Risk and Exposure Assessment to Support the 

Review of the NO2 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 NO2 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 from 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 concerning 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 (919-541-1167; 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 OVERVIEW...........................................................................................................................................................1

1.2 HISTORY ..............................................................................................................................................................5

1.2.1 History of the NO2 NAAQS..........................................................................................................................5

1.2.2 Health Evidence from Previous Review ......................................................................................................6

1.2.3 Assessment from Previous Review ..............................................................................................................7

1.3 SCOPE OF THE RISK AND EXPOSURE ASSESSMENT FOR THE CURRENT REVIEW ...................................................7

2. SOURCES, AMBIENT LEVELS, AND EXPOSURES ..................................................................................9

2.1 SOURCES OF NO2 .................................................................................................................................................9

2.2 AMBIENT LEVELS OF NO2....................................................................................................................................9

2.2.1 Background on NO2 monitoring network....................................................................................................9

2.2.2 Trends in ambient concentrations of NO2 .................................................................................................13

2.2.3 Uncertainty Associated with the Ambient NO2 Monitoring Method .........................................................14

2.3 EXPOSURE TO NO2.............................................................................................................................................15

2.3.1 Overview ...................................................................................................................................................15

2.3.2 Uncertainty Associated with Ambient Levels as a Surrogate for Exposure ..............................................16

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

3.1 OVERVIEW.........................................................................................................................................................17

3.2 SUSCEPTIBILITY: PRE-EXISTING DISEASE...........................................................................................................18

3.3 SUSCEPTIBILITY: AGE ........................................................................................................................................19

3.4 SUSCEPTIBILITY: GENETICS................................................................................................................................19

3.5 SUSCEPTIBILITY: GENDER ..................................................................................................................................20

3.6 VULNERABILITY: PROXIMITY (ON OR NEAR) TO ROADWAYS..............................................................................20

3.7 VULNERABILITY: SOCIOECONOMIC STATUS .......................................................................................................21

3.8 CONCLUSIONS....................................................................................................................................................21

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

4.1 INTRODUCTION ..................................................................................................................................................23

4.2ADVERSE RESPIRATORY EFFECTS FOLLOWING SHORT-TERM EXPOSURES.........................................................26

4.2.1 Overview ...................................................................................................................................................26

4.2.2 Respiratory Emergency Department Visits and Hospitalizations .............................................................27

4.2.3 Respiratory Symptoms...............................................................................................................................28

4.2.4 Lung Host Defenses 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 Evidence for Short-Term Respiratory Effects..........................................36

4.3 OTHER ADVERSE EFFECTS FOLLOWING SHORT-TERM EXPOSURES ...................................................................37

4.4 ADVERSE EFFECTS FOLLOWING LONG-TERM EXPOSURES ................................................................................38
4.4.1 Respiratory Morbidity...............................................................................................................................38

4.4.2 Mortality ...................................................................................................................................................40

4.4.3 Other Long-Term Effects...........................................................................................................................40

4.5 RELEVANCE OF SPECIFIC HEALTH EFFECTS TO THE NO2 RISK CHARACTERIZATION .........................................41

4.5.1 Overview ...................................................................................................................................................41

4.5.2 Epidemiology ............................................................................................................................................42

4.5.3 Controlled Human Exposure Studies ........................................................................................................44

5. IDENTIFICATION OF POTENTIAL ALTERNATIVE 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 INTRODUCTION ..................................................................................................................................................55

6.2 POTENTIAL HEALTH BENCHMARK LEVELS .........................................................................................................57

6.3 SIMULATING THE CURRENT AND ALTERNATIVE STANDARDS..............................................................................59

6.3.1 Adjustment of Ambient Air Quality ...........................................................................................................59

6.3.2 Adjustment of Potential Health Effect Benchmark Levels.........................................................................64

7. AMBIENT AIR QUALITY ASSESSMENT AND HEALTH RISK CHARACTERIZATION ................67

7.1 OVERVIEW.........................................................................................................................................................67

7.2 APPROACH.........................................................................................................................................................69

7.2.1 Air Quality Data Screen............................................................................................................................70

7.2.2 Selection of Locations for Air Quality Analysis ........................................................................................71

7.2.3 Site Characteristics of Ambient NO2 Monitors .........................................................................................73

7.2.4 Estimation of On-Road Concentrations using Ambient Concentrations...................................................80

7.2.5 Air Quality Concentration Metrics............................................................................................................83

7.3 AIR QUALITY AND HEALTH RISK CHARACTERIZATION RESULTS..............................................................85

7.3.1 Ambient Air Quality (As Is).......................................................................................................................85

7.3.2 On-Road Concentrations Derived From Ambient Air Quality (As Is) ......................................................97

7.3.3 Ambient Air Quality Adjusted to Just Meet the Current and Alternative Standards...............................102

7.3.4 On-Road Concentrations Derived From Ambient Air Quality Adjusted to Just Meet the Current and 

Alternative Standards.......................................................................................................................................114

7.4 UNCERTAINTY ANALYSIS ................................................................................................................................122

7.4.1 Air Quality Database ..............................................................................................................................124

7.4.2 Measurement Technique for Ambient NO2..............................................................................................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 Concentration Simulation........................................................................................................133

7.4.7 Health Benchmark...................................................................................................................................143

7.5 KEY OBSERVATIONS........................................................................................................................................144

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

8.1 OVERVIEW.......................................................................................................................................................146

8.2 OVERVIEW OF HUMAN EXPOSURE MODELING USING APEX...........................................................................149

8.3 CHARACTERIZATION OF STUDY AREA .............................................................................................................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 CHARACTERIZATION OF AMBIENT AIR QUALITY USING AERMOD.................................................................154

8.4.1 Overview .................................................................................................................................................154

 ii 
8.4.2 General Model Inputs .............................................................................................................................154

8.4.2.1 Meteorological Inputs ....................................................................................................................................... 154

8.4.2.2 Surface Characteristics and Land Use Analysis ................................................................................................ 155

8.4.2.4 Other AERMOD Input Specifications............................................................................................................... 155

8.4.3 Major Link On-Road Emission Estimates ...............................................................................................156

8.4.3.1 Emission Sources and Locations....................................................................................................................... 156

8.4.3.2 Emission Source Strength ................................................................................................................................. 158

8.4.3.3 Other Emission Parameters ............................................................................................................................... 160

8.4.4 Minor Link On-road Emission Estimates................................................................................................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 Emissions Preparation ...............................................................................................................165

8.4.7 Receptor Locations..................................................................................................................................167

8.4.8 Modeled Air Quality Evaluation .............................................................................................................168

8.4.8.1 Comparison of Hourly Cumulative Density Functions ..................................................................................... 168

8.4.8.2 Comparison of annual average diurnal concentration profiles .......................................................................... 170

8.4.8.3 Comparison of estimated on-road NO2 concentrations ..................................................................................... 173

8.4.8. Using unadjusted AERMOD predicted NO2 concentrations............................................................................... 177

8.5 SIMULATED POPULATION ................................................................................................................................177

8.6 CONSTRUCTION OF LONGITUDINAL ACTIVITY SEQUENCES .............................................................................179

8.7 CALCULATING MICROENVIRONMENTAL CONCENTRATIONS............................................................................179

8.7.1 Microenvironments Modeled...................................................................................................................180

8.7.2 Microenvironment Descriptions..............................................................................................................181

8.7.2.1 Microenvironment 1: Indoor-Residence............................................................................................................ 181

8.7.2.2 Microenvironments 2-7: All other indoor microenvironments.......................................................................... 184

8.7.2.3 Microenvironments 8 and 9: Outdoor Microenvironments ............................................................................... 184

8.7.2.4 Microenvironment 10: Outdoors-General ......................................................................................................... 185

8.7.2.5 Microenvironments 11 and 12: In Vehicle- Cars and Trucks, and Mass Transit............................................... 185

8.8 EXPOSURE MEASURES AND HEALTH RISK CHARACTERIZATION ..........................................................185

8.8.1 Adjustment for Just Meeting the Current and Alternative Standards......................................................187

8.9 EXPOSURE MODELING AND HEALTH RISK CHARACTERIZATION RESULTS ......................................................189

8.9.1 Overview .................................................................................................................................................189

8.9.2 Annual Average Exposure Concentrations (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 Exposures Above Selected Levels (as is) ........................................................... 197

8.9.4.3 Estimated Number of 1-hour Exposures Above Selected Levels (current standard)......................................... 207

8.9.4.4 Estimated Number of 1-hour Exposures Above Selected Levels (alternative standards).................................. 209

8.10 KEY OBSERVATIONS .....................................................................................................................................213

8.11 REPRESENTATIVENESS OF EXPOSURE RESULTS .............................................................................................214

8.11.1 Introduction..........................................................................................................................................214

8.11.2 Description of Data Compiled and Summarized..................................................................................214

8.11.2.1 HAPEM6 Near-Road Population Data Base .................................................................................................. 215

8.11.2.2 American Housing Survey (AHS) Data .......................................................................................................... 218

8.11.2.3 Federal Highway Administration (FHWA) Data ............................................................................................ 220

8.11.4 Discussion .............................................................................................................................................221

8.12 UNCERTAINTY ANALYSIS .............................................................................................................................223

8.12.1 Dispersion Modeling Uncertainties ......................................................................................................224

8.12.1.1 AERMOD Algorithms ................................................................................................................................... 224

8.12.1.2 Meteorological Inputs .................................................................................................................................... 226

8.12.1.3 Mobile Source Characterization..................................................................................................................... 228

8.12.1.4 On-Road Emissions Estimates ....................................................................................................................... 230

8.12.1.5 O3 Monitoring Data for OLM and PVMRM Options..................................................................................... 234

8.12.1.6 Use of Unadjusted AERMOD NO2 Concentrations....................................................................................... 235

8.12.2 Exposure Modeling Uncertainties........................................................................................................237

8.12.2.1 Population Data Base ...................................................................................................................................... 237

8.12.2.2 Commuting Data Base .................................................................................................................................... 238

8.12.2.2 Human Time-Location-Activity Pattern Data ................................................................................................. 239

 iii 
8.12.2.3 Longitudinal Profile ........................................................................................................................................ 240

8.12.2.4 Meteorological Data........................................................................................................................................ 242

8.12.2.5 Air Exchange Rates (AER) ............................................................................................................................. 243

8.12.2.6 Air Conditioning Prevalence........................................................................................................................... 246

8.12.2.7 Indoor Source Estimation................................................................................................................................ 248

9. CHARACTERIZATION OF HEALTH RISKS USING DATA FROM EPIDEMIOLOGICAL 

STUDIES .............................................................................................................................................................252

9.1 INTRODUCTION ................................................................................................................................................252

9.2 GENERAL APPROACH.......................................................................................................................................253

9.3 AIR QUALITY INFORMATION............................................................................................................................258

9.4 CONCENTRATION-RESPONSE FUNCTIONS ........................................................................................................259

9.5 BASELINE HEALTH EFFECTS INCIDENCE DATA................................................................................................261

9.6 ADDRESSING UNCERTAINTY AND VARIABILITY ..............................................................................................262

9.7 RISK ESTIMATES FOR EMERGENCY DEPARTMENT VISITS ..................................................................................269

10. EVIDENCE- AND EXPOSURE/RISK-BASED CONSIDERATIONS RELATED TO THE PRIMARY 

NO2 NAAQS ........................................................................................................................................................275

10.1 INTRODUCTION..............................................................................................................................................275

10.2 GENERAL APPROACH .....................................................................................................................................276

10.3 ADEQUACY OF THE CURRENT ANNUAL STANDARD........................................................................................278

10.3.1 Evidence-based considerations............................................................................................................279

10.3.2 Exposure- and risk-based considerations .............................................................................................282

10.3.2.1 Key uncertainties............................................................................................................................................ 284

10.3.2.2 Assessment results .......................................................................................................................................... 287

10.3.3 Conclusions regarding the adequacy of the current standard ..............................................................290

10.4 POTENTIAL ALTERNATIVE STANDARDS...........................................................................................291

10.4.1 Indicator................................................................................................................................................291

10.4.2 Averaging Time.....................................................................................................................................291

10.4.3 Form......................................................................................................................................................296

10.4.4 Level......................................................................................................................................................299

10.4.4.1 Evidence-based considerations........................................................................................................................ 299

10.4.4.2 Exposure- and risk-based considerations ........................................................................................................ 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 Distribution of Monitor Objectives ..................................................... 12 

Table 2-2. NOx Network Distribution across Measurement Scales............................................. 13 

Table 3-1. Overview of Susceptibility and Vulnerability Factors................................................ 18 

Table 4-1. Weight of Evidence for Causal Determination ........................................................... 24 

Table 4-2. Causality judgments made in the ISA for endpoints associated with short-term NO2

exposures...................................................................................................................... 25 

Table 4-3. Causality judgments made in the ISA for endpoints associated with long-term NO2

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-exposed 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 NO2 Air Quality Characterization, associated abbreviations, 

and values of selection criteria..................................................................................... 73 

Table 7-3. Percent of ambient NO2 monitors with selected monitoring objectives, using all valid 

site-years of historical air quality (1995-2000)............................................................ 75 

Table 7-4. Percent of ambient NO2 monitors with selected monitoring objectives, using all valid 

site-years of recent air quality (2001-2006)................................................................. 75 

Table 7-5. Percent of ambient NO2 monitors with selected measurement scales, using all valid 

site-years of historical air quality (1995-2000)............................................................ 76 

Table 7-6. Percent of ambient NO2 monitors with selected measurement scales, using all valid 

site-years of recent air quality (2001-2006)................................................................. 77 

Table 7-7. Percent of ambient NO2 monitors with selected land use, using all valid site-years of 

historical air quality (1995-2000)................................................................................. 77 

Table 7-8. Percent of ambient NO2 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 Cv/Cb ratios (m) for two season groups used for adjusting ambient NO2

concentrations to simulate on-road NO2 concentrations.............................................. 82 

Table 7-11. Monitoring site-years and annual average NO2 concentrations, using recent air 

quality data (as is) and monitors sited ≥100 m of a major road................................... 86 

Table 7-12. Monitoring site-years and annual average NO2 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-years and annual average NO2 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 exceedances of short-term (1-hour) potential health 

effect benchmarks in a year, using 2001-2003 recent NO2 air quality (as is) and 

monitors sited ≥100 m of a major road. ....................................................................... 91 

Table 7-15. Number of daily maximum exceedances of short-term (1-hour) potential health 

effect benchmarks in a year, using 2004-2006 recent NO2 air quality (as is) and 

monitors sited ≥100 m of a major road. ....................................................................... 92 

 v 
Table 7-16. Number of daily maximum exceedances of short-term (1-hour) potential health 

effect benchmarks in a year, using 2001-2003 recent NO2 air quality (as is) and 

monitors sited >20 m and <100 m of a major road...................................................... 93 

Table 7-17. Number of daily maximum exceedances of short-term (1-hour) potential health 

effect benchmarks in a year, using 2004-2006 recent NO2 air quality (as is) and 

monitors sited >20 m and <100 m of a major road...................................................... 94 

Table 7-18. Number of daily maximum exceedances of short-term (1-hour) potential health 

effect benchmarks in a year, using 2001-2003 recent NO2 air quality (as is) and 

monitors sited ≤20 m from a major road...................................................................... 95 

Table 7-19. Number of daily maximum exceedances of short-term (1-hour) potential health 

effect benchmarks in a year, using 2004-2006 recent NO2 air quality (as is) and 

monitors sited ≤20 m from a major road...................................................................... 96 

Table 7-20. Estimated annual average NO2 concentrations on-roads, using recent air quality data 

(as is) and an on-road adjustment factor...................................................................... 99 

Table 7-21. Estimated number of daily maximum exceedances of short-term (1-hour) potential 

health effect benchmarks on-roads in a year, using 2001-2003 recent NO2 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-roads in a year, using 2004-2006 recent NO2 air quality 

(as is) and an on-road adjustment factor.................................................................... 101 

Table 7-23. Estimated annual mean NO2 concentration and the number of daily maximum 

exceedances of short-term (1-hour) potential health 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 concentration and the number of daily maximum 

exceedances of short-term (1-hour) potential health 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 >20 m and <100 m from a major road. ....... 110 

Table 7-25. Estimated annual mean NO2 concentration and the number of daily maximum 

exceedances of short-term (1-hour) potential health 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 ≤20 m from a major road. ........................... 111 

Table 7-26. Estimated mean number of daily maximum exceedances of 100 ppb 1-hour NO2

concentrations in a year, using 2001-2003 air quality as is and that adjusted to just 

meeting the current and alternative standards (98th 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 NO2

concentrations in a year, using 2001-2003 air quality as is and air quality adjusted to 

just meeting the current and alternative standards (98th 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 NO2 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) adjusted to just meeting a 1-hour 100 ppb 

98th 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 NO2

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

 vi 
meeting the current and alternative standards (98th percentile) and an on-road 

adjustment factor........................................................................................................ 120 

Table 7-30. Estimated mean number of daily maximum exceedances of 150 ppb 1-hour NO2

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

meeting the current and alternative standards (98th percentile) and an on-road 

adjustment factor........................................................................................................ 121 

Table 7-31. Summary of qualitative uncertainty analysis for the air quality and health risk 

characterization. ......................................................................................................... 123 

Table 7-32. Percent difference in 1-hour NO2 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 NO2 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 adjustment factors used in on-road 

concentration estimation with a fitted lognormal distribution. .................................. 140 

Table 7-35. Absolute difference in the estimated number of exceedances of potential health 

effect benchmarks on-roads using either a fitted 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. ......................................... 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 major and minor links in Atlanta, 2002............................ 162 

Table 8-6. On-road vehicle emission strengths by county for Atlanta modeling domain: modeled 

vs NEI 2002. .............................................................................................................. 163 

Table 8-7. Summary statistics of estimated on-road hourly NO2 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 monitor 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 used 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 benchmark levels used by APEX to simulate just 

meeting the current standard and various alternative standards 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 prevalence and roadway distance statistics for housing units in 

several locations (AHS, 2008). .................................................................................. 219 

Table 8-16. Population and roadway statistics for several locations (FHWA, 2002)................ 220 

Table 8-17. Summary of qualitative uncertainty analysis for the exposure assessment............ 223 

Table 8-18. National vehicle miles traveled 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 results using a uniform versus lognormal NO2 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 Incidence of Respiratory ED Visits Associated 

with "As Is" NO2 Concentrations and NO2 Concentrations that Just Meet Alternative 

Standards in Atlanta, GA, Based on Adjusting 2005 NO2 Concentrations.*........... 270 

Table 9-3. Estimated Percent of Total Annual Incidence of Respiratory ED Visits Associated 

with "As Is" NO2 Concentrations and NO2 Concentrations that Just Meet Alternative 

Standards in Atlanta, GA, Based on Adjusting 2006 NO2 Concentrations.* ........... 271 

Table 9-4. Estimated Percent of Total Annual Incidence of Respiratory ED Visits Associated 

with "As Is" NO2 Concentrations and NO2 Concentrations that Just Meet Alternative 

Standards in Atlanta, GA, Based on Adjusting 2007 NO2 Concentrations.* ........... 272 

Table 10-1. Ratios of short-term to annual average NO2 concentrations.................................. 293 

Table 10-2. Ratios of 1-h daily maximum NO2 concentrations to 24-h average concentrations 

(ppm).......................................................................................................................... 295 

Table 10-3. Mean annual NO2 concentrations for 2004-2006 given just meeting alternative 1-h 

standards (98th percentile).......................................................................................... 296 

Table 10-4. NO2 concentrations (ppm) corresponding to 2nd-9th daily maximum and 98th/99th

percentile forms (2004-2006)..................................................................................... 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 NO2 concentrations ≥ 0.10 

ppm assuming 98th and 99th 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 analyses described in this document and their interconnections....... 4 

Figure 5-1. NO2 effect estimates (95% CI) for ED visits/HA and associated 1-h daily maximum 

NO2 levels (98th and 99th percentile values in boxes) .................................................. 52 

Figure 5-2. NO2 effect estimates for respiratory symptoms and associated 1-h daily maximum 

NO2 levels (98th and 99th percentile values in boxes) .................................................. 53 

Figure 6-1. Comparison of measured daily maximum NO2 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 concentrations at twelve ambient monitors in the 

Boston CMSA, as is (top) and air quality adjusted to just meet the current standard 

(bottom), Year 1995..................................................................................................... 62 

Figure 6-3. Comparison of adjusted 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 percentiles for where ambient monitoring NO2

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 NO2

concentration distributions are provided in Figure 6-3................................................ 66 

Figure 7-1. Illustration of three 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 year, using recent NO2 air quality (2001-2003) 

adjusted to just meeting the current annual 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 benchmarks in a year, using recent NO2 air quality (2004-2006) 

adjusted to just meeting the current annual 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 maximum exceedances of short-term (1-hour) potential 

health effect benchmarks (100 ppb, top; 200 ppb, bottom) in Chicago in a year, using 

recent NO2 air quality data (2001-2006) adjusted to just meeting alternative 1-hour 

standard levels (98th percentile, left; and 99th 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 maximum exceedances of 200 ppb in four locations 

(Phoenix, Los Angeles, Washington DC, and St. Louis) in a year, using recent NO2 air 

quality data (2001-2006) adjusted to just meeting alternative 1-hour 98th 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 NO2 air quality 

 ix 
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......................................................................................................................... 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 NO2 air quality (2001-2006) adjusted to just meeting alternative 1-

hour standard levels (98th percentile, left; and 99th 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, Washington DC, and St. Louis) in a year, using 

recent NO2 air quality (2001-2006) adjusted to just meeting alternative 1-hour 98th 

percentile standard levels and an on-road adjustment factor. .................................... 118 

Figre 7-9. Distribution of 1-hour NO2 concentrations for three modeled receptors in Atlanta at 

different vertical heights, using AERMOD predicted 2002 air quality. .................... 129 

Figure 7-10. Comparison of measured daily maximum NO2 concentration percentiles in 

Philadelphia for one high concentration years (1984) versus a low concentration years 

(2007) at four ambient monitors. ............................................................................... 133 

Figure 7-11. Comparison of the distribution of estimated Cv/Cb ratios or m for the not summer

category with fitted distributions. .............................................................................. 141 

Figure 7-12. Comparison of the distribution of estimated Cv/Cb ratios or m for the summer

category with fitted distributions. .............................................................................. 141 

Figure 8-1. General flow used for NO2 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 representing area sources for on-road mobile emissions 

that do not occur on major roadway links.................................................................. 162 

Figure 8-4. Location of major roadway 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 measured ambient monitor NO2 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 measured ambient monitor NO2 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 ratios developed from AERMOD concentration 

estimates for year 2002 and those derived from data reported in published NO2

measurement studies. ................................................................................................. 175 

Figure 8-9. Comparison of annual average AERMOD predicted NO2 concentrations (on-road 

and non-road receptors) and APEX modeled NO2 exposures (with and without 

modeled indoor sources) in Atlanta modeling domain for year 2002........................ 191 

Figure 8-10. Comparison of estimated annual average NO2 exposures for Years 2001-2003 in 

Atlanta modeling domain without modeled indoor sources. ..................................... 192 

Figure 8-11. Distribution of measured daily average personal NO2 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 NO2 exposures for individuals in Atlanta, 

stratified by two seasons (fall or spring) and with and without indoor sources, for Year 

2002 APEX simulation. Lower bound (2.5th percentile, 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 Atlanta model domain with at 

least one NO2 exposure at or above the potential health effect benchmark levels, using 

modeled 2001-2003 air quality (as is), without indoor sources................................. 199 

Figure 8-14. Estimated number of simulated asthmatic children in the Atlanta model domain 

with at least one NO2 exposure at or above the potential health effect benchmark 

levels, using modeled 2001-2003 air quality (as is), without modeled indoor sources.

.................................................................................................................................... 199 

Figure 8-15. Estimated number of all simulated asthmatics in the Atlanta model domain with at 

least one NO2 exposure at or above potential health effect benchmark levels, using 

modeled 2002 air quality (as is), both with and without modeled indoor sources..... 201 

Figure 8-16. Estimated number asthmatic person-days in the Atlanta model domain with an NO2

exposure at or above potential health effect 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 persons in the Atlanta model domain spend in the 

twelve microenvironments that corresponds with exceedances of the potential NO2

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 persons in the Atlanta model domain spend in the 

twelve microenvironments that corresponds with exceedances of the potential NO2

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 effect 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 

NO2 exposures above potential health effect 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 NO2 exposure at or above the potential health effect benchmark level, using 

modeled 2002 air quality just meeting the current standard (cur std), with and without 

modeled indoor sources.............................................................................................. 208 

Figure 8-22. Estimated percent of asthmatics in the Atlanta modeling domain with repeated NO2

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 asthmatics in the Atlanta modeling domain with NO2

exposures at or above potential health effect benchmark levels, using modeled 2002 

air quality adjusted to just meeting potential alternative standards, without indoor 

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

 xi 
Figure 8-24. Estimated percent of asthmatics in the Atlanta modeling domain with multiple NO2

exposures at or above potential health effect benchmark levels, using modeled 2002 

air quality adjusted to just meeting a 50 ppb level 99th percentile form alternative 

standard, without indoor sources. .............................................................................. 211 

Figure 8-25. Estimated percent of asthmatics in the Atlanta modeling domain with multiple NO2

exposures at or above potential health effect benchmark levels, using modeled 2002 

air quality adjusted to just meeting a 50 ppb level 99th percentile form alternative 

standard, with indoor sources..................................................................................... 212 

Figure 8-26. Estimated percent of asthmatics in the Atlanta modeling domain with multiple NO2

exposures at or above potential health effect benchmark levels, using modeled 2002 

air quality adjusted to just meeting a 100 ppb level 99th percentile form alternative 

standard, without indoor sources. .............................................................................. 212 

Figure 8-27. Comparison of estimated population and 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 NO2 concentrations (μg/m3) from 

mobile sources across four NO2 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 NO2

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 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). ................................................... 246 

Figure 9-1. Major components of nitrogen dioxide 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 Society (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 Factor 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 metropolitan 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 Regional Planning Council 

ECP Eosinophil cationic protein

EDR Emergency department 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 95th 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 Industrial 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 

NO2 Nitrogen dioxide 

NOx Oxides of nitrogen 

NO3

-

 Nitrate ion 

NWS National Weather Service 

NYC New York City 

NYDOH New York Department of Health 

O3 Ozone 

OAQPS Office of Air Quality Planning and Standards 

OR Odds ratio 

ORD Office of Research and Development 

ORIS Office of Regulatory Information 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 95th 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 (EPA) is conducting a review of the national 

ambient air quality standards (NAAQS) for nitrogen dioxide (NO2). Sections 108 and 109 of the 

Clean Air Act (The Act) govern the establishment and periodic review of the air quality criteria 

and the NAAQS. These standards are established for pollutants that may reasonably be 

anticipated to endanger public health or welfare, and whose presence in the ambient air results 

from numerous or diverse mobile or stationary 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 that 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 Administrator 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 

NO2 NAAQS, contains four major components. Each of these components, as they relate to the 

review of the NO2 primary NAAQS, is described below. 

The first of these components is an integrated review plan. This plan presents the 

schedule for the review, the process for conducting the review, and the key policy-relevant 

science issues that will guide the review. The integrated review plan for this review of the NO2

primary NAAQS is presented in the Integrated Review Plan for the Primary National Ambient 

Air Quality Standard for Nitrogen Dioxide (EPA, 2007a). The policy-relevant questions 

identified in this document to guide the review are: 

 1 
• Has new information altered the scientific support for the occurrence of health effects 

following short- and/or long-term exposure to levels of nitrogen oxides (NOx) found in 

the ambient air? 

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

effects of NOx? 

• At what levels of NOx exposure do health effects of concern occur? 

• Has new information altered conclusions from previous reviews regarding the plausibility 

of adverse health effects caused by NOx 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 between short-term and long-term exposures 

to NOx? 

Additional questions will become relevant if the evidence suggests that revision of the current 

standard might be appropriate. These questions are: 

• Is there evidence for the occurrence of adverse health effects at levels of NOx lower than 

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

associated with that evidence?

• Do exposure estimates suggest that exposures of concern for NOx-induced health effects 

will occur with current ambient levels of NO2 or with levels that just meet current, or 

potential alternative, standards? If so, are 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 uncertainties 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 evidence, the air quality assessment, and the 

risk/exposure assessments? What are the uncertainties and limitations in the evidence 

and the assessments?

 2 
• What is the range of forms supported by the evidence, the air quality assessment, and the 

exposure/risk assessments? What are the uncertainties and limitations in the evidence 

and the assessments? 

The second component of the review process 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 series of annexes that contain more detailed 

information about the scientific literature. The ISA to support this review of the NO2 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 review process is a risk and exposure assessment, which is 

described in this document. The purpose of this document is to communicate EPA’s assessment 

of exposures and risks associated with ambient NO2. In this assessment, we have developed 

estimates of human exposures and risks associated with current ambient levels of NO2, with 

levels that just meet the current standard, and with levels that just meet potential alternative 

standards. Figure 1-1 (below) presents a schematic overview of the analyses described in this 

document and how those analyses fit together. Each 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 rulemaking process, as discussed 

below in chapter 10. The draft plan for conducting the risk and exposure assessment to support 

the NO2 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 risk and exposure assessment is presented in Risk and 

Exposure Assessment to Support the Review of the NO2 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 NO2 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 final risk and exposure assessment as well as the scientific evidence 

(including evidence from the epidemiologic, controlled human exposure, and animal 

toxicological literatures) evaluated 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 NO2 NAAQS 

On April 30, 1971, EPA promulgated identical primary and secondary NAAQS for NO2

under section 109 of the Act. The standards were 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 NO2 standards were 

based. On February 23, 1984, EPA proposed to retain 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 review and comment (56 FR 59285). The draft 

document provided a comprehensive assessment of the available scientific and technical 

information on health and welfare effects associated with NO2 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 meeting 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 the effects of this pollutant and provides an 

adequate basis for EPA to make a decision as to the appropriate NAAQS for NO2” (Wolff, 

1993). The Air Quality Criteria Document for the Oxides of Nitrogen was then finalized (EPA, 

1993). 

The EPA also prepared a Staff Paper that summarized an air quality assessment for NO2

conducted by the Agency (McCurdy, 1994), summarized and integrated the key studies and 

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

critical elements to be considered in the review of the NO2 NAAQS. The CASAC reviewed two 

drafts of the Staff Paper and concluded in a closure letter to the Administrator (Wolff, 1995) that 

the document provided a “scientifically adequate basis for regulatory decisions on nitrogen 

dioxide.” In September of 1995, EPA finalized the Staff Paper entitled, “Review of the National 

Ambient Air Quality Standards for Nitrogen Dioxide: Assessment of Scientific and Technical 

Information” (EPA, 1995). 

In October 1995, the Administrator announced her proposed decision not to revise either 

the primary or secondary NAAQS for NO2 (60 FR 52874; October 11, 1995). A year later, the 

Administrator made a final determination not to revise the NAAQS for NO2 after careful 

evaluation of the comments received on the proposal (61 FR 52852, October 8, 1996). The level 

for both the existing primary and secondary NAAQS for NO2 is 0.053 parts per million (ppm) 

(100 micrograms per cubic meter of air [μg/m3

]), annual arithmetic average, calculated as the 

arithmetic mean of the 1-hour NO2 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 key health effects of greatest concern at ambient or near-ambient 

levels of NO2, increased airway responsiveness in asthmatic 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 NO2 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 drawn from epidemiologic studies that reported 

associations between respiratory symptoms and indoor exposures to NO2 in people living in 

homes with gas stoves. The biological plausibility of the epidemiologic results was supported by 

toxicological studies that detected changes in lung host defenses following NO2 exposure. 

Subpopulations considered potentially more susceptible to the effects of NO2 included 

individuals with preexisting respiratory disease, children, and the elderly. 

1.2.3 Assessment from Previous Review 

In the previous review of the NO2 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 human exposure studies. At the time of the review, 

a few studies indicated the possibility for adverse health effects due to short-term (e.g., 1-hour) 

exposures between 0.20 ppm and 0.30 ppm NO2. Therefore, the focus of the assessment was on 

the potential for short-term (i.e., 1-hour) exposures to NO2 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 exceedances of potential short-term health effect 

benchmarks. Predictive models were then constructed to relate the frequency of hourly 

concentrations above short-term health effect 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 concentrations at or above a potential health effect benchmark of 

0.20 ppm (1-h average) indicated that the existing annual standard 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 

NOx, for purposes of this document, include multiple gaseous (e.g., NO2, NO, HONO) 

and particulate (e.g., nitrate) species. As discussed in the integrated review plan (2007a), the 

current review of the NO2 NAAQS will focus on the gaseous species of NOx and will not 

consider health effects directly associated with particulate species of NOx. Of the gaseous 

 7 
species, EPA has historically determined it appropriate to specify the indicator of the standard in 

terms of NO2 because the majority of the information regarding health effects and exposures is 

for NO2. In the current review, staff notes that no alternative to NO2 has been advanced as being 

a more appropriate surrogate for ambient gaseous NOx. Controlled human exposure studies and 

animal toxicology studies provide specific evidence for health effects following exposure to 

NO2. Epidemiologic studies also typically report levels of NO2, as opposed to other gaseous 

NOx, though the degree to which monitored NO2 reflects actual NO2 levels, as opposed to NO2

plus other gaseous NOx, can vary (e.g.,. see section 2.2.3 of this document). Therefore, NO2 will 

be used as the indicator for the gaseous NOx in the risk and exposure assessments described in 

this document. 

 8 
2. SOURCES, AMBIENT LEVELS, AND EXPOSURES 

2.1 SOURCES OF NO2

Ambient levels of NO2 are the product of both direct NO2 emissions and emissions of 

other NOx (e.g, NO), which can then be converted to NO2 (for a more detailed discussion see the 

ISA, section 2.2). Nationally, anthropogenic sources account for approximately 87% of total 

NOx emissions. Mobile sources (both on-road and off-road) account for about 60% of total 

anthropogenic emissions of NOx, while stationary 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 

NOx including microbial activity in soils, lightning, and wildfires (ISA, section 2.2.1 and annex 

section 2.6.2). 

2.2 AMBIENT LEVELS OF NO2 

2.2.1 Background on NO2 monitoring network 

From the inception of the NO2 monitoring network in the late 1970’s through the present 

day, the number of monitoring sites has remained relatively stable (Watkins, 2008). As of 

October 2008, there were 409 NOx monitors within the United States actively reporting NO2 data 

into EPA’s Air Quality System (AQS). The NO2 network was originally established for 

implementation of the NO2 NAAQS promulgated in 1971. The first requirements for NO2

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

two NO2 national ambient monitoring stations (NAMS) were required in areas of the country 

with populations greater than 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 NO2 and would best be located in that 

part of the urban area where the emission density of NOx is the highest. The 

second station (category (b) urban scale), would be to measure the NO2 produced 

 9 
from the reaction of NO with O3 and should be downwind of the area peak NOx

emission areas. 

40 CFR Part 58, Appendix D, section 3.5. 

In October 2006, EPA revised the monitoring requirements for NO2 in light of the fact 

that there are no NO2 non-attainment areas under the current standards. The 2006 rule 

eliminated the minimum requirements for the number of NO2 monitoring sites. 40 CFR Part 58, 

Appendix D, section 4.3. However, the rule requires continued operation of existing State and 

local monitoring stations (SLAMS) until discontinuation is approved by the EPA Regional 

Administrator. The revised rule further requires that where SLAMS NO2 monitoring is ongoing, 

“at least one NO2 site in the area must be located to measure the maximum concentration of 

NO2.” 

As noted above, the size of the NO2 network has remained fairly stable through time, 

even though no minimum monitoring sites were required under the 2006 rule. The maintenance 

of the NO2 monitoring network has been driven by several factors, including the need to support 

ozone (O3) 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 NO2. 

 To characterize the current NO2 network, staff has reviewed the NO2 network meta-data. 

The data reviewed are those available from AQS in October 2008, for 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-characterized. While these files are useful 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-data fields. With this uncertainty in mind, staff 

has compiled information on the monitoring objectives and measurement scales for monitors in 

the NO2 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 welfare effects. As of October 2008, there were 489 records of NO2

monitor objective values (some monitors have multiple monitor objectives). Table 2-1 lists the 

distribution of monitoring objectives across the network. There are 12 categories of monitor 

objectives for NO2 monitors within AQS. The “other” category 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 Appendix D, there are six examples of NO2 site types: 

1. Sites located to determine the highest concentration 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 determine general background concentration levels (General 

Background). 

5. Sites located to determine the extent of regional pollutant transport among 

populated areas; and in support of secondary standards (Regional Transport). 

6. Sites located to measure air pollution impacts on visibility, vegetation damage, or 

other welfare-based impacts (Welfare Related Impacts). 

The remaining four categories available 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 characterize upwind background and transported 

O3 and its precursor concentrations entering the area and will identify those areas 

which are subjected to transport (Upwind Background). 

2. Type 2 sites are established to monitor 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 urban air toxic pollutants (Max. Precursor 

Impact). 

3. Type 3 sites are intended to monitor maximum O3 concentrations occurring 

downwind from the area of maximum precursor emissions (Max. O3

Concentration). 

 11 
4. Type 4 sites are established to characterize the downwind transported O3 and its 

precursor concentrations exiting the area and will identify those areas which are 

potentially contributing to overwhelming 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. O3 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 air 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 NO2 network to indicate what the measurement scale of a 

particular monitor represents. There are 386 NO2 monitor records in AQS with available 

measurement scale information. Table 2-2 shows the measurement scale distribution across all 

NO2 sites form the available data in AQS of monitors reporting 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 monitors 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 NO2 network is primarily targeting public health and photochemical process 

monitoring objectives. We note that nearly half of the monitor objective records are directly 

targeting public health through the population exposure (36.2%) and highest concentration 

(11.8%) categories alone. The other categories serve to inform public health concerns, but also 

address photochemistry issues where NOx serves as a precursor to ozone. Further, it appears that 

approximately 10% of NO2 monitors are in place to serve the PAMS network. In reality, a large 

majority of sites likely could serve both public health and photochemistry related objectives due 

to their proximity to urban areas. The exceptions would likely be categories such as upwind 

background, extreme downwind, regional transport, and possibly maximum O3 concentration. 

These four categories only represent approximately 7% of the NO2 network, and have a higher 

likelihood of being rural and likely regional in scale. 

2.2.2 Trends in ambient concentrations of NO2 

As noted above, NO2 is monitored largely in urban areas and, therefore, data from the 

NO2 monitoring network is generally more representative of urban areas than rural areas. 

According to monitoring data, nationwide levels of ambient NO2 (annual average) decreased 

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

concentrations of NO2 were about 15 ppb for averaging periods ranging from a day to a year. 

The average daily maximum hourly NO2 concentrations were approximately 30 ppb. These 

values are about twice as high as the 24-h averages. The highest maximum hourly concentrations 

(~200 ppb) between 2003 and 2005 are more than a factor of ten higher than the mean hourly or 

 13 
24-h concentrations (ISA, Figure 2.4-13). The highest levels of NO2 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 concentrations 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 estimated to range from only 0.1 ppb to 0.3 ppb (ISA, 

section 2.4.6). 

Ambient levels of NO2 exhibit both seasonal and diurnal variation. In southern cities, 

such as Atlanta, higher concentrations are found during winter, consistent with the lowest mixing 

layer heights being found during that time of the year. Lower concentrations are found during 

summer, consistent with higher mixing layer heights and increased rates of photochemical 

oxidation of NO2. For cities in the Midwest and Northeast, such as Chicago and New York City, 

higher levels tend to be found from late winter to early spring with lower levels occurring from 

summer though the fall. In Los Angeles the highest 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, NO2 levels typically peak during 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 pronounced over the course of a day than monitors 

located closer to traffic. 

2.2.3 Uncertainty Associated with the Ambient NO2 Monitoring Method 

The method for estimating ambient NO2 levels (i.e., subtraction of NO from a measure of 

total NOx) is subject to interference by NOx oxidation products (e.g., PAN, HNO3) (ISA, section 

2.3). Limited evidence suggests that these compounds result in an overestimate of NO2 levels by 

roughly 20 to 25% at typical ambient levels. Smaller relative errors are estimated to occur in 

measurements taken near strong NOx sources since most of the mass emitted as NO or NO2

would not yet have been further oxidized. Relatively larger errors appear in locations more 

distant from strong local NOx sources. Additionally, many NO2 monitors are elevated above 

ground level in the cores of large cities. Because most sources of NO2 are near ground level (i.e., 

combustion emissions from traffic), there is a gradient of NO2 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 NO2 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 source of uncertainty in exposure estimates can 

result from monitor location. NO2 monitors are sited for compliance with air quality standards 

rather than for capturing small-scale variability in NO2 concentrations near sources such as 

roadway traffic. Significant gradients in NO2 concentrations near roadways have been observed 

in several studies, and NO2 concentrations have been found to be correlated with distance from 

roadway and traffic volume (ISA, section 2.5.3.2). 

2.3 EXPOSURE TO NO2 

2.3.1 Overview 

Human exposure to an airborne pollutant can be characterized by contact between a 

person and the pollutant at a specific concentration for a specified period of time (ISA, section 

2.5.1). The integrated exposure of a person to a given pollutant is the time-weighted average of 

the exposures over all time intervals for all microenvironments in which the individual spends 

time. Microenvironments in which people are exposed to air pollutants such as NO2 typically 

include residential indoor environments and 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 variability in the time that individuals 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 time 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. Significant variability surrounds each of these broad 

estimates, particularly when considering influential personal attributes such as age or gender; 

when accounting for daily, weekly, or seasonal factors influencing personal behavior; or when 

characterizing individual variability in time spent in various locations (McCurdy and Graham, 

2003; Graham and McCurdy, 2004). Typically, the time spent outdoors or in vehicles could vary 

by 100% or more depending on which of these influential factors are considered. Exposure 

misclassification can result when the time spent in different microenvironments is not taken into 

consideration and may obscure the true relationship 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 NO2 concentrations as 

surrogates for personal exposure to ambient NO2. Results have been mixed regarding the 

appropriateness of using ambient levels of NO2 as a surrogate for personal exposures to ambient 

NO2. Studies examining the association between ambient NO2 and personal exposure to NO2

have generated mixed results due to 1) the prevalence of indoor sources of NO2; 2) the spatial 

heterogeneity of NO2 in study areas; 3) the seasonal and geographic variability in the infiltration 

of ambient NO2; 4) differences in the time spent in different microenvironments; and 5) 

differences in study design. As a result, some researchers have concluded that ambient NO2 may 

be a reasonable proxy for personal exposure, while others have noted that caution must be 

exercised (ISA, section 2.5.9). However, the possible consequences of this 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, effect estimates (ISA, section 5.2.2). 
3. AT RISK POPULATIONS 

3.1 OVERVIEW 

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

effects from NO2 exposure. This could occur because they are affected by lower levels of NO2

than the general population (susceptibility), because they experience a larger health impact than 

the general population to a given level of exposure (susceptibility), and/or because they are 

exposed to higher levels of NO2 than the general population (vulnerability). The term 

susceptibility generally encompasses innate (e.g., genetic or developmental) and/or acquired 

(e.g., age or disease) factors that make individuals more likely to experience effects with 

exposure to pollutants. Given the likely heterogeneity of individual 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. Factors that may influence susceptibility to the 

effects of air pollution include age (e.g., infants, children, elderly); gender; race/ethnicity; 

genetic factors; and pre-existing disease/condition (e.g., obesity, diabetes, respiratory disease, 

asthma, chonic obstructive pulmonary disease (COPD), cardiovascular disease, airway 

hyperresponsiveness, respiratory infection, adverse birth outcome) (ISA, sections 4.3.1, 4.3.5, 

and 5.3.2.8). In addition, some population groups are vulnerable to pollution-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., indoor versus outdoor) (ISA, section 4.3.5). The 

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

of the discussion devoted to factors for which NO2-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 pollution. In general, these include asthma, COPD, 

respiratory infection, conduction disorders, congestive heart failure (CHF), diabetes, past 

myocardial infarction (MI), obesity, coronary artery disease, low 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, may be at increased risk for experiencing 

respiratory symptoms (ISA, section 4.3.1). In considering NO2 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 exposure studies, supported by animal toxicology 

studies, have provided evidence for associations between NO2 exposure and respiratory effects in 

asthmatics (ISA, section 4.3.1.1). The ISA found evidence from epidemiologic studies for an 

association between ambient NO2 and children’s hospital admissions, emergency department 

(ED) visits, and calls to doctors for asthma. NO2 levels were associated with aggravation of 

asthma effects that include symptoms, medication use, and lung function. Time-series studies 

also demonstrated a relationship in children between hospital admissions or ED visits for asthma 

and ambient NO2 levels, even after adjusting for co-pollutants 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 NO2 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 NO2 concentrations than the concentrations that caused effects on 

other endpoints (ISA, sections 5.3.2.1-5.3.2.6). 

Compared to asthma, less evidence is available to support cardiovascular disease as a 

mediator of susceptibility to NO2. 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 NO2 concentrations (ISA, 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 

NO2 in persons with cardiovascular disease (ISA, section 4.3.1.2). 

3.3 SUSCEPTIBILITY: AGE 

The ISA identifies infants, children (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 NO2 concentrations (ISA, section 4.3.2). The ISA found 

evidence that associations of NO2 with respiratory ED visits and hospitalizations were stronger 

among children and older adults, though not all studies had comparable findings on this issue 

(ISA, section 4.3.2). In addition, long-term exposure studies suggest effects in children that 

include impaired lung function growth, increased respiratory symptoms and infections, and onset 

of asthma (ISA, section 3.4 and 4.3.2). In some studies, associations between NO2 and 

hospitalizations or ED visits for CVD have been observed in elderly populations. Among studies 

that observed positive associations between NO2 and mortality, a comparison indicated that, in 

general, the elderly population was more susceptible than the non-elderly population to NO2

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 must be satisfied in selecting 

and establishing useful links between 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, polymorphisms 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 must be carefully considered 

(ISA, section 4.3.4). 

Investigation of genetic susceptibility to NO2 effects has focused on the glutathione Stranferase (GST) gene. Several GST genes have common, functionally-important 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 individuals with genotypes that result in enzymes with 

reduced or absent peroxidase activity are likely to have reduced defenses against oxidative insult. 

This could potentially result in increased susceptibility to inhaled oxidants and radicals. 

However, data on genetic susceptibility to NO2 are only beginning to emerge and, while it 

remains plausible that there are genetic factors that can influence health responses to NO2, the 

few available studies do not provide specific support for genetic susceptibility to NO2 exposure 

(ISA, section 4.3.4). 

3.5 SUSCEPTIBILITY: GENDER 

As reported in the ISA, a limited number of NO2 studies have stratified results by gender. 

The results of these studies were mixed, and the ISA does not draw conclusions regarding the 

potential for gender to confer susceptibility to the effects of NO2 (ISA, section 4.3.3). 

3.6 VULNERABILITY: PROXIMITY (ON OR NEAR) TO ROADWAYS 

The ISA includes discussion of vulnerable populations that experience increased NO2

exposures on or near roadways (ISA, section 4.3.6). Large gradients in NOx concentrations near 

roadways lead to increased exposures for individuals residing, working, or attending school in 

the vicinity of roadways. Many studies find that indoor, personal, and outdoor NO2 levels are 

strongly associated with proximity to traffic or to traffic density (ISA, section 4.3.6). Due to 

high air exchange rates, NO2 levels inside a vehicle could rapidly approach levels outside the 

vehicle during commuting (ISA, section 4.3.6). Mean in-vehicle NO2 levels are between 2 and 3 

times ambient levels measured at fixed sites nearby (ISA, section 4.3.6). Therefore, individuals 

with occupations that require them to be in traffic or close to traffic (e.g., bus and taxi drivers, 

 20 
highway patrol officers, toll collectors) and individuals with long commutes could be exposed to 

relatively high levels of NO2 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 examined 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 (ISA, section 4.3.6). SES modification of NO2

associations has been examined in fewer studies. For example, in a study conducted in Seoul, 

South Korea, community-level SES indicators modified the association of air pollution with ED 

visits for asthma. Of the five criteria air pollutants evaluated, NO2 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 potential surrogate for SES) as a 

modifier of the effect of traffic-related air pollutants, including NO2, on childhood asthma. The 

authors reported an elevated risk of asthma with an increase in NO2 exposure solely among 

children with above-median exposure to violence in their neighborhoods (ISA, section 4.3.6). 

Although these recent studies have evaluated the impact of SES on vulnerability to NO2, they are 

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

3.8 CONCLUSIONS 

The population potentially affected by NO2 is large. A considerable fraction of the 

population resides, works, or attends school near major roadways, and these individuals are 

likely to have increased exposure to NO2 (ISA, section 4.4). Based on data from the American 

Housing Survey, approximately 36 million individuals live within 300 feet (~90 meters) of a 

four-lane highway, railroad, or airport (ISA, section 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 higher proportion of non-white and economically disadvantaged 

students attending those schools (ISA, section 4.4). Of this population, asthmatics and members 

of other susceptible groups discussed above will have even greater risks of health effects related 

 21 
 22 

to NO2. 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 among 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 that may be potentially at risk for NO2-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 proportions of the U.S. population are in age groups that 

are likely to have increased susceptibility and vulnerability for health effects from ambient NO2

exposure. The considerable size of the population groups at risk indicates that exposure to 

ambient NO2 could have a significant impact on public health in the United States. 
4. HEALTH EFFECTS 

4.1 INTRODUCTION 

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

assessment of the scientific evidence related to the health effects associated with NO2 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 relationship (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 experimental evidence, consistency of the observed 

association, biological plausibility, coherence of the evidence, temporal relationship of the 

observed association, and the presence of an exposure-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 rationale 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 ISA for endpoints associated with short-term NO2

exposures 

 25 
Table 4-3. Causality judgments made in the ISA for endpoints associated with long-term NO2

exposures 

4.2 ADVERSE RESPIRATORY EFFECTS FOLLOWING SHORT-TERM 

EXPOSURES 

4.2.1 Overview 

The ISA concludes that, taken together, recent studies provide scientific evidence that is 

sufficient to infer a likely causal relationship between short-term NO2 exposure and adverse 

effects on the respiratory system (ISA, section 5.3.2.1). This finding is supported by the large 

body of recent epidemiologic evidence as well as findings from human and animal experimental 

studies. These epidemiologic and experimental studies encompass a number of endpoints 

including ED visits and hospitalizations, respiratory symptoms, airway hyperresponsiveness, 

airway inflammation, and lung function. Effect estimates from epidemiologic studies conducted 

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

 increase in risks for ED visits and 

hospital admissions and higher risks for respiratory symptoms (ISA, section 5.4). The findings 

 

2

 Effect estimates in the ISA were standardized to a 30 ppb increase in NO2 for studies that evaluated 1-h daily 

maximum NO2 concentrations and to a 20 ppb increase for studies that evaluated 24-h average concentrations. 

 26 
relevant to these endpoints, which 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 NO2

concentrations below the current NAAQS with increased numbers of ED visits and hospital 

admissions for respiratory causes, especially asthma (ISA, section 5.3.2.1). Total respiratory 

causes for ED visits and hospitalizations typically include asthma, bronchitis and emphysema 

(collectively referred to as COPD), pneumonia, upper and lower respiratory infections, and other 

minor categories. Temporal associations between ED visits or hospital admissions for respiratory 

diseases and ambient levels of NO2 have been the subject of over 50 peer-reviewed research 

publications since the review of the NO2 NAAQS that was completed 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 NO2 with respiratory ED visits and hospital admissions were 

generally robust and independent of the effects of co-pollutants (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 evaluate host defense and immune system changes, 

airway inflammation, and airway responsiveness (see 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 NO2 using multi-pollutant models (Peel et al., 2005 and Tolbert et al., 2007 in Atlanta; New 

York Department of Health (NYDOH), 2006 and Ito 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 models, the authors reported a 2.4% (95% CI: 

0.9, 4.1) increase in respiratory ED visits associated with a 30-ppb increase in 1-h max NO2

 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 reanalyzed these data with 4 additional 

years of information and found essentially similar 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 PM10 or O3. In the study 

conducted by the New York Department of Health, investigators evaluated asthma ED visits in 

Bronx and Manhattan, New York over the period 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 NO2 and a 7% increase in visits per 30 ppb increase in daily 1-h 

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

that included PM2.5 or SO2. 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 NO2. 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%) increase in risk per 20 

ppb increase in 24-h ambient NO2. Risk estimates were robust and remained statistically 

significant in multi-pollutant models that included PM2.5, O3, CO, and SO2. With regard to the 

studies that evaluated only single pollutant models, Linn et al. (2000) detected a statisticallysignificant increase in hospital admissions and Jaffee et al. (2003) detected a positive, but 

statistically-nonsignificant, increase in ED visits associated with 24-h NO2 concentrations. 

4.2.3 Respiratory Symptoms 

Evidence for associations between NO2 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 plausibility and coherence for the epidemiologic 

results (ISA, section 5.3.2.1). Consistent evidence has been observed for an association of 

respiratory effects with indoor and personal NO2 exposures in children (ISA, sections 3.1.5.1 and 

5.3.2.1) and with ambient levels of NO2 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, NO2 associations 

in multicity studies are generally robust to adjustment for co-pollutants including O3, CO, and 

 28 
PM10 (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 NO2 

Epidemiologic studies using community ambient monitors have found associations 

between ambient NO2 concentrations and respiratory symptoms (ISA, sections 3.1.4.2 and 

5.3.2.1, Figure 3.1-6) in cities where NO2 concentrations were within the range of 24-h average 

concentrations observed in recent years. Several studies have been published since the 1996 

review of the NO2 NAAQS including single-city studies (e.g., Ostro et al., 2001; Delfino et al., 

2002) and multi-city studies in urban 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) studied 1,844 schoolchildren, followed for 1 year, as part of the Six 

Cities Study that included the cities of Watertown, MA, Baltimore, MD, Kingston-Harriman, 

TN, Steubenville, OH, Topeka, KS, and Portage, WI. Respiratory symptoms were recorded 

daily. The authors reported a significant association between 4-day mean NO2 levels and 

incidence of cough among all children 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 NO2. The incidence of cough 

increased up to approximately mean NO2 levels (~13 ppb) (p = 0.01), after which no further 

increase was observed. The significant association between cough and 4-day mean NO2 level 

remained unchanged in models that included O3 but lost statistical significance in two-pollutant 

models that included PM10 (OR = 1.37 [95% CI: 0.88, 2.13]) or SO2 (OR = 1.42 [95% CI: 0.90, 

2.28]). 

Mortimer et al. (2002) studied 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 evening asthma symptoms and peak flow recorded. 

The greatest effect was observed for morning symptoms using a 6-day moving average, with a 

reported OR of 1.48 (95% CI: 1.02, 2.16) per 20 ppb increase in NO2. Although the magnitudes 

of effect estimates were generally robust in multi-pollutant models that included O3 (OR for 20-

ppb increase in NO2 = 1.40 [95% CI: 0.93, 2.09]), O3 and SO2 (OR for NO2 = 1.31 [95% CI: 

 29 
0.87, 2.09]), or O3, SO2, and PM10 (OR for NO2 = 1.45 [95% CI: 0.63, 3.34]), they were not 

statistically-significant. 

Schildcrout et al. (2006) investigated the association between ambient NO2 and 

respiratory symptoms and rescue inhaler use as part of the Childhood Asthma Management 

Program (CAMP) study. The study reported on 990 asthmatic children living within 50 miles of 

an NO2 monitor in Boston, MA, Baltimore, MD, Toronto, ON, St. Louis, MO, Denver, CO, 

Albuquerque, NM, or San Diego, CA. Symptoms and use of rescue medication were recorded 

daily, resulting in each subject having an average of approximately two months of data. The 

authors reported the strongest association between NO2 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 NO2 occurring 2 days 

before measurement. Multi-pollutant models that included CO, PM10, or SO2 produced similar 

results (ISA, Figure 3.1-5, panel A). Additionally, increased NO2 exposure was associated with 

increased use of rescue medication, with the strongest association for a 2-day lag, both for 

single- and multi-pollutant models (e.g., for an increase of 20-ppb NO2 in the single-pollutant 

model, the RR for increased inhaler usage was 1.05 (95% CI: 1.01, 1.09). 

Studies of Indoor NO2 

Evidence supporting increased respiratory morbidity following NO2 exposures is also 

found in studies of indoor NO2 (ISA, section 3.1.4.1). For example, in a randomized 

intervention study in Australia (Pilotto et al., 2004), students attending schools that switched out 

unvented gas heaters, a major source of indoor NO2, experienced a decrease in both levels of 

NO2 and in respiratory symptoms (e.g., difficulty breathing, chest tightness, and asthma attacks) 

compared to students in schools that did not switch out unvented 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 NO2 had higher rates of respiratory symptoms (e.g., sore throat, 

cold) and absenteeism than students in classrooms with lower levels of NO2. This study detected 

a significant concentration-response relationship, strengthening the argument that NO2 is 

causally related to respiratory morbidity. A number of other indoor studies conducted in homes 

have also detected significant associations between indoor NO2 and respiratory symptoms (ISA, 

section 3.1.4.1). 

 30 
4.2.4 Lung Host Defenses and Immunity 

Impaired host-defense systems and increased risk of susceptibility to both viral and 

bacterial infections after NO2 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) provides evidence that increased personal exposure 

to NO2 worsened virus-associated symptoms and decreased lung function in children with 

asthma. The limited evidence from controlled human exposure studies indicates that NO2 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 NO2

exposure, with effects observed at concentrations of less than 1 ppm (ISA, section 3.1.7). When 

taken together, epidemiologic and experimental studies linking NO2 exposure with viral illnesses 

provide coherent and consistent evidence that NO2 exposure can result in lung host defense or 

immune system effects (ISA, sections 3.1.7 and 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. macrophage phagocytosis) could be the basis for the 

effects observed in epidemiologic studies, including increased respiratory illness or respiratory 

symptoms (ISA, section 5.3.2.1). Proposed mechanisms by which NO2, 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 smooth muscle contraction occurs quickly to 

narrow the airway in response to exposure to various stimuli including allergens or irritants. 

Bronchoconstriction is the dominant physiological event leading to clinical symptoms and 

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

such as NO2 may enhance the inherent responsiveness of the airway to a challenge by allergens 

and nonspecific agents (ISA, section 3.1.3). In the laboratory, airway responses can be measured 

by assessing changes in pulmonary function (e.g., decline in FEV1) or changes in the 

inflammatory response (e.g., using markers in bronchoalveolar lavage (BAL) fluid or induced 

sputum) (ISA, section 3.1.3). 

The ISA (section 5.3.2.1) draws two broad conclusions regarding the airway response 

following NO2 exposure. First, the ISA concludes that NO2 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 NO2 for 30 minutes (ISA, section 

5.3.2.1 and Figure 3.1-2). Second, exposure to NO2 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 general, small but significant increases in nonspecific airway 

responsiveness were observed in the range of 0.2 to 0.3 ppm NO2 for 30-minute exposures and at 

0.1 ppm NO2 for 60-minute exposures in asthmatics. These conclusions are consistent with 

results from animal toxicological studies which have detected 1) increased immune-mediated 

pulmonary inflammation in rats exposed to house dust mite allergen following exposure to 5 

ppm NO2 for 3-h and 2) increased responsiveness to non-specific challenges following subchronic (6-12 weeks) exposure to 1 to 4 ppm NO2 (ISA, section 5.3.2.1). Enhanced airway 

responsiveness could have important clinical implications for asthmatics since transient increases 

in airway responsiveness following NO2 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 NO2 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 alternative standards for evaluation (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 regarding this endpoint. 

Folinsbee (1992) conducted a meta-analysis using individual level data from 19 clinical 

NO2 exposure studies measuring airway responsiveness in asthmatics (ISA, section 3.1.3.2). 

These studies included NO2 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 responsiveness following exposure to NO2 levels between 0.2 and 

0.3 ppm. Results from an update of this meta-analysis (results combined 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-exposed asthmatics with increased nonspecific 

airway hyperresponsiveness3

As noted in Table 4-5, when exposed at rest 66% of subjects experienced an increase in 

airway responsiveness following exposure to 0.1 ppm NO2, 67% of subjects experienced an 

increase in airway responsiveness following exposure to NO2 concentrations between 0.1 and 

0.15 ppm (inclusively), 75% of subjects experienced an increase in airway responsiveness 

following exposure to NO2 concentrations between 0.2 and 0.3 ppm (inclusively), and 73% of 

subjects experienced an increase in airway responsiveness following exposure to NO2

concentrations above 0.3 ppm. Effects of NO2 exposure on the direction of airway 

responsiveness are statistically-significant at all of these levels. Because this meta-analysis 

evaluates only the direction of the change in airway responsiveness, it is not possible to discern 

the magnitude of the change from these data. However, the results do suggest that short-term 

exposures to NO2 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 since the 1996 review address the question of whether lowlevel exposures to NO2 enhance the response to specific allergen challenge in mild asthmatics 

(ISA, section 3.1.3.1). These recent studies suggest that NO2 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 NO2 increased the late phase response to allergen challenge 4 hours after exposure, as 

measured by changes in lung function. In a separate study (Strand et al., 1998), 4 daily repeated 

exposures to 0.26-ppm NO2 for 30 minutes increased both the early 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 study (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 NO2 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 performed BAL 19 hours after the allergen challenge to determine 

NO2 effects on the allergen-induced inflammatory response. Compared with air followed by 

allergen, NO2 followed by allergen caused an increase in the BAL recovery of 

polymorphonuclear (PMN) cells and 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 epithelial cells, and is thought to play a role in the pathogenesis of airway injury in 

asthma. Subsequently, Barck et al. (2005) exposed 18 mild asthmatics to air or 0.26 ppm NO2

for 15 minutes on day 1, followed by two 15 minute 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, NO2

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 rest. They used an adequate number of subjects, included air 

control exposures, randomized exposure order, and separated exposures by at least 2 weeks. 

Together, they indicate the possibility for effects on allergen responsiveness in some asthmatics 

following brief exposures to 0.26 ppm NO2. However, other recent studies have failed to find 

effects using similar, but not identical, approaches (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, the use of BAL versus sputum induction, exercise versus rest 

during exposure, and differences in subject susceptibility (ISA, section 3.1.3.1). 

4.2.6 Airway Inflammation 

Effects of NO2 on airway inflammation have been observed in controlled human 

exposure and animal toxicological studies at higher than ambient levels (0.4-5 ppm). The few 

available epidemiologic studies were suggestive of an association between ambient NO2

concentrations and inflammatory response in the airway in children, though the associations 

were inconsistent in the adult populations examined (ISA, section 3.1.2 and 5.3.2.1). Controlled 

human exposure studies provide evidence for increased airway inflammation at NO2

concentrations of <2.0 ppm. The onset of inflammatory responses in healthy subjects appears to 

be between 100 and 200 ppm-minutes, i.e., 1 ppm for 2 to 3 hours (ISA, Figure 3.1-1). Increases 

in biological markers of inflammation were not observed consistently in healthy animals at levels 

 35 
of less than 5 ppm; however, increased susceptibility to NO2 concentrations of as low as 0.4 ppm 

was observed when lung vitamin C was reduced (by diet) to levels that were <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 examined the association between ambient NO2

concentrations and lung function in children and adults generally produced inconsistent results 

(ISA, sections 3.1.5.1 and 5.3.2.1). Controlled human exposure studies generally did not find 

direct effects of NO2 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 NO2 on lung function also have been inconsistent at 

exposure concentrations of less than 1 ppm NO2. 

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 toxicological studies provide evidence that is sufficient to infer a 

likely causal relationship for respiratory effects following short-term NO2 exposure (ISA, 

sections 3.1.7 and 5.3.2.1). The ISA (section 5.4) concludes that the strongest evidence for an 

association between NO2 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 distribution of ambient 24-h average NO2 concentrations 

was below the current NAAQS level of 0.053 ppm (53 ppb) (annual average). The effect 

estimates from the U.S. and Canadian studies generally 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 respiratory effects can be characterized as 

consistent, in that associations are reported in studies conducted in numerous locations with a 

variety of methodological approaches. Considering this large body of epidemiologic studies 

alone, the findings are also coherent in the sense that the studies report associations with 

respiratory health outcomes that are logically linked together. In addition, a number of these 

 36 
associations are statistically-significant, particularly the more precise effect estimates (ISA, 

section 5.3.2.1). These epidemiologic studies are supported by evidence from toxicological and 

controlled human exposure studies, particularly by controlled human exposure studies that 

evaluate airway hyperresponsiveness in asthmatic individuals (ISA, section 5.4). Together, the 

epidemiologic and experimental data sets form a plausible, consistent, and coherent description 

of a relationship between NO2 exposures and an array of adverse respiratory health effects that 

range from the onset of respiratory symptoms to hospital admission. 

However, as noted in the ISA (section 5.4), it is difficult to determine “the extent to 

which NO2 is independently associated with respiratory effects or if NO2 is a marker for the 

effects of another traffic-related pollutant or mix of pollutants.” On-road vehicle exhaust 

emissions are a nearly ubiquitous source of combustion pollutant mixtures that include NOx and 

can be an important contributor to NO2 levels in near-road locations. Although this complicates 

the efforts to quantify specific NO2-related health effects, the evidence summarized in the ISA 

indicates that NO2 associations generally remain robust in multi-pollutant models and supports a 

direct effect of short-term NO2 exposure on respiratory morbidity at ambient concentrations 

below the current NAAQS level. The robustness of epidemiologic findings to adjustment for copollutants, coupled with data from animal and human experimental studies, support the 

determination that the relationship between NO2 and respiratory morbidity is likely causal, while 

still recognizing the relationship between NO2 and other traffic related pollutants and the 

potential for confounding. 

4.3 OTHER ADVERSE EFFECTS FOLLOWING SHORT-TERM 

EXPOSURES 

The ISA concludes that the epidemiologic evidence is suggestive but not sufficient to 

infer a causal relationship between short-term exposure to NO2 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 NO2 concentrations and the risk of all-cause (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 NO2 effect estimates were robust to adjustment for co-pollutants. Both 

 37 
cardiovascular and respiratory mortality have been associated with increased NO2 concentrations 

in epidemiologic studies (ISA, Figure 3.3-3); however, similar associations were observed for 

other pollutants, including PM and SO2. The range of risk estimates for excess mortality is 

generally smaller than that for other pollutants such as PM. In addition, while NO2 exposure, 

alone or in conjunction with other 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 biochemical, lung host defense, permeability, and 

inflammation changes with short-term exposures to NO2 provide limited evidence of plausible 

pathways by which risks of mortality may be increased, but no coherent picture is evident at this 

time (ISA, section 5.3.2.3). 

The ISA concludes that the available evidence on cardiovascular health effects following 

short-term exposure to NO2 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 cardiac rhythm disorders among heart patients with ischemic cardiac 

disease are inconsistent (ISA, section 5.3.2.2). In most studies, associations with PM were found 

to be similar or stronger than associations with NO2. Generally positive associations between 

ambient NO2 concentrations and hospital admissions or ED visits for cardiovascular disease have 

been reported in single-pollutant models (ISA, section 5.3.2.2); however, most of these effect 

estimate values were diminished in multi-pollutant models that also contained CO and PM 

indices (ISA, section 5.3.2.2). Mechanistic evidence of a role for NO2 in the development of 

cardiovascular diseases from studies of biomarkers of inflammation, cell adhesion, coagulation, 

and thrombosis is lacking (ISA, section 5.3.2.2). Furthermore, the effects of NO2 on various 

hematological parameters in animals are inconsistent and, thus, provide little biological 

plausibility for effects of NO2 on the cardiovascular system (ISA, section 5.3.2.2). 

4.4 ADVERSE EFFECTS FOLLOWING LONG-TERM EXPOSURES 

4.4.1 Respiratory Morbidity 

The ISA concludes that overall, the epidemiologic and experimental evidence is 

suggestive but not sufficient to infer a causal relationship between long-term NO2 exposure and 

respiratory morbidity (ISA, section 5.3.2.4). The available database evaluating the relationship 

between respiratory illness in children and long-term exposures to NO2 has increased since the 

 38 
1996 review of the NO2 NAAQS. A number of epidemiologic studies have examined the effects 

of long-term exposure to NO2 and reported positive associations with decrements in lung 

function and partially irreversible decrements in lung function growth (ISA, section 3.4.1, figures 

3.4-1 and 3.4-2). Specifically, results from the California-based Children’s Health Study, which 

evaluated NO2 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 NO2 concentration. Similar 

associations have been found for PM, O3, 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 estimate independent effects in these long-term 

exposure studies (ISA, section 5.3.2.4). With regard to asthma incidence and long-term NO2, 

two major cohort studies, the Children’s Health Study (Gauderman et al., 2005) and a birth 

cohort study in the Netherlands (Brauer et al., 2007), observed significant associations. 

However, several other studies failed to find consistent associations between long-term NO2

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

conducted in the United States and Europe have produced inconsistent results regarding an 

association between long-term exposure to NO2 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 across specific outcomes were inconsistent (ISA, 

section 5.3.2.4). 

Animal toxicological studies may provide biological plausibility for the chronic effects of 

NO2 that have been observed in epidemiologic studies (ISA, sections 3.4.5 and 5.3.2.4). The 

main biochemical targets of NO2 exposure appear to be antioxidants, membrane polyunsaturated 

fatty acids, and thiol groups. NO2 effects include changes in oxidant/antioxidant homeostasis 

and chemical alterations of lipids and proteins. Lipid peroxidation has been observed at NO2

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 longer durations of exposure. Other studies showed decreases in 

formation of key arachidonic acid metabolites in AMs following NO2 exposures of 0.5 ppm. 

NO2 has been shown to increase collagen synthesis rates at concentrations as low as 0.5 ppm. 

This could indicate increased total 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 NO2 exposures have been identified in animal studies 

that link to these increases in collagen synthesis and may provide plausibility for the deficits in 

lung function growth described in epidemiologic studies (ISA, section 3.4.5). 

4.4.2 Mortality 

The ISA concludes that the epidemiologic evidence is inadequate to infer the presence or 

absence of a causal relationship between long-term exposure to NO2 and mortality (ISA, section 

5.3.2.6). In the United States and European cohort studies examining the relationship between 

long-term exposure to NO2 and mortality, results have been inconsistent (ISA, section 5.3.2.6). 

Further, when associations were suggested, they were not specific to NO2 but also implicated PM 

and other traffic indicators. The relatively high correlations reported between NO2 and PM 

indices make it difficult to interpret these observed associations at this time (ISA, section 

5.3.2.6). 

4.4.3 Other Long-Term Effects 

The ISA concludes that the available epidemiologic and toxicological evidence is 

inadequate to infer the presence or absence of a causal relationship for carcinogenic, 

cardiovascular, and reproductive and developmental effects related to long-term NO2 exposure 

(ISA, section 5.3.2.5). Epidemiologic studies conducted in Europe have shown an association 

between long-term NO2 exposure and increased incidence of cancer (ISA, section 5.3.2.5). 

However, the animal toxicological studies have provided no clear evidence that NO2 acts as a 

carcinogen (ISA, section 5.3.2.5). The very limited epidemiologic and toxicological evidence 

does not suggest that long-term exposure to NO2 has cardiovascular effects (ISA, section 

5.3.2.5). The epidemiologic evidence is not consistent for associations between NO2 exposure 

and fetal growth retardation; however, some evidence is accumulating for effects on preterm 

delivery (ISA, section 5.3.2.5). Scant animal evidence supports a weak association between NO2

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 NO2 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 relationship (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 causal relationship, an association must have been 

observed between the pollutant and the outcome in studies where chance, bias, and confounding 

can be ruled out with reasonable confidence. Human clinical studies provide the strongest 

evidence for causality while other lines of evidence (e.g., epidemiologic studies) provide support 

for this determination. An inference of a causal 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 could be due to the difficulty associated with 

addressing chance, bias, and confounding and/or due to the fact that 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 group. 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 evidence 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 association, but the results of other studies are 

inconsistent (ISA, Table 1.3-2). For purposes of the quantitative characterization of NO2 health 

risks, staff has judged it appropriate to focus on endpoints for which 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 made in other recent NAAQS reviews (e.g., see EPA, 

 41 
2005) and it will help ensure that our risk characterization is based on endpoints for which a 

causal relationship with NO2 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 evidence is judged to be sufficient to infer 

either a causal or a likely causal relationship is respiratory morbidity following short-term NO2

exposure. Therefore, for purposes of characterizing health risks associated with NO2, we have 

focused on respiratory morbidity endpoints that have been associated with short-term NO2

exposures. Other endpoints (e.g., long-term effects) will be considered as part of the evidencebased evaluation of potential alternative standards during the rulemaking stage of the NAAQS 

review. In evaluating the appropriateness of specific endpoints for use in the NO2 risk 

characterization, we have considered both epidemiologic and controlled 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 locations 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 respiratory 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 NO2 and respiratory symptoms, hospitalization, 

and ED visits. A number of these associations are 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 the epidemiologic results: “It is difficult to 

determine from these new studies the extent to which NO2 is independently associated with 

respiratory effects or if NO2 is a marker for the effects of another 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 NO2-related effect from epidemiologic studies is that NO2 is a 

component of a complex air pollution mixture from traffic related sources that include CO and 

various forms of PM.” These caveats should be considered when interpreting a quantitative NO2

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 NO2-related 

health effects, the evidence summarized in this assessment indicates that NO2 associations 

 42 
generally remain robust in multi-pollutant models and supports a direct effect of short-term NO2

exposure on respiratory morbidity at ambient concentrations below the current NAAQS. The 

robustness of epidemiologic findings to adjustment for copollutants, coupled with data from 

animal and human experimental studies, support a determination that the relationship between 

NO2 and respiratory morbidity is likely causal, while still recognizing the relationship between 

NO2 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 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, activity patterns of the population, and characteristics of the 

healthcare system. Second, we judged that studies of ambient NO2 are preferable to those of 

indoor NO2. This does not suggest that indoor studies 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 causality. However, studies of indoor NO2 focus on 

individuals exposed to NO2 from indoor sources. These indoor sources can result in exposure 

patterns, NO2 levels, and co-pollutants that are different from those typically associated with 

ambient NO2. Because the purpose of a quantitative risk assessment based on the 

epidemiological literature would be to inform decisions regarding an ambient NO2 standard, the 

preferred approach would be to consider studies of ambient NO2. Third, we judged it 

appropriate to focus on studies of ED visits and hospital admissions. When compared to studies 

of respiratory symptoms, the public health significance of ED visits 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 appropriate to focus on studies that evaluated NO2 health 

effect associations using both single- and multi-pollutant models. Taking these factors into 

consideration, we have chosen to focus on the studies by Peel and colleagues (2005) and by 

Tolbert and colleagues (2007) in Atlanta, Georgia. The epidemiology-based risk assessment is 

described in more detail in subsequent sections of this document. 

 43 
4.5.3 Controlled Human Exposure Studies 

Controlled human exposure studies have addressed the consequences of short-term (e.g., 

30-minutes to several hours) NO2 exposures for a number of health endpoints including airway 

responsiveness, host defense and immunity, inflammation, and lung function (ISA, section 3.1). 

In identifying health endpoints from controlled human exposure studies on which to focus the 

characterization of NO2 health risks, staff judges it appropriate to focus on endpoints that occur 

at or near ambient levels of NO2 and endpoints that are of clinical significance. With regard to 

the NO2 levels at which different effects have been documented, the ISA concludes that 1) in 

asthmatics NO2 may increase the allergen-induced airway inflammatory response at exposures as 

low as 0.26-ppm for 30 min (ISA, Figure 3.1-2) and NO2 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 NO2 may 

increase susceptibility to injury by subsequent viral challenge following exposures of 0.6-1.5 

ppm for 3 hours; 3) evidence exists for increased airway inflammation at NO2 concentrations less 

than 2.0 ppm; and 4) the direct effects of NO2 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 reported health effects associations 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 NO2 in controlled human exposure studies, 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 reasons. First, the ISA concludes that “persons 

with preexisting pulmonary conditions are likely at greater risk from ambient NO2 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 

NO2-related airway hyperresponsiveness in asthmatics, the ISA concludes that “transient 

increases in airway responsiveness following NO2 exposure have the potential to increase 

symptoms and worsen asthma control” (ISA, sections 3.1.3 and 5.4). That this effect could have 

public health implications is suggested by the large size of the asthmatic population in the United 

States (see above and ISA, Table 4.4-1). Third, NO2 effects on airway responsiveness in 

asthmatics are part of the body of experimental evidence that provides plausibility and coherence 

 44 
for the effects observed on hospital admissions and ED visits in epidemiologic studies (ISA, 

section 5.3.2.1). As a result of these considerations, although studies on other endpoints 

evaluated in controlled human exposure studies provide qualitative support for the ability of NO2

to cause adverse effects on respiratory health, the focus for purpose of quantifying risks 

associated with ambient NO2 is airway responsiveness in asthmatics (see below). 

Because many of the studies of airway responsiveness evaluate only a single level of NO2

and because of methodological differences between the studies, staff has judged that the data are 

not sufficient to derive an exposure-response relationship in the range of interest. Therefore, the 

most appropriate approach to characterizing risks based on the controlled human exposure 

evidence for airway responsiveness is to compare estimated NO2 air quality and exposure levels 

with potential health effect benchmark levels. Estimates of hourly peak air quality 

concentrations and personal exposures to ambient NO2 concentrations at and above specified 

potential health effect benchmark levels provide some perspective on the potential health impacts 

of NO2 exposure. Staff recognizes that there is high inter-individual variability in NO2-induced 

effects on airway responsiveness such that only a subset of asthmatic individuals exposed at and 

above a given benchmark level may actually be expected to experience an adverse effect. 

Potential health benchmark levels and the approach 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 NO2 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 NO2; 2) NO2 levels associated with just meeting the current standard; and 3) 

NO2 levels associated with just meeting potential alternative standards. This section identifies 

potential alternative standards in terms of indicator, averaging time, form, and level and provides 

the rationale that was used to select them. 

5.2 INDICATOR 

The NOx, for purposes of this document, include multiple gaseous (e.g., NO2, NO) and 

particulate (e.g., nitrate) species. In considering the appropriateness of different indicators, we 

note that the health effects associated with particulate species of NOx have been considered 

within the context of the health effects of ambient particles in the Agency’s review of the 

NAAQS for PM. Thus, as discussed in the integrated review plan (2007a), the current review of 

the NO2 NAAQS is focused on the gaseous species of NOx and will not consider health effects 

directly associated with particulate species of NOx. Of the gaseous species, EPA has historically 

determined it appropriate to specify the indicator of the standard in terms of NO2 because the 

majority of the information regarding health effects and exposures is for NO2. The final ISA has 

found that this continues to be the case and, therefore, staff believes that NO2 remains the most 

appropriate indicator. 

5.3 AVERAGING TIME 

The current annual standard for NO2 was originally set in 1971 based on epidemiologic 

studies that supported a link between adverse respiratory effects and long-term exposure to lowlevels of NO2. Although the quantitative basis for the annual averaging time was later called into 

question (60 FR 52876), the annual standard was retained 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., emphysematous-like alterations in the lung and 

 46 
increased susceptibility to infection) came from animal studies conducted at concentrations well 

above those permitted in the ambient air by the 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 levels that had been shown in controlled human 

exposure studies to impact endpoints of potential concern (i.e., airway responsiveness). 

The issue of averaging time will be reconsidered in the current review. As described 

above, the ISA concludes that, when taken together, “recent studies provide scientific evidence 

that NO2 is associated with a range of respiratory effects and is sufficient to infer a likely causal 

relationship between short-term NO2 exposure and adverse effects 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 associations between short-term (e.g., 1-h, 24-h) 

NO2 concentrations and health effects. Many of these studies have been conducted in locations 

where long-term ambient levels of NO2 are well below the current annual standard. As a result, 

staff has concluded that it is appropriate to consider alternative averaging times for their ability 

to protect against health effects associated with short-term NO2 levels and/or exposures. 

In contrast to the conclusion in the ISA concerning respiratory morbidity associated with 

short-term exposures to NO2, the ISA concludes that the “evidence examining the effect of longterm exposure to NO2 on respiratory morbidity is suggestive 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 NO2 on other health outcomes (i.e., mortality, cancer, 

cardiovascular effects, reproductive and developmental 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 alternative long-term standards in the current assessment. 

In considering appropriate short-term averaging 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 reports 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 epidemiologic 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 NO2 exposures from less than 1 hour up to 3 hours can result in respiratory effects 

(section 5.3.2.7). Given that the 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 occurrence of effects following exposures 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 standard, staff recognizes that it is important 

to have a form that 1) reflects the health risks posed by elevated NO2 concentrations and 2) 

achieves a balance between limiting the occurrence of peak concentrations and providing a stable 

and robust regulatory target. Consistent with judgments made in recent reviews of the PM (71 

FR 61144) and O3 (73 FR 16436) NAAQS, staff judges that a concentration-based form 

averaged over 3 years for the NO2 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 NO2 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 same 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 NO2 concentrations and, in developing potential 

alternative standards for consideration, we have focused on standards with this concentrationbased form. The most recent review of the PM NAAQS (completed in 2006) judged that using a 

98th percentile form averaged over 3 years provides an appropriate balance between limiting the 

occurrence of peak concentrations and providing a stable regulatory target (71 FR 61144). In 

consideration of this balance, we have determined it appropriate in the current review to evaluate 

both the 98th and 99th percentile NO2 concentrations averaged over 3 years.4

 We have judged 

that these percentiles, when combined with the range of alternatives identified for the level of the 

 

4

 98th or 99th percentiles of the 1-h daily maximum NO2 concentrations would be calculated for each of 3 

consecutive years. The 98th or 99th 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 occurrence of peak concentrations. 

5.5 LEVEL 

In developing an approach to formulating an appropriate range of NO2 levels for analysis, 

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

review of the NO2 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 NO2. In general, these studies report positive 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 NO2 are well below the level of the current 

NAAQS (ISA, section 5.3.2.1). Second, controlled human exposure studies have detected 

effects of NO2 exposure on several health endpoints. Of these, only airway hyperresponsiveness 

is associated with exposures to NO2 concentrations at or near ambient levels. In fact, the NO2

exposure levels associated with increased airway responsiveness overlap the maximum ambient 

NO2 concentrations in some locations where associations with respiratory effects have been 

detected. Third, limitations in both epidemiologic studies (e.g., confounding by co-pollutants) 

and controlled human exposure studies (e.g., most sensitive populations likely not evaluated) 

suggest that an appropriate approach to identifying levels for potential alternative standards is to 

consider both types of studies. 

In considering both types of studies, we note that NO2 concentrations represent different 

metrics when reported in epidemiologic studies versus controlled human exposure studies. 

Concentrations of NO2 reported in epidemiologic studies are typically based on ambient 

monitoring data while NO2 levels reported in controlled human exposure studies represent the 

concentration of NO2 in the breathing zone of the individual. Therefore, consideration of NO2

levels from controlled human exposure studies when identifying alternative levels for an ambient 

standard introduces some uncertainty. For example, elevated NO2 monitors, particularly in inner 

cities, likely underestimate personal exposures that occur at lower elevations closer to traffic 

(ISA, section 5.2.2). In situations where personal exposure to ambient NO2 is higher than 

ambient levels measured at a monitor, ambient standard levels based on controlled exposure 

studies could be less health-protective than levels based on concentrations reported in 

 49 
epidemiologic studies at ambient monitors. However, in studies where it has been determined, 

the ratio of personal exposure to NO2 of ambient origin to the ambient NO2 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 NO2 are lower than the levels of NO2 measured at ambient 

monitors. In situations where personal exposure concentrations for ambient NO2 are lower than 

the levels measured at ambient monitors, an ambient standard level based on controlled exposure 

studies could be more health-protective than a level based on concentrations reported in 

epidemiologic studies at ambient monitors. Overall, because some individuals are likely exposed 

to NO2 levels higher than those measured at ambient monitors (ISA, section 5.2.2) while others 

are likely exposed to NO2 levels lower than those measured at ambient monitors (see ISA, tables 

2.5-4 and 2.5-5), we have chosen to use NO2 concentrations associated with both epidemiologic 

studies and controlled human exposure studies for purposes of selecting alternative levels for 

analysis. 

As a result of the above considerations, to determine the levels that should be evaluated 

staff has relied on both key epidemiologic studies conducted in the United States that evaluate 

associations between short-term levels of NO2 and respiratory morbidity (symptoms, hospital 

admissions, ED visits) and on controlled human exposure studies that evaluate airway 

hyperresponsiveness following NO2 exposure. Figures 5-1 and 5-2 below show standardized 

effect estimates5 and the 98th and 99th percentile concentrations of daily 1-h maximum NO2 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 included in figures 5-1 and 5-2, the highest 1-h 

NO2 concentrations were detected in the two studies conducted in Los Angeles (Linn et al., 

2000; Ostro et al., 2001). For these studies, the 98th and 99th percentile 1-h daily maximum 

concentrations of NO2 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 studies 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 NO2 at 0.2 to 0.3 ppm and given that the epidemiologic studies by 

Linn et al. (2000) and Ostro et al. (2001) are associated with 98th and 99th percentile 1-h daily 

maximum NO2 levels that are just below (Linn et al., 2000) and just above (99th percentile level 

for Ostro et al., 2001) 0.2 ppm (see 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 NO2 concentration of 0.20 

ppm. 
Figure 5-1. NO2 effect estimates6

 (95% CI) for ED visits/HA and associated 1-h daily 

maximum NO2 levels (98th and 99th percentile values in boxes7) 

 

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. NO2 effect estimates retained statistical-significance in the study by Ito, but not in the other 

studies. 

7

 Authors of relevant U.S. and Canadian studies were contacted and air quality statistics from the study monitor that 

recorded the highest NO2 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 identification information. In these cases, we attempted to 

reconstruct the air quality data set for the location and time of the study using EPA’s Air Quality System (AQS). 

Prior to identifying potential alternative standards, we did not receive air quality information from any of the 

Canadian authors contacted and we were unable to reconstruct the air quality data sets for the Canadian studies. 

Therefore, for purposes of identifying levels of potential alternative standards, our analysis was based on these key 

U.S. studies. Note that the NO2 concentrations reported in table 1 of the study by Jaffe are labeled as 24-hour 

concentrations, but the author indicated in a personal communication (Jaffe, 2008) that they actually represent 1-

hour daily maximum concentrations. 

 52 
Figure 5-2. NO2 effect estimates for respiratory symptoms and associated 1-h daily 

maximum NO2 levels (98th and 99th percentile values in boxes)

 In identifying additional standard levels that should be analyzed, staff has considered that 

1) health effect associations in epidemiologic studies are observed in locations with 1-h daily 

maximum levels of NO2 below 0.2 ppm (i.e., 99th percentile levels in several studies are close to 

0.1 ppm); 2) controlled human exposure studies that evaluate the ability of NO2 to elicit airway 

hyperresponsiveness have assessed mild asthmatics and more severely affected asthmatics could 

experience increased airway responsiveness at lower levels of NO2 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 NO2 exposures 

as low as 0.1 ppm. As a result of these considerations, staff judges that it would be appropriate 

to consider additional standard levels that provide a margin of safety relative to 0.20 ppm. 

Therefore, we will also consider daily maximum 1-h NO2 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 NO2 concentrations and respiratory morbidity in a location where 

the 98th and 99th percentile concentrations of the 1-h daily maximum levels of NO2 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 statistically-significant) levels of NO2 and asthma symptoms in a location 

where the 98th and 99th percentile 1-h daily maximum NO2 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 PM10. 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 NO2, with levels associated with just meeting 

the current NO2 NAAQS, and with levels associated with just meeting potential alternative 

standards (see chapter 5 of this document for discussion of potential alternative standards). To 

characterize health risks, we have employed three approaches. With each approach, we have 

characterized health risks associated with the air quality scenarios of interest (i.e., recent air 

quality unadjusted, air quality adjusted to simulate just meeting the current standard, and air 

quality adjusted to simulate just meeting potential alternative standards). In the first approach, 

NO2 air quality levels have been compared to potential 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, modeled estimates of actual exposures have been 

compared to potential health effect benchmarks. In the third approach, exposure-response 

relationships from epidemiologic studies have been used to estimate health impacts. An 

overview of the approaches to characterizing health 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 NO2 air quality with potential health effect 

benchmark levels for NO2. Scenario-driven air quality analyses have been performed using 

ambient NO2 concentrations for the years 1995 though 2006. With this approach, NO2 air 

quality serves as a surrogate for exposure. All U.S. monitoring sites where NO2 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 approach is its relative simplicity; however, there is 

uncertainty associated with the assumption that NO2 air quality can serve as an adequate 

surrogate for exposure to ambient NO2. Actual exposures might be influenced by factors not 

considered by this approach, such as the spatial and temporal variability in human activities. 

 55 
In the second approach, we have used an inhalation exposure model to generate more 

realistic estimates of personal exposures. Estimates of personal exposure have been compared to 

potential NO2 health benchmark levels. For this exposure analysis, a probabilistic approach was 

used to model individual exposures considering the time people spend in different 

microenvironments and the variable NO2 concentrations that occur within these 

microenvironments across time, space, and microenvironment type. This approach to assessing 

exposures was more resource intensive than using 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 analysis is restricted, the approach provides 

realistic estimates of NO2 exposures, particularly those exposures associated with important 

emission sources of NOx and NO2, 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 NO2 exposure levels that have 

been associated with increased airway responsiveness in asthmatics in controlled human 

exposure studies (ISA, section 5.3.2.1; see above for discussion). Benchmark values of 100, 

150, 200, 250, and 300 ppb have been compared to both NO2 air quality levels and to estimates 

of NO2 exposure. When NO2 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 

NO2 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 NO2 of ambient origin that exceed 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 studies clearly result from NO2 exposure. This is in contrast to 

health effects associated with NO2 in epidemiologic studies, which may also be associated with 

pollutants that co-occur with NO2 in the ambient air. Thus, when using epidemiologic studies as 

the basis for risk characterization, the unique contribution of NO2 to a particular health effect 

 

8

 In the document titled Risk and Exposure Assessment to Support the Review of the NO2 Primary National Ambient 

Air Quality Standard: First Draft, we have presented the results of an exposure analysis for Philadelphia. Based on 

CASAC comments received on that exposure analysis, we have 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 NO2 effect on airway responsiveness can vary considerably from individual to 

individual and not all asthmatics would be expected to respond to the same levels of NO2

exposure. Therefore, the public health impacts of NO2-induced airway hyperresponsiveness are 

difficult to quantify. 

In the third approach, we have estimated respiratory ED visits as a function of ambient 

levels of NO2 measured at a fixed-site monitor representing ambient air quality for an urban area. 

In this approach, concentration-response functions are derived from NO2 epidemiologic studies 

and are used to estimate the impact of ambient levels of NO2, 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 approach provides additional perspective on the 

potential public health impacts of NO2. Relative to the approaches that use controlled human 

exposure studies, this approach to characterizing health risks has several advantages. For 

example, the public health significance of the effect 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 concentration-response 

relationship reflects real-world levels of NO2 and co-pollutants present in ambient air. However, 

a disadvantage of this approach is the ambiguity and complexity associated with quantifying the 

contribution of NO2 to the reported health impacts relative to the contributions of co-occurring 

pollutants. 

6.2 POTENTIAL HEALTH BENCHMARK LEVELS 

As noted above (section 4.5.3 and 6.1), staff has 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 levels is to provide a basis for 

comparing NO2 concentrations shown to increase airway responsiveness in asthmatics (from 

controlled human exposure studies) to estimates of NO2 exposures derived from our air quality 

and exposure analyses. Because the purpose of the benchmarks is to provide a way to link 

estimates of NO2 exposure with levels known to produce respiratory effects in individual 

asthmatics, benchmark levels have not been developed from the epidemiologic literature. 

 57 
To identify potential health effect benchmarks, staff has relied on the ISA’s evaluation of 

the NO2 human exposures studies. Controlled human exposure studies involving allergen 

challenge in asthmatics suggest that NO2 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 NO2 for 30 min (ISA, Figure 3.1-2 and section 

5.3.2.1). Exposure to NO2 also has been found to enhance the 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 NO2 for 30 minute exposures and at 0.1 ppm NO2 for 1-h exposures (ISA, section 

5.3.2.1). Therefore, for the risk characterization, staff judges that 1-h NO2 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 have 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 provided evidence for the occurrence of NO2-induced 

airway hyperresponsiveness. 

In choosing this range, we recognize that uncertainties exist regarding the percentage of 

asthmatics expected to experience an increase in responsiveness following NO2 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 NO2 exposures in the range of 0.1 to 0.3 ppm. 

However, this meta-analysis provides information only on the direction of the NO2 effect and not 

on its magnitude. In addition, the NO2 controlled human exposure studies of airway 

responsiveness have focused primarily on mild asthmatics. It is possible that more severely 

affected asthmatics could experience a more severe response following NO2 exposures in this 

range. It is also possible that they could experience a response at lower levels of NO2 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 NO2 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 assessments is to evaluate the ability of the 

current NO2 standard (0.053 ppm annual average) and potential alternative 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 NO2 concentrations need to be 

adjusted such that they simulate levels of NO2 that just meet that standard. Such adjustments 

allow comparisons of the level of public health protection that could be associated with just 

meeting the current and potential alternative standards. All areas of the United States currently 

have ambient NO2 levels below the current annual standard. Therefore, to simulate just meeting 

the current annual standard, NO2 air quality levels must be adjusted upward. Similarly, to 

simulate a potential standard that is below current air quality levels, those current levels must be 

adjusted downward. This process of adjusting air quality to simulate just meeting a specific 

standard is described in more 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 NO2 concentrations were proportionally adjusted at each location 

using the maximum monitored concentration that occurred in each year. Policy-relevant 

background is defined as the distribution of NO2 concentrations that would be observed in the 

U.S. in the absence of anthropogenic (man-made) emissions of NO2 precursors in the U.S., 

Canada, and Mexico. Policy-relevant background for most of the continental U.S. is estimated to 

be less than 300 parts per trillion (ppt) (ISA, Section 2.4.6). In the Northeastern U.S. where 

present-day NO2 concentrations are highest, this amounts to a contribution of less than 1% 

percent of the total observed ambient NO2 concentration. This low contribution of PRB to NO2

concentrations provides support for 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 concentrations have declined significantly over the time period of 

analysis, the variability in the concentrations, both the annual average 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 NO2 1-hour 

concentration percentiles at the one ambient monitor 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 series of high concentration year data. As 

shown in the figure, the relationships between the low and high concentration years at each of 

the daily maximum concentration percentiles are mostly linear, with R2 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 minimum daily maximum 1-hour concentration. This 

indicates that the rate of decrease in ambient air quality concentrations at the mean value is 

consistent with the rate of change at the lower and upper daily maximum 1-hour concentration 

percentiles. This trend provides 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 NO2 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 CSF max,ij = / equation (6-1) 

where, 

Fij = NO2 concentration adjustment factor (unitless) in location i given the annual 

average standard and for each year j 

S = Current standard level (i.e., 53 ppb, annual average NO2 concentration) 

Cmax,ij = The maximum annual average NO2 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 NO2

concentrations using equation (6-2), it was assumed that the current temporal and spatial 

distribution of air concentrations (as characterized by the current air quality data) is maintained 

and increased NOx emissions contribute to increased NO2 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 quality adjustment factor used for each location 

evaluated in the air quality and risk characterization are given in Appendix A (section 7.2). For 

each location and calendar year, all the hourly NO2 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 NO2 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 concentrations measured at all monitoring sites in that 

location are then multiplied by 1.74, resulting in an upward scaling of hourly NO2 concentrations 

for that year. Therefore, one monitoring site in Boston for year 1995 would have an annual 

average concentration of 0.053 ppm, while all other monitoring sites would have an annual 

 61 
average concentration below that value, although still proportionally scaled up by 1.74 (Figure 6-

2, bottom). Then, using the adjusted hourly concentrations to simulate just meeting the current 

standard, the metrics of interest (e.g., annual mean NO2 concentration, the number of potential 

health effect benchmark exceedances) were estimated for each site-year. 

Figure 6-2. Distributions of hourly NO2 concentrations at twelve ambient monitors in the 

Boston CMSA, as is (top) and air quality adjusted to just meet the current standard 

(bottom), Year 1995. 

 62 
Proportional adjustment factors were also derived 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 provided in chapter 5 of this document. The 98th

and 99th percentile 1-hour NO2 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, 

Fikl = NO2 concentration adjustment factor (unitless) in location i given alternative 

standard percentile form k and standard level l across a 3-year period 

Sl = Standard level l (i.e., 50, 100, 150, 200 ppb 1-hour NO2 concentration (ppb)) 

Cijk = Selected percentile k (i.e., 98th or 99th) 1-hour daily maximum NO2

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 spatial distribution of air concentrations (as 

characterized by the current NO2 air quality data) is maintained and increased NOx emissions 

contribute to increased NO2 concentrations, with the highest monitor (in terms of the 3-year 

average at the 98th or 99th percentile) being adjusted so that it just meets the level of the 

particular 1-hour alternative standard. Since the alternative standard levels range from 50 ppb 

through 200 ppb, both proportional upward and downward adjustments were made to the 1-hour 

ambient NO2 concentrations. The values for each air quality adjustment factor used for each 

location evaluated in the air quality and risk characterization 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, only the more recent air quality data were used 

(i.e., years 2001-2006) and separated into two 3-year periods, 2001-2003 and 2004-2006. The 1-

hour ambient NO2 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 modify the air quality concentrations used for input to the 

exposure model, a proportional adjustment of the potential health effect benchmark level was 

performed. This was done to reduce the processing 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 selected benchmark level is mathematically equivalent to a 

proportional upward adjustment of the air quality concentrations. The same approach used in the 

air quality adjustment described above was used in the exposure modeling to scale the 

benchmark levels to simulate just meeting the current and potential alternative standards. For 

example, an adjustment factor of 2.27 was determined for Atlanta for year 2001 to simulate 

ambient concentrations just meeting the current standard, based on the maximum annual average 

NO2 concentration of 23.3 ppb observed at an ambient monitor for that year (see Appendix A, 

section 7.2). Therefore, the 1-hour potential health 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 presented 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 NO2 concentrations measured at the 

maximum ambient monitor (ID 1312100481) within the Atlanta modeling domain for year 2001. 

If we were interested in the number of exceedances 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 quality data are presented in Figure 6-4, only 

with expansion of the highest percentiles on the graph to allow for 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, there 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 current and alternative standards. Additional details 

regarding derivation of the adjusted benchmark levels used in the exposure modeling are 

provided in chapter 8 of this document. 

Figure 6-3. Comparison of adjusted 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 
99.7

99.8

99.9

100

50 100 150 200 250 300 350 400

1-hour NO2 (ppb)

Cumulative Percentile

ATL1312100481

ATL1312100481_CS

Adjust AQ up

Adjust Benchmark Down

Figure 6-4. Comparison of the upper percentiles for where ambient monitoring NO2

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 NO2

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 NO2 air quality across the U.S. This air quality data, as well as other 

NO2 concentrations derived from ambient levels, were used as a surrogate to estimate potential 

human exposure. While an ambient monitor measures NO2 concentrations at a stationary 

location, the monitor may well represent the concentrations that persons residing nearby are 

exposed to. The quality of the extrapolation of ambient monitor concentration to personal 

exposure will be dependent upon the spatial distribution 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 NO2 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 characterization was 1-hour, with 

concentration levels ranging from between 100 and 300 ppb. Since the current standard is based 

on annual average levels of NO2 while the most definitive health effects evidence is associated 

with short-term exposures (i.e., 30-minute to 1-hour, or one to several day), the air quality 

analysis required the development of a model that relates annual average and short-term levels of 

NO2. To characterize this relationship and to estimate the number of exceedances of the 

potential health effect benchmarks in specific locations, an empirical model, employing the 

annual average and hourly concentrations, was chosen to avoid some of the difficulties in 

extrapolating outside the range of the observed air quality. 

The available NO2 air quality were first divided into two six-year groups; one contained 

data from years 1995-2000, representing an historical data set; the other contained the 

monitoring years 2001-2006, representing recent ambient monitoring. Each of these monitoring 

year-groups were evaluated considering the NO2 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 within each year-group separated by monitor distance 

 67 
from a major road.1

 The ambient monitor data were categorized in this manner to account for 

the potential influence of vehicle emissions on concentrations measured 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 simulation approach (described below in section 

7.2.4) to estimate on-road NO2 concentrations for each of the year-groups. This scenari

developed by recognizing that vehicles are important emission sources of NOx and NO2 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 NO2 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 alternative standards considered here (98th

and 99th percentiles of the daily maximum concentrations 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 two broad analyses, one 

using air quality as is and the other where air quality was adjusted to just meeting the current and 

alternative standards. Within both of these analyses, an additional simulation was performed to 

estimate NO2 concentrations on roads. The first scenario 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 (scenario 2), concentrations that just meet a 

particular standard level (scenario 3), or both (scenario 4). 

Because many of the NO2 ambient monitoring sites used in this analysis are primarily 

targeting public health monitoring objectives, the results are considered a broad characterization 

of national air quality and potential human exposures that might be associated with these 

scenario-driven concentrations. The output of this air quality characterization is an estimate the 

1

 As part of our earlier analysis reported in the 2nd 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 1st draft REA, the historical data were evaluated using concentrations 

as is and for air quality adjusted to just meet the current standard. Only the recent 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 locations experience daily maximum levels3

 of NO2 that may 

cause adverse health effects in susceptible individuals. 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 used to generate a distribution of 

two exposure and risk characterization metrics; the annual average 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 median values were reported to represent the central 

tendency of each metric for the four 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 particular 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 percentiles generally included the 95th, 98th, and 99th percentiles of the 

distribution. As described for the minimum value, these upper percentile estimates could also 

represent 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, compiling, and screening the ambient air quality data collected since the 

prior review in 1995 to ensure consistency with the NO2 NAAQS requirements. Then, criteria 

based on the current standard and the potential health effect benchmark levels were used to 

identify specific locations for analysis using descriptive statistical analysis of the screened data 

set. All remaining monitoring data not identified by the selection criteria were grouped into one 

of two non-specific categories. 

 

3

 The historical data (1995-2000) were only evaluated for total number of exceedances, not for daily maximum 

exceedances as done for the recent air quality data (2001-2006). 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 potential health effect benchmark levels. Next, due to expected 

variable influence of road emissions on ambient monitor concentrations, the monitors within 

each of the named locations were categorized according 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 ambient concentrations (as is) and 

ambient concentrations just meeting the current and alternative standard levels, on-road NO2

concentrations were estimated in each location to approximate the potential exposure and risk 

metrics associated with these concentrations. And finally, air quality metrics of interest were 

calculated using air quality data from each scenario.

7.2.1 Air Quality Data Screen 

NO2 air quality data and associated documentation 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 occurrence code (POC), which 

gives a 10-digit monitor ID code. As required by the NO2 NAAQS, a valid year of monitoring 

data is needed to calculate the annual average concentration. A valid year at a monitoring site 

was comprised of 75% of valid days in a year, with 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 monitoring data used in 

the air quality characterization. 

Site-years of data are the total numbers of years the collective monitors in a location were 

in operation. Of a total of 5,243 site-years of data in the entire NO2 1-hour concentration 

database, 1,039 site-years did not meet the above completeness criterion and were excluded from 

any further analyses. In addition, since shorter term average concentrations are of interest, the 

remaining site-years of data were further screened 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 excluded, resulting in 4,177 complete site-years in the 

analytical database. Table 7-1 provides a summary of the site-years included in the analysis, 

 70 
relative to those excluded, by location and by two site-year groups.4

 The air quality data from 

AQS were separated into these 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 NO2 concentrations within each location. The selection of specific 

locations was a companion analysis to this data screening, and is discussed in section 7.2.2. 

Table 7-1. Counts of complete and incomplete site-years of NO2 ambient monitoring data. 

Number of Site-Years1 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 year 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 variable. 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 monitoring sites with high annual means and/or 

frequent exceedances of potential health effect benchmarks. Selected 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 NO2 level at a particular monitor greater than or equal to 25.7 ppb, 

which represents the 90th percentile across all locations and site-years, and/or had at least one 

reported 1-hour NO2 level greater than or equal to 200 ppb, the lowest level of the potential 

health effect benchmarks. 5

 A location in this context would include a geographic area that 

encompasses more than a single air quality monitor (e.g., particular city, metropolitan statistical 

area (MSA), or consolidated metropolitan statistical area or CMSA). First, all ambient monitors 

were identified as either belonging to a CMSA, a MSA, or neither. Then, locations of interest 

were identified through statistical analysis of the ambient NO2 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 monitors 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 200 ppb and contained one of 

the lowest maximum concentration-to-mean ratios. Therefore 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 NO2 Air Quality Characterization, associated abbreviations, and 

values of selection criteria. 

Location 

Type1 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; MSA is metropolitan statistical area according to the 

1999 Office of Management and Budget definitions (January 28, 2002 revision). 

* Indicates locations that satisfied both the annual average and exceedance criteria. 

7.2.3 Site Characteristics of Ambient NO2 Monitors 

The siting of the ambient monitors is of particular importance, recognizing that the 

purpose of the monitoring could have an influence on the measured NO2 concentrations and 

subsequent interpretation in the air quality characterization. Specific monitoring site 

characteristics provided in AQS were obtained, including the monitoring objective, measurement 

 73 
scale, and predominant land-use. Additional features such as proximity to NOx emission 

sources, including mobile and stationary sources, were estimated using each monitoring site and 

emission source geographic coordinates. Each of these attributes is summarized here to provide 

perspective on the representation the ambient NO2 monitoring network within each location. A 

more thorough discussion of the ambient monitoring network is provided in Chapter 2. 

Individual monitor site characteristics 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 the air quality characterization was performed for 

particular year-groups of data, the monitoring objectives were weighted 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 purposes of the REA, the four objective categories 

were used in the following order (i.e., population exposure, high concentration, 

general/background, unknown) to characterize the monitor with one objective. All other 

objectives were grouped into an “Other” category. Tables 7-3 and 7-4 summarize the monitoring 

objectives for each location using the historical and recent air quality data, respectively. Most 

locations contained monitoring site-years of data that would target public health through the 

population exposure and highest concentration categories. Where these categories were not the 

predominant objective, the monitoring objective was mainly unknown. 

 74 
Table 7-3. Percent of ambient NO2 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 NO2 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 site-years of air quality data. 

The measurement scale represents the air volumes associated with the monitoring area 

dimensions. While a monitor can have multiple 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). Where 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 use surrounding the monitors in each location using 

the historical and recent air quality data, respectively. Most locations contained monitoring siteyears of data from areas within residential and commercial areas, but were generally dominated 

by residential land use. Two locations however were characterized with site-years of data 

associated entirely with commercial land use (i.e., Jacksonville and Provo). 

Table 7-5. Percent of ambient NO2 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 NO2 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 NO2 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 NO2 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 contributors to nitrogen oxides (NOx) 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 meters or greater from a major road, however in 

locations with a large monitoring network such as Boston, Chicago, or New York, there were a 

few monitors sited within close proximity (<20 meters) of a road. Most of the monitors were 

sited at much greater distances to NOx stationary sources than major roads. In general, monitors 

were located at least 1 km from NOx stationary sources, with over half 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 categories 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. Distances 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 meter and 100 meters (>20 m, 

<100 m), and those located at least 100 meters from a major road (≥100 m) (Figure 7-1). 

Table 7-9. Distance of ambient monitors to the 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) NOx

stationary source 

Location n1 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 particular location between 1995 and 2006. The min, med, and 

max represent the minimum, median, and maximum percentiles of the distribution for the distance in meters 

(m) of the monitor to the nearby sources. Monitors >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 < 20 m to < 100 m ≥ 100 m

+

+

+

+ + ambient monitor location

major road

+

+

+

+

+

≤ 20 m < 20 m to < 100 m ≥ 100 m

+

+

+

+ + ambient monitor location

major road

+

+

+

+

+

Figure 7-1. Illustration of three roadway distance categories used to characterize ambient 

monitors in the Air Quality Characterization. 

7.2.4 Estimation of On-Road Concentrations using Ambient Concentrations 

Since mobile sources can account for a large part of personal exposures to ambient NO2

in some individuals, the potential impact of roadway levels of NO2 was evaluated. A strong 

relationship has been reported between NO2 levels measured on roadways and NO2 measured at 

increasing distance from the road. This relationship 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, 

Cx = NO2 concentration at a given distance (x) from a roadway (ppb) 

Cb = NO2 concentration (ppb) at a distance from a roadway, not directly influenced 

by road or non-road source emissions 

Cv = NO2 concentration contribution from vehicles on a roadway (ppb) 

k = Combined formation/decay constant describing NO2 with perpendicular 

distance from roadway (meters-1) 

x = Distance from roadway (meters) 

 80 
Based on the findings of several researchers, much of the decline in NO2 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 near ambient levels between 200 to 500 meters 

(Rodes and Holland, 1981; Bell and Ashenden, 1997; Gilbert et al., 2003; Pleijel 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 NO2 concentration and the concentration contribution expected from vehicle 

emissions on the roadway using 

 mCC )1( equation (7-2) ar +=

where, 

Cr = 1-hour on-road NO2 concentration (ppb) 

Ca = 1-hour ambient monitoring NO2 concentration (ppb) either as is or adjusted to 

just meet the current or alternative standards 

m = Ratio derived from estimates of Cv/Cb (from equation (7-1)) 

and assuming that Ca = Cb.

8

To estimate on-road NO2 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 identify published studies containing NO2 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 (Cv) to NO2

concentration at a distance from a roadway (Cb) (equation 7-1), generating a distribution of 

values for use in estimating on-road concentrations from the ambient monitor concentrations 

(Table 7-10). The distribution of derived m ratios were evaluated for possible stratification using 

potential influential factors reported in the collection 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 Ca may differ from Cb since Ca could include the influence of on-road as well as non-road sources. 

However, it is expected that for most monitors sited at a distance of ≥100 from a major road, the influence of onroad emissions would be neglibible so that Ca ≅ Cb.

 81 
data. In general, categorizing the data based on summer and not summer seasons were 

determined appropriate, containing 21 and 19 samples, respectively. Then, on-road NO2

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, section 8 for a detailed explanation of the m ratio derivation 

and the literature sources used. 

Table 7-10. Derived Cv/Cb ratios (m) for two season groups used for adjusting ambient NO2

concentrations to simulate on-road NO2 concentrations. 

Cv/Cb 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, NO2 concentrations can increase at a distance from the road due to 

chemical interaction of NOx with O3, the magnitude of which can be driven by certain 

meteorological conditions (e.g., wind direction). As such, the maximum NO2 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 NO2 roadway and near-road measurement studies was used to 

 82 
estimate NO2 concentrations that occur on the road and not used to estimate NO2 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 these concentrations, where such monitors are available in a 

location. Second, since there is potential for monitors that are sited near roadways to be 

influenced by vehicle emissions and equation (7-2) assumes the ambient concentration is 

approximating NO2 concentrations not directly influenced by the roadway, the ambient monitors 

<100 m were not used for estimating the on-road NO2 concentrations in this analysis. The 

uncertainty regarding any additional issues or assumptions and the potential effect on exposure 

estimates are discussed in section 7.4. 

To estimate on-road NO2 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 adjustment factors were assigned randomly because 

we expect the empirical relationship between Cv and Cb to vary from place to place and we do 

not have sufficient information to match specific ratios with any of the locations simulated in this 

assessment. Hourly NO2 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 distributions applied independently to the total siteyears of data (105). Following 100 simulations, a total of 10,500 site-years 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 characterization scenarios considered, two concentration 

metrics were calculated, including the annual average NO2 concentrations for each site-year and 

the number of exceedances of the potential health effect benchmark levels. To characterize this 

relationship and to estimate the number of exceedances of the potential health effect benchmarks 

in specific locations, several possible models were explored (i.e., exponential regression, logistic 

regression, a regression assuming a Poisson distribution, and an empirical model). An empirical 

model, employing the annual average and hourly NO2 concentrations, was chosen to avoid some 

 83 
of the difficulties in extrapolating outside the range of the measurement data. In addition, an 

empirical model could be used for any averaging time of interest. A detailed discussion 

justifying the selection of the empirical 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 are 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 years) and, when separated into the major road 

categories, there would be 6, 15, and 6 site-years 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 NO2 concentrations within each respective 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 for each of the road distance categories, the 98th and 99th

percentile values are essentially the same, i.e., the maximum site-year annual average 

concentration. The same approach was used for the counts of exceedances in a year, reporting 

each the central tendency values and the lower and upper percentiles for each location. 

In the second scenario, air quality metrics for the estimated on-road NO2 concentrations 

were generated in a similar manner. The concentration distributions for the annual average 

concentrations and the distributions for the number of exceedances of short-term potential health 

effect benchmark levels were calculated for each location and year-group. In using the 

hypothetical location described above (Figure 7-1) for years 2001-2003, 600 site-years of hourly 

on-road concentrations were simulated (6 site-years by 100 simulations each). Mean (average of 

the 600 site-year values) and median (the average of the 300th and 301st 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 percentiles also included the 98th and 99th percentiles of the distribution. 

The 98th and 99th percentiles were the 588th and the 594th 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 obtained from AQS were first separated into two sixyear groups, one representing historical data (1995-2000) and the other representing more recent 

data (2001-2006). The two broad six-year groups of data were compared using each location’s 

distribution of annual average 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 few 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 with the planned analysis of alternative standards 

(sections 7.3.3 and 7.3.4), the recent ambient monitoring data were separated into two three-year 

groups, 2001-2003 and 2004-2006. A summary of the descriptive statistics for the annual 

average ambient NO2 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, respectively. 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 sited <20 m and >100 m for 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 site-group indicates that the more recent 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 generally observed across all 

percentiles of the distribution. 

Table 7-11. Monitoring site-years and annual average NO2 concentrations, using recent air quality 

data (as is) and monitors sited ≥100 m of a major road. 

2001-2003 2004-2006 

Annual Mean NO2 (ppb)2 Annual Mean NO2 (ppb)2

Location

SiteYears1

mean min med p95 p98 p99

SiteYears1

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 within 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 based on all 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 

site-years across the monitoring period. The min, med, p95, p98, p99 represent the minimum, median, 95th, 98th, 

and 99th 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 NO2 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 NO2 (ppb)2 Annual Mean NO2 (ppb)2

Location

SiteYears1

mean min med p95 p98 p99

SiteYears1

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 within 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 based on all 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 

site-years across the monitoring period. The min, med, p95, p98, p99 represent the minimum, median, 95th, 98th, 

and 99th percentiles of the distribution for the annual means. 

ND no available monitoring data. 

 87 
Table 7-13. Monitoring site-years and annual average NO2 concentrations, using recent air quality 

data (as is) and monitors sited ≤20 m of a major road. 

2001-2003 2004-2006 

Annual Mean NO2 (ppb)2 Annual Mean NO2 (ppb)2

Location

SiteYears1

mean min med p95 p98 p99

SiteYears1

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 within 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 based on all 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 

site-years across the monitoring period. The min, med, p95, p98, p99 represent the minimum, median, 95th, 98th, 

and 99th 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 NO2 for 1-hour) is shown in Tables 7-14 through 7-19 

using recent as is ambient monitoring data and considering 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 tendency value for each location; it is the average 

number of days per year that there were daily maximum 1-hour NO2 concentration exceedances 

observed over the three year period in a location. The upper percentiles (98th and 99th) 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 monitor (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 NO2 concentration across each location was typically zero to a few, with one 

location (Provo) containing an average of fourteen 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 effect benchmark level at a few locations. For the 

locations with greater than one annual average exceedance, the numbers were primarily driven 

by a single site-year of data. For example, Detroit contained a largest 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 occurred at one monitor (ID 2616300192) during one 

year (2002). Provo contained that greatest number of daily maximum exceedances of both the 

100 and 150 ppb benchmark level (Table 7-15), associated with measurements from one monitor 

(ID 4904900021) in 2006. However, when considering the collective locations (both the named 

locations and aggregated), daily maximum exceedances 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 responsible 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 monitors 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 locations had no daily maximum 1-hour NO2 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 exceedances of 100 ppb (Tables 7-18 and 7-19). Of 

those, Denver, Los Angeles, and Phoenix contained 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 locations ranged from about 1-2%. As far as the 

higher benchmark levels, nearly all locations had no daily maximum 1-hour NO2 concentrations 

≥150 ppb. 
Table 7-14. Number of daily maximum exceedances of short-term (1-hour) potential health effect benchmarks in a 

recent NO2 air quality (as is) and monitors sited 

≥100 m of a major road. 

Exceedances of 100 ppb 1 Exceedances of 150 ppb 1 Exceedances of 200 ppb 1 Exceedances of 250 p

Location mean min med p98 p99 mean min med p98 p99 mean min med p98 p99 mean min med p98 pAtlanta 0 0 0 3 3 0 0 0 1 1 0 0 0 0 0 0 0 0 0 Boston 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Chicago 0 0 0 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Cleveland 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Denver 2 1 2 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Detroit 3 0 2 7 7 1 0 1 5 5 1 0 0 4 4 1 0 0 4 El Paso 0 0 0 1 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 Jacksonville 1 0 1 1 1 1 0 1 1 1 1 0 1 1 1 1 0 1 1 Las Vegas 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Los Angeles 4 0 1 17 18 0 0 0 1 8 0 0 0 0 4 0 0 0 0 Miami 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 New York 0 0 0 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Philadelphia 0 0 0 1 1 0 0 0 1 1 0 0 0 1 1 0 0 0 1 Phoenix 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Provo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 St. Louis 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Washington DC 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Other MSA 0 0 0 1 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Other Not MSA 0 0 0 5 5 0 0 0 1 1 0 0 0 0 1 0 0 0 0  Notes:  1 The mean number of exceedances represents the sum of the daily maximum exceedances occurring at all monitors in a 

the number of site-years across the monitoring period. The min, med, p98, and p99 represent the minimum, median, 98th, 

distribution for the number of daily maximum exceedances in any one year within the monitoring period. 

August 2008 - Draft 91 
Table 7-15. Number of daily maximum exceedances of short-term (1-hour) potential health effect benchmarks in a 

recent NO2 air quality (as is) and monitors sited 

≥100 m of a major road. 

Exceedances of 100 ppb 1 Exceedances of 150 ppb 1 Exceedances of 200 ppb 1 Exceedances of 250 p

Location mean min med p98 p99 mean min med p98 p99 mean min med p98 p99 mean min med p98 pAtlanta 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Boston 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Chicago 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Cleveland ND Denver 1 0 0 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Detroit 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 El Paso 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Jacksonville 2 0 2 3 3 1 0 1 2 2 1 0 1 1 1 0 0 0 0 Las Vegas 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Los Angeles 0 0 0 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Miami 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 New York 0 0 0 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Philadelphia 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Phoenix 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Provo 14 0 0 43 43 7 0 0 20 20 0 0 0 0 0 0 0 0 0 St. Louis 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Washington DC 0 0 0 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Other MSA 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Other Not MSA 0 0 0 2 3 0 0 0 0 2 0 0 0 0 1 0 0 0 0  Notes:  1 The mean number of exceedances represents the sum of the daily maximum exceedances occurring at all monitors in a 

the number of site-years across the monitoring period. The min, med, p98, and p99 represent the minimum, median, 98th, 

distribution for the number of daily maximum exceedances in any one year within the monitoring period. 

ND no available monitoring data. 

August 2008 - Draft 92 
Table 7-16. Number of daily maximum exceedances of short-term (1-hour) potential health effect benchmarks in a 

recent NO2 air quality (as is) and monitors sited >20 m and <100 m of a major road. 

Exceedances of 100 ppb 1 Exceedances of 150 ppb 1 Exceedances of 200 ppb 1 Exceedances of 250 p

Location mean min med p98 p99 mean min med p98 p99 mean min med p98 p99 mean min med p98 pAtlanta ND Boston 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Chicago 2 0 1 6 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Cleveland ND Denver ND Detroit ND El Paso 2 0 2 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Jacksonville ND Las Vegas 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Los Angeles 6 0 1 31 31 0 0 0 2 2 0 0 0 1 1 0 0 0 1 Miami 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 New York 1 0 0 8 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Philadelphia 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Phoenix 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Provo ND St. Louis 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Washington DC 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Other MSA ND Other Not MSA ND  Notes:  1 The mean number of daily maximum exceedances represents the sum of the daily maximum exceedances occurring at a

location divided by the number of site-years across the monitoring period. The min, med, p98, and p99 represent the minim

percentiles of the distribution for the number of daily maximum exceedances in any one year within the monitoring period. ND no available monitoring data. 

August 2008 - Draft 93 
Table 7-17. Number of daily maximum exceedances of short-term (1-hour) potential health effect benchmarks in a 

recent NO2 air quality (as is) and monitors sited >20 m and <100 m of a major road. 

Exceedances of 100 ppb 1 Exceedances of 150 ppb 1 Exceedances of 200 ppb 1 Exceedances of 250 p

Location mean min med p98 p99 mean min med p98 p99 mean min med p98 p99 mean min med p98 pAtlanta ND Boston 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Chicago 1 0 1 5 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Cleveland 1 0 1 1 1 1 0 1 1 1 0 0 0 0 0 0 0 0 0 Denver ND Detroit ND El Paso 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Jacksonville ND Las Vegas ND Los Angeles 1 0 1 9 9 0 0 0 1 1 0 0 0 0 0 0 0 0 0 Miami 1 0 1 2 2 1 0 1 1 1 1 0 1 1 1 1 0 1 1 New York 1 0 1 3 3 0 0 0 1 1 0 0 0 0 0 0 0 0 0 Philadelphia 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Phoenix 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Provo ND St. Louis 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Washington DC 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Other MSA ND Other Not MSA ND  Notes:  1 The mean number of exceedances represents the sum of the daily maximum exceedances occurring at all monitors in a 

the number of site-years across the monitoring period. The min, med, p98, and p99 represent the minimum, median, 98th, 

distribution for the number of daily maximum exceedances in any one year within the monitoring period. 

ND no available monitoring data. 

August 2008 - Draft 94 
Table 7-18. Number of daily maximum exceedances of short-term (1-hour) potential health effect benchmarks in a 

recent NO2 air quality (as is) and monitors sited 

≤20 m from a major road. 

Exceedances of 100 ppb 1 Exceedances of 150 ppb 1 Exceedances of 200 ppb 1 Exceedances of 250 p

Location mean min med p98 p99 mean min med p98 p99 mean min med p98 p99 mean min med p98 pAtlanta ND Boston 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Chicago 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Cleveland 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Denver 7 3 7 10 10 1 1 1 1 1 0 0 0 0 0 0 0 0 0 Detroit ND El Paso ND Jacksonville ND Las Vegas 1 0 0 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Los Angeles 6 0 6 9 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Miami 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 New York 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Philadelphia ND Phoenix 2 1 1 4 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Provo ND St. Louis 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Washington DC 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Other MSA ND Other Not MSA ND  Notes:  1 The mean number of exceedances represents the sum of the daily maximum exceedances occurring at all monitors in a 

the number of site-years across the monitoring period. The min, med, p98, and p99 represent the minimum, median, 98th, 

distribution for the number of daily maximum exceedances in any one year within the monitoring period. 

ND no available monitoring data. 

August 2008 - Draft 95 
August 2008 - Draft 96 

Table 7-19. Number of daily maximum exceedances of short-term (1-hour) potential health effect benchmarks in a 

recent NO2 air quality (as is) and monitors sited ≤20 m from a major road. 

Exceedances of 100 ppb 1 Exceedances of 150 ppb 1 Exceedances of 200 ppb 1 Exceedances of 250 p

Location mean min med p98 p99 mean min med p98 p99 mean min med p98 p99 mean min med p98 pAtlanta ND Boston 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Chicago 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Cleveland 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Denver 2 1 1 3 3 0 0 0 1 1 0 0 0 0 0 0 0 0 0 Detroit ND El Paso ND Jacksonville ND Las Vegas 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Los Angeles 3 0 2 8 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Miami 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 New York 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Philadelphia ND Phoenix 2 0 2 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Provo ND St. Louis 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Washington DC 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Other MSA/CMSA ND Other Not MSA ND  Notes:  1 The mean number of exceedances represents the sum of the daily maximum exceedances occurring at all monitors in a 

the number of site-years across the monitoring period. The min, med, p98, and p99 represent the minimum, median, 98th, 

distribution 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 From Ambient Air Quality (As Is) 

Descriptive statistics for estimated on-road NO2 concentrations for each recent three-year 

group are presented in Table 7-20. These estimated on-road concentrations were generated using 

the simulation procedure described above (section 7.2.3) and represent the second air quality 

scenario. For the 17 named locations, the calculation only used monitors sited at a distance ≥100 

m of a major road. The two aggregate locations (i.e., Other CMSA and Not MSA) did not have 

estimated monitor distances to major roads therefore all monitoring data available were used to 

estimate the distribution of on-road NO2 concentrations. 

The simulated on-road annual average NO2 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). Denver, Los Angeles, and Phoenix were predicted 

to have the highest on-road annual average NO2 levels. This is a direct result of these locations 

already containing some of the highest as-is NO2 concentrations prior to the on-road simulation 

(see Table 7-11). Estimated on-road annual average concentrations were greater by about 10% 

when using the 2001-2003 monitoring data compared with the 2004-2006 monitoring data. 

The median of the simulated concentration estimates for Los Angeles were compared 

with NO2 measurements provided by Westerdahl et al. (2005) for arterial roads and freeways in 

the same general location during spring 2003. Although 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 within the range of 31 ppb to 55 ppb identified 

by Westerdahl et al. (2005). 

On average, most locations are predicted to have fewer than 3 daily maximum 

exceedances of 1-hour NO2 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-groups) 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 averages 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 quality. 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 percentiles of the concentration 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 (98th and 99th) 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, there 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 98th and 99th 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 estimated that between about 10 and 30 daily maximum 

exceedances of 200 ppb could occur in a year in most locations as an upper bound estimate. The 

estimated upper percentile number of exceedances 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 NO2 concentrations on-roads, using recent air quality data 

(as is) and an on-road adjustment factor. 

2001-2003 2004-2006 

Annual Mean NO2 (ppb)2 Annual Mean NO2 (ppb)2

Location

SiteYears1

mean min med p95 p98 p99

SiteYears1

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 within 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 based 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 monitoring period. The min, med, p95, p98, p99 represent the 

minimum, median, 95th, 98th, and 99th 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 benchm

year, using 2001-2003 recent NO2 air quality (as is) and an on-road adjustment factor. 

Exceedances of 100 ppb 1 Exceedances of 150 ppb 1 Exceedances of 200 ppb 1 Exceedances of 250 p

Location mean min med p98 p99 mean min med p98 p99 mean min med p98 p99 mean min med p98 pAtlanta 23 0 5 130 169 4 0 0 36 51 1 0 0 8 13 0 0 0 3 Boston 5 0 1 36 40 0 0 0 3 5 0 0 0 1 1 0 0 0 0 Chicago 52 0 35 180 191 9 0 2 62 68 2 0 0 24 29 0 0 0 5 Cleveland 31 0 21 83 102 5 0 1 30 30 1 0 0 10 10 0 0 0 3 Denver 89 8 74 242 259 17 0 5 80 94 3 0 1 25 26 1 0 0 5 Detroit 41 1 30 130 141 9 0 5 44 46 3 0 2 16 18 2 0 1 7 El Paso 32 0 19 136 145 4 0 1 24 26 0 0 0 5 6 0 0 0 1 Jacksonville 13 0 7 55 56 1 0 1 6 7 1 0 1 1 1 1 0 1 1 Las Vegas 23 0 4 171 194 4 0 0 54 62 0 0 0 8 9 0 0 0 1 Los Angeles 71 0 57 231 251 17 0 6 94 108 5 0 0 41 48 1 0 0 16 Miami 7 0 2 50 64 1 0 0 6 10 0 0 0 1 2 0 0 0 0 New York 42 0 28 177 201 7 0 1 55 63 2 0 0 24 24 0 0 0 6 Philadelphia 37 0 19 149 172 6 0 1 49 62 1 0 0 11 24 0 0 0 3 Phoenix 101 1 83 280 315 16 0 2 113 124 2 0 0 17 20 0 0 0 4 Provo 61 1 38 248 289 9 0 0 62 64 1 0 0 11 11 0 0 0 2 St. Louis 25 0 12 128 139 3 0 0 28 37 0 0 0 5 6 0 0 0 1 Washington DC 36 0 17 169 205 6 0 0 46 54 1 0 0 9 14 0 0 0 1 Other MSA 16 0 3 105 129 2 0 0 22 32 0 0 0 4 7 0 0 0 1 Other Not MSA 4 0 0 43 62 1 0 0 9 15 0 0 0 2 5 0 0 0 1  Notes:  1 The mean number of exceedances represents the sum of the daily maximum exceedances occurring at all monitors in a 

the number of simulated site-years across the monitoring period. The min, med, p98, and p99 represent the minimum, me

of the distribution for the number of daily maximum exceedances in any one year within the monitoring period. 

August 2008 - Draft 100 
August 2008 - Draft 101 

Table 7-22. Estimated number of daily maximum exceedances of short-term (1-hour) potential health effect benchm

year, using 2004-2006 recent NO2 air quality (as is) and an on-road adjustment factor. 

Exceedances of 100 ppb 1 Exceedances of 150 ppb 1 Exceedances of 200 ppb 1 Exceedances of 250 p

Location mean min med p98 p99 mean min med p98 p99 mean min med p98 p99 mean min med p98 pAtlanta 17 0 2 114 120 2 0 0 26 27 0 0 0 6 7 0 0 0 1 Boston 2 0 0 18 21 0 0 0 1 1 0 0 0 0 0 0 0 0 0 Chicago 36 0 20 148 161 5 0 0 41 53 1 0 0 7 18 0 0 0 1 Cleveland ND Denver 63 2 49 190 195 10 0 4 47 52 2 0 0 11 14 0 0 0 3 Detroit 20 0 9 90 103 2 0 0 20 24 0 0 0 3 6 0 0 0 0 El Paso 24 0 12 114 143 3 0 0 20 23 0 0 0 4 4 0 0 0 1 Jacksonville 11 0 5 48 59 2 0 2 5 6 1 0 1 4 4 1 0 0 3 Las Vegas 15 0 0 133 148 2 0 0 54 64 0 0 0 6 8 0 0 0 0 Los Angeles 38 0 25 150 169 6 0 1 43 55 1 0 0 11 14 0 0 0 2 Miami 6 0 1 46 46 0 0 0 6 6 0 0 0 1 1 0 0 0 0 New York 35 0 23 149 171 5 0 1 40 45 1 0 0 13 15 0 0 0 3 Philadelphia 22 0 10 101 130 2 0 0 20 22 0 0 0 3 3 0 0 0 0 Phoenix 77 0 53 275 293 10 0 1 57 71 1 0 0 7 8 0 0 0 1 Provo 51 0 44 160 160 17 0 2 68 70 12 0 0 44 44 7 0 0 43 St. Louis 15 0 5 76 83 1 0 0 20 24 0 0 0 2 5 0 0 0 0 Washington DC 21 0 7 119 143 2 0 0 20 22 0 0 0 4 6 0 0 0 1 Other MSA 10 0 1 79 100 1 0 0 12 18 0 0 0 2 3 0 0 0 0 Other Not MSA 4 0 0 43 65 1 0 0 7 13 0 0 0 2 5 0 0 0 2  Notes:  1 The mean number of exceedances represents the sum of the daily maximum exceedances occurring at all monitors in a 

the number of simulated site-years across the monitoring period. The min, med, p98, and p99 represent the minimum, me

of 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 Current and Alternative 

Standards 

As described in section 6.2, each of the current and alternative standards 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 total 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 summarized 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) using 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 were 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 for each three-year group from what was expected. 

For example, a few of the locations had an estimated daily maximum number of exceedances of 

100 ppb that were slightly higher for the 2004-2006 year-group when compared 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 200 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 meeting the potential alternative 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 Chicago apply to the other locations, with a few 

exceptions. As expected, the estimated number of daily maximum exceedances is lower for a 

99th percentile form compared with each corresponding level using the 98th percentile form of 

alternative standard. In general, the number of estimated daily maximum exceedances of the 

potential health effect benchmark levels at monitoring 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 greater potential for vehicle emissions influencing ambient 

concentrations at near road monitors. There were also a slightly greater 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 health effect benchmark levels 

decreases with decreasing alternative standard level. Regardless of three-year group or 

monitoring group, an alternative standard level 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 locations, Phoenix, Los Angeles, Washington DC, 

and St. Louis. Again, there are trends in these results 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 from a major road. Also note that there were 

variable results when comparing year-groups across the different locations within the monitor 

site-group; sometimes the year 2001-2003 contained greater numbers of exceedances when 

compared with 2004-2006 (e.g., St. Louis), and other times it did not (e.g. Los Angeles). 

However, the alternative standard levels of either 100 or 150 ppb at either percentile consistently 

reduced the mean number of daily maximum exceedances of 200 ppb to about zero. 

Tables 7-23, 7-24, and 7-25 summarize the annual mean concentrations and estimated 

number of exceedances in a year given 2001-2003 air quality adjusted that just meets the 1-hour 

100 ppb 98th 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 provide a more comprehensive comparison of the 

numbers of daily maximum exceedances for the lowest 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-group 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 year-groups of air quality, 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 than 5 daily maximum exceedances of the 100 ppb 

concentration level, with upper percentile estimates 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 health 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 observed and estimated mean numbers of exceedances of 

100 ppb using the 2001-2003 as is air quality and air quality adjusted to just meet the current 

standard and the potential alternative 98th percentile standards at each location. The number of 

daily maximum exceedances for the as is air quality generally fell within the number of 

exceedances estimated using alternative 1-hour 98th percentile standards of 50 ppb and 100 ppb 

at each location. When the air quality was adjusted to just meeting the current annual average 

standard, the estimated number of daily maximum exceedances was generally near that estimated 

using the alternative 1-hour 98th 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 number of daily maximum exceedances using as is air 

quality in each location was most similar to that estimated using the alternative 1-hour 98th

percentile standard of 50 ppb, while estimates using the air quality adjusted to just meeting the 

current standard again approached the estimated numbers of exceedance using the alternative 1-

hour 98th percentile standard of 150 ppb at each location. 
Monitors Sited >=100 m from a Major Road

0 25 50 75 100 125 150 175 200 225

AtlantaBoston

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

Monitors Sited >20 m, <100 m from a Major Road

0 25 50 75 100 125 150 175 200 225

≥ 100 ppb≥ 150 ppb≥ 200 ppb

No dataNo dataNo dataNo dataNo dataNo dataNo dataNo data

Monitors Sited >=

0 25 50 75 

No dataNo dataNo dataNo dataNo dataNo dataNo dataNo data

Estimated mean number of daily maximum exceedances of 1-hour NO2 concentrations of 100

Figure 7-2. Estimated mean number of daily maximum exceedances of short-term (1-hour) potential healtin a year, using recent NO2 air quality (2001-2003) adjusted to just meeting the current annual standard (graph: monitors ≥100m from a major road; Middle graph: monitors >20 m and <100 m from a major roa

monitors ≤20 m from a major road. 

 105 
Figure 7-3. Estimated mean number of daily maximum exceedances of short-term (1-hour) potential healt

in a year, using recent NO2 air quality (2004-2006) adjusted to just meeting the current annual standard (0graph: monitors ≥100m from a major road; Middle graph: monitors >20 m and <100 m from a major roa

monitors ≤20 m from a major road).

Monitors Sited >=100 m from a Major Road

0 25 50 75 100 125 150 175 200 225

AtlantaBoston

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

No data

Monitors Sited >20 m, <100 m from a Major Road

0 25 50 75 100 125 150 175 200 225

≥ 100 ppb≥ 150 ppb≥ 200 ppb

No data

No dataNo data

No data

No data

No dataNo dataNo data

Monitors Site

0 25 50 

No dataNo dataNo dataNo dataNo dataNo dataNo dataNo data

Estimated mean number of daily maximum exceedances of 1-hour NO2 concentrations of 100, 1

 106 
Figure 7-4. Estimated number of daily maximum exceedances of short-term (1-hour) potential health effe

ppb, top; 200 ppb, bottom) in Chicago in a year, using recent NO2 air quality data (2001-2006) adjusted to 

alternative 1-hour standard levels (98th percentile, left; and 99th percentile, right) and monitors sited 

≥100 

m, 

≤ 20 m of major roads. 

2001-2003,<=20m2001-2003,>20m <100m2001-2003,>=100m2004-2006,<=20m2004-2006,>20m <100m2004-2006,>=100m

50 100 150 

99th Percentile Alternative 1-Hour NO2 S

2001-2003,<=20m2001-2003,>20m <100m2001-2003,>=100m2004-2006,<=20m2004-2006,>20m <100m2004-2006,>=100m

0

255075

100125

Number of Daily Maximum Excee

ppb NO2 1-hour in a Ye150

175200225

dances of 100 

ar

2001-2003,<=20m2001-2003,>20m <100m2001-2003,>=100m2004-2006,<=20m2004-2006,>20m <100m2004-2006,>=100m

012345678

50 100 150 200

98th Percentile Alternative 1-Hour NO2 Standard Level

Number of Daily Maximum Exceedances of 200 

ppb NO2 1-hour in a Year

2001-2003,<=20m2001-2003,>20m <100m2001-2003,>=100m2004-2006,<=20m2004-2006,>20m <100m2004-2006,>=100m

 107 
Figure 7-5. Estimated mean number of daily maximum exceedances of 200 ppb in four locations (Phoenix

Washington DC, and St. Louis) in a year, using recent NO2 air quality data (2001-2006) adjusted to just mhour 98th percentile standard levels and monitors sited ≥100 m, > 20 m and < 100 m, ≤ 20 m of major road

- Phoenix -

70123456

Number of Daily Maximum Exceedances 

of 200 ppb NO

2 1-hour in a Year

2001-2003,<=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 -

01234567

50 100 150 200

98th Percentile Alternative 1-Hour NO2 Standard Level

Number of Daily Maximum Exceedances 

of 200 ppb NO

2 1-hour in a Year 

- St. Louis -

50 100 150 

98th Percentile Alternative 1-Hour NO2 Stan

2001-2003,<=20m 2001-2003,<=20m2001-2003,>20m <100m 2001-2003,>20m <100m2001-2003,>=100m 2001-2003,>=100m2004-2006,<=20m 2004-2006,<=20m2004-2006,>20m <100m 2004-2006,>20m <100m2004-2006,>=100m 2004-2006,>=100m

 108 
Table 7-23. Estimated annual mean NO2 concentration and the number of daily maximum exceedances of short-ter

health effect benchmarks in a year, using 2001-2003 air quality adjusted to just meeting a 1-hour 100 ppb 98th percstandard and monitors sited 

≥ 100 m of a major road. 

Number of Daily Maximum Exceedances of 1-Hour L

Annual Mean NO2 (ppb)2 ≥100 ppb 

≥150 ppb 

≥ 2

Location 

SiteYears1 Mean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99 Mean Min 

Atlanta 14 15 5 19 29 29 2 0 0 15 15 0 0 0 1 1 0 0 Boston 6 13 7 15 16 16 0 0 0 1 1 0 0 0 0 0 0 0 Chicago 9 25 19 23 32 32 1 0 0 4 4 0 0 0 1 1 0 0 Cleveland 3 25 25 25 26 26 2 1 3 3 3 0 0 0 0 0 0 0 Denver 2 24 22 24 27 27 2 1 2 2 2 0 0 0 0 0 0 0 Detroit 6 23 21 23 26 26 3 1 3 7 7 2 0 1 7 7 1 0 El Paso 12 20 14 21 24 24 3 0 2 8 8 0 0 0 1 1 0 0 Jacksonville 2 26 26 26 26 26 7 3 7 10 10 1 0 1 1 1 1 0 Las Vegas 16 15 3 11 32 32 1 0 0 7 7 0 0 0 0 0 0 0 Los Angeles 51 18 4 19 29 29 1 0 0 5 10 0 0 0 0 5 0 0 Miami 6 16 13 16 19 19 4 0 3 14 14 0 0 0 2 2 0 0 New York 26 22 12 20 34 34 1 0 0 5 5 0 0 0 0 0 0 0 Philadelphia 14 27 20 25 39 39 3 0 2 15 15 0 0 0 1 1 0 0 Phoenix 5 31 26 33 34 34 1 0 1 2 2 0 0 0 0 0 0 0 Provo 3 37 34 38 39 39 6 4 6 9 9 0 0 0 0 0 0 0 St. Louis 9 27 22 26 32 32 3 0 1 15 15 0 0 0 1 1 0 0 Washington DC 18 26 12 29 35 35 4 0 3 11 11 0 0 0 0 0 0 0 Other MSA 612 13 1 13 23 25 0 0 0 1 4 0 0 0 0 0 0 0 Other Not MSA 127 7 1 7 17 18 0 0 0 6 6 0 0 0 1 1 0 0 Notes: 1 The average number of monitors operating per year within the three-year group is estimated by dividing the number of s2 Annual means for each monitor were first calculated based on all simulated hourly values in a year. Then the mean of th

means was estimated as the sum of all the annual means in a particular location divided by the number of simulated site-ymonitoring period. The min, med, p98, and p99 represent the minimum, median, 98th, and 99th percentiles of the distribut

annual average concentration in any one year within the monitoring period. 3 The mean number of exceedances represents the sum of daily maximum exceedances occurring at all monitors in a par

divided by the number of site-years across the monitoring period. The min, med, p98, and p99 represent the minimum, m

99th percentiles of the distribution for the number of daily maximum exceedances in any one year within the monitoring pe

 109 
Table 7-24. Estimated annual mean NO2 concentration and the number of daily maximum exceedances of short-ter

health effect benchmarks in a year, using 2001-2003 air quality adjusted to just meeting a 1-hour 100 pp

alternative standard and monitors sited >20 m and <100 m from a major road. 

Number of Daily Maximum Exceedances of 1-Hour L

Annual Mean NO2 (ppb)2 ≥ 100 ppb 

≥ 150 ppb 

≥ 2

Location 

SiteYears1 Mean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99 Mean Min 

Atlanta ND Boston 14 23 12 26 35 35 3 0 2 11 11 0 0 0 0 0 0 0 Chicago 6 35 33 35 37 37 7 1 4 21 21 1 0 0 3 3 0 0 Cleveland ND Denver ND Detroit ND El Paso 3 27 26 28 28 28 6 2 7 9 9 0 0 0 0 0 0 0 Jacksonville ND Las Vegas 3 8 4 8 12 12 1 0 0 4 4 0 0 0 0 0 0 0 Los Angeles 35 19 3 19 32 32 1 0 0 7 7 0 0 0 1 1 0 0 Miami 3 27 24 27 29 29 8 4 6 15 15 0 0 0 1 1 0 0 New York 13 33 23 33 44 44 3 0 1 13 13 0 0 0 2 2 0 0 Philadelphia 7 34 26 33 42 42 5 0 4 11 11 0 0 0 0 0 0 0 Phoenix 2 26 25 26 27 27 0 0 0 0 0 0 0 0 0 0 0 0 Provo ND St. Louis 11 22 13 18 38 38 2 0 0 11 11 0 0 0 0 0 0 0 Washington DC 10 29 20 31 36 36 2 0 2 5 5 0 0 0 0 0 0 0 Other MSA ND Other Not MSA ND Notes: 1 The average number of monitors operating per year within the three-year group is estimated by dividing the number of s2 Annual means for each monitor were first calculated based on all simulated hourly values in a year. Then the mean of th

means was estimated as the sum of all the annual means in a particular location divided by the number of simulated site-ymonitoring period. The min, med, p98, and p99 represent the minimum, median, 98th, and 99th percentiles of the distribut

annual average concentration in any one year within the monitoring period. 3 The mean number of exceedances represents the sum of daily maximum exceedances occurring at all monitors in a par

divided by the number of site-years across the monitoring period. The min, med, p98, and p99 represent the minimum, m

99th percentiles of the distribution for the number of daily maximum exceedances in any one year within the monitoring pe

ND no available monitoring data. 

 110 
Table 7-25. Estimated annual mean NO2 concentration and the number of daily maximum exceedances of short-ter

health effect benchmarks in a year, using 2001-2003 air quality adjusted to just meeting a 1-hour 100 pp

alternative standard and monitors sited 

≤20 m from a major road. 

Number of Daily Maximum Exceedances of 1-Hour L

Annual Mean NO2 (ppb)2 ≥ 100 ppb 

≥ 150 ppb 

≥ 2

Location 

SiteYears1 Mean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99 Mean Min 

Atlanta ND Boston 5 29 10 32 41 41 2 0 0 6 6 0 0 0 1 1 0 0 Chicago 4 26 25 26 27 27 2 0 1 6 6 0 0 0 0 0 0 0 Cleveland 3 32 31 32 34 34 7 3 7 10 10 0 0 0 0 0 0 0 Denver 2 37 37 37 38 38 7 3 7 10 10 2 1 2 3 3 0 0 Detroit ND El Paso ND Jacksonville ND Las Vegas 3 32 31 32 32 32 7 1 10 11 11 0 0 0 0 0 0 0 Los Angeles 9 23 18 23 29 29 1 0 0 3 3 0 0 0 0 0 0 0 Miami 3 12 11 12 12 12 1 0 1 1 1 0 0 0 0 0 0 0 New York 7 30 27 30 33 33 1 0 1 2 2 0 0 0 0 0 0 0 Philadelphia ND Phoenix 3 41 40 40 43 43 6 4 4 11 11 0 0 0 0 0 0 0 Provo ND St. Louis 6 28 25 29 30 30 3 1 3 6 6 0 0 0 1 1 0 0 Washington DC 4 33 28 34 36 36 6 3 6 9 9 0 0 0 1 1 0 0 Other MSA ND Other Not MSA ND Notes: 1 The average number of monitors operating per year within the three-year group is estimated by dividing the number of s2 Annual means for each monitor were first calculated based on all simulated hourly values in a year. Then the mean of th

means was estimated as the sum of all the annual means in a particular location divided by the number of simulated site-ymonitoring period. The min, med, p98, and p99 represent the minimum, median, 98th, and 99th percentiles of the distribut

annual average concentration in any one year within the monitoring period. 3 The mean number of exceedances represents the sum of daily maximum exceedances occurring at all monitors in a par

divided by the number of site-years across the monitoring period. The min, med, p98, and p99 represent the minimum, m

99th percentiles of the distribution for the number of daily maximum exceedances in any one year within the monitoring pe

ND no available monitoring data. 

 111 
Table 7-26. Estimated mean number of daily maximum exceedances of 100 ppb 1-hour NO2 concentrations in a yea

quality as is and that adjusted to just meeting the current and alternative standards (98th percentile) for 

m, >20 m and <100 m, and ≤20 m of a major road. 

Sites ≥100 m of a major road Sites >20 m and <100 m of a major road Sites ≤20 m 

Alternative 1-hour 98th

percentile standard 

Alternative 1-hour 98th

percentile standard 

Alt

p

Location 

As 

Is 

Cur 

Std 50 100 150 200 

As 

Is 

Cur 

Std 50 100 150 200 

As 

Is 

Cur 

Std 50 

Atlanta 0 80 0 2 20 65 ND ND Boston 0 3 0 0 3 34 0 38 0 3 35 116 0 19 0 Chicago 0 23 0 1 31 106 2 74 0 7 90 212 0 21 0 Cleveland 0 70 0 2 49 133 ND 0 111 0 Denver 2 99 0 2 37 149 ND 7 48 0 Detroit 3 99 1 3 17 73 ND ND El Paso 0 115 0 3 35 125 2 170 0 6 77 178 ND Jacksonville 1 152 1 7 74 152 ND ND Las Vegas 0 69 0 1 41 114 0 24 0 1 11 46 1 211 0 Los Angeles 4 21 0 1 9 37 6 31 0 1 15 53 6 42 0 Miami 0 106 0 4 41 107 0 152 0 8 73 155 0 78 0 New York 0 8 0 1 16 72 1 26 0 3 50 154 1 18 0 Philadelphia 0 29 0 3 53 171 0 44 0 5 84 216 ND Phoenix 0 32 0 1 89 213 0 4 0 0 49 210 2 72 0 Provo 0 162 0 6 200 327 ND ND St. Louis 0 65 0 3 60 175 0 41 0 2 37 119 0 56 0 Washington DC 0 61 0 4 58 153 0 57 0 2 57 168 0 78 0 Other MSA 0 16 0 0 3 18 ND ND Other Not MSA 0 37 0 0 1 6 ND ND Notes: The mean number of exceedances represents the sum of daily maximum exceedances occurring at all monitors in a partic

by the number of site-years across the monitoring period. ND No available monitoring data. 

 112 
 113 

Table 7-27. Estimated mean number of daily maximum exceedances of 150 ppb 1-hour NO2 concentrations in a yea

quality as is and air quality adjusted to just meeting the current and alternative standards (98th percent

≥100 m, >20 m and <100 m, and 

≤20 m of a major road. 

Sites 

≥100 m of a major road Sites >20 m and <100 m of a major road Sites 

≤20 m 

Alternative 1-hour 98th

percentile standard 

Alternative 1-hour 98th

percentile standard 

Alt

p

Location 

As 

Is 

Cur 

Std 50 100 150 200 

As 

Is 

Cur 

Std 50 100 150 200 

As 

Is 

Cur 

Std 50 

Atlanta 0 18 0 0 2 10 ND Boston 0 0 0 0 0 1 0 3 0 0 3 19 0 1 0 Chicago 0 1 0 0 1 14 0 5 0 1 7 53 0 1 0 Cleveland 0 5 0 0 2 27 ND 0 14 0 Denver 0 21 0 0 2 17 ND 1 5 0 Detroit 1 14 1 2 3 9 ND ND El Paso 0 13 0 0 3 16 0 32 0 0 6 40 ND Jacksonville 1 44 1 1 7 41 ND ND Las Vegas 0 6 0 0 1 19 0 2 0 0 1 5 0 22 0 Los Angeles 0 2 0 0 1 5 0 3 0 0 1 8 0 4 0 Miami 0 23 0 0 4 23 0 42 0 0 8 42 0 17 0 New York 0 0 0 0 1 7 0 1 0 0 3 25 0 1 0 Philadelphia 0 1 0 0 3 27 0 2 0 0 5 42 ND Phoenix 0 0 0 0 1 41 0 0 0 0 0 11 0 2 0 Provo 0 4 0 0 6 112 ND ND St. Louis 0 5 0 0 3 31 0 3 0 0 2 21 0 3 0 Washington DC 0 4 0 0 4 30 0 2 0 0 2 28 0 7 0 Other MSA 0 1 0 0 0 1 ND ND Other Not MSA 0 6 0 0 0 1 ND ND Notes: The mean number of exceedances represents the sum of daily maximum exceedances occurring at all monitors in a partic

by 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 NO2 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) based on the form of 

the potential alternative standards (i.e., a 3-year average) and represents the fourth air quality 

scenario. Results are presented in a manner consistent 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 quality 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 exceedances of 100 ppb, much greater than those 

estimated using either the 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 adjusted to just meeting the current standard. 

The effect of the potential alternative standards on the estimated on-road NO2

concentrations was also analyzed at each of the locations. Figure 7-7 illustrates each of the 

standard levels (50, 100, 150, and 200 ppb 1-hour) and the two forms (98th and 99th percentiles) 

evaluated, again using Chicago as an example to describe patterns in the number of exceedances. 

The patterns observed in Figure 7-2 and described 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-roads or sited at a distance from major roads. 

Estimated numbers of daily maximum concentrations above 100 ppb are 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 were estimated at other locations using air 

quality adjusted to just meeting the potential alternative standards. Figure 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 98th 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 comparison at this particular standard level (98th

percentile at 100 ppb) using 2001-2003 adjusted air quality at each of the locations. For most 

locations, the estimated mean number of daily maximum exceedances of 200 ppb on-roads was 

seven or less, with upper percentiles estimated to number about 30 to 70 of exceedances. The 

mean number of exceedances of 250 and 300 ppb were less, ranging from a few to tens of 

occurrences in a year. 

Tables 7-29 and 7-30 summarizes the observed 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 current standard and the potential alternative 98th percentile 

standards at each location. Patterns for the estimated on-road concentrations using as is air 

quality and air quality adjusted to just meet the current annual standard 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 maximum exceedances of 100 and 150 ppb using 2001-2003 

air quality). The estimated number of daily maximum exceedances on-roads using the as is air 

quality was within the range of estimates provided by the alternative 1-hour 98th 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 NO2 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 NO2

concentrations of 100, 150, 200 ppb on-roads in a Year
Figure 7-7. Estimated number of daily maximum exceedances of short-term (1-hour) potential health effec

ppb, top; 200 ppb, bottom) on-roads in Chicago in a year, using recent NO2 air quality (2001-2006) adjustealternative 1-hour standard levels (98th percentile, left; and 99th percentile) and an on-road adjustment fac

0

50

100150200250

Estimated Mean Number of Daily Maximum 

Exceedances of 100 ppb NO2 1-hour in a Year

300

2001-20032004-2006

2001-20032004-2006

0

102030405060708090

100

50 100 150 200

98th Percentile Alternative Standard Level (ppb)

Estimated Mean Number of Daily Maximum 

Exceedances of 200 ppb NO2 1-hour in a Year2001-2003 2001-2003 2004-2006 2004-2006

50 100 150 

99th Percentile Alternative Standard L

 117 
- Los Angeles -

Figure 7-8. Estimated mean number of daily maximum exceedances of 200 ppb on-roads in four locations Angeles, Washington DC, and St. Louis) in a year, using recent NO2 air quality (2001-2006) adjusted to ju

alternative 1-hour 98th percentile standard levels and an on-road adjustment factor. 

- Phoenix -

0

255075

100125175

Estimated Mean Number of Daily

Maximum Exceedances of 200 ppb NO150 2 1

-

hour in a Year

2001-20032004-2006

2001-20032004-2006

- Washington DC -

0

255075

100125150175

50 100 150 200

98th Percentile Alternative Standard Level (ppb)

Estimated Mean Number of Daily 

Maximum Exceedances of 200 ppb NO

2 1

- St. Louis -

50 100 150 

98th Percentile Alternative Standard Le

-

2001-2003 2001-20032004-2006 2004-2006

hour in a Year

 118 
Table 7-28. Estimated annual mean NO2 concentration and the number of daily maximum exceedances of short-ter

health effect benchmarks on-roads in a year, using recent air quality (2001-2003) adjusted to just meetin98th percentile alternative standard and an on-road adjustment factor. 

Number of Daily Maximum Exceedances of 1-Hour L

Annual Mean NO2 (ppb)2 ≥ 100 ppb ≥ 150 ppb ≥ 2

Location 

SiteYears1 Mean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99 Mean Min 

Atlanta 1400 27 6 29 57 65 46 0 22 190 229 10 0 1 70 104 2 0 Boston 600 24 9 24 40 41 26 0 13 125 131 3 0 0 22 25 0 0 Chicago 900 45 25 43 75 78 83 2 68 238 257 18 0 7 92 106 4 0 Cleveland 300 46 32 45 61 64 104 19 98 209 225 24 0 15 73 86 7 0 Denver 200 44 28 42 65 67 99 8 82 252 269 19 0 6 86 103 4 0 Detroit 600 41 26 40 59 63 59 4 46 170 186 13 1 8 56 57 5 0 El Paso 1200 36 17 35 56 58 89 4 79 231 249 17 0 8 80 91 3 0 Jacksonville 200 47 33 47 64 67 119 26 118 217 235 32 2 25 97 114 7 0 Las Vegas 1600 27 4 20 69 73 82 0 43 298 307 19 0 3 149 172 5 0 Los Angeles 5100 32 5 32 61 65 33 0 19 142 160 6 0 1 46 55 1 0 Miami 600 29 16 29 45 46 78 2 74 181 189 19 0 10 92 110 5 0 New York 2600 39 15 37 70 79 57 0 43 212 226 10 0 3 67 73 3 0 Philadelphia 1400 50 26 46 89 92 116 1 102 284 294 27 0 12 118 137 7 0 Phoenix 500 56 33 55 83 88 153 2 152 319 337 35 0 14 182 206 6 0 Provo 300 67 44 65 96 101 257 60 277 353 358 75 1 51 273 301 18 0 St. Louis 900 48 28 47 75 76 125 2 118 274 288 28 0 15 144 153 7 0 Washington DC 1800 47 15 48 82 88 109 0 99 287 310 27 0 10 139 168 7 0 Other MSA 61200 23 1 23 48 51 17 0 4 110 133 2 0 0 23 34 0 0 Other Not MSA 12700 13 1 12 34 37 6 0 0 64 86 1 0 0 12 17 0 0 Notes: 1 The average number of monitors operating per year within the three-year group is estimated by dividing the number of s2 Annual means for each monitor were first calculated based on all simulated hourly values in a year. Then the mean of th

means was estimated as the sum of all the annual means in a particular location divided by the number of simulated site-ymonitoring period. The min, med, p98, and p99 represent the minimum, median, 98th, and 99th percentiles of the distribut

annual average concentration in any one year within the monitoring period. 3 The mean number of exceedances represents the sum of daily maximum exceedances occurring at all monitors in a par

divided by the number of simulated site-years across the monitoring period. The min, med, p98, and p99 represent the m

98th, and 99th percentiles of the distribution for the number of daily maximum exceedances in any one year within the mon

 119 
Table 7-29. Estimated mean number of daily maximum exceedances of 100 ppb 1-hour NO2 concentrations on-road

quality as is and air quality adjusted to just meeting the current and alternative standards (98th percent

adjustment factor. 

2001-2003 Air Quality 2004-2006 

Alternative 1-hour 98th percentile 

standard 

Altern

Location As is 

Current 

std 50 100 150 200 As is 

Current 

std 50 

Atlanta 23 183 2 46 117 170 17 193 3 Boston 5 86 0 26 95 163 2 95 0 Chicago 52 193 4 83 205 281 36 189 2 Cleveland 31 264 7 104 235 305 ND Denver 89 267 4 99 232 288 63 257 9 Detroit 41 282 5 59 178 265 20 287 13 El Paso 32 272 3 89 216 278 24 281 5 Jacksonville 13 290 7 119 233 289 11 295 9 Las Vegas 23 189 5 82 167 218 15 177 6 Los Angeles 71 152 1 33 111 191 38 160 2 Miami 7 232 5 78 174 232 6 202 3 New York 42 129 3 57 169 249 35 147 3 Philadelphia 37 222 7 116 254 312 22 232 5 Phoenix 101 245 6 153 293 332 77 284 4 Provo 61 338 18 257 343 351 51 306 13 St. Louis 25 262 7 125 258 316 15 233 4 Washington DC 36 222 7 109 221 279 21 207 5 Other MSA 16 133 0 17 73 138 10 143 0 Other Not MSA 4 126 0 6 28 63 4 124 0 Notes: The mean number of exceedances represents the sum of daily maximum exceedances occurring at all monitors in a partic

number of site-years across the monitoring period. ND no available monitoring data 

 120 
 121 

Table 7-30. Estimated mean number of daily maximum exceedances of 150 ppb 1-hour NO2 concentrations on-road

quality as is and air quality adjusted to just meeting the current and alternative standards (98th percent

adjustment factor. 

2001-2003 Air Quality 2004-2006 

Alternative 1-hour 98th percentile 

standard 

Altern

Location As is 

Current 

std 50 100 150 200 As is 

Current 

std 50 

Atlanta 4 110 0 10 46 95 2 126 0 Boston 0 20 0 3 26 72 0 25 0 Chicago 9 74 0 18 83 168 5 69 0 Cleveland 5 134 1 24 104 196 ND Denver 17 157 0 19 99 198 10 134 1 Detroit 9 166 1 13 59 140 2 189 1 El Paso 4 168 0 17 89 180 3 198 0 Jacksonville 1 205 1 32 119 201 2 216 2 Las Vegas 4 106 0 19 82 143 2 99 1 Los Angeles 17 55 0 6 33 81 6 58 0 Miami 1 146 0 19 78 145 0 130 0 New York 7 36 0 10 57 132 5 45 0 Philadelphia 6 87 1 27 116 217 2 110 0 Phoenix 16 96 0 35 153 262 10 124 0 Provo 9 235 2 75 257 331 17 192 4 St. Louis 3 131 1 28 125 224 1 121 0 Washington DC 6 111 0 27 109 190 2 102 0 Other MSA 2 47 0 2 17 51 1 59 0 Other Not MSA 1 63 0 1 6 19 1 62 0 
7.4 UNCERTAINTY ANALYSIS 

Uncertainty refers to the lack of knowledge regarding 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 functions). 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 characterized, 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). First, 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 summary describing, for each source of uncertainty, the 

level and direction of influence the uncertainty may have on the air quality characterization 

results. 

The overall characterization of uncertainty is qualitatively evaluated 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 uncertainty would have only a small effect on the results, there is 

completeness and scientific consistency in the knowledge base, and decisions made regarding the 

particular source of uncertainty would 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 selection of inputs or models to choose from. 

A characterization of high implies that a small change in the source would have a large effect on 

 122 
 123 

results, there may be inconsistencies present in the scientific support, and assumptions made 

would be considered unusual 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 uncertainty identified in the air 

quality characterization, the level of uncertainty, and the overall judged bias of each. A 

discussion regarding each of these sources of uncertainty 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 

Air Quality Proportional approach used both Medium - High 

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 

Health Averaging time none Low - Medium 

Benchmarks Susceptibility under Medium 
7.4.1 Air Quality Database 

One basic assumption is that the AQS NO2 air quality data used are quality assured 

already. Methods exist for ensuring the precision 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 system, removed following determination of being of 

lower quality or flagged. There is likely no selective bias in retention 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 likely as low concentration data of poor quality. Given the numbers 

of measurements used for this analysis, it is likely that even if a few low quality data are present 

in the data set, they would not have any significant effect on the 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 expected that given the same methods and quality 

assurances, would be complimentary to the data existing in the AQS. Therefore, the air quality 

data and database used likely contributes minimally to the uncertainty level, there is low 

uncertainty in the knowledge base, and the uncertainty in the subjectivity of choices is also 

considered low. 

Temporally, the data are hourly measurements and appropriately account for variability 

in concentrations that are commonly observed for NO2 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 location. However, the degree of representativeness of the monitoring 

data used in this analysis can be evaluated from several perspectives, one of which is how well 

the temporal and spatial variability are represented. In particular, missing hourly measurements 

at a monitor may introduce bias (if different periods within a year or different years have 

different numbers of measured values) and increase 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 the monitors is expanded below. 

 124 
7.4.2 Measurement Technique for Ambient NO2 

One source of uncertainty for NO2 air quality data is due to interference with other 

oxidized nitrogen compounds. Nitrogen 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 interference when oxides of nitrogen other than 

NO2 are present (ISA section 2.3). Most commonly the interference is from HNO3 and has been 

reported to contribute to up to 50% the calculated NO2. This has been shown to occur primarily 

during the afternoon hours in the summer and would result in an overestimation of ambient NO2

concentrations. During winter, positive interference in the measurement of NO2 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 NOz compounds 

which are largely not measured. In addition, it is not known whether 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 NO2 concentrations, the level of which may range from affecting 

the concentrations minimally upwards to a moderate effect. While the science demonstrating the 

interference is consistent, there remain uncertainties about the application of the level of 

interference to individual monitoring sites. 

7.4.3 Temporal Representation 

Data are valid hourly measures and are of similar temporal scale as identified health 

effect benchmark concentrations. There are frequent 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 time of the day (e.g., night or day) are not equally represented. 

Since a 75 percent daily and hourly completeness rule was applied, some of these uncertainties 

and biases were reduced in these analyses. Additional validity criteria could have included 

completeness for monitoring based on quarters, rather 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 would likely exclude a few monitor site-years of 

data considered in the current analysis. 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 something 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 ambient monitoring concentrations. However, if 

a method were selected it would have to not simply interpolate the data, but accurately estimate 

the probability of peak concentrations as well. It was judged that if such 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, this 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 air quality characterization results if the years 

monitored vary significantly between locations. Although monitoring locations within a region 

do change over time, the NO2 network has been reasonably 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 and 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-groups included a different 3-year period, such as 

2002-2004 or 2003-2005. Deciding to bound the total period rather than characterize 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 areas between the ambient monitors that may or 

may not be influenced by similar local sources of NO2. For these reasons the uncertainty and 

bias due to the spatial network may be moderate, although the monitoring network design should 

have addressed these issues within the available resources and other monitoring constraints. Bias 

will depend on ambient monitoring objectives and scale and whether there is large variability in 

monitoring surface, i.e., areas of differing terrain 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 monitoring objectives or land use for individual monitors. Thus, 

there will be some lack of spatial representation and uncertainty due to the inclusion/exclusion of 

some monitors that are very near local sources (including mobile sources) potentially 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 monitor concentrations (ISA, 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, monitors positioned on building rooftops may underestimate NO2

concentrations at lower vertical heights and possibly at the standard breathing height of 1.8 m. 

In this REA, only 7 of the 17810 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 meters or less in height. 

In the aggregate locations (i.e., Other MSA, Other Not MSA), a total of 4 monitors of 34011

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 for this potential vertical gradient in NO2

concentrations may generate underestimates of exceedances for some sites, however the overall 

impact of inferences made for the locations included 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 relationship at heights below 4 meters is largely 

uncertain (e.g., a breathing height of 1.8 meters is commonly used) and therefore would add an 

additional unknown bias to the estimated NO2 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 heights 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 NO2

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 monitors. Consistent with the one 

study reported in the ISA, the estimated concentrations at the 15 meter monitoring height were 

the lowest, with progressively greater concentrations with decreasing receptor height. However, 

the level of differences in concentration at each of the different receptor heights were lower 

using the modeled concentrations in Atlanta than when compared with those reported for the 

South Bronx using the measured ambient concentrations. On average, the NO2 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%) occurred with comparison of the 0 m to the 15 m height (Table 7-32). 

When comparing concentrations for each of the receptor heights at the 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 (Table 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 monitor vertical siting height and NO2 concentration, only the 

magnitude of the relationship differs. The lack of a similar magnitude could be the result of 

several factors such differing influential features of the study area versus the modeled area (e.g., 

seasonal/meteorological factors, presence of nearby sources, terrain) 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 dispersion model predicted. Further, 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, staff did not adjust concentrations 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 NO2 (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 NO2 (ppb) 

0 m Height

1.8 m Height

5 m Height

15 m Height

Figre 7-9. Distribution of 1-hour NO2 concentrations for three modeled receptors in 

Atlanta at different vertical heights, using AERMOD predicted 2002 air quality. 

Table 7-32. Percent difference in 1-hour NO2 concentrations for three modeled receptors in 

Atlanta at different vertical heights, using AERMOD predicted 2002 air quality. 

% Difference in 1-hour NO2 

(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 NO2 concentrations above 100 ppb for three modeled receptors in 

Atlanta at different vertical heights, using AERMOD predicted 2002 air quality. 

Number of 1-hour NO2 ≥ 100 ppb 

(AERMOD Estimated NO2, 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 simulating 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 application of the approach to all ambient monitors 

within each location. 

Different sources have different 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, the proportional adjustment 

used an equivalent multiplicative factor derived from the annual mean concentration and equally 

applied to all portions of the concentration distribution, i.e., the upper tails were treated the same 

as the area of central tendency. This may not necessarily reflect changes in an overall emissions 

profile that may result from, for example, 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 affecting 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 observed at the mean, dependent largely on the type 

of sources and inherent operating conditions. Adjusting the ambient concentrations upwards to 

simulate the alternative standards also carries a similar level of uncertainty although the 

multiplicative factors were derived from the upper percentiles of the 1-hour NO2 concentrations, 

rather than the mean, and then applied to the 1-hour NO2 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 within six of the locations used in the air quality 

characterization to investigate how distributions of hourly nitrogen dioxide concentrations have 

changed over time (Rizzo, 2008). The analysis indicates that a proportional approach can be 

appropriate in simulating higher concentrations at most monitoring sites, since historically, NO2

concentrations have decreased linearly across the entire concentration distribution at each of the 

monitoring sites and locations evaluated. In addition, when adjusting concentrations downward 

(e.g., the alternative standard level of 50 ppb 1-hour, 99th percentile), the use of the proportional 

multiplicative adjustment derived from the upper tails and applied to concentration distribution 

may be less uncertain because NO2 concentrations have been observed to decrease linearly over 

time, and only assumes that the downward trend would continue similarly in the future with 

added source controls. 

At some of monitoring sites analyzed however, there were features not consistent with a 

completely proportional relationship, including deviation from linearity primarily at the 

maximum or minimum percentile concentrations, some indication of curvilinear relationships, 

and the presence of either a positive or negative regression intercept (Rizzo, 2008). Where 

multiple monitors were present in a location there 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 assumed 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 concentration 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 additional years of data were compared that similar 

relationships would be developed. Further, there is uncertainty in adjusting concentrations 

upwards or downwards considering assumptions regarding future source emission scenarios and 

how these would relate to observed trends in current and historical air quality. The uncertainty 

about future source emission scenarios is largely unknown. 

Universal application of the proportional simulation approach for each of the locations 

and within each location was done for consistency and was designed to preserve the inherent 

 131 
variability in the concentration distribution. There is uncertainty regarding emission changes 

that would affect the concentrations at the design monitor containing the highest concentration 

(annual mean, 98th or 99th percentile 1-hour) that may not necessarily affect lower concentration 

sites proportionately. This could result in either over- or under-estimations in the number of 

exceedances at lower concentration sites within a location where the current or alternative 

standard scenarios were evaluated. When comparing the low concentration years and the high 

concentration years at multiple ambient monitors within a location however, most monitors 

contained similar linear relationships (e.g., comparative regression slopes and intercepts). For 

example, Figure 7-10 shows the daily maximum NO2 concentration percentiles for four ambient 

monitors in Philadelphia, where each of 6 ambient monitors were in operation for years 1984 and 

2007. The similarity in slope for each of the monitors indicates that an adjustment factor derived 

from one ambient monitor can be applied to the other monitors in the monitoring network. 

Furthermore, when calculating the number of exceedances of the potential health effect 

benchmark levels, the greatest numbers 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 sites that contained very 

low concentrations and/or atypical variability in hourly concentrations. These few locations 

(e.g., Miami, Jacksonville, and Provo) may contain 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 uncertainty in the results is likely related to the magnitude of the 

extrapolation to the adjusted concentration level. This means that there is likely greater 

uncertainty in the results for evaluating the current annual and the 200 ppb 98th percentile 

alternative standards, than when considering the 50 ppb and 100 ppb 99th percentile alternative 

standard. 

Given the limited deviations in linearity and proportionality at each monitor site that may 

result in both over- or under-estimations in concentrations following either an adjustment 

upwards or downwards and the limited time and resources available to develop a new universal 

 132 
approach that addresses each of the observed deviations, staff judged the proportional approach 

used to simulate just meeting the current and alternative 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 years (1984) versus a low concentration years 

(2007) at four ambient monitors. 

 

7.4.6 On-Road Concentration Simulation 

On-road and ambient monitoring NO2 concentrations have been shown to be correlated 

significantly on a temporal basis (e.g., Cape et al., 2004) and motor vehicles are a significant 

emission source of NOx, providing support for estimating on-road concentrations using ambient 

monitoring data. The relationship used in this analysis to estimate on-road NO2 concentrations 

was derived from data reported in measurement studies containing mostly long-term averaging 

times, typically 7-14 days or greater in duration (e.g., Roorda-Knape, 1998; Pleijel et al., 2004; 

Cape et al, 2004). One study was conducted over 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 appropriate in this analysis to estimate 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 NO2 concentrations are correlated 

with 24-hour averages). While this should not impact the overall contribution relationship 

between vehicles and ambient concentrations on roads, the relationship will likely differ for 

shorter averaging times. However, the longer-term data used to develop the algorithm were 

likely collected during variable conditions (e.g., shifting wind direction, variable diurnal rate of 

transformation of NO to NO2) than would be observed across shorter 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 NO2 concentrations, 

depending on the time of day. In addition, the ambient concentration level could not be 

considered in the application of the on-road factor to the hourly concentration because the 

relationship between the derived adjustment factor and the ambient concentration on that 

temporal scale is unknown. If there is a concentration dependent relationship, this would bias 

the estimated on-road concentrations with unknown direction. Application of the on-road 

concentration estimation also assumes that concentration changes that occur on-roads and at the 

monitor are simultaneous (i.e., within the hour time period of estimation). While this may not be 

the case, because time-activity patterns of individuals 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 near 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 most of the data used were reported for urban 

areas (Appendix A, Table A-108). The factors developed should be appropriate for use in urban 

areas, as was done in this REA, however representation 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 analysis to account for in-vehicle penetration and decay. Therefore, invehicle concentrations would be overestimated if using the estimated on-road concentrations as a 

surrogate, given that reactive pollutants (e.g., PM2.5) tend to have a lower indoor/outdoor (I/O) 

concentration ratio (Rodes et al., 1998). One study reported mean (I/O) ratios of NO2 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 NO2 as it enters 

inside a vehicle. 

At locations where traffic counts are very low (e.g., on the order of hundreds/day) the onroad contribution has been shown to be negligible (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 NO2 concentrations when using equation (7-2) at these locations. 

This is not of great concern because most of the 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 sited 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 emissions (e.g., Beckerman et al., 2008), thus 

controlling for over-estimating the on-road concentrations. However, there is potential for 

influence by non-road source emissions on the measured concentrations at the monitors used (≥

100 m from a major road), contrary to an assumption that there is an absence of direct source 

influence (only that mobile sources were controlled for by selecting 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 Provo 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 commercial, 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 NO2 concentrations regards 

the presence of street canyons on roadways. These localized areas may be subject to highly 

variable and higher mean concentrations within a short span of a road, often defined by the 

presence of man-made structures, such as buildings, on both sides of the road. In one study, a 

comparison of street canyon measured NOx concentrations with those measured at a reference 

site (termed background) indicated that there is about a factor of 2.3 difference in the 

 135 
concentrations (Ghenu et. al, 2007). Vardoulakis et al. (2004) reported mean NO2 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 ambient concentrations, it is likely that some of the 

estimated on-road concentrations in the air quality characterization are similar in magnitude to 

those found in street canyons. 

To represent the relationship between the on-road concentrations and the concentrations 

measured at a distance, a simple exponential model was used. The selection of an exponential 

model was based on independent peer-reviewed studies that reported this type of relationship 

using NO2 measurements. There are uncertainties and possible biases with the selection of the 

exponential model. For example, NOx is primarily emitted as NO (e.g., Heeb et al., 2008; 

Shorter et al., 2005), with substantial secondary formation due predominantly to NO + O3 Æ

NO2 + O2. Numerous studies have demonstrated the O3 reduction that occurs near major roads, 

reflecting the transfer of odd oxygen to NO to form NO2, a process that can impact NO2

concentrations both on- and downwind of the road. Some studies report NO2 concentrations 

increasing just downwind of roadways and that are inversely correlated with O3 (e.g., Beckerman 

et al., 2008), suggesting that peak concentration of NO2 may not always occur on the road, but at 

a distance downwind. While an exponential model may fit well (or for portions of the data), the 

peak may be occurring at a distance from the road rather than on the road. Model convergence 

was one criteria used in selecting for useful parameter estimates. One of the principal reasons 

for lack of convergence is that the measurement data did not fit the exponential form considered. 

Therefore, if the study measurement data contained peak concentrations at a distance of the road 

and other lower concentrations closer to the road 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 distance 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 measurement, but in the absence of additional 

measurements closer to the road, the model assumes 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 combination with the form of the 

exponential model (the highest concentration occurs at zero distance from road) and the selection 

of studies that fit the exponential model, may also add a moderate 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 results. 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 the factors would be applied to the ambient 

monitoring data, it was decided that a two season 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 (spring, 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 rural 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 whether 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 uncertainty 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 either described as by the original 

researchers or inferred by staff as rural 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, given the schedule and resources available to 

produce the REA, the uncertainty in the categorization 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 selection. While there may be other distribution 

forms that could be alternatively selected, staff judged that use of a fitted 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 different from a normal distribution 

(p<0.01). While there is uncertainty associated with 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 lognormal distribution may underestimate the observed 

frequency of certain values of m (Figures 7-11 and 7-12), and while allowing for values outside 

of the empirical distribution, would still need to have realistic 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 adjustment factors outside of the 

range of the empirical data using a fitted distribution would have a low frequency such that the 

overall impact to the estimated on-road NO2 concentrations would likely be limited (Table 7-34). 

Furthermore, the m ratios were derived from measurement 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 (the number of samples, 

uncertainty in the limits and shape of the distribution, 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 selected, with lower and upper 

bounds of the on-road adjustment factor defined by the 0.5th and 99.5th percentiles (Table 7-34). 

On-road adjustment factors were obtained by sampling from the fitted m distribution for each 

season13, 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, the 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 standard, although differences were also present at the 

higher benchmark levels. In addition, the differences in the two simulations were variable at 

each location, that is, at some locations, the lognormal 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 using 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 Cv/Cb ratios or m for the not 

summer category with fitted distributions. 

Figure 7-12. Comparison of the distribution of estimated Cv/Cb 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 fitted 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 

Exceedances2

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 subtracting 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 the 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 representative of the U.S. fleet. 

Without detailed information characterizing the emissions patterns for the on-road study areas, 

there was no attempt to match the air quality characterization locations to specific on-road study 

areas, which might improve the precision of the 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) similar in range with the ratios estimated using 

data obtained from non-U.S. studies. This evidence implies that the level of uncertainty in 

 142 
 143 

applying the non-US studies for the purposes of this analysis may not be large, 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. For example, the potential health 

effect benchmarks used were based on studies where volunteers were exposed to NO2 for 

varying lengths of time. Typically, the NO2 exposure durations were between 30 minutes and 2 

hours. This introduces some uncertainty into the characterization of risk, which compared the 

potential health effect benchmarks to estimates of exposure over a 1-hour time period. 

Therefore, the use of a 1-hour averaging-time could either over- or under-estimate risks. 

In addition, the human exposure studies evaluated airways responsiveness in mild 

asthmatics. For ethical reasons, more severely affected asthmatics and asthmatic children were 

not included in these studies. Severe asthmatics and/or asthmatic children may be more 

susceptible than mildly asthmatic adults to the effects of NO2 exposure. Therefore, the potential 

health effect benchmarks based on these studies could underestimate risks in populations with 

greater susceptibility. 
7.5 KEY OBSERVATIONS 

Presented below are key observations resulting from the air quality characterization: 

• NO2 concentrations and estimates of benchmark 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 NO2 concentrations, based on simulated air 

quality, 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). 

• For unadjusted air quality, representing a recent year, many locations are 

estimated to have, on average, 0 days per year where the 1-hour daily maximum 

ambient NO2 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 NO2

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 NO2 concentrations typically ranged from 10 to 30 ppb (Tables 7-11 to 7-

13). In contrast, most locations are estimated to have between 10 and 50 days per 

year where 1-hour daily maximum NO2 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 NO2

concentrations are > 200 ppb (Table 7-21). The annual average of estimated onroad NO2 concentrations typically ranged from 15 to 45 ppb (Table 7-20). 

• When air quality is adjusted to simulate just meeting the current annual standard, 

a hypothetical scenario requiring air quality to be adjusted upward, all locations 

evaluated are estimated to have multiple days per year where 1-hour daily 

maximum ambient NO2 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 NO2 concentrations > 100 ppb, and six locations are estimated 

to have 100 days or more per year with 1-hour daily maximum ambient NO2

concentrations > 100 ppb. Fewer benchmark exceedances are estimated to occur 

with higher benchmark levels. For example, only two locations are estimated to 

have 10 or more days per year with 1-hour daily maximum ambient NO2

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 NO2 concentrations > 100 ppb and between 25 and 100 

days per year with 1-hour daily maximum on-road NO2 concentrations > 200 ppb 

(Figure 7-6). The corresponding annual average NO2 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 NO2

concentrations > 100 ppb (Tables 7-26 and 7-27). When considering the potential alternative 

standard levels of 0.05 and 0.10 ppm, corresponding annual average NO2 concentrations were 

typically between 10 and 30 ppb, similar to a range of concentrations using unadjusted air 

quality. When considering the potential alternative standard levels of 0.15 and 0.20 ppm, 

corresponding annual average NO2 concentrations were typically between 25 and 55 ppb, similar 

to the range of concentrations observed when using 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 NO2 conducted in support of the 

current review of the NO2 primary NAAQS. Two important components of the analysis include 

estimating temporally and spatially variable ambient NO2 concentrations and simulating human 

contact with these pollutant concentrations. 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 NO2 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 NO2

concentrations using emissions estimates from stationary 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 NO2 concentrations estimated by 

AERMOD. 

Exposure and potential health risk were characterized considering recent air quality 

conditions (as is), for air quality adjusted upward to just meet the current NO2 standard (0.053 

ppm annual average), and for just meeting several potential 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 benchmark levels identified in chapter 6. Specifically, the number of 

times an individual experienced a daily maximum 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 potential health effect benchmark levels. 

The approaches used for assessing exposures in Atlanta are described below. Detailed 

input data and supporting discussion of the Atlanta case-study is provided in Appendix B-4, in 

addition to containing the methodology and results for the first exposure modeling case-study 

 146 
conducted in Philadelphia County as part of the 1st 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 allow for a direct comparison of the estimated 

exposures with those estimated for Atlanta, therefore the approach and the exposure results for 

the Philadelphia County case-study are discussed entirely in Appendix B. 

Briefly, the discussion that follows here includes: 

• Description of the inhalation exposure model and associated input data used for 

Atlanta 

• Evaluation of estimated NO2 air quality concentrations and exposures 

• Assessment of the quality and limitations of the input data for supporting the goals of 

the NO2 NAAQS exposure and risk characterization. 

The overall flow of the exposure modeling process performed for this NO2 NAAQS 

review is illustrated in Figure 8-1. Several models were used in addition to APEX and 

AERMOD including emission factors, meteorological processing, and travel demand models, as 

well as a number of data bases and literature sources to populate the model input parameters. 

Each of these are described within this chapter, supplemented with additional details in 

Appendix B. 

 147 
Figure 8-1. General flow used for NO2 exposure assessment. 

 148 
8.2 OVERVIEW OF HUMAN EXPOSURE MODELING USING APEX 

The EPA has developed the Air Pollutants Exposure Model (APEX) model for estimating 

human population exposure to criteria and air toxic 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 O3 NAAQS review (EPA, 2007g; 2007h). 

APEX is a probabilistic model designed to account for sources of variability that affect 

people’s exposures. APEX simulates the movement of individuals through time and space and 

estimates their exposure to a given pollutant in indoor, outdoor, and in-vehicle 

microenvironments. The model stochastically generates 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-group, or block level, and 

a national commuting database based on 2000 census data provides home-to-work commuting 

flows. Any number of simulated individuals can be modeled, and collectively they approximate 

a random sample of people residing 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 the Consolidated Human Activity Database (CHAD) 

(McCurdy et al., 2000; EPA, 2002). The diaries are used to construct a sequence of activity 

events for simulated individuals consistent with their demographic characteristics, day type, and 

season of the year, as defined by ambient temperature regimes (Graham and McCurdy, 2004). 

The time-location-activity diaries input to APEX contain information regarding an individuals’ 

age, gender, race, employment status, occupation, day-of-week, daily maximum hourly average 

temperature, the location, start time, duration, and type of each activity 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 the important attributes contributing 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 from different seasons, different states of origin, 

etc.). 

 149 
APEX has a flexible approach for modeling microenvironmental concentrations, where 

the user can define the microenvironments to be modeled and their characteristics. Typical 

indoor microenvironments include residences, schools, 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 microenvironmental concentrations account for the 

contribution from ambient (outdoor) pollutant concentration 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 modeled, the available data to define each of the 

parameters, and the estimation method selected by the user. And, because the modeled 

individuals represent a random sample of the population of interest, the distribution of modeled 

individual exposures can be extrapolated to the larger population within the modeling domain. 

The exposure modeling simulations can be summarized by five steps, each of which is 

detailed in the subsequent sections of this document. Briefly, the five steps are as follows. 

1. Characterize the study area. APEX selects the census blocks within that study area 

– and thus identifies the potentially exposed 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 demographic data for the study area and 

estimates anthropometric and physiological parameters for the simulated individuals. 

3. Construct a sequence of activity events. APEX constructs an exposure event 

sequence spanning the period of the simulation 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 study area visit by assigning location codes in 

the activity pattern to the user-specified microenvironments. The model then 

calculates hourly pollutant concentrations in each of these microenvironments for the 

 150 
period of simulation, based on the user-provided microenvironment descriptions, the 

hourly air quality data, and for some of the indoor microenvironments, indoor sources 

of NO2. Microenvironmental concentrations are calculated for each of the simulated 

individuals. 

5. Estimate exposures. APEX estimates a concentration 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 and other input data, the 

desire to represent a range of geographic areas, population demographics, general climatology, 

and results of the ambient air quality characterization. 

Atlanta, along with several other locations, was initially selected as a location of interest 

through statistical analysis of the ambient NO2 air quality data (see section 7 and Appendix A). 

Briefly, criteria were established for selecting ambient monitoring sites having high annual mean 

concentrations and/or exceedances of potential health effect benchmark concentrations. The 90th

percentile served as the point of reference for 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 average concentration at or above the 90th percentile, of which 

Atlanta had one site-year (26.6 ppb annual average). A 1-hour potential health effect benchmark 

level of 200 ppb was selected as the second criteria for 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., with Atlanta as one of the locations in the southeastern U.S. 

EPA was also able to obtain measured daily NO2 exposures of several individuals residing in 

Atlanta (Suh, 2008), potentially for use in evaluating the APEX estimated exposures. Therefore, 

in considering each of these selection 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) geographic representation, Atlanta 

was selected as the second case-study location. 

8.3.2 Study Area Description 

The greater Atlanta metropolitan area covers the 13 counties within 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 quality and exposure modeling to be performed in this exposure 

assessment, the study location (or modeling domain) was designated as the four counties directly 

surrounding the city of Atlanta (i.e., Cobb, DeKalb, Fulton, and Gwinnet Counties) (see Figure 

8-2). These four counties comprise the urban center of the Atlanta MSA, and contain a large 

portion of the urbanized road systems 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 representativeness 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 variable. On a scale of high to low, in 2001 the 

temperature was about average (ranked 17th) though dry (28th lowest precipitation level), 2002 

had average precipitation (18th) although warmer than most years (12th), and 2003 was cooler 

(25th) and wetter (11th) than many other years (NCDC, 2007) 

8.3.4 Populations Analyzed 

The exposure assessment included the total population 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 were 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, individuals anticipated to be exposed more 

frequently to NO2 were assessed, including those commuting 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 were generated using AERMOD, a steady-state, 

Gaussian plume model (EPA, 2004). The following 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 characteristics, 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 Atlanta Hartsfield International Airport.1

3. Define receptor locations. Three sets of receptors were identified for the 

dispersion modeling, and included, ambient 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 sources at each of the defined receptors. 

A brief description of input data and approaches used for estimating source emissions are 

described below. Additional details on the inputs 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 AERMOD dispersion model simulations were 

processed with the AERMET meteorological preprocessor, version 06341. Raw meteorological 

 

1

 Fugitive emissions from major point sources in the Atlanta area were not included as was done in the Philadelphia 

County case study, since the NEI shows all emissions 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 meteorology for the AERMOD runs for the years 2001 

through 2003. Raw hourly surface meteorological data for the 2001 to 2003 period were 

obtained from the Integrated Surface Hourly (ISH) Database,2

 primarily for use in modeling the 

emissions from the Atlanta Hartsfield International 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 the data preparation and 

processing are given in Appendix B, Attachment 1. 

8.4.2.2 Surface Characteristics and Land Use Analysis 

In addition to the standard meteorological observations of wind, temperature, and cloud 

cover, AERMET analyzes three principal variables to help determine atmospheric stability and 

mixing heights: the Bowen ratio, surface albedo as a function of the solar 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, using the US Geological 

Survey (USGS) National Land Cover Data 2001 archives.3

 Details for the seasonal specification 

definitions, land-use sectors, and data processing are given in Appendix B, Attachment 1.

8.4.2.4 Other AERMOD Input Specifications 

All emission sources in the Atlanta modeling 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. 

NOx chemistry was applied to all sources to determine NO2 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 NOx to NO2 (Hanrahan, 1999a, 

1999b). The equilibrium value for the NO2:NOx ratio was taken as 75%, the national average 

ambient background ratio.5

 The initial NO2 fraction of NOx 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-Release/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 this estimate and used for all sources. 

Hourly surface O3 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 Research 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 O3 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 recent, 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 objectives. 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 provided 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 includes 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 description 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 endpoint location, number of lanes, link distance, and 

TDM-defined link daily capacity. 

First, all links with annual 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), which 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 that 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 scaling factors to allow for a smoothing of the 

distribution. The heavy-duty vehicle (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 activity was available, no seasonal variation was used in the simulations. 

However, seasonal variations in emission factors 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 annual daily traffic (AADT) volumes (one direction) for 

Atlanta AERMOD simulations. 

Statistic Road Type CBD1 Fringe Rural Suburban Urban 

 Arterial 229 180 14 1,299 1,221 

 Freeway 109 94 2 616 616 

Count 

 Local 41 60 168 250 

Minimum AADT Arterial 15,015 15,019 16,603 15,002 15,017 

 Freeway 15,049 16,745 23,569 15,111 15,025 

 157 
Statistic Road Type CBD1

 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 Period1 CBD2 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 vehicle registration distribution and corresponding to 

the 2008 O3 season. To maintain consistency with the recent ARC simulations and current 

modeling parameters and maximize temporal resolution, the ARC’s O3 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 included in the ARC files were those derived as 

average values over peak O3 days. To modify the focus from peak O3 to average summer days, 

these values in the input files were modified by converting to average daily minimum and 

maximum temperature and corresponding specific 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 winter and summer sulfur levels are identical. 

Finally, anti-tampering and inspection/maintenance programs, which were not included in the 

original ARC input files, were taken from MOBILE input files prepared by the State of Georgia 

for a previous project. 

The simulations were executed to calculate average running NOx 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 resulting tables were then consolidated 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 following the methodology of the Highway Capacity 

Manual.8

 Table 8-3 shows the resulting average speed for each functional class within each 

TDM region. The resulting emission factors were then coupled with the TDM-based activity 

estimates to calculate emissions from 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. 

 159 
Link Type CBD1 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 rectangular 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 sequential 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 whose length was zero after GIS adjustment of node 

location. This is assumed to be compensated by adjacent links whose length will have been 

expanded by a corresponding amount. Table 8-4 shows 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 temporally 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 factors are attributed to each roadway link to describe the emission 

strengths for 24 hours of each day of each of four 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 vehicle height, or about 2.6 meters for an average vehicle height of 

 160 
about 1.53 meters (5 feet), to account for the effects of vehicle-induced turbulence among other 

factors. The source release height is based on the midpoint of the initial vertical extent, or about 

1.3 meters. The initial vertical dispersion coefficient (sigma-Zo) 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 for an average vehicle 

height of about 4.0 meters. Similarly, source release heights were based on the midpoint of the 

initial vertical extent, or 3.4 meters. The initial sigma-Zo also based on the initial vertical extent 

divided by 2.15, was 3.2 meters. 

For effective source parameters representing 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 the vehicle mix for that roadway link. 

The total NOx emissions on the major roadways links 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 Atlanta 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, however 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 representing 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 source 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 NOx 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) 

FIPS1 Name Minor Link2 Major Link3 % Minor4

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. 

 162 
4 % 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 other 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), airport emissions (see section 8.4.7 below), and 

meteorological data. Therefore, to maintain temporal consistency 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 National 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 estimates 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 adjustment 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 NOx

emissions 

(tpy) 

Modeled 

minor link 

NOx

emissions 

(tpy) 

Total modeled 

on-road 

vehicle NOx

emissions 

(tpy) 

NEI on-road 

vehicle NOx

emissions for 

2002 (tpy) 

Ratio of NEI2002-tomodeled NOx

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 Emissions Inventory (NEI; US EPA, 2007e), Clean Air Markets 

Division (CAMD) Unit Level Emissions Database (US EPA, 2007f), and temporal emission 

profile information contained in the EMS-HAP (version 3.0) emissions model.9 The NEI 

database contains stack locations, emissions release parameters (i.e., height, diameter, exit 

temperature, exit velocity), and annual emissions for NOx-emitting facilities. The CAMD 

database contains information on hourly NOx 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 NOx emissions and are located either within the 4-county modeling 

domain or within 10 km of the modeling domain. Seven NOx-emitting facilities met these 

criteria (Figure 8-4). Stacks within the facilities that were listed separately in the NEI were 

combined for modeling purposes if they had identical stack physical parameters and were colocated within about 10 m. This resulted in 28 combined stacks (stack parameters are in 

Appendix B-4) and accounts for 16% of the total number of NOx point sources and 51% of the 

total NOx point source emissions in this buffered four county Atlanta area. 

The CAMD database was then queried for facilities 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 emissions. Only one of the 7 major facilities 

identified was found in the CAMD data base: the Georgia Power Company McDonough SteamGenerating Plant. The CAMD hourly emissions profiles for two units in this facility were 

summed together and then, after appropriate scaling, used to represent 2 major-facility combined 

stacks. 

For the remaining 26 major-facility combined stacks, hourly NOx emissions profiles were 

created based on the hourly profile typical of that stack’s Source Classification Codes (SCC), the 

season, and the day of week. These SCC-based 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/dispersion_related.htm#ems-hap 

 164 
 165 

 

User’s Guide, Section D-7.11 As with CAMD hourly emissions, these SCC-based emission 

profiles are scaled such that the annual total emissions are equal to those of NEI 2002. 

8.4.6 Airport Emissions Preparation 

The Atlanta-Hartsfield International Airport emissions were assigned to a polygon that 

defined an area source for simulation. The perimeter dimensions of the Atlanta-Hartsfield 

International Airport were determined by GIS analysis of aerial photograph. As with some point 

source emissions, the annual NOx emission totals were extracted from the NEI and the temporal 

profiles from the EPA’s EMS-HAP model. These seasonal, SCC-based emissions were scaled 

such that the annual total emissions are equal to those of NEI 2002: 5,761 tpy, with about 90% 

coming from commercial aircraft (see Figure 8-2 for airport location, 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 initial vertical extent and the initial vertical 

dispersion coefficient was estimated using the initial vertical extent 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 estimated as the release height 

divided by 2.15, or 1.47 m. For effective source parameters representing a mix of aircraft and 

cargo-handling equipment, the source release height and initial sigma-Zo were estimated using 

an emissions-weighted average with 92% of emissions contributed by aircraft. The aggregate 

value for release height was 4.85 m with an intial sigma- Zo of 4.22 m.

11 http://www.epa.gov/scram001/userg/other/emshapv2ug.pdf 
Figure 8-4. Location of major roadway links and major stationary emission sources in Atlanta modeling d

 166 
8.4.7 Receptor Locations 

Three sets of receptors were chosen to represent the locations of interest. The first set 

was selected to represent the locations of the residential population of the modeling domain. 

These receptors were the 27,315 US Census block centroids12. In an effort to make the Atlanta 

case-study more time efficient, a statistical analysis was performed on the Philadelphia exposure 

assessment results reported in the 1st draft REA (see Appendix B, section 3) to determine the 

degree of uncertainty introduced by modeling a subset of the block receptors only. The findings 

of that analysis indicated that the use of a random 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 based on using all the block receptors. It was 

judged that this uncertainty was minimal when compared to other uncertainties 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 along 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 estimates in comparison with a standard breathing height 

of 1.8 m is negligible (see section 7.4.4). Concentrations estimated 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 placed at the center of each of the 5,570 sources. 

Receptor concentration estimates were used by APEX, along with other factors, to estimate an 

individual’s on-road and in-vehicle microenvironmental 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 centroids 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 analysis are actually population-weighted locations 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 population 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 NOx/NO2

monitors. These receptors were used in evaluating the dispersion model performance. When 

considering the four Atlanta counties and period of analysis, there were three monitors within the 

modeling domain with valid ambient NO2 monitoring concentrations (Figure 8-5). 

Figure 8-5. Location of modeled receptors in Atlanta modeling domain. 

8.4.8 Modeled Air Quality Evaluation 

8.4.8.1 Comparison of Hourly Cumulative Density Functions 

The hourly NO2 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 NO2 monitors. Rather than compare concentrations just at the single 

modeled receptor point to the ambient monitor concentrations, a distribution of concentrations 

was developed for the predicted concentrations for 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 concentration 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, excluding 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 monitor concentration distribution. The 2.5th and 97.5th

percentiles from all monitor distribution percentiles15 were selected to create the lower and upper 

bounds of the envelope, while the 50th percentile concentrations were combined to create a 

distribution representing the central tendency (Figure 8-6). The distribution of the modeled 

values estimated for the monitor receptor is also presented, along with the complete hourly 

concentration distribution measured at each ambient 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 NO2

concentrations. The lower and upper bounds of the predicted concentration distributions 

surround the measured ambient concentration distribution at many of the percentiles. The actual 

modeled monitor receptor concentration distributions were generally above that of the 

corresponding measured concentrations, resulting in overestimation at some of the upper 

percentiles by about 20-50%. At monitor 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 monitor was 137 ppb, just 1 ppb above that measured 

(136 ppb). 

When considering the lowest potential health effect benchmark levels, the modeled 

monitor receptor had 19, 2, and 9 daily maximum predicted values above 100 ppb 1-hour, 

compared with 0, 0, and 3 of the measured 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 there are 1000 receptors surrounding a monitor, each receptor containing 8,760 hourly 

values used to create a concentration distribution. Then say the 73rd percentile concentration prediction is to be 

estimated for each receptor. The lower bound of the 73rd percentile of the modeled receptors would represented by 

the 2.5th percentile of all the calculated 73rd percentile concentration predictions, i.e., the 25th highest 73rd percentile 

concentration prediction across the 1000 73rd percentile values generated from all of the receptors. Note that, at any 

given percentile along either of the envelope bounds as well as at the central tendency distribution (the receptor 50th

percentile), the concentration from a different receptor may be used. 

 169 
131210048, respectively. There were only two predicted 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 NO2 concentrations 

above 200 ppb 1-hour as observed with the measurement 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 locations, 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 NO2 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 NO2 concentration for each hour at each receptor. 

Then a prediction envelope was constructed similar to that described above from modeled 

receptors located within 4 km of each ambient monitor. These modeled diurnal distributions, 

along with that of each ambient monitor hour-of-the-day annual average concentration are 

illustrated in Figure 8-7. A table providing the values used to generate the figure is provided in 

Appendix B-4. 

When comparing the modeled predicted and ambient 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 morning through many of the midday hours (8AM4PM). However, NO2 concentrations were overestimated at certain times of the day, generally 

between the hours of 4-6AM and 5-8PM. The overestimation in concentrations is not entirely 

unexpected given the results of the distribution of hourly concentrations illustrated in Figure 8-6. 

 170 
Monitor 130890002

0

10

20

30

40

50

60

70

80

90

100

Cumulative Percentile (%)

AMBIENT MONITOR

AERMOD P2.5

AERMOD P97.5

AERMOD MONITOR

Monitor 130893001

0

10

20

30

40

50

60

70

80

90

100

Cumulative 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

NO2 Concentration (ppb)

Cumulative Percentile (%)

AMBIENT MONITOR

AERMOD P2.5

AERMOD P97.5

AERMOD MONITOR

Figure 8-6. Comparison of measured ambient monitor NO2 concentration distribution 

with the modeled monitor receptor and receptors within 4 km of the monitors at three 

locations in Atlanta for Year 2002. 

 171 
Monitor 130890002

0

5

10

15

20

25

30

35

40

45

50

55

Annual Mean NO2 Concentration (ppb)

AERMOD P2.5

AERMOD P97.5

AMBIENT MONITOR

AERMOD MONITOR

Monitor 130893001

0

5

10

15

20

25

30

35

40

45

50

55

Annual Mean NO2 Concentration (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

0 612 18

Hour of the Day

Annual Mean NO2 Concentration (ppb)

AERMOD P2.5

AERMOD P97.5

AMBIENT MONITOR

AERMOD MONITOR

Figure 8-7. Comparison of measured ambient monitor NO2 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 NO2 concentrations

The two independent approaches used to estimate on-road NO2 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 NO2

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 factors was derived from on-road and near-road NO2

concentration measurements published in the extant literature. The derived empirical 

distribution was separated into two components, one for application to summertime ambient 

concentrations, and the second for all other seasons. The two empirical distributions are 

presented in Figure 8-8, and represent the factors that are multiplied by the ambient monitor 

concentration (i.e., at monitors ≥ 100 m from a major road) and used 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 NO2 concentrations 

estimated at AERMOD receptors ≥ 100 m from a major road were compared with the 

concentrations estimated at their closest on-road receptor to generate a similar ratio (i.e., onroad/non-road NO2 concentrations). In this case, 7-day averages were calculated using the 

hourly AERMOD concentration predictions. These 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 form 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 distribution percentiles, with the AERMOD ratios consistently below 

 173 
that of the empirically derived factors. This is likely due to the differences in the population of 

samples used to generate each type of distribution. The measurement study-derived distribution 

used data from on-road concentration measurements and from monitoring sites located at a 

distance from the road, sites that by design of the algorithm and the factor selection criteria are 

likely not under the influence of non-road NO2 emission sources. The measurement study 

derived ratios never fall below 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., concentrations 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 concentrations estimated at non-road 

receptors that are greater than corresponding on-road receptors. This is likely a more realistic 

depiction of the actual relationship between on-road and non-road receptors. It is possible that 

an NO2 emission source at a distance from a road could 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 lower to mid percentiles. Overlap of the two 

different approaches occurs through about the 40th percentile and tracks closely through the 90th

percentile. The AERMOD predicted ratio distributions then extend beyond the range of values 

offered by the measurement study derived ratios at about the 90th percentile. Given the greater 

number of receptors modeled by AERMOD, the AERMOD approach may be better representing 

the variability in NO2 concentrations than when using the on-road adjustment factor approach. 

While not directly comparable to Atlanta, a few U.S studies report similar concentrations 

for inside vehicles and near roads. For example, Riediker et al. (2003) measured NO2

concentrations inside North Carolina State patrol cars while on duty in Raleigh as well as at a 

fixed site monitor located near local major roadways. Mean concentrations inside the vehicles 

averaged over about 9 hours were 41.7 ppb (minimum 1.6, maximum 548.5 ppb), similar to the 

mean and range of concentrations estimated by AERMOD and using the on-road adjustment 

factor (Table 8-7). Reported roadside NO2 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., approximately 4-days) used 

 174 
for these roadside measurements. CARB (2003) measured NO2 concentrations inside school 

buses during several commutes on urban and suburban/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 NO2 70 ppb along an urban route (minimum 34, 

maximum 120 ppb). Rural/suburban areas averaged less, NO2 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 NO2 Concentration Ratio

Cumulative 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 year 2002 and those derived from data reported in published 

NO2 measurement studies. 

A second comparison was conducted using the hourly on-road NO2 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 receptor were rounded to the nearest 1 ppb. The 

second set of estimated on-road NO2 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 and 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 than 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 difference between the two approaches was prevalent 

throughout each of the percentiles and when considering 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 monitor simulation (n=800) and that the AERMOD 

generated on-road receptors could include locations with greater influence by roadway emissions 

that are not captured by the simplified approach conducted in the Air Quality Characterization. 

Table 8-7. Summary statistics of estimated on-road hourly NO2 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 monitor simulation approaches in Atlanta. 

On-Road Hourly 

NO2 (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

N1 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

 176 
On-Road Hourly 

NO2 (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 NO2 concentration, N is the number of 1-hour concentrations generated for each 

simulation. In the exceedance columns, N represents the number of AERMOD receptors or monitor siteyears simulated. 

8.4.8. Using unadjusted AERMOD predicted NO2 concentrations

The NO2 concentrations estimated using AERMOD may be biased upwards based on a 

comparison with measurement data from the three available ambient monitors. Given an 

apparent systematic bias, one could argue for 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 comparisons 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 number of comparisons available. 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 

O3 (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 information for years 2004 – 2005 (Blackwell and Kanny, 

2007). Table 8-8 provides a summary of the prevalence rates used in the exposure analysis by 

age and gender. Additional information on the variability in these prevalence rates is given in 

Appendix B-4. 

Table 8-8. Asthma prevalence rates by age and gender used for Atlanta. 

Asthma Prevalence2

Region 

(Study Area) Age1 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 simulated population of about 212,000 asthmatics. 

The model simulated approximately 500,000 children, of which there were about 64,000 

asthmatics. For comparison, the Georgia Department of Human Resources reports the 2001 

asthma prevalence for children in middle and 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 LONGITUDINAL 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 concentrations. To accurately model individuals and their 

exposure to pollutants, it is critical to understand their daily activities. EPA’s Consolidated 

Human Activity Database (CHAD) provides data for where people spend time and the activities 

performed. Typical time-activity pattern data available for inhalation exposure modeling consist 

of a sequence of location/activity combinations spanning 24-hours, with 1 to 3 diary-days for any 

single study individual. 

The exposure assessment performed here requires information on activity patterns over a 

full year. Long-term multi-day activity patterns were estimated 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 then 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 correlation (i.e., re-selection of diaries for each time 

period) and perfect correlation (i.e., selection of a single daily record to represent all days). 

Details regarding the algorithm and supporting evaluations are provided in Appendix B-4, 

Attachments 2 and 3. 

8.7 CALCULATING MICROENVIRONMENTAL 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 (outdoor) pollutant concentration and factors affecting indoor 

microenvironment, such as a penetration, air exchange 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 estimated for the relevant blocks/receptors, the 

user-specified algorithm, and input parameters specific to each microenvironment. The method 

used by APEX to estimate the microenvironmental 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 APEX calculates hourly concentrations 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 microenvironment due to deposition, filtration, and 

chemical degradation 

• Emissions from sources of a pollutant inside the microenvironment. 

A transfer factors approach is simpler than the mass balance model, however, most 

parameters are derived from distributions rather than single values to account for observed 

variability. It does not calculate concentration in a microenvironment 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 factor is used to account for proximity of the 

microenvironment to sources or sinks of pollution, or other systematic differences between 

concentrations just outside the microenvironment and the ambient concentrations (at the 

measurements site or modeled receptor). The second parameter, a penetration factor, quantifies 

the amount of outdoor pollutant penetrates into the microenvironment. 

8.7.1 Microenvironments Modeled 

In APEX, microenvironments represent the exposure locations for simulated individuals. 

For exposures to be estimated accurately, 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 transfer 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 microenvironment 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-deposition rate, PR=proximity factor, 

PE=penetration factor 

8.7.2 Microenvironment Descriptions 

8.7.2.1 Microenvironment 1: Indoor-Residence 

The Indoors-Residence microenvironment uses several variables that affect NO2

exposure: whether or not air conditioning is present, the average outdoor temperature, the NO2

removal rate, and an indoor concentration source. 

Air conditioning (A/C) status of an individual’s residential microenvironment was 

simulated randomly using the probability that a residence has an air conditioner. For the Atlanta 

modeling domain an air-conditioning prevalence of 97.0 % was used (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 O3 NAAQS review (EPA, 2007g; see Appendix B, 
 182 

Attachment 5). Briefly, AER data were reviewed, compiled, and evaluated from the extant 

literature to generate location-specific AER distributions 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 considering these influential factors. When categorizing 

by temperature, a range of temperatures was used to maintain a reasonable number of samples 

within each category to allow for some variability within the category, while still allowing for 

differences across categories. Several distribution forms were investigated (i.e., exponential, 

log-normal, normal, and Weibull) and in general, lognormal distributions provided the best fit. 

Fitted lognormal distributions were defined by a geometric mean (GM) and standard deviation 

(GSD). Because no fitted distribution was available specifically for Atlanta, distributions were 

selected from other locations thought to have similar characteristics, qualitatively considering 

factors that might influence AERs including the age composition of housing stock, construction 

methods, and other meteorological variables not explicitly 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 and maximum AER, respectively. Table 8-10 

summarizes the distributions used by A/C prevalence and temperature 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/C1

>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/C2

>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-California cities. See Appendix B, 

Attachment 5. 

The same NO2 removal rate distribution was used for 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 NO2 

exposure to gas cooking (hereafter referred to as “indoor sources”). This was the only indoor 

source considered in this assessment. Three types of data were used to generate the emission 

factor for this indoor source: (1) the fraction of households in the Atlanta MSA that use gas for 

cooking fuel, (2) the range of contributions to indoor NO2 concentrations that occur from 

cooking with gas, and (3) the diurnal 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 estimating the contribution to indoor NO2 concentrations that 

occur during cooking with gas fuel were derived from a study sponsored by the California Air 

Resources Board (CARB, 2001). A uniform distribution of concentration contributions for input 

to APEX was estimated as U{4, 188 ppb}. An analysis by Johnson et al (1999) of survey data 

on gas stove usage collected by Koontz et al (1992) showed an average 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 from 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 exchange rate distribution (GM = 1.109, GSD = 3.015, Min 

= 0.07, Max = 13.8) was based on an indoor air quality study (Persily et al, 2005). This is the 

same distribution in APEX used for the most recent O3 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 Indoor-Residence microenvironment (section 8.7.2.1). 

The Bars and Restaurants microenvironment included an estimated contribution from indoor 

sources as was described for the Indoor-Residence, only there was an assumed 100% prevalence 

rate for cooking with a gas appliance and 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 exposure. Penetration factors are not applicable to 

 184 
outdoor environments (effectively, PEN=1). The distribution for proximity factors were 

developed from the dispersion model estimated concentrations, using the relationship between 

on-road to receptor estimated concentrations. 

8.7.2.4 Microenvironment 10: Outdoors-General 

The general outdoor environment concentrations are represented by the AERMOD 

predicted concentrations. Therefore, both the penetration factor and proximity factor for this 

microenvironment were set to 1. 

8.7.2.5 Microenvironments 11 and 12: In Vehicle- Cars and Trucks, and Mass Transit 

Penetration factors were developed from data provided in Chan and Chung (2003). Since 

major roads were the focus of this assessment, reported indoor/outdoor ratios for highway and 

urban streets were used here. Mean values range from about 0.6 to just over 1.0, with higher 

values associated with increased ventilation (i.e., window open). A uniform distribution U{0.6, 

1.0} was selected for the penetration factor for Inside-Cars/Trucks due to the limited data 

available to describe a more formal 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 frequent opening and closing of doors, was assigned a 

uniform distribution U{0.8, 1.0} based on the reported mean values for fresh-air intake (0.796) 

and open windows (1.032) on urban streets. Proximity factors were developed from the 

dispersion model estimated concentrations, using the relationship between the on-road to 

receptor 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 HEALTH RISK CHARACTERIZATION 

APEX calculates the time series of exposure concentrations 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 present), 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 simulation period is determined using the following equation: 

 185 
T

tC

C

N

j

j

hourlymean

jME

i

∑= = 1

)()(

 equation (8-1) 

where, 

 Ci = Hourly exposure concentration at clock hour i of the simulation period 

(ppm) 

N = Number of events (i.e., microenvironments visited) in clock hour i of 

the simulation period. 

 = Hourly mean concentration in microenvironment j (ppm) hourlymean C jME )(

 t(j) = Time spent in microenvironment j (minutes) 

 T = 60 minutes 

From the hourly exposures, APEX calculates time series of 1-hour average exposure 

concentrations that a simulated individual would experience during the simulation period. 

APEX then statistically summarizes and tabulates the hourly (or daily, annual average) 

exposures. In this analysis, the exposure indicator is 1-hr exposures above selected health effect 

benchmark levels. From this, APEX can calculate two general types of exposure estimates: 

counts of the estimated number of people exposed at or above a specified NO2 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 potential health effect benchmark level of 

interest. APEX can also report 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 accumulates these estimates for the entire population 

residing in an area. 

APEX tabulates and displays the two measures for exposures above levels ranging from 

100 to 300 ppb by 50 ppb increments for 1-hour average 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 Characterization (Appendix A). In this case, instead 

of adjusting upward16 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 adjustment, 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 98th percentile averaged over three years), only the associated 

benchmarks are adjusted upwards (i.e., a higher threshold simulating lower exposures). 

Table 8-13. Adjusted potential health effect benchmark levels used by APEX to simulate just 

meeting the current standard and various alternative standards considered. 

Model Potential health effect benchmark level (ppb)

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 

98th %ile 163 nd 327 nd 490 50 

99th %ile 177 nd 355 nd 532 

98th %ile 82 nd 163 nd 245 100 

99th %ile 89 nd 177 nd 266 

98th %ile 54 nd 109 nd 163 150 

99th %ile 59 nd 118 nd 177 

98th %ile 41 nd 82 nd 123 

Alternative 

Standards 

1 hour 

200 

99th %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 contributions 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 justification 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 (if present) and this 

concentration can be either above or below a selected concentration level of interest. This is 

represented by the following equation: 

 Cexp osure aCambient indoor >+= CbC threshold

 where, 

Cexposure = individual exposure concentration (ppm) 

a = proportion of exposure concentration from ambient (unitless fraction) 

Cambient = ambient concentration in the absence of indoor sources 

b = proportion of exposure concentration from indoor (unitless fraction, 

equivalent to 1-a) 

Cindoor = direct indoor source concentration contribution in the absence of 

ambient influence (ppm) 

Cthreshold = an exposure concentration of interest (ppm) 

It follows that if we are interested in adjusting the ambient concentrations upwards by 

some proportional factor f (a unitless number), this can be described with the following: 

 ambient indoor >+ CbCCfa threshold

This is equivalent to 

/()/( fCfCbaC ) ambient + indoor > threshold

Therefore, if the potential health effect benchmark level and the indoor concentrations are 

both proportionally scaled downward by the same adjustment factor, the 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 ambient 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 characterization are presented here for the four 

county modeling domain in Atlanta. Several exposure scenarios were considered for the 

exposure assessment including an analysis of three averaging times for NO2 concentrations 

(annual, 24-hour, and 1-hour), an analysis of the contribution to NO2 exposures of both indoor 

and outdoor sources, and an analysis of NO2 exposures assuming air quality that just meets the 

current annual and several alternative 1-hour daily maximum standards. 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 persons, all children (ages 

5-17), all asthmatics, and asthmatic children (ages 5-17). 

The exposure results that are summarized below focus on asthmatics. Key results are 

presented in the next three subsections, 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, certain exposure data could only be output for the 

entire population modeled (i.e., all persons - includes asthmatics and healthy persons of all ages) 

rather than the particular subpopulation. The summary exposure results for the entire population 

(e.g., annual average exposure concentrations, time spent in microenvironments at or above a 

potential health effect benchmark level) is assumed representative of the asthmatic population in 

the modeling results because the asthmatic population does not have its microenvironmental 

concentrations and activities estimated any differently 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 findings 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 exposure concentrations for the total simulated 

population (i.e., both asthmatics and healthy individuals of all ages), considering the modeled 

year 2002 air quality (as is) and both with and without indoor sources. Also plotted on this 

figure is the distribution of the annual average NO2 concentrations predicted by AERMOD 

separated into two broad receptor categories. As a point of reference, the measured annual 

average concentration for the three ambient monitors in the Atlanta modeling domain ranged 

from 15 ppb to 19 ppb in year 2002. About one-half of the AERMOD predicted annual average 

NO2 concentrations for the non-road receptors were below the range of the ambient monitoring 

annual average concentrations, with most receptors 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 have a number of block centroids located near a major road. 

Consistent with what was observed in the air 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 estimated concentrations at non-road receptors. 

The hourly NO2 concentrations output from AERMOD were input into the exposure 

model, providing a wide range of estimated exposures calculated by APEX (Figure 8-9). All 

persons were estimated to experience exposures below an annual average exposure of 53 ppb, 

even when considering indoor source concentration 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 exposure estimation included indoor sources, and 

about 9 ppb less when annual average exposures were estimated without the indoor sources. In 

the absence of indoor source contributions, personal 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 consistent with studies reporting such a relationship 

based on measurements of personal exposure and 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 sources, indoor sources 

were estimated to contribute between 1 and 4 ppb to the total annual average exposures. This 

would correspond to indoor sources contributing approximately 1/3 of the annual average 

 190 
exposures for persons using gas cooking appliances. Again, while Figure 8-9 summarizes the 

entire population, the data are representative of what would be observed for the population of 

asthmatics or asthmatic children. 

Year-to year-variation was evaluated by comparing the estimated 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 exposures 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 NO2 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 NO2 concentrations (onroad and non-road receptors) and APEX modeled NO2 exposures (with and without 

modeled indoor sources) in Atlanta modeling domain for year 2002. 

 191 
0

10

20

30

40

50

60

70

80

90

100

0 5 10 15 20 25 30 35

Annual Average NO2 Exposure (ppb)

Cumulative Percentile (%)

2001 - No Indoor Sources

2002 - No Indoor Sources

2003 - No Indoor Sources

Figure 8-10. Comparison of estimated annual average NO2 exposures for Years 2001-2003 

in Atlanta modeling domain without modeled indoor sources. 

8.9.3 Daily Average Exposures (as is) 

As mentioned earlier, APEX is capable of providing exposure results across a variety of 

averaging times, including 24-hour average exposures. This averaging time serves as a good 

point of comparison with the personal exposures reported in the published 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 daily average concentration distribution (i.e., each 

person has a median daily average exposure, a 99th percentile daily average exposure, etc.). 

These modeled exposures were compared with personal NO2 measurement data obtained from 

Suh (2008) for the participants of an Atlanta epidemiological study conducted by Wheeler et al. 

(2006). The personal exposure measurements were collected across two seasons (fall and 

spring)17 and considered cooking fuel (gas or electric cooking) as an influential variable for 

personal exposures. A total of 30 individuals participated 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 reported 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 personal exposure measurements was available for each individual 

when stratified by season and cooking fuel (minimum number of days = 3, max = 7). Because 

there were few personal exposure measurements, an exposure distribution 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 personal daily average exposures higher during the 

spring by about 1 to 3 ppb when comparing the 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 highest in the spring and similar for both of the 

cooking fuel categories. 

Daily mean exposures estimated using APEX were 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 specific period from 1999-2000 was not modeled by 

APEX although this period was included in the personal exposure measurement study. The 

APEX simulation results for year 2002 were selected for comparison with the exposure 

measurements obtained from Suh (2008). A distribution of each person’s estimated daily 

exposure was constructed, using the median daily mean exposure to represent the central 

tendency and a 95 % prediction interval to represent the lower and upper bounds of exposure 

(i.e., the 2.5th and the 97.5th percentiles). This prediction interval 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 variability in exposures given the greater number of 

days in comparison with the personal exposure measures that contained at most 7 days of data 

per season. 

The daily mean exposures estimated using APEX, stratified by season 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 about 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 conducted 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 exposure distributions include exposures for all of the simulated 

individuals, some of which do not have gas cooking occurring at home. The APEX simulated 

daily mean exposures are similar to the measured 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 maximum daily exposure concentrations with the maximum 

observed daily personal exposure concentrations is considered 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. 
p50

minmax

0

102030405060708090

100

Cumulative Percent (%)

p50minmax

FALL 1999 SPRING 2

GAS COOKING ELECTRIC COOKING

0

102030405060708090

100

0.1 1 10 100

Daily Mean NO2 Exposure (ppb)

Cumulative Percent (%)

p50minmax

0.1 1 

Daily Mean NO2 Exposu

p50minmax

n = 17n = 7

n = 14n = 8

Figure 8-11. Distribution of measured daily average personal NO2 exposures for individuals in Atlanta, st

seasons (fall or spring) and cooking fuel (gas or electric). Minimum (min), median (p50), and maximum (m

from each individual’s multi-day exposure measurements. The figure generated here was based on personmeasurements obtained from Suh (2008).  195 
 196 

Figure 8-12. Distribution of estimated daily average NO2 exposures for individuals in Atlanta, stratified b

spring) and with and without indoor sources, for Year 2002 APEX simulation. Lower bound (2.5th percen

(p50), and upper bound (97.5th percentile, p97.5) were calculated from each simulated persons 365 days orandom sample of 5% of persons (about 2,500 individuals) is presented in each figure to limit the density o

FALL SPRING

With Indoor Sources No Indoor Sources

0

102030405060708090

100

Cumulative Percent (%)

p50p2.5p97.5

p50p2.5p97.5

0

102030405060708090

100

0.1 1 10 100

Daily Mean NO2 Exposure (ppb)

Cumulative Percent (%)

p50p2.5p97.5

0.1 1 

Daily Mean NO2 Exposure 

p50p2.5p97.5
8.9.4 One-Hour Exposures 

8.9.4.1 Overview 

Because the focus of the exposure and risk characterization is on short-term 1-hour daily 

maximum exposures, analyses were performed using the APEX estimated 1-hour exposure 

concentrations. The number of exposures above the selected potential health effect benchmark 

levels (i.e., 100, 150, 200, 250, and 300 ppb, 1-hour average) were estimated. An exceedance 

was recorded when the maximum exposure concentration 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 above 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 NO2 exposure, namely individuals with asthma. The potential health effect 

benchmark levels used are appropriate for characterizing the potential risk 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 exposure 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 population (such as time spent in particular 

microenvironments). The 1-hour exposure results are presented separately for three scenarios, 

(1) considering the exposures associated with as is air quality, (2) simulating exposures with air 

quality adjusted upwards to represent just meeting the current annual average standard, and (3) 

simulating exposures associated with air quality adjusted to represent just meeting alternative 1-

hour daily maximum standards. In addition, the presence (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 generated from the modeled air quality as input 

to APEX without any adjustment to the air concentrations or the potential health effect 

benchmark levels. Figure 8-13 summarizes the estimated 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., just over 212,000) were estimated to be exposed 

at least one time to a 1-hour daily maximum concentration of 100 ppb in a year. The number of 

asthmatics exposed to greater concentrations (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 potential 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 were estimated to be exposed one time to a 1-

hour daily maximum NO2 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 using 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%, respectively. The year-to-year variability in the 

number of asthmatic children exposed at or above 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 was also observed for exceedances of the 300 ppb 

benchmark (COV = 4.9%). 

 198 
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

5.0E+4

1.0E+5

1.5E+5

2.0E+5

2.5E+5

Estimated Number of Asthmatics 

Exposed 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 NO2 exposure at or above the potential 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+4

Estimated Number of Asthmatic 

Children Exposed to Selected Level in 

a Year

Potential Health Effect Benchmark Level (ppb)

Figure 8-14. Estimated number of simulated asthmatic children in the Atlanta model 

domain with at least one NO2 exposure at or above the potential health effect benchmark 

levels, using modeled 2001-2003 air quality (as is), without modeled indoor sources. 

 199 
Additional exposure estimates were generated using the modeled 2002 air quality (as is). 

Those estimates include an evaluation of the contribution of indoor sources. APEX allows for 

the same persons to be simulated (i.e., demographics of the population were conserved), as well 

as using the same individual time-location-activity 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 sources and without indoor sources included in the 

model runs. The number of asthmatics at or above the selected benchmark levels at least one 

time in a year is very similar when including indoor source concentration contributions (i.e., gas 

cooking) compared to the number of persons whose exposure estimates did not include indoor 

sources. The reduction in numbers of asthmatics exposed 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 concentration level was exceeded, representing the sum 

of all multiple exposures (in contrast to focusing 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 difficult to discern the effect that indoor sources 

had on the estimated exposures. Now it can be seen 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 selected benchmark level. It appears 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, respectively, 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 concentrations above the potential health effect 

benchmark levels. Currently, the output generated by APEX is limited to compiling the 

microenvironmental time for the total population (includes both asthmatic individuals and 

healthy persons) and the summaries provide the total time spent above the selected potential 

health effect benchmark levels. As mentioned above, the data still provide a reasonable 

approximation for each of the population subgroups (e.g., asthmatics or asthmatic children) 

 200 
because their microenvironmental concentrations and activities are not estimated any differently 

from those of the total population 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+5

Estimated Number of 

Asthmatics Exposed to 

Selected 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 NO2 exposure at or above potential health 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+7

Estimated Number of 

Asthmatic Person Days 

Exposed to Selected Level 

in a Year

Potential Health Effect Benchmark Level (ppb)

Figure 8-16. Estimated number asthmatic person-days in the Atlanta model domain with 

an NO2 exposure at or above potential health effect benchmark levels, using modeled 2002 

air quality (as is), both with and 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 (results 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 microenvironments contributed little to the occurrence 

of estimated exposures at or above the selected benchmark levels. Most indoor 

microenvironments contributed < 1% of exposures 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 outdoors (Outdoors-Near Road, 

Outdoors-Parking Lot, Outdoors-Other). The time spent outdoors near roadways exhibited an 

increase in contribution of exceedances of potential health benchmark levels, increasing from 

around 25 to 29% of time associated with concentrations of 100 and 300 ppb, respectively. The 

in-vehicle microenvironment showed 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, there is attenuation of the estimated on-road 

concentration that penetrates the in-vehicle 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 source contributions (Figure 8-18). While the 

transportation-associated microenvironments remained 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, contributing to approximately 26% (inside a home) and 

33% (inside bar/restaurant) of the time persons were exposed (Figure 8-18a). This is a result of 

the indoor source contribution to each individual’s exposure concentrations and is consistent 

with what was observed regarding the effect of indoor sources 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 indoor source contributions. The exposures at 

the higher benchmarks are associated mainly with the transportation microenvironments, 

increasing from about 39% of the time exposures occurred 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 selected 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 individuals are exposed to concentrations 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, the number of days with a 1-hour daily maximum 

exposure above a selected level is retained for each person. Figure 8-19 presents such an 

analysis for the year 2002, where the estimated exposures did not include indoor source NO2

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 NO2 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 corresponds with exceedances of the potential NO2

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 corresponds with exceedances of the potential NO2

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 NO2 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 experiencing 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 Exposed 

one or more Times to Selected Level in a 

Year

Potential Health Effect Benchmark Level 

(ppb)

Estimated Number 

of Repeated 

Exposures in a 

Year

Figure 8-19. Estimated percent of all asthmatics in the Atlanta modeling domain with 

repeated NO2 exposures above potential health effect benchmark levels, using modeled 

2002 air quality (as is), without indoor sources. 

 206 
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 Exposed 

one or more Times to Selected Level in a 

Year

Potential Health Effect Benchmark Level 

(ppb)

Estimated Number 

of Repeated 

Exposures in a 

Year

Figure 8-20. Estimated percent of all asthmatics in the Atlanta modeling domain with 

repeated NO2 exposures above potential health effect 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 NO2 standard, the potential health 

effect benchmark levels were adjusted in the exposure 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, estimates of short-term exposures (i.e., 1-hour daily 

maximum) were generated for the total population and population subgroups of interest (i.e., 

asthmatics and asthmatic children). 

When considering the estimated exposures associated with air quality simulated to just 

meet the current annual average NO2 standard, the number of persons experiencing 

concentrations at or above the potential health effect benchmarks is increased in comparison with 

as is air quality. Figure 8-21 illustrates the percent of asthmatics estimated to experience at least 

one exposure at or above the selected potential health effect benchmark concentrations, with air 

quality adjusted to just meet the current standard. The exposure results for both including and 

excluding indoor source contributions 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 estimated that nearly all asthmatics would experience at 

least one exposure above any of the potential health effect benchmark levels in a year when air 

quality is adjusted to just meet the current standard. Exposure estimates where indoor sources 

were included were not greatly different than the results without 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 illustrates 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+5

Estimated Number of 

Asthmatics Exposed to 

Selected 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 NO2 exposure at or above the potential health effect benchmark level, using 

modeled 2002 air quality just meeting the current standard (cur std), with and without 

modeled indoor sources. 

 

 208 
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 Exposed 

one or more Times to Selected Level in a 

Year

Potential Health Effect Benchmark Level 

(ppb)

Estimated Number 

of Repeated 

Exposures in a 

Year

Figure 8-22. Estimated percent of asthmatics in the Atlanta modeling domain with 

repeated NO2 exposures above health effect benchmark levels, using modeled 2002 air 

quality just meeting the current standard, 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 NO2 standards, the potential health effect 

benchmark level was adjusted in the exposure model, rather than adjusting all of the hourly 

concentrations for each receptor and year simulated (see section 8-8). Similar to exposure 

analyses performed with the as is air quality, estimates of short-term exposures (i.e., 1-hour daily 

maximum) were generated for the total population 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 potential health effect benchmark levels of 100, 200, 

300 ppb were evaluated for the alternative 1-hour daily maximum standards. 

In considering exposures estimated to occur associated with air quality simulated to just 

meet the alternative NO2 1-hour daily maximum standards, the 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 98th percentile or p98; 

99th 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 98th and 99th 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 standard. 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 approaches 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 exposed to 100 ppb at least 

once in a year as was observed in the above analyses, 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, 98th percentile standard. 

The estimated number of repeated NO2 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 illustrates the number of multiple exposures above the 

potential health effect benchmark levels using a 50 ppb, 99th percentile alternative 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 asthmatics 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 alternative standard (50 ppb, 99th percentile). Figure 8-25 illustrates what 

has been consistently shown in the above analyses, 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 potential health effect benchmark levels. 

In addition, for comparison with the results presented in Figure 8-24, the percent of 

asthmatics exposed to the selected health effect benchmark levels considering the 100 ppb, 99th

percentile alternative standard is presented in Figure 8-26. A greater proportion of asthmatics 

have multiple exposures at all of the 1-hour benchmarks, nearly all of which were estimated to 

have at least six exposures 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+5

Estimated Number of Asthmatics Exposed 

to Selected Level in a Year

Potential Health Effect 

Benchmark Level (ppb)

Figure 8-23. Estimated percent of asthmatics in the Atlanta modeling domain with NO2

exposures at or above potential health effect benchmark levels, using modeled 2002 air 

quality adjusted to just meeting potential alternative 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 Exposed 

one or more Times to Selected Level in a 

Year

Potential Health Effect 

Benchmark Level (ppb)

Estimated Number 

of Repeated 

Exposures in a 

Year

Figure 8-24. Estimated percent of asthmatics in the Atlanta modeling domain with 

multiple NO2 exposures at or above potential health effect benchmark levels, using 

modeled 2002 air quality adjusted to just meeting a 50 ppb level 99th percentile form 

alternative standard, without indoor sources. 

 211 
100 200

300

6

5

4

3

2

1

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Estimated Percent of Asthmatics Exposed 

one or more Times to Selected Level in a 

Year

Potential Health Effect 

Benchmark Level (ppb)

Estimated Number 

of Repeated 

Exposures in a 

Year

Figure 8-25. Estimated percent of asthmatics in the Atlanta modeling domain with 

multiple NO2 exposures at or above potential health effect benchmark levels, using 

modeled 2002 air quality adjusted to just meeting a 50 ppb level 99th 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 Exposed 

one or more Times to Selected Level in a 

Year

Potential Health Effect 

Benchmark Level (ppb)

Estimated Number 

of Repeated 

Exposures in a 

Year

Figure 8-26. Estimated percent of asthmatics in the Atlanta modeling domain with 

multiple NO2 exposures at or above potential health effect benchmark levels, using 

modeled 2002 air quality adjusted to just meeting a 100 ppb level 99th percentile form 

alternative standard, without indoor sources. 

 212 
8.10 KEY OBSERVATIONS 

Presented below are key observations resulting from the exposure assessment: 

• Modeled concentrations are reasonable given comparisons to available data 

o AERMOD predicted upper percentile NO2 concentrations are about 10 to 

50% (or 10 to 30 ppb) higher than ambient measurements at three fixedsite monitors 

o APEX modeled daily mean NO2 exposures in Atlanta (medians 3-24 ppb) 

are comparable to personal exposure measurements in Atlanta (median 3-

14 ppb) 

o APEX modeled annual average NO2 exposures, expressed as a percent of 

the ambient NO2 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-vehicle exposures, with the remainder 

associated with outdoor near-road exposures 

• When included, indoor sources contribute to the occurrence of NO2 exposures at 

or above 100 ppb (61%), but little to the occurrence of higher 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 considered, more than 90% of 

asthmatics in Atlanta are estimated to be exposed at least one time 

per year 

 Of the standard levels evaluated, 50 ppb was the only level 

estimated to reduce repeat exposures to NO2 concentrations above 

100 ppb compared to recent air quality levels 

o 200 ppb 

 Of all the air quality scenarios 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 scenarios 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 meets the current annual standard, virtually 

all asthmatics in Atlanta are estimated to experience six or more daily maximum 

1-hour exposures per year to NO2 concentrations above the highest benchmark 

level evaluated (300 ppb) 

• Using a 98th versus a 99th percentile form made a difference of approximately 5 to 

10% in the number of daily maximum benchmark exceedances, with the 99th

percentile form generating fewer exceedances. 

8.11 REPRESENTATIVENESS OF EXPOSURE RESULTS 

8.11.1 Introduction 

Due to time and resource constraints the exposure assessment evaluating the current and 

alternative standards was only applied to the Atlanta urban area. A natural question is how 

representative are the estimates from this assessment of exposures in Atlanta to other urban areas 

in the United States. To address this question, 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 exceedances were associated with the near-road or 

in-vehicle microenvironments, the analysis and discussion is centered 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 Atlanta 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 Administration (FHWA). 

 214 
8.11.2.1 HAPEM6 Near-Road Population Data Base 

The first type of data considered employs a data base developed for the Human Air 

Pollutants Exposure Model (HAPEM6) and is available as part of the HAPEM6 installation 

package.19 The data base provides estimates of the fraction 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 base 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 relative to concentrations at other outdoor locations. First, several 

measurement studies of near roadway concentration 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) and Rodes et al. (1998) studies were made at 

distances shorter than those of interest for this study. Available ratios for Zhu et al. (2002) were 

only for downwind distances, and did not represent ratios under more general meteorological 

conditions. The ability of regression models applied to the Relationships 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 concentrations 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 distributions of concentration ratios. A distribution was developed 

based on the ratio of concentrations within a distance D1 meters of a major roadway, to 

concentrations at locations greater than a second distance, D2 meters from a major roadway. A 

second distribution was developed using the ratio of concentrations at D1 - D2 meters of a major 

roadway to concentrations at locations greater than D2 meters from a major roadway. 

 

19 http://www.epa.gov/ttn/fera/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 distance-to-roadway bins, regression models were 

developed to estimate two distance terms D1 and D2 for each season and for the annual mean. 

Generally, the two-distance term regression models applied to the PATA CALPUFF data 

showed that the optimal road distances, based on R2 and Akaike Information Criterion (AIC) 

statistics, were D1 = 75 m and D2 = 300 m. The regression models favored the higher distances 

for D2 because the CALPUFF estimates for PATA continue to decrease significantly with 

distance from the road. However, the R2 and AIC values were not much different between the 

models with D2 = 200 or 300 m. Because a few other studies, including Zhu et al. (2002), have 

shown typical zones of influence for roadways no further than 200 m, distances of D1 = 75 m and 

D2 = 200 m were selected for development of the population data base. 

Then an analysis was conducted using the US Census block level populations stratified 

by age. The block level data were then aggregated up to the tract 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 extraction tool for Census file SF1 

• A geographic database for US Census block boundaries in Puerto 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 portion of the block containing residential 

populations. The block population was assumed to be uniformly distributed within the “clipped” 

block boundary. Next, a 75 m buffer and a 200 m buffer 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 each buffer was calculated. This area fraction was 

assumed to equal the population fraction that fell within each buffer, and the fractions were 

applied to each population subgroup. The 75 m buffer and the 200 m buffer were also overlaid 

 216 
on the unclipped block boundary to determine the fraction of the total block area that fell within 

each of the buffers. The block level fractions for area and populations were then aggregated up 

to the tract level. 

For each tract in the CMSA or MSA, the tract-level, age-stratified distance-to-roadway 

population fractions were combined with 2000 US Census tract-level, age-stratified residential 

populations to calculate the CMSA- or MSA-level distance-to-roadway population fractions, as 

follows. 

CMSA fraction = ∑tracts (tract-fraction*tract-population)/ ∑tracts (tract-population) 

Results of the estimated population within the selected roadway distances are provided in 

Table 8-14 for each of the 18 named locations identified in the air quality characterization. 

Based on the HAPEM6 data base, Atlanta has the lowest percent of its population 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 for Atlanta compared with all other locations. 

Atlanta also was estimated to have the greatest percent of the population living at distances ≥ 200 

m from a major road. Consideration of just this attribute would suggest that on a populationweighted basis, the number of daily maximum exposures of NO2 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 Road1

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% 

 217 
Percent of Population at Distance from a Major Road1

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 Development (HUD), collects data on the nation's housing 

(AHS, 2008). Relevant housing characteristic data, including housing units within 300 m of a 

major highway, railroad, or airport and residential prevalence of air conditioning are summarized 

for 14 of the named locations21 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 exposure modeling). Consistent with the pattern 

noted for the population living near roadways, Atlanta 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 slightly higher though, estimated to have 10.1, 11.2, 

and 11.4% of housing units, respectively < 300 m from these same locations. Again, 

consideration of this attribute alone, suggests daily maximum exposures to NO2 concentrations 

above selected benchmarks would tend to be lower for Atlanta than many of the other urban 

areas listed in Table 8-15. As noted previously, the estimated exceedances were dominated by 

exposure occurring in-vehicles. 

 The AHS also provides data on A/C prevalence 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 infiltration rate of outdoor air concentrations 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 Springs, El Paso, Jacksonville, and Las Vegas. 

 218 
rates (97.2%), though similar rates could be found in Miami, Phoenix, St. Louis, and Washington 

D.C. A few of the urban areas listed have much lower A/C prevalence 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 residential exposures to daily maximum NO2 concentrations 

above selected benchmarks would be greater compared to those estimated in Atlanta. However, 

results of this exposure assessment indicate the indoor residential microenvironment does not 

contribute to exceedances of the selected benchmarks (see Figure 8-17) even when considering 

alternative A/C prevalence (section 8.12.2.6). 

Table 8-15. Residential A/C prevalence and roadway distance statistics for housing units in several 

locations (AHS, 2008). 

Location

AHS 

Survey 

Year 

Housing units 

< 300 m from a 

mobile source1 

(%) 

A/C 

Use2

(%)

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 characterization and are presented in Table 8-16. We note that 

Atlanta contains the highest per capita daily vehicle miles traveled and also the highest miles of 

roadway per 1,000 persons, both attributes which would tend to result in greater population 

exposure to peak NO2 concentrations associated with roadway exposures. However, as shown in 

Figure 8-27, Atlanta is roughly in the middle of the distribution with respect to estimated 

population and total roadway miles when compared with the other locations examined. 

 

Table 8-16. Population and roadway statistics for several locations (FHWA, 2002). 

Location

Total 

roadway 

miles 

Estimated 

Population 

(thousands) 

Miles of 

roadway 

per 1,000 

persons 

Total 

DVMT1

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 

 220 
0

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 Miles

Estimated Population (Thousands)

Atlanta

Figure 8-27. Comparison of estimated population and total roadway miles in 18 locations 

(data from FHWA (2002) provided in Table 8-16). 

8.11.4 Discussion 

There are a number of important attributes to take into account in considering the 

representativeness of the exposure 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 selected distances of a major road (see Table 8-14) 

and percent of the population residing in housing units < 300 m from a mobile source (see Table 

8-15). Being on the low end with respect to these two attributes would likely result in Atlanta 

having a lower proportion of the population likely being exposed to daily maximum 1-hour NO2

concentrations above selected benchmark levels than other locations included in Tables 8-14 and 

8-15, in the absence of other influential attributes. However, as noted above, Atlanta contains 

 221 
the highest per capita daily vehicle miles traveled and also the highest miles of roadway per 

1,000 persons, both attributes which would tend to result in greater population exposure to NO2

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 roadway miles when compared with the other locations examined. Given 

that there are attributes that go in both directions 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 included in this comparison. Staff does 

recognize that the Atlanta exposure results are likely lower on a population-weighted basis 

compared to the largest urban areas such as Los Angeles, New York, and Chicago given the 

greater proximity of the population to mobile sources in these large urban areas. For example, 

64, 69, and 65%, respectively, of Los Angeles, New 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 Chicago), compared to only 9.7% for Atlanta. 

As discussed above, with respect to residential A/C prevalence, we expect that urban 

areas with lower A/C prevalence would tend to result in higher exposures to NO2 of ambient 

origin, all other factors being equal. We note that in comparing A/C prevalence rates for each 

location, Boston, Cleveland, Denver, and Los Angeles 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 percent of population exposed and person days with exposures 

above the selected health effect benchmarks for Atlanta may be somewhat lower than would be 

expected for other urban areas within 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 estimation of air pollutant exposure to individuals. 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 what they are doing), it has the ability to 

effectively approximate actual exposure 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 the input data that limit the realism and accuracy 

of the modeling results. 

Uncertainties and assumptions associated with NO2 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 are relevant to the NO2 NAAQS review are only summarized below. This 

includes a sensitivity analyses performed on the CHAD data base using O3 exposures and an 

analysis of the air exchange rate data. 

Following the same general approach described 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 identification of the important uncertainties, an 

indication of the potential bias 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 addressed separately here for clarity. 

Table 8-17. Summary of qualitative uncertainty 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 

O3 monitoring data over Low 

Use of unadjusted NO2

concentrations unknown Low – Medium 

AERMOD 

Inputs and 

Algorithms 

Meteorological data unknown Low – Medium 

APEX Population data base both Low 

Inputs and Commuting data base both Low - Medium 

 223 
CHAD 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 modeling was determined through use of EPA’s 

recommended regulatory air dispersion model, AERMOD (version 07026 (EPA, 2004)), with 

meteorological data discussed above and emissions 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 determined 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 accurate 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 the best approximation available 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 uncertainties in this context, it is also important to recognize 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 the 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 distances nominally up to 50 kilometers), based on 

a demonstration that the model produces largely unbiased 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 evaluations included low-level 

releases more typical of the dominant emissions category in this assessment. Moreover, the 

range of dispersion conditions represented by these evaluation studies 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 plume 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 adequately treat dispersion under conditions of 

atmospheric stagnation or recirculation 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 that each plume may impact the entire domain 

for each hour, regardless of whether the actual transport time for a particular source-receptor 

combination exceeds an hour. While these assumptions imply some degree of physically 

unrealistic behavior when considering the impacts 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 cumulative 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 presented in Chapter 8.4.8 is encouraging as to 

the viability of the approach in this application 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 based on the specifics of the application. 

Beyond the basic dispersion algorithms in AERMOD, another component of model 

formulation uncertainty for this application is the use of the Ozone Limiting Method (OLM) and 

Plume Volume Molar Ratio Method (PVMRM) algorithms for simulating the conversion of NO 

to NO2. Model performance evaluations for these NOx chemistry algorithms are more limited 

than for non-reactive plume dispersion. However, an assessment of the potential for bias 

 225 
associated with the PVMRM algorithm showed generally good performance based on the 

available evaluation data (MACTEC, 2005). 

While the AERMOD model algorithms are not considered to be a significant source of 

uncertainty for this assessment, the representativeness 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 contribute to bias and uncertainty are emission 

estimates and meteorological data. Use of the OLM and PVMRM chemistry algorithms also 

introduces the potential uncertainty associated with the dependence on ambient O3 concentration 

inputs since both algorithms treat the reaction of NO with O3 to produce NO2 as the primary 

mechanism for conversion of NOx emissions. These issues are addressed in the following 

sections. 

8.12.1.2 Meteorological Inputs 

Details regarding the representativeness of the meteorological data inputs for AERMOD 

are addressed separately in Attachment 1 to Appendix 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 meteorological tower site used to process the 

meteorological data for use in AERMOD relative to the surface roughness 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 mechanical shear-stress induced turbulence on 

dispersion for such sources. In particular, concerns were raised in preliminary modeling due the 

typically low surface roughness associated with the meteorological tower located at ATL airport 

compared with the much higher roughness environment of most low-level emission sources. 

Based on this concern, alternative meteorological data from the Jefferson Street (JST) Southeast 

Aerosol Research and Characterization study (SEARCH) site were determined to be more 

representative of the majority of NOx 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 Atlanta airport. Meteorological 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 NO2 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 presented in Figure 8-28, which shows a 

plot of high ranked 1-hour concentrations, unpaired in time and space, calculated at four Atlanta 

NO2 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 meteorological site. The overall peak 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 uncertainty for the REA, they may not be indicative 

of the degree of sensitivity to surface roughness for individual sources in other modeling 

contexts. 

 227 
10

100

1000

10000

10 100 1000

ATL_Met

JST_Met

Figure 8-28. Comparison of high ranked AERMOD 1-hour NO2 concentrations (μg/m3) 

from mobile sources across four NO2 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 several technical challenges and involves a number of 

uncertainties. Unlike typical stationary emission 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 represent an aggregate of emissions from nonstationary sources of various sizes, shapes, and speeds. Since mobile source emissions (other 

than aircraft) are emitted near the ground, the plumes can be significantly influenced by the 

turbulent wake associated with the emitting vehicle, as well as turbulence generated by nearby 

vehicles and other roughness elements such as sound 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 directions 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 NO2, 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 for slow moving vehicles on a cold day during rush hour. In 

addition to the influence of roughness elements, characteristics of the roadway itself may be a 

factor. For example, thermally-induced turbulence 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 for 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 NO2 modeling, may necessitate a 

different approach than characterizing mobile sources for a particular 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 characterizing emissions based on a reasonable estimate of 

the aggregate effect of these various factors is necessitated by practical limitations. 

The factor of 1.7 times the vehicle height used to account for vehicle-induced turbulence 

is cited in Gilles et al. (2005) based on some field measurements for an unpaved road. The factor 

of 1.7 is somewhat less than the typical formula 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 vehicle-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 orientation relative to the wind direction. These 

 229 
influences may include considerable variability both temporally and spatially, 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 uncertainties associated with source characterization will 

tend to cancel out in the cumulative impact assessment. 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 assess 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 possible. 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 came from segmenting. Spatial mismatch from 

segmenting could affect offroad concentrations relative to a different methodology where 

predicted concentrations from segments were mated to road locations rather than full TDM links 

due to both orientation of the areas and location relative to receptors. However, these segments 

were not considered individual links, but rather partial values to avoid numerical modeling issues 

and were maintained as consistent with the original 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 alternative methodology, where segments rather than links were mated 

to actual road locations, would differ due to orientation of the area relative to wind direction. 

However, as the onroad receptors are centered within the source, any 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 NOx inventories, and may contribute positive bias to diurnal patterns in AERMOD 

predictions. 

The truck fractions used in the traffic modeling 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) and a study conducted in Georgia (FHWA, 2007) 

indicate that the average truck fractions produced by the Atlanta Regional TDM are potentially 

higher, possibly contributing to overestimations in NOx emissions. Table 8-18 summarizes 

National estimates of the vehicle mile traveled (VMT) by two highway categories and vehicle 

types. Depending on roadway category, rural VMT 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, where 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 from 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 the 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) + Commercial (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 characterizations and computed composite emissions for light and 

heavy duty vehicles, where the heavy duty fraction is a VMT-weighted 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 commercial vehicles in the TDM, but this 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 HDV8B emission factor, this would have been 

corrected by the scaling 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 low, even when comparing with morning and 

afternoon HDV fractions in Table 8-2 which are based on broader time periods. 

 231 
Table 8-18. National vehicle miles traveled 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 

Rural Arterial 1,104 14,606 27,818 433,805 10 

Other 1,901 14,963 14,090 414,393 7 

Interstate 802 9,106 23,887 408,618 8 Urban1

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 Classes3

Roadway1

Peak 

Hour2 Direction 

6-7 

(MDT) 

8-13 

(HDT) 

Total 

Trucks4

Northbound 1 7 8 

AM Southbound 0 3 3 

Northbound 1 5 6 

I-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 northern 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 the diurnal pattern of on-road vehicle activity 

and emissions. Vehicle activity data was provided 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 hourly 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 realistic 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 actual hourly diurnal pattern. This uncertainty is important, 

because typical meteorological conditions 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 concentration impacts of emissions, so that any 

emission overestimates at those times are likely to result in disproportionate concentration 

overestimates. The model-to-monitor comparisons 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 profile 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 analysis of model-to-monitor comparisons for 

weekday vs. weekend periods was conducted to assess this potential source of uncertainty. 

Figure 8-29 compares the average ratios of predicted to observed NO2 concentrations at selected 

percentiles across the concentration distribution based on the four NO2 monitors (including the 

SEARCH JST monitor). While this analysis showed that model performance was significantly 

better for weekdays than weekends, the affect of this simplification on the overall concentration 

distributions was not significant. The absence of the morning rush hour peak in NO2

concentrations evident in the ambient concentrations 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. 

 233 
0

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

Percentile

Pred/Obs Conc

Pred/Obs Weekday

Pred/Obs Weekend

Figure 8-29. Comparison of the average ratios of predicted/observed concentrations of 

NO2 across four ambient monitors based on weekday vs. weekend only. 

Another potential source of uncertainty in relation to on-road emission estimates is the 

assumption of 0.1 as the “in-stack” ratio of NO2 to NOx emissions. While this ratio is considered 

to be generally representative of many sources of NOx emissions and is considered a default 

value for applications of the OLM option, modeled concentrations of NO2 will be sensitive to 

this user-specified value, especially 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 O3 Monitoring Data for OLM and PVMRM Options 

Monitored hourly O3 concentrations were used as input to the OLM and PVMRM 

atmospheric chemistry modules in AERMOD for conversion of NO to NO2. Since AERMOD is 

currently limited to the use of a single hourly O3 concentration for all sources within the modeled 

inventory, this may introduce some uncertainty in the modeled concentrations if there is 

significant spatial variability of O3 concentrations across the domain. One suggested source of 

 234 
this uncertainty and potential bias is the influence of an O3 deficit over major roadways due to 

the high concentration of NOx emissions depleting O3, especially during rush hour. Lacking 

more complete spatial coverage of O3 concentrations, it is difficult to quantify this potential 

cause of bias and uncertainty. If such spatial variability of O3 concentrations were present, it 

would likely contribute to more conservative modeled concentrations during periods of higher 

than average NOx emissions and lower than average O3 concentrations, which would limit 

conversion of NO to NO2. This may be one factor contributing to the apparent conservative bias 

that appears at all NO2 monitors during the morning rush hour period, in addition to the 

uncertainties regarding temporal profiles of mobile source emissions. The simple linear 

treatment of NOx chemistry in the AERMOD model options does not explicitly account for the 

O3 depleting effect of the NO-to-NO2 conversion. 

Another issue that has been raised as a potential source of uncertainty in relation to the 

NOx chemistry for this application is that the AERMOD modeling was conducted separately for 

mobile sources, point sources, and the ATL airport source. The OLM option was used for the 

mobile source and airport source emissions, while 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 modeling that separated the on-road and off-road 

components of mobile source emissions into separate AERMOD runs, the final application of 

AERMOD combined the on-road and off-road emissions into a single run. Given the relative 

contributions of these three categories of emissions, the potential impact of O3 depletion due to 

point source and airport emissions on total modeled concentrations, which are dominated by the 

mobile source category, is likely to be insignificant. 

8.12.1.6 Use of Unadjusted AERMOD NO2 Concentrations 

While the number and range of field study data bases used in AERMOD’s evaluation is 

large relative to evaluation of other 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 based 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 

 235 
evaluating model performance for such applications is complicated by a number of challenges 

and uncertainties. These include uncertainties regarding the temporal and spatial distribution of 

emissions, physical characteristics of the emission sources (such as release height and initial 

dilution parameters), and the influence of spatial variability of surface characteristics on 

dispersion of low-level plumes, among other factors. In addition, application of the AERMOD 

model to simulate NOx 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. Regulatory applications of AERMOD are generally 

motivated by a need to estimate the peak concentrations across a domain 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 modeled concentrations will 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 significance of the full concentration distribution 

predicted by the model. 

Despite these additional concerns and caveats 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 NO2 concentrations and available ambient monitored 

NO2 concentrations. The model evaluation results are consistent across the available ambient 

monitors and across all seasons. Bias in the predicted concentration distribution at each of the 

monitor locations is generally within the range expected of refined dispersion models across the 

cumulative concentration distribution. 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 proportion 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 results 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 available ambient monitored concentrations as a 

 236 
means of reducing overall uncertainty in the analysis. Two main factors have argued against 

such an approach for this REA. First, advances in the science of dispersion modeling, including 

the promulgation of AERMOD model and more refined techniques for treating the temporal and 

spatial variation of emissions, provide a basis for more confidence in the representativeness of 

modeled concentrations for the exposure assessment. Second, the limited number of available 

ambient NO2 monitors, the predominance of mobile source emissions of NOx within the 

inventory, and the significant horizontal gradients of concentration associated with low-level 

emissions from major roadways raise significant concerns regarding the spatial 

representativeness of the ambient NO2 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 distribution are not unusual in comparison with the other 

portions of the concentration distribution, it was determined that adjustment of the modeled air 

quality based on the three monitors was not necessary. Any effort to adjust concentration 

estimates based on monitored values would present a range of options and issues regarding how 

the modeled concentrations would be adjusted both temporally and spatially in relation to the 

observations. Based on these considerations, such an approach could actually increase the 

uncertainty of the REA in ways that are 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 from U.S. Census data from the year 

2000. This is a high quality data source for nationwide population data in the U.S. however, the 

data do have limitations. The Census used random 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 uncertainty to exposure results. The Census has a 

quality section (http://www.census.gov/quality/) that discusses these and other issues with 

Census data. It is likely the bias within this data would not affect the results in any particular 

direction, and given the use of the sampled demographics to represent the simulated population, 

it is expected that the uncertainty in the exposure results from this source is low. 

 237 
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 that they never travelled on roads, only that their 

home and work blocks were the same. This includes the population not employed outside the 

home, individuals indicated as commuting within their home block, and individuals that 

commute over 120 km a day. This could lead to either over- or under-estimations in exposure if 

they were in fact to visit a block with either higher or lower NO2 concentrations. Given that the 

number of individuals who meet these conditions is likely a small fraction of the total population 

and that the bias is likely in either direction, the overall uncertainty is considered low. 

Second, although several of the APEX microenvironments 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 possibility of passing through other census blocks 

during travel. This could also contribute to bias in either direction, dependent on the number of 

blocks the simulated individual would actually traverse and the spatial variability of the 

concentration across different blocks. This could 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 the actual number of persons and the number of 

blocks per commute and the spatial variability across blocks has not been quantified. In 

addition, the commuting route (i.e., which roads individuals are traveling 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-block 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 with lower density commercial/industrial density 

blocks, and 2) most persons commute to lower commercial/industrial density blocks. It should 

also be noted that recent surveys, notably the National Household Transportation Survey 

 238 
(NHTS), have found that most trips taken and most VMT accrued by households are non-work 

trips, particularly social/recreational and shopping-related travel (Hu and Reuscher, 2004). This 

constitutes an unquantified source of uncertainty that is not be addressed by the Census 

commuter dataset. 

8.12.2.2 Human Time-Location-Activity Pattern Data 

The CHAD time-location activity diaries used are the most comprehensive source of such 

data and realistically represent 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 exposure results as a result of the CHAD diaries 

used for simulating individuals may introduce uncertainty 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 may not have changed much over the years, it is 

certainly possible that some differences 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 person (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, resulting in under-estimation of exposures. 

Second, the CHAD data are taken from numerous 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 designed to not be representative 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 the survey population that 

are distinct from the simulated population. 

The CHAD diaries that are selected from APEX to represent 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 

 239 
population demographics (i.e., age and gender) for a particular location and temperature is used 

as a classification variable to account for its affect on human activities. 

A sensitivity analysis was performed to evaluate the affect of using different CHAD 

studies has on APEX results for the recent O3 NAAQS review (see Langstaff (2007) and EPA 

(2007g)). Briefly, O3 exposure results were generated using APEX with all of the CHAD diaries 

and compared with results generated from running 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), whether all of CHAD or only the NHAPS component of 

CHAD is used. The absolute difference in percent of persons above a particular 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 similar results would be 

obtained here for NO2 exposures, although it remains uncertain due to different averaging times 

(1-hour vs. 8-hour average). 

This is not to suggest that the uncertainty is low in using the CHAD data to represent the 

Atlanta area, but that similar results would be obtained in using the diaries available, so long as 

the population was appropriately stratified and certain characteristics influencing exposure were 

considered. One particular influential factor that is not modeled by APEX is the commute 

time/distances for the Atlanta population. The Atlanta population is spread over a larger area 

than most other locations and as a result, individual 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 population. Given the difference in Atlanta DVMT in 

comparison with other locations, it is possible that this underestimation is large. However, in 

considering this lack of accounting 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 representative 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 simulated person essentially 

 240 
becomes a composite of several actual people in the underlying activity data. Certain aspects of 

the personal profiles are held constant, 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 profiles was evaluated against a 

sequence of available multiday diaries sets collected as part of the Harvard Southern California 

Chronic Ozone Exposure Study (Xue et al. 2005, Geyh et al. 2000). Briefly, the activity pattern 

records were characterized according to time spent in each of 5 aggregate microenvironments: 

indoors-home, indoors-school, indoors-other, outdoors, and in-transit. 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 Appendix B, Attachment 2 and 3 for details. The 

evaluation indicated the cluster algorithm can replicate the observed sequential diary data, with 

some exceptions. The predicted time-in-microenvironment averages matched well with the 

observed values. For combinations of microenvironment/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 microenvironment ranges from -9% to +4%. Although, on 

occasion there were large differences in replicating variance across persons and within-person 

variance subsets, about two-thirds of the predictions for each case were within 30% of the 

observed time spent in each microenvironment. 

The longitudinal approach used in the exposure 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 with 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 O3 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 about 50%). The algorithm employing the D and A

statistics exhibited similar patterns, although were lower in magnitude 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 

 241 
the result of a greater correlation of diaries selected in comparison with the other two algorithms. 

This outcome conforms to an expectation of correlation between the daily activities of 

individuals. While the evaluation was performed using 8-hour O3 as the exposure output it is 

expected that similar results would be obtained for 1-hour NO2 exposures. That is, the 

characteristics of the diaries that contribute greatly to any pollutant exposure above a given 

threshold (e.g., time spent outdoors, vehicle driving 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 multi-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 algorithm and the evaluations performed. 

8.12.2.4 Meteorological Data 

Meteorological data are taken directly from monitoring 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 these data is that it is relatively easy to see significant errors if 

they appear in the data. Because 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 contributing 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 ASOS station at ATL were used to calculate 

hourly average wind speed and directions. This approach reduces the number of estimated zero 

concentrations that would be output by AERMOD 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 and indoor microenvironment air 

exchange rate. Because the model does not represent hour-to-hour variations in meteorological 

conditions throughout the day, there may be uncertainty in some of the exposure estimates for 

indoor microenvironments (see the next section). 

 242 
8.12.2.5 Air Exchange Rates (AER) 

The residential air exchange rate (AER) distributions used to estimate indoor exposures 

contribute to uncertainty in the exposure results. Three components of the AER analyzed 

previously by EPA (2007g) include 1) the extrapolation of air exchange rate distributions 

between-CMSAs, 2) analysis of within-CMSA uncertainty due to sampling variation, and 3) the 

uncertainty associated with estimating daily AER distributions from AER 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 assigned in the APEX model, as detailed in 

the indoors-residential microenvironment. 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 meteorological 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 represent the distribution of AERs in Atlanta (see 

Appendix B, Attachment 5). 

In order to assess the uncertainty associated with this extrapolation, between-location 

uncertainty was evaluated by examining the variation of the geometric means and standard 

deviations across several cities and studies. The evaluation showed a relatively wide variation 

across different cities in their AER geometric means and standard deviations, stratified by airconditioning status, and temperature 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 NO2 exposure estimates may be sensitive to use of an alternative distribution, 

say those in New York City, compared with results generated using the RTP AER distributions. 

It is likely though that the true distribution is more similar to the selected distribution from RTP 

than New York City or some other location given the population of available AER data. It is 

unclear as to the direction of bias given the limited number of data available for comparison. It 

 243 
should be noted that Houston, the only other “southern” location, generally 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 estimated 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 smaller than the observed variation across different 

CMSAs. This finding tends to support the approach 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 sampling 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 random sampling variation. They do not address other 

 244 
uncertainties such as the lack of representativeness of the available study data 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 uncertainties 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 represent 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 simulation 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 for the air exchange rates in the study data varied from 

one day to seven days, the analyses did not take the measurement duration into account and 

treated the data as if they were a set of statistically independent daily averages. To investigate 

the uncertainty of this assumption, correlations between consecutive 24-hour air exchange rates 

measured at the same house were investigated using 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 multi-day averaging periods as if they were 24-

hour averages. 

8.12.2.6 Air Conditioning Prevalence 

Because the selection of an air 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. For this study we used location-specific data for 

 246 
Atlanta AHS, 2004). EPA (2007g) details the specification 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 prevalence rates were 97% for Atlanta, with reported standard 

errors of 1.2% (AHS, 2004). Estimated 95% confidence intervals were also small and span 

approximately 4.6% (AHS, 2003). The RECS prevalence estimates for Atlanta and confidence 

intervals compared well with a value of 95.0% and a 95% confidence interval spanning 5.8%. 

This suggests that there is limited bias in the 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 the A/C prevalence form the actual Atlanta value 

used allows for a greater percentage of homes to use estimated 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 already 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 less to exposures above any of the 

benchmarks when compared with the estimated 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 additional 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 using 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 161918 

no 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 

(0.97 

ACprev0.55 

ACprev) 300 0 0 0 0 0 0 -605 -106 

8.12.2.7 Indoor Source Estimation 

Other indoor NO2 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 readily usable for characterization, modeling complexities 

regarding the assignment of particular sources to the simulated population (e.g, correlations of 

sources), a limited time to conduct analyses of potential data distributions including the analysis 

of their uncertainties, and limited resources allocated 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 much it would affect any estimates 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 exposure results when considering the form (i.e., 

uniform) and limits (limited by the bounds of the measurement data) of the distribution used to 

 248 
represent indoor decay. The measurement data used to develop the distribution were obtained 

from a single study, conducted in a single home, under limited temperature and humidity 

conditions (see Table 8-11). The experimental design included four different methods for 

introducing NO2 into the home, including direct injection and home appliances. Different 

homes, with varying construction materials and furnishings may have different NO2 decay rates, 

and as a result of the uniform distribution selected, the number of exposures may be either overor under-estimated depending on the extent of how far the true population of NO2 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 and 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 distribution for 2002 air quality 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 suggests that the simulated exposures above the selected 

benchmarks are not sensitive changes in the indoor decay rate NO2. Whether the same outcome 

would occur with additional alternative distributions of different forms and bounds or that the 

indoor microenvironment is not sensitive to indoor decay based on the algorithm used by APEX 

remains largely unknown. 

Table 8-21. Comparison of exposure results using a uniform versus lognormal NO2 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

 249 
150 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, possibly 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 does so less frequently than reported the 1992 survey population, 

then the number of such exposures may be over-estimated. 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 regarding each of these issues and how they may 

affect the exposure results is largely unknown. 

As noted in the microenvironmental description, it was assumed that the probability that a 

food preparation event included stove use was the same no matter what hour of the day the food 

preparation event occurred. If such probabilities differ, then 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 concern may be under- or over-estimated. For example, if gas stove 

usage and ambient concentrations are positively correlated (e.g., if cooking tends to occur during 

evening rush hour) and the diurnal allocation assumed 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 regarding the distribution 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 without 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, there already exist large uncertainties regarding the 

representation of these measured concentrations obtained under the limited experimental 

conditions to the population of all possible cooking conditions, the foods cooked, and the proper 

weighting of such cooking events for the simulated population in Atlanta. In the absence of such 

knowledge, it is likely that a fitted 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 exposures when cooking with 

gas, and implies a bias in underestimating indoor source contribution to indoor concentrations. 

Although it appears that the study was primarily designed to estimate upper percentile PM 

concentrations, it is possible that the uniform distribution selected for NO2 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 tests ranged from 21 minutes to 3 hours with 

an average of about 70 minutes. For implementation in APEX it was assumed that each cooking 

event lasts exactly an hour. That is, the randomly 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 one hour to the next, some of the indoor cooking 

impact will influence subsequent hours. However, 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 response to advice received from the CASAC NO2

Panel on the 1st draft REA, we have conducted a focused quantitative risk assessment in which 

estimates of respiratory ED visits as a function of ambient levels of NO2 have been developed for 

a single urban area (i.e., the Atlanta MSA). In this approach, concentration-response functions 

derived from NO2 epidemiological studies are used in conjunction with 1) ambient air quality 

data representing as is and alternative air quality scenarios and 2) baseline incidence data for 

respiratory ED visits to estimate the impact of ambient levels of NO2 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 information on the magnitude and potential changes in 

NO2-related public health impacts associated with recent air quality and alternative air quality 

scenarios simulating attainment of the current and alternative NO2 standards. We note that 

chapters 4 and 5 of this document provide additional qualitative assessment of the 

epidemiological evidence most relevant to characterizing NO2-related health effects in the 

United States; this includes both a discussion of respiratory-related ED visits as well as other 

health endpoints. We also note that integration of the scientific 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 presented in chapter 10. Chapter 10 also discusses 

staff’s assessment of how this information might be considered in evaluating the adequacy of the 

current NO2 NAAQS and the need for potential alternative primary NO2 standards. 

 Previous reviews of the NO2 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 O3 NAAQS reviews (Abt Associates, 2005; Abt 

Associates, 2007). Many of the same methodological issues are present in conducting a risk 

 252 
assessment for each of these criteria air pollutants where epidemiological studies provided the 

basis for the concentration-response relationships used in the quantitative risk assessment. 

The NO2 health risk assessment described in this chapter estimates the incidence of 

respiratory-related ED visits associated with short-term exposures to NO2 under recent (“as is”) 

air quality levels, upon just meeting the current NO2 standard of 0.053 ppm annual average, and 

upon just meeting several potential alternative NO2 primary NAAQS in the Atlanta MSA.1 As 

discussed in more detail in chapter 6 above, staff has elected to evaluate daily maximum 1-h 

standard levels of 0.05, 0.10, 0.15, and 0.20 ppm using both 98th and 99th percentile forms and 

averaged over a thee-year period.2

 The risk assessment is intended 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 the Administrator in judging whether the current 

primary standard protects public health with an adequate margin of safety, or whether revisions 

to the standard are appropriate. 

Section 9.2 describes the general approach used to conduct the 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 discussion 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 NO2-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 ISA. Since these studies estimate concentrationresponse functions using ambient air quality data from fixed-site, population-oriented monitors, 

the appropriate application of these functions in a risk assessment similarly requires the use of 

ambient air quality data at fixed-site, population-oriented monitors. 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 NO2 exposures, as well as the change in incidence of this health 

effect in that set of counties corresponding to a given simulated change in NO2 levels 

 

1

 The current NO2 standard refers to a two-year period and requires that the annual average NO2 level be less than or 

equal to 0.053 ppm in each of the two years. 

2

 As an example, for the alternative standards using the 98th percentile form, the standard is met when the average of 

the annual 98th percentile daily maximum 1-hour concentrations for a 3-year period is at or below the specified 

standard level. 

 253 
representing just meeting the current or alternative 1-h daily maximum NO2 standards, the 

following thee elements are required: 

• Air quality information including: (1) “as is” air quality data for NO2 from 

ambient monitors in the assessment location, and (2) “as is” concentrations adjusted 

to reflect patterns of air quality estimated to occur under a simulation where the 

area’s air quality is adjusted to just meet the specified standard. (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 NO2 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” 

NO2 levels in that location in that year.

Figure 9-1 provides a broad schematic depicting the role of these components in the NO2

risk assessment. Each of the key components (i.e., air quality information, estimated 

concentration-response functions, and baseline incidence) is discussed 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 NO2 levels. Although some epidemiological 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 NO2 level, y is the incidence of the health endpoint of interest at NO2

level x, β is the coefficient of ambient NO2 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 NO2. The 

relationship between a specified ambient NO2 level, x0, for example, and the incidence of a given 

health endpoint associated with that level (denoted as y0) is then 

0

0

x Bey β = . (Equation 9-2) 

 254 
If we let x0 denote the baseline (upper) NO2 level, and x1 denote the lower NO2 level, and 

y0 and y1 denote the corresponding incidences of the health effect, we can derive the following 

 255 
Figure 9-1. Major components of nitrogen dioxide health risk assessment for emergency 

department visits. 

 256 
relationship between the change in x, Δx= (x0- x1), and the corresponding change in y, Δy, from 

equation (9-1)3

: 

Δ Δyyy 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 associated with a particular air quality comparison. The risk of 

ED visits for respiratory illness at ambient NO2 level x0 relative to the risk of ED visits for 

respiratory illness at ambient NO2 level x1, for example, may be characterized by the ratio of the 

two rates: the rate of ED visits for respiratory illness among individuals when the ambient NO2

level is x0 and the rate of ED visits for respiratory illness among (otherwise identical) individuals 

when the ambient NO2 level is x1. This is the RR for ED visits for respiratory illness associated 

with the difference between the two ambient NO2 levels, x0 and x1. Given a concentrationresponse function of the form shown in equation (9-1) and a particular difference in ambient NO2

levels, Δx, the RR associated with that difference in ambient NO2, denoted as RRΔx, is equal to 

e

βΔx

. The difference in health effects incidence, Δy, corresponding to a given difference in 

ambient NO2 levels, Δx, can then be calculated based on this RRΔx as 

)]/1(1[)( 010 RR x yyyy =Δ − = − Δ . (Equation 9-4) 

Equations (9-3) and (9-4) are simply alternative ways of expressing the relationship 

between a given difference in ambient NO2 levels, Δx > 0, and the corresponding difference in 

health effects incidence, Δy. These health impact equations are the key equations that combine 

air quality information, concentration-response function information, and baseline health effects 

incidence information to estimate health risks related to changes in ambient NO2 concentrations. 

 

3 If Δx < 0 – i.e., if Δx = (x1- x0) – then the relationship between Δx and Δy can be shown to be 

. If Δx < 0, Δy will similarly be negative. However, the magnitude of Δy will be the 

same whether Δx > 0 or Δx < 0 – i.e., the absolute value of Δy does not depend on which equation is used. 

]1[)( 001 −=−=Δ Δx eyyyy β

 257 
9.3 AIR QUALITY INFORMATION 

As illustrated in Figure 9-1, and noted earlier, air quality information required to conduct 

the NO2 risk assessment includes (1) recent air quality data for NO2 from a suitable monitor for 

the assessment location and (2) air quality adjustment procedures to modify the recent data to 

simulate air quality data just meeting the current 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 NO2 (or equivalently, the change in health effect incidence, Δy, 

associated with a change in NO2 concentrations from “as is” levels of NO2 to 0 ppb). In the 

second part, we estimate the incidence of the health effect associated with NO2 concentrations 

simulated to just meet a specified standard (i.e., the current NO2 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” NO2 levels in a 

location, we need a time series of hourly “as is” NO2 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 epidemiological study from which we obtained the 

concentration-response functions (see section 9.4 below). Complete 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 NO2 concentrations were filled in, as described in section 3.5 of Appendix C. 

Because Tolbert et al. (2007) estimated a relationship between daily respiratory-related 

ED visits and the 3-day moving average (i.e., NO2 levels on the same day, the previous day, and 

the day before that) of the daily 1-h maximum NO2 concentrations, we calculated the 3-day 

moving average of the daily 1-h maximum NO2 concentrations at the monitor to provide the air 

quality input to the risk assessment. 

The calculations for the second part of the risk assessment, in which we estimated risks 

associated with NO2 levels simulated to just meet the current annual standard and potential 

alternative 1-h daily maximum standards were done analogously, using the monitor-specific 

series of adjusted daily maximum hourly concentrations rather than the monitor-specific series of 

“as is” daily maximum hourly concentrations. 

 258 
9.4 CONCENTRATION-RESPONSE FUNCTIONS 

As indicated in Figure 9-1, another key component in the risk assessment model is the set 

of concentration-response functions which provide estimates of the relationship between the 

health endpoint of interest and ambient NO2 concentrations. As discussed above, the health 

endpoint of interest for this focused quantitative risk assessment is respiratory-related ED visits. 

As discussed in sections 4.2.2 and 4.5.2 several community epidemiological studies have been 

conducted in the U.S. that examined the relationship between NO2 and other air pollutants and 

increased ED visits either for all respiratory causes or for asthma-related visits. Figure 5-1 in 

this document summarizes the single pollutant model effect estimates from these studies. As 

discussed in section 4.5.2, staff has 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 United 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, activity patterns of the population, and characteristics 

of the healthcare system. Second, we judged that studies of ambient NO2 are preferable to those 

of indoor NO2 given that studies of indoor NO2 focus on exposures in locations with indoor 

sources of NO2. These indoor sources can result in exposure patterns, NO2 levels, and copollutants that are different from those typically associated with ambient NO2. 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 incidence data are more readily available for these endpoints. 

Finally, we judged it appropriate to focus on studies that evaluated NO2 health effect associations 

using both single- and multi-pollutant models. Taking these factors into consideration, we have 

chosen to focus on the studies by Tolbert and colleagues (2007) in Atlanta, Georgia that address 

ED visits for respiratory causes as a case study to illustrate the magnitude and changes in 

estimated NO2-related risks for this endpoint for various air quality scenarios. 

Tolbert et al. (2007) estimated concentration-response functions using both single 

pollutant models (i.e., where NO2 was the only pollutant entered into the health effects model) 

and multi-pollutant models (i.e., where one or two co-pollutants (PM10, O3, CO) were entered 

into the health effects model). To the extent that any of the co-pollutants present in the ambient 

air may have contributed to the health effects attributed to NO2 in single pollutant models, risks 

 259 
attributed to NO2 might be overestimated where concentration-response functions are based on 

single pollutant models. However, if co-pollutants are highly correlated with NO2, their 

inclusion in an NO2 health effects model can lead to misleading conclusions in identifying a 

specific causal pollutant. When collinearity exists, inclusion of multiple pollutants in models 

often produces unstable and statistically insignificant effect estimates for both NO2 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 NO2 risk assessment. 

All of the models in Tolbert et al. (2007) used a 3-day moving 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 arise. 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 longer 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 NO2 exposure studies may not capture the possible impact of long-term 

exposures to NO2 is unknown. A number of epidemiologic studies have examined the effects of 

long-term exposure to NO2 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 epidemiological evidence was suggestive but not sufficient 

to infer a causal relationship between long-term NO2 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 respiratory ED visits associated with NO2 exposures, if any, 

and this uncertainty should be kept in mind as one considers the results from the short-term 

exposure NO2 risk assessment. 

 260 
9.5 BASELINE HEALTH EFFECTS INCIDENCE DATA 

As illustrated in Equation 9-1, the most common health risk model based on air pollution 

epidemiological studies expresses the reduction in health risk (Δy) associated with a given 

reduction in NO2 concentrations (Δx) as a percentage of the baseline incidence (y). To 

accurately assess the impact of changes in NO2 air quality on health risk in a given urban area, 

information on the baseline incidence of health effects in that location is therefore needed. For 

this assessment, baseline incidence is the incidence 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 conducted 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 these, 41 were able to provide incidence data for 

at least part of the study period (1993 – 2004). For purposes of the NO2 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 visits 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 NO2-

related risk. While there is some year-to-year variability in respiratory-related baseline incidence 

(e.g., there were roughly 130,000 and 140,000 respiratory-related ED visits in 2002 and 2003, 

respectively, in the Atlanta area), 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 for short-term daily concentration-response 

functions, were calculated by dividing the annual incidence by the number of days in the year for 

which the baseline incidences were obtained. To the extent that NO2 affects health, however, 

actual incidence rates would be expected to be somewhat higher than average on days with high 

NO2 concentrations; using an average daily incidence 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 department 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 496), upper respiratory illness (460 – 465, 460.0, and 477), 

pneumonia (480 – 486), and bronchiolitis (466.1, 466.11, and 466.19). 

 261 
somewhat lower than average on days with low NO2 concentrations; using an average daily 

incidence would, therefore, result in overestimating the changes in incidence on low NO2 days. 

Both effects would be expected to be small, however, 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 input variables (parameter uncertainty) and the 

physical systems or relationships (model uncertainty – e.g., the shape of the concentrationresponse functions). 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 

characterized, sometimes quantitatively. For example, for the NO2 risk assessment the statistical 

uncertainty surrounding the estimated NO2 coefficients in the concentration-response functions is 

reflected in the confidence intervals provided for the 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 population or variable of interest that is 

inherent and cannot be reduced through 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 are for the Atlanta MSA). Variability 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 being met. To minimize the degree to which values of inputs to the 

analysis may be different from the values of those inputs at that unspecified time: we have used 

recent input data – for example, air quality data for the period 2005-2007 and baseline incidence 

data for 2004. However, future changes in these 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 described in section 7.8 for the air quality characterization and in section 

8.12 for the exposure assessment in this document, staff have evaluated uncertainty in the 

 262 
 263 

respiratory-related ED visits risk assessment using an approach adapted from the recent 

guidelines for qualitative uncertainty characterization (WHO, 2008). Uncertainties have been 

qualitatively evaluated with respect to the level of uncertainty and the direction of bias. The level 

of uncertainty was evaluated 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. We have 

used a designation of low, medium, and high as described in WHO (2008). 

The bias direction indicates how the source of uncertainty was judged to influence 

estimated ED visits associated with NO2 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 direction 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. Table 9-1 provides a summary of the sources of 

uncertainty identified in the health risk assessment, the level of uncertainty, and the overall 

judged bias of each. A brief summary discussion regarding those sources of uncertainty not 

already examined in chapter 7 is included in the comments section of Table 9-1 and is elaborated 

on in the bulleted points below. 

• Causality. There is uncertainty about whether the association between NO2 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 NO2 itself or with NO2

serving as an indicator for itself and other components of ambient air associated with 

combustion processes. 

• Empirically estimated concentration-response relationships. In estimating the 

concentration-response relationships, there are uncertainties: (1) surrounding 

estimates of NO2 coefficients in concentration-response functions used in the 

assessment, (2) concerning the specification of the concentration-response model 

(including the shape of the relationships) and whether or not a population threshold or 

non-linear relationship exists within the range of concentrations examined in the 

studies, and (3) concerning the possible role of co-pollutants. The uncertainty 

resulting from the statistical uncertainty associated with the estimated NO2 coefficient 

in the concentration-response function has been characterized by confidence intervals 

reflecting sample size. These confidence intervals do not reflect the uncertainties 

related to the concentration-response functions, such as whether or not the model 
Table 9-1. Characterization of Key Uncertainties in the Emergency Department Visits Health Risk Assessment for 

Uncertainty Level of 

Uncertainty 

Direction of Bias Comments

Causality low Upward, if 

causality assumption isn’t true. 

Statistical association does not prove causation. Howeveassessment considers only a health endpoint for which th

of the evidence supports the assumption that NO2 is likel

related based on the totality of the health effects evidenceassumption of a causal relationship is incorrect, then a pocoefficient in the concentration-response function would biased, since it is greater than zero. 

Empirically estimated concentrationresponse relations 

medium No obvious bias, 

if concentrationresponse model is correctly specified. Otherwise, unclear. 

Because concentration-response functions are empirically 

there is uncertainty surrounding these estimates. If the mspecified, there is no bias in the coefficient estimates. If mis-specified, there can be bias. Omitted confounding vaexample, could cause upward bias in the estimated NO2 c

the omitted variables are positively correlated with both N

health effect. However, including potential confounding are highly correlated with one another can lead to unstablBecause both single- and multi-pollutant models were avwere used. 

Functional form of concentrationresponse relation 

medium Unclear Statistical significance of coefficients in an estimated con

response function does not necessarily mean that the matof the function is the best model of the true concentrationrelation. If the “true” functional relationship between NO

effect is different from the one specified, there can be biaresulting estimates of effect. The direction of the bias wihow the specified model differs from reality. For exampspecified concentration-response function is log-linear do

but there is actually a threshold in the true relationship, thwill be overstated by the model corresponding to levels o

threshold. 

 264 
Uncertainty Level of 

Uncertainty 

Direction of Bias Comments

Lag structure of concentrationresponse relation 

low Downward, if 

important lags are omitted (e.g., if concentrationresponse function includes a single lag, while “truth” is a distributed lag). Unclear, if concentrationresponse function includes a single lag, but it’s the wrong lag. 

The actual lag structure for short-term NO2 exposures is u

Omitted lags could cause an underestimation in the prediassociated with a given reduction in NO2 concentrations. uncertainty (in the sense of the impact of the uncertainty) 

the situation. For example, suppose the health effect is alargely by same-day NO2 concentrations but the model (i

includes only a 1-day lag. In this case, the impact on the analysis may be minimal if, as is likely, there is a high deautocorrelation in NO2 concentrations from day to day (syesterday’s NO2 level would act as a good proxy for toda

If, on the other hand, there is a distributed lag – e.g., if riseffect on day t depends on NO2 concentrations for the ent

leading up to day t – and the model includes only a single understatement of effect could be substantial. 

Transferability of concentrationresponse relations 

low No obvious bias. Concentration-response functions may not provide an ade

representation of the concentration-response relationship places other than those in which they were estimated. Fopopulations in the assessment location/time period may hfewer members of sensitive subgroups than the location/twhich functions were derived, which would introduce aduncertainty related to the use of a given concentration-res

in the analysis. This problem was minimized in the NO2 

however, because it relies on concentration-response funcin a recent study conducted in the assessment location. 

 265 
 266 

Uncertainty Level of 

Uncertainty 

Direction of Bias Comments

Extrapolation of concentrationresponse relations beyond the range of observed NO2

data 

low Unclear. A concentration-response relationship estimated by an ep

study may not be valid at concentrations outside the rangconcentrations observed during the study. This problem minimal in the NO2 risk assessment, however, because th

concentrations observed in the study from which C-R funobtained covered a wide range – from 1 ppb to 181 ppb. 

Adequacy of ambient NO2monitors as surrogate for population exposure 

low No obvious bias. Possible differences in how the spatial variation in ambie

across an urban area are characterized in the original epidstudy compared to the more recent ambient NO2 data use

current air quality would contribute to uncertainty in the estimates. The NO2 risk assessment uses the same monit

epidemiological study from which the C-R functions werwhich should minimize this source of uncertainty. 

Adjustment of air quality distributions to simulate just meeting current and alternative NO2 standards. 

medium to high 

No obvious bias. The pattern and extent of daily reductions in NO2 concen

would result if the current NO2 standard or alternative NO

were just met is not known. There remains significant unthe shape of the air quality distribution of hourly levels uan NO2 standard, especially for alternative standards that higher than recent NO2 air quality levels. 

Baseline health effects data 

Lowmedium Downward bias. Data on baseline incidence may be uncertain for a variety 

For example, location- and age-group-specific baseline raavailable in all cases. Baseline incidence may change ov

reasons unrelated to NO2. This source of uncertainty is re

in the NO2 risk assessment, however, because a baseline 

estimate has been obtained from the study authors for the area. There is a known downward bias to this estimate, hbecause it is based on an incomplete set of hospitals prov(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 whether or not a population threshold exists, the 

available epidemiological studies neither support nor refute the existence of 

thresholds at the population level. Concerning 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., PM10, O3) that produced the concentration-response 

functions that have been used 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 problem of possible confounding is particularly difficult. 

Single-pollutant models, which omit co-pollutants, may produce overestimates of the 

NO2 effect, if some of the effects are really due to one or more of the other pollutants. 

On the other hand, 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 the co-pollutants included in the model are highly correlated with 

NO2. As a result of these considerations, we report risk estimates based on both the 

single- and multi-pollutant models from Tolbert et al. (2007). It should be noted that 

use of a concentration-response 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 characteristics, land uses, source 

mixtures and other factors. 

• Adequacy of ambient NO2 monitors as surrogate for population exposure. The 

Tolbert et al. (2007) study used ambient concentrations at fixed-site monitors to 

represent ambient exposure and for several reasons this may or may not provide a 

good representation of ambient NO2 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 ambient concentrations at central monitoring 

sites; and (3) the difference between true and measured ambient concentrations (final 

ISA, section 1.3.2, p.1-5). While a concentration-response function may understate 

the effect of personal exposure to NO2 on the incidence of a health 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 NO2 is 

the causal agent, the understatement of the impact of personal exposures is not a 

concern, since NO2 NAAQS are expressed in terms of ambient, not personal 

exposure, levels. However, if NO2 is not the causal agent, and the effects are due to 

confounding copollutants or other factors, then reducing ambient NO2 levels might 

not result in the estimated reductions in the health effects. 

• Adjustment of air quality distributions to simulate just meeting the current annual 

standard and alternative 98th and 99th percentile daily maximum 1-h standards. The 

current annual standard and many of the alternative 1-h standards analyzed in the 

current risk assessment requires an upward adjustment of recent ambient NO2 levels. 

In adjusting air quality to simulate just meeting 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 distribution, we recognize 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 unspecified future year when air quality is 

adjusted to simulate just meeting the current and alternative standards; (2) the extent 

to which baseline incidence is underestimated because only 36 of the 42 emergency 

departments provided baseline incidence for 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 departments outside of the Atlanta MSA. 

As noted previously, the use of the available baseline incidence for 2004 results in 

some underestimation of the risk for the Atlanta 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). Concerning the use of annual baseline incidence to 

estimate daily incidence, to the extent that NO2 affects health, actual incidence would 

be expected to be somewhat higher than average on days with high NO2

concentrations and using an average daily incidence would result in underestimating 

the changes in incidence on such days. Similarly, actual incidence would be expected 

to be somewhat lower on days with low NO2 concentrations and using an average 

daily incidence would result in overestimating 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 uncertainty, we consider this to be a relatively 

minor uncertainty since most ED visits are likely 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 has 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 associated with several air quality scenarios, 

including three recent years of air quality as represented by 2005, 2006, and 2007 monitoring 

data. In addition, risk estimates are presented for a hypothetical scenario, where air quality from 

2006 and 2007 is adjusted upward to simulate just meeting the current annual NO2 standard, and 

for scenarios where the three year period (2005-2007) is adjusted (either up or down) to simulate 

just meeting potential alternative 98th and 99th percentile daily maximum 1-h standards. As 

discussed previously in chapter 5, potential alternative 1-h standards with 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 the uncertainty surrounding risk estimates 

resulting from the statistical uncertainty of the NO2 coefficients in the concentration-response 

functions used is characterized by ninety-five percent confidence intervals around estimates of 

incidence, incidence per 100,000 population, and percent of total incidence that is NO2-related. 

In some cases, the lower bound of a confidence interval falls below zero. This does not imply 

that additional exposure to NO2 has a beneficial effect but only that the estimated coefficient in 

the concentration-response function was not statistically significantly 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 present the risk estimates for NO2-related ED visits associated 

with recent air quality (2005, 2006, and 2007, respectively). Table 9-2 for 2005 also includes 

risk estimates for just meeting several alternative 1-h daily maximum standards based on 

adjusting 2005-2007 air quality data to simulate just meeting these alternative standards. 

Similarly, Tables 9-3 (based on 2006) and 9-4 (based on 2007) include risk estimates associated 

with just meeting these same alternative 1-h standards, as well as risk estimates associated with a 

simulation where air quality is adjusted upward to represent just meeting the current 0.053 ppm 

annual NO2 standard. Since attainment of the current annual standard is based on the most 

recent two year period, risk estimates for the annual standard are only included in the tables 

based on 2006 and 2007 air quality. 

 269 
Table 9-2. Estimated Percent of Total Annual Incidence of Respiratory ED Visits Associated with "As Is" NO2 Concentrations and NO2 

Concentrations that Just Meet Alternative Standards in Atlanta, GA, Based on Adjusting 2005 NO2 Concentrations.* 

0.05*** 0.1 0.15 0.2 0.05 0.1 0.15

none 3% 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%) (

CO 2.5% 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%) (

O3 1.5% 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%)

PM10 1.1% 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, O3 0.6% 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 NO2 coefficient.

Other 

Pollutants in 

Model

*Estimated incidences of respiratory emergency department visits are based on the concentration-response functions estimated in Tolbert et al. 

corresponding to Figure 2 in Tolbert et al. (2007) were obtained via personal communication with P. Tolbert]. All models use a 3-day moving average ofdaily 1-hr. maximum NO2 concentration and apply to all ages. 

***Alternative 1-hr daily maximum standards are characterized by a concentration of m ppm and an nth percentile, requiring that the average of the

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

(ppm) "as is"

Percent of Total Incidence of Respiratory Emergency Department Visits Associated with "As is" NO2 Concentrations and NO2 Conc

Just Meet Alternative Standards**

0.2

7.6%

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) [results 

 the 

 3 annual 

 standards 

entrations that 

 270 
Table 9-3. Estimated Percent of Total Annual Incidence of Respiratory ED Visits Associated with "As Is" NO2 Concentrations and NO2

Concentrations that Just Meet Alternative Standards in Atlanta, GA, Based on Adjusting 2006 NO2 Concentrations.* 

0.05*** 0.1 0.15 0.2 0.05 0.1 0.15

none 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%)

CO 2.6% 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%)

O3 1.6% 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%)

PM10 1.1% 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, O3 0.6% 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 NO2 coefficient.

Other 

Pollutants in 

Model

*Estimated incidences of respiratory emergency department visits are based on the concentration-response functions estimated in Tolbert et al. (2007) [results

to Figure 2 in Tolbert et al. (2007) were obtained via personal communication with P. Tolbert]. All models use a 3-day moving average of the daily 1-hr. maximconcentration and apply to all ages. ***Alternative 1-hr daily maximum standards are characterized by a concentration of m ppm and an nth percentile, requiring that the average of the 3 annual nth 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 maximu

(ppm) "as is"

Percent of Total Incidence of Respiratory Emergency Department Visits Associated with "As is" NO2 Concentrations and NO2 Concentrations that Just Me

Current and Alternative Standards**

current annual 

standard

0.2

7.9%

(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%)

 corresponding 

um NO2 

percentile 1-

m standards 

et the 

 271 
Table 9-4. Estimated Percent of Total Annual Incidence of Respiratory ED Visits Associated with "As Is" NO2 Concentrations and NO2

Concentrations that Just Meet Alternative Standards in Atlanta, GA, Based on Adjusting 2007 NO2 Concentrations.* 

0.05*** 0.1 0.15 0.2 0.05 0.1 0.15 0.2

none 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%)

CO 2.4% 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%)

O3 1.4% 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%)

PM10 1% 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, O3 0.6% 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 NO2 coefficient.

Other 

Pollutants in 

Model

*Estimated incidences of respiratory emergency department visits are based on the concentration-response functions estimated in Tolbert et al. (2007) [results corresponding 

to Figure 2 in Tolbert et al. (2007) were obtained via personal communication with P. Tolbert]. All models use a 3-day moving average of the daily 1-hr. maximum NO2 

concentration and apply to all ages. ***Alternative 1-hr daily maximum standards are characterized by a concentration of m ppm and an nth percentile, requiring that the 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" NO2 Concentrations and NO2 Concentrations that Just Meet the 

Current and Alternative Standards**

current annual 

standard

 272 
In Table 9-2, and similarly in Tables 9-3 and 9-4, the first row of percent of total annual 

incidence estimates is based on a single pollutant model (i.e., NO2 only) and results in the largest 

estimates for NO2-related respiratory ED visits. The next three rows present risk estimates based 

on two pollutant models (i.e., NO2 + CO, NO2 + O3, NO2 + PM10). The last row presents risk 

estimates based on a three pollutant model (i.e., NO2 + PM10 + O3). 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 the co-pollutants included 

in the model are highly correlated with NO2. In the case of this study in Atlanta, NO2 was 

moderately correlated with PM10 and O3 concentrations. The negative 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 concentrations and staff do not view these estimates as 

suggesting any health beneficial health effect of increasing NO2 exposure levels. 

 Tables 4-1, 4-2, and 4-3 in Appendix C present these same results expressed in terms of 

incidence of respiratory-related ED visits in the Atlanta MSA based 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 MSA 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 NO2 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 simulate just meeting the 

current annual NO2 standard and several alternative 1-hour daily maximum NO2 

standards. While we would expect some differences in estimated NO2-related ED 

respiratory visits across different locations due to differences in populations, land 

use patterns, access to medical facilities, co-pollutants and other factors affecting 

exposure and the concentration-response relationships, we believe that the risk 

estimates do provide a useful perspective on the likely overall magnitude and 

pattern of ED visits associated with various NO2 air quality scenarios in urban 

areas within the U.S. 

• The largest risk estimates were associated with single-pollutant NO2

concentration-response functions based on the effect estimates reported in Tolbert 

et al. (2007). Risk estimates based on various co-pollutant models with O3, CO, 

 273 
 274 

and PM10 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 reduction in risk estimates from the baseline 

of recent air quality for the three year period examined were the 98th and 99th

percentile 1-hour daily maximum standards 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 ppm to one set at 0.05 ppm reduces the 

estimated incidence of respiratory-related ED visits in Atlanta by about 49 percent 

in 2007 (from 4700 to 2400); however, changing 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 reduces 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 associated 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 NO2 and ED 

visits for respiratory causes actually reflect causal relationships; 

 - statistical uncertainty due to sampling error which is characterized in the 

assessment; 

 - uncertainties associated with the air quality adjustment procedure that was 

used to simulate just meeting the current annual and several alternative 1-h 

daily maximum standards; 

 -uncertainties associated with the estimated baseline incidence for ED 

respiratory visits; 

 - uncertainties related to how changes in population, 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 representative of other urban locations in the 

U.S.
10. EVIDENCE- AND EXPOSURE/RISK-BASED 

CONSIDERATIONS RELATED TO THE PRIMARY NO2

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 document as they relate to the 

adequacy of the current NO2 primary NAAQS and potential alternative primary NO2

standards. The available scientific evidence includes epidemiologic, controlled human 

exposure, and animal toxicological studies. The NO2 exposure and risk characterizations 

described in chapters 6-9 of this document include estimates of exposures and health 

risks associated with recent NO2 concentrations and with NO2 concentrations adjusted to 

simulate scenarios just meeting the current and potential alternative standards. In 

considering the scientific evidence 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 adequacy of the current standard and potential 

alternative standards. Section 10.3 focuses 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 alternative standards (in terms of the indicator, 

averaging time, form, and level). 

These considerations are intended to inform the Agency’s policy assessment of a 

range of options with regard to the NO2 NAAQS. We note that the final decision on 

retaining or revising the current NO2 primary standard, taking into account the Agency’s 

policy assessment, is largely a public health policy judgment. A final decision will draw 

upon scientific information and analyses about health effects, population exposure and 

risks, and policy judgments about the appropriate response to the range of uncertainties 

that are inherent in the scientific evidence and analyses. Our approach to informing these 

judgments, discussed more fully below, is based on a recognition that the available health 

effects evidence reflects a continuum consisting 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 

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is consistent with the requirements of the NAAQS provisions of the Act and with how 

EPA and the courts have historically interpreted the Act. These provisions require the 

Administrator to establish primary standards that, in the Administrator's judgment, are 

requisite to protect public health with an adequate margin of safety. In so doing, the 

Administrator seeks to establish standards that are neither more nor less stringent than 

necessary for this purpose. The Act does not require that primary standards be set at a 

zero-risk level but rather at a level that avoids unacceptable risks to public health, 

including the health of sensitive groups. 

10.2 GENERAL APPROACH 

This section describes the general approach that staff is taking to inform decisions 

regarding the need to retain or revise the current NO2 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 available scientific evidence for 

health effects associated with NO2 and on air quality information. With regard to these 

considerations, the Administrator noted that “a 0.053 ppm annual standard would keep 

annual NO2 concentrations considerably below the long-term levels for which serious 

chronic effects have been observed in animals” and that “Retaining the existing standard 

would also provide protection against short-term peak NO2 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 “the existing annual primary standard appears to be both 

adequate and necessary to protect human health against both long- and short-term NO2

exposures” and that “retaining the existing annual standard is consistent with the 

scientific data assessed in the Criteria Document (U.S. EPA, 1993) and the Staff Paper 

(U.S. EPA, 1995a) and with the advice and recommendations of CASAC” (61 FR 52852 

at 52854). 

To inform the range of options that the Agency will consider in this review of the 

current primary NO2 standard, the general approach we have adopted builds upon the 

approaches used in reviews of other criteria pollutants, including the most recent reviews 

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of the Pb, O3, 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 scientific evidence and the exposure/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 considering the scientific 

evidence and exposure/risk-based information. First, our consideration 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 exposure/risk-based information that has 

become available since the last review reinforce or call into question evidence for 

NO2-associated effects that were identified in the last review? 

• To what extent has evidence for different health effects and/or sensitive 

populations become available since the last review? 

• To what extent have uncertainties identified in the last review been reduced 

and/or have new uncertainties emerged? 

• To what extent does evidence and exposure/risk-based information that has 

become available since the last review reinforce 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 standard, we consider that evidence 

and information with regard to its support for consideration of a standard that is either 

more or less protective than the current standard. This evaluation is framed by the 

following questions: 

• Is there evidence that associations, especially causal or likely causal associations, 

extend to ambient NO2 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 estimated exposures and health 

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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 consideration of a revised standard, we then 

consider the specific elements of the standard (indicator for gaseous NOx, averaging time, 

form, and level) within the context of the currently available information. In so doing, 

we address the following questions: 

• Does the evidence provide support for considering a different indicator for 

gaseous NOx? 

• Does the evidence provide support for considering different averaging times? 

• What ranges of levels and forms of alternative 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 NO2, 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 scientific evidence and the exposure-/risk-based 

information specifically as they relate to the current and potential alternative standards. 

This discussion is intended to inform the Agency’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 notices. Section 10.3 considers the adequacy 

of the current standard while section 10.4 considers potential alternative standards in 

terms of indicator, averaging time, form, and level. Each of these sections considers key 

conclusions as well as the uncertainties associated with the evidence and/or exposure/risk 

analyses. 

10.3 ADEQUACY OF THE CURRENT ANNUAL STANDARD 

In the last review of the NO2 NAAQS, the AQCD for NOX concluded that there 

were two key health effects of greatest concern at ambient or near-ambient concentrations 

of NO2 (ISA, section 5.3.1). The first was increased airway hyperresponsiveness in 

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asthmatic individuals after short-term exposures. The second was increased respiratory 

illness among children associated with longer-term exposures to NO2. Evidence also was 

found for increased risk of emphysema, but this appeared to be of major concern only 

with exposures to levels of NO2 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 hyperresponsiveness and lung function changes while 

epidemiologic studies provided evidence for increased respiratory symptoms with 

increased indoor NO2 exposures. Animal toxicological findings of lung host defense 

system changes with NO2 exposure provided a biologically-plausible basis for the 

epidemiologic results. Subpopulations considered potentially more susceptible to the 

effects of NO2 exposure included persons with preexisting 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 AQCD (ISA, section 5.3.2). The epidemiologic 

evidence has grown substantially with the addition 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 NO2 exposures. 

As noted above, no epidemiologic studies were available in 1993 that assessed 

relationships between NO2 and outcomes such as hospital admissions, ED visits, or 

mortality. In contrast, dozens of epidemiologic 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 number of recent toxicological and human clinical studies also 

provide insights into relationships between NO2 exposure and health effects. 

In considering this evidence, we note that different scientific methodologies 

provide different types of information. For example, controlled human exposure studies 

provide information on health effects that are specifically associated with exposure to 

NO2 in the absence of the co-pollutants that are commonly found in ambient air. 

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However, these studies do not provide information directly related to the public health 

implications of real-world NO2 air quality. Epidemiologic studies provide information on 

the public health implications of real-world NO2 concentrations; however, interpretation 

of specific NO2-related effects in these studies is complicated by a number of factors, 

including the presence of co-pollutants 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) NO2 exposures to 

adverse health effects is judged to be either “suggestive but not sufficient to infer a causal 

relationship” (respiratory morbidity) or “inadequate to infer the presence or absence of a 

causal relationship” (mortality, cancer, cardiovascular effects, 

reproductive/developmental effects) (ISA, sections 5.3.2.4-5.3.2.6). In contrast, the 

evidence relating short-term (minutes to hours) NO2 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 NO2 concentrations with respiratory symptoms, ED 

visits, and hospital admissions. It suggests that, at a minimum, consideration of the 

adequacy of the current annual standard should take into account the extent to which that 

standard provides protection against respiratory effects associated with short-term NO2

exposures. Such an emphasis on health endpoints for which evidence has been judged 

sufficient to infer a likely causal relationship 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 NO2 is judged to be 

“more likely than not” (ISA, Table 1.3-2). 

Because there was concern in the 1996 review of the NO2 NAAQS about the 

potential for respiratory effects associated with short-term exposure to NO2

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 NO2 concentrations at this level was considered. In that review, the issue 

was examined with an air quality analysis that evaluated 1-h NO2 concentrations. The 

conclusion from that analysis was that locations meeting the current standard are unlikely 

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to experience 1-h concentrations exceeding levels (e.g., 0.2 ppm) that have been 

associated with respiratory effects in controlled human exposure studies (EPA, 1995). 

In the current review, a larger number of epidemiologic studies are available. In 

considering these epidemiologic studies, we note that annual average NO2 concentrations 

were below the level of the current annual NO2 NAAQS in many of the locations where 

positive associations with respiratory morbidity 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 and coherent. The evidence is consistent in 

that associations are reported in studies conducted in numerous locations and with a 

variety of methodological approaches (ISA, section 5.3.2.1). It is coherent in the sense 

that the studies report associations with respiratory 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 NO2 and respiratory 

symptoms, hospital admissions, and ED visits. A number of these associations are 

statistically significant, particularly the more precise effect estimates (ISA, section 

5.3.2.1). 

Interpretation of these NO2 epidemiologic studies is complicated by the fact that 

on-road vehicle exhaust emissions are a nearly ubiquitous source of combustion pollutant 

mixtures that include NO2. In recognition of this complication, the ISA notes that it is 

difficult to determine the extent to which NO2 is independently associated with 

respiratory effects versus being a marker for the effects of another traffic-related pollutant 

or mix of pollutants (see section 5.4). This uncertainty calls into question the extent to 

which effect estimates from epidemiologic studies reflect the independent contributions 

of NO2 to the adverse respiratory outcomes 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 NO2 exposure, and experimental studies. Specifically, the ISA notes that a 

number of NO2 epidemiologic studies have attempted to disentangle the effects of NO2

from those of co-occurring pollutants by employing multi-pollutant models. When 

evaluated as a whole, NO2 effect estimates in these models generally remained robust 

when co-pollutants were included. Therefore, despite uncertainties associated with 

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separating the effects of NO2 from those of co-occurring pollutants, the ISA (section 5.4, 

p. 5-16) concludes that “the evidence summarized in this assessment indicates that NO2

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

effect of short-term NO2 exposure on respiratory morbidity at ambient concentrations 

below the current NAAQS.” With regard to indoor studies, the ISA notes that these 

studies can test hypotheses related to NO2 specifically (ISA, section 3.1.4.1). Although 

confounding by indoor combustion sources is a concern, indoor studies are not 

confounded by the same mix of co-pollutants present in the ambient air or by the 

contribution of NO2 to the formation of secondary particles or O3 (ISA, section 3.1.4.1). 

The ISA notes that the findings of indoor NO2 studies are consistent with those of studies 

using ambient concentrations from central site monitors (also see chapter 4 of this 

document) and concludes that indoor studies 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 providing information on health effects that are 

specifically associated with exposure to NO2 in the absence of co-pollutants. The ISA 

concludes that the NO2 epidemiologic literature is supported by 1) evidence from 

controlled human exposure studies of airway hyperresponsiveness in asthmatics, 2) 

controlled human exposure and animal toxicological studies of impaired host-defense 

systems and increased risk of susceptibility to viral and bacterial infection, and 3) 

controlled human exposure and animal toxicological studies of airway inflammation 

(ISA, section 5.3.2.1 and 5.4). When taken together, the results of epidemiologic and 

experimental studies form a plausible and coherent data set that supports a relationship 

between NO2 exposures and respiratory endpoints, including symptoms and ED visits 

(ISA, section 5.4), at ambient concentrations that are present in areas that meet the 

current NO2 NAAQS. 

10.3.2 Exposure- and risk-based considerations 

In addition to the evidence-based considerations described above, staff has 

considered the extent to which exposure- and risk-based information can inform 

decisions regarding the adequacy of the current annual NO2 standard, taking into account 

key uncertainties associated with the estimated exposures and risks. For this review, 

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exposures have been addressed in two ways. In the first, NO2 air quality in 18 locations 

around the country has been used as a surrogate for exposure. In the second, exposures 

have been estimated for all asthmatics and for asthmatic children considering time spent 

in different microenvironments in one urban 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 exposure studies involving known NO2 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 NO2-related health risks that could exist. In 

another approach to characterizing NO2-related health risks, we have estimated the 

occurrences of NO2-related respiratory ED visits in Atlanta. This quantitative risk 

assessment is based on NO2 concentration-response relationships identified in an 

epidemiologic study of air pollution-related ED visits 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 NO2-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) and 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 measures, including ED visits, should be considered adverse. In 

addition, regarding airway responsiveness, we recognize the following: 

• NO2-related airway hyperresponsiveness has the potential to increase asthma 

symptoms and worsen asthma control (ISA, sections 5.3.2.1 and 5.4). 

• The majority of asthmatics may experience NO2-related airway 

hyperresponsiveness following short-term NO2 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 NO2-related airway hyperresponsiveness in 

any single individual (see below) and despite the fact that not all asthmatics are expected 

283
to respond to NO2 concentrations between 0.1 and 0.3 ppm, these considerations suggest 

that NO2-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 results will inform ultimate decisions 

regarding the NO2 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 analyses (air quality, Atlanta exposure, and 

Atlanta risk) are summarized below and are described 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 apply equally to consideration of alternative 

standards.

Air Quality Analyses

A number of key uncertainties should be considered when interpreting these 

results with regard to decisions on the standard. These uncertainties 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 NO2

concentrations was required. We note that this adjustment does not reflect a 

judgment that levels of NO2 are likely to increase under the current standard or 

any of the potential alternative standards under consideration. Rather, these 

adjustments reflect the fact that the current standard, as well as some of the 

alternatives under consideration, could allow for such increases in ambient NO2

concentrations. In adjusting air quality to simulate just meeting these standards, 

we have assumed that the overall shape of the distribution of NO2 concentrations 

would not change. While we believe this 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 required to simulate just meeting one or 

more of the standards. 

 

• In order to estimate NO2 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 NO2 concentrations given that these 

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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 collected under different conditions (e.g., with regard to 

meteorology, rate of transformation of NO to NO2). We do not know the extent 

to which it is appropriate to assume that these conditions are representative 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 NO2 concentrations 

reflect the actual relationship in the locations 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 mild 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 NO2 exposure (ISA, section 

3.1.3.2). Therefore, the potential health effect benchmarks based on these 

studies could underestimate risks in populations with greater susceptibility. 

Although approaches to classifying asthma 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 NO2-induced 

airway response, but not on the magnitude of the response. Therefore, 

although the ISA does conclude that increased airway responsiveness 

associated with NO2 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 NO2

exposures, in asthmatics, that exceed potential health benchmark levels. As with the air 

quality analyses, these exposures are considered for each of the air quality scenarios 

evaluated. A number of key uncertainties should be considered when interpreting these 

results with regard to decisions 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 the Atlanta exposure assessment are discussed 

briefly below. A more extensive 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 measurement data, predicted upper percentile NO2

concentrations from AERMOD may be 10-50% higher. Because these AERMOD 

outputs are used as inputs for our exposure modeling, this suggests the possibility 

that we are over-predicting upper percentile NO2 exposures. Other approaches 

used to evaluate our exposure results (i.e., comparison to personal exposure 

monitoring results and comparison of exposure-to-ambient concentration ratios 

with those identified in the ISA) suggest that exposure estimates are reasonable. 

However, we cannot rule out the possibility that we are over-predicting 

benchmark exceedances with our Atlanta exposure analysis. 

 

• The exposure assessment is limited to Atlanta 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 the Atlanta exposure estimates are likely 

representative of other moderate 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 estimates likely underestimate the fraction of 

asthmatics in these cities that is exposed to NO2 concentrations greater than or 

equal to potential health benchmark levels. 

Atlanta Risk Assessment 

For our risk assessment, we have considered the prevalence of NO2-related 

respiratory ED visits in Atlanta. As with the air quality and Atlanta exposure analyses, 

ED visits are considered for each of the air quality scenarios evaluated. A number of key 

uncertainties should be considered when interpreting these results with regard to 

decisions on the standard. Some of these, including the approach used to adjust air 

quality to simulate just meeting different standards and the appropriateness of 

generalizing results from Atlanta, are uncertainties shared with the air quality and/or 

Atlanta exposure analyses. Additional uncertainties associated with the Atlanta risk 

assessment are discussed briefly below. A more extensive discussion of uncertainties is 

provided in chapter 9. 

• There is uncertainty about whether the association between NO2 and ED visits 

actually reflects a causal relationship. Our judgment, that there exists at least a 

likely causal relationship with either short-term NO2 itself or with NO2 serving as 

an indicator for itself and other components of ambient air, draws on the 

conclusions in the ISA and is discussed in more detail in chapter 4. 

 

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• The statistical uncertainty associated with the estimated NO2 coefficient in the 

concentration-response function has been characterized by confidence intervals 

reflecting sample size. However, these confidence intervals do not reflect all of 

the uncertainties related to the concentration-response functions, such as whether 

or not the model used in the 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 NO2 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 NO2. 

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 NO2 standard was retained in 1996 based 

largely on an evaluation of short-term NO2 air quality. In that review, an air quality 

analysis demonstrated that locations meeting the current annual standard were unlikely to 

experience short-term ambient NO2 concentrations at central site monitors that have been 

associated with respiratory effects (i.e., airway hyperresponsiveness) in controlled human 

exposure studies (i.e., around 0.2 ppm). Therefore, 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 NO2 concentrations greater 

than or equal to 0.20 ppm are unlikely to occur 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., see tables 7-14 to 7-19). 

However, in the current review, in addition 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 NO2-

associated health risks. For example, in our exposure analyses, we have evaluated NO2

concentrations on roadways which are, on average, 80% higher than concentrations 

measured at central site monitors (section 7.3.2). Staff notes that high concentrations of 

NO2 on or near roadways could impact asthmatics living or walking nearby (e.g., as 

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would be common in an urban environment) or commuting in cars. In addition, we have 

adjusted NO2 concentrations to simulate NO2 air quality that could occur upon just 

meeting the current and potential alternative 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 options. For our exposure analyses, we have 

compared NO2 concentrations to potential health benchmark levels from 100 ppb to 300 

ppb, a range that extends beyond that considered in the 1996 review. We have also 

evaluated NO2-related ED visits in the current review. Epidemiological studies that form 

the basis for this analysis were not available in the 1996 review. When taken together, 

these analyses provide additional information, not available in the 1996 review, on which 

to base a decision regarding the adequacy of the current annual standard (and potential 

alternative standards) to protect the public health. 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 exposure 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 potential 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 results, 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 NO2 are likely to increase under the current standard. Rather, it 

reflects the fact that ambient NO2 concentrations could increase under the current 

standard. In situations where annual NO2 concentrations are adjusted upward to simulate 

just meeting the current standard, 1-h NO2 concentrations measured at fixed-site monitors 

in locations across the U.S. could exceed concentrations that have been associated with 

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increased airway responsiveness. Most locations are estimated to experience at least 50 

days per year with 1-h ambient NO2 concentrations at fixed-site monitors greater than or 

equal to 100 ppb (Figures 7-2 and 7-3) under 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 NO2 concentrations at 

central site monitors greater than or equal to 300 ppb, and none of those locations are 

estimated to experience more than 2 such days per year, on average (Appendix A). 

However, as noted above on-road NO2 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 benchmark levels, most locations are estimated to 

have exceedances on roadways. All locations evaluated except one (Boston) are 

estimated to experience on-road NO2 concentrations greater than or equal to 300 ppb 

(Appendix A). Four of these locations are estimated to experience an average of greater 

than 20 days per year with on-road NO2 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 adjusted upward to simulate just meeting the current standard. If 

NO2 concentrations were such that the Atlanta area just meets the current standard, nearly 

all asthmatics in Atlanta (>97%) would be estimated to experience six or more days per 

year with 1-h NO2 exposure concentrations greater than or equal to our highest 

benchmark level (0.3 ppm) (Figure 8-22). 

Risk Results 

 When considering the Atlanta risk assessment 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 NO2-related respiratory ED visits in Atlanta ranged from about 8-9% of total 

respiratory-related ED visits per year based on single pollutant models (or 9,800-10,900 

NO2-related incidences) when air quality is adjusted upward to simulate a situation where 

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Atlanta just meets the current standard. Central estimates of incidence of NO2-related 

respiratory ED visits ranged from 2.9-7.7% of total respiratory-related ED visits per year 

based on two-pollutant models (or 3,600-9,400 NO2-related incidences) In addition, 

inclusion of O3 and/or PM10 in multi-pollutant models results in the inclusion of an 

estimate of zero NO2-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 current 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 chapter 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 standard to protect the public health would 

clearly be called into question. Such a conclusion would provide support for 

consideration of an NO2 standard that would provide increased health protection for 

sensitive groups, including asthmatics and individuals who spend time on or near major 

roadways (see chapter 3), against health effects ranging from increased asthma symptoms 

to respiratory-related ED visits and hospital admissions associated with short-term 

exposures, as well as potential effects associated with long-term exposures. 

In examining the exposure- and risk-based information with regard to the 

adequacy of the current annual NO2 standard to protect the public 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 just meeting the current standard that can 

reasonably be judged important from a public health 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 protection, especially for sensitive groups, from NO2-

related adverse health effects associated with short-term, and potential long-term, 

exposures. 

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10.4 POTENTIAL ALTERNATIVE STANDARDS 

10.4.1 Indicator 

In the last review, EPA focused on NO2 as the most appropriate indicator for 

ambient NOx. In this review, while the presence of gaseous NOx species other than NO2

has been recognized (e.g., see section 1.3 of this document), no alternative to NO2 has 

been advanced as being a more appropriate surrogate for ambient gaseous NOx. 

Controlled human exposure studies and animal toxicology studies provide specific 

evidence for health effects following exposure to NO2. Epidemiologic studies also 

typically report levels of NO2, as opposed to other gaseous NOx, though the degree to 

which monitored NO2 reflects actual NO2 levels, as opposed to NO2 plus other gaseous 

NOx, can vary (e.g.,. see section 2.2.3 of this document). Because emissions that lead to 

the formation of NO2 generally also lead to the formation of other NOx oxidation 

products, measures leading to reductions in population exposures to NO2 can generally be 

expected to lead to reductions in population exposures to other gaseous NOx. Therefore, 

meeting an NO2 standard that protects the public health can also be expected to provide 

some degree of protection against potential health effects that may be independently 

associated with other gaseous NOx even though such effects are not discernable from 

currently available studies indexed by NO2 alone. Given these key points, staff judges 

that the available evidence supports the retention of NO2 as the indicator in the current 

review. 

10.4.2 Averaging Time 

The current annual averaging time for the NO2 NAAQS was originally set in 

1971, based on epidemiologic studies that supported a link between adverse respiratory 

effects and long-term exposure to low-levels of NO2. 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 

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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 causality judgments from the ISA, results from 

experimental and epidemiologic studies, and NO2 air quality correlations. These 

considerations are described in more detail below. 

To inform general decisions regarding averaging time (e.g., short-term versus 

long-term), we note the causality judgments made in the ISA regarding different health 

endpoints. As described in chapter 4 of this document, the evidence relating short-term 

(minutes to hours) NO2 exposures to respiratory morbidity is judged in the ISA to be 

“sufficient to infer a likely causal relationship” (ISA, section 5.3.2.1) while the evidence 

relating long-term (weeks to years) NO2 exposures to adverse health effects is judged to 

be either “suggestive but not sufficient to infer a causal relationship” (respiratory 

morbidity) or “inadequate to infer the presence or absence of a causal relationship” 

(mortality, cancer, cardiovascular effects, reproductive/developmental effects) (ISA, 

sections 5.3.2.4-5.3.2.6). These judgments most directly support an averaging time that 

focuses protection on short-term exposures to NO2. 

As has been done in past reviews, it is instructive to evaluate the potential for a 

standard based on annual average NO2 concentrations, as is the current standard, to 

provide protection against short-term NO2 exposures. To this end, Table 10-1 reports the 

ratios of short term to annual average NO2 concentrations. Ratios of 1-h daily maximum 

concentrations (98th and 99th percentile) to annual average concentrations range from 2.5 

to 8.7 while ratios of 24-h average concentrations to annual average concentrations range 

from 1.6 to 3.8 (see Thompson, 2008a for more details). The variability in these ratios 

across locations, particularly those for 1-h to annual average concentrations, suggests that 

a standard based on annual average NO2 concentrations would not likely be an effective 

or efficient approach to focus protection on short-term NO2 exposures. 

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Table 10-1. Ratios of short-term to annual average NO2 concentrations 

 

1-h Daily 

Max(99th):Annual 

1-h Daily 

Max(98th):Annual 

24-h 

Avg(99th):Annual 

24-h 

Avg(98th):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 relatively high ratio (e.g., 8), the current annual 

standard (0.053 ppm) would be expected to allow 1-h daily maximum NO2

concentrations of about 0.4 ppm. In contrast, in an area with a relatively low ratio (e.g., 

3), the current standard would be expected to allow 1-h daily maximum NO2

concentrations of about 0.15 ppm. Thus, for purposes of protecting against the range of 

1-h NO2 exposures considered in this review (i.e., 0.1 to 0.3 ppm), a standard based on 

annual average concentrations would likely require more control than necessary in some 

areas and less control than necessary in others, depending on the standard level selected. 

In considering the level of support available for specific short-term averaging 

times, we take note of evidence from both experimental and epidemiologic studies. 

Controlled human exposure studies and animal toxicological studies provide evidence 

that NO2 exposures with exposure durations from less than 1-h up to 3-h can result in 

respiratory effects such as increased airway responsiveness and inflammation (ISA, 

section 5.3.2.7). Specifically, the ISA concludes that NO2 exposures of 0.1 ppm for 1-h 

(or 0.2-0.3 ppm for 30-min) can result in small but significant increases in nonspecific 

airway responsiveness (ISA, section 5.3.2.1). In contrast, the epidemiologic literature 

does not provide clear support for one short-term averaging time versus another (ISA, 

section 5.3.2.7). A number of epidemiologic studies detect positive associations between 

respiratory morbidity and 1-h (daily maximum) and/or 24-h NO2 concentrations. A few 

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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 reveal 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 observed are attributable to average daily (or multiday) concentrations (24-h average) or high, peak exposures (1-h maximum) (ISA, section 

5.3.2.7). 

Given the above conclusions, the experimental 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 the level of protection 

provided against 1-h daily maximum NO2 concentrations. However, it is also worthwhile 

to consider the ability of averaging times under consideration to protect against 24-h 

average NO2 concentrations. To this end, Table 10-2 presents correlations between 1-h 

daily maximum NO2 concentrations and 24-h average NO2 concentrations (98th and 99th

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 annual average concentrations, suggesting that a 

standard based on 1-h daily maximum NO2 concentrations could also be effective at 

providing adequate protection against 24-h NO2 concentrations. 

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Table 10-2. Ratios of 1-h daily maximum NO2 concentrations to 24-h average 

concentrations (ppm) 

 1-h 24 h Ratio 1-h 24-h Ratio 

Location 99th 99th 98th 98th 

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 considered in this risk and 

exposure assessment document could have the effect of maintaining annual average NO2

concentrations below the level of the current standard (0.053 ppm). For example, in all 

locations evaluated, a 1-h standard with a level of 0.05 ppm is estimated to result in 

annual average NO2 concentrations less than or equal to approximately 0.02 ppm. A 1-h 

standard with a level of 0.10 ppm is estimated to result in annual average NO2

concentrations less than or equal to approximately 0.04 ppm. However, a 1-h standard 

with a level of 0.15 ppm could result in annual average NO2 concentrations up to 

approximately 0.06 ppm and a 1-h standard with a level of 0.20 ppm could result in 

annual average NO2 concentrations up to approximately 0.07 ppm (Table 10-3). 

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Table 10-3. Mean annual NO2 concentrations for 2004-2006 given just meeting alternative 

1-h standards (98th percentile) 

Annual NO2 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 conjunction with specific levels, staff 

considers the adequacy of the public health protection provided by the combination of 

level and form to be the foremost consideration. In addition, we recognize that it is 

296
important to have a form that is reasonably 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 reducing public health protection because shifting in 

and out of attainment due to meteorological conditions could disrupt an area’s ongoing 

implementation plans and associated control programs. 

Therefore, as noted in chapter 5 and consistent with recent reviews of the O3 and 

PM NAAQS, we have focused in the current review on concentration-based forms 

averaged over 3 years. As noted in the review of the O3 NAAQS (EPA, 2007h), 

concentration-based forms better reflect pollutant-associated health risks than forms 

based on expected exceedances because concentration-based forms give proportionally 

greater weight to periods of time when pollutant concentrations are well above the level 

of the standard than to times when the concentrations are just above the standard, while 

an expected exceedance form would give the same weight to periods of time with 

concentrations that just exceed the standard as to times when concentrations greatly 

exceed the standard. Concentration-based forms 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 number of days per year that an area could 

exceed the standard level and still attain the standard. Given this, we have focused on 

98th and 99th percentile forms averaged over 3 years. With regard to these alternative 

forms, staff notes that a 99th percentile form for a 1-h daily maximum standard would 

correspond to the 4th highest daily maximum concentration in a year while a 98th

percentile form would correspond approximately to the 7th to 8th highest daily maximum 

concentration in a year (Table 10-4; see Thompson, 2008 for methods). As noted in 

chapter 5, staff has judged that these forms would provide an appropriate balance 

between limiting peak NO2 concentrations and providing a stable regulatory target. This 

is consistent with judgments made in the 2006 review of the PM NAAQS (EPA, 2005). 

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Table 10-4. NO2 concentrations (ppm) corresponding to 2nd-9th daily maximum and 98th/99th

percentile forms (2004-2006) 

When considering the extent to which exposure and risk analyses inform 

judgments on standard form, staff notes that a 99th percentile form could be appreciably 

more protective than a 98th percentile form in some locations, as judged by the results of 

our air quality analyses. For example, in Boston, Philadelphia, and Washington, D.C. a 

99th percentile standard of 0.20 ppm is estimated to decrease benchmark exceedances, 

relative to a 98th percentile form, by approximately 50-70% (Table 10-5). However, in 

St. Louis, Detroit, and Las Vegas a 99th 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 98th percentile form versus a 99th

percentile form would have virtually no effect on exposure concentrations 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 99th percentile form is 

estimated to be associated with approximately 6% to 8% fewer NO2-related ED visits 

than a 98th 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 NO2

concentrations ≥ 0.10 ppm assuming 98th and 99th percentile forms of a 0.20 

ppm standard 

Exceedances 

Location Name 98th 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 results 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 standard levels that would provide greater protection than 

that afforded by the current standard against NO2-related adverse health effects, staff has 

taken into account scientific evidence from both experimental and epidemiologic studies, 

as well as the uncertainties and limitations in that evidence. In particular, we have 

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considered the extent to which controlled human 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 NO2 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. Therefore, while this section considers the evidence 

as it relates to alternative levels, such considerations assume particular averaging times 

and forms. Additional discussion of averaging 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 NO2 concentrations represent different metrics when reported in experimental studies 

versus epidemiologic studies. Concentrations of NO2 reported in epidemiologic studies 

are typically based on ambient monitoring data while NO2 levels reported in controlled 

human exposure studies represent the concentration of NO2 in the breathing zone of the 

individual. In some locations and at some points in time, individuals are likely exposed 

to NO2 concentrations that are higher than those measured at ambient monitors. The ISA 

concludes that elevated NO2 monitors (e.g., monitors sited on building roofs), particularly 

in inner cities, likely underestimate concentrations and personal exposures occurring at 

lower elevations, closer to motor vehicle emissions (ISA, section 5.2.2). In this situation, 

average NO2 concentrations measured at an elevated ambient monitor could be well 

below concentrations associated with effects in controlled human exposure studies while 

actual NO2 exposures on and/or near roadways could be comparable to, or higher than, 

these concentrations. We note that in this situation, where personal exposure 

concentrations to ambient NO2 are higher than ambient levels measured at a fixed-site 

monitor, ambient standards based principally 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 personal exposures have been measured, 

personal exposures to NO2 of ambient origin were generally lower than ambient NO2

concentrations based on fixed-site monitors (ISA, tables 2.5-4 and 2.5-5; section 5.2.2). 

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In this type of situation, where personal exposure concentrations to ambient NO2 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 consequences of short-term 

(e.g., 30-minutes to several hours) NO2 exposures for a number of health endpoints 

including airway responsiveness, host defense and immunity, inflammation, and lung 

function (ISA, section 3.1). In identifying 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 ambient levels of NO2 and 

endpoints that are of clinical significance. As described in more detail in section 4.5.3, 

the only endpoint to meet these criteria is increased airway responsiveness in asthmatics. 

The ISA concludes that NO2 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) and that “transient increases in airway 

responsiveness following NO2 exposure have the potential to increase symptoms and 

worsen asthma control” (ISA, sections 3.1.3 and 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, NO2 effects on airway responsiveness in 

asthmatics are part of the body of experimental evidence that provides plausibility and 

coherence for the observed NO2-related increased in hospital admissions and ED visits in 

epidemiologic studies (ISA, section 5.3.2.1). For all of these reasons, we have focused 

on increased airway responsiveness in asthmatics when considering the controlled human 

exposure literature in terms of its ability to inform decisions on alternative standard 

levels. 

With regard to controlled human exposure 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 

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experienced increased airway responsiveness following exposure to 0.1 ppm NO2, 67% 

experienced an increase following exposure to NO2 concentrations between 0.1 and 0.15 

ppm (inclusively), 75% experienced an increase following exposure to NO2

concentrations between 0.2 and 0.3 ppm (inclusively), and 73% experienced an increase 

following exposure to NO2 concentrations above 0.3 ppm. Effects of NO2 exposure on 

the direction of airway responsiveness are statistically-significant at all of these levels. 

As noted in section 10.3.2.1, one of the important uncertainties associated with these 

results is that, because the meta-analysis evaluated only the direction of the change in 

airway responsiveness, it is not possible to discern the magnitude of the change from 

these data. This limitation makes it particularly 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 that the ISA concludes that NO2 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 apparent 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 standard levels supported by the epidemiologic 

literature. When identifying this range, we focus on the higher percentiles of NO2

concentrations measured at the ambient monitors used in the study (i.e., 98th and 99th

percentiles), as these percentiles are likely most relevant for the health effects observed in 

epidemiologic studies. 

Figures 5-1 and 5-2 (see chapter 5) show standardized effect estimates and the 

98th and 99th percentile concentrations of daily 1-h maximum NO2 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). These key studies are associated with a range of 

98th/99th 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 NO2

concentrations observed in epidemiologic studies, there is increasing uncertainty as to 

302
whether observed health effects remain plausibly related to exposures to ambient NO2, as 

opposed to the broader mix of air pollutants 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 NO2

concentrations and respiratory morbidity in a location where NO2 concentrations were 

well below levels in most other key U.S. epidemiologic studies. This study reports 

positive associations between 1-h and 8-h (only 8-h associations were statisticallysignificant) levels of NO2 and asthma symptoms in a location where the 98th and 99th

percentile 1-h daily maximum NO2 concentrations were 0.05 and 0.053 ppm, 

respectively. Second, the controlled human exposure studies of airway responsiveness 

that formed the basis for the meta-analysis detected a lowest-observable-effect level of 

0.1 ppm NO2. However, these studies did not evaluate severe asthmatics. Most of the 

subjects included in these studies were mild asthmatics. More severely affected 

asthmatics may be more susceptible than mild asthmatics to the effects of NO2 exposure 

(ISA, section 3.1.3.2). As a result, staff judges that 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 increased 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 NO2

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 NO2 effect estimates were also observed in 

the two key U.S. studies associated with the highest 1-h NO2 concentrations (Linn 

et al., 2000; Ostro et al., 2001). These studies were associated with 98th and 99th

percentile 1-h daily maximum NO2 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 NO2 and 

respiratory morbidity beyond that provided by the studies noted above. 

• The meta-analysis of airway responsiveness presented in the ISA reports 

statistically-significant effects on the direction of airway responsiveness following 

short-term NO2 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 evidence 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 general uncertainties associated with quantifying 

the contributions of NO2 to respiratory effects in epidemiologic studies and uncertainties 

regarding the public health significance of NO2-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 maximum 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 risk-based information as it relates to 

alternative levels for the primary NO2 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 health. Therefore, we 

have judged it appropriate to consider a range of alternative levels that would improve 

upon the level of protection provided by the current 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 of the standard, given different levels (based on years 20

Percent of counties, total and by region, (and total population) not likely to meet standard and le

Alternative Standards 

and Levels (ppm) 

Total counties 

(population in 

millions) Northeast Southeast

Industrial 

Midwest 

Upper 

Midwest Southwest Northwest

Number of counties 

with monitors 

(population in 1000s) 138 (93.9) 31 32 20 12 9 20 

3 year 99th percentile daily 1 hour max: 0.20 0 0 0 0 0 0 0 0.15 0 0 0 0 0 0 0 0.10 0 0 0 0 0 0 0 0.05 59 (79.6) 77 41 80 17 56 55 3 year 98th percentile daily 1 hour max: 0.20 0 0 0 0 0 0 0 0.15 0 0 0 0 0 0 0 0.10 0 0 0 0 0 0 0 0.05 46 (63.4) 55 34 60 17 44 45 

305
The results of the air quality analysis are presented in chapter 7 of this document. In that 

chapter, we present estimates of the number of days per year with ambient (based on fixed-site 

ambient monitors) and on-road (based on on-road adjustment) NO2 concentrations at or above 

our potential health benchmark levels for the years 2001-2006. These estimates are based on asis air quality and air quality that has been adjusted to simulate just meeting the current and 

potential alternative standards. In considering the results presented chapter 7, we note the 

following key points: 

• Given unadjusted air quality for the years 2001-2006, it is estimated that 1-h NO2

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 adjusted 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 NO2

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 200 ppb (98th 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 

NO2 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 150 ppb (98th 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 

NO2 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 100 ppb (98th percentile), it is estimated that an average of up to 8 days per 

year could occur with ambient NO2 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 (98th percentile), it is estimated that an average of up to 1 day per year 

could occur with 1-h ambient NO2 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 NO2 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 fixed-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 98th percentile standards) 

(Table 7-29). 

The results of the Atlanta exposure 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 NO2 exposure concentrations at or above our potential health benchmark 

levels for the year 2002 (estimates were similar 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 results 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 NO2 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 NO2 concentrations greater than or equal to 0.3 ppm and that 

fewer than 10% could experience 6 or more exposures to NO2 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 

NO2 exposure concentrations greater than or equal to 0.3 ppm (Figure 8-22). 

• Given air quality adjusted to simulate just meeting a standard level of either 150 or 

200 ppb (98th or 99th percentile standard), it is estimated that more than 95% of 

Atlanta asthmatics could experience 1 or more days per year with NO2 exposure 

concentrations greater than or equal to 0.3 ppm (Figure 8-23). 

• Given air quality adjusted to simulate just meeting a standard level of 100 ppb (99th

percentile standard), it is estimated that approximately 70% of Atlanta asthmatics 

could experience 1 or more days per year with NO2 concentrations of 0.3 ppm or 

above. With a 99th percentile standard, approximately 10% of Atlanta asthmatics 

could experience 6 or more days per year with NO2 concentrations of 0.3 ppm or 

above (Figure 8-26). 

• Given air quality adjusted to simulate just meeting a standard level of 50 ppb (99th

percentile standard), it is estimated that fewer than 10% of asthmatics could 

experience 1 or more days per year with NO2 concentrations of 0.3 ppm or above. 

With a 99th percentile standard, virtually none are estimated to be exposed 6 or more 

times per year. However, this standard is estimated to result in over 90% of 

asthmatics being exposed 6 or more times per year to NO2 concentrations of 0.1 ppm 

or above (Figure 8-24). 

307
The results of the Atlanta risk assessment are presented in chapter 9 of this document. In 

Tables 9-2 through 9-4, we present estimates, for the years 2005-2007, of the percent of total 

annual respiratory ED visits in Atlanta associated with NO2. These results are also presented as 

incidence of NO2-associated respiratory ED visits in Appendix 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 NO2-related respiratory 

ED visits associated with recent air quality for the years 2005-2007 range from 2.8 to 

3.1% (or 3,400 to 3,800 NO2-related incidences per year). Based on multi-pollutant 

models, central estimates for this same time period range from 0.6 to 2.6% (or 700 to 

3,200 NO2-related incidences per year). 

• Central estimates of annual NO2-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 NO2-related incidences 

per year) based on single-pollutant models and from 1.7 to 7.7% (or 3,100 to 9,400 

NO2-related incidences per year) based on multi-pollutant models. . 

• Central estimates of annual NO2-related respiratory ED visits associated with air 

quality adjusted upward to simulate just meeting a 200 ppb, 1-h daily maximum, 98th

percentile standard (based 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 NO2-related respiratory ED visits associated with air 

quality adjusted upward to simulate just meeting a 150 ppb, 1-h daily maximum, 98th

percentile standard (based 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 NO2-related respiratory ED visits associated with air 

quality adjusted to simulate just meeting a 100 ppb, 1-h daily maximum, 98th

percentile standard (based 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 NO2-related respiratory ED visits associated with air 

quality adjusted to simulate just meeting a 50 ppb, 1-h daily maximum, 98th percentile 

standard (based on 2005-2007) ranges from 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 NO2-related respiratory ED visits associated with air 

quality adjusted to simulate 99th percentile 1-h daily maximum standards in the range 

of 50 to 200 ppb are generally on the order of 10% lower than the estimates 

summarized above for standards with a 98th percentile form. 

308
10.4.4.3 Conclusions regarding level 

 As noted in section 10.7.1, staff concludes 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-based information, we note that standard levels 

of 150 and 200 ppb are generally estimated to be associated 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 NO2-related ED visits, on average, than are associated with 

just meeting the current standard, though the reduction 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 scientific evidence is considered in conjunction with exposure 

and risk results, the strongest support is for standard levels between 50 and 100 ppb. This 

represents a range of levels that is consistent with the scientific evidence and that would be 

expected to provide improved public health protection relative to that provided by the current 

annual standard. 

309
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United States Office of Air Quality Planning and Standards 

Environmental Protection Health and Environmental Impacts Division N

Agency Research Triangle Park, NC 

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