vegas

Los Angeles

Miami

New York

Philadelphia

Phoenix

Provo

St. Louis

Washington DC

Other MSA

Other Not MSA ≥ 100 ppb

≥ 150 ppb

≥ 200 ppb 

### 2001 - 2003 2004 - 2006

≥ 100 ppb

≥ 150 ppb

r

0 50 100 150 200 250 300 350 ≥ 200 ppb

No data

Estimated mean number of daily maximum exceedances of 1-hour NO 2 concentrations of 100, 150, 200 ppb on-roads in a YearFigure 7-7. Estimated number of daily maximum exceedances of short-term (1-hour) potential health effect benchmarks (100ppb, top; 200 ppb, bottom) on-roads in Chicago in a year, using recent NO2air quality (2001-2006) adjusted to just meetingalternative 1-hour standard levels (98thpercentile, left; and 99thpercentile) and an on-road adjustment factor.050100150200250

Estimated Mean Number of Daily Maximum Exceedances of 100 ppb NO 2 1-hour in a Year3002001-20032004-20062001-20032004-200601020304050607080901005010015020098th Percentile Alternative Standard Level (ppb)

Estimated Mean Number of Daily Maximum Exceedances of 200 ppb NO 2 1-hour in a Year2001-20032001-20032004-20062004-20065010015020099th Percentile Alternative Standard Level (ppb)117- Los Angeles -Figure 7-8. Estimated mean number ofdaily maximum exceedances of 200 ppb on-roads in four locations (Phoenix, LosAngeles, Washington DC, and St.Louis) in a year, using recent NO2air quality (2001-2006) adjusted to just meetingalternative 1-hour 98thpercentile standard levels andan on-road adjustment factor.- Phoenix -0255075100125175

Estimated Mean Number of Daily Maximum Exceedances of 200 ppb N150

O 2 1 - hour in a Year2001-20032004-20062001-20032004-2006- Washington DC -02550751001251501755010015020098th Percentile Alternative Standard Level (ppb)

Estimated Mean Number of Daily Maximum Exceedances of 200 ppb NO 2 1- St. Louis -5010015020098th Percentile Alternative Standard Level (ppb)

-2001-20032001-20032004-20062004-2006

hour in a Year118Table 7-28. Estimated annual mean NO2concentration and the number of daily maximum exceedances of short-term (1-hour) potentialhealth effect benchmarks on-roads in a year, using recent air quality (2001-2003) adjusted to just meeting a 1-hour 100 ppb98thpercentile alternative standard and an on-road adjustment factor.Number of Daily Maximum Exceedances of 1-Hour Level3Annual Mean NO2(ppb)2≥100 ppb≥150 ppb≥200 ppbLocationSiteYears1MeanMinMedp98p99MeanMinMedp98p99MeanMinMedp98p99MeanMinMedp98p99Atlanta1400276295765460221902291001701042002638Boston60024924404126013125131300222500036Chicago9004525437578832682382571807921064004349Cleveland300463245616410419982092252401573867023738Denver2004428426567998822522691906861034013637Detroit600412640596359446170186131856575032628El Paso1200361735565889479231249170880913002324Jacksonville20047334764671192611821723532225971147043434Las Vegas16002742069738204329830719031491725006371Los Angeles51003253261653301914216060146551001520Miami60029162945467827418118919010921105013748New York2600391537707957043212226100367733003334Philadelphia140050264689921161102284294270121181377015468Phoenix50056335583881532152319337350141822066004448Provo300674465961012576027735335875151273301180486106St. Louis90048284775761252118274288280151441537014551Washington DC18004715488288109099287310270101391687005663Other MSA612002312348511704110133200233400058Other Not MSA127001311234376006486100121700048Notes:1The average number of monitors operating per year within the three-year group is estimated by dividing the number of site-years by 300.2Annual means for each monitor were first calculated based on allsimulated hourly values in a year. Then the mean of the annualmeans was estimated as the sum of all the annual means in a particular location divided by the number of simulated site-years across themonitoring period. The min, med, p98, and p99 represent the minimum, median, 98th, and 99thpercentiles of the distribution for theannual average concentration in any oneyear within the monitoring period.3The mean number of exceedances represents the sum of daily maximum exceedances occurring at all monitors in a particular locationdivided by the number of simulated site-years across the monitoring period. The min,med, p98, and p99 represent the minimum,median,98th, and 99thpercentiles of the distribution for the number of daily maximum exceedances in anyone year within the monitoring period.119Table 7-29. Estimated mean number ofdaily maximum exceedances of 100 ppb 1-hour NO2concentrations on-roads in a year, using airqualityas isand air quality adjusted to just meeting the current and alternative standards (98thpercentile) and an on-roadadjustment factor.2001-2003 Air Quality2004-2006 Air QualityAlternative 1-hour 98thpercentilestandardAlternative 1-hour 98thpercentilestandardLocationAsisCurrentstd50100150200As isCurrentstd50100150200Atlanta2318324611717017193358133181Boston5860269516329501984153Chicago5219348320528136189259176259Cleveland312647104235305NDDenver89267499232288632579148263296Detroit412825591782652028713165273313El Paso32272389216278242815108229281Jacksonville132907119233289112959127241293Las Vegas2318958216721815177683161210Los Angeles7115213311119138160254155227Miami72325781742326202356128182New York4212935716924935147375192264Philadelphia372227116254312222325112237295Phoenix1012456153293332772844146299338Provo6133818257343351513061363209298St. Louis252627125258316152334107226287Washington DC36222710922127921207596200260Other MSA16133017731381014302080143Other Not MSA41260628634124062860Notes:The mean number of exceedances represents the sum of daily maximum exceedances occurring at all monitors in a particular location divided by thenumber of site-years across the monitoring period.ND no available monitoring data120121Table 7-30. Estimated mean number ofdaily maximum exceedances of 150 ppb 1-hour NO2concentrations on-roads in a year, using airqualityas isand air quality adjusted to just meeting the current and alternative standards (98thpercentile) and an on-roadadjustment factor.2001-2003 Air Quality2004-2006 Air QualityAlternative 1-hour 98thpercentilestandardAlternative 1-hour 98thpercentilestandardLocationAsisCurrentstd50100150200As isCurrentstd50100150200Atlanta41100104695212601358111Boston020032672025011959Chicago9740188316856901059138Cleveland5134124104196NDDenver171570199919810134138148239Detroit9166113591402189150165249El Paso4168017891803198023108197Jacksonville12051321192012216235127211Las Vegas41060198214329912283138Los Angeles175506338165801054122Miami114601978145013001356106New York7360105713254501475157Philadelphia6871271162172110024112204Phoenix169603515326210124028146264Provo92352752573311719242063160St. Louis31311281252241121023107194Washington DC6111027109190210202296171Other MSA247021751159022057Other Not MSA1630161916201619

## 7.4 UNCERTAINTY ANALYSIS

Uncertainty refers to the lack of knowledge regard ing both the actual values of model input variables (parameter uncertainty ) and the physical systems or relationships (model uncertainty – e.g., the shape of the concentration-response functi ons). In any risk assessment, uncertainty is, ideally, reduced to the maximum extent possible, but significant uncertainty often remains. It can be reduced by improved measurement and improved model formulation. In addition, the degree of uncertainty can be char acterized, ranging from qualitative to quantitative assessments. Uncertainty can be distinct from variability, which commonly refers to the heterogeneity in a population or variable of interest that is inherent and cannot be reduced through further research. The approach for evaluating uncertainty was adapted from guidelines outlining how to conduct a qualitative uncertainty characterization (WHO, 2008). Fi rst, the key sources of the assessment that contribute to uncertainty are identified, and the rationale for why they are included is discussed. Second, a qualitative characterization follows for the types and components of uncertainty, resulting in a summar y describing, for each source of uncertainty, the level and direction of influence the uncertaintymay have on the air quality characterization

results. The overall characterization of uncertainty is qualitatively evalua ted by considering the degree of severity of the uncertainty, implied by the relationship between the source of the uncertainty and the output of the assessment. To the extent possible, an appraisal of the knowledge base (e.g., the accuracy of the data used, acknowledgement of data gaps) and evaluation of the decisions made (e.g., selection of particular model forms) is also included in this uncertainty rating. The characterization is subjectively scaled by the assessors using a designation of low, medium, and high. Briefly, a low level of uncertainty suggests large changes

within the source of uncertain ty would have only a small e ffect on the results, there is completeness and scientific consistency in the kn owledge base, and decisions made regarding the particular source of uncertainty woul d be widely accepted. A designation of medium implies that a change within the source of uncertainty would likely have a proportional effect on the results, there may be limited scientific backing, and limited se lection of inputs or mode ls to choose from.

A characterization of high implies that a small change in th e source would have a large effect on

122

### 123 results, there may be inconsiste ncies present in the scientific support, a nd assumptions made

would be considered unusua l and restrictive by others.

The bias direction indicates how the source of uncertainty has been judged to influence estimated concentrations, either the concentrations are likely over - or under-estimated . In the instance where two or more types or components of uncertainty are present that potentially offset the direction of influence, the bias has been judged as both . An unknown bias has been assigned where there was no evidence reviewed to judge the direction of uncertainty bias associated with the source. Table 7-31 provides a summary of the sources of un certainty identified in the air quality characterization, the level of uncerta inty, and the overall j udged bias of each. A discussion regarding each of these sources of unc ertainty and how conclusions were drawn is given in the sections that follow.

Table 7-31. Summary of qualitative uncertainty analysis for the air quality and health risk characterization.

Source Type Concentration/ Exceedance Bias Direction Characterization of Uncertainty Air Quality Data Database quality both Low Ambient Measurement Interference over Low - Medium Scale none Low Missing data both Low - Medium Years evaluated both Low Temporal Representation Emission source changes over Low - Medium Scale unknown Medium Monitor objectives both Medium Vertical siting of monitor under Low - Medium Spatial Representation Monitor extrapolation < 4m unknown Low - Medium Proportional approach used both Medium - High Air Quality Adjustment Spatial scale over Medium Temporal scale both Medium - High Scenario modeled over Low - Medium Spatial scale over Low - Medium Exponential model both Medium - High Influential factors unknown Medium Distribution form both Low - Medium On-Road Simulation

Non US studies used unknown Low - Medium Averaging time none Low - Medium Health Benchmarks Susceptibility under Medium

### 7.4.1 Air Quality Database One basic assumption is that the AQS NO 2 

### air quality data used are quality assured already. Methods exist for ensuring the precisi on and accuracy of the ambient monitoring data (e.g., EPA, 1983). Reported concentrations contain only valid measures, since values with quality limitations are not entered to the syst em, removed following determination of being of lower quality or flagged. There is likely no selective bias in rete ntion of data that is not of reasonable quality if the data are in error, it is assumed that selection of high concentration poor quality data would be just as li kely as low concentration data of poor quality. Given the numbers of measurements used for this analysis, it is likel y that even if a few low quality data are present in the data set, they would not have any significant effect on th e results presented here. There

are no alternative data sets available that are as comprehensive, and where monitoring data are available that are not included in the AQS, it is e xpected that given the same methods and quality assurances, would be complimentary to the data existing in the AQS. Ther efore, the air quality data and database used likely contributes mini mally to the uncertainty level, there is low uncertainty in the knowledge base, and the uncerta inty in the subjectivit y of choices is also

considered low. Temporally, the data are hourly measurements and appropriately account for variability in concentrations that are commonly observed for NO 2 

### and by definition are representative of an entire year. In addition, having more than one monitor does account for some of the spatial variability in a particular loca tion. However, the degree of repr esentativeness of the monitoring data used in this analysis can be evaluated from several persp ectives, one of which is how well the temporal and spatial variabil ity are represented. In particular, missing hourly measurements at a monitor may introduce bias (if different pe riods within a year or different years have different numbers of measured values) and increa se the uncertainty. Furthermore, the spatial representativeness will be poor if the monitoring network is not dense enough to resolve the spatial variability (causing increased uncertainty ) or if the monitors are not appropriately distributed to reflect population exposure (causing a bias). Additional uncertainty regarding temporal and spatial representation by th e monitors is expanded below.

124

### 7.4.2 Measurement Technique for Ambient NO 2 

### One source of uncertainty for NO 2 

### air quality data is due to interference with other oxidized nitrogen compounds. Nitr ogen dioxide is not directly measured. It is estimated by subtracting measured NO from total nitrogen oxides. The ISA identifies several studies conducted that have shown a constant positive in terference when oxides of nitrogen other than

NO 2 

### are present (ISA section 2.3). Most commonly the interference is from HNO 3 

### and has been reported to contribute to up to 50% the calculated NO 2 

### . This has been shown to occur primarily during the afternoon hours in th e summer and would result in an overestimation of ambient NO 2 

### concentrations. During wi nter, positive interference in the measurement of NO 2 

### is estimated to be less, generally at 10% or lower. At any one particular site, however, there is uncertainty in how much the interference will be, and is dependent on the presence of the NO z 

### compounds which are largely not measured. In addition, it is not known whet her there is a concentration dependence on the amount of interference. This is an important uncertainty when air quality concentrations adjusted upwards to just meet the current standard. Therefore, the bias would be

a consistent overestimation of NO 2 

### concentrations, the level of which may range from affecting the concentrations minimally upwards to a modera te effect. While the science demonstrating the interference is consistent, there remain uncerta inties about the applica tion of the level of interference to individual monitoring sites.

7.4.3 Temporal Representation Data are valid hourly measures and are of si milar temporal scale as identified health effect benchmark concentrations. There are freq uent missing values within a given valid year which contribute to the uncertainty as well as introducing a possible bias if some seasons, day types (e.g., weekday/weekend), or ti me of the day (e.g., night or day) are not equally represented. Since a 75 percent daily and hourly completeness rule was applie d, some of these uncertainties and biases were reduced in these analyses. A dditional validity criteria could have included completeness for monitoring based on quarters, rath er than the entire year. This would screen for air quality data potentially missing an entire season of monitoring. The use of validity criteria that included quarterly completeness wo uld likely exclude a few monitor site-years of

data considered in the current an alysis. This would likely have a greater effect at locations with

125

### fewer site-years of air quality if large numbers of missing data exist in a quarter, although in some locations, actual seasons may be somethi ng other than a quarterly classification. Ambient monitoring data were not interpolated to substitute for any missing values. It is assumed that missing values are not systematic, i.e ., high concentrations would be absent as well low concentrations in equal proportions. There are methods available that can account for timeof-day, day-of-week, and seasonal variation in am bient monitoring concentrations. However, if

a method were selected it would have to not si mply interpolate the data, but accurately estimate the probability of peak concentrations as well. It was judged that if su ch a method was available or one was developed to substitute data, it would likely add to a similar level of uncertainty as not choosing to substitute the data. Again, th is can be viewed as having a limited affect on uncertainty because using the validity criteria should select for the most representative and complete ambient monitoring data sets possible. There may be bias and uncertainty in the ai r quality characterization results if the years monitored vary significantly be tween locations. Although monito ring locations within a region do change over time, the NO 2 

### network has been reasonabl y stable over years 1995-2006, particularly at locations with larger monitoring networks. While it is possible for monitors to move from high concentration areas to low concentration areas or perhaps in the other direction, regulations exist that specifies the design a nd measurement requirements for these networks (e.g., 40 CFR Part 58). Given this, it is expected that the level of uncertainty in the specific monitors operating from year to year is low with a variable bias direction of over-estimation for some years and under-estimation for others.

It should also be noted that use of the older data in some of the analyses here assumes that the sources present at that time are the same as current sources, adding uncertainty to results if this is not the case. Separating the data into two 6-year groups (historical and recent for the as

is evaluation) and two additional subsets of the recent air quality (2001-2003 and 2004-2006) before analysis reduces the potential impact from changes in national- or location-specific source influences and is judged to have a minimal bias in representing air quality concentrations for those selected years. There is some variability expected from year-to-year, that is, there may be differences in the air quality results if the year-gro ups included a different 3-year period, such as 2002-2004 or 2003-2005. Deciding to bound the total pe riod rather than char acterize all possible

126

### 3-year combinations was judged appropriate, given the small differences in the observed results over time and the resources available for the analysis.

7.4.4 Spatial Representation Relative to the physical area, there are only a small number of monitors in each location. Even considering sparse siting, the monitoring data are assumed to be spatially representative of the locations analyzed here because the monitors are used in determining whether areas meet or do not meet the NAAQS. This could include area s between the ambient m onitors that may or may not be influenced by similar local sources of NO 2 

### . For these reasons the uncertainty and bias due to the spatial network may be mode rate, although the monitori ng network design should

have addressed these issues within the available resources and other monitoring constraints. Bias will depend on ambient monitoring objectives and scal e and whether there is large variability in monitoring surface, i.e., areas of differing terr ain that are not be well represented by the

distribution of monitors. The direction of this bias is largely unknown due to the differences in the true representativeness of the network and the particular terrain in each location. In addition, the air quality characterization used all monitors meeting the 75 percent completeness criteria, without taking into account the m onitoring objectives or land use for individual monitors. Thus, there will be some lack of spatial representation and uncertainty due to th e inclusion/exclusion of some monitors that are very near local sources (including mobile sources) potentiall y resulting in

either over- or under-estimations. According to one study conducted in the South Bronx, NY in November and December 2001, negative vertical gradients can exist for m onitor concentrations (IS A, section 2.5.3.3). On average, measured concentrations can be 2.5 times higher at 4 meters than at a 15 meter vertical monitor siting. Therefore, m onitors positioned on building rooftops may underestimate NO 2 

### concentrations at lower vertical heights and possibly at the sta ndard breathing height of 1.8 m. In this REA, only 7 of the 178 10 

### monitors in the named locations contained monitoring heights of 15 meters or greater, with 58% at 4 meters or less height, and 79% at 5 mete rs or less in height. In the aggregate locations (i.e., Other MSA

, Other Not MSA ), a total of 4 monitors of 340 11 

### contained monitoring heights of 15 meters or greater , with 50% at 4 meters or less in height, and

10 26 monitors in the named locations did not have height reported (therefore, 178 + 26 = 204 total number of monitors).

11 84 monitors in the aggregate locations did not have height reported (therefore, 340 + 84 = 424 total number of monitors).



### 127

### 73% at 5 meters above ground. Not accounting fo r this potential ver tical gradient in NO 2 

### concentrations may generate underestimates of exceedances for some sites, however the overall impact of inferences made for the locations in cluded in this assessment without considering vertical monitor height is likely minimal since most monitors used for analysis are sited at less than 4-5 meters above ground. In addition, the rela tionship at heights below 4 meters is largely uncertain (e.g., a breathing hei ght of 1.8 meters is commonly us ed) and therefore would add an

additional unknown bias to the estimated NO 2 

### concentrations above a benchmark when used as a surrogate for human exposure. Staff evaluated the potential bias in vertical siting of monitors by using the AERMOD predictions at four receptor hei ghts for each of the three ambient monitors located in the Atlanta exposure modeling domain (monitors 130890002, 130893001, and 131210048), each located at heights 0, 1.8, 5, and 15 meters above ground. An example of the predicted hourly NO 2 

### concentration distributions at each receptor height for one site (ID 131210048) is presented in Figure 7-9, and is similar to that predicted at each of the other m onitors. Consistent with the one study reported in the ISA, the estimated concentr ations at the 15 meter monitoring height were the lowest, with progressively gr eater concentrations with decreas ing receptor height. However,

the level of differences in concentration at each of the different recept or heights were lower using the modeled concentrations in Atlanta than when compar ed with those reported for the South Bronx using the measured ambient concentrations. On average, the NO 2 

### concentrations estimated at a 0 m height were >0.2% than those at a height of 1.8 m, while the largest difference in concentration (7.9%) o ccurred with comparison of the 0 m to the 15 m height (Table 7-32). When comparing concentrations for each of th e receptor heights at th e upper tails of the distribution, occasionally a similar pattern was observed, i.e., small increases in the numbers of exceedances of a selected benchmark level (T able 7-33). At one location however (ID

130893001 ), 

### there were no differences in the number of exceedances of the selected benchmark

level. These modeling results support the ISA cited measurement study in that there is an inverse relationship between monito r vertical siting height and NO 2 

### concentration, only the magnitude of the relationship differs. The lack of a similar magnitude could be the result of several factors such differing influential featur es of the study area vers us the modeled area (e.g., seasonal/meteorological factors, presence of nearby sources, te rrain) or perhaps a limited

128

### sensitivity of the model to variable receptor height. Based on these two limited evaluations,

there can be no clear determination as to whether the monitor vertical siting effect is as large as the single study estimated or as small as the disp ersion model predicted. Fu rther, since there are

limited measurement and model results available to inform a decision and that there were few monitors used above 5 m in vertical height, sta ff did not adjust concentr ations using vertical siting characteristics.

Monitor 131210048

0 10 20 30 40 50 60 70 80 90 100

0 20 40 60 80 100 120 140 160 

### AERMOD Predicted NO 2 

### (ppb)Cumulative Percentile

0 m Height 1.8 m Height 5 m Height 15 m Height 40 41 42 43 44 45

16 17 18 19 20 21 AERMOD Predicted NO 2 (ppb) 0 m Height 1.8 m Height 5 m Height 15 m Height



### Figre 7-9. Distribut ion of 1-hour NO 2 

### concentrations for three modeled receptors in Atlanta at different vertical heights, using AERMOD predic ted 2002 air quality.

Table 7-32. Percent difference in 1-hour NO 2 

### concentrations for three modeled receptors in Atlanta at different vertical heights, using AERMOD predicted 2002 air quality. % Difference in 1-hour NO 2 (Lower to higher vertical height) Monitor ID 0 m to 1.8 m 1.8 m to 5 m 0 m to 15 m 130890002 0.2 0.9 6.7 130893001 0.1 0.6 4.6 131210048 0.1 1.6 7.9



### 129

### Table 7-33. Number of 1-hour NO 2 

### concentrations above 100 ppb for three modeled receptors in Atlanta at different vertical heights, using AERMOD predicted 2002 air quality. Number of 1-hour NO 2 ≥ 100 ppb (AERMOD Estimated NO 2 , Year 2002) Monitor ID 0 m 1.8 m 5 m 15 m 130890002 21 21 21 19 130893001 2 2 2 2 131210048 12 12 11 10



### 7.4.5 Air Quality Adjustment Procedure There is uncertainty in the air quality adjustment procedures due to the uncertainty of the true relationship between the adjusted concentrations that are simulati ng a hypothetical scenario

and the as is air quality. The adjustment factors used for the current and alternative standards each assumed that all hourly concentrations will change proportionately at each ambient monitoring site. Two principal uncertainties are discussed, namely uncertainty regarding the proportional approach used and the universal applic ation of the approach to all ambient monitors

within each location.

Different sources have differe nt temporal emission profiles, so that equally applied changes to the concentrations at the ambient monitors to simulate hypothetical changes in emissions may not correspond well with all portions of the concentration distribution. When adjusting concentrations upward to just meeting the current standard, th e proportional adjustment used an equivalent multiplicative factor derive d from the annual mean concentration and equally applied to all portions of the c oncentration distribution, i.e., the upper tails were treated the same as the area of central tendency. This may not n ecessarily reflect changes in an overall emissions profile that may result from, for ex ample, an increase in the number of sources in a location. It is possible that while the mean concentration measured at an ambient monitor may increase with an

increase in the source emissions af fecting concentrations measured at the monitor, the tails of the hourly concentration distribution might not have the same proportional increase. The increase could be greater or it could be less than that ob served at the mean, dependent largely on the type of sources and inherent operati ng conditions. Adjusting the ambi ent concentrations upwards to

simulate the alternative standards also carri es a similar level of uncertainty although the multiplicative factors were derived from the upper percentiles of the 1-hour NO 2 

### concentrations, rather than the mean, and then applied to the 1-hour NO 2 

### concentrations equally.

130

### In each of these instances of adjusting the concentrations upwards, one could argue that there may be an associated over-estimation in the concentrations at the upper tails of the distributions, possibly leading to over-estimation in the numbers of exceedances of benchmark levels. An analysis was performed on monitors w ithin six of the locations used in the air quality characterization to investigate how distributions of hourly nitroge n dioxide concentrations have changed over time (Rizzo, 2008). The analysis i ndicates that a proportional approach can be appropriate in simulating higher co ncentrations at most monitori ng sites, since historically, NO 2 

### concentrations have decreased linearly across the entire concentration dist ribution at each of the monitoring sites and locations ev aluated. In addition, when adju sting concentrations downward (e.g., the alternative standard level of 50 ppb 1-hour, 99 th 

### percentile), the use of the proportional multiplicative adjustment derived from the upper ta ils and applied to concentration distribution may be less uncertain because NO 2 

### concentrations have been obser ved to decrease linearly over time, and only assumes that the downward trend w ould continue similarly in the future with

added source controls. At some of monitoring sites an alyzed however, there were feat ures not consistent with a completely proportional relationship, includi ng deviation from linearity primarily at the maximum or minimum percentile concentrations, some indication of curvilinear relationships, and the presence of either a pos itive or negative regression intercept (Rizzo, 2008). Where multiple monitors were present in a location th ere tended to be a mixture of each of these condition, including proportionality. Not all of the locations analyzed as part of the air quality

characterization were included in the evaluation. It was also assu med that the analysis conducted

at the six locations would reflect what would be observed at the other locations if evaluated for trends in concentration over long periods of time. High concentra tion year to low concentration year comparisons were also limited to 3-years to generate appropriate and a manageable number of comparisons. It was assumed that if additi onal years of data were compared that similar relationships would be developed. Further, there is uncertainty in adjusting concentrations upwards or downwards considering assumptions re garding future source emission scenarios and

how these would relate to observed trends in curr ent and historical air quality. The uncertainty

about future source emission scenarios is largely unknown. Universal application of the proportional si mulation approach for each of the locations

and within each location was done for consistency and was designe d to preserve the inherent

131

### variability in the concentrati on distribution. There is uncerta inty regarding emission changes

that would affect the concentrations at the de sign monitor containing th e highest concentration (annual mean, 98 th 

### or 99 th 

### percentile 1-hour) that may not nece ssarily affect lower concentration sites proportionately. This could result in either over- or unde r-estimations in the number of

exceedances at lower concentration sites within a location where the current or alternative

standard scenarios were evalua ted. When comparing the low c oncentration years and the high concentration years at multiple ambient monito rs within a location however, most monitors

contained similar linear relations hips (e.g., comparative regression slopes and intercepts). For example, Figure 7-10 shows the daily maximum NO 2 

### concentration percentiles for four ambient monitors in Philadelphia, where each of 6 ambien t monitors were in operation for years 1984 and 2007. The similarity in slope for each of the monito rs indicates that an ad justment factor derived from one ambient monitor can be applied to th e other monitors in the monitoring network. Furthermore, when calculating the number of exceedances of the potential health effect benchmark levels, the greatest num bers of exceedances typically were noted at the monitoring sites with the highest concentrations within the location (Appendix A, section 7), with little contribution from the low concentration sites within a location. A few locations though were noted that may have an exceptional number of estimated exceedances as a result of the air quality adjustment approach, particularly those locations with few monitoring site s that contained very low concentrations and/or atypi cal variability in hourly concen trations. These few locations (e.g., Miami, Jacksonville, and Provo) may contai n overestimations at the upper tails of the concentration distribution, leading to bias in estimated number of exceedances at both the upper percentiles and the mean when using the air quality simulated to just meet the current and most

of the alternative standards. It should also be noted that where deviations from proportionality occur, the magnitude of the uncerta inty in the results is likely related to the magnitude of the extrapolation to the adjusted concentration leve l. This means that there is likely greater uncertainty in the results for evalua ting the current annual and the 200 ppb 98 th 

### percentile alternative standards, than when considering the 50 ppb and 100 ppb 99 th 

### percentile alternative

standard. Given the limited deviations in linearity and pr oportionality at each m onitor site that may

result in both over- or under-estimations in concentrations following either an adjustment upwards or downwards and the limited time and re sources available to de velop a new universal

132

### approach that addresses each of the observed de viations, staff judged th e proportional approach used to simulate just meeting the current and a lternative standards as adequate and appropriate

for the scenario considered.

Figure 7-10. Comparison of measured daily maximum NO2 concentration percentiles in Philadelphia for one high concentration year s (1984) versus a low concentration years (2007) at four ambient monitors.

7.4.6 On-Road Concentration Simulation On-road and ambient monitoring NO 2 

### concentrations have been shown to be correlated significantly on a temporal basis (e.g., Cape et al., 2004) and motor vehi cles are a significant

emission source of NO x 

### , providing support for estimating on-ro ad concentrations using ambient monitoring data. The relationship used in this analysis to estimate on-road NO 2 

### concentrations was derived from data reported in measurement studies containing mostly long-term averaging times, typically 7-14 days or greater in durat ion (e.g., Roorda-Knape, 1998; Pleijel et al., 2004; Cape et al, 2004). One study was conducted ove r a one-hour time averaging period however the results were reported for time-averaging of at least 1-day (Rodes and Holland, 1981). Use of such data is considered appropria te in this analysis to estimat e on-road hourly concentrations from hourly ambient measures, assuming a direct relationship exists between the short-term

133

### peaks and time-averaged concentrations (e.g., hourly on-road NO 2 

### concentrations are correlated with 24-hour averages). While this should not impact the ove rall contribution relationship

between vehicles and ambient co ncentrations on roads, the rela tionship will likely differ for shorter averaging times. However, the longer- term data used to develop the algorithm were likely collected during variable c onditions (e.g., shifting wind direc tion, variable diurnal rate of

transformation of NO to NO 2 

### ) than would be observed across s horter time periods. Therefore, distribution of the adjustment factors based on 7-14 day averaging-times may be biased at the tails, that is, upper percentile values used may be biased low and the lower percentiles may be biased high. This could result in either over- or under-estimations of 1-hour NO 2 

### concentrations, depending on the time of day. In addition, th e ambient concentrati on level could not be considered in the application of the on-road factor to the hourly concentration because the relationship between the derived adjustment f actor and the ambient concentration on that temporal scale is unknown. If there is a concen tration dependent relationship, this would bias

the estimated on-road concentr ations with unknown direction. Application of the on-road

concentration estimation also assumes that concen tration changes that oc cur on-roads and at the monitor are simultaneous (i.e., with in the hour time period of estima tion). While this may not be the case, because time-activity patterns of indi viduals are not considered in this air quality benchmark characterization, there would be no bias in the number of estimated exceedances. If assessing personal exposures to individuals ne ar roadways or within vehicles that are traveling on a road, it is likely that their exposure concentrations would also vary due to differing roadway concentrations. There was limited data available in the development of the on-road adjustment factor for differing road-types and mo st of the data used were reported for urban areas (Appendix A, Table A-108). The factors deve loped should be appropriate for use in urban areas, as was done in this REA, however repr esentation of all road-types (e.g., freeways, arterials, or local roads) in the urban areas modeled is largely unknown. On-road concentrations were not adjusted in this analys is to account for in-vehicle pene tration and decay. Therefore, invehicle concentrations would be overestimated if using the estimated on-road concentrations as a surrogate, given that r eactive pollutants (e.g., PM 2.5 

### ) tend to have a lowe r indoor/outdoor (I/O) concentration ratio (Rodes et al., 1998). One study reported mean (I/O) ratios of NO 2 

### for a few roadways and driving conditions in Hong Kong (Chan and Chung, 2003). On highways and

134

### urban streets, the I/O is centered about 0.6 to 1.0, indicating removal of outdoor NO 2 

### as it enters

inside a vehicle.

At locations where traffic c ounts are very low (e.g., on the order of hundreds/day) the onroad contribution has been shown to be neglig ible (Bell and Ashenden, 1997; Cape et al., 2004), therefore any monitors sited in rural areas near roads with minimal traffic volumes could result

in small overestimations of NO 2 

### concentrations when using equa tion (7-2) at these locations. This is not of great concern because most of th e monitors used in the on-road simulation were sited within large CMSA/MSA, likely encompassing urban/suburban features of a location rather

than rural areas. Monitors site d within 100 m of the roadway in the named locations were not

used in the calculation of on-road concentrations due to the possibility of these monitors already accounting for notable impact from vehicle em issions (e.g., Beckerman et al., 2008), thus controlling for over-estimating the on-road concen trations. However, there is potential for influence by non-road source emissions on the meas ured concentrations at the monitors used ( ≥ 100 m from a major road), contrary to an assumpti on that there is an abse nce of direct source influence (only that mobile sources were controlled for by selecti ng these monitors). Therefore, if using ambient monitors directly affected by emissions from non-road sources, the simulated on-road concentrations may be over-estimated. For example, the estimated number of on-road exceedances was greater for Jacksonville and Prov o than at the other locations, even though all

on-road simulations used monitors sited ≥ 100 m from a major road. The predominant land use

however for both of these monitors was co mmercial, although monitoring objective and measurement scale were unknown. In addition, the ambient monitors in the aggregate locations (i.e., Other MSA, Other Not MSA) did not have distances to major roads calculated and may include a number of site-years of data from monitors <100 m from a major road. The estimated number of daily maximum on-road exceedances may be over-estimated for these aggregate

locations. Another source of uncertainty in the spatial heterogeneity of NO 2 

### concentrations regards the presence of street canyons on roadways. These localized ar eas may be subject to highly variable and higher mean concentrations within a short span of a ro ad, often defined by the presence of man-made structures, such as build ings, on both sides of the road. In one study, a comparison of street canyon measured NO x 

### concentrations with those measured at a reference site (termed background) indica ted that there is about a f actor of 2.3 difference in the

135

### concentrations (Ghenu et. al, 2007). Var doulakis et al. (2004) reported mean NO 2 

### concentrations at a major intersection can be a factor of about 2.1 times greater than on-road concentrations measured at a few hundred meters distance within a street canyon. 12 

### Because these factors are within the range of adjustment factors used here in estimating the on-road concentration, i.e., ranging from a factor of 1.2 to 3.7 times the ambien t concentrations, it is likely that some of the estimated on-road concentrations in the air qual ity characterization are similar in magnitude to those found in street canyons. To represent the relationship between the on-road concentrat ions and the concentrations measured at a distance, a simple exponential mo del was used. The sele ction of an exponential model was based on independent peer-reviewed studi es that reported this type of relationship using NO 2 

### measurements. There are uncertainties and possible biases with the selection of the exponential model. For example, NO x 

### is primarily emitted as NO (e.g., Heeb et al., 2008; Shorter et al., 2005), with substantial seconda ry formation due predominantly to NO + O 3 

### Æ

NO 2 

### + O 2 

### . Numerous studies have demonstrated the O 3 

### reduction that occurs near major roads, reflecting the transfer of odd oxygen to NO to form NO 2 

### , a process that can impact NO 2 

### concentrations both on- and downwind of the road. Some studies report NO 2 

### concentrations increasing just downwind of roadways a nd that are inversely correlated with O 3 

### (e.g., Beckerman et al., 2008), suggesting that peak concentration of NO 2 

### may not always occur on the road, but at a distance downwind. While an exponential model ma y fit well (or for portions of the data), the peak may be occurring at a distance from the ro ad rather than on the road. Model convergence was one criteria used in selecting for useful pa rameter estimates. One of the principal reasons for lack of convergence is that the measurement da ta did not fit the exponen tial form considered. Therefore, if the study measurement data contained peak concentrations at a distance of the road

and other lower concentrations closer to the ro ad and along the transect, it was likely that there were no valid parameters estimated for that study data. It follows that for studies where the nearest roadway distance was not at the edge of the road and the overall concentrations pattern is to increase with decreasing distan ce from the road, the estimated on-road adjustment factors may be biased high. This would occur when the concentration pattern followed the exponential model well, peaking at the nearest road meas urement, but in the absence of additional measurements closer to the road, the model assu mes a further increase in concentrations with

12 Ambient concentrations at a site not influenced by mobile sources were not reported by Vardoulakis et al. (2004).



### 136

### decreased distance to the road. Therefore, the uncertainty regarding where the peak concentration occurs (on-road or at a distance from the road) in combinati on with the form of the exponential model (the highest conc entration occurs at zero distan ce from road) and the selection

of studies that fit the exponential model, may also add a mo derate level of uncertainty in the

estimated on-road concentrations and the number of exceedances. The manner in which the on-road adjustment factor distribution was constructed and applied also introduces uncertainty to the resu lts. Based on the few influential variables available from the on-road studies, the number of values derived for the adjustment factors (n=41), a comparison of the distributions that would result in considering the potentially influential variables, and considering how th e factors would be applied to the ambient monitoring data, it was decided that a two s eason categorization was the most appropriate

characterization of the derived data (Appendix A, section 8.2). There is some bias in the application of the season categorization due to the presence of on-road adjustment factors derived from annual average data within the not summer distribution. Staff judged that on-road adjustment factors that included four seasons (s pring, fall, summer, and winter) would be more reflective of expected conditions during non-summer months rather than during summer months. Rather than excluding 27% of the derived ratios, staff decided to retain the ratios and include

them in the not summer category. Using an un-weighted mixture of urban and ru ral on-road adjustment factors within each season category assumes that the distribution of each appropriately reflects the balance of these factors within each location. First, some of the studies used in developing the on-road adjustment factors did not distinctly report whet her the data were from urban, suburban, or rural areas. There is a moderate level of uncertainty in the judgment by staff in characterizing the reported study data as urban or rural, an uncertain ty that was used to support the decision to not characterize the on-road adjustment factor distribution based on this potentially influential factor. Second, the values for the m ratios derived from studies eith er described as by the original researchers or inferred by staff as ru ral areas (min 0.36, median 0.89, max 2.44) were comparable to those described as by the original researchers or inferred by staff as urban areas (min 0.21, median 0.74, max 2.70). And finally, gi ven the schedule and resources available to produce the REA, the uncertainty in the categoriza tion by area type, and little difference between

137

### the potential distributions, staff decided not to stratify the on-road adjustment factors by area

type. Each season category was represented by an empirical distribution, with each value from the distribution having an equal probability of se lection. While there may be other distribution forms that could be alternatively selected, staff judged that use of a fitte d distribution would not improve the representation of the true population of m values compared with a empirical form, and that there would likely be minimal effect on the estimated number of exceedances. Use of an empirical distribution was done because neither season group of data could be assigned to a particular distribution type (e.g., normal, lognormal, exponential, gamma; see Figures 7-11 and 7-12), although the summer data set was significantly differe nt from a normal distribution (p<0.01). While there is uncertainty associated w ith the use of the empirically-derived data in representing the true population of m values, assuming a fitted distribution is not without its own uncertainties. For example, using a lognorma l distribution may underestimate the observed frequency of certain values of m (Figures 7-11 and 7-12), and wh ile allowing for values outside of the empirical distribution, w ould still need to have realis tic bounds placed on the minimum and maximum values, further adding to uncertainty regarding the shape and form of the fitted distribution. In addition, allowing for the selection of on-road adju stment factors outside of the range of the empirical data usi ng a fitted distribution would have a low frequency such that the overall impact to the estimated on-road NO 2 

### concentrations would likely be limited (Table 7-34). Furthermore, the m ratios were derived from meas urement data, they are not actual measurements but are measurement-based. Fitting a distribution from the modeled data may also add to the existing model uncertainty. Each of these factors mentioned (t he number of samples, uncertainty in the limits and shape of the distri bution, fitting distributions to modeled data) were considered and it was decided by staff that the empirical distribution derived from the measurement data would be most representative. Staff did however investigate the affect on the number of exceedances when using an

alternative fitted distribution. Lognormal distributions were se lected, with lower and upper bounds of the on-road adjustment factor defined by the 0.5 th 

### and 99.5 th 

### percentiles (Table 7-34). On-road adjustment factors were obt ained by sampling from the fitted m distribution for each

season 13 

### , and then adding 1 (see equation 7-2). The on-road simulation was performed using this

13 {geometric mean, geometric standard deviation}: Summer {0.989, 1.65}, Not summer {0.643, 1.73}.



### 138

### fitted distribution in the same manner done using the empirical distribution (section 7.2.4) using 2004-2006 air quality. The average number of estimated exceedances at 100, 200, and 300 ppb were compared with those generated using the empirical distribution by taking the difference of the two exposure estimates. In general, th e difference between the estimated number of exceedances obtained using the two different distributions was small when considering unadjusted air quality (Table 7-35). Most locations had between 0 to 3 additional daily maximum on-road exceedances at the 100 ppb benchmark using the fitted lognormal distribution, with no difference in daily maximum exceedances at the 200 ppb or 300 ppb level.

Differences between the two on-road simulations were similarly small considering air quality adjusted to just meet the current annual standa rd, although differences were also present at the higher benchmark levels. In addition, the differen ces in the two simulations were variable at each location, that is, at some locations, the logn ormal distribution generated a greater number of exceedances (e.g., El Paso, New York), while at other locations a fewer number of exceedances (e.g., Atlanta, Detroit) than when us ing the empirical distribution.

139

### Table 7-34. Comparison of empirical distribution of on-road adjustment factors used in on-road concentration estimation with a fitted lognormal distribution. Distribution Form Season Group Percentile Empirical Lognormal 0.5 1.22 1.16 1.0 1.22 1.18 2.3 1.22 1.22 2.5 1.22 1.22 5.0 1.22 1.26 94.4 2.50 2.54 95.0 2.54 2.59 97.5 2.54 2.88 99.0 2.54 3.30 Not Summer

99.5 2.54 3.64 0.5 1.49 1.27 1.0 1.49 1.31 2.5 1.49 1.37 5.0 1.51 1.44 7.8 1.51 1.49 95.0 3.45 3.25 97.5 3.70 3.63 97.8 3.70 3.70 99.0 3.70 4.16 Summer

99.5 3.70 4.58 Notes: Bold font indicates percentile where empirical distribution minimum and maximum intersect with a fitted lognormal distribution. On-road adjustment factors are 1+ m , see section 7.2.4 of REA.



### 140

### Figure 7-11. Comparison of the distribution of estimated C v 

### /C b 

### ratios or m for the not

summer category with fitted distributions.

Figure 7-12. Comparison of the distribution of estimated C v 

### /C b 

### ratios or m for the summer category with fitted distributions.

141

### Table 7-35. Absolute difference in the estimated number of exceedances of potential health effect benchmarks on-roads using either a fitte d lognormal distribution or empirical distribution of the on-road adjustment factors and 2004-2006 air quality as is and air quality adjusted to just meet the current annual standard. Difference in Mean Number of Daily Maximum Exceedances 2

As Is Current Annual Standard

Location ≥ 100 ppb ≥ 200 ppb ≥ 300 ppb ≥ 100 ppb ≥ 200 ppb ≥ 300 ppb Atlanta 0 0 0 0 2 1 Boston 0 0 0 0 2 0 Chicago -1 0 0 0 -2 0 Denver -1 0 0 0 0 -1 Detroit -1 0 0 1 2 3 El Paso -2 0 0 -1 -1 -1 Jacksonville 0 0 0 -1 -2 -4 Las Vegas 0 0 0 0 1 1 Los Angeles 0 0 0 0 1 0 Miami -2 0 0 -1 0 -1 New York -3 0 0 -1 -3 -1 Philadelphia -1 0 0 0 -1 -1 Phoenix 0 0 0 0 -3 2 Provo -9 -1 0 -3 -3 -11 St. Louis 1 0 0 0 3 -1 Washington DC 0 0 0 0 1 -1 Other MSA 0 0 0 0 0 0 Other Not MSA 0 0 0 0 1 1 Notes: 1 Differences are obtained by subtractin g on-road exposure results using fitted lognormal distribution from the results obtained using an empirical distribution.



### Another source of uncertainty is the extent to which the near-road study locations used to derive the on-road simulation factors represent th e locations in these analyses. The on-road and near-road data were collected in a few locations, most of them outside of the United States. The source mixes (i.e., the vehicle fleet) in study locations may not be repr esentative of the U.S. fleet.

Without detailed information char acterizing the emissions pattern s for the on-road study areas, there was no attempt to match the air quality ch aracterization locations to specific on-road study areas, which might improve the precision of th e estimates. When considering the two U.S. studies containing the required measurement data (Rodes et al. 1981; Singer et al.; 2004), three m ratios were estimated (i.e., 0.93, 1.54, and 2.43) sim ilar in range with the ratios estimated using

data obtained from non-U.S. studies. This evid ence implies that the le vel of uncertainty in

142

### 143 applying the non-US studies for the purposes of this analysis may not be la rge, although there is limited data available to make this judgment.

7.4.7 Health Benchmark The choice of potential health effect benchmarks, and the use of those benchmarks to assess risks, can introduce uncertainty into the risk assessment. Fo r example, the potential health

effect benchmarks used were based on st udies where volunteers were exposed to NO 2 

### for varying lengths of tim e. Typically, the NO 2 

### exposure durations were between 30 minutes and 2

hours. This introduces some uncer tainty into the char acterization of risk, which compared the potential health effect benchmarks to estim ates of exposure over a 1-hour time period. Therefore, the use of a 1-hour averaging-time could either over- or unde r-estimate risks. In addition, the human exposure studies ev aluated airways responsiveness in mild asthmatics. For ethical reasons, more severely affected asthmatics and asthmatic children were

not included in these studies. Severe asth matics and/or asthmatic children may be more susceptible than mildly asthmatic adults to the effects of NO 2 

### exposure. Therefore, the potential

health effect benchmarks based on these studie s could underestimate risk s in populations with greater susceptibility.

## 7.5 KEY OBSERVATIONS

Presented below are key observations resulti ng from the air quality characterization:

• NO 2 

### concentrations and estimates of be nchmark exceedances are typically higher

for monitors that are within 20 m of a major roadway than when monitors are farther (i.e., between 20 m and 100 m or ≥ 100 m) from a major roadway.

• Estimated on-road annual average NO 2 

### concentrations, based on simulated air quality, are, on average, 80% higher th an the respective ambient levels at

distances ≥ 100 m from a road. This falls with in the range of on-road to distant

monitor concentration ra tios reported in the ISA (about 2-fold higher concentrations on-roads) (ISA, section 2.5.4).

• For unadjusted air quality, representing a recent year, many locations are estimated to have, on average, 0 days pe r year where the 1-hour daily maximum

ambient NO 2 

### concentrations are > 100 ppb. Only one location is estimated to experience more than 10 such days, though results were from a monitor sited within a predominantly commercial area. Most locations are estimated to have, on average, 0 days per year with 1-hour daily maximum ambient NO 2 

### concentrations > 200-300 ppb. No location is estimated to have more than 1 such day per year, on average (Tables 7-14 to 7-19). The corresponding annual average NO 2 

### concentrations typically ranged from 10 to 30 ppb (Tables 7-11 to 7- 13). In contrast, most locations are estim ated to have between 10 and 50 days per year where 1-hour daily maximum NO 2 

### concentrations are > 100 ppb based on simulated on-road air quality. On average, most locations are estimated to have fewer than 5 days per year where 1-hour daily maximum on-road NO 2 

### concentrations are > 200 ppb (Table 7-21). The annual average of estimated onroad NO 2 

### concentrations typically ranged from 15 to 45 ppb (Table 7-20).

• When air quality is adjusted to simulate just meeting the cu rrent annual standard, a hypothetical scenario requiri ng air quality to be adjust ed upward, all locations evaluated are estimated to have multip le days per year where 1-hour daily

maximum ambient NO 2 

### concentrations are > 100 ppb. Most locations are estimated to have, on average, 50 days or more per year with 1-hour daily

144

### 145 maximum ambient NO 2 

### concentrations > 100 ppb, and six locations are estimated to have 100 days or more per year with 1-hour daily maximum ambient NO 2 

### concentrations > 100 ppb. Fewer benchmark exceedances are estimated to occur with higher benchmark levels. For exampl e, only two locations are estimated to have 10 or more days per year w ith 1-hour daily maximum ambient NO 2 

### concentrations that equal or exceed 200 ppb (Figures 7-2 and 7-3). Most locations are estimated to have between 100 and 300 days per year with 1-hour daily maximum on-road NO 2 

### concentrations > 100 ppb and between 25 and 100 days per year with 1-hour daily maximum on-road NO 2 

### concentrations > 200 ppb (Figure 7-6). The corre sponding annual average NO 2 

### concentrations were typically between 30 and 50 ppb (Table 7-28). In a number of locations, potential alternative standard levels of 0.05 and 0.10 ppm are estimated to result in far fewer days per year than standard levels of 0.15 and 0.20 ppm with NO 2 

### concentrations > 100 ppb (Tables 7-26 and 7-27). When c onsidering the potential alternative standard levels of 0.05 and 0.10 pp m, corresponding annual average NO 2 

### concentrations were typically between 10 and 30 ppb, si milar to a range of concentr ations using unadjusted air quality. When considering the potential alte rnative standard levels of 0.15 and 0.20 ppm, corresponding annual average NO 2 

### concentrations were typically between 25 and 55 ppb, similar to the range of concentrations observed when us ing adjusted air quality that just meets the

current annual standard.

# 8. EXPOSURE ASSESSMENT AND HEALTH RISK

CHARACTERIZATION

8.1 OVERVIEW

This section documents the methodology and data used in the inhalation exposure

assessment and associated health risk characterization for NO 2 

### conducted in support of the

current review of the NO 2 

### primary NAAQS. Two important components of the analysis include estimating temporally and spatially variable ambient NO 2 

### concentrations and simulating human contact with these pollutant c oncentrations. The approach was designed to better reflect exposures that occur nearby or on a roadway, not necessarily reflected by the existing ambient monitoring data. Both air quality and exposure modeling approaches have been used to generate estimates

of 1-hour NO 2 

### exposures within Atlanta, Georgia based on a 3-year period (2001-2003). AERMOD, an EPA recommended dispersion model, was used to estimate 1-hour ambient NO 2 

### concentrations using emissions estimates from st ationary and on-road mobile sources. The Air Pollutants Exposure (APEX) model, EPAs human exposure model, was then used to estimate population exposures using the hourly census block level NO 2 

### concentrations estimated by

AERMOD. Exposure and potential health risk were characterized cons idering recent air quality conditions ( as is ), for air quality adjusted upwar d to just meet the current NO 2 

### standard (0.053 ppm annual average), and for just meeting several pot ential alternative standards (see chapter 5). The estimated 1-hour exposures for each of these air quality scenarios were compared with the 1- hour potential health effect benchm ark levels identified in chapter 6. Specific ally, the number of times an individual experienced a daily maximu m 1-hour exposure concentration in excess of 100 ppb through 300 ppb was recorded. The exposures for each individual were estimated over an entire year therefore, multiple occurrences of exceedances were recorded, giving the number of days per year with an exceedance of the pot ential health effect be nchmark levels. The approaches used for assessing exposures in Atlanta are described below. Detailed input data and supporting discussi on of the Atlanta case-study is provided in Appendix B-4, in addition to containing the methodology and result s for the first exposure modeling case-study

146

### conducted in Philadelphia County as part of the 1 st 

### draft REA (EPA, 2008b). The Philadelphia County assessment is not included in this REA. There were a few major differences in the approaches used that would not necessarily a llow for a direct comparison of the estimated exposures with those estimated for Atlanta, ther efore the approach and the exposure results for the Philadelphia County case-study are di scussed entirely in Appendix B. Briefly, the discussion that follows here includes:

• Description of the inhalation exposure mode l and associated input data used for

Atlanta

• Evaluation of estimated NO 2 

### air quality concentrations and exposures

• Assessment of the quality and limitations of the input data for supporting the goals of

the NO 2 

### NAAQS exposure and risk characterization. The overall flow of the exposure mo deling process performed for this NO 2 

### NAAQS review is illustrated in Figure 8-1. Several models were used in addition to APEX and AERMOD including emission factors, meteorologi cal processing, and travel demand models, as

well as a number of data bases and literature s ources to populate the model input parameters. Each of these are described within this chap ter, supplemented with additional details in Appendix B.

147

### Figure 8-1. General flow used for NO 2 

### exposure assessment.

148

## 8.2 OVERVIEW OF HUMAN EXPO SURE MODELING USING APEX

The EPA has developed the Air Pollutants E xposure Model (APEX) model for estimating human population exposure to criteria and air to xic pollutants. APEX serves as the human inhalation exposure model within the Total Risk Integrated Methodology (TRIM) framework (EPA 2006a; 2006b) and was recently used to estimate population exposures in 12 urban areas

for the O 3 

### NAAQS review (EPA, 2007g; 2007h). APEX is a probabilistic model designed to acco unt for sources of variability that affect people’s exposures. APEX simulates the move ment of individuals through time and space and estimates their exposure to a given pollu tant in indoor, outdoo r, and in-vehicle microenvironments. The model stochastically gene rates a sample of simulated individuals using census-derived probability distributions for demographic characteristics. The population demographics are drawn from the year 2000 Census at the tract, block-gro up, or block level, and a national commuting database based on 2000 cen sus data provides home-to-work commuting flows. Any number of simulate d individuals can be modeled, a nd collectively they approximate a random sample of people residi ng in a particular study area. Daily activity patterns for individuals in a study area, an input to APEX, are obtained from detailed diaries that are compiled in th e Consolidated Human Ac tivity Database (CHAD) (McCurdy et al., 2000; EPA, 2002). The diaries ar e used to construct a sequence of activity

events for simulated individuals consistent with their demographic characteristics, day type, and season of the year, as define d by ambient temperature regime s (Graham and McCurdy, 2004). The time-location-activity diaries input to APEX contain information rega rding an individuals’ age, gender, race, employment status, occupa tion, day-of-week, daily maximum hourly average temperature, the location, start ti me, duration, and type of each act ivity performed. Much of this information is used to best match the activity diary with the generated personal profile, using age, gender, employment status, day of week, and temperature as first-order characteristics. The approach is designed to capture th e important attributes contributi ng to an individuals’ behavior, and of likely importance in this assessment (i.e., time spent outdoors) (Graham and McCurdy, 2004). Furthermore, these diary selection criteria give credence to the use of the variable data that comprise CHAD (e.g., data collected were fro m different seasons, different states of origin, etc.).

149

### APEX has a flexible approach for modeli ng microenvironmental concentrations, where

the user can define the microenv ironments to be modeled and their characteristics. Typical indoor microenvironments include residences, sc hools, and offices. Outdoor microenvironments include for example near roadways, at bus stops, and playgrounds. Inside cars, trucks, and mass

transit vehicles are microenvironments which are classified separately from indoors and outdoors. APEX probabilistically calculates the concentration in the microenvironment

associated with each event in an individual’s activity pattern and sums the event-specific exposures within each hour to obtain a continuous series of hourly exposures spanning the time period of interest. The estimated microenvi ronmental concentrati ons account for the contribution from ambient (outdoor) pollutant con centration and influential factors such as the penetration rate into indoor microenvironments , air exchange rates, decay/deposition rates, proximity to important outdoor sources, and indoor source emissions. Each of these influential factors are dependent on the microenvironment modele d, the available data to define each of the parameters, and the estimation method select ed by the user. And, because the modeled individuals represent a random sa mple of the population of intere st, the distribution of modeled individual exposures can be ex trapolated to the larger popula tion within the modeling domain. The exposure modeling simulations can be summa rized by five steps, each of which is detailed in the subsequent sections of this doc ument. Briefly, the five steps are as follows.

1. Characterize the study area . APEX selects the census bl ocks within that study area – and thus identifies the potentially e xposed population – based on user-defined criteria and availability of air quality and meteorological data for the area.

2. Generate simulated individuals . APEX stochastically generates a sample of hypothetical individuals based on the de mographic data for the study area and estimates anthropometric and physiological pa rameters for the simulated individuals.

3. Construct a sequence of activity events . APEX constructs an exposure event sequence spanning the period of the simulati on for each of the simulated individuals using the time-location- activity pattern data.

4. Calculate hourly concentrations in microenvironments . APEX users define microenvironments that people in the st udy area visit by assigni ng location codes in the activity pattern to the user-specifi ed microenvironments. The model then calculates hourly pollutant c oncentrations in each of thes e microenvironments for the

150

### period of simulation, based on the user-pr ovided microenvironment descriptions, the hourly air quality data, and for some of th e indoor microenvironments, indoor sources

of NO 2 

### . Microenvironmental concentrations ar e calculated for each of the simulated

individuals.

5. Estimate exposures . APEX estimates a concentrati on for each exposure event based on the microenvironment occupied during the event. These values can be averaged by clock hour to produce a sequence of hourly average exposures spanning the specified exposure period. These hourly values may be further aggregated to produce daily, monthly, and annual average exposure concentrations.



## 8.3 CHARACTERIZATION OF STUDY AREA

8.3.1 Study Area Selection

The selection of the location used for this exposure analysis was based on the location of field and epidemiology studies, the availability of ambient monitoring an d other input data, the desire to represent a range of geographic areas, population de mographics, general climatology,

and results of the ambient air quality characterization. Atlanta, along with several othe r locations, was initially selected as a location of interest through statistical analysis of the ambient NO 2 

### air quality data (see s ection 7 and Appendix A). Briefly, criteria were established for selecting ambient monitoring sites having high annual mean concentrations and/or exceedances of potential h ealth effect benchmark concentrations. The 90 th 

### percentile served as the point of reference fo r the annual mean concentrations and, across all complete site-years for 2001-2006, this value was 23.5 ppb. Seventeen locations had one or more site-years with an annual aver age concentration at or above the 90 th 

### percentile, of which Atlanta had one site-year (26.6 ppb annual average). A 1-hour potential hea lth effect benchmark level of 200 ppb was selected as the second criteria fo r location selection, and Atlanta had one

measured concentration above this level. An additional grouping of locations was done base on geographic regions of the U.S., w ith Atlanta as one of the locations in the southeastern U.S. EPA was also able to obtain measured daily NO 2 

### exposures of several individuals residing in Atlanta (Suh, 2008), potentially for use in evaluating the APEX estimated exposures. Therefore, in considering each of these select ion criteria, 1) the availability of health effects data associated with ambient concentrations (Tolbert et al., 2007), 2) the availability of personal exposure

151

### measurements, 3) the analysis of the air quality data, and 4) geographi c representation, Atlanta was selected as the second case-study location.

8.3.2 Study Area Description The greater Atlanta metropolitan area cove rs the 13 counties w ithin a radius of approximately 40 km about the Atlanta city center (33.65 °N 84.42 °W) in Fulton County. Due to the complexity of the air qua lity and exposure modeling to be performed in this exposure assessment, the study location (or modeling domain) was designated as the f our counties directly surrounding the city of Atlanta (i.e., Cobb, De Kalb, Fulton, and Gwinnet Counties) (see Figure 8-2). These four counties comprise the urban center of the Atlanta MS A, and contain a large portion of the urbanized road sy stems in the area. This four county modeling domain contains 27,315 U.S. Census blocks with a combined population of 2,678,078 (2000 Census), comprising approximately 65% of the Atlanta MSA population.

8.3.3 Time Period of Analysis Calendar years 2001 through 2003 were simulated to envelop the most recent year of emissions data available for the study location (i .e., 2002) and to include a total of 3 years of meteorological data to achieve a degree of re presentativeness in the dispersion and exposure model estimates. In considering the past 30-years of annual average temperature and precipitation in Atlanta, the three years were va riable. On a scale of hi gh to low, in 2001 the temperature was about average (ranked 17 th 

### ) though dry (28 th 

### lowest precipitation level), 2002 had average precipitation (18 th 

### ) although warmer than most years (12 th 

### ), and 2003 was cooler (25 th 

### ) and wetter (11 th 

### ) than many other years (NCDC, 2007)

8.3.4 Populations Analyzed The exposure assessment included the total popula tion residing in each modeled area and considered susceptible and vulnerable populations as identified in the ISA. These include population subgroups defined from either an exposure or health perspective. The population subgroups identified by the ISA (EPA, 2008b) that we re included and that can be modeled in the exposure assessment include:

• Children (5-18 years in age)

• Asthmatic children (5-18 years in age)

152

### • All persons (all ages)

• All Asthmatics (all ages)

In addition to these population subgroups, indi viduals anticipated to be exposed more frequently to NO 2 

### were assessed, including those commuti ng on roadways and persons residing near major roadways.

Figure 8-2. Four county modeling domain used for Atlanta exposure assessment.

153

## 8.4 CHARACTERIZATION OF AMBIENT AIR QUALITY USING

AERMOD

8.4.1 Overview Air quality data used for input to APEX we re generated using AERMOD, a steady-state, Gaussian plume model (EPA, 2004). The followi ng steps were performed to estimate air concentrations using AERMOD.

1. Collect and analyze general input parameters. Meteorological data, processing methodologies, and information on surface characteristics and land use were used to determine pollutant dispersion char acteristics, atmospheric stability, and mixing heights.

2. Define sources and estimate emissions . The emission sources modeled included major stationary emission sources, on-road emissions that occur on major and minor roadways, and emissions from Atla nta Hartsfield International Airport. 1



### 3. Define receptor locations . Three sets of receptors were identified for the dispersion modeling, and included, ambien t monitoring locations, census block centroids, and links along major roadways.

4. Estimate concentrations at receptors . Hourly concentrations were estimated for each year simulated (2001-2003) by combining the estimated concentration

contributions from each of the emission sour ces at each of the defined receptors. A brief description of input da ta and approaches used for estimating source emissions are

described below. Additional details on the i nputs and assumptions used in the dispersion modeling are provided in Appendix B-4.

8.4.2 General Model Inputs

8.4.2.1 Meteorological Inputs All meteorological data used for the AE RMOD dispersion model simulations were processed with the AERMET meteorological preprocessor, version 06341. Raw meteorological

1 Fugitive emissions from major point sources in the Atlant a area were not included as was done in the Philadelphia County case study, since the NEI shows all em issions to be accounted by stack totals.



### 154

### data from the Southeast Aerosol Research and Characterization study (SEARCH) site in Atlanta were used as the primary source of mete orology for the AERMOD runs for the years 2001 through 2003. Raw hourly surface meteorologi cal data for the 2001 to 2003 period were obtained from the Integrated Su rface Hourly (ISH) Database, 2 

### primarily for use in modeling the

emissions from the Atlanta Hartsfield Internat ional Airport (KATL). Upper air data in the Forecast System Laboratory (FSL) format was downloaded from the FSL, (now Global Systems Division) website, http://www.fsl.noaa.gov/ . Details regarding th e data preparation and processing are given in A ppendix B, Attachment 1.

8.4.2.2 Surface Characteristics and Land Use Analysis In addition to the standard meteorological observations of wind, te mperature, and cloud cover, AERMET analyzes three principal variable s to help determine atmospheric stability and mixing heights: the Bowen ratio, surface albedo as a function of the sola r angle, and surface roughness. A draft version of AERSURFACE (08256) was used to estimate land-use patterns and calculate these three variables as part of the AERMET processing, us ing the US Geological Survey (USGS) National La nd Cover Data 2001 archives. 3 

### Details for the seasonal specification definitions, land-use sectors, and data proces sing are given in Appendix B, Attachment 1.

8.4.2.4 Other AERMOD Input Specifications

All emission sources in the Atlanta m odeling domain were characterized as urban , using the 2000 census population of approximately 4.1 million people in the Atlanta MSA. 4 

### The

AERMOD toxics enhancements were also employed to speed calculations from area sources.

NO x 

### chemistry was applied to all sources to determine NO 2 

### concentrations. For the roadway and airport emission sources the Ozone Limiting Method (OLM) (EPA, 2006c) was used, with plumes considered grouped . For all point source simulations, the Plume Volume Molar Ratio

Method ( 

### PVMRM) was used to estimate the conversion of NO x 

### to NO 2 

### (Hanrahan, 1999a, 1999b). The equilibrium value for the NO 2 

### :NO x 

### ratio was taken as 75%, the national average ambient background ratio. 5 

### The initial NO 2 

### fraction of NO x 

### is anticipated to be about 10% or

2 National Climatic Data Center (NCDC), http://www1. ncdc.noaa.gov/pub/data/techrpts/tr200101/tr2001-01.pdf 3 http://seamless.usgs.gov/

4 http://www.census.gov/Press-Re lease/www/2001/sumfile1.html

5 Appendix W to CFR 51, page 466. http://www.epa.gov/scram001/ guidance/guide/appw_03.pdf

.



### 155

### less (Finlayson-Pitts and Pitts, 2000; Yao et al., 2005), therefore a conservative value of 10% was selected from the upper range of th is estimate and used for all sources. Hourly surface O 3 

### data for years 2001-2003 were obtained from five ambient monitors operating as part of EPA’s Air Quality System (AQS) 6 

### and from one ambient monitor operating as part of the South Eastern Aerosol Re search and Characterization (SEARCH) study. 7 

### Missing

data were substituted based on seasonal and time of day characteristics, and hourly values were averaged across each of the O 3 

### monitors which were available for a particular hour. None of the AQS monitors had data available for November , December, January, and February, for these months only the SEARCH monitor data were used. The locations of these monitors are shown in Figure 8-2.

8.4.3 Major Link On-Road Emission Estimates

Information on traffic data in the Atlanta area was obtained from the Atlanta Regional Commission (ARC) – the regional planning and intergovernmental coordination agency for the 10-county metropolitan area – via their most r ecent, baseline travel demand modeling (TDM) simulation for year 2005. Although considerable effort was expended to maintain consistency between the ARC approach to analysis of TDM data and that employed in this analysis, complete consistency was not possible due to the differing analysis objec tives. The ARC creates county emission inventories. This study created spatially and temporally resolved emission strengths for dispersion modeling. Information about expected differences in traffic between the 2005 data year and 2001-2003 modeled years was not prov ided by ARC, nor was information about

seasonal differences in MOBILE6.2 inputs. The approach used for estimating these major road

emissions is discussed further below.

8.4.3.1 Emission Sources and Locations The TDM simulation’s data file outputs include a description of the fixed information for the highway network links and traffic descriptors for four time periods: morning, afternoon, evening, and nighttime. Each period’s data incl udes free-flow speed, total vehicle count, total heavy duty truck count, total single occupancy vehicle count, and TDM-calculated congested

6 http://www.epa.gov/ttn/airs/airsaqs/detaildata/downloadaqsdata.htm

7 Ambient data were obtained from the Jefferson Street ozone monitor, maintained by Atmospheric Research & Analysis, Inc. Available at http://www.atmospheric-research .com/studies/SEARCH/index.html

.



### 156

### speeds for the period. The descri ption of the network consists of a series of nodes joining individual model links (i.e., roadway segments) to which the traffic volumes are assigned, and the characteristics of those links, such as endpoi nt location, number of lanes, link distance, and TDM-defined link daily capacity. First, all links with annu al average daily traffic ( AADT) values greater than 15,000 vehicles per day (one-way) were classified as major within the four counties (Cobb, DeKalb, Fulton, and Gwinnett) and a part of a fifth county (Clayton), whic h contains a small portion of the beltway in the MSA. Then, link locations from the TDM were modified through a GIS analysis to represent the best known locations of the actual roadways, since there was not always

a direct correlation between the two (see Appendix B-4.1.1). There were no hourly scaling factors provided for the ARC’s TDM predictions, therefore the total period volume was spread uniformly amongst all hours contributing to th at period (morning: 6AM-10AM, midday: 10AM3PM, afternoon: 3PM-7PM, or nighttime: 7PM-6AM) . A 5-hour rolling average, centered on the present hour, was applied to the emission sca ling factors to allow for a smoothing of the distribution. The heavy-duty ve hicle (HDV) fraction for each hour of each period was obtained by dividing the total period truck count by the total vehicle count, fixing the value as constant for all hours of the period, but allowing it to vary between periods and across links, according to the TDM parameterization. It should be noted that trucks, as defined in the TDM, include heavy and medium duty vehicles as well as commercial vehicles. Because no information on seasonal

variation in vehicle activ ity was available, no seasonal variation was used in the simulations. However, seasonal variations in emission f actors from MOBILE6.2 were implemented – see

section 8.4.3.2. The AADT and truck fraction from the ARC TDM used in the AERMOD simulations of major links are shown in Tables 8- 1 and 8-2, respectively. Note that no rural, local designated links meet the major link AADT criteria, and are thus omitted in Tables 8-1, 8- 2, and 8-3.

Table 8-1. Statistical summary of average annua l daily traffic (AADT) volumes (one direction) for Atlanta AERMOD simulations. Statistic Road Type CBD 1 Fringe Rural Suburban Urban Arterial 229 180 14 1,299 1,221 Freeway 109 94 2 616 616 Count

Local 41 60 168 250

Arterial 15,015 15,019 16,603 15,002 15,017 Minimum AADT Freeway 15,049 16,745 23,569 15,111 15,025



### 157Statistic Road Type CBD 1 Fringe Rural Suburban Urban

Local 15,442 15,052 15,111 15,017

Arterial 51,820 49,853 23,433 64,487 46,824 Freeway 150,047 109,204 24,028 144,434 155,083 Maximum AADT

Local 110,425 98,420 98,909 127,085

Arterial 24,814 21,732 19,016 21,383 22,434 Freeway 73,598 56,741 23,799 59,164 64,744 Average AADT

Local 25,737 26,536 23,781 25,745 Notes: 1 Central business district



### Table 8-2. Average heavy duty vehicle (HDV) fraction for Atlanta AERMOD simulations. Region Type Functional Class Time Period 1 CBD 2 Fringe Rural Suburban Urban Nighttime 12% 18% 15% 12% 13% Morning 14% 19% 18% 15% 15% Midday 17% 28% 27% 20% 20% Arterial

Afternoon 10% 16% 15% 12% 12% Nighttime 8% 20% 24% 19% 14% Morning 9% 20% 26% 19% 15% Midday 12% 27% 33% 26% 21% Freeway

Afternoon 7% 16% 21% 15% 12% Nighttime 10% 24% 18% 15% Morning 12% 24% 19% 16% Midday 14% 33% 25% 21% Local

Afternoon 9% 19% 15% 12% Notes: 1 morning: 6AM-10AM, midday: 10AM-3PM, afternoon: 3PM-7PM, or nighttime: 7PM-6AM 2 Central business district



### 8.4.3.2 Emission Source Strength

On-road mobile emission factors were derived from the MOBILE6.2 emissions model using ARC input files describing the 2002 vehicl e registration distribut ion and corresponding to

the 2008 O 3 

### season. To maintain consistency with the recent ARC simulations and current modeling parameters and maximize temporal resolution, the ARC’s O 3 

### season input files were used as a basis for all MOBILE6.2 simulations, but were modified as follows. First, the 24-hour

158

### series of temperature and humidity values in cluded in the ARC files were those derived as average values over peak O 3 

### days. To modify the focus from peak O 3 

### to average summer days, these values in the input files were modifi ed by converting to average daily minimum and maximum temperature and corresponding spec ific humidity, determined by the same meteorological record used in the dispersion simulations. Also, winter and summer-specific fuels for the Atlanta region were used for all years, which differ only until the phase-in of Georgia Phase 2 gasoline in 2003, at which point wi nter and summer sulfur levels are identical. Finally, anti-tampering and inspec tion/maintenance programs, which were not included in the original ARC input files, were taken from MOBI LE input files prepared by the State of Georgia for a previous project.

The simulations were executed to calculate average running NO x 

### emission factors in grams per mile for a specific functional class (Freeway, Arterial, Local, or Ramp), speed, and

season. Iterative MOBILE6.2 simulations were conducted to create tables of average Atlanta region emission factors resolved by speed (2.5 to 65 mph, in 1 mph increments from 3 to 65 mph), functional class, season, and year (2001, 2002, or 2003) for each of eight combined

MOBILE vehicle classes. The re sulting tables were then cons olidated into speed, functional class, and seasonal values for combined light- and heavy-duty vehicles. To create seasonalhourly resolved emissions, spring and fall values were taken as the average of corresponding summer and winter values. See Appendix B-4 for an example of the calculated emission factors for Summer, 2001. To determine the emission strengths for each link for each hour of the year, the Atlanta regional average MOBILE6.2 speed-resolved emissions factor tables were merged with the TDM link data, which had been processed to determine time-resolved speeds. The spatial-mean speed

of each link at each time was calculated followi ng the methodology of the Highway Capacity

Manual. 8 

### Table 8-3 shows the resulting average sp eed for each functional class within each TDM region. The resulting emission factors were then coupled with the TDM-based activity

estimates to calculate emissions fr om each of the major roadway links.

Table 8-3. Average calculated speed by link type in Atlanta modeling domain. Average Speed (mph)

8 As defined in Chapter 9 of Recommended Procedure for Long-Range Transporation Planning and Sketch Planning , NCHRP Report 387, National Academy Press, 1997. 151 pp., ISBN No: 0-309-060-58-3.



### 159Link Type CBD 1 Fringe Suburban Urban Rural Arterial 22 37 40 30 51 Freeway 54 62 60 57 64 Local 26 40 40 34 N/A Notes: 1 Central Business District



### 8.4.3.3 Other Emission Parameters Each roadway link is characterized as a rect angular area source with the width given by

the number of lanes and an assumed universal lane width of 12 ft (3.66 m). The length and orientation of each link is determined as the distance and angle between end nodes from the adjusted TDM locations. In cases where the distance is such that the aspect ratio is greater than 100:1, the links were disaggregated into sequent ial links, each with a ratio less than that

threshold. There were 737 links that exceeded this ratio and were converted to 1,776 segmented sources. Thus, the total number of area sources included in the dispersion simulations is 5,570. Note that there are some road segments whos e length was zero after GIS adjustment of node location. This is assumed to be compensate d by adjacent links whose length will have been expanded by a corresponding amount. Table 8-4 s hows the distribution of on-road area source

sizes.

Table 8-4. On-road area source sizes

.

Statistic Number of Lanes Segment Width (m) Segment Length (m) Minimum 1 3.7 0.0 Median 2 7.3 352.8 Mean 2.7 9.9 426.3 1- σ Deviation 1.2 4.5 330.0 Maximum 8 29.3 2218.1



### Resulting daily emission estimates were tem porally allocated to hour of the day and season using MOBILE6.2 emission factors, coupled with calculated hourly speeds from the postprocessed TDM and allocated into SEASHR emission profiles for the AERMOD dispersion model. That is, 96 emissions f actors are attributed to each road way link to describe the emission strengths for 24 hours of each day of each of f our seasons and written to the AERMOD input

control file. For light duty vehicles (LDV) it was assumed that the initial vertical extent of the plume is about 1.7 times the average vehi cle height, or about 2.6 meters fo r an average vehicle height of

160

### about 1.53 meters (5 feet), to account for the e ffects of vehicle-induced turbulence among other factors. The source release height is based on the midpoint of the in itial vertical extent, or about

1.3 meters. The initial vertical dispersion coefficient (sigma-Z o 

### ) was based on the initial vertical extent divided by 2.15, or 1.2 meters. For the heavy duty vehicles (HDV) as with LDVs , the initial vertical extent of the plume was assumed 1.7 times the average vehicle height, or about 6.8 meters fo r an average vehicle height of about 4.0 meters. Similarly, source re lease heights were based on the midpoint of the initial vertical extent, or 3.4 mete rs. The initial sigma- Zo also based on the in itial vertical extent divided by 2.15, was 3.2 meters. For effective source parameters representi ng a mix of LDV and HDV for a particular major roadway link, the source release height and initial sigma-Zo were then assigned using an emissions-weighted average based on th e vehicle mix for that roadway link.

The total NO x 

### emissions on the major roadways li nks were estimated to be 88,438 tons per year (tpy) or approximately 70% of the total on-road emissions.

8.4.4 Minor Link On-road Emission Estimates

On-road mobile emissions that do not occur on major roadway links were assigned to US

Census tracts and simulated as area sources represented by the tract polygons. There are 478 census tract area sources across the 4-county At lanta modeling domain, and a small part of Clayton County (Figure 8-3). Emission magnitudes and temporal profiles were derived with the same procedure as for the major roadway links, ho wever individual link values were not stored. Instead, each link was assigned to its respective tract and the combined emission total across

161

### Figure 8-3. The 478 U.S. Census tracts rep resenting area sources for on-road mobile emissions that do not occur on major roadway links.

links for a specific season and hour were determined for each tract. The resulting total seasonalhourly emissions profile for each tract area sour ce was then used in AERMOD. Tract-wide emission release parameters for minor links in the dispersion modeling were determined as emissions-weighted averages of light- and heavy- duty vehicle contributions to the tract total

values. Estimated NO x 

### emissions on the minor roadway links within the five counties was 38,039 tpy (Table 8-5).

Table 8-5. On-road emissions from major and minor links in Atlanta, 2002.

County Total On-Road Emissions (tpy)

FIPS 1 Name Minor Link 2 Major Link 3 % Minor 4

13063 Clayton 1,693 6,185 21% 13067 Cobb 8,329 15,816 34% 13089 DeKalb 7,134 19,871 26% 13121 Fulton 12,047 30,999 28% 13135 Gwinnett 8,835 15,568 36% Total 38,039 88,438 30% Notes: 1 Federal Information Processing Codes for each county

. 2 Minor links are those roads with ≤ 15,000 AADT. 3 Major links are those roads > 15,000 AADT.



### 1624 % Minor is the percent of minor roads in each county.



### 8.4.5 Adjustment of On-road Mobile Source Strengths to 2002 NEI Vehicle

Emissions

As noted above, the TDM data received from ARC specified traffic count projections for 2005 instead of the 2001-2003 target years for this analysis. All othe r model inputs were estimated for the target years, e.g., on-road mobile source emission factors, point source emissions (see section 8.4.6 below), airpor t emissions (see section 8.4.7 below), and meteorological data. Therefore, to maintain temporal consistenc y for all emissions used, the onroad emission strengths using the 2005 TDM data were adjusted to match 2002 totals for the 4- county modeling domain from the Nati onal Emissions Inventory (NEI). Table 8-6 compares the on-road mobile source emissions estimated for 2002 as described above (i.e., the 2005 traffic counts combined with 2002 emission factors) with the NEI estimates

for 2002. Note that the differences in these estima tes may be the result of differences in other factors in addition to the target year traffic counts, such as fleet mix and heavy-duty vehicle fractions. Based on this comparison, an adjustme nt factor of 0.78 was uniformly applied to all on-road mobile source emission strengths in the Atlanta modeling domain, for both major and

minor links.

Table 8-6. On-road vehicle emission strengths by county for Atlanta modeling domain: modeled vs NEI 2002.

County Modeled major link NO x

emissions (tpy) Modeled minor link NO x emissions (tpy) Total modeled on-road vehicle NO x emissions (tpy) NEI on-road vehicle NO x emissions for

2002 (tpy) Ratio of NEI2002-tomodeled NO x

emissions Cobb 15,816 8,329 24,145 18,754 0.78 DeKalb 19,871 7,134 27,006 21,715 0.80 Fulton 30,999 12,047 43,046 33,886 0.79 Gwinett 15,568 8,835 24,403 18,080 0.74 TOTAL 82,254 36,346 118,599 92,434 0.78



### 163

### 8.4.5 Stationary Sources Emissions Preparation Data for the parameterization of major point sources in Atlanta comes primarily from

three sources: the 2002 National Em issions Inventory (NEI; US EP A, 2007e), Clean Air Markets Division (CAMD) Unit Level Emissions Databa se (US EPA, 2007f), and temporal emission profile information contained in the EMS-HAP (version 3.0) emissions model. 9 

### The NEI

database contains stack locati ons, emissions release parameters (i.e., height, diameter, exit temperature, exit velocity), and annual emissions for NO x 

### -emitting facilities. The CAMD database contains information on hourly NO x 

### emission rates for units in the US, where the units are the boilers or equivalent, each of which can have multiple stacks. First, major stationary sources were selected from the NEI where stacks within facilities contain at least 100 tpy total NO x 

### emissions and are located eith er within the 4-county modeling domain or within 10 km of the modeling domain. Seven NO x 

### -emitting facilities met these criteria (Figure 8-4). Stacks with in the facilities that were listed separately in the NEI were combined for modeling purposes if they had iden tical stack physical pa rameters and were colocated within about 10 m. Th is resulted in 28 combined stacks (stack parameters are in Appendix B-4) and accounts for 16% of the total number of NO x 

### point sources and 51% of the

total NO x 

### point source emissions in this buffered four county Atlanta area. The CAMD database was then queried for fac ilities that matched the facilities identified from the NEI database using the facility name, the Office of Regulatory Information Systems (ORIS) identification code, and facility total em issions. Only one of the 7 major facilities

identified was found in the CAMD data ba se: the Georgia Power Company McDonough SteamGenerating Plant. The CAMD hourly emissions pr ofiles for two units in this facility were summed together and then, after ap propriate scaling, used to repr esent 2 major-facility combined

stacks. For the remaining 26 major-facility combined stacks, hourly NO x 

### emissions profiles were created based on the hourly profil e typical of that stack’s Sour ce Classification Codes (SCC), the season, and the day of week. These SCC-base d temporal profiles are year-independent, and were developed for the EPA’s EMS-HAP model, 10 

### described in the EMS-HAP model Version 2

9 http://www.epa.gov/ttn/chief/emch/projection/emshap30.html 10 http://www.epa.gov/scram001/disp ersion_related.htm#ems-hap



### 164

### 165 User’s Guide, Section D-7. 11 

### As with CAMD hourly emi ssions, these SCC-based emission profiles are scaled such that the annual total emissions ar e equal to those of NEI 2002.

8.4.6 Airport Emissions Preparation

The Atlanta-Hartsfield Intern ational Airport emissions we re assigned to a polygon that defined an area source for simulation. The peri meter dimensions of the Atlanta-Hartsfield International Airport were determined by GIS analys is of aerial photograph. As with some point source emissions, the annual NO x 

### emission totals were extracted from the NEI and the temporal profiles from the EPA’s EMS-HAP model. Thes e seasonal, SCC-based emissions were scaled such that the annual total emissions are equa l to those of NEI 2002: 5,761 tpy, with about 90% coming from commercial aircraft (see Figure 8-2 for airport locat ion, Appendix B-4 for depiction of area source polygon). The initial vertical extent of the plume for aircraft emissions was estimated as 10 m to account for typical emission heights and initial dilution parameters. A source release height of 5 m was selected based on the midpoint of the init ial vertical extent a nd the initial vertical dispersion coefficient was estimated using the initial vertical exte nt divided by 2.15, or 4.6 meters. For cargo-handling equipment a release height of 3.15 m was assumed, which is the average for cargo-handling equipment from a study by the California Air Resources Board (CARB 2006). The initial vertical dispersion coefficient was estim ated as the release height divided by 2.15, or 1.47 m. For effective source pa rameters representing a mix of aircraft and cargo-handling equipment, the source release hei ght and initial sigma-Zo were estimated using an emissions-weighted average wi th 92% of emissions contributed by aircraft. The aggregate value for release height was 4.85 m w ith an intial sigma- Zo of 4.22 m.

11 http://www.epa.gov/scram001/us erg/other/emshapv2ug.pdfFigure 8-4. Location of major roadway links and major stationary emission sources in Atlanta modeling domain.166

### 8.4.7 Receptor Locations Three sets of receptors were chosen to repres ent the locations of interest. The first set was selected to represent the locations of th e residential population of the modeling domain. These receptors were the 27,315 US Census block centroids 12 

### . In an effort to make the Atlanta case-study more time efficient, a statistical anal ysis was performed on the Philadelphia exposure assessment results reported in the 1 st 

### draft REA (see Appendix B, section 3) to determine the degree of uncertainty introduced by modeling a subset of the bloc k receptors only. The findings of that analysis indicated that the use of a ra ndom selection of 1/3 of the block centroids would provide estimates of exposure to exceedances of 200 ppb that were within 4% (90% confidence bounds = 0.3% - 10%) of the estimates obtained base d on using all the block receptors. It was judged that this uncertainty was minimal when co mpared to other uncertain ties in the analysis, and therefore, a random selection of 9,103 (1/3) of the block centroids was used for this analysis. These 9,103 Census block receptors are shown al ong with the other 18,212 block centroids are shown in Figure 8-5. For modeling efficiency, each receptor was assigned a height of 0.0 ft (0.0 m). The effect of this on the exposure estimate s in comparison with a standard breathing height of 1.8 m is negligible (see sect ion 7.4.4). Concentrations estima ted at these centroid receptors were used by APEX, along with other factors to estimate an individual’s microenvironmental concentrations (see section 8.7). The second set of receptors was chosen to represent the on-road microenvironment (Figure 8-5). For this set, one receptor was pl aced at the center of each of the 5,570 sources. Receptor concentration estimates were used by AP EX, along with other f actors, to estimate an

individual’s on-road and in-vehicle microenv ironmental exposures (see section 8.7). The

distribution of distances of the on-road and the block centroid receptors was estimated to determine the distance relationship between the on-road emissions and population-based receptors. Approximately 1% of the block centr oids are within 50 m of the center of a major roadway link and 26% within 400 m, with a geometric mean of the distribution between 750 m and 800 m (a detailed distribution is provided in Appendix B-4). 13 

### The population distribution

12 The block centroids used for this anal ysis are actually population-weighted lo cations reported in the ESRI data base. They were derived from geocoded addresses within the block taken from the Acxiom Corporation InfoBase household database (Skuta and Wombold 2008; ESRI 2008). These centroids differ from the “internal points” reported by the US Census, which are often referred to as centroids because they are designed to represent the approximate geographic center of the block. 13 The distance to the roadway edge is shorter by half the roadway width. As noted in Table 8-4 modeled roadway widths ranged from 3.7 m to 29.3 m with an average of 9.9 m and a median of 7.3 m.



### 167

### within close proximity of the road is actually a different value than the block centroids. For the population simulated in Atlanta, 17% of the populat ion resides within 75 m, 25% were between 75 and 200 m, and 58% were > 200 m of a major road. The third set of receptors included the locations of the available ambient NO x 

### /NO 2 

### monitors. These receptors were used in eval uating the dispersion model performance. When considering the four Atlanta count ies and period of analysis, there were three monitors within the modeling domain with valid ambient NO 2 

### monitoring concentrati ons (Figure 8-5).

Figure 8-5. Location of modeled re ceptors in Atlanta modeling domain.

8.4.8 Modeled Air Quality Evaluation

8.4.8.1 Comparison of Hourly Cumulative Density Functions The hourly NO 2 

### concentrations estimated from each of the four source categories were combined at each receptor. These concentration predictions were then compared with measured

concentrations at ambient NO 2 

### monitors. Rather than compare concentrations just at the single modeled receptor point to the ambient monitor c oncentrations, a distribution of concentrations was developed for the predicted concentrations fo r all receptors within a 4 km distance of the

168

### monitors, not including receptors within 100 m of a major road. Further, instead of a comparison of central tendency values alone for the number of receptors modeled (mean or median), the complete modeled and measurement concentrat ion distributions were used for comparison. As an initial comparison of modeled versus measured air quality, all modeled receptors within 4 km of each ambient monitor location, ex cluding those receptors on roadways or within 100 m of a major roadway, were used to generate a prediction envelope. 14 

### This envelope was constructed based on selected percentiles from the modeled concentration distribution at each receptor for comparison to the ambient mon itor concentration distribution. The 2.5 th 

### and 97.5 th 

### percentiles from all monito r distribution percentiles 15 

### were selected to cr eate the lower and upper bounds of the envelope, while the 50 th 

### percentile concentrations were combined to create a distribution representing the cen tral tendency (Figure 8-6). The distribution of the modeled

values estimated for the monito r receptor is also presented, along with the complete hourly

concentration distribution measured at each ambien t monitor. A table providing the values used to generate the figure is provided in Appendix B-4. The hourly concentration distributions modeled at receptors within 4 km of each of the ambient monitor locations provide a reasonable representation of the measured ambient NO 2 

### concentrations. The lower and upper bounds of the predicted concentration distributions

surround the measured ambient conc entration distribution at many of the percentiles. The actual modeled monitor receptor concentration distri butions were generally above that of the corresponding measured concentrations, resul ting in overestimation at some of the upper percentiles by about 20-50%. At mo nitor 131210048 however, the overestimate in concentrations was generally less than 10 ppb, or within 10-20% of that measured. In fact the

maximum estimated concentration at this mon itor was 137 ppb, just 1 ppb above that measured (136 ppb). When considering the lowest potential hea lth effect benchmark levels, the modeled monitor receptor had 19, 2, and 9 daily maxi mum predicted values above 100 ppb 1-hour, compared with 0, 0, and 3 of the meas ured values at monitors 130890002, 130893001, and

14 500 m to 4 km is the area of representation of a neighborhood-scale monitor, according to EPA guidance. 15 As an example, suppose th ere are 1000 receptor s surrounding a m onitor, each receptor co ntaining 8,760 hourly values used to create a concentra tion distribution. Then say the 73 rd percentile concentration prediction is to be estimated for each receptor. The lower bound of the 73 rd percentile of the modeled receptors would represented by the 2.5 th percentile of all the calculated 73 rd percentile concentration predictions, i.e., the 25 th highest 73 rd percentile concentration prediction across the 1000 73 rd percentile values generated from all of the receptors. Note that, at any given percentile along either of the en velope bounds as well as at the centra l tendency distribution (the receptor 50 th percentile), the concentration from a different receptor may be used.



### 169

### 131210048, respectively. There were only two pr edicted daily maximum exceedances of 150 ppb 1-hour at one monitor (ID 130890002), although there were no measured exceedances at this monitor. None of the modeled monitors had estimated daily maximum NO 2 

### concentrations above 200 ppb 1-hour as observed with the measur ement data for all three monitors. This indicates that while overestimating the number of exceedances of the lowest potential health effect benchmark level of 100 ppb at these three lo cations, there is not an overestimate of higher benchmark levels, such as those ≥ 200 ppb considering as is air quality.

8.4.8.2 Comparison of annual average diurnal concentration profiles A second comparison between the modeled and monitored data was performed to

evaluate the diurnal variation in NO 2 

### concentrations. For AERMOD, receptor concentrations during each hour-of-the-day were averaged (i.e ., 365 values for hour 1, 365 values for hour 2, and so on), to generate an annual average NO 2 

### concentration for each hour at each receptor. Then a prediction envelope was constructed si milar to that describe d above from modeled receptors located within 4 km of each ambient monitor. These modeled diurnal distributions, along with that of each ambient monitor hour-o f-the-day annual average concentration are illustrated in Figure 8-7. A table providing the valu es used to generate the figure is provided in Appendix B-4. When comparing the modeled predicted and am bient measured diurnal profiles, there was agreement between the patterns and several hours of the day where the observed values fell within the model prediction envelope. This occurred primarily during the late night (9PM12AM), early morning (1AM-3AM), and late mo rning through many of the midday hours (8AM4PM). However, NO 2 

### concentrations were overestimated at certain times of the day, generally

between the hours of 4-6AM and 5-8PM. The overe stimation in concentra tions is not entirely unexpected given the results of the distribution of hourly concentrati ons illustrated in Figure 8-6.

170Monitor 130890002

0 10 20 30 40 50 60 70 80 90 100Cumulative Percentile (%) AMBIENT MONITOR

AERMOD P2.5

AERMOD P97.5

AERMOD MONITOR

Monitor 130893001

0 10 20 30 40 50 60 70 80 90 100Cumulative Percentile (%) AMBIENT MONITOR

AERMOD P2.5

AERMOD P97.5

AERMOD MONITOR

Monitor 131210048

0 10 20 30 40 50 60 70 80 90 100

0 20 40 60 80 100 120 140 160 180 200 NO 2 Concentration (ppb)Cumulative Percentile (%) AMBIENT MONITOR

AERMOD P2.5

AERMOD P97.5

AERMOD MONITOR



### Figure 8-6. Comparison of measured ambient monitor NO 2 

### concentration distribution with the modeled monitor receptor and receptors within 4 km of the monitors at three locations in Atlanta for Year 2002.

171Monitor 130890002

0 5 10 15 20 25 30 35 40 45 50 55Annual Mean NO2Concentration (ppb) AERMOD P2.5 AERMOD P97.5 AMBIENT MONITOR AERMOD MONITOR

Monitor 130893001

0 5 10 15 20 25 30 35 40 45 50 55Annual Mean NO2Concentration (ppb) AERMOD P2.5 AERMOD P97.5 AMBIENT MONITOR AERMOD MONITOR

Monitor 131210048

0 5 10 15 20 25 30 35 40 45 50 55

0 61 2 1 8 Hour of the DayAnnual Mean NO2Concentration (ppb) AERMOD P2.5

AERMOD P97.5 AMBIENT MONITOR AERMOD MONITOR



### Figure 8-7. Comparison of measured ambient monitor NO 2 

### concentration diurnal profile with the modeled monitor receptor and receptors within 4 km of the monitors at three locations in Atlanta for Year 2002.

172

### 8.4.8.3 Comparison of estimated on-road NO 2 

### concentrations The two independent approaches used to estimate on-road NO 2 

### concentrations, one using

ambient monitor data combined with an on-road simulation factor (section 7) and the other using the AERMOD dispersion model, were compared to one another. There are no on-road NO 2 

### concentration measurements in Atlanta for the modeled data to be compared with, although it

should be noted that the data used to estimate the simulation factors and applied to the monitor

data were measurement based. First a comparison can be made between the adjustment factors used for estimating onroad concentrations in the air quality analysis and similar factors that can be generated using AERMOD estimated concentrations for year 2002. As described above in section 7, an empirical distribution of on-road simulation fact ors was derived from on-road and near-road NO 2 

### concentration measurements published in the extant literature. The derived empirical distribution was separated into two components, one for appl ication to summertime ambient concentrations, and the second for all other se asons. The two empirical distributions are presented in Figure 8-8, and represent the fact ors that are multiplied by the ambient monitor concentration (i.e., at monitors ≥ 100 m from a major road) and us ed to estimate the on-road concentration in the air quality characterization. The measurement data from which these were derived were mainly time-averaged over 7 to 14 days. The one-hour NO 2 

### concentrations estimated at AERMOD receptors ≥ 100 m from a major road were compared with the

concentrations estimated at their closest on-roadreceptor to generate a similar ratio (i.e., onroad/non-road NO 2 

### concentrations). In this case, 7-da y averages were calculated using the hourly AERMOD concentration predictions. Th ese 7-day average AERMOD generated ratios were also stratified into two seasonal categories, one containing the summer ratios (June, July, and August) and the other for all other times of the year. The AERMOD on-road factor distributions in semi-empirical fo rm are also presented in Figure 8-8. The values for each of the

methods and season distributions are provided in Appendix B-4.

Both the modeled and measurement derived distributions have similar seasonal relationships, that is the summer ratios are consistently greater than the non-summer ratios throughout the entire distribution. There are small differences when comparing the two approaches at the lowest dist ribution percentiles, with the AE RMOD ratios consistently below

173

### that of the empirically derived factors. This is likely due to the differenc es in the population of samples used to generate each type of distri bution. The measurement study-derived distribution

used data from on-road concentration measuremen ts and from monitoring sites located at a distance from the road, sites that by design of the algorithm and the factor selecti on criteria are likely not under the influence of non-road NO 2 

### emission sources. The measurement study

derived ratios never fall be low one, there are no on-road concentrations less than any corresponding non-road influenced concentrations. This is, by design, a function of the algorithm used to derive the ratio (i.e., concentra tions away from the road are always less). This was reasonable and conservative assumption used in estimating the on-road concentrations for the air quality characterization performed in section 7. The population of AERMOD receptors used to generate the depicted distribution however includes concen trations estimated at non-road receptors that are greater than corresponding on-road receptors. Th is is likely a more realistic depiction of the actual relations hip between on-road and non-road receptors. It is possible that

an NO 2 

### emission source at a distance from a road c ould contribute to local concentrations more

than a mobile source contributes to corresponding on-road concentrations.

There are some similarities that follow when comparing each of the AERMOD with the measurement study derived distributions the lowe r to mid percentiles. Overlap of the two different approaches occurs through about the 40 th 

### percentile and tracks closely through the 90 th 

### percentile. The AERMOD predicted ratio distribu tions then extend beyond the range of values offered by the measurement study derived ratios at about the 90 th 

### percentile. Given the greater number of receptors modeled by AERMOD, the AE RMOD approach may be better representing the variability in NO 2 

### concentrations than when using the on-road adjustment factor approach. While not directly comparable to Atlanta, a few U.S studies report si milar concentrations

for inside vehicles and near roads. For example, Riediker et al. (2003) measured NO 2 

### concentrations inside North Carolina State patrol cars while on duty in Raleigh as well as at a fixed site monitor located near local major roadwa ys. Mean concentrations inside the vehicles averaged over about 9 hours were 41.7 ppb (m inimum 1.6, maximum 548.5 ppb), similar to the mean and range of concentrations estimate d by AERMOD and using the on-road adjustment factor (Table 8-7). Reported roadside NO 2 

### concentrations were also comparable in mean concentration, (49.9 ppb), although they had a smaller range (minimum 13.0, maximum 212.1 ppb). This is likely a result of a much greater averaging time (i.e., appr oximately 4-days) used

174

### for these roadside measurements. CARB (2003) measured NO 2 

### concentrations inside school buses during several commutes on urban and suburb an/rural bus routes in Los Angeles, CA. Measurements were collected over an average commute time of approximately 85 minutes and corresponded to average concentrations of about NO 2 

### 70 ppb along an urban route (minimum 34, maximum 120 ppb). Rural/subur ban areas averaged less, NO 2 

### concentrations were about 45 ppb (minimum 23, maximum 68 ppb).

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

1

0.1 1 10 100 1000 

### On-Road/Non-Road NO 2 

### Concentration RatioCumulative Probability



### Not Summer - AERMOD Summer - AERMOD Summer - Studies Not Summer - Studies



### Figure 8-8. Comparison of on-road/non- road ratios developed from AERMOD concentration estimates for ye ar 2002 and those derived from data reported in published NO 2 

### measurement studies.

A second comparison was conducted using the hourly on-road NO 2 

### concentrations estimated by AERMOD for 3,259 on-road receptors in Atlanta for the years 2001-2003. The 24 hourly values modeled for each day at each rece ptor were rounded to the nearest 1 ppb. The

second set of estimated on-road NO 2 

### concentrations was generated as part of the Air Quality Characterization by applying randomly selected on-road adjustment factors to the ambient monitor concentrations in the Atlanta MSA, using the same three ambient monitors which were all located > 100 m from a major road. Table 8-7 compares the summary statistics of the hourly

175

### concentrations and the number of estimated exceedances of three potential health effect benchmark levels (i.e., 100, 150, and 200 ppb) using the two different approaches to estimate onroad concentrations. The AERMOD predicted and ambient monitor simulated concentration distributions have very similar variances, although the AERMOD estimated concentrations are about 40% greater at the mean a nd about 15 ppb higher at each of the percentiles (save the max). The AERMOD on-road receptors also consistently had a greater number of exceedances of potential health effect benchmark levels th an that estimated using the on-road monitor simulation. For example, the AERMOD receptors had an average of 241 exceedances of 100 ppb per site-year while the simulated on-road monitors had an average of 169 exceedances per year, a difference of about 40%. This differe nce between the two approaches was prevalent throughout each of the percentiles and when consid ering each of the 1-hour concentration levels. The differences could be due to the greater number of receptors modeled by AERMOD (n=3,259) compared with the on-road monito r simulation (n=800) and that the AERMOD generated on-road receptors could include locati ons with greater influence by roadway emissions that are not captured by the simplified approach conducted in the Air Qu ality Characterization.

Table 8-7. Summary statistics of estimated on-road hourly NO 2 

### concentrations (ppb) and the numbers of hourly concentratio ns above 100, 150, and 200 ppb in a year using both the AERMOD and the on-road ambient monitor simulation approaches in Atlanta. On-Road Hourly NO 2 (ppb) Number of hours >100 ppb Number of hours >150 ppb Number of hours >200 ppb

Statistic AERMOD AQ Monitors AERMOD AQ Monitors AERMOD AQ Monitors AERMOD AQ Monitors Mean 43 31 241 169 28 20 5 3 Std 25 25 307 227 51 43 14 7 Var 631 646 94,102 51,427 2,577 1,856 190 54 N 1 28,548,840 6,622,300 3,259 800 3,259 800 3,259 800 p0 0 0 0 0 0 0 0 0 p5 9 3 2 2 0 0 0 0 p10 15 6 4 8 0 0 0 0 p15 19 9 7 11 0 0 0 0 p20 23 11 11 16 0 0 0 0 p25 26 13 16 23 0 0 0 0 p30 29 15 23 33 0 0 0 0 p35 32 17 30 42 0 1 0 0 p40 34 20 41 55 0 1 0 0 p45 37 22 56 67 1 3 0 0 p50 40 25 79 87 1 3 0 0 p55 44 28 119 111 3 5 0 0 p60 46 31 185 132 6 8 0 1



### 176On-Road Hourly NO 2 (ppb) Number of hours >100 ppb Number of hours >150 ppb Number of hours >200 ppb

Statistic AERMOD AQ Monitors AERMOD AQ Monitors AERMOD AQ Monitors AERMOD AQ Monitors p65 49 35 253 160 12 9 1 1 p70 52 38 317 184 22 13 2 1 p75 56 43 399 220 37 21 4 2 p80 60 49 485 280 50 26 7 3 p85 66 56 584 353 71 36 11 3 p90 74 65 706 426 96 53 18 9 p95 88 81 879 649 132 110 31 17 p100 556 437 1,929 1,595 542 373 181 55 Notes: 1 For the on-road hourly NO 2 concentration, N is the number of 1-hour concentrations generated for each simulation. In the exceedance columns, N represent s the number of AERMOD receptors or monitor siteyears simulated.



### 8.4.8. Using unadjusted AERMOD predicted NO 2 

### concentrations

The NO 2 

### concentrations estimated using AERM OD may be biased upwards based on a comparison with measurement data from the th ree available ambient monitors. Given an apparent systematic bias, one could argue fo r adjusting concentrations to improve the comparison of the model predictions with the measurement data. However, data were not adjusted based on these model-to-monitor comparis ons for a few reasons, primarily regarding the confidence in the dispersion modeling system, the spatial representation of the monitors compared with receptors modeled, and the numbe r of comparisons availa ble. Details on the reasoning are provided in section 8.12.1.



## 8.5 SIMULATED POPULATION

One of the important population subgroups for the exposure assessment is asthmatics. Evaluating exposures for this population requires an estimation of both adult and children asthma prevalence rates. The proportion of the population of children characterized as being asthmatic was estimated by statistics on asthma prevalence rates recently used in the NAAQS review for

O 3 

### (US EPA, 2007g). See Appendix B, Attachment 4 for details in the derivation. Specifically, the analysis generated age and gender specific asthma prevalence rates for children ages 0-17 using data provided in the National Health Interview Survey (NHIS) for 2003 (CDC, 2007). Adult asthma prevalence rates for Atlanta were derived from the Behavioral Risk Factor

177

### Surveillance System (BRFSS) survey inform ation for years 2004 – 2005 (Blackwell and Kanny, 2007). Table 8-8 provides a summary of the prev alence rates used in th e exposure analysis by age and gender. Additional information on the va riability in these preval ence rates is given in Appendix B-4.

Table 8-8. Asthma prevalence rates by age and gender used for Atlanta.

Asthma Prevalence 2 Region (Study Area) Age 1 Female Male 0 0.034 0.041 1 0.052 0.070 2 0.071 0.102 3 0.088 0.129 4 0.099 0.144 5 0.119 0.165 6 0.122 0.164 7 0.112 0.133 8 0.093 0.138 9 0.091 0.168 10 0.108 0.178 11 0.132 0.162 12 0.123 0.145 13 0.097 0.143 14 0.095 0.153 15 0.100 0.151 16 0.115 0.140 17 0.145 0.122 Atlanta (South)

>17 0.083 0.050

Notes: 1 Ages 0-17 from the National Health Interview Survey (NHIS) for 2003 (CDC, 2007), ages >17 from the Behavioral Risk Factor Surveillance System (BRFSS) survey information (Blackwell and Kanny, 2007) 2 Asthma prevalence is given as fraction of the population. Multiply by 100 to obtain the percent.



### The total population simulated within the Atlanta model domain was approximately 2.68 million persons, of which there was a total si mulated population of about 212,000 asthmatics. The model simulated approximately 500,000 chil dren, of which there were about 64,000 asthmatics. For comparison, the Georgia Depa rtment of Human Res ources reports the 2001 asthma prevalence for children in middle a nd high school as ranging from 9.6 to 13.8%, though for Fulton County middle school estimates were higher (15.8%) (Blackwell et al, 2003).

178

### And when considering this population simulated in Atlanta and their proximity to roadways, 17% of the population resides within 75 m, 25% were between 75 and 200 m, and 58% were > 200 m of a major road.



## 8.6 CONSTRUCTION OF LONGIT UDINAL ACTIVITY SEQUENCES

Exposure models use human activity pattern data to predict and estimate exposure to pollutants. Different human activities, such as spending time outdoors, indoors, or driving, will result in varying pollutant exposure concentratio ns. To accurately model individuals and their exposure to pollutants, it is cr itical to understand their daily activities. EPA’s Consolidated Human Activity Database (CHAD) provides data fo r where people spend time and the activities performed. Typical time-activity pattern data av ailable for inhalation exposure modeling consist of a sequence of location/activity combinations sp anning 24-hours, with 1 to 3 diary-days for any single study individual. The exposure assessment performed here requi res information on activity patterns over a full year. Long-term multi-day activity patterns were estimate d from single days by combining the daily records using an algorithm that represents the day-to-day correlation of activities for individuals. The algorithm first uses cluster analysis to divide the daily activity pattern records into groups that are similar, and then select a single daily record from each group. This limited number of daily patterns is th en used to construct a long- term sequence for a simulated individual, based on empirically-derived transition probabilities. This approach is intermediate between an assumption of no day-to-day correlat ion (i.e., re-selection of diaries for each time period) and perfect correlation (i.e ., selection of a single daily reco rd to represent all days). Details regarding the algorithm and supporti ng evaluations are provi ded in Appendix B-4,

Attachments 2 and 3.



## 8.7 CALCULATING MICROENVIRO NMENTAL CONCENTRATIONS

Probabilistic algorithms are used to estimate the pollutant concentration associated with each exposure event. The estimated pollutant concentrations account for temporal and spatial variability in ambient (out door) pollutant concen tration and factors affecting indoor microenvironment, such as a penetration, air ex change rate, and pollutant decay or deposition

rate. APEX calculates air concentrations in the various microenvironments visited by the

179

### simulated person by using the ambient air data es timated for the relevant blocks/receptors, the user-specified algorithm, and input parameters specific to each microenvironment. The method used by APEX to estimate the microenvi ronmental concentration depends on the microenvironment, the data available for input to the algorithm, and the estimation method selected by the user. The current version of AP EX calculates hourly conc entrations in all the

microenvironments at each hour of the simulation for each of the simulated individuals using one of two methods: by mass balance or a transfer factors method. Details regarding the algorithms used for estimating specific microenvironments and associated input data derivations are provided in Appendix B. Briefly, the mass balance method simulates an enclosed microenvironment as a wellmixed volume in which the air concentration is spatially uniform at any specific time. The concentration of an air pollutant in such a microenvironment is estimated using the following

processes:

• Inflow of air into the microenvironment

• Outflow of air from the microenvironment

• Removal of a pollutant from the microe nvironment due to deposition, filtration, and chemical degradation

• Emissions from sources of a polluta nt inside the microenvironment.

A transfer factors approach is simpler th an the mass balance model, however, most parameters are derived from distributions rath er than single values to account for observed variability. It does not calculate concentration in a microenvi ronment from the concentration in the previous hour as is done by the mass balance method, and the transfer factors approach contains only two parameters. A proximity fact or is used to account for proximity of the

microenvironment to sources or sinks of pollution, or other sy stematic differences between concentrations just outside the microenvironm ent and the ambient concentrations (at the measurements site or modeled receptor). The s econd parameter, a penetr ation factor, quantifies the amount of outdoor pollutant penetr ates into the microenvironment.

8.7.1 Microenvironments Modeled In APEX, microenvironments repr esent the exposure locations for simulated individuals. For exposures to be estimated accu rately, it is important to have realistic microenvironments that

180

### 181 match closely to the locations where actual people spend time on a daily basis. As discussed above, the two methods available in APEX for calculating pollutant levels within

microenvironments were mass balance or a tran sfer factors approach. Table 8-9 lists the

microenvironments used in this study, the calculation method used, and the type of parameters

needed to calculate the micr oenvironment concentrations.

Table 8-9. List of microenvironments modeled and calculation methods used.

Microenvironment Calculation Method Parameter Types used 1 Indoors – Residence Mass balance AER and DE Indoors – Bars and restaurants Mass balance AER and DE Indoors – Schools Mass balance AER and DE Indoors – Day-care centers Mass balance AER and DE Indoors – Office Mass balance AER and DE Indoors – Shopping Mass balance AER and DE Indoors – Other Mass balance AER and DE Outdoors – Near road Factors PR Outdoors – Public garage - parking lot Factors PR Outdoors – Other Factors None In-vehicle – Cars and Trucks Factors PE and PR In-vehicle - Mass Transit (bus, subway, train) Factors PE and PR

1 AER=air exchange rate, DE=decay-depos ition rate, PR=proximity factor, PE=penetration factor



### 8.7.2 Microenvironment Descriptions

8.7.2.1 Microenvironment 1: Indoor-Residence

The Indoors-Residence microenvironment us es several variables that affect NO 2 

### exposure: whether or not air conditioning is pr esent, the average outdoor temperature, the NO 2 

### removal rate, and an ind oor concentration source. Air conditioning (A/C) status of an indivi dual’s residential mi croenvironment was simulated randomly using the probability that a re sidence has an air conditioner. For the Atlanta modeling domain an air-conditi oning prevalence of 97.0 % was us ed (American Housing Survey or AHS, 2004). Air exchange rate (AER) data for the indoor residential microenvironment were the same

used in APEX for the most recent O 3 

### NAAQS review (EPA, 2007g; see Appendix B,

### 182 Attachment 5). Briefly, AER data were revi ewed, compiled, and evaluated from the extant literature to generate location-specific AER di stributions categorized by influential factors, namely, location, temperature, and presence of A/ C. The AER data obtained was limited in the number of samples, particularly when considerin g these influential factor s. When categorizing by temperature, a range of temperatures was used to maintain a reasonable number of samples within each category to allow for some variabil ity within the category, while still allowing for differences across categories. Se veral distribution forms were investigated (i.e., exponential, log-normal, normal, and Weibull) and in general, lognormal distributions pr ovided the best fit. Fitted lognormal distributions we re defined by a geometric mean (GM) and standard deviation (GSD). Because no fitted distribution was availabl e specifically for Atlanta, distributions were selected from other locations thought to have similar characteristics, qualitatively considering factors that might influence AERs including th e age composition of hous ing stock, construction methods, and other meteorological variables not e xplicitly treated in the analysis, such as humidity and wind speed patterns. To avoid unusually extreme simulated AER values, bounds

of 0.1 and 10 were selected for minimum a nd maximum AER, resp ectively. Table 8-10

summarizes the distributions used by A/C prevalence and temperat ure categories. See Appendix B, Attachment 2 for additional details.

Table 8-10. Geometric means (GM) and standard deviations (GSD) for air exchange rates by A/C type and temperature range used for Atlanta exposure assessment.

A/C Type Temp (oC) N GM GSD <=10 61 0.9258 2.0836 10-20 87 0.7333 2.3299 No A/C 1

>20 44 1.3782 2.2757 <=10 157 0.9617 1.8094 10-20 320 0.5624 1.9058 20-25 196 0.3970 1.8887 Central or Room A/C 2

>25 145 0.3803 1.7092 Notes: 1 Distribution derived from Research Triangle Park study. See Appendix B, Attachment 5. 2 Distribution derived from non-Calif ornia cities. See Appendix B, Attachment 5.



### The same NO 2 

### removal rate distribution was used fo r all indoor microenvironments that

use the mass balance method. This removal rate is based on data provided by Spicer et al. (1993) and was approximated with a uniform distribution, U{1.02, 1.45 h -1 

### } based on the six reported values from a single house (Table 8-12).

### Table 8-11. Data used to estimate removal rate constant for indoor microenvironments.

Source Introduction House Temperature



### ◦ C Relative Humidity (%) Air Exchange Rate (h -1 ) Removal Constant k (h -1 ) injection 23 52 0.15 1.28 injection 21 54 0.16 1.02 radiant heater 26 54 0.15 1.15 convective heater 24 55 0.12 1.04 range 22 54 - 1.45 injection 22 55 0.15 1.13 Notes: Data from Table 1 of Spicer et al. (1993).



### An indoor emission source term was included in the APEX simulations to estimate NO 2 

### exposure to gas cooking (hereafter referred to as “indoor sources ”). This was the only indoor source considered in this assessment. Three type s of data were used to generate the emission factor for this indoor source: (1 ) the fraction of households in th e Atlanta MSA that use gas for cooking fuel, (2) the range of contributions to indoor NO 2 

### concentrations that occur from cooking with gas, and (3) the diurna l pattern of cooking in households.

The fraction of households in Atlanta that use gas cooking fuel (i.e., 39%) was obtained from AHS (2004). Data used for es timating the contribution to indoor NO 2 

### concentrations that occur during cooking with gas fuel were derive d from a study sponsored by the California Air Resources Board (CARB, 2001). A uniform distribu tion of concentration contributions for input to APEX was estimated as U{4, 188 ppb}. An anal ysis by Johnson et al ( 1999) of survey data on gas stove usage collected by Koontz et al (1 992) showed an aver age number of meals prepared each day with a gas stove of 1.4. The diurnal allocation of these cooking events was estimated using food preparation time obtained fr om CHAD diaries, stratified by hour of the day, and normalized to the expected value of daily food preparation events of 1.4 (Table 8-12).

Table 8-12. Probability of gas stove cooking by hour of the day.



### Hour of the Day Probability of Cooking (%) 1 0 0 1 0 2 0 3 0 4 0



### 183

### Hour of the Day Probability of Cooking (%) 1 5 5 6 10 7 10 8 10 9 5 10 5 11 5 12 10 13 5 14 5 15 5 16 15 17 20 18 15 19 10 20 5 21 5 22 0 23 0 Notes: 1 Values rounded to the nearest 5%. Data sum to 145% due to rounding convention and the scaling to represent 1.4 cooking events/day.



### 8.7.2.2 Microenvironments 2-7: All other indoor microenvironments The remaining five indoor microenvironments , which represent Bars and Restaurants, Schools, Day Care Centers, Office, Shopping, and Other environments, were all modeled using

the same data and functions. An air excha nge rate distribution (GM = 1.109, GSD = 3.015, Min = 0.07, Max = 13.8) was based on an indoor air qualit y study (Persily et al, 2005). This is the

same distribution in APEX used for the most recent O 3 

### NAAQS review (EPA, 2007g). See Appendix B, Attachment 5 for details in the data used and derivation. The removal rate is the

same uniform distribution used in the Indoo r-Residence microenvironment (section 8.7.2.1).

The Bars and Restaurants microenvironment in cluded an estimated contribution from indoor

sources as was described for the Indoor-Resid ence, only there was an assumed 100% prevalence rate for cooking with a gas appliance a nd it occurred at any hour of the day.

8.7.2.3 Microenvironments 8 and 9: Outdoor Microenvironments

Two outdoor microenvironments, the Near Road and Public Garage/Parking Lot, used the transfer factors method to calculate pollutant expo sure. Penetration factor s are not applicable to

184

### outdoor environments (effectively, PEN=1). Th e distribution for proximity factors were developed from the dispersion model estimated co ncentrations, using the relationship between on-road to receptor estimated concentrations.

8.7.2.4 Microenvironment 10: Outdoors-General The general outdoor environment concentr ations are represented by the AERMOD predicted concentrations. Theref ore, both the penetration factor and proximity factor for this

microenvironment were set to 1.

8.7.2.5 Microenvironments 11 and 12: In Vehi cle- Cars and Trucks, and Mass Transit Penetration factors were developed from da ta provided in Chan and Chung (2003). Since major roads were the focus of this assessment, reported indoor/outdoor ra tios for highway and

urban streets were used here. Mean values ra nge from about 0.6 to just over 1.0, with higher

values associated with increas ed ventilation (i.e., window open) . A uniform distribution U{0.6, 1.0} was selected for the penetration factor fo r Inside-Cars/Trucks due to the limited data

available to describe a more fo rmal distribution and the lack of data available to reasonably assign potentially influential characteristics such as use of vehicle ventilation systems for each location. Mass transit systems, due to the fre quent opening and closing of doors, was assigned a uniform distribution U{0.8, 1.0} based on the reporte d mean values for fresh-air intake (0.796) and open windows (1.032) on urban streets. Pr oximity factors were developed from the dispersion model estimated concentrations, usin g the relationship between the on-road toreceptor estimated concentrations. The proximity distributions were stratified based using timeof day and season bins and are provided in Appendix B-4.



## 8.8 EXPOSURE MEASURES AND HE ALTH RISK CHARACTERIZATION

APEX calculates the tim e series of exposure concentratio ns that a simulated individual experiences during the simulation period. APEX determines the exposure using hourly ambient air concentrations, calculated concentrations in each microenvironment based on these ambient air concentrations (and indoor sources if pres ent), and the minutes spent in a sequence of microenvironments visited according to the composite diary. The hourly exposure concentration at any clock hour during the si mulation period is determined using the following equation:

185

## T tC C N

j j hourlymean jME

i 

# ∑

= 

## = 1 )()( 

### equation (8-1)

where,

C i 

### = Hourly exposure concentration at clock hour i of the simulation period (ppm)

N

= Number of events (i.e., micr oenvironments visited) in clock hour i of the simulation period. = Hourly mean concentration in microenvironment j (ppm) hourlymean jME 

### C )(



### t (j) 

### = Time spent in microenvironment j (minutes)

T = 60 minutes

From the hourly exposures, APEX calculate s time series of 1-hour average exposure

concentrations that a simulated individual would experience during the simulation period. APEX then statistically summarizes and tabul ates the hourly (or daily, annual average) exposures. In this analysis, the exposure indicator is 1-hr exposure s above selected health effect benchmark levels. From this, APEX can calculate two general types of exposure estimates: counts of the estimated number of peopl e exposed at or above a specified NO 2 

### concentration level and the number of times per year that they are so exposed; the latter metric is in terms of person-occurrences or person-days. The former highlights the number of individuals exposed at

least one or more times per modeling period to the potenti al health effect benchmark level of interest. APEX can also repor t counts of individuals with multiple exposures. This personoccurrences measure estimates the number of times per season that individuals are exposed to the exposure indicator of interest and then accumula tes these estimates for the entire population residing in an area. APEX tabulates and displays the two measur es for exposures above levels ranging from 100 to 300 ppb by 50 ppb increments for 1-hour averag e exposures. These results are tabulated for the population and subpopulations of interest.

186

### 8.8.1 Adjustment for Just Meeting the Current and Alternative Standards We used a different approach to simulate just meeting the current and alternative standards than was used in the Air Quality Char acterization (Appendix A). In this case, instead of adjusting upward 16 

### the air quality concentrations, to reduce computer processing time, we adjusted the health effect benchmark levels by the same factors described for each specific location and simulated year (Table 8-13). Since it is a proportional adjustme nt, the end effect of adjusting concentrations upwards versus adjusting benchmark levels downward within the model

is the same. The same follows for where as is concentrations were in excess of an alternative standard level (e.g., 50 ppb for the 98 th 

### percentile averaged over three years), only the associated benchmarks are adjusted upwards (i.e., a hi gher threshold simulating lower exposures).

Table 8-13. Adjusted potential health effect be nchmark levels used by APEX to simulate just meeting the current standard and variou s alternative standards considered. Potential health effect benchmark level (ppb) Model Scenario Averaging Time Conc (ppb) Conditions 100 150 200 250 300 As-is 100 150 200 250 300 Year 2001 44 66 88 110 132 Year 2002 37 55 73 91 110 Current Standard Annual 53 Year 2003 31 46 62 77 93 98 th %ile 163 nd 327 nd 490 50 99 th %ile 177 nd 355 nd 532 98 th %ile 82 nd 163 nd 245 100 99 th %ile 89 nd 177 nd 266 98 th %ile 54 nd 109 nd 163 150 99 th %ile 59 nd 118 nd 177 98 th %ile 41 nd 82 nd 123 Alternative Standards 1 hour

200 99 th %ile 44 nd 89 nd 133

Notes: nd not done due to model constraints on number of levels possible in one model simulation.



### When modeling indoor sources, the indoor concentration cont ributions needed to be scaled by the similar proportions. This additional scaling was necessary so as not to affect the impact of the estimated indoor concentrations while adjusting the benchmark levels. The following presents the justificati on of why it can appropriate to use the same proportional factor to adjust the indoor source concentration contribution.

16 To evaluate the current and most of the alternative standards proposed, ambient concentrations were lower than air quality that would just meet the standards.



### 187

### Exposure concentrations an individual experiences are first defined as the sum of the

contribution from ambient concentrations and from indoor sources (i f present) and this

concentration can be either above or below a sele cted concentration level of interest. This is represented by the following equation:

threshold indoor ambient osure 

### CbC aC C >+=

exp



### where,



### C

exposure 

### = individual exposure concentration (ppm)



### a = proportion of exposure concentra tion from ambient (unitless fraction)



### C

ambient 

### = ambient concentration in the absence of indoor sources



### b = proportion of exposure concentra tion from indoor (unitless fraction, equivalent to 1-a)



### C indoor 

### = direct indoor sour ce concentration contribu tion in the absence of ambient influence (ppm)



### C

threshold 

### = an exposure concentration of interest (ppm)

It follows that if we are interested in ad justing the ambient con centrations upwards by some proportional factor 

### f (a unitless number), this can be described with the following:

threshold indoor ambient 

### CbCCfa >+

This is equivalent to

) /()/( 

### fCfCbaC threshold indoor ambient 

### > +

Therefore, if the potential heal th effect benchmark level and the indoor concentrations are both proportionally scaled downward by the same adjustment factor, th e contribution of both sources of exposure (i.e., ambient and indoor) are maintained and the same number of estimated

exceedances would be obtained as if the ambien t concentration were proportionally adjusted upwards by factor 

### f

.

188

## 8.9 EXPOSURE MODELING AND HEALTH RISK

CHARACTERIZATION RESULTS

8.9.1 Overview The results of the exposure and risk charac terization are presented here for the four county modeling domain in Atlanta. Several exposure scenarios were considered for the exposure assessment including an analys is of three averaging times for NO 2 

### concentrations (annual, 24-hour, and 1-hour), an an alysis of the contribution to NO 2 

### exposures of both indoor and outdoor sources, and an analysis of NO 2 

### exposures assuming air qual ity that just meets the

current annual and several altern ative 1-hour daily maximum standard s. The year 2002 served as the base year for all scenarios, while 2001 and 2003 were only evaluated for a limited number of scenarios. Exposures were simulated for four population groups; all pers ons, all children (ages 5-17), all asthmatics, and asthmatic children (ages 5-17). The exposure results that are summarized be low focus on asthmatics. Key results are presented in the next three subs ections, with complete results for each of these two population subgroups provided in Appendix B-4. In addition, due to limitations in the data summaries output from the current version of APEX, certa in exposure data could only be output for the entire population modeled (i.e., all persons - incl udes asthmatics and healt hy persons of all ages) rather than the particular subpopulation. The su mmary exposure results fo r the entire population (e.g., annual average exposure concentrations, time spent in microenvironments at or above a potential health effect benchmar k level) is assumed representati ve of the asthmatic population in the modeling results because the asthmatic popul ation does not have its microenvironmental concentrations and activities estimated any differe ntly from those of the total population. The assumption of modeling asthmatics similarly to healthy individuals (i.e ., using the same timelocation-activity profiles) is supported by the find ings of van Gent et al . (2007), at least when considering children 7-10 years in age. These researchers used three different activity-level measurement techniques; an accelerometer recording 1-minute time intervals, a written diary considering 15-minute time blocks, and a categorical scale of activity level. Based on analysis of 5-days of monitoring, van Gent et al. (2007) showed no difference in the activity data collection

methods used as well as no difference between asthmatic children and healthy children when comparing their activity levels.

189

### 8.9.2 Annual Average Exposure Concentrations (as is) Figure 8-9 illustrates the annual average expos ure concentrations fo r the total simulated population (i.e., both asthmatics and healthy individuals of all ag es), considering the modeled year 2002 air quality (as is) and both with and without indoor s ources. Also plotted on this figure is the distribution of the annual average NO 2 

### concentrations predicted by AERMOD separated into two broad receptor categories. As a point of reference, the measured annual average concentration for the three ambient m onitors in the Atlanta modeling domain ranged from 15 ppb to 19 ppb in year 2002. About one-h alf of the AERMOD predicted annual average

NO 2 

### concentrations for the non-road receptors we re below the range of the ambient monitoring annual average concentrations, with most recepto rs predicted to be less than 30 ppb. About 5% of these receptors had concentrations above this level. It should be noted that the non-road receptors included here could ha ve a number of block centroids located near a major road.

Consistent with what was observed in the ai r quality characterization data for on-road concentration estimates, the AERMOD long-term average concentrations predicted at the roadway links are about twice that of the estim ated concentrations at non-road receptors. The hourly NO 2 

### concentrations output from AE RMOD were input into the exposure model, providing a wide range of estimated expos ures calculated by APEX (Figure 8-9). All persons were estimated to experience exposur es below an annual average exposure of 53 ppb, even when considering indoor source concentrati on contributions. The estimated annual average exposures were below that of both the modeled receptors and the measured air quality. For example, the median annual average exposure was about 6 ppb less than the modeled median non-road receptor concentration when the e xposure estimation included indoor sources, and about 9 ppb less when annual average exposures we re estimated without the indoor sources. In the absence of indoor source contributions, person al exposure concentrations for most of the

simulated individuals are estimated to be about 40 to 70 percent that of the local ambient or

outdoor concentration. This estimate is consis tent with studies repor ting such a relationship based on measurements of personal exposure an d ambient concentrations that ranges from around 0.3 to 0.6 (Table AX3.5-1b, ISA ANNEX). In comparing the estimated exposures with and without indoor sour ces, indoor sources

were estimated to contribute between 1 and 4 ppb to the total annual average exposures. This would correspond to indoor sour ces contributing approximately 1/3 of the annual average

190

### exposures for persons using gas cooking applianc es. Again, while Figure 8-9 summarizes the entire population, the data are re presentative of what would be observed for the population of

asthmatics or asthmatic children. Year-to year-variation was evaluated by comparing the estimat ed annual average exposure distributions for each year simulated. Each simulated year of data was very similar, with estimated median annual average exposures at about 10 ppb and 95% of the simulated individuals’ annual average e xposures within the interval from 5.9 to 15.8 ppb (Figure 8-10).

0 10 20 30 40 50 60 70 80 90 100

0 10 20 30 40 50 60 70 8 Annual Average NO 2 Concentration (ppb)Cumulative Percentile (%)

0 AERMOD - Non Road Receptors

AERMOD - On-Road Receptors

APEX - Exposure - Indoor Sources

APEX - Exposure - No Indoor Sources



### Figure 8-9. Comparison of annual average AERMOD predicted NO 2 

### concentrations (onroad and non-road receptors) and APEX modeled NO 2 

### exposures (with and without modeled indoor sources) in Atlant a modeling domain for year 2002.

1910 10 20 30 40 50 60 70 80 90 100

0 5 10 15 20 25 30 35 Annual Average NO 2 Exposure (ppb)Cumulative Percentile (%)

2001 - No Indoor Sources

2002 - No Indoor Sources

2003 - No Indoor Sources



### Figure 8-10. Comparison of estimated annual average NO 2 

### exposures for Years 2001-2003 in Atlanta modeling domain wi thout modeled indoor sources.

8.9.3 Daily Average Exposures (as is) As mentioned earlier, APEX is capable of pr oviding exposure results across a variety of averaging times, including 24-hour average exposu res. This averaging time serves as a good point of comparison with the personal exposur es reported in the publis hed literature. As mentioned above regarding APEX default results, the daily mean exposures were estimated for the total simulated population. In this simulation, each person has 365 daily mean personal exposures, thus each individual experiences a dail y average concentration distribution (i.e., each person has a median daily average exposure, a 99 th 

### percentile daily aver age exposure, etc.). These modeled exposures were compared with personal NO 2 

### measurement data obtained from Suh (2008) for the participants of an Atlanta epidemiological study condu cted by Wheeler et al. (2006). The personal exposure measurements we re collected across tw o seasons (fall and spring) 17 

### and considered cooking fuel (g as or electric cooking) as an influential variable for personal exposures. A total of 30 individuals pa rticipated in the study, of whom 13 subjects had personal exposure measurements for both seasons, with no persons using both cooking fuels.

17 Fall was designated here for sample collection dates report ed in the months of September, October, and November 1999; Spring was designated where sample collection dates were reported in the months of April and May 2000.



### 192

### An average of 6 daily average pe rsonal exposure measurements was available for each individual when stratified by season and cooking fuel (min imum number of days = 3, max = 7). Because there were few personal exposure measurements, an exposure distributi on was constructed for each individual, simply using their minimum, median, and maximum daily mean exposures and are summarized in Figure 8-11. In comparing the median personal daily mean exposures using the two stratification variables, two patterns can be noted. First, the use of gas as a cooking fuel increased daily median personal exposures by about 3 to 10 ppb in both seasons. Second, seasonal differences were also present, with pe rsonal daily average expo sures higher during the spring by about 1 to 3 ppb when comparing th e individual median values for the persons employing gas or electric cooking. While these general patterns are noted, it should be added that the maximum daily average exposures were hi ghest in the spring and similar for both of the cooking fuel categories. Daily mean exposures estimated using APEX we re also evaluated in a similar manner, by stratifying the results based on the same seasons and whether or not indoor sources were included in the model simulation. The speci fic period from 1999-2000 was not modeled by APEX although this period was included in th e personal exposure measurement study. The APEX simulation results for year 2002 were selected for comparison with the exposure measurements obtained from Suh (2008). A di stribution of each pe rson’s estimated daily exposure was constructed, using the median da ily mean exposure to represent the central tendency and a 95 % prediction interval to re present the lower and upper bounds of exposure (i.e., the 2.5 th 

### and the 97.5 th 

### percentiles). This prediction inte rval was chosen rather than using the minimum and maximum as done with the personal measurements because APEX estimated 61 and 91 days of exposure for each individual in the spring and fall months, respectively. The APEX results would likely capture more variabil ity in exposures given the greater number of days in comparison with the pers onal exposure measures that cont ained at most 7 days of data

per season. The daily mean exposures estimated using AP EX, stratified by seas on and by inclusion of indoor sources, are presented in Figure 8-12. The distributions of median daily mean exposures are comparable to one another, although the fall season was a bout 1 ppb higher than the spring exposures. The range of estimated daily mean exposures, given by the 95% prediction interval, was also similar across the season categories. In comparing the simulations where indoor

193

### 194 sources were modeled to the simulations condu cted without indoor source contributions, the estimated exposures were between 1 to 4 ppb greater for the indoor source simulations. It should

be noted that the indoor source e xposure distributions include expos ures for all of the simulated individuals, some of which do not have gas c ooking occurring at home. The APEX simulated daily mean exposures are similar to the meas ured personal exposures (Figure 8-11) when considering the values and range of the median concentrations as well as the values and range of the bounding percentiles given by the 95% prediction intervals. 18

18 While a direct comparison of APEX estimated maxi mum daily exposure concentrations with the maximum observed daily personal exposure concentrations is consid ered questionable given the large discrepancy in sample sizes, it should be noted that approximately 99.1% of APEX simulated persons had their estimated maximum daily exposure concentrations within the maximum observed daily personal exposure measurement of 78.2 ppb without gas cooking. Approximately 97.5% of APEX simulated persons had their estimated maximum daily exposure concentrations within the maximum observed daily personal exposure measurement of 85.4 ppb with gas cooking.p50minmax0102030405060708090100 Cumulative Percent (%)p50minmaxFALL 1999SPRING 2000

GAS COOKING ELECTRIC COOKING01020304050607080901000.1110100Daily Mean NO2Exposure (ppb)

Cumulative Percent (%)p50minmax0.1110100Daily Mean NO2Exposure (ppb)p50minmaxn=17n=7n=14n=8Figure 8-11. Distribution of measured daily average personal NO2exposures for individuals inAtlanta, stratified by twoseasons (fall or spring) and cooking fuel(gas or electric). Minimum (min), median (p50), and maximum (max) were obtainedfrom each individual’s multi-day exposure measurements. The figure generated here was based on personal exposuremeasurements obtained from Suh (2008).195196Figure 8-12. Distribution of estimated daily average NO2exposures for individuals in Atlanta, stratified by two seasons (fall orspring) and with and without indoor sources, for Year 2002 APEXsimulation. Lower bound (2.5th percentile, p2.5), median(p50), and upper bound (97.5th percentile, p97.5) were calculatedfrom each simulated persons 365 days of exposure. Arandom sample of 5% of persons (about 2,500 individuals) is presented in each figure to limitthe density of the graphs.FALLSPRING

With Indoor Sources No Indoor Sources0102030405060708090100 Cumulative Percent (%)p50p2.5p97.5p50p2.5p97.501020304050607080901000.1110100Daily Mean NO2Exposure (ppb)

Cumulative Percent (%)p50p2.5p97.50.1110100Daily Mean NO2Exposure (ppb)p50p2.5p97.5

### 8.9.4 One-Hour Exposures

8.9.4.1 Overview Because the focus of the exposure and risk ch aracterization is on short-term 1-hour daily maximum exposures, analyses were performed using the APEX estimated 1-hour exposure

concentrations. The number of e xposures above the selected poten tial health effect benchmark levels (i.e., 100, 150, 200, 250, and 300 ppb, 1-hour aver age) were estimated. An exceedance was recorded when the maximum exposure concen tration estimated for the individual was above the selected benchmark level in a day. Estimates of repeated exposures are also recorded, that is where 1-hour exposure concentrations were abov e a selected benchmark level in a day added together across multiple days (therefore, the maximum number of multiple exceedances per individual is 365). Persons of interest in this exposure analysis are those with particular susceptibility to NO 2 

### exposure, namely individuals with asthma. The potentia l health effect benchmark levels used are appropriate for charac terizing the potential ri sk of adverse health effects for asthmatics. The majority of the results presented in this section are for the entire (i.e., all ages) simulated asthmatic population because the pattern of exposure results for asthmatic children were very similar. However, the expos ure analysis was also performed for the total population to assess numbers of persons exposed to these levels and to provide additional information relevant to the asthmatic populat ion (such as time spent in particular microenvironments). The 1-hour exposure results are presented separately for three scenarios, (1) considering the exposures asso ciated with as is air quality, (2) simulating exposures with air quality adjusted upwards to represent just mee ting the current annual average standard, and (3) simulating exposures associated w ith air quality adjusted to represent just meeting alternative 1- hour daily maximum standards. In addition, the pr esence (or not) of indoor sources was also

considered within each of these three scenarios.

8.9.4.2 Estimated Number of 1-hour Exposures Above Selected Levels (as is) The results presented in this section were ge nerated from the modeled air quality as input to APEX without any adjustment to the air c oncentrations or the pot ential health effect benchmark levels. Figure 8-13 summarizes the es timated number of asthmatics exposed at each of the potential health effect benchmark levels using the modeled air quality for each year,

197

### without any contribution from indoor sources. As observed with the annual average exposure concentrations, there is great similarity in the estimated numbers of exceedances for each of the three years modeled. Year-to-year variability in the number of asthmatics exposed as indicated by a coefficient of variation (COV=mean/standard deviation) was at most 3.3%, calculated for the 300 ppb benchmark level. All persons (i.e., ju st over 212,000) were estimated to be exposed at least one time to a 1-hour daily maximum con centration of 100 ppb in a year. The number of asthmatics exposed to greater c oncentrations (e.g., 200 or 300 ppb) drops only slightly and is estimated to be somewhere between 117,000 – 196,000 depending on the 1-hour concentration level and year of air quality simulated. Similar patterns across the benchmark levels were observed for simulated asthmatic children, albeit with lower total numbers of asthmatic children with exposures at or above the potentia l health effect benchmark levels.

The results for all asthmatics and asthmatic children were similar in terms of the proportion of the population exposed and the year-to year variability in numbers of exceedances. For example, nearly 61,000 asthmatic children we re estimated to be exposed one time to a 1- hour daily maximum NO 2 

### concentration of at least 200 ppb for year 2002, comprising about 95% of that subpopulation (Figure 8-14 ). The number of children with at least one exceedance of 300 ppb was less, estimated to be about 41,000 usi ng the 2002 air quality, or about 64% of all asthmatic children. As a comparison, the percent of all asthmatics experiencing exposures at or above 200 and 300 ppb was 92% and 59%, respectivel y. The year-to-year variability in the number of asthmatic children exposed at or abov e the selected benchmark levels was also small, although slightly higher than that estimated for all asthmatics. The highest COV for asthmatic children using the 3-year exposure estimates wa s also observed for exceedances of the 300 ppb benchmark (COV = 4.9%).

198100 150 200 250 300 2001 AQ (as is) - No Indoor 2002 AQ (as is) - No Indoor

2003 AQ (as is) - No Indoor 0.0E+0 5.0E+4 1.0E+5 1.5E+5 2.0E+5 2.5E+5Estimated Number of AsthmaticsExposed to Selected Level in a Year

Potential Health Effect Benchmark Level (ppb) 

### Figure 8-13. Estimated number of all simulated asthmatics in the Atlanta model domain with at least one NO 2 

### exposure at or above the potentia l health effect benchmark levels, using modeled 2001-2003 air quality (as is), without indoor sources.

100 150 200 250 300 2001 AQ (as is) - No Indoor 2002 AQ (as is) - No Indoor

2003 AQ (as is) - No Indoor 0.0E+0 1.0E+4 2.0E+4 3.0E+4 4.0E+4 5.0E+4 6.0E+4 7.0E+4Estimated Number of AsthmaticChildren Exposed to Selected Level ina Year

Potential Health Effect Benchmark Level (ppb) 

### Figure 8-14. Estimated number of simulate d asthmatic children in the Atlanta model domain with at least one NO 2 

### exposure at or above the pote ntial health effect benchmark levels, using modeled 2001-2003 air quality (as is), without modeled indoor sources.

199

### Additional exposure estimates we re generated using the modele d 2002 air quality (as is).

Those estimates include an evalua tion of the contribution of indoor sources. APEX allows for the same persons to be simulated (i.e., demograp hics of the population were conserved), as well as using the same individual time-location-activ ity profiles generated for each person. Figure 8-

15 illustrates the estimated number of asthmatics experiencing exposures above the potential health effect benchmarks, both with indoor sour ces and without indoor sources included in the

model runs. The number of asthmatics at or abov e the selected benchmark levels at least one time in a year is very similar when including in door source concentration contributions (i.e., gas cooking) compared to the number of persons wh ose exposure estimates did not include indoor

sources. The reduction in numbers of asthmatics e xposed at least once at or above any potential health effect benchmark level ranged from 0 to around 5,000 when indoor source contributions

were excluded. The number of person-days of exposure at or above a given potential benchmark level gives a different perspective on the contribution of indoor sources . Figure 8-16 illustrates the total number of days where the particular con centration level was exceed ed, representing the sum of all multiple exposures (in contrast to focu sing on persons as was done for example in Figure 8-13) for the simulated population in a given year. Since most individuals were exposed at least one time at many of the 1-hour levels, it was difficu lt to discern the eff ect that indoor sources had on the estimated exposures. Now it can be s een that the indoor source contribution increases not just the number of persons exposed, but more importantly how many times they would be exposed per year above the select ed benchmark level. It appear s that on average, there is an increase in the number of person-days by about a factor of 2.1 and 1.8 for the 100 and 150 ppb 1- hour concentration levels, respec tively, while the higher benchmark levels are largely unaffected by the presence of indoor sources. An evaluation of the time spent in the 12 microenvironments was performed to estimate where simulated individuals are exposed to conc entrations above the pot ential health effect benchmark levels. Currently, the output ge nerated by APEX is limited to compiling the

microenvironmental time for th e total population (inc ludes both asthmatic individuals and healthy persons) and the summari es provide the total time spen t above the selected potential

health effect benchmark levels. As menti oned above, the data still provide a reasonable approximation for each of the population subgro ups (e.g., asthmatics or asthmatic children)

200

### because their microenvironmental concentrations and activities are not estimated any differently from those of the total po pulation simulated by APEX.

100 150 200 250 300 2002 AQ (as is) no indoor 2002 AQ (as is) with indoor 0.0E+0 5.0E+4 1.0E+5 1.5E+5 2.0E+5 2.5E+5Estimated Number ofAsthmatics Exposed toSelected Level in a Year

Potential Health Effect Benchmark Level (ppb)



### Figure 8-15. Estimated number of all simulated asthmatics in the Atlanta model domain with at least one NO 2 

### exposure at or above potential heal th effect benchmark levels, using modeled 2002 air quality (as is), both with and without modeled indoor sources.

100 150 200 250 300 2002 AQ (as is) no indoor 2002 AQ (as is) with indoor 0.00E+0 2.50E+6 5.00E+6 7.50E+6 1.00E+7 1.25E+7 1.50E+7 1.75E+7Estimated Number ofAsthmatic Person DaysExposed to Selected Levelin a Year

Potential Health Effect Benchmark Level (ppb)



### Figure 8-16. Estimated number asthmatic pers on-days in the Atlanta model domain with an NO 2 

### exposure at or above potential health effect benchmark leve ls, using modeled 2002 air quality (as is), both with a nd without modeled indoor sources.

201

### As an example, Figure 8-17 (a, b, c) summarizes the percent of total time spent in each microenvironment for simulation year 2002 that was associated with estimated exposure concentrations at or above 100, 200, and 300 ppb (re sults for years 2001 and 2003 were similar). These estimated exposures summarized in this figure did not include the contribution from indoor sources. Time spent in the indoor microe nvironments contributed lit tle to the occurrence of estimated exposures at or above the selected benchmark levels. Most indoor microenvironments contributed < 1% of expos ures to 1-h concentrations above 100 ppb and none of them contributed at all to exceedances of the 200 and 300 ppb benchmark levels. Most of the time associated with the high short-term exposures was associated with the transportation microenvironments (In-Vehicle or In-Public Transport) or outdoor s (Outdoors-Near Road, Outdoors-Parking Lot, Outdoors-Other). The tim e spent outdoors near ro adways exhibited an

increase in contribution of ex ceedances of potential health benchmark levels, increasing from

around 25 to 29% of time associated with concentrations of 1 00 and 300 ppb, respectively. The

in-vehicle microenvironment s howed a corresponding decrease, estimated as contributing to 65% of the time associated with 100 ppb exceedances, while contributing to 58% of 1-hour daily maximum exposures at or above 300 ppb. While more persons are likely to spend more time inside a vehicle than outdoors near roads, th ere is attenuation of the estimated on-road concentration that penetrates the in-vehic le microenvironment, leading to lowered

concentrations. The result of this is that exposures above 300 ppb occur less frequently invehicles when compared with the outdoor near-road microenvironment that involves no

attenuation of concentrations. The microenvironments where the exposure exceedances occur were also identified for the estimated exposures that included indoor so urce contributions (Figure 8-18). While the transportation-associated microenvironments re mained important for exposures above each of the selected levels, the time spent in the indoor microenvironments was also important for exceedances of hourly levels of 100 ppb, contributi ng to approximately 26% (inside a home) and 33% (inside bar/restaurant ) of the time persons were exposed (F igure 8-18a). This is a result of

the indoor source contribution to each individual’s exposure concen trations and is consistent with what was observed regardi ng the effect of indoor source s on the total person-days of exposure. However, the importance of the indoor microenvironments decreases with the increasing benchmark levels. Exposures at or above 200-300 ppb occur rarely in the indoor

202

### 203 microenvironments, even when considering the i ndoor source contributions. The exposures at the higher benchmarks are associated mainly with the transportation microenvironments, increasing from about 39% of the time exposures occu rred at the lowest potential health effect benchmark level (100 ppb) to comprising 100% of the time exposures occurred at the highest benchmark level (300 ppb, Figure 8-18c). In the above analysis of persons exposed, the results show the number or percent of those with at least one day on which the 1-hour exposure was at or above the se lected potential health

effect benchmark level. Given that the benchmark is for a relatively short averaging time (i.e., 1- hour) it may be possible that indi viduals are exposed to concentra tions at or above the potential health effect benchmark levels on several days in a given year. Since APEX simulates the longitudinal diary profile for each individual, th e number of days with a 1-hour daily maximum exposure above a selected level is retained fo r each person. Figure 8-19 presents such an analysis for the year 2002, where the estimated exposures did not in clude indoor source NO 2 

### contributions. Nearly all simulated asthmatics (98.7%) experienced up to six exposures at or above 100 ppb, with nearly 78% experiencing at least six exposures at or above the 150 ppb level. Multiple exposures at or above the higher potential health effect benchmark levels were less frequent, with around 58, 28, and 12 percent of asthmatics exposed annually to four or more

1-hour NO 2 

### concentrations greater than or equal to 200, 250, and 300 ppb, respectively.Other

In-Residence

In-Bar & Restaurant

In-School In-Day Care

In-Office In-Shopping

Out-Other Out-Parking Lot Out-Near Road

In-Vehicle In-Public Trans In-Other 

### a) ≥ 100 ppb

Figure 8-17. Fraction of time all simulated persons in the Atlanta model domain spend in the twelve microenvironments that corresp onds with exceedances of the potential NO 2 

### health effect benchmark levels, a) ≥ 100 ppb, b) ≥ 200 ppb, and c) ≥ 300 ppb, year 2002 air quality (as is) without indoor sources. Other

In-Residence

In-Bar & Restaurant

In-School In-Day Care

In-Office In-Shopping

Out-Other Out-Parking Lot Out-Near Road

In-Vehicle In-Public Trans In-Other 

### b) ≥ 200 ppb

Other

In-Residence

In-Bar & Restaurant

In-School In-Day Care

In-Office In-Shopping

Out-Other Out-Parking Lot Out-Near Road

In-Vehicle In-Public Trans In-Other 

### c) ≥ 300 ppb

204

### a) ≥ 100 ppb

Out-Parking Lot

Out-Near Road In-Vehicle In-Public Trans

In-Other

In-School In-Residence Other

In-Bar & Restaurant Out-Other

In-Shopping In-Day Care

In-Office

Out-Parking Lot Out-Near Road

In-Vehicle In-Public Trans In-Other In-School

In-Residence

Other In-Bar & Restaurant

Out-Other In-Shopping In-Day Care

In-Office 

### b) ≥ 200 ppb

205 Out-Parking Lot Out-Near Road

In-Vehicle

In-Public Trans In-Other In-School

In-Residence

Other In-Bar & Restaurant

Out-Other In-Shopping In-Day Care

In-Office 

### c) ≥ 300 ppb

Figure 8-18. Fraction of time all simulated persons in the Atlanta model domain spend in the twelve microenvironments that corresp onds with exceedances of the potential NO 2 

### health effect benchmark levels, a) ≥ 100 ppb, b) ≥ 200 ppb, and c) ≥ 300 ppb, year 2002 air quality (as is) with indoor sources.

### The contribution of indoor sources to the occurrence of repeated exposure exceedances was also evaluated. Figure 8- 20 illustrates that nearly all asthmatics (about 93%) would be exposed at least six times to either the 1- hour daily maximum 100 ppb or 150 ppb concentration level in a year when considering exposure to ambient NO 2 

### combined with indoor source emissions. This is approximately 15% more persons than was estimated for the simulations without indoor source contributions. The percent of asthmatics experien cing multiple exposures above the 200, 250 and 300 ppb was only about 1-4% greater than that observed for asthmatics

without indoor sources. This is consistent with the person-day results that indicate the indoor source emissions contribute primarily to numbers of exposures experienced at or above the 100 or 150 ppb benchmark levels.

100 150 200 250 300 6 5 4 3 2 1

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%Estimated Percent of Asthmatics Exposedone or more Times to Selected Level in aYear

Potential Health Effect Benchmark Level (ppb) Estimated Number of Repeated Exposures in a Year



### Figure 8-19. Estimated percent of all asthmati cs in the Atlanta modeling domain with repeated NO2 exposures above potential health effect benchmark levels, using modeled 2002 air quality (as is), wi thout indoor sources.

206100 150 200 250 300 6 5 4 3 2 1

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%Estimated Percent of Asthmatics Exposedone or more Times to Selected Level in aYear

Potential Health Effect Benchmark Level (ppb) Estimated Number of Repeated Exposures in a Year



### Figure 8-20. Estimated percent of all asthmati cs in the Atlanta modeling domain with repeated NO 2 

### exposures above potential health e ffect benchmark levels, using modeled 2002 air quality (as is), with indoor sources.

8.9.4.3 Estimated Number of 1-hour Exposures Above Selected Levels (current standard) To simulate just meeting the current annual average NO 2 

### standard, the po tential health effect benchmark levels were adjusted in the ex posure model, rather than adjusting all of the hourly concentrations for each receptor and year simulated (see section 8.8). Similar to what was performed for the as is air quality, estimat es of short-term expos ures (i.e., 1-hour daily maximum) were generated for the total popula tion and population subgroups of interest (i.e., asthmatics and asthmatic children). When considering the estimated exposures asso ciated with air quality simulated to just meet the current annual average NO 2 

### standard, the number of persons experiencing concentrations at or above the potential health ef fect benchmarks is incr eased in comparison with as is air quality. Figure 8-21 illu strates the percent of asthmatics estimated to experience at least one exposure at or above the sele cted potential health effect be nchmark concentrations, with air quality adjusted to just meet the current sta ndard. The exposure results for both including and excluding indoor source contributio ns are presented. While it was estimated that about 92, 76,

207

### and 59% of asthmatics would be exposed to 200 , 250, and 300 ppb (1-hour average) at least once in a year for the as is air quality, it was estima ted that nearly all asthmatics would experience at least one exposure above any of th e potential health effect benchm ark levels in a year when air quality is adjusted to just m eet the current standard. Exposur e estimates where indoor sources were included were not greatly different than the results w ithout indoor source contributions, with nearly all asthmatics estimated to have at least one exposure at or above even the highest potential health effect benchmark level. For air quality simulated to just meet the current standard, repeat exposures at the selected potential health effect benchmarks are more frequent than that estimated for the modeled as is air quality. Figure 8-22 illustrate s this using the simulated asthmatic population for year 2002 data as an example. Nearly all asthmatics (>97%) were estimated to be exposed at or above any one of the selected levels for at least six times in a year . Results for asthmatics when exposures were estimated considering the contribution from indoor sources were similar, only slightly higher (data not shown).

100 150 200 250 300 2002 AQ (cur std) no indoor 2002 AQ (cur std) with indoor 0.0E+0 5.0E+4 1.0E+5 1.5E+5 2.0E+5 2.5E+5Estimated Number ofAsthmatics Exposed toSelected Level in a Year

Potential Health Effect Benchmark Level (ppb)



### Figure 8-21. Estimated number of all asthmatics in the Atlanta modeling domain with at least one NO 2 

### exposure at or above the potential health effect benchmark level, using modeled 2002 air quality just meeting the curr ent standard (cur std), with and without modeled indoor sources.

208100 150 200 250 300 6 5 4 3 2 1

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%Estimated Percent of Asthmatics Exposedone or more Times to Selected Level in aYear

Potential Health Effect Benchmark Level (ppb) Estimated Number of Repeated Exposures in a Year



### Figure 8-22. Estimated percent of asthmati cs in the Atlanta modeling domain with repeated NO 2 

### exposures above health effect benc hmark levels, using modeled 2002 air quality just meeting the current standa rd, without modeled indoor sources.

8.9.4.4 Estimated Number of 1-hour Exposures Above Selected Levels (alternative standards) To simulate just meeting the alternative NO 2 

### standards, the potential health effect benchmark level was adjusted in the exposure mode l, rather than adjus ting all of the hourly concentrations for each receptor and year simula ted (see section 8-8). Similar to exposure analyses performed with the as is air quality, es timates of short-term exposures (i.e., 1-hour daily maximum) were generated for the total popula tion and population subgroups of interest (i.e., asthmatics and asthmatic children). Due to limitations on the number of concentration levels allowed in an APEX simulation, only the potentia l health effect benchmark levels of 100, 200, 300 ppb were evaluated for the alterna tive 1-hour daily maximum standards. In considering exposures estimated to occur a ssociated with air quality simulated to just

meet the alternative NO 2 

### 1-hour daily maximum standards, th e number of persons experiencing concentrations at or above the potential health effect benchmarks varied, depending on the form and level of the standard. Figure 8-23 illustrates the different forms (a 98 th 

### percentile or p98;

99 th 

### percentile or p99) at various 1-hour concentration levels of the standard. The number of

209

### 210 persons exposed at least once at each of the 98 th 

### and 99 th 

### percentiles alternative standards and considering a potential benchmark level of 100 ppb is similar to that observed for the as is air quality and for air quality adjusted to just meet the current standa rd. That is, most persons are exposed at least once to 100 ppb in a year, regardless of the standard form and level chosen. It is

not until the level of the 1-hour daily maximum standard appro aches 50 ppb for either of the percentile forms that the number of persons exposed to the higher benchmark levels is substantially reduced. For example, while nearly all asthmatics are expos ed to 100 ppb at least once in a year as was observed in the above an alyses, the percent of asthmatics exposed to at

least one 1-hour concentration at or above the 200 or 300 ppb is reduced to about 49% and 14% of the subpopulation, respectively, when considering the 50 ppb, 98 th 

### percentile standard. The estimated number of repeated NO 2 

### exposures above selected levels can be sharply reduced for potential alternative standards at the lower end of the range of alternative standards considered. As an example, Figure 8-24 illust rates the number of multip le exposures above the potential health effect benc hmark levels using a 50 ppb, 99 th 

### percentile alternativ e standard. This is the first instance where multiple exposures of the 100 ppb benchmark are estimated to be reduced, with about 57% of asthmatics estimated to have greater than six in a year. A greater reduction in the number of multiple exposures is observed when considering the 200 ppb benchmark level. For example, only 5% of asthma tics are estimated to be exposed four or more times, compared with 58% using the 2002 air quality as is.

The effect of indoor source contributions to the exposures was also evaluated for the

same level and form of alte rnative standard (50 ppb, 99 th 

### percentile). Figure 8-25 illustrates what has been consistently shown in the above anal yses, the indoor sources primarily affect the numbers of persons and the number of times a person is exposed at or above 100 or 150 ppb, with limited contribution to the higher poten tial health effect benchmark levels. In addition, for comparison with the results presented in Figure 8-24, th e percent of asthmatics exposed to the select ed health effect benchmark levels considering the 100 ppb, 99 th 

### percentile alternative sta ndard is presented in Figure 8-26. A greater proportion of asthmatics have multiple exposures at all of the 1-hour benc hmarks, nearly all of which were estimated to have at least six exposur es at or above a 1-hour concentration of 100 ppb.100 200 300 2002 AQ (p99-50) no indoor 2002 AQ (p98-50) no indoor 2002 AQ (p99-100) no indoor 2002 AQ (p98-100) no indoor 2002 AQ (p99-150) no indoor

2002 AQ (p98-150) no indoor

0.0E+0 5.0E+4 1.0E+5 1.5E+5 2.0E+5 2.5E+5Estimated Number of Asthmatics Exposedto Selected Level in a Year

Potential Health Effect Benchmark Level (ppb) 

### Figure 8-23. Estimated percent of asthmatics in the Atlanta modeling domain with NO 2 

### exposures at or above potential health effe ct benchmark levels, using modeled 2002 air quality adjusted to just meeting potential al ternative standards, without indoor sources.

100 200 300 6 5 4 3 2 1

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%Estimated Percent of Asthmatics Exposedone or more Times to Selected Level in aYear

Potential Health Effect Benchmark Level (ppb) Estimated Number of Repeated Exposures in a Year



### Figure 8-24. Estimated percent of asthmati cs in the Atlanta modeling domain with multiple NO 2 

### exposures at or above potential he alth effect benchmark levels, using modeled 2002 air quality adjusted to just meeting a 50 ppb level 99 th 

### percentile form alternative standard, without indoor sources.

211100 200 300 6 5 4 3 2 1

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%Estimated Percent of Asthmatics Exposedone or more Times to Selected Level in aYear

Potential Health Effect Benchmark Level (ppb) Estimated Number of Repeated Exposures in a Year



### Figure 8-25. Estimated percent of asthmati cs in the Atlanta modeling domain with multiple NO 2 

### exposures at or above potential he alth effect benchmark levels, using modeled 2002 air quality adjusted to just meeting a 50 ppb level 99 th 

### percentile form alternative standard, with indoor sources.

100 200 300 6 5 4 3 2 1

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%Estimated Percent of Asthmatics Exposedone or more Times to Selected Level in aYear

Potential Health Effect Benchmark Level (ppb) Estimated Number of Repeated Exposures in a Year



### Figure 8-26. Estimated percent of asthmati cs in the Atlanta modeling domain with multiple NO 2 

### exposures at or above potential he alth effect benchmark levels, using modeled 2002 air quality adjusted to just meeting a 100 ppb level 99 th 

### percentile form alternative standard, without indoor sources.

212

## 8.10 KEY OBSERVATIONS

Presented below are key observations re sulting from the exposure assessment:



### • Modeled concentrations are reasonable given comparisons to available data

o AERMOD predicted upp er percentile NO 2 

### concentrations are about 10 to 50% (or 10 to 30 ppb) higher than ambi ent measurements at three fixedsite monitors

o APEX modeled daily mean NO 2 

### exposures in Atlanta (medians 3-24 ppb) are comparable to personal exposure measurements in Atlanta (median 3- 14 ppb)

o APEX modeled annual average NO 2 

### exposures, expressed as a percent of

the ambient NO 2 

### concentration (40 to 70%), are consistent with findings reported in the ISA (30 to 60%)



### • Estimated exposures above 1-hour potential health effect benchmark levels using APEX were due largely to roadway- related exposures (>99%). Of this, approximately 70% were from in-veh icle exposures, with the remainder associated with outdoor near-road exposures



### • When included, indoor sources co ntribute to the occurrence of NO 2 

### exposures at or above 100 ppb (61%), but little to the occurrence of hi gher exposures (i.e., above 200, 300 ppb)



### • The estimated effect of air quality on benchmark exceedances differs by

benchmark level considered:

o 100 ppb

 For all air quality scenarios c onsidered, more than 90% of

asthmatics in Atlanta are estimated to be exposed at least one time

per year

 Of the standard levels eval uated, 50 ppb was the only level estimated to reduce repeat exposures to NO 2 

### concentrations above 100 ppb compared to recent air quality levels

o 200 ppb

 Of all the air quality scenario s considered, only alternative standards set at 50 ppb are estimated to reduce the percent of

213

### asthmatics exposed at least one time (by approximately 40 to 50%) relative to recent air quality levels

o 300 ppb

 Of all the air quality scenario s considered, only alternative standards set at 50 ppb or 100 ppb are estimated to reduce the percent of asthmatics exposed at least one time (by approximately 80% and 15% respectively) relative to recent air quality levels



### • When simulating air quality that just meet s the current annual standard, virtually all asthmatics in Atlanta are estimated to experience six or more daily maximum 1-hour exposures per year to NO 2 

### concentrations above the highest benchmark level evaluated (300 ppb)



### • Using a 98 th 

### versus a 99 th 

### percentile form made a diffe rence of approximately 5 to 10% in the number of daily maximum benchmark exceedances, with the 99 th 

### percentile form generating fewer exceedances.



## 8.11 REPRESENTATIVENESS OF EXPOSURE RESULTS

8.11.1 Introduction

Due to time and resource constraints the e xposure assessment evaluating the current and alternative standards was only applied to the At lanta urban area. A na tural question is how representative are the estimates from this assessme nt of exposures in Atlanta to other urban areas in the United States. To address this questi on, additional data were compiled and analyzed to provide perspective on how representative the Atlanta exposure modeling results might be for

other urban areas. Because most estimated exceed ances were associated with the near-road or in-vehicle microenvironments, th e analysis and discussion is cen tered on a variety of population and road statistics to allow for comparison of Atlanta with several other urban locations.

8.11.2 Description of Data Compiled and Summarized Three sources were used for comparing Atla nta with several urban locations: 1) the Human Air Pollutants Exposure Model near-road population data base, 2) American Housing Survey (AHS) data, and 3) statistics from the Federal Highway Ad ministration (FHWA).

214

### 8.11.2.1 HAPEM6 Near-Road Population Data Base The first type of data considered employs a data base develo ped for the Human Air Pollutants Exposure Model (HAPEM6) and is ava ilable as part of the HAPEM6 installation package. 19 

### The data base provides estimates of the fr action of people in each of 6 age groups in each US Census tract living near major roadways. 20 

### The distance-from-roadway bins are: 0 m to 75 m, 75 m to 200 m, and > 200 m. Details regarding the data ba se development are provided in Appendix B, Attachment 6 and 7. Briefly, the development of the HAPEM6 near -road population data base was for use in estimating the enhancement near major roadways of air toxic pollutant concentrations from onroad motor vehicle emissions rela tive to concentrations at other outdoor locations. First, several measurement studies of near roadway concentr ation gradients were reviewed (Kwon, 2005; Meng et al., 2004; Riediker et al., 2003; Rodes et al ., 1998; Weisel et al., 2004; Zhu et al., 2002). None of these studies provided sufficient data to estimate the required concentration ratios. Measurements in the Riediker et al. (2003) a nd Rodes et al. (1998) st udies were made at

distances shorter than th ose of interest for this study. Available ratios for Zhu et al. (2002) were only for downwind distances, and did not repres ent ratios under more general meteorological conditions. The ability of regression models a pplied to the Relationshi ps of Indoor, Outdoor, and Personal Air (RIOPA) study data (Kwon et al ., 2005; Meng et al., 2004; and Weisel et al., 2004) to predict the near roadway concentrations was generally poor, likely due to the problem that the near roadway concentra tions are also impacted by other emissions sources that cannot be easily adjusted for. CALPUFF air dispersion model predictions from the Portland (OR) Air Toxics Assessment were analyzed (PATA; Cohen et al ., 2005) using summary statistics and regression modeling in order to obtain dist ributions of concentration ratios . A distribution was developed

based on the ratio of concentrations within a distance 

### D 1 

### meters of a major roadway, to

concentrations at lo cations greater than a second distance, 

### D 2 

### meters from a major roadway. A second distribution was developed usi ng the ratio of concentrations at 

### D 1 

### - 

### D 2 

### meters of a major roadway to concentrations at locations greater than 

### D 2 

### meters from a major roadway.

19 http://www.epa.gov/ttn/f era/human_hapem.html



### .

20 Ages 0-1, 2-4, 5-15, 16-17, 18-64, and ≥ 65.



### 215

### To determine the best choices for the distan ce-to-roadway bins, regression models were developed to estimate two distance terms 

### D 1 

### and 

### D 2 

### for each season and for the annual mean. Generally, the two-distance term regression m odels applied to the PATA CALPUFF data showed that the optimal ro ad distances, based on R 2 

### and Akaike Information Criterion (AIC) statistics, were 

### D 1 

### = 75 m and 

### D 2 

### = 300 m. The regression models favored the higher distances

for 

### D 2 

### because the CALPUFF estimates for PATA c ontinue to decrease significantly with distance from the road. However, the R 2 

### and AIC values were not much different between the

models with 

### D 2 

### = 200 or 300 m. Because a few other studi es, including Zhu et al. (2002), have shown typical zones of influence for roadwa ys no further than 200 m, distances of 

### D 1 

### = 75 m and



### D 2 

### = 200 m were selected for develo pment of the population data base. Then an analysis was conducted using the US Census block level populations stratified by age. The block level data we re then aggregated up to the tr act level for populations stratified by age for use in HAPEM6. The data bases used were:



### • The Environmental Sciences Research Center (ESRI) StreetMap US roadway geographic database (which includes NavTech, GDT and TeleAtlas rectified street data)



### • A geographic database of US Census block boundaries, extracted using the

PCensus 2000 Census data extractio n tool for Census file SF1



### • A geographic database for US Census bl ock boundaries in Pu erto Rico and the US Virgin Islands obtained from Proximity Because populations are not generally evenly distributed within blocks, it was assumed that the block populations all reside within 150 m of at least one road within the block of designation “local” or greater as defined by the Census Feature Class Codes (CFCC). Thus, the first step was to create a 150 m buffer around all roadways within the block. This buffer served as a “clipped” block boundary defining the po rtion of the block c ontaining residential populations. The block population wa s assumed to be uniformly dist ributed within the “clipped” block boundary. Next, a 75 m buffer and a 200 m buf fer were created around all major roadways within the block. These buffers were overlaid on the “clipped” block boundary, and the fraction of the “clipped” block area that fell within ea ch buffer was calculated. This area fraction was assumed to equal the population fr action that fell within each buffer, and the fractions were applied to each population subgr oup. The 75 m buffer and the 200 m buffer were also overlaid

216

### on the unclipped block boundary to determine the frac tion of the total block area that fell within

each of the buffers. The block le vel fractions for area and populat ions were then aggregated up

to the tract level. For each tract in the CMSA or MSA, the tr act-level, age-stratified distance-to-roadway population fractions were combined with 2000 US Ce nsus tract-level, age- stratified residential populations to calculate the CMSA- or MSA-leve l distance-to-roadway population fractions, as

follows.

CMSA fraction = ∑ tracts 

### (tract-fraction*tract-population)/ ∑ tracts 

### (tract-population)

Results of the estimated populat ion within the sel ected roadway distances are provided in

Table 8-14 for each of the 18 named locations id entified in the air quali ty characterization. Based on the HAPEM6 data base, Atlanta has the lo west percent of its p opulation living within each of the near-roadway distance bins. Absolute differences for the closest road distance bin ranged from 4 to 16 percentage points lower fo r Atlanta compared with all other locations. Atlanta also was estimated to have the greate st percent of the populat ion living at distances ≥ 200 m from a major road. Considera tion of just this attribute w ould suggest that on a populationweighted basis, the number of daily maximum exposures of NO 2 

### concentrations above benchmark levels may be lower for Atlanta than many of the other urban areas listed in Table 8- 14. However, note that most of the exceedances were associated with in-vehicle exposures.

Table 8-14. Percent of population within selected distances of a major road in several locations. Percent of Population at Distance from a Major Road 1 Location <75 m >75 m, <200 m ≥ 200 m Atlanta 16% 23% 62% Boston 27% 38% 35% Chicago 27% 38% 35% Cleveland 20% 36% 44% Colorado Springs 21% 32% 47% Denver 23% 34% 43% Detroit 22% 33% 45% El Paso 24% 35% 41% Jacksonville 21% 29% 49% Las Vegas 22% 33% 45% Los Angeles 26% 38% 37% Miami 26% 37% 37% New York 32% 37% 30%



### 217Percent of Population at Distance from a Major Road 1 Location <75 m >75 m, <200 m ≥ 200 m Philadelphia 25% 34% 42% Phoenix 22% 34% 44% Provo 19% 31% 51% St. Louis 20% 31% 49% Washington DC 25% 27% 48% Notes: 1 Major roadways were all roadways except those classified as local by the US Census Bureau.



### 8.11.2.2 American Housing Survey (AHS) Data The American Housing Survey (AHS), conducted by the Bureau of the Census for the Department of Housing and Urban Developmen t (HUD), collects data on the nation's housing (AHS, 2008). Relevant housing ch aracteristic data, including hous ing units within 300 m of a major highway, railroad, or airp ort and residential prevalence of air conditioning are summarized

for 14 of the named locations 21 

### evaluated in the air quality characterization, using the available metropolitan areas surveyed by the AHS (Table 8-15). Because survey years differ for each

location and some locations contained more than one survey, the most recent data or data closest to 2002 were selected (the base year for the e xposure modeling). Consis tent with the pattern noted for the population living near roadways, At lanta also has the lowest percent of housing units (9.7%) within 300 m of a 4 or more lane highway, railroad, or airport (AHS, 2008). Denver, Phoenix, and St. Louis were only slig htly higher though, estim ated to have 10.1, 11.2, and 11.4% of housing units, respectively < 300 m from these same locations. Again,

consideration of this at tribute alone, suggests dail y maximum exposures to NO 2 

### concentrations

above selected benchmarks would tend to be lo wer for Atlanta than many of the other urban areas listed in Table 8-15. As noted previous ly, the estimated exceedances were dominated by exposure occurring in-vehicles. The AHS also provides data on A/C preval ence rate for several urban areas (Table 8- 15). The A/C prevalence can vary greatly across urban areas, based largely on climate differences. The air conditioning prevalence can influence the air exchange rate in a residence, potentially affecting the infiltra tion rate of outdoor air concentrat ions into the indoors residential

microenvironment. Atlanta was estimated to have one of the highest air conditioning prevalence

21 There are no AHS data for Colorado Spri ngs, El Paso, Jacksonville, and Las Vegas.



### 218

### rates (97.2%), though similar rates could be found in Miami, Phoe nix, St. Louis, and Washington

D.C. A few of the urban areas listed have much lower A/C pr evalence rates, including Los Angeles with 57.4% and Boston with 63.1%. For locations having a low A/C prevalence, it is expected that the number of indoor resi dential exposures to daily maximum NO 2 

### concentrations above selected benchmarks would be greater compar ed to those estimated in Atlanta. However, results of this exposure assessment indicate th e indoor residential microenvironment does not

contribute to exceedances of the selected benchm arks (see Figure 8-17) even when considering alternative A/C prevalence (section 8.12.2.6).

Table 8-15. Residential A/C prevalence and roadw ay distance statistics for housing units in several locations (AHS, 2008).

Location AHS Survey

Year Housing units < 300 m from a mobile source 1 (%) A/C Use 2 (%) Atlanta 2004 9.7 97.2 Boston 1998 14.3 63.1 Chicago 2003 18.8 89.6 Cleveland 2004 19.0 75.8 Denver 2004 10.1 66.9 Detroit 2003 17.7 82.4 El Paso ND ND ND Jacksonville ND ND ND Las Vegas ND ND ND Los Angeles 2003 14.2 57.4 Miami 2002 15.8 98.1 New York 2003 14.8 83.3 Philadelphia 2003 14.3 91.4 Phoenix 2002 11.2 94.4 Provo ND ND ND St. Louis 2004 11.4 96.7 Washington DC 1998 17.6 96.0 Notes: ND No data available 1 Represents the percent of total year-round housing units located within 300 meters of a 4 or more lane highway, railroad, or airport (AHS, 2008). 2 Represents the percent of total year-round housing units having central or room unit air conditioners (AHS, 2008).



### 219

### 8.11.2.3 Federal Highway Administration (FHWA) Data The Federal Highway Administration (FHWA) produces annual highway statistics reports based on highway data submitted by individual States. Because 2002 served as the base year for the exposure assessment, these data were compiled for each of the named locations evaluated in the air quality ch aracterization and are presented in Table 8-16. We note that Atlanta contains the high est per capita daily vehicle miles trav eled and also the highest miles of roadway per 1,000 persons, both attributes whic h would tend to result in greater population exposure to peak NO 2 

### concentrations associated with road way exposures. However, as shown in Figure 8-27, Atlanta is roughly in the middle of the distributi on with respect to estimated population and total road way miles when compared with the other locations examined.

Table 8-16. Population and roadway stat istics for several locations (FHWA, 2002).

Location Total roadway

miles Estimated Population (thousands) Miles of roadway per 1,000 persons Total DVMT 1

per capita Atlanta 13,438 2,873 4.7 35.3 Boston 13,809 3,854 3.6 20.9 Chicago 23,832 7,702 3.1 21.5 Cleveland 6,975 1,785 3.9 20.6 Colorado Springs 1,887 439 4.3 20.4 Denver 7,261 1,989 3.7 22.9 Detroit 13,755 3,835 3.6 25.1 El Paso 2,225 657 3.4 17.3 Jacksonville 4,769 906 5.3 31.0 Las Vegas 3,206 1,456 2.2 18.1 Los Angeles 26,329 12,365 2.1 23.7 Miami 15,436 5,021 3.1 23.9 New York 37,854 17,307 2.2 15.9 Philadelphia 15,743 4,813 3.3 19.4 Phoenix 10,684 2,949 3.6 21.2 Provo 1,384 345 4.0 21.2 St. Louis 9,123 2,067 4.4 29.2 Washington DC 10,561 3,807 2.8 22.7



### 2200 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,000

0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 

### Total Roadway MilesEstimated Population (Thousands)



### A tlanta

Figure 8-27. Comparison of estimated populat ion and total roadway miles in 18 locations (data from FHWA (2002) pr ovided in Table 8-16).

8.11.4 Discussion There are a number of importa nt attributes to take into account in considering the representativeness of the exposur e modeling results from Atlanta with regard to other urban locations not modeled in the exposure assessment. As noted above, Atlanta falls on the low end

relative to the other urban areas examined (i.e, 18 locations in all listed in Table 8-14) with respect to percent of population living within sele cted distances of a major road (see Table 8-14) and percent of the population residing in housin g units < 300 m from a mobile source (see Table 8-15). Being on the low end with respect to thes e two attributes would likely result in Atlanta having a lower proportion of the population likel y being exposed to daily maximum 1-hour NO 2 

### concentrations above selected be nchmark levels than other locatio ns included in Tables 8-14 and 8-15, in the absence of other infl uential attributes. However, as noted above, Atlanta contains

221

### the highest per capita daily vehi cle miles traveled and also th e highest miles of roadway per 1,000 persons, both attributes which would tend to result in greater population exposure to NO 2 

### concentrations above selected benchmark levels associated with increased roadway exposures. While the miles of roads per person is generally accounted for by the current exposure modeling, the DVMT specific to Atlanta is not well represented and is likely more similar to a national

DVMT. Furthermore, Atlanta is roughly in the middle of the distribution with respect to estimated population and total roadwa y miles when compared with the other locations examined. Given that there are attributes that go in both directi ons with respect to the influence of on-road and other mobile sources on the representativeness of Atlanta relative to the other 17 urban areas examined in the air quality analysis, staff judges that the Atlanta exposure estimates are likely representative of other moderate to large urban areas includ ed in this comparison. Staff does recognize that the Atlanta expos ure results are likely lower on a population-weighted basis compared to the largest urban areas such as Los Angeles, Ne w York, and Chicago given the greater proximity of the population to mobile sources in these la rge urban areas. For example, 64, 69, and 65%, respectively, of Los Angeles, Ne w York, and Chicago’s populations live within 200 m of a major road compared to only 39% for Atlanta. Similarly, there is a much higher percentage of housing units within 300 m of a mobile source (i.e., 14.2, 14.8, and 18.8%, respectively, for Los Angeles, New York, and Chi cago), compared to only 9.7% for Atlanta. As discussed above, with respect to resident ial A/C prevalence, we expect that urban areas with lower A/C prevalence would te nd to result in higher exposures to NO 2 

### of ambient origin, all other factors being e qual. We note that in compari ng A/C prevalence rates for each location, Boston, Cleveland, Denver, and Los Ange les had between 20 to 40 percentage points less prevalence of A/C in residences than that observed for Atlanta. Thus, based on this consideration, the estimated pe rcent of population exposed and person days with exposures

above the selected health effect benchmarks fo r Atlanta may be somewhat lower than would be expected for other urban areas wi thin the parts of the U.S. (e.g., urban areas in the midwest, northeast, southern California, and northwest) with lower prevalence of residential A/C.

222

## 8.12 UNCERTAINTY ANALYSIS

The methods and the model used in this exposure assessment conform to the most contemporary modeling methodologies available. APEX is a powerful and flexible model that

allows for the realistic estimati on of air pollutant exposure to i ndividuals. Since it is based on human time-location-activity diaries and accounts for the most important variables known to affect exposure (where people are located and wh at they are doing), it has the ability to effectively approximate actual expos ure conditions. In addition, the input data selected were the best available data to generate the exposure results. However, there are constraints and uncertainties with the modeling approaches and th e input data that limit the realism and accuracy of the modeling results. Uncertainties and assumptions associated with NO 2 

### specific model inputs, their utilization, and application are discussed in the following sections. Analyses for certain components of APEX performed previously in other NAAQS reviews (see EPA, 2007g; Langstaff, 2007) that ar e relevant to the NO 2 

### NAAQS review are only summarized below. This includes a sensitivity analyses performed on the CHAD data base using O 3 

### exposures and an analysis of the air ex change rate data. Following the same general approach descri bed in section 7.8 and adapted from WHO (2008), a qualitative analysis of the components contributing to uncertainty in the exposure results was performed. This includes an iden tification of the important uncertainties, an indication of the potential bi as direction, and a scaling of the uncertainty using 

### low

, 

### medium , and 

### high categories. Even though uncertainties in AERMOD concentrations predictions are an APEX input uncertainty, they are addr essed separately here for clarity.

Table 8-17. Summary of qualitative uncertaint y analysis for the exposure assessment.

Source Type Concentration/ Exceedance Bias Direction Characterization of Uncertainty AERMOD formulations for mobile sources unknown Low On-road emissions over Low – Medium O 3 monitoring data over Low Use of unadjusted NO 2 concentrations unknown Low – Medium AERMOD Inputs and Algorithms

Meteorological data unknown Low – Medium Population data base both Low APEX Inputs and Commuting data base both Low - Medium



### 223CHAD data base under Low - High Meteorological data both Low Air exchange rates unknown Medium A/C prevalence none Low Indoor sources not modeled under Medium Indoor decay distribution under Low - Medium Indoor concentration distribution under Medium - High Algorithms

Longitudinal profile both Low



### 8.12.1 Dispersion Modeling Uncertainties Air quality data used in the exposure m odeling was determined through use of EPA’s recommended regulatory air dispersion model, AERMOD (version 07026 (EPA, 2004)), with meteorological data discussed above and emissi ons data based on the EPA’s National Emissions Inventory for 2002 (EPA, 2007e) and the CAMD Emissions Database (EPA, 2007f) for stationary sources and mobile sources determin ed from local travel demand modeling and EPA’s

MOBILE6.2 emission factor model. All of these are high quality data sources. Parameterization of meteorology and emissions in the model were made in as accu rate a manner as possible to ensure best representation of air quality for exposure modeling. Thus, the resulting air quality values are likely free of systematic errors to th e best approximation avai lable through application

of modeled data. An analysis of uncertainty associated with application of a model is generally broken down into two main categories of uncertainty: 1) model algorithms, and 2) model inputs. While

it is convenient to discuss uncertainti es in this context, it is also important to rec ognize that there is some interdependence between the two in the sense that an increase in the complexity of model algorithms may entail an increase in th e potential uncertainty associated with model inputs.

8.12.1.1 AERMOD Algorithms The AERMOD model was promulgated by in 2006 as a “refined” dispersion model for near-field applications (with plume transport di stances nominally up to 50 kilometers), based on

a demonstration that the model produces largely unbias ed estimates of ambient concentrations across a range of source characteristics, as well as a wide range of meteorological conditions and topographic settings (Perry, 

### et al. , 2005; EPA, 2003). While a majority of the 17 field study

224

### databases used in evaluating the performance of AERMOD are associated with elevated plumes from stationary sources (typically power plants ), a number of evaluati ons included low-level releases more typical of the dominant emissions category in this asse ssment. Moreover, the range of dispersion conditions re presented by these evaluation studi es provides some confidence that the fundamental dispersion formulations within the model will provide robust performance in other settings. AERMOD is a steady-state, straight-line plum e model, which implies limitations on the model’s ability to simulate certain aspects of plume dispersion. For example, AERMOD treats each hour of simulation as independent, with no memory of plume impacts from one hour to the next. As a result, AERMOD may not adequa tely treat dispersion under conditions of atmospheric stagnation or reci rculation when emissions may build up within a region over

several hours. This could lead to a bias toward under-prediction by AERMOD during such periods. On the other hand, AERMOD assumes th at each plume may impact the entire domain for each hour, regardless of whether the actual tr ansport time for a particular source-receptor

combination exceeds an hour. While these as sumptions imply some degree of physically unrealistic behavior when considering the imp acts of an individual plume simulation, their importance in terms of overall uncertainty will vary depending upon the application. The degree of uncertainty attributable to these basic model assumptions is likely to be more significant for individual plume simulations than for a cumula tive analysis based on a large inventory. This question deserves further investigation to better define the limits and capabilities of a modeling system such as AERMOD for large scale exposure assessments such as this. The evidence provided by the model-to-monitor comparisons pres ented in Chapter 8.4.8 is encouraging as to the viability of the approach in this applicati on when adequate meteorological and other inputs are available. However, each modeling domain and inventory will present its own challenges and will require a separate assessment ba sed on the specifics of the application. Beyond the basic dispersion al gorithms in AERMOD, anot her component of model formulation uncertainty for this application is the use of the Ozone Limiting Method (OLM) and Plume Volume Molar Ratio Method (PVMRM) al gorithms for simulating the conversion of NO

to NO 2 

### . Model performance evaluations for these NO x 

### chemistry algorithms are more limited than for non-reactive plume dispersion. Howeve r, an assessment of the potential for bias

225

### associated with the PVMRM algorithm show ed generally good performance based on the available evaluation data (MACTEC, 2005). While the AERMOD model algorithms are not c onsidered to be a si gnificant source of uncertainty for this assessment, the represen tativeness of modeled concentrations for any application are strongly dependent on the quality and representativeness of the model inputs. The main categories of model inputs that may c ontribute to bias and uncertainty are emission estimates and meteorological data. Use of the OLM and PVMRM chemistry algorithms also introduces the potential uncertainty asso ciated with the dependence on ambient O 3 

### concentration inputs since both algorithms tr eat the reaction of NO with O 3 

### to produce NO 2 

### as the primary

mechanism for conversion of NO x 

### emissions. These issues are addressed in the following

sections.

8.12.1.2 Meteorological Inputs Details regarding the representativeness of the meteorological da ta inputs for AERMOD are addressed separately in Attachment 1 to Appe ndix B-4 of the REA. One of the main issues associated with the representativeness for this application is the sensitivity of the AERMOD model to the surface roughness of the meteor ological tower site us ed to process the meteorological data for use in AERMOD relative to the surface roughne ss across the full domain

of sources. This issue has been shown to be more significant for low-level sources, including mobile sources, due to the importance of m echanical shear-stress induced turbulence on dispersion for such sources. In particular, concerns were raised in preliminary modeling due the typically low surface roughness associ ated with the meteorological tower located at ATL airport compared with the much higher roughness enviro nment of most low-level emission sources. Based on this concern, alternative meteorological data from the Jefferson Street (JST) Southeast Aerosol Research and Characterization study (SEA RCH) site were determined to be more representative of the majority of NO x 

### emissions across the modeling domain, as discussed in Attachment 1 to Appendix B-4. The meteorological data obtained for the JST site were used to model all emissions with the exception of the Atlant a airport. Meteorologi cal data for ATL were used to model the airport emissions. To assess the uncertainty associated with the sensitivity of AERMOD to surface roughness effects, a comparison was made between modeled NO 2 

### concentrations from mobile

226

### source emissions based on the JST meteorological data versus the ATL meteorological data. Surface roughness lengths were generally about an order of magnitude higher at the JST site

relative to the ATL site. Results of this comparison are pr esented in Figure 8-28, which shows a plot of high ranked 1-hour concen trations, unpaired in time and sp ace, calculated at four Atlanta

NO 2 

### monitoring locations for 2002. This figure shows relatively good agreement in modeled

concentrations based on the two sets of meteorological inputs, at least for the peak of the concentration distribution at these four receptor locations. This suggests that the sensitivity of

AERMOD model results to variations in surface roughness may be less significant than commonly believed, provided that meteorological data inputs are processed with surface characteristics appropriate for the meteorologica l site. The overall p eak concentration should

about a factor of 2 bias between the two sets of meteorological inputs, with the concentrations based on the higher roughness JST data showing higher impacts. While these results are encouraging in relation to the assessment of uncer tainty for the REA, they may not be indicative of the degree of sensitivity to surface r oughness for individual sources in other modeling

contexts.

22710 100 1000 10000

10 100 1000 ATL_MetJST_Met



### Figure 8-28. Comparison of high ranked AERMOD 1-hour NO 2 

### concentrations ( μ g/m 3 

### ) from mobile sources across four NO 2 

### monitoring locations based on JST vs. ATL meteorological inputs for 2002.

8.12.1.3 Mobile Source Characterization Given the predominance of mobile source emissions in this assessment, source characterization of non-stationary mobile sources is another important category to consider for the uncertainty analysis. Determining the most appropriate source characteristics for modeling emissions from mobile sources presents severa l technical challenges a nd involves a number of uncertainties. Unlike typical stationary em ission sources simulated by AERMOD, for which source characteristics such as release height and effluent parameters can be clearly defined and measured, emissions from mobile sources repr esent an aggregate of emissions from nonstationary sources of various si zes, shapes, and speeds. Since mobile source emissions (other than aircraft) are emitted near the ground, the pl umes can be significantly influenced by the turbulent wake associated with the emitting vehi cle, as well as turbulence generated by nearby vehicles and other roughness elements such as so und barriers, median barriers, trees, buildings, etc. Some of these effects may vary depending upon the orientation of the roadway to the wind

direction since the vehicle wake effect is most pronounced for wind direct ions parallel to the

228

### road, where the wakes of multiple vehicles can combine to enhance dispersion of the roadway

emissions. Representative source characteristics for mobile emissions may also depend on the pollutant of interest, and whether the pollutant is primarily associated with direct vehicle exhaust, as in the case of NO 2 

### , or includes mechanical sources, such as re-entrained road dust, tire wear and brake wear for PM. Emissions associated with vehicle exhaust may experience some buoyancy due to the exhaust temperature exceeding the ambient temperature. Such buoyancy effects might be negligible for vehicles moving at highway speeds, where mechanically-induced turbulence would likely dominate the initial plume characteristics, but could be a significant factor fo r slow moving vehicles on a co ld day during rush hour. In addition to the influence of roughness elements, ch aracteristics of the roadway itself may be a factor. For example, thermally-induced turbulen ce generated by direct sunlight on dark asphalt could enhance the initial plume spread compared to the same conditions for a more reflective concrete road surface. The best approach fo r determining source characteristics for mobile emissions may also depend on the scope and nature of the application. Characterizing mobile sources for a large-scale urban study, such as the Atlanta NO 2 

### modeling, may necessitate a different approach than characte rizing mobile sources for a partic ular highway project within a more localized modeling domain. A detailed treatment accounting for influences of specific local features may be possible for the latter. However, for larger scale applications such as this, a simplified approach with the goal of character izing emissions based on a reasonable estimate of the aggregate effect of these various factor s is necessitated by pract ical limitations. The factor of 1.7 times the vehicle height us ed to account for vehicle-induced turbulence is cited in Gilles et al. (2005) based on some fi eld measurements for an unpaved road. The factor of 1.7 is somewhat less than the typical formul a for the turbulent wake downwind of a building, which is 2.5 times the building height. This difference seems reasonable based on the more aerodynamic shape of vehicles as compared to buildings. While some differences may be expected between paved and unpaved roads, these differences are probably minor compared to

other uncertainties. However, the value used to estimate vehicl e-induced turbulence could be conservative in terms of modeled concentrations since it may not account for the other influences mentioned above, especially the influence of roadway orie ntation relative to the wind direction. These

229

### influences may include considerable variability bot h temporally and spatiall y, and this variability is difficult to quantify within the current model formulations. However, given the size of the domain, the number of sources included in the modeled inventory, and the length of period modeled, it is likely that some of the uncertainti es associated with source characterization will tend to cancel out in the cumulative impact asse ssment. This expectation is supported for offroad exposures by the generally good agreement in model-to-monitor comparisons presented in Chapter 8.4.8, but is more difficult to asse ss in relation to on-road exposures. The matching of TDM links, which are straight segments by their nature, to roads, which may be curvilinear, was done as objectively as possi ble. It is possible that once links were segmented to avoid aspect ratio issues in the modeling, these segments could deviate from the actual road layout more than they did prior to segmenting. However, the GIS analysis and matching was done before the segmenting and no revision was done after. Thus staff cannot quantify any additional spatial mismatch that ca me from segmenting. Spatial mismatch from segmenting could affect offroad concentrati ons relative to a di fferent methodology where predicted concentrations from segments were mate d to road locations rather than full TDM links

due to both orientation of the ar eas and location relative to recep tors. However, these segments

were not considered individual lin ks, but rather partial values to avoid numerical modeling issues and were maintained as consistent with the orig inal data source from which they were drawn (i.e., the TDM), which did not have data at the resolution of the individual segments. On-road concentrations using an altern ative methodology, where segments ra ther than links were mated to actual road locations, would differ due to orie ntation of the area rela tive to wind direction. However, as the onroad receptors are centered within the source, a ny differences are likely to be

small.

8.12.1.4 On-Road Emissions Estimates

It should be noted that free flow speed represents all traffic, that is, cars and trucks on each link are assigned the same speed. This adds to uncertainty due to the prominence of trucks

in the NO x 

### inventories, and may contribute positiv e bias to diurnal patterns in AERMOD predictions.

The truck fractions used in the traffic m odeling may also contribute to bias and uncertainty in the exposure results, although adjustments made based on the NEI on-road

230

### emissions may correct for the potential bias. A comparison with National estimates of vehicle miles traveled for year 2002 (FHWA, 2002) a nd a study conducted in Georgia (FHWA, 2007) indicate that the average truc k fractions produced by the Atlant a Regional TDM are potentially higher, possibly contributing to overestimations in NO x 

### emissions. Table 8-18 summarizes

National estimates of the vehicle mile travel ed (VMT) by two highway categories and vehicle types. Depending on roadway category, rural VM T consisted of about 7 to 20% trucks and urban road miles ranged from 4 to 8% trucks. Comparable results can be found in data reported by FHWA (2007) for a roadway in Atlanta, wher e urban traffic counts were estimated to be comprised of 3 to 8% trucks and rural traffic counts ranged from 10 to 20% trucks (Table 8-19).

The rural roads were estimated to have from 15 to 33% of VMT as trucks based on the Atlanta TDM modeling, while urban roads ranged fr om 12 to 20% (Table 8-2). The discrepancy is likely due to an inconsistency between the definitions of trucks between the present and

FHWA documents. The truck fraction used in th e exposure assessment is based directly on the total truck volume included in the TDM, defined as:

Total Trucks = Heavy (class 8-13) + Medium (class 4-7) + Comm ercial (any vehicle used for commercial purposes)

By definition this truck fraction contains a larger fraction of vehicles than those from the DEIS. To maintain internal consistency in our approach, we used the Atlanta-provided MOBILE6 input files and fleet ch aracterizations and computed co mposite emissions for light and heavy duty vehicles, where the h eavy duty fraction is a VMT-weight ed composite of all medium and heavy duty vehicles from MOBILE classes 2B to class 8B. It is possible that bias could

result from the addition of commer cial vehicles in the TDM, but th is was not resolvable with the data provided by ARC. However, even with a mismatched HDV fraction and emission factor, for example by coupling MDV+HDV VMT to an HDV8 B emission factor, this would have been corrected by the scali ng of the emissions down to the 2002 NEI on-road emissions to account for

differences between 2005 and 2001- 03 activity (see Table 8-6). In addition, the reported truck fractions in Table 8-18 are likely associated with peak counts for light duty vehicles during work commutes. Therefore these may be biased lo w, even when comparing with morning and

afternoon HDV fractions in Table 8-2 whic h are based on broader time periods.

231

### Table 8-18. National vehicle miles travel ed by roadway category and vehicle type. Millions of Vehicle-Miles Traveled

Area Roadway Buses Single Unit Trucks Combination Trucks All Vehicles % Trucks +

Buses Interstate 941 8,745 45,633 279,962 20 Arterial 1,104 14,606 27,818 433,805 10 Rural Other 1,901 14,963 14,090 414,393 7 Interstate 802 9,106 23,887 408,618 8 Urban 1 Other 2,101 28,467 27,215 1,318,978 4 Notes: 1 Urban consists of travel on all roads and streets in urban places with 5,000 or greater population. Data from table vm1.xls of FHWA (2002).



### Table 8-19. Observed peak hour truck percentages on Interstate 75 (I-75) using 2002 traffic count data. FHWA Vehicle Classes 3

Roadway 1 Peak Hour 2 Direction 6-7 (MDT) 8-13 (HDT) Total Trucks 4 Northbound 1 7 8 AM Southbound 0 3 3 Northbound 1 5 6I-75 N of I285 PM Southbound 1 6 6 Northbound 1 19 20 AM Southbound 1 11 12 Northbound 1 10 10 I-75 N of Wade Green Road PM Southbound 1 14 14 Notes: 1 I-75 N of I-285 is just east of Fulton county, likely represents an urban area. The roadway I-75 N of Wade Green Road in nor thern Cobb county likely represents a rural area. 2 Peak hours for AM were between 7:30-9 :15, PM peak hours were 4:45-5:14. 3 MDT and HDT are medium and heavy duty trucks, respectively. 4 Totals were summed before rounding, therefore MDT +HDT may not always sum to total trucks Data are from Table 2-4 of FHWA (2007).



### Another important source of uncertainty is th e diurnal pattern of on -road vehicle activity and emissions. Vehicle activity data was pr ovided by the Atlanta Regional Council (ARC) showing average daily traffic for 4 daily time periods on each roadway link (see section 8.4.3.1). However, the air dispersion model requires hourl y allocations of the data. Assigning equal vehicle activity levels to each hour in an ARC-specified time period would have created an unrealistic “step function” time series. In order to create a more realisti c diurnal pattern while minimizing alteration of the provided data, a 5- hour running averages of vehicle activity were used to smooth the “step function”. There is uncertainty about how well the smoothed diurnal

232

### pattern represents the unknown act ual hourly diurnal pattern. Th is uncertainty is important, because typical meteorological condi tions at various times of day have differential effects on the relationship between emissions and concentrations . For example, at dawn and dusk typical low mixing heights and low wind speeds enhance the con centration impacts of emissions, so that any emission overestimates at those times are likely to result in disproportionate concentration overestimates. The model-to-monitor compar isons in Figure 8-7 show disproportionate overestimates of concentrations during those hours of the day, suggesting that corresponding vehicle activity levels may be overestimated. Another temporal component of the emission profile for mobile sources worth noting is the variability by day-of-week. The temporal pr ofile of mobile source emissions used for the REA accounts for variability by season and hour-of-day, but does not distinguish between weekday and weekend emission profiles. An an alysis of model-to-monitor comparisons for weekday vs. weekend periods was conducted to a ssess this potential sour ce of uncertainty. Figure 8-29 compares the average ra tios of predicted to observed NO 2 

### concentrations at selected percentiles across the concentrati on distribution base d on the four NO 2 

### monitors (including the SEARCH JST monitor). While this analysis sh owed that model performance was significantly better for weekdays than weekends, the affect of this simplification on th e overall concentration distributions was not significant. The ab sence of the morning rush hour peak in NO 2 

### concentrations evident in the ambient concentr ations for the weekend comparison suggests that this factor may have contributed somewhat to the conservative bias in modeled concentrations for the morning rush hour period, but was not the major factor.

2330 0.2 0.4 0.6 0.8

1 1.2 1.4 1.6 1.8

2

0 10 20 30 40 50 60 70 80 90 100

PercentilePred/Obs Conc

Pred/Obs Weekday Pred/Obs Weekend



### Figure 8-29. Comparison of the average ratios of predicted/observed concentrations of NO 2 

### across four ambient monitors ba sed on weekday vs. weekend only.

Another potential source of un certainty in relation to on-road emission estimates is the assumption of 0.1 as the “in-stack” ratio of NO 2 

### to NO x 

### emissions. While this ratio is considered to be generally representa tive of many sources of NO x 

### emissions and is considered a default value for applications of the OLM option, modeled concentrations of NO 2 

### will be sensitive to this user-specified value, especi ally for modeled impacts close to low-level sources. As a result, the uncertainty associated with this issue is likely to be greater for on-road concentration estimates than for off-road locations. However, the overall uncertainty associated with this modeling input is expected to be low.

8.12.1.5 O 3 

### Monitoring Data for OLM and PVMRM Options Monitored hourly O 3 

### concentrations were used as input to the OLM and PVMRM atmospheric chemistry modules in AERMOD for conversion of NO to NO 2 

### . Since AERMOD is currently limited to the use of a single hourly O 3 

### concentration for all so urces within the modeled inventory, this may introduce some uncertainty in the modeled concentrations if there is significant spatial variability of O 3 

### concentrations across the doma in. One suggested source of

234

### this uncertainty and potential bias is the influence of an O 3 

### deficit over major roadways due to the high concentration of NO x 

### emissions depleting O 3 

### , especially during rush hour. Lacking more complete spatial coverage of O 3 

### concentrations, it is difficult to quantify this potential cause of bias and uncertainty. If such spatial variability of O 3 

### concentrations were present, it would likely contribute to more conservative modeled concentra tions during periods of higher than average NO x 

### emissions and lower than average O 3 

### concentrations, which would limit

conversion of NO to NO 2 

### . This may be one factor contributing to the apparent conservative bias that appears at all NO 2 

### monitors during the morning rush hour period, in addition to the uncertainties regarding temporal profiles of mobile source emissions. The simple linear

treatment of NO x 

### chemistry in the AERMOD model options does not explicitly account for the

O 3 

### depleting effect of the NO-to-NO 2 

### conversion. Another issue that has been raised as a poten tial source of uncertain ty in relation to the

NO x 

### chemistry for this applica tion is that the AERMOD modeli ng was conducted separately for mobile sources, point sources, a nd the ATL airport source. The OLM option was used for the mobile source and airport source emissions, wh ile the PVMRM option was used for the point sources. As a result, the ozone-limiting effects simulated by these chemistry options may have been underestimated and may lead to a conservative bias in modeled concentrations. While this may have been an issue with preliminary mode ling that separated the on-road and off-road components of mobile source emissions into se parate AERMOD runs, the final application of

AERMOD combined the on-road and off-road emi ssions into a single run. Given the relative contributions of these three categories of emissions, the potential impact of O 3 

### depletion due to point source and airport emissions on total mode led concentrations, which are dominated by the mobile source category, is lik ely to be insignificant.

8.12.1.6 Use of Unadjusted AERMOD NO 2 

### Concentrations While the number and range of field study da ta bases used in AERMOD’s evaluation is large relative to evaluation of ot her models, these field studies are predominantly associated with elevated buoyant releases from power plant stacks. In most of these cases emission rates are known with a high degree of accuracy, often base d on continuous emission monitors (CEMs) for operational facilities or based on controlled emissions in the case of tracer releases. Model performance for mobile source emissions across an urban area is not well documented, and

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### evaluating model performance for such applications is complicat ed by a number of challenges

and uncertainties. These include uncertainties re garding the temporal and spatial distribution of emissions, physical characteristics of the emission sources (such as rel ease height and initial dilution parameters), and the influence of spa tial variability of surf ace characteristics on dispersion of low-level plumes, among other fact ors. In addition, application of the AERMOD

model to simulate NO x 

### emissions across an urban domain in support of an exposure assessment imposes different requirements than a more typical application of AERMOD to support a specific regulatory permit application. Regul atory applications of AERMOD are generally motivated by a need to estimate the peak con centrations across a domai n of interest without regard to any specific spatial or temporal pattern of impacts. In contrast, the use of AERMOD to support an exposure assessment implies that mode led concentrations wi ll be coupled with population and other information in a way that implies some spatial and temporal pairing of impacts, and also places more emphasis on the si gnificance of the full concentration distribution predicted by the model. Despite these additional concerns and caveat s regarding the use of AERMOD in this REA, the evaluation of modeled air quality presented in Chapter 8.4.8 shows overall good agreement between AERMOD modeled NO 2 

### concentrations and available ambient monitored

NO 2 

### concentrations. The model evaluation results are consistent across the available ambient

monitors and across all seasons. Bias in the predicted concentra tion distribution at each of the monitor locations is generally w ithin the range expected of refi ned dispersion models across the

cumulative concentration distribut ion. While some systematic positive bias is evident in the diurnal profiles associated with morning rush hour peak in mobile source emissions, the degree

of bias in those cases is within the factor 2 commonly used to indicate relatively unbiased model performance. In considering that this upward bias occurs mainly in the early morning hours, it is possible that there may not be a large proporti on of the simulated population exposed at these times of the day. Therefore the upward bias may not have a large effect on the exposure results presented. The actual affect on the exposure resu lts from this bias remains uncertain, because the time-of-day simulated individuals were exposed was not generated by APEX.

Given the uncertainties associated with determining emission profiles and source characteristics for an urban-wide exposure assessment, previous assessments have often included adjustments to modeled concentrations based on av ailable ambient monitored concentrations as a

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### means of reducing overall uncertainty in the anal ysis. Two main factors have argued against such an approach for this REA. First, advances in the science of disp ersion modeling, including the promulgation of AERMOD model and more refi ned techniques for trea ting the temporal and spatial variation of emissions, provide a basis fo r more confidence in th e representativeness of

modeled concentrations for the exposure assessment. Second, th e limited number of available

ambient NO 2 

### monitors, the predominance of mobile source emissions of NO x 

### within the inventory, and the significant hori zontal gradients of concentrat ion associated with low-level emissions from major roadways raise signi ficant concerns regarding the spatial representativeness of the ambient NO 2 

### concentrations for purposes of the exposure assessment. Given that the air quality concentration estimates are estimated to be conservative, and that the values at the upper tails of the hourly distributio n are not unusual in co mparison with the other portions of the concentration dist ribution, it was determined that adjustment of the modeled air quality based on the three monitors was not nece ssary. Any effort to adjust concentration estimates based on monitored values would presen t a range of options and issues regarding how the modeled concentrations would be adjusted bo th temporally and spatially in relation to the observations. Based on these considerations, su ch an approach could actually increase the uncertainty of the REA in ways that ar e difficult to characterize or justify.

8.12.2 Exposure Modeling Uncertainties

8.12.2.1 Population Data Base The population and commuting data are drawn fr om U.S. Census data from the year 2000. This is a high quality data source for natio nwide population data in the U.S. however, the

data do have limitations. The Census used rando m sampling techniques instead of attempting to reach all households in the U.S., as it has in the past. While the sampling techniques are well established and trusted, they may introduce uncerta inty to exposure results. The Census has a quality section (http://www.censu s.gov/quality/) that discusses these and other issues with Census data. It is likely the bias within this da ta would not affect the results in any particular direction, and given the use of the sampled demographics to re present the simulated population, it is expected that the uncertainty in the exposur e results from this source is low.

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### 8.12.2.2 Commuting Data Base Commuting pattern data were derived from the 2000 U.S. Census. The commuting data address only home-to-work travel . A few simplifying assumptions needed to be made to allow for practical use of this data base to reflect a simulated individual ’s commute. First, there were a

few commuter identifications that necessitated a restriction of their movement from a home block to a work block. This is not to suggest th at they never travelled on roads, only that their

home and work blocks were the same. This includes the population no t employed outside the home, individuals indicated as commuting with in their home block, a nd individuals that commute over 120 km a day. This could lead to e ither over- or under-estimations in exposure if they were in fact to visit a bl ock with either higher or lower NO 2 

### concentrations. Given that the

number of individuals who meet these conditions is likely a sma ll fraction of the total population and that the bias is likely in either direction, the overall unc ertainty is considered low. Second, although several of the APEX microe nvironments account for time spent in travel, the travel is assumed to always occur in basically a composite of the home and work block. No other provision is made for the pos sibility of passing through other census blocks during travel. This could also cont ribute to bias in either dire ction, dependent on the number of blocks the simulated individual would actually traverse and the spatia l variability of the

concentration across different blocks. This c ould potentially affect a large portion of the population, since we expect that at the block level, many persons would have a commute transect that included more than two blocks, although th e actual number of persons and the number of blocks per commute and the spa tial variability across blocks has not been quantified. In addition, the commuting route (i.e., which road s individuals are tr aveling on during the commute) is not accounted for. This may bias the exposure results in either direction, with some

individual under-estimated and others over-estimated. Furthermore, the estimation of block-to-blo ck commuter flows relied on the assumption that the frequency of commuting to a workplace block within a tract is proportional to the

amount of commercial and industrial land in the block. This assumption may introduce a bias in overestimating exposures if 1) the blocks with greater commercial/industrial land density also have greater concentrations when compared w ith lower density commercial/industrial density blocks, and 2) most persons commute to lower commercial/industrial dens ity blocks. It should also be noted that recent surveys, notably the National Household Transportation Survey

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### (NHTS), have found that most trips taken a nd most VMT accrued by households are non-work trips, particularly social/recrea tional and shopping-relate d travel (Hu and Reus cher, 2004). This constitutes an unquantified source of uncertainty that is not be a ddressed by the Census

commuter dataset.

8.12.2.2 Human Time-Location-Activity Pattern Data The CHAD time-location activity diaries used ar e the most comprehensive source of such data and realistically represen t where individuals are located and what they are doing. The diaries are sequential records of each persons activities performed and microenvironments visited. There are however, uncertainties the expo sure results as a result of the CHAD diaries used for simulating individuals may introduce uncertain ty to the exposure results. First, much of the data used to generate the daily diaries were collected in surveys conducted over 20 years ago. While the trends in people’s daily activities ma y not have changed much over the years, it is certainly possible that some differe nces do exist. For example, it is estimated that between 1983 and 2001, the average miles traveled by people in the U.S. increased by 55%, corresponding to a 2.4% annual increase in miles traveled per pers on (Hu and Reuscher, 2004). Therefore, it is possible that the overall commute times in the CHAD diaries used to estimate in-vehicle exposures are biased low, resulti ng in under-estimation of exposures. Second, the CHAD data are taken from nume rous surveys that were performed for different purposes. Some of these surveys collected only a single diary-day while others went on for several days. Some of the studies were de signed to not be repres entative of the U.S. population, although a large portion of the data is from National surveys. In addition, study collection periods occur at different times of the year, possibly resulting in seasonal differences. This could add uncertainty to the results if there are characteristics of th e survey population that are distinct from the simulated population.

The CHAD diaries that are se lected from APEX to repres ent the Atlanta population are not all from Atlanta, the state of Georgia, or from the Southeast, albeit some of the diaries may be. As stated above, most of the diaries are from National surveys, therefore there are diaries

from locations other than Atlanta that are used to simulate the Atlanta population. A few of the

limitations associated with the use of diaries from different locations or seasons are corrected by the approaches used in the exposure modeling. For example, diaries used are weighted by

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### population demographics (i.e., age and gender) for a particular location and temperature is used

as a classification variable to accoun t for its affect on human activities. A sensitivity analysis was perf ormed to evaluate the aff ect of using different CHAD

studies has on APEX results for the recent O 3 

### NAAQS review (see Langstaff (2007) and EPA (2007g)). Briefly, O 3 

### exposure results were generated usi ng APEX with all of the CHAD diaries and compared with results generated from runni ng APEX using only the CHAD diaries from the National Human Activity Pattern Study (NHAPS), a nationally representative study in CHAD. There was agreement between the APEX exposure results for the 12 metropolitan areas evaluated (one of which was Atlanta), whethe r all of CHAD or only the NHAPS component of CHAD is used. The absolute difference in perc ent of persons above a pa rticular concentration level ranged from -1% to about 4%, indicating that the exposure model results are not being overly influenced by any single study in CHAD. It is likely that sim ilar results would be

obtained here for NO 2 

### exposures, although it remains uncertain due to different averaging times (1-hour vs. 8-hour average). This is not to suggest that th e uncertainty is low in using th e CHAD data to represent the Atlanta area, but that similar resu lts would be obtained in using th e diaries available, so long as the population was appropriately stratified and cert ain characteristics infl uencing exposure were considered. One particular in fluential factor that is not modeled by APEX is the commute time/distances for the Atlanta population. The At lanta population is spread over a larger area than most other locations and as a result, indivi dual spend more time driving (Table 8-16). Not taking this added drive time into account when using the CHAD diaries could lead to underestimation of exposures for the Atlanta populatio n. Given the difference in Atlanta DVMT in comparison with other locations, it is possible that this underestima tion is large. However, in considering this lack of accounti ng for the greater Atlanta commute times that exist and that an important driver for exposures above selected levels was from the in-vehicle microenvironment, the Atlanta exposure results may to some degree be representative of other locations in the U.S. with more nationally repr esentative commute times.

8.12.2.3 Longitudinal Profile APEX creates seasonal or annual sequences of daily activities for a simulated individual by sampling human activity data from more than one subject. Each simu lated person essentially

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### becomes a composite of several ac tual people in the underlying activity data. Certain aspects of the personal profiles are held cons tant, though in reality they change as an individual ages. This is only important for simulations with long timeframes, particularly when simulating young children (e.g., over a year or more). The cluster algorithm used in constructing longitudinal profil es was evaluated against a sequence of available multiday diaries sets collec ted as part of the Harvard Southern California Chronic Ozone Exposure Study (Xue et al. 2005, Ge yh et al. 2000). Briefly, the activity pattern records were characterized according to time spen t in each of 5 aggregate microenvironments: indoors-home, indoors-school, indoors- other, outdoors, and in-trans it. The predicted value for each stratum was compared to the value for the corresponding stratum in the actual diary data using a mean normalized bias statistic. See A ppendix B, Attachment 2 and 3 for details. The

evaluation indicated the cluster algorithm can replicate the observ ed sequential diary data, with some exceptions. The predicted time-in-microenvironment averages matched well with the

observed values. For combinations of microe nvironment/age/gender/season, the normalized bias ranges from –35% to +41%. Sixty percent of the predicted averages have bias between –9% and +9%, and the mean bias across any microenvi ronment ranges from -9% to +4%. Although, on occasion there were large differences in replic ating variance across persons and within-person variance subsets, about two-thirds of the pred ictions for each case were within 30% of the observed time spent in each microenvironment. The longitudinal approach used in the expos ure assessment was an intermediate between random selection of diaries (a new diary used for every day for each person in the year) and perfect correlation (same diary used for every day for each person in the year). The cluster algorithm used here was previously compared wi th two other algorithms, one that used random sampling and the other employing both diversity ( 

### D ) and autocorrelation ( 

### A ) statistics (see EPA, 2007g for details on this algorithm). The number of persons with at least one or more exposures to a given O 3 

### concentration was about 30% less when using the cluster algorithm than when using random sampling, while the number of multiple exposures for those persons exposed was greater using the cluster algorithm (by a bout 50%). The algorithm employing the 

### D and 

### A statistics exhibited similar pa tterns, although were lower in ma gnitude when compared with random sampling (about 5% fewer persons with one or more exposures, about 15% greater multiple exposures). These exposure results using the cluster algorithm in APEX appeared to be

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### the result of a greater correlation of diaries sele cted in comparison with the other two algorithms. This outcome conforms to an expectation of correlation between the daily activities of

individuals. While the evaluati on was performed using 8-hour O 3 

### as the exposure output it is expected that similar results would be obtained for 1-hour NO 2 

### exposures. That is, the

characteristics of the diaries th at contribute greatly to any pollutant exposure above a given threshold (e.g., time spent outdoors, vehicle driv ing time) are likely a strong component in developing each longitudinal profile. Given these results and that the REA in not necessarily focused on health effects resulting from mu lti-day exposures, the particular longitudinal approach used likely contributes minimally to uncertainty. See Appendix B, Attachments 2 and

3 for further details in the cluster al gorithm and the evaluations performed.

8.12.2.4 Meteorological Data Meteorological data are taken directly from moni toring stations in the assessment areas.

It is assumed that most of the data used are error free and have undergone required quality assurance review. One strength of th ese data is that it is relatively easy to see significant errors if they appear in the data. Becau se general climactic conditions are known for the simulated area, it would have been apparent upon review if there were outliers in the dataset, and at this time

none were identified. If there were a bias in the data, it would be expected to be limited in extend and randomly occurring, therefore contri buting to both under and over-estimations equally to a marginal degree. To reduce the number of calms and missing winds in the 1-hour MET data, archived one-minute winds for the AS OS station at ATL were used to calculate hourly average wind speed and directions. This approach reduces the nu mber of estimated zero concentrations that would be output by AERM OD if not supplemented by the additional wind data, thus preventing a downward bias in the predicted 1-hour concentrations. However, there are limitations in the use of these data. APEX only uses one temperature value per day in selecting an appropriate CHAD diary a nd indoor microenvironment air exchange rate. Because the model does not re present hour-to-hour variations in meteorological conditions throughout the day, there may be uncerta inty in some of the exposure estimates for indoor microenvironments (see the next section).

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### 8.12.2.5 Air Exchange Rates (AER) The residential air exchange rate (AER) dist ributions used to estimate indoor exposures contribute to uncertainty in the exposure re sults. Three components of the AER analyzed previously by EPA (2007g) include 1) the extrap olation of air exchange rate distributions between-CMSAs, 2) analysis of within-CMSA uncer tainty due to sampling variation, and 3) the uncertainty associated with estimating daily AE R distributions from AE R measurements with different averaging times. The results of those previous investigations are briefly summarized here. See Appendix B, Attachments 8 and 9 for details.

Extrapolation of AER among locations Air exchange rate (AER) distributions were as signed in the APEX model, as detailed in

the indoors-residential microe nvironment. Since specific AER data for Atlanta were not available, data from another location were used to represent AERs in Atlanta based on having potentially similar influential characteristics. Such factors include age composition of housing stock, construction methods used, and other meteorol ogical variables not explicitly treated in the analysis, such as humidity and wind speed patterns. AER data from measurements in Research Triangle Park, NC (RTP) were selected to repr esent the distribution of AERs in Atlanta (see Appendix B, Attachment 5).

In order to assess the uncerta inty associated with this extrapolation, between-location uncertainty was evaluated by examining the vari ation of the geometric means and standard

deviations across several cities and studies. Th e evaluation showed a re latively wide variation across different cities in their AER geometric me ans and standard deviations, stratified by airconditioning status, and temperat ure range. For example, Figure 8-30 Illustrates the GM and

GSD of AERs estimated for several cities in the U.S. where A/C was present and within the temperature range of 20-25 °C. The wide range in GM and GSD pairs implies that the modeling results may be very different if the matching of modeled location to study location was changed. For example, the NO 2 

### exposure estimates may be sensitive to use of an alternative distribution, say those in New York City, compared with resu lts generated using the RTP AER distributions. It is likely though that th e true distribution is more similar to the selected dist ribution from RTP than New York City or some other location give n the population of available AER data. It is unclear as to the direction of bias given the lim ited number of data available for comparison. It

243

### should be noted that Houston, the only other “s outhern” location, genera lly coincided with the

RTP AERs distribution in the 20 -25 °C and other temperature ranges for homes with A/C (see Appendix B, Attachment 8).

Figure 8-30. Example comparison of estima ted geometric mean and geometric standard deviations of AER (h-1) for homes with air conditioning in several cities.

Within location uncertainty

There is also variation in AERs within studies for the same location (e.g., Research Triangle Park, NC), but this is much smalle r than the observed variation across different CMSAs. This finding tends to support the approa ch of combining different studies for a CMSA,

where data were available. The within-city uncertainty was assessed by using a bootstrap distribution to estimate the effects of samp ling variation on the fitted geometric means and

standard deviations for the RTP data used to represent the Atlanta AERs. These bootstrap distributions assess the uncertainty due to rando m sampling variation. They do not address other

244

### uncertainties such as the lack of representativenes s of the available study da ta or the variation in the lengths of the AER monitoring periods. Because only the GM and GSD were used, the bootstrap analyses does not account for uncertaintie s about the true distributional shape, which may not necessarily be lognormal. One-thousand bootstrap samples were randomly generated for each AER subset (of size N), producing a set of 1,000 geometric mean (GM) and geometric standard deviation (GSD) pairs. The analysis of the RTP data used to re present Atlanta indicated that the GSD uncertainty for a given AER temperature group tended to have a range within ±0.25 fitted GSD (hr -1 

### ), with smaller intervals surrounding the GM (i.e, about ± 0.10 fitted GM (hr -1 

### ) (Figure 8-31). Broader ranges were generated from the bootstrap simula tion for AER distributions used for Atlanta homes without A/C (Figure 8-32) , although both still within ± 0.5 of the fitted GM and GSD values. See Appendix B, Attachment 8 for further details.

Figure 8-31. Example of boot strap simulation results used in evaluating random sampling variation of AER (h-1) distributions (RTP data).

245

### Figure 8-32. Example of boot strap simulation results used in evaluating random sampling variation of AER (h-1) distributions (outside CA).

Variation in AER measurement averaging times Although the averaging periods fo r the air exchange rates in the study data varied from one day to seven days, the analyses did not ta ke the measurement duration into account and treated the data as if they were a set of statis tically independent daily av erages. To investigate the uncertainty of this assumption, correlations between consecutive 24-hour air exchange rates measured at the same house were investigated us ing data from the Research Triangle Park Panel Study (Appendix B, Attachment 8). The results showed extremely strong correlations, providing support for the simplified approach of treating mu lti-day averaging periods as if they were 24- hour averages.

8.12.2.6 Air Conditioning Prevalence

Because the selection of an ai r exchange rate distribution is conditioned on the presence or absence of an air-conditioner, the air conditioning status of the residential microenvironment was simulated randomly using the probability that a residence has an air conditioner, i.e., the residential air conditioner prevalence rate. Fo r this study we used location-specific data for

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### Atlanta AHS, 2004). EPA (2007g) details the specifi cation of uncertainty estimates in the form of confidence intervals for the air conditioner prevalence rate, and compares these with prevalence rates and confidence intervals developed from the Residential Energy Consumption Survey (RECS) of 2001 for several aggregate geographic subdivision (e. g., states, multi-state Census divisions and regions) (EIA, 2001). Briefly, Air conditioning preval ence rates were 97% for Atlant a, with reported standard errors of 1.2% (AHS, 2004). Estimated 95% conf idence intervals were also small and span approximately 4.6% (AHS, 2003). The RECS prevalence estimates for A tlanta and confidence intervals compared well with a value of 95.0% and a 95% conf idence interval spanning 5.8%. This suggests that there is limited bias in th e A/C prevalence estimates used and that the uncertainty in the estimated value is likely low. A sensitivity analysis was performed to evaluate changes in the estimated exposures when using a lower A/C prevalence. Changing th e A/C prevalence form the actual Atlanta value used allows for a greater per centage of homes to use estima ted AER distributions for homes without A/C. An A/C prevalence of 0.55 (or 55%) was input to a new simulation using 2002 air quality without indoor sources, based on the lower bound of observed A/C prevalence rates in Table 8-15). Table 8-21 indicates that there is no change in the percent of asthmatics exposed at or above each of the potential health effect benchmark levels, whether considering a single exposure or up to six exposures in a year. There are however, several thousand additional person days, or additional days for persons that are alre ady exposed to daily maximum concentrations at or above the benchmarks when considering the simulation conducted using the lower prevalence rate. Only a few additional persons were exposed to benchmarks ≥ 200 ppb that did not have such exposures in the simulation using the higher A/C prevalence rate . These results suggest that

the indoor-residential microenvironment contributes much le ss to exposures above any of the benchmarks when compared with the estimat ed contribution from on-road and near-road microenvironments. Most individuals (>99%) were already estimated to experience at least one exposure at concentrations at or above 100 to 150 ppb through the roadway related exposures, one of the main reasons why there are no addition al persons exposed at these benchmark levels when considering the lower A/C prevalence. Even though there is a large fraction of the population not exposed to benchmark levels ≥ 200 ppb (18-41%) using the higher prevalence rate, the outdoor ambient concentrations rarely would exceed these concentrations. Thus only a

247

### few additional persons experience these higher benchmark level concentrations with the lower A/C prevalence.

Table 8-20. Comparison of exposure results us ing a 0.55 versus 0.97 A/C prevelance for 2002 air quality without indoor sources. Percent of All Asthmatics with Indicated Number of Multiple Daily Maximum 1-hour Exposures At or Above Benchmark Level

Simulation Benchmark Level (ppb) 1 2 3 4 5 6 Person Days Above Benchmark Number of Persons with at Least One Exposure 100 100% 100% 100% 99% 99% 99% 7393854 212426 150 99% 96% 93% 88% 83% 78% 2839603 209855 200 92% 80% 69% 58% 48% 40% 1271622 195766 250 76% 56% 40% 28% 20% 15% 618725 161863 no-indoor - 0.97 AC prevalence 300 59% 32% 19% 12% 8% 6% 323273 124531 100 100% 100% 100% 99% 99% 99% 7442239 212426 150 99% 96% 93% 88% 83% 78% 2849885 209855 200 92% 81% 69% 58% 48% 40% 1274646 195875 250 76% 56% 40% 28% 20% 15% 620237 161918no indoor0.55 AC prevalence 300 59% 33% 20% 12% 8% 6% 323878 124637 100 0 0 0 0 0 0 -48385 0 150 0 0 0 0 0 0 -10282 0 200 0 0 0 0 0 0 -3024 -109 250 0 0 0 0 0 0 -1512 -54 Absolute Difference (0.97ACprev0.55 ACprev) 300 0 0 0 0 0 0 -605 -106



### 8.12.2.7 Indoor Source Estimation

Other indoor NO 2 

### emission sources, such as emissions from gas pilot lights, gas heating, unvented gas fire places, gas water heaters, or gas clothes drying were not included in this analysis due to lack of adequate data readil y usable for characterizat ion, modeling complexities regarding the assignment of particular sources to the simulated populatio n (e.g, correlations of sources), a limited time to conduct analyses of pot ential data distributions including the analysis of their uncertainties, and limited resources alloca ted for inclusion in the review. Exclusion of

these sources would bias all indoor concentrations low when these sources are present, however, it is largely uncertain how mu ch it would affect any estimat es of the benchmark level exceedances, and the number of persons affected in Atlanta in the absence of source emission and prevalence data. There may be uncertainty added to the exposur e results when considering the form (i.e., uniform) and limits (limited by the bounds of the measurement data) of the distribution used to

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### represent indoor decay. The measurement data us ed to develop the distribution were obtained from a single study, conducted in a single hom e, under limited temperature and humidity conditions (see Table 8-11). The experimental design included four different methods for introducing NO 2 

### into the home, including direct inj ection and home appliances. Different homes, with varying construction material s and furnishings may have different NO 2 

### decay rates,

and as a result of the uniform distribution select ed, the number of exposures may be either overor under-estimated depending on the extent of how far the true population of NO 2 

### values extend outside the range of the uniform distribution used (i.e., 1.02-1.45). A sensitivity run was performed using an alternative fitted distribution, assuming a lognormal form with GM and GSD of 1.17 a nd 1.14, respectively, and lower and upper bounds defined by 50% of observed minimum (0.51) and 100% of observed maximum (2.9). The exposure results generated using the lognormal decay distribution were compared with the simulations performed using the uniform distri bution for 2002 air quali ty and without indoor sources (Table 8-21). There was no difference in the percent of asthmatics estimated to experience one through six daily maximum exposures in a year above any of the benchmarks. There were however a few additional person days above each of the benchmark levels except for <300 ppb and 54 additional persons exposed at or above 200 and 250 ppb when using the lognormal distribution. This s uggests that the simulated ex posures above the selected

benchmarks are not sensitive cha nges in the indoor decay rate NO 2 

### . Whether the same outcome

would occur with additional alte rnative distributions of differe nt forms and bounds or that the

indoor microenvironment is not sensitive to ind oor decay based on the algorithm used by APEX remains largely unknown.

Table 8-21. Comparison of exposure results using a uniform versus lognormal NO 2 

### indoor decay distribution for 2002 air quality without indoor sources. Percent of All Asthmatics with Indicated Number of Multiple Daily Maximum 1-hour Exposures at or Above Benchmark Level

Simulation Benchmark Level (ppb) 1 2 3 4 5 6 Person Days Above Benchmark Number of Persons with at Least One Exposure 100 100% 100% 100% 99% 99% 99% 7393854 212426 150 99% 96% 93% 88% 83% 78% 2839603 209855 200 92% 80% 69% 58% 48% 40% 1271622 195766 250 76% 56% 40% 28% 20% 15% 618725 161863 no-indoor uniform decay 300 59% 32% 19% 12% 8% 6% 323273 124531 no indoor- 100 100% 100% 100% 99% 99% 99% 7402926 212426



### 249150 99% 96% 93% 88% 83% 78% 2842022 209855 200 92% 80% 69% 58% 48% 40% 1272227 195821 250 76% 56% 40% 28% 20% 15% 619027 161918 lognormal decay

300 59% 32% 19% 12% 8% 6% 323273 124531 100 0 0 0 0 0 0 -9072 0 150 0 0 0 0 0 0 -2419 0 200 0 0 0 0 0 0

- 605 -54 250 0 0 0 0 0 0

- 302 -54 absolute difference(uniformlognormal) 300 0 0 0 0 0 0 0 0



### The data used to estimate the average number of daily food preparation events is older than the time period assessed (1992 versus 2001-2003) and may therefore be not reflect current conditions to some degree, possi bly leading to either under- or over-estimates of exposure to concentration exceedances. For example, if the population of Atlanta in 2003 that uses gas stoves to prepare food at home doe s so less frequently than repo rted the 1992 survey population, then the number of such exposures may be over-e stimated. The variability associated with the mean usage of 1.4 that was used in the model is also under-represented in that there are likely

some individuals that cook more or less than this value. Furthermore, the estimate is not specific for the Atlanta population. The uncertainty rega rding each of these issues and how they may affect the exposure re sults is largely unknown. As noted in the microenvironmental descripti on, it was assumed that the probability that a food preparation event included st ove use was the same no matter what hour of the day the food preparation event occurre d. If such probabilities differ, th en the diurnal allocation of cooking events may differ from the actual pattern. To the extent that the gas stove usage patterns may correlate with ambient concentration patterns, the number of exposures to exceedances of

threshold concentrations of con cern may be under- or over-estimate d. For example, if gas stove usage and ambient concentrations are positively correla ted (e.g., if cooking tends to occur during evening rush hour) and the diurnal allocation assu med here results in a lower correlation (e.g., if the diurnal allocation understates the probability of gas stove usage at times of high ambient concentrations) then the number of such exposures may be under-estimated. As another example, if the diurnal pattern allocation assumed here understates the probability of gas stove usage at times when simulated subjects are assumed to be at home, then the number of such exposures may be under-estimated.

250

### 251 There is also uncertainty re garding the distribu tion used to estimate the indoor source

concentration contribution. Concentrations were obtained from CARB (2001) from a variety of described cooking conditions (e.g., with or withou t ventilation, different pans) and foods cooked (e.g., bacon, french fries, broiled fish, lasagna) in a single test home. While an alternative distribution form may be fitted to such data, th ere already exist large uncertainties regarding the representation of these measured concentra tions obtained under the limited experimental conditions to the population of all possible cook ing conditions, the foods cooked, and the proper weighting of such cooking events for the simulate d population in Atlanta. In the absence of such knowledge, it is likely that a fitt ed distribution would be biased high. In considering these uncertainties, staff elected to use a uniform distribution, bounded by the upper and lower range of the experimental data. Use of this uniform distribution does exclude concentrations outside the upper value, suggesting that concentrations in excess of the upper bound are an impossibility. This is unlikely and may add to the uncertainty in the estimated expos ures when cooking with gas, and implies a bias in unde restimating indoor source contribu tion to indoor concentrations. Although it appears that the study was primarily designed to estimate upper percentile PM concentrations, it is possible that th e uniform distribution selected for NO 2 

### does capture the upper range of concentrations very well due to the presence of study-designed “worst-case” cooking scenarios. The durations of the CARB (2001) cooking test s ranged from 21 minutes to 3 hours with an average of about 70 minutes. For implementa tion in APEX it was assumed that each cooking event lasts exactly an hour. That is, the random ly selected net concentration contribution was added to hourly average indoor concentration for the hour it was selected to occur. Because the mass balance algorithm leads to carryover from on e hour to the next, some of the indoor cooking impact will influence subsequent hours. Howe ver, the affect of the cooking event may be overstated or understated for cooking events longer or shorter than 1 hour.

# 9. CHARACTERIZATION OF HEALTH RISKS USING DATA FROM EPIDEMIOLOGICAL STUDIES

9.1 INTRODUCTION

As mentioned above in chapter 6, in res ponse to advice received from the CASAC NO 2 

### Panel on the 1 st 

### draft REA, we have conducted a focuse d quantitative risk assessment in which estimates of respiratory ED visits as a function of ambient levels of NO 2 

### have been developed for a single urban area (i.e., the Atla nta MSA). In this approach, concentration-response functions

derived from NO 2 

### epidemiological studies are used in conjunction with 1) ambient air quality data representing as is and alte rnative air quality scenarios and 2) baseline incidence data for respiratory ED visits to estimate th e impact of ambient levels of NO 2 

### on ED visits associated with these air quality scenarios. The purpose of this chapter is to present the results for the current risk assessment which is an illustrative case study that provides info rmation on the magnitude and potential changes in

NO 2 

### -related public health impacts associated with recent air quality and alternative air quality scenarios simulating attainment of the current and alternative NO 2 

### standards. We note that chapters 4 and 5 of this document provide additional qualitative assessment of the epidemiological evidence most relevant to characterizing NO 2 

### -related health effects in the United States; this includes both a discussion of respiratory-related ED visi ts as well as other health endpoints. We also note that integration of the scientif ic evidence presented in the ISA (EPA, 2008a) with the air quality, exposure, and risk characterization results presented in chapters 7 through 9 of this document is presen ted in chapter 10. Chapter 10 also discusses

staff’s assessment of how this in formation might be considered in evaluating the adequacy of the

current NO 2 

### NAAQS and the need for pot ential alternative primary NO 2 

### standards.

Previous reviews of the NO 2 

### primary NAAQS, completed in 1985 and 1996, did not include quantitative health risk assessments. Thus, the risk assessment described in this document builds upon the methodology and lessons learned from the risk assessment work conducted for the recently concluded PM and O 3 

### NAAQS reviews (Abt Associates, 2005; Abt Associates, 2007). Many of the same methodologi cal issues are present in conducting a risk

252

### assessment for each of these crite ria air pollutants where epidemiological studies provided the basis for the concentration-response relationships used in the quantitat ive risk assessment.

The NO 2 

### health risk assessment described in this chapter estimates the incidence of respiratory-related ED visits associat ed with short-term exposures to NO 2 

### under recent (“as is”) air quality levels, upon just meeting the current NO 2 

### standard of 0.053 ppm annual average, and upon just meeting several potential alternative NO 2 

### primary NAAQS in the Atlanta MSA. 1 

### As discussed in more detail in chapter 6 above, st aff has elected to evaluate daily maximum 1-h standard levels of 0.05, 0.10, 0.15, and 0.20 ppm using both 98 th 

### and 99 th 

### percentile forms and averaged over a thee-year period. 2 

### The risk assessment is intende d as a tool that, together with

other information on this health endpoint and other health effects evaluated in the final ISA and discussed elsewhere in this document, can aid th e Administrator in judg ing whether the current primary standard protects public h ealth with an adequate margin of safety, or whether revisions to the standard are appropriate. Section 9.2 describes the genera l approach used to conduct th e risk assessment for ED visits. Sections 9.3, 9.4, and 9.5 discuss in more detail the three types of inputs required to conduct the assessment. Section 9.6 presents a di scussion of uncertainties and variability and section 9.7 presents a summary of results from the assessment and key observations.



## 9.2 GENERAL APPROACH

The general approach used for the NO 2 

### -related ED risk assessment is dictated by the fact that it is based on concentration-response functions which have been estimated in epidemiological studies evaluated in the final IS A. Since these studies estimate concentrationresponse functions using ambient air quality data from fixed-site, popula tion-oriented monitors, the appropriate applicati on of these functions in a risk assess ment similarly requires the use of ambient air quality data at fixe d-site, population-oriented monito rs. In order to estimate the incidence of respiratory-related ED visits associated with recent air quality conditions in a set of

counties attributable to ambient NO 2 

### exposures, as well as the change in incidence of this health effect in that set of counties correspo nding to a given simulated change in NO 2 

### levels

1 The current NO 2 standard refers to a two-year period and requires that the annual average NO 2 level be less than or equal to 0.053 ppm in each of the two years.

2 As an example, for the a lternative standards using the 98 th percentile form, the standard is met when the average of the annual 98 th percentile daily maximum 1-hour concentrations fo r a 3-year period is at or below the specified standard level.



### 253

### representing just meeting the curren t or alternative 1-h daily maximum NO 2 

### standards, the following thee elements are required:

• Air quality information including: (1) “as is” air quality data for NO 2 

### from ambient monitors in the assessment location, and (2) “as is” concentrations adjusted to reflect patterns of air quality esti mated to occur under a simulation where the area’s air quality is adjusted to just meet the specified standar d. (These air quality inputs are discussed in more detail in section 6.2 of this document).

• Concentration-response functions which provide an estimate of the relationship between the health endpoint of interest and ambient NO 2 

### concentrations.

• Baseline health effects incidence. The baseline incidence of the health effect in the assessment location in the target year is the incidence corresponding to “as is” NO 2 

### levels in that location in that year.

Figure 9-1 provides a broad schematic depicti ng the role of these components in the NO 2 

### risk assessment. Each of the key compone nts (i.e., air quality information, estimated concentration-response functions, an d baseline incidence) is disc ussed below, highlighting those points at which judgments have been made. These inputs are combined to estimate health effect incidence changes associated with specified changes in NO 2 

### levels. Although some epidemiologi cal studies have estimated linear or logistic concentration-response functions, by far the most common form, and the form relevant for the epidemiological study used in the current risk assessment is the exponential (or log-linear) form:

x 

### Bey β



### = , (Equation 9-1)

where x is the ambient NO 2 

### level, y is the incidence of the heal th endpoint of interest at NO 2 

### level x , β is the coefficient of ambient NO 2 

### concentration (describing the extent of change in y with a unit change in x ), and B is the incidence at x =0, i.e., when there is no ambient NO 2 

### . The relationship between a specified ambient NO 2 

### level, x 0 

### , for example, and the incidence of a given health endpoint associated w ith that level (denoted as y 0 

### ) is then

0 0 x 

### Bey β 

### = . (Equation 9-2)

254

### If we let 

### x 0 

### denote the baseline (upper) NO 2 

### level, and 

### x 1 

### denote the lower NO 2 

### level, and



### y 0 

### and 

### y 1 

### denote the corresponding incidences of the h ealth effect, we can derive the following

255

### Figure 9-1. Major components of nitrogen dioxi de health risk assessment for emergency department visits.

256

### relationship between the change in 

### x , Δ 

### x = ( 

### x 0 

### - x 1 

### ), and the corresponding change in 

### y , Δ 

### y , from equation (9-1) 3 

### :

Δ Δ



### yyy y e x



### =−= − −



### ( )[ 01 0 

### 1 β 

### ]. 

### (Equation 9-3)

Alternatively, the difference in health effects incidence can be calculated indirectly using relative risk. Relative risk (RR) is a measure commonly used by epidemiologists to characterize the comparative health effects a ssociated with a particular air quality comparison. The risk of ED visits for respiratory illness at ambient NO 2 

### level 

### x 0 

### relative to the risk of ED visits for respiratory illness at ambient NO 2 

### level 

### x 1 

### , for example, may be charac terized by the ratio of the two rates: the rate of ED visits for respirat ory illness among individua ls when the ambient NO 2 

### level is 

### x 0 

### and the rate of ED visits for respiratory illness among (otherwise identical) individuals

when the ambient NO 2 

### level is 

### x 1 

### . This is the RR for ED visits for respiratory illness associated

with the difference between the two ambient NO 2 

### levels, 

### x 0 

### and 

### x 1 

### . Given a concentrationresponse function of the form shown in equation (9-1) and a particular difference in ambient NO 2 

### levels, Δ 

### x , the RR associated with that difference in ambient NO 2 

### , denoted as RR Δ x 

### , is equal to

e βΔ x 

### . The difference in health effects incidence, Δ 

### y , corresponding to a given difference in

ambient NO 2 

### levels, Δ 

### x , can then be calculated based on this RR Δ x 

### as

)]/1(1[)( 010 x 

### RR yyyy Δ 

### − = − =Δ . (Equation 9-4)

Equations (9-3) and (9-4) are simply altern ative ways of expressing the relationship between a given difference in ambient NO 2 

### levels, Δ 

### x > 0, and the corresponding difference in health effects incidence, Δ 

### y . These health impact equations are the key equations that combine air quality information, concentr ation-response function informati on, and baseline health effects incidence information to estimate health risks related to changes in ambient NO 2 

### concentrations.

3 If Δ x < 0 – i.e., if Δ x = (x 1 - x 0 ) – then the relationship between Δ x and Δ y can be shown to be . If Δ x < 0, Δ y will similarly be negative. However, the magnitude of Δ y will be the same whether Δ x > 0 or Δ x < 0 – i.e., the absolute value of Δ y does not depend on which equation is used. ]1[)(

001 −=−=Δ Δ x eyyyy β



### 257

## 9.3 AIR QUALITY INFORMATION

As illustrated in Figure 9-1, a nd noted earlier, air quality in formation required to conduct

the NO 2 

### risk assessment includes (1) recent air quality data for NO 2 

### from a suitable monitor for the assessment location and (2) air quality adjustme nt procedures to modify the recent data to simulate air quality data just meeting the cu rrent annual and potential alternative 1-h daily maximum standards. The approach used to adjust air quality data to simulate meeting specified

standards is discussed above in section 6.2. In the first part of the risk assessment, we estimate the incidence of the health effect

associated with “as is” levels of NO 2 

### (or equivalently, the change in health effect incidence, Δ 

### y , associated with a change in NO 2 

### concentrations from “as is” levels of NO 2 

### to 0 ppb). In the second part, we estimate the incidence of the health effect associated with NO 2 

### concentrations simulated to just meet a specified standard (i.e., the current NO 2 

### standard of 0.053 ppm annual average as well as each of potential alternative 1-h daily maximum standards).

To estimate the incidence of a health effect associated with “as is” NO 2 

### levels in a location, we need a time series of hourly “as is” NO 2 

### concentrations for that location. We have used monitor data from the Georgia Tech monitor (monitor id =131210048), the monitor that was used in Tolbert et al. (2007), the epid emiological study from which we obtained the concentration-response functions (s ee section 9.4 below). Comple te hourly data were available on over 93 percent of the days – 348 days in 2005, 345 days in 2006, and 340 days in 2007. Missing NO 2 

### concentrations were filled in, as desc ribed in section 3.5 of Appendix C. Because Tolbert et al. (2007) estimated a rela tionship between daily respiratory-related ED visits and the 3-day moving average (i.e., NO 2 

### levels on the same day, the previous day, and the day before that) of the daily 1-h maximum NO 2 

### concentrations, we calculated the 3-day moving average of the daily 1-h maximum NO 2 

### concentrations at the monitor to provide the air quality input to the risk assessment. The calculations for the second part of the ri sk assessment, in which we estimated risks

associated with NO 2 

### levels simulated to just meet th e current annual sta ndard and potential alternative 1-h daily maximum standards were done analogously, usi ng the monitor-specific series of adjusted daily maximum hourly concentrations rather than the monitor-specific series of “as is” daily maximum h ourly concentrations.

258

## 9.4 CONCENTRATION-RESPONSE FUNCTIONS

As indicated in Figure 9-1, another key compone nt in the risk assessment model is the set of concentration-response functi ons which provide estimates of the relationship between the health endpoint of interest and ambient NO 2 

### concentrations. As discussed above, the health endpoint of interest for this focused quantitative risk assessment is respirat ory-related ED visits.

As discussed in sections 4.2.2 and 4.5.2 several co mmunity epidemiological studies have been

conducted in the U.S. that exam ined the relationship between NO 2 

### and other air pollutants and increased ED visits either for all respiratory cau ses or for asthma-related visits. Figure 5-1 in this document summarizes the single pollutant mode l effect estimates from these studies. As discussed in section 4.5.2, staff ha s considered several factors in selecting the urban area and epidemiological studies upon which the current risk assessment is based. First, we have judged

that studies conducted in the Un ited States are preferable to those conducted outside the United States given the potential for effect estimates to be impacted by factors such as the ambient pollutant mix, the placement of monitors, activ ity patterns of the population, and characteristics of the healthcare system. Second, we judged that studies of ambient NO 2 

### are preferable to those

of indoor NO 2 

### given that studies of indoor NO 2 

### focus on exposures in locations with indoor

sources of NO 2 

### . These indoor sources can result in exposure patterns, NO 2 

### levels, and copollutants that are different from thos e typically associated with ambient NO 2 

### . Third, we judged it appropriate to focus on studies of ED visits . When compared to studies of respiratory symptoms, the public health significance of ED visits is less ambiguous for the individuals affected. In addition, baseline in cidence data are more readily av ailable for these endpoints. Finally, we judged it appropriate to focus on studies that evaluated NO 2 

### health effect associations using both single- and multi-pollutant models. Ta king these factors into consideration, we have chosen to focus on the studies by Tolbert and coll eagues (2007) in Atlanta, Georgia that address ED visits for respiratory causes as a case st udy to illustrate the magnitude and changes in

estimated NO 2 

### -related risks for this endpoint fo r various air quality scenarios. Tolbert et al. (2007) estimated concentr ation-response functio ns using both single pollutant models (i.e., where NO 2 

### was the only pollutant entered into the health effects model) and multi-pollutant models (i.e., where one or two co-pollutants (PM 10 

### , O 3 

### , CO) were entered into the health effects model). To the extent th at any of the co-pollutant s present in the ambient air may have contributed to the health effects attributed to NO 2 

### in single pollutant models, risks

259

### attributed to NO 2 

### might be overestimated where concen tration-response functions are based on single pollutant models. However, if co -pollutants are highly correlated with NO 2 

### , their

inclusion in an NO 2 

### health effects model can lead to mi sleading conclusions in identifying a specific causal pollutant. When collinearity exis ts, inclusion of multiple pollutants in models often produces unstable and statistically in significant effect estimates for both NO 2 

### and the copollutants. Given that single and multi-pollutant models each have both potential advantages and disadvantages, with neither type clearly preferable over the other in all cases, we report risk estimates based on both single- and multi-pollutant models in the NO 2 

### risk assessment.

All of the models in Tolbert et al. (2007) used a 3-day movi ng average of pollution levels (i.e., the average of 0-, 1-, and 2-day lags), so the issue of which of several different lag

structures to select does not aris e. The issue of how well a given lag structure captures the actual relationship between the pollutant and the health effect, however, is still relevant. Models in which the pollutant-related incidence on a given day depends only on same-day or previous-day pollutant concentration (or some variant of those, such as a two- or thee-day average concentration) necessarily assume that the l onger pattern of pollutant levels preceding the pollutant concentration on a given day does not affect incidence of the health effect on that day. To the extent that a pollutant-related health effect on a given day is affected by pollutant concentrations over a longer period of time, then these models would be mis-specified, and this mis-specification would affect the predictions of daily incidence based on the model. The extent

to which short-term NO 2 

### exposure studies may not capture th e possible impact of long-term exposures to NO 2 

### is unknown. A number of epidemiologic studies have examined the effects of long-term exposure to NO 2 

### and observed associations with decrements in lung function and partially irreversible decrements in lung function growth. The final ISA (EPA, 2008a) concludes, however, that “overall, the epidemio logical evidence was suggestive but not sufficient to infer a causal relationship between long-term NO 2 

### exposure and respiratory morbidity” (ISA, section 3.4). Currently, there is insufficient information to adequately adjust for the potential impact of longer-term exposure on respir atory ED visits associated with NO 2 

### exposures, if any, and this uncertainty should be kept in mind as one considers the results from the short-term exposure NO 2 

### risk assessment.

260

## 9.5 BASELINE HEALTH EFFECTS INCIDENCE DATA

As illustrated in Equation 9-1, the most comm on health risk model based on air pollution epidemiological studies expresses the reduction in health risk ( Δ y) associated with a given

reduction in NO 2 

### concentrations ( Δ x) as a percentage of the baseline incidence ( 

### y ). To accurately assess the imp act of changes in NO 2 

### air quality on health risk in a given urban area,

information on the baseline incidence of health e ffects in that location is therefore needed. For this assessment, baseline incidence is the incidenc e under recent (“as is”) air quality conditions. We obtained annual estimates of the baseline incidence of respiratory ED visits in Atlanta, GA via personal communication with the authors of the study con ducted in the Atlanta area (Tolbert, 2007). Tolbert et al. (2007) notes that there are 42 hospitals with emergency departments in the 20-county Atlanta MSA. Of th ese, 41 were able to provide incidence data for at least part of the study period ( 1993 – 2004). For purposes of the NO 2 

### risk assessment, we need incidences for the years of the risk assessment (2005 – 2007). Assuming that baseline incidence of respiratory ED visits does not change appreciably in the span of a few years, we have used the incidence of respiratory ED vis its for the most recent year (i.e., 2004) in the Tolbert et al. study, which was 121,818 respiratory ED visits. 4 

### Because this baseline incidence estimate is based on 36 hospitals, rather than the total 42 hospitals in Atlanta, this will be an

underestimate of baseline incidence. This is a source of downward bias in our estimates of NO 2 

### - related risk. While there is some year-to-year va riability in respiratory-related baseline incidence (e.g., there were roughly 130,000 and 140,000 respirat ory-related ED visits in 2002 and 2003, respectively, in the Atlanta ar ea), the estimate used for the risk assessment based on 2004 was within 10% of the average for the most recent three year period available. Average daily baseline incidences, necessary fo r short-term daily concentration-response functions, were calculated by dividing the annual in cidence by the number of days in the year for

which the baseline incidences were obtained. To the extent that NO 2 

### affects health, however, actual incidence rates would be expected to be somewhat higher than average on days with high

NO 2 

### concentrations; using an average daily inci dence would therefore result in underestimating the changes in incidence on such days. Similarly, actual incidence rates would be expected to be

4 The specific definition of “respiratory-related” emergency de partment visits used in Tolbert et al. (2007) included visits with the following respiratory illnesses as the primary diagnosis (specified by ICD-9 diagnostic codes): asthma (493, 786.07, and 786.09), COPD (491, 492, and 49 6), upper respiratory illness (4 60 – 465, 460.0, and 477), pneumonia (480 – 486), and bronchio litis (466.1, 466.11, and 466.19).



### 261

### somewhat lower than average on days with low NO 2 

### concentrations; using an average daily incidence would, therefore, re sult in overestimating the changes in incidence on low NO 2 

### days. Both effects would be expected to be small, how ever, and should largely cancel one another out.



## 9.6 ADDRESSING UNCERTAINTY AND VARIABILITY

An important issue associated with any population health risk assessment is the characterization of uncertainties and variability. 

### Uncertainty refers to the lack of knowledge regarding both the actual values of model i nput variables (parameter uncertainty) and the physical systems or relationships (model uncerta inty – e.g., the shape of the concentrationresponse functions). In any risk assessment, uncertainty is, ideally, reduced to the maximum extent possible, but significan t uncertainty often remains. It can be reduced by improved measurement and improved model formulation. In addition, the degree of uncertainty can be characterized, sometimes quantitatively. For example, for the NO 2 

### risk assessment the statistical uncertainty surrounding the estimated NO 2 

### coefficients in the concen tration-response functions is reflected in the confidence intervals provided for th e risk estimates presented in this chapter and in Appendix C. Additional uncertainties are discussed briefly below and in more detail in Appendix C. 

### Variability refers to the heterogeneity in a popula tion or variable of interest that is

inherent and cannot be reduced th rough further research. The current risk assessment for Atlanta is based on locations-specific inputs (i.e., air quality data, baseline incidence data, and concentration-response functions ar e for the Atlanta MSA). Variab ility in air quality data is considered to some extent by the inclusion of thee years of data. Temporal variability is more difficult to address, because the risk assessment focuses on some unspecified time in the future when a given standard is just be ing met. To minimize the degree to which values of inputs to the analysis may be different from the values of thos e inputs at that unspecified time: we have used recent input data – for example, air quality da ta for the period 2005-2007 and baseline incidence data for 2004. However, future changes in th ese inputs have not been predicted (e.g., future population levels or changes in baseline incidence). A number of important sources of uncertainty have been addressed qualitatively. Using a similar approach to that describe d in section 7.8 for the air qualit y characterization and in section 8.12 for the exposure assessment in this document, staff have evaluate d uncertainty in the

262

### 263 respiratory-related ED visits risk assessmen t using an approach adapted from the recent guidelines for qualitative uncerta inty characterization (WHO, 2008) . Uncertainties have been qualitatively evaluated with respect to the level of uncertainty and the dire ction of bias. The level of uncertainty was evaluated by considering the de gree of severity of the uncertainty, implied by the relationship between the source of the uncertainty and the output of the assessment. We have used a designation of low, medium, and high as descri bed in WHO (2008).

The bias direction indicates how the source of uncertainty was judged to influence

estimated ED visits associated with NO 2 

### concentrations, either the estimated number or percent of ED visits are likely “over-“ or “under-estimated”. In the instance where two or more types or components of uncertainty result in offsetting di rection of influence, the bias was judged as “both”. An “unknown” bias was assigned where there was no evidence reviewed to judge the uncertainty associated with the source. Ta ble 9-1 provides a summar y of the sources of uncertainty identified in the health risk assessm ent, the level of uncertainty, and the overall judged bias of each. A brief su mmary discussion regarding those sources of uncertainty not already examined in chapter 7 is included in the comments section of Tabl e 9-1 and is elaborated on in the bulleted points below.



### • Causality. There is uncertainty about whet her the association between NO 2 

### and ED visits actually reflects a causal relationship. Our judgment, drawing on the conclusions in the ISA and as discussed in more detail in chapter 4, is that there is, at a minimum, a likely causal relationship with either short-term NO 2 

### itself or with NO 2 

### serving as an indicator for itself and other components of ambient air associated with combustion processes.



### • Empirically estimated concen tration-response relationships . In estimating the concentration-response relationships, th ere are uncertainti es: (1) surrounding estimates of NO 2 

### coefficients in concentration -response functions used in the assessment, (2) concerning the specificat ion of the concentration-response model (including the shape of the relationships) a nd whether or not a population threshold or non-linear relationship exists within the ra nge of concentrations examined in the studies, and (3) concerning th e possible role of co-pollu tants. The uncertainty resulting from the statistical uncertain ty associated with the estimated NO 2 

### coefficient in the concentration-respons e function has been characterized by confidence intervals reflecting sample size. These confidence intervals do not reflect the uncertainties related to the concentrati on-response functions, such as whether or not the modelTable 9-1. Characterization of Key Uncertainties in the EmergencyDepartment Visits Health Risk Assessment for the Atlanta Region.UncertaintyLevel ofUncertaintyDirection of BiasCommentsCausalitylowUpward, ifcausalityassumption isn’ttrue.Statistical associationdoes not prove causation. However, the riskassessment considers only a health endpoint for which the overall weightof the evidence supports the assumption that NO2is likely causallyrelated based on the totality of the health effects evidence. If theassumption of a causal relationship isincorrect, then a positive estimatedcoefficient in the concentration-response function would be upwardbiased, since it is greater than zero.EmpiricallyestimatedconcentrationresponserelationsmediumNo obvious bias,if concentrationresponse model iscorrectlyspecified.Otherwise,unclear.Because concentration-response functions are empirically estimated,there is uncertainty surrounding these estimates. If the model is correctlyspecified, there is no bias in the coefficient estimates. If the model ismis-specified, there can be bias.Omitted confounding variables, forexample, could cause upward bias in the estimated NO2coefficients ifthe omitted variables are positively correlated with both NO2and thehealth effect. However, includingpotential confounding variables thatare highly correlated with one another can lead to unstable estimators.Because both single- and multi-pollutant models were available, bothwere used.Functional formof concentrationresponse relationmediumUnclearStatistical significance of coefficients in an estimated concentrationresponse function does not necessarily mean that the mathematical formof the function is the best modelof the true concentration-responserelation. If the “true” functional relationship between NO2and a healtheffect is different from the one specified, there can be bias in theresulting estimates of effect. Thedirection of the bias will depend onhow the specified model differs fromreality. For example, if thespecified concentration-response function is log-linear down to 0 ug/m3,but there is actually a threshold in the true relationship, then the effectwill be overstated by the model corresponding to levels of NO2below thethreshold.264Uncertainty Level ofUncertaintyDirection of Bias CommentsLag structure ofconcentrationresponse relationlowDownward, ifimportant lags areomitted (e.g., ifconcentrationresponse functionincludes a singlelag, while “truth”is a distributedlag).Unclear, ifconcentrationresponse functionincludes a singlelag, but it’s thewrong lag.The actual lag structure for short-term NO2exposures is uncertain.Omitted lags could cause an underestimation in the predicted incidenceassociated with a given reduction in NO2concentrations. The level ofuncertainty (in the sense of theimpactof the uncertainty) may depend onthe situation. For example, suppose thehealth effect is actually affectedlargely by same-day NO2concentrations but the model (incorrectly)includes only a 1-day lag. In this case, the impact on the outcome of theanalysis may be minimal if, as islikely, there is a high degree ofautocorrelation in NO2concentrations from day to day (so thatyesterday’s NO2level would act asa good proxy for today’s NO2level).If, on the other hand, there is a distributed lag – e.g., if risk of the healtheffect on daytdepends on NO2concentrations for the entire weekleading up to dayt– and the model includes only a single lag, then theunderstatement of effect could be substantial.Transferabilityof concentrationresponserelationslowNo obvious bias.Concentration-responsefunctions may not provide an adequaterepresentation of the concentration-response relationship in times andplaces other than those in which they were estimated. For example,populations in the assessment location/time period may have more orfewer members of sensitive subgroupsthan the location/time period inwhich functions were derived, which would introduce additionaluncertainty related to the use of agiven concentration-response functionin the analysis. This problem was minimized in the NO2risk assessment,however, because it relies on concentration-response functions estimatedin a recent study conducted in the assessment location.265266Uncertainty Level ofUncertaintyDirection of Bias CommentsExtrapolation ofconcentrationresponserelations beyondthe range ofobserved NO2datalowUnclear.A concentration-response relationship estimated by an epidemiologicalstudy may not be valid at concentrations outside the range ofconcentrations observed during the study. This problem should beminimal in the NO2risk assessment, however, because the NO2concentrations observed in the study from which C-R functions wereobtained covered a wide range – from 1 ppb to 181 ppb.Adequacy ofambient NO2monitors assurrogate forpopulationexposurelowNo obvious bias.Possible differences in how the spatial variation in ambient NO2levelsacross an urban area are characterized in the original epidemiologicalstudy compared to the more recent ambient NO2data used to characterizecurrent air quality wouldcontribute to uncertainty in the health riskestimates. The NO2risk assessment uses the same monitor used in theepidemiological study from which the C-R functions were obtained,which should minimize this source of uncertainty.Adjustment ofair qualitydistributions tosimulate justmeeting currentand alternativeNO2standards.medium tohighNo obvious bias.The pattern and extent of daily reductions in NO2concentrations thatwould result if the current NO2standard or alternative NO2standardswere just met is not known. There remains significant uncertainty aboutthe shape of the air quality distribution of hourly levels upon just meetingan NO2standard, especially for alternative standards that are at levelshigher than recent NO2air quality levels.Baseline healtheffects dataLowmediumDownward bias.Data on baseline incidence may be uncertain for a variety of reasons.For example, location- and age-group-specific baseline rates may not beavailable in all cases. Baselineincidence may change over time forreasons unrelated to NO2.This source of uncertainty is relatively minorin the NO2risk assessment, however, because a baseline incidenceestimate has been obtained from the study authors for the assessmentarea. There is a known downward bias to this estimate, however,because it is based on an incomplete set of hospitals providing ED data(36 out of 42) in the Atlanta MSA.

### used in the epidemiological study is the correct model form. With respect to uncertainties about model form and whethe r or not a population threshold exists, the available epidemiological studies neithe r support nor refute the existence of thresholds at the population level. Concer ning the possible role of co-pollutants in the Tolbert et al. (2007) study, NO2 was only moderately correlated with the other pollutants considered (i.e., PM 10 

### , O 3 

### ) that produced the concentration-response functions that have been us ed in the risk assessment, although it was fairly highly correlated (r = 0.7) with CO. When a study, such as Tolbert et al. (2007) is conducted in a single location, the pr oblem of possible confounding is particularly difficult. Single-pollutant models, which omit co-pollu tants, may produce overestimates of the NO 2 

### effect, if some of the effect s are really due to one or mo re of the other pollutants. On the other hand, effect estimates based on a multi-pollutant model can be uncertain and even result in statistically insignif icant estimates where there is a true relationship, if the co-pollu tants included in the model are highly correlated with NO 2 

### . As a result of these considerations, we report risk estimates based on both the single- and multi-pollutant models from Tolber t et al. (2007). It should be noted that use of a concentration-re sponse relationship based on an epidemiological study conducted in the same location for this risk assessment reduces some potential uncertainties since it does not involve extrapolation of the relationship across different geographic areas with different population characte ristics, land uses, source mixtures and other factors.



### • Adequacy of ambient NO 2 

### monitors as surrogate for population exposure . The Tolbert et al. (2007) study used ambient c oncentrations at fixed-site monitors to represent ambient exposure and for severa l reasons this may or may not provide a good representation of ambient NO 2 

### exposure for the population. The final ISA identifies the following thee components to exposure measurement error: (1) the use of average population rather than individual exposure data ; (2) the difference between average personal ambient exposure and ambien t concentrations at central monitoring sites; and (3) the difference between true a nd measured ambient concentrations (final ISA, section 1.3.2, p.1-5). While a concen tration-response func tion may understate the effect of personal exposure to NO 2 

### on the incidence of a hea lth effect, it will give an unbiased estimate of the effect of ambient concentrations on the incidence of the health effect, if the ambient concentrations at monitoring stations provide an unbiased estimate of the ambient concentrations to which the population is exposed. If NO 2 

### is the causal agent, the understatement of the impact of personal exposures is not a concern, since NO 2 

### NAAQS are expressed in term s of ambient, not personal exposure, levels. However, if NO 2 

### is not the causal agent, and the effects are due to confounding copollutants or other f actors, then reducing ambient NO 2 

### levels might not result in the estimated reducti ons in the health effects.



### • Adjustment of air quality distributions to simulate just meeting the current annual standard and alternative 98 th 

### and 99 th 

### percentile daily maximum 1-h standards . The current annual standard and many of the al ternative 1-h standards analyzed in the current risk assessment requires an upward adjustment of recent ambient NO 2 

### levels. In adjusting air quality to simulate just m eeting these standards, we have assumed that the overall shape of the distribution of 1-h and 24-h concentrations would not change. While we believe this is a reasonable assumption in the absence of evidence

267

### 268 supporting a change in the di stribution, we recogni ze this as an important additional uncertainty, especially for those scenarios where considerable upward adjustment is required to simulate just meeting some of the standards.



### • Baseline incidence . There are uncertainties related to the baseline incidence including: (1) the extent to which baseline incidence varies between the year used in the assessment (i.e., 2004) and some unspeci fied future year when air quality is adjusted to simulate just meeting the current and alternative standards; (2) the extent to which baseline incidence is underestimat ed because only 36 of the 42 emergency departments provided baseline incidence fo r the study in 2004; (3) the use of annual incidence data to develop daily baseline incidence; and (4) the extent to which Atlanta area residents visited emergency de partments outside of the Atlanta MSA. As noted previously, the use of the availa ble baseline incidence for 2004 results in some underestimation of the risk for the A tlanta MSA since data were only available from 36 of the 42 emergency departments for that year (i.e., about 14% of emergency departments were not included). Concerni ng the use of annual baseline incidence to estimate daily incidence, to the extent that NO 2 

### affects health, act ual incidence would be expected to be somewhat higher than average on days with high NO 2 

### concentrations and using an average daily incidence would resu lt in underestimating the changes in incidence on such days. Sim ilarly, actual incidence would be expected to be somewhat lower on days with low NO 2 

### concentrations and using an average daily incidence would result in overestimati ng the changes in incidence on such days. Both of these effects would be expected to be small and should largely cancel each other out. With respect to the last uncerta inty, we consider this to be a relatively minor uncertainty since most ED visits are li kely to be made to the closest emergency department available, which, for residents of the Atlanta MSA are likely to be within that MSA. The baseline incidence data ha s not been adjusted for any future changes

### such as aging of the population over time or possible changes in ED visits due to increased in-migration of younger individuals.



## 9.7 RISK ESTIMATES FOR EMERGENCY DEPARTMENT VISITS

In this section, we present risk estimates a ssociated with several air quality scenarios, including three recent years of air qualit y as represented by 2005, 2006, and 2007 monitoring data. In addition, risk estimates are presented for a hypothetical s cenario, where air quality from 2006 and 2007 is adjusted upward to simulate just meeting the current annual NO 2 

### standard, and for scenarios where the three year period (2005-2007) is adjusted (e ither up or down) to simulate just meeting potential alternative 98 th 

### and 99 th 

### percentile daily maximu m 1-h standards. As discussed previously in chapter 5, potential alte rnative 1-h standards w ith levels set at 0.05, 0.10, 0.15, and 0.20 have been included in the risk assessment. Throughout this section and Appendix C th e uncertainty surroundi ng risk estimates resulting from the statistical uncertainty of the NO 2 

### coefficients in the concentration-response functions used is characterized by ninety-five percent confidence interv als around estimates of incidence, incidence per 100,000 population, and percent of total incidence that is NO 2 

### -related. In some cases, the lower bound of a confidence in terval falls below zero. This does not imply that additional exposure to NO 2 

### has a beneficial effect but only that the estimated coefficient in the concentration-response function was not statistically signi ficantly different from zero. Lack of statistical significance could reflect insufficient statistical power to detect a relationship that exists or could reflect that no relationship exists. Tables 9-2, 9-3, and 9-4 presen t the risk estimates for NO 2 

### -related ED visits associated with recent air quality ( 2005, 2006, and 2007, respectively). Ta ble 9-2 for 2005 also includes risk estimates for just meeting several alte rnative 1-h daily maximum standards based on adjusting 2005-2007 air quality data to simulate just meeting th ese alternative standards. Similarly, Tables 9-3 (based on 2006) and 9-4 (b ased on 2007) include risk estimates associated with just meeting these same alternative 1-h standa rds, as well as risk estim ates associated with a simulation where air quality is adjusted upward to represent ju st meeting the current 0.053 ppm

annual NO 2 

### standard. Since attainment of the curr ent annual standard is based on the most recent two year period, risk estimates for the an nual standard are only included in the tables based on 2006 and 2007 air quality.

269Table 9-2. Estimated Percent of Total Annual Incidence of Respiratory ED Visits Associated with "As Is" NO2 Concentrations andNO2Concentrations that Just Meet Alternative Standards in Atlanta, GA, Based on Adjusting 2005 NO2 Concentrations.*0.05***0.10.150.20.050.10.15none3%2.1% 4.2% 6.2% 8.1%2%3.9% 5.8%(1.6% - 4.3%) (1.1% - 3.1%) (2.2% - 6.1%) (3.3% - 8.9%) (4.4%- 11.7%) (1% - 2.9%) (2.1% - 5.7%) (3.1% - 8.3%) (CO2.5% 1.8% 3.6% 5.3%7%1.7% 3.3% 4.9%(0.8% - 4.2%) (0.6% - 3%) (1.2% - 5.9%) (1.8% - 8.7%) (2.4% -11.3%) (0.6% - 2.8%) (1.1% - 5.5%) (1.7% - 8.1%) (O31.5% 1.1% 2.1% 3.2% 4.2%1%2%2.9%(-0.1% - 3.1%) (0% - 2.2%) (-0.1% - 4.3%) (-0.1% - 6.3%) (-0.2% - 8.4%) (0% - 2%) (-0.1% - 4%) (-0.1% - 5.9%)PM101.1% 0.8% 1.5% 2.2%3%0.7% 1.4% 2.1%(-0.6% - 2.7%) (-0.4% - 1.9%) (-0.9% - 3.8%) (-1.3% - 5.6%) (-1.7% - 7.4%) (-0.4% - 1.8%) (-0.8% - 3.5%) (-1.2% - 5.2%)PM10, O30.6% 0.4% 0.9% 1.3% 1.7% 0.4% 0.8% 1.2%(-1.1% - 2.3%) (-0.8% - 1.7%) (-1.6% - 3.3%) (-2.5% - 4.9%) (-3.3% - 6.4%) (-0.8% - 1.5%) (-1.5% - 3%) (-2.3% - 4.5%)**Incidence was quantified down to 0 ppb. Percents are rounded to the nearest tenth.Note: Numbers in parentheses are 95% confidence intervals based on statistical uncertainty surrounding the NO2coefficient.OtherPollutants inModel*Estimated incidences of respiratory emergency department visitsare based on the concentration-response functions estimated inTolbert et al.corresponding to Figure 2 in Tolbert et al. (2007) were obtained via personal communication with P. Tolbert]. All models use a3-day moving average ofdaily 1-hr. maximum NO2concentration and apply to all ages.***Alternative 1-hr daily maximum standards are characterized bya concentration of m ppm and annth percentile, requiring thatthe average of thenth percentile 1-hr daily maxima over a 3-year period be at or below m ppm.Atternative 98th percentile 1-hr daily maximum standards(ppm)Alternative 99th percentile 1-hr daily maximum(ppm)"as is"Percent of Total Incidence of Respiratory Emergency Department Visits Associated with "As is" NO2Concentrations and NO2ConcJust Meet Alternative Standards**0.27.6%4.1% - 10.9%)6.5%2.2% - 10.6%)3.9%(-0.2% - 7.8%)2.8%(-1.6% - 6.9%)1.6%(-3.1% - 6%)(2007) [resultsthe3 annualstandardsentrations that270Table 9-3. Estimated Percent of Total Annual Incidenceof Respiratory ED Visits Associated with "As Is" NO2Concentrations and NO2Concentrations that Just Meet Alternative Standards in Atlanta, GA, Based on Adjusting 2006 NO2Concentrations.*0.05***0.10.150.20.050.10.15none 3.1%9%2.2% 4.3% 6.4% 8.5%2%4%6%(1.6% - 4.5%) (4.9% - 12.9%) (1.2% - 3.2%) (2.3%- 6.3%) (3.5% - 9.3%) (4.6% - 12.2%) (1.1%- 3%) (2.2% - 5.9%) (3.2% - 8.7%)CO2.6% 7.7% 1.9% 3.7% 5.5%7.3% 1.7% 3.4% 5.1%(0.9% - 4.4%) (2.6% - 12.5%) (0.6% - 3.1%) (1.2%- 6.1%) (1.8% - 9%) (2.5% - 11.8%) (0.6% -2.9%) (1.2% - 5.7%) (1.7% - 8.4%)O31.6% 4.6% 1.1% 2.2% 3.3%4.4%1%2.1% 3.1%(-0.1% - 3.2%) (-0.2% - 9.2%) (-0.1% - 2.3%) (-0.1% - 4.5%) (-0.2% - 6.6%) (-0.2% - 8.7%) (0% - 2.1%) (-0.1% - 4.1%) (-0.1% - 6.2%)PM101.1% 3.3% 0.8% 1.6% 2.3%3.1% 0.7% 1.4% 2.2%(-0.6% - 2.8%) (-1.9% - 8.2%) (-0.4% - 2%) (-0.9% - 3.9%) (-1.3% - 5.8%) (-1.8% - 7.7%) (-0.4% - 1.8%) (-0.8% - 3.7%) (-1.2% - 5.4%)PM10, O30.6% 1.9% 0.4% 0.9% 1.3%1.8% 0.4% 0.8% 1.2%(-1.2% - 2.4%) (-3.6% - 7.1%) (-0.8% - 1.7%) (-1.7% - 3.4%) (-2.5% - 5.1%) (-3.4% - 6.7%) (-0.8% - 1.6%) (-1.6% - 3.2%) (-2.4% - 4.7%)**Incidence was quantified down to 0 ppb. Percents are rounded to the nearest tenth.Note: Numbers in parentheses are 95% confidence intervals based on statistical uncertainty surrounding the NO2coefficient.OtherPollutants inModel*Estimated incidences of respiratory emergency department visitsare based on the concentration-response functions estimated inTolbert et al. (2007) [resultsto Figure 2 in Tolbert et al. (2007) were obtained via personalcommunication with P. Tolbert]. All models use a 3-day movingaverage of the daily 1-hr. maximconcentration and apply to all ages.***Alternative 1-hr daily maximum standards are characterizedby a concentration of m ppm andan nth percentile, requiring thatthe average of the 3 annual nthhr daily maxima over a 3-year period be at or below m ppm.Atternative 98th percentile 1-hr daily maximum standards(ppm)Alternative 99th percentile 1-hr daily maximu(ppm)"as is"Percent of Total Incidence of Respiratory Emergency Department Visits Associated with "As is" NO2Concentrations and NO2Concentrations that Just MeCurrent and Alternative Standards**current annualstandard0.27.9%(4.3% - 11.4%)6.8%(2.3% - 11%)4.1%(-0.2% - 8.1%)2.9%(-1.7% - 7.2%)1.6%(-3.2% - 6.2%)correspondingum NO2percentile 1-m standardset the271Table 9-4. Estimated Percent of Total Annual Incidenceof Respiratory ED Visits Associated with "As Is" NO2Concentrations and NO2Concentrations that Just Meet Alternative Standards in Atlanta, GA, Based on Adjusting 2007 NO2Concentrations.*0.05***0.10.150.20.050.10.150.2none 2.8% 8.1%2%3.9% 5.8%7.6% 1.8% 3.6% 5.4% 7.1%(1.5% - 4%) (4.4% - 11.6%) (1% - 2.9%) (2.1%- 5.7%) (3.1% - 8.4%) (4.1% - 11%) (1% - 2.7%) (1.9% - 5.3%) (2.9% - 7.8%) (3.8% - 10.2%)CO2.4% 6.9% 1.7% 3.3% 4.9%6.5% 1.6% 3.1% 4.6% 6.1%(0.8% - 3.9%) (2.3% - 11.3%) (0.6% - 2.8%) (1.1%- 5.5%) (1.7% - 8.1%) (2.2% - 10.6%) (0.5%- 2.6%) (1% - 5.1%) (1.5% - 7.5%) (2% - 9.9%)O31.4% 4.1%1%2%2.9% 3.9% 0.9% 1.8% 2.7% 3.6%(-0.1% - 2.8%) (-0.2% - 8.3%) (0% - 2%) (-0.1% - 4%) (-0.1% - 5.9%) (-0.2% - 7.8%) (0% - 1.9%) (-0.1% - 3.7%) (-0.1% - 5.5%) (-0.2% - 7.3%)PM101%2.9% 0.7% 1.4% 2.1% 2.8% 0.6% 1.3% 1.9% 2.6%(-0.6% - 2.5%) (-1.7% - 7.3%) (-0.4% - 1.8%) (-0.8% - 3.5%) (-1.2% - 5.2%) (-1.6% - 6.9%) (-0.4% - 1.7%) (-0.7% - 3.3%) (-1.1% - 4.9%) (-1.5% - 6.4%)PM10, O30.6% 1.7% 0.4% 0.8% 1.2%1.6% 0.4% 0.7% 1.1% 1.5%(-1.1% - 2.2%) (-3.2% - 6.4%) (-0.8% - 1.5%) (-1.5% - 3%) (-2.3% - 4.5%) (-3% - 6%) (-0.7% - 1.4%) (-1.4% - 2.8%) (-2.1% - 4.2%) (-2.8%- 5.6%)**Incidence was quantified down to 0 ppb. Percents are rounded to the nearest tenth.Note: Numbers in parentheses are 95% confidence intervals based on statistical uncertainty surrounding the NO2coefficient.OtherPollutants inModel*Estimated incidences of respiratory emergency department visitsare based on the concentration-response functions estimated inTolbert et al. (2007) [results correspondingto Figure 2 in Tolbert et al. (2007) were obtained via personalcommunication with P. Tolbert]. All models use a 3-day movingaverage of the daily 1-hr. maximum NO2concentration and apply to all ages.***Alternative 1-hr daily maximum standards are characterizedby a concentration of m ppm andan nth percentile, requiring thatthe average of the 3 annual nth percentile 1-hr daily maxima over a 3-year period be at or below m ppm.Atternative 98th percentile 1-hr daily maximum standards(ppm)Alternative 99th percentile 1-hr daily maximum standards(ppm)"as is"Percent of Total Incidence of Respiratory Emergency Department Visits Associated with "As is" NO2Concentrations and NO2Concentrations that Just Meet theCurrent and Alternative Standards**current annualstandard272

### In Table 9-2, and similarly in Tables 9-3 a nd 9-4, the first row of percent of total annual

incidence estimates is based on a single pollutant model (i.e., NO 2 

### only) and results in the largest

estimates for NO 2 

### -related respiratory ED visits . The next three rows pr esent risk estimates based on two pollutant models (i.e., NO 2 

### + CO, NO 2 

### + O 3 

### , NO 2 

### + PM 10 

### ). The last row presents risk

estimates based on a three pollutant model (i.e., NO 2 

### + PM 10 

### + O 3 

### ). As noted above in this chapter, effect estimates based on a multi-pollutant model can be uncertain and even result in statistically insignificant estimates where there is a true relationship, if th e co-pollutants included in the model are highly correlated with NO 2 

### . In the case of this study in Atlanta, NO 2 

### was moderately correlated with PM 10 

### and O 3 

### concentrations. The ne gative lower bounds of the confidence intervals for many of the risk estimates based on multi-pollutant models may in part be due to correlation in these pollutant concentr ations and staff do not view these estimates as suggesting any health beneficial health effect of increasing NO 2 

### exposure levels. Tables 4-1, 4-2, and 4-3 in Appendix C pres ent these same results expressed in terms of incidence of respiratory-related ED visits in the Atlanta MSA base d on recent air quality and just meeting altertative standards based on 2005, 2006, and 2007 air quality data. Finally, Tables 4- 4, 4-5, and 4-6 in Appendix C present these same risk estimates expressed in terms of incidence per 100,000 general population in the Atlanta MS A based on recent air quality and simulating just meeting alternative standards based on the same three years of air quality data. Key Observations Presented below are key observations resulting from the respiratory-related ED visits risk

assessment:



### • Respiratory-related ED visits estimated to result from exposures to NO 2 

### were estimated for a single urban area (i.e., Atlanta) for several recent years of air quality (2005-2007) and for air quality adjusted to si mulate just meeting the current annual NO 2 

### standard and several altern ative 1-hour daily maximum NO 2 

### standards. While we would expect some differences in estimated NO 2 

### -related ED respiratory visits across different locati ons due to differences in populations, land use patterns, access to medical facilities, co-pollutants and other factors affecting exposure and the concentration-response re lationships, we believe that the risk estimates do provide a useful perspec tive on the likely overall magnitude and pattern of ED visits a ssociated with various NO 2 

### air quality scenarios in urban areas within the U.S.



### • The largest risk estimates were associated with single-pollutant NO 2 

### concentration-response functions based on th e effect estimates reported in Tolbert et al. (2007). Risk estimates based on various co-pollutant models with O 3 

### , CO,

273

### 274 and PM 10 

### resulted in significant reduction in the risk estimates, often by a factor of two or greater and resulted in much wider confidence intervals.



### • The only standards that resulted in a reduc tion in risk estimates from the baseline of recent air quality for the three year period examined were the 98 th 

### and 99 th 

### percentile 1-hour daily maximum standa rds set at the level of 0.05 ppm.



### • The impact of changing the level of the alternative 1-hour daily maximum standards is substantially greater than the impact of changing from a 98th to a 99th percentile standard. For example, changing from a 98th percentile 1-hour daily maximum standard set at 0.10 pp m to one set at 0.05 ppm reduces the estimated incidence of respir atory-related ED visits in Atlanta by about 49 percent in 2007 (from 4700 to 2400); however, chan ging from a 98th percentile 1-hour daily maximum standard based on 0.05 ppm to a 99th percentile 1-hour daily maximum standard based on 0.05 r educes the incidence in 2007 by only about 8 percent (from 2400 to 2200).



### • The overall pattern of risk estimates is similar across the three years examined. For the three years examined, there was not significant year-to-year variability in the risk estimates.



### • Important uncertainties and limitations asso ciated with the risk assessment which were discussed above in section 9.6 and which should be kept in mind as one considers the quantitative risk estimates include: - uncertainty about the extent to which the associations between NO 2 

### and ED visits for respiratory causes actua lly reflect causal relationships; - statistical uncertainty due to samp ling error which is characterized in the assessment; - uncertainties associated with the ai r quality adjustment procedure that was used to simulate just meeting the cu rrent annual and several alternative 1-h daily maximum standards; -uncertainties associated with the estimated baseline incidence for ED respiratory visits; - uncertainties relate d to how changes in populat ion, activity patterns, air quality, and other factors over time might impact the risk estimates; - there is uncertainty about the extent to which the risk estimates presented for the Atlanta urban area are representa tive of other urba n locations in the U.S.

# 10. EVIDENCE- AND E XPOSURE/RISK-BASED CONSIDERATIONS RELATED TO THE PRIMARY NO 2 

# NAAQS

10.1 INTRODUCTION

This chapter considers the scientific evidence in the ISA (EPA, 2008a) and the exposure and risk characterization results presented in this docu ment as they relate to the adequacy of the current NO 2 

### primary NAAQS and potential alternative primary NO 2 

### standards. The available scientific eviden ce includes epidemiologic, controlled human exposure, and animal toxi cological studies. The NO 2 

### exposure and risk characterizations described in chapters 6-9 of this document include estimates of exposures and health

risks associated with recent NO 2 

### concentrations and with NO 2 

### concentrations adjusted to simulate scenarios just meeting the current and potential alternative standards. In considering the scientific ev idence and the exposure- and risk-based information, we

have also considered relevant uncertainties. Section 10.2 of this chapter presents our general approach to considering the adequ acy of the current standard and potential

alternative standards. Section 10.3 focu ses on evidence- and exposure-/risk-based

considerations related to the adequacy of the current standard, and section 10.4 focuses on such considerations related to potential alte rnative standards (in te rms of the indicator, averaging time, form, and level).

These considerations are intended to info rm the Agency’s policy assessment of a range of options with regard to the NO 2 

### NAAQS. We note that the final decision on retaining or revising the current NO 2 

### primary standard, taking in to account the Agency’s policy assessment, is largely a public health policy judgment. A final decision will draw upon scientific information and analyses a bout health effects, population exposure and risks, and policy judgments a bout the appropriate response to the range of uncertainties

that are inherent in the scientific evidence a nd analyses. Our approach to informing these judgments, discussed more fully below, is ba sed on a recognition that the available health

effects evidence reflects a continuum consisti ng of ambient levels at which scientists generally agree that health effects are likely to occur through lower levels at which the likelihood and magnitude of the response become increasingly uncertain. This approach

275

### is consistent with the requirements of the NAAQS provisions of the Act and with how EPA and the courts have historically interp reted the Act. These provisions require the Administrator to establish pr imary standards that, in the Administrator's judgment, are requisite to protect public health with an ad equate margin of safety. In so doing, the

Administrator seeks to establis h standards that are neither more nor less stringent than necessary for this purpose. The Act does not require that pr imary standards be set at a

zero-risk level but rather at a level that avoids unacceptable risks to public health, including the health of sensitive groups.



## 10.2 GENERAL APPROACH

This section describes the gene ral approach that staff is taking to inform decisions regarding the need to retain or revise the current NO 2 

### NAAQS. The current standard, which is an annual average of 0.053 ppm, was retained by the Administrator in the most recent review in 1996 (61 FR 52854 (October 8, 1996 )). The decision in that review to

retain the annual standard was based on consideration of ava ilable scientific evidence for

health effects associated with NO 2 

### and on air quality informati on. With regard to these considerations, the Administ rator noted that “a 0.053 ppm annual standard would keep

annual NO 2 

### concentrations considerably below the long-term levels for which serious

chronic effects have been observed in animal s” and that “Retaining the existing standard would also provide protection against short-term peak NO 2 

### concentrations at the levels associated with mild changes in pulmonary function and airway responsiveness observed in controlled human studies” (60 FR 52874, 52880 (Oct. 11, 1995)). As a result, the

Administrator concluded that “t he existing annual primary st andard appears to be both adequate and necessary to pr otect human health against bo th long- and short-term NO 2 

### exposures” and that “retaining the existing a nnual standard is c onsistent with the

scientific data assessed in the Criteria Do cument (U.S. EPA, 1993) and the Staff Paper (U.S. EPA, 1995a) and with the advice a nd recommendations of CASAC” (61 FR 52852 at 52854). To inform the range of options that the Ag ency will consider in this review of the current primary NO 2 

### standard, the general approach we have adopted builds upon the approaches used in reviews of other criteria pollutants, including th e most recent reviews

276

### of the Pb, O 3 

### , and PM NAAQS (EPA, 2008c; EPA, 2007h; EPA, 2005). As in these other reviews, we consider the implications of placing more or less weight or emphasis on different aspects of the sc ientific evidence and the expos ure/risk-based information, recognizing that the weight to be given to various elements of the evidence and exposure/risk information is part of the public health policy judgments that the Administrator will make in reaching decisions on the standard. A series of general questions frames our approach to consider ing the scientific evidence and exposure/risk-based information. First, our cons ideration of the scientific evidence and exposure/risk-based information with regard to the adequacy of the current standard is framed by the following questions:



### • To what extent does evidence and expos ure/risk-based information that has become available since the last review re inforce or call into question evidence for NO 2 

### -associated effects that were identified in the last review?



### • To what extent has evidence for differe nt health effects and/or sensitive populations become availabl e since the last review?



### • To what extent have uncertainties iden tified in the last review been reduced and/or have new uncertainties emerged?



### • To what extent does evidence and expos ure/risk-based information that has become available since the last review re inforce or call into question any of the basic elements of the current standard?

To the extent that the available evidence and exposure/risk-based information suggests it may be appropriate to consider revision of the current standa rd, we consider that evidence and information with regard to its support for consideration of a sta ndard that is either more or less protective than the current st andard. This evaluation is framed by the following questions:



### • Is there evidence that associations, especi ally causal or likely causal associations, extend to ambient NO 2 

### concentrations as low as, or lower than, the concentrations that have previously been associated with health effects? If so, what are the important uncertainties associated with that evidence?



### • Are exposures above benchmark levels and/ or health risks estimated to occur in areas that meet the current standard? If so, are the es timated exposures and health

277

### risks important from a public health perspective? What are the important uncertainties associated with the estimated risks?

To the extent that there is support for cons ideration of a revised standard, we then consider the specific elements of th e standard (indicat or for gaseous NO x 

### , averaging time, form, and level) within the context of the cu rrently available information. In so doing, we address the following questions:



### • Does the evidence provide support for considering a different indicator for gaseous NO x 

### ?



### • Does the evidence provide support for c onsidering different averaging times?



### • What ranges of levels and forms of a lternative standards are supported by the evidence, and what are the associated uncertainties and limitations?



### • To what extent do specific averaging times, levels, and forms of alternative standards reduce the estimated exposures above benchmark levels and risks attributable to NO 2 

### , and what are the uncertainties associated with the estimated exposure and risk reductions?

The following discussion addresses the questions outlined above and presents staff’s conclusions regarding the scientif ic evidence and the exposure-/risk-based information specifically as they relate to th e current and potential al ternative standards. This discussion is intended to inform the Ag ency’s consideration of policy options that will be presented in an Advanced Notice of Proposed Rulemaking (ANPR), together with the scientific support for such options, and which will be further considered in the Agency’s proposed and final rule-making noti ces. Section 10.3 considers the adequacy

of the current standard while section 10.4 c onsiders potential alternative standards in terms of indicator, averaging time, form, and le vel. Each of these sections considers key

conclusions as well as the uncer tainties associated with the evidence and/or exposure/risk analyses.



## 10.3 ADEQUACY OF THE CURRENT ANNUAL STANDARD

In the last review of the NO 2 

### NAAQS, the AQCD for NO X 

### concluded that there were two key health effects of greatest concern at ambient or near-ambient concentrations

of NO 2 

### (ISA, section 5.3.1). The first was in creased airway hyperresponsiveness in

278

### asthmatic individuals after short-term exposur es. The second was increased respiratory illness among children associated with longer-term exposures to NO 2 

### . Evidence also was found for increased risk of emphysema, but this appeared to be of major concern only with exposures to levels of NO 2 

### that were much higher than current ambient levels (ISA, section 5.3.1). Controlled human exposure and animal toxicological studies provided qualitative evidence for airway hyperrespons iveness and lung function changes while epidemiologic studies provided evidence for increased respiratory symptoms with

increased indoor NO 2 

### exposures. Animal toxicological findings of lung host defense system changes with NO 2 

### exposure provided a biologically-plausible basis for the epidemiologic results. Subpopul ations considered potentially more susceptible to the

effects of NO 2 

### exposure included persons with preex isting respiratory disease, children, and the elderly. The epidemiologic evidence for respiratory health effects was limited,

and no studies had considered effects such as hospital admissions, ED visits, or mortality (ISA, section 5.3.1).

10.3.1 Evidence-based considerations Evidence published since the last review generally has confirmed and extended the conclusions articulated in the 1993 AQC D (ISA, section 5.3.2). The epidemiologic evidence has grown substantially with the addi tion of field and panel studies, intervention studies, time-series studies of effects such as hospital admissions, and a substantial number of studies evaluating mortality risk associated with short-term NO 2 

### exposures. As noted above, no epidemiologic studies were available in 1993 that assessed relationships between NO 2 

### and outcomes such as hospital admissions, ED visits, or mortality. In contrast, dozens of epidem iologic studies on such outcomes are now included in this evaluation (ISA, chapter 3). While not as marked as the growth in the epidemiologic literature, a numbe r of recent toxicological and human clinical studies also provide insights into re lationships between NO 2 

### exposure and health effects. In considering this evidence, we note that different scientific methodologies provide different types of in formation. For example, controlled human exposure studies provide information on health effects that are specifically associated with exposure to

NO 2 

### in the absence of the co-pollutants that are commonly found in ambient air.

279

### However, these studies do not provide informa tion directly related to the public health implications of real-world NO 2 

### air quality. Epidemiologic studies provide information on the public health implications of real-world NO 2 

### concentrations; however, interpretation of specific NO 2 

### -related effects in these studies is complicated by a number of factors, including the presence of co-pollu tants in the ambient air. As an initial consideration with regard to the adequacy of the current standard, staff notes that the evidence relating long-term (weeks to years) NO 2 

### exposures to adverse health effects is judged to be either “suggestive but not suffi cient to infer a causal relationship” (respiratory morbid ity) or “inadequate to infer the presence or absence of a causal relationship” (mortality, can cer, cardiovascular effects, reproductive/developmental effects) (ISA, sections 5.3.2.4-5.3.2.6). In contrast, the evidence relating short-term (minutes to hours) NO 2 

### exposures to respiratory morbidity is judged to be “sufficient to infer a likely causal relationship” (ISA, section 5.3.2.1). This judgment is supported primarily by a large body of recent epidemiologic evidence that

evaluates associations of short-term NO 2 

### concentrations with re spiratory symptoms, ED visits, and hospital admissions. It suggests that, at a minimum, consideration of the adequacy of the current annual standard should take into acco unt the extent to which that standard provides protection against respirat ory effects associated with short-term NO 2 

### exposures. Such an emphasis on health e ndpoints for which evid ence has been judged sufficient to infer a likely causal relations hip would be consistent with other recent NAAQS reviews (e.g., EPA, 2005; EPA, 2007h; EPA, 2007i) and would ensure that decisions are based on endpoints for which a causal relationship with NO 2 

### is judged to be “more likely than not” (ISA, Table 1.3-2).

Because there was concern in the 1996 review of the NO 2 

### NAAQS about the potential for respiratory effects associated with short-term exposure to NO 2 

### concentrations around 0.2 ppm, the extent to which the then-current standard (which remains the current standard for purposes of this review) could be expected to afford protection from NO 2 

### concentrations at this level was c onsidered. In that review, the issue was examined with an air quality analysis that evaluated 1-h NO 2 

### concentrations. The conclusion from that analysis was that locati ons meeting the current standard are unlikely

280

### to experience 1-h concentrations exceed ing levels (e.g., 0.2 ppm) that have been associated with respiratory e ffects in controlled human exposur e studies (EPA, 1995). In the current review, a larger number of epidemiologic studies are available. In considering these epidemiologic stud ies, we note that annual average NO 2 

### concentrations

were below the level of the current annual NO 2 

### NAAQS in many of the locations where positive associations with respiratory morb idity endpoints have been detected (ISA, section 5.4). With regard to these studies , we note that the ISA characterizes the evidence for respiratory effects as consistent a nd coherent. The evidence is consistent in that associations are report ed in studies conducted in num erous locations and with a variety of methodological approaches (ISA, s ection 5.3.2.1). It is coherent in the sense that the studies report associations with re spiratory health outcomes that are logically linked together (ISA, section 5.3.2.1). When the epidemiologic literature is considered as a whole, there are generally positive associations between NO 2 

### and respiratory symptoms, hospital admissions, and ED visits . A number of these associations are statistically significant, particularly the mo re precise effect estimates (ISA, section 5.3.2.1). Interpretation of these NO 2 

### epidemiologic studies is complicated by the fact that on-road vehicle exhaust emissions are a nearly ubiquitous source of combustion pollutant

mixtures that include NO 2 

### . In recognition of this complication, the ISA notes that it is

difficult to determine the extent to which NO 2 

### is independently associated with respiratory effects versus being a marker for th e effects of another tr affic-related pollutant or mix of pollutants (see secti on 5.4). This uncertainty calls into question the extent to which effect estimates from epidemiologic st udies reflect the independent contributions

of NO 2 

### to the adverse respiratory outcom es assessed in these studies. In order to provide some perspective on this uncertainty, the ISA has evaluated epidemiologic studies that employed multi-pollutant models, epidemiologic studies of

indoor NO 2 

### exposure, and experimental studies. Specifically, the ISA notes that a

number of NO 2 

### epidemiologic studies have attempte d to disentangle the effects of NO 2 

### from those of co-occurring pollutants by employing multi-pollutant models. When evaluated as a whole, NO 2 

### effect estimates in these models generally remained robust when co-pollutants were included. Therefor e, despite uncertainti es associated with

281

### separating the effects of NO 2 

### from those of co-occurring pollutants, the ISA (section 5.4, p. 5-16) concludes that “the evidence summa rized in this assessment indicates that NO 2 

### associations generally remain robust in mu lti-pollutant models and supports a direct

effect of short-term NO 2 

### exposure on respiratory morbidity at ambient concentrations below the current NAAQS.” With regard to indoor studies, the ISA notes that these studies can test hypot heses related to NO 2 

### specifically (ISA, s ection 3.1.4.1). Although confounding by indoor combustion sources is a concern, indoor studies are not confounded by the same mix of co-pollutant s present in the ambient air or by the

contribution of NO 2 

### to the formation of s econdary particles or O 3 

### (ISA, section 3.1.4.1). The ISA notes that the findings of indoor NO 2 

### studies are consistent with those of studies using ambient concentrations from central si te monitors (also see chapter 4 of this document) and concludes that indoor studi es provide evidence of coherence for respiratory effects (ISA, section 3.1.4.1). With regard to experimental studies, we note that they have the advantage of providi ng information on health effects that are specifically associated with exposure to NO 2 

### in the absence of co-pollutants. The ISA

concludes that the NO 2 

### epidemiologic literature is supported by 1) evidence from controlled human exposure studies of airway hyperresponsiveness in asthmatics, 2) controlled human exposure and animal toxico logical studies of im paired host-defense systems and increased risk of susceptibility to viral and bacterial infection, and 3) controlled human exposure and animal toxico logical studies of airway inflammation (ISA, section 5.3.2.1 and 5.4). When taken toge ther, the results of epidemiologic and experimental studies form a plausible and c oherent data set that supports a relationship

between NO 2 

### exposures and respirator y endpoints, including symptoms and ED visits (ISA, section 5.4), at ambient concentrations that are present in areas that meet the

current NO 2 

### NAAQS.

10.3.2 Exposure- and risk-based considerations

In addition to the evidence-based cons iderations described above, staff has considered the extent to which exposure- and risk-based information can inform decisions regarding the adequ acy of the current annual NO 2 

### standard, taking into account key uncertainties associated with the estimated exposures and risks. For this review,

282

### exposures have been addressed in two ways. In the first, NO 2 

### air quality in 18 locations around the country has been used as a surroga te for exposure. In the second, exposures have been estimated for all asthmatics and for asthmatic children considering time spent

in different microenvironments in one urba n area, Atlanta, GA,. For both of these analyses, health risks have been characterized by comparing estimates of air quality or exposure to potential health benchmark levels (see chapters 4 and 6). The benchmarks

are based on controlled human e xposure studies involving known NO 2 

### exposure levels and measured airway hyperresponsiveness in asthmatics. The outputs of these analyses

are estimates of the occurrence of exposures greater than or equal to benchmark levels, which provide some perspective on the NO 2 

### -related health risks that could exist. In another approach to characterizing NO 2 

### -related health risks, we have estimated the

occurrences of NO 2 

### -related respiratory ED visits in Atlanta. This quantitative risk

assessment is based on NO 2 

### concentration-response rela tionships identified in an epidemiologic study of air pollution-related ED vi sits in Atlanta. We have selected these endpoints because they are considered adverse to the health of individuals and because the data necessary for the assessment are available. In making judgments as to whether NO 2 

### -related effects should be regarded as adverse to the health of individuals, staff has relied upon the guidelines published by the American Thoracic Society (ATS) (2000) a nd conclusions from the ISA. Of the morbidity endpoints used to characterize risks, ED visits are clearly indicative of effects

that are adverse to the health of the individual. The ATS notes that detectable effects of air pollution on clinical measur es, including ED visits, should be considered adverse. In addition, regarding airway responsiven ess, we recognize the following:



### • NO 2 

### -related airway hyperrespons iveness has the potentia l to increase asthma symptoms and worsen asthma control (ISA, sections 5.3.2.1 and 5.4).



### • The majority of asthmatics may experience NO 2 

### -related airway hyperresponsiveness following short-term NO 2 

### exposures between 0.1 ppm and 0.3 ppm (ISA, table 3.1-3).



### • Over 20 million people in the U.S. have asthma (ISA, table 4.4-1).

Despite uncertainty as to the magnitude of NO 2 

### -related airway hyperresponsiveness in any single individual (see below) and despite the fact that not all asthmatics are expected

283

### to respond to NO 2 

### concentrations between 0.1 and 0.3 pp m, these considerations suggest

that NO 2 

### -related airway hyperresponsiveness is an adverse effect when viewed from the perspective of the asthmatic population as a whole.

10.3.2.1 Key uncertainties The way in which exposure and risk re sults will inform ultimate decisions regarding the NO 2 

### standard will depend upon the weight placed on each of the analyses when uncertainties associated with those analyses are taken into consideration. The

uncertainties associated with each of the an alyses (air quality, Atlanta exposure, and Atlanta risk) are summarized below and are desc ribed in more detail in chapters 7-9 of this document. Although we are discussing these uncertainties within the context of the adequacy of the current standard, they appl y equally to consider ation of alternative

standards. 

### Air Quality Analyses A number of key uncertainties should be considered when interpreting these results with regard to decisi ons on the standard. These uncer tainties are discussed briefly below and in more detail in chapter 7.



### • In order to simulate just meeting the current annual standard and many of the alternative 1-h standards analyzed, an upward adjustment of recent ambient NO 2 

### concentrations was required. We note th at this adjustment does not reflect a judgment that levels of NO 2 

### are likely to increase unde r the current standard or any of the potential alternative standa rds under consideration. Rather, these adjustments reflect the fact that the curre nt standard, as well as some of the alternatives under consideration, could al low for such increases in ambient NO 2 

### concentrations. In adjusting air quality to simulate just meeting these standards, we have assumed that the overall shape of the distribution of NO 2 

### concentrations would not change. While we believe th is is a reasonable assumption in the absence of evidence supporting a different distribution and we note that available analyses support this approach (Rizzo, 2008), we recognize this as an important uncertainty. It may be an especially important uncertainty for those scenarios where considerable upward adjustment is re quired to simulate just meeting one or more of the standards.



### • In order to estimate NO 2 

### concentrations on roadways, empirically-derived relationships between ambient concentrations measured at fixed-site monitors and on-road concentrations were used. We have judged this to be an appropriate approach to estimating on-road NO 2 

### concentrations given that these

284

### concentrations have been shown to be correlated with concentrations measured at fixed-site monitors (Cape et al., 2004). However, the data used to develop the relationships were likely co llected under different conditions (e.g., with regard to meteorology, rate of transformation of NO to NO 2 

### ). We do not know the extent to which it is appropriate to assume that these conditions are re presentative of the times and places included in our analyses. Therefore, there is uncertainty in the degree to which the relationships used to estimate on-road NO 2 

### concentrations reflect the actual relationship in the locat ions and over the time periods of interest.



### • The potential health benchmark levels introduce sources of uncertainty including the following:

o The meta-analysis that formed a large part of the basis for potential health benchmark levels included primarily m ild asthmatics. For ethical reasons, more severely affected asthmatics were not included in the studies that formed the basis for the meta-analysis. Severe asthmatics may be more susceptible than mildly asthmatic individuals to the effects of NO 2 

### exposure (ISA, section 3.1.3.2). Therefore, the potential health effect benchmarks based on these studies could underestimate risks in popul ations with greater susceptibility. Although approaches to classifying asth ma severity differ, some estimates indicate that over half of asthmatics could be classified as moderate/severe (Fuhlbrigge et al., 2002; Stout et al., 2006).

o This meta-analysis provides information on the direction of the NO 2 

### -induced airway response, but not on the magnit ude of the response. Therefore, although the ISA does conclude that increased airway responsiveness associated with NO 2 

### exposure could increase symptoms and worsen asthma control (ISA, section 5.4), the full public health implications of benchmark exceedances are uncertain.



### Atlanta Exposure Assessment For our Atlanta exposure assessment, we have considered the occurrence of NO 2 

### exposures, in asthmatics, that exceed potential health benchmark levels. As with the air quality analyses, these exposures are consid ered for each of the air quality scenarios evaluated. A number of key uncertainties s hould be considered when interpreting these results with regard to decisi ons on the standard. Some of these uncertainties, including the approach used to adjust air quality to simulate just meeting different standards and

uncertainties associated with benchmark levels , are shared with the air quality analyses.

Additional uncertainties associated with th e Atlanta exposure assessment are discussed briefly below. A more extens ive discussion of uncertainties is provided in chapter 8.

285

### • A number of uncertainties are associated with exposure modeling, many of them with the activity data used in APEX.



### • When compared to ambient measuremen t data, predicted upper percentile NO 2 

### concentrations from AERMOD may be 10-50% higher. Because these AERMOD outputs are used as inputs for our exposur e modeling, this sugge sts the possibility that we are over-predicting upper percentile NO 2 

### exposures. Other approaches used to evaluate our exposure result s (i.e., comparison to personal exposure monitoring results and comparison of expos ure-to-ambient concentration ratios with those identified in the ISA) suggest that exposure estimates are reasonable. However, we cannot rule out the poss ibility that we are over-predicting benchmark exceedances with our Atlanta exposure analysis.



### • The exposure assessment is limited to A tlanta and the extent to which these results are representative of other locations in the U.S. is uncertain. As noted in section 8.11 above, staff has judged that th e Atlanta exposure estimates are likely representative of other m oderate to large urban areas . However, staff also recognizes that, given the greater proximity of the population to mobile sources in large urban areas such as Los Angeles, New York, and Chicago (see Tables 8-14 and 8-15), the Atlanta exposure estimate s likely underestimate the fraction of asthmatics in these cities that is exposed to NO 2 

### concentrations greater than or equal to potential health benchmark levels.



### Atlanta Risk Assessment For our risk assessment, we have considered the prevalence of NO 2 

### -related respiratory ED visits in Atlanta. As with the air quality and Atla nta exposure analyses,

ED visits are considered for each of the air quality scenarios evalua ted. A number of key

uncertainties should be considered when in terpreting these results with regard to decisions on the standard. Some of these, including the approach used to adjust air quality to simulate just meeting differ ent standards and th e appropriateness of generalizing results from Atlanta, are uncerta inties shared with the air quality and/or Atlanta exposure analyses. Additional uncer tainties associated with the Atlanta risk assessment are discussed briefly below. A mo re extensive discussion of uncertainties is provided in chapter 9.



### • There is uncertainty about whet her the association between NO 2 

### and ED visits actually reflects a causal relationship. Our judgment, that there exists at least a likely causal relationship with either short-term NO 2 

### itself or with NO 2 

### serving as an indicator for itself and other com ponents of ambient air, draws on the conclusions in the ISA and is discus sed in more detail in chapter 4.

286

### • The statistical uncertainty asso ciated with the estimated NO 2 

### coefficient in the concentration-response func tion has been characterized by confidence intervals reflecting sample size. However, these confidence intervals do not reflect all of the uncertainties related to the concentra tion-response functions , such as whether or not the model used in th e epidemiologic study is the correct model form.



### • Concerning the possible role of co-pollutants in the Tolbert et al. (2007) study, single-pollutant models may produce overestimates of the NO 2 

### effects if some of those effects are really due to one or more of the other pollutants. On the other hand, effect estimates based on multi-pollutant models can be uncertain, and can even result in statistically non-significant estimates where a true relationship exists, if the co-pollutants included in the model are highly correlated with NO 2 

### . As a result of these considerations, we report risk estimates based on both the single- and multi-pollutant models from Tolbert et al. (2007).

10.3.2.2 Assessment results As noted previously, the current annual NO 2 

### standard was retained in 1996 based largely on an evaluation of short-term NO 2 

### air quality. In that review, an air quality analysis demonstrated that locations meeting the current annual standard were unlikely to experience short-term ambient NO 2 

### concentrations at central s ite monitors that have been associated with respiratory effects (i.e., airw ay hyperresponsiveness) in controlled human exposure studies (i.e., around 0.2 ppm). Ther efore, the current annual standard was considered requisite to protect the public health against potential effects associated with short-term (as well as long-term) exposures. We note that a similar analysis of air quality in the current review produced similar results. That is, 1-h NO 2 

### concentrations greater than or equal to 0.20 ppm are unlikely to occu r in locations around the U.S., all of which

meet the current annual standard based on recent ambient air quality as measured at central site monitors (i.e., s ee tables 7-14 to 7-19). However, in the current review, in add ition to evaluating the adequacy of the current standard with ambient air quality as measured at central site monitors, we consider the results of additional analyses that provide perspective on potential NO 2 

### - associated health risks. For example, in our exposure analyses, we have evaluated NO 2 

### concentrations on roadways which are, on av erage, 80% higher th an concentrations

measured at central site mon itors (section 7.3.2). Staff notes that high concentrations of

NO 2 

### on or near roadways could impact asthmatics living or walking nearby (e.g., as

287

### would be common in an urban environment) or commuting in cars. In addition, we have adjusted NO 2 

### concentrations to simulate NO 2 

### air quality that could occur upon just meeting the current and potential alternat ive standards. As noted above (section 10.3.2.1), these adjustments provide information on potential health risks that could be allowed to occur under different standard opt ions. For our exposure analyses, we have compared NO 2 

### concentrations to pot ential health benchmark levels from 100 ppb to 300 ppb, a range that extends beyond that consider ed in the 1996 review. We have also

evaluated NO 2 

### -related ED visits in the current review . Epidemiological studies that form the basis for this analysis were not availa ble in the 1996 review. When taken together, these analyses provide additional informati on, not available in the 1996 review, on which to base a decision regarding th e adequacy of the current an nual standard (and potential alternative standards) to protect the public he alth. The uncertainties associated with these analyses (see 10.3.2.1 and chapter 7) should be carefully considered when interpreting

the results of these assessments. 

### Air Quality and Exposure Results The results of our air quality and exposur e assessments provide some perspective on the public health impacts of effects that we cannot currently evaluate in a quantitative risk assessment. As noted previously, we have addressed potentia l exposures with two approaches. In the first, we have estimated air quality exceedances of health benchmark

levels in 18 locations across the U.S. In the second, we have estimated exposure exceedances for asthmatics in Atlanta, GA. Results of these analyses, as they relate to the adequacy of the current standard, are discussed below. When considering the air quality-based re sults, where air quality serves as a surrogate for exposure, as they relate to the adequacy of the current standard, we note the

number of benchmark exceedances estimated to occur given air quality that just meets that standard. As noted above (section 10.3.2.1), this adjustment does not reflect a judgment that levels of NO 2 

### are likely to increase under th e current standard. Rather, it

reflects the fact that ambient NO 2 

### concentrations could in crease under the current

standard. In situa tions where annual NO 2 

### concentrations are adjust ed upward to simulate just meeting the current standard, 1-h NO 2 

### concentrations measured at fixed-site monitors

in locations across the U.S. could exceed concen trations that have been associated with

288

### increased airway responsiveness. Most loca tions are estimated to experience at least 50 days per year with 1-h ambient NO 2 

### concentrations at fixed-site monitors greater than or equal to 100 ppb (Figures 7-2 and 7-3) unde r this hypothetical scenario. Far fewer ambient exceedances are predicted for the higher benchmark levels. For example, only 5 areas are estimated to experience any days with 1-h ambient NO 2 

### concentrations at central site monitors greate r than or equal to 300 ppb, and none of those locations are estimated to experience more than 2 such days per year, on aver age (Appendix A). However, as noted above on-road NO 2 

### concentrations are estimated to be 80% higher (on average) than concentrations at fixed-site monitors. In the majority of locations, roadway exceedances of the 100 ppb benchmark level could occur on most days of the year when air quality is adjusted upward to simulate just meeting the current standard (Figure 7-6). Even for higher benchm ark levels, most locations are estimated to have exceedances on roadways. All locations evaluated except one (Boston) are estimated to experience on-road NO 2 

### concentrations greater than or equal to 300 ppb (Appendix A). Four of these lo cations are estimated to experience an average of greater than 20 days per year with on-road NO 2 

### concentrations greater than or equal to 300 ppb (Appendix A). When considering the Atlanta exposure results as they relate to the adequacy of the current standard, we note the number of benchmark exceedances estimated to occur given air quality that is adjust ed upward to simulate just m eeting the current standard. If

NO 2 

### concentrations were such that the Atlanta area just meets the current standard, nearly all asthmatics in Atlanta (>97%) would be estim ated to experience six or more days per year with 1-h NO 2 

### exposure concentrations greater than or equal to our highest benchmark level (0.3 ppm) (Figure 8-22).



### Risk Results When considering the Atlanta risk asse ssment results as they relate to the adequacy of the current standard, there was a range of central estimates since a two year period (2006-2007) was included in the assessment. We note that the central estimates of

incidence of NO 2 

### -related respiratory ED visits in Atla nta ranged from about 8-9% of total respiratory-related ED visits per year based on single pollutant models (or 9,800-10,900

NO 2 

### -related incidences) when air quality is adju sted upward to simulate a situation where

289

### Atlanta just meets the current standard. Central estimates of incidence of NO 2 

### -related respiratory ED visits ranged fr om 2.9-7.7% of total respirator y-related ED visits per year based on two-pollutant models (or 3,600-9,400 NO 2 

### -related incidences) In addition,

inclusion of O 3 

### and/or PM 10 

### in multi-pollutant models results in the inclusion of an

estimate of zero NO 2 

### -related respiratory ED visits within the 95% confidence intervals.

10.3.3 Conclusions regarding the adequacy of the current standard

As noted above, several lines of evidence are relevant to consider when making a decision regarding the adequacy of the curren t standard to protect the public health. These include causality judgments made in the ISA regarding the level of support for effects associated with short-term and long- term exposures, the epidemiologic evidence described in the ISA (and summarized in chap ter 4 of this document), the conclusions in the ISA regarding the robustness of this evidence, and the support provided for epidemiologic findings by experimental studies. To the extent that these considerations are emphasized, the adequacy of the current st andard to protect the public health would clearly be called into question. Such a conclusion would provide support for

consideration of an NO 2 

### standard that would provide increased health protection for sensitive groups, including as thmatics and individuals who spend time on or near major roadways (see chapter 3), against health eff ects ranging from increased asthma symptoms to respiratory-related ED visits and hosp ital admissions associated with short-term exposures, as well as potential effects a ssociated with long-term exposures. In examining the exposure- and risk-bas ed information with regard to the adequacy of the current annual NO 2 

### standard to protect the pub lic health, we note that the results described above (and in more detail in chapters 7-9) indicate risks associated with air quality adjusted upward to simulate ju st meeting the current standard that can reasonably be judged important from a public he alth perspective. Therefore, exposureand risk-based considerations reinforce the scientific evidence in supporting the conclusion that consideration should be given to revising the current standard so as to provide increased public health protecti on, especially for sensitive groups, from NO 2 

### - related adverse health effects associated with short-term, and potential long-term,

exposures.

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## 10.4 POTENTIAL ALTE RNATIVE STANDARDS

10.4.1 Indicator

In the last review, EPA focused on NO 2 

### as the most appropriate indicator for

ambient NO x 

### . In this review, while the presence of gaseous NO x 

### species other than NO 2 

### has been recognized (e.g., see section 1.3 of this document), no alternative to NO 2 

### has been advanced as being a more appropr iate surrogate for ambient gaseous NO x 

### . Controlled human exposure studies and animal toxicology studies provide specific evidence for health effects following exposure to NO 2 

### . Epidemiologic studies also typically report levels of NO 2 

### , as opposed to other gaseous NO x 

### , though the degree to

which monitored NO 2 

### reflects actual NO 2 

### levels, as opposed to NO 2 

### plus other gaseous

NO x 

### , can vary (e.g.,. see section 2.2.3 of this docum ent). Because emissions that lead to

the formation of NO 2 

### generally also lead to the formation of other NO x 

### oxidation products, measures leading to reduc tions in population exposures to NO 2 

### can generally be expected to lead to reductions in po pulation exposures to other gaseous NO x 

### . Therefore, meeting an NO 2 

### standard that protects the public hea lth can also be expected to provide some degree of protection agai nst potential health effects that may be independently associated with other gaseous NO x 

### even though such effects are not discernable from currently available studies indexed by NO 2 

### alone. Given these key points, staff judges

that the available evidence supports the retention of NO 2 

### as the indicator in the current

review.

10.4.2 Averaging Time The current annual averaging time for the NO 2 

### NAAQS was originally set in 1971, based on epidemiologic studies that suppo rted a link between adverse respiratory effects and long-term expos ure to low-levels of NO 2 

### . As noted in section 10.3.2.2, that annual standard was retained in subsequent reviews in part because an air quality assessment conducted by EPA concluded that areas that meet the annual standard would be unlikely to experience short-term ambient peaks above levels that had been shown in

291

### controlled human exposure studies to impact endpoints of potential concern (see section 10.3.2.2). Based on currently available evidence , the issue of averaging time is being considered in the current review, as discussed below. In order to inform judgments on averaging time, staff has considered causal ity judgments from the ISA, results from experimental and epidemiologic studies, and NO 2 

### air quality correlations. These

considerations are described in more detail below. To inform general decisi ons regarding averaging tim e (e.g., short-term versus long-term), we note the causality judgments ma de in the ISA regarding different health endpoints. As described in chapter 4 of th is document, the eviden ce relating short-term (minutes to hours) NO 2 

### exposures to respiratory morbid ity is judged in the ISA to be “sufficient to infer a likely causal relati onship” (ISA, section 5.3.2.1) while the evidence relating long-term (w eeks to years) NO 2 

### exposures to adverse hea lth effects is judged to be either “suggestive but not sufficient to infer a causal relationship” (respiratory morbidity) or “inadequate to infer the pres ence or absence of a causal relationship” (mortality, cancer, cardiovascular effects, reproductive/developmental effects) (ISA, sections 5.3.2.4-5.3.2.6). These judg ments most directly suppor t an averaging time that focuses protection on short-term exposures to NO 2 

### . As has been done in past revi ews, it is instructive to evaluate the potential for a standard based on annual average NO 2 

### concentrations, as is the current standard, to provide protection against short-term NO 2 

### exposures. To this end, Table 10-1 reports the ratios of short term to annual average NO 2 

### concentrations. Ratios of 1-h daily maximum concentrations (98 th 

### and 99 th 

### percentile) to annual average concentrations range from 2.5 to 8.7 while ratios of 24-h average concentra tions to annual average concentrations range from 1.6 to 3.8 (see Thompson, 2008a for more deta ils). The variability in these ratios across locations, particularly t hose for 1-h to annual average concentrations, suggests that a standard based on annual average NO 2 

### concentrations would not likely be an effective or efficient approach to focus protection on short-term NO 2 

### exposures.

292

### Table 10-1. Ratios of short-term to annual average NO 2 

### concentrations 1-h Daily Max(99 th ):Annual 1-h Daily Max(98 th ):Annual 24-h Avg(99 th ):Annual 24-h Avg(98 th ):Annual Location Atlanta 4.36 4.00 2.37 2.13 Boston 2.73 2.50 1.81 1.66 Chicago 3.03 2.86 1.68 1.62 Cleveland 3.35 3.03 1.99 1.78 Denver 4.22 3.78 2.44 2.25 El Paso 4.09 3.65 2.05 1.91 Las Vegas 8.65 8.21 2.84 2.65 Los Angeles 3.06 2.70 1.97 1.79 Miami 7.41 7.03 3.76 3.42 New York 2.90 2.60 1.88 1.75 Philadelphia 3.62 3.34 2.40 2.07 Phoenix 2.95 2.70 1.81 1.69 St. Louis 3.82 3.74 1.99 1.84 Washington DC 3.70 3.02 2.10 1.88



### For example, in an area with a relativ ely high ratio (e.g., 8), the current annual standard (0.053 ppm) would be expe cted to allow 1-h daily maximum NO 2 

### concentrations of about 0.4 ppm. In contrast , in an area with a re latively low ratio (e.g., 3), the current standard would be e xpected to allow 1-h daily maximum NO 2 

### concentrations of about 0.15 ppm. Thus, for purposes of protecting against the range of

1-h NO 2 

### exposures considered in this review (i .e., 0.1 to 0.3 ppm), a standard based on annual average concentrations would likely requ ire more control than necessary in some areas and less control than necessary in othe rs, depending on the standard level selected. In considering the level of support ava ilable for specific short-term averaging times, we take note of evidence from both e xperimental and epidemiologic studies. Controlled human exposure studies and animal toxicological studies provide evidence

that NO 2 

### exposures with exposure durations from less than 1-h up to 3-h can result in respiratory effects such as increased ai rway responsiveness and inflammation (ISA, section 5.3.2.7). Specifically, the ISA concludes that NO 2 

### exposures of 0.1 ppm for 1-h (or 0.2-0.3 ppm for 30-min) can result in sm all but significant increases in nonspecific airway responsiveness (ISA, section 5.3.2.1). In contrast, the epidem iologic literature does not provide clear support for one short-te rm averaging time versus another (ISA, section 5.3.2.7). A number of epidemiologic studies detect positive associations between respiratory morbidity and 1-h (d aily maximum) and/or 24-h NO 2 

### concentrations. A few

293

### epidemiologic studies have considered both 1-h and 24-h averaging times, allowing comparisons to be made. The ISA reports that such comparisons in studies that evaluate

asthma ED visits fail to re veal differences between effect estimates based on a 1-h averaging time and those based on a 24- h averaging time (ISA, section 5.3.2.7). Therefore, the ISA concludes that it is not possible, from the available epidemiologic evidence, to discern whether effects observedare attributable to average daily (or multiday) concentrations (24-h aver age) or high, peak exposures (1-h maximum) (ISA, section 5.3.2.7). Given the above conclusions, the experi mental evidence provides support for an averaging time of shorter duration than 24 hours (e.g., 1-h) while the epidemiologic evidence provides support for both 1-h and 24-h averaging times. At a minimum, this suggests that a primary concern with regard to averaging time is th e level of protection provided against 1-h daily maximum NO 2 

### concentrations. However, it is also worthwhile to consider the ability of averaging times under considera tion to protect against 24-h average NO 2 

### concentrations. To this end, Table 10-2 presents correlations between 1-h daily maximum NO 2 

### concentrations and 24-h average NO 2 

### concentrations (98 th 

### and 99 th 

### percentile) across 14 locations (see Thompson, 2008a for more detail). Typical ratios range from a 1.5 to 2.0, though one ratio (Las Vegas) is 3.1. These ratios are far less

variable than those discussed above for annua l average concentrati ons, suggesting that a standard based on 1-h daily maximum NO 2 

### concentrations could also be effective at providing adequate prot ection against 24-h NO 2 

### concentrations.

294

### Table 10-2. Ratios of 1-h daily maximum NO 2 

### concentrations to 24-h average concentrations (ppm) 1-h 24 h Ratio 1-h 24-h Ratio Location 99 th 99 th 98 th 98 th Atlanta 0.078 0.042 1.84 0.071 0.038 1.88 Boston 0.064 0.043 1.50 0.059 0.039 1.50 Chicago 0.093 0.052 1.80 0.088 0.050 1.77 Cleveland 0.072 0.043 1.68 0.065 0.038 1.70 Denver 0.086 0.050 1.73 0.077 0.046 1.68 El Paso 0.075 0.038 1.99 0.067 0.035 1.91 Las Vegas 0.039 0.013 3.04 0.037 0.012 3.10 Los Angeles 0.095 0.061 1.56 0.083 0.055 1.50 Miami 0.059 0.030 1.97 0.056 0.027 2.06 New York 0.093 0.060 1.55 0.083 0.056 1.48 Philadelphia 0.060 0.040 1.51 0.056 0.035 1.61 Phoenix 0.093 0.057 1.63 0.085 0.053 1.60 St. Louis 0.064 0.034 1.91 0.063 0.031 2.03 Washington DC 0.079 0.045 1.76 0.065 0.040 1.61



### As an additional matter, we note that a short-term standard (i.e., 1-h daily maximum) within the lower part of the range of standards co nsidered in this risk and exposure assessment document could have the effect of maintaining annual average NO 2 

### concentrations below the level of the current standard (0.053 ppm). For example, in all locations evaluated, a 1-h standard with a le vel of 0.05 ppm is estimated to result in annual average NO 2 

### concentrations less than or equa l to approximately 0.02 ppm. A 1-h standard with a level of 0.10 ppm is estim ated to result in annual average NO 2 

### concentrations less than or equal to approximately 0.04 ppm. However, a 1-h standard with a level of 0.15 ppm coul d result in annual average NO 2 

### concentrations up to approximately 0.06 ppm and a 1-h standard w ith a level of 0.20 ppm could result in annual average NO 2 

### concentrations up to approx imately 0.07 ppm (Table 10-3).

295

### Table 10-3. Mean annual NO 2 

### concentrations for 2004-2006 given just meeting alternative 1-h standards (98 th 

### percentile) Annual NO 2 Concentrations (ppm) for Different Standard Levels

Location Monitor Distance from Major Roadway 0.05 0.10 0.15 0.20 Atlanta >= 100 m 0.01 0.02 0.02 0.03 Boston <= 20 m 0.02 0.03 0.05 0.07 Boston > 20 and < 100 m 0.01 0.02 0.03 0.04 Boston >= 100 m 0.01 0.01 0.02 0.02 Chicago <= 20 m 0.01 0.02 0.03 0.04 Chicago > 20 and < 100 m 0.02 0.03 0.05 0.07 Chicago >= 100 m 0.01 0.02 0.03 0.04 Cleveland <= 20 m 0.02 0.03 0.05 0.06 Cleveland > 20 and < 100 m 0.01 0.02 0.04 0.05 Denver <= 20 m 0.02 0.04 0.06 0.07 Denver >= 100 m 0.01 0.03 0.04 0.05 Detroit >= 100 m 0.02 0.03 0.05 0.06 El Paso > 20 and < 100 m 0.01 0.02 0.03 0.04 El Paso >= 100 m 0.01 0.02 0.03 0.04 Jacksonville >= 100 m 0.01 0.03 0.04 0.05 Las Vegas <= 20 m 0.02 0.03 0.05 0.06 Las Vegas >= 100 m 0.01 0.01 0.02 0.03 Los Angeles <= 20 m 0.02 0.03 0.05 0.06 Los Angeles > 20 and < 100 m 0.01 0.03 0.04 0.05 Los Angeles >= 100 m 0.01 0.02 0.03 0.04 Miami <= 20 m 0.01 0.01 0.02 0.02 Miami > 20 and < 100 m 0.01 0.02 0.03 0.05 Miami >= 100 m 0.01 0.01 0.02 0.03 New York <= 20 m 0.02 0.03 0.05 0.07 New York > 20 and < 100 m 0.02 0.03 0.05 0.07 New York >= 100 m 0.01 0.02 0.04 0.05 Philadelphia > 20 and < 100 m 0.02 0.03 0.05 0.07 Philadelphia >= 100 m 0.01 0.03 0.04 0.05 Phoenix <= 20 m 0.01 0.03 0.04 0.06 Phoenix > 20 and < 100 m 0.01 0.02 0.03 0.05 Phoenix >= 100 m 0.01 0.03 0.04 0.06 Provo >= 100 m 0.01 0.03 0.04 0.05 St Louis <= 20 m 0.01 0.03 0.04 0.05 St Louis > 20 and < 100 m 0.01 0.02 0.03 0.04 St Louis >= 100 m 0.01 0.02 0.04 0.05 Washington <= 20 m 0.01 0.03 0.04 0.06 Washington > 20 and < 100 m 0.01 0.03 0.04 0.06 Washington >= 100 m 0.01 0.02 0.03 0.05



### 10.4.3 Form When evaluating alternative forms in c onjunction with specific levels, staff considers the adequacy of the public health protection provided by the combination of

level and form to be the foremost consider ation. In addition, we recognize that it is

296

### important to have a form that is reasonab ly stable and relatively insulated from the impacts of extreme meteorological events. A standard set with a high degree of instability could have the effect of reduci ng public health protection because shifting in and out of attainment due to meteorologica l conditions could disr upt an area’s ongoing implementation plans and associ ated control programs. Therefore, as noted in chapter 5 and c onsistent with recent reviews of the O 3 

### and PM NAAQS, we have focused in the curr ent review on concentration-based forms averaged over 3 years. As noted in the review of the O 3 

### NAAQS (EPA, 2007h), concentration-based forms better reflect pollu tant-associated health risks than forms based on expected exceedances because concentration-based forms give proportionally greater weight to periods of time when polluta nt concentrations are well above the level

of the standard than to times when the con centrations are just a bove the standard, while an expected exceedance form would give th e same weight to periods of time with concentrations that just exceed the standard as to times when concentrations greatly

exceed the standard. Concentration-based form s also provide greater regulatory stability than a form based on allowing only a single expected exceedance. In considering specific concentration-based forms on which to focus the current review, we note the need to minimize the num ber of days per year that an area could

exceed the standard level and still attain th e standard. Given this, we have focused on

98 th 

### and 99 th 

### percentile forms averaged over 3 years. With regard to these alternative forms, staff notes that a 99 th 

### percentile form for a 1-h daily maximum standard would correspond to the 4 th 

### highest daily maximum concentration in a year while a 98 th 

### percentile form would corres pond approximately to the 7 th 

### to 8 th 

### highest daily maximum concentration in a year (Table 10-4; s ee Thompson, 2008 for methods). As noted in chapter 5, staff has judged that these form s would provide an appropriate balance between limiting peak NO 2 

### concentrations and providing a st able regulatory target. This is consistent with judgments made in the 2006 review of the PM NAAQS (EPA, 2005).

297

### Table 10-4. NO 2 

### concentrations (ppm) corresponding to 2 nd 

### -9 th 

### daily maximum and 98 th 

### /99 th 

### percentile forms (2004-2006)



### When considering the extent to whic h exposure and risk analyses inform judgments on standard form, staff notes that a 99 th 

### percentile form could be appreciably more protective than a 98 th 

### percentile form in some locati ons, as judged by the results of our air quality analyses. For example, in Boston, Philadelphia, and Washington, D.C. a

99 th 

### percentile standard of 0.20 ppm is estim ated to decrease benchmark exceedances,

relative to a 98 th 

### percentile form, by approximately 50-70% (Table 10-5). However, in St. Louis, Detroit, and Las Vegas a 99 th 

### percentile form could decrease benchmark exceedances by only approximately 10% (Table 10-5). For most locations, the difference is estimated to be between approximately 10 and 50% (Table 10-5). With regard to the Atlanta exposure assessment, we note that adoption of a 98 th 

### percentile form versus a 99 th 

### percentile form would have virtually no effect on exposure concentra tions at or above 0.1 ppm. However, choice of form could make a difference of approximately 5-10% on the number of exposure concentrations at or above the 0.2 and 0.3 ppm benchmark levels (Figure 8-22). With regard to the Atlanta risk assessment, a 99 th 

### percentile form is

estimated to be associated with approximately 6% to 8% fewer NO 2 

### -related ED visits

than a 98 th 

### percentile form, across the levels of the potential 1-h standards examined (Tables 9-2 to 9-4).

298

### Table 10-5. Mean number of days per year (averaged over the 2004-2006 time period) estimated to have ambient (central site monitor) 1-h daily maximum NO 2 

### concentrations ≥ 0.10 ppm assuming 98 th 

### and 99 th 

### percentile forms of a 0.20 ppm standard

Exceedances

Location Name 98 th 99th Atlanta 81 66

Boston 29 9 Chicago 84 62

Denver 211 159 Detroit 209 194 El Paso 159 116 Jacksonville 178 53 Las Vegas 108 97 Los Angeles 70 50 Miami 74 64 New York 108 63 Philadelphia 163 77 Phoenix 224 163

Provo 84 74 St. Louis 146 134 Washington 133 69



### When considering these results as they relate to standard form, we note that a decision on form must be made in conjunction with selection of a particular standard level. The primary emphasis in such a decision will be on the level of public health protection provided by the combination of form and level. With regard to a decision on form, we note that the geographic heterogeneity of the impact of form, as indicated in the air quality analysis, suggests that caution should be exercised when using the Atlanta resu lts as a basis for selecting the most appropriate form. To the extent that a decision regarding form emphasizes this geographic uncertainty, it would likely place more weight on the air quality results.



### 10.4.4 Level

10.4.4.1 Evidence-based considerations In considering alternative st andard levels that would pr ovide greater protection than that afforded by the current standard against NO 2 

### -related adverse health effects, staff has

taken into account scientific evidence from both experiment al and epidemiologic studies,

as well as the uncertainties and limitations in that evidence. In particular, we have

299

### considered the extent to which controlled hum an exposure studies provide evidence for a

lowest-observed-effects level and the extent to which epidemiologic studies provide evidence for potential effect thresholds and/or for positive associations that extend down

to the lower levels of NO 2 

### concentrations observed in studies . We note that the scientific evidence can provide insights into alternative standard levels only within the context of specific averaging times and forms. Therefor e, while this section considers the evidence as it relates to alternative levels, such cons iderations assume part icular averaging times and forms. Additional discussion of averag ing time can be found in sections 5.3 and 10.5

of this document. Additional discussion of form can be found in sections 5.4 and 10.6 of

this document. When considering the scientific evidence as it relates to alternative levels, we note

that NO 2 

### concentrations represent different metrics when reported in experimental studies versus epidemiologic studie s. Concentrations of NO 2 

### reported in epidemiologic studies are typically based on ambient monitoring data while NO 2 

### levels reported in controlled human exposure studies repres ent the concentration of NO 2 

### in the breathing zone of the

individual. In some locations and at some points in time, individuals are likely exposed

to NO 2 

### concentrations that are hi gher than those measured at ambient monitors. The ISA

concludes that elevated NO 2 

### monitors (e.g., monitors sited on building roofs), particularly in inner cities, likely underestimate concentr ations and personal exposures occurring at lower elevations, closer to mo tor vehicle emissions (ISA, sec tion 5.2.2). In this situation, average NO 2 

### concentrations measured at an elevated ambient monitor could be well

below concentrations associated with effect s in controlled human exposure studies while

actual NO 2 

### exposures on and/or near roadways c ould be comparable to, or higher than,

these concentrations. We note that in this situation, where personal exposure

concentrations to ambient NO 2 

### are higher than ambient levels measured at a fixed-site monitor, ambient standards based principall y on controlled exposure studies could be less health-protective than standards primarily based on concentrations reported in epidemiologic studies at ambient monitors. However, the ISA also concludes that, in exposure measurement field studies where pe rsonal exposures have been measured, personal exposures to NO 2 

### of ambient origin were ge nerally lower than ambient NO 2 

### concentrations based on fixed-site monitors (ISA, tables 2.5-4 and 2.5-5; section 5.2.2).

300

### In this type of situation, where person al exposure concentrations to ambient NO 2 

### are lower than the levels measured at ambient monitors, we note that an ambient standard based principally on effects levels observed in controlled exposure studies could be more health-protective than a standard based primarily on concentrations reported in epidemiologic studies at ambient monitors. 

### Controlled Human Exposure Studies In considering the available controlled human exposure studies (see chapter 4 for more detail), we note that these studies have addressed the conseque nces of short-term (e.g., 30-minutes to several hours) NO 2 

### exposures for a number of health endpoints including airway responsiveness, host de fense and immunity, inflammation, and lung function (ISA, section 3.1). In identifyi ng health endpoints on which to focus for purposes of informing decisions about potential alternative standard levels, staff judges it appropriate to focus on those endpoints that occur at or near am bient levels of NO 2 

### and endpoints that are of c linical significance. As describe d in more detail in section 4.5.3, the only endpoint to meet thes e criteria is increased airway responsiveness in asthmatics.

The ISA concludes that NO 2 

### exposures between 0.2 and 0.3 ppm for 30 minutes or 0.1 ppm for 60-minutes can result in small but significant increases in nonspecific airway responsiveness (ISA, secti on 5.3.2.1) and that “transient increases in airway responsiveness following NO 2 

### exposure have the potential to increase symptoms and worsen asthma control” (ISA, sections 3.1.3 a nd 5.4). This effect could have important public health implications due to the large size of the asthmatic population in the United States (ISA, Table 4.4-1). In addition, NO 2 

### effects on airway responsiveness in asthmatics are part of the body of experiment al evidence that provi des plausibility and

coherence for the observed NO 2 

### -related increased in hospital admissions and ED visits in epidemiologic studies (ISA, section 5.3.2.1). Fo r all of these reasons, we have focused on increased airway responsiveness in asthma tics when considering the controlled human exposure literature in terms of its ability to inform decisions on alternative standard

levels. With regard to controlled human exposur e studies of airway responsiveness, we note that a meta-analysis of individual level data from 19 studies (section 3.1.3.2 and table 4-5 in chapter 4 of this document) indicates that 66% of resting asthmatics

301

### experienced increased airway responsiv eness following exposure to 0.1 ppm NO 2 

### , 67% experienced an increase following exposure to NO 2 

### concentrations between 0.1 and 0.15 ppm (inclusively), 75% experienced an increase following exposure to NO 2 

### concentrations between 0.2 and 0.3 ppm (inclusi vely), and 73% experienced an increase following exposure to NO 2 

### concentrations above 0.3 ppm. Effects of NO 2 

### exposure on the direction of airway responsiveness are stat istically-significant at all of these levels. As noted in section 10.3.2.1, one of the importa nt uncertainties associated with these results is that, because the meta-analysis ev aluated only the directi on of the change in airway responsiveness, it is not possible to discern the magnitude of the change from these data. This limitation makes it particular ly difficult to quantify the public health implications of these results. 

### Epidemiologic Studies When evaluating the epidemiologic literature for its potential to inform decisions on standard level, we note th at the ISA concludes that NO 2 

### epidemiologic studies provide “little evidence of any effect threshold” (section 5.3.2.9, p. 5-15). In studies that have evaluated concentration-response relationships , they appear linear within the observed range of data (ISA, section 5.3.2.9). In the absence of an appare nt threshold, we are focusing on the range of levels that have been associated with key U.S. studies for purposes of identifying the range of sta ndard levels supported by the epidemiologic literature. When identifying this range , we focus on the higher percentiles of NO 2 

### concentrations measured at the ambien t monitors used in the study (i.e., 98 th 

### and 99 th 

### percentiles), as these percentiles are likely mo st relevant for the health effects observed in epidemiologic studies. Figures 5-1 and 5-2 (see chapter 5) show standardized effect estimates and the

98 th 

### and 99 th 

### percentile concentrations of daily 1-h maximum NO 2 

### for locations and time periods that correspond to key U.S. epidemiol ogic studies identified in the ISA (see table 5.4-1 in ISA for a list of key studies). These key studies are associat ed with a range of

98 th 

### /99 th 

### percentile 1-h daily maximum levels from 0.05 ppm to 0.21 ppm. In considering this information, we note that, toward the lower end of the range of NO 2 

### concentrations observed in epidemiologic stud ies, there is increasing uncertainty as to

302

### whether observed health effects remain plau sibly related to expos ures to ambient NO 2 

### , as opposed to the broader mix of air polluta nts present in the ambient air. When considering an appropriate lower end of the range of levels that are supported by the evidence, staff has considered two primary factors. First, the study by Delfino et al., (2002) provides evidence for associations between short-term ambient NO 2 

### concentrations and respiratory morbidity in a location where NO 2 

### concentrations were well below levels in most other key U.S. ep idemiologic studies. This study reports positive associations between 1-h and 8-h ( only 8-h associations were statisticallysignificant) levels of NO 2 

### and asthma symptoms in a location where the 98 th 

### and 99 th 

### percentile 1-h daily maximum NO 2 

### concentrations were 0.05 and 0.053 ppm, respectively. Second, the c ontrolled human exposure studie s of airway responsiveness that formed the basis for the meta-analysis de tected a lowest-observa ble-effect level of 0.1 ppm NO 2 

### . However, these studies did not evalua te severe asthmatics. Most of the subjects included in these studies were m ild asthmatics. More severely affected asthmatics may be more susceptible than mild asthmatics to the effects of NO 2 

### exposure (ISA, section 3.1.3.2). As a result, staff judges th at it is appropriate to base the lower end of the range of alternative standard levels on the epidemiologic study by Delfino et al. (2002) and on providing increase d protection relative to the lowest-observed-effects level for airway hyperresponsiveness in asthmatics. Therefore, staff concludes that the lower end of the range of potential alternative 1-h daily maximum standards that is reasonably supported by the evidence is 0.05 ppm (50 ppb). When considering an appropriate upper end of the range of 1-h daily maximum standard levels that is supported by the evidence, we note the following:



### • Positive and statistically-significant associations were observed in several key U.S. epidemiologic studies associated with 1-h daily maximum levels of NO 2 

### close to 0.1 ppm (Peel et al ., 2005; NYDOH, 2006; Ito et al., 2007; Tolbert et al., 2007) (see Figure 5-1). In multi-pollutant models, effect estimates remained statistically-significant in the study by Ito and positive, but non-significant, in the other studies.



### • Positive and statistically-significant NO 2 

### effect estimates were also observed in the two key U.S. studies associ ated with the highest 1-h NO 2 

### concentrations (Linn et al., 2000; Ostro et al., 2001). Thes e studies were associated with 98 th 

### and 99 th 

### percentile 1-h daily maximum NO 2 

### concentrations from 0.18 ppm to 0.21 ppm. These studies did not evaluate multi-pollutant models. Therefore, they do not

303

### 304 provide additional support for an independent association between NO 2 

### and respiratory morbidity beyond that provi ded by the studies noted above.



### • The meta-analysis of airway responsiv eness presented in the ISA reports statistically-significant effects on the direction of airway responsiveness following short-term NO 2 

### exposures from 0.1 ppm to 0.3 ppm. The ISA does not draw distinctions between levels within this range with regard to the likely magnitude of the response or the percent of asthmatics expected to respond.

Given these observations, staff notes that the scientific ev idence provides strong support for a standard at or below 0.1 ppm (100 ppb). However, to the extent that a decision regarding standard level emphasizes the genera l uncertainties associated with quantifying

the contributions of NO 2 

### to respiratory effects in epid emiologic studies and uncertainties regarding the public h ealth significance of NO 2 

### -associated airway hyperresponsiveness (particularly at 0.1 ppm), a level as high as 0.2 could be supported. Therefore, staff concludes that the upper end of the range of 1-h daily maximu m standard levels that is reasonably supported by the evidence is 0.2 ppm (200 ppb).

10.4.4.2 Exposure- and risk-based considerations Staff’s consideration of exposure- and ri sk-based information as it relates to alternative levels for the primary NO 2 

### NAAQS builds upon our conclusion, discussed above in section 10.3, that the overall body of scientific evidence clearly calls into question the adequacy of the current standard to protect the public he alth. Therefore, we have judged it appropriate to consider a range of alternative levels that would improve upon the level of protection provided by the curren t standard. As noted in chapter 5, this range of levels (0.05-0.20 ppm) is based on results from controlled human exposure and epidemiologic studies. When considering this range of levels, we note that, given recent air quality, only a level of 0.05 ppm would be estimated to result in any counties in the

U.S. that are above the level of the standard (Table 10-6; Thompson, 2008).Table 10-6. Percent of counties that may be above the level ofthe standard, given different levels (based on years 2004-2006)Percent of counties, total and by region, (and total population) not likely to meet standard and levelAlternative Standardsand Levels (ppm)Total counties(population inmillions)NortheastSoutheastIndustrialMidwestUpperMidwestSouthwestNorthwestSouthernCAOutsideRegions**Number of countieswith monitors(population in 1000s)138 (93.9)313220129201313 year 99thpercentile daily 1 hour max:0.200000000000.150000000000.100000000000.0559 (79.6)7741801756557703 year 98thpercentile daily 1 hour max:0.200000000000.150000000000.100000000000.0546 (63.4)553460174445690305

### The results of the air quality analysis are presen ted in chapter 7 of this document. In that chapter, we present estimates of the number of da ys per year with ambient (based on fixed-site ambient monitors) and on-road (b ased on on-road adjustment) NO 2 

### concentrations at or aboveour potential health benchmark levels for the years 2001-2006. These estimates are based on asis air quality and air qu ality that has been adjusted to simulate just meeting the current and potential alternative standards. In consider ing the results presented chapter 7, we note the following key points:



### • Given unadjusted air quality for the year s 2001-2006, it is estimated that 1-h NO 2 

### concentrations would not exceed 0.1 ppm more than 14 days per year, on average, at fixed-site monitors in any location evaluated (Tables 7-13 through 7-18).



### • Given air quality for the years 2001-2003 adju sted to simulate just meeting the current standard, it is estimated that that an average of up to 211 days per year (most locations between 20 and 100 days per year) could occur with ambient NO 2 

### concentrations at fixed-site monito rs that exceed 0.1 ppm (Table 7-26).



### • Given air quality for the years 2001-2003 adjusted to simulate just meeting a standard level of 200 ppb (98 th 

### percentile), it is estimated that an average of up to 327 days per year (most locations between 50 and 200 days per year) could occur with ambient NO 2 

### concentrations at fixed-site monito rs that exceed 0.1 ppm (Table 7-26).



### • Given air quality for the years 2001-2003 adjusted to simulate just meeting a standard level of 150 ppb (98 th 

### percentile), it is estimated that an average of up to 200 days per year (most locations between 20 and 100 days per year) could occur with ambient NO 2 

### concentrations at fixed-site monito rs that exceed 0.1 ppm (Table 7-26).



### • Given air quality for the years 2001-2003 adjusted to simulate just meeting a standard level of 100 ppb (98 th 

### percentile), it is estimated that an average of up to 8 days per year could occur with ambient NO 2 

### concentrations at fixed-site monitors that exceed 0.1 ppm (Table 7-26).



### • Given air quality for the years 2001-2003 adjusted to simulate just meeting a standard level of 50 ppb (98 th 

### percentile), it is estimated that an average of up to 1 day per year could occur with 1-h ambient NO 2 

### concentrations at fixed-site monitors greater than or equal to 0.1 ppm. All of the locations evaluated except for 2 would be expected to experience 0 days per year with ambient NO 2 

### concentrations at fixed-site monitors that exceed this benchmark (Table 7-26).



### • Mean estimates of days per year with on -road exceedances are higher than estimates of ambient exceedances at fixe d-site monitors (Up to 18 days per year for a standard of 50 ppb, 257 for a standard of 100 ppb, 343 for a standard of 150 ppb, and 351 for a

306

### standard of 200 ppb based on the years 2001 to 2006 and 98 th 

### percentile standards) (Table 7-29).

The results of the Atlanta expos ure assessment are presented in chapter 8 of this document. In Figures 8-19 through 8-26, we present estimates of the percent of asthmatics in Atlanta expected to experience NO 2 

### exposure concentrations at or a bove our potential health benchmark levels for the year 2002 (estimates were sim ilar for the years 2001-2003 (Figure 8-5)), given unadjusted air quality and air quality that has been adjusted to simulate just meeting the current and potential alternative standards. In considering the result s presented in those figures, we note the following key points:



### • Given unadjusted air quality, it is estimated that virtually all asthmatics in Atlanta could experience 6 or more exposures to NO 2 

### concentrations greater than or equal to 0.1 ppm. It is estimated that just under 60% of Atlanta asthmatics could experience at least one exposure to NO 2 

### concentrations greater than or equal to 0.3 ppm and that fewer than 10% could experience 6 or more exposures to NO 2 

### concentrations greater than or equal to 0.3 ppm (Figure 8-19).



### • Given air quality adjusted to simulate just meeting the current standard, it is estimated that virtually all Atlanta asthmatics could experience 6 or more days per year with NO 2 

### exposure concentrations greater than or equal to 0.3 ppm (Figure 8-22).



### • Given air quality adjusted to simulate just meeting a standard le vel of either 150 or 200 ppb (98 th 

### or 99 th 

### percentile standard), it is estimated that more than 95% of Atlanta asthmatics could experience 1 or more days per year with NO 2 

### exposure concentrations greater than or e qual to 0.3 ppm (Figure 8-23).



### • Given air quality adjusted to simulate ju st meeting a standard level of 100 ppb (99 th 

### percentile standard), it is estimated that approximately 70% of Atlanta asthmatics could experience 1 or more days per year with NO 2 

### concentrations of 0.3 ppm or above. With a 99 th 

### percentile standard, approxima tely 10% of Atlanta asthmatics could experience 6 or more days per year with NO 2 

### concentrations of 0.3 ppm or above (Figure 8-26).



### • Given air quality adjusted to simulate ju st meeting a standard level of 50 ppb (99 th 

### percentile standard), it is estimated th at fewer than 10% of asthmatics could experience 1 or more days per year with NO 2 

### concentrations of 0.3 ppm or above. With a 99 th 

### percentile standard, virt ually none are estimated to be exposed 6 or more times per year. However, this standard is estimated to re sult in over 90% of asthmatics being exposed 6 or more times per year to NO 2 

### concentrations of 0.1 ppm or above (Figure 8-24).

307

### The results of the Atlanta risk assessment are presented in chap ter 9 of this document. In Tables 9-2 through 9-4, we pres ent estimates, for the years 20 05-2007, of the percent of total annual respiratory ED visits in Atlanta associated with NO 2 

### . These results are also presented as

incidence of NO 2 

### -associated respiratory ED visits in A ppendix C. In considering the results presented in Tables 9-2 through 9-4, we note the following key points:



### • Based on single-pollutant models , central estimates of annual NO 2 

### -related respiratory ED visits associated with recent air quali ty for the years 2005-2007 range from 2.8 to 3.1% (or 3,400 to 3,800 NO 2 

### -related incidences per year). Based on multi-pollutant models, central estimates for this same tim e period range from 0.6 to 2.6% (or 700 to 3,200 NO 2 

### -related incidences per year).



### • Central estimates of annual NO 2 

### -related respiratory ED visits associated with air quality adjusted upward to simulate just meeting the current annual standard (based on 2006-2007) range from 8.1 to 9.0% (or 9,800 to 10,900 NO 2 

### -related incidences per year) based on single-pol lutant models and from 1.7 to 7.7% (or 3,100 to 9,400 NO 2 

### -related incidences per year) based on multi-pollutant models. .



### • Central estimates of annual NO 2 

### -related respiratory ED visits associated with air quality adjusted upward to simulate ju st meeting a 200 ppb, 1-h daily maximum, 98 th 

### percentile standard (b ased on 2005-2007) ranges from 7.6 to 8.5% based on singlepollutant models and from 1.6 to 7.3% based on multi-pollutant models.



### • Central estimates of annual NO 2 

### -related respiratory ED visits associated with air quality adjusted upward to simulate ju st meeting a 150 ppb, 1-h daily maximum, 98 th 

### percentile standard (b ased on 2005-2007) ranges from 5.8 to 6.4% based on singlepollutant models and from 1.2 to 5.5% based on multi-pollutant models.



### • Central estimates of annual NO 2 

### -related respiratory ED visits associated with air quality adjusted to simulate just meeting a 100 ppb, 1-h daily maximum, 98 th 

### percentile standard (b ased on 2005-2007) ranges from 3.9 to 4.3% based on singlepollutant models and from 0.8 to 3.7% based on multi-pollutant models.



### • Central estimates of annual NO 2 

### -related respiratory ED visits associated with air quality adjusted to simulate just meeting a 50 ppb, 1-h daily maximum, 98 th 

### percentile standard (based on 2005-2007) ranges fr om 2.0 to 2.2% based on single-pollutant models and from 0.4 to 1.9% based on multi-pollutant models.



### • Central estimates of annual NO 2 

### -related respiratory ED visits associated with air quality adjusted to simulate 99 th 

### percentile 1-h daily maxi mum standards in the range of 50 to 200 ppb are generally on the orde r of 10% lower than the estimates summarized above for standards with a 98 th 

### percentile form.

308

### 10.4.4.3 Conclusions regarding level

As noted in section 10.7.1, staff conclude s that the scientific evidence reasonably supports a range of standard levels from 50 ppb to 200 ppb, with strong support for a level at or below 100 ppb. In also considering the exposure-ba sed information, we note that standard levels of 150 and 200 ppb are generally estimated to be asso ciated with a similar or greater number of benchmark exceedances than are associated with just meeting the current standard, with standard levels of 100 and 50 ppb providing appreciable reductions in estimated benchmark exceedances. In considering the risk-based information, we note that all of the standard levels evaluated are

estimated to be associated with fewer NO 2 

### -related ED visits, on average, than are associated with just meeting the current standard, though the redu ction associated with a standard level of 200 ppb is relatively small, with reductions notably increasing with standard levels going from 150 ppb down to 50 ppb. When the scien tific evidence is considered in conjunction with exposure and risk results, the stronges t support is for standard leve ls between 50 and 100 ppb. This represents a range of levels that is consistent with the scientif ic evidence and that would be expected to provide improved publ ic health protection relative to that provided by the current

annual standard.

309

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