Risk and Exposure Assessment to Support 

the Review of the NO2 Primary National 

Ambient Air Quality Standard: Appendices 
EPA document # EPA-452/R-08-008b

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). 
 i

Appendix A. Supplement to the NO2 Air Quality Characterization 
 ii

Table of Contents 

Appendix A. Supplement to the NO2 Air Quality Characterization.......................................... i 

A-1 Overview............................................................................................................................. 1 

A-2 Air Quality Data Screen...................................................................................................... 2 

A-2.1 Introduction................................................................................................................. 2 

A-2.2 Approach..................................................................................................................... 2 

A-2.3 Results......................................................................................................................... 2 

A-3 Selection of Locations......................................................................................................... 5 

A-3.1 Introduction................................................................................................................. 5 

A-3.2 Approach..................................................................................................................... 5 

A-3.3 Results......................................................................................................................... 5 

A-4 Ambient Monitoring Site Characteristics ........................................................................... 7 

A-4.1 Introduction................................................................................................................. 7 

A-4.2 Approach..................................................................................................................... 7 

A-4.3 Summary Results ........................................................................................................ 7 

A-4.4 Detailed Monitoring Site Characteristics.................................................................. 10 

A-5 Spatial and Temporal Air Quality Analyses ..................................................................... 25 

A-5.1 Introduction............................................................................................................... 25 

A-5.2 Approach................................................................................................................... 25 

A-5.3 Summary Results by Locations ................................................................................ 26 

A-5.4 Summary Results by Year ........................................................................................ 31 

A-5.5 Detailed Results by Year and Location .................................................................... 36 

A-6 Technical Memorandum on Regression Modeling........................................................... 80 

A-6.1 Summary................................................................................................................... 80 

A-6.2 Data Used.................................................................................................................. 80 

A-6.3 Regression Models.................................................................................................... 81 

A-6.4 Conclusion ................................................................................................................ 93 

A-6.5 Detailed Regression Model Predictions.................................................................... 94 

A-7 Adjustment of Air Quality to Just Meet the Current and Alternative Standards............ 100 

A-7.1 Introduction............................................................................................................. 100 

A-7.2 Approach................................................................................................................. 101 

A-8 Method for Estimating On-Road Concentrations ........................................................... 110 

A-8.1 Introduction............................................................................................................. 110 

A-8.2 Derivation of On-Road Ratios ................................................................................ 111 

A-8.3 Application of On-Road Factors............................................................................. 115 

A-9 Supplemental Results Tables to the REA ....................................................................... 117 

A-9.1 Annual average NO2 concentration data for 2001-2003......................................... 117 

A-9.2 Number of 1-hour NO2 exceedances in a year, 2001-2003 .................................... 139 

A-9.3 Annual average NO2 concentration data for 2004-2006......................................... 183 

A-9.4 Comparison of Historical and Recent Ambient Air Quality (As Is)....................... 250 

A-9.5 Comparison of On-Road Concentrations Derived From Historical and Recent 

Ambient Air Quality (As Is) ................................................................................................... 257 

A-9.6 Results Tables of Historical NO2 Ambient Monitoring Data (1995-2000) Adjusted to 

Just Meeting the Current Standard.......................................................................................... 262 
 iii

A-9.7 Results Tables of Recent NO2 Ambient Monitoring Data (2001-2006) As Is and Just 

Meeting the Current and Alternative Standards...................................................................... 266 

A-10 References................................................................................................................... 267 
 iv

List of Tables 

Table A-1. Example of ambient monitor years of operation, using the Boston CMSA................ 3 

Table A-2. Counts of complete site-years of NO2 monitoring data............................................... 4 

Table A-3. Locations selected for NO2 Air Quality Characterization, associated abbreviations, 

and values of selection criteria....................................................................................... 6 

Table A-4. Distribution of the distance of ambient monitors to the nearest major road in selected 

locations. ........................................................................................................................ 8 

Table A-5. Distribution of the distance of ambient monitors to stationary sources with NOx

emissions >5 tons per year and within a 10 kilometers radius....................................... 9 

Table A-6. Distribution of NOx emissions from stationary sources within 10 kilometers of 

monitoring site, where emissions were >5 tons per year. .............................................. 9 

Table A-7. Attributes of location-specific ambient monitors used for air quality characterization 

and the distance to nearest major roadway. ................................................................. 11 

Table A-8. Distance of location-specific ambient monitors to stationary sources emitting >5 tons 

of NOx per year, within a 10 kilometer distance of monitoring site. ........................... 20 

Table A-9. Statistical test results for spatial comparisons of all location parameter distributions.

...................................................................................................................................... 29 

Table A-10. Statistical test results for spatial comparisons of within-location parameter 

distributions.................................................................................................................. 30 

Table A-11. Distribution of annual average NO2 ambient concentrations (ppb) by year, Boston 

CMSA. ......................................................................................................................... 37 

Table A-12. Distribution of hourly NO2 ambient concentrations (ppb) by year, Boston CMSA.37 

Table A-13. Distribution of annual average NO2 ambient concentration (ppb) by monitor, 

Boston CMSA set A, 1995-2006. ................................................................................ 38 

Table A-14. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Boston CMSA 

set A, 1995-2006. ......................................................................................................... 38 

Table A-15. Distribution of annual average NO2 ambient concentration (ppb) by monitor, 

Boston CMSA set B, 1995-2006.................................................................................. 39 

Table A-16. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Boston CMSA 

set B, 1995-2006. ......................................................................................................... 39 

Table A-17. Distribution of annual average NO2 ambient concentrations (ppb) by year, Chicago 

CMSA. ......................................................................................................................... 40 

Table A-18. Distribution of hourly NO2 ambient concentrations (ppb) by year, Chicago CMSA.

...................................................................................................................................... 40 

Table A-19. Distribution of annual average NO2 ambient concentration (ppb) by monitor, 

Chicago CMSA, 1995-2006......................................................................................... 41 

Table A-20. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Chicago 

CMSA, 1995-2006. ...................................................................................................... 41 

Table A-21. Distribution of annual average NO2 ambient concentrations (ppb) by year, 

Cleveland CMSA. ........................................................................................................ 42 

Table A-22. Distribution of hourly NO2 ambient concentrations (ppb) by year, Cleveland 

CMSA. ......................................................................................................................... 42 

Table A-23. Distribution of annual average NO2 ambient concentration (ppb) by monitor, 

Cleveland CMSA, 1995-2006...................................................................................... 43 
 v

Table A-24. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Cleveland 

CMSA, 1995-2006. ...................................................................................................... 43 

Table A-25. Temporal distribution of annual average NO2 ambient concentrations (ppb) by year, 

Denver CMSA.............................................................................................................. 44 

Table A-26. Temporal distribution of hourly NO2 ambient concentrations (ppb) by year, Denver 

CMSA. ......................................................................................................................... 44 

Table A-27. Distribution of annual average NO2 ambient concentration (ppb) by monitor, 

Denver CMSA, 1995-2006. ......................................................................................... 45 

Table A-28. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Denver 

CMSA, 1995-2006. ...................................................................................................... 45 

Table A-29. Distribution of annual average NO2 ambient concentrations (ppb) by year, Detroit 

CMSA. ......................................................................................................................... 46 

Table A-30. Distribution of hourly NO2 ambient concentrations (ppb) by year, Detroit CMSA.46 

Table A-31. Distribution of annual average NO2 ambient concentration (ppb) by monitor, 

Detroit CMSA, 1995-2006........................................................................................... 47 

Table A-32. Distribution of annual average NO2 ambient concentration (ppb) by monitor, 

Detroit CMSA, 1995-2006........................................................................................... 47 

Table A-33. Distribution of annual average NO2 ambient concentrations (ppb) by year, Los 

Angeles CMSA. ........................................................................................................... 48 

Table A-34. Distribution of hourly NO2 ambient concentrations (ppb) by year, Los Angeles 

CMSA. ......................................................................................................................... 48 

Table A-35. Distribution of annual average NO2 ambient concentration (ppb) by monitor, Los 

Angeles CMSA set A, 1995-2006................................................................................ 49 

Table A-36. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Los Angeles 

CMSA set A, 1995-2006.............................................................................................. 49 

Table A-37. Distribution of annual average NO2 ambient concentration (ppb) by monitor, Los 

Angeles CMSA set B, 1995-2006................................................................................ 50 

Table A-38. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Los Angeles 

CMSA set B, 1995-2006.............................................................................................. 50 

Table A-39. Distribution of annual average NO2 ambient concentration (ppb) by monitor, Los 

Angeles CMSA set C, 1995-2006................................................................................ 51 

Table A-40. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Los Angeles 

CMSA set C, 1995-2006.............................................................................................. 51 

Table A-41. Distribution of annual average NO2 ambient concentrations (ppb) by year, Miami 

CMSA. ......................................................................................................................... 52 

Table A-42. Distribution of hourly NO2 ambient concentrations (ppb) by year, Miami CMSA. 52 

Table A-43. Distribution of annual average NO2 ambient concentration (ppb) by monitor, Miami 

CMSA, 1995-2006. ...................................................................................................... 53 

Table A-44. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Miami CMSA, 

1995-2006. ................................................................................................................... 53

Table A-45. Distribution of annual average NO2 ambient concentrations (ppb) by year, New 

York CMSA. ................................................................................................................ 54 

Table A-46. Distribution of hourly NO2 ambient concentrations (ppb) by year, New York 

CMSA. ......................................................................................................................... 54 

Table A-47. Distribution of annual average NO2 ambient concentration (ppb) by monitor, New 

York CMSA set A, 1995-2006. ................................................................................... 55 
 vi

Table A-48. Distribution of hourly NO2 ambient concentration (ppb) by monitor, New York 

CMSA set A, 1995-2006.............................................................................................. 55 

Table A-49. Distribution of annual average NO2 ambient concentration (ppb) by monitor, New 

York CMSA set B, 1995-2006..................................................................................... 56 

Table A-50. Distribution of hourly NO2 ambient concentration (ppb) by monitor, New York 

CMSA set B, 1995-2006.............................................................................................. 56 

Table A-51. Distribution of annual average NO2 ambient concentrations (ppb) by year, 

Philadelphia CMSA. .................................................................................................... 57 

Table A-52. Distribution of hourly NO2 ambient concentrations (ppb) by year, Philadelphia 

CMSA. ......................................................................................................................... 57 

Table A-53. Distribution of annual average NO2 ambient concentration (ppb) by monitor, 

Philadelphia CMSA, 1995-2006. ................................................................................. 58 

Table A-54. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Philadelphia 

CMSA, 1995-2006. ...................................................................................................... 58 

Table A-55. Distribution of annual average NO2 ambient concentrations (ppb) by year, 

Washington DC CMSA. .............................................................................................. 59 

Table A-56. Distribution of hourly NO2 ambient concentrations (ppb) by year, Washington DC 

CMSA. ......................................................................................................................... 59 

Table A-57. Distribution of annual average NO2 ambient concentration (ppb) by monitor, 

Washington DC CMSA set A, 1995-2006................................................................... 60 

Table A-58. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Washington 

DC CMSA set A, 1995-2006. ...................................................................................... 60 

Table A-59. Distribution of annual average NO2 ambient concentration (ppb) by monitor, 

Washington DC CMSA set B, 1995-2006. .................................................................. 61 

Table A-60. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Washington 

DC CMSA set B, 1995-2006. ...................................................................................... 61 

Table A-61. Distribution of annual average NO2 ambient concentrations (ppb) by year, Atlanta 

MSA. ............................................................................................................................ 62 

Table A-62. Distribution of hourly NO2 ambient concentrations (ppb) by year, Atlanta MSA.. 62 

Table A-63. Distribution of annual average NO2 ambient concentration (ppb) by monitor, 

Atlanta MSA, 1995-2006............................................................................................. 63 

Table A-64. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Atlanta MSA, 

1995-2006. ................................................................................................................... 63

Table A-65. Distribution of annual average NO2 ambient concentrations (ppb) by year, Colorado 

Springs MSA................................................................................................................ 64 

Table A-66. Distribution of hourly NO2 ambient concentrations (ppb) by year, Colorado Springs 

MSA. ............................................................................................................................ 64 

Table A-67. Distribution of annual average NO2 ambient concentration (ppb) by monitor, 

Colorado Springs MSA, 1995-2006............................................................................. 65 

Table A-68. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Colorado 

Springs MSA, 1995-2006. ........................................................................................... 65 

Table A-69. Distribution of annual average NO2 ambient concentrations (ppb) by year, El Paso 

MSA. ............................................................................................................................ 66 

Table A-70. Distribution of hourly NO2 ambient concentrations (ppb) by year, El Paso MSA. 66 

Table A-71. Distribution of annual average NO2 ambient concentration (ppb) by monitor, El 

Paso MSA, 1995-2006. ................................................................................................ 67 
 vii

Table A-72. Distribution of hourly NO2 ambient concentration (ppb) by monitor, El Paso MSA, 

1995-2006. ................................................................................................................... 67

Table A-73. Distribution of annual average NO2 ambient concentrations (ppb) by year, 

Jacksonville MSA. ....................................................................................................... 68 

Table A-74. Distribution of hourly NO2 ambient concentrations (ppb) by year, Jacksonville 

MSA. ............................................................................................................................ 68 

Table A-75. Distribution of annual average NO2 ambient concentration (ppb) by monitor, 

Jacksonville MSA, 1995-2006..................................................................................... 69 

Table A-76. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Jacksonville 

MSA, 1995-2006.......................................................................................................... 69 

Table A-77. Distribution of annual average NO2 ambient concentrations (ppb) by year, Las 

Vegas MSA. ................................................................................................................. 70 

Table A-78. Distribution of hourly NO2 ambient concentrations (ppb) by year, Las Vegas MSA.

...................................................................................................................................... 70 

Table A-79. Distribution of annual average NO2 ambient concentration (ppb) by monitor, Las 

Vegas MSA, 1995-2006............................................................................................... 71 

Table A-80. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Las Vegas 

MSA, 1995-2006.......................................................................................................... 71 

Table A-81. Distribution of annual average NO2 ambient concentrations (ppb) by year, Phoenix 

MSA. ............................................................................................................................ 72 

Table A-82. Distribution of hourly NO2 ambient concentrations (ppb) by year, Phoenix MSA. 72 

Table A-83. Distribution of annual average NO2 ambient concentration (ppb) by monitor, 

Phoenix MSA, 1995-2006............................................................................................ 73 

Table A-84. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Phoenix MSA, 

1995-2006. ................................................................................................................... 73

Table A-85. Distribution of annual average NO2 ambient concentrations (ppb) by year, Provo 

MSA. ............................................................................................................................ 74 

Table A-86. Distribution of hourly NO2 ambient concentrations (ppb) by year, Provo MSA.... 74 

Table A-87. Distribution of annual average NO2 ambient concentration (ppb) by monitor, Provo 

MSA, 1995-2006.......................................................................................................... 75 

Table A-88. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Provo MSA, 

1995-2006. ................................................................................................................... 75

Table A-89. Distribution of annual average NO2 ambient concentrations (ppb) by year, St. Louis 

MSA. ............................................................................................................................ 76 

Table A-90. Distribution of hourly NO2 ambient concentrations (ppb) by year, St. Louis MSA.

...................................................................................................................................... 76 

Table A-91. Distribution of annual average NO2 ambient concentration (ppb) by monitor, St. 

Louis MSA, 1995-2006................................................................................................ 77 

Table A-92. Distribution of hourly NO2 ambient concentration (ppb) by monitor, St. Louis 

MSA, 1995-2006.......................................................................................................... 77 

Table A-93. Distribution of annual average NO2 ambient concentrations (ppb) by year, Other 

MSA/CMSA................................................................................................................. 78 

Table A-94. Distribution of hourly NO2 ambient concentrations (ppb) by year, Other 

MSA/CMSA................................................................................................................. 78 

Table A-95. Distribution of annual average NO2 ambient concentrations (ppb) by year, Other 

Not MSA. ..................................................................................................................... 79
 viii

Table A-96. Distribution of hourly NO2 ambient concentrations (ppb) by year, Other Not MSA.

...................................................................................................................................... 79 

Table A-97. Goodness-of-fit statistics for eight generalized linear models. ............................... 82 

Table A-98. Parameters for Poisson exponential model stratified by location............................ 86 

Table A-99. Parameters for normal linear model stratified by location. ..................................... 87 

Table A-100. Predicted number of exceedances of of 1-hour NO2 concentrations of 150 ppb 

using a Poisson exponential model for the as-is and current-standard scenarios. ....... 91 

Table A-101. Predicted number of exceedances of of 1-hour NO2 concentrations of 150 ppb 

using a Normal linear model for the as-is and current-standard scenarios. ................. 92 

Table A-102. Comparison of predicted exceedances of 150 ppb using McCurdy (1994) for 

1988-1992 data and the Poisson exponential and normal linear models for 1995-2006 

data. .............................................................................................................................. 93 

Table A-103. Predicted number of exceedances of of 1-hour NO2 concentrations of 150 ppb 

using a Poisson exponential model and at several annual average concentrations...... 94 

Table A-104. Predicted number of exceedances of of 1-hour NO2 concentrations of 150 ppb 

using a Normal linear model and at several annual average concentrations................ 97 

Table A-105. Maximum annual average NO2 concentrations and air quality adjustment factors 

(F) to just meet the current standard, historical monitoring data............................... 104 

Table A-106. Maximum annual average NO2 concentrations and air quality adjustment factors 

(F) to just meet the current standard, recent monitoring data.................................... 105 

Table A-107. Air quality adjustment factors (F) to just meet the alternative 1-hour standards, 

using recent monitoring data...................................................................................... 106 

Table A-108. Studies reviewed containing NO2 concentrations at a distance from roadways.. 111 

Table A-109. Example data used to estimate on-road adjustment factor (m) obtained from 

Tables 1 and 4 reported in Singer et. al (2004).......................................................... 112 

Table A-110. Estimated on-road adjustment factors (Cv/Cb or m) for two season groups and 

potential influential factors. ....................................................................................... 113 

Table A-111. Estimated annual average NO2 concentrations for monitors ≥100 m from a major 

road using 2001-2003 air quality as is and air quality adjusted to just meet the current 

and alternative standards............................................................................................ 117 

Table A-112. Estimated annual average NO2 concentrations for monitors >20 m and <100 m 

from a major road using 2001-2003 air quality as is and air quality adjusted to just 

meet the current and alternative standards................................................................. 124 

Table A-113. Estimated annual average NO2 concentrations for monitors ≤20 m from a major 

road using 2001-2003 air quality as is and air quality adjusted to just meet the current 

and alternative standards............................................................................................ 128 

Table A-114. Estimated annual average NO2 concentrations on-roads using 2001-2003 air 

quality as is, air quality adjusted to just meet the current and alternative standards, and 

an on-road adjustment factor...................................................................................... 132 

Table A-115. Estimated number of exceedances of 1-hour concentration levels (100, 150, and 

200 ppb) for monitors ≥100 m from a major road using 2001-2003 air quality as is and 

air quality adjusted to just meet the current and alternative standards. ..................... 139 

Table A-116. Estimated number of exceedances of 1-hour concentration levels (250 and 300 

ppb) for monitors ≥100 m from a major road using 2001-2003 air quality as is and air 

quality adjusted to just meet the current and alternative standards............................ 146 
 ix

Table A-117. Estimated number of exceedances of 1-hour concentration levels (100, 150 and 

200 ppb) for monitors >20 m and <100 m from a major road using 2001-2003 air 

quality as is and air quality adjusted to just meet the current and alternative standards.

.................................................................................................................................... 151 

Table A-118. Estimated number of exceedances of 1-hour concentration levels (250 and 300 

ppb) for monitors >20 m and <100 m from a major road using 2001-2003 air quality 

as is and air quality adjusted to just meet the current and alternative standards. ...... 155 

Table A-119. Estimated number of exceedances of 1-hour concentration levels (100, 150 and 

200 ppb) for monitors ≤ 20 m from a major road using 2001-2003 air quality as is and 

air quality adjusted to just meet the current and alternative standards. ..................... 158 

Table A-120. Estimated number of exceedances of 1-hour concentration levels (250 and 300 

ppb) for monitors ≤ 20 m from a major road using 2001-2003 air quality as is and air 

quality adjusted to just meet the current and alternative standards............................ 162 

Table A-121. Estimated number of exceedances of 1-hour concentration levels (100, 150, and 

200 ppb) on-roads using 2001-2003 air quality as is and air quality adjusted to just 

meet the current and alternative standards and an on-road adjustment factor. .......... 165 

Table A-122. Estimated number of exceedances of 1-hour concentration levels (250, and 300 

ppb) on-roads using 2001-2003 air quality as is and air quality adjusted to just meet 

the current and alternative standards and an on-road adjustment factor.................... 178 

Table A-123. Estimated annual average NO2 concentrations for monitors ≥100 m from a major 

road using 2004-2006 air quality as is and adjusted to just meet the current and 

alternative standards................................................................................................... 183 

Table A-124. Estimated annual average NO2 concentrations for monitors >20 m and <100 m 

from a major road using 2004-2006 air quality as is and air quality adjusted to just 

meet the current and alternative standards................................................................. 189 

Table A-125. Estimated annual average NO2 concentrations for monitors ≤ 20 m from a major 

road using 2004-2006 air quality as is and air quality adjusted to just meet the current 

and alternative standards............................................................................................ 193 

Table A-126. Estimated annual average NO2 concentrations on-roads using 2004-2006 air 

quality as is, air quality adjusted to just meet the current and alternative standards, and 

an on-road adjustment factor...................................................................................... 197 

Table A-127. Estimated number of exceedances of 1-hour concentration levels (100, 150, and 

200 ppb) for monitors ≥100 m from a major road following adjustment to just meeting 

the current and alternative standards, 2004-2006 air quality. .................................... 203 

Table a-128. Estimated number of exceedances of 1-hour concentration levels (250 and 300 

ppb) for monitors ≥100 m from a major road following adjustment to just meeting the 

current and alternative standards, 2004-2006 air quality........................................... 211 

Table A-129. Estimated number of exceedances of 1-hour concentration levels (100, 150, and 

200 ppb) for monitors > 20 m and < 100 m from a major road following adjustment to 

just meeting the current and alternative standards, 2004-2006 air quality. ............... 218 

Table A-130. Estimated number of exceedances of 1-hour concentration levels (250 and 300 

ppb) for monitors > 20 m and < 100 m from a major road following adjustment to just 

meeting the current and alternative standards, 2004-2006 air quality. ...................... 224 

Table a-131. Estimated number of exceedances of 1-hour concentration levels (100, 150, and 

200 ppb) for monitors ≤ 20 m from a major road following adjustment to just meeting 

the current and alternative standards, 2004-2006 air quality. .................................... 228 
 x

Table A-132. Estimated number of exceedances of 1-hour concentration levels (250 and 300 

ppb) for monitors ≤ 20 m from a major road following adjustment to just meeting the 

current and alternative standards, 2004-2006 air quality........................................... 234 

Table A-133. Estimated number of exceedances of 1-hour concentration levels (100, 150, and 

200 ppb) on-roads following adjustment to just meeting the current and alternative 

standards, 2004-2006 air quality and an on-road road adjustment factor.................. 238 

Table A-134. Estimated number of exceedances of 1-hour concentration levels (250 and 300 

ppb) on-roads following adjustment to just meeting the current and alternative 

standards, 2004-2006 air quality and an on-road road adjustment factor.................. 244 

Table A-135. Monitoring site-years and annual average NO2 concentrations for two monitoring 

periods, historical and recent air quality data (as is) using monitors sited ≥100 m of a 

major road. ................................................................................................................. 250

Table A-136. Monitoring site-years and annual average NO2 concentrations for two monitoring 

periods, historical and recent air quality data (as is) using monitors sited <100 m of a 

major road. ................................................................................................................. 251

Table A-137. Total number of exceedances of short-term (1-hour) potential health effect 

benchmark levels in a year, 1995-2000 historical NO2 air quality (as is) using monitors 

sited ≥100 m of a major road. .................................................................................... 253 

Table A-138. Total number of exceedances of short-term (1-hour) potential health effect 

benchmark levels in a year, 2001-2006 recent NO2 air quality (as is) using monitors 

sited ≥100 m of a major road. .................................................................................... 254 

Table A-139. Total number of exceedances of short-term (1-hour) potential health effect 

benchmark levels in a year, 1995-2000 historical NO2 air quality (as is) using monitors 

sited <100 m of a major road. .................................................................................... 255 

Table A-140. Total number of exceedances of short-term (1-hour) potential health effect 

benchmark levels in a year, 2001-2006 recent NO2 air quality (as is) using monitors 

sited <100 m of a major road. .................................................................................... 256 

Table A-141. Estimated annual average on-road NO2 concentrations for two monitoring periods, 

historical and recent air quality data (as is)................................................................ 258 

Table A-142. Estimated total number of exceedances of short-term (1-hour) potential health 

effect benchmark levels in a year on-roads, 1995-2000 historical NO2 air quality (as 

is)................................................................................................................................ 260 

Table A-143. Estimated total number of exceedances of short-term (1-hour) potential health 

effect benchmark levels in a year on-roads, 2001-2006 recent NO2 air quality (as is).

.................................................................................................................................... 261 

Table A-144. Total number of exceedances of short-term (1-hour) potential health effect 

benchmark levels in a year, 1995-2000 historical NO2 air quality adjusted to just 

meeting the current annual average standard (0.053 ppm) using monitors sited ≥100 m 

of a major road. .......................................................................................................... 263 

Table 145. Total estimated number of exceedances of short-term (1-hour) potential health effect 

benchmark levels in a year, 1995-2000 historical NO2 air quality adjusted to just 

meeting the current annual average standard (0.053 ppm) using monitors sited <100 m 

of a major road. .......................................................................................................... 264 

Table A-146. Total estimated number of exceedances of short-term (1-hour) potential health 

effect benchmark levels in a year on-roads, 1995-2000 historical NO2 air quality 

adjusted to just meeting the current annual average standard (0.053 ppm)............... 265 
 xi

List of Figures 

Figure A-1. Distributions of annual mean NO2 ambient monitoring concentrations for selected 

CMSA locations, years 1995-2006. ............................................................................. 27 

Figure A-2. Distributions of annual mean NO2 ambient monitoring concentrations for selected 

MSA and grouped locations, years 1995-2006............................................................ 27 

Figure A-3. Distributions of hourly NO2 ambient monitoring concentrations for selected CMSA 

locations, years 1995-2006. ......................................................................................... 28 

Figure A-4. Distributions of hourly NO2 ambient concentration for selected CMSA locations, 

years 1995-2006........................................................................................................... 29 

Figure A-5. Distributions of annual average NO2 concentrations among 10 monitoring sites in 

Philadelphia CMSA, years 1995-2006......................................................................... 30 

Figure A-6. Distributions of annual mean NO2 concentrations for all monitors, years 1995-2006.

...................................................................................................................................... 32 

Figure A-7. Distributions of annual mean NO2 concentrations for the Philadelphia CMSA, years 

1995-2006. ................................................................................................................... 33

Figure A-8. Distributions of hourly NO2 concentrations in the Los Angeles CMSA, years 1995-

2006.............................................................................................................................. 34 

Figure A-9. Distributions of hourly NO2 concentrations in the Jacksonville MSA, years 1995-

2006, one monitor. ....................................................................................................... 35 

Figure A-10. Distributions of annual average NO2 concentrations in the Other Not MSA group 

location, years 1995-2006. ........................................................................................... 36 

Figure A-11. Distribution of annual average NO2 ambient concentrations (ppb) by year, Boston 

CMSA. ......................................................................................................................... 37 

Figure A-12. Distribution of hourly NO2 ambient concentrations (ppb) by year, Boston CMSA.

...................................................................................................................................... 37 

Figure A-13. Distribution of annual average NO2 ambient concentration (ppb) by monitor, 

Boston CMSA set A, 1995-2006. ................................................................................ 38 

Figure A-14. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Boston 

CMSA set A, 1995-2006.............................................................................................. 38 

Figure A-15. Distribution of annual average NO2 ambient concentration (ppb) by monitor, 

Boston CMSA set B, 1995-2006.................................................................................. 39 

Figure A-16. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Boston 

CMSA set B, 1995-2006.............................................................................................. 39 

Figure A-17. Distribution of annual average NO2 ambient concentrations (ppb) by year, Chicago 

CMSA. ......................................................................................................................... 40 

Figure A-18. Distribution of hourly NO2 ambient concentrations (ppb) by year, Chicago CMSA.

...................................................................................................................................... 40 

Figure A-19. Distribution of annual average NO2 ambient concentration (ppb) by monitor, 

Chicago CMSA, 1995-2006......................................................................................... 41 

Figure A-20. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Chicago 

CMSA, 1995-2006. ...................................................................................................... 41 

Figure A-21. Distribution of annual average NO2 ambient concentrations (ppb) by year, 

Cleveland CMSA. ........................................................................................................ 42 

Figure A-22. Temporal distribution of hourly NO2 ambient concentrations (ppb) by year, 

Cleveland CMSA. ........................................................................................................ 42 
 xii

Figure A-23. Distribution of annual average NO2 ambient concentration (ppb) by monitor, 

Cleveland CMSA, 1995-2006...................................................................................... 43 

Figure A-24. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Cleveland 

CMSA, 1995-2006. ...................................................................................................... 43 

Figure A-25. Distribution of annual average NO2 ambient concentrations (ppb) by year, Denver 

CMSA. ......................................................................................................................... 44 

Figure A-26. Distribution of hourly NO2 ambient concentrations (ppb) by year, Denver CMSA.

...................................................................................................................................... 44 

Figure A-27. Distribution of annual average NO2 ambient concentration (ppb) by monitor, 

Denver CMSA, 1995-2006. ......................................................................................... 45 

Figure A-28. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Denver 

CMSA, 1995-2006. ...................................................................................................... 45 

Figure A-29. Distribution of annual average NO2 ambient concentrations (ppb) by year, Detroit 

CMSA. ......................................................................................................................... 46 

Figure A-30. Distribution of hourly NO2 ambient concentrations (ppb) by year, Detroit CMSA.

...................................................................................................................................... 46 

Figure A-31. Distribution of annual average NO2 ambient concentration (ppb) by monitor, 

Detroit CMSA, 1995-2006........................................................................................... 47 

Figure A-32. Distribution of annual average NO2 ambient concentration (ppb) by monitor, 

Detroit CMSA, 1995-2006........................................................................................... 47 

Figure A-33. Distribution of annual average NO2 ambient concentrations (ppb) by year, Los 

Angeles CMSA. ........................................................................................................... 48 

Figure A-34. Distribution of hourly NO2 ambient concentrations (ppb) by year, Los Angeles 

CMSA. ......................................................................................................................... 48 

Figure A-35. Distribution of annual average NO2 ambient concentration (ppb) by monitor, Los 

Angeles CMSA set A, 1995-2006................................................................................ 49 

Figure A-36. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Los Angeles 

CMSA set A, 1995-2006.............................................................................................. 49 

Figure A-37. Distribution of annual average NO2 ambient concentration (ppb) by monitor, Los 

Angeles CMSA set B 1995-2006................................................................................. 50 

Figure A-38. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Los Angeles 

CMSA set B, 1995-2006.............................................................................................. 50 

Figure A-39. Distribution of annual average NO2 ambient concentration (ppb) by monitor, Los 

Angeles CMSA set C 1995-2006................................................................................. 51 

Figure A-40. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Los Angeles 

CMSA set C 1995-2006............................................................................................... 51 

Figure A-41. Distribution of annual average NO2 ambient concentrations (ppb) by year, Miami 

CMSA. ......................................................................................................................... 52 

Figure A-42. Distribution of hourly NO2 ambient concentrations (ppb) by year, Miami CMSA.

...................................................................................................................................... 52 

Figure A-43. Distribution of annual average NO2 ambient concentration (ppb) by monitor, 

Miami CMSA, 1995-2006. .......................................................................................... 53 

Figure A-44. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Miami 

CMSA, 1995-2006. ...................................................................................................... 53 

Figure A-45. Distribution of annual average NO2 ambient concentrations (ppb) by year, New 

York CMSA. ................................................................................................................ 54 
 xiii

Figure A-46. Distribution of hourly NO2 ambient concentrations (ppb) by year, New York 

CMSA. ......................................................................................................................... 54 

Figure A-47. Distribution of annual average NO2 ambient concentration (ppb) by monitor, New 

York CMSA set a, 1995-2006...................................................................................... 55 

Figure A-48. Distribution of hourly NO2 ambient concentration (ppb) by monitor, New York 

CMSA set a, 1995-2006............................................................................................... 55 

Figure A-49. Distribution of annual average NO2 ambient concentration (ppb) by monitor, New 

York CMSA set b, 1995-2006. .................................................................................... 56 

Figure A-50. Distribution of hourly NO2 ambient concentration (ppb) by monitor, New York 

CMSA set b, 1995-2006............................................................................................... 56 

Figure A-51. Distribution of annual average NO2 ambient concentrations (ppb) by year, 

Philadelphia CMSA. .................................................................................................... 57 

Figure A-52. Distribution of hourly NO2 ambient concentrations (ppb) by year, Philadelphia 

CMSA. ......................................................................................................................... 57 

Figure A-53. Distribution of annual average NO2 ambient concentration (ppb) by monitor, 

Philadelphia CMSA, 1995-2006. ................................................................................. 58 

Figure A-54. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Philadelphia 

CMSA, 1995-2006. ...................................................................................................... 58 

Figure A-55. Distribution of annual average NO2 ambient concentrations (ppb) by year, 

Washington DC CMSA. .............................................................................................. 59 

Figure A-56. Distribution of hourly NO2 ambient concentrations (ppb) by year, Washington DC 

CMSA. ......................................................................................................................... 59 

Figure A-57. Distribution of annual average NO2 ambient concentration (ppb) by monitor, 

Washington DC CMSA set A, 1995-2006................................................................... 60 

Figure A-58. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Washington 

DC CMSA set A, 1995-2006. ...................................................................................... 60 

Figure A-59. Distribution of annual average NO2 ambient concentration (ppb) by monitor, 

Washington DC CMSA set B, 1995-2006. .................................................................. 61 

Figure A-60. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Washington 

DC CMSA set B, 1995-2006. ...................................................................................... 61 

Figure A-61. Distribution of annual average NO2 ambient concentrations (ppb) by year, Atlanta 

MSA. ............................................................................................................................ 62 

Figure A-62. Distribution of hourly NO2 ambient concentrations (ppb) by year, Atlanta MSA. 62 

Figure A-63. Distribution of annual average NO2 ambient concentration (ppb) by monitor, 

Atlanta MSA, 1995-2006............................................................................................. 63 

Figure A-64. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Atlanta MSA, 

1995-2006. ................................................................................................................... 63

Figure A-65. Distribution of annual average NO2 ambient concentrations (ppb) by year, 

Colorado Springs MSA................................................................................................ 64 

Figure A-66. Distribution of hourly NO2 ambient concentrations (ppb) by year, Colorado 

Springs MSA................................................................................................................ 64 

Figure A-67. Distribution of annual average NO2 ambient concentration (ppb) by monitor, 

Colorado Springs MSA, 1995-2006............................................................................. 65 

Figure A-68. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Colorado 

Springs MSA, 1995-2006. ........................................................................................... 65 
 xiv

Figure A-69. Distribution of annual average NO2 ambient concentrations (ppb) by year, El Paso 

MSA. ............................................................................................................................ 66 

Figure A-70. Distribution of hourly NO2 ambient concentrations (ppb) by year, El Paso MSA.66 

Figure A-71. Distribution of annual average NO2 ambient concentration (ppb) by monitor, El 

Paso MSA, 1995-2006. ................................................................................................ 67 

Figure A-72. Distribution of hourly NO2 ambient concentration (ppb) by monitor, El Paso MSA, 

1995-2006. ................................................................................................................... 67

Figure A-73. Distribution of annual average NO2 ambient concentrations (ppb) by year, 

Jacksonville MSA. ....................................................................................................... 68 

Figure A-74. Distribution of hourly NO2 ambient concentrations (ppb) by year, Jacksonville 

MSA. ............................................................................................................................ 68 

Figure A-75. Distribution of annual average NO2 ambient concentration (ppb) by monitor, 

Jacksonville MSA, 1995-2006..................................................................................... 69 

Figure A-76. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Jacksonville 

MSA, 1995-2006.......................................................................................................... 69 

Figure A-77. Distribution of annual average NO2 ambient concentrations (ppb) by year, Las 

Vegas MSA. ................................................................................................................. 70 

Figure A-78. Distribution of hourly NO2 ambient concentrations (ppb) by year, Las Vegas 

MSA. ............................................................................................................................ 70 

Figure A-79. Distribution of annual average NO2 ambient concentration (ppb) by monitor, Las 

Vegas MSA, 1995-2006............................................................................................... 71 

Figure A-80. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Las Vegas 

MSA, 1995-2006.......................................................................................................... 71 

Figure A-81. Distribution of annual average NO2 ambient concentrations (ppb) by year, Phoenix 

MSA. ............................................................................................................................ 72 

Figure A-82. Distribution of hourly NO2 ambient concentrations (ppb) by year, Phoenix MSA.

...................................................................................................................................... 72 

Figure A-83. Distribution of annual average NO2 ambient concentration (ppb) by monitor, 

Phoenix MSA, 1995-2006............................................................................................ 73 

Figure A-84. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Phoenix 

MSA, 1995-2006.......................................................................................................... 73 

Figure A-85. Distribution of annual average NO2 ambient concentrations (ppb) by year, Provo 

MSA. ............................................................................................................................ 74 

Figure A-86. Temporal distribution of hourly NO2 ambient concentrations (ppb) by year, Provo 

MSA. ............................................................................................................................ 74 

Figure A-87. Distribution of annual average NO2 ambient concentration (ppb) by monitor, 

Provo MSA, 1995-2006. .............................................................................................. 75 

Figure A-88. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Provo MSA, 

1995-2006. ................................................................................................................... 75

Figure A-89. Distribution of annual average NO2 ambient concentrations (ppb) by year, St. 

Louis MSA................................................................................................................... 76 

Figure A-90. Temporal distribution of hourly NO2 ambient concentrations (ppb) by year, St. 

Louis MSA................................................................................................................... 76 

Figure A-91. Distribution of annual average NO2 ambient concentration (ppb) by monitor, St. 

Louis MSA, 1995-2006................................................................................................ 77 
 xv

Figure A-92. Distribution of hourly NO2 ambient concentration (ppb) by monitor, St. Louis 

MSA, 1995-2006.......................................................................................................... 77 

Figure A-93. Distribution of annual average NO2 ambient concentrations (ppb) by year, Other 

MSA/CMSA................................................................................................................. 78 

Figure A-94. Distribution of hourly NO2 ambient concentrations (ppb) by year, Other 

MSA/CMSA................................................................................................................. 78 

Figure A-95. Distribution of annual average NO2 ambient concentrations (ppb) by year, Other 

Not MSA. ..................................................................................................................... 79

Figure A-96. Distribution of hourly NO2 ambient concentrations (ppb) by year, Other Not MSA.

...................................................................................................................................... 79 

Figure A-97. Measured number of exceedances of 1-hour NO2 concentrations of 150 ppb versus 

annual mean NO2 concentrations (ppb) for CMSA locations...................................... 89 

Figure A-98. Predicted and observed exceedances of 1-hour NO2 concentrations of 150 ppb for 

CMSA locations using Poisson exponential model. .................................................... 89 

Figure A-99. Predicted and observed exceedances of 1-hour NO2 concentrations of 150 ppb for 

CMSA locations using normal linear model................................................................ 90 

Figure A-100. Trends in hourly and annual average NO2 ambient monitoring concentrations and 

their associated coefficients of variation (COV) for all monitors, years 1995-2006. 101 

Figure A-101. Distribution of estimated Cv/Cb ratios or m for two season groups. .................. 115 
 A-1

A-1 Overview 

This appendix contains supplemental descriptions of the data and methods used in the NO2

air quality characterization in support of the Risk and Exposure Assessment (REA) conducted 

for the NO2 NAAQS review. First, ambient monitoring data form years 1995 through 2006 have 

been characterized based on siting characteristics, proximity to stationary source emissions, and 

distance to roadways. Then, ambient NO2 concentration trends were evaluated considering the 

year of monitoring and distribution of monitors within a location. 

The primary output of the air quality characterization was the numbers of exceedances of 

potential health effect benchmark levels identified in the Integrated Science Assessment. The 

ambient NO2 concentrations were evaluated for the numbers of exceedances of the selected 

benchmarks in several locations and considering four scenarios. The first scenario considered as 

is air quality as obtained from EPA’s Air Quality System (US EPA, 2007a; 2007b). A second 

scenario used a portion of the as is air quality to estimate on-road NO2 concentrations. A third 

and fourth scenario followed in a similar manner, only these used air quality adjusted to just 

meeting the current and potential alternative standards. Each of these scenarios, in addition to 

the reasoning for the methods and data used, are described in detail in the sections that follow. 
 A-2

A-2 Air Quality Data Screen 

A-2.1 Introduction 

The current NO2 standard of 53 ppb annual arithmetic average was set in 1971 and has been 

retained since by subsequent reviews (i.e., 1985, 1995). Minor revisions to the standard made in 

1985 included an explicit rounding convention, stated annual averages would be determined on a 

calendar year basis, and indicated an explicit 75% completeness requirement for monitoring (60 

FR 52874). Each of these components of the standard were considered in characterizing the air 

quality monitoring data, beginning first with the selection of valid data. 

A-2.2 Approach 

NO2 air quality data from years 1995 through 2006 and associated documentation were 

downloaded from EPA’s Air Quality System (US EPA, 2007a; 2007b). As of the date of the 

first analyses were performed, hourly measurements for year 2006 were only available for 

January 1 through October 31, 2006. A site was defined by the state, county, site code, and 

parameter occurrence code (POC), which gives a 10-digit monitor ID code. The POC identifies 

collocated measurements at the same monitoring location, so that each measuring instrument is 

treated as a different site. Typically there was only one POC at a given monitoring location. 

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 is 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, a minimum of 4,932 or 4,950 hours). This 

served as a screening criterion for data to be used for analysis. 

Site-years of data are the total numbers of years the collective monitors in a location were in 

operation. For example, from years 1995-2006, the Boston CMSA had 27 total monitors in 

operation, some of which did not contain sufficient numbers of monitoring values, while others 

contained upwards of 11 years (Table A-1). Thus in summing the number of operating years, 

this particular location contained a total of 105 site-years of data across the monitoring period. 

In all of the subsequent analyses, where hourly values were missing they were treated as such. 

Reported values of zero (0) concentration were also retained as is. For certain illustrations, 

values of zero were substituted with 0.5 ppb, derived from one-half the lowest recorded 1-hour 

concentration (1 ppb). 

A-2.3 Results 

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 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 
 A-3

A-2 provides a summary of the site-years included in the analysis, relative to those excluded, by 

location and by two site-year groupings.1

 Location selection is defined in the Section A-1.2. 

 

Table A-1. Example of ambient monitor years of operation, using the Boston CMSA.

Year of monitoring (1995-2006) Totals 

Monitor ID 95 96 97 98 99 00 01 02 03 04 05 06 Complete Incomplete

2303130021 i c c c i c c c c i i 7 4 

2500510021 i 0 1 

2500510051 i c c i i i 2 4 

2500900051 i 0 1 

2500920061 c c c c i i c c c c c c 10 2 

2500940041 c c c c i i c i i i i i 5 7 

2500950051 i c c 2 1 

2502100091 c 1 0 

2502130031 i i i i i 0 5 

2502500021 c c c c c c c c i c c c 11 1 

2502500211 c c c c c c c c 8 0 

2502500351 c 1 0 

2502500361 c 1 0 

2502500401 c c c c c c c c c c c i 11 1 

2502500411 i i c i i i i i 1 7 

2502500421 i c c c c c c 6 1 

2502510031 c c c c c 5 0 

2502700201 c c c c c c c c i 8 1 

2502700231 c c c 3 0 

3301100161 c c c c i 4 1 

3301100191 i c i 1 2 

3301100201 i c c c c c 5 1 

3301110111 i i i 0 3 

3301500091 c c c c c i i 5 2 

3301500131 i c c c c i 4 2 

3301500141 i c c c 3 1 

3301500151 i c i 1 2 

Complete 12 10 11 11 7 7 10 10 5 7 8 7 105 

Incomplete 1 1 0 1 7 6 5 5 8 6 5 5 50

Notes: 

c = met criteria for valid year of monitoring data. 

i = did not met criteria for valid year of monitoring data.

 

1

 14 of 18 named locations and the 2 grouped locations contained enough data to be considered valid for year 2006. 
 A-4

Table A-2. Counts of complete site-years of NO2 monitoring data.

Number of 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 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:

ND no available monitoring data
 A-5

A-3 Selection of Locations 

A-3.1 Introduction 

The next step in this analysis was to identify similarities and differences in air quality among 

locations for the purpose of either aggregating or segregating data using a combination of 

descriptive statistics and health based criteria. Location in this context would include a 

geographic area that encompasses more than a single air quality monitor (e.g., particular city, 

consolidated metropolitan statistical area or CMSA). 

A-3.2 Approach 

Criteria were established for selecting sites with high annual means and/or frequent 

exceedances of potential health effect benchmarks. Selected locations were those that 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. 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 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. 

A-3.3 Results 

Fifteen locations met both selection criteria, that is, having at least one site-year annual mean 

above 25.7 ppb and at least one exceedance of 200 ppb. Upon further analysis of the more recent 

ambient data (2001-2006), four additional locations were observed to have met at least one of the 

criteria (either high annual mean and/or at least one exceedance of 200 ppb). New Haven, CT, 

while meeting the earlier criteria, did not have any recent exceedances of 200 ppb and contained 

one of the lowest maximum concentration-to-mean ratios, therefore was not separated out as a 

specific location. Thus, 14 locations were retained from the initial selection and 4 locations 

selected from a second screening to provide additional geographical representation. 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 Not MSA location contains all the sites that are 

not in an MSA or CMSA. The selected locations are summarized in Table A-3. 

The final database for analysis included air quality data from a total of 204 monitors within 

the named locations, 332 monitors in the Other CMSA group, and 92 monitors in the Not MSA 

group. Again, the monitors that were retained contained the criteria for estimating a valid annual 

average concentration described above.
 A-6

Table A-3. Locations selected for NO2 Air Quality Characterization, associated abbreviations, and values of selection crite

Location 

Type1 Code Description Abbreviation 

Maximum # 

Exceedance

of 200 ppb 

MSA 0520 Atlanta, GA Atlanta* 1 

CMSA 1122 Boston-Worcester-Lawrence, MA-NH-ME-CT Boston* 1 CMSA 1602 Chicago-Gary-Kenosha, IL-IN-WI Chicago 0 CMSA 1692 Cleveland-Akron, OH Cleveland* 1 

MSA 1720 Colorado Springs, CO 

Colorado Springs* 69 

CMSA 2082 Denver-Boulder-Greeley, CO Denver* 2 CMSA 2162 Detroit-Ann Arbor-Flint, MI Detroit* 12 

MSA 2320 El Paso, TX El Paso* 2 MSA 3600 Jacksonville, FL Jacksonville 2 MSA 4120 Las Vegas, NV-AZ Las Vegas* 11 

CMSA 4472 Los Angeles-Riverside-Orange County, CA Los Angeles* 5 CMSA 4992 Miami-Fort Lauderdale, FL Miami 3 CMSA 5602 New York-Northern New Jersey-Long Island, NY-NJ-CT-PA New York* 3 CMSA 6162 Philadelphia-Wilmington-Atlantic City, PA-NJ-DE-MD Philadelphia* 3 

MSA 6200 Phoenix-Mesa, AZ Phoenix* 37 MSA 6520 Provo-Orem, UT Provo 0 MSA 7040 St, Louis, MO-IL St. Louis* 8 

CMSA 8872 Washington-Baltimore, DC-MD-VA-WV Washington DC* 2 

MSA/CMSA - Other MSA/CMSA Other MSA 10 

- - Other Not MSA Other Not MSA 2 

1 CMSA is consolidated metropolitan statistical area; MSA is metropolitan statistical area according to the 1999 Office of Management and Budget definiti

revision). * Indicates locations that satisfied both the annual average and exceedance criteria.
A-7

A-4 Ambient Monitoring Site Characteristics 

A-4.1 Introduction 

Siting of monitors is of particular importance, recognizing that proximity of local sources 

could influence on measured NO2 concentrations. As part of the risk and exposure scope and 

methods document (US EPA, 2007c), both mobile and stationary sources (in particular power 

generating utilities using fossil fuels) were indicated as significant contributors to nitrogen oxides 

(NOx) emissions in the U.S. Analyses were performed to determine the distance of all locationspecific monitors to these source categories. In addition, emissions of NOx from stationary 

sources within close proximity of the location-specific monitoring sites were estimated. 

A-4.2 Approach 

Major road distances to each monitor were first determined using a Tele-Atlas roads database 

in a GIS application. For road-monitor pairs that showed particularly close distances, the values 

were refined using GoogleEarth® to estimate the distance to road edge. Distances of monitoring 

sites to stationary sources and those source’s emissions were estimated using data within the 2002 

National Emissions Inventory (NEI; US EPA, 2007d). The NEI database reports emissions of 

NOx in tons per year (tpy) for 131,657 unique emission sources at various points of release. The 

release locations were all taken from the latitude longitude values within the NEI. First, all NOx

emissions were summed for identical latitude and longitude entries while retaining source codes 

for the emissions (e.g., Standard Industrial Code (SIC), or North American Industrial 

Classification System (NAICS)). Therefore, any facility containing similar emission processes 

were summed at the stack location, resulting in 40,855 observations. These data were then 

screened for sources with emissions greater than 5 tpy, yielding 18,798 unique NOx emission 

sources. Locations of these stationary source emissions were compared with ambient monitoring 

locations using the following formula: 

d =arccos(sin( ) lat × sin(lat ) + cos(lat )×cos(lat )×cos(lon −lon ))×r 1 2 1 2 2 1

where 

d = distance (kilometers) 

lat1 = latitude of a monitor (radians) 

lat2 = latitude of source emission (radians) 

lon1 = longitude of monitor (radians) 

lon2 = longitude of source emission (radians) 

r = approximate radius of the earth (or 6,371 km) 

Location data for monitors and sources provided in the AQS and NEI data bases were given in 

units of degrees therefore, these were first converted to radians by dividing by 180/π. For each 

monitor, source emissions with estimated distances within 10 km were retained.

A-4.3 Summary Results 

Summary statistics for the monitoring site characteristics are presented in Tables A-4 through 

A-6 for the selected locations. Detailed results for the distance to major roadways, the distance 
A-8

and emissions from stationary sources for each ambient monitor are provided in section A-4.4, 

Tables A-7 and A-8. 

The distribution of the nearest distance of the ambient monitors to major roads for each of the 

named locations is summarized in Table A-4. On average, most 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 CMSA, there may be one or two monitors sited within 

close proximity (<20 meters) of a road. Since there is potential for roadway emissions to affect 

concentrations at monitors sited close to major roads, the ambient monitors were further 

categorized based on the monitor distance from major roads. Three proximity bins were 

identified, the first containing those monitors sited at or within 20 meters, (≤20 m), those between 

20 m and 100 m, and those located at least 100 meters from a major road (≥100 m).2

 

Table A-4. Distribution of the distance of ambient monitors to the nearest major road in selected locations. 

Distance (m) of monitor to nearest major road 

Location n mean std min 2.5 50 97.5 max 

Atlanta 4 488 283 134 134 505 809 809 

Boston 21 101 93 7 7 70 337 337 

Chicago 12 158 212 2 2 93 738 738 

Cleveland 4 114 90 2 2 134 187 187 

Colorado Springs 6 196 103 79 79 180 386 386 

Denver 7 166 260 18 18 65 748 748 

Detroit 3 382 39 339 339 393 415 415 

El Paso 7 282 266 33 33 128 718 718 

Jacksonville 1 144 

Las Vegas 10 244 286 1 1 181 914 914 

Los Angeles 43 155 150 1 2 89 522 570 

Miami 4 57 45 15 15 55 103 103 

New York 26 145 130 6 6 119 508 508 

Philadelphia 10 247 199 45 45 167 630 630 

Phoenix 7 190 177 7 7 141 433 433 

Provo 1 353 

St Louis 13 126 123 5 5 97 421 421 

Washington DC 16 129 104 14 14 83 338 338 

1

 n is the number of monitors operating in a particular location between 1995 and 2006. The min, 2.5, med, 97.5, and max 

represent the minimum, 2.5th, median, 97.5th, and maximum percentiles of the distribution for the distance in meters (m) to the 

nearest major road. Monitors >1km from road are not included. 

Table A-5 contains a summary of the distance of stationary source emissions to monitors 

within each named location. There were a number of sources emitting >5 tpy of NOx and located 

within a 10 km radius for many of the monitors. On average though, most monitors are placed at 

greater distances from stationary source emissions than roads with most sources at a distance of 

greater than 5 km. Most of the stationary source emissions of NOx within a 10 km radius of 

monitors were less than 50 tpy (Table A-6). Details regarding individual monitors are provided in 

Table A-8. 

 

2

 As part of our initial analysis, 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 for 

analysis. 
A-9

Table A-5. Distribution of the distance of ambient monitors to stationary sources with NOx emissions >5 

tons per year and within a 10 kilometers radius. 

Distance of monitor to NOx emission source (m)2

Location n

1 mean std min 2.5 50 97.5 max 

Atlanta 9 6522 3164 656 656 7327 9847 9847 

Boston 595 5333 2603 142 761 5363 9733 9988 

Chicago 394 6586 2657 411 770 7277 9834 9994 

Cleveland 19 7092 2439 956 956 7278 9884 9884 

Colorado Springs 66 6109 2632 782 1034 6340 9847 9933 

Denver 140 5655 2593 910 1029 5904 9862 9979 

Detroit 87 6889 2254 321 1963 7549 9974 9997 

El Paso 126 5694 3185 119 1384 6085 9945 9991 

Jacksonville 20 5125 2962 708 708 5720 9558 9558 

Las Vegas 18 6700 2184 3837 3837 7237 9950 9950 

Los Angeles 523 6003 2435 140 1483 6165 9801 9991 

Miami 11 6184 3151 1323 1323 7611 9117 9117 

New York 736 6101 2555 103 1383 6467 9818 9983 

Philadelphia 382 5837 2474 231 1299 5689 9754 9982 

Phoenix 59 6298 2279 833 1312 6355 9803 9890 

Provo 7 6558 3664 1214 1214 8178 9433 9433 

St Louis 253 6799 2337 396 1989 7120 9863 9990 

Washington DC 160 6173 2425 288 704 6254 9777 9973 

1

 n is the number of sources emitting >5 tons per year (tpy) NOx within a 10 kilometer radius of a monitor in a particular location. 2

 The min, 2.5, med, 97.5, and max represent the minimum, 2.5th, median, 97.5th, and maximum percentiles of the distribution for 

the distance in meters (m) to the source emission. 

Table A-6. Distribution of NOx emissions from stationary sources within 10 kilometers of monitoring site, 

where emissions were >5 tons per year.

Emissions (tpy) of NOx from sources within 10 km of monitor 2

Location n

1 mean std min 2.5 50 97.5 max 

Atlanta 9 709 1621 22 22 35 4895 4895 

Boston 595 128 344 5 5 10 1155 3794 

Chicago 394 204 919 5 5 10 2204 8985 

Cleveland 19 702 612 126 126 284 1476 1476 

Colorado Springs 66 387 1091 5 5 19 4205 4205 

Denver 140 252 1286 5 5 15 5404 9483 

Detroit 87 251 637 5 6 24 2398 3762 

El Paso 126 117 286 5 5 31 912 1679 

Jacksonville 20 201 407 5 5 31 1642 1642 

Las Vegas 18 483 636 18 18 84 1665 1665 

Los Angeles 523 70 310 5 5 12 577 4256 

Miami 11 24 16 8 8 22 51 51 

New York 736 284 1024 5 6 31 3676 9022 

Philadelphia 382 154 408 5 5 29 1304 4968 

Phoenix 59 85 234 5 5 14 1049 1049 

Provo 7 60 38 7 7 83 102 102 

St Louis 253 167 1032 5 5 16 848 14231 

Washington DC 160 320 1254 6 6 34 6009 10756 

1

 n is the number of sources emitting >5 tons per year (tpy) of NOx within a 10 kilometer radius of a monitor in a particular location. 2

 The min, 2.5, med, 97.5, and max represent the minimum, 2.5th, median, 97.5th, and maximum percentiles of the distribution for 
A-10

the source emissions.

A-4.4 Detailed Monitoring Site Characteristics 

Detailed physical attributes of each monitor used within the named locations (i.e., 18 specific 

locations were defined; it does not include the broadly grouped locations of “Other CMSA” or Not 

MSA). Each of these monitors met the criteria for containing a valid number of reported 

concentrations and were used throughout the air quality characterization. Data provided include 

monitor location and purpose, ground height and elevation above sea level, and distance to the 

nearest major roadway (Table A-7). In addition, the distances and emissions of stationary sources 

that emit >5 tons NOx per year were calculated for each monitor (Table A-8)
A-11

1 

Table A-7. Attributes of location-specific ambient monitors used for air quality characterization and the distance to nearest major 

M

Location Monitor ID Latitude Longitude Objective1 Setting2 Land Use3 Scale4 Years Atlanta 130890002 33.68801 -84.2903

POPULATION EXPOSURE SUBURBAN RESIDENTIAL URBAN SCALE 10 

Atlanta 130893001 33.84568 -84.2134 UNKNOWN RURAL RESIDENTIAL NEIGHBORHOOD 9 Atlanta 131210048 33.77919 -84.3958

POPULATION EXPOSURE 

URBAN AND CENTER CITY COMMERCIAL NEIGHBORHOOD 12 

Atlanta 132230003 33.92855 -85.0455

POPULATION EXPOSURE RURAL AGRICULTURAL URBAN SCALE 10 

Atlanta 132470001 33.59093 -84.0654

POPULATION EXPOSURE RURAL AGRICULTURAL URBAN SCALE 12 

Boston 230313002 43.08333 -70.75

POPULATION EXPOSURE SUBURBAN RESIDENTIAL NEIGHBORHOOD 7 

Boston 250051005 42.06306 -71.1489

POPULATION EXPOSURE RURAL AGRICULTURAL URBAN SCALE 2 

Boston 250092006 42.47467 -70.9714

POPULATION EXPOSURE 

URBAN AND CENTER CITY COMMERCIAL URBAN SCALE 10 

Boston 250094004 42.79027 -70.8083

POPULATION EXPOSURE SUBURBAN RESIDENTIAL URBAN SCALE 5 

Boston 250095005 42.77077 -71.1023

POPULATION EXPOSURE SUBURBAN RESIDENTIAL NEIGHBORHOOD 2 

Boston 250210009 42.31667 -71.1333 UNKNOWN 

URBAN AND CENTER CITY RESIDENTIAL MICROSCALE 1 

Boston 250250002 42.34887 -71.0972

HIGHEST CONCENTRATION 

URBAN AND CENTER CITY COMMERCIAL MICROSCALE 11 

Boston 250250021 42.37783 -71.0271

HIGHEST CONCENTRATION 

URBAN AND CENTER CITY RESIDENTIAL NEIGHBORHOOD 8 

Boston 250250035 42.33333 -71.1167 UNKNOWN 

URBAN AND CENTER CITY RESIDENTIAL 1 

Boston 250250036 42.33333 -71.1167 UNKNOWN 

URBAN AND CENTER CITY RESIDENTIAL 1 

Boston 250250040 42.34025 -71.0383

POPULATION EXPOSURE 

URBAN AND CENTER CITY INDUSTRIAL NEIGHBORHOOD 11 

Boston 250250041 42.31717 -70.9662

POPULATION EXPOSURE RURAL COMMERCIAL URBAN SCALE 1 

Boston 250250042 42.3294 -71.0825

POPULATION EXPOSURE 

URBAN AND CENTER CITY COMMERCIAL NEIGHBORHOOD 6 

Boston 250251003 42.40167 -71.0311

POPULATION EXPOSURE SUBURBAN RESIDENTIAL URBAN SCALE 5 

Boston 250270020 42.26722 -71.7989 UNKNOWN 

URBAN AND CENTER CITY COMMERCIAL 8 

Boston 250270023 42.26388 -71.7942

POPULATION EXPOSURE 

URBAN AND CENTER CITY COMMERCIAL URBAN SCALE 3 

Boston 330110016 42.99278 -71.4594 UNKNOWN 

URBAN AND CENTER CITY COMMERCIAL 4 

Boston 330110019 43.00056 -71.4681 UNKNOWN 

URBAN AND CENTER CITY COMMERCIAL 1 

Boston 330110020 43.00056 -71.4681 UNKNOWN 

URBAN AND CENTER CITY COMMERCIAL NEIGHBORHOOD 5 
A-12

Table A-7. Attributes of location-specific ambient monitors used for air quality characterization and the distance to nearest major 

M

Location Monitor ID Latitude Longitude Objective1 Setting2 Land Use3 Scale4 Years Boston 330150009 43.07806 -70.7628 UNKNOWN SUBURBAN COMMERCIAL 5 Boston 330150013 43 -71.2 OTHER RURAL RESIDENTIAL REGIONAL SCALE 4 Boston 330150014 43.07528 -70.7481

POPULATION EXPOSURE 

URBAN AND CENTER CITY RESIDENTIAL NEIGHBORHOOD 3 

Boston 330150015 43.0825 -70.7619

POPULATION EXPOSURE SUBURBAN COMMERCIAL NEIGHBORHOOD 1 

Chicago 170310037 41.97944 -87.67 UNKNOWN 

URBAN AND CENTER CITY RESIDENTIAL 1 

Chicago 170310063 41.87697 -87.6343

HIGHEST CONCENTRATION 

URBAN AND CENTER CITY MOBILE MIDDLE SCALE 12 

Chicago 170310064 41.79079 -87.6016

POPULATION EXPOSURE SUBURBAN RESIDENTIAL NEIGHBORHOOD 6 

Chicago 170310075 41.96417 -87.6586

POPULATION EXPOSURE SUBURBAN RESIDENTIAL NEIGHBORHOOD 4 

Chicago 170310076 41.7514 -87.7135

POPULATION EXPOSURE SUBURBAN RESIDENTIAL NEIGHBORHOOD 5 

Chicago 170313101 41.96525 -87.8763

HIGHEST CONCENTRATION SUBURBAN MOBILE MIDDLE SCALE 3 

Chicago 170313103 41.96519 -87.8763

HIGHEST CONCENTRATION SUBURBAN MOBILE MIDDLE SCALE 9 

Chicago 170314002 41.85524 -87.7525

POPULATION EXPOSURE SUBURBAN RESIDENTIAL NEIGHBORHOOD 12 

Chicago 170314201 42.14 -87.7992

POPULATION EXPOSURE SUBURBAN RESIDENTIAL URBAN SCALE 8 

Chicago 170318003 41.63139 -87.5681

POPULATION EXPOSURE SUBURBAN RESIDENTIAL NEIGHBORHOOD 8 

Chicago 171971011 41.22154 -88.191

GENERAL/BACKGROUND RURAL AGRICULTURAL REGIONAL SCALE 5 

Chicago 180890022 41.60667 -87.3047

HIGHEST CONCENTRATION 

URBAN AND CENTER CITY INDUSTRIAL NEIGHBORHOOD 8 

Chicago 180891016 41.60028 -87.3347

POPULATION EXPOSURE 

URBAN AND CENTER CITY RESIDENTIAL NEIGHBORHOOD 2 

Cleveland 390350043 41.46278 -81.5792

POPULATION EXPOSURE SUBURBAN RESIDENTIAL URBAN SCALE 2 

Cleveland 390350060 41.49396 -81.6785

POPULATION EXPOSURE 

URBAN AND CENTER CITY COMMERCIAL NEIGHBORHOOD 12 

Cleveland 390350066 41.46278 -81.5803

POPULATION EXPOSURE SUBURBAN RESIDENTIAL URBAN SCALE 6 

Cleveland 390350070 41.45694 -81.5922

POPULATION EXPOSURE 

URBAN AND CENTER CITY RESIDENTIAL NEIGHBORHOOD 2 

Colorado Springs 080416001 38.63361 -104.716 UNKNOWN RURAL INDUSTRIAL 6 Colorado Springs 080416004 38.92139 -104.813 UNKNOWN 

URBAN AND CENTER CITY RESIDENTIAL 6 

Colorado Springs 080416005 38.76333 -104.757 UNKNOWN 

URBAN AND CENTER CITY AGRICULTURAL 1 

Colorado Springs 080416006 38.9225 -104.996 UNKNOWN RURAL RESIDENTIAL 1 Colorado Springs 080416009 38.64083 -104.714 UNKNOWN RURAL INDUSTRIAL 1 
A-13

Table A-7. Attributes of location-specific ambient monitors used for air quality characterization and the distance to nearest major 

M

Location Monitor ID Latitude Longitude Objective1 Setting2 Land Use3 Scale4 Years Colorado Springs 080416011 38.84667 -104.827 UNKNOWN 

URBAN AND CENTER CITY RESIDENTIAL 6 

Colorado Springs 080416013 38.81056 -104.817 UNKNOWN 

URBAN AND CENTER CITY RESIDENTIAL 1 

Colorado Springs 080416018 38.81139 -104.751 UNKNOWN 

URBAN AND CENTER CITY COMMERCIAL 4 

Denver 080013001 39.83812 -104.95

POPULATION EXPOSURE RURAL AGRICULTURAL URBAN SCALE 11 

Denver 080050003 39.65722 -104.998

HIGHEST CONCENTRATION SUBURBAN COMMERCIAL NEIGHBORHOOD 1 

Denver 080310002 39.75118 -104.988

HIGHEST CONCENTRATION 

URBAN AND CENTER CITY COMMERCIAL NEIGHBORHOOD 9 

Denver 080590006 39.9129 -105.189 UNKNOWN RURAL INDUSTRIAL 3 Denver 080590008 39.87639 -105.166

GENERAL/BACKGROUND RURAL INDUSTRIAL NEIGHBORHOOD 4 

Denver 080590009 39.86194 -105.203

GENERAL/BACKGROUND RURAL INDUSTRIAL NEIGHBORHOOD 3 

Denver 080590010 39.89972 -105.24 UNKNOWN RURAL AGRICULTURAL NEIGHBORHOOD 5 Detroit 260990009 42.73139 -82.7935 UNKNOWN SUBURBAN COMMERCIAL 2 Detroit 261630016 42.35781 -83.096

HIGHEST CONCENTRATION 

URBAN AND CENTER CITY RESIDENTIAL NEIGHBORHOOD 11 

Detroit 261630019 42.43084 -83.0001

POPULATION EXPOSURE SUBURBAN RESIDENTIAL URBAN SCALE 11 

El Paso 481410027 31.76308 -106.487

GENERAL/BACKGROUND 

URBAN AND CENTER CITY COMMERCIAL URBAN SCALE 4 

El Paso 481410028 31.75361 -106.404 SOURCE ORIENTED SUBURBAN RESIDENTIAL MICROSCALE 1 El Paso 481410037 31.76828 -106.501

POPULATION EXPOSURE 

URBAN AND CENTER CITY COMMERCIAL NEIGHBORHOOD 11 

El Paso 481410044 31.76567 -106.455

HIGHEST CONCENTRATION 

URBAN AND CENTER CITY COMMERCIAL NEIGHBORHOOD 8 

El Paso 481410055 31.74676 -106.403

POPULATION EXPOSURE 

URBAN AND CENTER CITY COMMERCIAL 7 

El Paso 481410057 31.66219 -106.303

POPULATION EXPOSURE SUBURBAN RESIDENTIAL 7 

El Paso 481410058 31.89393 -106.426

POPULATION EXPOSURE 

URBAN AND CENTER CITY RESIDENTIAL NEIGHBORHOOD 6 

Jacksonville 120310032 30.35611 -81.6356 UNKNOWN SUBURBAN COMMERCIAL 10 Las Vegas 320030022 36.39078 -114.907 SOURCE ORIENTED RURAL INDUSTRIAL NEIGHBORHOOD 7 Las Vegas 320030023 36.80806 -114.061

POPULATION EXPOSURE RURAL RESIDENTIAL NEIGHBORHOOD 4 

Las Vegas 320030073 36.17306 -115.332

POPULATION EXPOSURE SUBURBAN RESIDENTIAL NEIGHBORHOOD 7 

Las Vegas 320030078 35.46505 -114.92

REGIONAL TRANSPORT RURAL DESERT REGIONAL SCALE 1 

Las Vegas 320030539 36.14444 -115.086

POPULATION EXPOSURE SUBURBAN MOBILE NEIGHBORHOOD 8 
A-14

Table A-7. Attributes of location-specific ambient monitors used for air quality characterization and the distance to nearest major 

M

Location Monitor ID Latitude Longitude Objective1 Setting2 Land Use3 Scale4 Years Las Vegas 320030557 36.15889 -115.11 UNKNOWN SUBURBAN RESIDENTIAL 2 Las Vegas 320030563 36.17639 -115.103

POPULATION EXPOSURE SUBURBAN RESIDENTIAL NEIGHBORHOOD 3 

Las Vegas 320030601 35.97889 -114.844

POPULATION EXPOSURE SUBURBAN COMMERCIAL NEIGHBORHOOD 5 

Las Vegas 320031019 35.78563 -115.357

GENERAL/BACKGROUND RURAL DESERT URBAN SCALE 7 

Las Vegas 320032002 36.19111 -115.122

HIGHEST CONCENTRATION 

URBAN AND CENTER CITY COMMERCIAL NEIGHBORHOOD 7 

Los Angeles 060370002 34.1365 -117.924

POPULATION EXPOSURE SUBURBAN RESIDENTIAL URBAN SCALE 12 

Los Angeles 060370016 34.14435 -117.85 UNKNOWN SUBURBAN RESIDENTIAL 12 Los Angeles 060370030 34.03528 -118.217 UNKNOWN 

URBAN AND CENTER CITY RESIDENTIAL 1 

Los Angeles 060370113 34.05111 -118.456 UNKNOWN 

URBAN AND CENTER CITY MOBILE 12 

Los Angeles 060370206 33.95833 -117.842 UNKNOWN 

URBAN AND CENTER CITY COMMERCIAL MIDDLE SCALE 1 

Los Angeles 060371002 34.17605 -118.317 UNKNOWN 

URBAN AND CENTER CITY COMMERCIAL 11 

Los Angeles 060371103 34.06659 -118.227

HIGHEST CONCENTRATION 

URBAN AND CENTER CITY RESIDENTIAL NEIGHBORHOOD 11 

Los Angeles 060371201 34.19925 -118.533 UNKNOWN SUBURBAN COMMERCIAL 12 Los Angeles 060371301 33.92899 -118.211 UNKNOWN 

URBAN AND CENTER CITY COMMERCIAL 12 

Los Angeles 060371601 34.01407 -118.061

POPULATION EXPOSURE SUBURBAN COMMERCIAL NEIGHBORHOOD 10 

Los Angeles 060371701 34.06703 -117.751 UNKNOWN SUBURBAN COMMERCIAL 12 Los Angeles 060372005 34.1326 -118.127 UNKNOWN 

URBAN AND CENTER CITY RESIDENTIAL 12 

Los Angeles 060374002 33.82376 -118.189 UNKNOWN SUBURBAN RESIDENTIAL 11 Los Angeles 060375001 33.92288 -118.37 UNKNOWN 

URBAN AND CENTER CITY COMMERCIAL 9 

Los Angeles 060375005 33.9508 -118.43

UPWIND BACKGROUND SUBURBAN RESIDENTIAL NEIGHBORHOOD 2 

Los Angeles 060376002 34.3875 -118.534

POPULATION EXPOSURE SUBURBAN COMMERCIAL MIDDLE SCALE 2 

Los Angeles 060376012 34.38344 -118.528 UNKNOWN SUBURBAN COMMERCIAL 5 Los Angeles 060379002 34.69 -118.132 UNKNOWN 

URBAN AND CENTER CITY COMMERCIAL MIDDLE SCALE 6 

Los Angeles 060379033 34.67139 -118.131 UNKNOWN 

URBAN AND CENTER CITY COMMERCIAL MIDDLE SCALE 5 

Los Angeles 060590001 33.83062 -117.938

POPULATION EXPOSURE SUBURBAN RESIDENTIAL URBAN SCALE 5 

Los Angeles 060590007 33.83062 -117.938

POPULATION EXPOSURE SUBURBAN RESIDENTIAL URBAN SCALE 4 
A-15

Table A-7. Attributes of location-specific ambient monitors used for air quality characterization and the distance to nearest major 

M

Location Monitor ID Latitude Longitude Objective1 Setting2 Land Use3 Scale4 Years Los Angeles 060591003 33.67464 -117.926 UNKNOWN SUBURBAN RESIDENTIAL MIDDLE SCALE 12 Los Angeles 060595001 33.92513 -117.953 UNKNOWN SUBURBAN RESIDENTIAL 11 Los Angeles 060650012 33.92086 -116.858

POPULATION EXPOSURE SUBURBAN COMMERCIAL NEIGHBORHOOD 9 

Los Angeles 060655001 33.85275 -116.541 UNKNOWN SUBURBAN RESIDENTIAL 12 Los Angeles 060658001 33.99958 -117.416 UNKNOWN SUBURBAN RESIDENTIAL 12 Los Angeles 060659001 33.67649 -117.331 UNKNOWN SUBURBAN RESIDENTIAL MIDDLE SCALE 12 Los Angeles 060710001 34.895 -117.024 UNKNOWN 

URBAN AND CENTER CITY COMMERCIAL 12 

Los Angeles 060710012 34.42611 -117.563 UNKNOWN RURAL COMMERCIAL 2 Los Angeles 060710014 34.5125 -117.33 UNKNOWN SUBURBAN RESIDENTIAL 5 Los Angeles 060710015 35.775 -117.367 UNKNOWN SUBURBAN INDUSTRIAL 2 Los Angeles 060710017 34.14194 -116.055 UNKNOWN 

URBAN AND CENTER CITY MOBILE 3 

Los Angeles 060710306 34.51 -117.331 UNKNOWN SUBURBAN RESIDENTIAL 7 Los Angeles 060711004 34.10374 -117.629

POPULATION EXPOSURE 

URBAN AND CENTER CITY RESIDENTIAL NEIGHBORHOOD 11 

Los Angeles 060711234 35.76389 -117.396 OTHER RURAL DESERT 9 Los Angeles 060712002 34.10002 -117.492 UNKNOWN SUBURBAN INDUSTRIAL 12 Los Angeles 060714001 34.41806 -117.285 UNKNOWN SUBURBAN RESIDENTIAL 3 Los Angeles 060719004 34.10688 -117.274

HIGHEST CONCENTRATION SUBURBAN COMMERCIAL URBAN SCALE 12 

Los Angeles 061110005 34.38694 -119.416

POPULATION EXPOSURE RURAL AGRICULTURAL 7 

Los Angeles 061110007 34.21 -118.869 UNKNOWN SUBURBAN RESIDENTIAL NEIGHBORHOOD 9 Los Angeles 061111003 34.44667 -119.27 UNKNOWN SUBURBAN MOBILE NEIGHBORHOOD 1 Los Angeles 061111004 34.44833 -119.23

POPULATION EXPOSURE SUBURBAN RESIDENTIAL URBAN SCALE 7 

Los Angeles 061112002 34.2775 -118.685

HIGHEST CONCENTRATION SUBURBAN RESIDENTIAL URBAN SCALE 12 

Los Angeles 061112003 34.2804 -119.314

GENERAL/BACKGROUND SUBURBAN RESIDENTIAL MIDDLE SCALE 9 

Los Angeles 061113001 34.255 -119.143

POPULATION EXPOSURE RURAL RESIDENTIAL URBAN SCALE 12 

Miami 120110003 26.28111 -80.2828

HIGHEST CONCENTRATION RURAL INDUSTRIAL NEIGHBORHOOD 3 

Miami 120110031 26.272 -80.295

HIGHEST CONCENTRATION SUBURBAN RESIDENTIAL URBAN SCALE 8 

Miami 120118002 26.087 -80.111

POPULATION EXPOSURE SUBURBAN RESIDENTIAL URBAN SCALE 11 

Miami 120860027 25.733 -80.162

POPULATION EXPOSURE SUBURBAN RESIDENTIAL NEIGHBORHOOD 11 

Miami 120864002 25.79833 -80.2103 HIGHEST URBAN AND COMMERCIAL NEIGHBORHOOD 11 
A-16

Table A-7. Attributes of location-specific ambient monitors used for air quality characterization and the distance to nearest major 

M

Location Monitor ID Latitude Longitude Objective1 Setting2 Land Use3 Scale4 Years 

CONCENTRATION CENTER CITY 

New York 090010113 41.18361 -73.1903

HIGHEST CONCENTRATION 

URBAN AND CENTER CITY COMMERCIAL NEIGHBORHOOD 3 

New York 090019003 41.11833 -73.3367

POPULATION EXPOSURE RURAL FOREST NEIGHBORHOOD 8 

New York 090090027 41.30111 -72.9028

POPULATION EXPOSURE 

URBAN AND CENTER CITY COMMERCIAL NEIGHBORHOOD 2 

New York 090091123 41.31083 -72.9169 UNKNOWN 

URBAN AND CENTER CITY RESIDENTIAL 9 

New York 340030001 40.80833 -73.9928 UNKNOWN SUBURBAN RESIDENTIAL 3 New York 340030005 40.89858 -74.0299

POPULATION EXPOSURE SUBURBAN RESIDENTIAL NEIGHBORHOOD 4 

New York 340130011 40.72667 -74.1442 UNKNOWN 

URBAN AND CENTER CITY INDUSTRIAL 5 

New York 340130016 40.72222 -74.1469

POPULATION EXPOSURE 

URBAN AND CENTER CITY INDUSTRIAL NEIGHBORHOOD 1 

New York 340131003 40.7575 -74.2005

HIGHEST CONCENTRATION 

URBAN AND CENTER CITY COMMERCIAL NEIGHBORHOOD 11 

New York 340170006 40.67025 -74.1261

POPULATION EXPOSURE 

URBAN AND CENTER CITY COMMERCIAL URBAN SCALE 11 

New York 340210005 40.28319 -74.7422

POPULATION EXPOSURE SUBURBAN RESIDENTIAL NEIGHBORHOOD 11 

New York 340230011 40.46218 -74.4294

POPULATION EXPOSURE RURAL AGRICULTURAL NEIGHBORHOOD 11 

New York 340273001 40.78763 -74.6763 UNKNOWN RURAL AGRICULTURAL 11 New York 340390004 40.64144 -74.2084

HIGHEST CONCENTRATION SUBURBAN INDUSTRIAL NEIGHBORHOOD 11 

New York 340390008 40.60083 -74.4419

POPULATION EXPOSURE SUBURBAN RESIDENTIAL NEIGHBORHOOD 3 

New York 360050080 40.83608 -73.9202

HIGHEST CONCENTRATION 

URBAN AND CENTER CITY RESIDENTIAL NEIGHBORHOOD 5 

New York 360050083 40.86586 -73.8808 UNKNOWN 

URBAN AND CENTER CITY COMMERCIAL 12 

New York 360050110 40.81616 -73.9021 UNKNOWN 

URBAN AND CENTER CITY RESIDENTIAL 6 

New York 360470011 40.73277 -73.9472

HIGHEST CONCENTRATION 

URBAN AND CENTER CITY INDUSTRIAL NEIGHBORHOOD 1 

New York 360590005 40.74316 -73.5855

HIGHEST CONCENTRATION SUBURBAN COMMERCIAL NEIGHBORHOOD 11 

New York 360610010 40.73944 -73.9861

HIGHEST CONCENTRATION 

URBAN AND CENTER CITY RESIDENTIAL NEIGHBORHOOD 4 

New York 360610056 40.75917 -73.9665

HIGHEST CONCENTRATION 

URBAN AND CENTER CITY COMMERCIAL MIDDLE SCALE 10 

New York 360810097 40.75527 -73.7586

GENERAL/BACKGROUND 

URBAN AND CENTER CITY RESIDENTIAL 3 

New York 360810098 40.7842 -73.8476 UNKNOWN 

URBAN AND CENTER CITY RESIDENTIAL 7 
A-17

Table A-7. Attributes of location-specific ambient monitors used for air quality characterization and the distance to nearest major 

M

Location Monitor ID Latitude Longitude Objective1 Setting2 Land Use3 Scale4 Years New York 360810124 40.7362 -73.8232

POPULATION EXPOSURE SUBURBAN RESIDENTIAL 5 

New York 361030009 40.8275 -73.0569 UNKNOWN SUBURBAN RESIDENTIAL 6 Philadelphia 100031003 39.76111 -75.4919

POPULATION EXPOSURE SUBURBAN RESIDENTIAL 5 

Philadelphia 100031007 39.55111 -75.7308 OTHER RURAL AGRICULTURAL 1 Philadelphia 100032004 39.73944 -75.5581 UNKNOWN 

URBAN AND CENTER CITY COMMERCIAL 4 

Philadelphia 340070003 39.92304 -75.0976

POPULATION EXPOSURE SUBURBAN RESIDENTIAL NEIGHBORHOOD 10 

Philadelphia 420170012 40.10722 -74.8822

POPULATION EXPOSURE SUBURBAN RESIDENTIAL NEIGHBORHOOD 12 

Philadelphia 420450002 39.83556 -75.3725

POPULATION EXPOSURE 

URBAN AND CENTER CITY INDUSTRIAL NEIGHBORHOOD 12 

Philadelphia 420910013 40.11222 -75.3092

POPULATION EXPOSURE SUBURBAN RESIDENTIAL NEIGHBORHOOD 11 

Philadelphia 421010004 40.00889 -75.0978

POPULATION EXPOSURE 

URBAN AND CENTER CITY RESIDENTIAL URBAN SCALE 11 

Philadelphia 421010029 39.95722 -75.1731

HIGHEST CONCENTRATION 

URBAN AND CENTER CITY COMMERCIAL NEIGHBORHOOD 10 

Philadelphia 421010047 39.94472 -75.1661

POPULATION EXPOSURE 

URBAN AND CENTER CITY RESIDENTIAL NEIGHBORHOOD 9 

Phoenix 040130019 33.48385 -112.143

POPULATION EXPOSURE SUBURBAN RESIDENTIAL NEIGHBORHOOD 10 

Phoenix 040133002 33.45793 -112.046

HIGHEST CONCENTRATION 

URBAN AND CENTER CITY RESIDENTIAL NEIGHBORHOOD 12 

Phoenix 040133003 33.47968 -111.917

POPULATION EXPOSURE SUBURBAN RESIDENTIAL URBAN SCALE 10 

Phoenix 040133010 33.46093 -112.117

POPULATION EXPOSURE SUBURBAN RESIDENTIAL MIDDLE SCALE 9 

Phoenix 040134005 33.4124 -111.935 UNKNOWN 

URBAN AND CENTER CITY RESIDENTIAL 1 

Phoenix 040134011 33.37005 -112.621 SOURCE ORIENTED RURAL AGRICULTURAL URBAN SCALE 2 Phoenix 040139997 33.50364 -112.095

POPULATION EXPOSURE 

URBAN AND CENTER CITY RESIDENTIAL 5 

Provo 490490002 40.25361 -111.663 UNKNOWN 

URBAN AND CENTER CITY COMMERCIAL 12 

St. Louis 171630010 38.61203 -90.1605

POPULATION EXPOSURE SUBURBAN INDUSTRIAL NEIGHBORHOOD 12 

St. Louis 291830010 38.57917 -90.8411 UNKNOWN RURAL AGRICULTURAL 3 St. Louis 291831002 38.8725 -90.2264

POPULATION EXPOSURE RURAL AGRICULTURAL URBAN SCALE 12 

St. Louis 291890001 38.52167 -90.3436 UNKNOWN SUBURBAN RESIDENTIAL 3 St. Louis 291890004 38.5325 -90.3828 UNKNOWN SUBURBAN RESIDENTIAL 6 St. Louis 291890006 38.61361 -90.4958 UNKNOWN RURAL RESIDENTIAL 11 St. Louis 291893001 38.64139 -90.3458 UNKNOWN SUBURBAN COMMERCIAL 11 
A-18

Table A-7. Attributes of location-specific ambient monitors used for air quality characterization and the distance to nearest major 

M

Location Monitor ID Latitude Longitude Objective1 Setting2 Land Use3 Scale4 Years St. Louis 291895001 38.76611 -90.2858 UNKNOWN SUBURBAN COMMERCIAL 10 St. Louis 291897002 38.72722 -90.3794 UNKNOWN SUBURBAN RESIDENTIAL 6 St. Louis 291897003 38.72092 -90.367

HIGHEST CONCENTRATION SUBURBAN RESIDENTIAL NEIGHBORHOOD 4 

St. Louis 295100072 38.62422 -90.1987 UNKNOWN 

URBAN AND CENTER CITY COMMERCIAL 10 

St. Louis 295100080 38.68283 -90.2468

HIGHEST CONCENTRATION 

URBAN AND CENTER CITY RESIDENTIAL NEIGHBORHOOD 5 

St. Louis 295100086 38.67227 -90.239

HIGHEST CONCENTRATION 

URBAN AND CENTER CITY RESIDENTIAL NEIGHBORHOOD 6 

Washington DC 110010017 38.90361 -77.0517

HIGHEST CONCENTRATION 

URBAN AND CENTER CITY COMMERCIAL NEIGHBORHOOD 1 

Washington DC 110010025 38.97528 -77.0228

POPULATION EXPOSURE 

URBAN AND CENTER CITY COMMERCIAL URBAN SCALE 12 

Washington DC 110010041 38.89722 -76.9528 UNKNOWN 

URBAN AND CENTER CITY RESIDENTIAL NEIGHBORHOOD 12 

Washington DC 110010043 38.91889 -77.0125

HIGHEST CONCENTRATION 

URBAN AND CENTER CITY COMMERCIAL URBAN SCALE 12 

Washington DC 240053001 39.31083 -76.4744

MAX PRECURSOR EMISSIONS IMPACT SUBURBAN RESIDENTIAL NEIGHBORHOOD 8 

Washington DC 245100040 39.29806 -76.6047

HIGHEST CONCENTRATION 

URBAN AND CENTER CITY RESIDENTIAL NEIGHBORHOOD 11 

Washington DC 245100050 39.31861 -76.5825

POPULATION EXPOSURE 

URBAN AND CENTER CITY RESIDENTIAL REGIONAL SCALE 1 

Washington DC 510130020 38.8575 -77.0592 UNKNOWN 

URBAN AND CENTER CITY COMMERCIAL 12 

Washington DC 510590005 38.89389 -77.4653

POPULATION EXPOSURE RURAL AGRICULTURAL NEIGHBORHOOD 11 

Washington DC 510590018 38.7425 -77.0775 UNKNOWN SUBURBAN RESIDENTIAL 3 Washington DC 510591004 38.86806 -77.1431 UNKNOWN SUBURBAN COMMERCIAL 6 Washington DC 510591005 38.83752 -77.1632

POPULATION EXPOSURE SUBURBAN RESIDENTIAL 4 

Washington DC 510595001 38.93194 -77.1989

POPULATION EXPOSURE SUBURBAN RESIDENTIAL NEIGHBORHOOD 10 

Washington DC 511071005 39.02444 -77.49

POPULATION EXPOSURE SUBURBAN RESIDENTIAL NEIGHBORHOOD 8 

Washington DC 511530009 38.85528 -77.6356

POPULATION EXPOSURE SUBURBAN RESIDENTIAL URBAN SCALE 12 

Washington DC 515100009 38.81083 -77.0447 UNKNOWN 

URBAN AND CENTER CITY RESIDENTIAL 12 

Notes: 1 Objective indicates the reason for measuring air quality by the monitor. Sites located to determine the highest concentration expected to occur in the area covere

Concentration), sites located to measure typical concentrations in areas of high population (Population Exposure), sites located to determine the impact of significaquality (Source Oriented), sites located to determine general background concentration levels (General Background), sites located to determine the extent of regioareas and in support of secondary standards (Regional Transport), sites located to measure air pollution impacts on visibility, vegetation damage, or other welfare

Impacts), sites are established to characterize upwind background and transported ozone and its precursor concentrations entering the area and will identify those 

(Upwind Background), sites are established to monitor the magnitude and type of precursor emissions in the area where maximum precursor emissions are expec
A-19

Table A-7. Attributes of location-specific ambient monitors used for air quality characterization and the distance to nearest major 

M

Location Monitor ID Latitude Longitude Objective1 Setting2 Land Use3 Scale4 Years monitoring of urban air toxic pollutants (Max. Precursor Impact), sites are intended to monitor maximum ozone concentrations occurring downwind from the area o

Ozone Concentration), and sites are established to characterize the downwind transported ozone and its precursor concentrations exiting the area and will identify 

contributing to overwhelming transport in other areas (Extreme Downwind). 2 Setting is the description of the environmental setting within which the site is located 3 Land use indicates the prevalent land use within 1/4 mile of that site. 

4 Scale indicates what the data from a monitor can represent in terms of air volumes associated with area dimensions. Micro - 0 to 100 meters; Middle - 100 to 50

4 kilometers; Urban Scale - 4 to 50 kilometers; Regional Scale - 50 kilometers up to 1000km. 5 Years is the number of valid site-years available for the monitor. Monitor probe height (Ht) and site elevation (Elev) above sea level are given in meters (m). 6 Distances (Dist) to nearest major roadway are given in meters (m). Major road types were defined as: 1=primary limited access or interstate, 2=primary US and 

and County, 4=freeway ramp, 5=other ramps. 
A-20

1 

Table A-8. Distance of location-specific ambient monitors to stationary sources emitting >5 tons of NOx per year, within a 10 kilometer d

Distance (km) to Source emissions >5 tpy and within 10 km Emissions (tpy) of Sources wit

Location ID n1 mean std min 2.5 50 97.5 max mean std min 2.5 Atlanta 130890002 1 4.9 4.9 4.9 4.9 4.9 4.9 34 34 34 Atlanta 130893001 3 7.2 4.0 2.7 2.7 9.2 9.8 9.8 34 2 32 32 Atlanta 131210048 5 6.4 3.3 0.7 0.7 7.3 8.9 8.9 1249 2106 22 22 Atlanta 132230003 0 Atlanta 132470001 0 Boston 230313002 5 3.5 1.5 1.0 1.0 3.8 4.9 4.9 642 769 31 31 Boston 250051005 3 6.7 1.6 5.5 5.5 6.0 8.5 8.5 9 4 5 5 Boston 250092006 12 6.8 2.7 2.5 2.5 7.4 9.9 9.9 439 1083 5 5 Boston 250094004 0 Boston 250095005 10 5.8 2.3 1.7 1.7 6.7 8.6 8.6 201 347 6 6 Boston 250210009 57 5.8 2.5 1.0 1.8 5.9 9.9 9.9 106 283 5 5 Boston 250250002 62 4.6 2.4 0.6 1.1 4.3 9.4 9.7 98 273 5 5 Boston 250250021 55 6.1 2.3 1.5 1.7 6.5 9.8 9.8 130 304 5 5 Boston 250250035 62 5.1 2.6 0.3 0.8 5.1 9.0 9.6 99 273 5 5 Boston 250250036 62 5.1 2.6 0.3 0.8 5.1 9.0 9.6 99 273 5 5 Boston 250250040 56 5.3 2.4 0.4 0.9 5.6 9.0 9.3 106 286 5 5 Boston 250250041 25 7.8 2.0 0.7 0.7 8.2 9.9 9.9 81 206 5 5 Boston 250250042 65 5.3 2.8 0.7 1.0 4.9 10.0 10.0 94 267 5 5 Boston 250251003 49 6.4 2.4 0.6 1.0 7.0 9.6 9.6 145 319 5 5 Boston 250270020 28 3.7 2.5 0.1 0.1 2.9 8.6 8.6 58 165 5 5 Boston 250270023 28 3.6 2.4 0.4 0.4 3.0 8.4 8.4 58 165 5 5 Boston 330110016 0 Boston 330110019 0 Boston 330110020 0 Boston 330150009 5 3.3 1.0 2.0 2.0 3.3 4.4 4.4 642 769 31 31 Boston 330150013 1 8.4 8.4 8.4 8.4 8.4 8.4 29 29 29 Boston 330150014 5 4.0 1.8 1.0 1.0 4.4 5.5 5.5 642 769 31 31 Boston 330150015 5 3.1 0.9 1.9 1.9 3.0 4.1 4.1 642 769 31 31 Chicago 170310037 17 5.6 2.7 0.7 0.7 5.7 9.5 9.5 18 31 5 5 Chicago 170310063 57 4.9 3.2 0.4 0.5 4.9 9.4 10.0 110 416 5 5 Chicago 170310064 33 6.9 2.5 1.2 1.2 6.9 10.0 10.0 94 428 5 5 Chicago 170310075 31 7.3 2.7 0.8 0.8 8.4 9.9 9.9 10 7 5 5 Chicago 170310076 46 7.8 2.3 1.3 1.6 8.4 9.8 9.9 170 463 5 5 Chicago 170313101 30 6.6 2.2 2.7 2.7 7.2 9.7 9.7 313 1638 5 5 Chicago 170313103 30 6.6 2.2 2.7 2.7 7.2 9.7 9.7 313 1638 5 5 Chicago 170314002 63 6.7 2.6 0.5 0.5 7.2 9.8 9.9 122 407 5 5 Chicago 170314201 7 6.5 1.5 4.0 4.0 6.6 9.0 9.0 8 3 5 5 Chicago 170318003 63 7.3 2.0 1.7 2.3 8.0 9.6 9.7 361 1201 5 5 Chicago 171971011 1 4.0 4.0 4.0 4.0 4.0 4.0 20 20 20 Chicago 180890022 8 5.1 3.8 0.8 0.8 4.1 9.4 9.4 815 1680 8 8 
A-21

Table A-8. Distance of location-specific ambient monitors to stationary sources emitting >5 tons of NOx per year, within a 10 kilometer d

Distance (km) to Source emissions >5 tpy and within 10 km Emissions (tpy) of Sources wit

Location ID n1 mean std min 2.5 50 97.5 max mean std min 2.5 Chicago 180891016 8 4.7 2.4 2.1 2.1 4.1 7.6 7.6 815 1680 8 8 Cleveland 390350043 5 8.1 1.9 5.2 5.2 8.3 9.9 9.9 673 664 126 126 Cleveland 390350060 4 4.1 2.4 1.0 1.0 4.4 6.4 6.4 810 681 165 165 Cleveland 390350066 5 8.0 1.9 5.2 5.2 8.3 9.8 9.8 673 664 126 126 Cleveland 390350070 5 7.6 1.8 5.5 5.5 7.3 9.7 9.7 673 664 126 126 Colorado Springs 080416001 4 5.1 4.4 0.8 0.8 5.1 9.1 9.1 780 1374 16 16 Colorado Springs 080416004 10 5.9 2.2 3.5 3.5 5.6 9.8 9.8 48 80 5 5 Colorado Springs 080416005 9 7.5 2.1 3.3 3.3 8.1 9.5 9.5 490 1393 5 5 Colorado Springs 080416006 0 Colorado Springs 080416009 4 5.2 4.3 1.0 1.0 5.3 9.3 9.3 780 1374 16 16 Colorado Springs 080416011 14 5.0 2.3 2.0 2.0 5.8 9.6 9.6 345 1113 5 5 Colorado Springs 080416013 14 6.3 2.9 2.1 2.1 6.9 9.9 9.9 346 1113 5 5 Colorado Springs 080416018 11 6.9 1.7 4.3 4.3 7.1 9.6 9.6 430 1254 5 5 Denver 080013001 34 5.3 1.8 1.6 1.6 4.7 9.5 9.5 310 1622 5 5 Denver 080050003 19 6.7 3.7 1.0 1.0 9.1 10.0 10.0 313 1233 5 5 Denver 080310002 52 5.3 2.5 0.9 0.9 5.8 9.7 9.8 319 1495 5 5 Denver 080590006 9 5.9 2.1 2.7 2.7 6.3 8.6 8.6 63 66 11 11 Denver 080590008 9 6.2 2.0 3.7 3.7 6.1 10.0 10.0 59 68 8 8 Denver 080590009 10 6.5 3.2 2.5 2.5 7.0 9.9 9.9 53 66 6 6 Denver 080590010 7 5.5 3.1 1.1 1.1 5.6 9.2 9.2 73 71 12 12 Detroit 260990009 4 4.9 3.2 0.3 0.3 5.7 7.7 7.7 63 70 7 7 Detroit 261630016 51 7.4 2.1 1.3 2.0 7.9 9.8 9.9 387 797 5 6 Detroit 261630019 32 6.3 2.2 2.6 2.6 6.5 10.0 10.0 57 168 5 5 El Paso 481410027 22 8.1 1.6 1.5 1.5 8.6 9.3 9.3 99 195 5 5 El Paso 481410028 24 2.2 1.9 0.9 0.9 1.6 9.3 9.3 127 338 5 5 El Paso 481410037 15 8.7 2.6 0.1 0.1 9.4 10.0 10.0 135 230 5 5 El Paso 481410044 25 5.9 1.2 4.4 4.4 5.6 9.5 9.5 158 366 5 5 El Paso 481410055 24 2.8 1.8 1.6 1.6 2.2 9.6 9.6 127 338 5 5 El Paso 481410057 0 El Paso 481410058 16 8.8 0.4 8.4 8.4 8.6 9.5 9.5 31 30 5 5 Jacksonville 120310032 20 5.1 3.0 0.7 0.7 5.7 9.6 9.6 201 407 5 5 Las Vegas 320030022 7 4.6 0.9 3.8 3.8 3.9 5.6 5.6 175 222 30 30 Las Vegas 320030023 0 Las Vegas 320030073 0 Las Vegas 320030078 0 Las Vegas 320030539 5 6.9 1.2 4.7 4.7 7.2 7.9 7.9 816 760 18 18 Las Vegas 320030557 4 9.1 1.2 7.3 7.3 9.7 9.7 9.7 807 877 18 18 Las Vegas 320030563 1 7.6 7.6 7.6 7.6 7.6 7.6 84 84 84 Las Vegas 320030601 0 Las Vegas 320031019 0 Las Vegas 320032002 1 9.9 9.9 9.9 9.9 9.9 9.9 84 84 84 Los Angeles 060370002 7 3.1 1.1 1.6 1.6 2.9 4.5 4.5 10 4 5 5 
A-22

Table A-8. Distance of location-specific ambient monitors to stationary sources emitting >5 tons of NOx per year, within a 10 kilometer d

Distance (km) to Source emissions >5 tpy and within 10 km Emissions (tpy) of Sources wit

Location ID n1 mean std min 2.5 50 97.5 max mean std min 2.5 Los Angeles 060370016 7 7.5 1.8 4.5 4.5 8.5 8.9 8.9 12 8 5 5 Los Angeles 060370030 35 5.5 2.3 2.1 2.1 5.2 9.8 9.8 23 27 5 5 Los Angeles 060370113 7 4.3 3.1 1.3 1.3 3.2 9.8 9.8 15 10 5 5 Los Angeles 060370206 11 5.6 2.2 2.3 2.3 5.8 9.2 9.2 32 31 6 6 Los Angeles 060371002 18 5.7 2.6 0.1 0.1 6.0 9.9 9.9 47 59 6 6 Los Angeles 060371103 31 6.5 2.7 1.8 1.8 7.2 10.0 10.0 18 21 5 5 Los Angeles 060371201 7 5.1 1.2 3.3 3.3 5.5 6.5 6.5 10 4 6 6 Los Angeles 060371301 45 6.8 2.1 1.2 2.5 7.1 9.7 10.0 22 24 5 5 Los Angeles 060371601 22 6.5 2.3 2.3 2.3 7.2 9.7 9.7 28 33 5 5 Los Angeles 060371701 13 6.1 3.0 1.1 1.1 7.0 9.7 9.7 22 20 5 5 Los Angeles 060372005 10 5.2 3.5 0.2 0.2 5.5 10.0 10.0 12 8 5 5 Los Angeles 060374002 55 6.4 2.3 1.7 2.2 6.2 9.9 9.9 76 159 5 5 Los Angeles 060375001 32 5.1 2.4 0.3 0.3 4.8 9.6 9.6 205 754 6 6 Los Angeles 060375005 25 4.6 2.4 1.4 1.4 4.6 9.9 9.9 224 850 6 6 Los Angeles 060376002 5 5.6 1.8 3.6 3.6 5.8 7.8 7.8 29 20 8 8 Los Angeles 060376012 6 6.2 2.5 3.0 3.0 6.8 9.7 9.7 26 19 8 8 Los Angeles 060379002 4 7.8 1.0 6.8 6.8 7.7 9.2 9.2 22 28 6 6 Los Angeles 060379033 4 6.3 0.8 5.3 5.3 6.4 7.1 7.1 22 28 6 6 Los Angeles 060590001 17 6.4 2.4 2.8 2.8 7.2 9.4 9.4 14 12 5 5 Los Angeles 060590007 17 6.4 2.4 2.8 2.8 7.2 9.4 9.4 14 12 5 5 Los Angeles 060591003 14 6.1 2.2 2.1 2.1 6.0 9.3 9.3 65 116 5 5 Los Angeles 060595001 16 7.9 1.6 3.4 3.4 8.2 9.5 9.5 19 26 6 6 Los Angeles 060650012 0 Los Angeles 060655001 0 Los Angeles 060658001 12 7.4 2.2 3.6 3.6 7.4 9.8 9.8 119 358 5 5 Los Angeles 060659001 2 4.6 5.9 0.4 0.4 4.6 8.7 8.7 11 9 5 5 Los Angeles 060710001 3 6.9 1.9 5.3 5.3 6.5 9.0 9.0 209 321 10 10 Los Angeles 060710012 0 Los Angeles 060710014 3 6.0 2.6 3.5 3.5 5.9 8.6 8.6 199 327 6 6 Los Angeles 060710015 3 4.4 4.6 1.7 1.7 1.8 9.7 9.7 752 1045 12 12 Los Angeles 060710017 0 Los Angeles 060710306 3 6.1 2.6 3.6 3.6 5.7 8.9 8.9 199 327 6 6 Los Angeles 060711004 19 7.3 1.7 4.3 4.3 7.4 9.8 9.8 57 120 5 5 Los Angeles 060711234 2 1.6 0.4 1.3 1.3 1.6 1.9 1.9 1122 1168 296 296 Los Angeles 060712002 20 5.7 2.2 2.0 2.0 5.8 9.6 9.6 44 65 5 5 Los Angeles 060714001 1 6.5 6.5 6.5 6.5 6.5 6.5 577 577 577 Los Angeles 060719004 8 5.8 2.5 1.5 1.5 5.7 9.0 9.0 171 438 5 5 Los Angeles 061110005 5 6.9 2.5 3.1 3.1 7.7 9.6 9.6 68 118 8 8 Los Angeles 061110007 20 4.7 2.2 1.7 1.7 4.2 9.3 9.3 25 20 5 5 Los Angeles 061111003 0 Los Angeles 061111004 0 Los Angeles 061112002 4 6.6 1.0 5.2 5.2 6.8 7.5 7.5 63 113 5 5 
A-23

Table A-8. Distance of location-specific ambient monitors to stationary sources emitting >5 tons of NOx per year, within a 10 kilometer d

Distance (km) to Source emissions >5 tpy and within 10 km Emissions (tpy) of Sources wit

Location ID n1 mean std min 2.5 50 97.5 max mean std min 2.5 Los Angeles 061112003 3 5.5 1.3 4.1 4.1 5.6 6.7 6.7 18 4 14 14 Los Angeles 061113001 7 5.1 2.3 1.9 1.9 5.9 7.4 7.4 35 51 5 5 Miami 120110003 0 Miami 120110031 0 Miami 120118002 0 Miami 120860027 3 4.1 4.2 1.6 1.6 1.8 8.9 8.9 31 19 14 14 Miami 120864002 8 7.0 2.6 1.3 1.3 7.8 9.1 9.1 22 15 8 8 New York 090010113 7 4.4 3.1 1.4 1.4 3.4 8.8 8.8 538 711 48 48 New York 090019003 3 6.3 2.0 4.0 4.0 7.4 7.5 7.5 127 179 12 12 New York 090090027 5 2.7 1.0 1.3 1.3 2.7 3.9 3.9 280 484 14 14 New York 090091123 6 3.3 2.8 1.2 1.2 2.4 8.9 8.9 234 447 7 7 New York 340030001 48 6.5 2.2 2.9 2.9 6.3 9.8 9.9 468 1506 6 7 New York 340030005 18 6.8 2.9 0.1 0.1 7.4 10.0 10.0 53 79 6 6 New York 340130011 43 5.4 2.9 0.7 0.8 5.8 9.4 9.5 273 1372 5 5 New York 340130016 44 5.5 2.8 0.1 1.0 6.3 9.4 9.6 267 1357 5 5 New York 340131003 32 6.4 2.0 2.1 2.1 6.8 9.3 9.3 77 149 5 5 New York 340170006 42 6.9 2.5 1.1 1.6 7.7 9.5 9.5 369 1420 5 6 New York 340210005 8 5.4 1.7 3.2 3.2 5.5 7.3 7.3 115 244 8 8 New York 340230011 20 6.1 2.8 1.0 1.0 7.0 9.5 9.5 95 175 6 6 New York 340273001 1 8.5 8.5 8.5 8.5 8.5 8.5 20 20 20 New York 340390004 46 6.3 2.4 0.7 0.9 6.6 9.6 9.7 134 341 5 6 New York 340390008 12 7.2 2.1 3.2 3.2 8.0 10.0 10.0 23 36 5 5 New York 360050080 54 6.4 2.3 1.8 1.8 6.4 9.9 9.9 241 776 6 6 New York 360050083 37 6.0 2.8 1.6 1.6 6.3 9.9 9.9 171 725 6 6 New York 360050110 55 5.9 2.2 2.1 2.6 5.7 9.6 9.9 236 769 6 6 New York 360470011 56 5.9 2.7 0.7 1.5 5.7 9.7 10.0 296 787 7 7 New York 360590005 7 6.3 3.4 1.9 1.9 8.1 9.8 9.8 372 500 7 7 New York 360610010 52 5.9 2.5 0.3 1.4 6.1 9.6 9.8 494 1453 5 7 New York 360610056 54 5.4 2.6 0.3 1.4 5.5 9.9 10.0 470 1429 7 7 New York 360810097 11 6.3 2.1 2.9 2.9 6.9 9.5 9.5 65 77 13 13 New York 360810098 48 7.1 2.3 1.6 2.8 7.8 9.8 9.8 262 820 6 7 New York 360810124 24 7.0 2.6 2.1 2.1 8.0 10.0 10.0 436 1136 8 8 New York 361030009 3 3.8 3.2 2.0 2.0 2.0 7.6 7.6 537 759 40 40 Philadelphia 100031003 39 5.5 2.5 1.6 1.6 6.2 9.7 9.7 282 481 5 5 Philadelphia 100031007 11 9.2 0.6 8.0 8.0 9.3 9.8 9.8 323 494 6 6 Philadelphia 100032004 32 4.8 1.9 0.7 0.7 4.7 8.4 8.4 223 403 5 5 Philadelphia 340070003 69 7.7 2.3 1.8 2.0 8.5 10.0 10.0 87 196 5 5 Philadelphia 420170012 10 4.1 2.3 1.2 1.2 4.2 9.4 9.4 85 96 11 11 Philadelphia 420450002 30 4.8 2.6 0.2 0.2 5.4 9.5 9.5 504 1055 5 5 Philadelphia 420910013 12 5.1 2.5 1.4 1.4 4.3 8.8 8.8 89 232 5 5 Philadelphia 421010004 32 5.9 2.5 1.0 1.0 5.6 9.9 9.9 58 111 5 5 Philadelphia 421010029 74 5.7 2.1 1.1 1.8 5.6 9.7 9.7 74 148 5 5 
A-24

Table A-8. Distance of location-specific ambient monitors to stationary sources emitting >5 tons of NOx per year, within a 10 kilometer d

Distance (km) to Source emissions >5 tpy and within 10 km Emissions (tpy) of Sources wit

Location ID n1 mean std min 2.5 50 97.5 max mean std min 2.5 Philadelphia 421010047 73 5.2 2.1 0.6 0.8 4.8 9.6 9.7 95 221 5 5 Phoenix 040130019 11 6.8 2.2 4.2 4.2 6.7 9.8 9.8 106 313 5 5 Phoenix 040133002 6 4.1 2.3 1.3 1.3 4.1 6.9 6.9 21 19 5 5 Phoenix 040133003 10 6.7 1.4 4.1 4.1 6.6 9.0 9.0 50 80 9 9 Phoenix 040133010 10 5.0 0.9 3.5 3.5 4.9 6.6 6.6 115 328 5 5 Phoenix 040134005 11 5.8 2.9 0.8 0.8 7.0 9.4 9.4 81 116 6 6 Phoenix 040134011 1 6.4 6.4 6.4 6.4 6.4 6.4 18 18 18 Phoenix 040139997 10 8.5 1.2 5.6 5.6 8.7 9.9 9.9 115 328 5 5 Provo 490490002 7 6.6 3.7 1.2 1.2 8.2 9.4 9.4 60 38 7 7 St Louis 171630010 48 7.0 2.8 1.3 1.9 8.0 9.8 9.9 112 178 5 5 St Louis 291830010 1 1.7 1.7 1.7 1.7 1.7 1.7 7821 7821 7821 St Louis 291831002 9 7.5 2.1 4.3 4.3 7.7 9.9 9.9 1868 4704 7 7 St Louis 291890001 10 7.7 1.3 6.2 6.2 7.4 9.8 9.8 24 20 5 5 St Louis 291890004 6 8.9 1.5 6.9 6.9 9.8 10.0 10.0 38 37 7 7 St Louis 291890006 8 7.0 1.7 4.2 4.2 7.9 8.7 8.7 25 34 6 6 St Louis 291893001 16 7.3 2.0 3.4 3.4 7.6 9.6 9.6 22 43 5 5 St Louis 291895001 11 7.5 1.7 4.3 4.3 7.7 9.7 9.7 46 62 5 5 St Louis 291897002 16 5.7 1.8 2.0 2.0 5.4 9.7 9.7 28 37 5 5 St Louis 291897003 16 6.2 2.0 2.5 2.5 6.0 9.6 9.6 24 33 5 5 St Louis 295100072 46 6.3 2.5 0.7 2.0 6.5 9.9 9.9 77 150 5 5 St Louis 295100080 31 6.9 2.2 0.4 0.4 7.3 10.0 10.0 98 176 5 5 St Louis 295100086 35 6.7 2.3 1.7 1.7 6.6 9.9 9.9 94 168 5 5 Washington DC 110010017 13 5.4 2.4 2.9 2.9 4.5 9.7 9.7 557 1643 11 11 Washington DC 110010025 6 6.4 1.0 4.8 4.8 6.5 7.6 7.6 40 35 11 11 Washington DC 110010041 10 6.1 2.4 0.6 0.6 6.1 9.8 9.8 124 137 11 11 Washington DC 110010043 12 5.0 3.2 0.3 0.3 4.6 9.8 9.8 109 129 11 11 Washington DC 240053001 11 7.5 2.1 2.6 2.6 7.9 9.7 9.7 1034 3225 6 6 Washington DC 245100040 26 5.0 2.5 0.3 0.3 4.9 9.5 9.5 122 220 6 6 Washington DC 245100050 24 6.2 2.1 2.4 2.4 6.0 10.0 10.0 129 227 6 6 Washington DC 510130020 14 6.2 2.6 1.5 1.5 5.4 9.8 9.8 558 1579 11 11 Washington DC 510590005 2 4.9 4.8 1.4 1.4 4.9 8.3 8.3 13 7 8 8 Washington DC 510590018 6 8.4 0.4 8.0 8.0 8.4 9.2 9.2 1104 2413 9 9 Washington DC 510591004 10 7.4 1.6 3.7 3.7 7.8 9.3 9.3 80 173 14 14 Washington DC 510591005 8 6.3 2.0 4.6 4.6 5.5 9.4 9.4 94 193 14 14 Washington DC 510595001 4 6.5 2.8 3.2 3.2 6.8 9.2 9.2 30 19 17 17 Washington DC 511071005 5 7.1 2.3 4.5 4.5 6.5 9.6 9.6 14 8 8 8 Washington DC 511530009 0 Washington DC 515100009 9 7.0 2.4 1.1 1.1 7.9 8.8 8.8 809 1959 14 14 Notes: 1 n is the number of sources emitting >5 tons per year (tpy) NOx within a 10 kilometer radius of a monitor in a particular location. 2 The min, 2.5, med, 97.5, and max represent the minimum, 2.5th, median, 97.5th, and maximum percentiles of the distribution for the distance in meters (m) to the source 
A-25

A-5 Spatial and Temporal Air Quality Analyses 

A-5.1 Introduction 

An analysis of the air quality was performed to determine spatial and temporal trends, 

considering locations, monitoring sites within locations, and time-averaging of ambient NO2

concentrations collected from 1995 through 2006. The purpose is to present relevant information 

on the air quality as it relates to both the current form of the standard (annual average 

concentration) and the exposure concentration and duration associated with adverse health 

effects (1-hour). 

A-5.2 Approach 

To evaluate variability in NO2 concentrations, temporal and spatial distributions of summary 

statistics were computed in addition to use of statistical tests to compare distributions between 

years and/or monitors and/or locations. For a given location, the variability within that location 

is defined by the distribution of the annual summary statistics across years and monitors and by 

the distribution of the hourly concentrations across hours and monitors. The summary statistics 

were compiled into tables and used to construct figures for visual comparison and for statistical 

analysis. 

Box-plots were constructed to display the distribution across sites and years (or hours for the 

hourly concentrations) for a single location. The box extends from the 25th to the 75th percentile, 

with the median shown as the line inside the box. The whiskers extend from the box to the 5th

and 95th percentiles. The extreme values in the upper and lower tails beyond the 5th and 95th

percentiles are not shown to allow for similar scaling along the y-axis for the plotted independent 

variables. The mean is plotted as a dot; typically it would appear inside the box, however it will 

fall outside the box if the distribution is highly skewed. All concentrations are shown in parts 

per billion (ppb). 

Q-Q plots also display the distribution in the calculated air quality metrics across sites and 

years (or hours for the hourly concentrations) for a single location. The Q-Q plot is used to 

compare the observed cumulative distribution to a standard statistical distribution. In this case 

the observed distributions are compared with a log-normal distribution, so that the vertical scale 

is logarithmic. The horizontal scale is the quantile of a standard normal distribution, so that if 

there are N observed values, then the kth highest value is plotted against the quantile probit(p), 

where probit is the inverse of the standard normal distribution function, and p is the plotting 

point. The plotting points were chosen as p = (k-3/8)/(N+1/4) for the annual statistics and p = 

k/(N+1) for the hourly concentrations. If the distribution were exactly log-normal, then the 

curve would be a straight line. The median value is the y-value when the normal quantile equals 

zero. The slope of the line is related to the standard deviation of the logarithms, so that the higher 

the slope, the higher the coefficient of variation (standard deviation divided by the mean for the 

raw data, before taking logarithms). 

In addition to the tabular and graphical comparisons of the summary statistics, the 

distributions of each variable were compared using various statistical tests. An F-Statistic 

comparison compares the mean values between locations using a one way analysis of variance 

(ANOVA). This test assumed that for each location, the site-year or site-hour variables are 
A-26

normally distributed, with a mean that may vary with the location and a constant variance (i.e., 

the same for each location). Statistical significance was assigned for p-values less than or equal 

to 0.05. The Kruskal-Wallis Statistics are non-parametric tests that are extensions of the more 

familiar Wilcoxon tests to two or more groups. The analysis is valid if the difference between 

the variable and the location median has the same distribution for each location. If so, this 

procedure tests whether the location medians are equal. The test is also consistent under weaker 

assumptions against more general alternatives. The Mood Statistic comparisons are nonparametric tests that compare the scale statistics for two or more groups. The scale statistic 

measures variation about the central value, which is a non-parametric generalization of the 

standard deviation. This test assumes that all the groups have the same median. Specifically, 

suppose there is a total of N values, summing across all the locations to be compared. These N 

values are ranked from 1 to N, and the jth highest value is given a score of {j - (N+1)/2}2

. The 

Mood statistic uses a one-way ANOVA statistic to compare the mean scores for each location. 

Thus the Mood statistic compares the variability between the different locations assuming that 

the medians are equal. 

A-5.3 Summary Results by Locations 

A summary of the important trends in NO2 concentrations is reported in this section. 

Detailed air quality results (i.e., by year and within-location) are presented in section A-5.4, 

containing both tabular and graphic summaries of the spatial and temporal concentration 

distributions. 

A broad view of the NO2 monitoring concentrations across locations is presented in Figures 

A-1 and A-2. In general there is variability in NO2 concentrations between the 20 locations. For 

example, in Los Angeles, the mean of annual means is approximately 24.3 ppb over the period of 

analysis, while considering the Not MSA grouping, the mean annual mean was about 7.0 ppb. 

Phoenix contained the highest mean annual mean of 27.3 ppb. Variability in the annual average 

concentrations was also present within locations, the magnitude of which varied by location. On 

average, the coefficient of variation in the annual mean concentrations was about 35%, however 

locations such as Jacksonville or Provo had COVs as low as 6% while locations such as Las 

Vegas and Not MSA contained COVs above 60%. Reasons for differing variability arise from 

the size of the monitoring network in a location, level of the annual mean concentration, 

underlying influence of temporal variability within particular locations, among others. 
A-27

Figure A-1. Distributions of annual mean NO2 ambient monitoring concentrations for selected CMSA 

locations, years 1995-2006. 

Figure A-2. Distributions of annual mean NO2 ambient monitoring concentrations for selected MSA and 

grouped locations, years 1995-2006. 

Differences in the distributions of hourly concentrations were of course consistent with that 

observed for the annual mean concentrations, and as expected there were differences in the 
A-28

COVs across locations, ranging from about 60 to 120%. However, in comparing the 90 percent 

intervals (from the 5th to the 95th percentiles) of hourly concentrations across locations, the 

ranges are somewhat similar (for example see Figure 3 for the CMSA locations). This means 

that the intervals for the annual mean differ more than that of the hourly concentrations between 

locations likely due to the influence of high 1-hour NO2 concentrations for certain locations. 

Figure A-3. Distributions3 of hourly NO2 ambient monitoring concentrations for selected CMSA locations, 

years 1995-2006.

This presence of extreme NO2 concentrations is best illustrated in Figure 4 using a Q-Q plot 

that captures the full concentration distribution for each CMSA location. The Q-Q plots are 

generally curved rather than straight, such that the distributions do not appear to be log-normal. 

However, the annual mean and hourly concentration curves do tend to be approximately straight 

and parallel for values above the median (normal quantile = 0) through the 3rd quantile, 

suggesting that these upper tails of the distributions are approximately log-normal with 

approximately the same coefficients of variation. Beyond the 3rd quantile though, each 

distribution similarly and distinctly curves upwards, indicating a number of uncharacteristic NO2

concentrations at each location when compared with the rest of their respective concentration 

distributions. 

 

3 The box-plots for hourly concentrations were created using a different procedure than for the annual 

statistics, because of the large number of hourly values and the inability of the graphing procedure to 

allow frequency weights. Therefore, the appropriate weighted percentiles and means were calculated 

and plotted as shown, but the vertical lines composing the sides of the box were omitted. 
A-29

Figure A-4. Distributions of hourly NO2 ambient concentration for selected CMSA locations, years 1995-

2006.

Distributions of each variable (annual means and hourly concentrations) were compared 

between the different locations using statistical tests. The results in Table A-9 show statistically 

significant differences between locations for both variables and all three summary statistics 

(means, medians, and scales). This supports the previous observation that the distributions for the 

different locations are dissimilar. 

Table A-9. Statistical test results for spatial comparisons of all location parameter distributions.

Means Comparison Central Values Comparison Scales Comparison 

Concentration 

Parameter F Statistic p-value Kruskal-Wallis p-value Mood p-value 

Annual Mean 148 <0.0001 1519 <0.0001 729 <0.0001 

Hourly 330272 <0.0001 5414056 <0.0001 1354075 <0.0001 

The distributions of NO2 concentrations within locations were also evaluated. As an 

example, Figure A-5 illustrates the distribution of the annual mean NO2 concentration at 10 

monitoring sites within Philadelphia. The mean annual means vary from a minimum of 14.8 ppb 

(site 1000310071) to a maximum of 30.5 ppb (site 4210100471). The range of within-site 

variability can be attributed to the number of monitoring years available coupled with the 

observed trends in temporal variability across the monitoring period (discussed below in Section 

2.4.4). 

Distributions of each variable (annual means and hourly NO2 concentrations) within 

locations (i.e., site distributions) were compared using statistical tests. The results in Table A-10 

indicate statistically significant differences within locations for both variables and the central 
A-30

tendency statistics (means and medians), while scales were statistically significant for 38 out of 

40 possible tests. This supports the previous observation that the distributions for the different 

locations are dissimilar. 

Figure A-5. Distributions of annual average NO2 concentrations among 10 monitoring sites in 

Philadelphia CMSA, years 1995-2006. 

Table A-10. Statistical test results for spatial comparisons of within-location parameter distributions. 

Means Comparison 

Central Values 

Comparison Scales Comparison 

Concentration 

Parameter Location 

F 

Statistic p-value 

KruskalWallis p-value Mood p-value 

Atlanta 119 <0.001 45.2 <0.001 28.6 <0.001

Boston 47.3 <0.001 96.5 <0.001 79.9 <0.001 

Chicago 123 <0.001 76.7 <0.001 68.5 <0.001 

Cleveland 12.1 <0.001 15.4 0.002 7.5 0.058 

Colorado Springs 8.7 <0.001 18.8 0.009 8.7 0.273 

Denver 85.3 <0.001 32.0 <0.001 23.0 0.001 

Detroit 13.2 <0.001 13.1 0.001 7.8 0.020 

El Paso 36.0 <0.001 31.6 <0.001 35.3 <0.001

Las Vegas 137 <0.001 45.4 <0.001 35.2 <0.001

Los Angeles 49.0 <0.001 325 <0.001 240 <0.001 

Miami 111 <0.001 36.2 <0.001 29.9 <0.001

New York 106 <0.001 163 <0.001 151 <0.001

Philadelphia 48.9 <0.001 68.8 <0.001 33.0 <0.001

Phoenix 20.4 <0.001 32.2 <0.001 23.6 0.001 

Annual Mean 

St. Louis 51.5 <0.001 82.1 <0.001 69.0 <0.001
A-31

Means Comparison 

Central Values 

Comparison Scales Comparison 

Concentration 

Parameter Location 

F 

Statistic p-value 

KruskalWallis p-value Mood p-value 

Washington DC 48.6 <0.001 104 <0.001 71.2 <0.001

Other MSA 82.5 <0.001 2152 <0.001 1934 <0.001

Other Not MSA 76.9 <0.001 424 <0.001 372 <0.001

Atlanta 35917 <0.001 137022 <0.001 17330 <0.001

Boston 17884 <0.001 312994 <0.001 59896 <0.001

Chicago 11611 <0.001 142034 <0.001 37224 <0.001

Cleveland 4191 <0.001 14102 <0.001 1985 <0.001

Denver 25130 <0.001 104800 <0.001 2864 <0.001

Colorado Springs 5541 <0.001 48252 <0.001 3921 <0.001

Detroit 4125 <0.001 10442 <0.001 424 <0.001

El Paso 10503 <0.001 57694 <0.001 18334 <0.001

Las Vegas 22567 <0.001 136455 <0.001 28972 <0.001

Los Angeles 27288 <0.001 1050310 <0.001 269190 <0.001

Miami 10669 <0.001 68580 <0.001 43090 <0.001

New York 20052 <0.001 404234 <0.001 91104 <0.001

Philadelphia 13759 <0.001 112129 <0.001 4903 <0.001

Phoenix 5626 <0.001 35645 <0.001 6747 <0.001

St. Louis 14807 <0.001 178180 <0.001 47842 <0.001

Washington 14262 <0.001 223040 <0.001 30974 <0.001

Other MSA 19557 <0.001 6306431 <0.001 2164452 <0.001

Hourly 

Other Not MSA 17630 <0.001 1580139 <0.001 491390 <0.001

A-5.4 Summary Results by Year 

A broad view of the trend of NO2 monitoring concentrations over time is presented in Figure 

A-6. The annual mean concentrations were calculated for each monitor site within each year to 

create a distribution of annual mean concentrations for each year. The distribution of annual 

mean concentrations generally decreases with each increasing year. On average, mean annual 

mean NO2 concentrations consistently decrease from a high of 17.5 ppb in 1995 to the most 

recent mean of 12.3 ppb. Also notable is the consistent pattern in the decreasing concentrations 

across each years distribution, the shape of each curve is similar indicating that while 

concentrations have declined, the variability within each year is similar from year to year. The 

variability within a given year is representing spatial differences in annual average 

concentrations across the 20 locations. 
A-32

Figure A-6. Distributions of annual mean NO2 concentrations for all monitors, years 1995-2006. 

In general, temporal trends within a location were also consistent with the trends observed in 

all monitors, particularly where the location’s monitoring network was comprised of several 

monitors. For example, Figure A-7 illustrates the temporal distributions of annual average NO2

concentration in the Philadelphia CMSA, each comprising between 4 and 8 monitors in operation 

per year. Clearly NO2 concentrations are decreased with increasing calendar year of monitoring 

with the lowest NO2 concentrations in the more recent years of monitoring. The pattern of 

variability in NO2 concentration within a year at this location is also similar when comparing 

across years based on similarities in the shape of each years respective curve. 
A-33

Figure A-7. Distributions of annual mean NO2 concentrations for the Philadelphia CMSA, years 1995-

2006. 

In general, temporal trends within a location considering the hourly concentration data were 

consistent with the above, particularly where the monitoring network was comprised of several 

monitors. For example, Figure A-8 illustrates the temporal distribution for hourly NO2

concentration in the Los Angeles CMSA, comprising between 26 and 36 monitors in operation 

per year. NO2 concentrations are decreased with increasing calendar year of monitoring with the 

distribution of hourly concentrations lowest in the more recent years of monitoring. The pattern 

of variability in NO2 concentration within a year at this location is also similar when comparing 

across years based on similarities in the shape of each years respective curve. 
A-34

Figure A-8. Distributions of hourly NO2 concentrations in the Los Angeles CMSA, years 1995-2006. 

These temporal trends were confirmed by statistical comparison tests. The means and 

medians of the annual means and hourly concentrations compared across the different years were 

statistically significant (all p<0.0001). A Mood test indicated that, for the annual means, the 

scales were also significantly different (both the annual and hourly p<0.001). Note, however, 

that the Mood test derivation assumes that the medians of the annual means are the same for each 

year, whereas the plots and the Kruskall-Wallis test result implies that the medians are not the 

same. As noted before, Figure A-8 indicates that the Q-Q curves for different years have similar 

slopes but different intercepts, which implies that the annual means for different years have 

different mean values but similar coefficients of variation. In fact the coefficients of variation of 

the annual means are nearly identical for different years, ranging from 52 % to 55 %. 

There were some exceptions to this temporal trend, particularly when considering the 

distribution of hourly concentrations and where a given location had only few monitors per year. 

Using Jacksonville as an example, Figure A-9 illustrates the same temporal trend in NO2

concentrations as was observed above for much of the distribution, however distinctions are 

noted at the upper tails of the distribution for two years of data, 2002 and 2004. For 

Jacksonville, each years’ hourly concentration distribution was based on only a single monitor. 

Where few monitors exist in a given location, atypical variability in one or a few monitors from 

year to year can greatly influence the distribution of short-term concentrations, particularly at the 

upper percentiles. 

The same follows for assignment of statistical significance to temporal trends within 

locations. While annual average concentrations are observed to have declined over time within a 

location, the number of sites were typically few thus limiting the power of the statistical tests. 
A-35

Only Los Angeles, El Paso, Phoenix, and Other CMSA were significant (p<0.05) for the central 

tendency tests, while only Los Angeles and Other CMSA were significant (p<0.05) for scale 

(data not shown). All hourly concentrations comparison tests for years within each location were 

statistically significant (p<0.05) for all three test statistics (mean, median, scale). 

 

 

Figure A-9. Distributions of hourly NO2 concentrations in the Jacksonville MSA, years 1995-2006, one 

monitor.

There is very little difference in annual average concentrations across the 1995-2006 

monitoring period for the grouped Not MSA location. While percentage-wise the reduction in 

concentration is about 25%, on a concentration basis this amounts to a reduction of about 2 ppb 

over the 11 year period (Figure A-10). When considering the last 5 years of data, the reduction 

in annual average concentration was only 0.5 ppb. This could indicate that many of these 

monitoring sites are affected less by local sources of NO2 (e.g., emissions from major roads and 

stationary sources) compared with the other locations. Therefore, the areas that these monitors 

represent may also be less likely to see significant benefit by changes in source emissions and/or 

NO2 standard levels compared with the named CMSA/MSA locations. 
A-36

Figure A-10. Distributions of annual average NO2 concentrations in the Other Not MSA group location, 

years 1995-2006. 

A-5.5 Detailed Results by Year and Location 

This section contains the ambient air quality analysis results by year for each of the named 

locations. Boxplots were constructed to display the annual average and hourly concentration 

distributions across years for a single location. The box extends from the 25th to the 75th

percentile, with the median shown as the line inside the box. The whiskers extend from the box 

to the 5th and 95th percentiles. The extreme values in the upper and lower tails beyond the 5th and 

95th percentiles are not shown to allow for similar scaling along the y-axis for the plotted 

independent variables. The mean is plotted as a dot; typically it would appear inside the box, 

however it will fall outside the box if the distribution is highly skewed. All concentrations are 

shown in parts per billion (ppb). The boxplots for hourly concentrations were created using a 

different procedure than for the annual statistics, because of the large number of hourly values 

and the inability of the graphing procedure to allow frequency weights. Therefore, the 

appropriate weighted percentiles and means were calculated and plotted as shown, but the 

vertical lines composing the sides of the box are essentially omitted. Tables are provided that 

summarize the complete distribution, with percentiles given in segments of 10. 
A-37

Figure A-11. Distribution of annual average NO2 ambient 

concentrations (ppb) by year, Boston CMSA.

Figure A-12. Distribution of hourly NO2 ambient concentrations 

(ppb) by year, Boston CMSA. 

Table A-11. Distribution of annual average NO2 ambie

year, Boston CMSA.

Year n Mean SD COV Min p10 p20 p30 p4

1995 12 20 7 34 6 12 14 16 21996 10 19 8 42 6 8 11 14 11997 11 17 8 44 6 9 11 13 11998 11 17 8 48 6 8 10 12 11999 7 19 9 45 6 6 9 20 22000 7 17 8 49 5 5 10 11 12001 10 16 8 50 7 7 8 10 12002 10 16 7 43 5 7 10 12 12003 5 15 6 42 9 9 10 11 12004 7 15 6 41 7 7 9 12 12005 8 14 6 39 7 7 10 10 12006 7 13 6 42 8 8 9 10 1

Table A-12. Distribution of hourly NO2 ambient conce

CMSA.

Year n Mean SD COV Min p10 p20 p30 

1995 99946 20 12 62 0 5 9 12 1996 83541 19 14 72 0 3 7 10 1997 90161 17 12 72 0 3 6 9 1998 89710 17 13 75 0 3 5 8 1999 54043 19 13 70 0 3 7 10 2000 56196 16 12 76 0 2 5 7 2001 82048 16 13 77 0 2 4 7 2002 80472 16 12 75 0 2 5 7 2003 41198 15 11 75 0 3 5 7 2004 56831 15 10 71 0 3 5 7 2005 66244 14 11 75 0 3 5 7 2006 57681 13 10 74 0 3 4 6 
A-38

Figure A-13. Distribution of annual average NO2 ambient 

concentration (ppb) by monitor, Boston CMSA set A, 1995-2006. 

Figure A-14. Distribution of hourly NO2 ambient concentration 

(ppb) by monitor, Boston CMSA set A, 1995-2006.

Table A-13. Distribution of annual average NO2 ambie

monitor, Boston CMSA set A, 1995-2006.

Monitor ID n Mean SD COV Min p10 p20 p30 

2303130021 7 10 1 9 9 9 9 9 2500510051 2 8 1 9 8 8 8 8 2500920061 10 12 3 22 9 9 10 10 2500940041 5 6 0 7 6 6 6 6 2500950051 2 9 1 8 9 9 9 9 2502100091 1 22 22 22 22 22 2502500021 11 28 3 11 23 23 25 25 2502500211 8 25 3 12 21 21 22 23 2502500351 1 23 23 23 23 23 2502500361 1 22 22 22 22 22 2502500401 11 21 2 10 16 18 20 21 2502500411 1 12 12 12 12 12 2502500421 6 21 3 16 17 17 19 19 2502510031 5 23 2 7 21 21 21 22 2502700201 8 19 1 6 17 17 18 19 

Table A-14. Distribution of hourly NO2 ambient conce

Boston CMSA set A, 1995-2006.

Monitor ID n Mean SD COV Min p10 p20 p

2303130021 58123 10 9 94 0 1 2 2500510051 16732 8 7 81 0 2 3 2500920061 80761 12 10 80 0 3 4 2500940041 41337 6 7 108 0 0 1 2500950051 16228 9 8 91 0 2 3 2502100091 8546 22 10 46 0 9 13 12502500021 87534 28 11 40 0 14 18 22502500211 63990 25 11 45 0 13 16 12502500351 8539 23 10 47 0 10 13 12502500361 8542 22 11 49 0 9 12 12502500401 91196 21 12 59 1 7 10 12502500411 8319 12 10 89 0 2 3 2502500421 48078 21 10 48 0 9 12 1
A-39

Figure A-15. Distribution of annual average NO2 ambient 

concentration (ppb) by monitor, Boston CMSA set B, 1995-2006.

Figure A-16. Distribution of hourly NO2 ambient concentration 

(ppb) by monitor, Boston CMSA set B, 1995-2006.

Table A-15. Distribution of annual average NO2 ambie

monitor, Boston CMSA set B, 1995-2006.

Monitor ID n Mean SD COV Min p10 p20 p30 

2502700231 3 15 0 3 15 15 15 15 3301100161 4 16 2 15 14 14 14 15 3301100191 1 11 11 11 11 11 3301100201 5 12 1 8 10 10 11 11 3301500091 5 12 1 12 9 9 11 12 3301500131 4 6 1 10 5 5 5 5 3301500141 3 8 0 6 7 7 7 7 3301500151 1 13 13 13 13 13 

Table A-16. Distribution of hourly NO2 ambient concen

CMSA set B, 1995-2006.

Monitor ID n Mean SD COV Min p10 p20 p

2502510031 40775 23 12 54 0 9 12 12502700201 63836 19 11 59 0 6 9 12502700231 24267 15 9 58 0 5 8 13301100161 33436 16 10 64 0 6 8 3301100191 8022 11 9 81 0 2 3 3301100201 41325 12 9 75 0 3 4 3301500091 40978 12 9 77 0 2 4 3301500131 33536 6 7 118 0 0 1 3301500141 25372 8 7 94 0 1 2 3301500151 8599 13 9 75 0 3 5 
A-40

Figure A-17. Distribution of annual average NO2 ambient 

concentrations (ppb) by year, Chicago CMSA. 

Figure A-18. Distribution of hourly NO2 ambient concentrations 

(ppb) by year, Chicago CMSA.

Table A-17. Distribution of annual average NO2 ambie

year, Chicago CMSA.

Year n Mean SD COV Min p10 p20 p30 p4

1995 7 28 3 12 23 23 24 26 21996 7 24 8 32 9 9 21 23 21997 6 25 8 34 9 9 23 23 21998 9 23 7 32 9 9 17 19 21999 9 23 7 29 10 10 17 19 22000 9 22 7 30 9 9 18 20 22001 7 25 5 21 18 18 18 24 22002 7 24 6 24 17 17 19 22 22003 5 26 5 19 20 20 21 22 22004 6 23 6 25 16 16 18 18 22005 6 23 5 23 17 17 18 18 22006 5 23 6 27 16 16 17 18 2

Table A-18. Distribution of hourly NO2 ambient conce

CMSA.

Year n Mean SD COV Min p10 p20 p30 

1995 58998 28 14 51 0 11 15 19 1996 59447 24 14 58 0 7 11 15 1997 51443 25 15 59 0 7 11 15 1998 76365 23 14 61 0 6 10 13 1999 74985 23 14 61 0 7 10 13 2000 75327 22 14 62 0 6 10 13 2001 58268 25 13 54 0 9 13 16 2002 58383 24 14 59 0 8 12 15 2003 42406 26 14 54 0 10 14 17 2004 49210 23 13 57 0 8 11 14 2005 51043 23 13 59 0 8 11 14 2006 42009 23 13 57 0 8 11 14 
A-41

Figure A-19. Distribution of annual average NO2 ambient 

concentration (ppb) by monitor, Chicago CMSA, 1995-2006.

Figure A-20. Distribution of hourly NO2 ambient concentration (ppb) 

by monitor, Chicago CMSA, 1995-2006.

Table A-19. Distribution of annual average NO2 ambie

monitor, Chicago CMSA, 1995-2006.

Monitor ID n Mean SD COV Min p10 p20 p30 

1703100371 1 29 29 29 29 29 1703100631 12 31 1 4 29 30 31 31 1703100641 6 23 2 8 21 21 22 22 1703100751 4 24 0 2 23 23 23 24 1703100761 5 20 2 9 18 18 19 20 1703131011 3 31 1 3 30 30 30 30 1703131031 9 29 1 5 28 28 28 28 1703140021 12 26 2 8 22 23 24 24 1703142011 4 17 1 4 17 17 17 17 1703142012 4 17 1 4 16 16 16 16 1703180031 8 23 1 4 22 22 22 23 1719710111 5 9 1 6 9 9 9 9 1808900221 8 19 1 4 18 18 18 18 1808910162 2 22 2 7 21 21 21 21 

Table A-20. Distribution of hourly NO2 ambient conce

Chicago CMSA, 1995-2006.

Monitor ID n Mean SD COV Min p10 p20 p

1703100371 8630 29 13 44 0 15 19 21703100631 101935 31 15 46 0 13 19 21703100641 52139 23 13 57 0 8 11 1703100751 34028 24 12 52 0 10 13 1703100761 42946 20 12 59 0 7 10 1703131011 25141 31 13 41 3 16 20 21703131031 75061 29 13 44 0 14 18 21703140021 102779 26 13 51 0 11 14 1703142011 32625 17 11 64 0 5 7 1703142012 32552 17 10 62 0 6 8 1703180031 68952 23 12 53 0 9 12 1719710111 41227 9 6 69 0 3 4 1808900221 63295 19 12 66 0 4 7 1808910162 16574 22 12 56 3 9 12 
A-42

Figure A-21. Distribution of annual average NO2 ambient 

concentrations (ppb) by year, Cleveland CMSA. 

Figure A-22. Temporal distribution of hourly NO2 ambient 

concentrations (ppb) by year, Cleveland CMSA. 

Table A-21. Distribution of annual average NO2 ambie

year, Cleveland CMSA.

Year n Mean SD COV Min p10 p20 p30 p40 

1995 2 24 5 19 21 21 21 21 21 1996 2 23 4 19 20 20 20 20 20 1997 1 28 0 28 28 28 28 28 1998 2 24 5 22 20 20 20 20 20 1999 2 21 5 26 17 17 17 17 17 2000 2 20 4 19 18 18 18 18 18 2001 2 21 4 17 19 19 19 19 19 2002 2 20 4 18 17 17 17 17 17 2003 2 20 3 15 17 17 17 17 17 2004 1 22 0 22 22 22 22 22 2005 2 19 3 17 17 17 17 17 17 2006 2 16 3 17 14 14 14 14 14 

Table A-22. Distribution of hourly NO2 ambient conce

Cleveland CMSA.

Year n Mean SD COV Min p10 p20 p30 p

1995 16042 24 13 53 2 10 13 16 11996 16593 23 12 52 1 9 13 15 11997 8300 28 17 59 0 12 15 18 21998 16680 24 13 53 0 9 13 16 11999 16743 21 12 58 0 7 10 13 12000 16399 20 11 55 0 8 10 13 12001 16566 21 12 56 0 8 10 13 12002 16464 20 11 56 1 8 10 12 12003 16948 20 11 57 0 7 10 13 12004 8484 22 11 51 0 10 13 15 12005 16558 19 12 60 0 7 9 12 12006 16853 16 10 64 0 5 8 10 1
A-43

Figure A-23. Distribution of annual average NO2 ambient 

concentration (ppb) by monitor, Cleveland CMSA, 1995-2006.

Figure A-24. Distribution of hourly NO2 ambient concentration (ppb) 

by monitor, Cleveland CMSA, 1995-2006.

Table A-23. Distribution of annual average NO2 ambie

monitor, Cleveland CMSA, 1995-2006.

Monitor ID n Mean SD COV Min p10 p20 p30 

3903500431 2 20 1 4 20 20 20 20 3903500601 12 24 3 12 18 22 22 22 3903500661 6 18 1 6 17 17 17 17 3903500701 2 15 2 12 14 14 14 14 

Table A-24. Distribution of hourly NO2 ambient conce

Cleveland CMSA, 1995-2006.

Monitor ID n Mean SD COV Min p10 p20 p

3903500431 16215 20 11 54 1 8 11 13903500601 99696 24 13 53 0 10 13 13903500661 50100 18 11 60 0 7 9 13903500701 16619 15 11 70 0 5 7 
A-44

Figure A-25. Distribution of annual average NO2 ambient 

concentrations (ppb) by year, Denver CMSA.

Figure A-26. Distribution of hourly NO2 ambient concentrations 

(ppb) by year, Denver CMSA. 

Table A-25. Temporal distribution of annual average N(ppb) by year, Denver CMSA.

Year n Mean SD COV Min p10 p20 p30 p40 

1995 3 28 6 23 23 23 23 23 26 1996 6 14 11 77 6 6 7 7 8 1997 6 15 11 74 6 6 8 8 9 1998 5 16 13 77 7 7 7 7 8 1999 3 12 6 52 8 8 8 8 9 2000 3 12 3 26 9 9 9 9 10 2001 2 31 8 25 26 26 26 26 26 2002 1 35 0 35 35 35 35 35 2003 1 21 0 21 21 21 21 21 2004 2 24 4 17 21 21 21 21 21 2005 2 24 5 21 20 20 20 20 20 2006 2 24 8 33 18 18 18 18 18 

Table A-26. Temporal distribution of hourly NO2 ambi

year, Denver CMSA.

Year n Mean SD COV Min p10 p20 p30 p

1995 23204 28 17 62 0 6 11 16 21996 46816 14 15 108 0 1 2 4 1997 45049 15 15 106 0 1 3 4 1998 40258 17 17 100 0 1 3 5 1999 23164 12 13 108 0 0 2 4 2000 24649 12 13 108 0 0 1 3 2001 15204 31 17 55 0 8 15 21 22002 7688 35 13 36 0 20 24 28 32003 6989 21 17 78 0 3 5 8 12004 15878 24 15 60 0 4 10 16 22005 15467 24 16 65 0 3 8 14 12006 13775 24 15 65 0 3 7 13 1
A-45

Figure A-27. Distribution of annual average NO2 ambient 

concentration (ppb) by monitor, Denver CMSA, 1995-2006.

Figure A-28. Distribution of hourly NO2 ambient concentration 

(ppb) by monitor, Denver CMSA, 1995-2006.

Table A-27. Distribution of annual average NO2 ambie

monitor, Denver CMSA, 1995-2006.

Monitor ID n Mean SD COV Min p10 p20 p30 

0800130011 11 21 3 14 15 18 19 20 0800500031 1 26 26 26 26 26 0803100021 9 33 4 11 27 27 28 29 0805900061 3 7 0 6 7 7 7 7 0805900081 4 9 1 7 9 9 9 9 0805900091 3 9 1 8 8 8 8 8 0805900101 5 7 1 19 6 6 6 6 

Table A-28. Distribution of hourly NO2 ambient conce

Denver CMSA, 1995-2006.

Monitor ID n Mean SD COV Min p10 p20 p

0800130011 83703 21 17 82 0 2 4 0800500031 7790 26 15 57 0 8 12 10803100021 68630 33 15 46 0 15 20 20805900061 22077 7 8 109 0 1 1 0805900081 32449 9 9 97 0 0 2 0805900091 24368 9 9 100 0 1 2 0805900101 39124 7 8 106 0 1 2 
A-46

Figure A-29. Distribution of annual average NO2 ambient 

concentrations (ppb) by year, Detroit CMSA.

Figure A-30. Distribution of hourly NO2 ambient concentrations 

(ppb) by year, Detroit CMSA. 

Table A-29. Distribution of annual average NO2 ambie

year, Detroit CMSA.

Year n Mean SD COV Min p10 p20 p30 p40 

1995 2 20 2 10 19 19 19 19 19 1996 3 18 5 28 12 12 12 12 20 1997 3 17 8 44 13 13 13 13 13 1998 2 21 3 14 19 19 19 19 19 1999 1 18 0 18 18 18 18 18 2000 1 24 0 24 24 24 24 24 2001 2 21 3 14 19 19 19 19 19 2002 2 20 1 7 19 19 19 19 19 2003 2 20 2 12 19 19 19 19 19 2004 2 17 3 16 15 15 15 15 15 2005 2 18 2 9 17 17 17 17 17 2006 2 15 1 9 14 14 14 14 14 

Table A-30. Distribution of hourly NO2 ambient conce

CMSA.

Year n Mean SD COV Min p10 p20 p30 p

1995 16629 20 12 58 0 8 10 12 11996 23600 18 13 74 0 4 7 9 11997 24117 17 16 94 0 2 5 7 11998 14863 21 14 68 0 5 9 12 11999 7110 18 13 73 0 4 7 9 12000 8590 24 13 56 0 8 12 15 12001 15154 21 13 61 0 7 9 12 12002 16623 20 15 73 0 7 10 12 12003 16569 20 13 62 0 7 9 12 12004 14779 17 11 66 0 5 7 9 12005 15827 19 12 63 0 6 8 10 12006 17273 15 10 64 0 4 6 8 1
A-47

Figure A-31. Distribution of annual average NO2 ambient 

concentration (ppb) by monitor, Detroit CMSA, 1995-2006.

Figure A-32. Distribution of annual average NO2 ambient 

concentration (ppb) by monitor, Detroit CMSA, 1995-2006.

Table A-31. Distribution of annual average NO2 ambie

monitor, Detroit CMSA, 1995-2006.

Monitor ID n Mean SD COV Min p10 p20 p30 

2609900091 2 12 0 3 12 12 12 12 2616300161 11 21 3 13 16 19 20 20 2616300192 11 18 3 14 13 14 15 17 

Table A-32. Distribution of annual average NO2 ambie

monitor, Detroit CMSA, 1995-2006.

Monitor ID n Mean SD COV Min p10 p20 p

2609900091 16523 12 9 75 0 3 5 2616300161 86487 21 13 62 0 7 10 12616300192 88124 18 13 75 0 5 7 
A-48

Figure A-33. Distribution of annual average NO2 ambient 

concentrations (ppb) by year, Los Angeles CMSA. 

Figure A-34. Distribution of hourly NO2 ambient concentrations 

(ppb) by year, Los Angeles CMSA.

Table A-33. Distribution of annual average NO2 ambie

year, Los Angeles CMSA.

Year n Mean SD COV Min p10 p20 p30 p40 

1995 36 29 13 47 5 8 18 20 23 1996 29 25 12 46 4 6 15 17 21 1997 33 25 12 47 4 8 14 16 20 1998 32 25 11 44 4 9 16 19 21 1999 31 27 12 44 5 10 18 20 23 2000 32 25 11 43 4 10 16 20 22 2001 31 25 11 43 4 9 17 19 24 2002 32 24 9 39 5 10 16 18 22 2003 32 23 9 37 5 11 15 18 21 2004 28 22 7 33 5 13 15 17 20 2005 28 21 7 34 5 12 14 16 19 2006 26 19 7 35 5 9 13 15 17 

Table A-34. Distribution of hourly NO2 ambient conce

Angeles CMSA.

Year n Mean SD COV Min p10 p20 p30 p

1995 290519 29 22 78 0 6 9 14 1996 232203 26 19 74 0 5 8 12 1997 263050 25 19 75 0 4 7 11 1998 257541 25 19 74 0 5 8 12 1999 253401 27 20 73 0 5 8 13 2000 263311 25 18 72 0 5 8 12 2001 251895 25 18 71 0 5 8 12 2002 258452 24 17 71 0 5 8 11 2003 259935 23 17 72 0 4 7 11 2004 225075 22 15 70 0 4 7 11 2005 227769 21 14 69 0 4 7 11 2006 184205 19 14 74 0 3 6 9 
A-49

Figure A-35. Distribution of annual average NO2 ambient 

concentration (ppb) by monitor, Los Angeles CMSA set A, 1995-2006.

Figure A-36. Distribution of hourly NO2 ambient concentration 

(ppb) by monitor, Los Angeles CMSA set A, 1995-2006.

Table A-35. Distribution of annual average NO2 ambie

monitor, Los Angeles CMSA set A, 1995-2006.

Monitor ID n Mean SD COV Min p10 p20 p30 

0603700022 12 33 7 22 20 25 25 29 0603700161 12 28 5 17 20 22 24 26 0603700301 1 38 38 38 38 38 0603701131 12 24 4 18 16 17 20 23 0603702061 1 45 45 45 45 45 0603710022 11 38 6 16 26 29 33 35 0603711031 11 36 6 16 27 27 32 33 0603712012 12 26 4 17 17 20 21 24 0603713012 12 37 6 16 28 30 31 31 0603716012 10 37 4 11 31 33 35 35 0603717012 12 39 7 17 30 31 31 35 0603720051 12 32 5 15 23 24 27 29 0603740022 11 30 5 16 20 24 28 29 0603750011 9 27 2 9 23 23 23 24 0603750051 2 13 0 1 13 13 13 13 

Table A-36. Distribution of hourly NO2 ambient conce

Angeles CMSA set A, 1995-2006.

Monitor ID n Mean SD COV Min p10 p20 p

0603700022 97734 33 20 59 0 11 16 20603700161 97838 28 18 63 0 8 13 10603700301 6817 38 17 44 8 21 25 20603701131 97124 24 16 67 0 7 9 10603702061 7604 45 25 56 0 19 25 30603710022 88656 38 19 49 0 17 23 20603711031 88425 36 19 52 0 15 20 20603712012 96922 26 16 64 0 7 11 10603713012 97352 37 17 45 0 19 24 20603716012 81411 37 18 48 0 17 23 20603717012 98551 39 18 47 0 19 25 20603720051 98151 32 17 54 0 13 18 20603740022 88730 30 17 58 0 12 16 10603750011 74014 27 19 72 0 5 9 10603750051 15047 13 15 114 0 0 1 
A-50

Figure A-37. Distribution of annual average NO2 ambient 

concentration (ppb) by monitor, Los Angeles CMSA set B 1995-2006.

Figure A-38. Distribution of hourly NO2 ambient concentration 

(ppb) by monitor, Los Angeles CMSA set B, 1995-2006.

Table A-37. Distribution of annual average NO2 ambie

monitor, Los Angeles CMSA set B, 1995-2006.

Monitor ID n Mean SD COV Min p10 p20 p30 

0603760021 2 27 4 14 25 25 25 25 0603760121 5 20 1 6 18 18 19 19 0603790021 6 16 2 12 14 14 15 15 0603790331 5 15 0 3 15 15 15 15 0605900015 5 33 3 8 29 29 31 32 0605900075 4 22 2 10 20 20 20 21 0605910031 12 18 3 16 12 13 16 17 0605950012 11 30 6 19 21 25 25 25 0606500121 9 19 3 14 15 15 16 17 0606550012 12 16 3 22 9 12 13 15 0606580012 12 23 4 16 17 19 21 22 0606590011 12 17 2 11 14 14 15 15 0607100011 12 23 1 6 20 21 22 22 0607100121 2 7 0 5 7 7 7 7 0607100141 5 21 1 6 20 20 20 21 

Table A-38. Distribution of hourly NO2 ambient concen

Angeles CMSA set B, 1995-2006.

Monitor ID n Mean SD COV Min p10 p20 p

0603760021 16534 27 15 57 0 10 14 10603760121 39399 20 12 61 0 4 9 10603790021 46871 16 11 69 0 5 7 0603790331 40341 15 11 73 0 5 6 0605900015 40987 33 17 53 0 14 19 20605900075 33847 22 15 70 0 5 9 10605910031 97546 18 15 85 0 4 6 0605950012 88510 30 16 54 0 12 17 20606500121 69857 19 17 91 0 3 5 0606550012 95624 16 12 73 0 4 6 0606580012 95642 23 16 67 0 6 10 10606590011 95010 17 13 75 0 4 6 0607100011 94741 23 17 76 0 5 7 0607100121 14753 7 5 69 0 2 4 0607100141 39719 21 14 67 0 7 9 1
A-51

Figure A-39. Distribution of annual average NO2 ambient 

concentration (ppb) by monitor, Los Angeles CMSA set C 1995-2006.

Figure A-40. Distribution of hourly NO2 ambient concentration 

(ppb) by monitor, Los Angeles CMSA set C 1995-2006.

Table A-39. Distribution of annual average NO2 ambie

monitor, Los Angeles CMSA set C, 1995-2006.

Monitor ID n Mean SD COV Min p10 p20 p30 

0607100151 2 7 2 28 5 5 5 5 0607100171 3 6 0 4 6 6 6 6 0607103061 7 21 1 5 19 19 20 21 0607110042 11 36 4 12 31 31 34 34 0607112341 9 5 1 12 5 5 5 5 0607120021 12 34 5 13 27 27 30 31 0607140011 3 17 1 4 16 16 16 16 0607190041 12 31 5 16 25 26 26 26 0611100051 7 4 0 8 4 4 4 4 0611100071 9 16 1 9 14 14 14 15 0611110031 1 10 10 10 10 10 0611110041 7 8 1 7 7 7 7 8 0611120021 12 18 4 20 13 14 15 15 0611120031 9 10 1 8 9 9 9 9 0611130011 12 13 2 16 9 10 11 11 

Table A-40. Distribution of hourly NO2 ambient conce

Angeles CMSA set C, 1995-2006.

Monitor ID n Mean SD COV Min p10 p20 p

0607100151 15531 7 6 82 0 2 3 0607100171 23713 6 5 84 0 2 3 0607103061 56831 21 15 70 0 5 8 10607110042 88766 36 17 48 0 17 22 20607112341 69325 5 5 103 0 1 2 0607120021 95054 34 18 54 0 12 19 20607140011 24587 17 11 68 0 6 7 0607190041 97785 31 16 51 0 12 18 20611100051 54034 4 4 89 0 0 1 0611100071 73031 16 12 74 0 4 6 0611110031 8240 10 5 52 0 4 6 0611110041 56869 8 5 66 0 3 4 0611120021 94238 18 13 70 0 4 7 0611120031 70332 10 8 85 0 1 2 0611130011 95263 13 8 65 0 4 6 
A-52

Figure A-41. Distribution of annual average NO2 ambient 

concentrations (ppb) by year, Miami CMSA.

Figure A-42. Distribution of hourly NO2 ambient concentrations 

(ppb) by year, Miami CMSA.

Table A-41. Distribution of annual average NO2 ambie

year, Miami CMSA.

Year n Mean SD COV Min p10 p20 p30 p40 

1995 4 10 3 31 7 7 7 9 9 1996 4 10 4 43 6 6 6 8 8 1997 4 10 4 43 7 7 7 9 9 1998 4 10 4 42 6 6 6 9 9 1999 4 11 4 42 6 6 6 10 10 2000 4 10 4 37 7 7 7 9 9 2001 4 10 4 42 6 6 6 9 9 2002 4 9 4 39 6 6 6 7 7 2003 4 9 3 29 7 7 7 8 8 2004 4 9 3 36 6 6 6 8 8 2005 4 9 3 38 6 6 6 7 7 

Table A-42. Distribution of hourly NO2 ambient conce

CMSA.

Year n Mean SD COV Min p10 p20 p30 p

1995 32713 10 10 95 0 1 2 3 1996 33086 10 10 103 0 1 2 3 1997 32754 10 10 97 0 1 2 3 1998 30849 10 10 98 0 1 2 3 1999 32721 11 11 99 0 1 2 3 2000 31833 10 10 99 0 1 2 4 2001 33063 10 10 98 0 1 2 3 2002 33755 9 9 96 0 1 2 3 2003 31031 9 9 97 0 1 2 3 2004 33625 9 10 117 0 1 2 2 2005 32342 9 10 109 0 0 1 2 
A-53

Figure A-43. Distribution of annual average NO2 ambient 

concentration (ppb) by monitor, Miami CMSA, 1995-2006.

Figure A-44. Distribution of hourly NO2 ambient concentration 

(ppb) by monitor, Miami CMSA, 1995-2006.

Table A-43. Distribution of annual average NO2 ambie

monitor, Miami CMSA, 1995-2006.

Monitor ID n Mean SD COV Min p10 p20 p30 

1201100031 3 9 1 7 8 8 8 8 1201100311 8 9 1 12 7 7 8 9 1201180021 11 9 1 11 7 8 8 8 1208600271 11 6 0 7 6 6 6 6 1208640022 11 15 1 9 13 13 14 14 

Table A-44. Distribution of hourly NO2 ambient conce

CMSA, 1995-2006.

Monitor ID n Mean SD COV Min p10 p20 p

1201100031 24440 9 7 81 0 2 3 1201100311 63306 9 7 78 0 2 3 1201180021 92241 9 11 128 0 0 1 1208600271 87068 6 8 132 0 1 1 1208640022 90717 15 10 67 0 5 7 
A-54

Figure A-45. Distribution of annual average NO2 ambient 

concentrations (ppb) by year, New York CMSA. 

Figure A-46. Distribution of hourly NO2 ambient concentrations 

(ppb) by year, New York CMSA.

Table A-45. Distribution of annual average NO2 ambie

year, New York CMSA.

Year n Mean SD COV Min p10 p20 p30 p40 

1995 16 28 8 28 12 16 24 25 26 1996 15 28 8 29 12 17 22 26 27 1997 16 27 8 30 12 17 23 24 26 1998 14 27 9 34 11 15 18 22 27 1999 16 27 9 31 11 17 19 24 26 2000 16 26 8 32 11 16 18 19 25 2001 14 25 8 32 11 17 17 21 24 2002 17 25 8 31 11 16 17 20 22 2003 15 23 6 28 12 14 16 18 21 2004 14 21 7 31 10 13 14 17 20 2005 16 23 7 31 11 13 16 18 22 2006 5 25 6 23 18 18 21 23 24 

Table A-46. Distribution of hourly NO2 ambient conce

York CMSA.

Year n Mean SD COV Min p10 p20 p30 p

1995 133504 28 16 56 0 9 14 18 1996 122074 28 16 57 0 8 13 18 1997 131144 27 15 56 0 9 13 17 1998 116748 27 16 58 0 8 13 17 1999 132646 27 16 57 0 8 13 17 2000 134037 26 15 58 0 8 12 16 2001 114478 25 15 61 0 7 10 15 2002 141480 24 15 60 0 7 11 14 2003 122724 23 14 61 0 6 10 13 2004 115578 21 13 64 0 5 8 12 2005 133856 23 14 63 1 6 9 13 2006 42223 25 13 51 0 10 13 17 
A-55

Figure A-47. Distribution of annual average NO2 ambient 

concentration (ppb) by monitor, New York CMSA set a, 1995-2006.

Figure A-48. Distribution of hourly NO2 ambient concentration 

(ppb) by monitor, New York CMSA set a, 1995-2006.

Table A-47. Distribution of annual average NO2 ambie

monitor, New York CMSA set A, 1995-2006.

Monitor ID n Mean SD COV Min p10 p20 p30 

0900101131 3 23 1 4 23 23 23 23 0900190031 8 17 2 11 14 14 15 16 0900900271 2 21 1 5 20 20 20 20 0900911231 9 26 1 4 24 24 25 25 3400300011 3 28 1 2 28 28 28 28 3400300051 4 21 1 5 20 20 20 20 3401300111 5 32 1 3 31 31 31 31 3401300161 1 29 29 29 29 29 3401310031 11 28 2 7 24 26 27 28 3401700061 11 25 2 6 22 23 23 25 3402100051 11 16 1 4 15 15 15 16 3402300111 11 18 1 6 16 17 18 18 3402730011 11 11 1 6 10 11 11 11 3403900042 11 38 4 12 30 32 32 39 3403900081 3 28 2 6 27 27 27 27 

Table A-48. Distribution of hourly NO2 ambient conce

York CMSA set A, 1995-2006.

Monitor ID n Mean SD COV Min p10 p20 p

0900101131 25148 23 13 55 0 9 12 10900190031 67123 17 13 75 0 4 6 0900900271 16002 21 14 65 0 6 8 10900911231 76418 26 13 50 0 11 14 13400300011 25620 28 14 50 3 11 15 13400300051 34090 21 14 66 3 5 8 13401300111 41642 32 16 50 3 12 17 23401300161 8368 29 15 52 3 11 15 13401310031 93578 28 14 51 3 11 15 13401700061 93886 25 14 56 2 9 12 13402100051 94591 16 11 67 2 4 7 3402300111 94366 18 12 65 3 5 8 13402730011 92642 11 9 82 0 3 3 3403900042 92472 38 15 41 3 19 25 23403900081 23611 28 13 47 3 11 16 2
A-56

Figure A-49. Distribution of annual average NO2 ambient 

concentration (ppb) by monitor, New York CMSA set b, 1995-2006.

Figure A-50. Distribution of hourly NO2 ambient concentration 

(ppb) by monitor, New York CMSA set b, 1995-2006.

Table A-49. Distribution of annual average NO2 ambie

monitor, New York CMSA set B, 1995-2006.

Monitor ID n Mean SD COV Min p10 p20 p30 

3600500801 5 35 1 4 33 33 34 35 3600500831 12 28 2 9 24 25 27 27 3600501101 6 30 2 6 26 26 29 29 3604700111 1 33 33 33 33 33 3605900052 11 23 2 10 18 20 21 22 3606100101 4 36 1 1 35 35 35 35 3606100561 10 39 2 6 34 35 37 38 3608100971 3 26 0 1 26 26 26 26 3608100981 7 29 1 4 27 27 28 28 3608101241 5 25 2 7 23 23 24 25 3610300092 6 15 2 14 13 13 13 13 

Table A-50. Distribution of hourly NO2 ambient conce

York CMSA set B, 1995-2006.

3600500801 41120 35 14 40 0 19 23 2

3600500831 95448 28 13 47 0 13 17 23600501101 46299 29 13 45 0 14 18 23604700111 8300 33 14 41 3 17 21 23605900052 89801 23 13 56 0 8 11 13606100101 30694 36 11 31 0 23 27 23606100561 81341 39 13 33 0 24 28 33608100971 24104 26 14 54 0 10 13 13608100981 56186 29 13 46 0 13 17 23608101241 39406 25 13 50 0 11 14 13610300092 48236 15 10 67 0 5 7 
A-57

Figure A-51. Distribution of annual average NO2 ambient 

concentrations (ppb) by year, Philadelphia CMSA.

Figure A-52. Distribution of hourly NO2 ambient concentrations 

(ppb) by year, Philadelphia CMSA.

Table A-51. Distribution of annual average NO2 ambie

year, Philadelphia CMSA.

Year n Mean SD COV Min p10 p20 p30 p40 

1995 8 23 6 27 15 15 17 20 20 1996 8 25 6 24 19 19 21 21 21 1997 8 24 6 25 18 18 19 20 20 1998 8 24 7 30 16 16 18 19 19 1999 8 23 6 28 16 16 17 18 18 2000 6 21 4 20 17 17 18 18 19 2001 7 23 5 24 16 16 18 19 19 2002 8 21 5 26 15 15 16 18 19 2003 6 20 4 19 16 16 17 17 18 2004 7 20 4 22 14 14 16 18 18 2005 7 19 4 19 16 16 17 17 17 2006 4 16 1 9 14 14 14 15 15 

Table A-52. Distribution of hourly NO2 ambient conce

Philadelphia CMSA.

Year n Mean SD COV Min p10 p20 p30 p

1995 65415 24 14 60 0 8 10 14 11996 67989 25 14 55 0 8 11 17 21997 68291 24 14 57 0 8 11 15 11998 66847 24 14 58 0 7 11 15 11999 64813 22 13 59 0 6 10 14 12000 51145 21 13 60 0 6 10 13 12001 59227 23 13 59 0 6 10 14 12002 66779 21 12 59 0 6 10 13 12003 49256 20 12 62 0 5 8 11 12004 58509 20 12 59 0 6 9 12 12005 56459 19 12 62 0 6 9 11 12006 32357 16 11 69 0 4 6 8 1
A-58

Figure A-53. Distribution of annual average NO2 ambient 

concentration (ppb) by monitor, Philadelphia CMSA, 1995-2006.

Figure A-54. Distribution of hourly NO2 ambient concentration 

(ppb) by monitor, Philadelphia CMSA, 1995-2006.

Table A-53. Distribution of annual average NO2 ambie

monitor, Philadelphia CMSA, 1995-2006.

Monitor ID n Mean SD COV Min p10 p20 p30 

1000310031 5 18 1 6 16 16 17 17 1000310071 1 15 15 15 15 15 1000320041 4 18 1 4 18 18 18 18 3400700032 10 21 1 7 19 20 20 20 4201700121 12 18 2 11 15 16 16 16 4204500021 12 19 2 8 16 17 17 18 4209100131 11 17 2 13 14 14 15 16 4210100043 11 26 3 10 22 23 24 24 4210100292 10 29 3 11 25 25 26 28 4210100471 9 31 3 10 26 26 26 29 

Table A-54. Distribution of hourly NO2 ambient conce

Philadelphia CMSA, 1995-2006. 

Monitor ID n Mean SD COV Min p10 p20 p

1000310031 40363 18 12 69 0 4 7 1000310071 6611 15 9 62 1 6 7 1000320041 31615 18 12 63 0 5 8 3400700032 84603 22 13 59 3 7 10 4201700121 102584 18 12 67 0 5 7 4204500021 100344 19 12 64 0 5 8 4209100131 93572 17 12 69 0 4 6 4210100043 90975 26 13 49 0 10 14 4210100292 81218 29 13 43 0 15 19 24210100471 75202 31 12 40 0 16 20 2
A-59

Figure A-55. Distribution of annual average NO2 ambient 

concentrations (ppb) by year, Washington DC CMSA.

Figure A-56. Distribution of hourly NO2 ambient concentrations 

(ppb) by year, Washington DC CMSA.

Table A-55. Distribution of annual average NO2 ambieyear, Washington DC CMSA.

Year n Mean SD COV Min p10 p20 p30 p40 

1995 12 21 5 25 11 11 19 19 22 1996 11 22 4 20 11 20 20 21 22 1997 11 20 5 27 10 11 17 19 21 1998 11 22 5 23 12 15 18 20 22 1999 12 20 5 25 11 12 14 18 20 2000 12 18 5 27 9 10 13 17 18 2001 11 19 5 28 9 11 14 19 20 2002 10 19 6 31 9 10 13 16 20 2003 11 20 6 28 10 12 16 18 18 2004 12 18 5 27 10 10 15 15 17 2005 12 17 5 28 9 10 14 15 17 2006 10 15 4 30 7 7 10 14 15 

Table A-56. Distribution of hourly NO2 ambient conce

Washington DC CMSA.

Year n Mean SD COV Min p10 p20 p30 p

1995 98349 21 13 59 0 7 10 13 1996 91551 22 12 57 0 7 11 14 1997 87646 20 12 62 0 6 9 12 1998 89335 22 12 57 0 8 11 14 1999 100112 20 12 61 0 6 9 12 2000 101494 18 12 64 0 5 8 11 2001 91594 19 12 62 0 6 9 11 2002 83969 19 12 64 0 6 9 11 2003 93111 20 12 61 0 6 9 12 2004 99370 18 11 63 0 5 8 10 2005 96396 17 12 68 0 5 7 10 2006 83691 15 11 73 0 4 6 7 
A-60

Figure A-57. Distribution of annual average NO2 ambient 

concentration (ppb) by monitor, Washington DC CMSA set A, 

1995-2006.

Figure A-58. Distribution of hourly NO2 ambient concentration 

(ppb) by monitor, Washington DC CMSA set A, 1995-2006.

Table A-57. Distribution of annual average NO2 ambie

monitor, Washington DC CMSA set A, 1995-2006.

Monitor ID n Mean SD COV Min p10 p20 p30 

1100100172 1 25 25 25 25 25 1100100251 12 22 2 11 17 19 20 21 1100100411 12 23 3 12 16 21 21 23 1100100431 12 20 2 12 17 18 18 18 2400530012 8 18 2 11 15 15 15 17 2451000401 11 25 2 7 22 23 23 23 2451000501 1 21 21 21 21 21 5101300201 12 23 2 10 18 21 21 22 5105900051 11 10 1 12 7 9 9 10 5105900181 3 19 1 3 19 19 19 19 5105910043 6 22 2 7 20 20 21 21 5105910051 4 17 1 9 15 15 15 17 5105950011 10 20 3 15 14 16 17 19 5110710051 8 14 1 6 13 13 13 14 5115300091 12 11 2 18 7 9 9 10 

Table A-58. Distribution of hourly NO2 ambient conce

Washington DC CMSA set A, 1995-2006.

Monitor ID n Mean SD COV Min p10 p20 p

1100100172 8584 25 11 45 4 12 15 1100100251 102444 22 12 55 0 9 11 1100100411 103173 23 12 53 0 9 12 1100100431 102217 20 13 64 0 6 9 2400530012 63983 18 12 65 0 5 7 2451000401 89589 25 11 44 0 12 15 2451000501 7872 21 12 60 0 6 9 5101300201 97517 23 13 56 0 8 11 5105900051 89964 10 7 73 0 3 4 5105900181 22689 19 11 60 0 6 9 5105910043 50294 22 11 52 0 10 12 5105910051 34022 17 11 63 0 6 8 5105950011 79051 20 12 61 0 6 9 5110710051 65327 14 9 65 0 5 7 5115300091 101671 11 7 68 0 3 5 
A-61

Figure A-59. Distribution of annual average NO2 ambient 

concentration (ppb) by monitor, Washington DC CMSA set B, 

1995-2006.

Figure A-60. Distribution of hourly NO2 ambient concentration 

(ppb) by monitor, Washington DC CMSA set B, 1995-2006.

Table A-59. Distribution of annual average NO2 ambie

monitor, Washington DC CMSA set B, 1995-2006.

Monitor ID n Mean SD COV Min p10 p20 p30 

5151000093 12 24 2 8 20 23 23 23 

Table A-60. Distribution of hourly NO2 ambient conce

Washington DC CMSA set B, 1995-2006.

Monitor ID n Mean SD COV Min p10 p20 p

5151000093 98221 24 12 48 0 11 14 
A-62

Figure A-61. Distribution of annual average NO2 ambient 

concentrations (ppb) by year, Atlanta MSA. 

Figure A-62. Distribution of hourly NO2 ambient concentrations 

(ppb) by year, Atlanta MSA.

Table A-61. Distribution of annual average NO2 ambie

year, Atlanta MSA.

Year n Mean SD COV Min p10 p20 p30 p40 

1995 3 13 6 46 7 7 7 7 15 1996 5 14 9 61 6 6 6 6 11 1997 4 15 7 47 8 8 8 15 15 1998 3 13 10 80 6 6 6 6 8 1999 4 14 9 61 7 7 7 7 7 2000 5 14 7 53 5 5 6 8 12 2001 5 14 8 56 4 4 6 8 12 2002 5 12 6 51 4 4 6 7 11 2003 4 11 6 56 5 5 5 6 6 2004 5 11 6 51 4 4 5 6 10 2005 5 11 6 51 4 4 5 6 10 2006 5 11 6 57 3 3 5 6 9 

Table A-62. Distribution of hourly NO2 ambient conce

MSA.

Year n Mean SD COV Min p10 p20 p30 p

1995 25213 13 12 89 1 3 3 5 1996 40576 15 13 89 1 3 3 5 1997 31069 15 13 86 1 3 5 7 1998 24142 12 13 105 0 1 3 4 1999 31121 14 14 99 0 2 4 5 2000 40584 14 14 97 1 1 3 5 2001 42761 14 14 98 1 1 3 5 2002 42076 12 12 95 1 1 3 5 2003 32215 11 11 101 0 1 2 3 2004 42124 11 11 98 1 1 3 4 2005 42279 11 11 96 1 1 3 4 2006 41052 11 11 98 1 2 3 4 
A-63

Figure A-63. Distribution of annual average NO2 ambient 

concentration (ppb) by monitor, Atlanta MSA, 1995-2006.

Figure A-64. Distribution of hourly NO2 ambient concentration 

(ppb) by monitor, Atlanta MSA, 1995-2006.

Table A-63. Distribution of annual average NO2 ambie

monitor, Atlanta MSA, 1995-2006.

Monitor ID n Mean SD COV Min p10 p20 p3

1308900021 10 16 2 11 14 14 15 15 1308930011 9 15 2 10 13 13 14 15 1312100481 12 21 4 17 16 17 17 18 1322300031 10 5 1 20 3 4 4 4 1324700011 12 7 1 11 6 6 6 6 

Table A-64. Distribution of hourly NO2 ambient conce

Atlanta MSA, 1995-2006.

Monitor ID n Mean SD COV Min p10 p20 p

1308900021 83891 16 12 77 0 3 5 1308930011 72029 15 11 73 1 4 6 1312100481 98975 21 15 73 0 5 8 1322300031 80168 5 5 108 0 1 1 1324700011 100149 7 6 81 0 2 3 
A-64

Figure A-65. Distribution of annual average NO2 ambient 

concentrations (ppb) by year, Colorado Springs MSA. 

Figure A-66. Distribution of hourly NO2 ambient concentrations 

(ppb) by year, Colorado Springs MSA. 

Table A-65. Distribution of annual average NO2 ambie

year, Colorado Springs MSA.

Year n Mean SD COV Min p10 p20 p30 p40 

1995 7 16 7 42 7 7 8 12 12 1996 3 16 9 53 7 7 7 7 18 1997 4 16 6 36 7 7 7 17 17 1998 4 16 6 37 7 7 7 17 17 1999 4 15 6 37 7 7 7 16 16 2000 4 19 11 58 9 9 9 16 16 

Table A-66. Distribution of hourly NO2 ambient conce

Colorado Springs MSA.

Year n Mean SD COV Min p10 p20 p30 p

1995 58569 16 14 91 0 2 4 6 1996 25387 16 16 101 0 2 4 6 1997 33469 16 13 80 0 3 5 6 1998 34509 16 12 76 0 3 5 7 1999 34472 15 12 82 0 3 4 6 2000 33956 19 20 106 0 3 6 8 1
A-65

Figure A-67. Distribution of annual average NO2 ambient 

concentration (ppb) by monitor, Colorado Springs MSA, 1995-2006.

Figure A-68. Distribution of hourly NO2 ambient concentration 

(ppb) by monitor, Colorado Springs MSA, 1995-2006.

Table A-67. Distribution of annual average NO2 ambie

monitor, Colorado Springs MSA, 1995-2006.

Monitor ID n Mean SD COV Min p10 p20 p3

0804160011 6 8 1 10 7 7 7 7 0804160041 6 17 2 10 16 16 16 16 0804160051 1 18 18 18 18 18 0804160061 1 7 7 7 7 7 0804160091 1 12 12 12 12 12 0804160111 6 21 3 12 17 17 19 19 0804160131 1 22 22 22 22 22 0804160181 4 22 8 37 18 18 18 18 

Table A-68. Distribution of hourly NO2 ambient conce

Colorado Springs MSA, 1995-2006.

Monitor ID n Mean SD COV Min p10 p20 p

0804160011 51373 8 7 94 0 1 2 0804160041 51288 17 11 66 0 4 6 0804160051 8345 18 13 74 1 3 5 0804160061 7993 7 7 99 0 1 2 0804160091 8282 12 10 89 0 2 3 0804160111 50707 21 16 77 0 5 7 0804160131 8637 22 14 62 0 5 8 0804160181 33737 23 21 94 0 5 7 
A-66

Figure A-69. Distribution of annual average NO2 ambient 

concentrations (ppb) by year, El Paso MSA. 

Figure A-70. Distribution of hourly NO2 ambient concentrations 

(ppb) by year, El Paso MSA. 

Table A-69. Distribution of annual average NO2 ambie

year, El Paso MSA.

Year n Mean SD COV Min p10 p20 p30 p40 

1995 1 23 0 23 23 23 23 23 1996 1 35 0 35 35 35 35 35 1997 3 26 7 27 21 21 21 21 23 1998 2 25 8 33 19 19 19 19 19 1999 3 22 6 25 17 17 17 17 23 2000 4 18 5 26 14 14 14 16 16 2001 5 16 4 26 10 10 12 14 16 2002 5 17 4 23 11 11 13 16 16 2003 5 16 3 21 11 11 13 15 16 2004 5 14 4 25 9 9 11 13 13 2005 5 14 3 21 10 10 11 13 14 2006 5 14 4 26 8 8 11 13 14 

Table A-70. Distribution of hourly NO2 ambient conce

MSA.

Year n Mean SD COV Min p10 p20 p30 p

1995 6960 23 13 58 3 9 12 14 11996 6627 35 15 43 2 20 23 27 21997 22888 26 15 58 0 10 13 16 21998 15523 25 15 61 0 7 12 15 11999 23447 22 13 60 0 6 10 14 12000 30772 17 13 72 0 3 5 8 12001 38020 16 12 77 0 3 5 7 12002 41466 17 13 77 0 4 5 7 12003 39968 16 13 80 0 3 5 7 2004 41952 14 12 83 0 2 4 6 2005 41496 14 12 86 0 2 4 5 2006 37203 14 12 84 0 2 4 6 
A-67

Figure A-71. Distribution of annual average NO2 ambient 

concentration (ppb) by monitor, El Paso MSA, 1995-2006.

Figure A-72. Distribution of hourly NO2 ambient concentration 

(ppb) by monitor, El Paso MSA, 1995-2006.

Table A-71. Distribution of annual average NO2 ambie

monitor, El Paso MSA, 1995-2006.

Monitor ID n Mean SD COV Min p10 p20 p3

4814100271 4 32 3 10 28 28 28 34814100281 1 23 23 23 23 23 4814100371 11 18 2 12 15 16 17 17 4814100441 8 19 4 22 13 13 13 18 4814100551 7 17 1 5 16 16 16 16 4814100571 7 14 1 6 13 13 13 14 4814100581 6 10 1 11 8 8 9 9 

Table A-72. Distribution of hourly NO2 ambient conce

Paso MSA, 1995-2006.

Monitor ID n Mean SD COV Min p10 p20 p

4814100271 29730 32 14 45 1 16 20 24814100281 8045 23 14 60 5 10 12 4814100371 87748 18 13 71 0 5 7 4814100441 62362 19 13 67 0 5 8 4814100551 53960 17 13 78 0 3 5 4814100571 57229 14 11 79 0 3 4 4814100581 47248 10 11 109 0 1 2 
A-68

Figure A-73. Distribution of annual average NO2 ambient 

concentrations (ppb) by year, Jacksonville MSA. 

Figure A-74. Distribution of hourly NO2 ambient concentrations 

(ppb) by year, Jacksonville MSA. 

Table A-73. Distribution of annual average NO2 ambie

year, Jacksonville MSA.

Year n Mean SD COV Min p10 p20 p30 p40 

1995 1 16 0 16 16 16 16 16 1996 1 15 0 15 15 15 15 15 1997 1 14 0 14 14 14 14 14 1998 1 15 0 15 15 15 15 15 1999 1 16 0 16 16 16 16 16 2000 1 15 0 15 15 15 15 15 2002 1 15 0 15 15 15 15 15 2003 1 14 0 14 14 14 14 14 2004 1 14 0 14 14 14 14 14 2005 1 13 0 13 13 13 13 13 

Table A-74. Distribution of hourly NO2 ambient conce

Jacksonville MSA.

Year n Mean SD COV Min p10 p20 p30 p40 

1995 7755 16 10 60 0 6 8 9 11 1996 8148 15 10 64 0 5 7 9 11 1997 8326 14 9 65 0 5 6 8 10 1998 8211 15 10 65 0 5 7 9 11 1999 7795 16 10 61 0 5 7 9 12 2000 7661 15 10 67 0 5 7 9 11 2002 7944 15 10 66 0 5 7 9 11 2003 7041 14 10 71 0 4 6 8 10 2004 7451 14 11 83 0 4 6 7 9 2005 7890 13 9 67 0 4 6 8 9 
A-69

Figure A-75. Distribution of annual average NO2 ambient 

concentration (ppb) by monitor, Jacksonville MSA, 1995-2006.

Figure A-76. Distribution of hourly NO2 ambient concentration 

(ppb) by monitor, Jacksonville MSA, 1995-2006.

Table A-75. Distribution of annual average NO2 ambie

monitor, Jacksonville MSA, 1995-2006.

Monitor ID n Mean SD COV Min p10 p20 p3

1203100322 10 15 1 6 13 14 14 14 

Table A-76. Distribution of hourly NO2 ambient conce

Jacksonville MSA, 1995-2006.

Monitor ID n Mean SD COV Min p10 p20 p

1203100322 78222 15 10 67 0 5 7 
A-70

Figure A-77. Distribution of annual average NO2 ambient 

concentrations (ppb) by year, Las Vegas MSA. 

Figure A-78. Distribution of hourly NO2 ambient concentrations 

(ppb) by year, Las Vegas MSA. 

Table A-77. Distribution of annual average NO2 ambie

year, Las Vegas MSA.

Year n Mean SD COV Min p10 p20 p30 p40 

1995 1 27 0 27 27 27 27 27 1996 1 27 0 27 27 27 27 27 1998 3 12 12 95 4 4 4 4 7 1999 5 14 10 71 4 4 6 8 8 2000 6 12 9 81 3 3 4 4 8 2001 6 11 9 84 2 2 5 5 6 2002 9 11 8 68 3 3 3 7 7 2003 7 12 8 66 2 2 6 8 8 2004 7 11 8 73 1 1 4 5 5 2005 6 10 8 76 2 2 5 5 6 

Table A-78. Distribution of hourly NO2 ambient conce

Vegas MSA.

Year n Mean SD COV Min p10 p20 p30 p

1995 7951 27 20 74 0 0 11 15 21996 8723 27 22 81 0 0 9 12 11998 25234 12 14 118 0 0 0 0 1999 43110 14 16 110 0 0 0 5 2000 46403 12 14 119 0 0 0 0 2001 49734 11 14 128 0 0 0 0 2002 74814 11 13 117 0 0 0 0 2003 58398 12 14 119 0 0 0 0 2004 57484 11 13 120 0 0 0 0 2005 48911 10 12 123 0 0 0 0 
A-71

Figure A-79. Distribution of annual average NO2 ambient 

concentration (ppb) by monitor, Las Vegas MSA, 1995-2006.

Figure A-80. Distribution of hourly NO2 ambient concentration 

(ppb) by monitor, Las Vegas MSA, 1995-2006.

Table A-79. Distribution of annual average NO2 ambie

monitor, Las Vegas MSA, 1995-2006.

Monitor ID n Mean SD COV Min p10 p20 p3

3200300221 7 5 1 26 4 4 4 4 3200300231 4 7 2 28 5 5 5 6 3200300731 7 8 1 9 7 7 7 8 3200300781 1 9 9 9 9 9 3200305391 8 23 3 12 19 19 20 23200305571 2 27 0 1 27 27 27 27 3200305631 3 19 0 1 19 19 19 19 3200306011 5 6 2 34 3 3 4 6 3200310191 7 3 1 38 1 1 2 2 3200320021 7 21 2 7 19 19 20 2

Table A-80. Distribution of hourly NO2 ambient conce

Vegas MSA, 1995-2006.

Monitor ID n Mean SD COV Min p10 p20 p

3200300221 58087 5 7 152 0 0 0 3200300231 34550 7 8 105 0 0 0 3200300731 56906 8 10 124 0 0 0 3200300781 8672 9 10 115 0 0 0 3200305391 64921 23 16 70 0 5 7 3200305571 16674 27 21 78 0 0 10 3200305631 25061 19 15 78 0 0 5 3200306011 42417 6 8 124 0 0 0 3200310191 57230 3 5 186 0 0 0 3200320021 56244 21 16 73 0 0 6 
A-72

Figure A-81. Distribution of annual average NO2 ambient 

concentrations (ppb) by year, Phoenix MSA. 

Figure A-82. Distribution of hourly NO2 ambient concentrations 

(ppb) by year, Phoenix MSA. 

Table A-81. Distribution of annual average NO2 ambie

year, Phoenix MSA.

Year n Mean SD COV Min p10 p20 p30 p4

1995 3 29 3 12 26 26 26 26 21996 3 28 3 12 25 25 25 25 21997 2 30 3 10 28 28 28 28 21998 4 29 5 15 24 24 24 28 21999 5 33 5 14 28 28 30 31 32000 5 30 4 13 26 26 27 29 22001 5 27 6 23 22 22 22 22 22002 3 29 6 19 24 24 24 24 22003 2 32 4 11 29 29 29 29 22004 5 25 4 18 19 19 21 23 22005 6 23 7 29 12 12 20 20 22006 6 22 7 30 11 11 19 19 2

Table A-82. Distribution of hourly NO2 ambient conce

MSA.

Year n Mean SD COV Min p10 p20 p30 

1995 23196 29 17 59 0 8 12 17 1996 23598 28 17 59 0 8 12 17 1997 14629 30 16 55 0 8 13 18 1998 32078 29 17 58 0 8 12 17 1999 40996 33 22 66 0 9 13 18 2000 41686 30 21 71 0 8 12 17 2001 40463 27 16 59 1 7 11 15 2002 25028 29 17 59 0 7 12 17 2003 14195 32 17 55 0 8 14 20 2004 42176 25 15 62 0 6 9 13 2005 50583 23 15 66 0 5 8 12 2006 48791 22 16 73 0 4 7 10 
A-73

Figure A-83. Distribution of annual average NO2 ambient 

concentration (ppb) by monitor, Phoenix MSA, 1995-2006.

Figure A-84. Distribution of hourly NO2 ambient concentration 

(ppb) by monitor, Phoenix MSA, 1995-2006.

Table A-83. Distribution of annual average NO2 ambie

monitor, Phoenix MSA, 1995-2006.

Monitor ID n Mean SD COV Min p10 p20 p3

0401300191 10 27 3 10 24 24 24 25 0401330026 12 29 3 10 25 25 26 29 0401330031 10 24 4 17 19 19 20 20401330101 9 35 3 9 31 31 31 32 0401340051 1 22 22 22 22 22 0401340111 2 12 1 6 11 11 11 10401399971 5 24 3 12 21 21 22 23 

Table A-84. Distribution of hourly NO2 ambient conce

Phoenix MSA, 1995-2006.

Monitor ID n Mean SD COV Min p10 p20 p

0401300191 81411 27 17 63 0 6 9 10401330026 97376 29 17 59 0 8 12 10401330031 80162 24 19 78 0 6 9 10401330101 73070 35 18 53 0 9 16 20401340051 7420 22 13 58 2 7 9 10401340111 16459 12 8 69 0 2 4 0401399971 41521 24 15 60 0 7 10 1
A-74

Figure A-85. Distribution of annual average NO2 ambient 

concentrations (ppb) by year, Provo MSA.

Figure A-86. Temporal distribution of hourly NO2 ambient 

concentrations (ppb) by year, Provo MSA. 

Table A-85. Distribution of annual average NO2 ambie

year, Provo MSA.

Year n Mean SD COV Min p10 p20 p30 p4

1995 1 23 0 23 23 23 23 21996 1 24 0 24 24 24 24 21997 1 23 0 23 23 23 23 21998 1 24 0 24 24 24 24 21999 1 24 0 24 24 24 24 22000 1 24 0 24 24 24 24 22001 1 24 0 24 24 24 24 22002 1 25 0 25 25 25 25 22003 1 22 0 22 22 22 22 22004 1 22 0 22 22 22 22 22005 1 21 0 21 21 21 21 22006 1 29 0 29 29 29 29 2

Table A-86. Distribution of hourly NO2 ambient conce

MSA.

Year n Mean SD COV Min p10 p20 p30 p

1995 8002 23 13 55 0 7 10 13 1996 8430 24 15 61 0 7 10 14 1997 7034 23 13 57 0 7 10 14 1998 8210 24 13 56 0 7 10 14 1999 8563 24 13 55 0 7 11 14 2000 8406 24 13 56 0 7 10 14 2001 8501 24 14 57 0 6 10 14 2002 8200 25 14 57 0 6 10 15 2003 7730 22 13 59 0 6 8 12 2004 8302 22 15 66 0 5 8 12 2005 8502 21 13 62 0 5 8 11 2006 6993 29 34 118 0 5 7 10 
A-75

Figure A-87. Distribution of annual average NO2 ambient 

concentration (ppb) by monitor, Provo MSA, 1995-2006.

Figure A-88. Distribution of hourly NO2 ambient concentration 

(ppb) by monitor, Provo MSA, 1995-2006.

Table A-87. Distribution of annual average NO2 ambie

monitor, Provo MSA, 1995-2006.

Monitor ID n Mean SD COV Min p10 p20 p30 

4904900021 12 24 2 9 21 22 22 23 

Table A-88. Distribution of hourly NO2 ambient conce

MSA, 1995-2006.

Monitor ID n Mean SD COV Min p10 p20 p

4904900021 96873 24 16 68 0 6 9 1
A-76

Figure A-89. Distribution of annual average NO2 ambient 

concentrations (ppb) by year, St. Louis MSA. 

Figure A-90. Temporal distribution of hourly NO2 ambient 

concentrations (ppb) by year, St. Louis MSA. 

Table A-89. Distribution of annual average NO2 ambie

year, St. Louis MSA.

Year n Mean SD COV Min p10 p20 p30 p4

1995 10 18 6 35 5 8 12 15 11996 10 17 6 33 6 8 12 16 11997 10 17 6 32 6 8 12 16 11998 8 19 5 25 11 11 13 18 11999 9 19 5 24 12 12 14 18 12000 9 18 5 29 9 9 12 16 12001 8 17 5 28 10 10 12 17 12002 9 16 4 26 10 10 11 14 12003 9 15 4 26 9 9 10 14 12004 9 14 4 31 8 8 10 12 12005 6 13 3 24 9 9 10 10 12006 2 12 5 40 8 8 8 8 8 

Table A-90. Distribution of hourly NO2 ambient conce

Louis MSA.

Year n Mean SD COV Min p10 p20 p30 

1995 85072 18 12 68 0 4 7 10 1996 86085 17 11 65 4 7 10 1997 86314 17 11 67 0 4 7 10 1998 68308 19 11 58 0 6 9 12 1999 77611 19 12 61 0 6 9 12 2000 77327 18 11 64 0 5 8 10 2001 67871 17 11 64 0 5 7 10 2002 76693 16 11 65 0 5 7 9 2003 77543 15 10 67 0 4 6 8 2004 75493 14 10 69 0 4 6 8 2005 49948 13 9 70 0 4 5 7 2006 16688 12 8 70 0 3 5 6 
A-77

Figure A-91. Distribution of annual average NO2 ambient 

concentration (ppb) by monitor, St. Louis MSA, 1995-2006.

Figure A-92. Distribution of hourly NO2 ambient concentration 

(ppb) by monitor, St. Louis MSA, 1995-2006.

Table A-91. Distribution of annual average NO2 ambie

monitor, St. Louis MSA, 1995-2006.

Monitor ID n Mean SD COV Min p10 p20 p30 

1716300102 12 18 2 12 15 15 16 16 2918300101 3 6 0 7 5 5 5 5 2918310021 12 10 1 13 8 8 9 9 2918900012 3 19 0 2 19 19 19 19 2918900041 6 15 2 15 12 12 13 13 2918900062 11 12 1 12 10 10 10 11 2918930012 11 20 2 11 17 17 18 19 2918950011 10 17 2 13 13 14 15 16 2918970022 6 20 1 6 19 19 20 20 2918970031 4 15 2 14 12 12 12 16 2951000722 10 25 2 9 20 21 23 24 2951000801 5 19 1 5 19 19 19 19 2951000861 6 19 2 11 15 15 18 18 

Table A-92. Distribution of hourly NO2 ambient conce

Louis MSA, 1995-2006.

Monitor ID n Mean SD COV Min p10 p20 p

1716300102 101236 18 9 52 0 8 10 2918300101 25873 6 6 98 0 1 2 2918310021 99623 10 8 81 0 2 3 2918900012 25801 19 11 58 0 7 9 2918900041 51987 15 10 68 0 4 6 2918900062 93770 12 9 79 0 3 4 2918930012 95589 20 11 52 0 8 11 2918950011 86912 17 11 62 0 6 8 2918970022 51777 20 11 54 0 8 11 2918970031 32235 15 10 66 0 4 7 2951000722 85643 25 11 46 0 11 15 2951000801 42884 19 11 59 0 7 10 2951000861 51623 19 12 62 0 6 9 
A-78

Figure A-93. Distribution of annual average NO2 ambient 

concentrations (ppb) by year, Other MSA/CMSA. 

Figure A-94. Distribution of hourly NO2 ambient concentrations 

(ppb) by year, Other MSA/CMSA. 

Table A-93. Distribution of annual average NO2 ambie

year, Other MSA/CMSA.

Year n Mean SD COV Min p10 p20 p30 p

1995 186 15 6 44 1 5 8 11 11996 186 14 6 43 1 5 9 11 11997 187 14 6 43 2 5 9 11 11998 185 14 6 43 1 5 10 11 11999 192 15 6 42 1 6 9 11 12000 199 14 6 41 1 5 8 11 12001 201 13 6 43 1 5 7 10 12002 209 12 6 45 1 5 7 9 12003 202 12 5 42 1 5 7 9 12004 211 11 5 44 1 5 7 9 12005 207 11 5 43 1 5 7 9 12006 147 10 4 41 1 4 6 9 

Table A-94. Distribution of hourly NO2 ambient conce

MSA/CMSA.

Year n Mean SD COV Min p10 p20 p30 

1995 186 15 6 44 1 5 8 11 1996 1520743 14 12 81 0 2 5 7 1997 1520290 14 11 82 0 2 4 6 1998 1503051 14 11 80 0 2 5 7 1999 1560074 15 12 83 0 3 5 7 2000 1630060 14 11 81 0 2 4 6 2001 1648640 13 11 84 0 2 4 6 2002 1713558 13 11 85 0 2 4 5 2003 1661992 12 10 84 0 2 4 5 2004 1738133 11 10 87 0 2 3 5 2005 1706730 11 10 87 0 2 3 5 2006 1168444 10 9 87 0 2 3 5 
A-79

Figure A-95. Distribution of annual average NO2 ambient 

concentrations (ppb) by year, Other Not MSA. 

Figure A-96. Distribution of hourly NO2 ambient concentrations 

(ppb) by year, Other Not MSA. 

Table A-95. Distribution of annual average NO2 ambie

year, Other Not MSA.

Year n Mean SD COV Min p10 p20 p30 p4

1995 28 8 5 59 1 2 4 5 7 1996 29 7 5 71 0 0 2 4 5 1997 35 7 5 67 0 1 3 4 5 1998 33 7 5 62 1 1 3 4 5 1999 36 8 5 67 0 1 3 4 5 2000 39 8 4 57 2 2 3 5 6 2001 41 7 4 60 1 2 3 4 5 2002 42 7 4 65 1 2 2 3 4 2003 44 7 4 61 1 2 3 3 4 2004 47 6 4 64 2 2 2 3 4 2005 43 7 4 63 1 2 2 3 5 2006 26 6 5 71 1 1 2 2 3 

Table A-96. Distribution of hourly NO2 ambient conce

Not MSA.

Year n Mean SD COV Min p10 p20 p30 

1995 225810 8 9 104 0 0 2 3 1996 234628 7 8 118 0 0 1 2 1997 278906 7 8 113 0 0 1 2 1998 264015 8 8 105 0 1 2 3 1999 290382 8 9 113 0 0 2 2 2000 316568 8 8 104 0 1 2 3 2001 328407 7 7 109 0 1 1 2 2002 340873 7 7 112 0 1 1 2 2003 351652 7 7 110 0 1 2 2 2004 375716 6 7 115 0 1 1 2 2005 353229 7 8 114 0 1 1 2 2006 207114 6 7 119 0 0 1 2 
A-80 

A-6 Technical Memorandum on Regression Modeling 

This section provides a technical memorandum submitted to EPA by ICF International. The 

memo has been formatted for consistency with the entire appendix. The work documented in 

section A-6 was part of the initial exploratory analyses conducted to develop and evaluate the 

relationship between the annual average concentration and the number of exceedances of 

potential health effect benchmark levels. Conceptually, the approach was based on analyses 

conducted for the last NO2 NAAQS review in 1995 (McCurdy, 1994). 

Staff found that use of a regression model applied to the 1995-2006 ambient air quality 

unsatisfactory, both because the models did not show a strong relationship between the annual 

means and the number of exceedances, and because the predicted numbers of exceedances for 

evaluating the current annual standard scenario were in many cases extremely high and 

uncertain. In addition, due to the lack of data containing a number of values at or above 200 ppb 

1-hour, staff decided to develop empirical exceedance estimates, as described in the REA. 

There have been no modifications or re-analyses to the regression models or results since 

they were first produced and documented in the 1st draft REA Technical Support Document 

(TSD). They do not have any relationship to the estimated concentrations or numbers of 

exceedances provided in the Final REA. Nevertheless, the regression models explored, 

developed, and applied are described with the following to give the reader justification as to why 

the regression model was not used and for why an empirical approach was ultimately used in the 

Final REA to estimate the number of exceedances of the short-term (1-hour) potential health 

effect benchmark levels. 

A-6.1 Summary 

This section describes the regression analyses of 1995 to 2006 NO2 hourly concentration 

data. Regression was used to estimate the annual number of exceedances of 150 ppb from the 

annual mean, in 20 locations (mostly large urban areas). Exposures to concentrations above 

certain thresholds may be associated with adverse health effects. These models were applied in 

an as-is scenario to estimate the annual exceedances at sites with annual means equal to the 

1995-2006 current average for their location. These models were also applied in a currentstandard scenario to predict the annual exceedances at sites with annual means equal to the 

current annual average NO2 standard of 0.053 ppm. The current-standard scenario is an 

extrapolation to higher annual means than currently observed; the maximum annual mean across 

all complete site-years was 51 ppb, in Los Angeles. 

A-6.2 Data Used 

All of the 1995 to 2006 NO2 hourly concentration data from AQS were compiled and annual 

summary statistics for each site-year combination were computed. Of particular interest is the 

long-term air quality measured by the annual mean and the short-term air quality measured by 

the annual numbers of hourly exceedances of selected levels 150, 200, 250 and 300 ppb. 

Exposures to concentrations above these thresholds may be associated with adverse health 

effects. To make the results temporally representative, we restricted the analyses to the 20 

percent of site-years that were 75 % complete, as defined by having data for 75 % of the hours in 

a year and having data for at least 75 % of the hours in a day (i.e., 18 hours or more) on at least 
A-81

75 % of the days in a year. We also spatially grouped the data into 18 urban areas with high 

annual means and high exceedances; these locations were all CMSAs or MSAs either with at 

least one site-year annual mean above 25.7 ppb (the 90th percentile) or with at least one 

exceedance of 200 ppb, as follows. 

• Atlanta 

• Boston 

• Chicago 

• Cleveland 

• Colorado Springs 

• Denver 

• Detroit 

• El Paso 

• Jacksonville 

• Las Vegas 

• Los Angeles 

• Miami 

• New York 

• Philadelphia 

• Phoenix 

• Provo 

• St. Louis 

• Washington DC 

The remaining site-years were analyzed as two additional location groups: “Other 

MSA/CMSA” site-years in an MSA or CMSA, and “Other Not MSA” site-years not in an MSA. 

Thus we have a total of 20 “locations.” 

A-6.3 Regression Models 

The regression modeling of the 1995-2006 NO2 data continues the analyses by McCurdy 

(1994)4

 of the 1988-1992 data. A regression model is used to estimate the mean number of 

exceedances from the annual mean. McCurdy (1994) assumed normally distributed exceedances 

and an exponential link function to estimate exceedances of 150, 200, 250, and 300 ppb based on 

the 1988-1992 data. In this section we present the results of the regression analyses for 

exceedances of 150 ppb using eight alternative models based on the 1995-2006 data. 

Throughout this discussion, “exceedances” will refer to annual numbers of hourly exceedances 

of 150 ppb, unless otherwise stated. 

Of the eight models, the two selected regression models were the Poisson exponential model 

and the normal linear model, stratified by location. The Poisson exponential model is of the 

form: 

• Number of exceedances has a Poisson distribution. 

• Mean exceedances = exp(a + b × annual mean). 

• The intercept a, and slope b, depend on the location. 

 

4

 McCurdy TR (1994). Analysis of high 1 hour NO2 values and associated annual averages using 1988-

1992 data. Report to the Office of Air Quality Planning and Standards, Durham NC. 
A-82

The normal linear model is of the form: 

• Number of exceedances has a normal distribution with standard deviation s. 

• Mean exceedances = a + b × annual mean. 

• The intercept a, slope b, and s all depend on the location. 

The first issue to be resolved was to decide whether to apply the regression analyses to the 

means and exceedances for each season separately or to each year. We examined the exceedance 

data for Colorado Springs, which had the highest maximum number of annual exceedances of 

200 ppb, 69, which occurred at site 804160181 in 2000. Of these 69 exceedances, 34 occurred in 

the winter on January 18-20, 2000, and 35 occurred in the summer on June 12-14, 2000. This 

limited analysis suggests that there is no clear pattern of seasonality in the exceedances. We 

decided to apply the regression modeling to the annual means and annual exceedances. 

Table 1 describes the eight regression models fitted. As described shortly, we fitted two 

distributions (normal and Poisson), two link functions (identity and exponential), and two 

stratifications (all data and stratified by location). The McCurdy (1994) analysis used a normal 

distribution, an exponential link, and stratified by location into Los Angeles and Not Los 

Angeles. 

We fitted generalized linear models where the number of exceedances has a given 

distribution (we fitted normal and Poisson distributions) and where the mean number of 

exceedances is a given function g of the annual mean. The function g(x) is called the link 

function. We can also define the link by defining the inverse link, i.e., the solution for x of the 

equation g(x) = y. 

We fitted two link functions, an identity link g(x) = x and a logarithmic link g(x) = log(x), 

where “log” denote the natural logarithm. The corresponding inverse links are the identity link, 

which we also call the “linear” function, and the exponential function. Thus, the linear inverse 

link models are of the form: 

Mean exceedances = a + b × annual mean. 

The exponential inverse link models are of the form: 

Mean exceedances = exp(a + b × annual mean). 

Table A-97. Goodness-of-fit statistics for eight generalized linear models.

Distribution Inverse Link 

Strata (a 

separate 

model is 

fitted in 

each 

stratum) 

R squared 

for all data

Min R 

squared 

among 

locations 

Max R 

squared 

among 

locations 

LogLikelihood 

Number of 

strata in 

final 

model 

Normal Linear All 0.033 -11527 1 

Normal Linear Location 0.244 0.006 0.616 -6065 13** 

Normal Exponential All 0.066 -11438 1 

Normal Exponential Location 0.401 0.005 0.981 -8734 11*** 
A-83

Poisson Linear All 0.025 -4737 1 

Poisson Linear Location 

Not 

Shown* Not Shown* Not Shown* Not Shown* 

Not 

Shown* 

Poisson Exponential All 0.064 -3660 1 

Poisson Exponential Location 0.406 0.004 0.976 -2694 13** 

Notes: 

* Model converged for only Cleveland, Atlanta, and “Other Not MSA” locations. Results are not shown since the model failed to converge for 

the “Other MSA” location, so the overall goodness-of-fit is not comparable to the other seven models. 

** “Other MSA” includes Chicago, Detroit, Philadelphia, Jacksonville, Las Vegas, Provo, St. Louis. 

*** “Other MSA” includes Chicago, Cleveland, Detroit, Philadelphia, Jacksonville, Las Vegas, Phoenix, Provo, St. Louis.

For each link function we fitted models using the normal distribution and the Poisson 

distribution. The normal model is at best an approximation since the numbers of exceedances 

must be positive or zero integers, but the normal distribution is continuous and includes negative 

values. T he Poisson model takes the form: 

 Prob(y exceedances) = (My/y!)e-M, y = 0, 1, 2, ..., 

where M is the mean exceedances. 

We fitted these four models (two links, two distributions) either to all the data or stratified by 

location. Thus the model fitted to all the data assumes that a and b have the same value for all 

site-years, and the model fitted by location assumes that a and b have the same value for all siteyears at the same location but these values may vary between locations. For the normal models, 

the variance of the number of exceedances is assumed to be the same for all site-years in each 

stratum. For the Poisson models, the variance equals the mean number of exceedances. 

The models stratified by location were fitted in two steps. First, each model was separately 

fitted to each of the 20 locations. For several models and locations, there were problem cases 

where the algorithm failed to converge to a solution, predicted a negative slope for the annual 

mean, or had only zero or one site-year with at least one exceedance. In the second case, if the 

slope is negative, then the model implies that exceedances decrease when the annual mean 

increases, which is unexpected and could lead to inconsistent results for projecting exceedances 

to the current-standard scenario. In the third case, there would be zero degrees of freedom and 

the model would be over-fitted for that location. To deal with these problem cases, we reallocated all the problem locations into the “Other MSA” combined location and refitted the 

models. The results in Table 1 stratified by location are for the refitted models. The re-allocated 

locations are listed in the footnotes. 

Table A-97 gives R squared and log-likelihood goodness-of-fit summary statistics. The R 

squared statistic is the squared Pearson correlation coefficient between the observed number of 

exceedances and the predicted mean number of exceedances. Negative predicted means are 

replaced by zero for this calculation. Values close to 1 indicate a good fit and values close to 

zero indicate a poor fit. For the models stratified by location, it is evident that the R squared 

value has a wide range across the locations, varying from a very poor fit at some locations to a 

very good fit at other locations. 

For these models the log-likelihood is a better overall goodness-of-fit statistic. The loglikelihood is defined as the logarithm of the fitted joint density function to all 4,177 site-years. 

The better-fitting models are those with the highest values of the log-likelihood. (The loglikelihood can only be used to compare different models; its value for a single statistical model is 
A-84

not meaningful). Of the various normal models, the best-fitting is stratified by location and uses 

a linear inverse link. Of the various Poisson models, the best-fitting is stratified by location and 

uses an exponential inverse link. The Poisson models fit better than the normal models, which is 

to be expected since the actual data are positive or zero discrete count data and the numbers of 

exceedances are frequently zero, implying a very small mean. 

We selected the Poisson exponential model stratified by location and the normal linear model 

stratified by location. The estimated parameter values for these models are displayed in Tables 

A-98 and A-99, respectively. 

The fitted models for the CMSA locations are displayed in Figures A-97 to A-99. Figure A97 and the first three attached plots show the number of exceedances plotted against the annual 

mean. These plots clearly show how weak the relationship between the exceedances and the 

annual mean is. Figure A-98 and the next three attached plots are for the Poisson exponential 

model, plotting predicted versus observed exceedances. Figure A-99 and the final three attached 

plots are for the normal linear model, plotting predicted versus observed exceedances (negative 

predictions were replaced by zero). Comparing the normal and Poisson model predictions, the 

normal model tends to under-predict the higher numbers of observed exceedances. 

Tables A-100 and A-101 indicate the predictions for a mean of 53 ppb and for the mean 

annual mean for each the Poisson exponential model and the normal linear model, respectively. 

The predictions for a mean of 53 ppb estimate the number of exceedances for a hypothetical siteyear with the highest annual mean concentration under the current-standard scenario, i.e., when 

the highest annual mean site-year for a given location just meets the annual standard. The 

predictions for a mean equal to the mean annual mean estimate the number of exceedances for 

the typical “as-is” scenario, i.e., for a hypothetical site-year with an annual mean that is the 

average annual mean for that location. 95 percent confidence and prediction intervals for the 

number of exceedances at given mean levels were also estimated using each model. In addition, 

exceedances were also estimated at alternative annual mean concentrations. Tables A-103 and 

A-104 give calculated predictions at annual mean values of 20, 30, 40, 50, 53, and 60 ppb and at 

the minimum, mean, and maximum annual mean value for each location using the Poisson 

exponential model and the normal linear model, respectively. 

The 95% confidence interval gives the uncertainty of the expected value, i.e., of the average 

number of exceedances over hypothetically infinitely many site-years with the same annual 

mean. The 95% prediction interval gives the uncertainty of the value for a single site-year, 

taking into account both the uncertainty of the estimated parameters and the variability of the 

number of exceedances in a given site-year about the overall mean. All prediction intervals were 

truncated to be greater than or equal to zero and less than or equal to 1,000. The maximum 

possible number of exceedances in a year is the maximum number of hours in a leap year, 8,784. 

The maximum observed exceedances in a year was 69. 

For annual means within the range of the data, the predicted numbers of exceedances are 

generally within the range of the observed numbers of exceedances. The normal model 

predictions tend to be lower than the Poisson model predictions. At annual mean levels above 

the range of the data, the Poisson model with the exponential inverse link sometimes gives 

extremely high estimates, well beyond the truncation limit of 1,000. This is mainly due to the 

exponential link; each increase of the annual mean by 1 ppb increases the predicted exceedances 

by a multiplicative factor of exp(b), where b >0. The upper bounds of the normal linear model 
A-85

prediction intervals are at most a more reasonable 202, but these predictions are less reliable 

because the Poisson model with an exponential inverse link fits the data much better. For the 

normal linear model, each increase of the annual mean by 1 ppb increases the predicted 

exceedances by b ppb. 

 

Not shown here are the results for the normal model with an exponential inverse link, which 

was the model formulation selected by McCurdy (1994). That model gives roughly similar 

predictions to the Poisson model with the exponential inverse link. 
A-86

Table A-98. Parameters for Poisson exponential model stratified by location.

Location Type Location Name Parameter* Estimate 

Standard Error 

Lower ConfidenBound 

MSA Atlanta,GA Intercept -5.081 1.917 -9MSA Atlanta,GA mean 0.140 0.099 -0MSA Atlanta,GA Scale 1.000 0.000 1CMSA Boston-Worcester-Lawrence, MA-NH-ME-CT CMSA Intercept -6.887 2.832 -14CMSA Boston-Worcester-Lawrence, MA-NH-ME-CT CMSA mean 0.144 0.116 -0CMSA Boston-Worcester-Lawrence, MA-NH-ME-CT CMSA Scale 1.000 0.000 1CMSA Cleveland-Akron, OH CMSA Intercept -14.209 4.374 -25CMSA Cleveland-Akron, OH CMSA mean 0.548 0.164 0CMSA Cleveland-Akron, OH CMSA Scale 1.000 0.000 1MSA Colorado Springs,CO Intercept -4.846 0.401 -5MSA Colorado Springs,CO mean 0.284 0.012 0MSA Colorado Springs,CO Scale 1.000 0.000 1CMSA Denver-Boulder-Greeley, CO CMSA Intercept -4.399 1.186 -7CMSA Denver-Boulder-Greeley, CO CMSA mean 0.137 0.038 0CMSA Denver-Boulder-Greeley, CO CMSA Scale 1.000 0.000 1MSA El Paso,TX Intercept -10.436 2.455 -16MSA El Paso,TX mean 0.350 0.074 0MSA El Paso,TX Scale 1.000 0.000 1CMSA Los Angeles-Riverside-Orange County, CA CMSA Intercept -5.628 0.253 -6CMSA Los Angeles-Riverside-Orange County, CA CMSA mean 0.181 0.006 0CMSA Los Angeles-Riverside-Orange County, CA CMSA Scale 1.000 0.000 1CMSA Miami-Fort Lauderdale, FL CMSA Intercept -5.780 1.641 -9CMSA Miami-Fort Lauderdale, FL CMSA mean 0.342 0.114 0CMSA Miami-Fort Lauderdale, FL CMSA Scale 1.000 0.000 1CMSA 

New York-Northern New Jersey-Long Island, NY-NJCT-PA CMS Intercept -6.800 1.269 -9

CMSA 

New York-Northern New Jersey-Long Island, NY-NJCT-PA CMS mean 0.147 0.037 0

CMSA 

New York-Northern New Jersey-Long Island, NY-NJCT-PA CMS Scale 1.000 0.000 1

MSA Phoenix-Mesa,AZ Intercept -1.568 0.400 -2MSA Phoenix-Mesa,AZ mean 0.106 0.013 0
A-87

Location Type Location Name Parameter* Estimate 

Standard Error 

Lower ConfidenBound 

MSA Phoenix-Mesa,AZ Scale 1.000 0.000 1CMSA Washington-Baltimore, DC-MD-VA-WV CMSA Intercept -6.559 3.054 -14CMSA Washington-Baltimore, DC-MD-VA-WV CMSA mean 0.145 0.135 -0CMSA Washington-Baltimore, DC-MD-VA-WV CMSA Scale 1.000 0.000 1MSA/CMSA Other MSA Intercept -5.137 0.222 -5MSA/CMSA Other MSA mean 0.152 0.010 0MSA/CMSA Other MSA Scale 1.000 0.000 1Not MSA Other Not MSA Intercept -4.672 0.467 -5Not MSA Other Not MSA mean 0.227 0.036 0Not MSA Other Not MSA Scale 1.000 0.000 1Notes: * using the report notation, a = “Intercept”, and b = “mean.” “Scale” equals 1, by definition, for this model. ** probability that the Chi-square test for that parameter = 0. 

Table A-99. Parameters for normal linear model stratified by location.

Location Type Location Name Parameter* Estimate 

Standard Error 

Lower ConfidenBound 

MSA Atlanta,GA Intercept -0.041 0.069 -0.MSA Atlanta,GA mean 0.008 0.005 -0.MSA Atlanta,GA Scale 0.226 0.022 0.CMSA Boston-Worcester-Lawrence, MA-NH-ME-CT CMSA Intercept -0.023 0.034 -0.CMSA Boston-Worcester-Lawrence, MA-NH-ME-CT CMSA mean 0.003 0.002 -0.CMSA Boston-Worcester-Lawrence, MA-NH-ME-CT CMSA Scale 0.135 0.009 0.CMSA Cleveland-Akron, OH CMSA Intercept -3.259 2.127 -7.CMSA Cleveland-Akron, OH CMSA mean 0.176 0.099 -0.CMSA Cleveland-Akron, OH CMSA Scale 1.755 0.265 1.MSA Colorado Springs,CO Intercept -36.358 11.812 -60.MSA Colorado Springs,CO mean 2.689 0.674 1.MSA Colorado Springs,CO Scale 22.519 3.123 17.CMSA Denver-Boulder-Greeley, CO CMSA Intercept -0.439 0.383 -1.CMSA Denver-Boulder-Greeley, CO CMSA mean 0.044 0.018 0.
A-88

Location Type Location Name Parameter* Estimate 

Standard Error 

Lower ConfidenBound 

CMSA Denver-Boulder-Greeley, CO CMSA Scale 1.097 0.129 0.MSA El Paso,TX Intercept -2.017 0.440 -2.MSA El Paso,TX mean 0.131 0.024 0.MSA El Paso,TX Scale 0.920 0.098 0.CMSA Los Angeles-Riverside-Orange County, CA CMSA Intercept -3.301 0.620 -4.CMSA Los Angeles-Riverside-Orange County, CA CMSA mean 0.194 0.023 0.CMSA Los Angeles-Riverside-Orange County, CA CMSA Scale 4.723 0.174 4.CMSA Miami-Fort Lauderdale, FL CMSA Intercept -0.496 0.384 -1.CMSA Miami-Fort Lauderdale, FL CMSA mean 0.070 0.037 -0.CMSA Miami-Fort Lauderdale, FL CMSA Scale 0.828 0.088 0.CMSA 

New York-Northern New Jersey-Long Island, NY-NJCT-PA CMS Intercept -0.230 0.104 -0.

CMSA 

New York-Northern New Jersey-Long Island, NY-NJCT-PA CMS mean 0.013 0.004 0.

CMSA 

New York-Northern New Jersey-Long Island, NY-NJCT-PA CMS Scale 0.407 0.022 0.

MSA Phoenix-Mesa,AZ Intercept -7.102 15.545 -38.MSA Phoenix-Mesa,AZ mean 0.423 0.557 -0.MSA Phoenix-Mesa,AZ Scale 22.513 2.274 18.CMSA Washington-Baltimore, DC-MD-VA-WV CMSA Intercept -0.032 0.069 -0.CMSA Washington-Baltimore, DC-MD-VA-WV CMSA mean 0.003 0.003 -0.CMSA Washington-Baltimore, DC-MD-VA-WV CMSA Scale 0.208 0.013 0.MSA/CMSA Other MSA Intercept -0.100 0.051 -0.MSA/CMSA Other MSA mean 0.013 0.003 0.MSA/CMSA Other MSA Scale 1.098 0.015 1.Not MSA Other Not MSA Intercept -0.064 0.049 -0.Not MSA Other Not MSA mean 0.021 0.006 0.Not MSA Other Not MSA Scale 0.549 0.018 0.Notes: 

Using the report notation, a = “Intercept”, b = “mean”, and standard deviation = “Scale.” ** probability that the Chi-square test for that parameter = 0. 
A-89

Figure A-97. Measured number of exceedances of 1-hour NO2 concentrations of 150 ppb versus annual 

mean NO2 concentrations (ppb) for CMSA locations. 

Figure A-98. Predicted and observed exceedances of 1-hour NO2 concentrations of 150 ppb for CMSA 

locations using Poisson exponential model. 
A-90

Figure A-99. Predicted and observed exceedances of 1-hour NO2 concentrations of 150 ppb for CMSA 

locations using normal linear model 
A-91

Table A-100. Predicted number of exceedances of of 1-hour NO2 concentrations of 150 ppb using a 

Poisson exponential model for the as-is and current-standard scenarios.

95% Confidence 

Interval for Mean 

Number of 

Exceedances 

95% Prediction 

Interval for 

Number of 

Exceedances 

Location

Annual 

Mean 

(ppb)

Observed 

Mean 

Exceedances

Observed 

Max 

Exceedances

Predicted 

Exceedances

Lower 

Bound 

Upper 

Bound 

Lower 

Bound 

Upper 

Bound 

Atlanta 53.0 0.057 1 10.242 0.012 1000.000 0 1000

Atlanta 12.9 0.057 1 0.038 0.008 0.181 0 1

Boston 53.0 0.019 1 2.081 0.002 1000.000 0 1000

Boston 16.8 0.019 1 0.011 0.001 0.091 0 0

Cleveland 53.0 0.455 9 1000.000 578.253 1000.000 364 1000

Cleveland 21.2 0.455 9 0.073 0.011 0.474 0 1

Colorado 

Springs 53.0 7.346 143 1000.000 1000.000 1000.000 1000 1000

Colorado 

Springs 16.3 7.346 143 0.792 0.528 1.189 0 3

Denver 53.0 0.389 6 17.140 2.958 99.308 2 98

Denver 18.7 0.389 6 0.158 0.057 0.438 0 1

El Paso 53.0 0.295 7 1000.000 177.602 1000.000 156 1000

El Paso 17.7 0.295 7 0.015 0.001 0.142 0 1

Los Angeles 53.0 1.403 44 53.244 44.092 64.297 37 73

Los Angeles 24.3 1.403 44 0.293 0.238 0.360 0 2

Miami 53.0 0.182 5 1000.000 35.520 1000.000 29 1000

Miami 9.7 0.182 5 0.086 0.026 0.281 0 1

New York 53.0 0.092 3 2.737 0.646 11.604 0 13

New York 25.5 0.092 3 0.048 0.022 0.104 0 1

Phoenix 53.0 4.469 147 56.901 31.702 102.130 26 106

Phoenix 27.3 4.469 147 3.760 3.221 4.389 0 8

Washington 53.0 0.030 2 3.038 0.001 1000.000 0 1000

Washington 19.4 0.030 2 0.023 0.007 0.082 0 0

Other MSA 53.0 0.079 39 18.369 9.388 35.940 7 41

Other MSA 13.9 0.079 39 0.048 0.040 0.058 0 1

Other Not 

MSA 53.0 0.081 7 1000.000 85.717 1000.000 75 1000

Other Not 

MSA 7.0 0.081 7 0.046 0.028 0.075 0 1
A-92

Table A-101. Predicted number of exceedances of of 1-hour NO2 concentrations of 150 ppb using a 

Normal linear model for the as-is and current-standard scenarios.

95% Confidence 

Interval for Mean 

Number of 

Exceedances 

95% Prediction 

Interval for 

Number of 

Exceedances 

Location Name

Annual 

Mean 

(ppb)

Observed 

Mean 

Exceedances

Observed 

Max 

Exceedances

Predicted 

Exceedances

Lower 

Bound 

Upper 

Bound 

Lower 

Bound 

Upper 

Bound 

Atlanta 53.0 0.057 1 0.360 0.000 0.739 0.000 0.957

Atlanta 12.9 0.057 1 0.057 0.000 0.117 0.000 0.514

Boston 53.0 0.019 1 0.111 0.000 0.245 0.000 0.412

Boston 16.8 0.019 1 0.019 0.000 0.045 0.000 0.289

Cleveland 53.0 0.455 9 6.046 0.000 12.267 0.000 13.612

Cleveland 21.2 0.455 9 0.455 0.000 1.188 0.000 4.198

Colorado 

Springs 53.0 7.346 143 106.169 56.853 155.486 36.477 175.862

Colorado 

Springs 16.3 7.346 143 7.346 0.000 16.002 0.000 54.709

Denver 53.0 0.389 6 1.906 0.645 3.168 0.000 4.490

Denver 18.7 0.389 6 0.389 0.031 0.747 0.000 2.648

El Paso 53.0 0.295 7 4.902 3.249 6.555 2.384 7.421

El Paso 17.7 0.295 7 0.295 0.024 0.567 0.000 2.172

Los Angeles 53.0 1.403 44 6.965 5.561 8.369 0.000 16.360

Los Angeles 24.3 1.403 44 1.403 0.921 1.884 0.000 10.703

Miami 53.0 0.182 5 3.199 0.024 6.375 0.000 6.871

Miami 9.7 0.182 5 0.182 0.000 0.426 0.000 1.871

New York 53.0 0.092 3 0.439 0.220 0.658 0.000 1.272

New York 25.5 0.092 3 0.092 0.031 0.152 0.000 0.897

Phoenix 53.0 4.469 147 15.339 0.000 44.043 0.000 69.369

Phoenix 27.3 4.469 147 4.469 0.000 10.773 0.000 50.219

Washington 53.0 0.030 2 0.136 0.000 0.364 0.000 0.608

Washington 19.4 0.030 2 0.030 0.000 0.065 0.000 0.443

Other MSA 53.0 0.079 39 0.584 0.324 0.844 0.000 2.752

Other MSA 13.9 0.079 39 0.079 0.037 0.120 0.000 2.232

Other Not MSA 53.0 0.081 7 1.036 0.505 1.566 0.000 2.238

Other Not MSA 7.0 0.081 7 0.081 0.030 0.132 0.000 1.161
A-93

We can compare these predictions with the predictions for Los Angeles from McCurdy 

(1994) based on 1988-1992 data. Table A-102 gives the McCurdy (1994) exceedance estimates 

for exceedances of 150 ppb together with our estimates for the 1995-2006 data based on the 

Poisson exponential model (see Table A-103) and the normal linear model (see Table A-104). It 

is easily seen that the McCurdy (1994) estimates agree reasonably well with our Poisson 

exponential model predictions, with predicted exceedances being a little lower for annual means 

up to 53 ppb, but a little higher at 60 ppb. The McCurdy (1994) model predicts 75 exceedances 

at 53 ppb, compared to our Poisson exponential model prediction of 53 exceedances. However, 

the McCurdy (1994) estimates are all much higher than our normal linear model predictions. For 

example, the McCurdy (1994) model predicts 75 exceedances at 53 ppb, compared to our normal 

linear model prediction of 7 exceedances. These findings are primarily due to the fact that 

McCurdy also used an exponential link function. 

Table A-102. Comparison of predicted exceedances of 150 ppb using McCurdy (1994) for 1988-1992 

data and the Poisson exponential and normal linear models for 1995-2006 data. 

Predicted Exceedances of 150 ppb 

Annual Mean (ppb) 

McCurdy (1994) 

Normal exponential 

1988-1992 data 

Current Analysis 

Poisson exponential 

1995-2006 data 

Current Analysis 

Normal linear 

1995-2006 data 

20 4 0 1 

30 9 1 3 

40 33 5 4 

50 57 31 6 

53 75 53 7 

60 142 189 8 

A-6.4 Conclusion 

These analyses found a poor relationship between the annual means and the exceedances of 

150 ppb, as well as frequently unrealistically high predictions of exceedances of 150 ppb for the 

current-standard scenario. The uncertainty at higher exceedance threshold concentration levels 

(200 to 300 ppb) would be expected to be even higher because the numbers of site-years with 

non-zero exceedances are even lower (which implies a much weaker numerical relationship 

between the annual mean and the annual exceedances). For example, for Los Angeles, the 

maximum number of exceedances of 150 ppb was 44, but the maximum number of exceedances 

of 200 ppb was only 5. Therefore we chose not to continue the regression analyses to higher 

exceedance threshold concentration levels. 
A-94

A-6.5 Detailed Regression Model Predictions 

Table A-103. Predicted number of exceedances of of 1-hour NO2 concentrations of 150 ppb using a 

Poisson exponential model and at several annual average concentrations.

95% Confidence 

Interval for Mean 

Number of 

Exceedances 

95% Prediction 

Interval for 

Number of 

Exceedances 

Location

Annual 

Mean 

(ppb)

Observed 

Mean 

Exceedances

Observed 

Max 

Exceedances

Predicted 

Exceedances

Lower 

Bound 

Upper 

Bound 

Lower 

Bound 

Upper 

Bound 

Atlanta 20.0 0.057 1 0.102 0.032 0.327 0 1

Atlanta 30.0 0.057 1 0.412 0.034 4.953 0 5

Atlanta 40.0 0.057 1 1.665 0.023 122.647 0 103

Atlanta 50.0 0.057 1 6.735 0.014 1000.000 0 1000

Atlanta 53.0 0.057 1 10.242 0.012 1000.000 0 1000

Atlanta 60.0 0.057 1 27.243 0.008 1000.000 0 1000

Atlanta 3.4 0.057 1 0.010 0.000 0.230 0 0

Atlanta 12.9 0.057 1 0.038 0.008 0.181 0 1

Atlanta 26.6 0.057 1 0.257 0.037 1.770 0 3

Boston 20.0 0.019 1 0.018 0.004 0.090 0 1

Boston 30.0 0.019 1 0.076 0.010 0.576 0 1

Boston 40.0 0.019 1 0.321 0.006 17.564 0 14

Boston 50.0 0.019 1 1.352 0.003 661.873 0 680

Boston 53.0 0.019 1 2.081 0.002 1000.000 0 1000

Boston 60.0 0.019 1 5.692 0.001 1000.000 0 1000

Boston 5.4 0.019 1 0.002 0.000 0.175 0 0

Boston 16.8 0.019 1 0.011 0.001 0.091 0 0

Boston 31.0 0.019 1 0.089 0.010 0.801 0 1

Cleveland 20.0 0.455 9 0.039 0.004 0.358 0 1

Cleveland 30.0 0.455 9 9.244 2.693 31.732 2 32

Cleveland 40.0 0.455 9 1000.000 29.509 1000.000 23 1000

Cleveland 50.0 0.455 9 1000.000 291.652 1000.000 184 1000

Cleveland 53.0 0.455 9 1000.000 578.253 1000.000 364 1000

Cleveland 60.0 0.455 9 1000.000 1000.000 1000.000 1000 1000

Cleveland 14.2 0.455 9 0.002 0.000 0.092 0 0

Cleveland 21.2 0.455 9 0.073 0.011 0.474 0 1

Cleveland 28.1 0.455 9 3.193 1.490 6.845 0 9

Colorado 

Springs 20.0 7.346 143 2.295 1.662 3.168 0 6

Colorado 

Springs 30.0 7.346 143 39.206 33.759 45.531 26 53

Colorado 

Springs 40.0 7.346 143 669.766 526.509 852.001 523 870

Colorado 

Springs 50.0 7.346 143 1000.000 1000.000 1000.000 1000 1000

Colorado 

Springs 53.0 7.346 143 1000.000 1000.000 1000.000 1000 1000

Colorado 

Springs 60.0 7.346 143 1000.000 1000.000 1000.000 1000 1000

Colorado 

Springs 6.8 7.346 143 0.054 0.029 0.102 0 1

Colorado 16.3 7.346 143 0.792 0.528 1.189 0 3
A-95

95% Confidence 

Interval for Mean 

Number of 

Exceedances 

95% Prediction 

Interval for 

Number of 

Exceedances 

Location

Annual 

Mean 

(ppb)

Observed 

Mean 

Exceedances

Observed 

Max 

Exceedances

Predicted 

Exceedances

Lower 

Bound 

Upper 

Bound 

Lower 

Bound 

Upper 

Bound 

Springs 

Colorado 

Springs 34.8 7.346 143 153.247 130.906 179.401 121 189

Denver 20.0 0.389 6 0.189 0.074 0.482 0 2

Denver 30.0 0.389 6 0.740 0.438 1.251 0 3

Denver 40.0 0.389 6 2.902 1.201 7.014 0 9

Denver 50.0 0.389 6 11.376 2.426 53.350 1 53

Denver 53.0 0.389 6 17.140 2.958 99.308 2 98

Denver 60.0 0.389 6 44.600 4.659 426.973 4 454

Denver 6.1 0.389 6 0.028 0.004 0.186 0 1

Denver 18.7 0.389 6 0.158 0.057 0.438 0 1

Denver 36.8 0.389 6 1.871 0.925 3.786 0 6

El Paso 20.0 0.295 7 0.032 0.005 0.230 0 1

El Paso 30.0 0.295 7 1.075 0.536 2.156 0 4

El Paso 40.0 0.295 7 35.703 11.290 112.906 11 119

El Paso 50.0 0.295 7 1000.000 95.081 1000.000 94 1000

El Paso 53.0 0.295 7 1000.000 177.602 1000.000 156 1000

El Paso 60.0 0.295 7 1000.000 757.520 1000.000 634 1000

El Paso 8.2 0.295 7 0.001 0.000 0.020 0 0

El Paso 17.7 0.295 7 0.015 0.001 0.142 0 1

El Paso 35.1 0.295 7 6.447 3.454 12.036 1 14

Los Angeles 20.0 1.403 44 0.135 0.104 0.174 0 1

Los Angeles 30.0 1.403 44 0.825 0.713 0.954 0 3

Los Angeles 40.0 1.403 44 5.050 4.632 5.505 1 10

Los Angeles 50.0 1.403 44 30.917 26.439 36.154 20 44

Los Angeles 53.0 1.403 44 53.244 44.092 64.297 37 73

Los Angeles 60.0 1.403 44 189.281 144.681 247.629 138 260

Los Angeles 3.6 1.403 44 0.007 0.004 0.011 0 0

Los Angeles 24.3 1.403 44 0.293 0.238 0.360 0 2

Los Angeles 50.6 1.403 44 34.208 29.084 40.236 22 48

Miami 20.0 0.182 5 2.882 0.636 13.069 0 13

Miami 30.0 0.182 5 88.023 2.282 1000.000 2 1000

Miami 40.0 0.182 5 1000.000 7.591 1000.000 7 1000

Miami 50.0 0.182 5 1000.000 24.900 1000.000 33 1000

Miami 53.0 0.182 5 1000.000 35.520 1000.000 29 1000

Miami 60.0 0.182 5 1000.000 81.274 1000.000 40 1000

Miami 5.5 0.182 5 0.020 0.003 0.154 0 1

Miami 9.7 0.182 5 0.086 0.026 0.281 0 1

Miami 16.8 0.182 5 0.970 0.380 2.475 0 4

New York 20.0 0.092 3 0.021 0.007 0.065 0 0

New York 30.0 0.092 3 0.092 0.052 0.163 0 1

New York 40.0 0.092 3 0.403 0.211 0.773 0 2

New York 50.0 0.092 3 1.760 0.507 6.107 0 7

New York 53.0 0.092 3 2.737 0.646 11.604 0 13

New York 60.0 0.092 3 7.677 1.121 52.548 0 53

New York 9.7 0.092 3 0.005 0.001 0.028 0 0

New York 25.5 0.092 3 0.048 0.022 0.104 0 1

New York 42.2 0.092 3 0.557 0.260 1.193 0 3
A-96

95% Confidence 

Interval for Mean 

Number of 

Exceedances 

95% Prediction 

Interval for 

Number of 

Exceedances 

Location

Annual 

Mean 

(ppb)

Observed 

Mean 

Exceedances

Observed 

Max 

Exceedances

Predicted 

Exceedances

Lower 

Bound 

Upper 

Bound 

Lower 

Bound 

Upper 

Bound 

Phoenix 20.0 4.469 147 1.731 1.287 2.329 0 5

Phoenix 30.0 4.469 147 4.988 4.367 5.698 1 10

Phoenix 40.0 4.469 147 14.375 10.922 18.919 7 24

Phoenix 50.0 4.469 147 41.422 24.843 69.066 21 71

Phoenix 53.0 4.469 147 56.901 31.702 102.130 26 106

Phoenix 60.0 4.469 147 119.362 55.901 254.864 56 254

Phoenix 11.1 4.469 147 0.673 0.404 1.119 0 3

Phoenix 27.3 4.469 147 3.760 3.221 4.389 0 8

Phoenix 40.5 4.469 147 15.110 11.361 20.098 7 25

Washington 20.0 0.030 2 0.026 0.008 0.081 0 1

Washington 30.0 0.030 2 0.109 0.011 1.044 0 2

Washington 40.0 0.030 2 0.463 0.004 55.438 0 57

Washington 50.0 0.030 2 1.968 0.001 1000.000 0 1000

Washington 53.0 0.030 2 3.038 0.001 1000.000 0 1000

Washington 60.0 0.030 2 8.368 0.000 1000.000 0 1000

Washington 6.9 0.030 2 0.004 0.000 0.256 0 1

Washington 19.4 0.030 2 0.023 0.007 0.082 0 0

Washington 27.2 0.030 2 0.072 0.014 0.366 0 1

Other MSA 20.0 0.079 39 0.122 0.107 0.140 0 1

Other MSA 30.0 0.079 39 0.559 0.442 0.707 0 2

Other MSA 40.0 0.079 39 2.552 1.681 3.874 0 6

Other MSA 50.0 0.079 39 11.648 6.317 21.480 4 25

Other MSA 53.0 0.079 39 18.369 9.388 35.940 7 41

Other MSA 60.0 0.079 39 53.171 23.650 119.541 20 116

Other MSA 0.5 0.079 39 0.006 0.004 0.010 0 0

Other MSA 13.9 0.079 39 0.048 0.040 0.058 0 1

Other MSA 34.0 0.079 39 1.025 0.756 1.391 0 4

Other Not MSA 20.0 0.081 7 0.878 0.459 1.681 0 3

Other Not MSA 30.0 0.081 7 8.514 2.297 31.556 1 32

Other Not MSA 40.0 0.081 7 82.532 11.133 611.822 10 573

Other Not MSA 50.0 0.081 7 799.989 53.545 1000.000 57 1000

Other Not MSA 53.0 0.081 7 1000.000 85.717 1000.000 75 1000

Other Not MSA 60.0 0.081 7 1000.000 256.785 1000.000 226 1000

Other Not MSA 0.3 0.081 7 0.010 0.004 0.025 0 0

Other Not MSA 7.0 0.081 7 0.046 0.028 0.075 0 1

Other Not MSA 19.7 0.081 7 0.823 0.438 1.547 0 3
A-97

Table A-104. Predicted number of exceedances of of 1-hour NO2 concentrations of 150 ppb using a 

Normal linear model and at several annual average concentrations.

95% Confidence 

Interval for Mean 

Number of 

Exceedances 

95% Prediction 

Interval for Number 

of Exceedances 

Location 

Name

Annual 

Mean

Observed 

Mean 

Exceedances

Observed 

Max 

Exceedances

Predicted 

Exceedances

Lower 

Bound 

Upper 

Bound 

Lower 

Bound 

Upper 

Bound 

Atlanta 20.0 0.057 1 0.110 0.020 0.201 0.000 0.573

Atlanta 30.0 0.057 1 0.186 0.015 0.357 0.000 0.672

Atlanta 40.0 0.057 1 0.262 0.001 0.522 0.000 0.787

Atlanta 50.0 0.057 1 0.337 0.000 0.689 0.000 0.916

Atlanta 53.0 0.057 1 0.360 0.000 0.739 0.000 0.957

Atlanta 60.0 0.057 1 0.413 0.000 0.857 0.000 1.055

Atlanta 3.4 0.057 1 0.000 0.000 0.092 0.000 0.452

Atlanta 12.9 0.057 1 0.057 0.000 0.117 0.000 0.514

Atlanta 26.6 0.057 1 0.161 0.019 0.303 0.000 0.637

Boston 20.0 0.019 1 0.027 0.000 0.056 0.000 0.297

Boston 30.0 0.019 1 0.052 0.000 0.107 0.000 0.327

Boston 40.0 0.019 1 0.078 0.000 0.166 0.000 0.361

Boston 50.0 0.019 1 0.103 0.000 0.226 0.000 0.399

Boston 53.0 0.019 1 0.111 0.000 0.245 0.000 0.412

Boston 60.0 0.019 1 0.128 0.000 0.287 0.000 0.441

Boston 5.4 0.019 1 0.000 0.000 0.039 0.000 0.263

Boston 16.8 0.019 1 0.019 0.000 0.045 0.000 0.289

Boston 31.0 0.019 1 0.055 0.000 0.113 0.000 0.330

Cleveland 20.0 0.455 9 0.252 0.000 1.019 0.000 4.003

Cleveland 30.0 0.455 9 2.008 0.141 3.874 0.000 6.173

Cleveland 40.0 0.455 9 3.763 0.035 7.492 0.000 9.163

Cleveland 50.0 0.455 9 5.519 0.000 11.163 0.000 12.553

Cleveland 53.0 0.455 9 6.046 0.000 12.267 0.000 13.612

Cleveland 60.0 0.455 9 7.275 0.000 14.846 0.000 16.125

Cleveland 14.2 0.455 9 0.000 0.000 0.769 0.000 3.243

Cleveland 21.2 0.455 9 0.455 0.000 1.188 0.000 4.198

Cleveland 28.1 0.455 9 1.667 0.140 3.194 0.000 5.673

Colorado 

Springs 20.0 7.346 143 17.426 7.454 27.398 0.000 65.075

Colorado 

Springs 30.0 7.346 143 44.318 24.197 64.439 0.000 95.397

Colorado 

Springs 40.0 7.346 143 71.210 38.662 103.758 13.462 128.958

Colorado 

Springs 50.0 7.346 143 98.102 52.682 143.522 31.411 164.793

Colorado 

Springs 53.0 7.346 143 106.169 56.853 155.486 36.477 175.862

Colorado 

Springs 60.0 7.346 143 124.994 66.550 183.438 47.873 202.115

Colorado 

Springs 6.8 7.346 143 0.000 0.000 0.000 0.000 31.109

Colorado 

Springs 16.3 7.346 143 7.346 0.000 16.002 0.000 54.709

Colorado 

Springs 34.8 7.346 143 57.235 31.241 83.228 3.296 111.173
A-98

95% Confidence 

Interval for Mean 

Number of 

Exceedances 

95% Prediction 

Interval for Number 

of Exceedances 

Location 

Name

Annual 

Mean

Observed 

Mean 

Exceedances

Observed 

Max 

Exceedances

Predicted 

Exceedances

Lower 

Bound 

Upper 

Bound 

Lower 

Bound 

Upper 

Bound 

Denver 20.0 0.389 6 0.446 0.085 0.807 0.000 2.706

Denver 30.0 0.389 6 0.888 0.353 1.424 0.000 3.185

Denver 40.0 0.389 6 1.331 0.499 2.163 0.000 3.720

Denver 50.0 0.389 6 1.773 0.613 2.934 0.000 4.306

Denver 53.0 0.389 6 1.906 0.645 3.168 0.000 4.490

Denver 60.0 0.389 6 2.216 0.716 3.716 0.000 4.933

Denver 6.1 0.389 6 0.000 0.000 0.402 0.000 2.136

Denver 18.7 0.389 6 0.389 0.031 0.747 0.000 2.648

Denver 36.8 0.389 6 1.189 0.458 1.920 0.000 3.543

El Paso 20.0 0.295 7 0.594 0.303 0.886 0.000 2.474

El Paso 30.0 0.295 7 1.900 1.270 2.529 0.000 3.866

El Paso 40.0 0.295 7 3.205 2.140 4.270 1.049 5.361

El Paso 50.0 0.295 7 4.511 2.994 6.027 2.085 6.936

El Paso 53.0 0.295 7 4.902 3.249 6.555 2.384 7.421

El Paso 60.0 0.295 7 5.816 3.844 7.789 3.065 8.568

El Paso 8.2 0.295 7 0.000 0.000 0.000 0.000 0.981

El Paso 17.7 0.295 7 0.295 0.024 0.567 0.000 2.172

El Paso 35.1 0.295 7 2.567 1.719 3.416 0.516 4.619

Los Angeles 20.0 1.403 44 0.573 0.053 1.093 0.000 9.876

Los Angeles 30.0 1.403 44 2.510 1.962 3.058 0.000 11.814

Los Angeles 40.0 1.403 44 4.447 3.579 5.315 0.000 13.776

Los Angeles 50.0 1.403 44 6.384 5.109 7.660 0.000 15.760

Los Angeles 53.0 1.403 44 6.965 5.561 8.369 0.000 16.360

Los Angeles 60.0 1.403 44 8.321 6.612 10.031 0.000 17.766

Los Angeles 3.6 1.403 44 0.000 0.000 0.000 0.000 6.747

Los Angeles 24.3 1.403 44 1.403 0.921 1.884 0.000 10.703

Los Angeles 50.6 1.403 44 6.492 5.193 7.792 0.000 15.871

Miami 20.0 0.182 5 0.899 0.108 1.689 0.000 2.757

Miami 30.0 0.182 5 1.596 0.092 3.099 0.000 3.873

Miami 40.0 0.182 5 2.293 0.065 4.521 0.000 5.131

Miami 50.0 0.182 5 2.990 0.034 5.947 0.000 6.463

Miami 53.0 0.182 5 3.199 0.024 6.375 0.000 6.871

Miami 60.0 0.182 5 3.687 0.001 7.373 0.000 7.834

Miami 5.5 0.182 5 0.000 0.000 0.281 0.000 1.607

Miami 9.7 0.182 5 0.182 0.000 0.426 0.000 1.871

Miami 16.8 0.182 5 0.677 0.103 1.250 0.000 2.449

New York 20.0 0.092 3 0.023 0.000 0.096 0.000 0.829

New York 30.0 0.092 3 0.149 0.079 0.218 0.000 0.955

New York 40.0 0.092 3 0.275 0.148 0.401 0.000 1.088

New York 50.0 0.092 3 0.401 0.204 0.598 0.000 1.228

New York 53.0 0.092 3 0.439 0.220 0.658 0.000 1.272

New York 60.0 0.092 3 0.527 0.256 0.798 0.000 1.375

New York 9.7 0.092 3 0.000 0.000 0.028 0.000 0.707

New York 25.5 0.092 3 0.092 0.031 0.152 0.000 0.897

New York 42.2 0.092 3 0.302 0.161 0.444 0.000 1.118

Phoenix 20.0 4.469 147 1.367 0.000 11.546 0.000 47.846

Phoenix 30.0 4.469 147 5.601 0.000 12.546 0.000 51.449

Phoenix 40.0 4.469 147 9.835 0.000 25.027 0.000 57.734
A-99

95% Confidence 

Interval for Mean 

Number of 

Exceedances 

95% Prediction 

Interval for Number 

of Exceedances 

Location 

Name

Annual 

Mean

Observed 

Mean 

Exceedances

Observed 

Max 

Exceedances

Predicted 

Exceedances

Lower 

Bound 

Upper 

Bound 

Lower 

Bound 

Upper 

Bound 

Phoenix 50.0 4.469 147 14.069 0.000 39.591 0.000 66.390

Phoenix 53.0 4.469 147 15.339 0.000 44.043 0.000 69.369

Phoenix 60.0 4.469 147 18.303 0.000 54.495 0.000 76.880

Phoenix 11.1 4.469 147 0.000 0.000 16.406 0.000 46.824

Phoenix 27.3 4.469 147 4.469 0.000 10.773 0.000 50.219

Phoenix 40.5 4.469 147 10.035 0.000 25.696 0.000 58.093

Washington 20.0 0.030 2 0.032 0.000 0.067 0.000 0.445

Washington 30.0 0.030 2 0.063 0.000 0.143 0.000 0.483

Washington 40.0 0.030 2 0.095 0.000 0.237 0.000 0.531

Washington 50.0 0.030 2 0.127 0.000 0.335 0.000 0.589

Washington 53.0 0.030 2 0.136 0.000 0.364 0.000 0.608

Washington 60.0 0.030 2 0.158 0.000 0.432 0.000 0.654

Washington 6.9 0.030 2 0.000 0.000 0.081 0.000 0.412

Washington 19.4 0.030 2 0.030 0.000 0.065 0.000 0.443

Washington 27.2 0.030 2 0.054 0.000 0.117 0.000 0.471

Other MSA 20.0 0.079 39 0.158 0.100 0.216 0.000 2.311

Other MSA 30.0 0.079 39 0.287 0.173 0.401 0.000 2.442

Other MSA 40.0 0.079 39 0.416 0.239 0.593 0.000 2.576

Other MSA 50.0 0.079 39 0.545 0.304 0.786 0.000 2.711

Other MSA 53.0 0.079 39 0.584 0.324 0.844 0.000 2.752

Other MSA 60.0 0.079 39 0.674 0.368 0.980 0.000 2.848

Other MSA 0.5 0.079 39 0.000 0.000 0.003 0.000 2.061

Other MSA 13.9 0.079 39 0.079 0.037 0.120 0.000 2.232

Other MSA 34.0 0.079 39 0.339 0.200 0.477 0.000 2.495

Other Not MSA 20.0 0.081 7 0.351 0.193 0.508 0.000 1.440

Other Not MSA 30.0 0.081 7 0.558 0.290 0.827 0.000 1.669

Other Not MSA 40.0 0.081 7 0.766 0.384 1.148 0.000 1.910

Other Not MSA 50.0 0.081 7 0.973 0.477 1.469 0.000 2.161

Other Not MSA 53.0 0.081 7 1.036 0.505 1.566 0.000 2.238

Other Not MSA 60.0 0.081 7 1.181 0.571 1.791 0.000 2.421

Other Not MSA 0.3 0.081 7 0.000 0.000 0.035 0.000 1.024

Other Not MSA 7.0 0.081 7 0.081 0.030 0.132 0.000 1.161

Other Not MSA 19.7 0.081 7 0.345 0.190 0.499 0.000 1.434

 
A-100

A-7 Adjustment of Air Quality to Just Meet the Current and 

Alternative Standards 

A-7.1 Introduction 

This section provides supplemental data and discussion on the approach used in adjusting air 

quality to just meet the current and alternative standards. As a reminder, every location across 

the U.S. meets the current NO2 annual standard (US EPA, 2007e). Even considering air quality 

data as far back as 1995, no location/monitoring site exceeded the current standard. Therefore, 

simulation of air quality data was required to evaluate just meeting the current standard or 

standards that are more stringent. 

In developing a simulation approach to adjust air quality to meet a particular standard level, 

policy-relevant background (PRB) levels in the U.S. were first considered. 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. Estimates of PRB have been reported in the draft ISA (Section 1.5.5) and 

the Annex (AX2.9), and for most of the continental U.S. the PRB is estimated to be less than 300 

parts per trillion (ppt). In the Northeastern U.S. where present-day NO2 concentrations are 

highest, this amounts to a contribution of about 1% percent of the total observed ambient NO2

concentration (AX2.9). 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. 

Next, the variability in NO2 concentrations was evaluated to determine whether a 

proportional approach would be reasonable if applied broadly across all years of data. Because 

the adjustment factor to meet the current standard would likely increase with increasing year, it 

was of interest to determine the trend in both the hourly concentrations and variability by year. 

Figure A-100 presents a summary of the annual average and hourly mean concentrations, as well 

as the coefficient of variation (COV, standard deviation as a percent of the mean) for each 

respective mean. Sample size for the annual average concentrations was about 350 per year, 

while hourly concentrations numbered about 3 million per year. 

As expected, there was no observed difference in the mean concentrations when comparing 

each concentration metric within a year. The mean of the annual averages of all monitors is 

nearly identical to the mean of the hourly concentrations. However, statistically significant 

decreases in concentration are evident from year-to-year (p<0.0001), with concentrations 

decreasing by about 30% across the monitoring period. Contrary to this, there is no apparent 

trend in the COV for the annual average concentrations across the 12 years of data, generally 

centered about 53%. The COV of the hourly concentrations is larger than the annual COV as 

expected, however it increases with increasing year. The hourly COV ranges from a low of 84% 

in 1998 to a high of 92% in 2006, amounting to a relative percent difference of only 10% across 

the entire monitoring period. A non-parametric Mann-Whitney U-test indicates that there is a 

significant difference in the COVs when comparing each year-group (p=0.004). This may result 

in a small upward bias in the number of estimated exceedances of short-term (1-hour) potential 

health benchmark levels if using a proportional roll-up on the more recent monitoring data 

relative to that estimated by rolling up the historical data to just meet the current standard. While 
A-101

the trend of increasing COV is apparent across the entire monitoring period, based on the limited 

difference in COV from year-to-year for both the annual and hourly concentration data within 

each year-group (each is <4%), it was concluded that a proportional method could be broadly 

applied to each data set. Additional analyses by Rizzo (2008) on the trends within six selected 

locations also support the findings here. 

10

11

12

13

14

15

16

17

18

19

20

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Year

Mean NO2 Concentration (ppb)

50

55

60

65

70

75

80

85

90

95

100

Coefficient of Variation (%)

Annual Mean

Hourly Mean

COV-Annual

COV-Hourly

Figure A-100. Trends in hourly and annual average NO2 ambient monitoring concentrations and their 

associated coefficients of variation (COV) for all monitors, years 1995-2006. 

A-7.2 Approach 

For the air quality characterization, data were first separated into two groups, an historical set 

of monitoring data (1995-2000) and one containing the most recent air quality (2001-2006). 

This grouping would further reduce any potential influential monitoring data affecting the 

variability in hourly concentrations that may exist in one year to the next within a location. The 

following air quality scenarios were considered for these sets of data: 

• “as is” representing the historical and recent ambient monitoring hourly concentration 

data as reported by US EPA’s Air Quality System (AQS). 

• “simulated” concentrations to just meet the current NO2 NAAQS (53 ppb annual 

average). 
A-102

Based on the form of the current standard and observed trends in ambient monitoring, such as 

the retention of similar hourly and annual COVs over time while annual average concentrations 

significantly decrease over the same time period, NO2 concentrations were proportionally 

modified at each location using the maximum annual average concentration that occurred in each 

year. To just meet the current standard adjustment factors F for each location (i) and year (j) 

were derived by the following 

Fij S Cmax,ij = / equation (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) 

Values for each air quality adjustment factor used for each location to simulate just meeting 

the current standard are given in Tables A-105 and A-106. It should be noted that a different 

monitor could have been used for each year to estimate F, the selection dependent only on 

whether the monitor contained the highest annual concentration for that year in the particular 

location. For each location and calendar year, all the hourly concentrations 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, for Boston in 1995, the maximum annual mean was 30.5 ppb, giving an 

adjustment factor of F = 53/30.5 = 1.74 using equation 1. All hourly concentrations in Boston in 

1995 were multiplied by 1.74. Then, using the adjusted hourly concentrations, the distributions 

of the annual means and annual number of exceedances are computed in the same manner as the 

as-is scenario.5

Following review of the NO2 ISA and summarization of relevant epidemiological and 

clinical health studies, alternative NO2 standards of differing averaging time, form, and level 

were also considered. Much of the discussion regarding the selection of each of these 

components of the standard is provided in Chapter 5 of the Final NO2 REA, with only the broad 

conclusions provided here. For averaging time, the epidemiological evidence does not provide 

clear guidance in choosing between 1-hour and 24-hour 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-hour), staff evaluated standards with 1-hour averaging 

times. For the form, we have focused on standards with statistical, concentration-based forms. 

Staff selected the 98th and 99th percentiles daily maximum concentration averaged over 3 years to 

balance the desire to provide a stable regulatory target with the desire to limit the occurrence of 

peak concentrations. Concentration levels ranging from 50 ppb to 200 ppb in increments of 50 

 

5 Because of the large database, we did not implement this procedure exactly as stated. For the annual means we 

computed and applied the adjustment factors directly to each annual mean. For the hourly concentrations we used 

the frequency distributions of the rounded hourly values, so that, in effect, we applied the adjustment factors to the 

hourly values after rounding them to the nearest integer. This has a negligible impact on the calculated number of 

exceedances. 
A-103

ppb were selected by staff based largely on the observed concentrations from both epidemiologic 

and controlled human exposure studies. Using these criteria for the investigated alternative 

standards, the following scenarios were considered using the most recent years of data (i.e., 

2001-2006) and divided into two three-year groups for analysis (years 2001-2003 and 2004-

2006): 

• “as is” representing the recent ambient monitoring hourly concentration data as reported 

by US EPA’s Air Quality System (AQS). 

• “simulated” concentrations to just meet the current NO2 NAAQS (53 ppb annual average 

as described above) and alternative 1-hour standards. 

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

ikl l

C

F S

max,

3

1

3

/

⎟

⎟

⎟

⎟

⎟

⎠

⎞

⎜

⎜

⎜

⎜

⎜

⎝

⎛

=

∑= equation (6-2) 

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

Values for each air quality adjustment factor used for each location and year-group to 

simulate just meeting the alternatives standards are given in Tables A-107 and A-108. It should 

be noted that a different monitor could have been used for each year group to estimate F, the 

selection dependent only on whether the monitor contained the highest 98th or 99th daily 

maximum 1-hour concentration averaged across the three year period in the particular location. 

For each location and year-group, all monitor hourly concentrations were multiplied by the same 

constant value F, whereas the monitor used to develop the adjustment factor would have a 3-year 

averaged daily maximum 1-hour concentration at the selected percentile equivalent to the level 

of the alternative standard. For example, for Atlanta in years 2001-2003, the maximum 3-year 

average 98th percentile of daily maximum 1-hour NO2 concentrations was 81.7 ppb, giving an 

adjustment factor F = 100/81.7 = 1.224 for the 1-hour alternative standard level of 200 ppb using 

equation (2). All hourly concentrations in Atlanta for each year in 2001-2003 were multiplied by 

1.224. Then, using the adjusted hourly concentrations, the distributions of the annual number of 

exceedances are computed in the same manner as the as-is scenario. 
A-104

Table A-105. Maximum annual average NO2 concentrations and air quality adjustment factors (F) to just 

meet the current standard, historical monitoring data.

Location Metric 1995 1996 1997 1998 1999 2000 

Atlanta Max Annual Mean 18.8 26.6 25.2 24.1 23.8 22.9 

Atlanta F 2.81 1.99 2.10 2.20 2.22 2.31 

Boston Max Annual Mean 30.5 31.0 30.4 30.7 29.7 29.0 

Boston F 1.74 1.71 1.74 1.73 1.79 1.83 

Chicago Max Annual Mean 32.2 32.0 33.6 32.2 31.5 32.0 

Chicago F 1.64 1.66 1.58 1.64 1.68 1.66 

Cleveland Max Annual Mean 27.3 25.9 28.1 27.3 24.5 23.1 

Cleveland F 1.94 2.04 1.89 1.94 2.16 2.30 

Colorado Springs Max Annual Mean 23.2 23.6 19.8 20.5 19.3 34.8 

Colorado Springs F 2.28 2.24 2.68 2.59 2.75 1.52 

Denver Max Annual Mean 34.8 33.1 33.9 35.3 19.4 14.9 

Denver F 1.52 1.60 1.56 1.50 2.73 3.55 

Detroit Max Annual Mean 21.6 21.5 25.9 22.9 18.0 23.9 

Detroit F 2.45 2.47 2.05 2.31 2.94 2.22 

El Paso Max Annual Mean 23.3 35.1 33.6 30.7 27.7 24.3 

El Paso F 2.27 1.51 1.58 1.72 1.91 2.18 

Jacksonville Max Annual Mean 15.8 14.9 14.4 15.0 15.9 15.4 

Jacksonville F 3.36 3.55 3.69 3.52 3.34 3.45 

Las Vegas Max Annual Mean 27.1 26.7 25.3 26.6 25.1 

Las Vegas F 1.96 1.99 2.09 1.99 2.12 

Los Angeles Max Annual Mean 46.2 42.3 43.2 43.4 50.6 43.9 

Los Angeles F 1.15 1.25 1.23 1.22 1.05 1.21 

Miami Max Annual Mean 14.7 16.0 16.6 15.2 16.8 15.7 

Miami F 3.60 3.30 3.19 3.49 3.15 3.37 

New York Max Annual Mean 41.7 42.2 41.1 41.9 41.5 40.6 

New York F 1.27 1.26 1.29 1.26 1.28 1.31 

Philadelphia Max Annual Mean 31.8 33.9 32.4 34.0 31.7 27.9 

Philadelphia F 1.67 1.56 1.63 1.56 1.67 1.90 

Phoenix Max Annual Mean 32.6 31.6 32.0 35.0 40.5 36.3 

Phoenix F 1.63 1.68 1.66 1.52 1.31 1.46 

Provo Max Annual Mean 22.6 24.3 23.3 23.9 24.1 23.6 

Provo F 2.35 2.18 2.27 2.22 2.20 2.25 

St. Louis Max Annual Mean 26.2 24.8 24.8 25.8 27.2 26.3 

St. Louis F 2.02 2.14 2.14 2.05 1.95 2.02 

Washington DC Max Annual Mean 26.2 26.9 25.9 27.2 25.4 23.5 

Washington DC F 2.02 1.97 2.05 1.95 2.09 2.26 

Other MSA Max Annual Mean 31.9 30.3 29.4 31.0 29.3 26.5 

Other MSA F 1.66 1.75 1.80 1.71 1.81 2.00 

Other Not MSA Max Annual Mean 19.1 14.5 19.7 18.8 19.7 18.7 

Other Not MSA F 2.78 3.66 2.69 2.82 2.69 2.83 
A-105

Table A-106. Maximum annual average NO2 concentrations and air quality adjustment factors (F) to just 

meet the current standard, recent monitoring data.

Location Metric 2001 2002 2003 2004 2005 2006 

Atlanta Max Annual Mean 23.3 19.4 16.4 17.0 17.4 17.9 

Atlanta F 2.27 2.73 3.23 3.12 3.05 2.96 

Boston Max Annual Mean 29.7 25.3 22.5 25.0 23.4 22.5 

Boston F 1.79 2.10 2.36 2.12 2.26 2.35 

Chicago Max Annual Mean 31.9 32.4 30.9 29.3 29.6 30.6 

Chicago F 1.66 1.63 1.72 1.81 1.79 1.73 

Cleveland Max Annual Mean 23.6 22.3 21.7 22.2 21.5 18.2 

Cleveland F 2.25 2.38 2.45 2.38 2.46 2.91 

Colorado Springs Max Annual Mean 

Colorado Springs F

Denver Max Annual Mean 36.8 35.4 21.4 27.2 27.6 29.1 

Denver F 1.44 1.50 2.47 1.95 1.92 1.82 

Detroit Max Annual Mean 23.2 21.4 22.0 18.9 19.6 15.9 

Detroit F 2.29 2.47 2.41 2.80 2.71 3.34 

El Paso Max Annual Mean 21.7 21.4 19.9 18.0 17.3 18.0 

El Paso F 2.45 2.48 2.66 2.94 3.06 2.94 

Jacksonville Max Annual Mean 14.6 14.3 13.7 13.3 

Jacksonville F 3.62 3.70 3.88 3.97 

Las Vegas Max Annual Mean 22.5 22.3 21.4 19.7 19.9 

Las Vegas F 2.35 2.38 2.48 2.69 2.67 

Los Angeles Max Annual Mean 41.2 40.2 35.3 33.7 30.9 29.7 

Los Angeles F 1.29 1.32 1.50 1.57 1.72 1.78 

Miami Max Annual Mean 15.8 14.3 12.9 13.0 13.5 

Miami F 3.35 3.71 4.12 4.08 3.92 

New York Max Annual Mean 40.3 39.7 32.0 30.5 36.5 34.2 

New York F 1.32 1.33 1.65 1.74 1.45 1.55 

Philadelphia Max Annual Mean 29.9 29.5 24.7 25.6 26.3 17.8 

Philadelphia F 1.77 1.80 2.15 2.07 2.02 2.98 

Phoenix Max Annual Mean 37.1 34.7 34.3 31.4 31.5 30.6 

Phoenix F 1.43 1.53 1.54 1.69 1.68 1.73 

Provo Max Annual Mean 24.1 24.8 21.8 22.3 20.5 28.9 

Provo F 2.20 2.14 2.43 2.37 2.58 1.83 

St. Louis Max Annual Mean 24.7 22.9 20.3 22.3 16.8 15.0 

St. Louis F 2.15 2.32 2.60 2.37 3.15 3.52 

Washington DC Max Annual Mean 24.3 24.8 26.0 24.0 24.1 19.6 

Washington DC F 2.18 2.14 2.04 2.20 2.20 2.70 

Other MSA Max Annual Mean 26.5 27.4 26.4 25.3 24.0 18.5 

Other MSA F 2.00 1.93 2.01 2.09 2.21 2.87 

Other Not MSA Max Annual Mean 16.5 16.4 15.5 15.8 17.1 15.6 

Other Not MSA F 3.21 3.23 3.42 3.36 3.11 3.39 
A-106

Table A-107. Air quality adjustment factors (F) to just meet the alternative 1-hour standards, using recent 

monitoring data.

98th Percentile 99th Percentile 

Year Group Location 

1-hour 

Standard 

Level 

Maximum 

Monitor 

Adjustment 

Factor1

Maximum 

Monitor 

Adjustment 

Factor1

2001-2003 Atlanta 50 1312100481 0.612 1312100481 0.564 

2001-2003 Atlanta 100 1312100481 1.224 1312100481 1.128 

2001-2003 Atlanta 150 1312100481 1.837 1312100481 1.692 

2001-2003 Atlanta 200 1312100481 2.449 1312100481 2.256 

2004-2006 Atlanta 50 1312100481 0.703 1312100481 0.641 

2004-2006 Atlanta 100 1312100481 1.405 1312100481 1.282 

2004-2006 Atlanta 150 1312100481 2.108 1312100481 1.923 

2004-2006 Atlanta 200 1312100481 2.810 1312100481 2.564 

2001-2003 Boston 50 2502500401 0.688 2502500401 0.622 

2001-2003 Boston 100 2502500401 1.376 2502500401 1.245 

2001-2003 Boston 150 2502500401 2.064 2502500401 1.867 

2001-2003 Boston 200 2502500401 2.752 2502500401 2.490 

2004-2006 Boston 50 2502500401 0.719 2502500401 0.613 

2004-2006 Boston 100 2502500401 1.439 2502500401 1.227 

2004-2006 Boston 150 2502500401 2.158 2502500401 1.840 

2004-2006 Boston 200 2502500401 2.878 2502500401 2.454 

2001-2003 Chicago 50 1703100631 0.577 1703131031 0.512 

2001-2003 Chicago 100 1703100631 1.154 1703131031 1.024 

2001-2003 Chicago 150 1703100631 1.731 1703131031 1.536 

2001-2003 Chicago 200 1703100631 2.308 1703131031 2.048 

2004-2006 Chicago 50 1703100631 0.570 1703100631 0.538 

2004-2006 Chicago 100 1703100631 1.141 1703100631 1.075 

2004-2006 Chicago 150 1703100631 1.711 1703100631 1.613 

2004-2006 Chicago 200 1703100631 2.281 1703100631 2.151 

2001-2003 Cleveland 50 3903500601 0.711 3903500601 0.664 

2001-2003 Cleveland 100 3903500601 1.422 3903500601 1.327 

2001-2003 Cleveland 150 3903500601 2.133 3903500601 1.991 

2001-2003 Cleveland 200 3903500601 2.844 3903500601 2.655 

2004-2006 Cleveland 50 3903500601 0.765 3903500601 0.691 

2004-2006 Cleveland 100 3903500601 1.531 3903500601 1.382 

2004-2006 Cleveland 150 3903500601 2.296 3903500601 2.074 

2004-2006 Cleveland 200 3903500601 3.061 3903500601 2.765 

2001-2003 Denver 50 0803100021 0.518 0803100021 0.459 

2001-2003 Denver 100 0803100021 1.036 0803100021 0.917 

2001-2003 Denver 150 0803100021 1.554 0803100021 1.376 

2001-2003 Denver 200 0803100021 2.073 0803100021 1.835 

2004-2006 Denver 50 0800130011 0.658 0800130011 0.584 

2004-2006 Denver 100 0800130011 1.316 0800130011 1.167 

2004-2006 Denver 150 0800130011 1.974 0800130011 1.751 

2004-2006 Denver 200 0800130011 2.632 0800130011 2.335 

2001-2003 Detroit 50 2616300192 0.554 2616300192 0.374 

2001-2003 Detroit 100 2616300192 1.107 2616300192 0.748 

2001-2003 Detroit 150 2616300192 1.661 2616300192 1.122 
A-107

98th Percentile 99th Percentile 

Year Group Location 

1-hour 

Standard 

Level 

Maximum 

Monitor 

Adjustment 

Factor1

Maximum 

Monitor 

Adjustment 

Factor1

2001-2003 Detroit 200 2616300192 2.214 2616300192 1.496 

2004-2006 Detroit 50 2616300161 0.915 2616300161 0.862 

2004-2006 Detroit 100 2616300161 1.829 2616300161 1.724 

2004-2006 Detroit 150 2616300161 2.744 2616300161 2.586 

2004-2006 Detroit 200 2616300161 3.659 2616300161 3.448 

2001-2003 El Paso 50 4814100441 0.655 4814100441 0.573 

2001-2003 El Paso 100 4814100441 1.310 4814100441 1.145 

2001-2003 El Paso 150 4814100441 1.965 4814100441 1.718 

2001-2003 El Paso 200 4814100441 2.620 4814100441 2.290 

2004-2006 El Paso 50 4814100551 0.743 4814100371 0.664 

2004-2006 El Paso 100 4814100551 1.485 4814100371 1.327 

2004-2006 El Paso 150 4814100551 2.228 4814100371 1.991 

2004-2006 El Paso 200 4814100551 2.970 4814100371 2.655 

2001-2003 Jacksonville 50 1203100322 0.901 1203100322 0.840 

2001-2003 Jacksonville 100 1203100322 1.802 1203100322 1.681 

2001-2003 Jacksonville 150 1203100322 2.703 1203100322 2.521 

2001-2003 Jacksonville 200 1203100322 3.604 1203100322 3.361 

2004-2006 Jacksonville 50 1203100322 0.962 1203100322 0.658 

2004-2006 Jacksonville 100 1203100322 1.923 1203100322 1.316 

2004-2006 Jacksonville 150 1203100322 2.885 1203100322 1.974 

2004-2006 Jacksonville 200 1203100322 3.846 1203100322 2.632 

2001-2003 Las Vegas 50 3200305391 0.718 3200305391 0.652 

2001-2003 Las Vegas 100 3200305391 1.435 3200305391 1.304 

2001-2003 Las Vegas 150 3200305391 2.153 3200305391 1.957 

2001-2003 Las Vegas 200 3200305391 2.871 3200305391 2.609 

2004-2006 Las Vegas 50 3200320021 0.820 3200305391 0.758 

2004-2006 Las Vegas 100 3200320021 1.639 3200305391 1.515 

2004-2006 Las Vegas 150 3200320021 2.459 3200305391 2.273 

2004-2006 Las Vegas 200 3200320021 3.279 3200305391 3.030 

2001-2003 Los Angeles 50 0603700301 0.394 0603700301 0.379 

2001-2003 Los Angeles 100 0603700301 0.787 0603700301 0.758 

2001-2003 Los Angeles 150 0603700301 1.181 0603700301 1.136 

2001-2003 Los Angeles 200 0603700301 1.575 0603700301 1.515 

2004-2006 Los Angeles 50 0603716012 0.549 0603711031 0.505 

2004-2006 Los Angeles 100 0603716012 1.099 0603711031 1.010 

2004-2006 Los Angeles 150 0603716012 1.648 0603711031 1.515 

2004-2006 Los Angeles 200 0603716012 2.198 0603711031 2.020 

2001-2003 Miami 50 1208640022 0.929 1208640022 0.817 

2001-2003 Miami 100 1208640022 1.858 1208640022 1.635 

2001-2003 Miami 150 1208640022 2.786 1208640022 2.452 

2001-2003 Miami 200 1208640022 3.715 1208640022 3.270 

2004-2006 Miami 50 1208640022 0.877 1208640022 0.826 

2004-2006 Miami 100 1208640022 1.754 1208640022 1.653 

2004-2006 Miami 150 1208640022 2.632 1208640022 2.479 

2004-2006 Miami 200 1208640022 3.509 1208640022 3.306 
A-108

98th Percentile 99th Percentile 

Year Group Location 

1-hour 

Standard 

Level 

Maximum 

Monitor 

Adjustment 

Factor1

Maximum 

Monitor 

Adjustment 

Factor1

2001-2003 New York 50 3403900042 0.542 3403900042 0.476 

2001-2003 New York 100 3403900042 1.083 3403900042 0.952 

2001-2003 New York 150 3403900042 1.625 3403900042 1.429 

2001-2003 New York 200 3403900042 2.166 3403900042 1.905 

2004-2006 New York 50 3403900042 0.613 3401310031 0.532 

2004-2006 New York 100 3403900042 1.227 3401310031 1.064 

2004-2006 New York 150 3403900042 1.840 3401310031 1.596 

2004-2006 New York 200 3403900042 2.454 3401310031 2.128 

2001-2003 Philadelphia 50 4210100471 0.694 4210100471 0.637 

2001-2003 Philadelphia 100 4210100471 1.389 4210100471 1.274 

2001-2003 Philadelphia 150 4210100471 2.083 4210100471 1.911 

2001-2003 Philadelphia 200 4210100471 2.778 4210100471 2.548 

2004-2006 Philadelphia 50 1000320041 0.758 1000320041 0.610 

2004-2006 Philadelphia 100 1000320041 1.515 1000320041 1.220 

2004-2006 Philadelphia 150 1000320041 2.273 1000320041 1.829 

2004-2006 Philadelphia 200 1000320041 3.030 1000320041 2.439 

2001-2003 Phoenix 50 0401330101 0.577 0401330101 0.526 

2001-2003 Phoenix 100 0401330101 1.154 0401330101 1.053 

2001-2003 Phoenix 150 0401330101 1.731 0401330101 1.579 

2001-2003 Phoenix 200 0401330101 2.308 0401330101 2.105 

2004-2006 Phoenix 50 0401330101 0.598 0401330101 0.538 

2004-2006 Phoenix 100 0401330101 1.195 0401330101 1.075 

2004-2006 Phoenix 150 0401330101 1.793 0401330101 1.613 

2004-2006 Phoenix 200 0401330101 2.390 0401330101 2.151 

2001-2003 Provo 50 4904900021 0.785 4904900021 0.735 

2001-2003 Provo 100 4904900021 1.571 4904900021 1.471 

2001-2003 Provo 150 4904900021 2.356 4904900021 2.206 

2001-2003 Provo 200 4904900021 3.141 4904900021 2.941 

2004-2006 Provo 50 4904900021 0.532 4904900021 0.521 

2004-2006 Provo 100 4904900021 1.064 4904900021 1.042 

2004-2006 Provo 150 4904900021 1.596 4904900021 1.563 

2004-2006 Provo 200 4904900021 2.128 4904900021 2.083 

2001-2003 St. Louis 50 2951000861 0.769 2951000861 0.704 

2001-2003 St. Louis 100 2951000861 1.538 2951000861 1.408 

2001-2003 St. Louis 150 2951000861 2.308 2951000861 2.113 

2001-2003 St. Louis 200 2951000861 3.077 2951000861 2.817 

2004-2006 St. Louis 50 2951000722 0.820 2951000722 0.794 

2004-2006 St. Louis 100 2951000722 1.639 2951000722 1.587 

2004-2006 St. Louis 150 2951000722 2.459 2951000722 2.381 

2004-2006 St. Louis 200 2951000722 3.279 2951000722 3.175 

2001-2003 Washington DC 50 2451000401 0.701 1100100411 0.633 

2001-2003 Washington DC 100 2451000401 1.402 1100100411 1.266 

2001-2003 Washington DC 150 2451000401 2.103 1100100411 1.899 

2001-2003 Washington DC 200 2451000401 2.804 1100100411 2.532 

2004-2006 Washington DC 50 2451000401 0.758 1100100411 0.617 
A-109

98th Percentile 99th Percentile 

Year Group Location 

1-hour 

Standard 

Level 

Maximum 

Monitor 

Adjustment 

Factor1

Maximum 

Monitor 

Adjustment 

Factor1

2004-2006 Washington DC 100 2451000401 1.515 1100100411 1.235 

2004-2006 Washington DC 150 2451000401 2.273 1100100411 1.852 

2004-2006 Washington DC 200 2451000401 3.030 1100100411 2.469 

2001-2003 Other MSA 50 4905700021 0.508 4905700021 0.439 

2001-2003 Other MSA 100 4905700021 1.015 4905700021 0.877 

2001-2003 Other MSA 150 4905700021 1.523 4905700021 1.316 

2001-2003 Other MSA 200 4905700021 2.030 4905700021 1.754 

2004-2006 Other MSA 50 0607320071 0.578 0607320071 0.532 

2004-2006 Other MSA 100 0607320071 1.156 0607320071 1.064 

2004-2006 Other MSA 150 0607320071 1.734 0607320071 1.596 

2004-2006 Other MSA 200 0607320071 2.312 0607320071 2.128 

2001-2003 Other Not MSA 50 0602500061 0.547 0602500061 0.466 

2001-2003 Other Not MSA 100 0602500061 1.095 0602500061 0.932 

2001-2003 Other Not MSA 150 0602500061 1.642 0602500061 1.398 

2001-2003 Other Not MSA 200 0602500061 2.190 0602500061 1.863 

2004-2006 Other Not MSA 50 5600508921 0.535 5600508921 0.429 

2004-2006 Other Not MSA 100 5600508921 1.070 5600508921 0.858 

2004-2006 Other Not MSA 150 5600508921 1.604 5600508921 1.288 

2004-2006 Other Not MSA 200 5600508921 2.139 5600508921 1.717 

Notes:

1

 The selected percentile (98th or 99th) in 1-hour daily maximum NO2 concentration at each monitor was 

averaged across the 3-years of data (either 2001-2003 or 2004-2006), with the highest concentration 

monitor retained for use in calculating the adjustment to just meet the alternative standard. 
A-110

A-8 Method for Estimating On-Road Concentrations 

A-8.1 Introduction 

As an additional step in the air quality characterization, the potential impact of motor 

vehicles on the surrogate exposure metrics was evaluated. Several studies have shown that 

concentrations of NO2 are at elevated levels when compared to ambient concentrations measured 

at increasing distances from the roadway (e.g., Rodes and Holland, 1981; Gilbert et al., 2003; 

Cape et al., 2004; Pleijel et al., 2004; Singer et al., 2004). On average, concentrations on or near 

a roadway can be from 2 to 3 times greater than ambient concentrations (ISA, section 2.5.4), but 

on occasion, as high as 7 times greater (Bell and Ashenden, 1997; Bignal et al., 2007). A strong 

relationship between measured on-road NO2 concentrations and those with increasing distance 

from the road has been reported under a variety of conditions (e.g., variable traffic counts, 

different seasons, wind direction) and can be described (e.g., Cape et al., 2004) with an 

exponential decay equation of the form 

 kx

x b v C C C e − = + equation (3) 

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 = Removal rate constant describing NO2 combined formation/decay with 

perpendicular distance from roadway (meters-1) 

x = Distance from roadway (meters) 

As a function of reported concentration measurements and the derived relationship, much of 

the decline in NO2 concentrations with distance from the road has been shown to occur within 

the first few meters (by 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 meters, referred to here as on-road, 

the equation reduces to the sum of the non-source influenced NO2 concentration and the 

concentration contribution expected from vehicle emissions on the roadway using 

 C C (1 m) r = a + equation (4) 

where, 

Cr = 1-hour on-road NO2 concentration (ppb) 

Ca = 1-hour ambient monitoring NO2 concentration (ppb) either as is or modified to 

just meet the current or alternative standards 

m = Ratio derived from estimates of Cv/Cb (from eq (1)) 
A-111

and assuming that Ca = Cb. 6

A-8.2 Derivation of On-Road Ratios 

A literature review was conducted to identify published studies containing NO2

concentrations both on-roads and with various distances from roadways. Principal criteria for 

inclusion in this analysis were that either tabular, graphical, or equations were provided in the 

paper that related distances from roadways and associated NO2 concentrations. Eleven papers 

were identified using these criteria, spanning several countries, various years, roadway locations, 

seasons, wind directions, and averaging times (Table A-108). The final data set contained 501 

data points, encompassing multiple NO2 measurements at a distance from a total of 56 individual 

roads, some of which were collected within 10 m of the road. 

Table A-108. Studies reviewed containing NO2 concentrations at a distance from roadways. 

First Author Year Country/State Season Type 

Wind 

Direction 

Averaging 

time 

Bell 1987 Wales Summer, winter Rural Up, down 7 days 

Bignal 2004 England Summer, fall Urban Combined 14 days 

Cape 2002 Scotland Annual Urban Combined 14 days 

Gilbert 2001 Quebec Summer Urban 

Down, up, 

Combined 7 days 

Maruo 2001 Japan Summer Urban Combined 14 days 

Monn 1995 Switzerland Summer, Winter Urban Combined 7 days 

Nitta 1982 Japan Not reported Urban Combined 7 days 

Pleijel 1994 Sweden Summer Rural Combined 30 days 

Rodes 1978 California Summer Urban Down > 1 day 

Roorda-Knape 1995 Holland Summer Urban Combined 14 days 

Singer 2001 California Spring through Fall Urban Up, Down 7 days 

Although there were, on occasion, data from several roads within a particular study, data for 

factors thought to influence on-road concentrations were very limited or were not distinctly 

defined for all studies. Factors that were reported and already noted as influential are the 

individual roadway (where the study included multiple roads) and the time of the year. Wind 

direction (upwind versus downwind) was also indicated as an important factor influencing 

concentrations at a distance from a roadway (Singer, 2004). Note however that the averaging 

time for measurements of 11 of the 12 studies is a week or more in length (Table A-108). Even 

for where the wind direction is reported as either downwind or upwind it is possible that the 

wind direction was variable (combined downwind and upwind) over the monitoring period. 

The relationship noted in equation (3) was iteratively solved using the data collected from the 

above reviewed literature and employing the SAS procedure proc nlin, generally as follows, 

proc nlin data=no2 maxiter=1000 noprint NOITPRINT; 

 parms Cb=0 to 80 by 1 

 Cv= 0 to 80 by 1 

 k= 0 to 1 by .025; 

 

6

 Note that Ca differs from Cb since Ca may include the influence of on-road as well as non-road sources. However, 

it is expected that for most monitors the influence of on-road emissions is minimal so that Ca ≅ Cb.
A-112

 model Cx=Cb + Cv*exp(-k*distance); 

 by author road season wind; 

 output out=outdata parms=Cb Cv k; 

run;

As an example, data were compiled from Table 1 and Table 4 reported in Singer et al. 

(2004) for NO2 measurements collected at six outdoor locations with varying distance from 

Interstate-880 in San Francisco, CA as follows: 

Table A-109. Example data used to estimate on-road adjustment factor (m) obtained from Tables 1 and 4 

reported in Singer et. al (2004). 

Distance to 

Road (m) 

Measured 

Concentration 

Cx (ppb) Season Wind Road

60 30 

SPRFALL D I-880

130 26 

SPRFALL D I-880

200 26 

SPRFALL D I-880

230 24 

SPRFALL D I-880

1200 21 

SPRFALL D I-880

1400 21 

SPRFALL D I-880

The non-linear procedure was applied to these data and all other individual roads identified 

within each study location listed in Table A-108. The results of this analysis were screened for 

data that yielded no unique solutions (lack of model convergence) or irrational parameters. 

Criteria for censoring data included the following, as well as the number of individual roads 

censored: 

• Model did not converge (number (n) of roads = 5) 

• k<0 (n=1) 

• k>1 (n=2) 

• Both k=0 and Cv =0 (n=1) 

• Extremely large Cv (>8,000 ppb; n=2) 

• Cb<0 (n=1) 

These data were then evaluated for trends using the limited influential factors reported in the 

collection of studies, considering the number of values of m available for potential groupings, 

and how the data were to be applied to the ambient monitoring data. In general, the 

measurements reported in the summer and resultant parameter estimates were observed as 

distinct from the measures and parameter estimates from other seasons, including data for where 

only annual averages were reported. The data were then grouped accordingly into two seasonal 

groups, summer and not summer, containing 23 and 21 samples, respectively. These two groups 

were also further censored for unusual parameter estimates. Resulting criteria for censoring the 

grouped data included the following: 
A-113

• An extreme value of k (0.354) in the summer category compared with others in group 

(mean=0.020, std=0.014) 

• Extreme values of estimated m (24.4 and 59.0) in the not summer category due to 

combined low estimated Cb (≤1 ppb) relative to high estimated Cv in comparison with 

the other derived m (mean=0.73, std=0.38) 

Therefore the final data set contained 19 and 22 values for use in the not summer and summer

categories, respectively (Table A-110). 

Table A-110. Estimated on-road adjustment factors (Cv/Cb or m) for two season groups and potential 

influential factors. 

Author Location 

Road 

Name Season1

Wind 

Direction2

Area 

Type3

Traffic 

Count Season2 

Cv/Cb

or m

Bell WAL A5 SU D R 5000 Summer 2.45 

Bell WAL A5 SU U R 5000 Summer 1.32 

Bell WAL A5 WI D R 2500 

Not 

Summer 1.14 

Bell WAL A5 WI U R 2500 

Not 

Summer 0.58 

Bell WAL B4547 SU D R 3500 Summer 0.90 

Bignal ENG M40 SU B U* 94000 Summer 2.70 

Bignal ENG M62 FA B U* 74000 

Not 

Summer 0.64 

Cape SCT d1 AN B U* 57786 

Not 

Summer 0.78 

Cape SCT d3 AN B U 85623 

Not 

Summer 0.86 

Cape SCT d5 AN B U* 20134 

Not 

Summer 0.59 

Cape SCT o1 AN B R 3433 

Not 

Summer 0.75 

Cape SCT o3 AN B U NR 

Not 

Summer 0.25 

Cape SCT o4 AN B R* 240 

Not 

Summer 1.50 

Cape SCT o5 AN B R 1299 

Not 

Summer 0.36 

Cape SCT t1 AN B R 11997 

Not 

Summer 0.79 

Cape SCT t2 AN B R* 3551 

Not 

Summer 1.08 

Cape SCT t4 AN B R* 9373 

Not 

Summer 0.79 

Cape SCT t5 AN B R 5052 

Not 

Summer 0.82 

Gilbert QUE HW15 SU B U 185000 Summer 0.78 

Gilbert QUE HW15 SU D U 185000 Summer 0.75 

Gilbert QUE HW15 SU U U 185000 Summer 0.94 

Maruo JAP L1L2 SU B U 24000 Summer 0.92 

Monn SWZ 1 SU B U 8800 Summer 0.74 
A-114

Author Location 

Road 

Name Season1

Wind 

Direction2

Area 

Type3

Traffic 

Count Season2 

Cv/Cb

or m

Nitta JAP a NR B U* 106000 

Not 

Summer 0.36 

Nitta JAP b NR B U* 106000 

Not 

Summer 0.22 

Nitta JAP e NR B U* 60000 

Not 

Summer 0.42 

Nitta JAP f NR B U* 60000 

Not 

Summer 0.47 

Pleijel SWE 1 SU D R 32500 Summer 1.21 

Pleijel SWE 2 SU D R 32500 Summer 1.19 

Pleijel SWE 3 SU D R 18900 Summer 0.51 

Pleijel SWE 4 SU D R 18900 Summer 1.13 

Pleijel SWE 5 SU D R 18900 Summer 0.79 

Rodes CAL HiO3 SU D U 200000 Summer 0.93 

Rodes CAL LowO3 SU D U 200000 Summer 2.43 

RoordaKnape HOL 1 SU B U 131907 Summer 0.78 

RoordaKnape HOL 1p2 SU B U 131907 Summer 0.67 

RoordaKnape HOL 1p3 SU B U 131907 Summer 0.70 

RoordaKnape HOL 1p4 SU B U 131907 Summer 0.49 

RoordaKnape HOL 2 SU B U 142512 Summer 0.52 

RoordaKnape HOL 2p2 SU B U 142512 Summer 1.95 

Singer CAL I880D SPFA D U* 200000 

Not 

Summer 1.54 

Notes: 

1

 Season: AN – Annual, SP – Spring, FA – Fall, SU – Summer, WI - Winter 

2

 Wind: B – Both, D – Downwind, U – Upwind 

3

 Type: R – Rural, U – Urban, * Inferred by staff using traffic count data 

NR – Not reported 

Two approaches were considered for estimating m from the Cv and Cb pairs in each season. 

The first approach considered was to regress Cb on Cv (either with or without an intercept) and 

use the fitted slope to estimate m. Ignoring meteorological effects, equation 3 implies that Cv

results solely from on-road emission sources and that Cb results solely from non-road emission 

sources. Since these two source types are likely to have quite different diurnal profiles, we 

expect the hourly Cv and Cb values to be approximately independent.7 Regressing Cb against Cv

would imply that there is some correlation between the values, which would be inconsistent with 

the conceptual model underlying equation (3). Further, if Cb were regressed against Cv using an 

intercept, the physical meaning of the intercept would be unclear. 

 

7

 Although the fact that Cv and Cb are subject to the same meteorology introduces some correlation, because 

meteorology tends to vary on a longer time scale than hourly, it is likely to have less influence than the emissions on 

the correlation between hourly concentrations. 
A-115

An empirical method was selected for the approach to characterize m based on the two 

seasonal sets of ratios of Cv/Cb. The resulting cumulative distribution for each group is depicted 

by Figure A-101 using the data from Table A-110. In applying the factors for use in equation 

(4), the selection criteria for a given m are based on equivalent probability (e.g., 1/19 for the not 

summer data). 

Means from the two seasons were tested for significant difference using a Student’s t 

(p=0.026), while the season distributions were compared using a Kolmogorov-Smirnov test (p= 

0.196). It was decided to retain the season-groups as separate to allow for some apportioning of 

variability resulting from an apparent seasonal influence, even though the statistical test results 

were mixed. 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0.0 0.5 1.0 1.5 2.0 2.5 3.0

On-Road Ratio (m )

Cumulative Percentile

Not Summer (n=19)

Summer (n=22)

Figure A-101. Distribution of estimated Cv/Cb ratios or m for two season groups. 

A-8.3 Application of On-Road Factors 

The purpose of this particular analysis was to estimate on-road concentrations using equation 

(4) above along with the required inputs, namely, the hourly ambient monitoring concentrations 

and derived on-road factors. The derived on-road factors for the two season groups could not be 

assigned a particular statistical distribution (e.g., normal, lognormal, gamma) with confidence. 

Therefore, an empirical approach was selected to still allow for some seasonal variability in the 

on-road concentration estimates. Summer months were first defined as June, July, August, while 

the remaining months were not summer. Although there may be distinctions among what may 

be designated as a summer month across the U.S., the reviewed data are not robust to allow for 

such an application. 

Each monitor site was then randomly assigned two on-road factors selected from the derived 

empirical distribution for a given year, one for summer months and one for the other months, 
A-116

using the appropriate distribution. Because the influence of on-road and non-road sources is 

likely different in each location and at each monitor, it would be expected that the empirical 

relationship between the two values Cv and Cb to vary from place to place. If source category 

emissions data for each study location were available to derive an equation (3) regression, that 

could have been used to match each of the study locations here, or, perhaps, each of the 

monitoring sites, to a similar equation (3) study area for assigning an appropriate ratio. 

However, since this information was not available, an empirical approach was used to randomly 

match the literature-derived ratios to the NO2 site-seasons. 

 

A particular summer on-road factor has a 1/22 chance of selection, while a specific not 

summer value has a 1/19 probability of selection, based on respective sample sizes. This random 

assignment was repeated for all site-years of data. Hourly NO2 concentrations were estimated 

for each site-year of data in a location using equation (4) and the randomly assigned on-road 

factors. Finally, 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 site-years 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). 

Simulated on-road NO2 concentrations were used to generate concentration distributions for 

the annual average concentrations and distributions for the number of exceedances of short-term 

potential health effect benchmark levels. Means and median values are reported to represent the 

central tendency of each parameter estimate. Since there were multiple simulations performed at 

each location using all available site-years of data, results for the upper percentiles were 

expanded to the 95th, 98th and 99th percentiles of the distribution. It is more appropriate to apply 

the parameter estimates outside the central tendencies to particular sites, areas within locations, 

or for certain conditions. Minimum values for the annual mean and annual number of 

exceedances were also estimated. One approach would have been to use the minimum values 

across the 100 simulations. However, that approach may not give the lowest possible value, 

because it is unlikely that in 100 simulations for a site-year there is a simulation where both 

seasonal adjustment factors are chosen to be the lowest values of 1 + m. To obtain the lowest 

value, two simulations were conducted for each site-year. The Summer adjustment factor was set 

to the lowest possible value (1.49) and the Not-Summer adjustment factor was the lowest 

possible value (1.22). The annual means and exceedances for those two separate simulations 

were used to compute the minimum values for each distribution. 

As part of the air quality characterization, these data were used to estimate the number of 

short-term concentrations above selected levels that might occur on roadways using the 

estimated hourly Cr values, associated with air quality as is. For evaluating just meeting the 

current annual and alternative standards, the approach described in Section A-7 to adjust the 

ambient concentrations was applied before estimating on-road NO2 concentrations. 
A-117

A-9 Supplemental Results Tables to the REA 

A-9.1 Annual average NO2 concentration data for 2001-2003 

Table A-111. Estimated annual average NO2 concentrations for monitors ≥100 m from a major road using 

2001-2003 air quality as is and air quality adjusted to just meet the current and alternative standards. 

Annual Average NO2 (ppb)3

Location Scenario1 Percentile2

SiteYears Mean Min Med p98 p99 

Atlanta As is 14 12 4 15 23 23 

Atlanta Current Std 14 33 9 39 53 53 

Atlanta 50 98 14 8 3 9 14 14 

Atlanta 50 99 14 7 2 9 13 13 

Atlanta 100 98 14 15 5 19 29 29 

Atlanta 100 99 14 14 5 17 26 26 

Atlanta 150 98 14 23 8 28 43 43 

Atlanta 150 99 14 21 7 26 39 39 

Atlanta 200 98 14 30 10 37 57 57 

Atlanta 200 99 14 28 9 34 53 53 

Atlanta 250 98 14 38 13 46 71 71 

Atlanta 250 99 14 35 12 43 66 66 

Atlanta 300 98 14 46 15 56 86 86 

Atlanta 300 99 14 42 14 51 79 79 

Boston As is 6 10 5 11 12 12 

Boston Current Std 6 19 11 21 26 26 

Boston 50 98 6 7 4 7 8 8 

Boston 50 99 6 6 3 7 7 7 

Boston 100 98 6 13 7 15 16 16 

Boston 100 99 6 12 7 14 15 15 

Boston 150 98 6 20 11 22 24 24 

Boston 150 99 6 18 10 20 22 22 

Boston 200 98 6 26 15 30 32 32 

Boston 200 99 6 24 13 27 29 29 

Boston 250 98 6 33 18 37 40 40 

Boston 250 99 6 30 17 34 36 36 

Boston 300 98 6 39 22 45 48 48 

Boston 300 99 6 36 20 41 44 44 

Chicago As is 9 22 17 20 28 28 

Chicago Current Std 9 36 27 34 47 47 

Chicago 50 98 9 12 10 12 16 16 

Chicago 50 99 9 11 9 10 14 14 

Chicago 100 98 9 25 19 23 32 32 

Chicago 100 99 9 22 17 20 28 28 

Chicago 150 98 9 37 29 35 48 48 

Chicago 150 99 9 33 26 31 43 43 

Chicago 200 98 9 50 39 46 64 64 

Chicago 200 99 9 44 34 41 57 57 
A-118

Annual Average NO2 (ppb)3

Location Scenario1 Percentile2

SiteYears Mean Min Med p98 p99 

Chicago 250 98 9 62 48 58 80 80 

Chicago 250 99 9 55 43 51 71 71 

Chicago 300 98 9 75 58 69 96 96 

Chicago 300 99 9 66 51 61 85 85 

Cleveland As is 3 18 17 17 19 19 

Cleveland Current Std 3 42 41 42 43 43 

Cleveland 50 98 3 13 12 12 13 13 

Cleveland 50 99 3 12 12 12 12 12 

Cleveland 100 98 3 25 25 25 26 26 

Cleveland 100 99 3 24 23 23 25 25 

Cleveland 150 98 3 38 37 37 40 40 

Cleveland 150 99 3 35 35 35 37 37 

Cleveland 200 98 3 51 49 50 53 53 

Cleveland 200 99 3 47 46 46 49 49 

Cleveland 250 98 3 63 62 62 66 66 

Cleveland 250 99 3 59 58 58 61 61 

Cleveland 300 98 3 76 74 75 79 79 

Cleveland 300 99 3 71 69 70 74 74 

Denver As is 2 24 21 24 26 26 

Denver Current Std 2 45 37 45 53 53 

Denver 50 98 2 12 11 12 13 13 

Denver 50 99 2 11 10 11 12 12 

Denver 100 98 2 24 22 24 27 27 

Denver 100 99 2 22 20 22 24 24 

Denver 150 98 2 37 33 37 40 40 

Denver 150 99 2 33 30 33 35 35 

Denver 200 98 2 49 44 49 53 53 

Denver 200 99 2 43 39 43 47 47 

Denver 250 98 2 61 56 61 67 67 

Denver 250 99 2 54 49 54 59 59 

Denver 300 98 2 73 67 73 80 80 

Denver 300 99 2 65 59 65 71 71 

Detroit As is 6 21 19 20 23 23 

Detroit Current Std 6 49 44 50 53 53 

Detroit 50 98 6 11 10 11 13 13 

Detroit 50 99 6 8 7 8 9 9 

Detroit 100 98 6 23 21 23 26 26 

Detroit 100 99 6 15 14 15 17 17 

Detroit 150 98 6 34 31 34 38 38 

Detroit 150 99 6 23 21 23 26 26 

Detroit 200 98 6 46 41 45 51 51 

Detroit 200 99 6 31 28 31 35 35 

Detroit 250 98 6 57 51 56 64 64 

Detroit 250 99 6 39 35 38 43 43 

Detroit 300 98 6 68 62 68 77 77 
A-119

Annual Average NO2 (ppb)3

Location Scenario1 Percentile2

SiteYears Mean Min Med p98 p99 

Detroit 300 99 6 46 42 46 52 52 

El Paso As is 12 15 10 16 18 18 

El Paso Current Std 12 38 26 40 48 48 

El Paso 50 98 12 10 7 11 12 12 

El Paso 50 99 12 9 6 9 10 10 

El Paso 100 98 12 20 14 21 24 24 

El Paso 100 99 12 17 12 18 21 21 

El Paso 150 98 12 30 21 32 36 36 

El Paso 150 99 12 26 18 28 31 31 

El Paso 200 98 12 40 27 42 47 47 

El Paso 200 99 12 35 24 37 41 41 

El Paso 250 98 12 49 34 53 59 59 

El Paso 250 99 12 43 30 46 52 52 

El Paso 300 98 12 59 41 63 71 71 

El Paso 300 99 12 52 36 55 62 62 

Jacksonville As is 2 14 14 14 15 15 

Jacksonville Current Std 2 53 53 53 53 53 

Jacksonville 50 98 2 13 13 13 13 13 

Jacksonville 50 99 2 12 12 12 12 12 

Jacksonville 100 98 2 26 26 26 26 26 

Jacksonville 100 99 2 24 24 24 25 25 

Jacksonville 150 98 2 39 39 39 40 40 

Jacksonville 150 99 2 36 36 36 37 37 

Jacksonville 200 98 2 52 52 52 53 53 

Jacksonville 200 99 2 49 48 49 49 49 

Jacksonville 250 98 2 65 64 65 66 66 

Jacksonville 250 99 2 61 60 61 61 61 

Jacksonville 300 98 2 78 77 78 79 79 

Jacksonville 300 99 2 73 72 73 74 74 

Las Vegas As is 16 10 2 7 22 22 

Las Vegas Current Std 16 25 5 18 53 53 

Las Vegas 50 98 16 8 2 5 16 16 

Las Vegas 50 99 16 7 1 5 14 14 

Las Vegas 100 98 16 15 3 11 32 32 

Las Vegas 100 99 16 14 3 10 29 29 

Las Vegas 150 98 16 23 5 16 48 48 

Las Vegas 150 99 16 21 4 15 43 43 

Las Vegas 200 98 16 30 6 21 64 64 

Las Vegas 200 99 16 27 6 19 58 58 

Las Vegas 250 98 16 38 8 27 79 79 

Las Vegas 250 99 16 34 7 24 72 72 

Las Vegas 300 98 16 45 9 32 95 95 

Las Vegas 300 99 16 41 8 29 87 87 

Los Angeles As is 51 22 5 24 36 37 

Los Angeles Current Std 51 31 7 32 48 52 
A-120

Annual Average NO2 (ppb)3

Location Scenario1 Percentile2

SiteYears Mean Min Med p98 p99 

Los Angeles 50 98 51 9 2 9 14 15 

Los Angeles 50 99 51 8 2 9 14 14 

Los Angeles 100 98 51 18 4 19 29 29 

Los Angeles 100 99 51 17 4 18 28 28 

Los Angeles 150 98 51 26 6 28 43 44 

Los Angeles 150 99 51 25 5 27 41 42 

Los Angeles 200 98 51 35 8 37 57 59 

Los Angeles 200 99 51 34 7 36 55 57 

Los Angeles 250 98 51 44 9 47 71 73 

Los Angeles 250 99 51 42 9 45 69 71 

Los Angeles 300 98 51 53 11 56 86 88 

Los Angeles 300 99 51 51 11 54 83 85 

Miami As is 6 9 7 9 10 10 

Miami Current Std 6 32 26 34 37 37 

Miami 50 98 6 8 6 8 9 9 

Miami 50 99 6 7 6 7 8 8 

Miami 100 98 6 16 13 16 19 19 

Miami 100 99 6 14 11 15 17 17 

Miami 150 98 6 24 19 25 28 28 

Miami 150 99 6 21 17 22 25 25 

Miami 200 98 6 32 26 33 38 38 

Miami 200 99 6 29 22 29 33 33 

Miami 250 98 6 41 32 41 47 47 

Miami 250 99 6 36 28 36 41 41 

Miami 300 98 6 49 38 49 56 56 

Miami 300 99 6 43 34 44 50 50 

New York As is 26 20 11 18 31 31 

New York Current Std 26 29 15 27 44 44 

New York 50 98 26 11 6 10 17 17 

New York 50 99 26 10 5 9 15 15 

New York 100 98 26 22 12 20 34 34 

New York 100 99 26 19 11 18 30 30 

New York 150 98 26 32 18 30 51 51 

New York 150 99 26 29 16 26 45 45 

New York 200 98 26 43 24 40 68 68 

New York 200 99 26 38 21 35 59 59 

New York 250 98 26 54 30 50 84 84 

New York 250 99 26 48 26 44 74 74 

New York 300 98 26 65 36 60 101 101 

New York 300 99 26 57 32 53 89 89 

Philadelphia As is 14 20 15 18 28 28 

Philadelphia Current Std 14 37 26 35 53 53 

Philadelphia 50 98 14 14 10 13 20 20 

Philadelphia 50 99 14 13 9 12 18 18 

Philadelphia 100 98 14 27 20 25 39 39 
A-121

Annual Average NO2 (ppb)3

Location Scenario1 Percentile2

SiteYears Mean Min Med p98 p99 

Philadelphia 100 99 14 25 19 23 36 36 

Philadelphia 150 98 14 41 30 38 59 59 

Philadelphia 150 99 14 38 28 35 54 54 

Philadelphia 200 98 14 55 40 51 79 79 

Philadelphia 200 99 14 50 37 46 72 72 

Philadelphia 250 98 14 68 50 63 98 98 

Philadelphia 250 99 14 63 46 58 90 90 

Philadelphia 300 98 14 82 61 76 118 118 

Philadelphia 300 99 14 75 56 70 108 108 

Phoenix As is 5 27 22 29 29 29 

Phoenix Current Std 5 40 32 41 45 45 

Phoenix 50 98 5 16 13 17 17 17 

Phoenix 50 99 5 14 12 15 15 15 

Phoenix 100 98 5 31 26 33 34 34 

Phoenix 100 99 5 28 23 30 31 31 

Phoenix 150 98 5 47 38 50 51 51 

Phoenix 150 99 5 43 35 45 46 46 

Phoenix 200 98 5 62 51 66 68 68 

Phoenix 200 99 5 57 47 60 62 62 

Phoenix 250 98 5 78 64 83 85 85 

Phoenix 250 99 5 71 58 75 77 77 

Phoenix 300 98 5 94 77 99 102 102 

Phoenix 300 99 5 85 70 90 93 93 

Provo As is 3 24 22 24 25 25 

Provo Current Std 3 53 53 53 53 53 

Provo 50 98 3 19 17 19 19 19 

Provo 50 99 3 17 16 18 18 18 

Provo 100 98 3 37 34 38 39 39 

Provo 100 99 3 35 32 35 37 37 

Provo 150 98 3 56 51 57 58 58 

Provo 150 99 3 52 48 53 55 55 

Provo 200 98 3 74 68 76 78 78 

Provo 200 99 3 69 64 71 73 73 

Provo 250 98 3 93 86 95 97 97 

Provo 250 99 3 87 80 89 91 91 

Provo 300 98 3 111 103 114 117 117 

Provo 300 99 3 104 96 106 110 110 

St. Louis As is 9 17 14 17 21 21 

St. Louis Current Std 9 41 36 38 49 49 

St. Louis 50 98 9 13 11 13 16 16 

St. Louis 50 99 9 12 10 12 14 14 

St. Louis 100 98 9 27 22 26 32 32 

St. Louis 100 99 9 24 20 24 29 29 

St. Louis 150 98 9 40 33 39 48 48 

St. Louis 150 99 9 36 30 36 43 43 
A-122

Annual Average NO2 (ppb)3

Location Scenario1 Percentile2

SiteYears Mean Min Med p98 p99 

St. Louis 200 98 9 53 44 52 63 63 

St. Louis 200 99 9 49 40 47 58 58 

St. Louis 250 98 9 66 55 65 79 79 

St. Louis 250 99 9 61 50 59 72 72 

St. Louis 300 98 9 80 66 78 95 95 

St. Louis 300 99 9 73 60 71 87 87 

Washington DC As is 18 18 9 21 25 25 

Washington DC Current Std 18 39 19 44 53 53 

Washington DC 50 98 18 13 6 15 17 17 

Washington DC 50 99 18 12 6 13 16 16 

Washington DC 100 98 18 26 12 29 35 35 

Washington DC 100 99 18 23 11 26 31 31 

Washington DC 150 98 18 39 18 44 52 52 

Washington DC 150 99 18 35 17 40 47 47 

Washington DC 200 98 18 52 25 59 70 70 

Washington DC 200 99 18 47 22 53 63 63 

Washington DC 250 98 18 64 31 73 87 87 

Washington DC 250 99 18 58 28 66 78 78 

Washington DC 300 98 18 77 37 88 104 104 

Washington DC 300 99 18 70 33 79 94 94 

Other MSA As is 612 13 1 13 22 24 

Other MSA Current Std 612 25 1 25 45 48 

Other MSA 50 98 612 6 0 7 11 12 

Other MSA 50 99 612 6 0 6 10 11 

Other MSA 100 98 612 13 1 13 23 25 

Other MSA 100 99 612 11 0 11 20 21 

Other MSA 150 98 612 19 1 20 34 37 

Other MSA 150 99 612 17 1 17 30 32 

Other MSA 200 98 612 25 1 26 46 49 

Other MSA 200 99 612 22 1 23 39 42 

Other MSA 250 98 612 32 1 33 57 61 

Other MSA 250 99 612 28 1 28 49 53 

Other MSA 300 98 612 38 2 39 68 74 

Other MSA 300 99 612 33 1 34 59 64 

Other Not MSA As is 127 7 1 6 15 16 

Other Not MSA Current Std 127 22 3 20 53 53 

Other Not MSA 50 98 127 4 1 3 8 9 

Other Not MSA 50 99 127 3 0 3 7 8 

Other Not MSA 100 98 127 7 1 7 17 18 

Other Not MSA 100 99 127 6 1 6 14 15 

Other Not MSA 150 98 127 11 2 10 25 27 

Other Not MSA 150 99 127 9 1 8 22 23 

Other Not MSA 200 98 127 15 2 13 34 36 

Other Not MSA 200 99 127 12 2 11 29 31 

Other Not MSA 250 98 127 18 3 16 42 45 
A-123

Annual Average NO2 (ppb)3

Location Scenario1 Percentile2

SiteYears Mean Min Med p98 p99 

Other Not MSA 250 99 127 16 2 14 36 38 

Other Not MSA 300 98 127 22 3 20 51 54 

Other Not MSA 300 99 127 19 3 17 43 46 

Notes: 

1

 Scenario: As is – unadjusted air quality, Current Std – air quality that just meets the current annual 

standard, All others – air quality that just meets 1-hour concentration level given percentile form of 

alternative standard. 

2

 Percentile: 98th or 99th percentile of daily maximum 1-hour concentration averaged over three years at 

maximum monitor in location. 

3 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, p98, 

p99 represent the minimum, median, 98th, and 99th percentiles of the distribution for the annual means. 
A-124

Table A-112. Estimated annual average NO2 concentrations for monitors >20 m and <100 m from a major 

road using 2001-2003 air quality as is and air quality adjusted to just meet the current and alternative 

standards. 

Annual Average NO2 (ppb)3

 Scenario1 Percentile2

SiteYears Mean Min Med p98 p99 

Boston As is 14 17 9 19 25 25 

Boston Current Std 14 35 16 36 53 53 

Boston 50 98 14 12 6 13 17 17 

Boston 50 99 14 11 5 12 16 16 

Boston 100 98 14 23 12 26 35 35 

Boston 100 99 14 21 11 23 32 32 

Boston 150 98 14 35 18 39 52 52 

Boston 150 99 14 32 16 35 47 47 

Boston 200 98 14 47 24 51 70 70 

Boston 200 99 14 42 22 46 63 63 

Boston 250 98 14 58 30 64 87 87 

Boston 250 99 14 53 27 58 79 79 

Boston 300 98 14 70 36 77 105 105 

Boston 300 99 14 63 32 70 95 95 

Chicago As is 6 31 28 31 32 32 

Chicago Current Std 6 51 47 52 53 53 

Chicago 50 98 6 18 16 18 19 19 

Chicago 50 99 6 16 15 16 17 17 

Chicago 100 98 6 35 33 35 37 37 

Chicago 100 99 6 31 29 31 33 33 

Chicago 150 98 6 53 49 53 56 56 

Chicago 150 99 6 47 44 47 50 50 

Chicago 200 98 6 71 66 70 75 75 

Chicago 200 99 6 63 58 63 66 66 

Chicago 250 98 6 88 82 88 94 94 

Chicago 250 99 6 78 73 78 83 83 

Chicago 300 98 6 106 99 106 112 112 

Chicago 300 99 6 94 87 94 100 100 

El Paso As is 3 21 20 21 22 22 

El Paso Current Std 3 53 53 53 53 53 

El Paso 50 98 3 14 13 14 14 14 

El Paso 50 99 3 12 11 12 12 12 

El Paso 100 98 3 27 26 28 28 28 

El Paso 100 99 3 24 23 25 25 25 

El Paso 150 98 3 41 39 42 43 43 

El Paso 150 99 3 36 34 37 37 37 

El Paso 200 98 3 55 52 56 57 57 

El Paso 200 99 3 48 46 49 50 50 

El Paso 250 98 3 69 65 70 71 71 

El Paso 250 99 3 60 57 61 62 62 

El Paso 300 98 3 82 78 84 85 85 

El Paso 300 99 3 72 68 74 74 74 
A-125

Annual Average NO2 (ppb)3

 Scenario1 Percentile2

SiteYears Mean Min Med p98 p99 

Las Vegas As is 3 6 3 6 9 9 

Las Vegas Current Std 3 14 7 14 21 21 

Las Vegas 50 98 3 4 2 4 6 6 

Las Vegas 50 99 3 4 2 4 6 6 

Las Vegas 100 98 3 8 4 8 12 12 

Las Vegas 100 99 3 8 4 8 11 11 

Las Vegas 150 98 3 13 6 12 19 19 

Las Vegas 150 99 3 11 6 11 17 17 

Las Vegas 200 98 3 17 9 17 25 25 

Las Vegas 200 99 3 15 8 15 23 23 

Las Vegas 250 98 3 21 11 21 31 31 

Las Vegas 250 99 3 19 10 19 28 28 

Las Vegas 300 98 3 25 13 25 37 37 

Las Vegas 300 99 3 23 12 23 34 34 

Los Angeles As is 35 24 4 24 41 41 

Los Angeles Current Std 35 33 5 33 53 53 

Los Angeles 50 98 35 10 2 9 16 16 

Los Angeles 50 99 35 9 2 9 16 16 

Los Angeles 100 98 35 19 3 19 32 32 

Los Angeles 100 99 35 18 3 18 31 31 

Los Angeles 150 98 35 29 5 28 49 49 

Los Angeles 150 99 35 28 5 27 47 47 

Los Angeles 200 98 35 38 7 38 65 65 

Los Angeles 200 99 35 37 6 36 62 62 

Los Angeles 250 98 35 48 8 47 81 81 

Los Angeles 250 99 35 46 8 46 78 78 

Los Angeles 300 98 35 57 10 57 97 97 

Los Angeles 300 99 35 55 10 55 94 94 

Miami As is 3 14 13 14 16 16 

Miami Current Std 3 53 53 53 53 53 

Miami 50 98 3 13 12 13 15 15 

Miami 50 99 3 12 11 12 13 13 

Miami 100 98 3 27 24 27 29 29 

Miami 100 99 3 23 21 23 26 26 

Miami 150 98 3 40 36 40 44 44 

Miami 150 99 3 35 32 35 39 39 

Miami 200 98 3 53 48 53 59 59 

Miami 200 99 3 47 42 47 52 52 

Miami 250 98 3 67 60 66 74 74 

Miami 250 99 3 59 53 58 65 65 

Miami 300 98 3 80 72 80 88 88 

Miami 300 99 3 70 63 70 78 78 

New York As is 13 31 21 30 40 40 

New York Current Std 13 44 30 46 53 53 

New York 50 98 13 17 11 16 22 22 

New York 50 99 13 15 10 14 19 19 
A-126

Annual Average NO2 (ppb)3

 Scenario1 Percentile2

SiteYears Mean Min Med p98 p99 

New York 100 98 13 33 23 33 44 44 

New York 100 99 13 29 20 29 38 38 

New York 150 98 13 50 34 49 65 65 

New York 150 99 13 44 30 43 58 58 

New York 200 98 13 66 45 65 87 87 

New York 200 99 13 58 40 57 77 77 

New York 250 98 13 83 57 82 109 109 

New York 250 99 13 73 50 72 96 96 

New York 300 98 13 100 68 98 131 131 

New York 300 99 13 88 60 86 115 115 

Philadelphia As is 7 24 19 24 30 30 

Philadelphia Current Std 7 46 34 45 53 53 

Philadelphia 50 98 7 17 13 17 21 21 

Philadelphia 50 99 7 16 12 15 19 19 

Philadelphia 100 98 7 34 26 33 42 42 

Philadelphia 100 99 7 31 24 31 38 38 

Philadelphia 150 98 7 51 39 50 62 62 

Philadelphia 150 99 7 47 36 46 57 57 

Philadelphia 200 98 7 68 53 67 83 83 

Philadelphia 200 99 7 62 48 61 76 76 

Philadelphia 250 98 7 85 66 84 104 104 

Philadelphia 250 99 7 78 60 77 95 95 

Philadelphia 300 98 7 102 79 100 125 125 

Philadelphia 300 99 7 93 72 92 114 114 

Phoenix As is 2 23 22 23 24 24 

Phoenix Current Std 2 33 31 33 36 36 

Phoenix 50 98 2 13 12 13 14 14 

Phoenix 50 99 2 12 11 12 12 12 

Phoenix 100 98 2 26 25 26 27 27 

Phoenix 100 99 2 24 23 24 25 25 

Phoenix 150 98 2 39 37 39 41 41 

Phoenix 150 99 2 36 34 36 37 37 

Phoenix 200 98 2 52 50 52 54 54 

Phoenix 200 99 2 47 45 47 50 50 

Phoenix 250 98 2 65 62 65 68 68 

Phoenix 250 99 2 59 57 59 62 62 

Phoenix 300 98 2 78 74 78 81 81 

Phoenix 300 99 2 71 68 71 74 74 

St. Louis As is 11 14 9 12 25 25 

St. Louis Current Std 11 34 21 27 53 53 

St. Louis 50 98 11 11 7 9 19 19 

St. Louis 50 99 11 10 6 8 17 17 

St. Louis 100 98 11 22 13 18 38 38 

St. Louis 100 99 11 20 12 16 35 35 

St. Louis 150 98 11 33 20 27 57 57 

St. Louis 150 99 11 30 18 24 52 52 
A-127

Annual Average NO2 (ppb)3

 Scenario1 Percentile2

SiteYears Mean Min Med p98 p99 

St. Louis 200 98 11 44 26 36 76 76 

St. Louis 200 99 11 40 24 33 70 70 

St. Louis 250 98 11 55 33 45 95 95 

St. Louis 250 99 11 50 30 41 87 87 

St. Louis 300 98 11 66 40 54 114 114 

St. Louis 300 99 11 60 36 49 104 104 

Washington DC As is 10 20 14 22 26 26 

Washington DC Current Std 10 43 30 47 53 53 

Washington DC 50 98 10 14 10 16 18 18 

Washington DC 50 99 10 13 9 14 16 16 

Washington DC 100 98 10 29 20 31 36 36 

Washington DC 100 99 10 26 18 28 33 33 

Washington DC 150 98 10 43 30 47 55 55 

Washington DC 150 99 10 39 27 42 49 49 

Washington DC 200 98 10 57 40 62 73 73 

Washington DC 200 99 10 52 36 56 66 66 

Washington DC 250 98 10 71 49 78 91 91 

Washington DC 250 99 10 65 45 70 82 82 

Washington DC 300 98 10 86 59 93 109 109 

Washington DC 300 99 10 77 54 84 99 99 

Notes: 

1

 Scenario: As is – unadjusted air quality, Current Std – air quality that just meets the current annual 

standard, All others – air quality that just meets 1-hour concentration level given percentile form of 

alternative standard. 

2

 Percentile: 98th or 99th percentile of daily maximum 1-hour concentration averaged over three years at 

maximum monitor in location. 

3 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, 

p98, p99 represent the minimum, median, 98th, and 99th percentiles of the distribution for the annual 

means. 
A-128

Table A-113. Estimated annual average NO2 concentrations for monitors ≤20 m from a major road using 

2001-2003 air quality as is and air quality adjusted to just meet the current and alternative standards. 

Annual Average NO2 (ppb)3

Location Scenario1 Percentile2

SiteYears Mean Min Med p98 p99 

Boston As is 5 21 7 23 30 30 

Boston Current Std 5 41 13 48 53 53 

Boston 50 98 5 15 5 16 20 20 

Boston 50 99 5 13 4 14 18 18 

Boston 100 98 5 29 10 32 41 41 

Boston 100 99 5 26 9 29 37 37 

Boston 150 98 5 44 14 48 61 61 

Boston 150 99 5 39 13 43 55 55 

Boston 200 98 5 58 19 63 82 82 

Boston 200 99 5 53 17 57 74 74 

Boston 250 98 5 73 24 79 102 102 

Boston 250 99 5 66 22 72 92 92 

Boston 300 98 5 87 29 95 122 122 

Boston 300 99 5 79 26 86 111 111 

Chicago As is 4 22 22 22 24 24 

Chicago Current Std 4 37 36 37 39 39 

Chicago 50 98 4 13 13 13 14 14 

Chicago 50 99 4 11 11 11 12 12 

Chicago 100 98 4 26 25 26 27 27 

Chicago 100 99 4 23 22 23 24 24 

Chicago 150 98 4 39 38 38 41 41 

Chicago 150 99 4 34 34 34 36 36 

Chicago 200 98 4 52 50 51 54 54 

Chicago 200 99 4 46 45 46 48 48 

Chicago 250 98 4 65 63 64 68 68 

Chicago 250 99 4 57 56 57 60 60 

Chicago 300 98 4 78 76 77 81 81 

Chicago 300 99 4 69 67 68 72 72 

Cleveland As is 3 23 22 22 24 24 

Cleveland Current Std 3 53 53 53 53 53 

Cleveland 50 98 3 16 15 16 17 17 

Cleveland 50 99 3 15 14 15 16 16 

Cleveland 100 98 3 32 31 32 34 34 

Cleveland 100 99 3 30 29 30 31 31 

Cleveland 150 98 3 48 46 48 50 50 

Cleveland 150 99 3 45 43 44 47 47 

Cleveland 200 98 3 64 62 63 67 67 

Cleveland 200 99 3 60 57 59 63 63 

Cleveland 250 98 3 80 77 79 84 84 

Cleveland 250 99 3 75 72 74 78 78 

Cleveland 300 98 3 96 92 95 101 101 

Cleveland 300 99 3 90 86 89 94 94 

Denver As is 2 36 35 36 37 37 

Denver Current Std 2 53 53 53 53 53 
A-129

Annual Average NO2 (ppb)3

Location Scenario1 Percentile2

SiteYears Mean Min Med p98 p99 

Denver 50 98 2 19 18 19 19 19 

Denver 50 99 2 17 16 17 17 17 

Denver 100 98 2 37 37 37 38 38 

Denver 100 99 2 33 32 33 34 34 

Denver 150 98 2 56 55 56 57 57 

Denver 150 99 2 50 49 50 51 51 

Denver 200 98 2 75 73 75 76 76 

Denver 200 99 2 66 65 66 68 68 

Denver 250 98 2 93 92 93 95 95 

Denver 250 99 2 83 81 83 84 84 

Denver 300 98 2 112 110 112 114 114 

Denver 300 99 2 99 97 99 101 101 

Las Vegas As is 3 22 21 22 23 23 

Las Vegas Current Std 3 53 53 53 53 53 

Las Vegas 50 98 3 16 15 16 16 16 

Las Vegas 50 99 3 14 14 15 15 15 

Las Vegas 100 98 3 32 31 32 32 32 

Las Vegas 100 99 3 29 28 29 29 29 

Las Vegas 150 98 3 47 46 48 48 48 

Las Vegas 150 99 3 43 42 44 44 44 

Las Vegas 200 98 3 63 61 64 65 65 

Las Vegas 200 99 3 57 56 58 59 59 

Las Vegas 250 98 3 79 77 80 81 81 

Las Vegas 250 99 3 72 70 73 73 73 

Las Vegas 300 98 3 95 92 96 97 97 

Las Vegas 300 99 3 86 84 87 88 88 

Los Angeles As is 9 30 23 29 37 37 

Los Angeles Current Std 9 41 30 39 53 53 

Los Angeles 50 98 9 12 9 12 15 15 

Los Angeles 50 99 9 11 9 11 14 14 

Los Angeles 100 98 9 23 18 23 29 29 

Los Angeles 100 99 9 23 17 22 28 28 

Los Angeles 150 98 9 35 27 35 44 44 

Los Angeles 150 99 9 34 26 33 42 42 

Los Angeles 200 98 9 47 36 46 58 58 

Los Angeles 200 99 9 45 35 45 56 56 

Los Angeles 250 98 9 59 45 58 73 73 

Los Angeles 250 99 9 56 44 56 70 70 

Los Angeles 300 98 9 70 54 69 87 87 

Los Angeles 300 99 9 68 52 67 84 84 

Miami As is 3 6 6 6 7 7 

Miami Current Std 3 23 19 23 27 27 

Miami 50 98 3 6 5 6 6 6 

Miami 50 99 3 5 5 5 5 5 

Miami 100 98 3 12 11 12 12 12 
A-130

Annual Average NO2 (ppb)3

Location Scenario1 Percentile2

SiteYears Mean Min Med p98 p99 

Miami 100 99 3 10 9 10 11 11 

Miami 150 98 3 17 16 18 18 18 

Miami 150 99 3 15 14 16 16 16 

Miami 200 98 3 23 22 24 25 25 

Miami 200 99 3 20 19 21 22 22 

Miami 250 98 3 29 27 29 31 31 

Miami 250 99 3 26 24 26 27 27 

Miami 300 98 3 35 32 35 37 37 

Miami 300 99 3 31 28 31 33 33 

New York As is 7 28 25 28 30 30 

New York Current Std 7 39 34 38 49 49 

New York 50 98 7 15 13 15 16 16 

New York 50 99 7 13 12 13 14 14 

New York 100 98 7 30 27 30 33 33 

New York 100 99 7 26 23 26 29 29 

New York 150 98 7 45 40 45 49 49 

New York 150 99 7 39 35 40 43 43 

New York 200 98 7 60 53 60 65 65 

New York 200 99 7 53 47 53 57 57 

New York 250 98 7 75 67 75 81 81 

New York 250 99 7 66 59 66 72 72 

New York 300 98 7 90 80 90 98 98 

New York 300 99 7 79 70 79 86 86 

Phoenix As is 3 35 34 35 37 37 

Phoenix Current Std 3 53 53 53 53 53 

Phoenix 50 98 3 20 20 20 21 21 

Phoenix 50 99 3 19 18 18 20 20 

Phoenix 100 98 3 41 40 40 43 43 

Phoenix 100 99 3 37 36 37 39 39 

Phoenix 150 98 3 61 59 60 64 64 

Phoenix 150 99 3 56 54 55 59 59 

Phoenix 200 98 3 82 79 80 86 86 

Phoenix 200 99 3 74 72 73 78 78 

Phoenix 250 98 3 102 99 100 107 107 

Phoenix 250 99 3 93 90 91 98 98 

Phoenix 300 98 3 122 119 120 128 128 

Phoenix 300 99 3 112 108 110 117 117 

St. Louis As is 6 18 16 19 20 20 

St. Louis Current Std 6 43 40 42 48 48 

St. Louis 50 98 6 14 13 14 15 15 

St. Louis 50 99 6 13 11 13 14 14 

St. Louis 100 98 6 28 25 29 30 30 

St. Louis 100 99 6 26 23 26 28 28 

St. Louis 150 98 6 42 38 43 45 45 

St. Louis 150 99 6 39 34 39 41 41 
A-131

Annual Average NO2 (ppb)3

Location Scenario1 Percentile2

SiteYears Mean Min Med p98 p99 

St. Louis 200 98 6 56 50 57 60 60 

St. Louis 200 99 6 51 46 52 55 55 

St. Louis 250 98 6 70 63 71 75 75 

St. Louis 250 99 6 64 57 65 69 69 

St. Louis 300 98 6 84 75 86 90 90 

St. Louis 300 99 6 77 69 78 83 83 

Washington DC As is 4 23 20 24 26 26 

Washington DC Current Std 4 50 43 52 53 53 

Washington DC 50 98 4 16 14 17 18 18 

Washington DC 50 99 4 15 12 15 16 16 

Washington DC 100 98 4 33 28 34 36 36 

Washington DC 100 99 4 30 25 30 33 33 

Washington DC 150 98 4 49 42 50 54 54 

Washington DC 150 99 4 44 37 46 49 49 

Washington DC 200 98 4 66 55 67 72 72 

Washington DC 200 99 4 59 50 61 65 65 

Washington DC 250 98 4 82 69 84 91 91 

Washington DC 250 99 4 74 62 76 82 82 

Washington DC 300 98 4 98 83 101 109 109 

Washington DC 300 99 4 89 75 91 98 98 

Notes: 

1

 Scenario: As is – unadjusted air quality, Current Std – air quality that just meets the current annual 

standard, All others – air quality that just meets 1-hour concentration level given percentile form of 

alternative standard. 

2

 Percentile: 98th or 99th percentile of daily maximum 1-hour concentration averaged over three years at 

maximum monitor in location. 

3 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, 

p98, p99 represent the minimum, median, 98th, and 99th percentiles of the distribution for the annual 

means. 
A-132

Table A-114. Estimated annual average NO2 concentrations on-roads using 2001-2003 air quality as is, air 

quality adjusted to just meet the current and alternative standards, and an on-road adjustment factor. 

Annual Average NO2 (ppb)3

Location Standard1 Percentile2

SiteYears Mean Min Med p98 p99 

Atlanta As is 1400 22 5 24 47 53 

Atlanta Current Std 1400 60 12 62 127 130 

Atlanta 50 98 1400 14 3 15 29 32 

Atlanta 50 99 1400 13 3 13 26 30 

Atlanta 100 98 1400 27 6 29 57 65 

Atlanta 100 99 1400 25 6 27 53 60 

Atlanta 150 98 1400 41 9 44 86 97 

Atlanta 150 99 1400 38 9 40 79 90 

Atlanta 200 98 1400 55 13 58 115 130 

Atlanta 200 99 1400 50 12 54 106 120 

Atlanta 250 98 1400 68 16 73 144 162 

Atlanta 250 99 1400 63 14 67 132 149 

Atlanta 300 98 1400 82 19 87 172 195 

Atlanta 300 99 1400 75 17 80 159 179 

Boston As is 600 17 7 18 29 30 

Boston Current Std 600 34 14 36 60 61 

Boston 50 98 600 12 5 12 20 21 

Boston 50 99 600 11 4 11 18 19 

Boston 100 98 600 24 9 24 40 41 

Boston 100 99 600 22 8 22 36 37 

Boston 150 98 600 36 14 37 59 62 

Boston 150 99 600 32 13 33 54 56 

Boston 200 98 600 48 19 49 79 82 

Boston 200 99 600 43 17 44 72 74 

Boston 250 98 600 59 23 61 99 103 

Boston 250 99 600 54 21 55 89 93 

Boston 300 98 600 71 28 73 119 123 

Boston 300 99 600 65 25 66 107 112 

Chicago As is 900 39 21 37 65 68 

Chicago Current Std 900 65 35 62 111 114 

Chicago 50 98 900 23 12 22 37 39 

Chicago 50 99 900 20 11 19 33 35 

Chicago 100 98 900 45 25 43 75 78 

Chicago 100 99 900 40 22 38 66 69 

Chicago 150 98 900 68 37 65 112 117 

Chicago 150 99 900 60 33 57 100 104 

Chicago 200 98 900 91 49 86 150 156 

Chicago 200 99 900 80 44 77 133 138 

Chicago 250 98 900 113 62 108 187 195 

Chicago 250 99 900 100 55 96 166 173 

Chicago 300 98 900 136 74 129 224 234 

Chicago 300 99 900 121 66 115 199 207 

Cleveland As is 300 32 22 32 43 45 

Cleveland Current Std 300 76 53 75 102 106 
A-133

Annual Average NO2 (ppb)3

Location Standard1 Percentile2

SiteYears Mean Min Med p98 p99 

Cleveland 50 98 300 23 16 23 30 32 

Cleveland 50 99 300 21 15 21 28 30 

Cleveland 100 98 300 46 32 45 61 64 

Cleveland 100 99 300 43 29 42 57 60 

Cleveland 150 98 300 69 47 68 91 96 

Cleveland 150 99 300 64 44 64 85 90 

Cleveland 200 98 300 92 63 91 122 128 

Cleveland 200 99 300 86 59 85 114 120 

Cleveland 250 98 300 115 79 113 152 160 

Cleveland 250 99 300 107 74 106 142 150 

Cleveland 300 98 300 138 95 136 183 192 

Cleveland 300 99 300 129 88 127 171 180 

Denver As is 200 42 27 40 63 64 

Denver Current Std 200 80 48 81 127 129 

Denver 50 98 200 22 14 21 33 33 

Denver 50 99 200 19 12 19 29 30 

Denver 100 98 200 44 28 42 65 67 

Denver 100 99 200 39 25 37 58 59 

Denver 150 98 200 66 42 63 98 100 

Denver 150 99 200 58 37 56 87 89 

Denver 200 98 200 88 56 84 131 134 

Denver 200 99 200 78 50 74 116 118 

Denver 250 98 200 110 70 105 163 167 

Denver 250 99 200 97 62 93 145 148 

Denver 300 98 200 131 84 126 196 200 

Denver 300 99 200 116 75 111 174 178 

Detroit As is 600 37 24 36 54 57 

Detroit Current Std 600 89 56 87 130 131 

Detroit 50 98 600 21 13 20 30 32 

Detroit 50 99 600 14 9 13 20 21 

Detroit 100 98 600 41 26 40 59 63 

Detroit 100 99 600 28 18 27 40 43 

Detroit 150 98 600 62 39 60 89 95 

Detroit 150 99 600 42 27 40 60 64 

Detroit 200 98 600 83 52 80 119 126 

Detroit 200 99 600 56 35 54 80 85 

Detroit 250 98 600 103 66 100 149 158 

Detroit 250 99 600 70 44 67 100 107 

Detroit 300 98 600 124 79 119 178 189 

Detroit 300 99 600 84 53 81 120 128 

El Paso As is 1200 27 13 27 43 44 

El Paso Current Std 1200 69 32 68 111 116 

El Paso 50 98 1200 18 9 18 28 29 

El Paso 50 99 1200 16 8 15 24 25 

El Paso 100 98 1200 36 17 35 56 58 
A-134

Annual Average NO2 (ppb)3

Location Standard1 Percentile2

SiteYears Mean Min Med p98 p99 

El Paso 100 99 1200 31 15 31 49 51 

El Paso 150 98 1200 54 26 53 84 87 

El Paso 150 99 1200 47 23 46 73 76 

El Paso 200 98 1200 71 34 70 112 116 

El Paso 200 99 1200 62 30 61 98 101 

El Paso 250 98 1200 89 43 88 140 145 

El Paso 250 99 1200 78 38 77 122 126 

El Paso 300 98 1200 107 52 105 168 174 

El Paso 300 99 1200 94 45 92 147 152 

Jacksonville As is 200 26 18 26 36 37 

Jacksonville Current Std 200 96 68 94 130 135 

Jacksonville 50 98 200 24 16 23 32 34 

Jacksonville 50 99 200 22 15 22 30 31 

Jacksonville 100 98 200 47 33 47 64 67 

Jacksonville 100 99 200 44 31 43 60 63 

Jacksonville 150 98 200 71 49 70 96 101 

Jacksonville 150 99 200 66 46 65 90 94 

Jacksonville 200 98 200 95 66 93 128 134 

Jacksonville 200 99 200 88 61 87 119 125 

Jacksonville 250 98 200 118 82 117 160 168 

Jacksonville 250 99 200 110 77 109 149 157 

Jacksonville 300 98 200 142 99 140 192 201 

Jacksonville 300 99 200 132 92 130 179 188 

Las Vegas As is 1600 19 3 14 48 51 

Las Vegas Current Std 1600 46 7 33 117 124 

Las Vegas 50 98 1600 14 2 10 35 36 

Las Vegas 50 99 1600 12 2 9 32 33 

Las Vegas 100 98 1600 27 4 20 69 73 

Las Vegas 100 99 1600 25 4 18 63 66 

Las Vegas 150 98 1600 41 6 29 104 109 

Las Vegas 150 99 1600 37 5 27 95 99 

Las Vegas 200 98 1600 54 8 39 139 146 

Las Vegas 200 99 1600 50 7 36 126 133 

Las Vegas 250 98 1600 68 10 49 174 182 

Las Vegas 250 99 1600 62 9 45 158 166 

Las Vegas 300 98 1600 82 12 59 208 219 

Las Vegas 300 99 1600 74 11 54 189 199 

Los Angeles As is 5100 41 6 40 77 82 

Los Angeles Current Std 5100 56 8 55 106 113 

Los Angeles 50 98 5100 16 2 16 31 32 

Los Angeles 50 99 5100 15 2 15 29 31 

Los Angeles 100 98 5100 32 5 32 61 65 

Los Angeles 100 99 5100 31 5 31 59 62 

Los Angeles 150 98 5100 48 7 48 92 97 

Los Angeles 150 99 5100 46 7 46 88 94 
A-135

Annual Average NO2 (ppb)3

Location Standard1 Percentile2

SiteYears Mean Min Med p98 p99 

Los Angeles 200 98 5100 64 10 63 122 130 

Los Angeles 200 99 5100 62 9 61 117 125 

Los Angeles 250 98 5100 80 12 79 153 162 

Los Angeles 250 99 5100 77 12 76 147 156 

Los Angeles 300 98 5100 97 14 95 183 194 

Los Angeles 300 99 5100 93 14 92 176 187 

Miami As is 600 16 9 15 24 25 

Miami Current Std 600 59 33 58 87 92 

Miami 50 98 600 15 8 14 22 23 

Miami 50 99 600 13 7 13 20 20 

Miami 100 98 600 29 16 29 45 46 

Miami 100 99 600 26 14 25 39 40 

Miami 150 98 600 44 24 43 67 68 

Miami 150 99 600 39 22 38 59 60 

Miami 200 98 600 59 33 57 89 91 

Miami 200 99 600 52 29 50 78 80 

Miami 250 98 600 73 41 71 111 114 

Miami 250 99 600 65 36 63 98 100 

Miami 300 98 600 88 49 86 134 137 

Miami 300 99 600 78 43 75 117 121 

New York As is 2600 36 14 34 65 73 

New York Current Std 2600 52 18 49 98 103 

New York 50 98 2600 20 8 18 35 39 

New York 50 99 2600 17 7 16 31 35 

New York 100 98 2600 39 15 37 70 79 

New York 100 99 2600 35 13 33 62 69 

New York 150 98 2600 59 23 55 105 118 

New York 150 99 2600 52 20 49 93 104 

New York 200 98 2600 79 30 74 140 158 

New York 200 99 2600 69 27 65 123 139 

New York 250 98 2600 98 38 92 175 197 

New York 250 99 2600 86 33 81 154 173 

New York 300 98 2600 118 45 111 211 237 

New York 300 99 2600 104 40 98 185 208 

Philadelphia As is 1400 36 18 33 64 66 

Philadelphia Current Std 1400 67 33 63 119 126 

Philadelphia 50 98 1400 25 13 23 44 46 

Philadelphia 50 99 1400 23 12 21 41 42 

Philadelphia 100 98 1400 50 26 46 89 92 

Philadelphia 100 99 1400 46 23 42 81 84 

Philadelphia 150 98 1400 74 38 69 133 138 

Philadelphia 150 99 1400 68 35 63 122 127 

Philadelphia 200 98 1400 99 51 92 177 184 

Philadelphia 200 99 1400 91 47 85 163 169 

Philadelphia 250 98 1400 124 64 115 222 230 
A-136

Annual Average NO2 (ppb)3

Location Standard1 Percentile2

SiteYears Mean Min Med p98 p99 

Philadelphia 250 99 1400 114 59 106 203 211 

Philadelphia 300 98 1400 149 77 138 266 276 

Philadelphia 300 99 1400 137 70 127 244 253 

Phoenix As is 500 49 28 47 72 77 

Phoenix Current Std 500 72 40 69 110 114 

Phoenix 50 98 500 28 16 27 42 44 

Phoenix 50 99 500 26 15 25 38 40 

Phoenix 100 98 500 56 33 55 83 88 

Phoenix 100 99 500 51 30 50 76 81 

Phoenix 150 98 500 84 49 82 125 133 

Phoenix 150 99 500 77 45 75 114 121 

Phoenix 200 98 500 113 65 109 166 177 

Phoenix 200 99 500 103 59 100 152 161 

Phoenix 250 98 500 141 81 137 208 221 

Phoenix 250 99 500 128 74 125 190 202 

Phoenix 300 98 500 169 98 164 250 265 

Phoenix 300 99 500 154 89 150 228 242 

Provo As is 300 43 28 41 61 64 

Provo Current Std 300 96 67 93 132 144 

Provo 50 98 300 33 22 32 48 50 

Provo 50 99 300 31 20 30 45 47 

Provo 100 98 300 67 44 65 96 101 

Provo 100 99 300 63 41 61 89 94 

Provo 150 98 300 100 65 97 143 151 

Provo 150 99 300 94 61 91 134 141 

Provo 200 98 300 134 87 129 191 201 

Provo 200 99 300 125 82 121 179 188 

Provo 250 98 300 167 109 162 239 252 

Provo 250 99 300 156 102 151 224 236 

Provo 300 98 300 200 131 194 287 302 

Provo 300 99 300 188 123 182 268 283 

St. Louis As is 900 31 18 30 48 50 

St. Louis Current Std 900 74 45 71 114 118 

St. Louis 50 98 900 24 14 23 37 38 

St. Louis 50 99 900 22 13 21 34 35 

St. Louis 100 98 900 48 28 47 75 76 

St. Louis 100 99 900 44 26 43 68 70 

St. Louis 150 98 900 72 42 70 112 114 

St. Louis 150 99 900 66 38 64 102 105 

St. Louis 200 98 900 96 56 94 149 152 

St. Louis 200 99 900 88 51 86 137 140 

St. Louis 250 98 900 120 70 117 186 190 

St. Louis 250 99 900 110 64 107 171 174 

St. Louis 300 98 900 145 84 141 224 229 

St. Louis 300 99 900 132 77 129 205 209 
A-137

Annual Average NO2 (ppb)3

Location Standard1 Percentile2

SiteYears Mean Min Med p98 p99 

Washington DC As is 1800 33 11 34 58 63 

Washington DC Current Std 1800 71 24 73 125 133 

Washington DC 50 98 1800 23 8 24 41 44 

Washington DC 50 99 1800 21 7 22 37 40 

Washington DC 100 98 1800 47 15 48 82 88 

Washington DC 100 99 1800 42 14 43 74 80 

Washington DC 150 98 1800 70 23 72 123 133 

Washington DC 150 99 1800 63 21 65 111 120 

Washington DC 200 98 1800 93 31 96 163 177 

Washington DC 200 99 1800 84 28 87 148 160 

Washington DC 250 98 1800 117 39 120 204 221 

Washington DC 250 99 1800 105 35 108 185 199 

Washington DC 300 98 1800 140 46 144 245 265 

Washington DC 300 99 1800 126 42 130 221 239 

Other MSA As is 61200 23 1 22 47 50 

Other MSA Current Std 61200 45 1 44 93 99 

Other MSA 50 98 61200 12 0 11 24 25 

Other MSA 50 99 61200 10 0 10 21 22 

Other MSA 100 98 61200 23 1 23 48 51 

Other MSA 100 99 61200 20 1 20 41 44 

Other MSA 150 98 61200 35 1 34 71 76 

Other MSA 150 99 61200 30 1 30 62 66 

Other MSA 200 98 61200 46 1 46 95 102 

Other MSA 200 99 61200 40 1 39 82 88 

Other MSA 250 98 61200 58 2 57 119 127 

Other MSA 250 99 61200 50 1 49 103 110 

Other MSA 300 98 61200 69 2 68 143 153 

Other MSA 300 99 61200 60 2 59 124 132 

Other Not MSA As is 12700 12 1 11 31 33 

Other Not MSA Current Std 12700 40 4 35 101 109 

Other Not MSA 50 98 12700 7 1 6 17 18 

Other Not MSA 50 99 12700 6 1 5 14 16 

Other Not MSA 100 98 12700 13 1 12 34 37 

Other Not MSA 100 99 12700 11 1 10 29 31 

Other Not MSA 150 98 12700 20 2 17 51 55 

Other Not MSA 150 99 12700 17 2 15 43 47 

Other Not MSA 200 98 12700 26 3 23 67 73 

Other Not MSA 200 99 12700 22 2 20 57 62 

Other Not MSA 250 98 12700 33 4 29 84 91 

Other Not MSA 250 99 12700 28 3 25 72 78 

Other Not MSA 300 98 12700 40 4 35 101 110 

Other Not MSA 300 99 12700 34 4 30 86 93 

Notes: 

1

 Scenario: As is – unadjusted air quality, Current Std – air quality that just meets the current annual 

standard, All others – air quality that just meets 1-hour concentration level given percentile form of 

alternative standard. 
A-138

Annual Average NO2 (ppb)3

Location Standard1 Percentile2

SiteYears Mean Min Med p98 p99 

2

 Percentile: 98th or 99th percentile of daily maximum 1-hour concentration averaged over three years at 

maximum monitor in location. 

3 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, 

p98, p99 represent the minimum, median, 98th, and 99th percentiles of the distribution for the annual 

means. 
A-139

A-9.2 Number of 1-hour NO2 exceedances in a year, 2001-2003 Table A-115. Estimated number of exceedances of 1-hour concentration levels (100, 150, and 200 ppb) for monitors ≥100 m fro2001-2003 air quality as is and air quality adjusted to just meet the current and alternative standards. 

Number of Daily Maximum Exceedances3

≥ 100 ppb ≥ 150 ppb 

Location Standard1 Percentile2

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99 Mean 

Atlanta As is 14 0 0 0 3 3 0 0 0 1 1 0 Atlanta Current Std 14 80 2 85 215 215 18 0 8 75 75 3 Atlanta 50 98 14 0 0 0 1 1 0 0 0 0 0 0 Atlanta 50 99 14 0 0 0 0 0 0 0 0 0 0 0 Atlanta 100 98 14 2 0 0 15 15 0 0 0 1 1 0 Atlanta 100 99 14 1 0 0 8 8 0 0 0 1 1 0 Atlanta 150 98 14 20 0 9 84 84 2 0 0 15 15 0 Atlanta 150 99 14 13 0 4 57 57 1 0 0 8 8 0 Atlanta 200 98 14 65 1 66 196 196 10 0 3 53 53 2 Atlanta 200 99 14 48 0 39 162 162 7 0 1 42 42 1 Boston As is 6 0 0 0 0 0 0 0 0 0 0 0 Boston Current Std 6 3 0 1 12 12 0 0 0 0 0 0 Boston 50 98 6 0 0 0 0 0 0 0 0 0 0 0 Boston 50 99 6 0 0 0 0 0 0 0 0 0 0 0 Boston 100 98 6 0 0 0 1 1 0 0 0 0 0 0 Boston 100 99 6 0 0 0 1 1 0 0 0 0 0 0 Boston 150 98 6 3 0 2 10 10 0 0 0 1 1 0 Boston 150 99 6 2 0 1 6 6 0 0 0 1 1 0 Boston 200 98 6 34 7 35 62 62 1 0 1 5 5 0 Boston 200 99 6 17 3 14 34 34 1 0 1 3 3 0 Chicago As is 9 0 0 0 2 2 0 0 0 0 0 0 Chicago Current Std 9 23 1 15 82 82 1 0 0 4 4 0 Chicago 50 98 9 0 0 0 0 0 0 0 0 0 0 0 Chicago 50 99 9 0 0 0 0 0 0 0 0 0 0 0 Chicago 100 98 9 1 0 0 4 4 0 0 0 1 1 0 Chicago 100 99 9 0 0 0 2 2 0 0 0 0 0 0 
A-140

Number of Daily Maximum Exceedances3

≥ 100 ppb ≥ 150 ppb 

Location Standard1 Percentile2

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99 Mean 

Chicago 150 98 9 31 4 24 86 86 1 0 0 4 4 0 Chicago 150 99 9 12 1 7 45 45 0 0 0 2 2 0 Chicago 200 98 9 106 51 104 193 193 14 1 8 51 51 1 Chicago 200 99 9 72 30 57 146 146 4 0 3 16 16 0 Cleveland As is 3 0 0 0 1 1 0 0 0 0 0 0 Cleveland Current Std 3 70 59 68 82 82 5 4 5 7 7 1 Cleveland 50 98 3 0 0 0 0 0 0 0 0 0 0 0 Cleveland 50 99 3 0 0 0 0 0 0 0 0 0 0 0 Cleveland 100 98 3 2 1 3 3 3 0 0 0 0 0 0 Cleveland 100 99 3 1 1 1 2 2 0 0 0 0 0 0 Cleveland 150 98 3 49 32 54 60 60 2 1 3 3 3 0 Cleveland 150 99 3 31 20 32 41 41 1 1 1 2 2 0 Cleveland 200 98 3 133 120 129 151 151 27 16 28 38 38 2 Cleveland 200 99 3 117 108 109 135 135 14 12 13 17 17 1 Denver As is 2 2 1 2 2 2 0 0 0 0 0 0 Denver Current Std 2 99 24 99 174 174 21 1 21 41 41 2 Denver 50 98 2 0 0 0 0 0 0 0 0 0 0 0 Denver 50 99 2 0 0 0 0 0 0 0 0 0 0 0 Denver 100 98 2 2 1 2 2 2 0 0 0 0 0 0 Denver 100 99 2 1 0 1 2 2 0 0 0 0 0 0 Denver 150 98 2 37 29 37 44 44 2 1 2 2 2 1 Denver 150 99 2 17 13 17 21 21 1 0 1 2 2 0 Denver 200 98 2 149 104 149 193 193 17 13 17 21 21 2 Denver 200 99 2 89 62 89 116 116 5 4 5 6 6 1 Detroit As is 6 3 0 2 7 7 1 0 1 5 5 1 Detroit Current Std 6 99 83 96 115 115 14 4 15 20 20 4 Detroit 50 98 6 1 0 1 4 4 1 0 0 3 3 0 Detroit 50 99 6 1 0 0 3 3 0 0 0 1 1 0 Detroit 100 98 6 3 1 3 7 7 2 0 1 7 7 1 Detroit 100 99 6 2 0 1 7 7 1 0 0 4 4 1 Detroit 150 98 6 17 7 18 23 23 3 1 3 7 7 2 
A-141

Number of Daily Maximum Exceedances3

≥ 100 ppb ≥ 150 ppb 

Location Standard1 Percentile2

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99 Mean 

Detroit 150 99 6 3 1 3 7 7 2 0 1 7 7 1 Detroit 200 98 6 73 46 70 99 99 9 2 10 14 14 3 Detroit 200 99 6 10 2 11 14 14 3 0 2 7 7 2 El Paso As is 12 0 0 0 1 1 0 0 0 1 1 0 El Paso Current Std 12 115 48 125 189 189 13 3 12 27 27 2 El Paso 50 98 12 0 0 0 1 1 0 0 0 0 0 0 El Paso 50 99 12 0 0 0 0 0 0 0 0 0 0 0 El Paso 100 98 12 3 0 2 8 8 0 0 0 1 1 0 El Paso 100 99 12 1 0 0 5 5 0 0 0 1 1 0 El Paso 150 98 12 35 11 34 60 60 3 0 2 8 8 0 El Paso 150 99 12 14 3 13 28 28 1 0 0 5 5 0 El Paso 200 98 12 125 59 129 174 174 16 4 14 30 30 3 El Paso 200 99 12 76 30 77 118 118 6 2 7 13 13 1 Jacksonville As is 2 1 0 1 1 1 1 0 1 1 1 1 Jacksonville Current Std 2 152 142 152 161 161 44 35 44 53 53 7 Jacksonville 50 98 2 1 0 1 1 1 1 0 1 1 1 1 Jacksonville 50 99 2 1 0 1 1 1 1 0 1 1 1 1 Jacksonville 100 98 2 7 3 7 10 10 1 0 1 1 1 1 Jacksonville 100 99 2 4 3 4 5 5 1 0 1 1 1 1 Jacksonville 150 98 2 74 70 74 77 77 7 3 7 10 10 1 Jacksonville 150 99 2 55 50 55 60 60 4 3 4 5 5 1 Jacksonville 200 98 2 152 142 152 161 161 41 35 41 46 46 7 Jacksonville 200 99 2 132 128 132 135 135 29 22 29 35 35 4 Las Vegas As is 16 0 0 0 1 1 0 0 0 0 0 0 Las Vegas Current Std 16 69 0 21 218 218 6 0 1 34 34 0 Las Vegas 50 98 16 0 0 0 0 0 0 0 0 0 0 0 Las Vegas 50 99 16 0 0 0 0 0 0 0 0 0 0 0 Las Vegas 100 98 16 1 0 0 7 7 0 0 0 0 0 0 Las Vegas 100 99 16 1 0 0 2 2 0 0 0 0 0 0 Las Vegas 150 98 16 41 0 5 143 143 1 0 0 7 7 0 Las Vegas 150 99 16 22 0 3 79 79 1 0 0 2 2 0 
A-142

Number of Daily Maximum Exceedances3

≥ 100 ppb ≥ 150 ppb 

Location Standard1 Percentile2

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99 Mean 

Las Vegas 200 98 16 114 1 61 293 293 19 0 3 70 70 1 Las Vegas 200 99 16 87 0 31 252 252 9 0 2 38 38 1 Los Angeles As is 51 4 0 1 17 18 0 0 0 1 8 0 Los Angeles Current Std 51 21 0 16 67 78 2 0 0 10 11 0 Los Angeles 50 98 51 0 0 0 0 0 0 0 0 0 0 0 Los Angeles 50 99 51 0 0 0 0 0 0 0 0 0 0 0 Los Angeles 100 98 51 1 0 0 5 10 0 0 0 0 5 0 Los Angeles 100 99 51 0 0 0 3 9 0 0 0 0 4 0 Los Angeles 150 98 51 9 0 4 43 46 1 0 0 5 10 0 Los Angeles 150 99 51 8 0 3 36 39 0 0 0 3 9 0 Los Angeles 200 98 51 37 0 31 110 128 5 0 1 26 27 1 Los Angeles 200 99 51 32 0 27 98 117 4 0 1 19 19 0 Miami As is 6 0 0 0 0 0 0 0 0 0 0 0 Miami Current Std 6 106 75 102 157 157 23 6 15 52 52 4 Miami 50 98 6 0 0 0 0 0 0 0 0 0 0 0 Miami 50 99 6 0 0 0 0 0 0 0 0 0 0 0 Miami 100 98 6 4 0 3 14 14 0 0 0 2 2 0 Miami 100 99 6 2 0 2 7 7 0 0 0 0 0 0 Miami 150 98 6 41 9 35 80 80 4 0 3 14 14 1 Miami 150 99 6 23 2 15 53 53 2 0 2 7 7 0 Miami 200 98 6 107 59 111 145 145 23 2 15 53 53 4 Miami 200 99 6 72 35 75 111 111 12 1 7 37 37 2 New York As is 26 0 0 0 3 3 0 0 0 0 0 0 New York Current Std 26 8 0 6 39 39 0 0 0 3 3 0 New York 50 98 26 0 0 0 0 0 0 0 0 0 0 0 New York 50 99 26 0 0 0 0 0 0 0 0 0 0 0 New York 100 98 26 1 0 0 5 5 0 0 0 0 0 0 New York 100 99 26 0 0 0 3 3 0 0 0 0 0 0 New York 150 98 26 16 0 7 42 42 1 0 0 5 5 0 New York 150 99 26 7 0 2 25 25 0 0 0 3 3 0 New York 200 98 26 72 9 68 141 141 7 0 2 25 25 1 
A-143

Number of Daily Maximum Exceedances3

≥ 100 ppb ≥ 150 ppb 

Location Standard1 Percentile2

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99 Mean 

New York 200 99 26 40 3 30 87 87 3 0 2 15 15 0 Philadelphia As is 14 0 0 0 1 1 0 0 0 1 1 0 Philadelphia Current Std 14 29 4 21 75 75 1 0 1 4 4 0 Philadelphia 50 98 14 0 0 0 1 1 0 0 0 1 1 0 Philadelphia 50 99 14 0 0 0 1 1 0 0 0 1 1 0 Philadelphia 100 98 14 3 0 2 15 15 0 0 0 1 1 0 Philadelphia 100 99 14 1 0 1 5 5 0 0 0 1 1 0 Philadelphia 150 98 14 53 18 41 140 140 3 0 2 15 15 1 Philadelphia 150 99 14 30 7 19 91 91 1 0 1 5 5 0 Philadelphia 200 98 14 171 99 176 265 265 27 6 16 83 83 3 Philadelphia 200 99 14 121 52 120 215 215 16 1 9 58 58 1 Phoenix As is 5 0 0 0 0 0 0 0 0 0 0 0 Phoenix Current Std 5 32 2 29 65 65 0 0 0 0 0 0 Phoenix 50 98 5 0 0 0 0 0 0 0 0 0 0 0 Phoenix 50 99 5 0 0 0 0 0 0 0 0 0 0 0 Phoenix 100 98 5 1 0 1 2 2 0 0 0 0 0 0 Phoenix 100 99 5 0 0 0 1 1 0 0 0 0 0 0 Phoenix 150 98 5 89 14 111 146 146 1 0 1 2 2 0 Phoenix 150 99 5 45 7 56 88 88 0 0 0 1 1 0 Phoenix 200 98 5 213 115 227 272 272 41 4 52 79 79 1 Phoenix 200 99 5 171 71 190 235 235 17 2 22 31 31 0 Provo As is 3 0 0 0 0 0 0 0 0 0 0 0 Provo Current Std 3 162 148 160 177 177 4 3 4 6 6 0 Provo 50 98 3 0 0 0 0 0 0 0 0 0 0 0 Provo 50 99 3 0 0 0 0 0 0 0 0 0 0 0 Provo 100 98 3 6 4 6 9 9 0 0 0 0 0 0 Provo 100 99 3 4 3 4 5 5 0 0 0 0 0 0 Provo 150 98 3 200 156 221 223 223 6 4 6 9 9 0 Provo 150 99 3 144 107 160 164 164 4 3 4 5 5 0 Provo 200 98 3 327 295 341 345 345 112 81 122 133 133 6 Provo 200 99 3 316 281 332 335 335 65 42 63 91 91 4 
A-144

Number of Daily Maximum Exceedances3

≥ 100 ppb ≥ 150 ppb 

Location Standard1 Percentile2

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99 Mean 

St. Louis As is 9 0 0 0 1 1 0 0 0 0 0 0 St. Louis Current Std 9 65 22 44 128 128 5 0 1 18 18 0 St. Louis 50 98 9 0 0 0 0 0 0 0 0 0 0 0 St. Louis 50 99 9 0 0 0 0 0 0 0 0 0 0 0 St. Louis 100 98 9 3 0 1 15 15 0 0 0 1 1 0 St. Louis 100 99 9 1 0 0 5 5 0 0 0 1 1 0 St. Louis 150 98 9 60 20 51 118 118 3 0 1 15 15 0 St. Louis 150 99 9 37 7 24 81 81 1 0 0 5 5 0 St. Louis 200 98 9 175 109 171 245 245 31 5 18 70 70 3 St. Louis 200 99 9 141 76 133 215 215 15 1 7 38 38 1 Washington DC As is 18 0 0 0 1 1 0 0 0 0 0 0 Washington DC Current Std 18 61 0 66 146 146 4 0 3 16 16 0 Washington DC 50 98 18 0 0 0 0 0 0 0 0 0 0 0 Washington DC 50 99 18 0 0 0 0 0 0 0 0 0 0 0 Washington DC 100 98 18 4 0 3 11 11 0 0 0 0 0 0 Washington DC 100 99 18 1 0 1 5 5 0 0 0 0 0 0 Washington DC 150 98 18 58 0 67 131 131 4 0 3 11 11 0 Washington DC 150 99 18 34 0 34 87 87 1 0 1 5 5 0 Washington DC 200 98 18 153 10 196 251 251 30 0 28 78 78 4 Washington DC 200 99 18 117 4 150 214 214 15 0 16 43 43 1 Other MSA As is 612 0 0 0 1 4 0 0 0 0 0 0 Other MSA Current Std 612 16 0 8 78 94 1 0 0 9 12 0 Other MSA 50 98 612 0 0 0 0 0 0 0 0 0 0 0 Other MSA 50 99 612 0 0 0 0 0 0 0 0 0 0 0 Other MSA 100 98 612 0 0 0 1 4 0 0 0 0 0 0 Other MSA 100 99 612 0 0 0 1 1 0 0 0 0 0 0 Other MSA 150 98 612 3 0 0 25 30 0 0 0 1 4 0 Other MSA 150 99 612 1 0 0 9 14 0 0 0 1 1 0 Other MSA 200 98 612 18 0 8 81 102 1 0 0 10 16 0 Other MSA 200 99 612 8 0 2 50 56 0 0 0 4 6 0 Other Not MSA As is 127 0 0 0 5 5 0 0 0 1 1 0 
A-145

Number of Daily Maximum Exceedances3

≥ 100 ppb ≥ 150 ppb 

Location Standard1 Percentile2

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99 Mean 

Other Not MSA Current Std 127 37 0 12 170 192 6 0 0 74 80 1 Other Not MSA 50 98 127 0 0 0 1 1 0 0 0 0 0 0 Other Not MSA 50 99 127 0 0 0 0 0 0 0 0 0 0 0 Other Not MSA 100 98 127 0 0 0 6 6 0 0 0 1 1 0 Other Not MSA 100 99 127 0 0 0 3 5 0 0 0 1 1 0 Other Not MSA 150 98 127 1 0 0 23 32 0 0 0 6 6 0 Other Not MSA 150 99 127 1 0 0 14 16 0 0 0 3 5 0 Other Not MSA 200 98 127 6 0 0 78 79 1 0 0 16 18 0 Other Not MSA 200 99 127 3 0 0 49 52 0 0 0 10 11 0 Notes: 1 Scenario: As is – unadjusted air quality, Current Std – air quality that just meets the current annual standard, All others – 

meets 1-hour concentration level given percentile form of alternative standard. 2 Percentile: 98th or 99th percentile of daily maximum 1-hour concentration averaged over three years at maximum monitor 

3 The mean number of exceedances represents the sum of daily maximum exceedances occurring at all monitors in a part

by the number of site-years across the monitoring period. The min, med, p98, and p99 represent the minimum, median, 98

of the distribution for the number of daily maximum exceedances in any one year within the monitoring period. 
A-146

Table A-116. Estimated number of exceedances of 1-hour concentration levels (250 and 300 ppb) for 

monitors ≥100 m from a major road using 2001-2003 air quality as is and air quality adjusted to just meet the 

current and alternative standards. 

Number of Daily Maximum Exceedances3

≥ 250 ppb ≥ 300 ppb 

Location Standard1 Percentile2 Site-Years Mean Min Med p98 p99 Mean Min Med p98 p99

Atlanta As is 14 0 0 0 0 0 0 0 0 0 0

Atlanta Current Std 14 1 0 0 4 4 0 0 0 1 1

Atlanta 50 98 14 0 0 0 0 0 0 0 0 0 0

Atlanta 50 99 14 0 0 0 0 0 0 0 0 0 0

Atlanta 100 98 14 0 0 0 0 0 0 0 0 0 0

Atlanta 100 99 14 0 0 0 0 0 0 0 0 0 0

Atlanta 150 98 14 0 0 0 1 1 0 0 0 1 1

Atlanta 150 99 14 0 0 0 1 1 0 0 0 0 0

Atlanta 200 98 14 0 0 0 2 2 0 0 0 1 1

Atlanta 200 99 14 0 0 0 1 1 0 0 0 1 1

Boston As is 6 0 0 0 0 0 0 0 0 0 0

Boston Current Std 6 0 0 0 0 0 0 0 0 0 0

Boston 50 98 6 0 0 0 0 0 0 0 0 0 0

Boston 50 99 6 0 0 0 0 0 0 0 0 0 0

Boston 100 98 6 0 0 0 0 0 0 0 0 0 0

Boston 100 99 6 0 0 0 0 0 0 0 0 0 0

Boston 150 98 6 0 0 0 0 0 0 0 0 0 0

Boston 150 99 6 0 0 0 0 0 0 0 0 0 0

Boston 200 98 6 0 0 0 0 0 0 0 0 0 0

Boston 200 99 6 0 0 0 0 0 0 0 0 0 0

Chicago As is 9 0 0 0 0 0 0 0 0 0 0

Chicago Current Std 9 0 0 0 0 0 0 0 0 0 0

Chicago 50 98 9 0 0 0 0 0 0 0 0 0 0

Chicago 50 99 9 0 0 0 0 0 0 0 0 0 0

Chicago 100 98 9 0 0 0 0 0 0 0 0 0 0

Chicago 100 99 9 0 0 0 0 0 0 0 0 0 0

Chicago 150 98 9 0 0 0 0 0 0 0 0 0 0

Chicago 150 99 9 0 0 0 0 0 0 0 0 0 0

Chicago 200 98 9 0 0 0 1 1 0 0 0 1 1

Chicago 200 99 9 0 0 0 1 1 0 0 0 0 0

Cleveland As is 3 0 0 0 0 0 0 0 0 0 0

Cleveland Current Std 3 0 0 0 0 0 0 0 0 0 0

Cleveland 50 98 3 0 0 0 0 0 0 0 0 0 0

Cleveland 50 99 3 0 0 0 0 0 0 0 0 0 0

Cleveland 100 98 3 0 0 0 0 0 0 0 0 0 0

Cleveland 100 99 3 0 0 0 0 0 0 0 0 0 0

Cleveland 150 98 3 0 0 0 0 0 0 0 0 0 0

Cleveland 150 99 3 0 0 0 0 0 0 0 0 0 0

Cleveland 200 98 3 1 0 1 1 1 0 0 0 0 0

Cleveland 200 99 3 0 0 0 1 1 0 0 0 0 0

Denver As is 2 0 0 0 0 0 0 0 0 0 0

Denver Current Std 2 1 0 1 2 2 1 0 1 1 1
A-147

Number of Daily Maximum Exceedances3

≥ 250 ppb ≥ 300 ppb 

Location Standard1 Percentile2 Site-Years Mean Min Med p98 p99 Mean Min Med p98 p99

Denver 50 98 2 0 0 0 0 0 0 0 0 0 0

Denver 50 99 2 0 0 0 0 0 0 0 0 0 0

Denver 100 98 2 0 0 0 0 0 0 0 0 0 0

Denver 100 99 2 0 0 0 0 0 0 0 0 0 0

Denver 150 98 2 0 0 0 0 0 0 0 0 0 0

Denver 150 99 2 0 0 0 0 0 0 0 0 0 0

Denver 200 98 2 1 0 1 1 1 0 0 0 0 0

Denver 200 99 2 0 0 0 0 0 0 0 0 0 0

Detroit As is 6 1 0 0 4 4 0 0 0 2 2

Detroit Current Std 6 2 0 1 7 7 2 0 1 7 7

Detroit 50 98 6 0 0 0 0 0 0 0 0 0 0

Detroit 50 99 6 0 0 0 0 0 0 0 0 0 0

Detroit 100 98 6 1 0 0 4 4 1 0 0 3 3

Detroit 100 99 6 0 0 0 2 2 0 0 0 1 1

Detroit 150 98 6 1 0 1 5 5 1 0 1 4 4

Detroit 150 99 6 1 0 0 4 4 1 0 0 3 3

Detroit 200 98 6 2 0 1 7 7 2 0 1 7 7

Detroit 200 99 6 1 0 1 5 5 1 0 0 4 4

El Paso As is 12 0 0 0 0 0 0 0 0 0 0

El Paso Current Std 12 0 0 0 3 3 0 0 0 1 1

El Paso 50 98 12 0 0 0 0 0 0 0 0 0 0

El Paso 50 99 12 0 0 0 0 0 0 0 0 0 0

El Paso 100 98 12 0 0 0 0 0 0 0 0 0 0

El Paso 100 99 12 0 0 0 0 0 0 0 0 0 0

El Paso 150 98 12 0 0 0 1 1 0 0 0 1 1

El Paso 150 99 12 0 0 0 1 1 0 0 0 0 0

El Paso 200 98 12 1 0 0 3 3 0 0 0 1 1

El Paso 200 99 12 0 0 0 1 1 0 0 0 1 1

Jacksonville As is 2 1 0 1 1 1 0 0 0 0 0

Jacksonville Current Std 2 3 2 3 3 3 1 0 1 1 1

Jacksonville 50 98 2 1 0 1 1 1 0 0 0 0 0

Jacksonville 50 99 2 0 0 0 0 0 0 0 0 0 0

Jacksonville 100 98 2 1 0 1 1 1 1 0 1 1 1

Jacksonville 100 99 2 1 0 1 1 1 1 0 1 1 1

Jacksonville 150 98 2 1 0 1 1 1 1 0 1 1 1

Jacksonville 150 99 2 1 0 1 1 1 1 0 1 1 1

Jacksonville 200 98 2 2 1 2 2 2 1 0 1 1 1

Jacksonville 200 99 2 1 1 1 1 1 1 0 1 1 1

Las Vegas As is 16 0 0 0 0 0 0 0 0 0 0

Las Vegas Current Std 16 0 0 0 0 0 0 0 0 0 0

Las Vegas 50 98 16 0 0 0 0 0 0 0 0 0 0

Las Vegas 50 99 16 0 0 0 0 0 0 0 0 0 0

Las Vegas 100 98 16 0 0 0 0 0 0 0 0 0 0

Las Vegas 100 99 16 0 0 0 0 0 0 0 0 0 0

Las Vegas 150 98 16 0 0 0 0 0 0 0 0 0 0
A-148

Number of Daily Maximum Exceedances3

≥ 250 ppb ≥ 300 ppb 

Location Standard1 Percentile2 Site-Years Mean Min Med p98 p99 Mean Min Med p98 p99

Las Vegas 150 99 16 0 0 0 0 0 0 0 0 0 0

Las Vegas 200 98 16 0 0 0 1 1 0 0 0 0 0

Las Vegas 200 99 16 0 0 0 1 1 0 0 0 0 0

Los Angeles As is 51 0 0 0 0 0 0 0 0 0 0

Los Angeles Current Std 51 0 0 0 0 4 0 0 0 0 1

Los Angeles 50 98 51 0 0 0 0 0 0 0 0 0 0

Los Angeles 50 99 51 0 0 0 0 0 0 0 0 0 0

Los Angeles 100 98 51 0 0 0 0 0 0 0 0 0 0

Los Angeles 100 99 51 0 0 0 0 0 0 0 0 0 0

Los Angeles 150 98 51 0 0 0 0 3 0 0 0 0 0

Los Angeles 150 99 51 0 0 0 0 2 0 0 0 0 0

Los Angeles 200 98 51 0 0 0 0 7 0 0 0 0 5

Los Angeles 200 99 51 0 0 0 0 6 0 0 0 0 4

Miami As is 6 0 0 0 0 0 0 0 0 0 0

Miami Current Std 6 1 0 1 3 3 0 0 0 0 0

Miami 50 98 6 0 0 0 0 0 0 0 0 0 0

Miami 50 99 6 0 0 0 0 0 0 0 0 0 0

Miami 100 98 6 0 0 0 0 0 0 0 0 0 0

Miami 100 99 6 0 0 0 0 0 0 0 0 0 0

Miami 150 98 6 0 0 0 0 0 0 0 0 0 0

Miami 150 99 6 0 0 0 0 0 0 0 0 0 0

Miami 200 98 6 2 0 1 6 6 0 0 0 2 2

Miami 200 99 6 1 0 0 3 3 0 0 0 0 0

New York As is 26 0 0 0 0 0 0 0 0 0 0

New York Current Std 26 0 0 0 0 0 0 0 0 0 0

New York 50 98 26 0 0 0 0 0 0 0 0 0 0

New York 50 99 26 0 0 0 0 0 0 0 0 0 0

New York 100 98 26 0 0 0 0 0 0 0 0 0 0

New York 100 99 26 0 0 0 0 0 0 0 0 0 0

New York 150 98 26 0 0 0 0 0 0 0 0 0 0

New York 150 99 26 0 0 0 0 0 0 0 0 0 0

New York 200 98 26 0 0 0 1 1 0 0 0 0 0

New York 200 99 26 0 0 0 1 1 0 0 0 0 0

Philadelphia As is 14 0 0 0 1 1 0 0 0 0 0

Philadelphia Current Std 14 0 0 0 1 1 0 0 0 1 1

Philadelphia 50 98 14 0 0 0 0 0 0 0 0 0 0

Philadelphia 50 99 14 0 0 0 0 0 0 0 0 0 0

Philadelphia 100 98 14 0 0 0 1 1 0 0 0 1 1

Philadelphia 100 99 14 0 0 0 1 1 0 0 0 1 1

Philadelphia 150 98 14 0 0 0 1 1 0 0 0 1 1

Philadelphia 150 99 14 0 0 0 1 1 0 0 0 1 1

Philadelphia 200 98 14 1 0 1 1 1 0 0 0 1 1

Philadelphia 200 99 14 0 0 0 1 1 0 0 0 1 1

Phoenix As is 5 0 0 0 0 0 0 0 0 0 0

Phoenix Current Std 5 0 0 0 0 0 0 0 0 0 0
A-149

Number of Daily Maximum Exceedances3

≥ 250 ppb ≥ 300 ppb 

Location Standard1 Percentile2 Site-Years Mean Min Med p98 p99 Mean Min Med p98 p99

Phoenix 50 98 5 0 0 0 0 0 0 0 0 0 0

Phoenix 50 99 5 0 0 0 0 0 0 0 0 0 0

Phoenix 100 98 5 0 0 0 0 0 0 0 0 0 0

Phoenix 100 99 5 0 0 0 0 0 0 0 0 0 0

Phoenix 150 98 5 0 0 0 0 0 0 0 0 0 0

Phoenix 150 99 5 0 0 0 0 0 0 0 0 0 0

Phoenix 200 98 5 0 0 0 0 0 0 0 0 0 0

Phoenix 200 99 5 0 0 0 0 0 0 0 0 0 0

Provo As is 3 0 0 0 0 0 0 0 0 0 0

Provo Current Std 3 0 0 0 0 0 0 0 0 0 0

Provo 50 98 3 0 0 0 0 0 0 0 0 0 0

Provo 50 99 3 0 0 0 0 0 0 0 0 0 0

Provo 100 98 3 0 0 0 0 0 0 0 0 0 0

Provo 100 99 3 0 0 0 0 0 0 0 0 0 0

Provo 150 98 3 0 0 0 0 0 0 0 0 0 0

Provo 150 99 3 0 0 0 0 0 0 0 0 0 0

Provo 200 98 3 0 0 0 1 1 0 0 0 0 0

Provo 200 99 3 0 0 0 0 0 0 0 0 0 0

St. Louis As is 9 0 0 0 0 0 0 0 0 0 0

St. Louis Current Std 9 0 0 0 1 1 0 0 0 0 0

St. Louis 50 98 9 0 0 0 0 0 0 0 0 0 0

St. Louis 50 99 9 0 0 0 0 0 0 0 0 0 0

St. Louis 100 98 9 0 0 0 0 0 0 0 0 0 0

St. Louis 100 99 9 0 0 0 0 0 0 0 0 0 0

St. Louis 150 98 9 0 0 0 1 1 0 0 0 0 0

St. Louis 150 99 9 0 0 0 1 1 0 0 0 0 0

St. Louis 200 98 9 0 0 0 2 2 0 0 0 1 1

St. Louis 200 99 9 0 0 0 1 1 0 0 0 1 1

Washington 

DC 

As is 18 0 0 0 0 0 0 0 0 0 0

Washington 

DC 

Current Std 18 0 0 0 0 0 0 0 0 0 0

Washington 

DC 

50 98 18 0 0 0 0 0 0 0 0 0 0

Washington 

DC 

50 99 18 0 0 0 0 0 0 0 0 0 0

Washington 

DC 

100 98 18 0 0 0 0 0 0 0 0 0 0

Washington 

DC 

100 99 18 0 0 0 0 0 0 0 0 0 0

Washington 

DC 

150 98 18 0 0 0 0 0 0 0 0 0 0

Washington 

DC 

150 99 18 0 0 0 0 0 0 0 0 0 0

Washington 

DC 

200 98 18 0 0 0 2 2 0 0 0 0 0
A-150

Number of Daily Maximum Exceedances3

≥ 250 ppb ≥ 300 ppb 

Location Standard1 Percentile2 Site-Years Mean Min Med p98 p99 Mean Min Med p98 p99

Washington 

DC 

200 99 18 0 0 0 1 1 0 0 0 0 0

Other MSA As is 612 0 0 0 0 0 0 0 0 0 0

Other MSA Current Std 612 0 0 0 1 1 0 0 0 0 0

Other MSA 50 98 612 0 0 0 0 0 0 0 0 0 0

Other MSA 50 99 612 0 0 0 0 0 0 0 0 0 0

Other MSA 100 98 612 0 0 0 0 0 0 0 0 0 0

Other MSA 100 99 612 0 0 0 0 0 0 0 0 0 0

Other MSA 150 98 612 0 0 0 0 0 0 0 0 0 0

Other MSA 150 99 612 0 0 0 0 0 0 0 0 0 0

Other MSA 200 98 612 0 0 0 1 1 0 0 0 0 0

Other MSA 200 99 612 0 0 0 0 1 0 0 0 0 0

Other Not 

MSA 

As is 127 0 0 0 0 0 0 0 0 0 0

Other Not 

MSA 

Current Std 127 1 0 0 12 12 0 0 0 6 6

Other Not 

MSA 

50 98 127 0 0 0 0 0 0 0 0 0 0

Other Not 

MSA 

50 99 127 0 0 0 0 0 0 0 0 0 0

Other Not 

MSA 

100 98 127 0 0 0 0 0 0 0 0 0 0

Other Not 

MSA 

100 99 127 0 0 0 0 0 0 0 0 0 0

Other Not 

MSA 

150 98 127 0 0 0 1 1 0 0 0 1 1

Other Not 

MSA 

150 99 127 0 0 0 1 1 0 0 0 0 0

Other Not 

MSA 

200 98 127 0 0 0 3 3 0 0 0 1 1

Other Not 

MSA 

200 99 127 0 0 0 1 1 0 0 0 1 1

Notes: 

1

 Scenario: As is – unadjusted air quality, Current Std – air quality that just meets the current annual standard, All 

others – air quality that just meets 1-hour concentration level given percentile form of alternative standard. 

2

 Percentile: 98th or 99th percentile of daily maximum 1-hour concentration averaged over three years at 

maximum monitor in location. 

3

 The mean number of exceedances represents the sum of daily maximum exceedances occurring at all monitors 

in a particular location divided by the number of site-years across the monitoring period. The min, med, p98, and 

p99 represent the minimum, median, 98th, and 99th percentiles of the distribution for the number of daily 

maximum exceedances in any one year within the monitoring period. 
A-151

1 Table A-117. Estimated number of exceedances of 1-hour concentration levels (100, 150 and 200 ppb) for monitors >20 m and <100 m f2 2003 air quality as is and air quality adjusted to just meet the current and alternative standards. 

Number of Daily Maximum Exceedance

≥ 100 ppb ≥ 150 ppb 

Location Standard1 Percentile2

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99 M

Boston As is 14 0 0 0 0 0 0 0 0 0 0 Boston Current Std 14 38 0 24 145 145 3 0 1 20 20 Boston 50 98 14 0 0 0 0 0 0 0 0 0 0 Boston 50 99 14 0 0 0 0 0 0 0 0 0 0 Boston 100 98 14 3 0 2 11 11 0 0 0 0 0 Boston 100 99 14 1 0 0 6 6 0 0 0 0 0 Boston 150 98 14 35 2 27 89 89 3 0 2 11 11 Boston 150 99 14 20 0 14 64 64 1 0 0 6 6 Boston 200 98 14 116 21 120 226 226 19 0 13 59 59 Boston 200 99 14 82 11 79 182 182 9 0 6 28 28 Chicago As is 6 2 0 1 6 6 0 0 0 0 0 Chicago Current Std 6 74 34 74 111 111 5 1 4 9 9 Chicago 50 98 6 0 0 0 0 0 0 0 0 0 0 Chicago 50 99 6 0 0 0 0 0 0 0 0 0 0 Chicago 100 98 6 7 1 4 21 21 1 0 0 3 3 Chicago 100 99 6 2 0 1 7 7 0 0 0 2 2 Chicago 150 98 6 90 48 86 144 144 7 1 4 21 21 Chicago 150 99 6 50 20 48 95 95 2 0 1 7 7 Chicago 200 98 6 212 147 216 280 280 53 24 51 99 99 Chicago 200 99 6 164 108 163 242 242 24 6 21 51 51 El Paso As is 3 2 0 2 3 3 0 0 0 0 0 El Paso Current Std 3 170 128 187 196 196 32 17 35 44 44 El Paso 50 98 3 0 0 0 0 0 0 0 0 0 0 El Paso 50 99 3 0 0 0 0 0 0 0 0 0 0 El Paso 100 98 3 6 2 7 9 9 0 0 0 0 0 El Paso 100 99 3 4 1 5 5 5 0 0 0 0 0 El Paso 150 98 3 77 54 71 105 105 6 2 7 9 9 El Paso 150 99 3 38 23 34 57 57 4 1 5 5 5 El Paso 200 98 3 178 141 174 218 218 40 25 34 62 62 
A-152

Number of Daily Maximum Exceedance

≥ 100 ppb ≥ 150 ppb 

Location Standard1 Percentile2

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99 M

El Paso 200 99 3 137 104 137 169 169 16 12 16 19 19 Las Vegas As is 3 0 0 0 0 0 0 0 0 0 0 Las Vegas Current Std 3 24 0 4 67 67 2 0 0 5 5 Las Vegas 50 98 3 0 0 0 0 0 0 0 0 0 0 Las Vegas 50 99 3 0 0 0 0 0 0 0 0 0 0 Las Vegas 100 98 3 1 0 0 4 4 0 0 0 0 0 Las Vegas 100 99 3 1 0 0 2 2 0 0 0 0 0 Las Vegas 150 98 3 11 0 0 34 34 1 0 0 4 4 Las Vegas 150 99 3 5 0 0 16 16 1 0 0 2 2 Las Vegas 200 98 3 46 1 25 113 113 5 0 0 14 14 Las Vegas 200 99 3 32 0 10 86 86 3 0 0 9 9 Los Angeles As is 35 6 0 1 31 31 0 0 0 2 2 Los Angeles Current Std 35 31 0 25 82 82 3 0 0 21 21 Los Angeles 50 98 35 0 0 0 1 1 0 0 0 0 0 Los Angeles 50 99 35 0 0 0 0 0 0 0 0 0 0 Los Angeles 100 98 35 1 0 0 7 7 0 0 0 1 1 Los Angeles 100 99 35 1 0 0 4 4 0 0 0 1 1 Los Angeles 150 98 35 15 0 2 59 59 1 0 0 7 7 Los Angeles 150 99 35 13 0 2 49 49 1 0 0 4 4 Los Angeles 200 98 35 53 0 63 131 131 8 0 1 36 36 Los Angeles 200 99 35 48 0 47 124 124 7 0 1 33 33 Miami As is 3 0 0 0 0 0 0 0 0 0 0 Miami Current Std 3 152 137 154 166 166 42 38 43 44 44 Miami 50 98 3 0 0 0 0 0 0 0 0 0 0 Miami 50 99 3 0 0 0 0 0 0 0 0 0 0 Miami 100 98 3 8 4 6 15 15 0 0 0 1 1 Miami 100 99 3 2 0 1 6 6 0 0 0 0 0 Miami 150 98 3 73 48 75 97 97 8 4 6 15 15 Miami 150 99 3 42 27 38 62 62 2 0 1 6 6 Miami 200 98 3 155 113 166 187 187 42 27 38 62 62 Miami 200 99 3 114 80 122 140 140 25 19 20 35 35 
A-153

Number of Daily Maximum Exceedance

≥ 100 ppb ≥ 150 ppb 

Location Standard1 Percentile2

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99 M

New York As is 13 1 0 0 8 8 0 0 0 0 0 New York Current Std 13 26 1 16 48 48 1 0 1 4 4 New York 50 98 13 0 0 0 0 0 0 0 0 0 0 New York 50 99 13 0 0 0 0 0 0 0 0 0 0 New York 100 98 13 3 0 1 13 13 0 0 0 2 2 New York 100 99 13 1 0 0 5 5 0 0 0 0 0 New York 150 98 13 50 10 42 121 121 3 0 1 13 13 New York 150 99 13 25 2 20 67 67 1 0 0 5 5 New York 200 98 13 154 66 148 258 258 25 2 20 67 67 New York 200 99 13 102 23 95 197 197 12 1 8 33 33 Philadelphia As is 7 0 0 0 1 1 0 0 0 0 0 Philadelphia Current Std 7 44 10 48 73 73 2 0 2 5 5 Philadelphia 50 98 7 0 0 0 0 0 0 0 0 0 0 Philadelphia 50 99 7 0 0 0 0 0 0 0 0 0 0 Philadelphia 100 98 7 5 0 4 11 11 0 0 0 0 0 Philadelphia 100 99 7 2 0 1 4 4 0 0 0 0 0 Philadelphia 150 98 7 84 36 80 136 136 5 0 4 11 11 Philadelphia 150 99 7 47 18 32 89 89 2 0 1 4 4 Philadelphia 200 98 7 216 155 219 275 275 42 16 30 82 82 Philadelphia 200 99 7 168 107 172 231 231 22 6 15 51 51 Phoenix As is 2 0 0 0 0 0 0 0 0 0 0 Phoenix Current Std 2 4 3 4 5 5 0 0 0 0 0 Phoenix 50 98 2 0 0 0 0 0 0 0 0 0 0 Phoenix 50 99 2 0 0 0 0 0 0 0 0 0 0 Phoenix 100 98 2 0 0 0 0 0 0 0 0 0 0 Phoenix 100 99 2 0 0 0 0 0 0 0 0 0 0 Phoenix 150 98 2 49 39 49 59 59 0 0 0 0 0 Phoenix 150 99 2 15 13 15 17 17 0 0 0 0 0 Phoenix 200 98 2 210 190 210 229 229 11 6 11 15 15 Phoenix 200 99 2 162 140 162 184 184 2 0 2 3 3 St. Louis As is 11 0 0 0 0 0 0 0 0 0 0 
A-154

Number of Daily Maximum Exceedance

≥ 100 ppb ≥ 150 ppb 

Location Standard1 Percentile2

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99 M

St. Louis Current Std 11 41 0 26 120 120 3 0 0 13 13 St. Louis 50 98 11 0 0 0 0 0 0 0 0 0 0 St. Louis 50 99 11 0 0 0 0 0 0 0 0 0 0 St. Louis 100 98 11 2 0 0 11 11 0 0 0 0 0 St. Louis 100 99 11 1 0 0 5 5 0 0 0 0 0 St. Louis 150 98 11 37 0 24 120 120 2 0 0 11 11 St. Louis 150 99 11 24 0 11 82 82 1 0 0 5 5 St. Louis 200 98 11 119 28 90 265 265 21 0 10 74 74 St. Louis 200 99 11 90 11 63 235 235 11 0 4 42 42 Washington DC As is 10 0 0 0 0 0 0 0 0 0 0 Washington DC Current Std 10 57 5 67 103 103 2 0 2 6 6 Washington DC 50 98 10 0 0 0 0 0 0 0 0 0 0 Washington DC 50 99 10 0 0 0 0 0 0 0 0 0 0 Washington DC 100 98 10 2 0 2 5 5 0 0 0 0 0 Washington DC 100 99 10 1 0 0 2 2 0 0 0 0 0 Washington DC 150 98 10 57 4 64 111 111 2 0 2 5 5 Washington DC 150 99 10 31 0 34 69 69 1 0 0 2 2 Washington DC 200 98 10 168 56 201 271 271 28 0 30 65 65 Washington DC 200 99 10 124 29 149 221 221 12 0 12 34 34 Notes: 1 Scenario: As is – unadjusted air quality, Current Std – air quality that just meets the current annual standard, All others – air qua

concentration level given percentile form of alternative standard. 2 Percentile: 98th or 99th percentile of daily maximum 1-hour concentration averaged over three years at maximum monitor in locat

3 The mean number of exceedances represents the sum of daily maximum exceedances occurring at all monitors in a particular lo

of site-years across the monitoring period. The min, med, p98, and p99 represent the minimum, median, 98th, and 99th percentiles 

number of daily maximum exceedances in any one year within the monitoring period. 
A-155

Table A-118. Estimated number of exceedances of 1-hour concentration levels (250 and 300 ppb) for 

monitors >20 m and <100 m from a major road using 2001-2003 air quality as is and air quality adjusted to 

just meet the current and alternative standards. 

Number of Daily Maximum Exceedances3

≥ 250 ppb ≥ 300 ppb 

 

Location

 

 

Standar

d1

 

 

Percentile

2

 

 

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99 

Boston As is 14 0 0 0 0 0 0 0 0 0 0

Boston 

Current 

Std 14 0 0 0 0 0 0 0 0 0 0

Boston 50 98 14 0 0 0 0 0 0 0 0 0 0

Boston 50 99 14 0 0 0 0 0 0 0 0 0 0

Boston 100 98 14 0 0 0 0 0 0 0 0 0 0

Boston 100 99 14 0 0 0 0 0 0 0 0 0 0

Boston 150 98 14 0 0 0 0 0 0 0 0 0 0

Boston 150 99 14 0 0 0 0 0 0 0 0 0 0

Boston 200 98 14 0 0 0 2 2 0 0 0 0 0

Boston 200 99 14 0 0 0 0 0 0 0 0 0 0

Chicago As is 6 0 0 0 0 0 0 0 0 0 0

Chicago 

Current 

Std 6 0 0 0 0 0 0 0 0 0 0

Chicago 50 98 6 0 0 0 0 0 0 0 0 0 0

Chicago 50 99 6 0 0 0 0 0 0 0 0 0 0

Chicago 100 98 6 0 0 0 0 0 0 0 0 0 0

Chicago 100 99 6 0 0 0 0 0 0 0 0 0 0

Chicago 150 98 6 0 0 0 2 2 0 0 0 0 0

Chicago 150 99 6 0 0 0 0 0 0 0 0 0 0

Chicago 200 98 6 1 0 0 5 5 1 0 0 3 3

Chicago 200 99 6 1 0 0 5 5 0 0 0 2 2

El Paso As is 3 0 0 0 0 0 0 0 0 0 0

El Paso 

Current 

Std 3 2 0 2 5 5 0 0 0 0 0

El Paso 50 98 3 0 0 0 0 0 0 0 0 0 0

El Paso 50 99 3 0 0 0 0 0 0 0 0 0 0

El Paso 100 98 3 0 0 0 0 0 0 0 0 0 0

El Paso 100 99 3 0 0 0 0 0 0 0 0 0 0

El Paso 150 98 3 0 0 0 0 0 0 0 0 0 0

El Paso 150 99 3 0 0 0 0 0 0 0 0 0 0

El Paso 200 98 3 3 0 3 5 5 0 0 0 0 0

El Paso 200 99 3 0 0 0 0 0 0 0 0 0 0

Las Vegas As is 3 0 0 0 0 0 0 0 0 0 0

Las Vegas 

Current 

Std 3 0 0 0 0 0 0 0 0 0 0

Las Vegas 50 98 3 0 0 0 0 0 0 0 0 0 0

Las Vegas 50 99 3 0 0 0 0 0 0 0 0 0 0

Las Vegas 100 98 3 0 0 0 0 0 0 0 0 0 0

Las Vegas 100 99 3 0 0 0 0 0 0 0 0 0 0

Las Vegas 150 98 3 0 0 0 0 0 0 0 0 0 0

Las Vegas 150 99 3 0 0 0 0 0 0 0 0 0 0

Las Vegas 200 98 3 0 0 0 0 0 0 0 0 0 0

Las Vegas 200 99 3 0 0 0 0 0 0 0 0 0 0

Los Angeles As is 35 0 0 0 1 1 0 0 0 0 0

Los Angeles Current 35 0 0 0 1 1 0 0 0 1 1
A-156

Number of Daily Maximum Exceedances3

≥ 250 ppb ≥ 300 ppb 

 

Location

 

 

Standar

d1

 

 

Percentile

2

 

 

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99 

Std 

Los Angeles 50 98 35 0 0 0 0 0 0 0 0 0 0

Los Angeles 50 99 35 0 0 0 0 0 0 0 0 0 0

Los Angeles 100 98 35 0 0 0 0 0 0 0 0 0 0

Los Angeles 100 99 35 0 0 0 0 0 0 0 0 0 0

Los Angeles 150 98 35 0 0 0 1 1 0 0 0 1 1

Los Angeles 150 99 35 0 0 0 1 1 0 0 0 0 0

Los Angeles 200 98 35 0 0 0 2 2 0 0 0 1 1

Los Angeles 200 99 35 0 0 0 2 2 0 0 0 1 1

Miami As is 3 0 0 0 0 0 0 0 0 0 0

Miami 

Current 

Std 3 1 0 1 1 1 0 0 0 1 1

Miami 50 98 3 0 0 0 0 0 0 0 0 0 0

Miami 50 99 3 0 0 0 0 0 0 0 0 0 0

Miami 100 98 3 0 0 0 0 0 0 0 0 0 0

Miami 100 99 3 0 0 0 0 0 0 0 0 0 0

Miami 150 98 3 0 0 0 0 0 0 0 0 0 0

Miami 150 99 3 0 0 0 0 0 0 0 0 0 0

Miami 200 98 3 2 0 1 4 4 0 0 0 1 1

Miami 200 99 3 0 0 0 1 1 0 0 0 0 0

New York As is 13 0 0 0 0 0 0 0 0 0 0

New York 

Current 

Std 13 0 0 0 0 0 0 0 0 0 0

New York 50 98 13 0 0 0 0 0 0 0 0 0 0

New York 50 99 13 0 0 0 0 0 0 0 0 0 0

New York 100 98 13 0 0 0 0 0 0 0 0 0 0

New York 100 99 13 0 0 0 0 0 0 0 0 0 0

New York 150 98 13 0 0 0 0 0 0 0 0 0 0

New York 150 99 13 0 0 0 0 0 0 0 0 0 0

New York 200 98 13 1 0 0 4 4 0 0 0 2 2

New York 200 99 13 0 0 0 3 3 0 0 0 0 0

Philadelphia As is 7 0 0 0 0 0 0 0 0 0 0

Philadelphia 

Current 

Std 7 0 0 0 0 0 0 0 0 0 0

Philadelphia 50 98 7 0 0 0 0 0 0 0 0 0 0

Philadelphia 50 99 7 0 0 0 0 0 0 0 0 0 0

Philadelphia 100 98 7 0 0 0 0 0 0 0 0 0 0

Philadelphia 100 99 7 0 0 0 0 0 0 0 0 0 0

Philadelphia 150 98 7 0 0 0 0 0 0 0 0 0 0

Philadelphia 150 99 7 0 0 0 0 0 0 0 0 0 0

Philadelphia 200 98 7 1 0 1 2 2 0 0 0 0 0

Philadelphia 200 99 7 0 0 0 1 1 0 0 0 0 0

Phoenix As is 2 0 0 0 0 0 0 0 0 0 0

Phoenix 

Current 

Std 2 0 0 0 0 0 0 0 0 0 0

Phoenix 50 98 2 0 0 0 0 0 0 0 0 0 0

Phoenix 50 99 2 0 0 0 0 0 0 0 0 0 0

Phoenix 100 98 2 0 0 0 0 0 0 0 0 0 0

Phoenix 100 99 2 0 0 0 0 0 0 0 0 0 0
A-157

Number of Daily Maximum Exceedances3

≥ 250 ppb ≥ 300 ppb 

 

Location

 

 

Standar

d1

 

 

Percentile

2

 

 

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99 

Phoenix 150 98 2 0 0 0 0 0 0 0 0 0 0

Phoenix 150 99 2 0 0 0 0 0 0 0 0 0 0

Phoenix 200 98 2 0 0 0 0 0 0 0 0 0 0

Phoenix 200 99 2 0 0 0 0 0 0 0 0 0 0

St. Louis As is 11 0 0 0 0 0 0 0 0 0 0

St. Louis 

Current 

Std 11 0 0 0 0 0 0 0 0 0 0

St. Louis 50 98 11 0 0 0 0 0 0 0 0 0 0

St. Louis 50 99 11 0 0 0 0 0 0 0 0 0 0

St. Louis 100 98 11 0 0 0 0 0 0 0 0 0 0

St. Louis 100 99 11 0 0 0 0 0 0 0 0 0 0

St. Louis 150 98 11 0 0 0 0 0 0 0 0 0 0

St. Louis 150 99 11 0 0 0 0 0 0 0 0 0 0

St. Louis 200 98 11 0 0 0 1 1 0 0 0 0 0

St. Louis 200 99 11 0 0 0 0 0 0 0 0 0 0

Washington 

DC As is 10 0 0 0 0 0 0 0 0 0 0

Washington 

DC 

Current 

Std 10 0 0 0 0 0 0 0 0 0 0

Washington 

DC 50 98 10 0 0 0 0 0 0 0 0 0 0

Washington 

DC 50 99 10 0 0 0 0 0 0 0 0 0 0

Washington 

DC 100 98 10 0 0 0 0 0 0 0 0 0 0

Washington 

DC 100 99 10 0 0 0 0 0 0 0 0 0 0

Washington 

DC 150 98 10 0 0 0 0 0 0 0 0 0 0

Washington 

DC 150 99 10 0 0 0 0 0 0 0 0 0 0

Washington 

DC 200 98 10 0 0 0 1 1 0 0 0 0 0

Washington 

DC 200 99 10 0 0 0 0 0 0 0 0 0 0

Notes: 

1

 Scenario: As is – unadjusted air quality, Current Std – air quality that just meets the current annual standard, All 

others – air quality that just meets 1-hour concentration level given percentile form of alternative standard. 

2

 Percentile: 98th or 99th percentile of daily maximum 1-hour concentration averaged over three years at 

maximum monitor in location. 

3

 The mean number of exceedances represents the sum of daily maximum exceedances occurring at all 

monitors in a particular location divided by the number of site-years across the monitoring period. The min, med, 

p98, and p99 represent the minimum, median, 98th, and 99th percentiles of the distribution for the number of daily 

maximum exceedances in any one year within the monitoring period. 
A-158

1 

2 Table A-119. Estimated number of exceedances of 1-hour concentration levels (100, 150 and 200 ppb) for monitors ≤ 20 m from a majo3 quality as is and air quality adjusted to just meet the current and alternative standards. 

Number of Daily Maximum Exceedan

≥ 100 ppb ≥ 150 ppb 

Location Standard1 Percentile2 Site-Years Mean Min Med p98 p99 Mean Min Med p98 p99 

Boston As is 5 0 0 0 1 1 0 0 0 0 0 Boston Current Std 5 19 0 26 38 38 1 0 0 3 3 Boston 50 98 5 0 0 0 0 0 0 0 0 0 0 Boston 50 99 5 0 0 0 0 0 0 0 0 0 0 Boston 100 98 5 2 0 0 6 6 0 0 0 1 1 Boston 100 99 5 1 0 0 4 4 0 0 0 0 0 Boston 150 98 5 29 0 22 91 91 2 0 0 6 6 Boston 150 99 5 17 0 16 46 46 1 0 0 4 4 Boston 200 98 5 104 9 60 249 249 15 0 15 39 39 Boston 200 99 5 72 3 46 196 196 8 0 7 23 23 Chicago As is 4 0 0 0 0 0 0 0 0 0 0 Chicago Current Std 4 21 14 23 26 26 1 0 1 4 4 Chicago 50 98 4 0 0 0 0 0 0 0 0 0 0 Chicago 50 99 4 0 0 0 0 0 0 0 0 0 0 Chicago 100 98 4 2 0 1 6 6 0 0 0 0 0 Chicago 100 99 4 0 0 0 1 1 0 0 0 0 0 Chicago 150 98 4 28 19 30 35 35 2 0 1 6 6 Chicago 150 99 4 14 7 14 20 20 0 0 0 1 1 Chicago 200 98 4 119 107 118 132 132 15 9 14 23 23 Chicago 200 99 4 76 72 76 80 80 7 4 6 13 13 Cleveland As is 3 0 0 0 0 0 0 0 0 0 0 Cleveland Current Std 3 111 103 107 122 122 14 13 15 15 15 Cleveland 50 98 3 0 0 0 0 0 0 0 0 0 0 Cleveland 50 99 3 0 0 0 0 0 0 0 0 0 0 Cleveland 100 98 3 7 3 7 10 10 0 0 0 0 0 Cleveland 100 99 3 4 2 4 7 7 0 0 0 0 0 Cleveland 150 98 3 79 68 77 92 92 7 3 7 10 10 Cleveland 150 99 3 57 46 55 69 69 4 2 4 7 7 Cleveland 200 98 3 187 177 179 204 204 50 40 48 62 62 
A-159

Number of Daily Maximum Exceedan

≥ 100 ppb ≥ 150 ppb 

Location Standard1 Percentile2 Site-Years Mean Min Med p98 p99 Mean Min Med p98 p99 

Cleveland 200 99 3 166 161 167 171 171 30 19 28 43 43 Denver As is 2 7 3 7 10 10 1 1 1 1 1 Denver Current Std 2 48 43 48 53 53 5 3 5 7 7 Denver 50 98 2 0 0 0 0 0 0 0 0 0 0 Denver 50 99 2 0 0 0 0 0 0 0 0 0 0 Denver 100 98 2 7 3 7 10 10 2 1 2 3 3 Denver 100 99 2 4 2 4 6 6 0 0 0 0 0 Denver 150 98 2 68 54 68 81 81 7 3 7 10 10 Denver 150 99 2 33 24 33 42 42 4 2 4 6 6 Denver 200 98 2 224 224 224 224 224 33 24 33 42 42 Denver 200 99 2 157 144 157 170 170 18 11 18 24 24 Las Vegas As is 3 1 0 0 2 2 0 0 0 0 0 Las Vegas Current Std 3 211 203 212 218 218 22 15 22 28 28 Las Vegas 50 98 3 0 0 0 0 0 0 0 0 0 0 Las Vegas 50 99 3 0 0 0 0 0 0 0 0 0 0 Las Vegas 100 98 3 7 1 10 11 11 0 0 0 0 0 Las Vegas 100 99 3 2 0 2 4 4 0 0 0 0 0 Las Vegas 150 98 3 141 112 142 168 168 7 1 10 11 11 Las Vegas 150 99 3 85 65 89 102 102 2 0 2 4 4 Las Vegas 200 98 3 280 261 261 319 319 76 59 78 92 92 Las Vegas 200 99 3 248 229 236 278 278 46 36 40 63 63 Los Angeles As is 9 6 0 6 9 9 0 0 0 0 0 Los Angeles Current Std 9 42 25 37 77 77 4 0 2 9 9 Los Angeles 50 98 9 0 0 0 0 0 0 0 0 0 0 Los Angeles 50 99 9 0 0 0 0 0 0 0 0 0 0 Los Angeles 100 98 9 1 0 0 3 3 0 0 0 0 0 Los Angeles 100 99 9 0 0 0 2 2 0 0 0 0 0 Los Angeles 150 98 9 21 7 20 32 32 1 0 0 3 3 Los Angeles 150 99 9 17 5 18 28 28 0 0 0 2 2 Los Angeles 200 98 9 72 46 55 118 118 7 3 9 11 11 Los Angeles 200 99 9 62 35 49 110 110 6 1 6 10 10 Miami As is 3 0 0 0 0 0 0 0 0 0 0 
A-160

Number of Daily Maximum Exceedan

≥ 100 ppb ≥ 150 ppb 

Location Standard1 Percentile2 Site-Years Mean Min Med p98 p99 Mean Min Med p98 p99 

Miami Current Std 3 78 56 79 100 100 17 14 15 23 23 Miami 50 98 3 0 0 0 0 0 0 0 0 0 0 Miami 50 99 3 0 0 0 0 0 0 0 0 0 0 Miami 100 98 3 1 0 1 1 1 0 0 0 0 0 Miami 100 99 3 0 0 0 0 0 0 0 0 0 0 Miami 150 98 3 28 25 30 30 30 1 0 1 1 1 Miami 150 99 3 16 13 15 19 19 0 0 0 0 0 Miami 200 98 3 76 68 79 80 80 16 13 15 19 19 Miami 200 99 3 53 46 52 61 61 8 5 6 13 13 New York As is 7 1 0 1 1 1 0 0 0 0 0 New York Current Std 7 18 6 17 52 52 1 0 0 2 2 New York 50 98 7 0 0 0 0 0 0 0 0 0 0 New York 50 99 7 0 0 0 0 0 0 0 0 0 0 New York 100 98 7 1 0 1 2 2 0 0 0 0 0 New York 100 99 7 0 0 0 1 1 0 0 0 0 0 New York 150 98 7 38 18 42 55 55 1 0 1 2 2 New York 150 99 7 18 4 17 31 31 0 0 0 1 1 New York 200 98 7 143 107 141 168 168 18 4 17 31 31 New York 200 99 7 88 53 96 117 117 7 3 6 12 12 Phoenix As is 3 2 1 1 4 4 0 0 0 0 0 Phoenix Current Std 3 72 58 67 90 90 2 1 1 4 4 Phoenix 50 98 3 0 0 0 0 0 0 0 0 0 0 Phoenix 50 99 3 0 0 0 0 0 0 0 0 0 0 Phoenix 100 98 3 6 4 4 11 11 0 0 0 0 0 Phoenix 100 99 3 4 2 4 6 6 0 0 0 0 0 Phoenix 150 98 3 160 135 170 174 174 6 4 4 11 11 Phoenix 150 99 3 96 79 102 107 107 4 2 4 6 6 Phoenix 200 98 3 293 266 297 316 316 88 67 97 100 100 Phoenix 200 99 3 260 236 259 284 284 39 31 40 46 46 St. Louis As is 6 0 0 0 1 1 0 0 0 0 0 St. Louis Current Std 6 56 28 47 112 112 3 0 2 6 6 St. Louis 50 98 6 0 0 0 0 0 0 0 0 0 0 
A-161

Number of Daily Maximum Exceedan

≥ 100 ppb ≥ 150 ppb 

Location Standard1 Percentile2 Site-Years Mean Min Med p98 p99 Mean Min Med p98 p99 

St. Louis 50 99 6 0 0 0 0 0 0 0 0 0 0 St. Louis 100 98 6 3 1 3 6 6 0 0 0 1 1 St. Louis 100 99 6 1 0 1 4 4 0 0 0 1 1 St. Louis 150 98 6 46 21 50 68 68 3 1 3 6 6 St. Louis 150 99 6 26 16 23 40 40 1 0 1 4 4 St. Louis 200 98 6 175 126 192 209 209 23 15 22 36 36 St. Louis 200 99 6 136 95 149 169 169 10 6 9 15 15 Washington DC As is 4 0 0 0 1 1 0 0 0 0 0 Washington DC Current Std 4 78 61 81 88 88 7 6 7 8 8 Washington DC 50 98 4 0 0 0 0 0 0 0 0 0 0 Washington DC 50 99 4 0 0 0 0 0 0 0 0 0 0 Washington DC 100 98 4 6 3 6 9 9 0 0 0 1 1 Washington DC 100 99 4 2 0 2 4 4 0 0 0 0 0 Washington DC 150 98 4 73 47 77 92 92 6 3 6 9 9 Washington DC 150 99 4 43 25 45 55 55 2 0 2 4 4 Washington DC 200 98 4 209 158 217 243 243 38 22 38 53 53 Washington DC 200 99 4 154 106 162 184 184 22 15 20 32 32 Notes: 1 Scenario: As is – unadjusted air quality, Current Std – air quality that just meets the current annual standard, All others – air qualit

concentration level given percentile form of alternative standard. 2 Percentile: 98th or 99th percentile of daily maximum 1-hour concentration averaged over three years at maximum monitor in locatio

3 The mean number of exceedances represents the sum of daily maximum exceedances occurring at all monitors in a particular loc

site-years across the monitoring period. The min, med, p98, and p99 represent the minimum, median, 98th, and 99th percentiles of 

daily maximum exceedances in any one year within the monitoring period. 

1 
A-162

Table A-120. Estimated number of exceedances of 1-hour concentration levels (250 and 300 ppb) for 

monitors ≤ 20 m from a major road using 2001-2003 air quality as is and air quality adjusted to just meet the 

current and alternative standards. 

 Number of Daily Maximum Exceedances3

 ≥ 250 ppb ≥ 300 ppb 

Location Standard1 Percentile2

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99

Boston As is 5 0 0 0 0 0 0 0 0 0 0

Boston 

Current 

Std 5 0 0 0 0 0 0 0 0 0 0

Boston 50 98 5 0 0 0 0 0 0 0 0 0 0

Boston 50 99 5 0 0 0 0 0 0 0 0 0 0

Boston 100 98 5 0 0 0 0 0 0 0 0 0 0

Boston 100 99 5 0 0 0 0 0 0 0 0 0 0

Boston 150 98 5 0 0 0 0 0 0 0 0 0 0

Boston 150 99 5 0 0 0 0 0 0 0 0 0 0

Boston 200 98 5 0 0 0 2 2 0 0 0 1 1

Boston 200 99 5 0 0 0 1 1 0 0 0 0 0

Chicago As is 4 0 0 0 0 0 0 0 0 0 0

Chicago 

Current 

Std 4 0 0 0 0 0 0 0 0 0 0

Chicago 50 98 4 0 0 0 0 0 0 0 0 0 0

Chicago 50 99 4 0 0 0 0 0 0 0 0 0 0

Chicago 100 98 4 0 0 0 0 0 0 0 0 0 0

Chicago 100 99 4 0 0 0 0 0 0 0 0 0 0

Chicago 150 98 4 0 0 0 0 0 0 0 0 0 0

Chicago 150 99 4 0 0 0 0 0 0 0 0 0 0

Chicago 200 98 4 0 0 0 0 0 0 0 0 0 0

Chicago 200 99 4 0 0 0 0 0 0 0 0 0 0

Cleveland As is 3 0 0 0 0 0 0 0 0 0 0

Cleveland 

Current 

Std 3 0 0 0 0 0 0 0 0 0 0

Cleveland 50 98 3 0 0 0 0 0 0 0 0 0 0

Cleveland 50 99 3 0 0 0 0 0 0 0 0 0 0

Cleveland 100 98 3 0 0 0 0 0 0 0 0 0 0

Cleveland 100 99 3 0 0 0 0 0 0 0 0 0 0

Cleveland 150 98 3 0 0 0 0 0 0 0 0 0 0

Cleveland 150 99 3 0 0 0 0 0 0 0 0 0 0

Cleveland 200 98 3 1 1 1 2 2 0 0 0 0 0

Cleveland 200 99 3 0 0 0 0 0 0 0 0 0 0

Denver As is 2 0 0 0 0 0 0 0 0 0 0

Denver 

Current 

Std 2 0 0 0 0 0 0 0 0 0 0

Denver 50 98 2 0 0 0 0 0 0 0 0 0 0

Denver 50 99 2 0 0 0 0 0 0 0 0 0 0

Denver 100 98 2 0 0 0 0 0 0 0 0 0 0

Denver 100 99 2 0 0 0 0 0 0 0 0 0 0

Denver 150 98 2 0 0 0 0 0 0 0 0 0 0

Denver 150 99 2 0 0 0 0 0 0 0 0 0 0

Denver 200 98 2 3 1 3 4 4 2 1 2 3 3

Denver 200 99 2 2 1 2 3 3 0 0 0 0 0

Las Vegas As is 3 0 0 0 0 0 0 0 0 0 0
A-163

 Number of Daily Maximum Exceedances3

 ≥ 250 ppb ≥ 300 ppb 

Location Standard1 Percentile2

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99

Las Vegas 

Current 

Std 3 1 0 0 2 2 0 0 0 0 0

Las Vegas 50 98 3 0 0 0 0 0 0 0 0 0 0

Las Vegas 50 99 3 0 0 0 0 0 0 0 0 0 0

Las Vegas 100 98 3 0 0 0 0 0 0 0 0 0 0

Las Vegas 100 99 3 0 0 0 0 0 0 0 0 0 0

Las Vegas 150 98 3 0 0 0 0 0 0 0 0 0 0

Las Vegas 150 99 3 0 0 0 0 0 0 0 0 0 0

Las Vegas 200 98 3 1 0 0 2 2 0 0 0 0 0

Las Vegas 200 99 3 1 0 0 2 2 0 0 0 0 0

Los Angeles As is 9 0 0 0 0 0 0 0 0 0 0

Los Angeles 

Current 

Std 9 0 0 0 0 0 0 0 0 0 0

Los Angeles 50 98 9 0 0 0 0 0 0 0 0 0 0

Los Angeles 50 99 9 0 0 0 0 0 0 0 0 0 0

Los Angeles 100 98 9 0 0 0 0 0 0 0 0 0 0

Los Angeles 100 99 9 0 0 0 0 0 0 0 0 0 0

Los Angeles 150 98 9 0 0 0 0 0 0 0 0 0 0

Los Angeles 150 99 9 0 0 0 0 0 0 0 0 0 0

Los Angeles 200 98 9 0 0 0 0 0 0 0 0 0 0

Los Angeles 200 99 9 0 0 0 0 0 0 0 0 0 0

Miami As is 3 0 0 0 0 0 0 0 0 0 0

Miami 

Current 

Std 3 0 0 0 0 0 0 0 0 0 0

Miami 50 98 3 0 0 0 0 0 0 0 0 0 0

Miami 50 99 3 0 0 0 0 0 0 0 0 0 0

Miami 100 98 3 0 0 0 0 0 0 0 0 0 0

Miami 100 99 3 0 0 0 0 0 0 0 0 0 0

Miami 150 98 3 0 0 0 0 0 0 0 0 0 0

Miami 150 99 3 0 0 0 0 0 0 0 0 0 0

Miami 200 98 3 0 0 0 0 0 0 0 0 0 0

Miami 200 99 3 0 0 0 0 0 0 0 0 0 0

New York As is 7 0 0 0 0 0 0 0 0 0 0

New York 

Current 

Std 7 0 0 0 0 0 0 0 0 0 0

New York 50 98 7 0 0 0 0 0 0 0 0 0 0

New York 50 99 7 0 0 0 0 0 0 0 0 0 0

New York 100 98 7 0 0 0 0 0 0 0 0 0 0

New York 100 99 7 0 0 0 0 0 0 0 0 0 0

New York 150 98 7 0 0 0 0 0 0 0 0 0 0

New York 150 99 7 0 0 0 0 0 0 0 0 0 0

New York 200 98 7 0 0 0 0 0 0 0 0 0 0

New York 200 99 7 0 0 0 0 0 0 0 0 0 0

Phoenix As is 3 0 0 0 0 0 0 0 0 0 0

Phoenix 

Current 

Std 3 0 0 0 0 0 0 0 0 0 0

Phoenix 50 98 3 0 0 0 0 0 0 0 0 0 0

Phoenix 50 99 3 0 0 0 0 0 0 0 0 0 0

Phoenix 100 98 3 0 0 0 0 0 0 0 0 0 0
A-164

 Number of Daily Maximum Exceedances3

 ≥ 250 ppb ≥ 300 ppb 

Location Standard1 Percentile2

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99

Phoenix 100 99 3 0 0 0 0 0 0 0 0 0 0

Phoenix 150 98 3 0 0 0 0 0 0 0 0 0 0

Phoenix 150 99 3 0 0 0 0 0 0 0 0 0 0

Phoenix 200 98 3 0 0 0 1 1 0 0 0 0 0

Phoenix 200 99 3 0 0 0 0 0 0 0 0 0 0

St. Louis As is 6 0 0 0 0 0 0 0 0 0 0

St. Louis 

Current 

Std 6 0 0 0 1 1 0 0 0 1 1

St. Louis 50 98 6 0 0 0 0 0 0 0 0 0 0

St. Louis 50 99 6 0 0 0 0 0 0 0 0 0 0

St. Louis 100 98 6 0 0 0 0 0 0 0 0 0 0

St. Louis 100 99 6 0 0 0 0 0 0 0 0 0 0

St. Louis 150 98 6 0 0 0 1 1 0 0 0 0 0

St. Louis 150 99 6 0 0 0 1 1 0 0 0 0 0

St. Louis 200 98 6 1 0 1 2 2 0 0 0 1 1

St. Louis 200 99 6 1 0 1 2 2 0 0 0 1 1

Washington 

DC As is 4 0 0 0 0 0 0 0 0 0 0

Washington 

DC 

Current 

Std 4 0 0 0 0 0 0 0 0 0 0

Washington 

DC 50 98 4 0 0 0 0 0 0 0 0 0 0

Washington 

DC 50 99 4 0 0 0 0 0 0 0 0 0 0

Washington 

DC 100 98 4 0 0 0 0 0 0 0 0 0 0

Washington 

DC 100 99 4 0 0 0 0 0 0 0 0 0 0

Washington 

DC 150 98 4 0 0 0 0 0 0 0 0 0 0

Washington 

DC 150 99 4 0 0 0 0 0 0 0 0 0 0

Washington 

DC 200 98 4 1 0 0 2 2 0 0 0 1 1

Washington 

DC 200 99 4 0 0 0 1 1 0 0 0 0 0

Notes: 

1

 Scenario: As is – unadjusted air quality, Current Std – air quality that just meets the current annual 

standard, All others – air quality that just meets 1-hour concentration level given percentile form of 

alternative standard. 

2

 Percentile: 98th or 99th percentile of daily maximum 1-hour concentration averaged over three years at 

maximum monitor in location. 

3

 The mean number of exceedances represents the sum of daily maximum exceedances occurring at all 

monitors in a particular location divided by the number of site-years across the monitoring period. The 

min, med, p98, and p99 represent the minimum, median, 98th, and 99th percentiles of the distribution for 

the number of daily maximum exceedances in any one year within the monitoring period. 
A-165

Table A-121. Estimated number of exceedances of 1-hour concentration levels (100, 150, and 200 ppb) on-roads using 2001-2003 air quadjusted to just meet the current and alternative standards and an on-road adjustment factor. 

Number of Daily Maximum Exceedances3

≥ 100 ppb ≥ 150 ppb 

Location Standard1 Percentile2

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99 Me

Atlanta As is 1400 23 0 5 130 169 4 0 0 36 51 Atlanta Current Std 1400 183 12 228 341 348 110 1 103 290 299 Atlanta 50 98 1400 2 0 0 26 38 0 0 0 4 5 Atlanta 50 99 1400 2 0 0 20 25 0 0 0 1 3 Atlanta 100 98 1400 46 0 22 190 229 10 0 1 70 104 Atlanta 100 99 1400 36 0 13 166 203 7 0 0 53 76 Atlanta 150 98 1400 117 0 123 295 312 46 0 22 190 229 Atlanta 150 99 1400 102 0 96 282 298 36 0 13 166 203 Atlanta 200 98 1400 170 5 202 339 344 95 0 83 273 289 Atlanta 200 99 1400 156 2 178 331 340 80 0 62 254 275 Boston As is 600 5 0 1 36 40 0 0 0 3 5 Boston Current Std 600 86 1 83 209 227 20 0 7 96 105 Boston 50 98 600 0 0 0 3 6 0 0 0 0 0 Boston 50 99 600 0 0 0 3 3 0 0 0 0 0 Boston 100 98 600 26 0 13 125 131 3 0 0 22 25 Boston 100 99 600 16 0 4 89 99 1 0 0 13 18 Boston 150 98 600 95 3 87 219 248 26 0 13 125 131 Boston 150 99 600 76 0 67 200 220 16 0 4 89 99 Boston 200 98 600 163 27 166 283 293 72 0 60 196 220 Boston 200 99 600 139 15 141 263 279 53 0 38 180 188 Chicago As is 900 52 0 35 180 191 9 0 2 62 68 Chicago Current Std 900 193 24 192 328 332 74 1 56 235 250 Chicago 50 98 900 4 0 0 43 49 0 0 0 4 7 Chicago 50 99 900 2 0 0 31 34 0 0 0 1 3 Chicago 100 98 900 83 2 68 238 257 18 0 7 92 106 Chicago 100 99 900 56 0 40 188 197 10 0 2 65 74 Chicago 150 98 900 205 45 207 331 336 83 2 68 238 257 Chicago 150 99 900 167 18 161 316 321 56 0 40 188 197 Chicago 200 98 900 281 129 285 354 356 168 18 163 316 322 
A-166

Number of Daily Maximum Exceedances3

≥ 100 ppb ≥ 150 ppb 

Location Standard1 Percentile2

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99 Me

Chicago 200 99 900 253 91 257 344 350 130 9 118 291 301 Cleveland As is 300 31 0 21 83 102 5 0 1 30 30 Cleveland Current Std 300 264 154 277 332 335 134 31 134 241 264 Cleveland 50 98 300 7 0 2 37 38 1 0 0 7 8 Cleveland 50 99 300 5 0 1 30 30 0 0 0 3 3 Cleveland 100 98 300 104 19 98 209 225 24 0 15 73 86 Cleveland 100 99 300 84 12 80 179 192 19 0 10 62 70 Cleveland 150 98 300 235 114 240 320 324 104 19 98 209 225 Cleveland 150 99 300 212 84 214 307 313 84 12 80 179 192 Cleveland 200 98 300 305 220 314 348 348 196 66 198 298 304 Cleveland 200 99 300 291 181 301 342 344 174 50 173 283 295 Denver As is 200 89 8 74 242 259 17 0 5 80 94 Denver Current Std 200 267 137 286 315 316 157 8 162 282 284 Denver 50 98 200 4 0 1 36 37 0 0 0 1 1 Denver 50 99 200 2 0 1 14 15 0 0 0 1 1 Denver 100 98 200 99 8 82 252 269 19 0 6 86 103 Denver 100 99 200 66 4 52 209 221 11 0 3 66 73 Denver 150 98 200 232 90 238 317 318 99 8 82 252 269 Denver 150 99 200 197 55 192 311 312 66 4 52 209 221 Denver 200 98 200 288 209 288 325 326 198 55 192 313 315 Denver 200 99 200 270 158 276 323 323 157 29 143 293 303 Detroit As is 600 41 1 30 130 141 9 0 5 44 46 Detroit Current Std 600 282 181 285 345 349 166 52 167 299 307 Detroit 50 98 600 5 0 3 26 28 1 0 1 7 7 Detroit 50 99 600 1 0 1 7 7 1 0 0 4 5 Detroit 100 98 600 59 4 46 170 186 13 1 8 56 57 Detroit 100 99 600 14 1 8 57 59 3 0 2 16 18 Detroit 150 98 600 178 52 183 301 308 59 4 46 170 186 Detroit 150 99 600 62 6 50 176 191 14 1 8 57 59 Detroit 200 98 600 265 131 271 334 343 140 31 131 278 286 Detroit 200 99 600 143 35 134 284 287 41 1 30 125 137 
A-167

Number of Daily Maximum Exceedances3

≥ 100 ppb ≥ 150 ppb 

Location Standard1 Percentile2

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99 Me

El Paso As is 1200 32 0 19 136 145 4 0 1 24 26 El Paso Current Std 1200 272 122 284 339 348 168 13 168 291 301 El Paso 50 98 1200 3 0 0 23 24 0 0 0 3 3 El Paso 50 99 1200 1 0 0 10 12 0 0 0 1 1 El Paso 100 98 1200 89 4 79 231 249 17 0 8 80 91 El Paso 100 99 1200 56 1 42 190 201 8 0 3 46 49 El Paso 150 98 1200 216 47 222 313 327 89 4 79 231 249 El Paso 150 99 1200 174 15 176 297 306 56 1 42 190 201 El Paso 200 98 1200 278 135 288 342 348 180 15 180 298 313 El Paso 200 99 1200 255 97 265 334 342 136 10 133 274 288 Jacksonville As is 200 13 0 7 55 56 1 0 1 6 7 Jacksonville Current Std 200 290 227 278 338 340 205 103 206 307 310 Jacksonville 50 98 200 7 0 4 34 34 1 0 1 3 3 Jacksonville 50 99 200 5 0 3 25 25 1 0 1 2 2 Jacksonville 100 98 200 119 26 118 217 235 32 2 25 97 114 Jacksonville 100 99 200 100 19 98 201 212 23 1 18 79 86 Jacksonville 150 98 200 233 135 232 323 325 119 26 118 217 235 Jacksonville 150 99 200 214 111 212 314 317 100 19 98 201 212 Jacksonville 200 98 200 289 227 278 338 340 201 96 201 306 310 Jacksonville 200 99 200 279 200 273 336 337 182 81 182 290 297 Las Vegas As is 1600 23 0 4 171 194 4 0 0 54 62 Las Vegas Current Std 1600 189 2 194 335 338 106 0 71 309 316 Las Vegas 50 98 1600 5 0 0 63 71 0 0 0 6 7 Las Vegas 50 99 1600 3 0 0 49 58 0 0 0 2 3 Las Vegas 100 98 1600 82 0 43 298 307 19 0 3 149 172 Las Vegas 100 99 1600 64 0 27 276 292 13 0 1 102 120 Las Vegas 150 98 1600 167 0 161 330 333 82 0 43 298 307 Las Vegas 150 99 1600 148 0 127 325 329 64 0 27 276 292 Las Vegas 200 98 1600 218 5 237 343 344 143 0 118 324 328 Las Vegas 200 99 1600 201 3 212 337 339 123 0 89 318 321 Los Angeles As is 5100 71 0 57 231 251 17 0 6 94 108 
A-168

Number of Daily Maximum Exceedances3

≥ 100 ppb ≥ 150 ppb 

Location Standard1 Percentile2

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99 Me

Los Angeles Current Std 5100 152 0 152 319 330 55 0 40 196 225 Los Angeles 50 98 5100 1 0 0 15 20 0 0 0 1 3 Los Angeles 50 99 5100 1 0 0 13 17 0 0 0 0 2 Los Angeles 100 98 5100 33 0 19 142 160 6 0 1 46 55 Los Angeles 100 99 5100 29 0 16 132 148 5 0 0 42 49 Los Angeles 150 98 5100 111 0 101 280 299 33 0 19 142 160 Los Angeles 150 99 5100 100 0 89 266 288 29 0 16 132 148 Los Angeles 200 98 5100 191 0 204 338 346 81 0 69 246 262 Los Angeles 200 99 5100 181 0 189 334 344 73 0 59 236 254 Miami As is 600 7 0 2 50 64 1 0 0 6 10 Miami Current Std 600 232 147 233 316 323 146 25 147 243 253 Miami 50 98 600 5 0 1 37 48 0 0 0 5 7 Miami 50 99 600 3 0 0 21 31 0 0 0 2 3 Miami 100 98 600 78 2 74 181 189 19 0 10 92 110 Miami 100 99 600 53 0 48 151 173 11 0 4 67 84 Miami 150 98 600 174 44 178 267 276 78 2 74 181 189 Miami 150 99 600 144 21 150 237 245 53 0 48 151 173 Miami 200 98 600 232 139 228 317 322 145 21 150 242 253 Miami 200 99 600 208 84 202 299 307 114 9 115 210 225 New York As is 2600 42 0 28 177 201 7 0 1 55 63 New York Current Std 2600 129 0 124 298 310 36 0 22 157 196 New York 50 98 2600 3 0 0 33 34 0 0 0 3 4 New York 50 99 2600 1 0 0 19 22 0 0 0 1 2 New York 100 98 2600 57 0 43 212 226 10 0 3 67 73 New York 100 99 2600 35 0 22 156 179 6 0 1 48 56 New York 150 98 2600 169 5 170 316 324 57 0 43 212 226 New York 150 99 2600 129 0 119 294 307 35 0 22 156 179 New York 200 98 2600 249 40 266 344 351 132 0 125 294 307 New York 200 99 2600 216 20 229 332 340 94 0 80 263 279 Philadelphia As is 1400 37 0 19 149 172 6 0 1 49 62 Philadelphia Current Std 1400 222 29 231 339 347 87 0 71 250 280 
A-169

Number of Daily Maximum Exceedances3

≥ 100 ppb ≥ 150 ppb 

Location Standard1 Percentile2

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99 Me

Philadelphia 50 98 1400 7 0 1 54 68 1 0 0 8 20 Philadelphia 50 99 1400 4 0 1 41 56 0 0 0 4 10 Philadelphia 100 98 1400 116 1 102 284 294 27 0 12 118 137 Philadelphia 100 99 1400 91 0 75 256 275 18 0 5 93 105 Philadelphia 150 98 1400 254 65 261 346 350 116 1 102 284 294 Philadelphia 150 99 1400 228 42 235 335 347 91 0 75 256 275 Philadelphia 200 98 1400 312 192 317 364 364 217 31 223 332 342 Philadelphia 200 99 1400 299 152 304 359 359 187 21 191 325 332 Phoenix As is 500 101 1 83 280 315 16 0 2 113 124 Phoenix Current Std 500 245 23 266 345 352 96 1 77 286 299 Phoenix 50 98 500 6 0 0 44 48 0 0 0 2 3 Phoenix 50 99 500 3 0 0 19 24 0 0 0 1 1 Phoenix 100 98 500 153 2 152 319 337 35 0 14 182 206 Phoenix 100 99 500 118 1 103 290 323 22 0 4 135 156 Phoenix 150 98 500 293 92 297 356 360 153 2 152 319 337 Phoenix 150 99 500 269 48 283 351 356 118 1 103 290 323 Phoenix 200 98 500 332 245 345 362 363 262 41 278 350 354 Phoenix 200 99 500 325 199 334 361 362 231 22 250 343 349 Provo As is 300 61 1 38 248 289 9 0 0 62 64 Provo Current Std 300 338 293 347 363 364 235 55 258 351 355 Provo 50 98 300 18 0 4 86 106 2 0 0 18 18 Provo 50 99 300 13 0 2 75 82 1 0 0 9 9 Provo 100 98 300 257 60 277 353 358 75 1 51 273 301 Provo 100 99 300 229 38 248 352 355 56 1 33 235 283 Provo 150 98 300 343 289 352 364 364 257 60 277 353 358 Provo 150 99 300 337 264 347 363 364 229 38 248 352 355 Provo 200 98 300 351 321 362 365 365 331 236 342 363 364 Provo 200 99 300 350 320 360 365 365 322 204 329 362 363 St. Louis As is 900 25 0 12 128 139 3 0 0 28 37 St. Louis Current Std 900 262 112 268 346 350 131 4 124 276 295 St. Louis 50 98 900 7 0 1 45 51 1 0 0 7 10 
A-170

Number of Daily Maximum Exceedances3

≥ 100 ppb ≥ 150 ppb 

Location Standard1 Percentile2

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99 Me

St. Louis 50 99 900 4 0 0 35 41 0 0 0 4 4 St. Louis 100 98 900 125 2 118 274 288 28 0 15 144 153 St. Louis 100 99 900 96 1 85 256 263 19 0 6 105 114 St. Louis 150 98 900 258 92 262 345 351 125 2 118 274 288 St. Louis 150 99 900 233 63 237 335 347 96 1 85 256 263 St. Louis 200 98 900 316 211 322 358 361 224 52 229 329 341 St. Louis 200 99 900 301 166 306 356 357 195 30 197 315 329 Washington DC As is 1800 36 0 17 169 205 6 0 0 46 54 Washington DC Current Std 1800 222 6 268 348 353 111 0 100 293 312 Washington DC 50 98 1800 7 0 0 56 63 0 0 0 6 8 Washington DC 50 99 1800 5 0 0 38 45 0 0 0 2 2 Washington DC 100 98 1800 109 0 99 287 310 27 0 10 139 168 Washington DC 100 99 1800 83 0 65 260 291 18 0 4 102 123 Washington DC 150 98 1800 221 4 269 347 352 109 0 99 287 310 Washington DC 150 99 1800 194 1 227 339 344 83 0 65 260 291 Washington DC 200 98 1800 279 38 325 360 362 190 1 221 336 343 Washington DC 200 99 1800 262 20 313 357 360 161 0 176 326 334 Other MSA As is 61200 16 0 3 105 129 2 0 0 22 32 Other MSA Current Std 61200 133 0 129 320 336 47 0 25 208 239 Other MSA 50 98 61200 0 0 0 5 8 0 0 0 0 1 Other MSA 50 99 61200 0 0 0 2 3 0 0 0 0 0 Other MSA 100 98 61200 17 0 4 110 133 2 0 0 23 34 Other MSA 100 99 61200 8 0 1 68 85 1 0 0 11 17 Other MSA 150 98 61200 73 0 52 258 287 17 0 4 110 133 Other MSA 150 99 61200 47 0 25 208 238 8 0 1 68 85 Other MSA 200 98 61200 138 0 136 324 338 51 0 29 216 247 Other MSA 200 99 61200 104 0 91 298 320 30 0 12 161 191 Other Not MSA As is 12700 4 0 0 43 62 1 0 0 9 15 Other Not MSA Current Std 12700 126 0 104 333 343 63 0 26 255 282 Other Not MSA 50 98 12700 0 0 0 4 8 0 0 0 1 1 Other Not MSA 50 99 12700 0 0 0 2 4 0 0 0 1 1 
A-171

Number of Daily Maximum Exceedances3

≥ 100 ppb ≥ 150 ppb 

Location Standard1 Percentile2

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99 Me

Other Not MSA 100 98 12700 6 0 0 64 86 1 0 0 12 17 Other Not MSA 100 99 12700 3 0 0 33 44 0 0 0 6 13 Other Not MSA 150 98 12700 28 0 6 172 199 6 0 0 64 86 Other Not MSA 150 99 12700 16 0 2 127 149 3 0 0 33 44 Other Not MSA 200 98 12700 63 0 26 254 280 19 0 3 139 164 Other Not MSA 200 99 12700 42 0 12 207 235 10 0 1 92 118 Atlanta As is 1500 17 0 2 114 120 2 0 0 26 27 Atlanta Current Std 1500 193 4 259 337 341 126 0 148 285 300 Atlanta 50 98 1500 3 0 0 29 31 0 0 0 4 5 Atlanta 50 99 1500 2 0 0 21 23 0 0 0 1 2 Atlanta 100 98 1500 58 0 39 206 218 13 0 1 93 100 Atlanta 100 99 1500 45 0 21 185 198 8 0 0 64 75 Atlanta 150 98 1500 133 0 162 295 304 58 0 39 206 218 Atlanta 150 99 1500 116 0 131 276 288 45 0 21 185 198 Atlanta 200 98 1500 181 3 241 328 334 111 0 123 266 281 Atlanta 200 99 1500 167 1 219 319 326 94 0 91 249 262 Boston As is 800 2 0 0 18 21 0 0 0 1 1 Boston Current Std 800 95 4 91 207 221 25 0 15 104 109 Boston 50 98 800 0 0 0 1 2 0 0 0 0 0 Boston 50 99 800 0 0 0 0 1 0 0 0 0 0 Boston 100 98 800 19 0 8 92 99 1 0 0 12 13 Boston 100 99 800 8 0 2 54 65 0 0 0 3 5 Boston 150 98 800 84 4 78 189 207 19 0 8 92 99 Boston 150 99 800 52 1 42 150 162 8 0 2 54 65 Boston 200 98 800 153 32 151 249 267 59 1 50 161 174 Boston 200 99 800 114 14 111 215 238 34 0 24 119 129 Chicago As is 800 36 0 20 148 161 5 0 0 41 53 Chicago Current Std 800 189 25 187 329 339 69 0 54 225 241 Chicago 50 98 800 2 0 0 24 30 0 0 0 0 0 Chicago 50 99 800 1 0 0 15 24 0 0 0 0 0 Chicago 100 98 800 59 0 43 201 220 10 0 2 63 78 
A-172

Number of Daily Maximum Exceedances3

≥ 100 ppb ≥ 150 ppb 

Location Standard1 Percentile2

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99 Me

Chicago 100 99 800 47 0 31 181 193 8 0 1 54 67 Chicago 150 98 800 176 20 169 320 329 59 0 43 201 220 Chicago 150 99 800 157 14 148 313 320 47 0 31 181 193 Chicago 200 98 800 259 94 261 352 355 138 9 127 302 309 Chicago 200 99 800 244 64 248 348 353 119 7 108 291 296 Denver As is 300 63 2 49 190 195 10 0 4 47 52 Denver Current Std 300 257 135 259 314 320 134 12 133 260 264 Denver 50 98 300 9 0 2 41 45 1 0 0 5 9 Denver 50 99 300 5 0 1 23 25 0 0 0 2 3 Denver 100 98 300 148 20 151 263 269 38 0 23 132 150 Denver 100 99 300 106 10 100 236 242 23 0 11 87 95 Denver 150 98 300 263 170 264 315 321 148 20 151 263 269 Denver 150 99 300 239 114 243 309 313 106 10 100 236 242 Denver 200 98 300 296 241 308 330 332 239 122 243 309 313 Denver 200 99 300 286 217 287 323 327 204 66 209 294 299 Detroit As is 600 20 0 9 90 103 2 0 0 20 24 Detroit Current Std 600 287 166 286 350 352 189 35 193 300 315 Detroit 50 98 600 13 0 4 57 66 1 0 0 12 18 Detroit 50 99 600 10 0 2 52 53 1 0 0 9 14 Detroit 100 98 600 165 29 166 293 296 50 0 37 165 180 Detroit 100 99 600 146 18 143 275 284 40 0 27 146 163 Detroit 150 98 600 273 163 271 341 346 165 29 166 293 296 Detroit 150 99 600 261 141 260 337 342 146 18 143 275 284 Detroit 200 98 600 313 237 318 355 359 249 117 251 331 337 Detroit 200 99 600 307 234 309 354 357 235 90 237 327 332 El Paso As is 1200 24 0 12 114 143 3 0 0 20 23 El Paso Current Std 1200 281 137 285 349 354 198 22 205 309 320 El Paso 50 98 1200 5 0 1 32 37 0 0 0 4 4 El Paso 50 99 1200 3 0 0 20 23 0 0 0 2 2 El Paso 100 98 1200 108 3 102 254 263 23 0 11 113 137 El Paso 100 99 1200 75 2 62 225 234 13 0 5 72 86 
A-173

Number of Daily Maximum Exceedances3

≥ 100 ppb ≥ 150 ppb 

Location Standard1 Percentile2

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99 Me

El Paso 150 98 1200 229 63 235 323 327 108 3 102 254 263 El Paso 150 99 1200 199 26 205 309 317 75 2 62 225 234 El Paso 200 98 1200 281 137 286 349 353 197 26 205 308 317 El Paso 200 99 1200 264 97 271 340 346 162 12 166 289 296 Jacksonville As is 200 11 0 5 48 59 2 0 2 5 6 Jacksonville Current Std 200 295 229 295 341 342 216 114 223 309 318 Jacksonville 50 98 200 9 0 5 39 50 2 0 2 5 5 Jacksonville 50 99 200 2 0 2 5 5 1 0 1 4 4 Jacksonville 100 98 200 127 30 124 256 269 35 0 23 138 151 Jacksonville 100 99 200 39 1 27 145 158 6 0 4 25 29 Jacksonville 150 98 200 241 145 243 320 327 127 30 124 256 269 Jacksonville 150 99 200 135 33 131 265 279 39 1 27 145 158 Jacksonville 200 98 200 293 229 294 339 340 211 104 214 302 312 Jacksonville 200 99 200 218 118 223 309 318 102 12 95 232 253 Las Vegas As is 1100 15 0 0 133 148 2 0 0 54 64 Las Vegas Current Std 1100 177 2 171 343 346 99 0 55 317 324 Las Vegas 50 98 1100 6 0 0 77 79 1 0 0 17 19 Las Vegas 50 99 1100 4 0 0 71 73 0 0 0 6 8 Las Vegas 100 98 1100 83 0 39 305 312 22 0 1 174 187 Las Vegas 100 99 1100 70 0 24 288 304 16 0 0 142 151 Las Vegas 150 98 1100 161 1 146 342 343 83 0 39 305 312 Las Vegas 150 99 1100 145 0 118 337 339 70 0 24 288 304 Las Vegas 200 98 1100 210 6 224 350 350 138 0 108 335 336 Las Vegas 200 99 1100 198 3 205 348 349 123 0 83 329 333 Los Angeles As is 5400 38 0 25 150 169 6 0 1 43 55 Los Angeles Current Std 5400 160 2 163 314 326 58 0 45 187 215 Los Angeles 50 98 5400 2 0 0 18 24 0 0 0 1 2 Los Angeles 50 99 5400 1 0 0 12 15 0 0 0 1 1 Los Angeles 100 98 5400 54 0 41 188 208 10 0 2 62 75 Los Angeles 100 99 5400 40 0 27 153 176 6 0 1 44 55 Los Angeles 150 98 5400 155 2 155 314 325 54 0 41 188 208 
A-174

Number of Daily Maximum Exceedances3

≥ 100 ppb ≥ 150 ppb 

Location Standard1 Percentile2

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99 Me

Los Angeles 150 99 5400 132 0 127 296 310 40 0 27 153 176 Los Angeles 200 98 5400 227 13 240 349 352 122 0 115 285 304 Los Angeles 200 99 5400 208 8 217 342 348 100 0 91 258 281 Miami As is 400 6 0 1 46 46 0 0 0 6 6 Miami Current Std 400 202 100 201 284 286 130 30 139 197 204 Miami 50 98 400 3 0 0 24 24 0 0 0 1 1 Miami 50 99 400 2 0 0 18 21 0 0 0 1 1 Miami 100 98 400 56 0 47 144 148 13 0 4 72 77 Miami 100 99 400 46 0 34 134 140 9 0 2 62 65 Miami 150 98 400 128 24 135 194 199 56 0 47 144 148 Miami 150 99 400 117 17 123 187 192 46 0 34 134 140 Miami 200 98 400 182 75 188 260 262 106 12 111 178 183 Miami 200 99 400 171 63 178 248 250 94 7 96 170 174 New York As is 2200 35 0 23 149 171 5 0 1 40 45 New York Current Std 2200 147 0 148 308 321 45 0 31 188 202 New York 50 98 2200 3 0 0 32 34 0 0 0 3 4 New York 50 99 2200 2 0 0 17 23 0 0 0 1 1 New York 100 98 2200 75 0 62 225 248 14 0 6 73 88 New York 100 99 2200 45 0 32 175 196 7 0 2 49 53 New York 150 98 2200 192 2 204 326 333 75 0 62 225 248 New York 150 99 2200 149 0 151 305 312 45 0 32 175 196 New York 200 98 2200 264 38 284 350 353 157 0 162 310 317 New York 200 99 2200 232 20 250 339 343 115 0 108 278 294 Philadelphia As is 1200 22 0 10 101 130 2 0 0 20 22 Philadelphia Current Std 1200 232 54 242 321 326 110 2 99 269 280 Philadelphia 50 98 1200 5 0 1 37 42 0 0 0 4 4 Philadelphia 50 99 1200 1 0 0 14 14 0 0 0 0 1 Philadelphia 100 98 1200 112 5 102 258 276 24 0 10 109 139 Philadelphia 100 99 1200 53 0 39 187 214 8 0 1 49 53 Philadelphia 150 98 1200 237 76 245 325 329 112 5 102 258 276 Philadelphia 150 99 1200 172 24 178 297 311 53 0 39 187 214 
A-175

Number of Daily Maximum Exceedances3

≥ 100 ppb ≥ 150 ppb 

Location Standard1 Percentile2

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99 Me

Philadelphia 200 98 1200 295 186 302 339 345 204 51 213 313 319 Philadelphia 200 99 1200 256 105 264 329 332 134 7 130 277 296 Phoenix As is 900 77 0 53 275 293 10 0 1 57 71 Phoenix Current Std 900 284 127 296 357 359 124 2 111 307 325 Phoenix 50 98 900 4 0 0 26 35 0 0 0 1 1 Phoenix 50 99 900 2 0 0 12 14 0 0 0 0 0 Phoenix 100 98 900 146 3 140 320 332 28 0 7 159 171 Phoenix 100 99 900 103 0 84 293 312 16 0 2 101 107 Phoenix 150 98 900 299 151 309 358 360 146 3 140 320 332 Phoenix 150 99 900 268 95 280 353 357 103 0 84 293 312 Phoenix 200 98 900 338 227 347 363 364 264 92 277 352 356 Phoenix 200 99 900 328 204 337 361 363 225 39 236 345 352 Provo As is 300 51 0 44 160 160 17 0 2 68 70 Provo Current Std 300 306 153 331 360 361 192 44 187 331 340 Provo 50 98 300 13 0 0 45 45 4 0 0 42 42 Provo 50 99 300 13 0 0 44 45 4 0 0 42 42 Provo 100 98 300 63 0 48 187 187 20 0 5 78 79 Provo 100 99 300 60 0 46 182 183 19 0 3 78 79 Provo 150 98 300 209 38 214 328 335 63 0 48 187 187 Provo 150 99 300 201 30 210 328 334 60 0 46 182 183 Provo 200 98 300 298 217 300 356 359 160 7 157 306 314 Provo 200 99 300 294 204 290 354 356 151 6 147 299 306 St. Louis As is 400 15 0 5 76 83 1 0 0 20 24 St. Louis Current Std 400 233 82 228 341 347 121 1 113 280 300 St. Louis 50 98 400 4 0 0 35 41 0 0 0 9 10 St. Louis 50 99 400 3 0 0 31 35 0 0 0 4 9 St. Louis 100 98 400 107 3 100 238 249 23 0 12 105 108 St. Louis 100 99 400 98 2 90 227 240 20 0 10 96 101 St. Louis 150 98 400 226 96 221 331 340 107 3 100 238 249 St. Louis 150 99 400 217 82 211 328 340 98 2 90 227 240 St. Louis 200 98 400 287 166 291 357 357 194 62 190 308 320 
A-176

Number of Daily Maximum Exceedances3

≥ 100 ppb ≥ 150 ppb 

Location Standard1 Percentile2

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99 Me

St. Louis 200 99 400 282 162 283 355 356 186 49 181 305 317 Washington DC As is 1700 21 0 7 119 143 2 0 0 20 22 Washington DC Current Std 1700 207 10 238 340 345 102 0 88 270 289 Washington DC 50 98 1700 5 0 1 37 42 0 0 0 5 6 Washington DC 50 99 1700 2 0 0 15 16 0 0 0 2 2 Washington DC 100 98 1700 96 0 81 269 283 22 0 8 126 150 Washington DC 100 99 1700 50 0 29 209 226 8 0 1 52 66 Washington DC 150 98 1700 200 2 232 337 345 96 0 81 269 283 Washington DC 150 99 1700 148 0 157 309 322 50 0 29 209 226 Washington DC 200 98 1700 260 42 299 358 362 171 0 191 323 335 Washington DC 200 99 1700 220 8 256 346 352 117 0 108 288 298 Other MSA As is 56500 10 0 1 79 100 1 0 0 12 18 Other MSA Current Std 56500 143 0 143 322 336 59 0 36 233 257 Other MSA 50 98 56500 0 0 0 5 8 0 0 0 0 1 Other MSA 50 99 56500 0 0 0 2 5 0 0 0 0 0 Other MSA 100 98 56500 20 0 5 123 152 2 0 0 26 37 Other MSA 100 99 56500 14 0 2 96 121 1 0 0 17 25 Other MSA 150 98 56500 80 0 62 264 288 20 0 5 123 152 Other MSA 150 99 56500 63 0 43 237 263 14 0 2 96 121 Other MSA 200 98 56500 143 0 144 322 337 57 0 36 228 255 Other MSA 200 99 56500 125 0 119 311 328 44 0 23 201 228 Other Not MSA As is 11600 4 0 0 43 65 1 0 0 7 13 Other Not MSA Current Std 11600 124 0 100 331 339 62 0 24 257 272 Other Not MSA 50 98 11600 0 0 0 2 6 0 0 0 1 2 Other Not MSA 50 99 11600 0 0 0 2 3 0 0 0 0 2 Other Not MSA 100 98 11600 6 0 0 61 86 1 0 0 10 17 Other Not MSA 100 99 11600 2 0 0 23 35 0 0 0 3 7 Other Not MSA 150 98 11600 28 0 4 173 198 6 0 0 61 86 Other Not MSA 150 99 11600 13 0 1 113 134 2 0 0 23 35 Other Not MSA 200 98 11600 60 0 22 249 269 19 0 2 141 164 Other Not MSA 200 99 11600 34 0 6 193 216 8 0 0 80 104 
A-177

Number of Daily Maximum Exceedances3

≥ 100 ppb ≥ 150 ppb 

Location Standard1 Percentile2

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99 Me

Notes: 1 Scenario: As is – unadjusted air quality, Current Std – air quality that just meets the current annual standard, All others – air qual

concentration level given percentile form of alternative standard. 2 Percentile: 98th or 99th percentile of daily maximum 1-hour concentration averaged over three years at maximum monitor in locat

3 The mean number of exceedances represents the sum of daily maximum exceedances occurring at all monitors in a particular lo

number of site-years across the monitoring period. The min, med, p98, and p99 represent the minimum, median, 98th, and 99th pe

for the number of daily maximum exceedances in any one year within the monitoring period. 
A-178

Table A-122. Estimated number of exceedances of 1-hour concentration levels (250, and 300 ppb) on-roads 

using 2001-2003 air quality as is and air quality adjusted to just meet the current and alternative standards 

and an on-road adjustment factor. 

Number of Daily Maximum Exceedances3

≥ 250 ppb ≥ 300 ppb

Location 

Standard

1

Percentile

2 Site-Years Mean Min Med p98 p99 Mean Min Med p98 p99

Atlanta As is 1400 0 0 0 3 4 0 0 0 1 1

Atlanta Current 

Std 

1400 29 0 7 157 189 15 0 1 102 125

Atlanta 50 98 1400 0 0 0 0 0 0 0 0 0 0

Atlanta 50 99 1400 0 0 0 0 0 0 0 0 0 0

Atlanta 100 98 1400 1 0 0 8 10 0 0 0 4 5

Atlanta 100 99 1400 0 0 0 6 8 0 0 0 1 3

Atlanta 150 98 1400 6 0 0 47 70 2 0 0 26 38

Atlanta 150 99 1400 4 0 0 39 53 2 0 0 20 25

Atlanta 200 98 1400 21 0 4 122 165 10 0 1 70 104

Atlanta 200 99 1400 15 0 2 101 137 7 0 0 53 76

Boston As is 600 0 0 0 0 0 0 0 0 0 0

Boston Current 

Std 

600 1 0 0 11 12 0 0 0 3 3

Boston 50 98 600 0 0 0 0 0 0 0 0 0 0

Boston 50 99 600 0 0 0 0 0 0 0 0 0 0

Boston 100 98 600 0 0 0 1 1 0 0 0 0 0

Boston 100 99 600 0 0 0 1 1 0 0 0 0 0

Boston 150 98 600 1 0 0 13 18 0 0 0 3 6

Boston 150 99 600 1 0 0 7 13 0 0 0 3 3

Boston 200 98 600 9 0 1 59 67 3 0 0 22 25

Boston 200 99 600 5 0 1 33 37 1 0 0 13 18

Chicago As is 900 0 0 0 5 12 0 0 0 1 3

Chicago Current 

Std 

900 9 0 2 64 69 4 0 0 37 45

Chicago 50 98 900 0 0 0 0 0 0 0 0 0 0

Chicago 50 99 900 0 0 0 0 0 0 0 0 0 0

Chicago 100 98 900 1 0 0 15 21 0 0 0 4 7

Chicago 100 99 900 0 0 0 8 13 0 0 0 1 3

Chicago 150 98 900 11 0 2 68 76 4 0 0 43 49

Chicago 150 99 900 6 0 1 49 56 2 0 0 31 34

Chicago 200 98 900 38 0 23 150 164 18 0 7 92 106

Chicago 200 99 900 23 0 11 109 125 10 0 2 65 74

Cleveland As is 300 0 0 0 3 3 0 0 0 1 1

Cleveland Current 

Std 

300 24 0 15 70 83 10 0 3 44 48

Cleveland 50 98 300 0 0 0 0 0 0 0 0 0 0

Cleveland 50 99 300 0 0 0 0 0 0 0 0 0 0

Cleveland 100 98 300 3 0 0 19 19 1 0 0 7 8

Cleveland 100 99 300 2 0 0 15 16 0 0 0 3 3

Cleveland 150 98 300 15 0 8 57 63 7 0 2 37 38

Cleveland 150 99 300 11 0 4 48 54 5 0 1 30 30

Cleveland 200 98 300 50 4 44 123 136 24 0 15 73 86

Cleveland 200 99 300 40 2 32 105 116 19 0 10 62 70

Denver As is 200 1 0 0 5 5 0 0 0 1 1

Denver Current 

Std 

200 36 0 22 156 163 16 0 7 94 97
A-179

Number of Daily Maximum Exceedances3

≥ 250 ppb ≥ 300 ppb

Location 

Standard

1

Percentile

2 Site-Years Mean Min Med p98 p99 Mean Min Med p98 p99

Denver 50 98 200 0 0 0 1 1 0 0 0 0 0

Denver 50 99 200 0 0 0 0 0 0 0 0 0 0

Denver 100 98 200 1 0 0 5 5 0 0 0 1 1

Denver 100 99 200 0 0 0 2 2 0 0 0 1 1

Denver 150 98 200 12 0 3 71 77 4 0 1 36 37

Denver 150 99 200 6 0 1 50 52 2 0 1 14 15

Denver 200 98 200 43 1 27 162 165 19 0 6 86 103

Denver 200 99 200 26 0 10 105 121 11 0 3 66 73

Detroit As is 600 2 0 1 7 7 1 0 1 7 7

Detroit Current 

Std 

600 34 1 25 109 120 17 1 11 66 72

Detroit 50 98 600 1 0 0 4 4 0 0 0 4 4

Detroit 50 99 600 0 0 0 2 3 0 0 0 2 2

Detroit 100 98 600 2 0 1 11 12 1 0 1 7 7

Detroit 100 99 600 1 0 1 5 7 1 0 0 4 5

Detroit 150 98 600 9 0 5 43 46 5 0 3 26 28

Detroit 150 99 600 2 0 1 11 12 1 0 1 7 7

Detroit 200 98 600 26 1 18 80 100 13 1 8 56 57

Detroit 200 99 600 7 0 3 33 37 3 0 2 16 18

El Paso As is 1200 0 0 0 1 1 0 0 0 0 1

El Paso Current 

Std 

1200 33 0 20 160 168 14 0 7 76 86

El Paso 50 98 1200 0 0 0 0 0 0 0 0 0 0

El Paso 50 99 1200 0 0 0 0 0 0 0 0 0 0

El Paso 100 98 1200 1 0 0 6 7 0 0 0 3 3

El Paso 100 99 1200 0 0 0 3 3 0 0 0 1 1

El Paso 150 98 1200 10 0 4 51 56 3 0 0 23 24

El Paso 150 99 1200 5 0 1 27 31 1 0 0 10 12

El Paso 200 98 1200 39 0 24 159 171 17 0 8 80 91

El Paso 200 99 1200 21 0 11 104 118 8 0 3 46 49

Jacksonville As is 200 1 0 1 1 1 1 0 1 1 1

Jacksonville Current 

Std 

200 67 3 64 153 168 34 2 26 103 114

Jacksonville 50 98 200 1 0 1 1 1 1 0 1 1 1

Jacksonville 50 99 200 1 0 1 1 1 1 0 1 1 1

Jacksonville 100 98 200 2 0 1 11 11 1 0 1 3 3

Jacksonville 100 99 200 1 0 1 6 7 1 0 1 2 2

Jacksonville 150 98 200 20 0 11 68 74 7 0 4 34 34

Jacksonville 150 99 200 14 0 8 55 56 5 0 3 25 25

Jacksonville 200 98 200 64 3 60 145 168 32 2 25 97 114

Jacksonville 200 99 200 50 3 42 127 147 23 1 18 79 86

Las Vegas As is 1600 0 0 0 1 1 0 0 0 0 0

Las Vegas Current 

Std 

1600 20 0 3 155 172 9 0 0 76 90

Las Vegas 50 98 1600 0 0 0 0 0 0 0 0 0 0

Las Vegas 50 99 1600 0 0 0 0 0 0 0 0 0 0

Las Vegas 100 98 1600 1 0 0 24 30 0 0 0 6 7

Las Vegas 100 99 1600 1 0 0 13 15 0 0 0 2 3

Las Vegas 150 98 1600 12 0 1 97 116 5 0 0 63 71
A-180

Number of Daily Maximum Exceedances3

≥ 250 ppb ≥ 300 ppb

Location 

Standard

1

Percentile

2 Site-Years Mean Min Med p98 p99 Mean Min Med p98 p99

Las Vegas 150 99 1600 8 0 0 73 82 3 0 0 49 58

Las Vegas 200 98 1600 42 0 13 231 264 19 0 3 149 172

Las Vegas 200 99 1600 28 0 7 186 215 13 0 1 102 120

Los Angeles As is 5100 1 0 0 16 23 0 0 0 7 10

Los Angeles Current 

Std 

5100 7 0 1 53 59 3 0 0 29 34

Los Angeles 50 98 5100 0 0 0 0 0 0 0 0 0 0

Los Angeles 50 99 5100 0 0 0 0 0 0 0 0 0 0

Los Angeles 100 98 5100 0 0 0 5 7 0 0 0 1 3

Los Angeles 100 99 5100 0 0 0 4 6 0 0 0 0 2

Los Angeles 150 98 5100 3 0 0 33 39 1 0 0 15 20

Los Angeles 150 99 5100 3 0 0 28 34 1 0 0 13 17

Los Angeles 200 98 5100 14 0 3 79 92 6 0 1 46 55

Los Angeles 200 99 5100 11 0 2 71 84 5 0 0 42 49

Miami As is 600 0 0 0 0 0 0 0 0 0 0

Miami Current 

Std 

600 40 0 33 143 154 19 0 10 97 103

Miami 50 98 600 0 0 0 0 0 0 0 0 0 0

Miami 50 99 600 0 0 0 0 0 0 0 0 0 0

Miami 100 98 600 1 0 0 12 18 0 0 0 5 7

Miami 100 99 600 0 0 0 5 9 0 0 0 2 3

Miami 150 98 600 12 0 5 68 85 5 0 1 37 48

Miami 150 99 600 7 0 2 47 61 3 0 0 21 31

Miami 200 98 600 40 0 31 127 156 19 0 10 92 110

Miami 200 99 600 24 0 14 105 123 11 0 4 67 84

New York As is 2600 0 0 0 6 9 0 0 0 1 2

New York Current 

Std 

2600 4 0 0 38 47 1 0 0 19 27

New York 50 98 2600 0 0 0 0 0 0 0 0 0 0

New York 50 99 2600 0 0 0 0 0 0 0 0 0 0

New York 100 98 2600 1 0 0 11 14 0 0 0 3 4

New York 100 99 2600 0 0 0 4 7 0 0 0 1 2

New York 150 98 2600 6 0 1 53 58 3 0 0 33 34

New York 150 99 2600 3 0 0 36 40 1 0 0 19 22

New York 200 98 2600 23 0 12 117 137 10 0 3 67 73

New York 200 99 2600 13 0 4 73 87 6 0 1 48 56

Philadelphia As is 1400 0 0 0 3 5 0 0 0 1 1

Philadelphia Current 

Std 

1400 10 0 2 69 75 4 0 1 41 46

Philadelphia 50 98 1400 0 0 0 1 1 0 0 0 1 1

Philadelphia 50 99 1400 0 0 0 1 1 0 0 0 1 1

Philadelphia 100 98 1400 2 0 0 26 40 1 0 0 8 20

Philadelphia 100 99 1400 1 0 0 14 27 0 0 0 4 10

Philadelphia 150 98 1400 17 0 4 90 100 7 0 1 54 68

Philadelphia 150 99 1400 11 0 2 69 83 4 0 1 41 56

Philadelphia 200 98 1400 56 0 38 198 225 27 0 12 118 137

Philadelphia 200 99 1400 40 0 24 158 184 18 0 5 93 105

Phoenix As is 500 0 0 0 4 5 0 0 0 0 0

Phoenix Current 500 8 0 0 58 69 2 0 0 18 22
A-181

Number of Daily Maximum Exceedances3

≥ 250 ppb ≥ 300 ppb

Location 

Standard

1

Percentile

2 Site-Years Mean Min Med p98 p99 Mean Min Med p98 p99

Std 

Phoenix 50 98 500 0 0 0 0 0 0 0 0 0 0

Phoenix 50 99 500 0 0 0 0 0 0 0 0 0 0

Phoenix 100 98 500 1 0 0 11 12 0 0 0 2 3

Phoenix 100 99 500 0 0 0 5 5 0 0 0 1 1

Phoenix 150 98 500 20 0 3 135 147 6 0 0 44 48

Phoenix 150 99 500 11 0 1 81 88 3 0 0 19 24

Phoenix 200 98 500 76 1 54 259 284 35 0 14 182 206

Phoenix 200 99 500 52 0 27 221 244 22 0 4 135 156

Provo As is 300 0 0 0 2 2 0 0 0 0 0

Provo Current 

Std 

300 37 0 16 174 204 15 0 2 74 90

Provo 50 98 300 0 0 0 0 0 0 0 0 0 0

Provo 50 99 300 0 0 0 0 0 0 0 0 0 0

Provo 100 98 300 7 0 0 52 56 2 0 0 18 18

Provo 100 99 300 5 0 0 38 40 1 0 0 9 9

Provo 150 98 300 47 0 24 208 253 18 0 4 86 106

Provo 150 99 300 34 0 14 161 203 13 0 2 75 82

Provo 200 98 300 151 11 128 331 348 75 1 51 273 301

Provo 200 99 300 121 4 97 318 342 56 1 33 235 283

St. Louis As is 900 0 0 0 1 1 0 0 0 1 1

St. Louis Current 

Std 

900 19 0 7 103 122 7 0 1 50 59

St. Louis 50 98 900 0 0 0 1 1 0 0 0 0 1

St. Louis 50 99 900 0 0 0 1 1 0 0 0 0 1

St. Louis 100 98 900 2 0 0 22 27 1 0 0 7 10

St. Louis 100 99 900 1 0 0 12 18 0 0 0 4 4

St. Louis 150 98 900 17 0 6 99 103 7 0 1 45 51

St. Louis 150 99 900 11 0 2 68 70 4 0 0 35 41

St. Louis 200 98 900 61 0 47 215 230 28 0 15 144 153

St. Louis 200 99 900 44 0 28 181 200 19 0 6 105 114

Washington 

DC 

As is 1800 0 0 0 1 1 0 0 0 0 0

Washington 

DC 

Current 

Std 

1800 18 0 4 107 128 8 0 0 60 65

Washington 

DC 

50 98 1800 0 0 0 0 0 0 0 0 0 0

Washington 

DC 

50 99 1800 0 0 0 0 0 0 0 0 0 0

Washington 

DC 

100 98 1800 2 0 0 22 28 0 0 0 6 8

Washington 

DC 

100 99 1800 1 0 0 10 14 0 0 0 2 2

Washington 

DC 

150 98 1800 18 0 4 100 123 7 0 0 56 63

Washington 

DC 

150 99 1800 11 0 1 72 84 5 0 0 38 45

Washington 

DC 

200 98 1800 56 0 36 213 251 27 0 10 139 168

Washington 

DC 

200 99 1800 38 0 18 175 211 18 0 4 102 123
A-182

Number of Daily Maximum Exceedances3

≥ 250 ppb ≥ 300 ppb

Location 

Standard

1

Percentile

2 Site-Years Mean Min Med p98 p99 Mean Min Med p98 p99

Other MSA As is 61200 0 0 0 1 2 0 0 0 0 0

Other MSA Current 

Std 

61200 5 0 0 47 62 2 0 0 21 30

Other MSA 50 98 61200 0 0 0 0 0 0 0 0 0 0

Other MSA 50 99 61200 0 0 0 0 0 0 0 0 0 0

Other MSA 100 98 61200 0 0 0 1 2 0 0 0 0 1

Other MSA 100 99 61200 0 0 0 0 1 0 0 0 0 0

Other MSA 150 98 61200 1 0 0 13 22 0 0 0 5 8

Other MSA 150 99 61200 0 0 0 6 9 0 0 0 2 3

Other MSA 200 98 61200 6 0 0 51 66 2 0 0 23 34

Other MSA 200 99 61200 3 0 0 28 39 1 0 0 11 17

Other Not 

MSA 

As is 12700 0 0 0 1 2 0 0 0 1 1

Other Not 

MSA 

Current 

Std 

12700 13 0 1 107 130 6 0 0 63 82

Other Not 

MSA 

50 98 12700 0 0 0 0 0 0 0 0 0 0

Other Not 

MSA 

50 99 12700 0 0 0 0 0 0 0 0 0 0

Other Not 

MSA 

100 98 12700 0 0 0 2 3 0 0 0 1 1

Other Not 

MSA 

100 99 12700 0 0 0 1 1 0 0 0 1 1

Other Not 

MSA 

150 98 12700 1 0 0 9 14 0 0 0 4 8

Other Not 

MSA 

150 99 12700 0 0 0 5 8 0 0 0 2 4

Other Not 

MSA 

200 98 12700 2 0 0 25 35 1 0 0 12 17

Other Not 

MSA 

200 99 12700 1 0 0 14 18 0 0 0 6 13

Notes: 

1

 Scenario: As is – unadjusted air quality, Current Std – air quality that just meets the current annual standard, All 

others – air quality that just meets 1-hour concentration level given percentile form of alternative standard. 

2

 Percentile: 98th or 99th percentile of daily maximum 1-hour concentration averaged over three years at 

maximum monitor in location. 

3

 The mean number of exceedances represents the sum of daily maximum exceedances occurring at all 

monitors in a particular location divided by the number of site-years across the monitoring period. The min, med, 

p98, and p99 represent the minimum, median, 98th, and 99th percentiles of the distribution for the number of daily 

maximum exceedances in any one year within the monitoring period. 
A-183

1 A-9.3 Annual average NO2 concentration data for 2004-2006 

2 Table A-123. Estimated annual average NO2 concentrations for monitors ≥100 m from a major road using 

3 2004-2006 air quality as is and adjusted to just meet the current and alternative standards. 

Annual Average NO2 (ppb)3

Location Scenario1 Percentile2

SiteYears Mean Min med p98 p99 

Atlanta As is 15 11 3 14 18 18 

Atlanta Current Std 15 34 10 44 53 53 

Atlanta 50 98 15 8 2 10 13 13 

Atlanta 50 99 15 7 2 9 11 11 

Atlanta 100 98 15 16 5 20 25 25 

Atlanta 100 99 15 14 4 18 23 23 

Atlanta 150 98 15 24 7 30 38 38 

Atlanta 150 99 15 22 7 28 34 34 

Atlanta 200 98 15 32 10 40 50 50 

Atlanta 200 99 15 29 9 37 46 46 

Atlanta 250 98 15 39 12 51 63 63 

Atlanta 250 99 15 36 11 46 57 57 

Atlanta 300 98 15 47 14 61 75 75 

Atlanta 300 99 15 43 13 55 69 69 

Boston As is 8 9 7 9 10 10 

Boston Current Std 8 20 15 20 23 23 

Boston 50 98 8 6 5 6 7 7 

Boston 50 99 8 5 4 5 6 6 

Boston 100 98 8 12 10 13 14 14 

Boston 100 99 8 11 9 11 12 12 

Boston 150 98 8 19 16 19 21 21 

Boston 150 99 8 16 13 16 18 18 

Boston 200 98 8 25 21 25 29 29 

Boston 200 99 8 21 18 21 24 24 

Boston 250 98 8 31 26 31 36 36 

Boston 250 99 8 27 22 27 30 30 

Boston 300 98 8 37 31 38 43 43 

Boston 300 99 8 32 27 32 36 36 

Chicago As is 8 19 16 18 24 24 

Chicago Current Std 8 35 28 32 44 44 

Chicago 50 98 8 11 9 10 14 14 

Chicago 50 99 8 10 9 10 13 13 

Chicago 100 98 8 22 18 21 28 28 

Chicago 100 99 8 21 17 19 26 26 

Chicago 150 98 8 33 27 31 42 42 

Chicago 150 99 8 31 26 29 39 39 

Chicago 200 98 8 44 36 41 55 55 

Chicago 200 99 8 42 34 39 52 52 

Chicago 250 98 8 56 45 51 69 69 

Chicago 250 99 8 52 43 48 65 65 

Chicago 300 98 8 67 54 62 83 83 
A-184

Annual Average NO2 (ppb)3

Location Scenario1 Percentile2

SiteYears Mean Min med p98 p99 

Chicago 300 99 8 63 51 58 78 78 

Denver As is 3 20 18 20 21 21 

Denver Current Std 3 38 33 39 42 42 

Denver 50 98 3 13 12 13 14 14 

Denver 50 99 3 12 11 12 13 13 

Denver 100 98 3 26 24 27 28 28 

Denver 100 99 3 23 21 24 25 25 

Denver 150 98 3 39 36 40 42 42 

Denver 150 99 3 35 32 36 38 38 

Denver 200 98 3 53 48 53 57 57 

Denver 200 99 3 47 42 47 50 50 

Denver 250 98 3 66 59 67 71 71 

Denver 250 99 3 58 53 59 63 63 

Denver 300 98 3 79 71 80 85 85 

Denver 300 99 3 70 63 71 75 75 

Detroit As is 6 17 14 17 20 20 

Detroit Current Std 6 49 42 50 53 53 

Detroit 50 98 6 15 13 15 18 18 

Detroit 50 99 6 14 12 14 17 17 

Detroit 100 98 6 31 26 30 36 36 

Detroit 100 99 6 29 24 29 34 34 

Detroit 150 98 6 46 38 46 54 54 

Detroit 150 99 6 43 36 43 51 51 

Detroit 200 98 6 61 51 61 72 72 

Detroit 200 99 6 58 48 57 68 68 

Detroit 250 98 6 77 64 76 90 90 

Detroit 250 99 6 72 60 72 84 84 

Detroit 300 98 6 92 77 91 107 107 

Detroit 300 99 6 87 72 86 101 101 

El Paso As is 12 14 8 15 18 18 

El Paso Current Std 12 42 24 45 53 53 

El Paso 50 98 12 10 6 11 13 13 

El Paso 50 99 12 9 5 10 12 12 

El Paso 100 98 12 21 12 22 27 27 

El Paso 100 99 12 19 11 20 24 24 

El Paso 150 98 12 31 18 33 40 40 

El Paso 150 99 12 28 16 30 36 36 

El Paso 200 98 12 42 24 45 54 54 

El Paso 200 99 12 37 22 40 48 48 

El Paso 250 98 12 52 30 56 67 67 

El Paso 250 99 12 46 27 50 60 60 

El Paso 300 98 12 62 36 67 80 80 

El Paso 300 99 12 56 33 60 72 72 

Jacksonville As is 2 14 13 14 14 14 

Jacksonville Current Std 2 53 53 53 53 53 
A-185

Annual Average NO2 (ppb)3

Location Scenario1 Percentile2

SiteYears Mean Min med p98 p99 

Jacksonville 50 98 2 13 13 13 13 13 

Jacksonville 50 99 2 9 9 9 9 9 

Jacksonville 100 98 2 26 26 26 26 26 

Jacksonville 100 99 2 18 18 18 18 18 

Jacksonville 150 98 2 39 38 39 39 39 

Jacksonville 150 99 2 27 26 27 27 27 

Jacksonville 200 98 2 52 51 52 53 53 

Jacksonville 200 99 2 36 35 36 36 36 

Jacksonville 250 98 2 65 64 65 66 66 

Jacksonville 250 99 2 44 44 44 45 45 

Jacksonville 300 98 2 78 77 78 79 79 

Jacksonville 300 99 2 53 53 53 54 54 

Las Vegas As is 11 9 1 6 20 20 

Las Vegas Current Std 11 24 4 16 53 53 

Las Vegas 50 98 11 7 1 5 16 16 

Las Vegas 50 99 11 7 1 5 15 15 

Las Vegas 100 98 11 15 2 10 32 32 

Las Vegas 100 99 11 14 2 9 30 30 

Las Vegas 150 98 11 22 3 15 48 48 

Las Vegas 150 99 11 20 3 14 45 45 

Las Vegas 200 98 11 29 5 20 65 65 

Las Vegas 200 99 11 27 4 18 60 60 

Las Vegas 250 98 11 37 6 25 81 81 

Las Vegas 250 99 11 34 5 23 75 75 

Las Vegas 300 98 11 44 7 30 97 97 

Las Vegas 300 99 11 41 6 28 90 90 

Los Angeles As is 54 18 5 18 31 31 

Los Angeles Current Std 54 30 8 31 48 53 

Los Angeles 50 98 54 10 2 10 17 17 

Los Angeles 50 99 54 9 2 9 15 16 

Los Angeles 100 98 54 20 5 20 34 34 

Los Angeles 100 99 54 18 5 18 31 31 

Los Angeles 150 98 54 30 7 29 50 51 

Los Angeles 150 99 54 27 7 27 46 47 

Los Angeles 200 98 54 40 10 39 67 68 

Los Angeles 200 99 54 37 9 36 62 62 

Los Angeles 250 98 54 50 12 49 84 85 

Los Angeles 250 99 54 46 11 45 77 78 

Los Angeles 300 98 54 60 15 59 101 102 

Los Angeles 300 99 54 55 14 54 92 94 

Miami As is 4 8 7 8 8 8 

Miami Current Std 4 31 28 31 32 32 

Miami 50 98 4 7 6 7 7 7 

Miami 50 99 4 6 6 6 7 7 

Miami 100 98 4 13 13 14 14 14 
A-186

Annual Average NO2 (ppb)3

Location Scenario1 Percentile2

SiteYears Mean Min med p98 p99 

Miami 100 99 4 13 12 13 13 13 

Miami 150 98 4 20 19 20 21 21 

Miami 150 99 4 19 18 19 20 20 

Miami 200 98 4 27 25 27 28 28 

Miami 200 99 4 25 24 26 26 26 

Miami 250 98 4 34 32 34 35 35 

Miami 250 99 4 32 30 32 33 33 

Miami 300 98 4 40 38 41 42 42 

Miami 300 99 4 38 36 39 39 39 

New York As is 22 19 10 20 27 27 

New York Current Std 22 30 16 32 43 43 

New York 50 98 22 12 6 12 16 16 

New York 50 99 22 10 5 11 14 14 

New York 100 98 22 23 12 25 33 33 

New York 100 99 22 20 10 21 28 28 

New York 150 98 22 35 18 37 49 49 

New York 150 99 22 30 15 32 42 42 

New York 200 98 22 47 24 49 65 65 

New York 200 99 22 41 21 43 57 57 

New York 250 98 22 59 30 62 82 82 

New York 250 99 22 51 26 54 71 71 

New York 300 98 22 70 36 74 98 98 

New York 300 99 22 61 31 64 85 85 

Philadelphia As is 12 17 14 16 25 25 

Philadelphia Current Std 12 39 29 39 51 51 

Philadelphia 50 98 12 13 11 12 19 19 

Philadelphia 50 99 12 11 9 10 15 15 

Philadelphia 100 98 12 26 21 25 37 37 

Philadelphia 100 99 12 21 17 20 30 30 

Philadelphia 150 98 12 39 32 37 56 56 

Philadelphia 150 99 12 32 26 30 45 45 

Philadelphia 200 98 12 52 43 50 75 75 

Philadelphia 200 99 12 42 34 40 60 60 

Philadelphia 250 98 12 65 53 62 94 94 

Philadelphia 250 99 12 53 43 50 75 75 

Philadelphia 300 98 12 79 64 75 112 112 

Philadelphia 300 99 12 63 52 60 90 90 

Phoenix As is 9 24 21 24 26 26 

Phoenix Current Std 9 41 36 40 44 44 

Phoenix 50 98 9 14 12 14 16 16 

Phoenix 50 99 9 13 11 13 14 14 

Phoenix 100 98 9 29 25 29 31 31 

Phoenix 100 99 9 26 22 26 28 28 

Phoenix 150 98 9 43 37 43 47 47 

Phoenix 150 99 9 38 33 39 42 42 
A-187

Annual Average NO2 (ppb)3

Location Scenario1 Percentile2

SiteYears Mean Min med p98 p99 

Phoenix 200 98 9 57 50 57 63 63 

Phoenix 200 99 9 51 45 51 56 56 

Phoenix 250 98 9 71 62 71 78 78 

Phoenix 250 99 9 64 56 64 70 70 

Phoenix 300 98 9 86 74 86 94 94 

Phoenix 300 99 9 77 67 77 85 85 

Provo As is 3 24 21 22 29 29 

Provo Current Std 3 53 53 53 53 53 

Provo 50 98 3 13 11 12 15 15 

Provo 50 99 3 12 11 12 15 15 

Provo 100 98 3 25 22 24 31 31 

Provo 100 99 3 25 21 23 30 30 

Provo 150 98 3 38 33 36 46 46 

Provo 150 99 3 37 32 35 45 45 

Provo 200 98 3 51 44 48 62 62 

Provo 200 99 3 50 43 47 60 60 

Provo 250 98 3 64 55 59 77 77 

Provo 250 99 3 62 53 58 75 75 

Provo 300 98 3 76 65 71 92 92 

Provo 300 99 3 75 64 70 90 90 

St. Louis As is 4 15 12 14 18 18 

St. Louis Current Std 4 38 29 36 49 49 

St. Louis 50 98 4 12 10 12 14 14 

St. Louis 50 99 4 12 10 11 14 14 

St. Louis 100 98 4 24 20 23 29 29 

St. Louis 100 99 4 23 20 23 28 28 

St. Louis 150 98 4 36 30 35 43 43 

St. Louis 150 99 4 35 29 34 42 42 

St. Louis 200 98 4 48 40 47 58 58 

St. Louis 200 99 4 46 39 45 56 56 

St. Louis 250 98 4 60 50 58 72 72 

St. Louis 250 99 4 58 49 56 70 70 

St. Louis 300 98 4 72 61 70 87 87 

St. Louis 300 99 4 69 59 68 84 84 

Washington DC As is 17 15 7 16 22 22 

Washington DC Current Std 17 36 19 42 51 51 

Washington DC 50 98 17 12 5 12 17 17 

Washington DC 50 99 17 9 4 10 14 14 

Washington DC 100 98 17 23 10 24 33 33 

Washington DC 100 99 17 19 8 20 27 27 

Washington DC 150 98 17 35 16 36 50 50 

Washington DC 150 99 17 28 13 30 41 41 

Washington DC 200 98 17 46 21 48 67 67 

Washington DC 200 99 17 38 17 39 54 54 

Washington DC 250 98 17 58 26 60 84 84 
A-188

Annual Average NO2 (ppb)3

Location Scenario1 Percentile2

SiteYears Mean Min med p98 p99 

Washington DC 250 99 17 47 21 49 68 68 

Washington DC 300 98 17 69 31 72 100 100 

Washington DC 300 99 17 56 25 59 82 82 

Other MSA As is 565 11 1 11 21 23 

Other MSA Current Std 565 26 2 26 49 52 

Other MSA 50 98 565 6 0 6 12 13 

Other MSA 50 99 565 6 0 6 11 12 

Other MSA 100 98 565 13 1 13 24 27 

Other MSA 100 99 565 12 1 12 22 25 

Other MSA 150 98 565 19 1 19 36 40 

Other MSA 150 99 565 18 1 18 33 37 

Other MSA 200 98 565 26 2 26 48 54 

Other MSA 200 99 565 24 2 24 44 50 

Other MSA 250 98 565 32 2 32 59 67 

Other MSA 250 99 565 29 2 30 55 62 

Other MSA 300 98 565 38 3 39 71 81 

Other MSA 300 99 565 35 3 36 66 74 

Other Not MSA As is 116 7 1 6 16 16 

Other Not MSA Current Std 116 21 3 19 53 53 

Other Not MSA 50 98 116 3 0 3 8 8 

Other Not MSA 50 99 116 3 0 3 7 7 

Other Not MSA 100 98 116 7 1 7 17 17 

Other Not MSA 100 99 116 6 1 5 13 14 

Other Not MSA 150 98 116 10 1 10 25 25 

Other Not MSA 150 99 116 8 1 8 20 20 

Other Not MSA 200 98 116 14 2 13 33 34 

Other Not MSA 200 99 116 11 1 10 27 27 

Other Not MSA 250 98 116 17 2 16 42 42 

Other Not MSA 250 99 116 14 2 13 34 34 

Other Not MSA 300 98 116 21 3 20 50 51 

Other Not MSA 300 99 116 17 2 16 40 41 

Notes: 

1

 Scenario: As is – unadjusted air quality, Current Std – air quality that just meets the current annual 

standard, All others – air quality that just meets 1-hour concentration level given percentile form of 

alternative standard. 

2

 Percentile: 98th or 99th percentile of daily maximum 1-hour concentration averaged over three years at 

maximum monitor in location. 

3 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, 

p98, p99 represent the minimum, median, 98th, and 99th percentiles of the distribution for the annual 

means. 

1 
A-189

1 

2 Table A-124. Estimated annual average NO2 concentrations for monitors >20 m and <100 m from a major 

3 road using 2004-2006 air quality as is and air quality adjusted to just meet the current and alternative 

4 standards. 

Annual Average NO2 (ppb)3

Location Scenario1

 Percentile2

SiteYears Mean Min med p98 p99 

Boston As is 11 15 10 16 19 19 

Boston Current Std 11 34 24 35 44 44 

Boston 50 98 11 11 7 11 13 13 

Boston 50 99 11 9 6 10 11 11 

Boston 100 98 11 22 15 23 27 27 

Boston 100 99 11 19 12 19 23 23 

Boston 150 98 11 33 22 34 40 40 

Boston 150 99 11 28 19 29 34 34 

Boston 200 98 11 44 29 45 54 54 

Boston 200 99 11 37 25 38 46 46 

Boston 250 98 11 55 36 56 67 67 

Boston 250 99 11 47 31 48 57 57 

Boston 300 98 11 66 44 68 81 81 

Boston 300 99 11 56 37 58 69 69 

Chicago As is 6 29 28 29 31 31 

Chicago Current Std 6 52 48 52 53 53 

Chicago 50 98 6 17 16 16 17 17 

Chicago 50 99 6 16 15 16 16 16 

Chicago 100 98 6 33 31 33 35 35 

Chicago 100 99 6 31 30 31 33 33 

Chicago 150 98 6 50 47 49 52 52 

Chicago 150 99 6 47 44 47 49 49 

Chicago 200 98 6 66 63 66 70 70 

Chicago 200 99 6 62 59 62 66 66 

Chicago 250 98 6 83 79 82 87 87 

Chicago 250 99 6 78 74 78 82 82 

Chicago 300 98 6 99 94 99 105 105 

Chicago 300 99 6 93 89 93 99 99 

Cleveland As is 2 15 14 15 17 17 

Cleveland Current Std 2 41 41 41 41 41 

Cleveland 50 98 2 12 11 12 13 13 

Cleveland 50 99 2 11 10 11 12 12 

Cleveland 100 98 2 24 22 24 26 26 

Cleveland 100 99 2 21 20 21 23 23 

Cleveland 150 98 2 36 33 36 39 39 

Cleveland 150 99 2 32 29 32 35 35 

Cleveland 200 98 2 47 43 47 51 51 

Cleveland 200 99 2 43 39 43 46 46 

Cleveland 250 98 2 59 54 59 64 64 

Cleveland 250 99 2 54 49 54 58 58 

Cleveland 300 98 2 71 65 71 77 77 
A-190

Annual Average NO2 (ppb)3

Location Scenario1

 Percentile2

SiteYears Mean Min med p98 p99 

Cleveland 300 99 2 64 59 64 70 70 

El Paso As is 3 15 13 13 18 18 

El Paso Current Std 3 44 39 40 53 53 

El Paso 50 98 3 11 10 10 13 13 

El Paso 50 99 3 10 9 9 12 12 

El Paso 100 98 3 22 19 20 27 27 

El Paso 100 99 3 20 17 17 24 24 

El Paso 150 98 3 33 29 29 40 40 

El Paso 150 99 3 29 26 26 36 36 

El Paso 200 98 3 44 39 39 53 53 

El Paso 200 99 3 39 35 35 48 48 

El Paso 250 98 3 55 48 49 67 67 

El Paso 250 99 3 49 43 44 60 60 

El Paso 300 98 3 66 58 59 80 80 

El Paso 300 99 3 59 52 52 72 72 

Los Angeles As is 22 25 9 27 34 34 

Los Angeles Current Std 22 41 15 47 53 53 

Los Angeles 50 98 22 14 5 15 19 19 

Los Angeles 50 99 22 12 4 14 17 17 

Los Angeles 100 98 22 27 9 30 37 37 

Los Angeles 100 99 22 25 9 27 34 34 

Los Angeles 150 98 22 41 14 44 56 56 

Los Angeles 150 99 22 37 13 41 51 51 

Los Angeles 200 98 22 54 19 59 74 74 

Los Angeles 200 99 22 50 17 54 68 68 

Los Angeles 250 98 22 68 23 74 93 93 

Los Angeles 250 99 22 62 22 68 85 85 

Los Angeles 300 98 22 81 28 89 111 111 

Los Angeles 300 99 22 75 26 82 102 102 

Miami As is 2 13 13 13 14 14 

Miami Current Std 2 53 53 53 53 53 

Miami 50 98 2 12 11 12 12 12 

Miami 50 99 2 11 11 11 11 11 

Miami 100 98 2 23 23 23 24 24 

Miami 100 99 2 22 21 22 22 22 

Miami 150 98 2 35 34 35 36 36 

Miami 150 99 2 33 32 33 34 34 

Miami 200 98 2 46 46 46 47 47 

Miami 200 99 2 44 43 44 45 45 

Miami 250 98 2 58 57 58 59 59 

Miami 250 99 2 55 54 55 56 56 

Miami 300 98 2 70 68 70 71 71 

Miami 300 99 2 66 64 66 67 67 

New York As is 11 28 18 29 36 36 

New York Current Std 11 43 28 42 53 53 
A-191

Annual Average NO2 (ppb)3

Location Scenario1

 Percentile2

SiteYears Mean Min med p98 p99 

New York 50 98 11 17 11 18 22 22 

New York 50 99 11 15 10 15 19 19 

New York 100 98 11 34 22 35 45 45 

New York 100 99 11 30 20 31 39 39 

New York 150 98 11 51 34 53 67 67 

New York 150 99 11 44 29 46 58 58 

New York 200 98 11 68 45 71 90 90 

New York 200 99 11 59 39 61 78 78 

New York 250 98 11 85 56 88 112 112 

New York 250 99 11 74 49 77 97 97 

New York 300 98 11 102 67 106 134 134 

New York 300 99 11 89 59 92 116 116 

Philadelphia As is 6 22 18 22 26 26 

Philadelphia Current Std 6 48 36 50 53 53 

Philadelphia 50 98 6 17 13 17 20 20 

Philadelphia 50 99 6 13 11 14 16 16 

Philadelphia 100 98 6 33 27 34 40 40 

Philadelphia 100 99 6 27 22 27 32 32 

Philadelphia 150 98 6 50 40 50 60 60 

Philadelphia 150 99 6 40 32 41 48 48 

Philadelphia 200 98 6 66 54 67 80 80 

Philadelphia 200 99 6 54 43 54 64 64 

Philadelphia 250 98 6 83 67 84 100 100 

Philadelphia 250 99 6 67 54 68 80 80 

Philadelphia 300 98 6 100 80 101 120 120 

Philadelphia 300 99 6 80 65 81 96 96 

Phoenix As is 3 19 19 19 20 20 

Phoenix Current Std 3 33 33 33 33 33 

Phoenix 50 98 3 12 12 12 12 12 

Phoenix 50 99 3 10 10 10 11 11 

Phoenix 100 98 3 23 23 23 24 24 

Phoenix 100 99 3 21 21 21 21 21 

Phoenix 150 98 3 35 35 35 35 35 

Phoenix 150 99 3 31 31 31 32 32 

Phoenix 200 98 3 47 46 47 47 47 

Phoenix 200 99 3 42 41 42 42 42 

Phoenix 250 98 3 58 58 58 59 59 

Phoenix 250 99 3 52 52 52 53 53 

Phoenix 300 98 3 70 69 70 71 71 

Phoenix 300 99 3 63 62 63 63 63 

St. Louis As is 8 12 8 10 22 22 

St. Louis Current Std 8 32 19 30 53 53 

St. Louis 50 98 8 9 7 8 18 18 

St. Louis 50 99 8 9 6 8 18 18 

St. Louis 100 98 8 19 13 16 37 37 
A-192

Annual Average NO2 (ppb)3

Location Scenario1

 Percentile2

SiteYears Mean Min med p98 p99 

St. Louis 100 99 8 18 13 16 35 35 

St. Louis 150 98 8 28 20 25 55 55 

St. Louis 150 99 8 27 19 24 53 53 

St. Louis 200 98 8 38 27 33 73 73 

St. Louis 200 99 8 37 26 32 71 71 

St. Louis 250 98 8 47 33 41 92 92 

St. Louis 250 99 8 46 32 40 89 89 

St. Louis 300 98 8 57 40 49 110 110 

St. Louis 300 99 8 55 39 48 106 106 

Washington DC As is 12 18 13 18 24 24 

Washington DC Current Std 12 43 30 43 53 53 

Washington DC 50 98 12 14 10 13 18 18 

Washington DC 50 99 12 11 8 11 15 15 

Washington DC 100 98 12 28 20 27 37 37 

Washington DC 100 99 12 23 17 22 30 30 

Washington DC 150 98 12 42 30 40 55 55 

Washington DC 150 99 12 34 25 33 45 45 

Washington DC 200 98 12 55 41 53 73 73 

Washington DC 200 99 12 45 33 43 60 60 

Washington DC 250 98 12 69 51 67 91 91 

Washington DC 250 99 12 56 41 54 74 74 

Washington DC 300 98 12 83 61 80 110 110 

Washington DC 300 99 12 68 50 65 89 89 

Notes: 

1

 Scenario: As is – unadjusted air quality, Current Std – air quality that just meets the current annual 

standard, All others – air quality that just meets 1-hour concentration level given percentile form of 

alternative standard. 

2

 Percentile: 98th or 99th percentile of daily maximum 1-hour concentration averaged over three years at 

maximum monitor in location. 

3 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, 

p98, p99 represent the minimum, median, 98th, and 99th percentiles of the distribution for the annual 

means. 

1 
A-193

1 Table A-125. Estimated annual average NO2 concentrations for monitors ≤ 20 m from a major road using 

2 2004-2006 air quality as is and air quality adjusted to just meet the current and alternative standards. 

Annual Average NO2 (ppb)3

Location Scenario1 Percentile2

SiteYears Mean Min med p98 p99 

Boston As is 3 24 23 23 25 25 

Boston Current Std 3 53 53 53 53 53 

Boston 50 98 3 17 16 17 18 18 

Boston 50 99 3 15 14 14 15 15 

Boston 100 98 3 34 32 34 36 36 

Boston 100 99 3 29 28 29 31 31 

Boston 150 98 3 51 49 51 54 54 

Boston 150 99 3 44 41 43 46 46 

Boston 200 98 3 68 65 67 72 72 

Boston 200 99 3 58 55 57 61 61 

Boston 250 98 3 85 81 84 90 90 

Boston 250 99 3 73 69 72 77 77 

Boston 300 98 3 102 97 101 108 108 

Boston 300 99 3 87 83 86 92 92 

Chicago As is 3 19 18 20 20 20 

Chicago Current Std 3 34 31 36 36 36 

Chicago 50 98 3 11 10 11 11 11 

Chicago 50 99 3 10 10 11 11 11 

Chicago 100 98 3 22 20 23 23 23 

Chicago 100 99 3 21 19 21 22 22 

Chicago 150 98 3 33 31 34 34 34 

Chicago 150 99 3 31 29 32 32 32 

Chicago 200 98 3 44 41 45 46 46 

Chicago 200 99 3 41 38 43 43 43 

Chicago 250 98 3 55 51 57 57 57 

Chicago 250 99 3 52 48 53 54 54 

Chicago 300 98 3 66 61 68 69 69 

Chicago 300 99 3 62 58 64 65 65 

Cleveland As is 3 21 18 22 22 22 

Cleveland Current Std 3 53 53 53 53 53 

Cleveland 50 98 3 16 14 16 17 17 

Cleveland 50 99 3 14 13 15 15 15 

Cleveland 100 98 3 32 28 33 34 34 

Cleveland 100 99 3 29 25 30 31 31 

Cleveland 150 98 3 47 42 49 51 51 

Cleveland 150 99 3 43 38 45 46 46 

Cleveland 200 98 3 63 56 66 68 68 

Cleveland 200 99 3 57 50 60 61 61 

Cleveland 250 98 3 79 70 82 85 85 

Cleveland 250 99 3 71 63 74 77 77 

Cleveland 300 98 3 95 84 99 102 102 

Cleveland 300 99 3 86 75 89 92 92 

Denver As is 3 28 27 28 29 29 

Denver Current Std 3 53 53 53 53 53 
A-194

Annual Average NO2 (ppb)3

Location Scenario1 Percentile2

SiteYears Mean Min med p98 p99 

Denver 50 98 3 18 18 18 19 19 

Denver 50 99 3 16 16 16 17 17 

Denver 100 98 3 37 36 36 38 38 

Denver 100 99 3 33 32 32 34 34 

Denver 150 98 3 55 54 54 57 57 

Denver 150 99 3 49 48 48 51 51 

Denver 200 98 3 74 72 73 76 76 

Denver 200 99 3 65 64 64 68 68 

Denver 250 98 3 92 90 91 96 96 

Denver 250 99 3 82 79 81 85 85 

Denver 300 98 3 110 107 109 115 115 

Denver 300 99 3 98 95 97 102 102 

Las Vegas As is 2 19 19 19 20 20 

Las Vegas Current Std 2 52 51 52 53 53 

Las Vegas 50 98 2 16 16 16 16 16 

Las Vegas 50 99 2 15 14 15 15 15 

Las Vegas 100 98 2 32 31 32 33 33 

Las Vegas 100 99 2 30 29 30 30 30 

Las Vegas 150 98 2 48 47 48 49 49 

Las Vegas 150 99 2 44 43 44 45 45 

Las Vegas 200 98 2 64 63 64 65 65 

Las Vegas 200 99 2 59 58 59 60 60 

Las Vegas 250 98 2 80 78 80 81 81 

Las Vegas 250 99 2 74 72 74 75 75 

Las Vegas 300 98 2 96 94 96 98 98 

Las Vegas 300 99 2 89 87 89 90 90 

Los Angeles As is 6 27 20 29 31 31 

Los Angeles Current Std 6 46 36 47 53 53 

Los Angeles 50 98 6 15 11 16 17 17 

Los Angeles 50 99 6 14 10 15 16 16 

Los Angeles 100 98 6 30 22 32 34 34 

Los Angeles 100 99 6 28 20 29 32 32 

Los Angeles 150 98 6 45 33 47 52 52 

Los Angeles 150 99 6 41 30 44 47 47 

Los Angeles 200 98 6 60 44 63 69 69 

Los Angeles 200 99 6 55 40 58 63 63 

Los Angeles 250 98 6 75 55 79 86 86 

Los Angeles 250 99 6 69 51 73 79 79 

Los Angeles 300 98 6 90 66 95 103 103 

Los Angeles 300 99 6 83 61 87 95 95 

Miami As is 2 6 6 6 6 6 

Miami Current Std 2 24 24 24 24 24 

Miami 50 98 2 5 5 5 5 5 

Miami 50 99 2 5 5 5 5 5 

Miami 100 98 2 10 10 10 11 11 
A-195

Annual Average NO2 (ppb)3

Location Scenario1 Percentile2

SiteYears Mean Min med p98 p99 

Miami 100 99 2 10 10 10 10 10 

Miami 150 98 2 16 15 16 16 16 

Miami 150 99 2 15 14 15 15 15 

Miami 200 98 2 21 20 21 21 21 

Miami 200 99 2 20 19 20 20 20 

Miami 250 98 2 26 25 26 26 26 

Miami 250 99 2 24 24 24 25 25 

Miami 300 98 2 31 30 31 32 32 

Miami 300 99 2 29 29 29 30 30 

New York As is 2 28 27 28 28 28 

New York Current Std 2 44 40 44 49 49 

New York 50 98 2 17 17 17 17 17 

New York 50 99 2 15 15 15 15 15 

New York 100 98 2 34 33 34 35 35 

New York 100 99 2 30 29 30 30 30 

New York 150 98 2 51 50 51 52 52 

New York 150 99 2 44 44 44 45 45 

New York 200 98 2 68 67 68 69 69 

New York 200 99 2 59 58 59 60 60 

New York 250 98 2 85 84 85 87 87 

New York 250 99 2 74 73 74 75 75 

New York 300 98 2 102 100 102 104 104 

New York 300 99 2 89 87 89 90 90 

Phoenix As is 5 23 11 31 32 32 

Phoenix Current Std 5 40 19 53 53 53 

Phoenix 50 98 5 14 7 18 19 19 

Phoenix 50 99 5 13 6 16 17 17 

Phoenix 100 98 5 28 13 37 38 38 

Phoenix 100 99 5 25 12 33 34 34 

Phoenix 150 98 5 42 20 55 57 57 

Phoenix 150 99 5 38 18 49 51 51 

Phoenix 200 98 5 56 26 73 75 75 

Phoenix 200 99 5 50 24 66 68 68 

Phoenix 250 98 5 70 33 92 94 94 

Phoenix 250 99 5 63 30 82 85 85 

Phoenix 300 98 5 84 40 110 113 113 

Phoenix 300 99 5 75 36 99 102 102 

St. Louis As is 5 16 15 16 17 17 

St. Louis Current Std 5 46 38 46 53 53 

St. Louis 50 98 5 13 12 13 14 14 

St. Louis 50 99 5 13 12 13 14 14 

St. Louis 100 98 5 26 24 26 28 28 

St. Louis 100 99 5 25 23 25 27 27 

St. Louis 150 98 5 39 36 39 42 42 

St. Louis 150 99 5 38 35 38 41 41 
A-196

Annual Average NO2 (ppb)3

Location Scenario1 Percentile2

SiteYears Mean Min med p98 p99 

St. Louis 200 98 5 52 48 52 56 56 

St. Louis 200 99 5 51 47 50 55 55 

St. Louis 250 98 5 65 60 65 70 70 

St. Louis 250 99 5 63 58 63 68 68 

St. Louis 300 98 5 78 72 78 85 85 

St. Louis 300 99 5 76 70 75 82 82 

Washington DC As is 5 19 14 18 23 23 

Washington DC Current Std 5 43 36 39 50 50 

Washington DC 50 98 5 14 11 13 17 17 

Washington DC 50 99 5 12 9 11 14 14 

Washington DC 100 98 5 28 22 27 35 35 

Washington DC 100 99 5 23 18 22 28 28 

Washington DC 150 98 5 42 33 40 52 52 

Washington DC 150 99 5 35 27 33 42 42 

Washington DC 200 98 5 57 44 53 69 69 

Washington DC 200 99 5 46 36 44 57 57 

Washington DC 250 98 5 71 55 67 87 87 

Washington DC 250 99 5 58 45 54 71 71 

Washington DC 300 98 5 85 66 80 104 104 

Washington DC 300 99 5 69 54 65 85 85 

Notes: 

1

 Scenario: As is – unadjusted air quality, Current Std – air quality that just meets the current annual 

standard, All others – air quality that just meets 1-hour concentration level given percentile form of 

alternative standard. 

2

 Percentile: 98th or 99th percentile of daily maximum 1-hour concentration averaged over three years at 

maximum monitor in location. 

3 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, 

p98, p99 represent the minimum, median, 98th, and 99th percentiles of the distribution for the annual 

means. 

1 
A-197

1 Table A-126. Estimated annual average NO2 concentrations on-roads using 2004-2006 air quality as is, air 

2 quality adjusted to just meet the current and alternative standards, and an on-road adjustment factor. 

Annual Average NO2 (ppb)3

Location Scenario1 Percentile2

SiteYears Mean Min Med p98 p99 

Atlanta As is 1500 20 4 22 40 42 

Atlanta Current Std 1500 62 13 68 124 128 

Atlanta 50 98 1500 14 3 16 28 30 

Atlanta 50 99 1500 13 3 14 26 27 

Atlanta 100 98 1500 28 6 31 57 59 

Atlanta 100 99 1500 26 6 29 52 54 

Atlanta 150 98 1500 43 9 47 85 89 

Atlanta 150 99 1500 39 8 43 78 81 

Atlanta 200 98 1500 57 12 63 114 118 

Atlanta 200 99 1500 52 11 57 104 108 

Atlanta 250 98 1500 71 15 78 142 148 

Atlanta 250 99 1500 65 14 71 130 135 

Atlanta 300 98 1500 85 18 94 170 177 

Atlanta 300 99 1500 78 17 86 155 162 

Boston As is 800 16 9 15 24 24 

Boston Current Std 800 35 19 34 54 57 

Boston 50 98 800 11 7 11 17 18 

Boston 50 99 800 10 6 9 14 15 

Boston 100 98 800 23 13 22 34 35 

Boston 100 99 800 19 11 19 29 30 

Boston 150 98 800 34 20 33 51 53 

Boston 150 99 800 29 17 28 43 45 

Boston 200 98 800 45 26 43 68 70 

Boston 200 99 800 38 22 37 58 60 

Boston 250 98 800 56 33 54 85 88 

Boston 250 99 800 48 28 46 72 75 

Boston 300 98 800 68 40 65 102 106 

Boston 300 99 800 58 34 56 87 90 

Chicago As is 800 35 20 33 57 60 

Chicago Current Std 800 63 35 59 103 107 

Chicago 50 98 800 20 12 19 33 34 

Chicago 50 99 800 19 11 18 31 32 

Chicago 100 98 800 40 23 38 65 68 

Chicago 100 99 800 38 22 36 61 64 

Chicago 150 98 800 60 35 57 98 102 

Chicago 150 99 800 57 33 54 92 96 

Chicago 200 98 800 80 46 76 130 136 

Chicago 200 99 800 75 43 72 123 128 

Chicago 250 98 800 100 58 95 163 170 

Chicago 250 99 800 94 54 89 153 160 

Chicago 300 98 800 120 69 114 195 204 

Chicago 300 99 800 113 65 107 184 193 

Denver As is 300 36 23 36 51 53 

Denver Current Std 300 69 42 68 99 103 
A-198

Annual Average NO2 (ppb)3

Location Scenario1 Percentile2

SiteYears Mean Min Med p98 p99 

Denver 50 98 300 24 15 24 33 35 

Denver 50 99 300 21 13 21 30 31 

Denver 100 98 300 48 30 47 67 69 

Denver 100 99 300 43 27 42 59 62 

Denver 150 98 300 72 46 71 100 104 

Denver 150 99 300 64 40 63 89 92 

Denver 200 98 300 96 61 95 134 139 

Denver 200 99 300 85 54 84 119 123 

Denver 250 98 300 120 76 118 167 173 

Denver 250 99 300 106 67 105 148 154 

Denver 300 98 300 144 91 142 201 208 

Denver 300 99 300 128 81 126 178 185 

Detroit As is 600 31 18 30 45 47 

Detroit Current Std 600 90 54 88 128 141 

Detroit 50 98 600 28 16 27 41 43 

Detroit 50 99 600 27 15 26 39 41 

Detroit 100 98 600 56 33 55 83 86 

Detroit 100 99 600 53 31 52 78 81 

Detroit 150 98 600 85 49 82 124 129 

Detroit 150 99 600 80 46 78 117 122 

Detroit 200 98 600 113 65 110 166 172 

Detroit 200 99 600 106 61 103 156 162 

Detroit 250 98 600 141 81 137 207 215 

Detroit 250 99 600 133 77 129 196 203 

Detroit 300 98 600 169 98 164 249 258 

Detroit 300 99 600 160 92 155 235 243 

El Paso As is 1200 25 10 25 42 43 

El Paso Current Std 1200 75 30 75 124 127 

El Paso 50 98 1200 19 8 19 31 32 

El Paso 50 99 1200 17 7 17 28 28 

El Paso 100 98 1200 37 15 37 62 63 

El Paso 100 99 1200 33 14 33 55 57 

El Paso 150 98 1200 56 23 56 93 95 

El Paso 150 99 1200 50 21 50 83 85 

El Paso 200 98 1200 75 31 74 124 127 

El Paso 200 99 1200 67 27 66 111 113 

El Paso 250 98 1200 93 38 93 155 159 

El Paso 250 99 1200 83 34 83 138 142 

El Paso 300 98 1200 112 46 112 186 190 

El Paso 300 99 1200 100 41 100 166 170 

Jacksonville As is 200 24 17 23 36 37 

Jacksonville Current Std 200 96 67 93 143 145 

Jacksonville 50 98 200 23 16 23 35 35 

Jacksonville 50 99 200 16 11 15 24 24 

Jacksonville 100 98 200 47 32 45 70 71 

Jacksonville 100 99 200 32 22 31 48 49 
A-199

Annual Average NO2 (ppb)3

Location Scenario1 Percentile2

SiteYears Mean Min Med p98 p99 

Jacksonville 150 98 200 70 49 68 105 106 

Jacksonville 150 99 200 48 33 46 72 73 

Jacksonville 200 98 200 94 65 90 140 142 

Jacksonville 200 99 200 64 44 62 96 97 

Jacksonville 250 98 200 117 81 113 175 177 

Jacksonville 250 99 200 80 55 77 119 121 

Jacksonville 300 98 200 141 97 136 209 213 

Jacksonville 300 99 200 96 67 93 143 146 

Las Vegas As is 1100 16 2 11 44 46 

Las Vegas Current Std 1100 43 5 30 118 123 

Las Vegas 50 98 1100 13 1 9 36 38 

Las Vegas 50 99 1100 12 1 8 33 35 

Las Vegas 100 98 1100 26 3 18 72 76 

Las Vegas 100 99 1100 24 3 17 67 70 

Las Vegas 150 98 1100 40 4 27 108 113 

Las Vegas 150 99 1100 37 4 25 100 105 

Las Vegas 200 98 1100 53 6 36 145 151 

Las Vegas 200 99 1100 49 5 34 134 140 

Las Vegas 250 98 1100 66 7 45 181 189 

Las Vegas 250 99 1100 61 7 42 167 174 

Las Vegas 300 98 1100 79 9 54 217 227 

Las Vegas 300 99 1100 73 8 50 200 209 

Los Angeles As is 5400 33 6 32 60 65 

Los Angeles Current Std 5400 56 10 54 102 109 

Los Angeles 50 98 5400 18 3 18 33 36 

Los Angeles 50 99 5400 17 3 16 31 33 

Los Angeles 100 98 5400 36 6 35 66 72 

Los Angeles 100 99 5400 33 6 32 61 66 

Los Angeles 150 98 5400 54 9 53 100 107 

Los Angeles 150 99 5400 50 9 49 92 99 

Los Angeles 200 98 5400 73 13 71 133 143 

Los Angeles 200 99 5400 67 12 65 122 131 

Los Angeles 250 98 5400 91 16 88 166 179 

Los Angeles 250 99 5400 83 15 81 153 164 

Los Angeles 300 98 5400 109 19 106 199 215 

Los Angeles 300 99 5400 100 17 97 183 197 

Miami As is 400 14 9 13 19 20 

Miami Current Std 400 55 35 53 77 80 

Miami 50 98 400 12 8 12 17 17 

Miami 50 99 400 11 7 11 16 16 

Miami 100 98 400 24 16 23 34 34 

Miami 100 99 400 23 15 22 32 32 

Miami 150 98 400 36 24 35 51 51 

Miami 150 99 400 34 22 33 48 48 

Miami 200 98 400 48 32 47 68 69 

Miami 200 99 400 45 30 44 64 65 
A-200

Annual Average NO2 (ppb)3

Location Scenario1 Percentile2

SiteYears Mean Min Med p98 p99 

Miami 250 98 400 60 40 59 85 86 

Miami 250 99 400 57 37 55 80 81 

Miami 300 98 400 72 48 70 102 103 

Miami 300 99 400 68 45 66 96 97 

New York As is 2200 35 12 35 58 61 

New York Current Std 2200 55 20 55 94 99 

New York 50 98 2200 21 7 21 36 37 

New York 50 99 2200 18 6 18 31 32 

New York 100 98 2200 42 15 42 71 75 

New York 100 99 2200 37 13 37 62 65 

New York 150 98 2200 64 22 64 107 112 

New York 150 99 2200 55 19 55 93 97 

New York 200 98 2200 85 30 85 142 150 

New York 200 99 2200 74 26 74 123 130 

New York 250 98 2200 106 37 106 178 187 

New York 250 99 2200 92 32 92 154 162 

New York 300 98 2200 127 45 127 214 225 

New York 300 99 2200 110 39 110 185 195 

Philadelphia As is 1200 31 18 30 51 59 

Philadelphia Current Std 1200 70 37 68 112 123 

Philadelphia 50 98 1200 24 14 23 39 45 

Philadelphia 50 99 1200 19 11 18 31 36 

Philadelphia 100 98 1200 47 27 45 78 89 

Philadelphia 100 99 1200 38 22 36 63 72 

Philadelphia 150 98 1200 71 41 68 117 134 

Philadelphia 150 99 1200 57 33 55 94 108 

Philadelphia 200 98 1200 95 54 91 155 178 

Philadelphia 200 99 1200 76 44 73 125 143 

Philadelphia 250 98 1200 118 68 113 194 223 

Philadelphia 250 99 1200 95 54 91 156 179 

Philadelphia 300 98 1200 142 81 136 233 267 

Philadelphia 300 99 1200 114 65 109 188 215 

Phoenix As is 900 43 26 42 64 65 

Phoenix Current Std 900 73 45 71 107 109 

Phoenix 50 98 900 26 16 25 38 39 

Phoenix 50 99 900 23 14 23 34 35 

Phoenix 100 98 900 51 31 50 76 77 

Phoenix 100 99 900 46 28 45 68 70 

Phoenix 150 98 900 77 47 75 114 116 

Phoenix 150 99 900 69 42 68 103 105 

Phoenix 200 98 900 103 63 100 152 155 

Phoenix 200 99 900 92 56 90 137 139 

Phoenix 250 98 900 128 78 125 190 194 

Phoenix 250 99 900 116 70 113 171 174 

Phoenix 300 98 900 154 94 150 228 232 

Phoenix 300 99 900 139 85 135 205 209 
A-201

Annual Average NO2 (ppb)3

Location Scenario1 Percentile2

SiteYears Mean Min Med p98 p99 

Provo As is 300 43 26 41 70 71 

Provo Current Std 300 94 67 93 129 131 

Provo 50 98 300 23 14 22 37 38 

Provo 50 99 300 22 14 21 37 37 

Provo 100 98 300 45 28 43 75 76 

Provo 100 99 300 45 27 42 73 74 

Provo 150 98 300 68 41 65 112 114 

Provo 150 99 300 67 41 64 110 111 

Provo 200 98 300 91 55 87 149 151 

Provo 200 99 300 89 54 85 146 148 

Provo 250 98 300 114 69 108 186 189 

Provo 250 99 300 111 68 106 183 185 

Provo 300 98 300 136 83 130 224 227 

Provo 300 99 300 134 81 127 219 222 

St. Louis As is 400 27 16 26 41 42 

St. Louis Current Std 400 68 38 66 114 119 

St. Louis 50 98 400 22 13 21 34 34 

St. Louis 50 99 400 21 13 20 33 33 

St. Louis 100 98 400 43 26 42 67 68 

St. Louis 100 99 400 42 25 41 65 66 

St. Louis 150 98 400 65 39 63 101 102 

St. Louis 150 99 400 63 38 61 98 99 

St. Louis 200 98 400 87 52 84 134 136 

St. Louis 200 99 400 84 50 81 130 132 

St. Louis 250 98 400 109 65 105 168 171 

St. Louis 250 99 400 105 63 102 163 165 

St. Louis 300 98 400 130 78 126 201 205 

St. Louis 300 99 400 126 75 122 195 198 

Washington DC As is 1700 28 9 28 51 52 

Washington DC Current Std 1700 64 23 66 114 121 

Washington DC 50 98 1700 21 7 21 39 40 

Washington DC 50 99 1700 17 5 17 31 32 

Washington DC 100 98 1700 42 13 42 77 79 

Washington DC 100 99 1700 34 11 35 63 65 

Washington DC 150 98 1700 63 20 64 116 119 

Washington DC 150 99 1700 51 16 52 94 97 

Washington DC 200 98 1700 84 26 85 154 158 

Washington DC 200 99 1700 68 21 69 126 129 

Washington DC 250 98 1700 104 33 106 193 198 

Washington DC 250 99 1700 85 27 86 157 161 

Washington DC 300 98 1700 125 39 127 231 238 

Washington DC 300 99 1700 102 32 104 189 194 

Other MSA As is 56500 20 1 20 41 45 

Other MSA Current Std 56500 47 3 45 97 105 

Other MSA 50 98 56500 12 1 11 24 26 

Other MSA 50 99 56500 11 1 10 22 24 
A-202

Annual Average NO2 (ppb)3

Location Scenario1 Percentile2

SiteYears Mean Min Med p98 p99 

Other MSA 100 98 56500 23 1 23 48 52 

Other MSA 100 99 56500 21 1 21 44 48 

Other MSA 150 98 56500 35 2 34 72 77 

Other MSA 150 99 56500 32 2 31 66 71 

Other MSA 200 98 56500 46 2 45 96 103 

Other MSA 200 99 56500 43 2 42 88 95 

Other MSA 250 98 56500 58 3 57 120 129 

Other MSA 250 99 56500 53 3 52 110 119 

Other MSA 300 98 56500 70 4 68 144 155 

Other MSA 300 99 56500 64 3 62 132 143 

Other Not MSA As is 11600 12 1 10 31 33 

Other Not MSA Current Std 11600 39 3 34 100 109 

Other Not MSA 50 98 11600 6 1 6 16 18 

Other Not MSA 50 99 11600 5 0 5 13 14 

Other Not MSA 100 98 11600 13 1 11 33 35 

Other Not MSA 100 99 11600 10 1 9 26 28 

Other Not MSA 150 98 11600 19 2 17 49 53 

Other Not MSA 150 99 11600 15 1 14 39 43 

Other Not MSA 200 98 11600 25 2 22 65 71 

Other Not MSA 200 99 11600 20 2 18 53 57 

Other Not MSA 250 98 11600 32 3 28 82 88 

Other Not MSA 250 99 11600 25 2 23 66 71 

Other Not MSA 300 98 11600 38 3 34 98 106 

Other Not MSA 300 99 11600 30 3 27 79 85 

Notes: 

1

 Scenario: As is – unadjusted air quality, Current Std – air quality that just meets the current annual 

standard, All others – air quality that just meets 1-hour concentration level given percentile form of 

alternative standard. 

2

 Percentile: 98th or 99th percentile of daily maximum 1-hour concentration averaged over three years at 

maximum monitor in location. 

3 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, p98, 

p99 represent the minimum, median, 98th, and 99th percentiles of the distribution for the annual means. 
A-203

1 Table A-127. Estimated number of exceedances of 1-hour concentration levels (100, 150, and 200 ppb) for monitors ≥100 m fro2 following adjustment to just meeting the current and alternative standards, 2004-2006 air quality. 

Number of Daily Maximum Exceeda

≥ 100 ppb ≥ 150 ppb 

Location Standard Percentile Site-Years Mean Min Med p98 p99 Mean Min Med p98 p99 

Atlanta As is 15 0 0 0 1 1 0 0 0 0 0 Atlanta Current Std 15 96 1 121 188 188 24 0 15 73 73 Atlanta 50 98 15 0 0 0 0 0 0 0 0 0 0 Atlanta 50 99 15 0 0 0 0 0 0 0 0 0 0 Atlanta 100 98 15 2 0 0 17 17 0 0 0 1 Atlanta 100 99 15 1 0 0 7 7 0 0 0 1 Atlanta 150 98 15 28 0 16 83 83 2 0 0 17 17 Atlanta 150 99 15 19 0 10 65 65 1 0 0 7 7 Atlanta 200 98 15 81 0 90 168 168 15 0 6 58 58 Atlanta 200 99 15 66 0 67 144 144 8 0 2 39 39 Atlanta 250 98 15 122 3 171 235 235 47 0 43 114 114 Atlanta 250 99 15 105 1 133 205 205 32 0 19 89 89 Atlanta 300 98 15 157 8 228 281 281 81 0 90 168 168 Atlanta 300 99 15 143 4 209 266 266 66 0 67 144 144 Boston As is 8 0 0 0 0 0 0 0 0 0 0 Boston Current Std 8 5 0 5 10 10 0 0 0 0 0 Boston 50 98 8 0 0 0 0 0 0 0 0 0 0 Boston 50 99 8 0 0 0 0 0 0 0 0 0 0 Boston 100 98 8 0 0 0 0 0 0 0 0 0 0 Boston 100 99 8 0 0 0 0 0 0 0 0 0 0 Boston 150 98 8 2 0 2 7 7 0 0 0 0 0 Boston 150 99 8 0 0 0 0 0 0 0 0 0 0 Boston 200 98 8 29 13 26 54 54 0 0 0 2 2 Boston 200 99 8 9 2 8 20 20 0 0 0 0 0 Boston 250 98 8 72 32 67 102 102 7 1 6 13 13 Boston 250 99 8 38 15 35 67 67 1 0 1 6 6 Boston 300 98 8 105 56 102 138 138 29 13 26 54 54 Boston 300 99 8 72 32 67 102 102 9 2 8 20 20 Chicago As is 8 0 0 0 0 0 0 0 0 0 0 Chicago Current Std 8 23 7 14 69 69 1 0 0 3 3 
A-204

Number of Daily Maximum Exceeda

≥ 100 ppb ≥ 150 ppb 

Location Standard Percentile Site-Years Mean Min Med p98 p99 Mean Min Med p98 p99 

Chicago 50 98 8 0 0 0 0 0 0 0 0 0 0 Chicago 50 99 8 0 0 0 0 0 0 0 0 0 0 Chicago 100 98 8 0 0 0 1 1 0 0 0 0 0 Chicago 100 99 8 0 0 0 0 0 0 0 0 0 0 Chicago 150 98 8 18 5 9 56 56 0 0 0 1 Chicago 150 99 8 13 2 5 42 42 0 0 0 0 0 Chicago 200 98 8 84 32 69 158 158 9 2 4 32 32 Chicago 200 99 8 62 22 49 123 123 5 0 2 21 2Chicago 250 98 8 161 90 149 240 240 34 10 20 83 83 Chicago 250 99 8 138 69 123 215 215 25 7 14 69 69 Chicago 300 98 8 227 149 231 298 298 84 32 69 158 158 Chicago 300 99 8 214 132 217 285 285 62 22 49 123 123 Denver As is 3 1 0 0 3 3 0 0 0 0 0 Denver Current Std 3 60 41 67 73 73 4 2 4 7 7 Denver 50 98 3 0 0 0 0 0 0 0 0 0 0 Denver 50 99 3 0 0 0 0 0 0 0 0 0 0 Denver 100 98 3 6 4 6 9 9 0 0 0 0 0 Denver 100 99 3 3 1 1 7 7 0 0 0 0 0 Denver 150 98 3 76 67 80 81 81 6 4 6 9 9 Denver 150 99 3 40 30 41 50 50 3 1 1 7 7 Denver 200 98 3 211 184 215 233 233 45 32 49 53 53 Denver 200 99 3 159 141 153 183 183 15 6 17 22 22 Denver 250 98 3 263 232 276 281 281 124 111 120 142 142 Denver 250 99 3 231 199 247 248 248 68 57 73 73 73 Denver 300 98 3 289 255 305 306 306 211 184 215 233 233 Denver 300 99 3 274 240 289 294 294 159 141 153 183 183 Detroit As is 6 0 0 0 0 0 0 0 0 0 0 Detroit Current Std 6 142 91 145 174 174 13 7 13 21 2Detroit 50 98 6 0 0 0 0 0 0 0 0 0 0 Detroit 50 99 6 0 0 0 0 0 0 0 0 0 0 Detroit 100 98 6 8 1 6 18 18 0 0 0 1 Detroit 100 99 6 5 0 4 10 10 0 0 0 0 0 
A-205

Number of Daily Maximum Exceeda

≥ 100 ppb ≥ 150 ppb 

Location Standard Percentile Site-Years Mean Min Med p98 p99 Mean Min Med p98 p99 

Detroit 150 98 6 107 67 100 164 164 8 1 6 18 18 Detroit 150 99 6 88 52 84 137 137 5 0 4 10 10 Detroit 200 98 6 209 172 201 265 265 67 40 61 108 108 Detroit 200 99 6 194 159 188 247 247 44 19 42 73 73 Detroit 250 98 6 273 241 274 320 320 149 116 143 208 208 Detroit 250 99 6 253 220 250 307 307 127 90 120 188 188 Detroit 300 98 6 297 263 302 335 335 209 172 201 265 265 Detroit 300 99 6 290 258 293 329 329 194 159 188 247 247 El Paso As is 12 0 0 0 0 0 0 0 0 0 0 El Paso Current Std 12 157 79 172 216 216 26 5 24 58 58 El Paso 50 98 12 0 0 0 0 0 0 0 0 0 0 El Paso 50 99 12 0 0 0 0 0 0 0 0 0 0 El Paso 100 98 12 4 0 4 10 10 0 0 0 0 0 El Paso 100 99 12 2 0 1 4 4 0 0 0 0 0 El Paso 150 98 12 52 12 48 111 111 4 0 4 10 10 El Paso 150 99 12 26 6 23 59 59 2 0 1 4 4 El Paso 200 98 12 159 79 172 227 227 26 6 23 59 59 El Paso 200 99 12 116 45 123 192 192 12 2 11 25 25 El Paso 250 98 12 228 130 238 290 290 84 27 85 156 156 El Paso 250 99 12 188 94 198 251 251 46 11 42 100 100 El Paso 300 98 12 259 163 265 314 314 159 79 172 227 227 El Paso 300 99 12 236 133 245 296 296 116 45 123 192 192 Jacksonville As is 2 2 0 2 3 3 1 0 1 2 2 Jacksonville Current Std 2 178 161 178 194 194 51 42 51 60 60 Jacksonville 50 98 2 2 0 2 3 3 1 0 1 2 2 Jacksonville 50 99 2 1 0 1 2 2 0 0 0 0 0 Jacksonville 100 98 2 7 5 7 9 9 2 0 2 4 4 Jacksonville 100 99 2 2 0 2 4 4 2 0 2 3 3 Jacksonville 150 98 2 78 65 78 90 90 7 5 7 9 9 Jacksonville 150 99 2 9 6 9 11 11 2 0 2 4 4 Jacksonville 200 98 2 178 161 178 194 194 48 42 48 54 54 Jacksonville 200 99 2 53 46 53 60 60 4 2 4 6 6 
A-206

Number of Daily Maximum Exceeda

≥ 100 ppb ≥ 150 ppb 

Location Standard Percentile Site-Years Mean Min Med p98 p99 Mean Min Med p98 p99 

Jacksonville 250 98 2 237 226 237 247 247 109 97 109 121 12Jacksonville 250 99 2 117 103 117 131 131 15 12 15 18 18 Jacksonville 300 98 2 271 262 271 279 279 178 161 178 194 194 Jacksonville 300 99 2 178 161 178 194 194 53 46 53 60 60 Las Vegas As is 11 0 0 0 0 0 0 0 0 0 0 Las Vegas Current Std 11 72 0 12 249 249 6 0 0 26 26 Las Vegas 50 98 11 0 0 0 0 0 0 0 0 0 0 Las Vegas 50 99 11 0 0 0 0 0 0 0 0 0 0 Las Vegas 100 98 11 2 0 0 9 9 0 0 0 0 0 Las Vegas 100 99 11 1 0 0 4 4 0 0 0 0 0 Las Vegas 150 98 11 55 0 5 209 209 2 0 0 9 9 Las Vegas 150 99 11 40 0 3 165 165 1 0 0 4 4 Las Vegas 200 98 11 108 2 43 305 305 31 0 2 128 128 Las Vegas 200 99 11 97 1 31 286 286 17 0 0 66 66 Las Vegas 250 98 11 150 7 105 327 327 77 0 15 257 257 Las Vegas 250 99 11 134 5 81 322 322 60 0 7 219 219 Las Vegas 300 98 11 186 18 167 339 339 108 2 43 305 305 Las Vegas 300 99 11 176 16 150 337 337 97 1 31 286 286 Los Angeles As is 54 0 0 0 2 2 0 0 0 0 0 Los Angeles Current Std 54 19 0 17 55 85 1 0 0 4 6 Los Angeles 50 98 54 0 0 0 0 0 0 0 0 0 0 Los Angeles 50 99 54 0 0 0 0 0 0 0 0 0 0 Los Angeles 100 98 54 1 0 0 5 8 0 0 0 0 0 Los Angeles 100 99 54 0 0 0 2 2 0 0 0 0 0 Los Angeles 150 98 54 17 0 15 66 75 1 0 0 5 8 Los Angeles 150 99 54 10 0 8 45 46 0 0 0 2 2 Los Angeles 200 98 54 70 2 68 176 186 8 0 6 29 32 Los Angeles 200 99 54 50 0 46 135 157 4 0 2 18 2Los Angeles 250 98 54 139 12 153 279 284 31 0 30 98 112 Los Angeles 250 99 54 112 10 120 248 257 19 0 18 68 79 Los Angeles 300 98 54 194 30 209 322 328 70 2 68 176 186 Los Angeles 300 99 54 176 24 190 306 318 50 0 46 135 157 
A-207

Number of Daily Maximum Exceeda

≥ 100 ppb ≥ 150 ppb 

Location Standard Percentile Site-Years Mean Min Med p98 p99 Mean Min Med p98 p99 

Miami As is 4 0 0 0 0 0 0 0 0 0 0 Miami Current Std 4 102 57 104 143 143 33 7 33 59 59 Miami 50 98 4 0 0 0 0 0 0 0 0 0 0 Miami 50 99 4 0 0 0 0 0 0 0 0 0 0 Miami 100 98 4 3 0 2 8 8 0 0 0 1 Miami 100 99 4 2 0 1 5 5 0 0 0 1 Miami 150 98 4 33 7 30 64 64 3 0 2 8 8 Miami 150 99 4 23 2 23 45 45 2 0 1 5 5 Miami 200 98 4 74 36 72 115 115 18 1 18 34 34 Miami 200 99 4 64 29 62 104 104 12 1 12 25 25 Miami 250 98 4 122 82 124 157 157 44 12 44 77 77 Miami 250 99 4 105 62 108 143 143 37 10 35 66 66 Miami 300 98 4 157 129 160 181 181 74 36 72 115 115 Miami 300 99 4 139 106 139 171 171 64 29 62 104 104 New York As is 22 0 0 0 2 2 0 0 0 0 0 New York Current Std 22 11 0 12 36 36 0 0 0 2 2 New York 50 98 22 0 0 0 0 0 0 0 0 0 0 New York 50 99 22 0 0 0 0 0 0 0 0 0 0 New York 100 98 22 1 0 1 4 4 0 0 0 1 New York 100 99 22 1 0 0 4 4 0 0 0 1 New York 150 98 22 27 0 31 56 56 1 0 1 4 4 New York 150 99 22 12 0 13 28 28 1 0 0 4 4 New York 200 98 22 108 8 132 178 178 13 0 14 35 35 New York 200 99 22 63 0 79 111 111 5 0 5 15 15 New York 250 98 22 185 46 213 266 266 52 0 67 96 96 New York 250 99 22 135 17 162 211 211 21 0 24 52 52 New York 300 98 22 233 75 257 312 312 108 8 132 178 178 New York 300 99 22 196 52 226 275 275 63 0 79 111 11Philadelphia As is 12 0 0 0 0 0 0 0 0 0 0 Philadelphia Current Std 12 54 7 38 138 138 4 0 2 14 14 Philadelphia 50 98 12 0 0 0 0 0 0 0 0 0 0 Philadelphia 50 99 12 0 0 0 0 0 0 0 0 0 0 
A-208

Number of Daily Maximum Exceeda

≥ 100 ppb ≥ 150 ppb 

Location Standard Percentile Site-Years Mean Min Med p98 p99 Mean Min Med p98 p99 

Philadelphia 100 98 12 3 0 2 10 10 0 0 0 1 Philadelphia 100 99 12 0 0 0 2 2 0 0 0 0 0 Philadelphia 150 98 12 52 21 46 96 96 3 0 2 10 10 Philadelphia 150 99 12 12 1 12 33 33 0 0 0 2 2 Philadelphia 200 98 12 163 103 149 246 246 24 4 21 54 54 Philadelphia 200 99 12 77 40 67 134 134 4 0 3 14 14 Philadelphia 250 98 12 231 186 229 293 293 85 42 75 146 146 Philadelphia 250 99 12 163 103 149 246 246 24 4 21 54 54 Philadelphia 300 98 12 278 239 281 325 325 163 103 149 246 246 Philadelphia 300 99 12 220 165 218 290 290 77 40 67 134 134 Phoenix As is 9 0 0 0 1 1 0 0 0 0 0 Phoenix Current Std 9 40 19 29 78 78 1 0 0 2 2 Phoenix 50 98 9 0 0 0 0 0 0 0 0 0 0 Phoenix 50 99 9 0 0 0 0 0 0 0 0 0 0 Phoenix 100 98 9 1 0 1 5 5 0 0 0 0 0 Phoenix 100 99 9 0 0 0 2 2 0 0 0 0 0 Phoenix 150 98 9 65 37 55 105 105 1 0 1 5 5 Phoenix 150 99 9 25 10 23 43 43 0 0 0 2 2 Phoenix 200 98 9 224 167 235 245 245 21 8 18 36 36 Phoenix 200 99 9 163 126 160 195 195 6 0 7 10 10 Phoenix 250 98 9 298 208 307 324 324 116 80 100 158 158 Phoenix 250 99 9 265 188 275 292 292 65 37 55 105 105 Phoenix 300 98 9 330 249 337 352 352 224 167 235 245 245 Phoenix 300 99 9 317 230 326 338 338 163 126 160 195 195 Provo As is 3 14 0 0 43 43 7 0 0 20 20 Provo Current Std 3 129 56 137 194 194 19 3 12 43 43 Provo 50 98 3 0 0 0 0 0 0 0 0 0 0 Provo 50 99 3 0 0 0 0 0 0 0 0 0 0 Provo 100 98 3 14 0 0 43 43 10 0 0 29 29 Provo 100 99 3 14 0 0 43 43 8 0 0 23 23 Provo 150 98 3 19 1 14 43 43 14 0 0 43 43 Provo 150 99 3 19 1 12 43 43 14 0 0 43 43 
A-209

Number of Daily Maximum Exceeda

≥ 100 ppb ≥ 150 ppb 

Location Standard Percentile Site-Years Mean Min Med p98 p99 Mean Min Med p98 p99 

Provo 200 98 3 84 64 90 98 98 16 0 6 43 43 Provo 200 99 3 74 50 82 91 91 16 0 5 43 43 Provo 250 98 3 209 198 213 216 216 25 5 22 49 49 Provo 250 99 3 196 191 194 202 202 23 3 20 47 47 Provo 300 98 3 281 242 296 305 305 84 64 90 98 98 Provo 300 99 3 281 242 296 305 305 74 50 82 91 9St. Louis As is 4 0 0 0 0 0 0 0 0 0 0 St. Louis Current Std 4 67 15 50 154 154 6 0 1 22 22 St. Louis 50 98 4 0 0 0 0 0 0 0 0 0 0 St. Louis 50 99 4 0 0 0 0 0 0 0 0 0 0 St. Louis 100 98 4 1 0 1 3 3 0 0 0 0 0 St. Louis 100 99 4 1 0 1 2 2 0 0 0 0 0 St. Louis 150 98 4 58 31 54 94 94 1 0 1 3 3 St. Louis 150 99 4 49 23 42 88 88 1 0 1 2 2 St. Louis 200 98 4 146 112 143 186 186 25 2 22 55 55 St. Louis 200 99 4 134 100 128 179 179 16 1 14 36 36 St. Louis 250 98 4 211 160 206 272 272 88 57 83 127 127 St. Louis 250 99 4 201 153 197 256 256 82 49 78 121 12St. Louis 300 98 4 255 191 258 313 313 146 112 143 186 186 St. Louis 300 99 4 255 191 258 313 313 134 100 128 179 179 Washington DC As is 17 0 0 0 2 2 0 0 0 0 0 Washington DC Current Std 17 52 0 61 149 149 4 0 3 14 14 Washington DC 50 98 17 0 0 0 0 0 0 0 0 0 0 Washington DC 50 99 17 0 0 0 0 0 0 0 0 0 0 Washington DC 100 98 17 3 0 2 12 12 0 0 0 2 2 Washington DC 100 99 17 1 0 0 6 6 0 0 0 0 0 Washington DC 150 98 17 50 0 52 120 120 3 0 2 12 12 Washington DC 150 99 17 16 0 14 46 46 1 0 0 6 6 Washington DC 200 98 17 133 12 149 246 246 26 0 25 67 67 Washington DC 200 99 17 69 0 69 160 160 5 0 3 20 20 Washington DC 250 98 17 190 42 230 299 299 77 0 80 171 17Washington DC 250 99 17 133 12 149 246 246 29 0 28 74 74 
A-210

Number of Daily Maximum Exceeda

≥ 100 ppb ≥ 150 ppb 

Location Standard Percentile Site-Years Mean Min Med p98 p99 Mean Min Med p98 p99 

Washington DC 300 98 17 237 90 278 333 333 133 12 149 246 246 Washington DC 300 99 17 190 42 230 299 299 69 0 69 160 160 Other MSA As is 565 0 0 0 1 1 0 0 0 0 0 Other MSA Current Std 565 28 0 12 141 156 2 0 0 23 27 Other MSA 50 98 565 0 0 0 0 0 0 0 0 0 0 Other MSA 50 99 565 0 0 0 0 0 0 0 0 0 0 Other MSA 100 98 565 0 0 0 2 4 0 0 0 0 Other MSA 100 99 565 0 0 0 1 1 0 0 0 0 Other MSA 150 98 565 4 0 1 29 37 0 0 0 2 4 Other MSA 150 99 565 2 0 0 18 23 0 0 0 1 Other MSA 200 98 565 24 0 12 112 128 2 0 0 15 19 Other MSA 200 99 565 16 0 7 85 110 1 0 0 8 10 Other MSA 250 98 565 65 0 53 212 228 8 0 2 52 63 Other MSA 250 99 565 48 0 34 170 191 4 0 1 31 4Other MSA 300 98 565 109 0 104 267 300 24 0 12 112 128 Other MSA 300 99 565 86 0 77 242 274 16 0 7 85 110 Other Not MSA As is 116 0 0 0 2 3 0 0 0 0 2 Other Not MSA Current Std 116 37 0 11 192 195 7 0 0 69 78 Other Not MSA 50 98 116 0 0 0 0 1 0 0 0 0 0 Other Not MSA 50 99 116 0 0 0 0 1 0 0 0 0 0 Other Not MSA 100 98 116 0 0 0 3 5 0 0 0 0 2 Other Not MSA 100 99 116 0 0 0 2 2 0 0 0 0 2 Other Not MSA 150 98 116 1 0 0 16 19 0 0 0 3 5 Other Not MSA 150 99 116 0 0 0 7 9 0 0 0 2 2 Other Not MSA 200 98 116 7 0 0 71 80 1 0 0 12 14 Other Not MSA 200 99 116 2 0 0 26 26 0 0 0 3 6 Other Not MSA 250 98 116 18 0 2 116 147 2 0 0 30 3Other Not MSA 250 99 116 7 0 0 71 80 1 0 0 12 15 Other Not MSA 300 98 116 34 0 8 165 199 7 0 0 71 80 Other Not MSA 300 99 116 16 0 1 112 144 2 0 0 26 26 
A-211

1 Table a-128. Estimated number of exceedances of 1-hour concentration levels (250 and 300 ppb) for monitors 

2 ≥100 m from a major road following adjustment to just meeting the current and alternative standards, 2004-

3 2006 air quality. 

Number of Daily Maximum Exceedances 

≥ 250 ppb ≥ 300 ppb 

Location Standard Percentile

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99

Atlanta As is 15 0 0 0 0 0 0 0 0 0 0

Atlanta Current Std 15 0 0 0 4 4 0 0 0 1 1

Atlanta 50 98 15 0 0 0 0 0 0 0 0 0 0

Atlanta 50 99 15 0 0 0 0 0 0 0 0 0 0

Atlanta 100 98 15 0 0 0 0 0 0 0 0 0 0

Atlanta 100 99 15 0 0 0 0 0 0 0 0 0 0

Atlanta 150 98 15 0 0 0 1 1 0 0 0 0 0

Atlanta 150 99 15 0 0 0 0 0 0 0 0 0 0

Atlanta 200 98 15 0 0 0 2 2 0 0 0 1 1

Atlanta 200 99 15 0 0 0 1 1 0 0 0 1 1

Atlanta 250 98 15 2 0 0 17 17 0 0 0 2 2

Atlanta 250 99 15 1 0 0 7 7 0 0 0 1 1

Atlanta 300 98 15 8 0 1 36 36 2 0 0 17 17

Atlanta 300 99 15 4 0 0 22 22 1 0 0 7 7

Boston As is 8 0 0 0 0 0 0 0 0 0 0

Boston Current Std 8 0 0 0 0 0 0 0 0 0 0

Boston 50 98 8 0 0 0 0 0 0 0 0 0 0

Boston 50 99 8 0 0 0 0 0 0 0 0 0 0

Boston 100 98 8 0 0 0 0 0 0 0 0 0 0

Boston 100 99 8 0 0 0 0 0 0 0 0 0 0

Boston 150 98 8 0 0 0 0 0 0 0 0 0 0

Boston 150 99 8 0 0 0 0 0 0 0 0 0 0

Boston 200 98 8 0 0 0 0 0 0 0 0 0 0

Boston 200 99 8 0 0 0 0 0 0 0 0 0 0

Boston 250 98 8 0 0 0 0 0 0 0 0 0 0

Boston 250 99 8 0 0 0 0 0 0 0 0 0 0

Boston 300 98 8 0 0 0 0 0 0 0 0 0 0

Boston 300 99 8 0 0 0 0 0 0 0 0 0 0

Chicago As is 8 0 0 0 0 0 0 0 0 0 0

Chicago Current Std 8 0 0 0 0 0 0 0 0 0 0

Chicago 50 98 8 0 0 0 0 0 0 0 0 0 0

Chicago 50 99 8 0 0 0 0 0 0 0 0 0 0

Chicago 100 98 8 0 0 0 0 0 0 0 0 0 0

Chicago 100 99 8 0 0 0 0 0 0 0 0 0 0

Chicago 150 98 8 0 0 0 0 0 0 0 0 0 0

Chicago 150 99 8 0 0 0 0 0 0 0 0 0 0

Chicago 200 98 8 0 0 0 0 0 0 0 0 0 0

Chicago 200 99 8 0 0 0 0 0 0 0 0 0 0

Chicago 250 98 8 0 0 0 1 1 0 0 0 0 0

Chicago 250 99 8 0 0 0 0 0 0 0 0 0 0

Chicago 300 98 8 2 0 2 10 10 0 0 0 1 1

Chicago 300 99 8 1 0 1 5 5 0 0 0 0 0
A-212

Number of Daily Maximum Exceedances 

≥ 250 ppb ≥ 300 ppb 

Location Standard Percentile

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99

Denver As is 3 0 0 0 0 0 0 0 0 0 0

Denver Current Std 3 0 0 0 0 0 0 0 0 0 0

Denver 50 98 3 0 0 0 0 0 0 0 0 0 0

Denver 50 99 3 0 0 0 0 0 0 0 0 0 0

Denver 100 98 3 0 0 0 0 0 0 0 0 0 0

Denver 100 99 3 0 0 0 0 0 0 0 0 0 0

Denver 150 98 3 0 0 0 0 0 0 0 0 0 0

Denver 150 99 3 0 0 0 0 0 0 0 0 0 0

Denver 200 98 3 2 0 1 4 4 0 0 0 0 0

Denver 200 99 3 0 0 0 1 1 0 0 0 0 0

Denver 250 98 3 5 4 5 7 7 2 0 1 5 5

Denver 250 99 3 3 1 1 7 7 1 0 0 2 2

Denver 300 98 3 17 8 20 24 24 6 4 6 9 9

Denver 300 99 3 9 4 11 11 11 3 1 1 7 7

Detroit As is 6 0 0 0 0 0 0 0 0 0 0

Detroit Current Std 6 0 0 0 0 0 0 0 0 0 0

Detroit 50 98 6 0 0 0 0 0 0 0 0 0 0

Detroit 50 99 6 0 0 0 0 0 0 0 0 0 0

Detroit 100 98 6 0 0 0 0 0 0 0 0 0 0

Detroit 100 99 6 0 0 0 0 0 0 0 0 0 0

Detroit 150 98 6 0 0 0 0 0 0 0 0 0 0

Detroit 150 99 6 0 0 0 0 0 0 0 0 0 0

Detroit 200 98 6 1 0 1 1 1 0 0 0 1 1

Detroit 200 99 6 1 0 1 1 1 0 0 0 0 0

Detroit 250 98 6 8 1 6 18 18 1 0 1 2 2

Detroit 250 99 6 5 0 4 10 10 1 0 1 1 1

Detroit 300 98 6 32 14 32 61 61 8 1 6 18 18

Detroit 300 99 6 19 6 19 38 38 5 0 4 10 10

El Paso As is 12 0 0 0 0 0 0 0 0 0 0

El Paso Current Std 12 1 0 0 2 2 0 0 0 0 0

El Paso 50 98 12 0 0 0 0 0 0 0 0 0 0

El Paso 50 99 12 0 0 0 0 0 0 0 0 0 0

El Paso 100 98 12 0 0 0 0 0 0 0 0 0 0

El Paso 100 99 12 0 0 0 0 0 0 0 0 0 0

El Paso 150 98 12 0 0 0 0 0 0 0 0 0 0

El Paso 150 99 12 0 0 0 0 0 0 0 0 0 0

El Paso 200 98 12 0 0 0 2 2 0 0 0 0 0

El Paso 200 99 12 0 0 0 1 1 0 0 0 0 0

El Paso 250 98 12 4 0 4 10 10 1 0 1 2 2

El Paso 250 99 12 2 0 1 4 4 0 0 0 1 1

El Paso 300 98 12 12 2 11 25 25 4 0 4 10 10

El Paso 300 99 12 6 1 7 12 12 2 0 1 4 4

Jacksonville As is 2 0 0 0 0 0 0 0 0 0 0

Jacksonville Current Std 2 3 1 3 5 5 2 0 2 4 4

Jacksonville 50 98 2 0 0 0 0 0 0 0 0 0 0
A-213

Number of Daily Maximum Exceedances 

≥ 250 ppb ≥ 300 ppb 

Location Standard Percentile

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99

Jacksonville 50 99 2 0 0 0 0 0 0 0 0 0 0

Jacksonville 100 98 2 1 0 1 2 2 1 0 1 2 2

Jacksonville 100 99 2 1 0 1 1 1 0 0 0 0 0

Jacksonville 150 98 2 2 0 2 4 4 2 0 2 3 3

Jacksonville 150 99 2 1 0 1 2 2 1 0 1 2 2

Jacksonville 200 98 2 3 0 3 5 5 2 0 2 4 4

Jacksonville 200 99 2 2 0 2 4 4 2 0 2 3 3

Jacksonville 250 98 2 7 5 7 9 9 3 1 3 5 5

Jacksonville 250 99 2 2 0 2 4 4 2 0 2 4 4

Jacksonville 300 98 2 25 23 25 27 27 7 5 7 9 9

Jacksonville 300 99 2 3 1 3 5 5 2 0 2 4 4

Las Vegas As is 11 0 0 0 0 0 0 0 0 0 0

Las Vegas Current Std 11 0 0 0 0 0 0 0 0 0 0

Las Vegas 50 98 11 0 0 0 0 0 0 0 0 0 0

Las Vegas 50 99 11 0 0 0 0 0 0 0 0 0 0

Las Vegas 100 98 11 0 0 0 0 0 0 0 0 0 0

Las Vegas 100 99 11 0 0 0 0 0 0 0 0 0 0

Las Vegas 150 98 11 0 0 0 0 0 0 0 0 0 0

Las Vegas 150 99 11 0 0 0 0 0 0 0 0 0 0

Las Vegas 200 98 11 0 0 0 0 0 0 0 0 0 0

Las Vegas 200 99 11 0 0 0 0 0 0 0 0 0 0

Las Vegas 250 98 11 1 0 0 7 7 0 0 0 1 1

Las Vegas 250 99 11 1 0 0 4 4 0 0 0 0 0

Las Vegas 300 98 11 14 0 0 52 52 2 0 0 9 9

Las Vegas 300 99 11 7 0 0 30 30 1 0 0 4 4

Los Angeles As is 54 0 0 0 0 0 0 0 0 0 0

Los Angeles Current Std 54 0 0 0 0 0 0 0 0 0 0

Los Angeles 50 98 54 0 0 0 0 0 0 0 0 0 0

Los Angeles 50 99 54 0 0 0 0 0 0 0 0 0 0

Los Angeles 100 98 54 0 0 0 0 0 0 0 0 0 0

Los Angeles 100 99 54 0 0 0 0 0 0 0 0 0 0

Los Angeles 150 98 54 0 0 0 0 0 0 0 0 0 0

Los Angeles 150 99 54 0 0 0 0 0 0 0 0 0 0

Los Angeles 200 98 54 0 0 0 1 2 0 0 0 0 0

Los Angeles 200 99 54 0 0 0 0 0 0 0 0 0 0

Los Angeles 250 98 54 1 0 0 5 8 0 0 0 1 2

Los Angeles 250 99 54 0 0 0 2 2 0 0 0 0 1

Los Angeles 300 98 54 4 0 2 17 19 1 0 0 5 8

Los Angeles 300 99 54 2 0 1 10 11 0 0 0 2 2

Miami As is 4 0 0 0 0 0 0 0 0 0 0

Miami Current Std 4 2 0 1 4 4 0 0 0 1 1

Miami 50 98 4 0 0 0 0 0 0 0 0 0 0

Miami 50 99 4 0 0 0 0 0 0 0 0 0 0

Miami 100 98 4 0 0 0 0 0 0 0 0 0 0

Miami 100 99 4 0 0 0 0 0 0 0 0 0 0
A-214

Number of Daily Maximum Exceedances 

≥ 250 ppb ≥ 300 ppb 

Location Standard Percentile

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99

Miami 150 98 4 0 0 0 0 0 0 0 0 0 0

Miami 150 99 4 0 0 0 0 0 0 0 0 0 0

Miami 200 98 4 0 0 0 1 1 0 0 0 1 1

Miami 200 99 4 0 0 0 1 1 0 0 0 1 1

Miami 250 98 4 3 0 2 8 8 1 0 1 1 1

Miami 250 99 4 2 0 1 5 5 0 0 0 1 1

Miami 300 98 4 10 0 10 21 21 3 0 2 8 8

Miami 300 99 4 6 0 5 13 13 2 0 1 5 5

New York As is 22 0 0 0 0 0 0 0 0 0 0

New York Current Std 22 0 0 0 1 1 0 0 0 0 0

New York 50 98 22 0 0 0 0 0 0 0 0 0 0

New York 50 99 22 0 0 0 0 0 0 0 0 0 0

New York 100 98 22 0 0 0 0 0 0 0 0 0 0

New York 100 99 22 0 0 0 0 0 0 0 0 0 0

New York 150 98 22 0 0 0 1 1 0 0 0 0 0

New York 150 99 22 0 0 0 0 0 0 0 0 0 0

New York 200 98 22 0 0 0 2 2 0 0 0 1 1

New York 200 99 22 0 0 0 1 1 0 0 0 1 1

New York 250 98 22 1 0 1 4 4 0 0 0 2 2

New York 250 99 22 1 0 0 4 4 0 0 0 1 1

New York 300 98 22 7 0 7 18 18 1 0 1 4 4

New York 300 99 22 2 0 1 8 8 1 0 0 4 4

Philadelphia As is 12 0 0 0 0 0 0 0 0 0 0

Philadelphia Current Std 12 0 0 0 0 0 0 0 0 0 0

Philadelphia 50 98 12 0 0 0 0 0 0 0 0 0 0

Philadelphia 50 99 12 0 0 0 0 0 0 0 0 0 0

Philadelphia 100 98 12 0 0 0 0 0 0 0 0 0 0

Philadelphia 100 99 12 0 0 0 0 0 0 0 0 0 0

Philadelphia 150 98 12 0 0 0 0 0 0 0 0 0 0

Philadelphia 150 99 12 0 0 0 0 0 0 0 0 0 0

Philadelphia 200 98 12 0 0 0 2 2 0 0 0 1 1

Philadelphia 200 99 12 0 0 0 0 0 0 0 0 0 0

Philadelphia 250 98 12 3 0 2 10 10 1 0 0 2 2

Philadelphia 250 99 12 0 0 0 2 2 0 0 0 1 1

Philadelphia 300 98 12 12 1 12 33 33 3 0 2 10 10

Philadelphia 300 99 12 2 0 1 7 7 0 0 0 2 2

Phoenix As is 9 0 0 0 0 0 0 0 0 0 0

Phoenix Current Std 9 0 0 0 0 0 0 0 0 0 0

Phoenix 50 98 9 0 0 0 0 0 0 0 0 0 0

Phoenix 50 99 9 0 0 0 0 0 0 0 0 0 0

Phoenix 100 98 9 0 0 0 0 0 0 0 0 0 0

Phoenix 100 99 9 0 0 0 0 0 0 0 0 0 0

Phoenix 150 98 9 0 0 0 0 0 0 0 0 0 0

Phoenix 150 99 9 0 0 0 0 0 0 0 0 0 0

Phoenix 200 98 9 0 0 0 0 0 0 0 0 0 0
A-215

Number of Daily Maximum Exceedances 

≥ 250 ppb ≥ 300 ppb 

Location Standard Percentile

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99

Phoenix 200 99 9 0 0 0 0 0 0 0 0 0 0

Phoenix 250 98 9 1 0 1 5 5 0 0 0 0 0

Phoenix 250 99 9 0 0 0 2 2 0 0 0 0 0

Phoenix 300 98 9 6 0 7 10 10 1 0 1 5 5

Phoenix 300 99 9 2 0 1 7 7 0 0 0 2 2

Provo As is 3 0 0 0 0 0 0 0 0 0 0

Provo Current Std 3 10 0 0 31 31 0 0 0 1 1

Provo 50 98 3 0 0 0 0 0 0 0 0 0 0

Provo 50 99 3 0 0 0 0 0 0 0 0 0 0

Provo 100 98 3 0 0 0 0 0 0 0 0 0 0

Provo 100 99 3 0 0 0 0 0 0 0 0 0 0

Provo 150 98 3 2 0 0 7 7 0 0 0 0 0

Provo 150 99 3 1 0 0 3 3 0 0 0 0 0

Provo 200 98 3 13 0 0 40 40 10 0 0 29 29

Provo 200 99 3 13 0 0 40 40 8 0 0 23 23

Provo 250 98 3 14 0 0 43 43 14 0 0 42 42

Provo 250 99 3 14 0 0 43 43 14 0 0 41 41

Provo 300 98 3 15 0 2 43 43 14 0 0 43 43

Provo 300 99 3 15 0 2 43 43 14 0 0 43 43

St. Louis As is 4 0 0 0 0 0 0 0 0 0 0

St. Louis Current Std 4 0 0 0 0 0 0 0 0 0 0

St. Louis 50 98 4 0 0 0 0 0 0 0 0 0 0

St. Louis 50 99 4 0 0 0 0 0 0 0 0 0 0

St. Louis 100 98 4 0 0 0 0 0 0 0 0 0 0

St. Louis 100 99 4 0 0 0 0 0 0 0 0 0 0

St. Louis 150 98 4 0 0 0 0 0 0 0 0 0 0

St. Louis 150 99 4 0 0 0 0 0 0 0 0 0 0

St. Louis 200 98 4 0 0 0 0 0 0 0 0 0 0

St. Louis 200 99 4 0 0 0 0 0 0 0 0 0 0

St. Louis 250 98 4 1 0 1 3 3 0 0 0 0 0

St. Louis 250 99 4 1 0 1 2 2 0 0 0 0 0

St. Louis 300 98 4 12 0 9 28 28 1 0 1 3 3

St. Louis 300 99 4 9 0 6 25 25 1 0 1 2 2

Washington 

DC 

As is 17 0 0 0 0 0 0 0 0 0 0

Washington 

DC 

Current Std 17 0 0 0 2 2 0 0 0 1 1

Washington 

DC 

50 98 17 0 0 0 0 0 0 0 0 0 0

Washington 

DC 

50 99 17 0 0 0 0 0 0 0 0 0 0

Washington 

DC 

100 98 17 0 0 0 0 0 0 0 0 0 0

Washington 

DC 

100 99 17 0 0 0 0 0 0 0 0 0 0

Washington 150 98 17 0 0 0 1 1 0 0 0 0 0
A-216

Number of Daily Maximum Exceedances 

≥ 250 ppb ≥ 300 ppb 

Location Standard Percentile

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99

DC 

Washington 

DC 

150 99 17 0 0 0 0 0 0 0 0 0 0

Washington 

DC 

200 98 17 1 0 0 5 5 0 0 0 2 2

Washington 

DC 

200 99 17 0 0 0 2 2 0 0 0 0 0

Washington 

DC 

250 98 17 3 0 2 12 12 1 0 0 6 6

Washington 

DC 

250 99 17 1 0 0 6 6 0 0 0 2 2

Washington 

DC 

300 98 17 13 0 10 39 39 3 0 2 12 12

Washington 

DC 

300 99 17 2 0 1 10 10 1 0 0 6 6

Other MSA As is 565 0 0 0 0 0 0 0 0 0 0

Other MSA Current Std 565 0 0 0 1 1 0 0 0 0 1

Other MSA 50 98 565 0 0 0 0 0 0 0 0 0 0

Other MSA 50 99 565 0 0 0 0 0 0 0 0 0 0

Other MSA 100 98 565 0 0 0 0 0 0 0 0 0 0

Other MSA 100 99 565 0 0 0 0 0 0 0 0 0 0

Other MSA 150 98 565 0 0 0 0 1 0 0 0 0 0

Other MSA 150 99 565 0 0 0 0 0 0 0 0 0 0

Other MSA 200 98 565 0 0 0 1 1 0 0 0 0 1

Other MSA 200 99 565 0 0 0 0 1 0 0 0 0 1

Other MSA 250 98 565 0 0 0 2 4 0 0 0 1 1

Other MSA 250 99 565 0 0 0 1 1 0 0 0 1 1

Other MSA 300 98 565 1 0 0 7 9 0 0 0 2 4

Other MSA 300 99 565 0 0 0 4 7 0 0 0 1 1

Other Not 

MSA 

As is 116 0 0 0 0 1 0 0 0 0 0

Other Not 

MSA 

Current Std 116 0 0 0 7 9 0 0 0 3 5

Other Not 

MSA 

50 98 116 0 0 0 0 0 0 0 0 0 0

Other Not 

MSA 

50 99 116 0 0 0 0 0 0 0 0 0 0

Other Not 

MSA 

100 98 116 0 0 0 0 1 0 0 0 0 0

Other Not 

MSA 

100 99 116 0 0 0 0 0 0 0 0 0 0

Other Not 

MSA 

150 98 116 0 0 0 0 2 0 0 0 0 1

Other Not 

MSA 

150 99 116 0 0 0 0 1 0 0 0 0 1

Other Not 

MSA 

200 98 116 0 0 0 2 2 0 0 0 0 2

Other Not 200 99 116 0 0 0 0 2 0 0 0 0 2
A-217

Number of Daily Maximum Exceedances 

≥ 250 ppb ≥ 300 ppb 

Location Standard Percentile

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99

MSA 

Other Not 

MSA 

250 98 116 0 0 0 3 5 0 0 0 2 2

Other Not 

MSA 

250 99 116 0 0 0 2 2 0 0 0 0 2

Other Not 

MSA 

300 98 116 0 0 0 7 9 0 0 0 3 5

Other Not 

MSA 

300 99 116 0 0 0 3 3 0 0 0 2 2

1 

2 

3 
A-218

1 Table A-129. Estimated number of exceedances of 1-hour concentration levels (100, 150, and 200 ppb) for monitors > 20 m and 2 road following adjustment to just meeting the current and alternative standards, 2004-2006 air quality. 

Number of Daily Maximum Exceedances 

≥ 100 ppb ≥ 150 ppb 

Location Standard Percentile

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99 Mea

Boston As is 11 0 0 0 1 1 0 0 0 0 0 Boston Current Std 11 25 2 12 73 73 2 0 0 10 10 Boston 50 98 11 0 0 0 0 0 0 0 0 0 0 Boston 50 99 11 0 0 0 0 0 0 0 0 0 0 Boston 100 98 11 1 0 0 9 9 0 0 0 1 1 Boston 100 99 11 1 0 0 4 4 0 0 0 0 0 Boston 150 98 11 19 1 11 67 67 1 0 0 9 9 Boston 150 99 11 8 0 2 34 34 1 0 0 4 4 Boston 200 98 11 84 20 65 153 153 9 0 3 42 42 Boston 200 99 11 42 7 32 102 102 4 0 1 18 18 Boston 250 98 11 165 78 143 247 247 36 6 28 91 91 Boston 250 99 11 103 30 79 175 175 16 0 8 62 62 Boston 300 98 11 214 115 208 291 291 84 20 65 153 153 1Boston 300 99 11 165 78 143 247 247 42 7 32 102 102 Chicago As is 6 1 0 1 5 5 0 0 0 0 0 Chicago Current Std 6 94 39 90 152 152 6 0 5 15 15 Chicago 50 98 6 0 0 0 0 0 0 0 0 0 0 Chicago 50 99 6 0 0 0 0 0 0 0 0 0 0 Chicago 100 98 6 4 0 3 15 15 0 0 0 1 1 Chicago 100 99 6 3 0 1 12 12 0 0 0 0 0 Chicago 150 98 6 80 34 74 147 147 4 0 3 15 15 Chicago 150 99 6 64 17 62 118 118 3 0 1 12 12 Chicago 200 98 6 206 161 200 267 267 44 10 43 88 88 Chicago 200 99 6 178 133 172 239 239 30 4 30 62 62 Chicago 250 98 6 277 248 273 314 314 122 78 112 190 190 2Chicago 250 99 6 261 230 257 299 299 99 58 90 164 164 1Chicago 300 98 6 324 309 325 338 338 206 161 200 267 267 8Chicago 300 99 6 320 302 319 338 338 178 133 172 239 239 6Cleveland As is 2 1 0 1 1 1 1 0 1 1 1 Cleveland Current Std 2 97 86 97 108 108 11 10 11 12 12 
A-219

Number of Daily Maximum Exceedances 

≥ 100 ppb ≥ 150 ppb 

Location Standard Percentile

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99 Mea

Cleveland 50 98 2 1 0 1 1 1 0 0 0 0 0 Cleveland 50 99 2 1 0 1 1 1 0 0 0 0 0 Cleveland 100 98 2 4 2 4 6 6 1 0 1 1 1 Cleveland 100 99 2 2 2 2 2 2 1 0 1 1 1 Cleveland 150 98 2 60 48 60 71 71 4 2 4 6 6 Cleveland 150 99 2 33 18 33 47 47 2 2 2 2 2 Cleveland 200 98 2 153 140 153 166 166 33 18 33 47 47 Cleveland 200 99 2 110 95 110 124 124 16 7 16 24 24 Cleveland 250 98 2 208 199 208 217 217 83 68 83 97 97 2Cleveland 250 99 2 188 180 188 195 195 60 48 60 71 71 1Cleveland 300 98 2 259 258 259 259 259 153 140 153 166 166 6Cleveland 300 99 2 230 227 230 233 233 110 95 110 124 124 3El Paso As is 3 0 0 0 0 0 0 0 0 0 0 El Paso Current Std 3 160 137 143 201 201 25 16 17 42 42 El Paso 50 98 3 0 0 0 0 0 0 0 0 0 0 El Paso 50 99 3 0 0 0 0 0 0 0 0 0 0 El Paso 100 98 3 6 4 4 9 9 0 0 0 0 0 El Paso 100 99 3 3 2 3 4 4 0 0 0 0 0 El Paso 150 98 3 57 33 56 83 83 6 4 4 9 9 El Paso 150 99 3 26 15 20 42 42 3 2 3 4 4 El Paso 200 98 3 160 132 147 201 201 26 15 20 42 42 El Paso 200 99 3 118 90 106 157 157 14 8 10 24 24 El Paso 250 98 3 239 220 225 273 273 88 59 82 124 124 1El Paso 250 99 3 197 167 181 243 243 51 27 51 76 76 1El Paso 300 98 3 276 263 267 297 297 160 132 147 201 201 5El Paso 300 99 3 251 234 238 281 281 118 90 106 157 157 2Los Angeles As is 22 1 0 1 9 9 0 0 0 1 1 Los Angeles Current Std 22 50 0 53 116 116 3 0 2 12 12 Los Angeles 50 98 22 0 0 0 0 0 0 0 0 0 0 Los Angeles 50 99 22 0 0 0 0 0 0 0 0 0 0 Los Angeles 100 98 22 3 0 1 17 17 0 0 0 2 2 Los Angeles 100 99 22 1 0 1 11 11 0 0 0 1 1 
A-220

Number of Daily Maximum Exceedances 

≥ 100 ppb ≥ 150 ppb 

Location Standard Percentile

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99 Mea

Los Angeles 150 98 22 46 0 51 101 101 3 0 1 17 17 Los Angeles 150 99 22 30 0 30 82 82 1 0 1 11 11 Los Angeles 200 98 22 139 9 143 235 235 22 0 23 63 63 Los Angeles 200 99 22 107 2 113 190 190 12 0 11 43 43 Los Angeles 250 98 22 225 41 242 295 295 75 0 77 143 143 1Los Angeles 250 99 22 196 27 202 282 282 51 0 55 107 107 Los Angeles 300 98 22 272 69 302 330 330 139 9 143 235 235 4Los Angeles 300 99 22 259 64 283 323 323 107 2 113 190 190 3Miami As is 2 1 0 1 2 2 1 0 1 1 1 Miami Current Std 2 173 171 173 175 175 76 75 76 76 76 2Miami 50 98 2 1 0 1 2 2 1 0 1 1 1 Miami 50 99 2 1 0 1 1 1 1 0 1 1 1 Miami 100 98 2 8 6 8 9 9 1 0 1 2 2 Miami 100 99 2 4 2 4 5 5 1 0 1 2 2 Miami 150 98 2 77 73 77 80 80 8 6 8 9 9 Miami 150 99 2 57 52 57 62 62 4 2 4 5 5 Miami 200 98 2 139 132 139 145 145 47 42 47 52 52 Miami 200 99 2 122 112 122 132 132 36 34 36 37 37 Miami 250 98 2 203 197 203 209 209 93 86 93 100 100 3Miami 250 99 2 179 175 179 183 183 83 76 83 89 89 2Miami 300 98 2 249 238 249 260 260 139 132 139 145 145 7Miami 300 99 2 225 218 225 232 232 122 112 122 132 132 5New York As is 11 1 0 1 3 3 0 0 0 1 1 New York Current Std 11 30 2 32 64 64 1 0 0 4 4 New York 50 98 11 0 0 0 0 0 0 0 0 0 0 New York 50 99 11 0 0 0 0 0 0 0 0 0 0 New York 100 98 11 5 1 6 8 8 0 0 0 1 1 New York 100 99 11 2 0 2 4 4 0 0 0 1 1 New York 150 98 11 69 14 62 126 126 5 1 6 8 8 New York 150 99 11 31 4 29 51 51 2 0 2 4 4 New York 200 98 11 198 84 199 285 285 35 5 33 60 60 New York 200 99 11 134 36 128 211 211 15 2 14 28 28 
A-221

Number of Daily Maximum Exceedances 

≥ 100 ppb ≥ 150 ppb 

Location Standard Percentile

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99 Mea

New York 250 98 11 281 190 287 339 339 115 26 106 190 190 2New York 250 99 11 232 121 235 315 315 58 9 53 104 104 1New York 300 98 11 317 245 331 356 356 198 84 199 285 285 6New York 300 99 11 290 207 302 345 345 134 36 128 211 211 3Philadelphia As is 6 1 0 1 1 1 0 0 0 0 0 Philadelphia Current Std 6 70 28 56 173 173 6 1 3 25 25 Philadelphia 50 98 6 0 0 0 0 0 0 0 0 0 0 Philadelphia 50 99 6 0 0 0 0 0 0 0 0 0 0 Philadelphia 100 98 6 6 3 6 10 10 1 0 1 1 1 Philadelphia 100 99 6 2 1 2 4 4 0 0 0 0 0 Philadelphia 150 98 6 93 62 96 125 125 6 3 6 10 10 Philadelphia 150 99 6 25 15 24 39 39 2 1 2 4 4 Philadelphia 200 98 6 216 169 216 267 267 45 28 49 60 60 Philadelphia 200 99 6 121 76 127 166 166 11 6 10 16 16 Philadelphia 250 98 6 272 227 272 313 313 131 83 138 173 173 3Philadelphia 250 99 6 216 169 216 267 267 45 28 49 60 60 Philadelphia 300 98 6 307 274 308 342 342 216 169 216 267 267 9Philadelphia 300 99 6 266 220 265 310 310 121 76 127 166 166 2Phoenix As is 3 0 0 0 0 0 0 0 0 0 0 Phoenix Current Std 3 14 13 13 15 15 0 0 0 0 0 Phoenix 50 98 3 0 0 0 0 0 0 0 0 0 0 Phoenix 50 99 3 0 0 0 0 0 0 0 0 0 0 Phoenix 100 98 3 0 0 0 0 0 0 0 0 0 0 Phoenix 100 99 3 0 0 0 0 0 0 0 0 0 0 Phoenix 150 98 3 29 28 29 30 30 0 0 0 0 0 Phoenix 150 99 3 7 4 9 9 9 0 0 0 0 0 Phoenix 200 98 3 174 171 174 178 178 5 2 6 6 6 Phoenix 200 99 3 120 114 121 126 126 1 0 2 2 2 Phoenix 250 98 3 257 239 264 268 268 68 55 66 83 83 Phoenix 250 99 3 217 207 221 222 222 29 28 29 30 30 Phoenix 300 98 3 304 285 309 317 317 174 171 174 178 178 2Phoenix 300 99 3 284 263 294 294 294 120 114 121 126 126 
A-222

Number of Daily Maximum Exceedances 

≥ 100 ppb ≥ 150 ppb 

Location Standard Percentile

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99 Mea

St. Louis As is 8 0 0 0 1 1 0 0 0 0 0 St. Louis Current Std 8 42 1 35 108 108 2 0 1 6 6 St. Louis 50 98 8 0 0 0 1 1 0 0 0 0 0 St. Louis 50 99 8 0 0 0 1 1 0 0 0 0 0 St. Louis 100 98 8 1 0 0 8 8 0 0 0 1 1 St. Louis 100 99 8 1 0 0 4 4 0 0 0 1 1 St. Louis 150 98 8 26 1 13 124 124 1 0 0 8 8 St. Louis 150 99 8 22 1 8 115 115 1 0 0 4 4 St. Louis 200 98 8 83 23 65 242 242 12 0 3 78 78 St. Louis 200 99 8 75 19 57 231 231 10 0 2 66 66 St. Louis 250 98 8 142 71 116 319 319 42 3 26 174 174 St. Louis 250 99 8 131 62 105 304 304 39 3 23 164 164 St. Louis 300 98 8 187 114 157 344 344 83 23 65 242 242 2St. Louis 300 99 8 187 114 157 344 344 75 19 57 231 231 2Washington DC 

As is 12 0 0 0 1 1 0 0 0 0 0 

Washington DC 

Current Std 12 66 10 74 123 123 3 0 3 7 7 

Washington DC 

50 98 12 0 0 0 0 0 0 0 0 0 0 

Washington DC 

50 99 12 0 0 0 0 0 0 0 0 0 0 

Washington DC 

100 98 12 3 0 2 7 7 0 0 0 1 1 

Washington DC 

100 99 12 0 0 0 1 1 0 0 0 1 1 

Washington DC 

150 98 12 59 16 54 115 115 3 0 2 7 7 

Washington DC 

150 99 12 15 0 12 38 38 0 0 0 1 1 

Washington DC 

200 98 12 165 80 174 245 245 26 2 23 61 61 

Washington DC 

200 99 12 85 29 82 154 154 5 0 5 14 14 
A-223

Number of Daily Maximum Exceedances 

≥ 100 ppb ≥ 150 ppb 

Location Standard Percentile

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99 Mea

Washington DC 

250 98 12 224 127 241 303 303 94 35 93 169 169 1

Washington DC 

250 99 12 165 80 174 245 245 30 2 25 66 66 

Washington DC 

300 98 12 266 182 277 338 338 165 80 174 245 245 5

Washington DC 

300 99 12 224 127 241 303 303 85 29 82 154 154 1

1 
A-224

Table A-130. Estimated number of exceedances of 1-hour concentration levels (250 and 300 ppb) for 

monitors > 20 m and < 100 m from a major road following adjustment to just meeting the current and 

alternative standards, 2004-2006 air quality. 

Number of Daily Maximum Exceedances 

≥ 250 ppb ≥ 300 ppb 

Location Standard Percentile

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99

Boston As is 11 0 0 0 0 0 0 0 0 0 0

Boston Current Std 11 0 0 0 1 1 0 0 0 0 0

Boston 50 98 11 0 0 0 0 0 0 0 0 0 0

Boston 50 99 11 0 0 0 0 0 0 0 0 0 0

Boston 100 98 11 0 0 0 0 0 0 0 0 0 0

Boston 100 99 11 0 0 0 0 0 0 0 0 0 0

Boston 150 98 11 0 0 0 0 0 0 0 0 0 0

Boston 150 99 11 0 0 0 0 0 0 0 0 0 0

Boston 200 98 11 0 0 0 2 2 0 0 0 1 1

Boston 200 99 11 0 0 0 1 1 0 0 0 0 0

Boston 250 98 11 1 0 0 9 9 1 0 0 4 4

Boston 250 99 11 1 0 0 4 4 0 0 0 1 1

Boston 300 98 11 5 0 1 29 29 1 0 0 9 9

Boston 300 99 11 2 0 0 12 12 1 0 0 4 4

Chicago As is 6 0 0 0 0 0 0 0 0 0 0

Chicago Current Std 6 0 0 0 0 0 0 0 0 0 0

Chicago 50 98 6 0 0 0 0 0 0 0 0 0 0

Chicago 50 99 6 0 0 0 0 0 0 0 0 0 0

Chicago 100 98 6 0 0 0 0 0 0 0 0 0 0

Chicago 100 99 6 0 0 0 0 0 0 0 0 0 0

Chicago 150 98 6 0 0 0 0 0 0 0 0 0 0

Chicago 150 99 6 0 0 0 0 0 0 0 0 0 0

Chicago 200 98 6 0 0 0 2 2 0 0 0 1 1

Chicago 200 99 6 0 0 0 2 2 0 0 0 0 0

Chicago 250 98 6 4 0 3 15 15 1 0 0 3 3

Chicago 250 99 6 2 0 1 9 9 0 0 0 2 2

Chicago 300 98 6 21 1 18 53 53 4 0 3 15 15

Chicago 300 99 6 14 1 11 39 39 3 0 1 12 12

Cleveland As is 2 0 0 0 0 0 0 0 0 0 0

Cleveland Current Std 2 1 0 1 1 1 1 0 1 1 1

Cleveland 50 98 2 0 0 0 0 0 0 0 0 0 0

Cleveland 50 99 2 0 0 0 0 0 0 0 0 0 0

Cleveland 100 98 2 1 0 1 1 1 0 0 0 0 0

Cleveland 100 99 2 0 0 0 0 0 0 0 0 0 0

Cleveland 150 98 2 1 0 1 1 1 1 0 1 1 1

Cleveland 150 99 2 1 0 1 1 1 1 0 1 1 1

Cleveland 200 98 2 1 1 1 1 1 1 0 1 1 1

Cleveland 200 99 2 1 0 1 1 1 1 0 1 1 1

Cleveland 250 98 2 4 2 4 6 6 1 1 1 1 1

Cleveland 250 99 2 2 2 2 2 2 1 0 1 1 1

Cleveland 300 98 2 16 7 16 24 24 4 2 4 6 6

Cleveland 300 99 2 7 4 7 10 10 2 2 2 2 2

El Paso As is 3 0 0 0 0 0 0 0 0 0 0
A-225

Number of Daily Maximum Exceedances 

≥ 250 ppb ≥ 300 ppb 

Location Standard Percentile

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99

El Paso Current Std 3 2 1 2 3 3 0 0 0 0 0

El Paso 50 98 3 0 0 0 0 0 0 0 0 0 0

El Paso 50 99 3 0 0 0 0 0 0 0 0 0 0

El Paso 100 98 3 0 0 0 0 0 0 0 0 0 0

El Paso 100 99 3 0 0 0 0 0 0 0 0 0 0

El Paso 150 98 3 0 0 0 0 0 0 0 0 0 0

El Paso 150 99 3 0 0 0 0 0 0 0 0 0 0

El Paso 200 98 3 2 1 2 2 2 0 0 0 0 0

El Paso 200 99 3 0 0 0 1 1 0 0 0 0 0

El Paso 250 98 3 6 4 4 9 9 2 2 2 3 3

El Paso 250 99 3 3 2 3 4 4 1 0 1 1 1

El Paso 300 98 3 14 8 10 24 24 6 4 4 9 9

El Paso 300 99 3 7 4 5 13 13 3 2 3 4 4

Los Angeles As is 22 0 0 0 0 0 0 0 0 0 0

Los Angeles Current Std 22 0 0 0 0 0 0 0 0 0 0

Los Angeles 50 98 22 0 0 0 0 0 0 0 0 0 0

Los Angeles 50 99 22 0 0 0 0 0 0 0 0 0 0

Los Angeles 100 98 22 0 0 0 0 0 0 0 0 0 0

Los Angeles 100 99 22 0 0 0 0 0 0 0 0 0 0

Los Angeles 150 98 22 0 0 0 1 1 0 0 0 0 0

Los Angeles 150 99 22 0 0 0 0 0 0 0 0 0 0

Los Angeles 200 98 22 0 0 0 3 3 0 0 0 2 2

Los Angeles 200 99 22 0 0 0 2 2 0 0 0 1 1

Los Angeles 250 98 22 3 0 1 17 17 0 0 0 5 5

Los Angeles 250 99 22 1 0 1 9 9 0 0 0 3 3

Los Angeles 300 98 22 11 0 10 41 41 3 0 1 17 17

Los Angeles 300 99 22 5 0 4 25 25 1 0 1 11 11

Miami As is 2 1 0 1 1 1 1 0 1 1 1

Miami Current Std 2 3 0 3 5 5 1 0 1 2 2

Miami 50 98 2 1 0 1 1 1 1 0 1 1 1

Miami 50 99 2 1 0 1 1 1 1 0 1 1 1

Miami 100 98 2 1 0 1 1 1 1 0 1 1 1

Miami 100 99 2 1 0 1 1 1 1 0 1 1 1

Miami 150 98 2 1 0 1 2 2 1 0 1 2 2

Miami 150 99 2 1 0 1 2 2 1 0 1 1 1

Miami 200 98 2 1 0 1 2 2 1 0 1 2 2

Miami 200 99 2 1 0 1 2 2 1 0 1 2 2

Miami 250 98 2 8 6 8 9 9 1 0 1 2 2

Miami 250 99 2 4 2 4 5 5 1 0 1 2 2

Miami 300 98 2 28 25 28 31 31 8 6 8 9 9

Miami 300 99 2 19 17 19 20 20 4 2 4 5 5

New York As is 11 0 0 0 0 0 0 0 0 0 0

New York Current Std 11 0 0 0 1 1 0 0 0 0 0

New York 50 98 11 0 0 0 0 0 0 0 0 0 0

New York 50 99 11 0 0 0 0 0 0 0 0 0 0
A-226

Number of Daily Maximum Exceedances 

≥ 250 ppb ≥ 300 ppb 

Location Standard Percentile

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99

New York 100 98 11 0 0 0 0 0 0 0 0 0 0

New York 100 99 11 0 0 0 0 0 0 0 0 0 0

New York 150 98 11 0 0 0 1 1 0 0 0 0 0

New York 150 99 11 0 0 0 0 0 0 0 0 0 0

New York 200 98 11 1 0 1 3 3 0 0 0 1 1

New York 200 99 11 0 0 0 2 2 0 0 0 1 1

New York 250 98 11 5 1 6 8 8 1 0 2 3 3

New York 250 99 11 2 0 2 4 4 1 0 0 2 2

New York 300 98 11 19 2 20 33 33 5 1 6 8 8

New York 300 99 11 7 2 7 14 14 2 0 2 4 4

Philadelphia As is 6 0 0 0 0 0 0 0 0 0 0

Philadelphia Current Std 6 1 0 0 3 3 0 0 0 0 0

Philadelphia 50 98 6 0 0 0 0 0 0 0 0 0 0

Philadelphia 50 99 6 0 0 0 0 0 0 0 0 0 0

Philadelphia 100 98 6 0 0 0 0 0 0 0 0 0 0

Philadelphia 100 99 6 0 0 0 0 0 0 0 0 0 0

Philadelphia 150 98 6 0 0 0 1 1 0 0 0 0 0

Philadelphia 150 99 6 0 0 0 0 0 0 0 0 0 0

Philadelphia 200 98 6 2 1 2 4 4 1 0 1 1 1

Philadelphia 200 99 6 0 0 0 1 1 0 0 0 0 0

Philadelphia 250 98 6 6 3 6 10 10 2 1 2 4 4

Philadelphia 250 99 6 2 1 2 4 4 1 0 1 1 1

Philadelphia 300 98 6 25 15 24 39 39 6 3 6 10 10

Philadelphia 300 99 6 4 2 5 6 6 2 1 2 4 4

Phoenix As is 3 0 0 0 0 0 0 0 0 0 0

Phoenix Current Std 3 0 0 0 0 0 0 0 0 0 0

Phoenix 50 98 3 0 0 0 0 0 0 0 0 0 0

Phoenix 50 99 3 0 0 0 0 0 0 0 0 0 0

Phoenix 100 98 3 0 0 0 0 0 0 0 0 0 0

Phoenix 100 99 3 0 0 0 0 0 0 0 0 0 0

Phoenix 150 98 3 0 0 0 0 0 0 0 0 0 0

Phoenix 150 99 3 0 0 0 0 0 0 0 0 0 0

Phoenix 200 98 3 0 0 0 0 0 0 0 0 0 0

Phoenix 200 99 3 0 0 0 0 0 0 0 0 0 0

Phoenix 250 98 3 0 0 0 0 0 0 0 0 0 0

Phoenix 250 99 3 0 0 0 0 0 0 0 0 0 0

Phoenix 300 98 3 1 0 2 2 2 0 0 0 0 0

Phoenix 300 99 3 0 0 0 1 1 0 0 0 0 0

St. Louis As is 8 0 0 0 0 0 0 0 0 0 0

St. Louis Current Std 8 0 0 0 1 1 0 0 0 1 1

St. Louis 50 98 8 0 0 0 0 0 0 0 0 0 0

St. Louis 50 99 8 0 0 0 0 0 0 0 0 0 0

St. Louis 100 98 8 0 0 0 0 0 0 0 0 0 0

St. Louis 100 99 8 0 0 0 0 0 0 0 0 0 0

St. Louis 150 98 8 0 0 0 1 1 0 0 0 1 1
A-227

Number of Daily Maximum Exceedances 

≥ 250 ppb ≥ 300 ppb 

Location Standard Percentile

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99

St. Louis 150 99 8 0 0 0 1 1 0 0 0 1 1

St. Louis 200 98 8 0 0 0 1 1 0 0 0 1 1

St. Louis 200 99 8 0 0 0 1 1 0 0 0 1 1

St. Louis 250 98 8 1 0 0 5 5 0 0 0 1 1

St. Louis 250 99 8 1 0 0 4 4 0 0 0 1 1

St. Louis 300 98 8 7 0 1 45 45 1 0 0 8 8

St. Louis 300 99 8 4 0 0 33 33 1 0 0 4 4

Washington 

DC 

As is 12 0 0 0 0 0 0 0 0 0 0

Washington 

DC 

Current Std 12 0 0 0 1 1 0 0 0 1 1

Washington 

DC 

50 98 12 0 0 0 0 0 0 0 0 0 0

Washington 

DC 

50 99 12 0 0 0 0 0 0 0 0 0 0

Washington 

DC 

100 98 12 0 0 0 0 0 0 0 0 0 0

Washington 

DC 

100 99 12 0 0 0 0 0 0 0 0 0 0

Washington 

DC 

150 98 12 0 0 0 1 1 0 0 0 0 0

Washington 

DC 

150 99 12 0 0 0 0 0 0 0 0 0 0

Washington 

DC 

200 98 12 0 0 0 1 1 0 0 0 1 1

Washington 

DC 

200 99 12 0 0 0 1 1 0 0 0 1 1

Washington 

DC 

250 98 12 3 0 2 7 7 0 0 0 1 1

Washington 

DC 

250 99 12 0 0 0 1 1 0 0 0 1 1

Washington 

DC 

300 98 12 13 0 11 33 33 3 0 2 7 7

Washington 

DC 

300 99 12 2 0 1 6 6 0 0 0 1 1
A-228

Table a-131. Estimated number of exceedances of 1-hour concentration levels (100, 150, and 200 ppb) for monitors ≤ 20 m from 

adjustment to just meeting the current and alternative standards, 2004-2006 air quality. 

Number of Daily Maximum Exceedances 

≥ 100 ppb ≥ 150 ppb 

Location Standard Percentile

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99 Mea

Boston As is 3 0 0 0 0 0 0 0 0 0 0 Boston Current Std 3 68 64 66 75 75 3 1 3 5 5 Boston 50 98 3 0 0 0 0 0 0 0 0 0 0 Boston 50 99 3 0 0 0 0 0 0 0 0 0 0 Boston 100 98 3 2 1 3 3 3 0 0 0 0 0 Boston 100 99 3 0 0 0 0 0 0 0 0 0 0 Boston 150 98 3 55 33 60 72 72 2 1 3 3 3 Boston 150 99 3 18 10 21 22 22 0 0 0 0 0 Boston 200 98 3 197 179 195 216 216 24 14 26 31 31 Boston 200 99 3 113 86 123 129 129 5 3 6 7 7 Boston 250 98 3 291 281 288 304 304 103 76 110 123 123 1Boston 250 99 3 227 207 222 253 253 43 29 43 57 57 Boston 300 98 3 323 314 322 334 334 197 179 195 216 216 5Boston 300 99 3 291 281 288 304 304 113 86 123 129 129 1Chicago As is 3 0 0 0 0 0 0 0 0 0 0 Chicago Current Std 3 22 5 23 38 38 1 0 1 1 1 Chicago 50 98 3 0 0 0 0 0 0 0 0 0 0 Chicago 50 99 3 0 0 0 0 0 0 0 0 0 0 Chicago 100 98 3 0 0 0 1 1 0 0 0 0 0 Chicago 100 99 3 0 0 0 0 0 0 0 0 0 0 Chicago 150 98 3 15 4 15 26 26 0 0 0 1 1 Chicago 150 99 3 10 1 11 18 18 0 0 0 0 0 Chicago 200 98 3 93 66 104 109 109 7 0 10 12 12 Chicago 200 99 3 69 40 75 91 91 5 0 5 9 9 Chicago 250 98 3 167 138 179 184 184 33 15 32 51 51 Chicago 250 99 3 144 112 155 165 165 23 8 23 38 38 Chicago 300 98 3 235 211 243 250 250 93 66 104 109 109 1Chicago 300 99 3 222 197 232 238 238 69 40 75 91 91 1Cleveland As is 3 0 0 0 0 0 0 0 0 0 0 
A-229

Number of Daily Maximum Exceedances 

≥ 100 ppb ≥ 150 ppb 

Location Standard Percentile

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99 Mea

Cleveland Current Std 3 119 96 119 142 142 16 11 14 22 22 Cleveland 50 98 3 0 0 0 0 0 0 0 0 0 0 Cleveland 50 99 3 0 0 0 0 0 0 0 0 0 0 Cleveland 100 98 3 7 1 9 10 10 0 0 0 0 0 Cleveland 100 99 3 3 0 4 6 6 0 0 0 0 0 Cleveland 150 98 3 72 57 76 83 83 7 1 9 10 10 Cleveland 150 99 3 46 29 48 60 60 3 0 4 6 6 Cleveland 200 98 3 185 162 184 210 210 46 29 48 60 60 Cleveland 200 99 3 136 115 138 154 154 25 15 27 32 32 Cleveland 250 98 3 258 244 257 274 274 113 93 114 131 131 3Cleveland 250 99 3 228 217 224 244 244 72 57 76 83 83 1Cleveland 300 98 3 313 298 320 321 321 185 162 184 210 210 7Cleveland 300 99 3 281 260 289 294 294 136 115 138 154 154 4Denver As is 3 2 1 1 3 3 0 0 0 1 1 Denver Current Std 3 82 79 80 87 87 2 1 2 4 4 Denver 50 98 3 0 0 0 1 1 0 0 0 0 0 Denver 50 99 3 0 0 0 0 0 0 0 0 0 0 Denver 100 98 3 4 1 4 7 7 2 1 1 3 3 Denver 100 99 3 2 1 1 3 3 1 0 0 2 2 Denver 150 98 3 108 92 109 122 122 4 1 4 7 7 Denver 150 99 3 42 35 44 48 48 2 1 1 3 3 Denver 200 98 3 241 221 249 252 252 52 38 55 63 63 Denver 200 99 3 195 176 200 210 210 14 12 14 16 16 Denver 250 98 3 295 287 295 304 304 164 142 170 181 181 2Denver 250 99 3 265 256 269 269 269 95 79 96 109 109 Denver 300 98 3 324 302 332 337 337 241 221 249 252 252 10Denver 300 99 3 310 293 318 319 319 195 176 200 210 210 4Las Vegas As is 2 0 0 0 0 0 0 0 0 0 0 Las Vegas Current Std 2 202 196 202 208 208 19 17 19 21 21 Las Vegas 50 98 2 0 0 0 0 0 0 0 0 0 0 Las Vegas 50 99 2 0 0 0 0 0 0 0 0 0 0 
A-230

Number of Daily Maximum Exceedances 

≥ 100 ppb ≥ 150 ppb 

Location Standard Percentile

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99 Mea

Las Vegas 100 98 2 8 6 8 10 10 0 0 0 0 0 Las Vegas 100 99 2 3 1 3 5 5 0 0 0 0 0 Las Vegas 150 98 2 162 162 162 162 162 8 6 8 10 10 Las Vegas 150 99 2 123 117 123 128 128 3 1 3 5 5 Las Vegas 200 98 2 282 279 282 285 285 96 93 96 99 99 Las Vegas 200 99 2 263 260 263 265 265 50 43 50 57 57 Las Vegas 250 98 2 312 311 312 313 313 216 213 216 218 218 6Las Vegas 250 99 2 306 304 306 307 307 177 170 177 183 183 3Las Vegas 300 98 2 322 320 322 324 324 282 279 282 285 285 16Las Vegas 300 99 2 318 316 318 319 319 263 260 263 265 265 12Los Angeles As is 6 3 0 2 8 8 0 0 0 0 0 Los Angeles Current Std 6 53 26 48 82 82 4 0 4 9 9 Los Angeles 50 98 6 0 0 0 0 0 0 0 0 0 0 Los Angeles 50 99 6 0 0 0 0 0 0 0 0 0 0 Los Angeles 100 98 6 3 0 2 9 9 0 0 0 0 0 Los Angeles 100 99 6 3 0 2 8 8 0 0 0 0 0 Los Angeles 150 98 6 50 19 60 65 65 3 0 2 9 9 Los Angeles 150 99 6 31 8 39 47 47 3 0 2 8 8 Los Angeles 200 98 6 136 59 148 190 190 23 3 26 33 33 Los Angeles 200 99 6 107 44 118 150 150 14 1 14 23 23 Los Angeles 250 98 6 216 115 224 285 285 77 29 86 112 112 1Los Angeles 250 99 6 188 91 200 250 250 54 20 65 73 73 Los Angeles 300 98 6 270 170 270 335 335 136 59 148 190 190 5Los Angeles 300 99 6 252 152 256 319 319 107 44 118 150 150 3Miami As is 2 0 0 0 0 0 0 0 0 0 0 Miami Current Std 2 87 85 87 89 89 36 36 36 36 36 Miami 50 98 2 0 0 0 0 0 0 0 0 0 0 Miami 50 99 2 0 0 0 0 0 0 0 0 0 0 Miami 100 98 2 3 1 3 4 4 0 0 0 0 0 Miami 100 99 2 1 0 1 1 1 0 0 0 0 0 Miami 150 98 2 36 33 36 38 38 3 1 3 4 4 
A-231

Number of Daily Maximum Exceedances 

≥ 100 ppb ≥ 150 ppb 

Location Standard Percentile

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99 Mea

Miami 150 99 2 27 25 27 28 28 1 0 1 1 1 Miami 200 98 2 75 72 75 77 77 20 18 20 22 22 Miami 200 99 2 66 64 66 68 68 12 9 12 14 14 Miami 250 98 2 98 89 98 107 107 48 43 48 52 52 1Miami 250 99 2 88 85 88 91 91 39 36 39 42 42 Miami 300 98 2 122 110 122 133 133 75 72 75 77 77 3Miami 300 99 2 109 99 109 119 119 66 64 66 68 68 2New York As is 2 1 0 1 1 1 0 0 0 0 0 New York Current Std 2 38 17 38 59 59 2 0 2 3 3 New York 50 98 2 0 0 0 0 0 0 0 0 0 0 New York 50 99 2 0 0 0 0 0 0 0 0 0 0 New York 100 98 2 4 4 4 4 4 0 0 0 0 0 New York 100 99 2 1 0 1 2 2 0 0 0 0 0 New York 150 98 2 71 60 71 81 81 4 4 4 4 4 New York 150 99 2 34 33 34 35 35 1 0 1 2 2 New York 200 98 2 185 175 185 194 194 37 35 37 38 38 New York 200 99 2 134 132 134 135 135 10 9 10 11 11 New York 250 98 2 274 260 274 288 288 114 112 114 115 115 2New York 250 99 2 214 201 214 227 227 59 52 59 66 66 New York 300 98 2 312 296 312 327 327 185 175 185 194 194 7New York 300 99 2 283 269 283 297 297 134 132 134 135 135 3Phoenix As is 5 2 0 2 3 3 0 0 0 0 0 Phoenix Current Std 5 61 0 83 126 126 3 0 2 8 8 Phoenix 50 98 5 0 0 0 0 0 0 0 0 0 0 Phoenix 50 99 5 0 0 0 0 0 0 0 0 0 0 Phoenix 100 98 5 5 0 6 11 11 0 0 0 1 1 Phoenix 100 99 5 2 0 2 6 6 0 0 0 0 0 Phoenix 150 98 5 82 0 132 143 143 5 0 6 11 11 Phoenix 150 99 5 48 0 67 97 97 2 0 2 6 6 Phoenix 200 98 5 182 7 285 311 311 43 0 55 92 92 Phoenix 200 99 5 150 2 241 256 256 21 0 25 44 44 
A-232

Number of Daily Maximum Exceedances 

≥ 100 ppb ≥ 150 ppb 

Location Standard Percentile

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99 Mea

Phoenix 250 98 5 225 41 335 359 359 121 0 197 203 203 2Phoenix 250 99 5 205 18 315 340 340 82 0 132 143 143 1Phoenix 300 98 5 263 120 349 365 365 182 7 285 311 311 8Phoenix 300 99 5 241 73 341 364 364 150 2 241 256 256 4St. Louis As is 5 0 0 0 0 0 0 0 0 0 0 St. Louis Current Std 5 109 30 119 178 178 11 0 11 28 28 St. Louis 50 98 5 0 0 0 0 0 0 0 0 0 0 St. Louis 50 99 5 0 0 0 0 0 0 0 0 0 0 St. Louis 100 98 5 1 0 1 1 1 0 0 0 0 0 St. Louis 100 99 5 0 0 0 0 0 0 0 0 0 0 St. Louis 150 98 5 49 37 44 65 65 1 0 1 1 1 St. Louis 150 99 5 41 33 38 56 56 0 0 0 0 0 St. Louis 200 98 5 158 135 143 187 187 20 16 19 24 24 St. Louis 200 99 5 145 119 130 178 178 12 11 11 15 15 St. Louis 250 98 5 245 228 239 263 263 86 66 78 107 107 1St. Louis 250 99 5 233 218 222 254 254 74 53 71 98 98 St. Louis 300 98 5 298 285 289 319 319 158 135 143 187 187 4St. Louis 300 99 5 298 285 289 319 319 145 119 130 178 178 4Washington DC 

As is 5 0 0 0 0 0 0 0 0 0 0 

Washington DC 

Current Std 5 60 40 63 76 76 4 0 2 11 11 

Washington DC 

50 98 5 0 0 0 0 0 0 0 0 0 0 

Washington DC 

50 99 5 0 0 0 0 0 0 0 0 0 0 

Washington DC 

100 98 5 4 2 2 13 13 0 0 0 0 0 

Washington DC 

100 99 5 0 0 0 1 1 0 0 0 0 0 

Washington DC 

150 98 5 63 30 59 92 92 4 2 2 13 13 
A-233

Number of Daily Maximum Exceedances 

≥ 100 ppb ≥ 150 ppb 

Location Standard Percentile

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99 Mea

Washington DC 

150 99 5 17 5 12 43 43 0 0 0 1 1 

Washington DC 

200 98 5 173 122 155 222 222 32 12 30 56 56 

Washington DC 

200 99 5 85 43 76 118 118 6 2 4 17 17 

Washington DC 

250 98 5 247 197 224 307 307 93 54 80 129 129 2

Washington DC 

250 99 5 173 122 155 222 222 36 13 36 62 62 

Washington DC 

300 98 5 293 256 277 336 336 173 122 155 222 222 6

Washington DC 

300 99 5 247 197 224 307 307 85 43 76 118 118 1
A-234

Table A-132. Estimated number of exceedances of 1-hour concentration levels (250 and 300 ppb) for 

monitors ≤ 20 m from a major road following adjustment to just meeting the current and alternative 

standards, 2004-2006 air quality. 

Number of Daily Maximum Exceedances 

≥ 250 ppb ≥ 300 ppb 

Location Standard Percentile

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99

Boston As is 3 0 0 0 0 0 0 0 0 0 0

Boston Current 

Std 

 3 0 0 0 0 0 0 0 0 0 0

Boston 50 98 3 0 0 0 0 0 0 0 0 0 0

Boston 50 99 3 0 0 0 0 0 0 0 0 0 0

Boston 100 98 3 0 0 0 0 0 0 0 0 0 0

Boston 100 99 3 0 0 0 0 0 0 0 0 0 0

Boston 150 98 3 0 0 0 0 0 0 0 0 0 0

Boston 150 99 3 0 0 0 0 0 0 0 0 0 0

Boston 200 98 3 0 0 0 0 0 0 0 0 0 0

Boston 200 99 3 0 0 0 0 0 0 0 0 0 0

Boston 250 98 3 2 1 3 3 3 0 0 0 0 0

Boston 250 99 3 0 0 0 0 0 0 0 0 0 0

Boston 300 98 3 11 4 11 18 18 2 1 3 3 3

Boston 300 99 3 3 1 3 4 4 0 0 0 0 0

Chicago As is 3 0 0 0 0 0 0 0 0 0 0

Chicago Current 

Std 

 3 0 0 0 0 0 0 0 0 0 0

Chicago 50 98 3 0 0 0 0 0 0 0 0 0 0

Chicago 50 99 3 0 0 0 0 0 0 0 0 0 0

Chicago 100 98 3 0 0 0 0 0 0 0 0 0 0

Chicago 100 99 3 0 0 0 0 0 0 0 0 0 0

Chicago 150 98 3 0 0 0 0 0 0 0 0 0 0

Chicago 150 99 3 0 0 0 0 0 0 0 0 0 0

Chicago 200 98 3 0 0 0 0 0 0 0 0 0 0

Chicago 200 99 3 0 0 0 0 0 0 0 0 0 0

Chicago 250 98 3 0 0 0 1 1 0 0 0 0 0

Chicago 250 99 3 0 0 0 0 0 0 0 0 0 0

Chicago 300 98 3 4 0 3 9 9 0 0 0 1 1

Chicago 300 99 3 1 0 2 2 2 0 0 0 0 0

Cleveland As is 3 0 0 0 0 0 0 0 0 0 0

Cleveland Current 

Std 

 3 0 0 0 0 0 0 0 0 0 0

Cleveland 50 98 3 0 0 0 0 0 0 0 0 0 0

Cleveland 50 99 3 0 0 0 0 0 0 0 0 0 0

Cleveland 100 98 3 0 0 0 0 0 0 0 0 0 0

Cleveland 100 99 3 0 0 0 0 0 0 0 0 0 0

Cleveland 150 98 3 0 0 0 0 0 0 0 0 0 0

Cleveland 150 99 3 0 0 0 0 0 0 0 0 0 0

Cleveland 200 98 3 1 0 1 1 1 0 0 0 0 0

Cleveland 200 99 3 0 0 0 0 0 0 0 0 0 0

Cleveland 250 98 3 7 1 9 10 10 1 0 1 2 2

Cleveland 250 99 3 3 0 4 6 6 0 0 0 0 0
A-235

Number of Daily Maximum Exceedances 

≥ 250 ppb ≥ 300 ppb 

Location Standard Percentile

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99

Cleveland 300 98 3 25 15 27 32 32 7 1 9 10 10

Cleveland 300 99 3 11 6 14 14 14 3 0 4 6 6

Denver As is 3 0 0 0 0 0 0 0 0 0 0

Denver Current 

Std 

 3 0 0 0 1 1 0 0 0 1 1

Denver 50 98 3 0 0 0 0 0 0 0 0 0 0

Denver 50 99 3 0 0 0 0 0 0 0 0 0 0

Denver 100 98 3 0 0 0 0 0 0 0 0 0 0

Denver 100 99 3 0 0 0 0 0 0 0 0 0 0

Denver 150 98 3 1 0 0 2 2 0 0 0 1 1

Denver 150 99 3 0 0 0 1 1 0 0 0 0 0

Denver 200 98 3 2 1 1 3 3 2 1 1 3 3

Denver 200 99 3 2 1 1 3 3 1 0 0 2 2

Denver 250 98 3 3 1 3 6 6 2 1 1 3 3

Denver 250 99 3 2 1 1 3 3 2 1 1 3 3

Denver 300 98 3 16 15 16 18 18 4 1 4 7 7

Denver 300 99 3 5 4 5 7 7 2 1 1 3 3

Las Vegas As is 2 0 0 0 0 0 0 0 0 0 0

Las Vegas Current 

Std 

 2 0 0 0 0 0 0 0 0 0 0

Las Vegas 50 98 2 0 0 0 0 0 0 0 0 0 0

Las Vegas 50 99 2 0 0 0 0 0 0 0 0 0 0

Las Vegas 100 98 2 0 0 0 0 0 0 0 0 0 0

Las Vegas 100 99 2 0 0 0 0 0 0 0 0 0 0

Las Vegas 150 98 2 0 0 0 0 0 0 0 0 0 0

Las Vegas 150 99 2 0 0 0 0 0 0 0 0 0 0

Las Vegas 200 98 2 0 0 0 0 0 0 0 0 0 0

Las Vegas 200 99 2 0 0 0 0 0 0 0 0 0 0

Las Vegas 250 98 2 7 4 7 9 9 1 0 1 1 1

Las Vegas 250 99 2 3 1 3 5 5 0 0 0 0 0

Las Vegas 300 98 2 43 37 43 49 49 8 6 8 10 10

Las Vegas 300 99 2 24 19 24 29 29 3 1 3 5 5

Los Angeles As is 6 0 0 0 0 0 0 0 0 0 0

Los Angeles Current 

Std 

 6 0 0 0 0 0 0 0 0 0 0

Los Angeles 50 98 6 0 0 0 0 0 0 0 0 0 0

Los Angeles 50 99 6 0 0 0 0 0 0 0 0 0 0

Los Angeles 100 98 6 0 0 0 0 0 0 0 0 0 0

Los Angeles 100 99 6 0 0 0 0 0 0 0 0 0 0

Los Angeles 150 98 6 0 0 0 0 0 0 0 0 0 0

Los Angeles 150 99 6 0 0 0 0 0 0 0 0 0 0

Los Angeles 200 98 6 1 0 0 2 2 0 0 0 0 0

Los Angeles 200 99 6 0 0 0 1 1 0 0 0 0 0

Los Angeles 250 98 6 3 0 2 9 9 1 0 0 2 2

Los Angeles 250 99 6 3 0 2 8 8 0 0 0 1 1
A-236

Number of Daily Maximum Exceedances 

≥ 250 ppb ≥ 300 ppb 

Location Standard Percentile

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99

Los Angeles 300 98 6 13 1 13 23 23 3 0 2 9 9

Los Angeles 300 99 6 6 1 5 13 13 3 0 2 8 8

Miami As is 2 0 0 0 0 0 0 0 0 0 0

Miami Current 

Std 

 2 1 0 1 1 1 0 0 0 0 0

Miami 50 98 2 0 0 0 0 0 0 0 0 0 0

Miami 50 99 2 0 0 0 0 0 0 0 0 0 0

Miami 100 98 2 0 0 0 0 0 0 0 0 0 0

Miami 100 99 2 0 0 0 0 0 0 0 0 0 0

Miami 150 98 2 0 0 0 0 0 0 0 0 0 0

Miami 150 99 2 0 0 0 0 0 0 0 0 0 0

Miami 200 98 2 0 0 0 0 0 0 0 0 0 0

Miami 200 99 2 0 0 0 0 0 0 0 0 0 0

Miami 250 98 2 3 1 3 4 4 0 0 0 0 0

Miami 250 99 2 1 0 1 1 1 0 0 0 0 0

Miami 300 98 2 10 7 10 12 12 3 1 3 4 4

Miami 300 99 2 7 4 7 9 9 1 0 1 1 1

New York As is 2 0 0 0 0 0 0 0 0 0 0

New York Current 

Std 

 2 0 0 0 0 0 0 0 0 0 0

New York 50 98 2 0 0 0 0 0 0 0 0 0 0

New York 50 99 2 0 0 0 0 0 0 0 0 0 0

New York 100 98 2 0 0 0 0 0 0 0 0 0 0

New York 100 99 2 0 0 0 0 0 0 0 0 0 0

New York 150 98 2 0 0 0 0 0 0 0 0 0 0

New York 150 99 2 0 0 0 0 0 0 0 0 0 0

New York 200 98 2 1 0 1 1 1 0 0 0 0 0

New York 200 99 2 0 0 0 0 0 0 0 0 0 0

New York 250 98 2 4 4 4 4 4 1 0 1 2 2

New York 250 99 2 1 0 1 2 2 0 0 0 0 0

New York 300 98 2 19 18 19 19 19 4 4 4 4 4

New York 300 99 2 5 4 5 5 5 1 0 1 2 2

Phoenix As is 5 0 0 0 0 0 0 0 0 0 0

Phoenix Current 

Std 

 5 0 0 0 0 0 0 0 0 0 0

Phoenix 50 98 5 0 0 0 0 0 0 0 0 0 0

Phoenix 50 99 5 0 0 0 0 0 0 0 0 0 0

Phoenix 100 98 5 0 0 0 0 0 0 0 0 0 0

Phoenix 100 99 5 0 0 0 0 0 0 0 0 0 0

Phoenix 150 98 5 0 0 0 0 0 0 0 0 0 0

Phoenix 150 99 5 0 0 0 0 0 0 0 0 0 0

Phoenix 200 98 5 1 0 0 2 2 0 0 0 1 1

Phoenix 200 99 5 0 0 0 2 2 0 0 0 0 0

Phoenix 250 98 5 5 0 6 11 11 2 0 2 3 3

Phoenix 250 99 5 2 0 2 5 5 0 0 0 2 2
A-237

Number of Daily Maximum Exceedances 

≥ 250 ppb ≥ 300 ppb 

Location Standard Percentile

SiteYears Mean Min Med p98 p99 Mean Min Med p98 p99

Phoenix 300 98 5 21 0 25 44 44 5 0 6 11 11

Phoenix 300 99 5 9 0 13 20 20 2 0 2 6 6

St. Louis As is 5 0 0 0 0 0 0 0 0 0 0

St. Louis Current 

Std 

 5 0 0 0 0 0 0 0 0 0 0

St. Louis 50 98 5 0 0 0 0 0 0 0 0 0 0

St. Louis 50 99 5 0 0 0 0 0 0 0 0 0 0

St. Louis 100 98 5 0 0 0 0 0 0 0 0 0 0

St. Louis 100 99 5 0 0 0 0 0 0 0 0 0 0

St. Louis 150 98 5 0 0 0 0 0 0 0 0 0 0

St. Louis 150 99 5 0 0 0 0 0 0 0 0 0 0

St. Louis 200 98 5 0 0 0 0 0 0 0 0 0 0

St. Louis 200 99 5 0 0 0 0 0 0 0 0 0 0

St. Louis 250 98 5 0 0 0 0 0 0 0 0 0 0

St. Louis 250 99 5 0 0 0 0 0 0 0 0 0 0

St. Louis 300 98 5 6 5 6 7 7 1 0 1 1 1

St. Louis 300 99 5 4 1 4 6 6 0 0 0 0 0

Washington 

DC 

As is 5 0 0 0 0 0 0 0 0 0 0

Washington 

DC 

Current 

Std 

 5 0 0 0 0 0 0 0 0 0 0

Washington 

DC 

50 98 5 0 0 0 0 0 0 0 0 0 0

Washington 

DC 

50 99 5 0 0 0 0 0 0 0 0 0 0

Washington 

DC 

100 98 5 0 0 0 0 0 0 0 0 0 0

Washington 

DC 

100 99 5 0 0 0 0 0 0 0 0 0 0

Washington 

DC 

150 98 5 0 0 0 0 0 0 0 0 0 0

Washington 

DC 

150 99 5 0 0 0 0 0 0 0 0 0 0

Washington 

DC 

200 98 5 0 0 0 1 1 0 0 0 0 0

Washington 

DC 

200 99 5 0 0 0 0 0 0 0 0 0 0

Washington 

DC 

250 98 5 4 2 2 13 13 0 0 0 1 1

Washington 

DC 

250 99 5 0 0 0 1 1 0 0 0 0 0

Washington 

DC 

300 98 5 15 4 10 38 38 4 2 2 13 13

Washington 

DC 

300 99 5 3 1 2 11 11 0 0 0 1 1
A-238

Table A-133. Estimated number of exceedances of 1-hour concentration levels (100, 150, and 200 ppb) on-roads following adjuthe current and alternative standards, 2004-2006 air quality and an on-road road adjustment factor. 

Number of Daily Maximum Exce

≥ 100 ppb ≥ 150 ppb 

Location Standard Percentile Site-Years Mean Min Med p98 p99 Mean Min Med p98 p

Atlanta As is 1500 17 0 2 114 120 2 0 0 26 Atlanta Current Std 1500 193 4 259 337 341 126 0 148 285 Atlanta 50 98 1500 3 0 0 29 31 0 0 0 4 Atlanta 50 99 1500 2 0 0 21 23 0 0 0 1 Atlanta 100 98 1500 58 0 39 206 218 13 0 1 93 Atlanta 100 99 1500 45 0 21 185 198 8 0 0 64 Atlanta 150 98 1500 133 0 162 295 304 58 0 39 206 Atlanta 150 99 1500 116 0 131 276 288 45 0 21 185 Atlanta 200 98 1500 181 3 241 328 334 111 0 123 266 Atlanta 200 99 1500 167 1 219 319 326 94 0 91 249 Boston As is 800 2 0 0 18 21 0 0 0 1 Boston Current Std 800 95 4 91 207 221 25 0 15 104 Boston 50 98 800 0 0 0 1 2 0 0 0 0 Boston 50 99 800 0 0 0 0 1 0 0 0 0 Boston 100 98 800 19 0 8 92 99 1 0 0 12 Boston 100 99 800 8 0 2 54 65 0 0 0 3 Boston 150 98 800 84 4 78 189 207 19 0 8 92 Boston 150 99 800 52 1 42 150 162 8 0 2 54 Boston 200 98 800 153 32 151 249 267 59 1 50 161 Boston 200 99 800 114 14 111 215 238 34 0 24 119 Chicago As is 800 36 0 20 148 161 5 0 0 41 Chicago Current Std 800 189 25 187 329 339 69 0 54 225 Chicago 50 98 800 2 0 0 24 30 0 0 0 0 Chicago 50 99 800 1 0 0 15 24 0 0 0 0 Chicago 100 98 800 59 0 43 201 220 10 0 2 63 Chicago 100 99 800 47 0 31 181 193 8 0 1 54 Chicago 150 98 800 176 20 169 320 329 59 0 43 201 Chicago 150 99 800 157 14 148 313 320 47 0 31 181 Chicago 200 98 800 259 94 261 352 355 138 9 127 302 Chicago 200 99 800 244 64 248 348 353 119 7 108 291 
A-239

Number of Daily Maximum Exce

≥ 100 ppb ≥ 150 ppb 

Location Standard Percentile Site-Years Mean Min Med p98 p99 Mean Min Med p98 p

Denver As is 300 63 2 49 190 195 10 0 4 47 Denver Current Std 300 257 135 259 314 320 134 12 133 260 Denver 50 98 300 9 0 2 41 45 1 0 0 5 Denver 50 99 300 5 0 1 23 25 0 0 0 2 Denver 100 98 300 148 20 151 263 269 38 0 23 132 Denver 100 99 300 106 10 100 236 242 23 0 11 87 Denver 150 98 300 263 170 264 315 321 148 20 151 263 Denver 150 99 300 239 114 243 309 313 106 10 100 236 Denver 200 98 300 296 241 308 330 332 239 122 243 309 Denver 200 99 300 286 217 287 323 327 204 66 209 294 Detroit As is 600 20 0 9 90 103 2 0 0 20 Detroit Current Std 600 287 166 286 350 352 189 35 193 300 Detroit 50 98 600 13 0 4 57 66 1 0 0 12 Detroit 50 99 600 10 0 2 52 53 1 0 0 9 Detroit 100 98 600 165 29 166 293 296 50 0 37 165 Detroit 100 99 600 146 18 143 275 284 40 0 27 146 Detroit 150 98 600 273 163 271 341 346 165 29 166 293 Detroit 150 99 600 261 141 260 337 342 146 18 143 275 Detroit 200 98 600 313 237 318 355 359 249 117 251 331 Detroit 200 99 600 307 234 309 354 357 235 90 237 327 El Paso As is 1200 24 0 12 114 143 3 0 0 20 El Paso Current Std 1200 281 137 285 349 354 198 22 205 309 El Paso 50 98 1200 5 0 1 32 37 0 0 0 4 El Paso 50 99 1200 3 0 0 20 23 0 0 0 2 El Paso 100 98 1200 108 3 102 254 263 23 0 11 113 El Paso 100 99 1200 75 2 62 225 234 13 0 5 72 El Paso 150 98 1200 229 63 235 323 327 108 3 102 254 El Paso 150 99 1200 199 26 205 309 317 75 2 62 225 El Paso 200 98 1200 281 137 286 349 353 197 26 205 308 El Paso 200 99 1200 264 97 271 340 346 162 12 166 289 Jacksonville As is 200 11 0 5 48 59 2 0 2 5 Jacksonville Current Std 200 295 229 295 341 342 216 114 223 309 
A-240

Number of Daily Maximum Exce

≥ 100 ppb ≥ 150 ppb 

Location Standard Percentile Site-Years Mean Min Med p98 p99 Mean Min Med p98 p

Jacksonville 50 98 200 9 0 5 39 50 2 0 2 5 Jacksonville 50 99 200 2 0 2 5 5 1 0 1 4 Jacksonville 100 98 200 127 30 124 256 269 35 0 23 138 Jacksonville 100 99 200 39 1 27 145 158 6 0 4 25 Jacksonville 150 98 200 241 145 243 320 327 127 30 124 256 Jacksonville 150 99 200 135 33 131 265 279 39 1 27 145 Jacksonville 200 98 200 293 229 294 339 340 211 104 214 302 Jacksonville 200 99 200 218 118 223 309 318 102 12 95 232 Las Vegas As is 1100 15 0 0 133 148 2 0 0 54 Las Vegas Current Std 1100 177 2 171 343 346 99 0 55 317 Las Vegas 50 98 1100 6 0 0 77 79 1 0 0 17 Las Vegas 50 99 1100 4 0 0 71 73 0 0 0 6 Las Vegas 100 98 1100 83 0 39 305 312 22 0 1 174 Las Vegas 100 99 1100 70 0 24 288 304 16 0 0 142 Las Vegas 150 98 1100 161 1 146 342 343 83 0 39 305 Las Vegas 150 99 1100 145 0 118 337 339 70 0 24 288 Las Vegas 200 98 1100 210 6 224 350 350 138 0 108 335 Las Vegas 200 99 1100 198 3 205 348 349 123 0 83 329 Los Angeles As is 5400 38 0 25 150 169 6 0 1 43 Los Angeles Current Std 5400 160 2 163 314 326 58 0 45 187 Los Angeles 50 98 5400 2 0 0 18 24 0 0 0 1 Los Angeles 50 99 5400 1 0 0 12 15 0 0 0 1 Los Angeles 100 98 5400 54 0 41 188 208 10 0 2 62 Los Angeles 100 99 5400 40 0 27 153 176 6 0 1 44 Los Angeles 150 98 5400 155 2 155 314 325 54 0 41 188 Los Angeles 150 99 5400 132 0 127 296 310 40 0 27 153 Los Angeles 200 98 5400 227 13 240 349 352 122 0 115 285 Los Angeles 200 99 5400 208 8 217 342 348 100 0 91 258 Miami As is 400 6 0 1 46 46 0 0 0 6 Miami Current Std 400 202 100 201 284 286 130 30 139 197 Miami 50 98 400 3 0 0 24 24 0 0 0 1 Miami 50 99 400 2 0 0 18 21 0 0 0 1 
A-241

Number of Daily Maximum Exce

≥ 100 ppb ≥ 150 ppb 

Location Standard Percentile Site-Years Mean Min Med p98 p99 Mean Min Med p98 p

Miami 100 98 400 56 0 47 144 148 13 0 4 72 Miami 100 99 400 46 0 34 134 140 9 0 2 62 Miami 150 98 400 128 24 135 194 199 56 0 47 144 Miami 150 99 400 117 17 123 187 192 46 0 34 134 Miami 200 98 400 182 75 188 260 262 106 12 111 178 Miami 200 99 400 171 63 178 248 250 94 7 96 170 New York As is 2200 35 0 23 149 171 5 0 1 40 New York Current Std 2200 147 0 148 308 321 45 0 31 188 New York 50 98 2200 3 0 0 32 34 0 0 0 3 New York 50 99 2200 2 0 0 17 23 0 0 0 1 New York 100 98 2200 75 0 62 225 248 14 0 6 73 New York 100 99 2200 45 0 32 175 196 7 0 2 49 New York 150 98 2200 192 2 204 326 333 75 0 62 225 New York 150 99 2200 149 0 151 305 312 45 0 32 175 New York 200 98 2200 264 38 284 350 353 157 0 162 310 New York 200 99 2200 232 20 250 339 343 115 0 108 278 Philadelphia As is 1200 22 0 10 101 130 2 0 0 20 Philadelphia Current Std 1200 232 54 242 321 326 110 2 99 269 Philadelphia 50 98 1200 5 0 1 37 42 0 0 0 4 Philadelphia 50 99 1200 1 0 0 14 14 0 0 0 0 Philadelphia 100 98 1200 112 5 102 258 276 24 0 10 109 Philadelphia 100 99 1200 53 0 39 187 214 8 0 1 49 Philadelphia 150 98 1200 237 76 245 325 329 112 5 102 258 Philadelphia 150 99 1200 172 24 178 297 311 53 0 39 187 Philadelphia 200 98 1200 295 186 302 339 345 204 51 213 313 Philadelphia 200 99 1200 256 105 264 329 332 134 7 130 277 Phoenix As is 900 77 0 53 275 293 10 0 1 57 Phoenix Current Std 900 284 127 296 357 359 124 2 111 307 Phoenix 50 98 900 4 0 0 26 35 0 0 0 1 Phoenix 50 99 900 2 0 0 12 14 0 0 0 0 Phoenix 100 98 900 146 3 140 320 332 28 0 7 159 Phoenix 100 99 900 103 0 84 293 312 16 0 2 101 
A-242

Number of Daily Maximum Exce

≥ 100 ppb ≥ 150 ppb 

Location Standard Percentile Site-Years Mean Min Med p98 p99 Mean Min Med p98 p

Phoenix 150 98 900 299 151 309 358 360 146 3 140 320 Phoenix 150 99 900 268 95 280 353 357 103 0 84 293 Phoenix 200 98 900 338 227 347 363 364 264 92 277 352 Phoenix 200 99 900 328 204 337 361 363 225 39 236 345 Provo As is 300 51 0 44 160 160 17 0 2 68 Provo Current Std 300 306 153 331 360 361 192 44 187 331 Provo 50 98 300 13 0 0 45 45 4 0 0 42 Provo 50 99 300 13 0 0 44 45 4 0 0 42 Provo 100 98 300 63 0 48 187 187 20 0 5 78 Provo 100 99 300 60 0 46 182 183 19 0 3 78 Provo 150 98 300 209 38 214 328 335 63 0 48 187 Provo 150 99 300 201 30 210 328 334 60 0 46 182 Provo 200 98 300 298 217 300 356 359 160 7 157 306 Provo 200 99 300 294 204 290 354 356 151 6 147 299 St. Louis As is 400 15 0 5 76 83 1 0 0 20 St. Louis Current Std 400 233 82 228 341 347 121 1 113 280 St. Louis 50 98 400 4 0 0 35 41 0 0 0 9 St. Louis 50 99 400 3 0 0 31 35 0 0 0 4 St. Louis 100 98 400 107 3 100 238 249 23 0 12 105 St. Louis 100 99 400 98 2 90 227 240 20 0 10 96 St. Louis 150 98 400 226 96 221 331 340 107 3 100 238 St. Louis 150 99 400 217 82 211 328 340 98 2 90 227 St. Louis 200 98 400 287 166 291 357 357 194 62 190 308 St. Louis 200 99 400 282 162 283 355 356 186 49 181 305 Washington DC As is 1700 21 0 7 119 143 2 0 0 20 Washington DC Current Std 1700 207 10 238 340 345 102 0 88 270 Washington DC 50 98 1700 5 0 1 37 42 0 0 0 5 Washington DC 50 99 1700 2 0 0 15 16 0 0 0 2 Washington DC 100 98 1700 96 0 81 269 283 22 0 8 126 Washington DC 100 99 1700 50 0 29 209 226 8 0 1 52 Washington DC 150 98 1700 200 2 232 337 345 96 0 81 269 Washington DC 150 99 1700 148 0 157 309 322 50 0 29 209 
A-243

Number of Daily Maximum Exce

≥ 100 ppb ≥ 150 ppb 

Location Standard Percentile Site-Years Mean Min Med p98 p99 Mean Min Med p98 p

Washington DC 200 98 1700 260 42 299 358 362 171 0 191 323 Washington DC 200 99 1700 220 8 256 346 352 117 0 108 288 Other MSA As is 56500 10 0 1 79 100 1 0 0 12 Other MSA Current Std 56500 143 0 143 322 336 59 0 36 233 Other MSA 50 98 56500 0 0 0 5 8 0 0 0 0 Other MSA 50 99 56500 0 0 0 2 5 0 0 0 0 Other MSA 100 98 56500 20 0 5 123 152 2 0 0 26 Other MSA 100 99 56500 14 0 2 96 121 1 0 0 17 Other MSA 150 98 56500 80 0 62 264 288 20 0 5 123 Other MSA 150 99 56500 63 0 43 237 263 14 0 2 96 Other MSA 200 98 56500 143 0 144 322 337 57 0 36 228 Other MSA 200 99 56500 125 0 119 311 328 44 0 23 201 Other Not MSA As is 11600 4 0 0 43 65 1 0 0 7 Other Not MSA Current Std 11600 124 0 100 331 339 62 0 24 257 Other Not MSA 50 98 11600 0 0 0 2 6 0 0 0 1 Other Not MSA 50 99 11600 0 0 0 2 3 0 0 0 0 Other Not MSA 100 98 11600 6 0 0 61 86 1 0 0 10 Other Not MSA 100 99 11600 2 0 0 23 35 0 0 0 3 Other Not MSA 150 98 11600 28 0 4 173 198 6 0 0 61 Other Not MSA 150 99 11600 13 0 1 113 134 2 0 0 23 Other Not MSA 200 98 11600 60 0 22 249 269 19 0 2 141 Other Not MSA 200 99 11600 34 0 6 193 216 8 0 0 80 
A-244

Table A-134. Estimated number of exceedances of 1-hour concentration levels (250 and 300 ppb) on-roads 

following adjustment to just meeting the current and alternative standards, 2004-2006 air quality and an onroad road adjustment factor. 

Number of Daily Maximum Exceedances

≥ 250 ppb ≥ 300 ppb

Location Standard Percentile Site-Years Mean Min Med p98 p99 Mean Min Med p98 p99

Atlanta As is 1500 0 0 0 1 1 0 0 0 0 0

Atlanta Current 

Std 

 1500 38 0 15 169 179 19 0 3 117 127

Atlanta 50 98 1500 0 0 0 0 0 0 0 0 0 0

Atlanta 50 99 1500 0 0 0 0 0 0 0 0 0 0

Atlanta 100 98 1500 1 0 0 13 15 0 0 0 4 5

Atlanta 100 99 1500 0 0 0 8 8 0 0 0 1 2

Atlanta 150 98 1500 8 0 0 61 74 3 0 0 29 31

Atlanta 150 99 1500 5 0 0 44 49 2 0 0 21 23

Atlanta 200 98 1500 28 0 6 145 160 13 0 1 93 100

Atlanta 200 99 1500 20 0 3 119 131 8 0 0 64 75

Boston As is 800 0 0 0 0 0 0 0 0 0 0

Boston Current 

Std 

 800 1 0 0 11 12 0 0 0 2 2

Boston 50 98 800 0 0 0 0 0 0 0 0 0 0

Boston 50 99 800 0 0 0 0 0 0 0 0 0 0

Boston 100 98 800 0 0 0 0 0 0 0 0 0 0

Boston 100 99 800 0 0 0 0 0 0 0 0 0 0

Boston 150 98 800 1 0 0 5 7 0 0 0 1 2

Boston 150 99 800 0 0 0 1 2 0 0 0 0 1

Boston 200 98 800 6 0 1 39 51 1 0 0 12 13

Boston 200 99 800 2 0 0 15 19 0 0 0 3 5

Chicago As is 800 0 0 0 1 2 0 0 0 0 0

Chicago Current 

Std 

 800 8 0 1 54 63 3 0 0 33 38

Chicago 50 98 800 0 0 0 0 0 0 0 0 0 0

Chicago 50 99 800 0 0 0 0 0 0 0 0 0 0

Chicago 100 98 800 0 0 0 5 11 0 0 0 0 0

Chicago 100 99 800 0 0 0 2 6 0 0 0 0 0

Chicago 150 98 800 6 0 0 48 55 2 0 0 24 30

Chicago 150 99 800 4 0 0 39 45 1 0 0 15 24

Chicago 200 98 800 25 0 11 121 132 10 0 2 63 78

Chicago 200 99 800 19 0 6 99 111 8 0 1 54 67

Denver As is 300 0 0 0 3 3 0 0 0 0 0

Denver Current 

Std 

 300 21 0 10 79 91 8 0 1 38 43

Denver 50 98 300 0 0 0 0 0 0 0 0 0 0

Denver 50 99 300 0 0 0 0 0 0 0 0 0 0

Denver 100 98 300 3 0 0 16 18 1 0 0 5 9

Denver 100 99 300 1 0 0 7 10 0 0 0 2 3

Denver 150 98 300 25 0 11 93 103 9 0 2 41 45

Denver 150 99 300 13 0 4 59 64 5 0 1 23 25

Denver 200 98 300 75 4 64 210 216 38 0 23 132 150

Denver 200 99 300 49 1 34 158 174 23 0 11 87 95
A-245

Number of Daily Maximum Exceedances

≥ 250 ppb ≥ 300 ppb

Location Standard Percentile Site-Years Mean Min Med p98 p99 Mean Min Med p98 p99

Detroit As is 600 0 0 0 0 0 0 0 0 0 0

Detroit Current 

Std 

 600 42 0 28 147 185 18 0 7 73 103

Detroit 50 98 600 0 0 0 0 0 0 0 0 0 0

Detroit 50 99 600 0 0 0 0 0 0 0 0 0 0

Detroit 100 98 600 4 0 0 33 40 1 0 0 12 18

Detroit 100 99 600 3 0 0 25 30 1 0 0 9 14

Detroit 150 98 600 32 0 20 130 145 13 0 4 57 66

Detroit 150 99 600 24 0 12 101 113 10 0 2 52 53

Detroit 200 98 600 96 4 84 231 246 50 0 37 165 180

Detroit 200 99 600 79 1 66 206 226 40 0 27 146 163

El Paso As is 1200 0 0 0 1 1 0 0 0 0 0

El Paso Current 

Std 

 1200 52 0 35 191 203 24 0 11 119 133

El Paso 50 98 1200 0 0 0 0 0 0 0 0 0 0

El Paso 50 99 1200 0 0 0 0 0 0 0 0 0 0

El Paso 100 98 1200 1 0 0 11 14 0 0 0 4 4

El Paso 100 99 1200 0 0 0 5 8 0 0 0 2 2

El Paso 150 98 1200 13 0 6 72 92 5 0 1 32 37

El Paso 150 99 1200 7 0 2 47 52 3 0 0 20 23

El Paso 200 98 1200 50 0 32 189 201 23 0 11 113 137

El Paso 200 99 1200 32 0 18 141 163 13 0 5 72 86

Jacksonville As is 200 1 0 0 3 3 1 0 0 3 3

Jacksonville Current 

Std 

 200 72 6 61 202 227 38 1 26 141 158

Jacksonville 50 98 200 1 0 0 3 3 0 0 0 2 2

Jacksonville 50 99 200 0 0 0 2 2 0 0 0 1 1

Jacksonville 100 98 200 3 0 4 9 10 2 0 2 5 5

Jacksonville 100 99 200 1 0 1 4 4 1 0 1 4 4

Jacksonville 150 98 200 22 0 9 96 112 9 0 5 39 50

Jacksonville 150 99 200 3 0 4 12 13 2 0 2 5 5

Jacksonville 200 98 200 68 4 58 196 221 35 0 23 138 151

Jacksonville 200 99 200 15 0 5 67 81 6 0 4 25 29

Las Vegas As is 1100 0 0 0 0 1 0 0 0 0 0

Las Vegas Current 

Std 

 1100 21 0 1 170 181 9 0 0 86 96

Las Vegas 50 98 1100 0 0 0 0 0 0 0 0 0 0

Las Vegas 50 99 1100 0 0 0 0 0 0 0 0 0 0

Las Vegas 100 98 1100 2 0 0 52 59 1 0 0 17 19

Las Vegas 100 99 1100 1 0 0 31 49 0 0 0 6 8

Las Vegas 150 98 1100 14 0 0 123 138 6 0 0 77 79

Las Vegas 150 99 1100 10 0 0 89 100 4 0 0 71 73

Las Vegas 200 98 1100 46 0 10 247 265 22 0 1 174 187

Las Vegas 200 99 1100 34 0 5 219 235 16 0 0 142 151

Los Angeles As is 5400 0 0 0 2 3 0 0 0 1 1

Los Angeles Current 5400 6 0 1 45 55 2 0 0 21 28
A-246

Number of Daily Maximum Exceedances

≥ 250 ppb ≥ 300 ppb

Location Standard Percentile Site-Years Mean Min Med p98 p99 Mean Min Med p98 p99

Std 

Los Angeles 50 98 5400 0 0 0 0 0 0 0 0 0 0

Los Angeles 50 99 5400 0 0 0 0 0 0 0 0 0 0

Los Angeles 100 98 5400 0 0 0 4 6 0 0 0 1 2

Los Angeles 100 99 5400 0 0 0 2 3 0 0 0 1 1

Los Angeles 150 98 5400 6 0 1 42 54 2 0 0 18 24

Los Angeles 150 99 5400 3 0 0 31 38 1 0 0 12 15

Los Angeles 200 98 5400 22 0 11 102 126 10 0 2 62 75

Los Angeles 200 99 5400 15 0 5 80 96 6 0 1 44 55

Miami As is 400 0 0 0 0 0 0 0 0 0 0

Miami Current 

Std 

 400 42 0 32 126 128 22 0 11 98 100

Miami 50 98 400 0 0 0 0 0 0 0 0 0 0

Miami 50 99 400 0 0 0 0 0 0 0 0 0 0

Miami 100 98 400 1 0 0 7 8 0 0 0 1 1

Miami 100 99 400 0 0 0 5 6 0 0 0 1 1

Miami 150 98 400 7 0 2 59 59 3 0 0 24 24

Miami 150 99 400 5 0 1 44 46 2 0 0 18 21

Miami 200 98 400 27 0 15 108 110 13 0 4 72 77

Miami 200 99 400 21 0 11 97 101 9 0 2 62 65

New York As is 2200 0 0 0 3 4 0 0 0 1 1

New York Current 

Std 

 2200 4 0 0 41 45 2 0 0 20 26

New York 50 98 2200 0 0 0 0 0 0 0 0 0 0

New York 50 99 2200 0 0 0 0 0 0 0 0 0 0

New York 100 98 2200 1 0 0 12 14 0 0 0 3 4

New York 100 99 2200 0 0 0 5 8 0 0 0 1 1

New York 150 98 2200 9 0 2 54 61 3 0 0 32 34

New York 150 99 2200 4 0 0 36 40 2 0 0 17 23

New York 200 98 2200 33 0 20 140 158 14 0 6 73 88

New York 200 99 2200 17 0 8 88 99 7 0 2 49 53

Philadelphia As is 1200 0 0 0 0 0 0 0 0 0 0

Philadelphia Current 

Std 

 1200 16 0 5 101 115 6 0 1 53 58

Philadelphia 50 98 1200 0 0 0 0 0 0 0 0 0 0

Philadelphia 50 99 1200 0 0 0 0 0 0 0 0 0 0

Philadelphia 100 98 1200 1 0 0 11 14 0 0 0 4 4

Philadelphia 100 99 1200 0 0 0 2 2 0 0 0 0 1

Philadelphia 150 98 1200 14 0 4 73 83 5 0 1 37 42

Philadelphia 150 99 1200 4 0 0 32 36 1 0 0 14 14

Philadelphia 200 98 1200 52 0 38 187 213 24 0 10 109 139

Philadelphia 200 99 1200 20 0 8 94 119 8 0 1 49 53

Phoenix As is 900 0 0 0 1 1 0 0 0 0 0

Phoenix Current 

Std 

 900 11 0 1 74 87 3 0 0 21 22

Phoenix 50 98 900 0 0 0 0 0 0 0 0 0 0
A-247

Number of Daily Maximum Exceedances

≥ 250 ppb ≥ 300 ppb

Location Standard Percentile Site-Years Mean Min Med p98 p99 Mean Min Med p98 p99

Phoenix 50 99 900 0 0 0 0 0 0 0 0 0 0

Phoenix 100 98 900 1 0 0 5 7 0 0 0 1 1

Phoenix 100 99 900 0 0 0 2 2 0 0 0 0 0

Phoenix 150 98 900 16 0 2 101 107 4 0 0 26 35

Phoenix 150 99 900 8 0 1 44 62 2 0 0 12 14

Phoenix 200 98 900 65 0 35 256 274 28 0 7 159 171

Phoenix 200 99 900 41 0 13 196 227 16 0 2 101 107

Provo As is 300 7 0 0 43 43 3 0 0 40 40

Provo Current 

Std 

 300 40 0 43 166 167 24 0 17 69 69

Provo 50 98 300 0 0 0 0 0 0 0 0 0 0

Provo 50 99 300 0 0 0 0 0 0 0 0 0 0

Provo 100 98 300 9 0 0 43 43 4 0 0 42 42

Provo 100 99 300 8 0 0 43 43 4 0 0 42 42

Provo 150 98 300 16 0 1 61 64 13 0 0 45 45

Provo 150 99 300 16 0 1 57 62 13 0 0 44 45

Provo 200 98 300 31 0 23 105 105 20 0 5 78 79

Provo 200 99 300 29 0 22 101 101 19 0 3 78 79

St. Louis As is 400 0 0 0 0 0 0 0 0 0 0

St. Louis Current 

Std 

 400 19 0 8 124 140 8 0 0 80 87

St. Louis 50 98 400 0 0 0 0 0 0 0 0 0 0

St. Louis 50 99 400 0 0 0 0 0 0 0 0 0 0

St. Louis 100 98 400 1 0 0 17 22 0 0 0 9 10

St. Louis 100 99 400 1 0 0 17 20 0 0 0 4 9

St. Louis 150 98 400 13 0 4 67 75 4 0 0 35 41

St. Louis 150 99 400 11 0 3 61 69 3 0 0 31 35

St. Louis 200 98 400 51 0 41 159 167 23 0 12 105 108

St. Louis 200 99 400 45 0 35 147 162 20 0 10 96 101

Washington 

DC 

As is 1700 0 0 0 1 2 0 0 0 0 0

Washington 

DC 

Current 

Std 

 1700 15 0 3 93 110 6 0 1 43 56

Washington 

DC 

50 98 1700 0 0 0 0 0 0 0 0 0 0

Washington 

DC 

50 99 1700 0 0 0 0 0 0 0 0 0 0

Washington 

DC 

100 98 1700 1 0 0 14 15 0 0 0 5 6

Washington 

DC 

100 99 1700 0 0 0 4 6 0 0 0 2 2

Washington 

DC 

150 98 1700 13 0 3 81 102 5 0 1 37 42

Washington 

DC 

150 99 1700 5 0 1 34 36 2 0 0 15 16

Washington 

DC 

200 98 1700 47 0 26 202 216 22 0 8 126 150

Washington 200 99 1700 20 0 7 115 141 8 0 1 52 66
A-248

Number of Daily Maximum Exceedances

≥ 250 ppb ≥ 300 ppb

Location Standard Percentile Site-Years Mean Min Med p98 p99 Mean Min Med p98 p99

DC 

Other MSA As is 56500 0 0 0 0 1 0 0 0 0 0

Other MSA Current 

Std 

 56500 8 0 0 70 90 3 0 0 32 45

Other MSA 50 98 56500 0 0 0 0 0 0 0 0 0 0

Other MSA 50 99 56500 0 0 0 0 0 0 0 0 0 0

Other MSA 100 98 56500 0 0 0 1 2 0 0 0 0 1

Other MSA 100 99 56500 0 0 0 1 1 0 0 0 0 0

Other MSA 150 98 56500 1 0 0 15 23 0 0 0 5 8

Other MSA 150 99 56500 1 0 0 9 14 0 0 0 2 5

Other MSA 200 98 56500 7 0 0 58 76 2 0 0 26 37

Other MSA 200 99 56500 4 0 0 41 56 1 0 0 17 25

Other Not 

MSA 

As is 11600 0 0 0 2 2 0 0 0 0 2

Other Not 

MSA 

Current 

Std 

 11600 14 0 1 119 137 6 0 0 66 86

Other Not 

MSA 

50 98 11600 0 0 0 0 1 0 0 0 0 0

Other Not 

MSA 

50 99 11600 0 0 0 0 0 0 0 0 0 0

Other Not 

MSA 

100 98 11600 0 0 0 2 3 0 0 0 1 2

Other Not 

MSA 

100 99 11600 0 0 0 0 2 0 0 0 0 2

Other Not 

MSA 

150 98 11600 0 0 0 6 11 0 0 0 2 6

Other Not 

MSA 

150 99 11600 0 0 0 2 5 0 0 0 2 3

Other Not 

MSA 

200 98 11600 2 0 0 23 34 1 0 0 10 17

Other Not 

MSA 

200 99 11600 1 0 0 8 15 0 0 0 3 7
A-249
A-250

A-9.4 Comparison of Historical and Recent Ambient Air Quality 

(As Is) 

This section presents the preliminary results using the ambient monitoring data obtained from 

AQS that were separated into two six-year groups, one representing historical data (1995-2000) 

and the other representing more recent data (2001-2006). This initial analysis performed in the 

1st draft REA used the total number of exceedances of the potential benchmark levels of 150, 

200, 250, and 300 ppb, for monitors sited ≥100 m and <100 m from a major road. It differs from 

the analyses performed in Chapter 7 of the final REA where the number of times the daily 

maximum exceeded the potential benchmark levels was recorded (including a benchmark level of 

100 ppb) for different monitor road categories (≥100 m, 20 m< x <100 m, and ≤20 m from a 

major road) and for two three-year groups (2001-2003 and 2004-2006). It is presented here 

mainly for comparison of the two six-year groups of data, because the historical data set was not 

re-analyzed using the added benchmark level, was not separated into three monitor-to-major road 

categories, and did not calculate the number of daily maximum exceedances in a year. 

A summary of the descriptive statistics for the annual average ambient NO2 concentrations at 

each selected location is provided in Tables A-111 and A-112 for monitors sited ≥100 m and 

<100 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. The highest 

observed annual average NO2 concentrations were measured in Los Angeles and Phoenix during 

the historical monitoring period and considering the monitors ≥100 m from a major road. 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 NO2

concentrations were greater at the monitors <100 m of a major road (in 23 of 27 possible 

location/year-group combinations). Four locations (Denver, Los Angeles, Phoenix, St. Louis) 

contained higher concentrations at the more distant monitors for one year-group when compared 

with the monitors <100 m from a major road. Where concentrations were greater at the near 

road monitors, the concentrations were on average about 20-25% higher when compared with the 

more distant monitors in each corresponding location, regardless of year-group. A comparison 

of the year-group of data within each monitor site-group indicates that the more recent 

monitoring concentrations were lower, on average by about 13-15%. These average trends in 

concentration across year-group and monitor site group were generally observed across all 

percentiles of the distribution. 

Table A-135. Monitoring site-years and annual average NO2 concentrations for two monitoring periods, 

historical and recent air quality data (as is) using monitors sited ≥100 m of a major road. 

1995-2000 2001-2006 

Annual Mean (ppb) 1 Annual Mean (ppb) 1

Location

SiteYears mean min med p95 p98 p99SiteYears mean min med p95 p98 p99

Atlanta 24 14 5 15 25 27 27 29 12 3 14 19 23 23 

Boston 18 18 5 18 25 25 25 14 9 5 9 12 12 12 

Chicago 28 20 9 22 27 28 28 17 21 16 19 28 28 28 

Cleveland 5 19 17 20 21 21 21 3 18 17 17 19 19 19 

Colorado Springs 25 16 7 17 24 35 35 - - - - - - - 

Denver 7 22 15 23 26 26 26 5 21 18 21 26 26 26 
A-251

1995-2000 2001-2006 

Annual Mean (ppb) 1 Annual Mean (ppb) 1

Location

SiteYears mean min med p95 p98 p99SiteYears mean min med p95 p98 p99

Detroit 12 19 12 19 26 26 26 12 19 14 19 23 23 23 

El Paso 8 19 14 18 23 23 23 24 15 8 16 18 18 18 

Jacksonville 6 15 14 15 16 16 16 4 14 13 14 15 15 15 

Las Vegas 8 10 3 6 24 24 24 27 10 1 7 22 22 22 

Los Angeles 92 27 6 28 40 46 46 105 20 5 20 33 34 36 

Miami 9 9 9 9 10 10 10 10 8 7 8 10 10 10 

New York 47 24 11 26 35 36 36 48 20 10 19 28 31 31 

Philadelphia 35 21 15 20 33 33 33 26 19 14 18 28 28 28 

Phoenix 14 30 26 29 34 34 34 14 25 21 24 29 29 29 

Provo 6 24 23 24 24 24 24 6 24 21 23 29 29 29 

St. Louis 18 17 5 19 21 21 21 13 16 12 16 21 21 21 

Washington DC 33 18 9 19 25 26 26 35 17 7 18 24 25 25 

Other MSA 1135 14 1 14 24 26 28 1177 12 1 12 20 22 24 

Other Not MSA 200 8 0 7 16 19 19 243 7 1 6 14 16 16 

1

 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 mean. 

2

 Colorado Springs monitoring data were collected as part of short-term study completed in September 2001, 

therefore there are no 2001-2006 data. 

Table A-136. Monitoring site-years and annual average NO2 concentrations for two monitoring periods, 

historical and recent air quality data (as is) using monitors sited <100 m of a major road. 

1995-2000 2001-2006 

Annual Mean (ppb) 1 Annual Mean (ppb) 1

Location

SiteYears mean min med p95 p98 p99SiteYears mean min med p95 p98 p99

Boston 40 18 6 20 31 31 31 33 18 7 18 25 30 30 

Chicago 19 29 22 31 34 34 34 19 27 18 28 32 32 32 

Cleveland 6 26 23 27 28 28 28 8 20 14 22 24 24 24 

Colorado Springs 1 18 18 18 18 18 18 

Denver 19 14 6 9 35 35 35 5 31 27 29 37 37 37 

El Paso 6 29 23 29 35 35 35 6 18 13 19 22 22 22 

Las Vegas 8 19 7 25 27 27 27 8 15 3 19 23 23 23 

Los Angeles 101 25 4 23 45 46 46 72 25 4 27 37 40 41 

Miami 15 11 6 9 17 17 17 10 10 6 10 16 16 16 

New York 46 31 22 29 42 42 42 33 29 18 28 40 40 40 

Philadelphia 11 30 26 29 34 34 34 13 23 18 24 30 30 30 

Phoenix 8 31 24 30 40 40 40 13 25 11 24 37 37 37 

St. Louis 38 18 9 19 26 27 27 30 15 8 15 23 25 25 

Washington DC 36 23 13 23 27 27 27 31 20 13 20 26 26 26 

1

 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 mean. 

2

 Colorado Springs monitoring data were collected as part of short-term study completed in September 2001, 

therefore there are no 2001-2006 data. 
A-252

The estimated total number of exceedances of four potential health effect benchmark levels 

(150, 200, 250, and 300 ppb NO2 for 1-hr) is shown in Tables A-113 and A-114 for the historical 

and recent ambient monitoring data, respectively, and where the monitors were sited ≥100 m 

from a major road. The number of exceedances of each benchmark were summed for the year at 

each monitor; a single monitor value of 10 could represent ten 1-hr exceedances that occurred in 

one day, 10 exceedances in 10 days, or some combination of multiple hours or days that totaled 

10 exceedances for the year. In general, the number of benchmark exceedances was low across 

all locations and considering both year-groups of the as is air quality. The average number of 

exceedances of the lowest 1-hour concentration level of 150 ppb across each location was 

typically none or one. Considering that there are 8,760 hours in a year, this number of 

exceedances amounts to a small fraction of the year (0.01%) containing an exceedance of the 

potential health effect benchmark level. For locations with greater than 1 yearly average 

exceedance, the numbers were primarily driven by a single site-year of data. For example, the 

Colorado Springs mean is 3 exceedances per year for the years 1995-2000; however, this mean 

was driven by a single site-year that contained 69 exceedances of 200 ppb. That particular 

monitor (ID 0804160181) does not appear to have any unusual attributes (e.g., the closest major 

road is beyond a distance of 160 meters and the closest stationary source emitting >5 tons per 

year (tpy) is at a distance >4 km) except that a power generating utility (NAICS code 221112) 

located 7.2 km from the monitor has estimated emissions of 4,205 tpy. It is not known at this 

time whether this particular facility is influencing the observed concentration exceedances at this 

specific monitoring site. Similarly, Detroit contained the largest number of excedances of 200 

ppb (a maximum of 12) for as is air quality data from years 2001-2006 (Table A-112). Again, 

all of those exceedances occurred at one monitor (ID 2616300192) during one year (2002). The 

number of exceedances of higher potential benchmark concentration levels at each of the 

locations was less than that observed at the 200 ppb level. Most locations had no exceedances of 

250 or 300 ppb, with higher numbers confined to the same aforementioned cities where 

exceedances of 200 ppb were observed. 

When considering the historical data and monitors sited <100 m of a major road (Table A115), a few locations contained exceedances of the potential health effect benchmark levels, 

driven mainly by observations from one or two monitors. For example, in Phoenix a single year 

from one monitor (ID 0401330031) was responsible for all observed exceedances of 200 ppb. 

This monitor is located 78 m from a major road along with 10 stationary sources located within 

10 km of this monitor, 9 of which contained estimated emissions of less than 60 tpy (one source 

emitted 272 tpy, see Appendix A, section 4). It is not known if observed exceedances of 200 ppb 

at this monitor are a result of proximity of major roads or the stationary sources. There were 

fewer locations with observed exceedances of the benchmark levels at the monitors sited within 

100 m of a major road considering the more recent as is air quality. Eleven of thirteen total 

locations contained an average of zero exceedances of the 150 ppb benchmark level (Table A116).
August 2008 - Draft 253 

1 

2 Table A-137. Total number of exceedances of short-term (1-hour) potential health effect benchmark levels in a year, 1995-2000 3 quality (as is) using monitors sited 

≥100 m of a major road. 

Exceedances of 150 ppb 1 Exceedances of 200 ppb 1 Exceedances of 250 ppb 1 

Location mean min med p95 P98 p99 mean min med p95 p98 p99 mean min med p95 p98 p99 mAtlanta 0 0 0 1 1 1 0 0 0 0 1 1 0 0 0 0 0 0 Boston 0 0 0 1 1 1 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 Cleveland 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Colorado Springs 8 0 0 47 143 143 3 0 0 3 69 69 1 0 0 0 23 23 Denver 1 0 0 4 4 4 0 0 0 2 2 2 0 0 0 0 0 0 Detroit 1 0 0 10 10 10 0 0 0 3 3 3 0 0 0 1 1 1 El Paso 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Jacksonville 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Las Vegas 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Los Angeles 3 0 0 22 42 44 0 0 0 2 2 4 0 0 0 0 1 2 Miami 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 New York 0 0 0 0 3 3 0 0 0 0 0 0 0 0 0 0 0 0 Philadelphia 0 0 0 0 10 10 0 0 0 0 3 3 0 0 0 0 0 0 Phoenix 0 0 0 2 2 2 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 St. Louis 1 0 0 12 12 12 0 0 0 8 8 8 0 0 0 4 4 4 Washington DC 0 0 0 1 2 2 0 0 0 1 2 2 0 0 0 1 1 1 Other MSA 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 Other Not MSA 0 0 0 0 2 4 0 0 0 0 0 1 0 0 0 0 0 0  Notes:  1 The mean number of exceedances represents the number of exceedances occurring at all monitors in a particular locatio

years across the monitoring period. The min, med, p95, p98, and p99 represent the minimum, median, 95th, 98th, and 99th 

the number of exceedances in any one year within the monitoring period. 

4 
August 2008 - Draft 254 

1 2 Table A-138. Total number of exceedances of short-term (1-hour) potential health effect benchmark levels in a year, 2001-2006 3 (as is) using monitors sited 

≥100 m of a major road. 

Exceedances of 150 ppb 1 Exceedances of 200 ppb 1 Exceedances of 250 ppb 1 

Location mean min med p95 P98 p99 mean min med p95 p98 p99 mean min med p95 p98 p99 mAtlanta 0 0 0 0 1 1 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 Chicago 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 Denver 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Detroit 2 0 0 16 16 16 1 0 0 12 12 12 1 0 0 8 8 8 El Paso 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 Jacksonville 2 0 1 6 6 6 1 0 1 2 2 2 0 0 0 1 1 1 Las Vegas 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Los Angeles 0 0 0 0 1 1 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 New York 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Philadelphia 0 0 0 0 1 1 0 0 0 0 1 1 0 0 0 0 1 1 Phoenix 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Provo 7 0 0 39 39 39 0 0 0 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 Washington DC 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Other MSA 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Other Not MSA 0 0 0 0 1 2 0 0 0 0 0 1 0 0 0 0 0 1  Notes:  1 The mean number of exceedances represents the number of exceedances occurring at all monitors in a particular locatio

years across the monitoring period. The min, med, p95, p98, and p99 represent the minimum, median, 95th, 98th, and 99th 

the number of exceedances in any one year within the monitoring period. 

2 Colorado Springs monitoring data were collected as part of short-term study completed in September 2001, therefore the

4 5 6 
August 2008 - Draft 255 

1 2 Table A-139. Total number of exceedances of short-term (1-hour) potential health effect benchmark levels in a year, 1995-2000 3 quality (as is) using monitors sited <100 m of a major road. 

Exceedances of 150 ppb 1 Exceedances of 200 ppb 1 Exceedances of 250 ppb 1 

Location mean min med p95 P98 p99 mean min med p95 p98 p99 mean min med p95 p98 p99 mBoston 0 0 0 0 1 1 0 0 0 0 1 1 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 Cleveland 2 0 0 9 9 9 0 0 0 1 1 1 0 0 0 1 1 1 Colorado Springs 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Denver 0 0 0 6 6 6 0 0 0 1 1 1 0 0 0 1 1 1 El Paso 2 0 1 7 7 7 0 0 0 2 2 2 0 0 0 0 0 0 Las Vegas 1 0 0 11 11 11 1 0 0 11 11 11 0 0 0 3 3 3 Los Angeles 2 0 0 11 18 33 0 0 0 1 2 2 0 0 0 0 0 0 Miami 0 0 0 3 3 3 0 0 0 1 1 1 0 0 0 0 0 0 New York 0 0 0 2 3 3 0 0 0 0 3 3 0 0 0 0 0 0 Philadelphia 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 Phoenix 27 0 1 147 147 147 5 0 0 37 37 37 0 0 0 3 3 3 St. Louis 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Washington DC 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0  Notes:  1 The mean number of exceedances represents the number of exceedances occurring at all monitors in a particular locatio

years across the monitoring period. The min, med, p95, p98, and p99 represent the minimum, median, 95th, 98th, and 99th 

the number of exceedances in any one year within the monitoring period. 

4 
August 2008 - Draft 256 

1 

2 Table A-140. Total number of exceedances of short-term (1-hour) potential health effect benchmark levels in a year, 2001-2006 3 (as is) using monitors sited <100 m of a major road. 

Exceedances of 150 ppb 1 Exceedances of 200 ppb 1 Exceedances of 250 ppb 1 

Location mean min med p95 P98 p99 mean min med p95 p98 p99 mean min med p95 p98 p99 mBoston 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 Cleveland 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 Denver 1 0 1 1 1 1 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 Las Vegas 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 6 0 0 0 0 1 1 0 0 0 0 1 1 Miami 1 0 0 5 5 5 0 0 0 3 3 3 0 0 0 3 3 3 New York 0 0 0 1 1 1 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 Phoenix 0 0 0 0 0 0 0 0 0 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 Washington DC 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0  Notes:  1 The mean number of exceedances represents the number of exceedances occurring at all monitors in a particular locatio

years across the monitoring period. The min, med, p95, p98, and p99 represent the minimum, median, 95th, 98th, and 99th 

the number of exceedances in any one year within the monitoring period. 
August 2008 - Draft 257 

1 A-9.5 Comparison of On-Road Concentrations Derived From 

2 Historical and Recent Ambient Air Quality (As Is) 

3 This section presents the preliminary results using the ambient monitoring data obtained from 

4 AQS that were separated into two six-year groups, one representing historical data (1995-2000) 

5 and the other representing more recent data (2001-2006). These estimated on-road 

6 concentrations were generated by applying the simulation procedure described above in section 

7 A-8 to ambient monitors ≥100 m of a major road and represent the second scenario evaluated. 

8 This initial analysis used the total number of exceedances of the potential benchmark levels of 

9 150, 200, 250, and 300 ppb. It differs from the analyses performed in Chapter 7 of the final REA 

10 where the number of times the daily maximum exceeded the potential benchmark levels was 

11 recorded (also including a benchmark level of 100 ppb). It is presented here mainly for 

12 comparison of the two six-year groups, because the historical data set was not re-analyzed using 

13 the added benchmark level and did have the number of daily maximum exceedances in a year 

14 calculated. 

15 

16 Descriptive statistics for estimated on-road NO2 concentrations are presented in Table A-117. 

17 For the 18 named locations, the calculation only used monitors sited at a distance ≥100 m of a 

18 major road. The two grouped locations (i.e., “Other CMSA” and “Not MSA”) did not have 

19 estimated monitor distances to major roads therefore all monitoring data available were used to 

20 estimate the distribution of on-road NO2 concentrations. 

21 

22 The simulated on-road annual average NO2 concentrations are, on average, a factor of 1.8 

23 higher than their respective ambient levels. This falls within the range of ratios reported in the 

24 ISA (about 2-fold higher concentrations on roads) (ISA, section 2.5.4). Los Angeles, New York, 

25 and Phoenix were predicted to have the highest on-road NO2 levels. This is a direct result of 

26 these locations already containing some of the highest “as-is” NO2 concentrations prior to the 

27 on-road simulation (see Table A-111). 

28 

29 The median of the simulated concentration estimates for Los Angeles were compared with 

30 NO2 measurements provided by Westerdahl et al. (2005) for arterial roads and freeways in the 

31 same general location during spring 2003. Although the averaging time is not exactly the same, 

comparison of the medians is judged to be appropriate.8 32 The estimated median on-road 

33 concentration for 2001-2006 is 36 ppb which falls within the range of 31 ppb to 55 ppb identified 

34 by Westerdahl et al. (2005). 

35 

36 On average, most locations are predicted to have fewer than 10 exceedances per year for the 

37 200 ppb potential health effect benchmark while the median frequency of exceedances in most 

38 locations is estimated to be 1 or less per year (Tables A-118 and A-119). When considering the 

39 lower 1-hour benchmark of 150 ppb, most locations (17 out of 20) were estimated to have less 

40 than 50/year, on average. There are generally fewer predicted mean exceedances of the potential 

41 health effect benchmark levels when considering recent air quality compared with the historical 

 

8

 Table A-118 considers annual average of hourly measurements while Westerdahl et al. (2005) reported between 2 

to 4 hour average concentrations. 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 258 

1 air quality. Areas with a relatively high number of estimated exceedances (e.g., Provo) are likely 

2 influenced by the presence of a small number of monitors and one or a few exceptional site-years 

3 where there were unusually high concentrations at the upper percentiles of the concentration 

4 distribution. 

5 

6 The upper percentiles for estimated number of exceedances of the 150 ppb, 1-hr average 

7 level in most locations using the historical ambient monitoring data was between 100 and 300 

8 per year, while a few locations were estimated to contain up to a several hundred exceedances 

9 (e.g., Los Angeles, New York, and Phoenix). There were lower numbers of estimated 

10 exceedances considering the 2001-2006 air quality compared with the historical data, with most 

locations containing under 200 estimated exceedances of 150 ppb per year at the 98th and 99th 11 

12 percentiles. As expected, the frequency of benchmark exceedances at all locations was lower 

13 when considering any of the higher benchmark levels (i.e., 200, 250, 300 ppb, 1-hr average) 

14 compared with 150 ppb. 

15 

16 The number of predicted benchmark exceedances across large urban areas may be used to 

17 broadly represent particular locations within those types of areas. For example, Chicago, New 

18 York, and Los Angeles are large CMSAs, have several monitoring sites, and have a large number 

19 of roadways. Each of these locations was estimated to have, on average, about 10 exceedances 

20 of 200 ppb per year on-roads. Assuming that the on-road exceedances distribution generated 

21 from the existing monitoring is proportionally representing the distribution of roadways within 

22 each location, about one-half of the roads in these areas would not have any on-road 

23 concentrations in excess of 200 ppb. This is because the median value for exceedances of 200 

24 ppb in most locations was estimated as zero. However, Tables A-118 and A-119 indicate that 

25 there is also a possibility of tens to just over a hundred exceedances of 200 ppb in a year as an 

26 upper bound estimate on certain roads/sites in a particular year. 

27 

28 Table A-141. Estimated annual average on-road NO2 concentrations for two monitoring periods, historical 

29 and recent air quality data (as is). 

1995-2000 2001-2006 

Annual Mean (ppb) 1 Annual Mean (ppb) 1

Location

SiteYears mean min med p95 p98 p99SiteYears mean min med p95 p98 p99

Atlanta 2400 26 6 25 49 57 60 2900 21 4 23 40 43 47 

Boston 1800 32 7 32 51 55 57 1400 16 7 16 25 28 29 

Chicago 2800 37 11 39 59 63 66 1700 37 20 35 57 64 66 

Cleveland 500 35 22 34 47 49 53 300 32 22 32 42 43 45 

Colorado 

Springs2 2500 30 9 30 52 64 73 - - - - - - - 

Denver 700 39 19 38 55 58 62 500 39 23 38 54 61 62 

Detroit 1200 35 15 34 52 57 59 1200 34 18 34 47 52 54 

El Paso 800 34 17 33 49 54 57 2400 26 10 26 39 43 43 

Jacksonville 600 28 18 27 37 39 41 400 25 17 25 34 36 37 

Las Vegas 800 17 4 11 45 50 55 2700 18 2 13 43 46 50 

Los Angeles 9200 50 8 49 83 91 97 10500 37 6 36 63 72 77 

Miami 900 17 11 17 23 25 26 1000 15 9 14 21 24 24 

New York 4700 43 14 42 73 78 83 4800 35 12 34 56 62 66 

Philadelphia 3500 39 19 36 63 73 77 2600 34 18 32 52 60 64 
August 2008 - Draft 259 

1995-2000 2001-2006 

Annual Mean (ppb) 1 Annual Mean (ppb) 1

Location

SiteYears mean min med p95 p98 p99SiteYears mean min med p95 p98 p99

Phoenix 1400 54 33 52 75 78 80 1400 45 26 43 63 70 72 

Provo 600 43 29 42 58 62 64 600 43 26 41 61 69 70 

St. Louis 1800 31 7 33 47 50 52 1300 30 16 29 41 46 49 

Washington DC 3300 33 12 33 53 58 61 3500 31 9 31 51 56 59 

Other MSA 113500 26 1 25 47 53 57 117700 21 1 21 39 45 48 

Other Not MSA 20000 14 0 12 31 35 39 24300 12 1 11 27 31 33 

1

 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 mean. 2

 Colorado Springs monitoring data were collected as part of short-term study completed in September 2001, 

therefore there are no 2001-2006 data. 

1 

2 
August 2008 - Draft 260 

1 

2 Table A-142. Estimated total number of exceedances of short-term (1-hour) potential health effect benchmark levels in a year o3 historical NO2 air quality (as is). 

Exceedances of 150 ppb 1 Exceedances of 200 ppb 1 Exceedances of 250 ppb 1 

Location mean min med p95 P98 p99 mean min med p95 p98 p99 mean min med p95 p98 p99 mAtlanta 24 0 1 160 271 357 4 0 0 31 57 87 1 0 0 3 11 21 Boston 11 0 1 79 106 125 1 0 0 9 20 24 0 0 0 1 4 7 Chicago 39 0 2 212 338 385 7 0 0 41 97 118 1 0 0 6 23 30 Cleveland 15 0 1 108 130 146 2 0 0 19 27 31 0 0 0 1 5 5 Colorado Springs 45 0 0 267 447 626 21 0 0 171 264 325 12 0 0 111 183 219 Denver 48 0 17 185 230 288 8 0 4 36 46 53 2 0 1 10 12 15 Detroit 39 0 19 158 207 270 10 0 2 48 72 86 4 0 1 21 34 35 El Paso 21 0 8 96 141 149 4 0 0 20 31 39 1 0 0 5 7 8 Jacksonville 3 0 0 13 30 36 0 0 0 1 2 4 0 0 0 0 1 1 Las Vegas 14 0 0 95 294 306 2 0 0 5 34 36 0 0 0 0 6 6 Los Angeles 166 0 54 738 1023 1268 43 0 6 213 348 508 12 0 0 63 118 188 Miami 3 0 0 13 27 27 0 0 0 2 4 5 0 0 0 0 0 1 New York 63 0 8 397 560 685 13 0 0 92 155 212 3 0 0 21 44 55 Philadelphia 25 0 2 124 311 369 4 0 0 20 45 63 1 0 0 4 11 15 Phoenix 104 0 31 447 630 670 14 0 2 65 89 102 2 0 0 13 21 27 Provo 21 0 0 112 195 245 2 0 0 9 33 34 0 0 0 0 1 4 St. Louis 14 0 0 74 121 132 2 0 0 15 25 28 1 0 0 10 13 14 Washington DC 21 0 1 128 208 240 3 0 0 20 39 56 1 0 0 2 8 11 Other MSA 10 0 0 55 109 168 1 0 0 6 18 32 0 0 0 1 3 6 Other Not MSA 2 0 0 11 31 55 1 0 0 2 7 14 0 0 0 1 2 4  Notes:  1 The mean number of exceedances represents the number of exceedances occurring at all monitors in a particular locatio

years across the monitoring period. The min, med, p95, p98, and p99 represent the minimum, median, 95th, 98th, and 99th 

the number of exceedances in any one year within the monitoring period. 
August 2008 - Draft 261 

1 

2 Table A-143. Estimated total number of exceedances of short-term (1-hour) potential health effect benchmark levels in a year o3 recent NO2 air quality (as is). 

Exceedances of 150 ppb 1 Exceedances of 200 ppb 1 Exceedances of 250 ppb 1 

Location2 mean min med p95 P98 p99 mean min med p95 p98 p99 mean min med p95 p98 p99 mAtlanta 8 0 0 52 101 121 1 0 0 8 16 25 0 0 0 1 3 6 Boston 0 0 0 1 2 10 0 0 0 0 0 1 0 0 0 0 0 0 Chicago 24 0 1 160 211 337 4 0 0 17 44 69 0 0 0 1 5 10 Cleveland 14 0 3 79 89 89 2 0 0 16 23 23 0 0 0 4 5 6 Denver 41 0 6 171 270 379 4 0 0 25 40 53 0 0 0 3 6 7 Detroit 20 0 3 116 149 171 5 0 0 29 44 45 2 0 0 16 22 28 El Paso 6 0 0 34 45 54 1 0 0 4 8 9 0 0 0 1 1 1 Jacksonville 7 0 2 29 53 53 3 0 1 15 23 24 2 0 0 8 15 15 Las Vegas 9 0 0 39 169 205 1 0 0 3 14 15 0 0 0 0 0 2 Los Angeles 42 0 4 227 405 546 7 0 0 37 87 129 1 0 0 7 20 28 Miami 1 0 0 4 9 16 0 0 0 0 1 2 0 0 0 0 0 0 New York 21 0 1 129 210 280 3 0 0 22 45 72 1 0 0 3 10 16 Philadelphia 12 0 1 62 110 211 1 0 0 5 12 30 0 0 0 1 1 7 Phoenix 37 0 2 184 302 350 3 0 0 14 28 44 0 0 0 1 3 4 Provo 117 0 1 658 702 703 70 0 0 547 662 662 33 0 0 234 606 612 St. Louis 7 0 0 48 84 102 1 0 0 3 10 14 0 0 0 0 2 2 Washington DC 11 0 0 81 130 141 1 0 0 7 14 21 0 0 0 0 1 2 Other MSA 4 0 0 17 44 76 0 0 0 1 5 10 0 0 0 0 1 1 Other Not MSA 1 0 0 4 14 27 0 0 0 1 4 8 0 0 0 0 2 3  Notes:  1 The mean number of exceedances represents the number of exceedances occurring at all monitors in a particular locatio

years across the monitoring period. The min, med, p95, p98, and p99 represent the minimum, median, 95th, 98th, and 99th 

the number of exceedances in any one year within the monitoring period. 

2 Colorado Springs monitoring data were collected as part of short-term study completed in September 2001, therefore the
August 2008 - Draft 262 

1 

2 A-9.6 Results Tables of Historical NO2 Ambient Monitoring Data 

3 (1995-2000) Adjusted to Just Meeting the Current Standard 

4 

5 This section presents the preliminary results using the historical ambient monitoring data 

6 (1995-2000) adjusted to just meet the current annual average standard only. This initial analysis 

7 calculated the total number of exceedances of the potential benchmark levels of 150, 200, 250, 

8 and 300 ppb, for ambient monitors sited ≥100 m and <100 m from a major road. These results 

9 are presented in Tables A-120 and A-121, respectively. In addition on-road concentrations were 

10 also estimated using the adjusted air quality concentrations, using the same procedure described 

11 in section A-8. The total estimated number of exceedances of the potential health effect 

12 benchmark levels on-roads given just meeting the current standard is provided in Table A-122. 

13 Each of the result tables presented in this section differs from the analyses performed in Chapter 

14 7 of the final REA where the number of times the daily maximum exceeded the potential 

15 benchmark levels was recorded (including a benchmark level of 100 ppb) for different monitor 

16 road categories (≥100 m, 20 m< x <100 m, and ≤20 m from a major road), and for not just the 

17 current standard but all of the potential alternative standards. It is presented here mainly as a 

18 companion to the as is air quality results presented in sections A-9.1 and A-9.2. 
August 2008 - Draft 263 

1 

2 Table A-144. Total number of exceedances of short-term (1-hour) potential health effect benchmark levels in a year, 1995-2000 3 quality adjusted to just meeting the current annual average standard (0.053 ppm) using monitors sited 

≥100 m of a major road

Exceedances of 150 ppb 1 Exceedances of 200 ppb 1 Exceedances of 250 ppb 1 

Location mean min med p95 P98 p99 mean min med p95 p98 p99 mean min med p95 p98 p99 mAtlanta 42 0 2 197 233 233 4 0 0 19 21 21 0 0 0 2 3 3 Boston 1 0 0 7 7 7 0 0 0 1 1 1 0 0 0 1 1 1 Chicago 1 0 1 5 7 7 0 0 0 1 1 1 0 0 0 0 0 0 Cleveland 2 0 1 7 7 7 0 0 0 0 0 0 0 0 0 0 0 0 Colorado Springs 50 0 3 283 318 318 32 0 0 180 241 241 16 0 0 123 135 135 Denver 141 1 12 648 648 648 24 0 2 141 141 141 5 0 1 28 28 28 Detroit 75 2 65 162 162 162 13 0 13 25 25 25 4 0 2 15 15 15 El Paso 16 1 9 69 69 69 2 0 1 14 14 14 0 0 0 2 2 2 Jacksonville 122 82 137 147 147 147 12 2 15 20 20 20 2 0 1 7 7 7 Las Vegas 3 0 1 11 11 11 0 0 0 1 1 1 0 0 0 0 0 0 Los Angeles 9 0 2 56 83 96 1 0 0 4 6 8 0 0 0 1 2 2 Miami 72 4 91 133 133 133 10 0 10 27 27 27 1 0 0 6 6 6 New York 1 0 0 4 7 7 0 0 0 0 2 2 0 0 0 0 0 0 Philadelphia 2 0 0 10 18 18 0 0 0 0 12 12 0 0 0 0 9 9 Phoenix 8 0 5 26 26 26 0 0 0 4 4 4 0 0 0 0 0 0 Provo 16 2 4 71 71 71 1 0 0 5 5 5 0 0 0 0 0 0 St. Louis 4 0 1 16 16 16 1 0 0 15 15 15 1 0 0 14 14 14 Washington DC 9 0 3 34 38 38 1 0 0 3 4 4 0 0 0 2 3 3 Other MSA 2 0 0 13 28 40 0 0 0 1 3 6 0 0 0 0 1 1 Other Not MSA 20 0 0 116 241 336 4 0 0 18 53 87 1 0 0 4 15 42  Notes:  1 The mean number of exceedances represents the number of exceedances occurring at all monitors in a particular locatio

years across the monitoring period. The min, med, p95, p98, and p99 represent the minimum, median, 95th, 98th, and 99th 

the number of exceedances in any one year within the monitoring period. 
August 2008 - Draft 264 

1 2 Table 145. Total estimated number of exceedances of short-term (1-hour) potential health effect benchmark levels in a year, 193 air quality adjusted to just meeting the current annual average standard (0.053 ppm) using monitors sited <100 m of a major ro

Exceedances of 150 ppb 1 Exceedances of 200 ppb 1 Exceedances of 250 ppb 1 

Location mean min med p95 P98 p99 mean min med p95 p98 p99 mean min med p95 p98 p99 mBoston 2 0 0 11 22 22 0 0 0 1 2 2 0 0 0 0 1 1 Chicago 4 0 2 16 16 16 0 0 0 0 0 0 0 0 0 0 0 0 Cleveland 35 9 16 110 110 110 5 0 1 24 24 24 2 0 0 10 10 10 Colorado Springs 7 7 7 7 7 7 2 2 2 2 2 2 1 1 1 1 1 1 Denver 12 0 0 77 77 77 1 0 0 10 10 10 0 0 0 5 5 5 El Paso 23 5 24 36 36 36 6 0 7 13 13 13 2 0 1 6 6 6 Las Vegas 47 0 25 226 226 226 6 0 1 28 28 28 3 0 0 13 13 13 Los Angeles 8 0 0 42 56 79 1 0 0 6 8 9 0 0 0 0 1 2 Miami 70 2 56 161 161 161 9 0 7 34 34 34 2 0 0 15 15 15 New York 1 0 0 6 10 10 0 0 0 1 3 3 0 0 0 0 3 3 Philadelphia 5 0 3 26 26 26 0 0 0 3 3 3 0 0 0 1 1 1 Phoenix 77 0 9 339 339 339 32 0 1 198 198 198 12 0 0 92 92 92 St. Louis 2 0 1 11 13 13 0 0 0 1 1 1 0 0 0 0 0 0 Washington DC 12 0 9 47 61 61 1 0 0 9 17 17 0 0 0 0 3 3  Notes:  1 The mean number of exceedances represents the number of exceedances occurring at all monitors in a particular locatio

years across the monitoring period. The min, med, p95, p98, and p99 represent the minimum, median, 95th, 98th, and 99th 

the number of exceedances in any one year within the monitoring period. 
August 2008 - Draft 265 

1 2 Table A-146. Total estimated number of exceedances of short-term (1-hour) potential health effect benchmark levels in a year 3 historical NO2 air quality adjusted to just meeting the current annual average standard (0.053 ppm). 

Exceedances of 150 ppb 1 Exceedances of 200 ppb 1 Exceedances of 250 ppb 1 

Location mean min med p95 P98 p99 mean min med p95 p98 p99 mean min med p95 p98 p99 mAtlanta 597 0 215 2122 2566 2778 251 0 42 109414721640 106 0 7 535 843 947 4Boston 231 0 108 930 1282 1394 53 0 11 299 369 390 14 0 1 95 132 161 Chicago 386 0 242 1288 1609 1802 111 0 32 498 615 707 36 0 2 195 289 364 Cleveland 526 42 407 1305 1568 1762 157 1 83 457 586 700 51 0 13 215 269 306 Colorado Springs 866 0 565 2666 3106 3332 308 0 80 134817921902 123 0 11 574 803 934 6Denver 980 15 585 2765 3021 3149 497 0 111 209723042451 254 0 26 146716951930 1Detroit 982 5 860 2413 2771 2882 405 2 284 122714391589 175 2 97 576 776 872 8El Paso 488 19 317 1443 2106 2391 152 0 67 545 997 1126 54 0 16 186 440 485 2Jacksonville 1381 365 1328 2485 2677 3110 610 40 549 142615151801 263 2 195 773 839 1002 1Las Vegas 348 0 47 1618 2108 2908 106 0 6 663 894 1248 38 0 1 318 526 596 Los Angeles 323 0 154 1219 1555 1935 97 0 24 427 671 865 32 0 4 158 264 366 Miami 802 33 788 1637 1885 2043 359 2 289 985 12011353 159 0 95 550 683 797 7New York 199 0 64 950 1251 1384 50 0 5 313 475 602 14 0 0 103 175 230 Philadelphia 362 0 174 1352 1967 2536 86 0 21 400 689 865 24 0 2 125 245 341 Phoenix 811 15 605 2493 2818 2922 229 0 88 954 12931375 63 0 12 304 436 544 Provo 1434 84 1363 3215 3526 3729 443 1 230 164318712058 135 0 32 543 697 817 4St. Louis 486 0 368 1402 1630 1843 144 0 51 523 693 728 46 0 9 232 289 323 Washington 562 0 358 1843 2409 2563 176 0 64 721 949 1073 60 0 9 316 411 478 2Other MSA 199 0 65 858 1262 1572 52 0 6 268 444 592 15 0 0 84 156 231 Other Not MSA 247 0 45 1234 1771 2130 95 0 7 549 928 1203 39 0 1 221 438 635  Notes:  1 The mean number of exceedances represents the number of exceedances occurring at all monitors in a particular locatio

years across the monitoring period. The min, med, p95, p98, and p99 represent the minimum, median, 95th, 98th, and 99th 

the number of exceedances in any one year within the monitoring period. 
August 2008 - Draft 266 

1 A-9.7 Results Tables of Recent NO2 Ambient Monitoring Data 

2 (2001-2006) As Is and Just Meeting the Current and Alternative 

3 Standards 

4 
August 2008 - Draft 267 

1 

2 A-10 References 

3 Bell S and Ashenden TW. (1997). Spatial and temporal variation in nitrogen dioxide pollution 

4 adjacent to rural roads. Water Air Soil Pollut. 95:87-98. 

5 Beckerman B, Jerrett M, Brook JR, Verma DK, Arain MA, Finkelstein MM. (2008). 

6 Correlation of nitrogen dioxide with other traffic pollutants near a major expressway. Atmos 

7 Environ. 42:275-290. 

8 Bignal KL, Ashmore MR, Headley AD, Stewart K, Weigert K. (2007). Ecological impacts of 

9 air pollution from road transport on local vegetation. Applied Geochemistry. 22:1265-1271. 

10 Cape JN, Tang YS, van Dijk N, Love L, Sutton MA, Palmer SCF. (2004). Concentrations of 

11 ammonia and nitrogen dioxide at roadside verges, and their contribution to nitrogen 

12 deposition. Environ Pollut. 132:469-478. 

13 Chan AT and Chung MW. (2003). Indoor-outdoor air quality relationships in vehicle: effect of 

14 driving environment and ventilation modes. Atmos Environ. 37: 3795-3808. 

15 FHWA. (2005). Highway Statistics 2005, Urbanized Areas - 2005, Miles and Daily Vehicle16 Miles of Travel (Table HM-71). Available at: 

17 http://www.fhwa.dot.gov/policy/ohim/hs05/htm/hm71.htm. 

18 Gilbert NL, Woodhouse S, Stieb DM, Brook JR. (2003). Ambient nitrogen dioxide and distance 

19 from a major highway. Sci Total Environ. 312:43-46. 

20 Heeb NV, Saxer CJ, Forss A-M, Brühlmann S. (2008). Trends of NO-, NO2-, and NH3-

21 emissions from gasoline-fueled Euro-3- to Euro-4-passenger cars. Atmos Environ. 

22 42(10):2543-2554. 

23 Maruo YY, Ogawa S, Ichino T, Murao N, Uchiyama M. (2003). Measurement of local 

24 variations in atmospheric nitrogen dioxide levels in Sapporo, Japan using a new method with 

25 high spatial and high temporal resolution. Atmos Environ. 37:1065-1074. 

26 McCurdy TR. (1994). Analysis of high 1 hour NO2 values and associated annual averages 

27 using 1988-1992 data. Report to the Office of Air Quality Planning and Standards, Durham 

28 NC. 

29 Monn Ch, Carabias V, Junker M, Waeber R, Karrer M, Wanner HU. (1997). Small-scale spatial 

30 variability of particulate matter <10 μm (PM10) and nitrogen dioxide. Atmos Environ. 

31 31(15)2243-2247. 

32 Nitta H, Sato T, Nakai S, Maeda K, Aoki S, Ono M. (1993). Respiratory health associated with 

33 exposure to automobile exhaust. I. Results of cross-section studies in 1979, 1982, 1983. 

34 Arch Environ Health. 48(1):53-58. 

35 Pleijel H, Karlsson GP, Gerdin EB. (2004). On the logarithmic relationship between NO2

36 concentration and the distance from a highroad. Sci Total Environ. 332:261-264. 

37 Rizzo (2008). Investigation of how distributions of hourly nitrogen dioxide concentrations have 

38 changed over time in six cities. Nitrogen Dioxide NAAQS Review Docket (EPA–HQ–

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40 Rodes C, Sheldon L, Whitaker D, Clayton A, Fitzgerald K, Flanagan J, DiGenova F, Hering S, 

41 Frazier C. (1998). Measuring Concentrations of Selected Air Pollutants Inside California 

42 Vehicles. California Environmental Protection Agency, Air Resources Board. Final Report, 

43 December 1998. 

44 Rodes CE and Holland DM. (1981). Variations of NO, NO2 and O3 concentrations downwind 

45 of a Los Angeles freeway. Atmos Environ. 15:243-250. 
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1 Roorda-Knape MC, Janssen NAH, De Hartog JJ, Van Vliet PHN, Harssema H, Brunekreef B. 

2 (1998). Air Pollution from traffic in city districts near major roadways. Atmos Environ. 

3 32(11)1921-1930. 

4 Shorter JH, Herndon S, Zahniser MS, Nelson DD, Wormhoudt J, Demerjian KL, Kolb CE. 

5 (2005). Real-Time measurements of nitrogen oxide emissions from in-use New York City 

6 transit buses using a chase vehicle. Environ Sci Technol. 39:7991-8000. 

7 Singer BC, Hodgson AT, Hotchi T, Kim JJ (2004). Passive measurement of nitrogen oxides to 

8 assess traffic-related pollutant exposure for the East Bay Children’s Respiratory Health 

9 Study. Atmos Environ. 38:393-403. 

10 US EPA. (2007a). US EPA Air Quality System (AQS). Download Detailed AQS Data. 

11 Available at: http://www.epa.gov/ttn/airs/airsaqs/detaildata/downloadaqsdata.htm. 

12 US EPA. (2007b). Field Guide to Air Quality Data (v1.0.0). February 28, 2007. Available at: 

13 http://www.epa.gov/ttn/airs/aqsdatamart/documentation/index.htm. 

14 US EPA. (2007c). Nitrogen Dioxide Health Assessment Plan: Scope and Methods for Exposure 

15 and Risk Assessment. September 2007. Office of Air Quality Planning and Standards. 

16 Available at: http://www.epa.gov/ttn/naaqs/standards/nox/s_nox_cr_pd.html. 

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18 http://www.epa.gov/ttn/chief/net/2002inventory.html#inventorydata. 

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20 Westerdahl D, Fruin S, Sax T, Fine PM, Sioutas C. (2005). Mobile platform measurements of 

21 ultrafine particles and associated pollutant concentrations on freeways and residential streets 

22 in Los Angeles. Atmos Environ. 39:3597-3610. 

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25 National Center for Environmental Assessment. Available at: 

26 http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=181712. 

27 

28 

29 

30 
 i

Appendix B. Supplement to the NO2 Exposure Assessment 
 ii

Table of Contents 

Appendix B. ............................................................. Supplement to the NO2 Exposure Assessment

.......................................................................................................................................................... i 

B-1 Overview......................................................................................................................... 1 

B-2 Human Exposure Modeling using APEX....................................................................... 3 

B-2.1 History............................................................................................................................. 3 

B-2.2 APEX Model Overview.................................................................................................. 4 

B-2.2.1 Study Area Characterization............................................................................... 5 

B-2.2.2 Simulated Individuals ......................................................................................... 7 

B-2.2.3 Activity Pattern Sequences ............................................................................... 10 

B-2.2.4 Calculating Microenvironmental Concentrations............................................. 14 

B-2.2.5 Exposure Calculations ...................................................................................... 19 

B-2.2.6 Exposure Model Output.................................................................................... 19 

B-3..........................................................................Philadelphia Exposure Assessment Case-Study

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

B-3.1 Study Area Selection and Description .......................................................................... 21 

B-3.2 Exposure Period of Analysis......................................................................................... 22 

B-3.3 Populations Analyzed ................................................................................................... 22

B-3.4 Simulated Individuals ................................................................................................... 22 

B-3.4.1 Asthma Prevalence Rates.................................................................................. 23 

B-3.5 Air Quality Data Generated by AERMOD................................................................... 24 

B-3.5.1 Meteorological Inputs ....................................................................................... 24 

B-3.5.2 Surface Characteristics and Land Use Analysis ............................................... 27 

B-3.5.3 Meteorological Data Analysis........................................................................... 30 

B-3.5.4 On-Road Emissions Preparation....................................................................... 31 

B-3.5.5 Stationary Sources Emissions Preparation........................................................ 38 

B-3.5.6 Fugitive and Airport Emissions Preparation..................................................... 43 

B-3.5.7 Receptor Locations ........................................................................................... 47 

B-3.5.8 Other AERMOD Specifications ....................................................................... 48 

B-3.5.9 Air Quality Concentration Adjustment............................................................. 49 

B-3.5.10 Meteorological Data Used By APEX ............................................................... 50 

B-3.5.11 Microenvironment Descriptions ....................................................................... 50 

B-3.5.12 Adjustment for Just Meeting the Current Standard .......................................... 56 

B-3.6 Philadelphia Exposure Modeling Results ..................................................................... 58 

B-3.6.1 Overview........................................................................................................... 58 

B-3.6.2 Evaluation of Modeled NO2 Air Quality Concentrations (as is) ...................... 58 

B-3.6.3 Comparison of estimated on-road NO2 concentrations .................................... 61 

B-3.6.4 Annual Average Exposure Concentrations (as is) ............................................ 64 

B-3.6.5 One-Hour Exposures (as is).............................................................................. 65 

B-3.6.6 One-Hour Exposures Associated with Just Meeting the Current Standard ...... 75 

B-3.6.7 Additional Exposure Results............................................................................. 77 

B-4 Atlanta Exposure Assessment Case-Study ................................................................... 90 

B-4.1 Supplemental AERMOD Modeling Inputs and Discussion ......................................... 91 

B-4.1.1 Major Link On-Road Emission Estimates ........................................................ 91 

B-4.1.2 Stationary Sources Emissions Preparation........................................................ 93 
 iii

B-4.1.3 Airport Emissions Preparation.......................................................................... 94 

B-4.1.4 Receptor Locations ........................................................................................... 97 

B-4.1.5 Data used to generate dispersion model-to-monitor comparison figures in REA.

 98 

B-4.1.6 Comparison of estimated on-road NO2 concentrations .................................. 101 

B-4.2 Supplemental APEX Modeling Inputs and Discussion .............................................. 105 

B-4.2.1 Simulated Individuals ..................................................................................... 105 

B-4.2.2 Asthma Prevalence Rates................................................................................ 105 

B-4.2.3 Meteorological Data Used by APEX.............................................................. 106 

B-4.2.4 Method Used for Indoor Source Contributions .............................................. 106 

B-4.2.5 Method Used for Cooking Probabilities ......................................................... 106 

B-4.2.6 In-vehicle and Near-Road PROX factors ....................................................... 107 

B-4.2.7 Supplemental Exposure Results...................................................................... 108 

B-4.2.6 Supplemental Exposure Results............................................................................. 108 

B-5 References................................................................................................................... 125 

Attachments

Attachment 1: Technical Memorandum on Meteorological Data Preparation for AERMOD for 

NO2 REA for Atlanta, GA 2001-2003........................................................................................ 130 

Attachment 2: Technical Memorandum on Longitudinal Diary Construction Approach ......... 154 

Attachment 2: Technical Memorandum on Longitudinal Diary Construction Approach ......... 154 

Attachment 3: Technical Memorandum on the Evaluation Cluster-Markov Algorithm........... 161 

Attachment 4. Technical Memorandum on the Analysis of NHIS Asthma Prevalence Data ... 175 

Attachment 5: Technical Memorandum on Analysis of Air Exchange Rate Data .................... 199 

Attachment 6: Technical Memorandum on HAPEM Near Road Population Data Base 

Development (from Task 2. Near roadway concentrations (revised))........................................ 219 

Attachment 7: Technical Memorandum on HAPEM Near Road Population Data Base 

Development (Estimating near roadway populations and areas for HAPEM6)......................... 237 

Attachment 8. Technical Memorandum on the Uncertainty Analysis Of Residential Air 

Exchange Rate Distributions....................................................................................................... 244 

Attachment 9. Technical Memorandum on the Distributions of Air Exchange Rate Averages 

Over Multiple Days..................................................................................................................... 275 
 iv

List of Tables 

Table B-1. Examples of profile variables in APEX....................................................................... 7 

Table B-2. Summary of activity pattern studies used in CHAD.................................................. 12 

Table B-3. Mass balance model parameters. ............................................................................... 14 

Table B-4. Factors model parameters. ......................................................................................... 15 

Table B-5. List of microenvironments and calculation methods used......................................... 16 

Table B-6. Mapping of CHAD activity locations to APEX microenvironments. ....................... 17 

Table B-7. Example of APEX output files. ................................................................................. 20 

Table B-8. Asthma prevalence rates by age and gender used for Philadelphia........................... 23 

Table B-9. Number of AERMET raw hourly surface meteorology observations, percent 

acceptance rate, 2001-2003.......................................................................................... 24 

Table B-10. Number of calms reported by AERMET by year for Philadelphia. ........................ 25 

Table B-11. Number and AERMET acceptance rate of upper-air observations 2001-2003....... 25 

Table B-12. Seasonal definitions and specifications for Philadelphia......................................... 27 

Table B-13. Monthly precipitation compared to NCDC 30-year climatic normal for 

Philadelphia, 2001-2003. ............................................................................................. 31 

Table B-14. Hourly scaling factors (in percents) applied to Philadelphia County AADT 

volumes. ....................................................................................................................... 33 

Table B-15. Seasonal scaling factors applied to Philadelphia County AADT volumes.............. 34 

Table B-16. Signals per mile, by link type, applied to Philadelphia County AADT volumes. ... 34 

Table B-17. Statistical summary of AADT volumes (one direction) for Philadelphia County 

AERMOD simulations................................................................................................. 34 

Table B-18. Average calculated speed by link type. ................................................................... 36 

Table B-19. On-road area source sizes. ........................................................................................ 37 

Table B-20. Combined stacks parameters for stationary NOx emission sources in Philadelphia 

County.......................................................................................................................... 40 

Table B-21. Matched stacks between the CAMD and NEI database. ......................................... 41 

Table B-22. Emission parameters for the three Philadelphia County fugitive NOx area emission 

sources.......................................................................................................................... 43 

Table B-23. Philadelphia International airport (PHL) NOx emissions. ....................................... 46 

Table B-24. Philadelphia County NOx monitors......................................................................... 47 

Table B-25. Comparison of ambient monitoring and AERMOD predicted NO2 concentrations in 

Philadelphia.................................................................................................................. 49 

Table B-26. Air conditioning prevalence estimates with 95% confidence intervals................... 50 

Table B-27. Geometric means (GM) and standard deviations (GSD) for air exchange rates by 

city, A/C type, and temperature range. ........................................................................ 51 

Table B-28. Probability of gas stove cooking by hour of the day. .............................................. 53 

Table B-29. Adjustment factors and potential health effect benchmark levels used by APEX to 

simulate just meeting the current standard................................................................... 56 

Table B-30. Summary statistics of on-road hourly NO2 concentrations (ppb) and the numbers of 

potential health effect benchmark levels using AERMOD and the on-road ambient 

monitor simulation approaches in Philadelphia. .......................................................... 63 

Table B-31. Estimated number of asthmatics in Philadelphia County exposed at or above 

potential health effect benchmark levels (1 to 6 times per year), using modeled air 

quality (as is) and with just meeting the current standard (std), and with and without 

indoor sources. ............................................................................................................. 78 
 v

Table B-32. Estimated percent of asthmatics in Philadelphia County exposed at or above 

potential health effect benchmark levels (1 to 6 times per year), using modeled air 

quality (as is) and with just meeting the current standard (std), and with and without 

indoor sources. ............................................................................................................. 79 

Table B-33. Estimated number of asthmatic children in Philadelphia County exposed at or above 

potential health effect benchmark levels (1 to 6 times per year), using modeled air 

quality (as is) and with just meeting the current standard (std), and with and without 

indoor sources. ............................................................................................................. 84 

Table B-34. Estimated percent of asthmatic children in Philadelphia County exposed at or above 

potential health effect benchmark levels (1 to 6 times per year), using modeled air 

quality (as is) and with just meeting the current standard (std), and with and without 

indoor sources. ............................................................................................................. 85 

Table B-35. 30 year annual average temperature and precipitation summary for Atlanta, GA. . 90 

Table B-36. The major-facility combined stacks within 10 km of the Atlanta modeling domain.

...................................................................................................................................... 95 

Table B-37. Data used to generate cumulative density functions plotted in Figure 8-6 of REA. 98 

Table B-38. Data used to generate diurnal variation plotted in Figure 8-7 of REA.................... 99 

Table B-39. On-road/non-road NO2 concentration ratios using AERMOD roadway link 

concentration prediction and nearest corresponding receptor concentration ≥ 100 m of 

a major road. .............................................................................................................. 101 

Table B-40. Estimated on-road/non-road NO2 concentration ratios using m ratio derived from 

data reported in published NO2 measurement studies................................................ 103 

Table B-41. Mean asthma prevalence rates, along with lower and upper 95% confidence limits, 

by age and gender used for Atlanta............................................................................ 105 

Table B-42. In-vehicle and near-road PROX factors used in APEX......................................... 107 

Table B-43. Estimated number of asthmatics in the Atlanta modeling domain exposed at or 

above potential health effect benchmark levels (1 to 6 times per year), using 2001 

modeled air quality (as is), with just meeting the current standard (cs), and potential 

alternative standards, without indoor sources. ........................................................... 109 

Table B-44. Estimated percent of asthmatics in the Atlanta modeling domain exposed at or 

above potential health effect benchmark levels (1 to 6 times per year), using 2001 

modeled air quality (as is), with just meeting the current standard (cs), and potential 

alternative standards, without indoor sources. ........................................................... 110 

Table B-45. Estimated number of asthmatic children in the Atlanta modeling domain exposed at 

or above potential health effect benchmark levels (1 to 6 times per year), using 2001 

modeled air quality (as is), with just meeting the current standard (cs), and potential 

alternative standards, without indoor sources. ........................................................... 111 

Table B-46. Estimated percent of asthmatic children in the Atlanta modeling domain exposed at 

or above potential health effect benchmark levels (1 to 6 times per year), using 2001 

modeled air quality (as is), with just meeting the current standard (cs), and potential 

alternative standards, without indoor sources. ........................................................... 112 

Table 47. Estimated number of asthmatics in the Atlanta modeling domain exposed at or above 

potential health effect benchmark levels (1 to 6 times per year), using 2002 modeled 

air quality (as is), with just meeting the current standard (cs), and potential alternative 

standards, without indoor sources.............................................................................. 113 
 vi

Table B-48. Estimated percent of asthmatics in the Atlanta modeling domain exposed at or above 

potential health effect benchmark levels (1 to 6 times per year), using 2002 modeled 

air quality (as is), with just meeting the current standard (cs), and potential alternative 

standards, without indoor sources.............................................................................. 114 

Table B-49. Estimated number of asthmatic children in the Atlanta modeling domain exposed at 

or above potential health effect benchmark levels (1 to 6 times per year), using 2002 

modeled air quality (as is), with just meeting the current standard (cs), and potential 

alternative standards, without indoor sources. ........................................................... 115 

Table B-50. Estimated percent of asthmatic children in the Atlanta modeling domain exposed at 

or above potential health effect benchmark levels (1 to 6 times per year), using 2002 

modeled air quality (as is), with just meeting the current standard (cs), and potential 

alternative standards, without indoor sources. ........................................................... 116 

Table B-51. Estimated number of asthmatic in the Atlanta modeling domain exposed at or above 

potential health effect benchmark levels (1 to 6 times per year), using 2003 modeled 

air quality (as is), with just meeting the current standard (cs), and potential alternative 

standards, without indoor sources.............................................................................. 117 

Table B-52. Estimated percent of asthmatics in the Atlanta modeling domain exposed at or 

above potential health effect benchmark levels (1 to 6 times per year), using 2003 

modeled air quality (as is), with just meeting the current standard (cs), and potential 

alternative standards, without indoor sources. ........................................................... 118 

Table B-53. Estimated number of asthmatic children in the Atlanta modeling domain exposed at 

or above potential health effect benchmark levels (1 to 6 times per year), using 2003 

modeled air quality (as is), with just meeting the current standard (cs), and potential 

alternative standards, without indoor sources. ........................................................... 119 

Table B-54. Estimated percent of asthmatic children in the Atlanta modeling domain exposed at 

or above potential health effect benchmark levels (1 to 6 times per year), using 2003 

modeled air quality (as is), with just meeting the current standard (cs), and potential 

alternative standards, without indoor sources. ........................................................... 120 

Table B-55. Estimated number of asthmatics in the Atlanta modeling domain exposed at or 

above potential health effect benchmark levels (1 to 6 times per year), using 2002 

modeled air quality (as is), with just meeting the current standard (cs), and potential 

alternative standards, with indoor sources. ................................................................ 121 

Table B-56. Estimated percent of asthmatics in the Atlanta modeling domain exposed at or 

above potential health effect benchmark levels (1 to 6 times per year), using 2002 

modeled air quality (as is), with just meeting the current standard (cs), and potential 

alternative standards, with indoor sources. ................................................................ 122 

Table B-57. Estimated number of asthmatic children in the Atlanta modeling domain exposed at 

or above potential health effect benchmark levels (1 to 6 times per year), using 2002 

modeled air quality (as is), with just meeting the current standard (cs), and potential 

alternative standards, with indoor sources. ................................................................ 123 

Table B-58. Estimated percent of asthmatic children in the Atlanta modeling domain exposed at 

or above potential health effect benchmark levels (1 to 6 times per year), using 2002 

modeled air quality (as is), with just meeting the current standard (cs), and potential 

alternative standards, with indoor sources. ................................................................ 124 
 vii

List of Figures 

Figure B-1. Example of a profile function file for A/C prevalence............................................. 10 

Figure B-2. Land-use and sectors around the Philadelphia-area surface meteorological station 

(KPHL). Sector borders are 80, 184, 262, and 312 degrees from geographic North. 

Philadelphia city center is labeled................................................................................ 29 

Figure B-3. Estimated z0 values for the Philadelphia case-study analysis using visual and 

AERSURFACE land-use estimations.......................................................................... 30 

Figure B-4. Example of Light- and heavy-duty vehicle NOx emissions grams/mile (g/mi) for 

arterial and freeway functional classes, 2001. ............................................................. 36 

Figure B-5. Differences in facility-wide annual NOx emission totals between NEI and CAMD 

data bases for Philadelphia County 2002..................................................................... 43 

Figure B-6. Locations of the four ancillary area sources. Also shown are centroid receptor 

locations. ...................................................................................................................... 45 

Figure B-7. Centroid locations within fixed distances to major point and mobile sources in 

Philadelphia county...................................................................................................... 47 

Figure B-8. Frequency distribution of distance between each Census receptor and its nearest 

road-centered receptor in Philadelphia County............................................................ 48 

Figure B-9. Example input file from APEX for Indoors-residence microenvironment. ............. 52 

Figure B-10. Example input file from APEX for all Indoors microenvironments (non-residence).

...................................................................................................................................... 54 

Figure B-11. Example input file from APEX for outdoor near road microenvironment. ........... 55 

Figure B-12 . Distribution of AERMOD estimated annual average NO2 concentrations at each of 

the 16,857 receptors in Philadelphia County for years 2001-2003.............................. 59 

Figure B-13. Measured and modeled diurnal pattern of NO2 concentrations at three ambient 

monitor sites................................................................................................................. 60 

Figure B-14. Comparison of on-road/non-road ratios developed from AERMOD concentration 

estimates and those derived from published NO2 measurement studies...................... 62 

Figure B-15. Estimated annual average total NO2 exposure concentrations for all simulated 

persons in Philadelphia County, using modeled 2001-2003 air quality (as is), with 

modeled indoor sources................................................................................................ 64 

Figure B-16. Comparison of AERMOD predicted and ambient monitoring annual average NO2

concentrations (as is) and APEX exposure concentrations (with and without modeled 

indoor sources) in Philadelphia County for year 2002................................................. 65 

Figure B-17. Estimated maximum NO2 exposure concentration for all simulated persons in 

Philadelphia County, using modeled 2001-2003 air quality (as is), with and without 

modeled indoor sources. Values above the 99th percentile are not shown................. 67 

Figure B-18. Estimated number of all simulated asthmatics in Philadelphia County with at least 

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

modeled 2001-2003 air quality (as is), with modeled indoor sources. ........................ 67 

Figure B-19. Estimated number of simulated asthmatic children in Philadelphia County with at 

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

modeled 2001-2003 air quality (as is), with modeled indoor sources. ........................ 68 

Figure B-20. Comparison of the estimated number of all simulated asthmatics in Philadelphia 

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

levels, using modeled 2002 air quality (as is) , with and without modeled indoor 

sources.......................................................................................................................... 68 

Figure B-21. Fraction of time all simulated persons in Philadelphia County spend in the twelve 

microenvironments associated with the potential NO2 health effect benchmark levels, 

a) ≥ 200 ppb, b) ≥ 250 ppb, and c) ≥ 300 ppb, year 2002 simulation with indoor 

sources.......................................................................................................................... 71 

Figure B-22. Fraction of time all simulated persons in Philadelphia County spend in the twelve 

microenvironments associated with the potential NO2 health effect benchmark levels, 

a) ≥ 200 ppb, b) ≥ 250 ppb, and c) ≥ 300 ppb, year 2002 simulation without indoor 

sources.......................................................................................................................... 72 

Figure B-23. Estimated percent of all asthmatics in Philadelphia County with repeated NO2

exposures above potential health effect benchmark levels, using 2003 modeled air 

quality (as is), with modeled indoor sources................................................................ 74 

Figure B-24. Estimated percent of all asthmatics in Philadelphia County with repeated NO2

exposures above potential health effect benchmark levels, using modeled 2002 air 

quality (as is), with and without indoor sources. ......................................................... 74 

Figure B-25. Estimated percent of all asthmatics in Philadelphia with at least one exposure at or 

above the potential health effect benchmark level, using modeled 2001-2003 air 

quality just meeting the current standard, with modeled indoor sources..................... 76 

Figure B-26. Estimated number of all asthmatics in Philadelphia with at least one exposure at or 

above the potential health effect benchmark level, using modeled 2002 air quality just 

meeting the current standard, with and without modeled indoor sources.................... 76 

Figure B-27. Estimated percent of asthmatics in Philadelphia County with repeated exposures 

above health effect benchmark levels, using modeled 2002 air quality just meeting the 

current standard, with and without modeled indoor sources........................................ 77 

Figure B-28. Estimated percent of all asthmatics in Philadelphia County with at least one NO2

exposure at or above potential health effect benchmark level, using 2001-2003 

modeled air quality (as is), with modeled indoor sources............................................ 80 

Figure B-29. Estimated percent of all asthmatics in Philadelphia County with at least one NO2

exposure at or above potential health effect benchmark level, using 2001-2003 

modeled air quality (as is), with no indoor sources. .................................................... 80 

Figure B-30. Estimated percent of all asthmatics in Philadelphia County with at least one NO2 

exposure at or above potential health effect benchmark level, using 2001-2003 

modeled air quality just meeting the current standard (std), with modeled indoor 

sources.......................................................................................................................... 81 

Figure B-31. Estimated percent of all asthmatics in Philadelphia County with at least one NO2 

exposure at or above potential health effect benchmark level, using 2001-2003 

modeled air quality just meeting the current standard (std), with no indoor sources. . 81 

Figure B-32. Estimated percent of all asthmatics in Philadelphia County with repeated NO2

exposures at or above 200 ppb 1-hr, using 2001-2003 modeled air quality (as is), with 

modeled indoor sources................................................................................................ 82 

Figure B-33. Estimated percent of all asthmatics in Philadelphia County with repeated NO2 

exposures at or above 200 ppb 1-hr, using 2001-2003 modeled air quality (as is), 

without indoor sources. ................................................................................................ 82 
 ix

Figure B-34. Estimated percent of all asthmatics in Philadelphia County with repeated NO2

exposures at or above 200 ppb 1-hour, using 2001-2003 modeled air quality just 

meeting the current standard (std), with modeled indoor sources. .............................. 83 

Figure B-35. Estimated percent of all asthmatics in Philadelphia County with repeated NO2

exposures at or above 200 ppb 1-hour, using 2001-2003 modeled air quality just 

meeting the current standard (std), with no indoor sources. ........................................ 83 

Figure B-36. Estimated percent of asthmatic children in Philadelphia County with at least one 

NO2 exposure at or above potential health effect benchmark level, using 2001-2003 

modeled air quality (as is), with modeled indoor sources............................................ 86 

Figure B-37. Estimated percent of asthmatic children in Philadelphia County with at least one 

NO2 exposure at or above potential health effect benchmark level, using 2001-2003 

modeled air quality (as is), with no indoor sources. .................................................... 86 

Figure B-38. Estimated percent of asthmatic children in Philadelphia County with at least one 

NO2 exposure at or above potential health effect benchmark level, using 2001-2003 

modeled air quality just meeting the current standard (std), with modeled indoor 

sources.......................................................................................................................... 87 

Figure B-39. Estimated percent of asthmatic children in Philadelphia County with at least one 

NO2 exposure at or above potential health effect benchmark level, using 2001-2003 

modeled air quality just meeting the current standard (std), with no indoor sources. . 87 

Figure B-40. Estimated percent of asthmatic children in Philadelphia County with repeated NO2 

exposures at or above 200 ppb 1-hr, using 2001-2003 modeled air quality (as is), with 

modeled indoor sources................................................................................................ 88 

Figure B-41. Estimated percent of asthmatic children in Philadelphia County with repeated NO2 

exposures at or above 200 ppb 1-hr, using 2001-2003 modeled air quality (as is), with 

no indoor sources. ........................................................................................................ 88 

Figure B-42. Estimated percent of asthmatic children in Philadelphia County with repeated NO2 

exposures at or above 200 ppb 1-hr, using 2001-2003 modeled air quality meeting the 

current standard (std), with modeled indoor sources. .................................................. 89 

Figure B-43. Estimated percent of asthmatic children in Philadelphia County with repeated NO2 

exposures at or above 200 ppb 1-hr, using 2001-2003 modeled air quality meeting the 

current standard (std), with no indoor sources............................................................. 89 

Figure B-44. Example of Light- and heavy-duty vehicle NOx emissions grams/mile (g/mi) for 

arterial and freeway functional classes, 2001. ............................................................. 93 

Figure B-45. Polygon representing the Atlanta-Hartsfield International Airport area source..... 94 

Figure B-46. Frequency distribution of distance between each Census block receptor and its 

nearest major-roadway-link-centered receptor. ........................................................... 97 
 B-1

B-1 Overview 

This appendix contains supplemental descriptions of the methods and data used in the NO2

exposure assessment, as well as detailed results from the exposure analyses performed. First, a 

broad description of the exposure modeling approach is described, applicable to the two 

exposure modeling case-studies conducted to date: Philadelphia and Atlanta. This is followed 

with details regarding the required inputs for the model and the assumptions made for both of the 

case-study assessments. The primary output for each exposure assessment was the numbers of 

exceedances of short-term (1-hour) potential health effect benchmark levels experienced by the 

asthmatic population residing within each location. 

The first simulation location included Philadelphia County and was summarized in the 1st

draft Risk and Exposure Assessment (REA). The results from this assessment are presented here 

as they existed in that document and the draft Technical Support Document draft (TSD) and no 

adjustments were made to modeling approach used to generate the exposure results. However, 

additional comparative analyses are presented here to clarify certain issues raised in the review 

of this case-study by CASAC in May, 2008. These include additional comparisons of the 

AERMOD modeled air quality with the available ambient monitor data (section 3.6.2) as well as 

a comparison of the two on-road concentration estimation approaches used (section 3.6.3). 

It should be noted that due to the differences in the approach used in the Philadelphia 

analysis, the results are not directly comparable to the Atlanta case-study. In the dispersion 

modeling approach used for Philadelphia, minor roadway link emissions were not estimated. 

This lack of accounting for a potentially large emission source could have been responsible for 

the underestimations in modeled ambient concentrations when compared with available ambient 

monitoring data. It followed that the modeled air quality was then adjusted to account for the 

difference in concentration using the monitored data. This was another difference in the 

approach used for Philadelphia that was not used in Atlanta. The results for the Philadelphia 

analysis are still included here since they still estimate exposures for the population within the 

County, only with different uncertainties in the results when compared with the Atlanta data due 

to the differing approach used. Most of the uncertainties in the results described in the Atlanta 

REA can be similarly applied to the Philadelphia assessment (e.g., uncertainty in the CHAD data 

base, population data bases, asthma prevalence rate, etc.), however, this Appendix does not 

include a full characterization of uncertainty in the Philadelphia results since it was not used in 

the final REA. 

As mentioned above second case-study was conducted in portions of the Atlanta 

Metropolitan Statistical Area (MSA) that included four counties. This is the exposure 

assessment case study included in the final REA. Supplemental data and discussion not included 

in the final REA regarding the dispersion modeling and exposure modeling approaches for the 

Atlanta exposure case-study are provided here. 

The discussion that follows includes three main sections. First is a broad overview of the 

APEX model that was used in this NO2 National Ambient Air Quality Standard (NAAQS) 

review to estimate human exposures. This is followed with a description of the Philadelphia 

County approach, data inputs, and results. And third, additional data and discussion regarding 

the Atlanta exposure assessment are described. This is then followed with a series of 
 B-2

Attachments, further documenting some of the data sources and modeling approaches used, as 

well as previously conducted uncertainty analyses on selected input parameters. 
 B-3

B-2 Human Exposure Modeling using APEX 

The Air Pollutants Exposure model (APEX) is a personal computer (PC)-based program 

designed to estimate human exposure to criteria and air toxic pollutants at the local, urban, and 

consolidated metropolitan levels. APEX, also known as TRIM.Expo, is the human inhalation 

exposure module of EPA’s Total Risk Integrated Methodology (TRIM) model framework (US 

EPA, 1999), a modeling system with multimedia capabilities for assessing human health and 

ecological risks from hazardous and criteria air pollutants. It is being developed to support 

evaluations with a scientifically sound, flexible, and user-friendly methodology. Additional 

information on the TRIM modeling system, as well as downloads of the APEX Model, user’s 

guide, and other supporting documentation, can be found on EPA’s Technology Transfer 

Network (TTN) at http://www.epa.gov/ttn/fera. 

B-2.1 History 

APEX was derived from the National Ambient Air Quality Standards (NAAQS) Exposure 

Model (NEM) series of models, developed to estimate exposure to the criteria pollutants (e.g., 

carbon monoxide (CO), ozone O3). In 1979, EPA began by assembling a database of human 

activity patterns that could be used to estimate exposures to indoor and outdoor pollutants 

(Roddin et al., 1979). These data were then combined with measured outdoor concentrations in 

NEM to estimate exposures to CO (Biller et al., 1981; Johnson and Paul, 1983). In 1988, 

OAQPS began to incorporate probabilistic elements into the NEM methodology and use activity 

pattern data based on various human activity diary studies to create an early version of 

probabilistic NEM for O3 (i.e., pNEM/O3). In 1991, a probabilistic version of NEM was 

extended to CO (pNEM/CO) that included a one-compartment mass-balance model to estimate 

CO concentrations in indoor microenvironments. The application of this model to Denver, 

Colorado has been documented in Johnson et al. (1992). Additional enhancements to pNEM/O3

in the early- to mid-1990’s allowed for probabilistic exposure assessments in nine urban areas for 

the general population, outdoor children, and outdoor workers (Johnson et al., 1996a; 1996b; 

1996c). Between 1999 and 2001, updated versions of pNEM/CO (versions 2.0 and 2.1) were 

developed that relied on activity diary data from EPA’s Consolidated Human Activities Database 

(CHAD) and enhanced algorithms for simulating gas stove usage, estimating alveolar ventilation 

rate (a measure of human respiration), and modeling home-to-work commuting patterns. 

The first version of APEX was essentially identical to pNEM/CO (version 2.0) except that it 

was capable of running on a PC instead of a mainframe. The next version, APEX2, was 

substantially different, particularly in the use of a personal profile approach (i.e., simulation of 

individuals) rather than a cohort simulation (i.e., groups of similar persons). APEX3 introduced 

a number of new features including automatic site selection from national databases, a series of 

new output tables providing summary exposure and dose statistics, and a thoroughly reorganized 

method of describing microenvironments and their parameters. Most of the spatial and temporal 

constraints of pNEM and APEX1 were removed or relaxed by version 3. 

The version of APEX used in this exposure assessment is APEX4, described in the APEX 

User’s Guide and the APEX Technical Support Document (US EPA, 2006a; 2006b) and referred 

to here as the APEX User’s Guide and TSD. 
 B-4

B-2.2 APEX Model Overview 

APEX estimates human exposure to criteria and toxic air 

pollutants at the local, urban, or consolidated metropolitan 

area levels using a stochastic, microenvironmental approach. 

The model randomly selects data for a sample of hypothetical 

individuals from an actual population database and simulates 

each hypothetical individual’s movements through time and 

space (e.g., at home, in vehicles) to estimate their exposure to 

a pollutant. APEX simulates commuting, and thus exposures 

that occur at home and work locations, for individuals who 

work in different areas than they live. 

APEX can be conceptualized as a simulated field study that would involve selecting an actual 

sample of specific individuals who live in (or work and live in) a geographic area and then 

continuously monitoring their activities and subsequent inhalation exposure to a specific air 

pollutant during a specific period of time. 

The main differences between APEX and an actual field study are that in APEX: 

• The sample of individuals is a virtual sample, not actual persons. However, the 

population of individuals appropriately balanced according to various demographic 

variables and census data using their relative frequencies, in order to obtain a 

representative sample (to the extent possible) of the actual people in the study area 

• The activity patterns of the sampled individuals (e.g., the specification of indoor and 

other microenvironments visited and the time spent in each) are assumed by the model to 

be comparable to individuals with similar demographic characteristics, according to 

activity data such as diaries compiled in EPA’s Consolidated Human Activity Database 

(or CHAD; US EPA, 2002; McCurdy et al., 2000) 

• The pollutant exposure concentrations are estimated by the model using a set of userinput ambient outdoor concentrations (either modeled or measured) and information on 

the behavior of the pollutant in various microenvironments; 

• Variation in ambient air quality levels can be simulated by either adjusting air quality 

concentrations to just meet alternative ambient standards, or by reducing source 

emissions and obtaining resulting air quality modeling outputs that reflect these potential 

emission reductions, and 

• The model accounts for the most significant factors contributing to inhalation exposure – 

the temporal and spatial distribution of people and pollutant concentrations throughout 

the study area and among microenvironments – while also allowing the flexibility to 

adjust some of these factors for alternative scenarios and sensitivity analyses. 

APEX is designed to simulate human population exposure to criteria and air toxic pollutants 

at local, urban, and regional scales. The user specifies the geographic area to be modeled and the 

number of individuals to be simulated to represent this population. APEX then generates a 

personal profile for each simulated person that specifies various parameter values required by the 

model. The model next uses diary-derived time/activity data matched to each personal profile to 

generate an exposure event sequence (also referred to as activity pattern or diary) for the 

modeled individual that spans a specified time period, such as one year. Each event in the 

A microenvironment is a threedimensional space in which human 

contact with an environmental 

pollutant takes place and which can 

be treated as a well-characterized, 

relatively homogeneous location 

with respect to pollutant 

concentrations for a specified time 

period. 
 B-5

sequence specifies a start time, exposure duration, geographic location, microenvironment, and 

activity performed. Probabilistic algorithms are used to estimate the pollutant concentration 

associated with each exposure event. The estimated pollutant concentrations account for the 

effects of ambient (outdoor) pollutant concentration, penetration factors, air exchange rates, 

decay/deposition rates, and proximity to emission sources, depending on the microenvironment, 

available data, and estimation method selected by the user. 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. The model simulation can be broadly 

described in five steps that follow: 

1. Characterize the study area. APEX selects census tracts within a 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 census data for the study area and human profile 

distribution data (such as age-specific employment probabilities). 

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 and based on 

the activity pattern data. 

4. Calculate hourly concentrations in microenvironments. APEX users define 

microenvironments that people in the study area would visit by assigning location codes 

in the activity pattern to the user-specified microenvironments. The model then 

calculates hourly concentrations of a pollutant in each of these microenvironments for the 

period of simulation, based on the user-provided microenvironment descriptions and 

hourly air quality data. 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 values. 

B-2.2.1 Study Area Characterization 

The APEX study area has traditionally been on the scale of a city or slightly larger 

metropolitan area, although it is now possible to model larger areas such as combined statistical 

areas (CSAs). In the exposure analyses performed as part of this NAAQS review, the study area 

is defined by either a single or a few counties. The demographic data used by the model to 

create personal profiles is provided at the census block level. For each block the model requires 

demographic information representing the distribution of age, gender, race, and work status 

within the study population. Each block has a location specified by latitude and longitude for 

some representative point (e.g., geographic center). The current release of APEX includes input 

files that already contain this demographic and location data for all census tracts, block groups, 

and blocks in the 50 United States, based on the 2000 Census. In this assessment, exposures 

were evaluated at the block level. 
 B-6

B-2.2.1.1 Air Quality Data 

Air quality data can be input to the model as measured data from an ambient monitor or that 

generated by air quality modeling. This exposure analysis used modeled air quality data, whereas 

the principal emission sources included both mobile and stationary sources as well as fugitive 

emissions. Air quality data used for input to APEX were generated using AERMOD, a steadystate, Gaussian plume model (EPA, 2004). The following steps were performed using 

AERMOD. 

1. Collect and analyze general input parameters. Meteorological data, processing 

methodologies used to derive input meteorological fields (e.g., temperature, wind 

speed, precipitation), and information on surface characteristics and land use are 

needed to help determine pollutant dispersion characteristics, atmospheric 

stability and mixing heights. 

2. Estimate emissions. The emission sources modeled included, major stationary 

emission sources, on-road emissions that occur on major roadways, and fugitive 

emissions. 

3. Define receptor locations. Three sets of receptors were identified for the 

dispersion modeling, including ambient monitoring locations, census block 

centroids, and links along major roadways. 

4. Estimate concentrations at receptors. Hourly concentrations were estimated for 

each year of the simulation (years 2001 through 2003) by combining 

concentration contributions from each of the emission sources and accounting for 

sources not modeled. 

In APEX, the ambient air quality data are assigned to geographic areas called districts. The 

districts are used to assign pollutant concentrations to the blocks/tracts and microenvironments 

being modeled. The ambient air quality data are provided by the user as hourly time series for 

each district. As with blocks/tracts, each district has a representative location (latitude and 

longitude). APEX calculates the distance from each block/tract to each district center, and 

assigns the block/tract to the nearest district, provided the block/tract representative location 

point (e.g., geographic center) is in the district. Each block/tract can be assigned to only one 

district. In this assessment the district was synonymous with the receptor modeled in the 

dispersion modeling. 

B-2.2.1.2 Meteorological Data 

Ambient temperatures are input to APEX for different sites (locations). As with districts, 

APEX calculates the distance from each block to each temperature site and assigns each block to 

the nearest site. Hourly temperature data are from the National Climatic Data Center Surface 

Airways Hourly TD-3280 dataset (NCDC Surface Weather Observations). Daily average and 1-

hour maxima are computed from these hourly data. 

There are two files that are used to provide meteorological data to APEX. One file, the 

meteorological station location file, contains the locations of meteorological data recordings 

expressed in latitude and longitude coordinates. This file also contains start and end dates for the 

data recording periods. The temperature data file contains the data from the locations in the 
 B-7

temperature zone location file. This file contains hourly temperature readings for the period 

being modeled for the meteorological stations in and around the study area. 

B-2.2.2 Simulated Individuals 

APEX stochastically generates a user-specified number of simulated persons to represent the 

population in the study area. Each simulated person is represented by a personal profile, a 

summary of personal attributes that define the individual. APEX generates the simulated person 

or profile by probabilistically selecting values for a set of profile variables (Table B-1). The 

profile variables could include: 

• Demographic variables, generated based on the census data; 

• Physical variables, generated based on sets of distribution data; 

• Other daily varying variables, generated based on literature-derived distribution data that 

change daily during the simulation period. 

APEX first selects demographic and physical attributes for each specified individual, and 

then follows the individual over time and calculates his or her time series of exposure. 

Table B-1. Examples of profile variables in APEX. 

Variable 

Type Profile Variables Description

Age Age (years) 

Gender Male or Female 

Home block Block in which a simulated person lives 

Work tract Tract in which a simulated person works 

Demographic 

Employment status Indicates employment outside home 

Physical Air conditioner Indicates presence of air conditioning at home 

Gas Stove Indicates presence of gas stove at home 

B-2.2.2.1 Population Demographics 

APEX takes population characteristics into account to develop accurate representations of 

study area demographics. Specifically, population counts by area and employment probability 

estimates are used to develop representative profiles of hypothetical individuals for the 

simulation. 

APEX is flexible in the resolution of population data provided. As long as the data are 

available, any resolution can be used (e.g., county, census tract, census block). For this 

application of the model, census block level data were used. Block-level population counts come 

from the 2000 Census of Population and Housing Summary File 1 (SF-1). This file contains the 

100-percent data, which is the information compiled from the questions asked of all people and 

about every housing unit. 

As part of the population demographics inputs, it is important to integrate working patterns 

into the assessment. In the 2000 U.S. Census, estimates of employment were developed by 
 B-8

census information (US Census Bureau, 2007). The employment statistics are broken down by 

gender and age group, so that each gender/age group combination is given an employment 

probability fraction (ranging from 0 to 1) within each census tract. The age groupings used are: 

16-19, 20-21, 22-24, 25-29, 30-34, 35-44, 45-54, 55-59, 60-61, 62-64, 65-69, 70-74, and >75. 

Children under 16 years of age were assumed to be not employed. 

Since this analysis was conducted at the census block level, block level employment 

probabilities were required. It was assumed that the employment probabilities for a census tract 

apply uniformly to the constituent census blocks. 

B-2.2.2.2 Commuting 

In addition to using estimates of employment by tract, APEX also incorporates home-towork commuting data. Commuting data were originally derived from the 2000 Census and were 

collected as part of the Census Transportation Planning Package (CTPP) (US DOT, 2007). The 

data used contain counts of individuals commuting from home to work locations at a number of 

geographic scales. These data were processed to calculate fractions for each tract-to-tract flow to 

create the national commuting data distributed with APEX. This database contains commuting 

data for each of the 50 states and Washington, D.C. 

Commuting within the Home Tract 

The APEX data set does not differentiate people that work at home from those that 

commute within their home tract. 

Commuting Distance Cutoff 

A preliminary data analysis of the home-work counts showed that a graph of log(flows) 

versus log(distance) had a near-constant slope out to a distance of around 120 kilometers. 

Beyond that distance, the relationship also had a fairly constant slope but it was flatter, meaning 

that flows were not as sensitive to distance. A simple interpretation of this result is that up to 

120 km, the majority of the flow was due to persons traveling back and forth daily, and the 

numbers of such persons decrease fairly rapidly with increasing distance. Beyond 120 km, the 

majority of the flow is made up of persons who stay at the workplace for extended times, in 

which case the separation distance is not as crucial in determining the flow. 

To apply the home-work data to commuting patterns in APEX, a simple rule was chosen. It 

was assumed that all persons in home-work flows up to 120 km are daily commuters, and no 

persons in more widely separated flows commute daily. This meant that the list of destinations 

for each home tract was restricted to only those work tracts that are within 120 km of the home 

tract. When the same cutoff was performed on the 1990 census data, it resulted in 4.75% of the 

home-work pairs in the nationwide database being eliminated, representing 1.3% of the workers. 

The assumption is that this 1.3% of workers do not commute from home to work on a daily 

basis. It is expected that the cutoff reduced the 2000 data by similar amounts. 

Eliminated Records 
 B-9

A number of tract-to-tract pairs were eliminated from the database for various reasons. A 

fair number of tract-to-tract pairs represented workers who either worked outside of the U.S. 

(9,631 tract pairs with 107,595 workers) or worked in an unknown location (120,830 tract pairs 

with 8,940,163 workers). An additional 515 workers in the commuting database whose data 

were missing from the original files, possibly due to privacy concerns or errors, were also 

deleted. 

Commuting outside the study area 

APEX allows for some flexibility in the treatment of persons in the modeled population who 

commute to destinations outside the study area. By specifying “KeepLeavers = No” in the 

simulation control parameters file, people who work inside the study area but live outside of it 

are not modeled, nor are people who live in the study area but work outside of it. By specifying 

“KeepLeavers = Yes,” these commuters are modeled. This triggers the use of two additional 

parameters, called LeaverMult and LeaverAdd. While a commuter is at work, if the workplace is 

outside the study area, then the ambient concentration is assumed to be related to the average 

concentration over all air districts at the same point in time, and is calculated as: 

Ambient Concentrat ion = LeaverMult × avg (t) + LeaverAdd equation (1) 

where: 

 Ambient Concentration = Calculated ambient air concentrations for locations outside 

of the study area (ppm or ppm) 

 LeaverMult = Multiplicative factor for city-wide average concentration, 

applied when working outside study area 

 avg(t) = Average ambient air concentration over all air districts in 

study area, for time t (ppm or ppm)

 LeaverAdd = Additive term applied when working outside study area 

All microenvironmental concentrations for locations outside of the study area are determined 

from this ambient concentration by the same function as applies inside the study area. 

Block-level commuting 

For census block simulations, APEX requires block-level commuting file. A special software 

preprocesser was created to generate these files for APEX on the basis of the tract-level 

commuting data and finely-resolved land use data. The software calculates commuting flows 

between census blocks for the employed population according equation (2). 

Flowblock =Flow tract×Fpop×Fland equation (2) 

where: 

Flow block = flow of working population between a home block and a work block. 

Flow tract = flow of working population between a home tract and a work tract. 

F pop = fraction of home tract’s working population residing in the home block. 

F land = fraction of work tract’s commercial/industrial land area in the work block 
 B-10

Thus, it is assumed 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. 

B-2.2.2.3 Profile Functions 

A Profile Functions file contains settings used to generate results for variables related to 

simulated individuals. While certain settings for individuals are generated automatically by 

APEX based on other input files, including demographic characteristics, others can be specified 

using this file. For example, the file may contain settings for determining whether the profiled 

individual’s residence has an air conditioner, a gas stove, etc. As an example, the Profile 

Functions file contains fractions indicating the prevalence of air conditioning in the cities 

modeled in this assessment (Figure B-1). APEX uses these fractions to stochastically generate 

air conditioning status for each individual. The derivation of particular data used in specific 

microenvironments is provided below. 

 

AC_Home 

! Has air conditioning at home 

TABLE 

INPUT1 PROBABILITY 2 “A/C probabilities” 

0.85 0.15 

RESULT INTEGER 2 “Yes/No” 

1 2 

# 

Figure B-1. Example of a profile function file for A/C prevalence. 

B-2.2.3 Activity Pattern 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 

have varying pollutant exposure concentrations. To accurately model individuals and their 

exposure to pollutants, it is critical to understand their daily activities. 

The Consolidated Human Activity Database (CHAD) provides data for where people spend 

time and the activities performed. CHAD was designed to provide a basis for conducting multiroute, multi-media exposure assessments (McCurdy et al., 2000). The data contained within 

CHAD come from multiple activity pattern surveys with varied structures (Table B-2), however 

the surveys have commonality in containing daily diaries of human activities and personal 

attributes (e.g., age and gender). 

There are four CHAD-related input files used in APEX. Two of these files can be 

downloaded directly from the CHADNet (http://www.epa.gov/chadnet1), and adjusted to fit into 

the APEX framework. These are the human activity diaries file and the personal data file, and 

are discussed below. A third input file contains metabolic information for different activities 

listed in the diary file, these are not used in this exposure analysis. The fourth input file maps 

five-digit location codes used in the diary file to APEX microenvironments; this file is discussed 

in the section describing microenvironmental calculations (Section B-2.2.4.4). 

B-2.2.3.1 Personal Information file 
 B-11

 Personal attribute data are contained in the CHAD questionnaire file that is distributed with 

APEX. This file also has information for each day individuals have diaries. The different 

variables in this file are: 

• The study, person, and diary day identifiers 

• Day of week 

• Gender 

• Employment status 

• Age in years 

• Maximum temperature in degrees Celsius for this diary day 

• Mean temperature in degrees Celsius for this diary day 

• Occupation code 

• Time, in minutes, during this diary day for which no data are included in the database 

B-2.2.3.2 Diary Events file 

The human activity diary data are contained in the events file that is distributed with APEX. 

This file contains the activities for the nearly 23,000 people with intervals ranging from one 

minute to one hour. An individuals’ diary varies in length from one to 15 days. This file 

contains the following variables: 

• The study, person, and diary day identifiers 

• Start time of this activity 

• Number of minutes for this activity 

• Activity code (a record of what the individual was doing) 

• Location code (a record of where the individual was) 
 B-12

Table B-2. Summary of activity pattern studies used in CHAD. 

B-2.2.3.3 Construction of Longitudinal Activity Sequences 

Typical time-activity pattern data available for inhalation exposure modeling consist of a 

sequence of location/activity combinations spanning a 24-hour duration, with 1 to 3 diary-days 

for any single individual. Exposure modeling requires information on activity patterns over 

longer periods of time, e.g., a full year. For example, even for pollutant health effects with short 

averaging times (e.g., NO2 1-hour average concentration) it may be desirable to know the 

frequency of exceedances of a concentration over a long period of time (e.g., the annual number 

of exceedances of a 1-hour average NO2 concentration of 200 ppb for each simulated individual). 

Long-term multi-day activity patterns can be estimated from single days by combining the 

daily records in various ways, and the method used for combining them will influence the 

variability of the long-term activity patterns across the simulated population. This in turn will 

influence the ability of the model to accurately represent either long-term average high-end 

exposures, or the number of individuals exposed multiple times to short-term high-end 

concentrations. 

Study Name Location 

Study 

time 

period Ages Persons 

Person

-days 

Diary type 

/study 

design Reference 

Baltimore A single 

building in 

Baltimore 

01/1997-

02/1997, 

07/1998-

08/1998 

72-93 26 292 Diary Williams et al. (2000) 

California 

Adolescents 

and Adults 

(CARB) 

California 10/1987-

09/1988 

12-17 

18-94 

181 

1,552 

181 

1,552 

Recall 

/Random 

Robinson et al. 

(1989); 

Wiley et al. (1991a) 

California 

Children 

(CARB) 

California 04/1989- 

02/1990 

0-11 1,200 1,200 Recall 

/Random 

Wiley et al. (1991b) 

Cincinnati 

(EPRI) 

Cincinnati 

MSA 

03/1985-

04/1985, 

08/1985 

0-86 888 2,587 Diary 

/Random 

Johnson (1989) 

Denver 

(EPA) 

Denver 

MSA 

11/1982- 

02/1983 

18-70 432 791 Diary 

/Random 

Johnson (1984); 

Akland et al. (1985) 

Los Angeles: 

Elementary 

School 

Children 

Los 

Angeles 

10/1989 10-12 17 51 Diary Spier et al. (1992) 

Los Angeles: 

High School 

Adolescents 

Los 

Angeles 

09/1990-

10/1990 

13-17 19 42 Diary Spier et al. (1992) 

National: 

NHAPS-Air 

National 09/1992-

10/1994 

0-93 4,326 4,326 Recall 

/Random 

Klepeis et al. (1996); 

Tsang and Klepeis 

(1996) 

National: 

NHAPSWater 

National 09/1992-

10/1994 

0-93 4,332 4,332 Recall 

/Random 

Klepeis et al. (1996); 

Tsang and Klepeis 

(1996) 

Washington, 

D.C. (EPA) 

Wash. DC 

MSA 

11/1982-

02/1983 

18-98 639 639 Diary 

/Random 

Hartwell et al. (1984); 

Akland et al. (1985) 
 B-13

A common approach for constructing long-term activity patterns from short-term records is 

to re-select a daily activity pattern from the pool of data for each day, with the implicit 

assumption that there is no correlation between activities from day to day for the simulated 

individual. This approach tends to result in long-term activity patterns that are very similar 

across the simulated population. Thus, the resulting exposure estimates are likely to 

underestimate the variability across the population, and therefore, underestimate the high-end 

exposure concentrations or the frequency of exceedances. 

A contrasting approach is to select a single activity pattern (or a single pattern for each 

season and/or weekday-weekend) to represent a simulated individual’s activities over the 

duration of the exposure assessment. This approach has the implicit assumption that an 

individual’s day-to-day activities are perfectly correlated. This approach tends to result in longterm activity patterns that are very different across the simulated population, and therefore may 

over-estimate the variability across the population. 

Cluster-Markov Algorithm 

A new algorithm has been developed and incorporated into APEX to represent the day-today correlation of activities for individuals. The algorithms first use 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 the assumption of no day-to-day correlation (i.e., re-selection 

for each time period) and perfect correlation (i.e., selection of a single daily record to represent 

all days). 

The steps in the algorithm are as follows. 

1. For each demographic group (age, gender, employment status), temperature range, and 

day-of-week combination, the associated time-activity records are partitioned into 3 

groups using cluster analysis. The clustering criterion is a vector of 5 values: the time 

spent in each of 5 microenvironment categories (indoors – residence; indoors – other 

building; outdoors – near road; outdoors – away from road; in vehicle). 

2. For each simulated individual, a single time-activity record is randomly selected from 

each cluster. 

3. A Markov process determines the probability of a given time-activity pattern occurring 

on a given day based on the time-activity pattern of the previous day and cluster-tocluster transition probabilities. The cluster-to-cluster transition probabilities are 

estimated from the available multi-day time-activity records. If insufficient multi-day 

time-activity records are available for a demographic group, season, day-of-week 

combination, then the cluster-to-cluster transition probabilities are estimated from the 

frequency of time-activity records in each cluster in the CHAD data base. 

Details regarding the Cluster-Markov algorithm and supporting evaluations are provided in 

Attachment 1. 
 B-14

B-2.2.4 Calculating Microenvironmental Concentrations 

Probabilistic algorithms are used to estimate the pollutant concentration associated with each 

exposure event. The estimated pollutant concentrations account for the effects of ambient 

(outdoor) pollutant concentration, penetration factor, air exchange rate, decay/deposition rate, 

and proximity to microenvironments can use the transfer factors method while the others use the 

mass balance emission sources, depending on the microenvironment, available data, and the 

estimation method selected by the user. 

APEX calculates air concentrations in the various microenvironments visited by the 

simulated person by using the ambient air data for the relevant blocks, the user-specified 

estimation method, and input parameters specific to each microenvironment. 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. 

B-2.2.4.1 Mass Balance Model 

The mass balance method simulates an enclosed microenvironment as a well-mixed 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. 

Table B-3 lists the parameters required by the mass balance method to calculate 

concentrations in a microenvironment. A proximity factor (fproximity) is used to account for 

differences in ambient concentrations between the geographic location represented by the 

ambient air quality data (e.g., a regional fixed-site monitor or modeled concentration) and the 

geographic location of the microenvironment (e.g., near a roadway). This factor could take a 

value either greater than or less than 1. Emission source (ES) represents the emission rate for the 

emission source and concentration source (CS) is the mean air concentration resulting from the 

source. Rremoval is defined as the removal rate of a pollutant from a microenvironment due to 

deposition, filtration, and chemical reaction. The air exchange rate (Rair exchange) is expressed in 

air changes per hour. 

Table B-3. Mass balance model parameters. 

Variable Definition Units Value Range

f proximity Proximity factor unitless f proximity ≥ 0 

CS Concentration source ppb CS ≥ 0 

R removal Removal rate due to deposition, 

filtration, and chemical reaction 

1/hr Rremoval ≥ 0 

R air exchange Air exchange rate 1/hr Rair exchange ≥ 0 

V Volume of microenvironment m3 V > 0 

The mass balance equation for a pollutant in a microenvironment is described by: 
 B-15

= ΔCin − ΔCout − ΔCremoval + ΔCsource dt

dC ME (t) equation (3) 

where: 

 dCME(t) = Change in concentration in a microenvironment at time t (ppb), 

ΔCin = Rate of change in microenvironmental concentration due to influx 

of air (ppb/hour), 

ΔCout = Rate of change in microenvironmental concentration due to outflux 

of air (ppb/hour), 

ΔCremoval = Rate of change in microenvironmental concentration due to 

removal processes (ppb/hour), and 

ΔCsource = Rate of change in microenvironmental concentration due to an 

emission source inside the microenvironment (ppb/hour). 

Within the time period of an hour each of the rates of change, ΔCin, ΔCout, ΔCremoval, and 

ΔCsource, is assumed to be constant. At each hour time step of the simulation period, APEX 

estimates the hourly equilibrium, hourly ending, and hourly mean concentrations using a series 

of equations that account for concentration changes expected to occur due to these physical 

processes. Details regarding these equations are provided in the APEX User’s Guide. APEX 

reports hourly mean concentration as hourly concentration for a specific hour. The calculation 

then continues to the next hour by using the end concentration for the previous hour as the initial 

microenvironmental concentration. A description of the input parameters estimates used for 

microenvironments using the mass balance approach is provided below. 

 B-2.2.4.2 Factors Model 

The factors method is simpler than the mass balance method. It does not calculate 

concentration in a microenvironment from the concentration in the previous hour and it has 

fewer parameters. Table B-4 lists the parameters required by the factors method to calculate 

concentrations in a microenvironment without emissions sources. 

Table B-4. Factors model parameters. 

Variable Definition Units Value Range

f proximity Proximity factor unitless f proximity ≥ 0 

f penetration Penetration factor unitless 0 ≤ f penetration ≤ 1 

The factors method uses the following equation to calculate hourly mean concentration in a 

microenvironment from the user-provided hourly air quality data: 

 ambient proximity penetration

hourlymean

ME C =C x f x f equation (4) 

where: 

hourlymean CME = Hourly concentration in a microenvironment (ppb) 

Cambient = Hourly concentration in ambient environment (ppb) 

fproximity = Proximity factor (unitless) 

fpenetration = Penetration factor (unitless) 
 B-16

The ambient NO2 concentrations are from the air quality data input file. The proximity factor 

is a unitless parameter that represents the proximity of the microenvironment to a monitoring 

station. The penetration factor is a unitless parameter that represents the fraction of pollutant 

entering a microenvironment from outside the microenvironment via air exchange. The 

development of the specific proximity and penetration factors used in this analysis are discussed 

below for each microenvironment using this approach. 

B-2.2.4.3 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 

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 are: 1) factors and 2) mass balance. A list of microenvironments used in this 

study, the calculation method used, and the parameters used to calculate the microenvironment 

concentrations can be found in Table B-5. 

Table B-5. List of microenvironments and calculation methods used. 

Microenvironment 

No. Name 

Calculation 

Method 

Parameter 

Types used 1

1 Indoors – Residence Mass balance AER and DE 

2 Indoors – Bars and restaurants Mass balance AER and DE 

3 Indoors – Schools Mass balance AER and DE 

4 Indoors – Day-care centers Mass balance AER and DE 

5 Indoors – Office Mass balance AER and DE 

6 Indoors – Shopping Mass balance AER and DE 

7 Indoors – Other Mass balance AER and DE 

8 Outdoors – Near road Factors PR 

9 Outdoors – Public garage - parking lot Factors PR 

10 Outdoors – Other Factors None 

11 In-vehicle – Cars and Trucks Factors PE and PR 

12 In-vehicle - Mass Transit (bus, subway, train) Factors PE and PR 

0 Not modeled 

1 AER=air exchange rate, DE=decay-deposition rate, PR=proximity factor, PE=penetration 

factor 

Each of the microenvironments is designed to simulate an environment in which people spend 

time during the day. CHAD locations are linked to the different microenvironments in the 

Microenvironment Mapping File (see below). There are many more CHAD locations than 

microenvironment locations (there are 113 CHAD codes versus 12 microenvironments in this 

assessment), therefore most of the microenvironments have multiple CHAD locations mapped to 

them. 

B-2.2.4.4 Mapping of APEX Microenvironments to CHAD Diaries 
 B-17

The Microenvironment Mapping file matches the APEX Microenvironments to CHAD 

Location codes. Table B-6 gives the mapping used for the APEX simulations. 

Table B-6. Mapping of CHAD activity locations to APEX microenvironments. 

CHAD Loc. Description APEX micro 

--------- ------------------------------------------------- 

U Uncertain of correct code = -1 Unknown 

X No data = -1 Unknown 

30000 Residence, general = 1 Indoors-Residence 

30010 Your residence = 1 Indoors-Residence 

30020 Other residence = 1 Indoors-Residence 

30100 Residence, indoor = 1 Indoors-Residence 

30120 Your residence, indoor = 1 Indoors-Residence 

30121 ..., kitchen = 1 Indoors-Residence 

30122 ..., living room or family room = 1 Indoors-Residence 

30123 ..., dining room = 1 Indoors-Residence 

30124 ..., bathroom = 1 Indoors-Residence 

30125 ..., bedroom = 1 Indoors-Residence 

30126 ..., study or office = 1 Indoors-Residence 

30127 ..., basement = 1 Indoors-Residence 

30128 ..., utility or laundry room = 1 Indoors-Residence 

30129 ..., other indoor = 1 Indoors-Residence 

30130 Other residence, indoor = 1 Indoors-Residence 

30131 ..., kitchen = 1 Indoors-Residence 

30132 ..., living room or family room = 1 Indoors-Residence 

30133 ..., dining room = 1 Indoors-Residence 

30134 ..., bathroom = 1 Indoors-Residence 

30135 ..., bedroom = 1 Indoors-Residence 

30136 ..., study or office = 1 Indoors-Residence 

30137 ..., basement = 1 Indoors-Residence 

30138 ..., utility or laundry room = 1 Indoors-Residence 

30139 ..., other indoor = 1 Indoors-Residence 

30200 Residence, outdoor = 10 Outdoors-Other 

30210 Your residence, outdoor = 10 Outdoors-Other 

30211 ..., pool or spa = 10 Outdoors-Other 

30219 ..., other outdoor = 10 Outdoors-Other 

30220 Other residence, outdoor = 10 Outdoors-Other 

30221 ..., pool or spa = 10 Outdoors-Other 

30229 ..., other outdoor = 10 Outdoors-Other 

30300 Residential garage or carport = 7 Indoors-Other 

30310 ..., indoor = 7 Indoors-Other 

30320 ..., outdoor = 10 Outdoors-Other 

30330 Your garage or carport = 1 Indoors-Residence 

30331 ..., indoor = 1 Indoors-Residence 

30332 ..., outdoor = 10 Outdoors-Other 

30340 Other residential garage or carport = 1 Indoors-Residence 

30341 ..., indoor = 1 Indoors-Residence 

30342 ..., outdoor = 10 Outdoors-Other 

30400 Residence, none of the above = 1 Indoors-Residence 

31000 Travel, general = 11 In Vehicle-Cars_and_Trucks 

31100 Motorized travel = 11 In Vehicle-Cars_and_Trucks 

31110 Car = 11 In Vehicle-Cars_and_Trucks 

31120 Truck = 11 In Vehicle-Cars_and_Trucks 

31121 Truck (pickup or van) = 11 In Vehicle-Cars_and_Trucks 

31122 Truck (not pickup or van) = 11 In Vehicle-Cars_and_Trucks 

31130 Motorcycle or moped = 8 Outdoors-Near_Road 

31140 Bus = 12 In Vehicle-Mass_Transit 

31150 Train or subway = 12 In Vehicle-Mass_Transit 

31160 Airplane = 0 Zero_concentration 

31170 Boat = 10 Outdoors-Other 

31171 Boat, motorized = 10 Outdoors-Other 
 B-18

31172 Boat, other = 10 Outdoors-Other 

31200 Non-motorized travel = 10 Outdoors-Other 

31210 Walk = 10 Outdoors-Other 

31220 Bicycle or inline skates/skateboard = 10 Outdoors-Other 

31230 In stroller or carried by adult = 10 Outdoors-Other 

31300 Waiting for travel = 10 Outdoors-Other 

31310 ..., bus or train stop = 8 Outdoors-Near_Road 

31320 ..., indoors = 7 Indoors-Other 

31900 Travel, other = 11 In Vehicle-Cars_and_Trucks 

31910 ..., other vehicle = 11 In Vehicle-Cars_and_Trucks 

32000 Non-residence indoor, general = 7 Indoors-Other 

32100 Office building/ bank/ post office = 5 Indoors-Office 

32200 Industrial/ factory/ warehouse = 5 Indoors-Office 

32300 Grocery store/ convenience store = 6 Indoors-Shopping 

32400 Shopping mall/ non-grocery store = 6 Indoors-Shopping 

32500 Bar/ night club/ bowling alley = 2 Indoors-Bars_and_Restaurants 

32510 Bar or night club = 2 Indoors-Bars_and_Restaurants 

32520 Bowling alley = 2 Indoors-Bars_and_Restaurants 

32600 Repair shop = 7 Indoors-Other 

32610 Auto repair shop/ gas station = 7 Indoors-Other 

32620 Other repair shop = 7 Indoors-Other 

32700 Indoor gym /health club = 7 Indoors-Other 

32800 Childcare facility = 4 Indoors-Day_Care_Centers 

32810 ..., house = 1 Indoors-Residence 

32820 ..., commercial = 4 Indoors-Day_Care_Centers 

32900 Large public building = 7 Indoors-Other 

32910 Auditorium/ arena/ concert hall = 7 Indoors-Other 

32920 Library/ courtroom/ museum/ theater = 7 Indoors-Other 

33100 Laundromat = 7 Indoors-Other 

33200 Hospital/ medical care facility = 7 Indoors-Other 

33300 Barber/ hair dresser/ beauty parlor = 7 Indoors-Other 

33400 Indoors, moving among locations = 7 Indoors-Other 

33500 School = 3 Indoors-Schools 

33600 Restaurant = 2 Indoors-Bars_and_Restaurants 

33700 Church = 7 Indoors-Other 

33800 Hotel/ motel = 7 Indoors-Other 

33900 Dry cleaners = 7 Indoors-Other 

34100 Indoor parking garage = 7 Indoors-Other 

34200 Laboratory = 7 Indoors-Other 

34300 Indoor, none of the above = 7 Indoors-Other 

35000 Non-residence outdoor, general = 10 Outdoors-Other 

35100 Sidewalk, street = 8 Outdoors-Near_Road 

35110 Within 10 yards of street = 8 Outdoors-Near_Road 

35200 Outdoor public parking lot /garage = 9 Outdoors-Public_Garage-Parking 

35210 ..., public garage = 9 Outdoors-Public_Garage-Parking 

35220 ..., parking lot = 9 Outdoors-Public_Garage-Parking 

35300 Service station/ gas station = 10 Outdoors-Other 

35400 Construction site = 10 Outdoors-Other 

35500 Amusement park = 10 Outdoors-Other 

35600 Playground = 10 Outdoors-Other 

35610 ..., school grounds = 10 Outdoors-Other 

35620 ..., public or park = 10 Outdoors-Other 

35700 Stadium or amphitheater = 10 Outdoors-Other 

35800 Park/ golf course = 10 Outdoors-Other 

35810 Park = 10 Outdoors-Other 

35820 Golf course = 10 Outdoors-Other 

35900 Pool/ river/ lake = 10 Outdoors-Other 

36100 Outdoor restaurant/ picnic = 10 Outdoors-Other 

36200 Farm = 10 Outdoors-Other 

36300 Outdoor, none of the above = 10 Outdoors-Other 
 B-19

B-2.2.5 Exposure Calculations 

APEX calculates exposure as a 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: 

T

C t

C

N

j

j

hourlymean

ME j

i

∑= = 1

( ) ( )

 equation (5) 

where: 

 Ci = Hourly exposure concentration at clock hour i of the simulation period 

(ppb) 

N = Number of events (i.e., microenvironments visited) in clock hour i of 

the simulation period. 

hourlymean CME( j) = Hourly mean concentration in microenvironment j (ppm) 

 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 to a specified NO2 concentration level and the 

number of times per year that they are so exposed; the latter metric is in terms of personoccurrences or person-days. The former highlights the number of individuals exposed at least 

one or more times per modeling period to the health effect benchmark level of interest. APEX 

can also report counts of individuals with multiple exposures. This person-occurrences 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 200 

to 300 ppb by 50 ppb increments for 1-hour average exposures. These results are tabulated for 

the population and subpopulations of interest. 

B-2.2.6 Exposure Model Output 

All of the output files written by APEX are ASCII text files. Table B-7 lists each of the 

output data files written for these simulations and provides descriptions of their content. 

Additional output files that can produced by APEX are given in Table 5-1 of the APEX User’s 
 B-20

Guide, and include hourly exposure, ventilation, and energy expenditures, and even detailed 

event-level information, if desired. The names and locations, as well as the output table levels 

(e.g., output percentiles, cut-points), for these output files are specified by the user in the 

simulation control parameters file. 

Table B-7. Example of APEX output files. 

Output File Type Description

Log The Log file contains the record of the APEX model simulation as it progresses. 

If the simulation completes successfully, the log file indicates the input files and 

parameter settings used for the simulation and reports on a number of different 

factors. If the simulation ends prematurely, the log file contains error messages 

describing the critical errors that caused the simulation to end. 

Profile Summary The Profile Summary file provides a summary of each individual modeled in the 

simulation. 

Microenvironment 

Summary

The Microenvironment Summary file provides a summary of the time and 

exposure by microenvironment for each individual modeled in the simulation. 

Sites The Sites file lists the tracts, districts, and zones in the study area, and identifies 

the mapping between them. 

Output Tables The Output Tables file contains a series of tables summarizing the results of the 

simulation. The percentiles and cut-off points used in these tables are defined 

in the simulation control parameters file. 
 B-21

B-3 Philadelphia Exposure Assessment Case-Study 

This section documents detailed methodology and input data used in the Philadelphia 

inhalation exposure assessment for NO2 conducted in support of the current review of the NO2

primary NAAQS. As mentioned in the Overview (section B-1), the Philadelphia analyses were 

not updated since the 1st draft REA and not used in the final REA. One major difference in the 

Philadelphia County assessment compared with that performed for Atlanta was the lack of 

accounting for minor road emissions 

Two important components of the analysis include the approach for estimating temporally 

and spatially variable NO2 concentrations and simulating contact of humans with these pollutant 

concentrations. A combined air quality and exposure modeling approach has been used here to 

generate estimates of 1-hour NO2 exposures within Philadelphia. Details on the approaches used 

are provided below and include the following: 

• Description of the area assessed and populations considered 

• Summary of the air quality modeling methodology and associated input data 

• Description of the inhalation exposure model and associated input data 

• Evaluation of estimated NO2 exposures using modeling methodology 

B-3.1 Study Area Selection and Description 

The selection of areas to include in the exposure analysis takes into consideration 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. 

Philadelphia was selected as a location of interest through a similar statistical analysis of the 

ambient NO2 air quality data described in Appendix A for each monitoring site within a location. 

Criteria were established for selecting sites with high annual means and/or high numbers of 

exceedances of potential health effect benchmark concentrations. The analysis considered all 

data combined, as well as the more recent air quality data (2001-2006) separately. 

The 90th percentile served as the point of reference for the annual means, and across all 

complete site-years for 2001-2006, this value was 23.5 ppb. Seventeen locations contained one 

or more site-years with an annual average concentration at or above the 90th percentile. When 

combined with the number of 1-hour NO2 concentrations at or above 200 ppb, only two locations 

fit these criteria, Philadelphia and Los Angeles. In comparing the size of the potential modeling 

domains and the anticipated complexity in modeling influence of roadway exposures, 

Philadelphia was determined to be a more manageable case-study. 

Philadelphia County is comprised of 17,315 blocks containing a population of 1,517,550 

persons. For this analysis the population studied was limited those residents of Philadelphia 

County residing in census blocks that were either within 400 meters of a major roadway or 
 B-22

within 10 km of a major emission source (see section B-3.5 for definition). This was done to 

maintain balance between the representation of the study area/objectives and the computational 

load regarding file size and processing time. There were 16,857 such blocks containing a 

population of 1,475,651. 

B-3.2 Exposure Period of Analysis 

The exposure periods modeled were 2001 through 2003 to envelop the most recent year of 

travel demand modeling (TDM) data available for the respective study locations (i.e., 2002) and 

to include a 3 years of meteorological data to achieve a degree of stability in the dispersion and 

exposure model estimates. 

B-3.3 Populations Analyzed 

A detailed consideration of the population residing in each modeled area was included where 

the exposure modeling was performed. The assessment includes the general population (All 

Persons) 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 (US EPA, 2007a) that were 

included and that can be modeled in the exposure assessment include: 

• Children (ages 5-18) 

• Asthmatic children (ages 5-18) 

• All persons (all ages) 

• All Asthmatics (all ages) 

In addition to these population subgroups, individuals anticipated to be exposed more 

frequently to NO2 were considered, including those commuting on roadways and persons 

residing near major roadways. To date, this document provides a summary of the subpopulations 

of interest (all asthmatics and asthmatic children), supplemented with additional exposure and 

risk results for the total population where appropriate. 

B-3.4 Simulated Individuals 

Due to the large size of the air quality input files, the modeled area was separated into three 

sections. The number of simulated persons in each model run (3 sections per 3 years) was set to 

50,000, yielding a total of 150,000 persons simulated for each year. The parameters controlling 

the location and size of the simulated area were set to include the county(s) in the selected study 

area. The settings that allow for replacement of CHAD data that are missing gender, 

employment or age values were all set to preclude replacing missing data. The width of the age 

window was set to 20 percent to increase the pool of diaries available for selection. The variable 

that controls the use of additional ages outside the target age window was set to 0.1 to further 

enhance variability in diary selection. See the APEX User’s Guide for further explanation of 

these parameters. The total population simulated for Philadelphia County was approximately 

1.48 million persons, of which there a total simulated population of 163,000 asthmatics. The 

model simulated approximately 281,000 children, of which there were about 48,000 asthmatics. 
 B-23

Due to random sampling, the actual number of specific subpopulations modeled varied slightly 

by year. 

B-3.4.1 Asthma Prevalence Rates 

One of the important population subgroups for the exposure assessment is asthmatic children. 

Evaluation of the exposure of this group with APEX requires the estimation of children’s 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, 2007d; 2007e). 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). These asthma rates were characterized by geographic 

regions, namely Midwest, Northeast, South, and West. Adult asthma prevalence rates for 

Philadelphia County were obtained from the Behavioral Risk Factor Surveillance System 

(BRFSS) survey information (PA DOH, 2008). The average rates for adult males and females in 

Philadelphia for 2001-2003 were 7% and 12%, respectively. These rates were assumed to apply 

to all adults uniformly. Table B-8 provides a summary of the prevalence rates used in the 

exposure analysis by age and gender. 

Table B-8. Asthma prevalence rates by age and gender used for Philadelphia. 

 Females Males 

Region 

(Study Area) Age Prevalence se L95 U95 Prevalence se L95 U95 

0 0.068 0.066 0.007 0.442 0.048 0.033 0.010 0.200 

1 0.072 0.038 0.021 0.221 0.046 0.018 0.019 0.108 

2 0.075 0.022 0.038 0.145 0.052 0.015 0.027 0.097 

3 0.077 0.020 0.042 0.138 0.068 0.018 0.037 0.120 

4 0.082 0.023 0.043 0.151 0.100 0.023 0.059 0.164 

5 0.116 0.030 0.063 0.205 0.149 0.029 0.094 0.226 

6 0.161 0.037 0.092 0.266 0.207 0.042 0.129 0.316 

7 0.185 0.041 0.108 0.298 0.228 0.045 0.143 0.343 

8 0.171 0.040 0.096 0.284 0.222 0.043 0.142 0.332 

9 0.145 0.035 0.080 0.246 0.212 0.041 0.136 0.316 

10 0.135 0.031 0.078 0.223 0.177 0.037 0.108 0.275 

11 0.141 0.031 0.084 0.227 0.166 0.035 0.102 0.259 

12 0.166 0.034 0.102 0.259 0.183 0.036 0.116 0.276 

13 0.174 0.034 0.109 0.266 0.171 0.031 0.113 0.250 

14 0.151 0.029 0.095 0.232 0.170 0.029 0.115 0.244 

15 0.146 0.028 0.093 0.221 0.182 0.029 0.127 0.254 

16 0.146 0.031 0.088 0.232 0.204 0.032 0.142 0.284 

17 0.157 0.054 0.068 0.322 0.242 0.061 0.133 0.399 

Northeast 

(Philadelphia) 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

18+ 0.070 0.040 0.140 0.120 0.090 0.150 

Notes: 

se – Standard error 

L95 – Lower limit on 95th confidence interval 

U95 – Upper limit on 95th confidence interval 
 B-24

B-3.5 Air Quality Data Generated by AERMOD 

Air quality data input to the model were generated by air quality modeling using AERMOD. 

Principal emission sources included both mobile and stationary sources as well as fugitive 

emissions. The methodology is described below. 

B-3.5.1 Meteorological Inputs 

All meteorological data used for the AERMOD dispersion model simulations were processed 

with the AERMET meteorological preprocessor, version 06341. This section describes the input 

data and processing methodologies used to derive input meteorological fields for each of the five 

regions of interest. 

 B-3.5.1.1 Data Selection 

Raw surface meteorological data for the 2001 to 2003 period were obtained from the 

Integrated Surface Hourly (ISH) Database,1 maintained by the National Climatic Data Center 

(NCDC). The ISH data used for this study consists of typical hourly surface parameters 

(including air and dew point temperature, atmospheric pressure, wind speed and direction, 

precipitation amount, and cloud cover) from hourly Automated Surface Observing System 

(ASOS) stations. No on-site observations were used. 

The surface meteorological station used for this analysis is located at Philadelphia 

International (KPHL) airport. The selection of surface meteorological stations minimized the 

distance from the station to city center, minimized missing data, and maximized land-use 

representativeness of the station site compared to the city center. 

The total number of surface observations and the percentage of those observations accepted 

by AERMET (i.e., those observations that were both not missing and within the expected ranges 

of values), are shown by Table B-9. Note that instances of calm winds are not rejected by the 

AERMET processor, but are later treated as calms in the dispersion analysis. There were 1,772 

hours in Philadelphia (7%) with calm winds (see Table B-10). 

Table B-9. Number of AERMET raw hourly surface meteorology observations, percent acceptance rate, 

2001-2003. 

Philadelphia (KPHL) 

Surface Variable n=26,268 

% Accepted a

Precipitation 100 

Station Pressure 99 

Cloud Height 99 

Sky Cover 95 

Horizontal Visibility 99 

Temperature 99 * 

Dew Point 

Temperature 99 

Relative Humidity 99 

 

1

 http://www1.ncdc.noaa.gov/pub/data/techrpts/tr200101/tr2001-01.pdf 
 B-25

Wind Direction 97 

Wind Speed 99 

Notes: 

a

 Percentages are rounded down to the nearest integer. 

* The majority of unaccepted records are due to values 

being out of range. 

Table B-10. Number of calms reported by AERMET by year for Philadelphia. 

Year Number of Calms 

2001 610 

2002 470 

2003 692 

Total 1772 

Mandatory and significant levels of upper-air data were obtained from the NOAA 

Radiosonde Database.2

 Upper air observations show less spatial variation than do surface 

observations; thus they are both representative of larger areas and measured with less spatial 

frequency than are surface observations. The selection of upper-air station locations for each 

city minimized both the proximity of the station to city center and the amount of missing data in 

the records. The selected stations for Philadelphia was Washington Dulles Airport (KIAD). The 

total number of upper-air observations per station per height interval, and the percentage of those 

observations accepted by AERMET, are shown in Table B-11. 

Table B-11. Number and AERMET acceptance rate of upper-air observations 2001-2003. 

Height Philadelphia (KIAD) 

Level Variable 

n % Accepted 

Pressure 2152 100 

Height 2152 100 

Temperature 2152 100 

DewPoint Temperature 2152 100 

WindDirection 2152 100 

Surface 

WindSpeed 2152 85 * 

Pressure 4320 100 

Height 4320 100 

Temperature 4320 100 

DewPoint Temperature 4320 99 

WindDirection 4320 63 

0-500m 

WindSpeed 4320 62 

Pressure 3702 100 

Height 3702 100 

Temperature 3702 100 

DewPointTemperature 3702 99 * 

WindDirection 3702 73 

500-

1000m 

WindSpeed 3702 73 

Pressure 4204 100 

Height 4204 100 

Temperature 4204 100 

1000-

1500m 

DewPointTemperature 4204 97 * 

 

2

 http://raob.fsl.noaa.gov/ 
 B-26

Height Philadelphia (KIAD) 

Level Variable 

n % Accepted 

WindDirection 4204 71 

WindSpeed 4204 71 

Pressure 3354 100 

Height 3354 100 

Temperature 3354 100 

DewPointTemperature 3354 95 * 

WindDirection 3354 50 

1500-

2000m 

WindSpeed 3354 50 

Pressure 3246 100 

Height 3246 100 

Temperature 3246 100 

DewPointTemperature 3246 93 * 

WindDirection 3246 50 

2000-

2500m 

WindSpeed 3246 50 

Pressure 3736 100 

Height 3736 100 

Temperature 3736 100 

DewPointTemperature 3736 90 * 

WindDirection 3736 64 

2500-

3000m 

WindSpeed 3736 64 

Pressure 3614 100 

Height 3614 100 

Temperature 3614 100 

DewPointTemperature 3614 90 * 

WindDirection 3614 65 

3000-

3500m 

WindSpeed 3614 65 

Pressure 2830 100 

Height 2830 100 

Temperature 2830 100 

DewPointTemperature 2830 87 * 

WindDirection 2830 50 

3500-

4000m 

WindSpeed 2830 50 

Pressure 7619 88 * 

Height 7619 71 * 

Temperature 7619 99 * 

DewPointTemperature 7619 79 * 

WindDirection 7619 55 

>4000

m 

WindSpeed 7619 55 

Notes: 

a

 Percentages are rounded down to the nearest integer. 

* The majority of unaccepted records are due to values 

being out of range. 

Shading: 

≤95 of observations were accepted. 

≤75 of observations were accepted. 

 ≤50 of observations were accepted. 
 B-27

B-3.5.2 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 ratio3

, surface albedo4 as a function of the solar angle, and surface 

roughness. 5 

The January 2008 version of AERSURFACE was used to estimate land-use patterns and 

calculate the Bowen ratio, surface albedo, and surface roughness as part of the AERMET 

processing. AERSURFACE uses the US Geological Survey (USGS) National Land Cover Data 

1992 archives (NLCD92). 6 Three to four land-use sectors were manually identified around the 

surface meteorological station using this land-use data. These land-use sectors are used to 

identify the Bowen ratio and surface albedo, which are assumed to represent an area around the 

station of radius 10 km, and to calculate surface roughness by wind direction. 

A monthly temporal resolution was used for the Bowen ratio, albedo, and surface roughness 

at the meteorological site. Because the site was located at an airport, a lower surface roughness 

was calculated for the ‘Commercial/Industrial/Transportation’ land-use type to reflect the 

dominance of transportation land cover rather than commercial buildings. Philadelphia has at 

least one winter month of continuous snow cover, which tends to increase albedo, decrease 

Bowen ratio, and decrease surface roughness for most land-use types during the winter months 

compared to a snow-free area. Seasons were assigned based on 1971-2000 NCDC 30-year 

climatic normals and on input from the state climatologist (Table B-12). 

Table B-12. Seasonal definitions and specifications for Philadelphia. 

Location 

Winter 

(continuous 

snow) 

Winter 

(no snow) Spring Summer Fall 

Philadelphia Dec, Jan, Feb Mar, Apr, May Jun, Jul, Aug Sep, Oct, Nov 

Season definitions provided by the AERSURFACE manual as follows: 

 Winter (continuous snow): Winter with continuous snow on ground 

 Winter (no snow): Late autumn after frost and harvest, or winter with no snow 

 Spring: Transitional spring with partial green coverage or short annuals 

 Summer: Midsummer with lush vegetation 

 Fall: Autumn with unharvested cropland 

Figure B-2 illustrates show the manually created land-use sectors around the application site; 

a 1.9 mile (3 km) radius circle was used. Data are from the NLCD92 database. Prior to the 

 

3 For any moist surface, the Bowen Ratio is the ratio of heat energy used for sensible heating (conduction and 

convection) to the heat energy used for latent heating (evaporation of water or sublimation of snow). The Bowen 

ratio ranges from about 0.1 for the ocean surface to more than 2.0 for deserts. Bowen ratio values tend to decrease 

with increasing surface moisture for most land-use types. 

4

 The ratio of the amount of electromagnetic radiation reflected by the earth's surface to the amount incident upon it. 

Value varies with surface composition. For example, snow and ice vary from 80% to 85% and bare ground from 

10% to 20%. 

5

 The presence of buildings, trees, and other irregular land topography that is associated with its efficiency as a 

momentum sink for turbulent air flow, due to the generation of drag forces and increased vertical wind shear. 6

 http://seamless.usgs.gov/ 
 B-28

release of AERSURFACE, the user was required to manually pull values of Bowen ratio (β0), 

albedo (α), and surface roughness (z0) per season and per land-use sector from look-up tables in 

the AERMET User’s Guide. Using the look-up tables, values of these three surface 

characteristics vary by the four seasons and by eight basic land-use categories. Furthermore, the 

AERMOD Implementation Guide was somewhat ambiguous about whether Bowen ratio values 

should also vary with wind direction sector, as does the surface roughness. AERSURFACE 

resolves these issues by providing a uniform methodology for calculation of surface effects on 

dispersion; it also only varies surface roughness by wind direction. 

Before AERSURFACE, without an automated algorithm to determine land-use patterns, it 

was simplest for the user to visually estimate land usage by sector. With AERSURFACE, the 

land-use is automatically determined. The proximity of the meteorological site to an airport and 

whether the site was located in an arid region were previously not explicitly accounted for as 

they now are in AERSURFACE. Snow cover, too, is critical for determination of α, but was 

largely left to user’s discretion regarding its presence. With AERSURFACE, the lookup tables 

have separate columns for winter without much snow and for winter with abundant snow. The 

user determines if winter at a particular location contains at least one month of continuous snow 

cover, and AERSURFACE will pull values of the surface characteristics from the appropriate 

winter column. 

We conducted a sensitivity test to evaluate the impacts of using this new tool on the present 

analysis. Figure B-3 shows a sample comparison of surface roughness values at the Philadelphia 

site with and without the use of AERSURFACE. In the Figure, estimated surface roughness 

values using visual land-use estimations and look-up table values are shown in muted shades and 

AERSURFACE values in dark shades. Monthly season definitions are the same in both cases. 

However, in the AERSURFACE case, winter was specified as having a one-month period of 

snow cover. Also, in the AERSURFACE case the site was specified as being at an airport. 

In this case, z0 values are much lower with AERSURFACE than with a visual estimation of 

land-use. In the AERSURFACE tool, Philadelphia was noted as being at an airport, tending to 

represent the lower building heights in the region and the inverse distance weighting 

implemented in the tool. Thus, lower z0 values were obtained over most developed-area sectors 

in this scenario. The indication that at least one month of continuous snow cover is present also 

tends to lower wintertime z0 values. In addition to these systematic differences, the automated 

AERSURFACE land-use analysis for Philadelphia tended to identify less urban coverage and 

more water coverage, lowering roughness values, but it also tended to identify more forest cover 

and less cultivated land cover than our visual analysis, increasing some z0 values. 

β0 and α also varied significantly between the scenarios. However, this was largely due to 

two practical matters: First, the independence of these variables of wind direction in the 

AERSURFACE case and secondly the use of monthly-varying moisture conditions in one test 

case and not another. Thus we have not presented those results 
 B-29

 Figure B-2. Land-use and sectors around the Philadelphia-area surface meteorological station (KPHL). 

Sector borders are 80, 184, 262, and 312 degrees from geographic North. Philadelphia city center is labeled. 
 B-30

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0 50 100 150 200 250 300 350

Wind direction (degrees)

Roughness Length (m)

Minimum (old) Maximum (old) Average (old) Minimum (new) Maximum (new) Average (new)

Figure B-3. Estimated z0 values for the Philadelphia case-study analysis using visual and AERSURFACE 

land-use estimations. 

B-3.5.3 Meteorological Data Analysis 

The AERMET application location and elevation were taken as the center of the modeled 

city, estimated using Google Earth version 4.2.0198.2451 (beta) and defined as 39.952 °N, 

75.164 °W, 12 m. The 2001-2003 AERSURFACE processing was run three times – once 

assuming the entire period was drier than normal, once assuming the entire period was wetter 

than normal, and once assuming the entire period was of average precipitation accumulation. 

These precipitation assumptions influence the Bowen ratio, discussed above. 

To create meteorological input records that best represent the city for each of the three years, 

the resulting surface output files for each site were then pieced together on a month-by-month 

basis, with selection based on the relative amount of precipitation in each month. Any month 

where the actual precipitation amount received was at least twice the 1971-2000 NCDC 30-year 

climatic normal monthly precipitation amount was considered wetter than normal, while any 

month that received less than half the normal amount of precipitation amount was considered 

drier than normal; all other months were considered to have average surface moisture conditions. 

Table B-13 indicates the surface moisture condition for each month evaluated in this 

Philadelphia case-study. 
 B-31

Table B-13. Monthly precipitation compared to NCDC 30-year climatic normal for Philadelphia, 2001-2003. 

Year Jan Feb Mar Apr May Jun 

74.8% 103.6% 144.2% 43.9% 102.9% 180.1% 

2001 Jul Aug Sep Oct Nov Dec 

29.9% 26.0% 67.1% 30.6% 17.9% 64.6% 

Jan Feb Mar Apr May Jun 

69.9% 17.7% 96.4% 52.7% 89.2% 93.9% 

Jul Aug Sep Oct Nov Dec 2002

51.0% 59.0% 89.1% 202.7% 94.2% 117.9% 

Jan Feb Mar Apr May Jun 

53.2% 165.0% 102.7% 62.0% 108.5% 246.2% 

Jul Aug Sep Oct Nov Dec 2003

46.5% 86.1% 120.8% 162.8% 92.9% 158.6% 

Shading: 

 Less than or equal to half the normal monthly precipitation amount 

Less than twice the normal precipitation level and greater than half the 

normal amount 

 At least twice the normal precipitation level 

B-3.5.4 On-Road Emissions Preparation 

Information on traffic data in the Philadelphia area was obtained from the Delaware Valley 

Regional Planning Council (DVRPC7) via their most recent, baseline travel demand modeling 

(TDM) simulation – that is, the most recent simulation calibrated to match observed traffic data. 

DVRPC provided the following files. 

• Shapefiles of TDM outputs for the 2002 baseline year for all links in their network. 

• Input files for the MOBILE6.2 emissions model that characterize local inputs that differ 

from national defaults, including fleet registration distribution information. 

• Postprocessing codes they employ for analysis of TDM outputs into emission inventory 

data, to ensure as much consistency as possible between the methodology used for this 

study and that of DVRPC. These include DVRPC’s versions of the local SVMT.DEF, 

HVMT.DEF, and FVMT.DEF MOBILE6.2 input files describing the vehicle miles 

traveled (VMT) by speed, hour, and facility, respectively, by county in the Delaware 

Valley area. 

• A lookup table used to translate average annual daily traffic (AADT) generated by the 

TDM into hourly values. 

Although considerable effort was expended to maintain consistency between the DVRPC 

approach to analysis of TDM data and that employed in this analysis, including several personal 

communications with agency staff on data interpretation, complete consistency was not possible 

due to the differing analysis objectives. The DVRPC creates countywide emission inventories. 

This study created spatially and temporally resolved emission strengths for dispersion modeling. 

B-3.5.4.1 Emission Sources and Locations 

 

7

 http://www.dvrpc.org/ 
 B-32

The TDM simulation’s shapefile outputs include annual average daily traffic (AADT) 

volumes and a description of the loaded highway network. 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.8 

To reduce the scope of the analysis, the full set of links in the DVRPC network was first 

filtered to include only those roadway types considered major (i.e., freeway, parkway, major 

arterial, ramp), and that had AADT values greater than 15,000 vehicles per day (one direction). 

However, the locations of links in the model do not necessarily agree well with the roads 

they are attempting to represent. While the exact locations of the links may not be mandatory for 

DVRPC’s travel demand modeling, the impacts of on-road emissions on fixed receptors is 

crucially linked to the distance between the roadways and receptors. Hence, it was necessary to 

modify the link locations from the TDM to the best known locations of the actual roadways. The 

correction of link locations was done based on the locations of the nodes that define the end 

points of links with a GIS analysis, as follows. 

A procedure was developed to relocate TDM nodes to more realistic locations. The 

nodes in the TDM represent the endpoints of links in the transportation planning network and are 

specified in model coordinates. The model coordinate system is a Transverse Mercator 

projection of the TranPlan Coordinate System with a false easting of 31068.5, false northing of -

200000.0, central meridian: -75.00000000, origin latitude of 0.0, scale factor of 99.96, and in 

units of miles. The procedure moved the node locations to the true road locations and translated 

to dispersion model coordinates. The Pennsylvania Department of Transportation (PA DOT) 

road network database9

 was used as the specification of the true road locations. The nodes were 

moved to coincide with the nearest major road of the corresponding roadway type using a builtin function of ArcGIS. Once the nodes had been placed in the corrected locations, a line was 

drawn connecting each node pair to represent a link of the adjusted planning network. 

To determine hourly traffic on each link, the AADT volumes were converted to hourly 

values by applying DVRPC’s seasonal and hourly scaling factors. To determine hourly traffic 

on each link, the AADT volumes were converted to hourly values by applying DVRPC’s 

seasonal and hourly scaling factors. The heavy-duty vehicle fraction – which is assumed by 

DVRPC to be about 6% in all locations and times – was also applied.10 Another important 

 

8

 The TDM capacity specifications are not the same as those defined by the Highway Capacity Manual (HCM). 

Following consultation with DVRPC, the HCM definition of capacity was used in later calculations discussed 

below. 

9

 http://www.pasda.psu.edu/ 

10 As shown by Figure B-4 NOx emissions from HDVs tend to be higher than their LDV counterparts by about a 

factor of 10. However, the HDV fraction is less than 10% of the total VMT in most circumstances, mitigating their 

influence on composite emission factors, although this mitigating effect is less pronounced at some times than 

others. For example, nighttimes on freeways tend to show a smaller reduction in HDV volume than in total volume, 

and thus an increased HDV fraction. This effect is not captured in most TDMs or emission postprocessors and – 

both to maintain consistency with the local MPO’s vehicle characterizations and emissions modeling and due to lack 

of other relevant data – was also not included here. The net result of this is likely to be slightly underestimated 

emissions from major freeways during late-night times. 
 B-33

variable, the number of traffic signals occurring on a given link, was taken from the TDM linkdescription information. 

Several of these parameters are shown in the following set of tables. 

• Table B-14 hourly scaling factors 

• Table B-15 seasonal scaling factors 

• Table B-16 number of signals per roadway mile 

• Table B-17 statistical summaries of AADT volumes for links included in the study. 

Table B-14. Hourly scaling factors (in percents) applied to Philadelphia County AADT volumes. 

Road 

Type Region 0:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:00 11:00

CBD 1.23 0.86 0.74 0.84 1.23 2.50 4.87 6.52 6.47 5.75 4.99 5.02 

Fringe 1.23 0.86 0.74 0.84 1.23 2.50 4.87 6.52 6.47 5.75 4.99 5.02 

Urban 1.23 0.86 0.74 0.84 1.23 2.50 4.87 6.52 6.47 5.75 4.99 5.02 

Suburban 0.96 0.64 0.54 0.61 0.90 2.16 5.39 7.33 6.85 5.52 4.90 4.94 

Freeway 

Rural 0.71 0.48 0.38 0.48 0.95 2.54 6.05 7.77 6.79 5.22 4.64 4.78 

CBD 1.43 0.96 0.61 0.50 0.58 1.17 2.89 5.50 6.87 5.87 5.37 5.17 

Fringe 1.53 0.97 0.62 0.47 0.54 1.10 2.99 5.77 6.53 5.60 5.14 4.86 

Urban 1.13 0.68 0.52 0.45 0.63 1.68 4.26 6.68 6.86 5.47 5.09 5.17 

Suburban 0.70 0.40 0.32 0.33 0.55 1.71 4.51 7.04 6.84 5.37 4.95 5.36 

Arterial 

Rural 0.60 0.36 0.34 0.41 0.77 2.29 5.47 7.37 6.62 5.36 5.09 5.35 

CBD 1.11 0.71 0.45 0.37 0.41 0.97 2.39 4.82 6.72 6.50 4.60 4.93 

Fringe 1.00 0.55 0.37 0.21 0.39 0.98 1.98 5.31 5.91 5.78 5.14 5.19 

Urban 1.19 0.74 0.53 0.43 0.54 1.32 3.37 6.54 6.86 5.09 4.65 4.95 

Suburban 0.53 0.29 0.21 0.20 0.37 1.25 3.94 7.51 7.50 5.24 4.66 5.22 

Local 

Rural 0.55 0.32 0.25 0.30 0.57 1.89 5.26 7.93 6.84 4.94 4.57 4.89 

CBD 1.23 0.86 0.74 0.84 1.23 2.50 4.87 6.52 6.47 5.75 4.99 5.02 

Fringe 1.23 0.86 0.74 0.84 1.23 2.50 4.87 6.52 6.47 5.75 4.99 5.02 

Urban 1.23 0.86 0.74 0.84 1.23 2.50 4.87 6.52 6.47 5.75 4.99 5.02 

Suburban 0.96 0.64 0.54 0.61 0.90 2.16 5.39 7.33 6.85 5.52 4.90 4.94 

Ramp 

Rural 0.71 0.48 0.38 0.48 0.95 2.54 6.05 7.77 6.79 5.22 4.64 4.78 

Road 

Type Region 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00

CBD 4.97 5.77 6.40 6.60 7.02 6.76 6.27 4.20 3.52 3.06 2.50 1.92 

Fringe 4.97 5.77 6.40 6.60 7.02 6.76 6.27 4.20 3.52 3.06 2.50 1.92 

Urban 4.97 5.77 6.40 6.60 7.02 6.76 6.27 4.20 3.52 3.06 2.50 1.92 

Suburban 5.05 5.19 5.90 6.80 7.58 7.67 6.51 4.27 3.34 2.97 2.32 1.66 

Freeway 

Rural 4.92 5.01 5.75 7.12 7.88 8.18 6.27 4.31 3.45 2.97 2.10 1.27 

CBD 5.27 5.57 5.95 6.63 7.39 7.81 6.36 4.78 4.05 3.74 3.18 2.36 

Fringe 5.52 5.40 6.08 6.88 7.36 8.08 6.24 4.98 4.21 3.82 3.13 2.19 

Urban 5.42 5.54 6.16 7.04 7.39 7.42 6.08 4.74 3.77 3.31 2.61 1.93 

Suburban 5.75 5.71 6.12 7.05 7.66 7.98 6.42 4.81 3.83 3.13 2.15 1.34 

Arterial 

Rural 5.55 5.50 6.00 7.11 7.82 7.98 6.26 4.48 3.50 2.80 1.88 1.11 

CBD 6.26 6.74 6.88 6.78 7.64 8.10 6.57 4.96 3.96 3.02 2.88 2.25 

Fringe 6.31 5.64 6.64 7.32 7.85 9.52 6.25 5.50 5.29 2.87 2.46 1.56 

Local 

Urban 5.25 5.40 6.44 7.35 7.80 7.85 6.41 5.02 4.04 3.46 2.79 2.01 
 B-34

Suburban 5.78 5.57 6.01 7.11 8.20 8.98 6.83 5.02 3.83 2.90 1.82 1.05 

Rural 5.20 5.11 5.89 7.41 8.53 8.93 6.75 4.82 3.64 2.70 1.73 0.99 

CBD 4.97 5.77 6.40 6.60 7.02 6.76 6.27 4.20 3.52 3.06 2.50 1.92 

Fringe 4.97 5.77 6.40 6.60 7.02 6.76 6.27 4.20 3.52 3.06 2.50 1.92 

Urban 4.97 5.77 6.40 6.60 7.02 6.76 6.27 4.20 3.52 3.06 2.50 1.92 

Suburban 5.05 5.19 5.90 6.80 7.58 7.67 6.51 4.27 3.34 2.97 2.32 1.66 

Ramp 

Rural 4.92 5.01 5.75 7.12 7.88 8.18 6.27 4.31 3.45 2.97 2.10 1.27 

Table B-15. Seasonal scaling factors applied to Philadelphia County AADT volumes. 

Season 

Road 

Type Factor 

Winter Freeway 0.945 

Spring Freeway 1.006 

Summer Freeway 1.041 

Autumn Freeway 1.009 

Winter Arterial 0.942 

Spring Arterial 1.004 

Summer Arterial 1.041 

Autumn Arterial 1.013 

Winter Local 0.933 

Spring Local 1.012 

Summer Local 1.05 

Autumn Local 1.004 

Winter Ramp 0.944 

Spring Ramp 1.005 

Summer Ramp 1.041 

Autumn Ramp 1.011 

Table B-16. Signals per mile, by link type, applied to Philadelphia County AADT volumes. 

 Region Type 

Functional Class CBD Fringe Rural Suburban Urban 

Freeway 0 0 0 0 0 

Local 8 6 1.5 3 5 

Major Arterial 8 6 1 2 4 

Minor Arterial 8 6 1.3 2 4 

Parkway 4 2 0.5 1 1.5 

Ramp 0 0 0 0 0 

Table B-17. Statistical summary of AADT volumes (one direction) for Philadelphia County AERMOD 

simulations. 

Statistic Road Type CBD Fringe Suburban Urban

Arterial 186 58 210 580

Freeway 11 10 107 98

Count 

Ramp 0 4 3 1

Arterial 15088 15282 15010 15003

Freeway 15100 18259 15102 15100

Minimum 

AADT 

Ramp 16796 15679 16337

Maximum Arterial 44986 44020 48401 44749

AADT Freeway 39025 56013 68661 68661
 B-35

Ramp 40538 24743 16337

Arterial 21063 21196 20736 22368

Freeway 25897 40168 33979 31294

Average 

AADT 

Ramp 24468 18814 16337

B-3.5.4.2 Emission Source Strength 

On-road mobile emission factors were derived from the MOBILE6.2 emissions model as 

follows. The DVRPC-provided external data files describing the vehicle miles traveled (VMT) 

distribution by speed, functional class, and hour, as well as the registration distribution and Post1994 Light Duty Gasoline Implementation for Philadelphia County were all used in the model 

runs without modification. To further maintain consistency with the recent DVRPC inventory 

simulations and maximize temporal resolution, the DVRPC’s seasonal particulate matter (PM) 

MOBILE6 input control files were also used. These files include county-specific data describing 

the vehicle emissions inspection and maintenance (I/M) programs, on-board diagnostics (OBD) 

start dates, VMT mix, vehicle age distributions, default diesel fractions, and representative 

minimum and maximum temperatures, humidity, and fuel parameters. The simulations are 

designed to calculate average running NOx emission factors.11 

These input files were modified for the current project to produce running NOx emissions in 

grams per mile for a specific functional class (Freeway, Arterial, or Ramp) and speed. Iterative 

MOBILE6.2 simulations were conducted to create tables of average Philadelphia County 

emission factors resolved by speed (2.5 to 65 mph), functional class, season, and year (2001, 

2002, or 2003) for each of the eight combined MOBILE vehicle classes (LDGV, LDGT12, 

LDGT34, HDGV, LDDV, LDDT, HDDV, and MC)12. The resulting tables were then 

consolidated into speed, functional class, and seasonal values for combined light- and heavy-duty 

vehicles. Figure B-4 shows an example of the calculated emission factors for Autumn, 2001. 

 

11 Basing the present emissions model input files on MPO-provided PM, rather than NOx input files should not cause 

confusion. MPO-provided PM files were used because they contain quarterly rather than annual or biannual 

information. In all cases the output species were modified to produce gaseous emissions. Further, many of the 

specified input parameters do not affect PM emissions, but were included by the local MPO to best represent local 

conditions, which were preserved in the present calculations of NOx emissions. This usage is consistent with the 

overall approach of preserving local information wherever possible. 

12 HDDV - Heavy-Duty Diesel Vehicle, HDGV - Heavy-Duty Gasoline Vehicle, LDDT - Light-Duty Diesel Truck, 

LDDV - Light-Duty Diesel Vehicle, LDGT12 - Light-Duty Gasoline Truck with gross vehicle weight rating ≤ 6,000 

lbs and a loaded vehicle weight of ≤ 5,750 lbs, LDGT 34 - Light-Duty Gasoline Truck with gross vehicle weight 

rating between 6,001 - 8,500 and a loaded vehicle weight of ≤ 5,750 lbs, LDGV - Light-Duty Gasoline Vehicle, MC 

- Motorcycles. 
 B-36

0

2

4

6

8

10

12

14

16

18

20

0 10 20 30 40 50 60 70

Average Speed (mph)

Average NOx Emissions (g/mi)

Fall Arterial LDV

Fall Freeway LDV

Fall Arterial HDV

Fall Freeway HDV

Figure B-4. Example of Light- and heavy-duty vehicle NOx emissions grams/mile (g/mi) for arterial and 

freeway functional classes, 2001. 

To determine the emission strengths for each link for each hour of the year, the Philadelphia 

County 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 speed calculations 

were made as follows. 

The spatial-mean speed of each link at each time was calculated following the methodology 

of the Highway Capacity Manual.13 Generally, the spatial-mean speed calculation is a function of 

the time-resolved volume-to-capacity ratio, with capacity the limiting factor. In the case of 

freeway calculations, this is determined by the HDV fraction, posted speed, and the general 

hilliness of the terrain, which was assumed to be uniformly flat for this region. The case of 

arterials without intersections is similar, but also considers urban effects. The case of arterials 

with intersections further considers the number of signals and length of each link and 

signalization parameters. It was assumed that all signals are identical, operating with a 120-

second cycle and a protected left turn phase. Each link’s speed is calculated independently. For 

example, a series of adjacent arterial links could show very different spatial-mean speeds if one 

link contains one or more intersections. That is, no up- or down-stream impacts are considered 

on individual link speeds. Speeds were assumed to be equal for light- and heavy-duty vehicles. 

Table B-18 shows the resulting average speed for each functional class within each TDM 

region. Several values are shown as N/A, due to the focus only on major links as discussed 

above. 

Table B-18. Average calculated speed by link type. 

 Average Speed (mph) 

 CBD Fringe Suburban Urban Rural 

 

13 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. 
 B-37

Ramp N/A 35 35 35 N/A 

Arterial 34 31 44 32 N/A 

Freeway 51 62 66 62 N/A 

The resulting emission factors were then coupled with the TDM-based activity estimates to 

calculate emissions from each of the 1,268 major roadway links. However, many of the links 

were two sides of the same roadway segment. To speed model execution time, those links that 

could be combined into a single emission source were merged together. This was done only for 

the 628 links (314 pairs) where opposing links were paired in space and exhibited similar activity 

levels within 20% of each other. 

B-3.5.4.3 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 27 links that exceeded this ratio and were converted to 55 segmented 

sources. Thus, the total number of area sources included in the dispersion simulations is 982. 

Table B-19 shows the distribution of on-road area source sizes. 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 B-19. On-road area source sizes. 

Segment 

Width (m) Lanes 

Segment 

Length (m) 

Minimum 3.7 1.0 0.0 

Median 11.0 3.0 220.6 

Average 13.7 3.8 300.2 

1-σ Deviation 7.7 2.1 259.5 

Maximum 43.9 12.0 1340.2 

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. 

The release height of each source was determined as the average of the light- and heavy-duty 

vehicle fractions, with an assumed light- and heavy-duty emission release heights of 1.0 ft 

(0.3048 m) and 13.1 ft (4.0 m), respectively.14 Because AERMOD only accepts a single release 

height for each source, the 24-hour average of the composite release heights is used in the 

modeling. Since surface-based mobile emissions are anticipated to be terrain following, no 

 

14 4.0 m includes plume rise from truck exhaust stacks. See Diesel Particulate Matter Exposure Assessment Study 

for the Ports of Los Angeles and Long Beach, State of California Air Resources Board, Final Report, April 2006. 
 B-38

elevated or complex terrain was included in the modeling. That is, all sources are assumed to lie 

in a flat plane. 

B-3.5.5 Stationary Sources Emissions Preparation 

Data for the parameterization of major point sources in Philadelphia comes primarily from 

two sources: the 2002 National Emissions Inventory (NEI; US EPA, 2007b) and Clean Air 

Markets Division (CAMD) Unit Level Emissions Database (US EPA, 2007c). These two 

databases have complimentary information. 

The NEI database contains stack locations, emissions release parameters (i.e., height, 

diameter, exit temperature, exit velocity), and annual emissions for 707 NOx-emitting stacks 

(206 of which are considered fugitive release points) in Philadelphia County. The CAMD 

database, on the other hand, has information on hourly NOx emission rates for all the units in the 

US, where the units are the boilers or equivalent, each of which can have multiple stacks. The 

alignment of facilities between the two databases is not exact, however. Some facilities listed in 

the NEI, are not included in the CAMD database. Of those facilities that do match, in many cases 

there is no clear pairing between the individual stacks assigned within the databases. 

B-3.5.5.1 Data Source Alignment 

To align the information between the two databases and extract the useful portion of each for 

dispersion modeling, the following methodology was used. 

1. Attention was limited stacks within the NEI data base that (a) lie within Philadelphia 

County and (b) were part of a facility with total emissions from all stacks exceeding 

100 tpy NOx. 

2. Individual stacks that had identical stack physical parameters and were co-located 

within about 10 m were combined to be simulated as a single stack with their 

emissions summed. 

3. All fugitive releases were removed from the list, to be analyzed as a separate source 

group. 

The resulting 19 distinct, combined stacks from the NEI are shown in Table B-20. 

The CAMD database was then queried for facilities that matched the facilities identified from 

the NEI database. Facility matching was done on the facility name, Office of Regulatory 

Information Systems (ORIS) identification code (when provided) and facility total emissions to 

ensure a best match between the facilities. Once facilities were paired, individual units and 

stacks in the data bases were paired, based on annual emission totals. Table B-21 shows the 

matching scheme for the seven major facilities in Philadelphia County.15 

 

15 Note that Jefferson Smurfit does not exist in the CAMD database. The matching here was based on facility types 

as follows. Smurfit in PA was taken as a packaging/recycling facility, and the stack assumed to be a Cogen facility, 

based on information in the NEEDS database (http://www.epa.gov/interstateairquality/pdfs/NEEDS-NODA.xls). 

The best matched cogen plant in Philadelphia County in both the NEEDS and CAMD database is the Gray’s Ferry 

Cogen Partnership (ORIS 54785), which was a reasonable match for Smurfit’s total emissions. It was assumed that 

the hourly emission profile also matches well. 
 B-39

In Table B-21, there are sometimes multiple CAMD units that pair with a single NEI 

combined stack. In these cases the hourly emission rates from the matching CAMD units are 

summed for each hour. For example, in the case of stack 859 for “Sunoco, Inc – Philadelphia” 

five CAMD hourly records are summed into a single hourly record. Then each resulting hourly 

value is scaled by a factor of 1032.8 / 938.9 = 1.10, so that the annual total matches the NEI 

annual total. 

Similarly, there are sometimes multiple combined stacks that pair with single units. In this 

case the CAMD values are disaggregated according to NEI-defined stack contributions. For 

example, “Sunoco, Inc – Philadelphia” stack 855’s profile is determined by taking the hourly 

profile from CAMD unit number 52106-150101, and scaling each value by a factor of 26.2 tpy / 

48.2 tpy total = 0.54. Then each resulting hourly value is scaled by a factor of 48.2/162.1 = 0.3 

so that the sum of the annual totals for the 4 stacks corresponding to unit number 52106-150101 

matches the NEI total. For consistency, in each case the 2001 and 2003 hourly emission profiles 

were determined using the same scaling factors, but applied to the respective CAMD emission 

profile. 

It is clear from Table B-21 that most facilities agree well in total annual NOx emissions 

between the two databases. However, in the case of the “Sunoco Chemicals (Former Allied 

Signal)” facility, nearly half of the NEI emissions (without fugitives) do not appear in the 

CAMD database. The reason for this is unknown and no information was readily available on 

the relative accuracy of the two databases. 

Figure B-5 illustrates the discrepancy versus fraction of hours with positive emissions, 

according to the CAMD data base. The figure suggests that the discrepancies are not primarily 

the result of facilities with episodic emissions (i.e., “peak load” facilities). Although there is 

good agreement on facility-wide emissions between the two data bases, there are larger 

discrepancies between CAMD unit emissions and NEI stack emissions. This is to be expected 

given the discrepancy in resolution between the two data bases. 

 
 B-40

Table B-20. Combined stacks parameters for stationary NOx emission sources in Philadelphia County. 

Stack 

No 

NEI Site ID Facility Name SIC Code NAICS Code 

ORIS Facility Code 

Stack Emissions (tpy) 

Stack X (deg) 

Stack Y (deg) 

Stack Ht (m) 

Exit Tem(K) 

817 NEIPA2218 

EXELON GENERATION CO - DELAWARE STATION 4911 221112 3160 4.82 -75.1358 39.96769 49 5

818 NEIPA2218 

EXELON GENERATION CO - DELAWARE STATION 4911 221112 3160 287.8 -75.1358 39.96769 64 38

819 NEI40720 

JEFFERSON SMURFIT CORPORATION (U S) 2631 32213 0.148 -75.2391 40.03329 16 47

820 NEI40720 

JEFFERSON SMURFIT CORPORATION (U S) 2631 32213 113.8 -75.2391 40.03329 53 42

821 NEI40720 

JEFFERSON SMURFIT CORPORATION (U S) 2631 32213 114.46 -75.2391 40.03329 53 47

855 NEI40723 Sunoco Inc. - Philadelphia 2911 32411 26.2 -75.2027 39.92535 24 45856 NEI40723 Sunoco Inc. - Philadelphia 2911 32411 1.3 -75.2003 39.91379 24 64857 NEI40723 Sunoco Inc. - Philadelphia 2911 32411 1.4 -75.203 39.92539 25 5858 NEI40723 Sunoco Inc. - Philadelphia 2911 32411 19.3 -75.2027 39.92535 25 52859 NEI40723 Sunoco Inc. - Philadelphia 2911 32411 1032.8 -75.2124 39.90239 61 48860 NEI7330 

SUNOCO CHEMICALS (FORMER ALLIED SIGNAL) 2869 325998 0.033 -75.0715 40.00649 5 47

861 NEI7330 

SUNOCO CHEMICALS (FORMER ALLIED SIGNAL) 2869 325998 49.1 -75.0715 40.00649 41 42

862 NEI7330 

SUNOCO CHEMICALS (FORMER ALLIED SIGNAL) 2869 325998 34.6 -75.0715 40.00649 42 42

863 NEI7330 

SUNOCO CHEMICALS (FORMER ALLIED SIGNAL) 2869 325998 77.2 -75.0715 40.00649 42 42

864 NEIPA101353 TRIGEN - SCHUYLKILL 4961 22 128.6 -75.1873 39.94239 69 45865 NEIPA101353 TRIGEN - SCHUYLKILL 4961 22 61.5 -75.1873 39.94239 78 45866 NEIPA101356 

GRAYS FERRY COGENERATION PARTNERS 4911 22 54785 143.2 -75.1873 39.94239 78 39

867 NEIPA101356 

GRAYS FERRY COGENERATION PARTNERS 4911 22 54785 90.3 -75.1873 39.94239 85 44

868 NEIPA2222 TRIGEN - EDISON 4961 62 130.5 -75.1569 39.94604 78 58
 B-41

Table B-21. Matched stacks between the CAMD and NEI database. 

NEI Facility 

Name 

NEI 

Comb. 

Stack

Number 

NEI 

Comb. 

Stack

Emiss 

(tpy) 

NEI 

Unit 

Emiss 

(tpy) 

NEI 

Facility 

Emiss 

(tpy, 

w/out 

Fugitive) 

CAMD 

Facility 

Name 

CAMD 

Units * 

CAMD 

Unit 

Emiss 

(tpy) * 

CAMD 

Comb. 

Unit 

Totals 

(tpy) 

CAMD 

Facility 

Totals 

(tpy) 

Sta

(

rel

CA

va

817 4.8 4.8 3160-9 1.542 1.542 21

3160-71 123.8 

Exelon 

Generation Co 

- Delaware 

Station 

818 287.8 287.8 

292.6 Delaware 

3160-81 164 

287.8 

289.3 

0

855 26.2 856 1.3 857 1.4 858 19.3 

48.2 52106- 150101 162.1 162.1 -7

52106-

150137 194.2 

52106-

150110 162.1 

52106-

150138 194.2 

52106-

150139 194.2 

Sunoco Inc. - 

Philadelphia 

859 1032.8 1032.8 

1081.0 Philadelphia Refinery 

52106-

150140 194.2 

938.9 

1101.0 

1

860 0.0 861 49.1 862 34.6 

Sunoco 

Chemicals 

(Former Allied 

Signal) 863 77.2 

160.9 160.9 

Sunoco 

Chemicals 

Frankford 

Plant 

880007-52 84.5 84.5 84.5 9

864 128.6 128.6 50607-23 163.1 163.1 -2

Trigen - Schuylkill 

865 61.5 61.5 

190.1 Trigen Energy - 

Schuykill 50607-24 2.9 15.6 

178.7 

29
 B-42

NEI Facility 

Name 

NEI 

Comb. 

Stack

Number 

NEI 

Comb. 

Stack

Emiss 

(tpy) 

NEI 

Unit 

Emiss 

(tpy) 

NEI 

Facility 

Emiss 

(tpy, 

w/out 

Fugitive) 

CAMD 

Facility 

Name 

CAMD 

Units * 

CAMD 

Unit 

Emiss 

(tpy) * 

CAMD 

Comb. 

Unit 

Totals 

(tpy) 

CAMD 

Facility 

Totals 

(tpy) 

Sta

(

rel

CA

va

50607-26 12.7 

866 143.2 143.2 54785-2 143.2 143.2 0

Grays Ferry 

Cogeneration 

Partners 

867 90.3 90.3 

233.5 

Grays Ferry 

Cogen 

Partnership 

54785-25 90.3 90.3 

233.5 

0

880006-1 19.8 880006-2 17.3 880006-3 36.1 

Trigen - 

Edison 868 130.5 130.5 130.5 

Trigen 

Energy 

CorporationEdison St 880006-4 37.8 111 111.0 1

819 0.1 54785-2 143.2 820 113.8 

Jefferson 

Smurfit 

Corporation 

(U S) *** 821 114.5 

228.4 228.4 

54785-25 90.3 

233.5 233.5 -2

Notes: * In the format "ORIS ID - UNIT ID" ** All CAMD values are for 2002 *** Jefferson Smurfit not in CAMD; will use Grays Ferry as surrogate 
 B-43

Facility-Wide 2002 NOx Emission Frequency 

versus 

Difference in 2002 NOx Emission Mass Between the CAMD and NEI Databases

(ORIS ID labeled)

3160

50607 880006

54785(Grays Ferry)

52106

880007

54785( Jef f erso n Smurf it )

-80

-60

-40

-20

0

20

40

60

80

10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

NOx Emission Frequency for 2002 

(% hours in 2002 where NOx was emitted)

2002 Annual NOx Emission 

Mass [NEI-CAMD] Difference 

(short tons)

Figure B-5. Differences in facility-wide annual NOx emission totals between NEI and CAMD data bases for 

Philadelphia County 2002. 

B-3.5.6 Fugitive and Airport Emissions Preparation 

Fugitive emission releases in Philadelphia County, as totaled in the NEI database, were 

modeled as area sources with the profile of these releases determined by the overall facility 

profile of emissions. In addition, emissions associated with the Philadelphia International 

Airport were estimated. 

B-3.5.6.1 Fugitive Releases 

Thirty five combined stacks were identified during the point source analysis (see previous 

section) that were associated with facilities considered major emitters, but where the emissions 

from the stacks are labeled Fugitive in the NEI. These stacks have zero stack diameter, zero 

emission velocity, and exit temperature equal to average ambient conditions (295 K). Thus, we 

determined it was not appropriate to include these in the point source group simulation. 

These 35 stacks occur at only two facilities in the County: Exelon Generation Co – Delaware 

Station (NEI Site ID: NEIPA2218) and Sunoco Inc. – Philadelphia (NEI Site ID: NEI40723). 

Consequently, they were grouped by facility. The Sunoco emissions further fall into two distinct 

categories based on release heights. Thus, to accommodate all these sources most efficiently, we 

created three area source groups: one for Sunoco emissions at 3.0 m, one for Sunoco emissions 

greater than 23.0 m, and one for Exelon. The “stacks” within the NEI and their parameters 

comprising each of these sources are shown in Table B-22 along with their groupings and the 

resulting combined area source parameters. 

Table B-22. Emission parameters for the three Philadelphia County fugitive NOx area emission sources. 

Scaled Emissions (tpy) 2

Grp. 

No. 

NEI 

Site ID Facility Name 

NEI 2002 

Emissions 

(tpy) Stack X Stack Y 

Stack 

Height 

(m) 

Stacks 

Used for 

Emission 

Profile 1 2001 2002 2003 
 B-44

Scaled Emissions (tpy) 2

Grp. 

No. 

NEI 

Site ID Facility Name 

NEI 2002 

Emissions 

(tpy) Stack X Stack Y 

Stack 

Height 

(m) 

Stacks 

Used for 

Emission 

Profile 1 2001 2002 2003 

0.1 -75.13582 39.96769 5 

5.1 -75.12528 39.96680 8 

1

NEIPA

2218 

 

EXELON 

GENERATIO

N CO - 

DELAWARE 

STATION 5.2 6.5 817+818 

 

4.8 

 

5.2 

 

6.4 

65.3 -75.21408 39.90811 3 

350.9 -75.21300 39.90878 3 

12.7 -75.20972 39.90467 3 

355.7 -75.20945 39.90778 3 

31.1 -75.20876 39.90185 3 

6.2 -75.20845 39.90708 3 

182.4 -75.20809 39.91580 3 

1.1 -75.20707 39.90946 3 

7.5 -75.20651 39.90988 3 

1.0 -75.20301 39.91362 3 

2.0 -75.20114 39.91273 3 

49.4 -75.20090 39.91621 3 

106.3 -75.20079 39.91615 3 

188.5 -75.20047 39.91366 3 

87.8 -75.20043 39.91377 3 

36.1 -75.20024 39.91406 3 

9.7 -75.20020 39.91410 3 

61.2 -75.19995 39.91596 3 

13.6 -75.19766 39.91696 3 

17.0 -75.19751 39.91696 3 

17.2 -75.19735 39.91590 3 

12.2 -75.19723 39.91597 3 

12.6 -75.19720 39.91698 3 

23.7 -75.19713 39.91596 3 

19.2 -75.19699 39.91599 3 

NEI40

723 

Sunoco Inc. - 

Philadelphia 

10.0 -75.19644 39.91493 3 

2 1,680.4 3.0 

855+856+ 

857+858+ 

859 

 

1,873.

8 

 

1,681.

4 

 

2,202

.4 

79.5 -75.21322 39.90899 23 

13.1 -75.20833 39.90278 26 

Sunoco Inc. - 

Philadelphia 

15.3 -75.20850 39.90246 27 

2.5 -75.20844 39.90239 27 

10.2 -75.20838 39.90231 27 

19.0 -75.20828 39.90237 27 

NEI40

723 

211.2 -75.20889 39.90279 30 

 

 

 

 

 

 

 

3 350.8 26.7 

855+856+ 

857+858+ 

859 

 

391.2 

 

351.0 

 

459.8 

1

 See Table B-20 for stack definitions. 

2

 Scaled emissions are determined by summing the scaled, hourly values 

from the CAMD database, as used in the dispersion modeling.
 B-45

In the case of the Sunoco emissions, the vertices of the area sources were determined by a 

convex hull encapsulating all the points. In the case of Excelon, only two points are provided, 

which is insufficient information to form a closed polygon. Instead, the boundary of the facility 

was digitized into a 20-sided polygon. Figure B-6 shows the locations of these polygons. 

Emission profiles for the fugitive releases were determined from the CAMD hourly emission 

database in a method similar to that for the point sources. We determined scaling factors based 

on the ratio of the 2002 fugitive releases described by the NEI to the total, non-fugitive point 

source releases from the same facility. All stacks within that facility were combined on an 

hourly basis for each year and the fugitive to non-fugitive scaling factor applied, ensuring that 

the same temporal emission profile was used for fugitives as for other releases from the facility, 

since the origins of the emissions should be parallel. We created external hourly emissions files 

for each of the three fugitive area sources with appropriate units (grams per second per square 

meter). 

Figure B-6. Locations of the four ancillary area sources. Also shown are centroid receptor locations. 

B-3.5.6.2 Philadelphia International Airport Emissions 

Another significant source of NOx emissions in Philadelphia County not captured in the 

earlier simulations is from operation of the Philadelphia International Airport (PHL). PHL is the 

only major commercial airport in the County and is the largest airport in the Delaware Valley. 
 B-46

The majority of NOx emissions in the NEI16 database attributable to airports in Philadelphia 

County are from non-road mobile sources, specifically ground support equipment. There is 

another airport in the County: Northeast Philadelphia Airport. However, because it serves 

general aviation, is generally much smaller in operations than PHL, and has little ground support 

equipment activity – which is associated primarily with commercial aviation – all airport 

emissions in the County were attributed to PHL. The PHL emissions were taken from the nonroad section of the 2002 NEI, and are shown by Table B-23. 

Table B-23. Philadelphia International airport (PHL) NOx emissions. 

State and 

County SCC 

NOx 

(tpy) 

SCC Level 1 

Description 

SCC Level 3 

Description 

SCC Level 6 

Description 

SCC Level 8 

Description 

2265008005 4.6

Mobile 

Sources

Off-highway 

Vehicle 

Gasoline, 4-

Stroke

Airport 

Ground 

Support 

Equipment 

Airport 

Ground 

Support 

Equipment

2267008005 5.1

Mobile 

Sources LPG

Airport 

Ground 

Support 

Equipment 

Airport 

Ground 

Support 

Equipment

2270008005 196.2

Mobile 

Sources

Off-highway 

Vehicle 

Diesel

Airport 

Ground 

Support 

Equipment 

Airport 

Ground 

Support 

Equipment

2275020000 0.01

Mobile 

Sources Aircraft

Commercial 

Aircraft 

Total: All 

Types

Philadelphia, 

PA 

2275050000 2.5

Mobile 

Sources Aircraft

General 

Aviation Total

PHL Total 208.4 

As with the fugitive sources discussed above, the airport emissions are best parameterized as 

area sources. The boundary of the area source was taken as the region of operation of baggage 

handling equipment, including the terminal building and the region surrounding the gates. This 

region was digitized into an 18-sided polygon of size 1,326,000 m2

, and included in the 

AERMOD input control file. 

The activity profile for PHL was taken to have seasonal and hourly variation (SEASHR), 

based on values from the EMS-HAP model.17 These factors are disaggregated in the EMS-HAP 

model database based on source classification codes (SCCs), which were linked to those from 

the NEI database. The EMS-HAP values provide hourly activity factors by season, day type, and 

hour; to compress to simple SEASHR modeling, the hourly values from the three individual day 

types were averaged together. The total emissions for each SCC were then disaggregated into 

seasonal and hourly components and the resulting components summed to create total PHL 

emissions for each hour of the four annual seasons. These parameterized emissions were then 

normalized to the total cargo handling operational area, to produce emission factors in units of 

grams per second per square meter and included in the AERMOD input file. Figure B-6 also 

illustrates the location of the PHL area source. 

 

16 http://www.epa.gov/ttn/chief/net/2002inventory.html 

17 EPA 2004, User's Guide for the Emissions Modeling System for Hazardous Air Pollutants (EMS-HAP) Version 

3.0, EPA-454/B-03-006. 
 B-47

B-3.5.7 Receptor Locations 

Three sets of receptors were chosen to represent the locations of interest. First, all NOx 

monitor locations, shown by Table B-24, within the Philadelphia county were included as 

receptor locations. Although all receptors are assumed to be on a flat plane, they are placed at 

the standard breathing height of 5.9 ft (1.8 m). 

Table B-24. Philadelphia County NOx monitors. 

Site ID Latitude Longitude 

421010004 40.0089 -75.0978 

421010029 39.9572 -75.1731 

421010047 39.9447 -75.1661 

The second receptor locations were selected to represent the locations of census block 

centroids near major NOx sources. GIS analysis was used to determine all block centroids in 

Philadelphia County that lie within a 0.25 mile (400 m) of the roadway segments and also all 

block centroids that lie within 6.2 miles (10 km) of any major point source. 12,982 block 

centroids were selected due to their proximity to major roadways; 16,298 centroids were selected 

due to their proximity to major sources. The union of these sets produced 16,857 unique block 

centroid receptor locations, each of which was assigned a height of 5.9 ft (1.8 m). The locations 

of centroids that met either distance criteria – and were thus included in the modeling – is shown 

by Figure B-7. 

Figure B-7. Centroid locations within fixed distances to major point and mobile sources in Philadelphia 

county. 
 B-48

The third set of receptors was chosen to represent the on-road microenvironment. For this 

set, one receptor was placed at the center of each of the 982 sources. 

The distance relationship between the road segments and block centroids can be estimated by 

looking at the distance between the road-centered and the block centroid receptors. Figure B-8 

shows the histogram of the shortest distance between each centroid receptor and its nearest 

roadway-centered receptor. 

p

0

100

200

300

400

500

600

700

0

175350525700875

105012251400157517501925210022752450262528002975

Distance (m)

Frequency

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

CDF (%)

Figure B-8. Frequency distribution of distance between each Census receptor and its nearest road-centered 

receptor in Philadelphia County. 

The block centroids selected were those within 10 km of any major point source or 400 m 

from any receptor edge, so the distances to the nearest major road segment can be significantly 

greater than 400 m. The mode of the distribution is about 150 m and the median distance to the 

closest roadway segment center is about 450 m. However, these values represent the distances 

of the block centroids to road centers instead of road edges, so that they overestimate the actual 

distances to the zone most influenced by roadway by an average of 14 m and a range of 4 m to 

44 m (see Table B-19 above). 

B-3.5.8 Other AERMOD Specifications 

Since each of the case-study locations were MSA/CMSAs, all emission sources were 

characterized as urban. 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 each of the roadway, fugitive, and airport emission sources, the ozone 

limiting method (OLM) was used, with plumes considered ungrouped. Because an initial NO2

fraction of NOx is anticipated to be about 10% or less (Finlayson-Pitts and Pitts, 2000; Yao et al., 

2005), a conservative value of 10% for all sources was selected. For all point source simulations 
 B-49

the Plume Volume Molar Ratio Method (PVMRM) was used to estimate the conversion of NOx

to NO2, with the following settings: 

1. Hourly series of O3 concentrations were taken from EPA’s AQS database18. The 

complete national hourly record of monitored O3 concentrations were filtered for the 

four monitors within Philadelphia County (stations 421010004, 421010014, 

421010024, and 421010136). The hourly records of these stations were then 

averaged together to provide an average Philadelphia County concentrations of O3 for 

each hour of 2001-2003. 

2. The equilibrium value for the NO2:NOx ratio was taken as 75%, the national average 

ambient ratio.19 

3. The initial NO2 fraction of NOx is anticipated to be about 10% or less. A default 

value of 10% was used for all stacks (Finlayson-Pitts and Pitts, 2000). 

B-3.5.9 Air Quality Concentration Adjustment 

The hourly concentrations estimated from each of the three source categories were combined 

at each receptor. Then a local concentration, reflecting the concentration contribution from 

emission sources not included in the simulation, was added to the sum of the concentration 

contributions from each of these sources at each receptor. The local concentration was estimated 

from the difference between the model predictions at the local NO2 monitors and the observed 

values. It should be noted that this local concentration may also include any model error present 

in estimating concentration at the local monitoring sites. Table B-25 presents a summary of the 

estimated local concentration added to the AERMOD hourly concentration data. 

Table B-25. Comparison of ambient monitoring and AERMOD predicted NO2 concentrations in 

Philadelphia. 

Annual Average NO2 concentration (ppb) 

Year and 

Monitor ID Monitor

AERMOD 

Inititial Difference1

AERMOD 

Final2

2001

4210100043 26 7 18 19 

4210100292 28 22 6 33 

4210100471 30 20 10 32 

mean 11 

2002

4210100043 24 7 17 18 

4210100292 28 21 7 32 

4210100471 29 19 10 31 

mean 11 

2003

4210100043 24 7 17 13 

4210100292 25 22 3 28 

4210100471* 25 26 -1 32 

 

18 http://www.epa.gov/ttn/airs/airsaqs/detaildata/downloadaqsdata.htm 

19 Appendix W to CFR 51, page 466. http://www.epa.gov/scram001/guidance/guide/appw_03.pdf. 
 B-50

mean 6 

1 the difference represents concentrations attributed to sources 

not modeled by AERMOD and model error.

2

 the mean difference between measured and modeled was 

added uniformly at each receptor hourly concentration to 

generate the AERMOD final concentrations. 

* monitor did not meet completeness criteria used in the air 

quality characterization. 

B-3.5.10 Meteorological Data Used By APEX 

APEX used the same meteorological data that was used for the AERMOD modeling, the 

station located at Philadelphia International (KPHL) airport. 

B-3.5.11 Microenvironment Descriptions 

B-3.5.11.1 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. The first two of these variables affect the air exchange 

rate. 

Since the selection of an air exchange rate distribution is conditioned on the presence or 

absence of an air-conditioner, for each modeled area the air conditioning status of the residential 

microenvironments is simulated randomly using the probability that a residence has an air 

conditioner. For this study, location-specific air conditioning prevalence was taken from the 

American Housing Survey of 2003 (AHS, 2003a; 2003b). Previous analyses (US EPA, 2007d) 

detail the specification of uncertainty estimates in the form of confidence intervals for the air 

conditioner prevalence using the following: 

Confidence Interval( ) 1.96 Standard Error ( )

, 3850 1 Standard Error ( )

P P P

N

 P ( P) P

= ± ×

− =

where P is the estimated percentage and N is the estimated total number of housing units. 

Table B-26 contains the values for air conditioning prevalence used for each modeled location. 

Table B-26. Air conditioning prevalence estimates with 95% confidence intervals. 

AHS 

Survey 

Housing 

Units 

A/C 

Prevalence

(%) se L95 U95 

Philadelphia 1,943,492 90.6 1.3 88.1 93.2 

Notes: 

se – Standard error 

L95 – Lower limit on 95th confidence interval 

U95 – Upper limit on 95th confidence interval 
 B-51

Air exchange rate data for the indoor residential microenvironment were obtained from US 

EPA (2007d). Briefly, residential air exchange rate (AER) data were obtained from several 

studies (Avol et al., 1998; Williams et al., 2003a, 2003b; Meng et al., 2004; Weisel et al., 2004; 

Chillrud at al, 2004; Kinney et al., 2002; Sax et al., 2004; Wilson et al., 1986, 1996; Colome et 

al., 1993, 1994; Murray and Burmaster, 1995). Influential characteristics (e.g., temperature, air 

conditioning), where reported in the study, were also compiled for use in statistical analyses. 

Descriptive statistics were generated for each location/variable type and evaluated using 

statistical comparison testing (e.g., ANOVA). Based on the summary statistics and the statistical 

comparisons, different AER distributions were fit for each combination of A/C type, city, and 

temperature. In general, lognormal distributions provided the best fit, and are defined by a 

geometric mean (GM) and standard deviation (GSD). To avoid unusually extreme simulated 

AER values, bounds of 0.1 and 10 were selected for minimum and maximum AER, respectively. 

For Philadelphia, a distribution was selected from a location thought to have similar 

characteristics to the city to be modeled, qualitatively considering factors that might influence 

AERs. These factors include 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. The distributions used for Philadelphia are provided in Table B-27. 

Table B-27. Geometric means (GM) and standard deviations (GSD) for air exchange rates by city, A/C type, 

and temperature range. 

Area 

Modeled Study City A/C Type 

Temp 

(oC) N GM GSD 

<=10 20 0.7108 2.0184 

10-25 42 1.1392 2.6773 

Central or 

Room A/C 

>25 19 1.2435 2.1768 

<=10 48 1.0165 2.1382 

10-20 59 0.7909 2.0417 

Philadelphia New York 

City 

No A/C 

>20 32 1.6062 2.1189 

For this analysis, the same NO2 removal rate distribution was used for all microenvironments 

that use the mass balance method. This removal rate is based on data provided by Spicer et al. 

(1993). A total of 6 experiments, under variable source emission characteristics including 

operation of gas stove, were conducted in an unoccupied test house. A distribution could not be 

described with the limited data set, therefore a uniform distribution was approximated by the 

bounds of the 6 values, a minimum of 1.02 and a maximum of 1.45 h-1. 

An excerpt from the APEX input file describing the indoor residential microenvironment is 

provided in Figure B-9. The first section of the input file excerpt specifies the air exchange rate 

distributions for the microenvironment. Average temperature and air conditioning presence, 

which are city-specific, were coded into air exchange rate conditional variables, C1 and C2, 

respectively. Average temperatures were separated into five categories (variable C1, numbered 

1-5): 50 o F, 50-68 o F, 68-77 o F, 77-86 o F, and 86 o F and above. For variable C2, air 

conditioning status can range from 1 to 2 (1 for having air conditioning, 2 for not having it). The 

air exchange rate estimates generated previously in the form of lognormal distributions were 

entered into the appropriate temperature and A/C category for each location for a total of ten 

distributions (i.e., 5 temperature distributions by 2 air conditioning distributions). In the input 

file example however, there are actually four AER distributions for homes with an air 
 B-52

conditioner and three for those without; the last few distributions for each air conditioning setting 

were the same due to the available data to populate the field. The parameter estimates for the 

removal factor (DE) is also shown following the AER data. 

Micro number = 1 ! Indoors - residence - AIR EXCHANGE RATES 

Parameter Type = AER 

Condition # 1 = AvgTempCat 

Condition # 2 = AC_Home 

ResampHours = NO 

ResampDays = YES 

ResampWork = YES 

Block DType Season Area C1 C2 C3 Shape Par1 Par2 Par3 Par4 LTrunc UTrunc 

1 1 1 1 1 1 1 Lognormal 0.711 2.018 0 . 0.1 10 

1 1 1 1 2 1 1 Lognormal 1.139 2.677 0 . 0.1 10 

1 1 1 1 3 1 1 Lognormal 1.139 2.677 0 . 0.1 10 

1 1 1 1 4 1 1 Lognormal 1.244 2.177 0 . 0.1 10 

1 1 1 1 5 1 1 Lognormal 1.244 2.177 0 . 0.1 10 

1 1 1 1 1 2 1 Lognormal 1.016 2.138 0 . 0.1 10 

1 1 1 1 2 2 1 Lognormal 0.791 2.042 0 . 0.1 10 

1 1 1 1 3 2 1 Lognormal 1.606 2.119 0 . 0.1 10 

1 1 1 1 4 2 1 Lognormal 1.606 2.119 0 . 0.1 10 

1 1 1 1 5 2 1 Lognormal 1.606 2.119 0 . 0.1 10 

Micro number = 1 ! DECAY RATES 

Pollutant = 1 

Parameter Type = DE 

ResampHours = NO 

ResampDays = NO 

ResampWork = YES 

Block DType Season Area C1 C2 C3 Shape Par1 Par2 Par3 Par4 LTrunc UTrunc 

1 1 1 1 1 1 1 Uniform 1.02 1.45 . . 1.02 1.45 

Figure B-9. Example input file from APEX for Indoors-residence microenvironment.

Indoor source contributions 

A number of studies, as described in the NOx ISA, have noted the importance of gas cooking 

appliances as sources of NO2 emissions. An indoor emission source term was included in the 

APEX simulations to estimate exposure to indoor sources of NO2. Three types of data were used 

to implement this factor: 

• The fraction of households in the Philadelphia MSA that use gas for cooking fuel 

• The range of contributions to indoor NO2 concentrations that occur from cooking 

with gas 

• The diurnal pattern of cooking in households. 

The fraction of households in Philadelphia County that use gas cooking fuel (i.e., 55%) was 

taken from the US Census Bureau’s American Housing Survey for the Philadelphia Metropolitan 

Area: 2003. 
 B-53

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). For this study a test house was set up for continuous measurements of 

NO2 indoors and outdoors, among several other parameters, and conducted under several 

different cooking procedures and stove operating conditions. A uniform distribution of 

concentration contributions for input to APEX was estimated as follows. 

• The concurrent outdoor NO2 concentration measurement was subtracted from each 

indoor concentration measurement, to yield net indoor concentrations 

• Net indoor concentrations for duplicate cooking tests (same food cooked the same 

way) were averaged for each indoor room, to yield average net indoor concentrations 

• The minimum and maximum average net indoor concentrations for any test in any 

room were used as the lower and upper bounds of a uniform distribution 

This resulted in a minimum average net indoor concentration of 4 ppb and a maximum net 

average indoor concentration of 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 as follows. 

• Food preparation time obtained from CHAD diaries was stratified by hour of the day, 

and summed for each hour, and summed for total preparation time. 

• The fraction of food preparation occurring in each hour of the day was calculated as 

the total number of minutes for that hour divided by the overall total preparation time. 

The result was a measure of the probability of food preparation taking place during 

any hour, given one food preparation event per day. 

• Each hourly fraction was multiplied by 1.4, to normalize the expected value of daily 

food preparation events to 1.4. 

The estimated probabilities of cooking by hour of the day are presented in Table B-28. For 

this analysis it was assumed that the probability that food preparation would include stove usage 

was the same for each hour of the day, so that the diurnal allocation of food preparation events 

would be the same as the diurnal allocation of gas stove usage. It was also assumed that each 

cooking event lasts for exactly 1 hour, implying that the average total daily gas stove usage is 1.4 

hours. 

Table B-28. Probability of gas stove cooking by hour of the day. 

Hour of Day Probability of Cooking 

(%)1

0 0 

1 0 

2 0 

3 0 

4 0 

5 5 

6 10 

7 10 

8 10 

9 5 
 B-54

Hour of Day Probability of Cooking 

(%)1

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 

1 Values rounded to the nearest 5%. Data sum to 

145% due to rounding and scaling to 1.4 cooking 

events/day. 

B-3.5.11.2 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, are all modeled using the 

same data and functions (Figure B-10). As with the Indoor-Residence microenvironment, these 

microenvironments use both air exchange rates and removal rates to calculate exposures within 

the microenvironment. The air exchange rate distribution (GM = 1.109, GSD = 3.015, Min = 

0.07, Max = 13.8) was developed based on an indoor air quality study (Persily et al, 2005; see 

US EPA, 2007d for details in derivation). The decay rate is the same as used in the IndoorResidence microenvironment discussed previously. The Bars and Restaurants microenvironment 

included an estimated contribution from indoor sources as was described for the IndoorResidence, only there was an assumed 100% prevalence rate and the cooking with the gas 

appliance occurred at any hour of the day. 

Micro number = 2 ! Bars & restaurants - AIR EXCHANGE RATES 

Parameter Type = AER 

ResampHours = NO 

ResampDays = YES 

ResampWork = YES 

Block DType Season Area C1 C2 C3 Shape Par1 Par2 Par3 Par4 LTrunc UTrunc 

1 1 1 1 1 1 1 LogNormal 1.109 3.015 0 . 0.07 13.8 

Micro number = 2 ! DECAY RATES 

Pollutant = 1 

Parameter Type = DE 

ResampHours = NO 

ResampDays = YES 

ResampWork = YES 

Block DType Season Area C1 C2 C3 Shape Par1 Par2 Par3 Par4 LTrunc UTrunc 

1 1 1 1 1 1 1 Uniform 1.02 1.45 . . 1.02 1.45 

 

Figure B-10. Example input file from APEX for all Indoors microenvironments (non-residence). 
 B-55

Microenvironments 8 and 9: Outdoor microenvironments 

Two outdoor microenvironments, the Near Road and Public Garage/Parking Lot, used the 

factors method to calculate pollutant exposure. Penetration factors are not applicable to outdoor 

environments (effectively, PEN=1). Proximity factors were developed from the AERMOD 

concentration predictions, i.e., the block-centroid-to-nearest-roadway concentration ratios. Based 

on the resulting sets of ratio values, the ratio distributions were stratified by hour of the day into 

3 groups as indicated by the “hours-block” specification in the example file in Figure B-11. The 

lower and upper bounds for sampling were specified as the 5th and 95th percentile values, 

respectively, of each distribution. 

Micro number = 8 ! Outdoor near road PROXIMITY FACTOR 

Pollutant = 1 

Parameter Type = PR 

Hours - Block = 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 1 1 

ResampHours = YES 

ResampDays = YES 

ResampWork = YES 

Block DType Season Area C1 C2 C3 Shape Par1 Par2 Par3 Par4 LTrunc UTrunc ResampOut 

1 1 1 1 1 1 1 LogNormal 1.251 1.478 0. . 0.86 2.92 Y 

2 1 1 1 1 1 1 LogNormal 1.555 1.739 0. . 0.83 4.50 Y 

3 1 1 1 1 1 1 LogNormal 1.397 1.716 0. . 0.73 4.17 Y 

 

Figure B-11. Example input file from APEX for outdoor near road microenvironment. 

B-3.5.11.3 Microenvironment 10: Outdoors-General. 

 The general outdoor environment concentrations are well represented by the modeled 

concentrations. Therefore, both the penetration factor and proximity factor for this 

microenvironment were set to 1. 

B-3.5.11.4 Microenvironments 11 and 12: In Vehicle- Cars and Trucks, and Mass Transit 

Penetration factors were developed from data provided in Chan and Chung (2003). Insidevehicle and outdoor NO2 concentrations were measured with for three ventilation conditions, airrecirculation, fresh air intake, and with windows opened. 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 was selected for the penetration factor 

for Inside-Cars/Trucks (ranging from 0.6 to 1.0) 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 ranging from 0.8 to 1.0 based on the reported mean values for fresh air intake and 

open windows. Proximity factors were developed as described above for Microenvironments 8 

and 9. 

 
 B-56

B-3.5.12 Adjustment for Just Meeting the Current Standard 

To simulate just meeting the current standard, dispersion modeled concentration were not 

rolled-up as was done for the monitor concentrations used in the air quality characterization. A 

proportional approach was used as done in the Air Quality Characterization, but to reduce 

computer processing time, the health effect benchmark levels were proportionally reduced by the 

similar factors described for each specific location and simulated year. 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 difference in the exposure and risk modeling 

was that the modeled air quality concentrations were used to generate the adjustment factors. 

Table B-29 provides the adjustment factors used and the adjusted potential health effect 

benchmark concentrations to simulate just meeting the current standard. When modeling indoor 

sources, the indoor concentration contributions needed to be scaled downward by the same 

proportions. 

Table B-29. Adjustment factors and potential health effect benchmark levels used by APEX to simulate just 

meeting the current standard. 

Potential Health 

Effect Benchmark 

Level (ppb) 

Simulated 

Year 

(factor) Actual Adjusted 

150 94 

200 126 

250 157 

2001 

(1.59) 

300 189 

150 92 

200 122 

250 153 

2002 

(1.63) 

300 184 

150 91 

200 122 

250 152 

2003 

(1.64) 

300 183 

When considering the indoor sources, an additional scaling was performed so as not to 

affect their estimated concentrations while adjusting the benchmark levels downward. To clarify 

how this was done, 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: 

 Cesposure = A×Cambient + B×Cindoor >Cthreshold equation (6) 

 where, 

 

Cexposure = individual exposure concentration 

A = proportion of exposure concentration from ambient 

Cambient = ambient concentration in the absence of indoor sources 
 B-57

B = proportion of exposure concentration from indoor 

Cindoor = indoor source concentration contribution 

Cthreshold = an exposure concentration of interest 

It follows that if we are interested in adjusting the ambient concentrations upwards by 

some proportional factor F, this can be described with the following: 

F × A×Cambient + B×Cindoor >Cthreshold equation (7) 

This is equivalent to 

 ) A×Cambient + B×(Cindoor / F) > (Cthreshold / F equation (8) 

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. 
 B-58

B-3.6 Philadelphia Exposure Modeling Results 

B-3.6.1 Overview 

The results of the exposure and risk characterization are presented here for Philadelphia 

County. These results are not to be directly compared with the results in the final REA, due to 

differences in the model approach used here. The main difference that does not allow for 

comparison with the Atlanta exposure assessment is that the minor road emissions were not 

modeled in the Philadelphia County assessment. 

Several scenarios were considered for the exposure assessment, including two averaging time 

for NO2 concentrations (annual and 1-hour), inclusion of indoor sources, and for evaluating just 

meeting the current standard. To date, year 2002 served as the base year for all scenarios, years 

2001 and 2003 were only evaluated for a limited number of scenarios. Exposures were 

simulated for four groups; children and all persons, and the asthmatic population within each of 

these. 

The exposure results summarized below focus on the population group where exposure 

estimations are of greatest interest, namely asthmatic individuals. The complete results for each 

of these two population subgroups are provided in section B-3.6.7. However, due to certain 

limitations in the data summaries output from the current version of APEX, some exposure data 

could only be output for the entire population modeled (i.e., all persons - includes asthmatics and 

healthy persons of all ages). The summary data for the entire population (e.g., annual average 

exposure concentrations, time spent in microenvironments at or above a potential health effect 

benchmark level) can be representative of the asthmatic population since the asthmatic 

population does not have its microenvironmental concentrations and activities estimated any 

differently from those of the total population. 

B-3.6.2 Evaluation of Modeled NO2 Air Quality Concentrations (as is) 

Since the current NO2 standard is 0.053 ppm annual average, the predicted air quality 

concentrations were first summarized by calculating annual average concentration. The 

distribution for the AERMOD predicted NO2 concentrations at each of the 16,857 receptors for 

years 2001 through 2003 are illustrated in Figure B-12. Variable concentrations were estimated 

by the dispersion model over the three year period (2001-2003). The NO2 concentration 

distribution was similar for years 2001 and 2002, with mean annual average concentrations of 

about 21 ppb and a COV of just over 30%. On average, NO2 annual average concentrations 

were lowest during simulated year 2003 (mean annual average concentration was about 16 ppb), 

largely a result of the comparably lower local concentration added (Table B-28). While the 

mean annual average concentrations were lower than those estimated for 2001 and 2002, a 

greater number of annual average concentrations were estimated above 53 ppb for year 2003. In 

addition, year 2003 also contained greater variability in annual average concentrations as 

indicated by a COV of 53%. 
 B-59

0

20

40

60

80

100

120

140

160

180

200

-5 -4 -3 -2 -1 0 1 2 3 4 5

Normal Quantile

AERMOD Predicted Annual Average NO2 Concentration (ppb))

2003

2002

2001

Figure B-12 . Distribution of AERMOD estimated annual average NO2 concentrations at each of the 16,857 

receptors in Philadelphia County for years 2001-2003. 

Diurnal variability in NO2 concentrations was evaluated by comparing the modeled 

concentrations at the monitor receptors with the measured concentrations at the ambient 

monitors. Figure B-13 presents the annual average NO2 concentration at each hour of the day for 

the three monitors located in Philadelphia County. The diurnal distributions among the modeled 

versus measured concentrations are similar at all of the monitors, with peak NO2 concentrations 

generally coinciding with the typical peak commute times of 6:00-9:00 AM and 5:00-8:00 PM. 

The pattern is represented best at monitor 4210100043 (top graph in Figure B-13), however the 

AERMOD concentrations are approximately 8 ppb lower at the earlier times of the day following 

the adjustment for sources not modeled (section B-3.5.9). There is greater variability in the 

modeled NO2 concentrations at the other two monitors when compared with the measured data 

(middle and bottom graphs of Figure B-13), although the patterns are still similar. The greatest 

difference in NO2 concentrations occurs during the later commute period, most notable at 

monitor 4210100292. Given the concentration adjustment to correct for sources not modeled 

was applied to all receptors equally across the entire modeling domain, it is not surprising that 

the modeled concentrations are higher in some instances while others not. The pattern in the 

concentrations is the important feature to replicate, of which AERMOD does reasonably, and 

based on these three receptors, may slightly overestimate peak concentrations more times than 

underestimate them. 

 
 B-60

 

 Figure B-13. Measured and modeled diurnal pattern of NO2 concentrations at three ambient monitor sites.

0

10

20

30

40

50

60

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour of Day

Annual Average NO 2 Concentration (ppb)

Monitor - 4210100471

AERMOD

AERMOD + Correction

0

10

20

30

40

50

60

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour of Day

Annual Average NO 2 Concentration (ppb)

Monitor - 4210100292

AERMOD

AERMOD + Correction

0

5

10

15

20

25

30

35

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour of Day

Annual Average NO 2 Concentration (ppb)

Monitor - 4210100043

AERMOD

AERMOD + Correction
 B-61

B-3.6.3 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 adjustment factor (section A-8) and the other 

using the AERMOD dispersion model (section B-3.5), were compared to one another. There are 

no on-road NO2 concentration measurements in Philadelphia for the modeled data to be 

compared with, although it should be noted that the data used to estimate the adjustment factors 

and applied to the monitor data are measurement based. 

First a comparison can be made between the factor used for estimating on-road 

concentrations in the air quality analysis and similar factors calculated using AERMOD 

estimated concentrations. As described in section A-8, an empirical distribution of on-road 

adjustment 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 B-14, and represent the 

on-road adjustment factors multiplied by the ambient monitor concentration (> 100 m from a 

major road) and used to estimate the on-road concentration in the air quality characterization 

(chapter 7 of the REA). The one-hour NO2 concentrations estimated at every AERMOD 

receptor in Philadelphia were compared with the concentrations estimated at their closest on-road 

receptor to generate a similar ratio (i.e., on-road/non-road concentrations). These 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 B-14. There are similarities in comparing 

each of the AERMOD with the measurement study derived distributions, most importantly at the 

upper percentiles. Intersection of the two approaches occurs at about the 70th percentile and 

continues through the 90th percentile. While the two seasonal distributions for AERMOD are 

very similar to one another, they diverge at the upper percentiles, with the summer ratios 

containing greater values at the same percentiles. This is similar to what was observed in the 

measurement derived distribution, although the summer ratio distribution consistently contained 

greater values at all percentiles compared with the non-summer distribution. 

 There are differences that exist when comparing the two approaches at the mid to lower 

percentiles, with the AERMOD ratios consistently lower than 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. Thus, the measurement study derived ratios never fall below a value of 

one, there are no on-road concentrations less than any corresponding non-road influenced 

concentrations. This was, by design, a reasonable assumption for estimating the on-road 

concentrations for the air quality characterization. The AERMOD receptors however, include all 

types of emission sources such that there are possibilities for concentrations at non-road 

receptors that are greater than on-road, a more realistic depiction of the actual relationship 

between on-road and non-road receptors. Furthermore, the AERMOD distribution extends 
 B-62

beyond the range of values offered by the measurement study derived ratios at the very upper 

percentiles. One issue with this comparison is that the AERMOD developed ratios are from a 1-

hour averaging time, while the study derived ratios were from averaging times of mostly 7-14 

days. This could be why the AERMOD ratios have much greater variability than with the study 

derived ratios. 

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.1 1.0 10.0 100.0

On-Road/Non-Road Ratio

Cumulative Probability

Summer - AERMOD

Not Summer - AERMOD

Summer - Studies

Not Summer - Studies

Figure B-14. Comparison of on-road/non-road ratios developed from AERMOD concentration estimates and 

those derived from published NO2 measurement studies. 

Briefly for the second comparison, hourly on-road NO2 concentrations were estimated using 

AERMOD for 979 on-road receptors in Philadelphia for the year 2002. The 24 hourly values 

modeled for each day at each receptor were rounded to the nearest 1 ppb and then adjusted for 

sources not modeled using the ambient monitor data (Table B-25). 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 Philadelphia CMSA. 

Table B-30 compares the summary statistics of the hourly concentrations and the number of 

exceedances of the potential health effect benchmark levels. The AERMOD predicted and 

ambient monitor simulated concentration distributions have very similar means and percentiles. 

However the variance of the modeled values is about 60 % higher than the variance of the 

simulated on-road monitor concentrations. This variance difference is largely a function of 

differences in the extreme upper tails of the distributions and most notable when comparing the 

numbers of exceedances of the potential health effect benchmark levels. The AERMOD on-road 

receptors consistently have 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 35 exceedances of 200 ppb per site-year while the simulated on-road 
 B-63

monitors had an average of 2 exceedances per year. The maximum number of exceedances per 

site-year was 530 for the AERMOD modeled data and 59 for the simulated on-road monitor data. 

The apparent contradiction between the similarity of the hourly concentration distributions 

and the large differences in the exceedance distributions can be explained by the fact that 200 

ppb is the 99.605th percentile of the AERMOD hourly concentrations and is the 99.974th

percentile of the simulated on-road monitor concentrations. Thus on average, 0.395 % of hourly 

AERMOD values exceed 200 ppb per year and 0.026 % of hourly simulated on-road monitored 

values exceed 200 ppb per year. These differences could be due to the greater number of 

receptors modeled by AERMOD (n=979) compared with the on-road monitor simulation (n=5). 

Again, the AERMOD generated data could include locations greatly influenced by roadway 

emissions that are not captured by the simplified approach conducted in the Air Quality 

Characterization. 

Table B-30. Summary statistics of on-road hourly NO2 concentrations (ppb) and the numbers of potential 

health effect benchmark levels using AERMOD and the on-road ambient monitor simulation approaches in 

Philadelphia. 

1-hour NO2

concentrations 

Exceedances of 

150 ppb 

Exceedances of 

200 ppb 

Exceedances of 

250 ppb 

Statistic AERMOD 

Monitor 

Simulation AERMOD

Monitor 

Simulation AERMOD

Monitor 

Simulation AERMOD

Monitor 

Simulation

N 8,576,040 4,183,900 979 500 979 500 979 500

Mean 36.2 35.4 113 18 35 2 12 0.6

Stdev 32.1 24.9 142 47 61 8 30 1.6

Variance 1,030 620 20,171 2,187 3,751 61 900 2.6

p0 12 0 0 0 0 0 0 0

p5 12 5 2 0 0 0 0 0

p10 12 9 8 0 0 0 0 0

p15 13 11 13 0 1 0 0 0

p20 14 14 21 0 2 0 0 0

p25 15 16 27 1 3 0 0 0

p30 17 19 32 1 4 0 0 0

p35 18 22 39 1 6 0 1 0

p40 20 25 45 1 8 0 1 0

p45 22 27 56 1 10 0 2 0

p50 25 30 65 1 13 0 2 0

p55 28 34 73 1 15 0 3 0

p60 31 38 86 2 20 1 4 0

p65 35 41 106 3 24 1 5 0

p70 40 45 122 6 31 1 7 0

p75 45 49 143 8 39 1 10 1

p80 52 54 176 15 56 1 15 1

p85 61 60 216 24 72 1 21 1

p90 75 68 267 63 95 4 31 1

p95 98 81 390 92 148 11 58 1

p100 707 681 1,072 278 530 59 299 11

 
 B-64

B-3.6.4 Annual Average Exposure Concentrations (as is) 

The hourly NO2 concentrations output from AERMOD were input into the exposure model, 

providing a range of estimated exposures output by APEX. Figure B-15 illustrates the annual 

average exposure concentrations for the entire simulated population (both asthmatics and healthy 

individual of all ages), for each of the years analyzed and where indoor sources were modeled. 

While years 2001 and 2002 contained very similar population exposure concentration 

distributions, the modeled year 2003 contained about 20% lower annual average concentrations. 

The lower exposure concentrations for year 2003 are similar to what was observed for the 

predicted air quality (Figure B-12), however, all persons were estimated to contain exposures 

below an annual average concentration of 53 ppb, even considering indoor source concentration 

contributions. Again, while Figure B-15 summarizes the entire population, the data are 

representative of what would be observed for the population of asthmatics or asthmatic children. 

0

10

20

30

40

50

60

70

80

90

100

0 5 10 15 20 25 30 35 40 45

Annual Average NO2 Exposure (ppb)

Population Percentile

2001 with indoor sources

2002 with indoor sources

2003 with indoor sources

Figure B-15. Estimated annual average total NO2 exposure concentrations for all simulated persons in 

Philadelphia County, using modeled 2001-2003 air quality (as is), with modeled indoor sources. 

The AERMOD predicted air quality and the estimated exposures for year 2002 were 

compared using their respective annual average NO2 concentrations (Figure B-16). As a point of 

reference, the annual average concentration for 2002 ambient monitors ranged from 24 ppb to 29 

ppb. Many of the AERMOD predicted annual average concentrations were below that of the 

lowest ambient monitoring concentration of 24 ppb, although a few of the receptors contained 

concentrations above the highest measured annual average concentration. Estimated exposure 

concentrations were below that of both the modeled and measured air quality. For example, 

exposure concentrations were about 5 ppb less than the modeled air quality when the exposure 
 B-65

estimation included indoor sources, and about 10 ppb less for when exposures were estimated 

without indoor sources. In comparing the estimated exposures with and without indoor sources, 

indoor sources were estimated to contribute between 1 and 5 ppb to the total annual average 

exposures. 

0

10

20

30

40

50

60

70

80

90

100

0 5 10 15 20 25 30 35 40 45

Annual Average NO2 Cocentrations (ppb)

 Percentile

AERMOD Predicted 2002 air quality (as is)

APEX Exposure 2002 no indoor sources

APEX Exposure 2002 with indoor sources

Figure B-16. Comparison of AERMOD predicted and ambient monitoring annual average NO2

concentrations (as is) and APEX exposure concentrations (with and without modeled indoor sources) in 

Philadelphia County for year 2002. 

B-3.6.5 One-Hour Exposures (as is) 

Since there is interest in short-term exposures, a few analyses were performed using the 

APEX estimated exposure concentrations. As part of the standard analysis, APEX reports the 

maximum exposure concentration for each simulated individual in the simulated population. 

This can provide insight into the proportion of the population experiencing any NO2 exposure 

concentration level of interest. In addition, exposures are estimated for each of the selected 

potential health effect benchmark levels (200, 250, and 300 ppb, 1-hour average). An 

exceedance was recorded when the maximum exposure concentration observed for the individual 

was above the selected level in a day (therefore, the maximum number of exceedances is 365 for 

a single person). Estimates of repeated exposures are also recorded, that is where 1-hour 

exposure concentrations were above a selected level in a day added together across multiple days 

(therefore, the maximum number of multiple exceedances is also 365). Persons of interest in this 

exposure analysis are those with particular susceptibility to NO2 exposure, namely individuals 

with asthma. The health effect benchmark levels are appropriate for estimating the potential risk 

of adverse health effects for asthmatics. The majority of the results presented in this section are 

for the simulated asthmatic population. However, the exposure analysis was performed for the 

total population to assess numbers of persons exposed to these levels and to provide additional 
 B-66

information relevant to the asthmatic population (such as time spent in particular 

microenvironments). 

B-3.6.5.1 Maximum Estimated Exposure Concentrations 

A greater variability was observed in maximum exposure concentrations for the 2003 year 

simulation compared with years 2001 and 2002 (Figure B-17). While annual average exposure 

concentrations for the total population were the lowest of the 3-year simulation, year 2003 

contained a greater number of individual maximum exposures at and above the lowest potential 

health effect benchmark level. When indoor sources are not modeled however, over 90% of the 

simulated persons do not have an occurrence of a 1-hour exposure above 200 ppb in a year. 

B-3.6.5.2 Number of Estimated Exposures above Selected Levels 

When considering the total asthmatic population simulated in Philadelphia County and using 

current air quality of 2001-2003, nearly 50,000 persons were estimated to be exposed at least one 

time to a one-hour concentration of 200 ppb in a year (Figure B-18). These exposures include 

both the NO2 of ambient origin and that contributed by indoor sources. The number of 

asthmatics exposed to greater concentrations (e.g., 250 or 300 ppb) drops dramatically and is 

estimated to be somewhere between 1,000 – 15,000 depending on the 1-hour concentration level 

and the year of air quality data used. Exposures simulated for year 2003 contained the greatest 

number of asthmatics exposed in a year consistently for all potential health effect benchmark 

levels, while year 2002 contained the lowest number of asthmatics. Similar trends across the 

benchmark levels and the simulation years were observed for asthmatic children, albeit with 

lower numbers of asthmatic children with exposures at or above the potential health effect 

benchmark levels. 
 B-67

0

10

20

30

40

50

60

70

80

90

100

0 50 100 150 200 250 300 350 400 450 500

Maximum 1-hour Exposure (ppb) in a Year

Population Percentile

2001 with indoor sources

2002 with indoor sources

2003 with indoor sources

2002 no indoor sources

Figure B-17. Estimated maximum NO2 exposure concentration for all simulated persons in Philadelphia 

County, using modeled 2001-2003 air quality (as is), with and without modeled indoor sources. Values above 

the 99th percentile are not shown. 

200

250

300

2001 AQ (as is) - with indoor sources

2002 AQ (as is) - with indoor sources

2003 AQ (as is) - with indoor souces

0.0E+0

1.0E+4

2.0E+4

3.0E+4

4.0E+4

5.0E+4

6.0E+4

Estimated Number of Asthmatics

Exposed to Selected Level in a Year

Potential Health Effect Benchmark Level (ppb)

Simulated Year - Scenario

Figure B-18. Estimated number of all simulated asthmatics in Philadelphia County with at least one NO2

exposure at or above the potential health effect benchmark levels, using modeled 2001-2003 air quality (as is), 

with modeled indoor sources. 
 B-68

200

250

300

2001 AQ (as is) - with indoor sources

2002 AQ (as is) - with indoor sources

2003 AQ (as is) - with indoor souces

0.0E+0

2.0E+3

4.0E+3

6.0E+3

8.0E+3

1.0E+4

1.2E+4

1.4E+4

Estimated Number of Asthmatic Children

Exposed to Selected 1-hour Level in a Year

Potential Health Effect Benchmark Level (ppb)

Simulated Year - Scenario

Figure B-19. Estimated number of simulated asthmatic children in Philadelphia County with at least one 

NO2 exposure at or above the potential health effect benchmark levels, using modeled 2001-2003 air quality 

(as is), with modeled indoor sources. 

200

250

300

2002 AQ (as is) - no indoor sources

2002 AQ (as is) - with indoor sources

47651

4430

9505 1240

2275

974

0.0E+0

1.0E+4

2.0E+4

3.0E+4

4.0E+4

5.0E+4

Estimated Number of Asthmatics

Exposed to Selected Level in Year 

2002

Potential Health Effect Benchmark Level (ppb)

Simulated Year - Scenario

Figure B-20. Comparison of the estimated number of all simulated asthmatics in Philadelphia County with at 

least one NO2 exposure at or above potential health effect benchmark levels, using modeled 2002 air quality 

(as is) , with and without modeled indoor sources. 
 B-69

For example, nearly 12,000 were estimated to be exposed to at least a one-hour NO2

concentration of 200 ppb in a year (Figure B-19). Additional exposure estimates were generated 

using the modeled 2002 air quality (as is) and where the contribution from indoor sources was 

not included in the exposure concentrations. APEX allows for the same persons to be simulated, 

i.e., demographics of the population were conserved, as well as using the same individual timelocation-activity profiles generated for each person. Figure B-20 compares 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 concentrations is reduced by between 50-80%, depending on 

benchmark level, when not including indoor source (i.e., gas cooking) concentration 

contributions. 

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 is summarized to 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) since their 

microenvironmental concentrations and activities are not estimated any differently from those of 

the total population by APEX. 

As an example, Figure B-21 (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 200, 250, and 300 ppb (results for years 2001 and 2003 were similar). 

Estimated exposures included the contribution from one major category of indoor sources (i.e., 

gas cooking). The time spent in the indoor residence and bars/restaurants were the most 

important for concentrations ≥200 ppb, contributing to approximately 75% of the time persons 

were exposed (Figure B-21a). This is likely a result of the indoor source concentration 

contribution to each individual’s exposure concentrations. The importance of the particular 

microenvironment however changes with differing potential health effect benchmark levels. 

This is evident when considering the in-vehicle and outdoor near-road microenvironments, 

progressing from about 19% of the time exposures were at the lowest potential health effect 

benchmark level (200 ppb) to a high of 64% of the time exposures were at the highest 

benchmark level (300 ppb, Figure B-21c). 

The microenvironments where higher exposure concentrations occur were also evaluated for 

the exposure estimates generated without indoor source contributions. Figure B-22 illustrates 

that the time spent in the indoor microenvironments contributes little to the estimated exposures 

above the selected benchmark levels. The contribution of these microenvironments varied only 

slightly with increasing benchmark concentration, ranging from about 2-5%. Most of the time 

associated with high exposures was associated with the transportation microenvironments (InVehicle or In-Public Transport) or outdoors (Out-Near Road, Out-Parking Lot, Out-Other). The 

importance of time spent outdoors near roadways exhibited the greatest change in contribution 

with increased health benchmark level, increasing from around 30 to 44% of time associated 

with concentrations of 200 and 300 ppb, respectively. While more persons are likely to spend 
 B-70

time inside a vehicle than outdoors near roads, there is attenuation of the on-road concentration 

that penetrates the in-vehicle microenvironment, leading to lowered concentrations, occurring 

less frequently above 300 ppb than outdoors near roads. 
 B-71

In-Residence

In-Bar & Restaurant

In-School

In-Day Care

In-Office

In-Shopping

In-Other

Out-Near Road

Out-Parking Lot

Out-Other

In-Vehicle

In-Public Trans

Other

In-Residence

In-Bar & Restaurant

In-School

In-Day Care

In-Office

In-Shopping

In-Other

Out-Near Road

Out-Parking Lot

Out-Other

In-Vehicle

In-Public Trans

Other

In-School

In-Day Care

In-Office

In-Shopping

Out-Near Road

Out-Parking Lot

Out-Other

In-Vehicle

In-Residence

In-Bar & Restaurant

In-Other

In-Public Trans

Other

a) ≥ 200 ppb

b) ≥ 250 ppb

c) ≥ 300 ppb

In-Residence

In-Bar & Restaurant

In-School

In-Day Care

In-Office

In-Shopping

In-Other

Out-Near Road

Out-Parking Lot

Out-Other

In-Vehicle

In-Public Trans

Other

In-Residence

In-Bar & Restaurant

In-School

In-Day Care

In-Office

In-Shopping

In-Other

Out-Near Road

Out-Parking Lot

Out-Other

In-Vehicle

In-Public Trans

Other

In-School

In-Day Care

In-Office

In-Shopping

Out-Near Road

Out-Parking Lot

Out-Other

In-Vehicle

In-Residence

In-Bar & Restaurant

In-Other

In-Public Trans

Other

a) ≥ 200 ppb

b) ≥ 250 ppb

c) ≥ 300 ppb

Figure B-21. Fraction of time all simulated persons in Philadelphia County spend in the twelve 

microenvironments associated with the potential NO2 health effect benchmark levels, a) ≥ 200 ppb, b) ≥ 250 

ppb, and c) ≥ 300 ppb, year 2002 simulation with indoor sources. 
 B-72

Out-Near Road

Out-Parking Lot

Out-Other

In-Vehicle

In-Public TransOther

In-Residence In-School

In-Day Care

In-Office

In-Shopping

In-Other

In-Bar & Restaurant

Out-Near Road

Out-Parking Lot Out-Other

In-Vehicle

In-Public TransOther

In-Residence

In-School

In-Day Care

In-Office

In-Shopping

In-Other

In-Bar & Restaurant

Out-Near Road

Out-Parking Lot

Out-Other

In-Vehicle

In-Public Trans Other

In-Residence

In-School

In-Day Care

In-Office

In-Shopping

In-Other

In-Bar & Restaurant

a) ≥ 200 ppb

b) ≥ 250 ppb

c) ≥ 300 ppb

Out-Near Road

Out-Parking Lot

Out-Other

In-Vehicle

In-Public TransOther

In-Residence In-School

In-Day Care

In-Office

In-Shopping

In-Other

In-Bar & Restaurant

Out-Near Road

Out-Parking Lot Out-Other

In-Vehicle

In-Public TransOther

In-Residence

In-School

In-Day Care

In-Office

In-Shopping

In-Other

In-Bar & Restaurant

Out-Near Road

Out-Parking Lot

Out-Other

In-Vehicle

In-Public Trans Other

In-Residence

In-School

In-Day Care

In-Office

In-Shopping

In-Other

In-Bar & Restaurant

a) ≥ 200 ppb

b) ≥ 250 ppb

c) ≥ 300 ppb

Figure B-22. Fraction of time all simulated persons in Philadelphia County spend in the twelve 

microenvironments associated with the potential NO2 health effect benchmark levels, a) ≥ 200 ppb, b) ≥ 250 

ppb, and c) ≥ 300 ppb, year 2002 simulation without indoor sources. 
Draft-Do Not Quote or Cite B-73

B-3.6.5.3 Number of Repeated Exposures Above Selected Levels

In the analysis of persons exposed, the results show the number or percent of those with at 

least one exposure at or above the selected potential health effect benchmark level. Given that 

the benchmark is for a small averaging time (i.e., one-hour) it may be possible that individuals 

are exposed to concentrations at or above the potential health effect benchmark levels more than 

once in a given year. Since APEX simulates the longitudinal diary profile for each individual, 

the number of times above a selected level is retained for each person. Figure B-23 presents 

such an analysis for the year 2003, the year containing the greatest number of exposure 

concentrations at or above the selected benchmarks. Estimated exposures include both those 

resulting from exposures to NO2 of ambient origin and those resulting from indoor source NO2

contributions. While a large fraction of individuals experience at least one exposure to 200 ppb 

or greater over a 1-hour time period in a year (about 32 percent), only around 14 percent were 

estimated to contain at least 2 exposures. Multiple exposures at or above the selected 

benchmarks greater than or equal to 3 or more times per year are even less frequent, with around 

5 percent or less of asthmatics exposed to 1-hour concentrations greater than or equal to 200 ppb 

3 or more times in a year. 

Exposure estimates for year 2002 are presented to provide an additional perspective, 

including a lower bound of repeated exposures for this population subgroup and for exposure 

estimates generated with and without modeled indoor sources (Figure B-24). Most asthmatics 

exposed to a 200 ppb concentration are exposed once per year and only around 11 percent would 

experience 2 or more exposures at or above 200 ppb when including indoor source contributions. 

The percent of asthmatics experiencing multiple exposures a and abovet 250 and 300 ppb is 

much lower, typically less than 1 percent of all asthmatics are exposed at the higher potential 

benchmark levels. Also provided in Figure B-24 are the percent of asthmatics exposed to 

selected levels in the absence of indoor sources. Again, without the indoor source contribution, 

there are reduced occurrences of multiple exposures at all of the potential health effect 

benchmark levels compared with when indoor sources were modeled. 
Draft-Do Not Quote or Cite B-74

200

250

300 6

5

4

3

2

1

0

5

10

15

20

25

30

35

Estimated Percent of Asthmatics Exposed

one or more times to Selected 1-hour Level in Year 2003

Potential Health Effect Benchmark 

Level (ppb)

Estimated Number of

Repeated Exposures in a Year

Figure B-23. Estimated percent of all asthmatics in Philadelphia County with repeated NO2 exposures above 

potential health effect benchmark levels, using 2003 modeled air quality (as is), with modeled indoor sources. 

200 - with

indoor

soucrces

250 - with

indoor

sources

300 - with

indoor

sources

200 - no

indoor

sources

250 - no

indoor

sources

300 - no

indoor

sources

6

5

4

3

2

1

29

3

1

6

1 1

11

1 1

5

1 3

2

1 0

5

10

15

20

25

30

Estimated Percent of Asthmatics Exposed 

one or more Times to Selected 1-hour Level in Year 2002

Health Effect Benchmark Level (ppb)

Estimated Number of 

Repeated Exposures

in a Year

Figure B-24. Estimated percent of all asthmatics in Philadelphia County with repeated NO2 exposures above 

potential health effect benchmark levels, using modeled 2002 air quality (as is), with and without indoor 

sources. 
Draft-Do Not Quote or Cite B-75

B-3.6.6 One-Hour Exposures Associated with Just Meeting the Current 

Standard 

 To simulate just meeting the current NO2 standard, 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. Similar estimates of short-term exposures 

(i.e., 1-hour) were generated for the total population and population subgroups of interest (i.e., 

asthmatics and asthmatic children). 

B-3.6.6.1 Number of Estimated Exposures above Selected Levels 

In considering exposures estimated to occur 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 increased. To allow for 

reasonable comparison, the number of persons affected considering each scenario is expressed as 

the percent of the subpopulation of interest. Figure B-25 illustrates the percent of asthmatics 

estimated to experience at least one exposure at or above the selected potential health effect 

benchmark concentrations, with just meeting the current standard and including indoor source 

contributions. While it was estimated that about 30% percent of asthmatics would be exposed to 

200 ppb (1-hour average) at least once in a year for as is air quality, it was estimated that around 

80 percent of asthmatics would experience at least one concentration above the lowest potential 

health effect benchmark level in a year representing just meeting the current standard. Again, 

estimates for asthmatic children exhibited a similar trend, with between 75 to 80 percent exposed 

to a concentration at or above the lowest potential health effect benchmark level at least once per 

year for a year just meeting the current standard (data not shown). The percent of all asthmatics 

experiencing the higher benchmark levels is reduced to between 31 and 45 percent for the 250 

ppb, 1-hour benchmark, and between 10 and 24 percent for the 300 ppb, 1-hour benchmark level 

associated with air quality representing just meeting the current annual average standard. 
Draft-Do Not Quote or Cite B-76

200

250

300

2001 AQ (std) - with indoor soucrces

2002 AQ (std) - with indoor soucrces

2003 AQ (std) - with indoor soucrces

0

10

20

30

40

50

60

70

80

90

Estimated Number of Asthmatics Exposed to

Selected Level in Years 2001-2003

with Just Meeting the Current Standard

Potential Health Effect Benchmark Level (ppb)

Simulated Year - Scenario

Figure B-25. Estimated percent of all asthmatics in Philadelphia with at least one exposure at or above the 

potential health effect benchmark level, using modeled 2001-2003 air quality just meeting the current 

standard, with modeled indoor sources. 

In evaluating the influence of indoor source contribution for the scenario just meeting the 

current standard, the numbers of individuals exposed at selected levels are reduced without 

indoor sources, ranging from about 26 percent lower for the 200 ppb level to around 11 percent 

for the 300 ppb level when compared with exposure estimates that accounted for indoor sources 

(Figure B-26). 

200

250

300

2002 AQ (std) - no indoor sources

2002 AQ (std) - with indoor sources

133524

53367

18828

98850

43972

16693

0.0E+0

3.0E+4

6.0E+4

9.0E+4

1.2E+5

1.5E+5

Estimated Number of Asthmatics

Exposed to Selected Level in 

Year 2002 with Just Meeting the 

Current Standard

Potential Health Effect Benchmark Level (ppb)

Simulated Year - Scenario

Figure B-26. Estimated number of all asthmatics in Philadelphia with at least one exposure at or above the 

potential health effect benchmark level, using modeled 2002 air quality just meeting the current standard, 

with and without modeled indoor sources. 
Draft-Do Not Quote or Cite B-77

B-3.6.6.2 Number of Repeated Exposures Above Selected Levels

For air quality simulated to just meet the current standard, repeated exposures at the selected 

potential health effect benchmarks are more frequent than that estimated for the modeled as is air 

quality. Figure B-27 illustrates this using the simulated asthmatic population for year 2002 data 

as an example. Many asthmatics that are exposed at or above the selected levels are exposed 

more than one time. Repeated exposures above the potential health effect benchmark levels are 

reduced however, when not including the contribution from indoor sources. The percent of 

asthmatics exposed drops with increasing benchmark level, with progressively fewer persons 

experiencing multiple exposures for each benchmark level. 

200 - with

indoor

soucrces

250 - with

indoor

sources

300 - with

indoor

sources

200 - no

indoor

sources

250 - no

indoor

sources

300 - no

indoor

sources

6

5

4

3

2

1

82

33

12

61

27

10

63

13

3

37

10

3

48

6 1

23

5 1

35

4 1

14

2

1

26

2

1

10

2

20

2 7

1

0

10

20

30

40

50

60

70

80

90

Estimated Percent of Asthmatics Exposed 

one or more Times to Selected 1-hour Level in Year 2002

with Just Meeting the Current Standard

Health Effect Benchmark Level (ppb)

Estimated Number of 

Repeated Exposures

in a Year

Figure B-27. Estimated percent of asthmatics in Philadelphia County with repeated exposures above health 

effect benchmark levels, using modeled 2002 air quality just meeting the current standard, with and without 

modeled indoor sources. 

B-3.6.7 Additional Exposure Results 

This section provides supplemental exposure and risk characterization results for two 

subpopulations, all asthmatics and asthmatic children. The data are presented in series of 

summary tables and figures across each of the scenarios investigated (i.e. with modeled air 

quality as is and simulating just meeting the current standard), with and without modeled indoor 

sources (i.e., gas stoves), for each of the potential health effect benchmark levels (i.e., 200, 250, 

300 ppb 1-hour), and across three years of modeled air quality (i.e., 2001 to 2003). Repeated 

exposures are presented only for the lowest potential health effect benchmark level (i.e., 200 ppb 

1-hour). 
Draft-Do Not Quote or Cite B-78

B-3.6.7.1 All Asthmatics 

Table B-31. Estimated number of asthmatics in Philadelphia County exposed at or above potential health 

effect benchmark levels (1 to 6 times per year), using modeled air quality (as is) and with just meeting the 

current standard (std), and with and without indoor sources. 

Persons with Number of Repeated Exposures 

Year (AQ) 

Indoor 

Source 

Level 

(ppb) 1 2 3 4 5 6 

200 49796 19544 8959 4516 2666 1732 

250 4867 1414 658 381 265 157 

Yes 

300 1388 404 157 108 59 39 

200 10544 2577 1230 795 520 422 

250 2584 765 413 295 186 118 

2001 (as 

is) 

No 

300 1013 344 177 98 39 29 

200 128147 96119 70079 50253 35965 26167 

250 49632 18322 8523 4808 3095 2152 

Yes 

300 16805 4480 1828 1219 866 638 

200 90211 51600 31720 19805 12899 8938 

250 40466 14362 6155 3225 2141 1414 

2001 (std) 

No 

300 15100 3590 1595 1003 755 569 

200 47652 17720 8056 4170 2662 1765 

250 4430 1173 530 274 166 127 

Yes 

300 1240 393 147 88 69 49 

200 9505 2411 1240 706 401 323 

250 2276 778 332 185 117 88 

2002 (as 

is) 

No 

300 975 304 137 59 49 49 

200 133524 102861 77512 57152 42473 31800 

250 53367 20737 9855 5784 3489 2623 

Yes 

300 18828 5220 2324 1447 925 648 

200 98849 60056 36913 23238 15850 10875 

250 43972 16367 7370 4066 2680 1734 

2002 (std) 

No 

300 16693 4389 1950 1131 766 510 

200 52639 22084 11950 7441 4863 3457 

250 14407 5040 2599 1577 935 650 

Yes 

300 6568 1892 887 512 335 245 

200 26120 10007 5857 3783 2609 1842 

250 11142 3927 2040 1261 777 550 

2003 (as 

is) 

No 

300 5605 1627 778 462 285 206 

200 132640 102034 76909 58857 44719 34990 

250 73387 38505 22953 15416 11101 8499 

Yes 

300 39283 16213 9280 6175 4374 3259 

200 109726 73489 51133 36551 27509 21181 

250 65437 33096 18948 12710 8964 6862 

2003 (std) 

No 

300 35948 14502 8474 5654 4098 2935 
Draft-Do Not Quote or Cite B-79

Table B-32. Estimated percent of asthmatics in Philadelphia County exposed at or above potential health 

effect benchmark levels (1 to 6 times per year), using modeled air quality (as is) and with just meeting the 

current standard (std), and with and without indoor sources. 

Percent (%) of Persons With Repeated Exposures 

Year (AQ) 

Indoor 

Source 

Level 

(ppb) 1 2 3 4 5 6 

200 31 12 6 3 2 1 

250 3 1 0 0 0 0 

Yes 

300 1 0 0 0 0 0 

200 6 2 1 0 0 0 

250 2 0 0 0 0 0 

2001 

(as is) 

No 

300 1 0 0 0 0 0 

200 79 59 43 31 22 16 

250 31 11 5 3 2 1 

Yes 

300 10 3 1 1 1 0 

200 55 32 20 12 8 5 

250 25 9 4 2 1 1 

2001 

(std) 

No 

300 9 2 1 1 0 0 

200 29 11 5 3 2 1 

250 3 1 0 0 0 0 

Yes 

300 1 0 0 0 0 0 

200 6 1 1 0 0 0 

250 1 0 0 0 0 0 

2002 

(as is) 

No 

300 1 0 0 0 0 0 

200 82 63 48 35 26 20 

250 33 13 6 4 2 2 

Yes 

300 12 3 1 1 1 0 

200 61 37 23 14 10 7 

250 27 10 5 2 2 1 

2002 

(std) 

No 

300 10 3 1 1 0 0 

200 32 14 7 5 3 2 

250 9 3 2 1 1 0 

Yes 

300 4 1 1 0 0 0 

200 16 6 4 2 2 1 

250 7 2 1 1 0 0 

2003 

(as is) 

No 

300 3 1 0 0 0 0 

200 81 63 47 36 27 21 

250 45 24 14 9 7 5 

Yes 

300 24 10 6 4 3 2 

200 67 45 31 22 17 13 

250 40 20 12 8 6 4 

2003 

(std) 

No 

300 22 9 5 3 3 2 
Draft-Do Not Quote or Cite B-80

200

250

300

2001 AQ (as is) - with indoor soucrces

2002 AQ (as is) - with indoor soucrces

2003 AQ (as is) - with indoor soucrces

0

5

10

15

20

25

30

35

Estimated Percent of Asthmatics Exposed to

Selected Level in Years 2001-2003 (as is)

Potential Health Effect Benchmark Level (ppb)

Simulated Year - Scenario

Figure B-28. Estimated percent of all asthmatics in Philadelphia County with at least one NO2 exposure at or 

above potential health effect benchmark level, using 2001-2003 modeled air quality (as is), with modeled 

indoor sources. 

200

250

300

2001 AQ (as is) - no indoor soucrces

2002 AQ (as is) - no indoor soucrces

2003 AQ (as is) - no indoor soucrces

0

5

10

15

20

25

30

35

Estimated Percent of Asthmatics Exposed to

Selected Level in Years 2001-2003 (as is)

Potential Health Effect Benchmark Level (ppb)

Simulated Year - Scenario

Figure B-29. Estimated percent of all asthmatics in Philadelphia County with at least one NO2 exposure at or 

above potential health effect benchmark level, using 2001-2003 modeled air quality (as is), with no indoor 

sources. 
Draft-Do Not Quote or Cite B-81

200

250

300

2001 AQ (std) - with indoor soucrces

2002 AQ (std) - with indoor soucrces

2003 AQ (std) - with indoor soucrces

0

10

20

30

40

50

60

70

80

90

100

Estimated Percent of Asthmatics Exposed to

Selected Level in Years 2001-2003

with Just Meeting the Current Standard

Potential Health Effect Benchmark Level (ppb)

Simulated Year - Scenario

Figure B-30. Estimated percent of all asthmatics in Philadelphia County with at least one NO2 exposure at or 

above potential health effect benchmark level, using 2001-2003 modeled air quality just meeting the current 

standard (std), with modeled indoor sources. 

200

250

300

2001 AQ (std) - no indoor soucrces

2002 AQ (std) - no indoor soucrces

2003 AQ (std) - no indoor soucrces

0

10

20

30

40

50

60

70

80

90

100

Estimated Percent of Asthmatics Exposed to

Selected Level in Years 2001-2003

with Just Meeting the Current Standard

Potential Health Effect Benchmark Level (ppb)

Simulated Year - Scenario

Figure B-31. Estimated percent of all asthmatics in Philadelphia County with at least one NO2 exposure at or 

above potential health effect benchmark level, using 2001-2003 modeled air quality just meeting the current 

standard (std), with no indoor sources. 
Draft-Do Not Quote or Cite B-82

2003 (as is)

2002 (as is)

2001 (as is) 6

5

4

3

2

1

0

5

10

15

20

25

30

35

Estimated Percent of Asthmatics Exposed,

Years 2001 to 2003, with Indoor Sources

Simulated Year - Scenario

Estimated Number of

Repeated Exposures to 

200 ppb 1-hour in a Year

Figure B-32. Estimated percent of all asthmatics in Philadelphia County with repeated NO2 exposures at or 

above 200 ppb 1-hr, using 2001-2003 modeled air quality (as is), with modeled indoor sources. 

2003 (as is)

2002 (as is)

2001 (as is) 6

5

4

3

2

1

0

5

10

15

20

25

30

35

Estimated Percent of Asthmatics Exposed,

Years 2001 to 2003, No Indoor Sources

Simulated Year - Scenario

Estimated Number of

Repeated Exposures to 

200 ppb 1-hour in a Year

Figure B-33. Estimated percent of all asthmatics in Philadelphia County with repeated NO2 exposures at or 

above 200 ppb 1-hr, using 2001-2003 modeled air quality (as is), without indoor sources. 
Draft-Do Not Quote or Cite B-83

2003 (std)

2002 (std)

2001 (std) 6

5

4

3

2

1

0

10

20

30

40

50

60

70

80

90

100

Estimated Percent of Asthmatics Exposed,

Years 2001 to 2003, with Indoor Sources Simulated Year - Scenario

Estimated Number of

Repeated Exposures to 

200 ppb 1-hour in a Year

Figure B-34. Estimated percent of all asthmatics in Philadelphia County with repeated NO2 exposures at or 

above 200 ppb 1-hour, using 2001-2003 modeled air quality just meeting the current standard (std), with 

modeled indoor sources. 

2003 (std)

2002 (std)

2001 (std) 6

5

4

3

2

1

0

10

20

30

40

50

60

70

80

90

100

Estimated Percent of Asthmatics Exposed,

Years 2001 to 2003, No Indoor Sources Simulated Year - Scenario

Estimated Number of

Repeated Exposures to 

200 ppb 1-hour in a Year

Figure B-35. Estimated percent of all asthmatics in Philadelphia County with repeated NO2 exposures at or 

above 200 ppb 1-hour, using 2001-2003 modeled air quality just meeting the current standard (std), with no 

indoor sources. 
Draft-Do Not Quote or Cite B-84

B-3.6.7.2 Asthmatic Children 

Table B-33. Estimated number of asthmatic children in Philadelphia County exposed at or above potential 

health effect benchmark levels (1 to 6 times per year), using modeled air quality (as is) and with just meeting 

the current standard (std), and with and without indoor sources. 

Persons With Number of Repeated Exposures 

Year (AQ) 

Indoor 

Source 

Level 

(ppb) 1 2 3 4 5 6 

200 11351 3649 1418 709 424 267 

250 709 167 68 49 20 10 

Yes 

300 128 49 10 10 0 0 

200 2329 401 147 98 58 58 

250 393 97 39 20 0 0 

2001 

(as is) 

No 

300 97 29 10 10 0 0 

200 36656 26353 18272 12133 8271 5783 

250 13543 4530 1877 926 533 295 

Yes 

300 3909 768 236 187 128 88 

200 27511 16067 9890 6094 3757 2430 

250 11282 3735 1413 500 333 197 

2001 

(std) 

No 

300 3440 638 187 128 109 79 

200 10636 3338 1439 800 494 346 

250 692 139 49 30 0 0 

Yes 

300 70 10 0 0 0 0 

200 1771 315 158 79 10 0 

250 158 49 20 10 0 0 

2002 

(as is) 

No 

300 30 10 0 0 0 0 

200 38834 28678 20840 14308 10063 6996 

250 14855 4887 1978 1086 652 514 

Yes 

300 4203 947 336 228 119 79 

200 30548 18685 11394 7063 4336 2782 

250 12487 3775 1288 738 493 365 

2002 

(std) 

No 

300 3736 670 276 158 99 39 

200 12525 4693 2736 1712 1100 797 

250 3541 1240 678 423 247 178 

Yes 

300 1545 423 237 138 89 39 

200 6724 2526 1515 984 708 492 

250 2784 1032 531 335 188 128 

2003 

(as is) 

No 

300 1368 355 208 119 69 39 

200 37931 28305 20344 15230 11013 8483 

250 20044 9893 6016 4088 2888 2253 

Yes 

300 10562 4100 2381 1643 1211 906 

200 32066 21662 14938 10326 7647 6018 

250 18770 8897 4974 3371 2388 1859 

2003 

(std) 

No 

300 9547 3704 2223 1496 1072 817 
Draft-Do Not Quote or Cite B-85

Table B-34. Estimated percent of asthmatic children in Philadelphia County exposed at or above potential 

health effect benchmark levels (1 to 6 times per year), using modeled air quality (as is) and with just meeting 

the current standard (std), and with and without indoor sources. 

Percent (%) of Persons With Repeated Exposures 

Year (AQ) 

Indoor 

Source 

Level 

(ppb) 1 2 3 4 5 6 

200 23 8 3 1 1 1 

250 1 0 0 0 0 0 

Yes 

300 0 0 0 0 0 0 

200 5 1 0 0 0 0 

250 1 0 0 0 0 0 

2001 

(as is) 

No 

300 0 0 0 0 0 0 

200 75 54 38 25 17 12 

250 28 9 4 2 1 1 

Yes 

300 8 2 0 0 0 0 

200 57 33 20 13 8 5 

250 23 8 3 1 1 0 

2001 

(std) 

No 

300 7 1 0 0 0 0 

200 22 7 3 2 1 1 

250 1 0 0 0 0 0 

Yes 

300 0 0 0 0 0 0 

200 4 1 0 0 0 0 

250 0 0 0 0 0 0 

2002 

(as is) 

No 

300 0 0 0 0 0 0 

200 81 60 43 30 21 15 

250 31 10 4 2 1 1 

Yes 

300 9 2 1 0 0 0 

200 64 39 24 15 9 6 

250 26 8 3 2 1 1 

2002 

(std) 

No 

300 8 1 1 0 0 0 

200 26 10 6 4 2 2 

250 7 3 1 1 1 0 

Yes 

300 3 1 0 0 0 0 

200 14 5 3 2 1 1 

250 6 2 1 1 0 0 

2003 

(as is) 

No 

300 3 1 0 0 0 0 

200 79 59 43 32 23 18 

250 42 21 13 9 6 5 

Yes 

300 22 9 5 3 3 2 

200 67 45 31 22 16 13 

250 39 19 10 7 5 4 

2003 

(std) 

No 

300 20 8 5 3 2 2 
Draft-Do Not Quote or Cite B-86

200

250

300

2001 AQ (as is) - with indoor soucrces

2002 AQ (as is) - with indoor soucrces

2003 AQ (as is) - with indoor soucrces

0

5

10

15

20

25

30

Estimated Percent of AsthmaticChildren Exposed to

Selected Level in Years 2001-2003 (as is)

Potential Health Effect Benchmark Level (ppb)

Simulated Year - Scenario

Figure B-36. Estimated percent of asthmatic children in Philadelphia County with at least one NO2 exposure 

at or above potential health effect benchmark level, using 2001-2003 modeled air quality (as is), with modeled 

indoor sources. 

200

250

300

2001 AQ (as is) - no indoor soucrces

2002 AQ (as is) - no indoor soucrces

2003 AQ (as is) - no indoor soucrces

0

5

10

15

20

25

30

Estimated Percent of Asthmatic Children Exposed to

Selected Level in Years 2001-2003 (as is)

Potential Health Effect Benchmark Level (ppb)

Simulated Year - Scenario

Figure B-37. Estimated percent of asthmatic children in Philadelphia County with at least one NO2 exposure 

at or above potential health effect benchmark level, using 2001-2003 modeled air quality (as is), with no 

indoor sources. 
Draft-Do Not Quote or Cite B-87

200

250

300

2001 AQ (std) - with indoor soucrces

2002 AQ (std) - with indoor soucrces

2003 AQ (std) - with indoor soucrces

0

10

20

30

40

50

60

70

80

90

100

Estimated Percent of Asthmatics Children Exposed 

to

Selected Level in Years 2001-2003

with Just Meeting the Current Standard

Potential Health Effect Benchmark Level (ppb)

Simulated Year - Scenario

Figure B-38. Estimated percent of asthmatic children in Philadelphia County with at least one NO2 exposure 

at or above potential health effect benchmark level, using 2001-2003 modeled air quality just meeting the 

current standard (std), with modeled indoor sources. 

200

250

300

2001 AQ (std) - no indoor soucrces

2002 AQ (std) - no indoor soucrces

2003 AQ (std) - no indoor soucrces

0

10

20

30

40

50

60

70

80

90

100

Estimated Percent of Asthmatic Children Exposed to

Selected Level in Years 2001-2003

with Just Meeting the Current Standard

Potential Health Effect Benchmark Level (ppb)

Simulated Year - Scenario

Figure B-39. Estimated percent of asthmatic children in Philadelphia County with at least one NO2 exposure 

at or above potential health effect benchmark level, using 2001-2003 modeled air quality just meeting the 

current standard (std), with no indoor sources. 
Draft-Do Not Quote or Cite B-88

2003 (as is)

2002 (as is)

2001 (as is) 6

5

4

3

2

1

0

5

10

15

20

25

30

Estimated Percent of Asthmatic Children Exposed,

Years 2001 to 2003, with Indoor Sources

Simulated Year - Scenario

Estimated Number of

Repeated Exposures to 

200 ppb 1-hour in a Year

Figure B-40. Estimated percent of asthmatic children in Philadelphia County with repeated NO2 exposures at 

or above 200 ppb 1-hr, using 2001-2003 modeled air quality (as is), with modeled indoor sources. 

2003 (as is)

2002 (as is)

2001 (as is) 6

5

4

3

2

1

0

5

10

15

20

25

30

Estimated Percent of Asthmatic Children Exposed,

Years 2001 to 2003, No Indoor Sources

Simulated Year - Scenario

Estimated Number of

Repeated Exposures to 

200 ppb 1-hour in a Year

Figure B-41. Estimated percent of asthmatic children in Philadelphia County with repeated NO2 exposures at 

or above 200 ppb 1-hr, using 2001-2003 modeled air quality (as is), with no indoor sources. 
Draft-Do Not Quote or Cite B-89

2003 (std)

2002 (std)

2001 (std) 6

5

4

3

2

1

0

10

20

30

40

50

60

70

80

90

100

Estimated Percent of Asthmatic Children Exposed,

Years 2001 to 2003, with Indoor Sources Simulated Year - Scenario

Estimated Number of

Repeated Exposures to 

200 ppb 1-hour in a Year

Figure B-42. Estimated percent of asthmatic children in Philadelphia County with repeated NO2 exposures at 

or above 200 ppb 1-hr, using 2001-2003 modeled air quality meeting the current standard (std), with modeled 

indoor sources. 

2003 (std)

2002 (std)

2001 (std) 6

5

4

3

2

1

0

10

20

30

40

50

60

70

80

90

100

Estimated Percent of Asthmatic Children Exposed,

Years 2001 to 2003, No Indoor Sources Simulated Year - Scenario

Estimated Number of

Repeated Exposures to 

200 ppb 1-hour in a Year

Figure B-43. Estimated percent of asthmatic children in Philadelphia County with repeated NO2 exposures at 

or above 200 ppb 1-hr, using 2001-2003 modeled air quality meeting the current standard (std), with no 

indoor sources. 
Draft-Do Not Quote or Cite B-90

B-4 Atlanta Exposure Assessment Case-Study 

This section provides supplemental discussion on methodology and additional detailed 

input data used in the Atlanta inhalation exposure assessment for NO2 conducted in support of 

the current review of the NO2 primary NAAQS. The general exposure modeling approach has 

been broadly defined in Appendix section B-2. 

In defining the years modeled, 3-years (2001-2003) were selected to allow for reasonable 

representation of variability in meteorology. Table B-35 summarizes the temperature and 

precipitation in Atlanta over the last 30 years, with years 2001-2003 showing a range of values 

across each variable considered. 

Table B-35. 30 year annual average temperature and precipitation summary for Atlanta, GA. 

Year 

Annual 

Temperature 

(◦ F) Year 

Annual 

Precipitation

(inches) 

1990 65 1989 63.3 

2007 64.4 1992 60.1 

1994 64.2 1994 60.0 

1991 63.7 1990 57.6 

1986 63.7 2005 56.4 

2006 63.4 1982 56.2 

1993 63.4 1991 55.9 

1998 63.4 1984 55.4 

1980 63.2 1979 54.7 

1995 63.1 2004 53.6 

1999 63.1 2003 52.9 

2002 62.8 1995 52.8 

1987 62.8 1997 51.7 

1989 62.7 1983 51.6 

2004 62.7 1985 49.8 

1996 62.7 2006 48.5 

2001 62.5 1993 48.1 

2005 62.3 2002 47.8 

1985 62.3 1980 46.9 

1988 62.3 1987 46.2 

2000 62.1 1998 46.2 

1992 61.9 1988 45.9 

1982 61.9 1996 44.6 

1984 61.9 1981 41.9 

2003 61.7 1978 41.4 

1981 61.7 1986 40.5 

1979 61.6 1999 38.9 

1997 61.1 2001 38.4 

1978 60.9 2000 35.6 

1983 60.1 2007 31.9 

average 62.6 average 49.2 

Notes: 

Both temperature and precipitation are ordered by 
Draft-Do Not Quote or Cite B-91

maximum to minimum values. 

B-4.1 Supplemental AERMOD Modeling Inputs and Discussion 

Air quality data input to the APEX exposure model were generated by air quality 

modeling using AERMOD. Principal emission sources included both mobile and stationary 

sources as well as emissions from Atlanta Hartsfield International Airport.20 The supplemental 

data used for estimating the emission sources, in addition to other AERMOD parameters used for 

the Atlanta exposure analysis are described below.

B-4.1.1 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 – that is, the most recent simulation calibrated to match observed traffic data. ARC 

provided the following files. 

• Excel TM files of loaded network TDM outputs for the 2005 ARC baseline year for all 

links in the 13 county network domain. 

• Excel TM data file of node end point locations. 

• Arterial and freeway MOBILE6.2 emissions model input files for the 2008 summer 

ozone season, characterizing local inputs that differ from national defaults, and 2002 

registration distribution. 

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 countywide 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, nor was information about seasonal 

differences in MOBILE6.2 inputs. These are discussed further below. 

B-4.1.1.1 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 freeflow speed, total vehicle count, total 

heavy duty truck count, total single occupancy vehicle count, and TDM-calculated congested 

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.21 

 

20 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. 21 The TDM capacity specifications are not the same as those defined by the Highway Capacity Manual (HCM). 

Following previous analyses, the HCM definition of capacity was used in later calculations, as discussed below. 
Draft-Do Not Quote or Cite B-92

The full set of links in the 13 county regional network was filtered to include only those 

roadway links that are considered major as determined by TDM- based vehicle counts and within 

the four part of a fifth county (Clayton), which contains a small portion of the beltway. That is, 

all links with AADT values greater than 15,000 vehicles per day (one direction) in Cobb, 

DeKalb, Fulton, and Gwinnett were included, and those with greater than 15,000 AADT in 

Clayton County that lie north of 3,717,036 m N in the UTM Zone 16, WGS84 datum were also 

included. The treatment of non-major links is discussed below. 

 

Link locations from the TDM were modified to represent the best known locations of the 

actual roadways, since there was not always a direct correlation between the two. The correction 

of link locations was done based on the locations of the nodes that define the end points of links 

with a GIS analysis, as follows. 

A procedure was developed to relocate TDM nodes to more realistic locations. The 

nodes in the TDM represent the endpoints of links in the transportation planning network and are 

specified by node indices, cross-referenced to locations in the Georgia West Stateplane. The 

procedure moved the node locations to the true road locations and translated to dispersion model 

coordinates. The ESRI StreetMapTM Pro road network database, an enhanced version of the Tele 

Atlas North America, Inc database was used as the specification of the true road locations. The 

nodes were moved to coincide with the nearest major road of the corresponding roadway type 

using a built in function of ArcGIS. Once the nodes had been placed in the corrected locations, a 

line was drawn connecting each node pair to represent a link of the adjusted planning network. 

B-4.1.1.2 Emission Source Strength 

On-road mobile emission factors were derived from the MOBILE6.2 emissions. 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) and speed. Iterative 

MOBILE6.2 simulations were conducted to create tables of average Atlanta region emission 

factors resolved by speed (2.5 to 65 mph), functional class, season, and year (2001, 2002, or 

2003) for each of the eight combined MOBILE vehicle classes.22 The resulting tables were then 

consolidated into speed, functional class, and seasonal values for combined light- and heavy-duty 

vehicles. To create seasonal-hourly resolved emissions, spring and fall values were taken as the 

average of corresponding summer and winter values. Figure B-44 shows an example of the 

calculated emission factors for Summer, 2001. 

The resulting emission factors were then coupled with the TDM-based activity estimates 

to calculate emissions from each of the 4,899 major roadway links. However, many of the links 

were two sides of the same roadway segment. To speed model execution time, those links that 

could be combined into a single emission source were merged together. This was done only for 

the 734 links (367 pairs) where opposing links were paired in space and exhibited similar activity 

levels within 20% of each other. 

 

22 HDDV - Heavy-Duty Diesel Vehicle, HDGV - Heavy-Duty Gasoline Vehicle, LDDT - Light-Duty Diesel Truck, 

LDDV - Light-Duty Diesel Vehicle, LDGT12 - Light-Duty Gasoline Truck with gross vehicle weight rating ≤ 6,000 

lbs and a loaded vehicle weight of ≤ 5,750 lbs, LDGT 34 - Light-Duty Gasoline Truck with gross vehicle weight 

rating between 6,001 - 8,500 and a loaded vehicle weight of ≤ 5,750 lbs, LDGV - Light-Duty Gasoline Vehicle, MC 

- Motorcycles. 
Draft-Do Not Quote or Cite B-93

0

5

10

15

20

25

0 10 20 30 40 50 60 70

Average Speed (mph)

Average NOx Emissions (g/mi)

Summer Arterial LDV

Summer Freeway LDV

Summer Arterial HDV

Summer Freeway

HDV

Figure B-44. Example of Light- and heavy-duty vehicle NOx emissions grams/mile (g/mi) for arterial and 

freeway functional classes, 2001. 

B-4.1.2 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, 2007b), Clean Air Markets 

Division (CAMD) Unit Level Emissions Database (US EPA, 2007c), and temporal emission 

profile information contained in the EMS-HAP (version 3.0) emissions model.23 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, on the other hand, has 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. The 

alignment of facilities between the two databases is not exact, however. Some facilities listed in 

the NEI, are not included in the CAMD database. Of those facilities that do match, in many cases 

there is no clear pairing between the individual stacks assigned within the databases. 

Major stationary sources for this analysis were selected from the NEI according to the 

following criteria: 

(1) Stacks within facilities whose total NOx emissions are at least 100 tpy, and 

(2) Stacks within facilities located either within the 4-county modeling domain or within 

10 km of the modeling domain. 

There are 7 NOx-emitting facilities in the NEI that meet these criteria. 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 co-located within about 10 m. This process 

resulted in 28 combined stacks, listed in Table B-36. These 28 major-facility combined stacks 

 

23 http://www.epa.gov/ttn/chief/emch/projection/emshap30.html 
Draft-Do Not Quote or Cite B-94

account for 16% of the 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. Facility matching was done on the facility name, Office of Regulatory 

Information Systems (ORIS) identification code (when provided) and facility total emissions to 

ensure a best match between the facilities. However, because Georgia was not part of many of 

the market-based reduction programs that constitute the CAMD emissions database, only one of 

the 7 major facilities in the four-county focus area was found in the CAMD data base:the 

Georgia Power Company McDonough Steam-Generating Plant. The CAMD hourly emissions 

profiles for these two units are 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 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,24 described in the EMS-HAP model Version 2 User’s Guide, Section D-7.25 

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. 

B-4.1.3 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 photographs, and the polygon 

representing the airport is estimated to have an area of 3 km2 (see Figure B-45). 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. 

Figure B-45. Polygon representing the Atlanta-Hartsfield International Airport area source. 

 

24 http://www.epa.gov/scram001/dispersion_related.htm#ems-hap 25 http://www.epa.gov/scram001/userg/other/emshapv2ug.pdf 
Draft-Do Not Quote or Cite B-95

Table B-36. The major-facility combined stacks within 10 km of the Atlanta modeling domain.

County NEI Site ID Facility Name SCC1 Lat. Lon. 

StackTotal NOx 

Emiss. 

(TPY) 

FacilityTotal 

Emiss. 

(TPY) 

Stack 

Hght.2

(m) 

Exit 

Gas 

Temp

2 (K) 

Clayton NEI2GA300105 Delta Air Lines Inc TOC 

20200102, 20200401 33.6425 -84.41556 1.23 101.6 8 52

Clayton NEI2GA300105 Delta Air Lines Inc TOC 20400110 33.64417 -84.41805 0.04 101.6 9 97Clayton NEI2GA300105 Delta Air Lines Inc TOC 20400110 33.64361 -84.41805 67.51 101.6 14 44Clayton NEI2GA300105 Delta Air Lines Inc TOC 

10200502, 10200602, 10200603 

33.64194 -84.41278 32.82 101.6 18 59

Cobb NEI12840 Georgia Power Company McDonough Steam- Electric Generating Plant 20100101, 20100201 33.82472 -84.475 11.91 4895.3 17 66Cobb NEI12840 Georgia Power Company McDonough Steam- Electric Generating Plant 10100212 33.82472 -84.475 4883.4 4895.3 255 40Cobb NEI2GA700022 Caraustar Mill Group Inc 

30790001, 30790003 33.81778 -84.64889 1.81 364.1 13 36

Cobb NEI2GA700022 Caraustar Mill Group Inc 

10200202, 10200501, 10200601 

33.81778 -84.64889 362.3 364.1 38 45

Fulton NEIGA1210021 Owens Corning - Fairburn Plant 30501299 33.53861 -84.61694 2.14 602.1 16 35Fulton NEIGA1210021 Owens Corning - Fairburn Plant 

30501204, 30501205, 30501299 

33.53861 -84.61694 12 602.1 19 34

Fulton NEIGA1210021 Owens Corning - Fairburn Plant 

30501204, 30501205, 30501299 

33.53861 -84.61694 13.29 602.1 19 34

Fulton NEIGA1210021 Owens Corning - Fairburn Plant 

30501204, 30501205, 30501299 

33.53861 -84.61694 5.63 602.1 19 39

Fulton NEIGA1210021 Owens Corning - Fairburn Plant 30501203 33.53861 -84.61694 327 602.1 21 31Fulton NEIGA1210021 Owens Corning - Fairburn Plant 30501203 33.53861 -84.61694 242 602.1 204 32Fulton NEIGA1210401 Lafarge Building Materials 30500606 33.8225 -84.47 943 1252.9 20 58
Draft-Do Not Quote or Cite B-96

County NEI Site ID Facility Name SCC1 Lat. Lon. 

StackTotal NOx 

Emiss. 

(TPY) 

FacilityTotal 

Emiss. 

(TPY) 

Stack 

Hght.2

(m) 

Exit 

Gas 

Temp

2 (K) 

Fulton NEIGA1210401 Lafarge Building Materials 

30500606, 30500613 33.8225 -84.47 309.89 1252.9 24 33

Fulton NEIGA1210020 Owens-Brockway Glass Container Inc - Atlanta GA plant 10200602 33.66972 -84.41861 10.06 710.5 18 49Fulton NEIGA1210020 Owens-Brockway Glass Container Inc - Atlanta GA plant 10200602 33.67083 -84.42083 208.49 710.5 27 58Fulton NEIGA1210020 Owens-Brockway Glass Container Inc - Atlanta GA plant 10200602 33.67083 -84.42083 402.49 710.5 27 58Fulton NEIGA1210020 Owens-Brockway Glass Container Inc - Atlanta GA plant 10200602 33.67083 -84.42083 89.42 710.5 27 64Henry NEIGA1315100 Transcontinental Gas Pipe Line - Station 120 20200202 33.56944 -84.255 7.88 2347.4 5 74Henry NEIGA1315100 Transcontinental Gas Pipe Line - Station 120 20200252 33.56944 -84.255 642.88 2347.4 8 62Henry NEIGA1315100 Transcontinental Gas Pipe Line - Station 120 20200252 33.56944 -84.255 184.17 2347.4 8 62Henry NEIGA1315100 Transcontinental Gas Pipe Line - Station 120 20200252 33.56944 -84.255 945.58 2347.4 8 63Henry NEIGA1315100 Transcontinental Gas Pipe Line - Station 120 20200202 33.56944 -84.255 36.6 2347.4 8 66Henry NEIGA1315100 Transcontinental Gas Pipe Line - Station 120 20200252 33.56944 -84.255 280.57 2347.4 8 67Henry NEIGA1315100 Transcontinental Gas Pipe Line - Station 120 20200252 33.56944 -84.255 218.68 2347.4 9 62Henry NEIGA1315100 Transcontinental Gas Pipe Line - Station 120 20200201 33.56944 -84.255 31.08 2347.4 10 741 Combined stacks may have multiple Source Classification Codes (SCCs) 2 The physical stack parameters are converted from English units into metric units. The stack height, exit gas temperature, and exit gas velocity are rounded to integers, and th

rounded to one decimal place. 
 B-97

B-4.1.4 Receptor Locations 

The distance relationship between the major roadway link and block centroids receptors 

can be estimated by looking at the distance between the road-centered and the block centroid 

receptors. Figure B-46 presents the histogram of the shortest distance between each centroid 

receptor and its nearest major-roadway-link-centered receptor. Approximately 1% of the blocks 

are within 50 m of a major roadway link and the geometric mean of the distribution is between 

750 m and 800 m. Approximately 26% of the blocks are within 400 m of a major roadway link 

center. However, these values represent the distances of the block centroids to road centers 

instead of road edges, so that they overestimate the actual distances to the zone most influenced 

by roadway by an average of 10 m and a range of 4 m to 29 m (based on the distribution of the 

on-road area source widths). 

0

50

100

150

200

250

300

350

400

450

0

200400600800

100012001400160018002000220024002600280030003200340036003800400042004400460048005000

Distance (m)

Frequency

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

CDF (%)

Figure B-46. Frequency distribution of distance between each Census block receptor and its nearest majorroadway-link-centered receptor. 
 B-98

B-4.1.5 Data used to generate dispersion model-to-monitor comparison figures in REA. Table B-37. Data used to generate cumulative density functions plotted in Figure 8-6 of REA. 

Percentile Concentration (ppb) 

Monitor Receptor(s) 100 99 95 90 80 70 60 50 

AERMOD P2.5 108 63 49 41 30 21 14 9 AERMOD P50 137 70 51 46 34 28 22 17 AERMOD P97.5 169 85 68 59 50 44 38 33 AMBIENT MONITOR 90 53 39 32 24 19 15 12 

130890002 

AERMOD MONITOR 160 79 60 51 39 29 22 16 

AERMOD P2.5 94 56 47 40 28 20 14 10 AERMOD P50 106 60 49 45 34 26 19 14 AERMOD P97.5 145 82 65 57 49 43 36 29 AMBIENT MONITOR 66 49 38 32 24 19 16 13 

130893001 

AERMOD MONITOR 103 58 48 45 32 24 17 12 

AERMOD P2.5 111 61 48 43 33 26 21 17 AERMOD P50 122 70 52 47 39 31 25 20 AERMOD P97.5 157 96 71 61 53 47 40 33 AMBIENT MONITOR 136 63 47 39 30 24 19 16 

131210048 

AERMOD MONITOR 137 72 52 47 40 31 26 22 
 B-99

Table B-38. Data used to generate diurnal variation plotted in Figure 8-7 of REA. 

Annual Average NO2 Concentration at Given Receptor 

Monitor ID 

Hour 

of Day 

AERMOD 

P2.5 

AERMOD 

P50 

AERMOD 

P97.5 

AMBIENT 

MONITOR

AERMOD 

MONITOR 

0 15 22 31 20 21 

1 16 22 31 18 23 

2 15 21 30 17 21 

3 16 21 30 16 23 

4 20 28 35 16 27 

5 23 33 38 16 31 

6 23 29 35 16 29 

7 19 23 32 17 24 

8 16 18 29 18 19 

9 13 15 31 15 16 

10 11 13 34 11 15 

11 10 13 36 9 15 

12 10 12 38 8 15 

13 10 12 40 7 16 

14 9 12 40 7 15 

15 9 12 41 8 15 

16 9 12 42 8 15 

17 15 19 45 10 21 

18 19 26 44 14 26 

19 27 39 47 18 34 

20 22 32 40 20 27 

21 15 22 32 21 21 

22 16 23 32 22 22 

130890002 

23 15 22 30 21 22 

0 15 20 30 18 19 

1 16 21 30 16 19 

2 15 19 29 16 18 

3 15 20 30 15 18 

4 20 25 35 15 24 

5 23 28 38 17 27 

6 23 27 35 19 25 

7 18 21 28 19 20 

8 16 17 23 18 16 

9 12 14 21 14 13 

10 11 13 23 12 12 

11 10 13 25 10 11 

12 10 12 27 9 11 

13 9 12 29 9 10 

14 9 11 29 9 10 

15 9 12 27 10 10 

16 10 13 28 11 11 

17 15 18 34 13 17 

18 20 24 39 17 23 

19 29 34 47 20 33 

130893001 

20 23 28 40 22 27 
 B-100

Annual Average NO2 Concentration at Given Receptor 

Monitor ID 

Hour 

of Day 

AERMOD 

P2.5 

AERMOD 

P50 

AERMOD 

P97.5 

AMBIENT 

MONITOR

AERMOD 

MONITOR 

21 15 20 31 22 19 

22 16 21 31 21 21 

23 16 20 29 19 19 

0 22 26 35 24 27 

1 22 26 35 23 27 

2 21 25 33 21 26 

3 21 25 34 20 27 

4 28 32 40 20 33 

5 32 34 45 21 36 

6 28 31 40 23 31 

7 22 24 31 24 24 

8 18 20 27 23 21 

9 15 18 26 20 18 

10 13 17 27 16 17 

11 12 16 30 13 16 

12 11 16 32 12 16 

13 11 15 33 11 15 

14 10 15 33 11 15 

15 11 16 33 11 15 

16 12 17 35 12 17 

17 18 23 41 14 23 

18 26 31 45 18 31 

19 39 43 54 23 44 

20 33 37 46 26 38 

21 23 28 37 26 29 

22 23 28 38 26 30 

131210048 

23 22 26 35 25 28 
 B-101

B-4.1.6 Comparison of estimated on-road NO2 concentrations 

Table B-39 provides the semi-empirical distribution derived from the relationship of the on-road 

concentrations estimated by AERMOD and the concentrations at receptors located at least 100 

meters form a major road. The data were separated in to two season categories, summer (June, 

July and August) and not summer (all other months). Table B-40 contains the values for each of 

the same distribution types, however were derived from measurement data reported in published 

literature sources (see Appendix A-8 for details). Each of the distributions were illustrated in 

Figure 8-8 of the final REA. 

 

Table B-39. On-road/non-road NO2 concentration ratios using AERMOD roadway link concentration 

prediction and nearest corresponding receptor concentration ≥ 100 m of a major road. 

AERMOD Predicted 

On-road/Non-road 

Probability Not Summer Summer 

0 0.46 0.30 

0.01 1.05 1.10 

0.02 1.12 1.22 

0.03 1.17 1.29 

0.04 1.20 1.35 

0.05 1.23 1.40 

0.06 1.25 1.44 

0.07 1.28 1.47 

0.08 1.30 1.50 

0.09 1.31 1.53 

0.1 1.33 1.55 

0.11 1.35 1.58 

0.12 1.36 1.60 

0.13 1.38 1.62 

0.14 1.39 1.64 

0.15 1.40 1.66 

0.16 1.42 1.68 

0.17 1.43 1.70 

0.18 1.45 1.72 

0.19 1.46 1.73 

0.2 1.47 1.75 

0.21 1.49 1.77 

0.22 1.50 1.79 

0.23 1.51 1.81 

0.24 1.53 1.82 

0.25 1.54 1.84 

0.26 1.55 1.86 

0.27 1.57 1.87 

0.28 1.58 1.89 

0.29 1.59 1.91 

0.3 1.61 1.93 
 B-102

AERMOD Predicted 

On-road/Non-road 

Probability Not Summer Summer 

0.31 1.62 1.94 

0.32 1.63 1.96 

0.33 1.65 1.98 

0.34 1.66 2.00 

0.35 1.67 2.02 

0.36 1.69 2.03 

0.37 1.70 2.05 

0.38 1.72 2.07 

0.39 1.73 2.09 

0.4 1.75 2.11 

0.41 1.76 2.13 

0.42 1.77 2.15 

0.43 1.79 2.17 

0.44 1.81 2.19 

0.45 1.82 2.21 

0.46 1.84 2.23 

0.47 1.85 2.25 

0.48 1.87 2.27 

0.49 1.89 2.29 

0.5 1.90 2.31 

0.51 1.92 2.34 

0.52 1.94 2.36 

0.53 1.96 2.38 

0.54 1.97 2.40 

0.55 1.99 2.43 

0.56 2.01 2.45 

0.57 2.03 2.48 

0.58 2.05 2.51 

0.59 2.07 2.53 

0.6 2.09 2.56 

0.61 2.11 2.59 

0.62 2.14 2.62 

0.63 2.16 2.65 

0.64 2.18 2.68 

0.65 2.21 2.71 

0.66 2.23 2.74 

0.67 2.26 2.78 

0.68 2.28 2.82 

0.69 2.31 2.85 

0.7 2.34 2.89 

0.71 2.37 2.93 

0.72 2.40 2.97 

0.73 2.43 3.01 

0.74 2.46 3.05 

0.75 2.50 3.10 

0.76 2.54 3.14 
 B-103

AERMOD Predicted 

On-road/Non-road 

Probability Not Summer Summer 

0.77 2.57 3.19 

0.78 2.61 3.25 

0.79 2.66 3.30 

0.8 2.70 3.36 

0.81 2.75 3.43 

0.82 2.80 3.50 

0.83 2.86 3.57 

0.84 2.92 3.65 

0.85 2.98 3.73 

0.86 3.05 3.83 

0.87 3.13 3.94 

0.88 3.22 4.05 

0.89 3.31 4.19 

0.9 3.43 4.36 

0.91 3.56 4.55 

0.92 3.72 4.77 

0.93 3.90 5.06 

0.94 4.12 5.45 

0.95 4.41 5.90 

0.96 4.81 6.51 

0.97 5.41 7.53 

0.98 6.44 8.89 

0.99 9.32 12.8 

1 122 215 

Notes: 

This ratio was calculated from 7-day averaged 

concentrations for the on-road and non-road 

receptors to allow for a better comparison 

with the study-derived ratios (Table B-40) that 

were based on 7-14 day averages. 

Table B-40. Estimated on-road/non-road NO2 concentration ratios using m ratio derived from data reported 

in published NO2 measurement studies. 

Measurement Derived 

Season Probability1 On-Road/Non-Road2

0.03 1.22 

0.08 1.25 

0.14 1.36 

0.19 1.36 

0.24 1.42 

0.29 1.47 

0.34 1.58 

0.40 1.59 

0.45 1.64 

Not 

Summer 

0.50 1.75 
 B-104

Measurement Derived 

Season Probability1 On-Road/Non-Road2

0.55 1.78 

0.60 1.79 

0.66 1.79 

0.71 1.82 

0.76 1.86 

0.81 2.08 

0.86 2.14 

0.92 2.50 

0.97 2.54 

0.03 1.49 

0.07 1.51 

0.12 1.52 

0.16 1.67 

0.21 1.70 

0.25 1.74 

0.30 1.75 

0.34 1.78 

0.39 1.78 

0.43 1.79 

0.48 1.90 

0.52 1.92 

0.57 1.93 

0.61 1.94 

0.66 2.13 

0.70 2.19 

0.75 2.21 

0.79 2.32 

0.84 2.95 

0.88 3.43 

0.93 3.45 

Summer 

0.97 3.70 

Notes: 

1

 In the figure presentation, the n ratios for each 

season are plotted as the ith value against (i3/8)/(n+1/4), which is the Blom normal score. The 

lowest value has i=1. It was only used for the 

plotting. 

2

 This value is obtained by (1+m) and is what was 

used to estimate on-road concentrations from 

ambient monitor concentrations, effectively 

representing the ratio of on-Road/non-road 

concentrations. See Appendix A, Section 8. 
 B-105

B-4.2 Supplemental APEX Modeling Inputs and Discussion 

B-4.2.1 Simulated Individuals 

The number of simulated persons in each model run was set to 50,000 persons simulated 

for each year. The parameters controlling the location and size of the simulated area were set to 

include the counties in the selected study area. The settings that allow for replacement of CHAD 

data that are missing gender, employment or age values were all set to preclude replacing 

missing data. The width of the age window was set to 20 percent to increase the pool of diaries 

available for selection. The variable that controls the use of additional ages outside the target 

age window was set to 0.1 to further enhance variability in diary selection. See the APEX User’s 

Guide for further explanation of these parameters. 

B-4.2.2 Asthma Prevalence Rates 

One of the important population subgroups for the exposure assessment is asthmatic 

children. Evaluation of the exposure of this group with APEX requires the estimation of 

children’s 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 (EPA, 2007d; 2007e). 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). These asthma rates were 

characterized by geographic regions, namely Midwest, Northeast, South, and West. Adult 

asthma prevalence rates for Atlanta were derived from the Behavioral Risk Factor Surveillance 

System (BRFSS) survey information for year 2004-2005 (Blackwell and Kanny, 2007; Georgia 

Department of Human Resources, 2007). Average rates for adult males and females in Atlanta 

were derived from reported county prevalence rates for both genders. First each of the four 

county prevalence rates was weighted by their population, then averaged, and finally stratified by 

gender using the statewide reported gender prevalence. The adult prevalence rates were assumed 

to apply to all individuals uniformly. Table B-38 provides a summary of the prevalence rates 

used in the exposure analysis by age and gender. 

Table B-41. Mean asthma prevalence rates, along with lower and upper 95% confidence limits, by age and 

gender used for Atlanta. 

 Females Males 

Region 

(Study Area) Age Prevalence1

 se L95 U95 Prevalence1 se L95 U95 

0 0.034 0.013 0.015 0.077 0.041 0.019 0.015 0.110 

1 0.052 0.012 0.031 0.085 0.070 0.016 0.041 0.116 

2 0.071 0.014 0.046 0.109 0.102 0.017 0.070 0.146 

3 0.088 0.017 0.056 0.134 0.129 0.021 0.088 0.184 

4 0.099 0.019 0.064 0.150 0.144 0.024 0.099 0.205 

5 0.119 0.022 0.079 0.175 0.165 0.024 0.118 0.224 

6 0.122 0.023 0.079 0.182 0.164 0.025 0.116 0.226 

7 0.112 0.022 0.072 0.170 0.133 0.023 0.090 0.194 

8 0.093 0.019 0.059 0.144 0.138 0.023 0.095 0.197 

9 0.091 0.018 0.059 0.139 0.168 0.025 0.121 0.230 

10 0.108 0.020 0.071 0.162 0.178 0.025 0.130 0.240 

Atlanta 

(South) 

11 0.132 0.023 0.090 0.191 0.162 0.022 0.119 0.218 
 B-106

 Females Males 

Region 

(Study Area) Age Prevalence1

 se L95 U95 Prevalence1 se L95 U95 

12 0.123 0.020 0.085 0.175 0.145 0.020 0.106 0.195 

13 0.097 0.017 0.065 0.142 0.143 0.019 0.105 0.192 

14 0.095 0.016 0.064 0.137 0.153 0.019 0.116 0.200 

15 0.100 0.016 0.070 0.141 0.151 0.017 0.116 0.194 

16 0.115 0.016 0.084 0.156 0.140 0.018 0.105 0.185 

17 0.145 0.029 0.091 0.223 0.122 0.026 0.075 0.193 

17+ 0.083 0.050 

Notes: 

1

 prevalence is given in fraction of the population. Multiply by 100 to obtain the percent. 

se – Standard error 

L95 – Lower limit on 95th confidence interval 

U95 – Upper limit on 95th confidence interval 

B-4.2.3 Meteorological Data Used by APEX 

APEX used meteorological data from the station located at Atlanta Hartsfield 

International (KATL) airport. This was one of the stations used for the AERMOD simulations. 

B-4.2.4 Method Used for Indoor Source Contributions 

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). For this study a test house was set up for continuous measurements of 

NO2 indoors and outdoors, among several other parameters, and conducted under several 

different cooking procedures and stove operating conditions. A uniform distribution of 

concentration contributions for input to APEX was estimated as follows. 

• The concurrent outdoor NO2 concentration measurement was subtracted from each in 

door concentration measurement, to yield net indoor concentrations 

• Net indoor concentrations for duplicate cooking tests (same food cooked the same 

way) were averaged for each indoor room, to yield average net indoor concentrations 

• The minimum and maximum average net indoor concentrations for any test in any 

room were used as the lower and upper bounds of a uniform distribution.

This resulted in a minimum average net indoor concentration of 4 ppb and a maximum 

net average indoor concentration of 188 ppb. 

B-4.2.5 Method Used for Cooking Probabilities

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 as follows. 

• Food preparation time obtained from CHAD diaries was stratified by hour of the day, 

and summed for each hour, and summed for total preparation time. 
 B-107

• The fraction of food preparation occurring in each hour of the day was calculated as 

the total number of minutes for that hour divided by the overall total preparation time. 

The result was a measure of the probability of food preparation taking place during 

any hour, given one food preparation event per day. 

• Each hourly fraction was multiplied by 1.4, to normalize the expected value of daily 

food preparation events to 1.4. 

This resulted in estimated probabilities of cooking by hour of the day. For this analysis it 

was assumed that the probability that food preparation would include stove usage was the same 

for each hour of the day, so that the diurnal allocation of food preparation events would be the 

same as the diurnal allocation of gas stove usage. It was also assumed that each cooking event 

lasts for exactly 1 hour, implying that the average total daily gas stove usage is 1.4 hours. 

B-4.2.6 In-vehicle and Near-Road PROX factors 

These data were used for the in-vehicle and near-road PROX factors used by APEX. They were 

developed from the 1-hour on-road to non-road receptor concentrations predicted by AERMOD. 

The data were stratified by two seasons (summer: June, July, and August) and by hour of the day 

(Table B-42). 

Table B-42. In-vehicle and near-road PROX factors used in APEX. 

Parameter Estimates1

Season Hour of Day GM GSD 

Lower 

Bound 

Upper 

Bound 

11PM-6AM 1.942 2.093 1.000 9.4 

6AM-7PM 2.989 2.549 1.021 18.8 Not 

Summer 

7PM-11PM 1.879 2.085 1.000 9.0 

11PM-6AM 1.992 2.149 1.000 10.2 

Summer 6AM-7PM 4.619 2.820 1.067 30.0 

7PM-11PM 1.965 2.177 1.000 10.4 

Notes: 

1

 A lognormal distribution was selected to fit the data, represented by the 

geometric mean (GM), geometric standard deviation (GSD). Lower and 

upper bounds were approximated by the 5th and 95th percentiles of the 

fitted distribution. 

 
 B-108

B-4.2.7 Supplemental Exposure Results 

B-4.2.6 Supplemental Exposure Results 

This section provides complete exposure and risk characterization results for the two 

subpopulations, all asthmatics and asthmatic children. The data are presented in series of 

summary tables across each of the scenarios investigated (i.e. with modeled air quality as is and 

simulating just meeting the current and alternative standards), with and without modeled indoor 

sources (i.e., gas stoves), for each of the potential health effect benchmark levels (i.e., 100, 150, 

200, 250, 300 ppb 1-hour), and across three years of modeled air quality (i.e., 2001 to 2003). 

Due to limits on the number of benchmarks allowed by APEX per simulation, only the 

benchmarks of 100, 200, and 300 ppb were evaluated for the potential alternative standards. 

When evaluating the indoor source contributions, the 99th percentile form was used for each the 

50, 100, and 150 ppb 1-hour standard levels, the 98th percentile form was evaluated only at a 100 

ppb 1-hour standard level for comparison with the 99th form. 
 B-109

B-4.2.6.1 All Asthmatics, Year 2001, No Indoor sources 

Table B-43. Estimated number of asthmatics in the Atlanta modeling domain exposed at or above potential 

health effect benchmark levels (1 to 6 times per year), using 2001 modeled air quality (as is), with just meeting 

the current standard (cs), and potential alternative standards, without indoor sources. 

Air Quality 

Adjustment Persons With Number of Repeated Exposures 

Level1

(ppb) Form2

1-hour 

Benchmark 

(ppb) 1 2 3 4 5 6 

100 98 100 212426 212426 212426 212372 212212 211997

100 98 200 207070 197375 185109 170648 155436 140760

100 98 300 162453 118639 87359 63524 47402 35511

100 99 100 212426 212426 212265 212051 211997 211515

100 99 200 203267 187734 170380 150883 133154 118586

100 99 300 145688 96733 66202 44510 31869 22657

150 98 100 212426 212426 212426 212426 212426 212426

150 98 200 212319 211783 211033 209908 208623 205945

150 98 300 207070 197375 185109 170648 155436 140760

150 99 100 212426 212426 212426 212426 212426 212426

150 99 200 211944 211462 210123 208087 204927 200910

150 99 300 203267 187734 170380 150883 133154 118586

200 98 100 212426 212426 212426 212426 212426 212426

200 98 200 212426 212426 212426 212372 212212 211997

200 98 300 211837 210980 208998 205784 201981 197107

200 99 100 212426 212426 212426 212426 212426 212426

200 99 200 212426 212426 212265 212051 211997 211515

200 99 300 211301 209159 205409 200053 193197 186609

50 98 100 207070 197375 185109 170648 155436 140760

50 98 200 97322 49063 25710 14890 8838 5410

50 98 300 23621 5035 1553 750 428 268

50 99 100 203267 187734 170380 150883 133154 118586

50 99 200 77290 34654 16551 8195 5142 2892

50 99 300 15640 2678 911 536 268 161

asis asis 000 212426 212426 212426 212426 212426 212426

asis asis 100 212426 212051 211837 211408 210658 209801

asis asis 150 209426 203963 195018 185217 174343 162078

asis asis 200 191912 167166 141135 117997 100053 83449

asis asis 250 158650 112587 81200 58757 43171 31816

asis asis 300 118104 66738 39636 24960 15801 10337

cs01 cs01 100 212426 212426 212426 212426 212426 212426

cs01 cs01 150 212426 212426 212426 212426 212426 212372

cs01 cs01 200 212426 212426 212319 212158 211997 211730

cs01 cs01 250 212212 211730 210926 209801 208087 205731

cs01 cs01 300 211355 209266 205731 200696 194643 187734

1

 value is the 1-hour concentration that air quality is adjusted considering particular form; cs is the 

current annual average value of 0.053 ppm. 

2

 asis – current air quality, not adjusted; 98 – 98th percentile 1-hour concentration averaged across 

three years; 99 – 99th 1-hour concentration averaged across three years; cs – current annual 

average standard. 
 B-110

Table B-44. Estimated percent of asthmatics in the Atlanta modeling domain exposed at or above potential 

health effect benchmark levels (1 to 6 times per year), using 2001 modeled air quality (as is), with just meeting 

the current standard (cs), and potential alternative standards, without indoor sources. 

Air Quality 

Adjustment Percent of Persons With Number of Repeated Exposures 

Level1

(ppb) Form2

1-hour 

Benchmark 

(ppb) 1 2 3 4 5 6 

100 98 100 100% 100% 100% 100% 100% 100%

100 98 200 97% 93% 87% 80% 73% 66%

100 98 300 76% 56% 41% 30% 22% 17%

100 99 100 100% 100% 100% 100% 100% 100%

100 99 200 96% 88% 80% 71% 63% 56%

100 99 300 69% 46% 31% 21% 15% 11%

150 98 100 100% 100% 100% 100% 100% 100%

150 98 200 100% 100% 99% 99% 98% 97%

150 98 300 97% 93% 87% 80% 73% 66%

150 99 100 100% 100% 100% 100% 100% 100%

150 99 200 100% 100% 99% 98% 96% 95%

150 99 300 96% 88% 80% 71% 63% 56%

200 98 100 100% 100% 100% 100% 100% 100%

200 98 200 100% 100% 100% 100% 100% 100%

200 98 300 100% 99% 98% 97% 95% 93%

200 99 100 100% 100% 100% 100% 100% 100%

200 99 200 100% 100% 100% 100% 100% 100%

200 99 300 99% 98% 97% 94% 91% 88%

50 98 100 97% 93% 87% 80% 73% 66%

50 98 200 46% 23% 12% 7% 4% 3%

50 98 300 11% 2% 1% 0% 0% 0%

50 99 100 96% 88% 80% 71% 63% 56%

50 99 200 36% 16% 8% 4% 2% 1%

50 99 300 7% 1% 0% 0% 0% 0%

asis asis 000 100% 100% 100% 100% 100% 100%

asis asis 100 100% 100% 100% 100% 99% 99%

asis asis 150 99% 96% 92% 87% 82% 76%

asis asis 200 90% 79% 66% 56% 47% 39%

asis asis 250 75% 53% 38% 28% 20% 15%

asis asis 300 56% 31% 19% 12% 7% 5%

cs01 cs01 100 100% 100% 100% 100% 100% 100%

cs01 cs01 150 100% 100% 100% 100% 100% 100%

cs01 cs01 200 100% 100% 100% 100% 100% 100%

cs01 cs01 250 100% 100% 99% 99% 98% 97%

cs01 cs01 300 99% 99% 97% 94% 92% 88%

1

 value is the 1-hour concentration that air quality is adjusted considering particular form; cs is the 

current annual average value of 0.053 ppm. 

2

 asis – current air quality, not adjusted; 98 – 98th percentile 1-hour concentration averaged across 

three years; 99 – 99th 1-hour concentration averaged across three years; cs – current annual 

average standard. 
 B-111

B-4.2.6.2 Asthmatic Children, Year 2001, No Indoor Sources 

Table B-45. Estimated number of asthmatic children in the Atlanta modeling domain exposed at or above 

potential health effect benchmark levels (1 to 6 times per year), using 2001 modeled air quality (as is), with 

just meeting the current standard (cs), and potential alternative standards, without indoor sources. 

Air Quality 

Adjustment Persons With Number of Repeated Exposures 

Level1

(ppb) Form2

1-hour 

Benchmark 

(ppb) 1 2 3 4 5 6 

100 98 100 64113 64113 64113 64113 64113 64113

100 98 200 62881 60953 58275 54847 51366 48313

100 98 300 51794 39957 31226 23514 17622 14194

100 99 100 64113 64113 64113 64113 64113 64060

100 99 200 61917 58596 54847 50241 45635 41617

100 99 300 47456 33476 23567 16015 12159 9213

150 98 100 64113 64113 64113 64113 64113 64113

150 98 200 64113 64060 64006 63738 63578 63042

150 98 300 62881 60953 58275 54847 51366 48313

150 99 100 64113 64113 64113 64113 64113 64113

150 99 200 64060 64006 63899 63578 63042 62185

150 99 300 61917 58596 54847 50241 45635 41617

200 98 100 64113 64113 64113 64113 64113 64113

200 98 200 64113 64113 64113 64113 64113 64113

200 98 300 64060 63953 63738 63042 62346 61435

200 99 100 64113 64113 64113 64113 64113 64113

200 99 200 64113 64113 64113 64113 64113 64060

200 99 300 64006 63685 62721 61435 60150 58864

50 98 100 62881 60953 58275 54847 51366 48313

50 98 200 32030 17086 9373 5892 3321 2089

50 98 300 7177 1660 321 107 54 0

50 99 100 61917 58596 54847 50241 45635 41617

50 99 200 25656 12587 6535 3053 1821 857

50 99 300 4499 857 107 54 0 0

asis asis 000 64113 64113 64113 64113 64113 64113

asis asis 100 64113 64113 64113 64060 64060 63899

asis asis 150 63685 62560 60525 58168 56722 53883

asis asis 200 59025 54044 46866 41350 36476 31119

asis asis 250 51044 38564 29191 22067 15908 12748

asis asis 300 37868 22924 13444 9320 6374 4338

cs01 cs01 100 64113 64113 64113 64113 64113 64113

cs01 cs01 150 64113 64113 64113 64113 64113 64113

cs01 cs01 200 64113 64113 64113 64113 64113 64060

cs01 cs01 250 64113 64006 63953 63738 63524 63042

cs01 cs01 300 64006 63685 62881 61542 60364 59079

1

 value is the 1-hour concentration that air quality is adjusted considering particular form; cs is the 

current annual average value of 0.053 ppm. 

2

 asis – current air quality, not adjusted; 98 – 98th percentile 1-hour concentration averaged across 

three years; 99 – 99th 1-hour concentration averaged across three years; cs – current annual 

average standard. 
 B-112

Table B-46. Estimated percent of asthmatic children in the Atlanta modeling domain exposed at or above 

potential health effect benchmark levels (1 to 6 times per year), using 2001 modeled air quality (as is), with 

just meeting the current standard (cs), and potential alternative standards, without indoor sources. 

Air Quality 

Adjustment Percent of Persons With Number of Repeated Exposures 

Level1

(ppb) Form2

1-hour 

Benchmark 

(ppb) 1 2 3 4 5 6 

100 98 100 100% 100% 100% 100% 100% 100%

100 98 200 98% 95% 91% 86% 80% 75%

100 98 300 81% 62% 49% 37% 27% 22%

100 99 100 100% 100% 100% 100% 100% 100%

100 99 200 97% 91% 86% 78% 71% 65%

100 99 300 74% 52% 37% 25% 19% 14%

150 98 100 100% 100% 100% 100% 100% 100%

150 98 200 100% 100% 100% 99% 99% 98%

150 98 300 98% 95% 91% 86% 80% 75%

150 99 100 100% 100% 100% 100% 100% 100%

150 99 200 100% 100% 100% 99% 98% 97%

150 99 300 97% 91% 86% 78% 71% 65%

200 98 100 100% 100% 100% 100% 100% 100%

200 98 200 100% 100% 100% 100% 100% 100%

200 98 300 100% 100% 99% 98% 97% 96%

200 99 100 100% 100% 100% 100% 100% 100%

200 99 200 100% 100% 100% 100% 100% 100%

200 99 300 100% 99% 98% 96% 94% 92%

50 98 100 98% 95% 91% 86% 80% 75%

50 98 200 50% 27% 15% 9% 5% 3%

50 98 300 11% 3% 1% 0% 0% 0%

50 99 100 97% 91% 86% 78% 71% 65%

50 99 200 40% 20% 10% 5% 3% 1%

50 99 300 7% 1% 0% 0% 0% 0%

asis asis 000 100% 100% 100% 100% 100% 100%

asis asis 100 100% 100% 100% 100% 100% 100%

asis asis 150 99% 98% 94% 91% 88% 84%

asis asis 200 92% 84% 73% 64% 57% 49%

asis asis 250 80% 60% 46% 34% 25% 20%

asis asis 300 59% 36% 21% 15% 10% 7%

cs01 cs01 100 100% 100% 100% 100% 100% 100%

cs01 cs01 150 100% 100% 100% 100% 100% 100%

cs01 cs01 200 100% 100% 100% 100% 100% 100%

cs01 cs01 250 100% 100% 100% 99% 99% 98%

cs01 cs01 300 100% 99% 98% 96% 94% 92%

1

 value is the 1-hour concentration that air quality is adjusted considering particular form; cs is the 

current annual average value of 0.053 ppm. 

2

 asis – current air quality, not adjusted; 98 – 98th percentile 1-hour concentration averaged across 

three years; 99 – 99th 1-hour concentration averaged across three years; cs – current annual 

average standard. 
 B-113

B-4.2.6.3 All Asthmatics, Year 2002, No Indoor sources 

Table B-47. Estimated number of asthmatics in the Atlanta modeling domain exposed at or above potential 

health effect benchmark levels (1 to 6 times per year), using 2002 modeled air quality (as is), with just meeting 

the current standard (cs), and potential alternative standards, without indoor sources. 

Air Quality 

Adjustment Persons With Number of Repeated Exposures 

Level1

(ppb) Form2

1-hour 

Benchmark 

(ppb) 1 2 3 4 5 6 

100 98 100 212426 212426 212426 212265 212051 211944

100 98 200 207820 199089 187252 172576 157954 143813

100 98 300 165345 123674 89555 64756 48045 35351

100 99 100 212426 212372 212319 212051 211944 211676

100 99 200 204070 190037 172469 153883 136797 120246

100 99 300 150776 100268 68184 45045 32191 23192

150 98 100 212426 212426 212426 212426 212426 212426

150 98 200 212372 212051 211301 210016 208891 206909

150 98 300 207820 199089 187252 172576 157954 143813

150 99 100 212426 212426 212426 212426 212426 212426

150 99 200 212212 211408 210123 208248 205677 202356

150 99 300 204070 190037 172469 153883 136797 120246

200 98 100 212426 212426 212426 212426 212426 212426

200 98 200 212426 212426 212426 212265 212051 211944

200 98 300 211997 210658 209319 206588 203481 199143

200 99 100 212426 212426 212426 212426 212426 212426

200 99 200 212426 212372 212319 212051 211944 211676

200 99 300 211301 209319 206213 201124 194804 188430

50 98 100 207820 199089 187252 172576 157954 143813

50 98 200 103535 49920 29352 17300 10391 6963

50 98 300 29620 7392 2785 1178 696 321

50 99 100 204070 190037 172469 153883 136797 120246

50 99 200 83824 36904 19496 11141 6160 4178

50 99 300 21264 4285 1500 803 268 107

asis asis 000 212426 212426 212426 212426 212426 212426

asis asis 100 212426 212265 211997 211301 210819 209748

asis asis 150 209855 204713 197429 187359 176540 164756

asis asis 200 195768 170862 146063 122281 102196 85431

asis asis 250 161864 117997 84199 59293 43171 32405

asis asis 300 124531 68988 41350 25870 17782 11944

cs02 cs02 100 212426 212426 212426 212426 212426 212426

cs02 cs02 150 212426 212426 212426 212426 212426 212426

cs02 cs02 200 212426 212426 212426 212426 212372 212319

cs02 cs02 250 212426 212372 212319 211997 211890 211301

cs02 cs02 300 212372 212051 211140 209962 208516 206695

1

 value is the 1-hour concentration that air quality is adjusted considering particular form; cs is the 

current annual average value of 0.053 ppm. 

2

 asis – current air quality, not adjusted; 98 – 98th percentile 1-hour concentration averaged across 

three years; 99 – 99th 1-hour concentration averaged across three years; cs – current annual 

average standard. 
 B-114

Table B-48. Estimated percent of asthmatics in the Atlanta modeling domain exposed at or above potential 

health effect benchmark levels (1 to 6 times per year), using 2002 modeled air quality (as is), with just meeting 

the current standard (cs), and potential alternative standards, without indoor sources. 

Air Quality 

Adjustment Percent of Persons With Number of Repeated Exposures 

Level1

(ppb) Form2

1-hour 

Benchmark 

(ppb) 1 2 3 4 5 6 

100 98 100 100% 100% 100% 100% 100% 100% 

100 98 200 98% 94% 88% 81% 74% 68% 

100 98 300 78% 58% 42% 30% 23% 17% 

100 99 100 100% 100% 100% 100% 100% 100% 

100 99 200 96% 89% 81% 72% 64% 57% 

100 99 300 71% 47% 32% 21% 15% 11% 

150 98 100 100% 100% 100% 100% 100% 100% 

150 98 200 100% 100% 99% 99% 98% 97% 

150 98 300 98% 94% 88% 81% 74% 68% 

150 99 100 100% 100% 100% 100% 100% 100% 

150 99 200 100% 100% 99% 98% 97% 95% 

150 99 300 96% 89% 81% 72% 64% 57% 

200 98 100 100% 100% 100% 100% 100% 100% 

200 98 200 100% 100% 100% 100% 100% 100% 

200 98 300 100% 99% 99% 97% 96% 94% 

200 99 100 100% 100% 100% 100% 100% 100% 

200 99 200 100% 100% 100% 100% 100% 100% 

200 99 300 99% 99% 97% 95% 92% 89% 

50 98 100 98% 94% 88% 81% 74% 68% 

50 98 200 49% 23% 14% 8% 5% 3% 

50 98 300 14% 3% 1% 1% 0% 0% 

50 99 100 96% 89% 81% 72% 64% 57% 

50 99 200 39% 17% 9% 5% 3% 2% 

50 99 300 10% 2% 1% 0% 0% 0% 

asis asis 000 100% 100% 100% 100% 100% 100% 

asis asis 100 100% 100% 100% 99% 99% 99% 

asis asis 150 99% 96% 93% 88% 83% 78% 

asis asis 200 92% 80% 69% 58% 48% 40% 

asis asis 250 76% 56% 40% 28% 20% 15% 

asis asis 300 59% 32% 19% 12% 8% 6% 

cs02 cs02 100 100% 100% 100% 100% 100% 100% 

cs02 cs02 150 100% 100% 100% 100% 100% 100% 

cs02 cs02 200 100% 100% 100% 100% 100% 100% 

cs02 cs02 250 100% 100% 100% 100% 100% 99% 

cs02 cs02 300 100% 100% 99% 99% 98% 97% 

1

 value is the 1-hour concentration that air quality is adjusted considering particular form; cs is the 

current annual average value of 0.053 ppm. 

2

 asis – current air quality, not adjusted; 98 – 98th percentile 1-hour concentration averaged across 

three years; 99 – 99th 1-hour concentration averaged across three years; cs – current annual 

average standard. 
 B-115

B-4.2.6.4 Asthmatic Children, Year 2002, No Indoor Sources 

Table B-49. Estimated number of asthmatic children in the Atlanta modeling domain exposed at or above 

potential health effect benchmark levels (1 to 6 times per year), using 2002 modeled air quality (as is), with 

just meeting the current standard (cs), and potential alternative standards, without indoor sources. 

Air Quality 

Adjustment Persons With Number of Repeated Exposures 

Level1

(ppb) Form2

1-hour 

Benchmark 

(ppb) 1 2 3 4 5 6 

100 98 100 64113 64113 64113 64113 64006 63953

100 98 200 63149 61221 58918 55758 52973 49437

100 98 300 53347 42099 31816 22603 16711 12855

100 99 100 64113 64113 64113 64060 64006 63899

100 99 200 62667 59400 55704 51259 47349 43224

100 99 300 49330 34976 24049 15051 10873 7552

150 98 100 64113 64113 64113 64113 64113 64113

150 98 200 64113 64113 63846 63685 63417 63256

150 98 300 63149 61221 58918 55758 52973 49437

150 99 100 64113 64113 64113 64113 64113 64113

150 99 200 64060 64006 63738 63310 62828 61971

150 99 300 62667 59400 55704 51259 47349 43224

200 98 100 64113 64113 64113 64113 64113 64113

200 98 200 64113 64113 64113 64113 64006 63953

200 98 300 64060 63846 63578 62881 62399 61435

200 99 100 64113 64113 64113 64113 64113 64113

200 99 200 64113 64113 64113 64060 64006 63899

200 99 300 64006 63471 62614 61757 60632 59025

50 98 100 63149 61221 58918 55758 52973 49437

50 98 200 34387 16604 9480 5249 3267 2035

50 98 300 8784 1768 428 161 107 54

50 99 100 62667 59400 55704 51259 47349 43224

50 99 200 27263 12051 5999 3321 1928 964

50 99 300 6052 911 107 107 0 0

asis asis 000 64113 64113 64113 64113 64113 64113

asis asis 100 64113 64113 64006 63846 63792 63578

asis asis 150 63524 62506 60900 58971 56775 54097

asis asis 200 60632 54740 48688 43171 37172 31869

asis asis 250 52598 40493 30262 20568 14890 11516

asis asis 300 40975 23996 13819 8034 5731 3428

cs02 cs02 100 64113 64113 64113 64113 64113 64113

cs02 cs02 150 64113 64113 64113 64113 64113 64113

cs02 cs02 200 64113 64113 64113 64113 64113 64113

cs02 cs02 250 64113 64113 64113 64060 64006 63792

cs02 cs02 300 64113 64113 63846 63685 63363 63256

1

 value is the 1-hour concentration that air quality is adjusted considering particular form; cs is the 

current annual average value of 0.053 ppm. 

2

 asis – current air quality, not adjusted; 98 – 98th percentile 1-hour concentration averaged across 

three years; 99 – 99th 1-hour concentration averaged across three years; cs – current annual 

average standard. 
 B-116

Table B-50. Estimated percent of asthmatic children in the Atlanta modeling domain exposed at or above 

potential health effect benchmark levels (1 to 6 times per year), using 2002 modeled air quality (as is), with 

just meeting the current standard (cs), and potential alternative standards, without indoor sources. 

Air Quality 

Adjustment Percent of Persons With Number of Repeated Exposures 

Level1

(ppb) Form2

1-hour 

Benchmark 

(ppb) 1 2 3 4 5 6 

100 98 100 100% 100% 100% 100% 100% 100%

100 98 200 98% 95% 92% 87% 83% 77%

100 98 300 83% 66% 50% 35% 26% 20%

100 99 100 100% 100% 100% 100% 100% 100%

100 99 200 98% 93% 87% 80% 74% 67%

100 99 300 77% 55% 38% 23% 17% 12%

150 98 100 100% 100% 100% 100% 100% 100%

150 98 200 100% 100% 100% 99% 99% 99%

150 98 300 98% 95% 92% 87% 83% 77%

150 99 100 100% 100% 100% 100% 100% 100%

150 99 200 100% 100% 99% 99% 98% 97%

150 99 300 98% 93% 87% 80% 74% 67%

200 98 100 100% 100% 100% 100% 100% 100%

200 98 200 100% 100% 100% 100% 100% 100%

200 98 300 100% 100% 99% 98% 97% 96%

200 99 100 100% 100% 100% 100% 100% 100%

200 99 200 100% 100% 100% 100% 100% 100%

200 99 300 100% 99% 98% 96% 95% 92%

50 98 100 98% 95% 92% 87% 83% 77%

50 98 200 54% 26% 15% 8% 5% 3%

50 98 300 14% 3% 1% 0% 0% 0%

50 99 100 98% 93% 87% 80% 74% 67%

50 99 200 43% 19% 9% 5% 3% 2%

50 99 300 9% 1% 0% 0% 0% 0%

asis asis 000 100% 100% 100% 100% 100% 100%

asis asis 100 100% 100% 100% 100% 99% 99%

asis asis 150 99% 97% 95% 92% 89% 84%

asis asis 200 95% 85% 76% 67% 58% 50%

asis asis 250 82% 63% 47% 32% 23% 18%

asis asis 300 64% 37% 22% 13% 9% 5%

cs02 cs02 100 100% 100% 100% 100% 100% 100%

cs02 cs02 150 100% 100% 100% 100% 100% 100%

cs02 cs02 200 100% 100% 100% 100% 100% 100%

cs02 cs02 250 100% 100% 100% 100% 100% 99%

cs02 cs02 300 100% 100% 100% 99% 99% 99%

1

 value is the 1-hour concentration that air quality is adjusted considering particular form; cs is the 

current annual average value of 0.053 ppm. 

2

 asis – current air quality, not adjusted; 98 – 98th percentile 1-hour concentration averaged across 

three years; 99 – 99th 1-hour concentration averaged across three years; cs – current annual 

average standard. 
 B-117

B-4.2.6.5 All Asthmatics, Year 2003, No Indoor sources 

Table B-51. Estimated number of asthmatic in the Atlanta modeling domain exposed at or above potential 

health effect benchmark levels (1 to 6 times per year), using 2003 modeled air quality (as is), with just meeting 

the current standard (cs), and potential alternative standards, without indoor sources. 

Air Quality 

Adjustment Persons With Number of Repeated Exposures 

Level1

(ppb) Form2

1-hour 

Benchmark 

(ppb) 1 2 3 4 5 6 

100 98 100 212426 212426 212372 212319 212158 212051

100 98 200 206534 197429 184360 168827 154526 139261

100 98 300 162721 117514 84520 61596 45099 33958

100 99 100 212426 212426 212319 212104 211622 210980

100 99 200 202731 187520 169148 149973 131923 115908

100 99 300 143653 94911 63203 44349 31762 22228

150 98 100 212426 212426 212426 212426 212426 212426

150 98 200 212212 211837 210980 209587 207927 205998

150 98 300 206534 197429 184360 168827 154526 139261

150 99 100 212426 212426 212426 212426 212426 212426

150 99 200 211944 211248 209373 207284 204338 199250

150 99 300 202731 187520 169148 149973 131923 115908

200 98 100 212426 212426 212426 212426 212426 212426

200 98 200 212426 212426 212372 212319 212158 212051

200 98 300 211676 210337 208516 205249 201017 195072

200 99 100 212426 212426 212426 212426 212426 212426

200 99 200 212426 212426 212319 212104 211622 210980

200 99 300 211087 208837 205838 199625 193037 184413

50 98 100 206534 197429 184360 168827 154526 139261

50 98 200 98072 48366 26406 15265 8784 5570

50 98 300 25924 5892 2571 857 268 54

50 99 100 202731 187520 169148 149973 131923 115908

50 99 200 79057 33958 16926 8570 5035 2946

50 99 300 17836 3749 1446 428 107 0

asis asis 000 212426 212426 212426 212426 212426 212426

asis asis 100 212426 212158 211837 211194 210016 209051

asis asis 150 209105 203963 194804 183824 172522 160257

asis asis 200 192447 165452 139582 117568 97429 80450

asis asis 250 158114 111730 78843 57204 41296 30744

asis asis 300 117461 66470 39261 25228 15158 9695

cs03 cs03 100 212426 212426 212426 212426 212426 212426

cs03 cs03 150 212426 212426 212426 212426 212426 212426

cs03 cs03 200 212426 212426 212426 212426 212426 212426

cs03 cs03 250 212426 212426 212426 212319 212265 212265

cs03 cs03 300 212426 212372 212212 211997 211408 210712

1

 value is the 1-hour concentration that air quality is adjusted considering particular form; cs is the 

current annual average value of 0.053 ppm. 

2

 asis – current air quality, not adjusted; 98 – 98th percentile 1-hour concentration averaged across 

three years; 99 – 99th 1-hour concentration averaged across three years; cs – current annual 

average standard. 
 B-118

Table B-52. Estimated percent of asthmatics in the Atlanta modeling domain exposed at or above potential 

health effect benchmark levels (1 to 6 times per year), using 2003 modeled air quality (as is), with just meeting 

the current standard (cs), and potential alternative standards, without indoor sources. 

Air Quality 

Adjustment Percent of Persons With Number of Repeated Exposures 

Level1

(ppb) Form2

1-hour 

Benchmark 

(ppb) 1 2 3 4 5 6 

100 98 100 100% 100% 100% 100% 100% 100%

100 98 200 97% 93% 87% 79% 73% 66%

100 98 300 77% 55% 40% 29% 21% 16%

100 99 100 100% 100% 100% 100% 100% 99%

100 99 200 95% 88% 80% 71% 62% 55%

100 99 300 68% 45% 30% 21% 15% 10%

150 98 100 100% 100% 100% 100% 100% 100%

150 98 200 100% 100% 99% 99% 98% 97%

150 98 300 97% 93% 87% 79% 73% 66%

150 99 100 100% 100% 100% 100% 100% 100%

150 99 200 100% 99% 99% 98% 96% 94%

150 99 300 95% 88% 80% 71% 62% 55%

200 98 100 100% 100% 100% 100% 100% 100%

200 98 200 100% 100% 100% 100% 100% 100%

200 98 300 100% 99% 98% 97% 95% 92%

200 99 100 100% 100% 100% 100% 100% 100%

200 99 200 100% 100% 100% 100% 100% 99%

200 99 300 99% 98% 97% 94% 91% 87%

50 98 100 97% 93% 87% 79% 73% 66%

50 98 200 46% 23% 12% 7% 4% 3%

50 98 300 12% 3% 1% 0% 0% 0%

50 99 100 95% 88% 80% 71% 62% 55%

50 99 200 37% 16% 8% 4% 2% 1%

50 99 300 8% 2% 1% 0% 0% 0%

asis asis 000 100% 100% 100% 100% 100% 100%

asis asis 100 100% 100% 100% 99% 99% 98%

asis asis 150 98% 96% 92% 87% 81% 75%

asis asis 200 91% 78% 66% 55% 46% 38%

asis asis 250 74% 53% 37% 27% 19% 14%

asis asis 300 55% 31% 18% 12% 7% 5%

cs03 cs03 100 100% 100% 100% 100% 100% 100%

cs03 cs03 150 100% 100% 100% 100% 100% 100%

cs03 cs03 200 100% 100% 100% 100% 100% 100%

cs03 cs03 250 100% 100% 100% 100% 100% 100%

cs03 cs03 300 100% 100% 100% 100% 100% 99%

1

 value is the 1-hour concentration that air quality is adjusted considering particular form; cs is the 

current annual average value of 0.053 ppm. 

2

 asis – current air quality, not adjusted; 98 – 98th percentile 1-hour concentration averaged across 

three years; 99 – 99th 1-hour concentration averaged across three years; cs – current annual 

average standard. 
 B-119

B-4.2.6.6 Asthmatic Children, Year 2003, No Indoor Sources 

Table B-53. Estimated number of asthmatic children in the Atlanta modeling domain exposed at or above 

potential health effect benchmark levels (1 to 6 times per year), using 2003 modeled air quality (as is), with 

just meeting the current standard (cs), and potential alternative standards, without indoor sources. 

Air Quality 

Adjustment Persons With Number of Repeated Exposures 

Level1

(ppb) Form2

1-hour 

Benchmark 

(ppb) 1 2 3 4 5 6 

100 98 100 64113 64113 64113 64060 63953 63899

100 98 200 62935 60846 58061 54579 51312 47723

100 98 300 52008 39582 28977 21907 16818 13444

100 99 100 64113 64113 64060 64006 63846 63846

100 99 200 61864 58864 54526 49812 45045 40921

100 99 300 46492 32405 22603 15747 11355 8570

150 98 100 64113 64113 64113 64113 64113 64113

150 98 200 64006 63953 63738 63471 63363 63149

150 98 300 62935 60846 58061 54579 51312 47723

150 99 100 64113 64113 64113 64113 64113 64113

150 99 200 63953 63846 63471 63256 62560 61596

150 99 300 61864 58864 54526 49812 45045 40921

200 98 100 64113 64113 64113 64113 64113 64113

200 98 200 64113 64113 64113 64060 63953 63899

200 98 300 63953 63685 63363 62560 62024 60632

200 99 100 64113 64113 64113 64113 64113 64113

200 99 200 64113 64113 64060 64006 63846 63846

200 99 300 63899 63417 62774 61435 59989 58275

50 98 100 62935 60846 58061 54579 51312 47723

50 98 200 31334 16818 9373 5463 2999 2035

50 98 300 7981 1821 643 161 0 0

50 99 100 61864 58864 54526 49812 45045 40921

50 99 200 25335 11569 5678 3107 1928 857

50 99 300 5142 1071 321 0 0 0

asis asis 000 64113 64113 64113 64113 64113 64113

asis asis 100 64113 64006 63899 63738 63524 63417

asis asis 150 63578 62292 59936 57900 55543 53133

asis asis 200 59239 52758 45956 39957 34922 30102

asis asis 250 50830 37600 27316 20193 15158 12051

asis asis 300 37547 23192 14676 9373 5249 3214

cs03 cs03 100 64113 64113 64113 64113 64113 64113

cs03 cs03 150 64113 64113 64113 64113 64113 64113

cs03 cs03 200 64113 64113 64113 64113 64113 64113

cs03 cs03 250 64113 64113 64113 64060 64006 64006

cs03 cs03 300 64113 64060 64006 63953 63846 63738

1

 value is the 1-hour concentration that air quality is adjusted considering particular form; cs is the 

current annual average value of 0.053 ppm. 

2

 asis – current air quality, not adjusted; 98 – 98th percentile 1-hour concentration averaged across 

three years; 99 – 99th 1-hour concentration averaged across three years; cs – current annual 

average standard. 
 B-120

Table B-54. Estimated percent of asthmatic children in the Atlanta modeling domain exposed at or above 

potential health effect benchmark levels (1 to 6 times per year), using 2003 modeled air quality (as is), with 

just meeting the current standard (cs), and potential alternative standards, without indoor sources. 

Air Quality 

Adjustment Percent of Persons With Number of Repeated Exposures 

Level1

(ppb) Form2

1-hour 

Benchmark 

(ppb) 1 2 3 4 5 6 

100 98 100 100% 100% 100% 100% 100% 100%

100 98 200 98% 95% 91% 85% 80% 74%

100 98 300 81% 62% 45% 34% 26% 21%

100 99 100 100% 100% 100% 100% 100% 100%

100 99 200 96% 92% 85% 78% 70% 64%

100 99 300 73% 51% 35% 25% 18% 13%

150 98 100 100% 100% 100% 100% 100% 100%

150 98 200 100% 100% 99% 99% 99% 98%

150 98 300 98% 95% 91% 85% 80% 74%

150 99 100 100% 100% 100% 100% 100% 100%

150 99 200 100% 100% 99% 99% 98% 96%

150 99 300 96% 92% 85% 78% 70% 64%

200 98 100 100% 100% 100% 100% 100% 100%

200 98 200 100% 100% 100% 100% 100% 100%

200 98 300 100% 99% 99% 98% 97% 95%

200 99 100 100% 100% 100% 100% 100% 100%

200 99 200 100% 100% 100% 100% 100% 100%

200 99 300 100% 99% 98% 96% 94% 91%

50 98 100 98% 95% 91% 85% 80% 74%

50 98 200 49% 26% 15% 9% 5% 3%

50 98 300 12% 3% 1% 0% 0% 0%

50 99 100 96% 92% 85% 78% 70% 64%

50 99 200 40% 18% 9% 5% 3% 1%

50 99 300 8% 2% 1% 0% 0% 0%

asis asis 000 100% 100% 100% 100% 100% 100%

asis asis 100 100% 100% 100% 99% 99% 99%

asis asis 150 99% 97% 93% 90% 87% 83%

asis asis 200 92% 82% 72% 62% 54% 47%

asis asis 250 79% 59% 43% 31% 24% 19%

asis asis 300 59% 36% 23% 15% 8% 5%

cs03 cs03 100 100% 100% 100% 100% 100% 100%

cs03 cs03 150 100% 100% 100% 100% 100% 100%

cs03 cs03 200 100% 100% 100% 100% 100% 100%

cs03 cs03 250 100% 100% 100% 100% 100% 100%

cs03 cs03 300 100% 100% 100% 100% 100% 99%

1

 value is the 1-hour concentration that air quality is adjusted considering particular form; cs is the 

current annual average value of 0.053 ppm. 

2

 asis – current air quality, not adjusted; 98 – 98th percentile 1-hour concentration averaged across 

three years; 99 – 99th 1-hour concentration averaged across three years; cs – current annual 

average standard. 

 
 B-121

B-4.2.6.7 All Asthmatics, Year 2002, With Indoor Sources 

Table B-55. Estimated number of asthmatics in the Atlanta modeling domain exposed at or above potential 

health effect benchmark levels (1 to 6 times per year), using 2002 modeled air quality (as is), with just meeting 

the current standard (cs), and potential alternative standards, with indoor sources. 

Air Quality 

Adjustment Percent of Persons With Number of Repeated Exposures 

Level1

(ppb) Form2

1-hour 

Benchmark 

(ppb) 1 2 3 4 5 6 

asis asis 0 212426 212426 212426 212426 212426 212426

asis asis 100 212426 212426 212319 212319 212265 212212

asis asis 150 211890 210873 208516 205570 201231 196679

asis asis 200 197268 175843 152383 129191 109694 92930

asis asis 250 166952 121960 87520 62989 46438 33905

asis asis 300 127156 72630 44242 26995 17943 11409

cs02 cs02 100 212426 212426 212426 212426 212426 212426

cs02 cs02 150 212426 212426 212426 212426 212426 212426

cs02 cs02 200 212426 212426 212426 212426 212426 212426

cs02 cs02 250 212426 212426 212372 212319 211890 211462

cs02 cs02 300 212372 211944 211515 210712 209373 207605

50 99 100 211890 210980 209801 207766 205463 202838

50 99 200 86556 37707 18532 10070 6535 3910

50 99 300 20514 3856 1071 375 107 0

100 99 100 212426 212426 212426 212426 212426 212319

100 99 200 205731 193786 179110 160792 144938 127370

100 99 300 154204 104070 70594 48313 33637 24049

100 98 100 212426 212426 212426 212426 212426 212426

100 98 200 208677 201017 190948 177718 164649 151205

100 98 300 170273 126191 92394 68077 50134 37386

150 99 100 212426 212426 212426 212426 212426 212426

150 99 200 212158 211569 210980 209641 207605 205356

150 99 300 204284 191001 175147 157097 140278 123674

1

 value is the 1-hour concentration that air quality is adjusted considering particular form; cs is the 

current annual average value of 0.053 ppm. 

2

 asis – current air quality, not adjusted; 98 – 98th percentile 1-hour concentration averaged across 

three years; 99 – 99th 1-hour concentration averaged across three years; cs – current annual 

average standard. 
 B-122

Table B-56. Estimated percent of asthmatics in the Atlanta modeling domain exposed at or above potential 

health effect benchmark levels (1 to 6 times per year), using 2002 modeled air quality (as is), with just meeting 

the current standard (cs), and potential alternative standards, with indoor sources. 

Air Quality 

Adjustment Percent of Persons With Number of Repeated Exposures 

Level1

(ppb) Form2

1-hour 

Benchmark 

(ppb) 1 2 3 4 5 6 

asis asis 0 100% 100% 100% 100% 100% 100%

asis asis 100 100% 100% 100% 100% 100% 100%

asis asis 150 100% 99% 98% 97% 95% 93%

asis asis 200 93% 83% 72% 61% 52% 44%

asis asis 250 79% 57% 41% 30% 22% 16%

asis asis 300 60% 34% 21% 13% 8% 5%

cs02 cs02 100 100% 100% 100% 100% 100% 100%

cs02 cs02 150 100% 100% 100% 100% 100% 100%

cs02 cs02 200 100% 100% 100% 100% 100% 100%

cs02 cs02 250 100% 100% 100% 100% 100% 100%

cs02 cs02 300 100% 100% 100% 99% 99% 98%

50 99 100 100% 99% 99% 98% 97% 95%

50 99 200 41% 18% 9% 5% 3% 2%

50 99 300 10% 2% 1% 0% 0% 0%

100 99 100 100% 100% 100% 100% 100% 100%

100 99 200 97% 91% 84% 76% 68% 60%

100 99 300 73% 49% 33% 23% 16% 11%

100 98 100 100% 100% 100% 100% 100% 100%

100 98 200 98% 95% 90% 84% 78% 71%

100 98 300 80% 59% 43% 32% 24% 18%

150 99 100 100% 100% 100% 100% 100% 100%

150 99 200 100% 100% 99% 99% 98% 97%

150 99 300 96% 90% 82% 74% 66% 58%

1

 value is the 1-hour concentration that air quality is adjusted considering particular form; cs is the 

current annual average value of 0.053 ppm. 

2

 asis – current air quality, not adjusted; 98 – 98th percentile 1-hour concentration averaged across 

three years; 99 – 99th 1-hour concentration averaged across three years; cs – current annual 

average standard. 
 B-123

B-4.2.6.8 Asthmatic Children, Year 2002, With Indoor Sources 

Table B-57. Estimated number of asthmatic children in the Atlanta modeling domain exposed at or above 

potential health effect benchmark levels (1 to 6 times per year), using 2002 modeled air quality (as is), with 

just meeting the current standard (cs), and potential alternative standards, with indoor sources. 

Air Quality 

Adjustment Percent of Persons With Number of Repeated Exposures 

Level1

(ppb) Form2

1-hour 

Benchmark 

(ppb) 1 2 3 4 5 6 

asis asis 0 64113 64113 64113 64113 64113 64113

asis asis 100 64113 64113 64060 64060 64006 64006

asis asis 150 64006 63738 63203 62292 61221 59507

asis asis 200 60471 55651 50348 44563 39261 34065

asis asis 250 52812 40653 31012 23085 16979 12694

asis asis 300 41028 24638 15212 9534 5785 3696

cs02 cs02 100 64113 64113 64113 64113 64113 64113

cs02 cs02 150 64113 64113 64113 64113 64113 64113

cs02 cs02 200 64113 64113 64113 64113 64113 64113

cs02 cs02 250 64113 64113 64113 64060 64006 63953

cs02 cs02 300 64113 64006 63953 63738 63524 63524

50 99 100 63792 63363 62774 61971 60739 59561

50 99 200 27852 12694 6106 2946 1553 696

50 99 300 5517 1018 214 107 54 0

100 99 100 64113 64113 64113 64113 64113 64006

100 99 200 62560 59882 57150 52544 48848 44403

100 99 300 49170 35297 25067 17729 12105 8570

100 98 100 64113 64113 64113 64113 64113 64113

100 98 200 63363 61757 59882 56775 53722 50723

100 98 300 53508 41725 32351 24960 18532 13819

150 99 100 64113 64113 64113 64113 64113 64113

150 99 200 64060 63899 63792 63524 63363 62989

150 99 300 62292 59239 56400 51848 47777 43974

1

 value is the 1-hour concentration that air quality is adjusted considering particular form; cs is the 

current annual average value of 0.053 ppm. 

2

 asis – current air quality, not adjusted; 98 – 98th percentile 1-hour concentration averaged across 

three years; 99 – 99th 1-hour concentration averaged across three years; cs – current annual 

average standard. 
 B-124

Table B-58. Estimated percent of asthmatic children in the Atlanta modeling domain exposed at or above 

potential health effect benchmark levels (1 to 6 times per year), using 2002 modeled air quality (as is), with 

just meeting the current standard (cs), and potential alternative standards, with indoor sources. 

Air Quality 

Adjustment Percent of Persons With Number of Repeated Exposures 

Level1

(ppb) Form2

1-hour 

Benchmark 

(ppb) 1 2 3 4 5 6 

asis asis 0 100% 100% 100% 100% 100% 100%

asis asis 100 100% 100% 100% 100% 100% 100%

asis asis 150 100% 99% 99% 97% 95% 93%

asis asis 200 94% 87% 79% 70% 61% 53%

asis asis 250 82% 63% 48% 36% 26% 20%

asis asis 300 64% 38% 24% 15% 9% 6%

cs02 cs02 100 100% 100% 100% 100% 100% 100%

cs02 cs02 150 100% 100% 100% 100% 100% 100%

cs02 cs02 200 100% 100% 100% 100% 100% 100%

cs02 cs02 250 100% 100% 100% 100% 100% 100%

cs02 cs02 300 100% 100% 100% 99% 99% 99%

50 99 100 99% 99% 98% 97% 95% 93% 

50 99 200 43% 20% 10% 5% 2% 1% 

50 99 300 9% 2% 0% 0% 0% 0% 

100 99 100 100% 100% 100% 100% 100% 100%

100 99 200 98% 93% 89% 82% 76% 69%

100 99 300 77% 55% 39% 28% 19% 13%

100 98 100 100% 100% 100% 100% 100% 100%

100 98 200 99% 96% 93% 89% 84% 79%

100 98 300 83% 65% 50% 39% 29% 22%

150 99 100 100% 100% 100% 100% 100% 100%

150 99 200 100% 100% 99% 99% 99% 98%

150 99 300 97% 92% 88% 81% 75% 69%

1

 value is the 1-hour concentration that air quality is adjusted considering particular form; cs is the 

current annual average value of 0.053 ppm. 

2

 asis – current air quality, not adjusted; 98 – 98th percentile 1-hour concentration averaged across 

three years; 99 – 99th 1-hour concentration averaged across three years; cs – current annual 

average standard. 
 B-125

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 B-130

Attachment 1: Technical Memorandum on Meteorological Data 

Preparation for AERMOD for NO2 REA for Atlanta, GA 2001-2003
October 2, 2008 

 B-131

Meteorological data preparation for AERMOD for NO2 REA for Atlanta, GA 

2001-2003 

James Thurman and Roger Brode 

U.S. EPA, OAQPS, AQAD 

Air Quality Modeling Group 

1. Introduction 

While National Weather Service (NWS) surface observational data are often used as the source 

of meteorological inputs for AERMOD, sometimes the data are not truly representative of the 

modeling domain, especially for urban applications. Often the meteorological data is from an 

airport, which has different surface characteristics than the sources being modeled. The airport 

meteorological tower is often located in open spaces while the sources are located in urban areas 

with trees, buildings, and other obstacles. For the Atlanta study, the airport, Atlanta Hartsfield 

Airport was initially chosen as the representative meteorological location. The sources used in 

the study are located in urban areas. Therefore, the airport data, due to lower surface roughness 

at the airport, may not adequately represent conditions at the sources. 

To address the concern regarding representativeness of the Atlanta NWS data for this study, 

meteorological 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. Figure 1a shows the locations of the SEARCH site, located at 

Jefferson St, and hereafter referenced as JST, and Hartsfield International Airport, hereafter 

referenced as ATL. The JST site is located in an urban area, while the airport is on the outskirts 

of the city. Figure 1b provides a closer look at the JST site and it can be clearly seen that the site 

is in an urban setting. 

The methodologies used to prepare meteorological data for AERMOD are described below, 

including the analysis of surface characteristics data, and AERMET processing for the JST site 

and ATL. Also discussed is the methodology used to process upper air data from Peachtree 

City, GA and Birmingham, AL. 

Another potential concern related to the use of NWS meteorological data for dispersion 

modeling is the often high incidence of calms and variable wind conditions. The AERMOD 

model currently cannot simulate dispersion under these conditions. To reduce the number of 

calms and missing winds in the ATL data, archived one-minute winds for the ASOS station at 

ATL were used to calculate hourly average wind speed and directions, which were used to 

supplement the standard archive of winds reported for ATL in the Integrated Surface Hourly 

(ISH) database. Details regarding this procedure are described below. 

Section 2 describes preparation of the JST data, Section 3 describes the preparation of data and 

calculation of hourly winds from one-minute ASOS data for ATL, Section 4 describes 

preparation of upper air data from Peachtree City and Birmingham, Section 5 describes 

AERSURFACE processing for surface characteristics, and Section 6 describes the AERMET 
October 2, 2008 

 B-132

processing. Section 7 describes an additional adjustment that was made to the processed 

meteorological data to address an issue regarding AERMOD formulation for the urban option 

that contributed to anomalous modeled concentrations from a preliminary analysis. Section 8 

provides a brief analysis of the AERMET output for JST and ATL. References are listed in 

Section 9. 
October 2, 2008 

 B-133

Figure 1. a) location of JST (red dot) relative to ATL (red airplane) and b) zoomed in view of 

JST (white dot). 
October 2, 2008 

 B-134

2. SEARCH data preparation 

SEARCH data for the Jefferson St. monitor (JST) in Atlanta was downloaded from the public 

archive section of the SEARCH website, http://www.atmosphericresearch.com/public/index.html, for 2001 through 2003. Trace gas and met data were chosen. 

The data in the SEARCH spreadsheets were reported on a 0 to 23 hour basis, with the reported 

time represented the beginning of the observational hour. The convention for meteorological 

data input to dispersion models is that the reported time represents the end of the averaging 

period. The AERMOD model also requires meteorological inputs on a 1 to 23 hour basis. After 

adjusting the JST data to conform to the AERMOD model conventions, missing values for wind 

speed, wind direction, and temperature were reset to the missing values of those variables as 

described in AERMET Appendix B, Table B-3. (U.S. EPA, 2004). The anemometer height for 

the JST data was set to 10 m. 

Since data quality is an important consideration for meteorological inputs to dispersion models, 

the JST data were reviewed for completeness and reasonableness. Specifically, hourly wind 

speeds, wind directions, and temperatures for JST were compared to the values for Atlanta 

Hartsfield Airport (ATL) for the three years of 2001 through 2003. Analysis of the wind 

directions showed generally good agreement between JST and ATL data throughout most of the 

period. However, this comparison identified somewhat anomalous directions for the period of 

May 2 through May 8, 2001 (Figure 2). The original wind directions for the JST data (red lines), 

revealed an approximate 180 degree shift in wind direction when compared to the ATL wind 

directions (blue lines). This shift followed a significant period of missing data for JST from late 

April to early May 2001. After May 8, the wind directions appeared to be in better agreement 

with airport wind directions. A similar problem had been encountered for a SEARCH site in 

Birmingham as part of another study, and was later confirmed to be a 120-degree offset. Based 

on this review and prior experience with a similar problem, it was decided to shift the JST wind 

direction by 180 degrees for the period beginning with 1700 LST May 2 and ending at 1500 LST 

May 8. Figure 3 shows the resulting directions (green line), which are more in line with the 

airport directions. After correcting for the wind directions, the hourly winds and temperatures 

were written to text files for input in to AERMET. Figure 4 shows the wind roses for each year 

for JST. Winds were predominantly from the northwest with a secondary maximum from the 

east. 

The number of calms and missing hours (winds or temperature) for JST were compiled for each 

year to determine if data substitution from the airport was necessary in AERMET processing. 

Table 1 lists the number of calms and missing winds and temperatures for the JST site for 2001 

through 2003. Note that a wind speed threshold of 0.28 m/s was used in processing the JST data 

through AERMET. As a result, any wind speed reported less than 0.28 m/s was treated as a calm 

hour. Unlike NWS surface observations, which treat any wind speed below 3 knots as a calm, 

the JST data are based on a sonic anemometer, which has virtually no threshold since the 

observations are not dependent on mechanical parts. Several manufacturers of sonic 

anemometers report starting thresholds of 0.01 m/s. While such low winds speeds may be a 

reasonable starting threshold for an instantaneous wind speed sample from a sonic anemometer, 

it may not be appropriate as a threshold for defining a valid hourly average wind speed to be 

used in a steady-state plume model such as AERMOD, with a single hourly average wind 
October 2, 2008 

 B-135

direction. Under conditions that would result in an hourly average wind speed on the order of 

0.01 m/s, there would be no well-defined transport direction. The AERMOD model formulation 

includes adjustments to the minimum wind speed to account for turbulence effects under very 

light wind conditions, with the minimum effective wind speed that will be used for dilution in 

AERMOD of about 0.2828 m/s. Based on these considerations, a threshold of 0.28 m/s was 

selected as the most appropriate value to be applied for the JST data, with any hourly average 

wind speeds below that threshold being classified as calm. Note that the current meteorological 

monitoring guidance for dispersion modeling applications (EPA, 2000) specifies a maximum 

acceptable starting threshold of 0.5 m/s for site-specific meteorological monitoring programs. 

Table 1. Number of calms, missing winds, and missing temperatures for each year for 2001 

through 2003 for the JST site. 

Variable Year 

2001 2002 2003 

Calms# 427 287 19

Missing winds* 165 497 792

Missing temperature 187 205 379

# anything less than 0.28 m/s was considered calm 

* missing wind speed and/or wind direction. 
 B-136

Figure 2. May 2001 a) wind directions for the SEARCH monitor (red line) and Hartsfield International Airport (wind direction differences (airport – SEARCH). 
 B-137

Figure 3. Hourly wind directions for original SEARCH (red), airport (blue) and shifted SEARCH (green) for Ma

2001. 
 B-138

Figure 4. Annual wind roses for JST for a) 2001, b) 2002, and c) 2003. 
October 2, 2008 

 B-139

3. Surface airport data 

Surface data from an NWS site was needed to supplement the data from the SEARCH site. For 

AERMOD, the most representative data for an NWS site should be used, most often the nearest 

location. For Atlanta, Atlanta Hartsfield Airport (ATL) was chosen as the site. Integrated 

Surface Hourly (ISH) data was downloaded from the National Climatic Data Center (NCDC) for 

2001, 2002, and 2003. 

Surface data from NWS locations often contain a large number of calms and variable winds. 

This is due to the METAR reporting method used for NWS observations. Currently, the wind 

speed and direction used to represent the hour in AERMOD is a single two-minute average, 

usually reported about 10 minutes before the hour. The METAR system reports winds of less 

than three knots as calm, and winds up to six knots will be reported as variable when the 

variation in the 2-minute wind direction is more than 60 degrees. This variable wind is reported 

as a non-zero wind speed with a missing wind direction. The number of calms and variable 

winds can influence concentration calculations in AERMOD because concentrations are not 

calculated for calms or variable wind hours. For daily or annual averages, this can result in 

underestimated concentrations. This is especially of concern for applications involving low-level 

releases since the worst-case dispersion conditions for such sources are associated with low wind 

speeds, and the hours being discarded as calm or variable are biased toward this condition. 

Recently, NCDC began archiving the two-minute average wind speeds for each minute of the 

hour for most ASOS stations. These values have not been subjected to the METAR coding for 

calm and variable winds. Recent work in AQMG has focused on utilizing these 1 minute winds 

to calculate hourly average winds to reduce the number of calms and variable winds for a given 

station and year. For data input into AERMOD, one minute winds for ATL were used to 

calculate hourly average winds for 2001 through 2003. These winds would be input to 

AERMET and replace the winds reported for the hour from the ISH dataset. Following is the 

methodology used to calculate the hourly average winds: 

One minute data files are monthly, so each month for 2001 through 2003 was downloaded. The 

program used to calculate hourly average winds is executed for each year. 

1. Each line of the data file was read and QA performed on the format of the line to check if 

the line is valid data line. Currently, the one minute data files loosely follow a fixed 

format, but there are numerous exceptions. The program performed several checks on 

the line to ensure that wind direction and wind speed were in the correct general location. 

If a minute was listed twice, the second line for that minute was assumed to be the correct 

line. In the files, wind directions were recorded at the nearest whole degree and wind 

speed to the nearest whole knot. 

2. If the reported wind speed was less than 2 knots, the wind speed was reset to 1 knot. This 

was done because anything less than 2 knots was considered below the instrument 

threshold (if the anemometer is not a sonic anemometer, which was the case for ATL 

prior to April 2007). So a reported wind speed of 0 knots may not necessarily be a calm 

wind. This also conforms to the meteorological monitoring guidance recommendation of 
October 2, 2008 

 B-140

applying a wind speed of one half the threshold value to each wind sample below 

threshold when processing samples to obtain hourly averages. At the same time, the xand y-components of the wind direction were calculated using equations 1 and 2 below, 

which are the functions inside the summation of equations 6.2.17 and 6.2.18 of the 

meteorological guidance document (U.S. EPA, 2000). The components were only 

calculated for minutes that did not require resetting. 

 vx = −sinθ (1) 

 vy = −cosθ (2) 

 where vx and vy are the x- and y-components of the one minute wind direction θ. 

3. For all minutes that passed the QA check in step 1, the wind speeds were converted from 

knots to m/s. 

4. Before calculating hourly averages, the number of valid minutes (those with wind 

directions) was checked for each hour. An hourly average would be calculated if the 

there were at least two valid minutes for the hour. This could be even minutes, odd 

minutes, or a mixture of non-overlapping even and odd minutes. Even minutes were 

given priority over odd. If at least two valid minutes were found, then all available 

minutes would be used to calculate hourly averages. The most observations that could be 

used were 30 2-minute values (30 even or 30 odd). 

5. For wind speed averages, all available non-overlapping minutes’ speeds were used, even 

those subject to resets as described in step 2. The hourly wind speed was an arithmetic 

average of the wind speeds used. 

 

6. For wind directions, the x- and y-components were summed according to equations 

6.2.17 and 6.2.18 of the meteorological monitoring guidance (U.S. EPA, 2000), 

summarized in equations 3 and 4 below with vxi and vyi calculated in equations 1 and 2. 

The hourly wind direction was calculated using equation 6.2.19 of the meteorological 

monitoring guidance (U.S.EPA, 2000), summarized in equation 5. The one minute 

average wind directions do not use the flow correction as shown in equation 6.2.19, since 

the calculated direction is the direction from which the wind was blowing, not the 

direction in which it is blowing, as shown by the flow correction in 6.2.19. Instead, the 

one minute program corrected for the direction from which the wind was blowing. 

 ∑=

=

N

i

X xi v

N

V

1

1 (3) 

 ∑=

=

N

i

Y yi v

N

V

1

1 (4) 

 CORR V

V Arc

Y

X ⎟ +

⎠

⎞ ⎜

⎝

⎛ θ = tan (5) 
October 2, 2008 

 B-141

Where Vx and Vy are the hourly averaged x- and y-components of the wind, θ is the 

hourly averaged wind direction, N is the number of observations used for the hour, and 

= 180 for Vx > 0 and Vy > 0 or Vx < 0 and Vy > 0 

CORR = 0 for Vx < 0 and Vy < 0 

= 360 for Vx ≥ 0 and Vy < 0 
October 2, 2008 

 B-142

4. Upper air data 

For AERMET processing, an upper air station must be paired with the surface station. For both 

JST and ATL, the Peachtree City upper air station, FFC, was chosen as the most representative 

upper air site. 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/. The data 

period chosen was January 1, 2001 through December 31, 2003 for all times and all levels. The 

selected wind speed units were chosen as tenths of a meter per second. The data was 

downloaded as one file for all three years. 

Analysis of the data revealed 31 occurrences of missing 1200 UTC soundings for the three years, 

mostly in 2001. The AERMOD processor requires a 1200 UTC sounding in order to calculate 

the convective mixing height for the day. As a result, if the 1200 UTC sounding is missing, all 

of the daytime convective hours for that day will be considered as missing by the AERMOD 

model. In order to minimize missing data as much as possible, these gaps in the data were filled 

with data from the Birmingham, AL upper air station, BMX or from the FFC data itself. Table 2 

lists the missing dates and method of data substitution. These substitutions should have very 

limited impact on the Atlanta NO2 modeling since BMX is reasonably representative of Atlanta, 

and modeling results for low-level releases, such as mobile sources, are not very sensitive to the 

convective mixing heights in AERMOD. 

Table 2. Missing 1200 UTC sounding dates in upper air data with substitution 

method. Unless specified otherwise, substitution times are the same as the missing 

date/time. 

Date/time Substitution Date/time Substitution 

03/11/01 BMX 04/17/02 BMX 

03/12/01 BMX 04/18/02 BMX 

03/13/01 BMX 04/19/02 BMX 

05/06/01 BMX 04/20/02 BMX 

06/13/01 BMX 04/21/02 BMX 

06/14/01 BMX 04/22/02 BMX 

06/15/01 BMX 04/26/02 BMX 

08/11/01 BMX 04/27/02 BMX 

11/21/01 BMX 06/14/02 BMX 

11/22/01 BMX 06/23/02 BMX 

11/23/01 BMX 07/21/02 FFC 07/20/02 

01/11/02 BMX 09/08/02 BMX 

02/19/02 BMX 09/09/02 BMX 

03/23/02 BMX 01/22/03 BMX 

03/24/02 BMX 03/09/03 BMX 

03/25/02 BMX 06/26/03 BMX 
October 2, 2008 

 B-143

5. AERSURFACE 

The AERSURFACE tool (U.S. EPA, 2008a) was used to determine surface characteristics 

(albedo, Bowen ratio, and surface roughness) for input to AERMET. Surface characteristics 

were calculated for the JST meteorological tower site (33.77753° N, 84.41666° W) and for the 

ATL meteorological tower (33.63° N 84.44167° W). As noted in the AERSURFACE User’s 

Guide (U.S. EPA, 2008), AERSURFACE should be run for the location of the actual 

meteorological tower to ensure accurate representation of the conditions around the site. 

A draft version of AERSURFACE (08256) that utilizes 2001 NLCD was used to determine the 

surface characteristics for this application since the 2001 land cover data will be more 

representative of this modeling period than the 1992 NLCD data supported by the current version 

of AERSURFACE available on EPA’s SCRAM website. Both meteorological data sites were 

run according to the methodology in Section 3.2.2 of the 1st draft NO2 risk and exposure 

assessment technical support document (U.S. EPA, 2008b): both sites were run as non-arid 

regions, ATL was considered “at an airport” for the low, medium, and high intensity developed 

categories, default seasonal assignments to each month, and no continuous snow cover. 

Moisture conditions for Bowen ratio (average, dry, or wet) were assigned to each month based 

on the analysis shown in Table 30 of the technical support document (U.S. EPA, 2008b). 

Months with at least twice the normal precipitation level were denoted as wet, those with less 

than one-half the normal precipitation level were assigned dry and all others were average. This 

resulted in three AERSURFACE runs for each site with average, dry, or wet conditions because 

AERSURFACE can not assign moisture conditions to individual months within one 

AERSURFACE run. Table 3 shows the assignment to each month for each year. Figures 5 and 

6 show the sectors used for surface roughness for JST and ATL. 

After running AERSURFACE, a year specific set of surface characteristics was generated for 

each year by merging results for the appropriate moisture condition for each month for the year, 

i.e. for 2001, the average moisture surface characteristics for January through June were 

concatenated with the dry July and August surface characteristics, average September surface 

characteristics, dry October and November surface characteristics, and average December 

surface characteristics. These merged AERSURFACE results were used in Stage 3 of 

AERMET. 
October 2, 2008 

 B-144

Figure 5. 2001 NLCD for JST with surface roughness 1 km radius and sectors (denoted by 

numbers 1 through 4). Numbers outside 1 km radius are the starting directions of each sector. 
October 2, 2008 

 B-145

Figure 6. 2001 NLCD for ATL with surface roughness 1 km radius and sectors (denoted by 

numbers 1 through 5). Numbers outside 1 km radius are the starting directions of each sector. 
October 2, 2008 

 B-146

Table 3. Assignment of average, dry, or wet conditions for 

each month for ATL and JST for 2001, 2002, and 2003. 

Month Year 

2001 2002 2003 

January Average Average Dry 

February Average Average Average 

March Average Average Average 

April Average Average Average 

May Average Average Wet 

June Average Average Average 

July Dry Average Average 

August Dry Dry Average 

September Average Average Average 

October Dry Average Dry 

November Dry Average Average 

December Average Average Average 
October 2, 2008 

 B-147

6. AERMET 

The meteorological data files (upper air, ATL ISH data, JST surface data, and ATL one minute 

data) were processed in AERMET, which includes three “Stages” for processing of 

meteorological data. Stage 1 was used to read in all the data files and perform initial QA. The 

upper air data was processed via the UPPERAIR pathway. The ATL ISH data was processed via 

the SURFACE pathway, and the JST surface data and ATL one minute hourly average winds 

were processed via the ONSITE pathway. Winds and temperatures were read into AERMET for 

the JST data and hourly averaged winds were read into AERMET for the ATL one minute hourly 

average winds. For JST, the THRESHOLD keyword was set to 0.28 m/s as described in Section 

2. For the hourly averaged one minute ATL winds, the threshold was set to 0.01 m/s. 

For each year, there were two separate runs of Stage 2 of AERMET, the merging of surface data 

and upper air data; one for ATL and one for JST. For ATL, the Stage 1 upper air output, ATL 

ISH output, and ATL one minute output were merged together via the MERGE pathway. For 

JST, the upper air output, ATL ISH output, and JST output were merged together. 

As with Stage 2, there were two separate Stage 3 runs for each year. First, for ATL, the output 

from Stage 2 was processed. For each year, the year specific surface characteristics created by 

concatenating the appropriate surface characteristics for each month were used. The ATL one 

minute hourly averaged winds would be the primary source of wind data. All other variables 

would come from the ATL ISH data. ATL ISH winds would be used only when the ATL one 

minute hourly averaged winds were missing. The substitution was done via the SUBNWS 

keyword in the Stage 3 input file. The anemometer height was set to 10 m (keyword 

NWS_HGT). 

The second run was for JST. The JST winds and temperature would be the primary source of 

data. Other variables would come from the ATL ISH data and the ATL winds or temperature 

would be used only when the values were missing for JST for a particular hour. Surface 

characteristics were the year specific surface characteristics for JST. For later post-processing, 

the NWS_HGT keyword was set to 9.9 m. This would allow for identification of hours where 

the ATL winds were used. For hours with valid data at the JST site, the 10 m height read into 

AERMET from the JST met file in stage 1 would be used. Note that even for hours using ATL 

data, surface characteristics for JST were used. 

After AERMET processing for each year for JST and ATL, a FORTRAN program was used to 

substitute the records from the ATL *.SFC and *.PFL files into the JST *.SFC and *.PFL files 

when ATL data was substituted for missing values in the JST data (anemometer heights of 9.9 

m). This substitution was done so that the ATL hours that were substituted into the JST data 

would have data based on the ATL surface characteristics. The entire record, including 

anemometer heights, was substituted. The resulting files were a hybrid of JST data and ATL 

hybrid data. The number of hours substituted with ATL data were 165, 497, and 792 for 2001, 

2002, and 2003 respectively. 
October 2, 2008 

 B-148

7. Adjustment of mechanical mixing heights 

Preliminary model-to-monitor comparisons using the processed meteorological data for JST 

should generally good agreement between modeled and observed concentrations. However, 

several spuriously high 1-hour modeled concentrations were also noted. Examination of the 

meteorological conditions associated with these high modeled concentrations indicated a 

consistent pattern of occurring on the first convective hour of the day. This was indicative of an 

issue with the AERMOD model formulation for the urban option that has been identified, but has 

not been addressed yet. The urban option in AERMOD currently applies only to nighttime stable 

hours when the urban heat island effect is expected to increase turbulence relative to the 

surrounding rural areas. The issue that contributes to these high modeled concentrations for 

Atlanta is that the urban-enhanced turbulence disappears once the atmosphere becomes 

convective, with no transitional period to account for residual enhanced turbulence that is likely 

to occur during the transition from night to day. As a result, low-level releases may be subjected 

to very limited mixing conditions for the first convective hour of the day, which may lead to 

unrealistically high concentrations. Every outlier examined was consistent with this pattern, and 

no such anomalies occurred at other hours of the day. In one case, the 1-hour concentration for 

the last stable hour was about an order of magnitude lower then the concentration for the first 

convective hour, with very similar wind speeds and directions. 

In order to minimize the impact that these anomalously high 1-hour concentrations may have on 

the exposure assessment for Atlanta, an adjustment was made to the mechanical mixing heights 

in the processed meteorological data files for the first convective hour of each day. Morning 

mechanical mixing heights for both JST and ATL were adjusted for the first convective hour of 

each day to apply a minimum value of 240 meters. If the mechanical mixing height calculated 

by AERMET was less than 240 meters, it was reset to 240 meters, and if it was larger than 240 

meters then no change was made. This adjustment was intended to account for some limited 

residual mixing from the urban nighttime boundary layer for the first convective hour. The value 

of 240 meters is about one half of the urban nighttime boundary layer for a city with the 

population of Atlanta. Modifying only the mechanical mixing height is considered a reasonable 

approach to account for residual turbulence since the convective mixing height is driven directly 

by the daytime solar heating. This adjustment may underestimate the amount of residual mixing 

that could occur, but is considered to be a reasonable compromise for this application, and 

subsequent modeling comparisons indicated much better agreement between modeled and 

monitored concentrations.
October 2, 2008 

 B-149

8. Analysis of processed meteorology 

Table 4 lists the number of hours that were based on one-minute hourly averaged winds for ATL. 

Table 4 also lists the number of calms and missing winds for the hybrid ATL data and ISH data 

for ATL. For each year, over 90% of the winds were hourly averaged winds from the oneminute data and the number of calms and missing winds were dramatically reduced. 

Table 4. Number of hours using hourly averaged one minute winds and number of calms and 

missing winds for ATL hybrid data and ATL ISH data. 

One minute ISH 

Year One minute hours calms Missing calms missing 

2001 8028 (92%) 118 48 917 645 

2002 7959 (91%) 85 43 856 492 

2003 8171 (93%) 123 19 765 277 

Wind roses and histograms of wind speed for JST and ATL inputs into AERMOD are shown in 

Figures 7 through 9 for 2001, 2002, and 2003. Both sites exhibit similar wind roses, with 

predominant wind directions from the northwest and secondary peaks generally from the east or 

southwest. 

Both the wind roses and histograms show a larger number of lower wind speeds for the JST site 

than for the ATL site, even with the one minute hourly averaged winds included in the ATL data. 

This is consistent with expected influence on wind speeds of the higher surface roughness 

surrounding the JST site as compared to the ATL site. 
October 2, 2008 

 B-150

Figure 7. 2001 wind roses and wind speed histograms for a) ATL hybrid, b) JST hybrid, c) ATL 

hybrid and d) JST hybrid. 
October 2, 2008 

 B-151

Figure 8. 2002 wind roses and wind speed histograms for a) ATL hybrid, b) JST hybrid, c) ATL 

hybrid and d) JST hybrid. 
October 2, 2008 

 B-152

Figure 9. 2002 wind roses and wind speed histograms for a) ATL hybrid, b) JST hybrid, c) ATL 

hybrid and d) JST hybrid. 
 

 B-153

9. References 

U.S. EPA, 2000: Meteorological Monitoring Guidance for Regulatory Modeling 

Applications. EPA-454/R-99-005. U.S. Environmental Protection Agency, 

Research Triangle Park, NC 27711. 

U.S. EPA, 2004: User’s Guide for the AERMOD Meteorological Preprocessor 

(AERMET). EPA-454/B-03-002. U.S. Environmental Protection Agency, 

Research Triangle Park, NC 27711. 

U.S. EPA, 2008a: AERSURFACE User’s Guide. EPA-454/B-08-001. U.S. 

Environmental Protection Agency, Research Triangle Park, NC 27711. 

U.S. EPA, 2008b: Risk and Exposure Assessment to Support the Review of the NO2 

Primary National Ambient Air Quality Standard: Technical Support Document. 

EPA-452/P-08-002. U.S. Environmental Protection Agency, Research Triangle 

Park, NC 27711. 
 

 B-154

Attachment 2: Technical Memorandum on Longitudinal Diary 

Construction Approach 
B-155 

TECHNICAL MEMORANDUM 

TO: Stephen Graham and John Langstaff, US EPA 

FROM: Arlene Rosenbaum 

DATE: February 29, 2008 

SUBJECT: The Cluster-Markov algorithm in APEX

Background 

The goals of population exposure assessment generally include an accurate estimate of 

both the average exposure concentration and the high end of the exposure distribution. One of 

the factors influencing the number of exposures at the high end of the concentration distribution 

is time-activity patterns that differ from the average, e.g., a disproportionate amount of time 

spent near roadways. Whether a model represents these exposure scenarios well depends on 

whether the treatment of activity pattern data accurately characterizes differences among 

individuals. 

Human time-activity data for population exposure models are generally derived from 

demographic surveys of individuals’ daily activities, the amount of time spent engaged in those 

activities, and the ME locations where the activities occur. Typical time-activity pattern data 

available for inhalation exposure modeling consist of a sequence of location/activity 

combinations spanning a 24-hour duration, with 1 to 3 records for any single individual. But 

modeling assessments of exposure to air pollutants typically require information on activity 

patterns over long periods of time, e.g., a full year. For example, even for pollutant health 

effects with short averaging times (e.g., ozone 8-hour average) it may be desirable to know the 

frequency of exceedances of a threshold concentration over a long period of time (e.g., the 

annual number of exceedances of an 8-hour average ozone concentration of 0.07 ppm for each 

simulated individual). 

Long-term activity patterns can be estimated from daily ones by combining the daily 

records in various ways, and the method used for combining them will influence the variability 

of the long-term activity patterns across the simulated population. This in turn will influence the 

ability of the model to accurately represent either long-term average high-end exposures, or the 

number of individuals exposed multiple times to short-term high-end concentrations. 

A common approach for constructing long-term activity patterns from short-term records 

is to re-select a daily activity pattern from the pool of data for each day, with the implicit 

assumption that there is no correlation between activities from day to day for the simulated 

individual. This approach tends to result in long-term activity patterns that are very similar 

across the simulated population. Thus, the resulting exposure estimates are likely to 
 

 B-156

underestimate the variability across the population, and therefore, underestimate the high-end 

concentrations. 

A contrasting approach is to select a single activity pattern (or a single pattern for each 

season and/or weekday-weekend) to represent a simulated individual’s activities over the 

modeling period. This approach has the implicit assumption that an individual’s day to day 

activities are perfectly correlated. This approach tends to result in long-term activity patterns 

that are very different across the simulated population, and therefore may over-estimate the 

variability across the population. 

The Cluster-Markov Algorithm 

Recently, a new algorithm has been developed and incorporated into APEX that attempts 

to more realistically represent the day-to-day correlation of activities for individuals. The 

algorithms first use 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 the 

assumption of no day-to-day correlation (i.e., re-selection for each time period) and perfect 

correlation (i.e., selection of a single daily record to represent all days). 

The steps in the algorithm are as follows. 

• For each demographic group (age, gender, employment status), temperature range, 

and day-of-week combination, the associated time-activity records are partitioned into 

3 groups using cluster analysis. The clustering criterion is a vector of 5 values: the 

time spent in each of 5 microenvironment categories (indoors – residence; indoors – 

other building; outdoors – near road; outdoors – away from road; in vehicle). 

• For each simulated individual, a single time-activity record is randomly selected from 

each cluster. 

• Next the Markov process determines the probability of a given time-activity pattern 

occurring on a given day based on the time-activity pattern of the previous day and 

cluster-to-cluster transition probabilities. The cluster-to-cluster transition 

probabilities are estimated from the available multi-day time-activity records. (If 

insufficient multi-day time-activity records are available for a demographic group, 

season, day-of-week combination, then the cluster-to-cluster transition probabilities 

are estimated from the frequency of time-activity records in each cluster in the CHAD 

data base.). 

Figure 1 illustrates the Cluster-Markov algorithm in flow chart format. 
B-157 

Figure 1. Flow chart of Cluster-Markov algorithm used for constructing longitudinal time-activity diaries. 
B-158 

Evaluation of modeled diary profiles versus observed diary profiles 

The Cluster-Markov algorithm is also incorporated into the Hazardous Air 

Pollutant Exposure Model (HAPEM). Rosebaum and Cohen (2004) incorporated the 

algorithm in HAPEM and tested modeled longitudinal profiles with multi-day diary data 

sets collected as part of the Harvard Southern California Chronic Ozone Exposure Study 

(Xue et al. 2005, Geyh et al. 2000). In this study, 224 children in ages between 7 and 12 

yr were followed for 1 year from June 1995 to May 1996, for 6 consecutive days each 

month. The subjects resided in two separate areas of San Bernardino County: urban 

Upland CA, and the small mountain towns of Lake Arrowhead, Crestline, and Running 

Springs, CA. 

For purposes of clustering 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. For purposes of defining diary 

pools and for clustering and calculating transition probabilities the activity pattern 

records were divided by day type (i.e., weekday, weekend), season (i.e., summer or ozone 

season, non-summer or non-ozone season), age (7-10 and 11-12), and gender. 

Week-long sequences (Wednesday through Tuesday) for each of 100 people in 

each age/gender group for each season were simulated. To evaluate the algorithm the 

following statistics were calculated for the predicted multi-day activity patterns and 

compared them with the actual multi-day diary data. 

• For each age/gender group for each season, the average time in each 

microenvironment 

• For each simulated person-week and microenvironment, the average of the 

within-person variance across all simulated persons. (The within-person 

variance was defined as the variance of the total time per day spent in the 

microenvironment across the week.) 

• For each simulated person-week the variance across persons of the mean time 

spent in each microenvironment. 

In each case the predicted statistic for the stratum was compared to the statistic for 

the corresponding stratum in the actual diary data. The mean normalized bias for the 

statistic, which is a common performance measure used in dispersion model performance 

and was also calculated as follows. 

∑ − =

N

observed

predicted observed

N

NBIAS

1

100 ( )

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%. Fourteen predictions have positive bias and 23 have negative bias. 
 

 B-159

For the variance across persons for the average time spent in each 

microenvironment, the bias ranged from –40% to +120% for any 

microenvironment/age/gender/season. Sixty-five percent of the predicted variances had 

bias between –22% and +24%. The mean normalized bias across any microenvironment 

ranged from –10% to +28%. Eighteen predictions had positive bias and 20 had negative 

bias. 

For the within-person variance for time spent in each microenvironment, the bias 

ranged from –47% to +150% for any microenvironment/age/gender/season. Seventy 

percent of the predicted variances had bias between –25% and +30%. The mean 

normalized bias across any microenvironment ranged from –11% to +47%. Twenty-eight 

predictions had positive bias and 12 had negative bias, suggesting some tendency for 

overprediction of this variance measure. 

The overall conclusion was that the proposed algorithm appeared to be able to 

replicate the observed data reasonably well. Although some discrepancies were rather 

large for some of the “variance across persons” and “within-person variance” subsets, 

about two-thirds of the predictions for each case were within 30% of the observed value. 

A detailed description of the evaluation using HAPEM is presented in Attachment 3. 

Comparison of Cluster-Markov approach with other algorithms 

As part of the application of APEX in support of US EPA’s recent review of the 

ozone NAAQS several sensitivity analyses were conducted (US EPA, 2007). One of 

these was to make parallel simulations using each of the three algorithms for constructing 

multi-day time-activity sequences that are incorporated into APEX. 

Table 1 presents the results for the number of persons in Atlanta population 

groups with moderate exertion exposed to 8-hour average concentrations exceeding 0.07 

ppm. The results show that the predictions made with alternative algorithm ClusterMarkov algorithm are substantially different from those made with simple re-sampling or 

with the Diversity-Autocorrelation algorithm (“base case”). Note that for the cluster 

algorithm approximately 30% of the individuals with 1 or more exposure have 3 or more 

exposures. The corresponding values for the other algorithms range from about 13% to 

21%. 

Table 2 presents the results for the mean and standard deviation of number of 

days/person with 8-hour average exposures exceeding 0.07 ppm with moderate or greater 

exertion. The results show that although the mean for the Cluster-Markov algorithm is 

similar to the other approaches, the standard deviation is substantially higher, i.e., the 

Cluster-Markov algorithm results in substantially higher inter-individual variability. 
 

 B-160

Table 1. Sensitivity to longitudinal diary algorithm: 2002 simulated counts of Atlanta 

general population and children (ages 5-18) with any or three or more 8-hour ozone 

exposures above 0.07 ppm concomitant with moderate or greater exertion (after US EPA 

2007). 

One or more exposures Three or more exposures 

Population 

Group Simple 

re-sampling 

DiversityAutocorrelationClusterMarkov Simple 

re-sampling 

DiversityAutocorrelationClusterMarkov 

General 

Population 

979,533 939,663 

(-4%) 

668,004 

(-32%) 

124,687 144,470 

(+16%) 

188,509 

(+51%) 

Children (5-18) 411,429 389,372 

(-5%) 

295,004 

(-28%) 

71,174 83,377 

(+17%) 

94,216 

(+32%) 

Table 2. Sensitivity to longitudinal diary algorithm: 2002 days per person with 8-hour 

ozone exposures above 0.07 ppm concomitant with moderate or greater exertion for 

Atlanta general population and children (ages 5-18) (after US EPA 2007). 

Mean Days/Person Standard Deviation 

Population 

Group Simple 

re-sampling Base case 

ClusterMarkov Simple resampling Base case ClusterMarkov 

General 

Population 

0.332 0.335 

(+1%) 

0.342 

(+3%) 

0.757 0.802 

(+6%) 

1.197 

(+58%) 

Children (5-18) 0.746 0.755 

(+1%) 

0.758 

(+2%) 

1.077 1.171 

(+9%) 

1.652 

(+53%) 

References 

Geyh AS, Xue J, Ozkaynak H, Spengler JD. (2000). The Harvard Southern California 

chronic ozone exposure study: Assessing ozone exposure of grade-school-age 

children in two Southern California communities. Environ Health Persp. 108:265-

270. 

Rosenbaum AS and Cohen JP. (2004). Evaluation of a multi-day activity pattern 

algorithm for creating longitudinal activity patterns. Memorandum prepared for Ted 

Palma, US EPA OAQPS, by ICF International. 

US EPA. (2007). Ozone Population Exposure Analysis for Selected Urban Areas. EPA452/R-07-010. Available at: 

http://www.epa.gov/ttn/naaqs/standards/ozone/data/2007-01_o3_exposure_tsd.pdf. 

Xue J, Liu SV, Ozkaynak H, Spengler J. (2005). Parameter evaluation and model 

validation of ozone exposure assessment using Harvard Southern California Chronic 

Ozone Exposure Study Data. J. Air & Waste Manage Assoc. 55:1508–1515.
B-161 

Attachment 3: Technical Memorandum on the Evaluation 

Cluster-Markov Algorithm 
B-162 

TECHNICAL MEMORANDUM 

TO: Ted Palma, US EPA 

FROM: Arlene Rosenbaum and Jonathan Cohen, ICF Consulting 

DATE: November 4, 2004 

SUBJECT: Evaluation of a multi-day activity pattern algorithm for creating longitudinal 

activity patterns.

BACKGROUND 

In previous work ICF reviewed the HAPEM4 modeling approach for developing 

annual average activity patterns from the CHAD database and recommended an approach to 

improve the model’s pattern selection process to better represent the variability among 

individuals. This section summarizes the recommended approach. (For details see 

Attachment 2) 

Using cluster analysis, first the CHAD daily activity patterns are grouped into either 

two or three categories of similar patterns for each of the 30 combinations of day type 

(summer weekday, non-summer weekday, and weekend) and demographic group (males or 

females; age groups: 0-4, 5-11, 12-17, 18-64, 65+). Next, for each combination of day type 

and demographic group, category-to-category transition probabilities are defined by the 

relative frequencies of each second-day category associated with each given first-day 

category, where the same individual was observed for two consecutive days. (Consecutive 

day activity pattern records for a single individual constitute a small subset of the CHAD 

data.) 

To implement the proposed algorithm, for each day type and demographic group, one 

daily activity pattern per category is randomly selected from the corresponding CHAD data 

to represent that category. That is, if there are 3 cluster categories for each of 3 day types, 9 

unique activity patterns are selected to be averaged together to create an annual average 

activity pattern to represent an individual in a given demographic group and census tract. 

The weighting for each of the 9 activity patterns used in the averaging process is 

determined by the product of two factors. The first is the relative frequency of its day type, 

i.e., 0.18 for summer weekdays, 0.54 for non-summer weekdays, and 0.28 for weekends. 

The second factor in the weighting for the selected activity pattern is determined by 

simulating a sequence of category-types as a one-stage Markov chain process using the 

transition probabilities. The category for the first day is selected according to the relative 

frequencies of each category. The category for the second day is selected according to the 

category-to-category transition probabilities for the category selected for the first day. The 

category for the third day is selected according to the transition probabilities for the category 
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selected for the second day. This is repeated for all days in the day type (65 for summer 

weekdays, 195 for non-summer weekdays, 104 for weekends), producing a sequence of daily 

categories. The relative frequency of the category-type in the sequence associated with the 

selected activity pattern is the second factor in the weighting. 

PROPOSED ALGORITHM STEPS 

The proposed algorithm is summarized in Figure 1. Each step is explained in this 

section. 

Data Preparation 

Step 1: Each daily activity pattern in the CHAD data base is summarized by the total 

minutes in each of five micro-environments: indoors – residence; indoors – other 

building; outdoors – near road; outdoors – away from road; in vehicle. These five 

numbers are assumed to represent the most important features of the activity pattern 

for their exposure impact. 

Step 2: All CHAD activity patterns for a given day-type and demographic group are 

subjected to cluster analysis, resulting in 2 or 3 cluster categories. Each daily activity 

pattern is tagged with a cluster category. 

Step 3: For each day-type and demographic group, the relative frequency of each daytype in the CHAD data base is determined. 

Step 4: All CHAD activity patterns for a given day-type and demographic group that 

are consecutive days for a single individual, are analyzed to determine the categoryto-category transition frequencies in the CHAD data base. These transition 

frequencies are used to calculate category-to-category transition probabilities. 

For example, if there are 2 categories, A and B, then 

PAA = the probability that a type A pattern is followed by a type A pattern, 

PAB = the probability that a type A pattern is followed by a type B pattern (PAB = 1 – 

PAA), 

PBB = the probability that a type B pattern is followed by a type B pattern, and 

PBA = the probability that a type B pattern is followed by a type A pattern (PBA = 1 – 

PBB). 

Activity Pattern Selection 

For each day-type and demographic group in each census tract: 

Step 5: One activity pattern is randomly selected from each cluster category group 

(i.e., 2 to 3 activity patterns) 

Creating Weights for Day-type Averaging 

For each day-type and demographic group in each census tract: 
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Step 6: A cluster category is selected for the first day of the day-type sequence, 

according to the relative frequency of the cluster category days in the CHAD data set. 

Step 7: A cluster category is selected for each subsequent day in the day-type 

sequence day by day using the category-to-category transition probabilities. 

Step 8: The relative frequency of each cluster category in the day-type sequence is 

determined. 

Step 9: The activity patterns selected for each cluster category (Step 5) are averaged 

together using the cluster category frequencies (Step 8) as weights, to create a daytype average activity pattern. 

Creating Annual Average Activity Patterns 

For each demographic group in each census tract: 

Step 10: The day-type average activity patterns are averaged together using the 

relative frequency of day-types as weights, to create an annual average activity 

pattern. 

Creating Replicates 

For each demographic group in each census tract: 

Step 11: Steps 5 through 10 are repeated 29 times to create 30 annual average activity 

patterns. 

EVALUATING THE ALGORITHM 

The purpose of this study is to evaluate how well the proposed one-stage Markov 

chain algorithm can reproduce observed multi-day activity patterns with respect to 

demographic group means and inter-individual variability, while using one-day selection. 

In order to accomplish this we propose to apply the algorithm to observed multi-day 

activity patterns provided by the WAM, and compare the means and variances of the 

predicted multi-day patterns with the observed patterns. 

 

Current APEX Algorithm 

Because the algorithm is being considered for incorporation into APEX, we would 

like the evaluation to be consistent with the approach taken in APEX for selection of activity 

patterns for creating multi-day sequences. The APEX approach for creating multi-day 

activity sequences is as follows. 

Step1: A profile for a simulated individual is generated by selection of gender, age 

group, and home sector from a given set of distributions consistent with the 

population of the study area. 
 B-165

Step 2: A specific age within the age group is selected from a uniform distribution. 

Step 3: The employment status is simulated as a function of the age. 

Step 4: For each simulated day, the user defines an initial pool of possible diary days 

based on a user-specified function of the day type (e.g., weekday/weekend) and 

temperature. 

Step 5: The pool is further restricted to match the target gender and employment 

status exactly and the age within 2A years for some parameter A. The diary days 

within the pool are assigned a weight of 1 if the age is within A years of the target age 

and a weight of w (user-defined parameter) if the age difference is between A and 2A 

years. For each simulated day, the probability of selecting a given diary day is equal 

to the age weight divided by the total of the age weights for all diary days in the pool 

for that day. 

Approach to Incorporation of Day-to-Day Dependence into APEX Algorithm 

If we were going to incorporate day-to-day dependence of activity patterns into the 

APEX model, we would propose preparing the data with cluster analysis and transition 

probabilities as described in Steps 1-4 for the proposed HAPEM 5 algorithm, with the 

following modifications. 

• For Step 2 the activity patterns would be divided into groups based on day-type 

(weekday, weekend), temperature, gender, employment status, and age, with 

cluster analysis applied to each group. However, because the day-to-day 

transitions in the APEX activity selection algorithm can cross temperature bins, 

we would propose to use broad temperature bins for the clustering and transition 

probability calculations so that the cluster definitions would be fairly uniform 

across temperature bins. Thus we would probably define the bins according to 

season (e.g., summer, non-summer). 

• In contrast to HAPEM, the sequence of activity patterns may be important in 

APEX. Therefore, for Step 4 transition probabilities would be specified for 

transitions between days with the same day-type and season, as in HAPEM, and 

also between days with different day-types and/or seasons. For example, 

transition probabilities would be specified for transitions between summer 

weekdays of each category and summer weekends of each category. 

Another issue for dividing the CHAD activity records for the purposes of clustering 

and calculating transition probabilities is that the diary pools specified for the APEX activity 

selection algorithm use varying and overlapping age ranges. One way to address this 

problem would be to simply not include consideration of age in the clustering process, under 

the assumption that cluster categories are similar across age groups, even if the frequency of 

each cluster category varies by age group. This assumption could be tested by examination 

of the cluster categories stratified by age group that were developed for HAPEM5. If the 

assumption is found to be valid, then the cluster categories could be pre-determined for input 

to APEX, while the transition probabilities could be calculated within APEX during the 

simulation for each age range specified for dairy pools. 
 B-166

If the assumption is found to be invalid, then an alternative approach could be 

implemented that would create overlapping age groups for purposes of clustering as follows. 

APEX age group ranges and age window percentages would be constrained to some 

maximum values. Then a set of overlapping age ranges that would be at least as large as the 

largest possible dairy pool age ranges would be defined for the purposes of cluster analysis 

and transition probability calculation. The resulting sets of cluster categories and transition 

probabilities would be pre-determined for input into APEX and the appropriate set used by 

APEX for each diary pool used during the simulation. 

The actual activity pattern sequence selection would be implemented as follows. The 

activity pattern for first day in the year would be selected exactly as is currently done in 

APEX, as described above. For the selecting the second day’s activity pattern, each age 

weight would be multiplied by the transition probability PAB where A is the cluster for the 

first day’s activity pattern and B is the cluster for a given activity pattern in the available pool 

of diary days for day 2. (Note that day 2 may be a different day-type and/or season than day 

1). The probability of selecting a given diary day on day 2 is equal to the age weight times 

PAB divided by the total of the products of age weight and PAB for all diary days in the pool 

for day 2. Similarly, for the transitions from day 2 to day 3, day 3 to day 4, etc. 

 

Testing the Approach with the Multi-day Data set 

We tested this approach using the available multi-day data set. For purposes of 

clustering we characterized the activity pattern records according to time spent in each of 5 

microenvironments: indoors-home, indoors-school, indoors-other, outdoors (aggregate of the 

3 outdoor microenvironments), and in-transit. 

For purposes of defining diary pools and for clustering and calculating transition 

probabilities we divided the activity pattern records by day type (i.e., weekday, weekend), 

season (i.e., summer or ozone season, non-summer or non-ozone season), age (6-10 and 11-

12), and gender. Since all the subjects are 6-12 years of age and all are presumably 

unemployed, we need not account for differences in employment status. For each day type, 

season, age, and gender, we found that the activity patterns appeared to group in three 

clusters. 

In this case, we simulated week-long sequences (Wednesday through Tuesday) for 

each of 100 people in each age/gender group for each season, using the transition 

probabilities. To evaluate the algorithm we calculated the following statistics for the 

predicted multi-day activity patterns for comparison with the actual multi-day diary data. 

• For each age/gender group for each season, the average time in each 

microenvironment 

• For each age/gender group, season, and microenvironment, the average of the 

within-person variance across all simulated persons (We defined the withinperson variance as the variance of the total time per day spent in the 

microenvironment across the week.) 

• For each age/gender group, season, and microenvironment, the variance across 

persons of the mean time spent in that microenvironment 
 B-167

In each case we compared the predicted statistic for the stratum to the statistic for the 

corresponding stratum in the actual diary data.26 

We also calculated the mean normalized bias for the statistic, which is a common 

performance measure used in dispersion model performance and which is calculated as 

follows.

∑ − =

N

observed

predicted observed

N

NBIAS

1

100 ( )

 % 

RESULTS 

Comparisons of simulated and observed data for time in each of the 5 

microenvironments are presented in Tables 1 – 3 and Figures 2-5. 

Average Time in Microenvironment 

Table 1 and Figure 2 show the comparisons for the average time spent in each of the 

5 microenvironments for each age/gender group and season. Figure 3 shows the comparison 

for all the microenvironments except indoor, home in order to highlight the lower values. 

Table 1 and the figures show that the predicted time-in-microenvironment averages 

match 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%. Fourteen predictions have positive bias and 

23 have negative bias. A Wilcoxon signed rank test that the median bias across the 40 

combinations = 0 % was not significant (p-value = 0.40) supporting the conclusion of no 

overall bias. 

Variance Across Persons 

Table 2 and Figure 4 show the comparisons for the variance across persons for the 

average time spent in each microenvironment. In this case the bias ranges from –40% to 

+120% for any microenvironment/age/gender/season. Sixty-five percent of the predicted 

variances have bias between –22% and +24%. The mean normalized bias across any 

microenvironment ranges from –10% to +28%. Eighteen predictions have positive bias and 

20 have negative bias. Figure 4 suggests a reasonably good match of predicted to observed 

variance in spite of 2 or 3 outliers. A Wilcoxon signed rank test that the median bias across 

the 40 combinations = 0 % was not significant (p-value = 0.93) supporting the conclusion of 

no overall bias. 

Within-Person Variance for Persons 

 

26 For the diary data, because the number of days per person varies, the average of the within-person variances 

was calculated as a weighted average, where the weight is the degrees of freedom, i.e., one less than the number 

of days simulated. Similarly, the variance across persons of the mean time was appropriately adjusted for the 

different degrees of freedom using analysis of variance. 
 B-168

Table 3 and Figure 5 show the comparisons for the within-person variance for time 

spent in each microenvironment. In this case the bias ranges from –47% to +150% for any 

microenvironment/age/gender/season. Seventy percent of the predicted variances have bias 

between –25% and +30%. The mean normalized bias across any microenvironment ranges 

from –11% to +47%. Twenty-eight predictions have positive bias and 12 have negative bias, 

suggesting some tendency for overprediction of this variance measure. And indeed a 

Wilcoxon signed rank test that the median bias across the 40 combinations = 0 % was very 

significant (p-value = 0.01) showing that the within-person variance was significantly 

overpredicted. Still, Figure 4 suggests a reasonably good match of predicted to observed 

variance in most cases, with a few overpredicting outliers at the higher end of the 

distribution. So although the positive bias is significant in a statistical sense (i.e., the variance 

is more likely to be overpredicted than underpredicted), it is not clear whether the bias is 

large enough to be important. 

CONCLUSIONS 

The proposed algorithm appears to be able to replicate the observed data reasonably 

well, although the within-person variance is somewhat overpredicted. 

It would be informative to compare this algorithm with the earlier alternative 

approaches in order to gain perspective on the degree of improvement, if any, afforded by 

this approach. 

Two earlier approaches were: 

1. Select a single activity pattern for each day-type/season combination from the 

appropriate set, and use that pattern for every day in the multi-day sequence that 

corresponds to that day-type and season. 

2. Re-select an activity pattern for each day in the multi-day sequence from the 

appropriate set for the corresponding day-type and season. 

Goodness-of-fit statistics could be developed to compare the three approaches and find 

which model best fits the data for a given stratum.
 B-169

Table 1. Average time spent in each microenvironment: comparison of predicted and observed. 

Microenvironment 

Demographic 

Group Season 

Observed 

(hours/day) 

Predicted 

(hours/day) 

Normalized 

Bias 

Indoor, home Girls, 6-10 Summer 15.5 16.5 6% 

 Not Summer 15.8 15.5 -2% 

 Boys, 6-10 Summer 15.7 15.2 -3% 

 Not Summer 15.8 16.4 4% 

 Girls, 11-12 Summer 16.2 15.3 -5% 

 Not Summer 16.5 16.5 0% 

 Boys, 11-12 Summer 16.0 15.6 -3% 

 Not Summer 16.2 16.1 -1% 

 MEAN -1% 

Indoor, school Girls, 6-10 Summer 0.7 0.7 -9% 

 Not Summer 2.3 2.5 7% 

 Boys, 6-10 Summer 0.8 0.5 -34% 

 Not Summer 2.2 2.2 0% 

 Girls, 11-12 Summer 0.7 0.7 6% 

 Not Summer 2.1 2.4 13% 

 Boys, 11-12 Summer 0.6 0.9 38% 

 Not Summer 2.4 2.7 11% 

 MEAN 4% 

Indoor, other Girls, 6-10 Summer 2.9 2.4 -14% 

 Not Summer 2.4 2.7 13% 

 Boys, 6-10 Summer 2.2 2.7 21% 

 Not Summer 1.9 1.8 -3% 

 Girls, 11-12 Summer 2.2 1.6 -25% 

 Not Summer 2.2 2.1 -2% 

 Boys, 11-12 Summer 2.3 2.2 -5% 

 Not Summer 1.9 2.0 4% 

 MEAN -2% 

Outdoors Girls, 6-10 Summer 3.7 3.5 -6% 

 Not Summer 2.5 2.5 0% 

 Boys, 6-10 Summer 4.1 4.3 4% 

 Not Summer 3.1 2.7 -12% 

 Girls, 11-12 Summer 3.7 5.2 41% 

 Not Summer 2.3 2.1 -5% 

 Boys, 11-12 Summer 3.9 4.3 9% 

 Not Summer 2.6 2.4 -7% 

 MEAN 3% 

In-vehicle Girls, 6-10 Summer 1.1 0.9 -20% 

 Not Summer 1.0 0.9 -13% 

 Boys, 6-10 Summer 1.1 1.3 13% 

 Not Summer 1.0 0.9 -16% 

 Girls, 11-12 Summer 1.2 1.1 -12% 

 Not Summer 0.9 0.8 -15% 

 Boys, 11-12 Summer 1.1 1.0 -5% 

 Not Summer 0.9 0.8 -7% 

 MEAN -9% 
 B-170

Table 2. Variance across persons for time spent in each microenvironment: comparison of 

predicted and observed. 

Microenvironment 

Demographic 

Group Season 

Observed 

(hours/day)2 

Predicted 

(hours/day)2

Normalized 

Bias 

Indoor, home Girls, 6-10 Summer 70 42 -40% 

 Not Summer 67 60 -9% 

 Boys, 6-10 Summer 54 49 -9% 

 Not Summer 35 30 -12% 

 Girls, 11-12 Summer 56 47 -17% 

 Not Summer 42 38 -10% 

 Boys, 11-12 Summer 57 63 12% 

 Not Summer 39 42 8% 

 MEAN -10% 

Indoor, school Girls, 6-10 Summer 6.0 5.2 -13% 

 Not Summer 9.5 5.9 -38% 

 Boys, 6-10 Summer 5.6 3.8 -32% 

 Not Summer 5.3 8.2 53% 

 Girls, 11-12 Summer 4.9 5.5 11% 

 Not Summer 5.4 5.3 -1% 

 Boys, 11-12 Summer 5.6 6.0 6% 

 Not Summer 9.2 11 23% 

 MEAN 1% 

Indoor, other Girls, 6-10 Summer 46 32 -30% 

 Not Summer 44 46. 6% 

 Boys, 6-10 Summer 34 33 -4% 

 Not Summer 23 16 -27% 

 Girls, 11-12 Summer 21 18 -15% 

 Not Summer 28 22 -22% 

 Boys, 11-12 Summer 33 31 -6% 

 Not Summer 30 30 0% 

 MEAN -12% 

Outdoors Girls, 6-10 Summer 17 23 37% 

 Not Summer 9.3 6.8 -27% 

 Boys, 6-10 Summer 17 18 3% 

 Not Summer 8.3 7.6 -8% 

 Girls, 11-12 Summer 22 22 0% 

 Not Summer 9.0 9.1 1% 

 Boys, 11-12 Summer 13 29 120% 

 Not Summer 10 11 8% 

 MEAN 17% 

In-vehicle Girls, 6-10 Summer 1.9 2.3 24% 

 Not Summer 1.8 1.6 -11% 

 Boys, 6-10 Summer 2.5 4.7 93% 

 Not Summer 1.5 1.6 9% 

 Girls, 11-12 Summer 3.5 4.7 34% 

 Not Summer 2.8 2.0 -28% 

 Boys, 11-12 Summer 3.2 5.4 69% 

 Not Summer 1.3 1.7 35% 

 MEAN 28% 
 B-171

Table 3. Average within person variance for time spent in each microenvironment: comparison 

of predicted and observed. 

Microenvironment 

Demographic 

Group Season 

Observed 

(hours/day)2

Predicted 

(hours/day)2

Normalized 

Bias 

Indoor, home Girls, 6-10 Summer 20 29 49% 

 Not Summer 18 23 25% 

 Boys, 6-10 Summer 17 30 75% 

 Not Summer 15 24 64% 

 Girls, 11-12 Summer 22 42 93% 

 Not Summer 22 25 13% 

 Boys, 11-12 Summer 21 24 16% 

 Not Summer 17 24 38% 

 MEAN 47% 

Indoor, school Girls, 6-10 Summer 2.3 2.4 5% 

 Not Summer 7.3 6.4 -12% 

 Boys, 6-10 Summer 2.0 1.5 -25% 

 Not Summer 6.7 5.8 -14% 

 Girls, 11-12 Summer 1.7 2.1 29% 

 Not Summer 7.4 7.6 3% 

 Boys, 11-12 Summer 1.4 2.9 101% 

 Not Summer 7.3 7.8 6% 

 MEAN 12% 

Indoor, other Girls, 6-10 Summer 14 14 -4% 

 Not Summer 14 18 30% 

 Boys, 6-10 Summer 12 17 42% 

 Not Summer 10 13 26% 

 Girls, 11-12 Summer 10 10 1% 

 Not Summer 14 15 7% 

 Boys, 11-12 Summer 11 14 26% 

 Not Summer 12 13 7% 

 MEAN 17% 

Outdoors Girls, 6-10 Summer 8.4 9.5 13% 

 Not Summer 3.4 3.2 -3% 

 Boys, 8-10 Summer 6.7 9.5 42% 

 Not Summer 3.4 4.4 28% 

 Girls, 11-12 Summer 10 25 150% 

 Not Summer 4.0 4.5 11% 

 Boys, 11-12 Summer 9.2 7.4 -20% 

 Not Summer 4.3 3.7 -15% 

 MEAN 26% 

In-vehicle Girls, 6-10 Summer 1.0 0.90 -13% 

 Not Summer 0.90 0.48 -47% 

 Boys, 6-10 Summer 1.1 1.4 31% 

 Not Summer 0.81 0.71 -12% 

 Girls, 11-12 Summer 1.3 1.3 4% 

 Not Summer 1.3 1.1 -16% 

 Boys, 11-12 Summer 2.4 1.6 -34% 

 Not Summer 0.85 0.85 1% 

 MEAN -11% 
B-172 

Figure 1. Flow diagram of proposed algorithm for creating annual average activity patterns for HAPEM5. 

Consolidated Human Activity Database - CHAD (CHAD) 

Cluster Analysis 

Cluster 1 

Pattern 

Group 

Cluster 2 

Pattern 

Group 

Cluster 3 

Pattern 

Group

Transition Analysis 

Transition 

Probabilities

Markov Selection

Individual Annual Average Activity Pattern

Winter Weekday 

Pattern Group

Summer Weekday 

Pattern Group

Weekend 

Pattern Group

Cluster 1 

Pattern 

Cluster 2 

Pattern 

Cluster 3 

Pattern

Weights Average Winter Weekday Pattern 

0.54 

Cluster Analysis Cluster Analysi
 B-173

0

5

10

15

20

0 5 10 15 20

Observed (hours/day)

Predicted (hours/day)

Indoor, home

Indoor, school

indoor, other

Outdoor

In vehicle

Figure 2. Comparison of predicted and observed average time in each of 5 microenvironments 

for age/gender groups and seasons. 

0

1

2

3

4

5

012345

Observed (hours/day)

Predicted (hours/day)

Indoor, school

indoor, other

Outdoor

In vehicle

Figure 3. Comparison of predicted and observed average time in each of 4 microenvironments 

for age/gender groups and seasons. 
 B-174

0

10

20

30

40

50

60

70

80

0 20 40 60 80

Observed

Simulated

girls, 6-10, summer

girls, 6-10, w inter

boys , 6-10, summer

boys, 6-10, w inter

girls, 11-12, summer

girls, 11-12, w inter

boys, 11-12, summer

boys, 11-12, w inter

Figure 4. Comparison of predicted and observed variance across persons for time spent in each 

of 5 microenvironments for age/gender groups and seasons. 

0

5

10

15

20

25

30

0 10 20 30

Observed

Simulated

girls, 6-10, summer

girls, 6-10, w inter

boys , 6-10, summer

boys, 6-10, w inter

girls, 11-12, summer

girls, 11-12, w inter

boys, 11-12, summer

boys, 11-12, w inter

Figure 5. Comparison of predicted and observed the average within-person variance for time 

spent in each of 5 microenvironments by age/gender groups and seasons. 
 B-175

Attachment 4. Technical Memorandum on the Analysis of NHIS 

Asthma Prevalence Data 
 B-176

DRAFT MEMORANDUM 

To: John Langstaff 

From: Jonathan Cohen, Arlene Rosenbaum 

Date: September 30, 2005 

Re: EPA 68D01052, Work Assignment 3-08. Analysis of NHIS Asthma Prevalence 

Data 

This memorandum describes our analysis of children’s asthma prevalence data from the National 

Health Interview Survey (NHIS) for 2003. Asthma prevalence rates for children aged 0 to 17 

years were calculated for each age, gender, and region. The regions defined by NHIS are 

“Midwest,” “Northeast,” “South,” and “West.” For this project, asthma prevalence was defined 

as the probability of a Yes response to the question CASHMEV: “Ever been told that ... had 

asthma?” among those that responded Yes or No to this question. The responses were weighted 

to take into account the complex survey design of the NHIS survey. Standard errors and 

confidence intervals for the prevalence were calculated using a logistic model, taking into 

account the survey design. Prevalence curves showing the variation of asthma prevalence 

against age for a given gender and region were plotted. A scatterplot smoothing technique using 

the LOESS smoother was applied to smooth the prevalence curves and compute the standard 

errors and confidence intervals for the smoothed prevalence estimates. Logistic analysis of the 

prevalence curves shows statistically significant differences in prevalence by gender and by 

region. Therefore we did not combine the prevalence rates for different genders or regions. 

Logistic Models

NHIS survey data for 2003 were provided by EPA. One obvious approach to calculate 

prevalence rates and their uncertainties for a given gender, region, and age is to calculate the 

proportion of Yes responses among the Yes and No responses for that demographic group, 

weighting each response by the survey weight. Although that approach was initially used, two 

problems are that the distributions of the estimated prevalence rates are not well approximated by 

normal distributions, and that the estimated confidence intervals based on the normal 

approximation often extend outside the [0, 1] interval. A better approach is to use a logistic 

transformation and fit a model of the form: 

Prob (asthma) = exp(beta) / (1 + exp(beta) ), 

where beta may depend on the explanatory variables for age, gender, or region. This is 

equivalent to the model: 
 B-177

 Beta = logit {prob (asthma) } = log { prob (asthma) / [1 – prob (asthma)] }. 

The distribution of the estimated values of beta is more closely approximated by a normal 

distribution than the distribution of the corresponding estimates of prob (asthma). By applying a 

logit transformation to the confidence intervals for beta, the corresponding confidence intervals 

for prob (asthma) will always be inside [0, 1]. Another advantage of the logistic modeling is that 

it can be used to compare alternative statistical models, such as models where the prevalence 

probability depends upon age, region, and gender, or on age and region but not gender. 

A variety of logistic models for asthma prevalence were fit and compared, where the transformed 

probability variable beta is a given function of age, gender, and region. SAS’s 

SURVEYLOGISTIC procedure was used to fit the logistic models, taking into account the NHIS 

survey weights and survey design (stratification and clustering). 

The following Table G-1 lists the models fitted and their log-likelihood goodness-of-fit 

measures. 16 models were fitted. The Strata column lists the four possible stratifications: no 

stratification, by gender, by region, by region and gender. For example, “4. region, gender” 

means that separate prevalence estimates were made for each combination of region and gender. 

As another example, “2. gender” means that separate prevalence estimates were made for each 

gender, so that for each gender, the prevalence is assumed to be the same for each region. The 

prevalence estimates are independently calculated for each stratum. 

Table G-1. Alternative logistic models for asthma prevalence. 

Model Description Strata - 2 Log Likelihood DF

1 1. logit(prob) = linear in age 1. none 54168194.62 2

2 1. logit(prob) = linear in age 2. gender 53974657.17 4

3 1. logit(prob) = linear in age 3. region 54048602.57 8

4 1. logit(prob) = linear in age 4. region, gender 53837594.97 16

5 2. logit(prob) = quadratic in age 1. none 53958021.20 3

6 2. logit(prob) = quadratic in age 2. gender 53758240.99 6

7 2. logit(prob) = quadratic in age 3. region 53818198.13 12

8 2. logit(prob) = quadratic in age 4. region, gender 53593569.84 24

9 3. logit(prob) = cubic in age 1. none 53849072.76 4

10 3. logit(prob) = cubic in age 2. gender 53639181.24 8

11 3. logit(prob) = cubic in age 3. region 53694710.66 16

12 3. logit(prob) = cubic in age 4. region, gender 53441122.98 32

13 4. logit(prob) = f(age) 1. none 53610093.48 18

14 4. logit(prob) = f(age) 2. gender 53226610.02 36

15 4. logit(prob) = f(age) 3. region 53099749.33 72

16 4. logit(prob) = f(age) 4. region, gender 52380000.19 144
 B-178

The Description column describes how beta depends upon the age: 

 Linear in age: Beta = α + β × age, where α and β vary with the strata. 

Quadratic in age: Beta = α + β × age + γ × age2

 where α β and γ vary with the 

strata. 

Cubic in age: Beta = α + β × age + γ × age2

 + δ × age3 where α β, γ, and δ vary 

with the strata. 

f(age) Beta = arbitrary function of age, with different functions for 

different strata 

The category f(age) is equivalent to making age one of the stratification variables, and is also 

equivalent to making beta a polynomial of degree 16 in age (since the maximum age for children 

is 17), with coefficients that may vary with the strata. 

The fitted models are listed in order of complexity, where the simplest model (1) is an 

unstratified linear model in age and the most complex model (16) has a prevalence that is an 

arbitrary function of age, gender, and region. Model 16 is equivalent to calculating independent 

prevalence estimates for each of the 144 combinations of age, gender, and region. 

Table G-1 also includes the -2 Log Likelihood, a goodness-of-fit measure, and the degrees of 

freedom, DF, which is the total number of estimated parameters. Two models can be compared 

using their -2 Log Likelihood values; lower values are preferred. If the first model is a special 

case of the second model, then the approximate statistical significance of the first model is 

estimated by comparing the difference in the -2 Log Likelihood values with a chi-squared 

random variable with r degrees of freedom, where r is the difference in the DF. This is a 

likelihood ratio test. For all pairs of models from Table G-1, all the differences are at least 

70,000 and the likelihood ratios are all extremely statistically significant at levels well below 5 

percent. Therefore the model 16 is clearly preferred and was used to model the prevalences. 

The SURVEYLOGISTIC model predictions are tabulated in Table G-2 below and plotted in 

Figures 1 and 3 below. Also shown in Table G-2 and in Figures 2 and 4 are results for smoothed 

curves calculated using a LOESS scatterplot smoother, as discussed below. 

The SURVEYLOGISTIC procedure produces estimates of the beta values and their 95 % 

confidence intervals for each combination of age, region, and gender. Applying the inverse logit 

transformation, 

Prob (asthma) = exp( beta) / (1 + exp(beta) ), 

converted the beta values and 95 % confidence intervals into predictions and 95 % confidence 

intervals for the prevalence, as shown in Table G-2 and Figures 1 and 3. The standard error for 

the prevalence was estimated as 

Std Error {Prob (asthma)} = Std Error (beta) × exp(- beta) / (1 + exp(beta) )2

, 

which follows from the delta method (a first order Taylor series approximation). 

Loess Smoother
 B-179

The estimated prevalence curves shows that the prevalence is not a smooth function of age. The 

linear, quadratic, and cubic functions of age modeled by SURVEYLOGISTIC were one strategy 

for smoothing the curves, but they did not provide a good fit to the data. One reason for this 

might be due to the attempt to fit a global regression curve to all the age groups, which means 

that the predictions for age A are affected by data for very different ages. We instead chose to 

use a local regression approach that separately fits a regression curve to each age A and its 

neighboring ages, giving a regression weight of 1 to the age A, and lower weights to the 

neighboring ages using a tri-weight function: 

 Weight = {1 – [ |age – A| / q ] 3

}, where | age – A| <= q. 

The parameter q defines the number of points in the neighborhood of the age a. Instead of calling 

q the smoothing parameter, SAS defines the smoothing parameter as the proportion of points in 

each neighborhood. We fitted a quadratic function of age to each age neighborhood, separately 

for each gender and region combination. We fitted these local regression curves to the beta 

values, the logits of the asthma prevalence estimates, and then converted them back to estimated 

prevalence rates by applying the inverse logit function exp(beta) / (1 + exp(beta) ). In addition to 

the tri-weight variable, each beta value was assigned a weight of 

1 / [std error (beta)]2

, to account for their uncertainties. 

The SAS LOESS procedure was applied to estimate smoothed curves for beta, the logit of the 

prevalence, as a function of age, separately for each region and gender. We fitted curves using 

the choices 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, and 1.0 for the smoothing parameter in an effort to 

determine the optimum choice based on various regression diagnostics.27,28 

Quantities predicted in these smoothing parameter tests were the predicted value, standard error, 

confidence interval lower bound and confidence interval upper bound for the betas, and the 

corresponding values for the prevalence rates. 

The polygonal curves joining values for different ages show the predicted values with vertical 

lines indicating the confidence intervals in Figures 3 and 4 for smoothing parameters 0 (i.e., no 

smoothing) and 0.5, respectively. Note that the confidence intervals are not symmetric about the 

predicted values because of the inverse logit transformation. 

 

27 Two outlier cases were adjusted to avoid wild variations in the “smoothed” curves: For the West region, males, 

age 0, there were 97 children surveyed that all gave No answers to the asthma question, leading to an estimated 

value of -15.2029 for beta with a standard error of 0.14. For the Northeast region, females, age 0, there were 29 

children surveyed that all gave No answers to the asthma question, leading to an estimated value of -15.2029 for 

beta with a standard error of 0.19. In both cases the raw probability of asthma equals zero, so the corresponding 

estimated beta would be negative infinity, but SAS’s software gives -15.2029 instead. To reduce the impact of these 

outlier cases, we replaced their estimated standard errors by 4, which is approximately four times the maximum 

standard error for all other region, gender, and age combinations. 

28 With only 18 points, a smoothing parameter of 0.2 cannot be used because the weight function assigns zero 

weights to all ages except age A, and a quadratic model cannot be uniquely fitted to a single value. A smoothing 

parameter of 0.3 also cannot be used because that choice assigns a neighborhood of 5 points only (0.3 × 18 = 5, 

rounded down), of which the two outside ages have assigned weight zero, making the local quadratic model fit 

exactly at every point except for the end points (ages 0, 1, 16 and 17). Usually one uses a smoothing parameter 

below one so that not all the data are used for the local regression at a given x value. 
 B-180

Note that in our application of LOESS, we used weights of 1 / [std error (beta)] 2, so that σ

2

 = 1 

for this application. The LOESS procedure estimates σ

2

 from the weighted sum of squares. Since 

in our application we assume σ

2

 = 1, we multiplied the estimated standard errors by 1 / 

estimated σ, and adjusted the widths of the confidence intervals by the same factor. 

Additionally, because the true value of σ equals 1, the best choices of smoothing parameter 

should give residual standard errors close to one. Using this criterion the best choice varies with 

gender and region between smoothing parameters 0.4 (3 cases), 0.5 (2 cases), 0.6 (1 case), and 

0.7 (1 case). 

 As a further regression diagnostic the residual errors from the LOESS model were divided by 

std error (beta) to make their variances approximately constant. These approximately studentized 

residuals, ‘student,’ should be approximately normally distributed with a mean of zero and a 

variance of σ

2

 = 1. To test this assumption, normal probability plots of the residuals were 

created for each smoothing parameter, combining all the studentized residuals across genders, 

regions, and ages. The plots for smoothing parameters seem to be equally straight for each 

smoothing parameter. 

The final regression diagnostic is a plot of the studentized residuals against the smoothed beta 

values. Ideally there should be no obvious pattern and an average studentized residual close to 

zero. The plots indeed showed no unusual patterns, and the results for smoothing parameters 0.5 

and 0.6 seem to showed a fitted LOESS close to the studentized residual equals zero line. 

The regression diagnostics suggested the choice of smoothing parameter as 0.4 or 0.5. Normal 

probability plots did not suggest any preferred choices. The plots of residuals against smoothed 

predictions suggest the choices of 0.5 or 0.6. We therefore chose the final value of 0.5. These 

predictions, standard errors, and confidence intervals are presented in tabular form below as 

Table G-2. 
 B-181

region=Midwest

gender Female Male

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 B-182

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 B-183

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gender Female Male

predprob

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 B-184

region=South

gender Female Male

predprob

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0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

region=West

gender Female Male

predprob

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 B-185

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gender Female Male

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region=Northeast

gender Female Male

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 B-186

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gender Female Male

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 B-187

region=Midwest

gender Female Male

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0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

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 B-188

region=South

gender Female Male

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 B-189

Table G-2. Raw and smoothed prevalence rates, with confidence intervals, by region, 

gender, and age. 

Obs Region Gender Age Smoothed Prevalence 

Std 

Error 

95 % Conf 

Interval – 

Lower 

Bound 

95 % Conf 

Interval – 

Upper 

Bound 

1 Midwest Female 0 No 0.04161 0.02965 0.01001 0.15717

2 Midwest Female 0 Yes 0.06956 0.03574 0.02143 0.20330

3 Midwest Female 1 No 0.10790 0.04254 0.04840 0.22336

4 Midwest Female 1 Yes 0.07078 0.01995 0.03736 0.13008

5 Midwest Female 2 No 0.05469 0.02578 0.02131 0.13325

6 Midwest Female 2 Yes 0.07324 0.01778 0.04228 0.12395

7 Midwest Female 3 No 0.06094 0.03474 0.01936 0.17579

8 Midwest Female 3 Yes 0.07542 0.01944 0.04205 0.13163

9 Midwest Female 4 No 0.09049 0.03407 0.04233 0.18298

10 Midwest Female 4 Yes 0.08100 0.02163 0.04417 0.14393

11 Midwest Female 5 No 0.08463 0.03917 0.03317 0.19942

12 Midwest Female 5 Yes 0.09540 0.02613 0.05106 0.17131

13 Midwest Female 6 No 0.14869 0.08250 0.04643 0.38520

14 Midwest Female 6 Yes 0.09210 0.02854 0.04534 0.17808

15 Midwest Female 7 No 0.04757 0.02927 0.01389 0.15051

16 Midwest Female 7 Yes 0.09032 0.02563 0.04728 0.16571

17 Midwest Female 8 No 0.10444 0.03638 0.05160 0.19997

18 Midwest Female 8 Yes 0.08612 0.02181 0.04842 0.14857

19 Midwest Female 9 No 0.09836 0.04283 0.04062 0.21943

20 Midwest Female 9 Yes 0.11040 0.02709 0.06298 0.18643

21 Midwest Female 10 No 0.10916 0.04859 0.04400 0.24600

22 Midwest Female 10 Yes 0.16190 0.03486 0.09838 0.25484

23 Midwest Female 11 No 0.27341 0.06817 0.16112 0.42437

24 Midwest Female 11 Yes 0.19597 0.03920 0.12296 0.29763

25 Midwest Female 12 No 0.10055 0.04780 0.03816 0.23952

26 Midwest Female 12 Yes 0.21214 0.03957 0.13724 0.31309

27 Midwest Female 13 No 0.22388 0.05905 0.12907 0.35959

28 Midwest Female 13 Yes 0.16966 0.03371 0.10716 0.25807
 B-190

Obs Region Gender Age Smoothed Prevalence 

Std 

Error 

95 % Conf 

Interval – 

Lower 

Bound 

95 % Conf 

Interval – 

Upper 

Bound 

29 Midwest Female 14 No 0.10511 0.04233 0.04637 0.22104

30 Midwest Female 14 Yes 0.14020 0.02603 0.09164 0.20857

31 Midwest Female 15 No 0.12026 0.03805 0.06327 0.21670

32 Midwest Female 15 Yes 0.13341 0.02266 0.09056 0.19226

33 Midwest Female 16 No 0.13299 0.03933 0.07288 0.23037

34 Midwest Female 16 Yes 0.14040 0.02235 0.09764 0.19777

35 Midwest Female 17 No 0.17497 0.04786 0.09970 0.28884

36 Midwest Female 17 Yes 0.16478 0.04037 0.09320 0.27468

37 Midwest Male 0 No 0.06419 0.03612 0.02068 0.18227

38 Midwest Male 0 Yes 0.03134 0.01537 0.01042 0.09046

39 Midwest Male 1 No 0.02824 0.01694 0.00859 0.08879

40 Midwest Male 1 Yes 0.06250 0.01751 0.03321 0.11457

41 Midwest Male 2 No 0.05102 0.02343 0.02040 0.12189

42 Midwest Male 2 Yes 0.10780 0.02078 0.06960 0.16328

43 Midwest Male 3 No 0.18650 0.04864 0.10898 0.30057

44 Midwest Male 3 Yes 0.15821 0.02705 0.10696 0.22775

45 Midwest Male 4 No 0.24649 0.05823 0.15035 0.37686

46 Midwest Male 4 Yes 0.21572 0.03661 0.14543 0.30774

47 Midwest Male 5 No 0.11609 0.04818 0.04973 0.24793

48 Midwest Male 5 Yes 0.17822 0.03525 0.11280 0.27003

49 Midwest Male 6 No 0.14158 0.05280 0.06576 0.27873

50 Midwest Male 6 Yes 0.12788 0.02799 0.07751 0.20375

51 Midwest Male 7 No 0.09726 0.03614 0.04588 0.19448

52 Midwest Male 7 Yes 0.12145 0.02642 0.07391 0.19317

53 Midwest Male 8 No 0.16718 0.05814 0.08134 0.31276

54 Midwest Male 8 Yes 0.12757 0.02700 0.07864 0.20031

55 Midwest Male 9 No 0.13406 0.04783 0.06458 0.25769

56 Midwest Male 9 Yes 0.14718 0.02976 0.09254 0.22603

57 Midwest Male 10 No 0.13986 0.04422 0.07331 0.25050

58 Midwest Male 10 Yes 0.17728 0.02996 0.12020 0.25366

59 Midwest Male 11 No 0.23907 0.05031 0.15449 0.35075
 B-191

Obs Region Gender Age Smoothed Prevalence 

Std 

Error 

95 % Conf 

Interval – 

Lower 

Bound 

95 % Conf 

Interval – 

Upper 

Bound 

60 Midwest Male 11 Yes 0.18961 0.03044 0.13100 0.26639

61 Midwest Male 12 No 0.13660 0.04784 0.06668 0.25946

62 Midwest Male 12 Yes 0.19487 0.03078 0.13541 0.27221

63 Midwest Male 13 No 0.18501 0.04498 0.11230 0.28945

64 Midwest Male 13 Yes 0.16939 0.02841 0.11528 0.24195

65 Midwest Male 14 No 0.16673 0.05094 0.08886 0.29104

66 Midwest Male 14 Yes 0.16795 0.02631 0.11734 0.23459

67 Midwest Male 15 No 0.14583 0.04241 0.08054 0.24967

68 Midwest Male 15 Yes 0.17953 0.02561 0.12951 0.24347

69 Midwest Male 16 No 0.24965 0.06037 0.15033 0.38489

70 Midwest Male 16 Yes 0.20116 0.03048 0.14187 0.27721

71 Midwest Male 17 No 0.21152 0.06481 0.11131 0.36490

72 Midwest Male 17 Yes 0.23741 0.05816 0.13243 0.38835

73 Northeast Female 0 No 0.00000 0.00000 0.00000 0.00000

74 Northeast Female 0 Yes 0.06807 0.06565 0.00670 0.44174

75 Northeast Female 1 No 0.12262 0.07443 0.03476 0.35164

76 Northeast Female 1 Yes 0.07219 0.03765 0.02088 0.22109

77 Northeast Female 2 No 0.07217 0.03707 0.02561 0.18713

78 Northeast Female 2 Yes 0.07522 0.02212 0.03764 0.14468

79 Northeast Female 3 No 0.08550 0.03991 0.03324 0.20269

80 Northeast Female 3 Yes 0.07709 0.02021 0.04162 0.13840

81 Northeast Female 4 No 0.08704 0.03804 0.03596 0.19592

82 Northeast Female 4 Yes 0.08171 0.02252 0.04269 0.15080

83 Northeast Female 5 No 0.07597 0.03754 0.02801 0.18998

84 Northeast Female 5 Yes 0.11603 0.03012 0.06258 0.20515

85 Northeast Female 6 No 0.19149 0.06960 0.08937 0.36372

86 Northeast Female 6 Yes 0.16106 0.03737 0.09219 0.26629

87 Northeast Female 7 No 0.22034 0.07076 0.11195 0.38783

88 Northeast Female 7 Yes 0.18503 0.04087 0.10844 0.29764

89 Northeast Female 8 No 0.11002 0.05128 0.04241 0.25654

90 Northeast Female 8 Yes 0.17054 0.04039 0.09628 0.28407
 B-192

Obs Region Gender Age Smoothed Prevalence 

Std 

Error 

95 % Conf 

Interval – 

Lower 

Bound 

95 % Conf 

Interval – 

Upper 

Bound 

91 Northeast Female 9 No 0.17541 0.07488 0.07159 0.36981

92 Northeast Female 9 Yes 0.14457 0.03538 0.08042 0.24618

93 Northeast Female 10 No 0.12980 0.04964 0.05930 0.26087

94 Northeast Female 10 Yes 0.13487 0.03098 0.07799 0.22319

95 Northeast Female 11 No 0.15128 0.05287 0.07366 0.28547

96 Northeast Female 11 Yes 0.14072 0.03068 0.08367 0.22704

97 Northeast Female 12 No 0.11890 0.04426 0.05568 0.23597

98 Northeast Female 12 Yes 0.16615 0.03375 0.10211 0.25877

99 Northeast Female 13 No 0.22638 0.06285 0.12650 0.37158

100 Northeast Female 13 Yes 0.17374 0.03402 0.10861 0.26626

101 Northeast Female 14 No 0.15807 0.05513 0.07694 0.29719

102 Northeast Female 14 Yes 0.15137 0.02946 0.09519 0.23220

103 Northeast Female 15 No 0.07460 0.03409 0.02971 0.17506

104 Northeast Female 15 Yes 0.14564 0.02761 0.09279 0.22127

105 Northeast Female 16 No 0.13603 0.05328 0.06081 0.27686

106 Northeast Female 16 Yes 0.14601 0.03095 0.08805 0.23241

107 Northeast Female 17 No 0.19074 0.07382 0.08451 0.37568

108 Northeast Female 17 Yes 0.15662 0.05374 0.06784 0.32151

109 Northeast Male 0 No 0.03904 0.03829 0.00547 0.23095

110 Northeast Male 0 Yes 0.04768 0.03299 0.00991 0.20023

111 Northeast Male 1 No 0.05533 0.03425 0.01596 0.17461

112 Northeast Male 1 Yes 0.04564 0.01831 0.01850 0.10821

113 Northeast Male 2 No 0.05525 0.03119 0.01781 0.15872

114 Northeast Male 2 Yes 0.05161 0.01505 0.02680 0.09709

115 Northeast Male 3 No 0.03842 0.02923 0.00840 0.15853

116 Northeast Male 3 Yes 0.06766 0.01784 0.03734 0.11955

117 Northeast Male 4 No 0.07436 0.02906 0.03393 0.15522

118 Northeast Male 4 Yes 0.09964 0.02330 0.05859 0.16441

119 Northeast Male 5 No 0.17601 0.04519 0.10393 0.28234

120 Northeast Male 5 Yes 0.14854 0.02948 0.09428 0.22623

121 Northeast Male 6 No 0.23271 0.09319 0.09832 0.45756
 B-193

Obs Region Gender Age Smoothed Prevalence 

Std 

Error 

95 % Conf 

Interval – 

Lower 

Bound 

95 % Conf 

Interval – 

Upper 

Bound 

122 Northeast Male 6 Yes 0.20731 0.04235 0.12875 0.31640

123 Northeast Male 7 No 0.13074 0.05195 0.05785 0.26922

124 Northeast Male 7 Yes 0.22820 0.04524 0.14338 0.34311

125 Northeast Male 8 No 0.33970 0.08456 0.19726 0.51855

126 Northeast Male 8 Yes 0.22240 0.04298 0.14157 0.33157

127 Northeast Male 9 No 0.13761 0.05024 0.06507 0.26785

128 Northeast Male 9 Yes 0.21238 0.04071 0.13589 0.31617

129 Northeast Male 10 No 0.21785 0.06659 0.11464 0.37465

130 Northeast Male 10 Yes 0.17652 0.03731 0.10824 0.27460

131 Northeast Male 11 No 0.11448 0.05849 0.04005 0.28601

132 Northeast Male 11 Yes 0.16617 0.03516 0.10200 0.25907

133 Northeast Male 12 No 0.17736 0.05489 0.09349 0.31067

134 Northeast Male 12 Yes 0.18279 0.03589 0.11611 0.27581

135 Northeast Male 13 No 0.19837 0.05450 0.11222 0.32635

136 Northeast Male 13 Yes 0.17078 0.03078 0.11288 0.25000

137 Northeast Male 14 No 0.16201 0.04973 0.08618 0.28386

138 Northeast Male 14 Yes 0.17033 0.02889 0.11547 0.24408

139 Northeast Male 15 No 0.11894 0.04584 0.05417 0.24139

140 Northeast Male 15 Yes 0.18246 0.02858 0.12740 0.25438

141 Northeast Male 16 No 0.24306 0.05798 0.14759 0.37326

142 Northeast Male 16 Yes 0.20406 0.03216 0.14187 0.28447

143 Northeast Male 17 No 0.22559 0.06980 0.11748 0.38930

144 Northeast Male 17 Yes 0.24185 0.06066 0.13291 0.39898

145 South Female 0 No 0.02459 0.01116 0.01002 0.05906

146 South Female 0 Yes 0.03407 0.01282 0.01465 0.07723

147 South Female 1 No 0.08869 0.03373 0.04118 0.18067

148 South Female 1 Yes 0.05182 0.01167 0.03127 0.08472

149 South Female 2 No 0.05097 0.02373 0.02012 0.12319

150 South Female 2 Yes 0.07110 0.01386 0.04584 0.10869

151 South Female 3 No 0.08717 0.03240 0.04122 0.17500

152 South Female 3 Yes 0.08759 0.01718 0.05624 0.13394
 B-194

Obs Region Gender Age Smoothed Prevalence 

Std 

Error 

95 % Conf 

Interval – 

Lower 

Bound 

95 % Conf 

Interval – 

Upper 

Bound 

153 South Female 4 No 0.11010 0.03209 0.06113 0.19035

154 South Female 4 Yes 0.09897 0.01914 0.06387 0.15025

155 South Female 5 No 0.09409 0.02943 0.05015 0.16968

156 South Female 5 Yes 0.11870 0.02157 0.07855 0.17548

157 South Female 6 No 0.15318 0.04317 0.08611 0.25777

158 South Female 6 Yes 0.12150 0.02282 0.07925 0.18182

159 South Female 7 No 0.09608 0.03538 0.04565 0.19105

160 South Female 7 Yes 0.11192 0.02171 0.07204 0.16985

161 South Female 8 No 0.09955 0.03288 0.05111 0.18493

162 South Female 8 Yes 0.09287 0.01897 0.05850 0.14436

163 South Female 9 No 0.07477 0.02719 0.03606 0.14864

164 South Female 9 Yes 0.09117 0.01786 0.05855 0.13929

165 South Female 10 No 0.10602 0.03214 0.05750 0.18732

166 South Female 10 Yes 0.10821 0.02026 0.07077 0.16201

167 South Female 11 No 0.14411 0.04267 0.07875 0.24907

168 South Female 11 Yes 0.13237 0.02251 0.08989 0.19071

169 South Female 12 No 0.12646 0.02981 0.07860 0.19723

170 South Female 12 Yes 0.12346 0.02004 0.08543 0.17519

171 South Female 13 No 0.11376 0.03270 0.06365 0.19510

172 South Female 13 Yes 0.09653 0.01717 0.06458 0.14190

173 South Female 14 No 0.02915 0.01339 0.01174 0.07054

174 South Female 14 Yes 0.09469 0.01619 0.06436 0.13721

175 South Female 15 No 0.11985 0.03357 0.06801 0.20259

176 South Female 15 Yes 0.09988 0.01586 0.06978 0.14099

177 South Female 16 No 0.14183 0.03685 0.08366 0.23028

178 South Female 16 Yes 0.11501 0.01620 0.08365 0.15612

179 South Female 17 No 0.13141 0.03007 0.08280 0.20226

180 South Female 17 Yes 0.14466 0.02946 0.09067 0.22291

181 South Male 0 No 0.01164 0.00852 0.00275 0.04790

182 South Male 0 Yes 0.04132 0.01867 0.01487 0.10956

183 South Male 1 No 0.10465 0.03216 0.05629 0.18635
 B-195

Obs Region Gender Age Smoothed Prevalence 

Std 

Error 

95 % Conf 

Interval – 

Lower 

Bound 

95 % Conf 

Interval – 

Upper 

Bound 

184 South Male 1 Yes 0.06981 0.01623 0.04125 0.11576

185 South Male 2 No 0.11644 0.03486 0.06353 0.20382

186 South Male 2 Yes 0.10189 0.01672 0.07024 0.14557

187 South Male 3 No 0.10794 0.03253 0.05874 0.19005

188 South Male 3 Yes 0.12852 0.02139 0.08793 0.18405

189 South Male 4 No 0.08480 0.02973 0.04190 0.16410

190 South Male 4 Yes 0.14393 0.02379 0.09861 0.20534

191 South Male 5 No 0.22243 0.04227 0.15052 0.31592

192 South Male 5 Yes 0.16450 0.02373 0.11821 0.22430

193 South Male 6 No 0.13908 0.03392 0.08485 0.21964

194 South Male 6 Yes 0.16386 0.02460 0.11613 0.22617

195 South Male 7 No 0.10695 0.04272 0.04747 0.22347

196 South Male 7 Yes 0.13329 0.02322 0.08951 0.19392

197 South Male 8 No 0.13660 0.03841 0.07712 0.23049

198 South Male 8 Yes 0.13818 0.02276 0.09484 0.19702

199 South Male 9 No 0.15978 0.03742 0.09920 0.24720

200 South Male 9 Yes 0.16839 0.02450 0.12062 0.23012

201 South Male 10 No 0.21482 0.04702 0.13676 0.32086

202 South Male 10 Yes 0.17848 0.02453 0.13021 0.23972

203 South Male 11 No 0.15078 0.03440 0.09492 0.23112

204 South Male 11 Yes 0.16247 0.02224 0.11881 0.21820

205 South Male 12 No 0.13727 0.03260 0.08489 0.21438

206 South Male 12 Yes 0.14480 0.01976 0.10610 0.19453

207 South Male 13 No 0.14136 0.03119 0.09049 0.21409

208 South Male 13 Yes 0.14318 0.01928 0.10537 0.19165

209 South Male 14 No 0.16110 0.03444 0.10438 0.24037

210 South Male 14 Yes 0.15339 0.01875 0.11612 0.19992

211 South Male 15 No 0.16172 0.03519 0.10394 0.24291

212 South Male 15 Yes 0.15088 0.01746 0.11598 0.19398

213 South Male 16 No 0.15836 0.03879 0.09614 0.24974

214 South Male 16 Yes 0.14038 0.01773 0.10533 0.18467
 B-196

Obs Region Gender Age Smoothed Prevalence 

Std 

Error 

95 % Conf 

Interval – 

Lower 

Bound 

95 % Conf 

Interval – 

Upper 

Bound 

215 South Male 17 No 0.11156 0.02737 0.06810 0.17746

216 South Male 17 Yes 0.12247 0.02596 0.07537 0.19286

217 West Female 0 No 0.00983 0.00990 0.00135 0.06802

218 West Female 0 Yes 0.01318 0.00987 0.00248 0.06700

219 West Female 1 No 0.02367 0.01862 0.00497 0.10522

220 West Female 1 Yes 0.03105 0.01312 0.01204 0.07769

221 West Female 2 No 0.08097 0.03759 0.03170 0.19166

222 West Female 2 Yes 0.05440 0.01482 0.02948 0.09825

223 West Female 3 No 0.07528 0.03851 0.02679 0.19404

224 West Female 3 Yes 0.07444 0.01842 0.04257 0.12701

225 West Female 4 No 0.09263 0.03196 0.04621 0.17703

226 West Female 4 Yes 0.07696 0.02064 0.04194 0.13701

227 West Female 5 No 0.01976 0.01347 0.00513 0.07302

228 West Female 5 Yes 0.07737 0.02123 0.04157 0.13949

229 West Female 6 No 0.15792 0.07301 0.06009 0.35487

230 West Female 6 Yes 0.07298 0.01985 0.03947 0.13107

231 West Female 7 No 0.06955 0.02567 0.03321 0.13989

232 West Female 7 Yes 0.08146 0.01987 0.04691 0.13776

233 West Female 8 No 0.07753 0.02825 0.03731 0.15417

234 West Female 8 Yes 0.09062 0.01994 0.05507 0.14558

235 West Female 9 No 0.13440 0.04481 0.06802 0.24832

236 West Female 9 Yes 0.10215 0.02347 0.06061 0.16709

237 West Female 10 No 0.06573 0.03719 0.02102 0.18736

238 West Female 10 Yes 0.12152 0.02660 0.07376 0.19374

239 West Female 11 No 0.15354 0.04584 0.08329 0.26584

240 West Female 11 Yes 0.12719 0.02688 0.07852 0.19950

241 West Female 12 No 0.10120 0.03594 0.04934 0.19631

242 West Female 12 Yes 0.13054 0.02498 0.08440 0.19650

243 West Female 13 No 0.14759 0.04125 0.08346 0.24769

244 West Female 13 Yes 0.11968 0.02369 0.07629 0.18284

245 West Female 14 No 0.08748 0.03284 0.04105 0.17675
 B-197

Obs Region Gender Age Smoothed Prevalence 

Std 

Error 

95 % Conf 

Interval – 

Lower 

Bound 

95 % Conf 

Interval – 

Upper 

Bound 

246 West Female 14 Yes 0.11063 0.02132 0.07145 0.16744

247 West Female 15 No 0.10099 0.03841 0.04674 0.20471

248 West Female 15 Yes 0.11236 0.02051 0.07428 0.16645

249 West Female 16 No 0.12538 0.04343 0.06188 0.23755

250 West Female 16 Yes 0.12224 0.02210 0.08108 0.18021

251 West Female 17 No 0.14672 0.04582 0.07743 0.26052

252 West Female 17 Yes 0.14371 0.03992 0.07558 0.25621

253 West Male 0 No 0.00000 0.00000 0.00000 0.00000

254 West Male 0 Yes 0.03075 0.02534 0.00437 0.18642

255 West Male 1 No 0.05457 0.02662 0.02056 0.13695

256 West Male 1 Yes 0.04584 0.01889 0.01729 0.11595

257 West Male 2 No 0.07833 0.02789 0.03833 0.15342

258 West Male 2 Yes 0.06254 0.01442 0.03627 0.10573

259 West Male 3 No 0.05897 0.02530 0.02500 0.13281

260 West Male 3 Yes 0.07844 0.01913 0.04398 0.13607

261 West Male 4 No 0.07267 0.03354 0.02870 0.17208

262 West Male 4 Yes 0.09122 0.02482 0.04765 0.16763

263 West Male 5 No 0.19732 0.10033 0.06632 0.45969

264 West Male 5 Yes 0.11262 0.02937 0.06021 0.20092

265 West Male 6 No 0.13335 0.04859 0.06322 0.25970

266 West Male 6 Yes 0.12119 0.02916 0.06799 0.20680

267 West Male 7 No 0.08881 0.03493 0.04015 0.18508

268 West Male 7 Yes 0.12691 0.02806 0.07464 0.20758

269 West Male 8 No 0.15183 0.05484 0.07210 0.29200

270 West Male 8 Yes 0.13161 0.02705 0.08037 0.20811

271 West Male 9 No 0.17199 0.05164 0.09260 0.29715

272 West Male 9 Yes 0.15079 0.02837 0.09590 0.22915

273 West Male 10 No 0.12897 0.03747 0.07151 0.22159

274 West Male 10 Yes 0.16356 0.02584 0.11192 0.23279

275 West Male 11 No 0.19469 0.04002 0.12785 0.28505

276 West Male 11 Yes 0.16965 0.02623 0.11699 0.23956
 B-198

Obs Region Gender Age Smoothed Prevalence 

Std 

Error 

95 % Conf 

Interval – 

Lower 

Bound 

95 % Conf 

Interval – 

Upper 

Bound 

277 West Male 12 No 0.13214 0.04542 0.06547 0.24865

278 West Male 12 Yes 0.17494 0.02738 0.12002 0.24792

279 West Male 13 No 0.19947 0.04814 0.12127 0.31029

280 West Male 13 Yes 0.16217 0.02773 0.10747 0.23732

281 West Male 14 No 0.10759 0.03838 0.05220 0.20880

282 West Male 14 Yes 0.16487 0.02644 0.11214 0.23582

283 West Male 15 No 0.18459 0.05348 0.10138 0.31235

284 West Male 15 Yes 0.17018 0.02480 0.11996 0.23578

285 West Male 16 No 0.19757 0.04862 0.11892 0.30993

286 West Male 16 Yes 0.17888 0.02540 0.12718 0.24569

287 West Male 17 No 0.18078 0.04735 0.10548 0.29227

288 West Male 17 Yes 0.19218 0.04291 0.11118 0.31153

 
 B-199

Attachment 5: Technical Memorandum on Analysis of Air Exchange 

Rate Data 
 B-200

DRAFT MEMORANDUM 

To: John Langstaff 

From: Jonathan Cohen, Hemant Mallya, Arlene Rosenbaum 

Date: September 30, 2005 

Re: EPA 68D01052, Work Assignment 3-08. Analysis of Air Exchange Rate Data 

 

EPA is planning to use the APEX exposure model to estimate ozone exposure in 12 cities / 

metropolitan areas: Atlanta, GA; Boston, MA; Chicago, IL; Cleveland, OH; Detroit, MI; Houston, 

TX; Los Angeles, CA; New York, NY; Philadelphia, PA; Sacramento, CA; St. Louis, MO-IL; 

Washington, DC. As part of this effort, ICF Consulting has developed distributions of residential 

and non-residential air exchange rates (AER) for use as APEX inputs for the cities to be 

modeled. This memorandum describes the analysis of the AER data and the proposed APEX 

input distributions. Also included in this memorandum are proposed APEX inputs for penetration 

and proximity factors for selected microenvironments. 

Residential Air Exchange Rates

Studies. Residential air exchange rate (AER) data were obtained from the following seven 

studies: 

Avol: Avol et al, 1998. In this study, ozone concentrations and AERs were measured at 

126 residences in the greater Los Angeles metropolitan area between February and 

December, 1994. Measurements were taken in four communities: Lancaster, Lake 

Gregory, Riverside, and San Dimas. Data included the daily average outdoor 

temperature, the presence or absence of an air conditioner (either central or room), and 

the presence or absence of a swamp (evaporative) cooler. Air exchange rates were 

computed based on the total house volume and based on the total house volume 

corrected for the furniture. These data analyses used the corrected AERs. 

RTP Panel: Williams et al, 2003a, 2003b. In this study particulate matter concentrations 

and daily average AERs were measured at 37 residences in central North Carolina 

during 2000 and 2001 (averaging about 23 AER measurements per residence). The 

residences belong to two specific cohorts: a mostly Caucasian, non-smoking group aged 

at least 50 years having cardiac defibrillators living in Chapel Hill; a group of nonsmoking, African Americans aged at least 50 years with controlled hypertension living in 

a low-to-moderate SES neighborhood in Raleigh. Data included the daily average 

outdoor temperature, and the number of air conditioner units (either central or room). 

Every residence had at least one air conditioner unit. 

RIOPA: Meng et al, 2004, Weisel et al, 2004. The Relationship of Indoor, Outdoor, and 

Personal Air (RIOPA) study was undertaken to estimate the impact of outdoor sources of 

air toxics to indoor concentrations and personal exposures. Volatile organic compounds, 

carbonyls, fine particles and AERs were measured once or twice at 310 non-smoking 

residences from summer 1999 to spring 2001. Measurements were made at residences 

in Elizabeth, NJ, Houston TX, and Los Angeles CA. Residences in California were 
 B-201

randomly selected. Residences in New Jersey and Texas were preferentially selected to 

be close (< 0.5 km) to sources of air toxics. The AER measurements (generally over 48 

hours) used a PMCH tracer. Data included the daily average outdoor temperature, and 

the presence or absence of central air conditioning, room air conditioning, or a swamp 

(evaporative) cooler. 

TEACH: Chillrud at al, 2004, Kinney et al, 2002, Sax et al, 2004. The Toxic Exposure 

Assessment, a Columbia/Harvard (TEACH) study was designed to characterize levels of 

and factors influencing exposures to air toxics among high school students living in 

inner-city neighborhoods of New York City and Los Angeles, CA. Volatile organic 

compounds, aldehydes, fine particles, selected trace elements, and AER were 

measured at 87 high school student’s residences in New York City and Los Angeles in 

1999 and 2000. Data included the presence or absence of an air conditioner (central or 

room) and hourly outdoor temperatures (which were converted to daily averages for 

these analyses). 

Wilson 1984: Wilson et al, 1986, 1996. In this 1984 study, AER and other data were 

collected at about 600 southern California homes with three seven-day tests (in March 

and July 1984, and January, 1985) for each home. We obtained the data directly from 

Mr. Wilson. The available data consisted of the three seven-day averages, the month, 

the residence zip code, the presence or absence of a central air conditioner, and the 

presence or absence of a window air conditioner. We matched these data by month and 

zip code to the corresponding monthly average temperatures obtained from EPA’s 

SCRAM website as well as from the archives in www.wunderground.com (personal and 

airport meteorological stations). Residences more than 25 miles away from the nearest 

available meteorological station were excluded from the analysis. For our analyses, the 

city/location was defined by the meteorological station, since grouping the data by zip 

code would not have produced sufficient data for most of the zip codes. 

Wilson 1991: Wilson et al, 1996. Colome et al, 1993, 1994. In this 1991 study, AER and 

other data were collected at about 300 California homes with one two-day test in the 

winter for each home. We obtained the data directly from Mr. Wilson. The available data 

consisted of the two-day averages, the date, city name, the residence zip code, the 

presence or absence of a central air conditioner, the presence or absence of a swamp 

(evaporative) cooler, and the presence or absence of a window air conditioner . We 

matched these data by date, city, and zip code to the corresponding daily average 

temperatures obtained from EPA’s SCRAM website as well as from the archives in 

www.wunderground.com (personal and airport meteorological stations). Residences 

more than 25 miles away from the nearest available meteorological station were 

excluded from the analysis. For our analyses, the city/location was defined by the 

meteorological station, since grouping the data by zip code would not have produced 

sufficient data for most of the zip codes. 

 

Murray and Burmaster: Murray and Burmaster (1995). For this article, Murray and 

Burmaster corrected and compiled nationwide residential AER data from several studies 

conducted between 1982 and 1987. These data were originally compiled by the 

Lawrence Berkeley National Laboratory. We acknowledge Mr. Murray’s assistance in 

obtaining these data for us. The available data consisted of AER measurements, dates, 

cities, and degree-days. Information on air conditioner presence or absence was not 

available. 

Table A-1 summarizes these studies. 
 B-202

For each of the studies, air conditioner usage, window status (open or closed), and fan status 

(on or off) was not part of the experimental design, although some of these studies included 

information on whether air conditioners or fans were used (and for how long) and whether 

windows were closed during the AER measurements (and for how long). 

As described above, in the following studies the homes were deliberately sampled from specific 

subsets of the population at a given location rather than the entire population: The RTP Panel 

study selected two specific cohorts of older subjects with specific diseases. The RIOPA study 

was biased towards residences near air toxics sources. The TEACH study focused on inner-city 

neighborhoods. Nevertheless, we included all these studies because we determined that any 

potential bias would be likely to be small and we preferred to keep as much data as possible. 
B-203 

Table A-1. Summary of Studies of Residential Air Exchange Rates 

Avol RTP Panel RIOPA TEACH Wilson 1984 Wilson 1991 

Locations 

Lancaster, Lake Gregory, Riverside, San Dimas. All in Southern CA 

Research Triangle Park, NC CA; NJ; TX 

Los Angeles, CA; New York City, NY Southern CA Southern CA 

Years 1994 2000; 2001 1999; 2000; 2001 1999; 2000 1984, 1985 1984 Months/Seasons 

Feb; Mar; Apr; May; Jun; Jul; Aug; Sep; Oct; Nov 

2000 (Jun; Jul; Aug; Sep; Oct; Nov), 2001 (Jan; Feb; Apr; May) 

1999 (July to Dec); 2000 (all months); 2001 (Jan and Feb) 

1999 (Feb; Mar; Apr; Jul; Aug); 2000 (Jan; Feb; Mar; Sep; Oct) 

Mar 1984, Jul 1984, Jan 1985 Jan, Mar, Jul 

Number of Homes 86 37 284 85 581 288 Total AER Measurements 161 854 524 151 1,362 316 Average Number of Measurements per Home 1.87 23.08 1.85 1.78 2.34 1.10 Measurement Duration 

Not Available 24 hour 24 to 96 hours 

Sample time (hours) reported. Ranges from about 1 to 7 days. 7 days 7 days 

Measurement Technique Not Available 

Perflourocarbon tracer. PMCH tracer 

Perflourocarbon tracer. Perflourocarbon tracer. Perflourocarbon tracer. 

Min AER Value 0.01 0.02 0.08 0.12 0.03 0.01 Max AER Value 2.70 21.44 87.50 8.87 11.77 2.91 Mean AER Value 0.80 0.72 1.41 1.71 1.05 0.57 Min Temperature (C) -0.04 -2.18 -6.82 -1.36 11.00 3.00 
 B-204

Avol RTP Panel RIOPA TEACH Wilson 1984 Wilson 1991 

Max Temperature (C) 36.25 30.81 32.50 32.00 28.00 25.00 Air Conditioner Categories 

No A/C; Central or Room A/C; Swamp Cooler only; Swamp + [Central or Room] 

Central or Room A/C (Y/N) 

Window A/C (Y/N); Evap Coolers (Y/N) 

Central or Room A/C (Y/N) 

Central A/C (Y/N); Room A/C (Y/N); 

Central A/C (Y/N); Room A/C (Y/N); Swamp Cooler(Y/N) 

Air Conditioner Measurements A/C use in minutes Not Available 

Duration measurements in Hrs and Mins Not Available Not Available Not Available 

Fan Categories Not available Fan (Y/N) Fan (Y/N) Not Available Not Available Not Available Fan Measurements 

Time on or off for various fan types during sampling was recorded, but not included in database provided. Not Available 

Duration measurements in Hrs and Mins Not Available Not Available Not Available 

Window Open/ Closed Data 

Duration open between times 6am-12 pm; 12pm - 6 pm; and 6pm - 6am 

Windows (open / closed along with duration open in inch-hours units 

Windows (Open / Closed) along with window open duration measurements Not Available Not Available Not Available 

Comments 

CA sample was a random sample of homes. NJ and TX homes were deliberately chosen to be near to ambient sources. 

Restricted to innercity homes with high school students. 

Contemporaneous temperature data obtained for these analyses from SCRAM and www.wunderground.com meteorological data. 

Contemporaneous temperature data obtained for these analyses from SCRAM and www.wunderground.com meteorological data. 
 B-205

We compiled the data from these seven studies to create the following variables, of which some 

had missing values: 

• Study 

• Date 

• Time – Time of the day that the AER measurement was made 

• House_ID – Residence identifier 

• Measurement_ID – Uniquely identifies each AER measurement for a given study 

• AER – Air Exchange Rate (per hour) 

• AER_Duration – Length of AER measurement period 

• Have_AC – Indicates if the residence has any type of air conditioner (A/C), either a room 

A/C or central A/C or swamp cooler or any of them in combination. “Y” = “Yes.” “N” = 

“No.” 

• Type_of_AC1 – Indicates the types of A/C or swamp cooler available in each house 

measured. Possible values: “Central A/C” “Central and Room A/C” “Central or Room 

A/C” “No A/C” “Swamp + (Central or Room)” “Swamp Cooler only” “Window A/C” 

“Window and Evap” 

• Type_of_AC2 – Indicates if a house measured has either no A/C or some A/C. Possible 

values are “No A/C” and “Central or Room A/C.” 

• Have_Fan – Indicates if the house studied has any fans 

• Mean_Temp – Daily average outside temperature 

• Min_Temp – Minimum hourly outside temperature 

• Max_Temp – Maximum hourly outside temperature 

• State 

• City 

• Location – Two character abbreviation 

• Flag – Data status. Murray and Burmaster study: “Used” or “Not Used.” Other studies: 

“Used”; “Missing” (missing values for AER, Type_of_AC2, and/or Mean_Temp); 

“Outlier”. 

The main data analysis was based on the first six studies. The Murray and Burmaster data were 

excluded because of the absence of information on air conditioner presence. (However, a 

subset of these data was used for a supplementary analysis described below.) . 

Based on our review of the AER data we excluded seven outlying high AER values – above 10 

per hour. The main data analysis used all the remaining data that had non-missing values for 

AER, Type_of_AC2, and Mean_Temp. We decided to base the A/C type variable on the broad 

characterization “No A/C” versus “Central or Room A/C” since this variable could be calculated 

from all of the studies (excluding Murray and Burmaster). Information on the presence or 

absence of swamp coolers was not available from all the studies, and, also importantly, the 

corresponding information on swamp cooler prevalence for the subsequent ozone modeling 

cities was not available from the American Housing Survey. It is plausible that AER distributions 

depend upon the presence or absence of a swamp cooler. It is also plausible that AER 

distributions also depend upon whether the residence specifically has a central A/C, room or 

window A/C, or both. However we determined to use the broader A/C type definition, which in 

effect assumes that the exact A/C type and the presence of a swamp cooler are approximately 

proportionately represented in the surveyed residences. 
 B-206

Most of the studies had more than one AER measurement for the same house. It is reasonable 

to assume that the AER varies with the house as well as other factors such as the temperature. 

(The A/C type can be assumed to be the same for each measurement of the same house). We 

expected the temperature to be an important factor since the AER will be affected by the use of 

the available ventilation (air conditioners, windows, fans), which in turn will depend upon the 

outside meteorology. Therefore it is not appropriate to average data for the same house under 

different conditions, which might have been one way to account for dependence between 

multiple measurements on the same house. To simplify the data analysis, we chose to ignore 

possible dependence between measurements on the same house on different days and treat all 

the AER values as if they were statistically independent. 

Summary Statistics. We computed summary statistics for AER and its natural logarithm 

LOG_AER on selected strata defined from the study, city, A/C type, and mean temperature. 

Cities were defined as in the original databases, except that for Los Angeles we combined all 

the data in the Los Angeles ozone modeling region, i.e. the counties of Los Angeles, Orange, 

Ventura, Riverside, and San Bernardino. A/C type was defined from the Type_of_AC2 variable, 

which we abbreviated as “NA” = “No A/C” and “AC” = “Central or Room A/C.” The mean 

temperature was grouped into the following temperature bins: -10 to 0 oC, 0 to 10 oC, 10 to 20 

oC, 20 to 25 oC, 25 to 30 oC, 30 to 40 oC.(Values equal to the lower bounds are excluded from 

each interval.) Also included were strata defined by study = “All” and/or city = “All,” and/or A/C 

type = “All” and/or temperature bin = “All.” The following summary statistics for AER and 

LOG_AER were computed: 

• Number of values 

• Arithmetic Mean 

• Arithmetic Standard Deviation 

• Arithmetic Variance 

• Deciles (Min, 10th, 20th ... 90th percentiles, Max) 

These calculations exclude all seven outliers and results are not used for strata with 10 or fewer 

values, since those summary statistics are extremely unreliable. 

Examination of these summary tables clearly demonstrates that the AER distributions vary 

greatly across cities and A/C types and temperatures, so that the selected AER distributions for 

the modeled cities should also depend upon the city, A/C type and temperature. For example, 

the mean AER for residences with A/C ranges from 0.39 for Los Angeles between 30 and 40 oC 

to 1.73 for New York between 20 and 25 oC. The mean AER for residences without A/C ranges 

from 0.46 for San Francisco between 10 and 20 oC to 2.29 for New York between 20 and 25 oC. 

The need to account for the city as well as the A/C type and temperature is illustrated by the 

result that for residences with A/C and between 20 and 25 oC, the mean AER ranges from 0.52 

for Research Triangle Park to 1.73 for New York. Statistical comparisons are described below. 

Statistical Comparisons. Various statistical comparisons were carried out between the 

different strata, for the AER and its logarithm. The various strata are defined as in the Summary 

Statistics section, excluding the “All” cases. For each analysis, we fixed one or two of the 

variables Study, City, A/C type, temperature, and tested for statistically significant differences 

among other variables. The comparisons are listed in Table A-2. 

Table A-2. Summary of Comparisons of Means 
 B-207

Cases with significantly 

different means (5 % 

level) 

Comparison 

Analysis 

Number. 

Comparison 

Variable(s) 

“Groups 

Compared” 

Stratification 

Variable(s) 

(not missing in 

worksheet) 

Total 

Comparisons

AER Log AER 

1. City Type of A/C AND 

Temp. Range 

12 8 8 

2. Temp. Range Study AND City 12 5 5 

3. Type of A/C Study AND City 15 5 5 

4. City Type of A/C 2 2 2 

5. City Temp. Range 6 5 6 

6. Type of A/C 

AND Temp. 

Range 

Study AND City 17 6 6 

For example, the first set of comparisons fix the Type of A/C and the temperature range; there 

are twelve such combinations. For each of these twelve combinations, we compare the AER 

distributions across different cities. This analysis determines whether the AER distribution is 

appropriately defined by the A/C type and temperature range, without specifying the city. 

Similarly, for the sixth set of comparisons, the study and city are held fixed (17 combinations) 

and in each case we compare AER distributions across groups defined by the combination of 

the A/C type and the temperature range. 

The F Statistic comparisons compare the mean values between groups using a one way 

analysis of variance (ANOVA). This test assumes that the AER or log(AER) values are normally 

distributed with a mean that may vary with the comparison variable(s) and a constant variance. 

We calculated the F Statistic and its P-value. P-values above 0.05 indicate cases where all the 

group means are not statistically significantly different at the 5 percent level. Those results are 

summarized in the last two columns of the above table “Summary of Comparisons of Means” 

which gives the number of cases where the means are significantly different. Comparison 

analyses 2, 3, and 6 show that for a given study and city, slightly less than half of the 

comparisons show significant differences in the means across temperature ranges, A/C types, 

or both. Comparison analyses 1, 4, and 5 show that for the majority of cases, means vary 

significantly across cities, whether you first stratify by temperature range, A/C type, or both. 

The Kruskal-Wallis Statistic comparisons are non-parametric tests that are extensions of the 

more familiar Wilcoxon tests to two or more groups. The analysis is valid if the AER minus the 

group median has the same distribution for each group, and tests whether the group medians 

are equal. (The test is also consistent under weaker assumptions against more general 

alternatives) The P-values show similar patterns to the parametric F test comparisons of the 

means. Since the logarithm is a strictly increasing function and the test is non-parametric, the 

Kruskal-Wallis tests give identical results for AER and Log (AER). 

The Mood Statistic comparisons are non-parametric tests that compare the scale statistics for 

two or more groups. The scale statistic measures variation about the central value, which is a 

non-parametric generalization of the standard deviation. Specifically, suppose there is a total of 

N AER or log(AER) values, summing across all the groups. These N values are ranked from 1 

to N, and the j’th highest value is given a score of {j - (N+1)/2}2

. The Mood statistic uses a one 

way ANOVA statistic to compare the total scores for each group. Generally, the Mood statistics 

show that in most cases the scale statistics are not statistically significantly different. Since the 
 B-208

logarithm is a strictly increasing function and the test is non-parametric, the Mood tests give 

identical results for AER and Log (AER). 

Fitting Distributions. Based on the summary statistics and the statistical comparisons, the 

need to fit different AER distributions to each combination of A/C type, city, and temperature is 

apparent. For each combination with a minimum of 11 AER values, we fitted and compared 

exponential, log-normal, normal, and Weibull distributions to the AER values. 

The first analysis used the same stratifications as in the above “Summary Statistics” and 

“Statistical Comparisons” sections. Results are not reported for all strata because of the 

minimum data requirement of 11 values. Results for each combination of A/C type, city, and 

temperature (i.e., A, C, and T) were analyzed. Each combination has four rows, one for each 

fitted distribution. For each distribution we report the fitted parameters (mean, standard 

deviation, scale, shape) and the p-value for three standard goodness-of-fit tests: KolmogorovSmirnov (K-S), Cramer-Von-Mises (C-M), Anderson-Darling (A-D). Each goodness-of-fit test 

compares the empirical distribution of the AER values to the fitted distribution. The K-S and C-M 

tests are different tests examining the overall fit, while the Anderson-Darling test gives more 

weight to the fit in the tails of the distribution. For each combination, the best-fitting of the four 

distributions has the highest p-value and is marked by an x in the final three columns. The mean 

and standard deviation (Std_Dev) are the values for the fitted distribution. The scale and shape 

parameters are defined by: 

 

• Exponential: density = σ

-1 exp(-x/σ), where shape = mean = σ

• Log-normal: density = {σx√(2π)}-1 exp{ -(log x - ζ)

2

 / (2σ

2

)}, where shape = σ and scale = 

ζ. Thus the geometric mean and geometric standard deviation are given by exp(ζ) and 

exp(σ), respectively. 

• Normal: density = {σ√(2π)}-1 exp{ -(x - μ)

2

 / (2σ

2

)}, where mean = μ and standard 

deviation = σ

• Weibull: density = (c/σ) (x/σ)

c-1 exp{-(x/σ)c

}, where shape = c and scale = σ

Generally, the log-normal distribution was the best-fitting of the four distributions, and so, for 

consistency, we recommend using the fitted log-normal distributions for all the cases. 

One limitation of the initial analysis was that distributions were available only for selected cities, 

and yet the summary statistics and comparisons demonstrate that the AER distributions depend 

upon the city as well as the temperature range and A/C type. As one option to address this 

issue, we considered modeling cities for which distributions were not available by using the AER 

distributions across all cities and dates for a given temperature range and A/C type. 

Another important limitation of the initial analysis was that distributions were not fitted to all of 

the temperature ranges due to inadequate data. There are missing values between temperature 

ranges, and the temperature ranges are all bounded. To address this issue, the temperature 

ranges were regrouped to cover the entire range of temperatures from minus to plus infinity, 

although obviously the available data to fit these ranges have finite temperatures. Stratifying by 

A/C type, city, and the new temperature ranges produces results for four cities: Houston (AC 

and NA); Los Angeles (AC and NA); New York (AC and NA); Research Triangle Park (AC). For 

each of the fitted distributions we created histograms to compare the fitted distributions with the 

empirical distributions. 
 B-209

AER Distributions for The First Nine Cities. Based upon the results for the above four cities 

and the corresponding graphs, we propose using those fitted distributions for the three cities 

Houston, Los Angeles, and New York. For another 6 of the cities to be modeled, we propose 

using the distribution for one of the four cities thought to have similar characteristics to the city 

to be modeled with respect to factors that might influence AERs. These factors include 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. The distributions 

proposed for these cities are as follows: 

• Atlanta, GA, A/C: Use log-normal distributions for Research Triangle Park. Residences 

with A/C only. 

• Boston, MA: Use log-normal distributions for New York 

• Chicago, IL: Use log-normal distributions for New York 

• Cleveland, OH: Use log-normal distributions for New York 

• Detroit, MI: Use log-normal distributions for New York 

• Houston, TX: Use log-normal distributions for Houston 

• Los Angeles, CA: Use log-normal distributions for Los Angeles 

• New York, NY: Use log-normal distributions for New York 

• Philadelphia, PA: Use log-normal distributions for New York 

Since the AER data for Research Triangle Park was only available for residences with air 

conditioning, AER distributions for Atlanta residences without air conditioning are discussed 

below. 

To avoid unusually extreme simulated AER values, we propose to set a minimum AER value of 

0.01 and a maximum AER value of 10. 

Obviously, we would be prefer to model each city using data from the same city, but this 

approach was chosen as a reasonable alternative, given the available AER data. 

AER Distributions for Sacramento and St. Louis. For these two cities, a direct mapping to 

one of the four cities Houston, Los Angeles, New York, and Research Triangle Park is not 

recommended because the cities are likely to be too dissimilar. Instead, we decided to use the 

distribution for the inland parts of Los Angeles to represent Sacramento and to use the 

aggregate distributions for all cities outside of California to represent St. Louis. The results for 

the city Sacramento were obtained by combining all the available AER data for Sacramento, 

Riverside, and San Bernardino counties. The results for the city St. Louis were obtained by 

combining all non-California AER data. 

AER Distributions for Washington DC. Washington DC was judged likely to have similar 

characteristics both to Research Triangle Park and to New York City. To choose between these 

two cities, we compared the Murray and Burmaster AER data for Maryland with AER data from 

each of those cities. The Murray and Burmaster study included AER data for Baltimore and for 

Gaithersburg and Rockville, primarily collected in March. April, and May 1987, although there is 

no information on mean daily temperatures or A/C type. We collected all the March, April, and 

May AER data for Research Triangle Park and for New York City, and compared those 

distributions with the Murray and Burmaster Maryland data for the same three months. 

 

The results for the means and central values show significant differences at the 5 percent level 

between the New York and Maryland distributions. Between Research Triangle Park and 
 B-210

Maryland, the central values and the mean AER values are not statistically significantly different, 

and the differences in the mean log (AER) values are much less statistically significant than 

between New York and Maryland. The scale statistic comparisons are not statistically 

significantly different between New York and Maryland, but were statistically significantly 

different between Research Triangle Park and Maryland. Since matching central and mean 

values is generally more important than matching the scales, we propose to model Washington 

DC residences with air conditioning using the Research Triangle Park distributions, stratified by 

temperature: 

• Washington DC, A/C: Use log-normal distributions for Research Triangle Park. 

Residences with A/C only. 

Since the AER data for Research Triangle Park was only available for residences with air 

conditioning, the estimated AER distributions for Washington DC residences without air 

conditioning are discussed below. 

AER Distributions for Washington DC and Atlanta GA Residences With No A/C. For 

Atlanta and Washington DC we have proposed to use the AER distributions for Research 

Triangle Park. However, all the Research Triangle Park data (from the RTP Panel study) were 

from houses with air conditioning, so there are no available distributions for the “No A/C” cases. 

For these two cities, one option is to use AER distributions fitted to all the study data for 

residences without A/C, stratified by temperature. We propose applying the “No A/C” 

distributions for modeling these two cities for residences without A/C. However, since Atlanta 

and Washington DC residences are expected to be better represented by residences outside of 

California, we instead propose to use the “No A/C” AER distributions aggregated across cities 

outside of California, which is the same as the recommended choice for the St. Louis “No A/C” 

AER distributions.

A/C Type and Temperature Distributions. Since the proposed AER distribution is conditional 

on the A/C type and temperature range, these values also need to be simulated using APEX in 

order to select the appropriate AER distribution. Mean daily temperatures are one of the 

available APEX inputs for each modeled city, so that the temperature range can be determined 

for each modeled day according to the mean daily temperature. To simulate the A/C type, we 

obtained estimates of A/C prevalence from the American Housing Survey. Thus for each 

city/metropolitan area, we obtained the estimated fraction of residences with Central or Room 

A/C (see Table A-3), which gives the probability p for selecting the A/C type “Central or Room 

A/C.” Obviously, 1-p is the probability for “No A/C.” For comparison with Washington DC and 

Atlanta, we have included the A/C type percentage for Charlotte, NC (representing Research 

Triangle Park, NC). As discussed above, we propose modeling the 96-97 % of Washington DC 

and Atlanta residences with A/C using the Research Triangle Park AER distributions, and 

modeling the 3-4 % of Washington DC and Atlanta residences without A/C using the combined 

study No A/C AER distributions. 

Table A-3. Fraction of residences with central or room A/C (from American Housing 

Survey) 

 

CITY SURVEY AREA & YEAR PERCENTAGE 

Atlanta Atlanta, 2003 97.01 

Boston Boston, 2003 85.23 

Chicago Chicago, 2003 87.09 
 B-211

Cleveland Cleveland, 2003 74.64 

Detroit Detroit, 2003 81.41 

Houston Houston, 2003 98.70 

Los Angeles Los Angeles, 2003 55.05 

New York New York, 2003 81.57 

Philadelphia Philadelphia, 2003 90.61 

Sacramento Sacramento, 2003 94.63 

St. Louis St. Louis, 2003 95.53 

Washington DC Washington DC, 2003 96.47 

Research Triangle Park Charlotte, 2002 96.56 

Other AER Studies 

We recently became aware of some additional residential and non-residential AER studies that 

might provide additional information or data. Indoor / outdoor ozone and PAN distributions were 

studied by Jakobi and Fabian (1997). Liu et al (1995) studied residential ozone and AER 

distributions in Toronto, Canada. Weschler and Shields (2000) describes a modeling study of 

ventilation and air exchange rates. Weschler (2000) includes a useful overview of residential 

and non-residential AER studies. 

AER Distributions for Other Indoor Environments

To estimate AER distributions for non-residential, indoor environments (e.g., offices and 

schools), we obtained and analyzed two AER data sets: “Turk” (Turk et al, 1989); and “Persily” 

(Persily and Gorfain 2004; Persily et al. 2005). 

The earlier “Turk” data set (Turk et al, 1989) includes 40 AER measurements from offices (25 

values), schools (7 values), libraries (3 values), and multi-purpose (5 values), each measured 

using an SF6 tracer over two- or four-hours in different seasons of the year. 

The more recent “Persily” data (Persily and Gorfain 2004; Persily et al. 2005) were derived 

from the U.S. EPA Building Assessment Survey and Evaluation (BASE) study, which was 

conducted to assess indoor air quality, including ventilation, in a large number of randomly 

selected office buildings throughout the U.S. The data base consists of a total of 390 AER 

measurements in 96 large, mechanically ventilated offices; each office was measured up to four 

times over two days, Wednesday and Thursday AM and PM. The office spaces were relatively 

large, with at least 25 occupants, and preferably 50 to 60 occupants. AERs were measured both 

by a volumetric method and by a CO2 ratio method, and included their uncertainty estimates. For 

these analyses, we used the recommended “Best Estimates” defined by the values with the lower 

estimated uncertainty; in the vast majority of cases the best estimate was from the volumetric 

method. 

Another study of non-residential AERs was performed by Lagus Applied Technology (1995) 

using a tracer gas method. That study was a survey of AERs in 16 small office buildings, 6 large 

office buildings, 13 retail establishments, and 14 schools. We plan to obtain and analyze these 

data and compare those results with the Turk and Persily studies. 
 B-212

Due to the small sample size of the Turk data, the data were analyzed without stratification by 

building type and/or season. For the Persily data, the AER values for each office space were 

averaged, rather using the individual measurements, to account for the strong dependence of 

the AER measurements for the same office space over a relatively short period. 

Summary statistics of AER and log (AER) for the two studies are presented in Table A-4. 

Table A-4. AER summary statistics for offices and other non-residential buildings 

Study Variable N Mean Std Dev Min 25th %ile Median 75th %ile Max 

Persily AER 96 1.9616 2.3252 0.0712 0.5009 1.0795 2.7557 13.8237 

Turk AER 40 1.5400 0.8808 0.3000 0.8500 1.5000 2.0500 4.1000 

Persily Log(AER) 96 0.1038 1.1036 -2.6417 -0.6936 0.0765 1.0121 2.6264 

Turk Log(AER) 40 0.2544 0.6390 -1.2040 -0.1643 0.4055 0.7152 1.4110 

 

The mean values are similar for the two studies, but the standard deviations are about twice as 

high for the Persily data. The proposed AER distributions were derived from the more recent 

Persily data only. 

Similarly to the analyses of the residential AER distributions, we fitted exponential, log-normal, 

normal, and Weibull distributions to the 96 office space average AER values. The results are 

shown in Table A-5. 

Table A-5. Best fitting office AER distributions from the Persily et al. (2004, 2005) 

Scale Shape Mean Std_Dev Distribution

P-Value 

KolmogorovSmirnov 

P-Value 

Cramervon 

Mises 

P-Value 

AndersonDarling 

1.9616 1.9616 1.9616 Exponential 0.13 0.04 0.05 

0.1038 1.1036 2.0397 3.1469 Lognormal 0.15 0.46 0.47 

 1.9616 2.3252 Normal 0.01 0.01 0.01 

1.9197 0.9579 1.9568 2.0433 Weibull 0.01 0.01 

(For an explanation of the Kolmogorov-Smirnov, Cramer-von Mises, and Anderson-Darling Pvalues see the discussion residential AER distributions above.) According to all three goodnessof-fit measures the best-fitting distribution is the log-normal. Reasonable choices for the lower 

and upper bounds are the observed minimum and maximum AER values. 

We therefore propose the following indoor, non-residential AER distributions. 

• AER distribution for indoor, non-residential microenvironments: Lognormal, with scale 

and shape parameters 0.1038 and 1.1036, i.e., geometric mean = 1.1094, geometric 

standard deviation = 3.0150. Lower Bound = 0.07. Upper bound = 13.8. 

Proximity and Penetration Factors For Outdoors, In-vehicle, and Mass Transit

For the APEX modeling of the outdoor, in-vehicle, and mass transit micro-environments, an 

approach using proximity and penetration factors is proposed, as follows. 
 B-213

Outdoors Near Road

Penetration factor = 1. 

For the Proximity factor, we propose using ratio distributions developed from the Cincinnati 

Ozone Study (American Petroleum Institute, 1997, Appendix B; Johnson et al. 1995). The field 

study was conducted in the greater Cincinnati metropolitan area in August and September, 

1994. Vehicle tests were conducted according to an experimental design specifying the vehicle 

type, road type, vehicle speed, and ventilation mode. Vehicle types were defined by the three 

study vehicles: a minivan, a full-size car, and a compact car. Road types were interstate 

highways (interstate), principal urban arterial roads (urban), and local roads (local). Nominal 

vehicle speeds (typically met over one minute intervals within 5 mph) were at 35 mph, 45 mph, 

or 55 mph. Ventilation modes were as follows: 

• Vent Open: Air conditioner off. Ventilation fan at medium. Driver’s window half open. 

Other windows closed. 

• Normal A/C. Air conditioner at normal. All windows closed. 

• Max A/C: Air conditioner at maximum. All windows closed. 

Ozone concentrations were measured inside the vehicle, outside the vehicle, and at six fixed 

site monitors in the Cincinnati area. 

The proximity factor can be estimated from the distributions of the ratios of the outside-vehicle 

ozone concentrations to the fixed-site ozone concentrations, reported in Table 8 of Johnson et 

al. (1995). Ratio distributions were computed by road type (local, urban, interstate, all) and by 

the fixed-site monitor (each of the six sites, as well as the nearest monitor to the test location). 

For this analysis we propose to use the ratios of outside-vehicle concentrations to the 

concentrations at the nearest fixed site monitor, as shown in Table A-6. 

Table A-6. Ratio of outside-vehicle ozone to ozone at nearest fixed site1

Road 

Type1

Number 

of 

cases1

Mean1 Standard 

Deviation1

25th

Percentile1

50th

Percentile1

75th

Percentile1

Estimated 

5th

Percentile2

Local 191 0.755 0.203 0.645 0.742 0.911 0.422 

Urban 299 0.754 0.243 0.585 0.722 0.896 0.355 

Interstate 241 0.364 0.165 0.232 0.369 0.484 0.093 

All 731 0.626 0.278 0.417 0.623 0.808 0.170 

 

1. From Table 8 of Johnson et al. (1995). Data excluded if fixed-site concentration < 40 

ppb. 

2. Estimated using a normal approximation as Mean – 1.64 × Standard Deviation 

For the outdoors-near- road microenvironment, we recommend using the distribution for local 

roads, since most of the outdoors-near-road ozone exposure will occur on local roads. The 

summary data from the Cincinnati Ozone Study are too limited to allow fitting of distributions, 

but the 25th and 75th percentiles appear to be approximately equidistant from the median (50th

percentile). Therefore we propose using a normal distribution with the observed mean and 
 B-214

standard deviation. A plausible upper bound for the proximity factor equals 1. Although the 

normal distribution allows small positive values and can even produce impossible, negative 

values (with a very low probability), the titration of ozone concentrations near a road is limited. 

Therefore, as an empirical approach, we recommend a lower bound of the estimated 5th

percentile, as shown in the final column of the above table. Therefore in summary we propose: 

• Penetration factor for outdoors, near road: 1.

• Proximity factor for outdoors, near road: Normal distribution. Mean = 0.755. Standard 

Deviation = 0.203. Lower Bound = 0.422. Upper Bound = 1.

Outdoors, Public Garage / Parking Lot

This micro-environment is similar to the outdoors-near-road microenvironment. We therefore 

recommend the same distributions as for outdoors-near-road: 

• Penetration factor for outdoors, public garage / parking lot: 1.

• Proximity factor for outdoors, public garage / parking lot: Normal distribution. Mean = 

0.755. Standard Deviation = 0.203. Lower Bound = 0.422. Upper Bound = 1.

Outdoors, Other

The outdoors, other ozone concentrations should be well represented by the ambient monitors. 

Therefore we propose: 

• Penetration factor for outdoors, other: 1.

• Proximity factor for outdoors, other: 1.

In-Vehicle

For the proximity factor for in-vehicle, we also recommend using the results of the Cincinnati 

Ozone Study presented in Table A-6. For this microenvironment, the ratios depend upon the 

road type, and the relative prevalences of the road types can be estimated by the proportions of 

vehicle miles traveled in each city. The proximity factors are assumed, as before, to be normally 

distributed, the upper bound to be 1, and the lower bound to be the estimated 5th percentile. 

• Proximity factor for in-vehicle, local roads: Normal distribution. Mean = 0.755. Standard 

Deviation = 0.203. Lower Bound = 0.422. Upper Bound = 1.

• Proximity factor for in-vehicle, urban roads: Normal distribution. Mean = 0.754. Standard 

Deviation = 0.243. Lower Bound = 0.355. Upper Bound = 1.

• Proximity factor for in-vehicle, interstates: Normal distribution. Mean = 0.364. Standard 

Deviation = 0.165. Lower Bound = 0.093. Upper Bound = 1.

To complete the specification, the distribution of road type needs to be estimated for each city to 

be modeled. Vehicle miles traveled (VMT) in 2003 by city (defined by the Federal-Aid urbanized 

area) and road type were obtained from the Federal Highway Administration. 

(http://www.fhwa.dot.gov/policy/ohim/hs03/htm/hm71.htm). For local and interstate road types, 

the VMT for the same DOT categories were used. For urban roads, the VMT for all other road 

types was summed (Other freeways/expressways, Other principal arterial, Minor arterial, 

Collector). The computed VMT ratios for each city are shown in Table A-7. 
 B-215

Table A-7. Vehicle Miles Traveled by City and Road Type in 2003 (FHWA, October 2004) 

FRACTION VMT BY ROAD TYPE 

FEDERAL-AID URBANIZED 

AREA INTERSTATE URBAN LOCAL 

Atlanta 0.38 0.45 0.18 

Boston 0.31 0.55 0.14 

Chicago 0.30 0.59 0.12 

Cleveland 0.39 0.45 0.16 

Detroit 0.26 0.63 0.11 

Houston 0.24 0.72 0.04 

Los Angeles 0.29 0.65 0.06 

New York 0.18 0.67 0.15 

Philadelphia 0.23 0.65 0.11 

Sacramento 0.21 0.69 0.09 

St. Louis 0.36 0.45 0.19 

Washington 0.31 0.61 0.08 

Note that a “Federal-Aid Urbanized Area" is an area with 50,000 or more persons that at a 

minimum encompasses the land area delineated as the urbanized area by the Bureau of the 

Census. Urbanized areas that have been combined with others for reporting purposes are not 

shown separately. The Illinois portion of Round Lake Beach-McHenry-Grayslake has been 

reported with Chicago. 

 

Thus to simulate the proximity factor in APEX, we propose to first select the road type according 

to the above probability table of road types, then select the AER distribution (normal) for that 

road type as defined in the last set of bullets. 

For the penetration factor for in-vehicle, we recommend using the inside-vehicle to outsidevehicle ratios from the Cincinnati Ozone Study. The ratio distributions were summarized for all 

the data and for stratifications by vehicle type, vehicle speed, road type, traffic (light, moderate, 

or heavy), and ventilation. The overall results and results by ventilation type are shown in Table 

A-8.

Table A-8. Ratio of inside-vehicle ozone to outside-vehicle ozone1

Ventilation1

Number 

of 

cases1

Mean1 Standard 

Deviation1

25th

Percentile1

50th

Percentile1

75th

Percentile1

Estimated 

5th

Percentile2

Vent Open 226 0.361 0.217 0.199 0.307 0.519 0.005 

Normal A/C 332 0.417 0.211 0.236 0.408 0.585 0.071 

Maximum 

A/C 254 0.093 0.088 0.016 0.071 0.149 0.0003

All 812 0.300 0.232 0.117 0.251 0.463 0.0003

 

1. From Table 7 of Johnson et al.(1995). Data excluded if outside-vehicle concentration < 

20 ppb. 
 B-216

2. Estimated using a normal approximation as Mean – 1.64 × Standard Deviation 

3. Negative estimate (impossible value) replaced by zero. 

Although the data in Table A-8 indicate that the inside-to-outside ozone ratios strongly depend 

upon the ventilation type, it would be very difficult to find suitable data to estimate the ventilation 

type distributions for each modeled city. Furthermore, since the Cincinnati Ozone Study was 

scripted, the ventilation conditions may not represent real-world vehicle ventilation scenarios. 

Therefore, we propose to use the overall average distributions. 

• Penetration factor for in-vehicle: Normal distribution. Mean = 0.300. Standard Deviation 

= 0.232. Lower Bound = 0.000. Upper Bound = 1. 

Mass Transit

The mass transit microenvironment is expected to be similar to the in-vehicle microenvironment. 

Therefore we recommend using the same APEX modeling approach: 

• Proximity factor for mass transit, local roads: Normal distribution. Mean = 0.755. 

Standard Deviation = 0.203. Lower Bound = 0.422. Upper Bound = 1.

• Proximity factor for mass transit, urban roads: Normal distribution. Mean = 0.754. 

Standard Deviation = 0.243. Lower Bound = 0.355. Upper Bound = 1.

• Proximity factor for mass transit, interstates: Normal distribution. Mean = 0.364. 

Standard Deviation = 0.165. Lower Bound = 0.093. Upper Bound = 1.

• Road type distributions for mass transit: See Table A-6 

• Penetration factor for mass transit: Normal distribution. Mean = 0.300. Standard 

Deviation = 0.232. Lower Bound = 0.000. Upper Bound = 1. 

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 B-219

Attachment 6: Technical Memorandum on HAPEM Near Road 

Population Data Base Development (from Task 2. Near roadway 

concentrations (revised)) 
 B-220

MEMORANDUM 

To: Chad Bailey and Rich Cook 

From: Jonathan Cohen and Arlene Rosenbaum 

Date: September 30, 2005 

Re: Task 2. Near roadway concentrations (revised) 

 

The objective of this task was to estimate the enhancement near major roadways of air toxic 

pollutant concentrations from onroad motor vehicle emissions relative to concentrations at other 

outdoor locations. 

For this task, we reviewed several studies of near roadway concentration gradients (Cohen et 

al, 2005; Kwon, 2005; Meng et al, 2004; Riediker et al, 2003; Rodes et al, 1998; Weisel et al, 

2004; Zhu et al, 2002). We analyzed the available data using summary statistics and regression 

modeling in order to obtain distributions of concentration ratios. One distribution describes the 

ratio of concentrations within D1 meters of a major roadway to concentrations at locations 

greater than D2 meters from a major roadway. A second distribution describes the ratio of 

concentrations D1 - D2 meters of a major roadway to concentrations at locations greater than 

D2 meters from a major roadway. We chose distances D1 = 75 m and D2 = 200 m to best 

represent the near roadway concentration gradient. These ratio distributions were used in Task 

3 to estimate the spatial distribution of concentrations within a census tract from the ASPEN 

concentration prediction. 

Rodes and Riediker Studies 

In order to stratify the concentration ratios according distance from major roadways we required 

concentration databases that specify those distances. EPA provided data from the Riediker et al 

(2003) study of concentrations inside patrol cars, near roadways, and at fixed ambient 

monitoring sites in Wake County, NC. We also evaluated the study by Rodes et al (1998), which 

includes near roadway monitoring in Sacramento and Los Angeles, CA. However, in neither 

study do the distances of the near roadway concentration measurements from the roadways 

span the range required for this analysis. The measurements in the Riediker study were taken 

within 20 feet of the roadway. The report for the Rodes study states that permission for placing 

the roadside monitors was obtained from the California Transportation Agency, implying they 

were located within the right-of-way of the road. The goal of this task was to estimate 

concentration ratios for concentrations within about 50 to 150 m and within about 150 to 300 m 

to concentrations further away from the roadway. Therefore, we determined that the results from 

the Rodes and Riediker studies could not be used for the Task 2 analyses since those studies 

had concentration measurements much nearer to the roadway. 

Zhu et al (2002) Study

Zhu et al (2002) measured concentrations of black carbon (BC), carbon monoxide (CO), and 

particle number at various distances upwind and downwind from the 710 and 405 freeways in 

Los Angeles. We used these data to calculate mean CO concentrations and concentration 
 B-221

ratios at different distances from the freeways. For freeway 710, the distances were 17, 20, 30, 

90, 150, and 300 m downwind and 200m upwind. For freeway 405, the distances were 30, 60, 

90, 150, and 300 m downwind and 300m upwind. Three measurements per day were taken at 

approximately the same time (various times between 10 am and 4:30 pm) for all downwind 

distances. One measurement was made on the same day at the upwind distance. We 

calculated the daily average concentrations and used them to calculate the distribution of the 

mean concentration upwind or downwind as a function of the distance to the freeway. These 

results for each freeway, season, and overall, are shown in Table 1. The concentrations drop 

very sharply as the distances increase. For Table 2, we calculated the ratios of the average 

concentration between 0 and 50 m from the road to the average concentration greater than 150 

m from the road and of the average concentration between 50 and 150 m from the road to the 

average concentration greater than 150 m from the road. The distributions of these ratios are 

shown in Table 2. 

After reviewing the Zhu et al (2002) study and the analyses of Tables 1 and 2, we determined 

that the results of the Zhu et al (2002) could not be used for this project because the available 

ratios were only for the downwind distances, and did not represent ratios under more general 

meteorological conditions. 

RIOPA Study 

The Relationship of Indoor, Outdoor, and Personal Air (RIOPA) study (Meng et al, 2004; Weisel 

et al, 2004) was undertaken to estimate the impact of outdoor sources of air toxics to indoor 

concentrations and personal exposures. Volatile organic compounds, carbonyls, fine particles 

and air exchange ratios were measured once or twice at 310 non-smoking residences from 

summer 1999 to spring 2001. Measurements were made at residences in Elizabeth, NJ, 

Houston TX, and Los Angeles CA. Residences in California were randomly selected. 

Residences in New Jersey and Texas were preferentially selected to be close (< 0.5 km) to 

sources of air toxics. 

Since the residences studied were at various distances from major roads, we analyzed the 

results of this study to estimate the relationship between the concentration (outside the 

residence) and the distance from the roadway. We obtained the relevant data from the 

Appendix of Kwon (2005), who used GIS mapping to calculate distances from the residences to 

major roads, gas stations, and other important emissions sources. Kwon (2005) used these data 

in various regression models to estimate the concentration as a function of these distances. 

Our analyses used a similar regression approach. but our modeling only used the distances to 

the major roadways. For the main analyses, we excluded residences within 150 m of a gas 

station to avoid confounding our analysis of roadway emissions with the effects of gas station 

emissions. Unlike Kwon (2005) we chose not to eliminate any values as potential outliers, since 

there were no “obvious” outlier data values. 

For the preliminary analysis, all the available data, including residences within 150 m of a gas 

station, were included. For each residence, two-day average pollutant concentrations were 

measured for benzene, carbon tetrachloride, ethylbenzene, MTBE, PCE (perchloroethylene), 

toluene, m & p-xylene, and o-xylene. We computed the distributions of the pollutant 

concentrations for residences within 50 m of a major roadway, between 50 and 150 m of a 

major roadway, and more than 150 m from a major roadway. These distances, from Kwon 

(2005), are the distances to the nearest roadway among functional classes FC11 (urban 

interstate highways), FC12 (urban other freeways), and FC14 (urban major arterials). The 

results are tabulated in Table 3. 

Also shown in Table 3 are the ratios of the average concentration between 0 and 50 m from the 

road to the average concentration greater than 150 m from the road, and of the average 
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concentration between 50 and 150 m from the road to the average concentration greater than 

150 m from the road. The standard deviation of the ratio was estimated using the delta method 

(first order Taylor series approximation). The CV (standard deviation divided by mean) is also 

presented. Of particular interest is the finding that for all of the pollutants except toluene and the 

xylenes, the mean concentrations are higher in the 50 to 150 m range than the concentrations in 

the 0 to 50 m and greater than 150 m ranges. For toluene and the xylenes, the mean 

concentrations decrease with distance. This preliminary analysis does not account for 

seasonality and meteorology, and it also ignores the possible confounding effect of the distance 

to the nearest gas station, an important emissions source. 

For the main data analyses of the RIOPA data, we first removed all data from residences within 

150 m of a gas station. 

 

The attached file graphs.riopa.doc contains graphs of the concentrations versus distance to the 

roadway. Four distance definitions are used, depending upon the road type: FC11 = distance to 

functional class FC11, urban interstate highways; FC12 = distance to functional class FC12, 

urban other freeways and expressways; FC14 = distance to functional class FC14, urban major 

arterials; Min = minimum (FC11, FC12, FC14). Also shown on the graphs is a cubic regression 

curve for log(concentration) against distance. The graphs show significant scatter, and no clear 

tendency for concentrations to decrease with distance. 

We then fitted various regression models to log (concentration) using a stepwise regression 

procedure to determine the best model. 

The attached Excel spreadsheet file riopa.two distance intervals.xls contains regression 

analyses where the distance effect is represented by one indicator term for the 0-D1 distance 

(i.e., a dummy variable that equals 1 for residences within D1 of the roadway and equals zero 

for other residences), and another indicator term for the 0-D2 distance. The fitted regression 

model always has an intercept and coefficients for the 0-D1 distance (to the nearest major road) 

and for the 0-D2 distance. Other terms that could be in the "best" model were season indicators 

(for spring, summer, or fall), wind speed, temperature, RH, Precipitation, Mixing Height, 

Stability, log wind speed, log temperature, log RH, log Mixing Height, log Stability. 

The spreadsheet shows results for the best-fitting models for each distance type and each pair 

of distances, when either a) there are no season terms, or b) all three season terms are forced 

into the model. The predicted values for the logarithm of the concentration are given by 

Log(concentration) = Intercept + a×Indic(0-D1) + b×Indic(0-D2) + Meteorological terms 

 

= Intercept + a + b + Met terms, if distance < D1 

= Intercept + b + Met terms, if D1 <= distance < D2 

= Intercept + Met terms, if distance >= D2 

so this model is mathematically equivalent to having different coefficients for the ranges 0-D1, 

D1-D2, D2 - infinity. 

The spreadsheet gives D1 and D2 (columns X and Y), all the coefficients, the R squared 

statistic (column U), and the Akaike Information Criterion, AIC, a good-of-fit measure. For these 

analyses, the AIC is a better measure of the goodness-of-fit than R squared, since the R 

squared will obviously improve if you add terms to the model, but the AIC used for SAS's 

regression procedure adds a penalty term to the negative log-likelihood for the number of fitted 

parameters. There is no absolute scale to decide what AIC values are good, but the models with 
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the lowest AIC are the best ones according to this statistic. The final column indicates the model 

with the lowest AIC for each HAP. 

None of these models fit the data very well. The R-Squared values range from 0.17 for MTBE, 

0.25 for ethyl-benzene, 0.26 for toluene, 0.28 for m&p-xylene, 0.27 for benzene, and 0.31 for oxylene, which are all quite poor, but consistent with Kwon's results. For the "best" models, the 

estimated values for D1 are either 25 m or 450 m. 

As a second approach, we fitted models with indicator terms for 12 distance intervals instead of 

2. The results are shown in the attached Excel spreadsheet riopa.all distance intervals.xls. The 

same approach was used except that instead of just having indicators for 0-D1 and 0-D2, we 

have indicators for each interval group: 0-25, 25-50, 50-75, 75-100, 100-150, 150-200, ... 450-

500 m. In most cases the coefficient is high for the 0-25 m group, then decreases, and then 

increases again. The coefficients at the high distances are almost as high as for 0-25 m (for 

MTBE they are even higher). This is not the expected pattern of coefficients decreasing with 

distance, reflecting the expected tendency for concentrations to decrease with distance (if there 

are no other sources). 

As a third approach, we developed the models shown in the Excel spreadsheet riopa.three 

times two distance intervals.xls. In this approach, instead of fitting separate two-distance models 

for each functional class, we fit a model with six distance indicators, two for each functional 

class, defining combinations of distances to the nearest FC11, FC12, and FC14 road. Using the 

same D1 and D2 for each functional class, the R squared goodness-of-fit measures are a slight 

improvement over the first approach (based on a single functional class), but the AIC statistics 

show no improvement after accounting for the 4 extra terms in the model. 

The ability of the RIOPA regression models to predict the near roadway concentrations was 

generally poor, as discussed above. This is likely due to the problem that the near roadway 

concentrations are also impacted by other emissions sources that cannot be easily adjusted for. 

Furthermore, the true relationship between the concentrations and the distance from the road, 

meteorology and season is known to be very much more complicated than these simple 

regression model formulations. In view of these findings we chose to use modeled data rather 

than measured values, as discussed in the next section. 

Portland Air Toxics Assessment Study 

The Portland Air Toxics Assessment (PATA) Study (e.g., Cohen et al (2005)) was a recent air 

toxics modeling study in the Portland area, funded by the Oregon Department of Environmental 

Quality and U.S. EPA. PATA evaluated the air quality impact in Calendar Year 1999 of 

emissions from over 1000 roadway links using the CALPUFF dispersion model. CALPUFF is a 

non-steady state Gaussian puff model, and was selected for modeling in Portland because of its 

capability for handling complex terrain and coastal interaction effects. For these Task 2 

analyses, we used the CALPUFF predictions of the benzene, 1,3-butadiene, and diesel 

particulate matter (PM) from major on-road sources only. We used the predicted quarterly and 

annual mean concentrations together with the distances from the receptor (block group 

centroid) to the nearest major road. 

We restricted these analyses to block group receptors only, and to the 211 block groups in the 

54 tracts that had at least one block group within 300 m of a road and at least one block group 

more than 300 m from a road. The idea was to restrict the analysis to the census tracts where 

the HAPEM adjustments to the ASPEN predictions would be applied. 300 m was chosen as a 

maximum possible realistic value for the far distance, D2. 
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The statistical regression analyses of the PATA data were similar to the regression analyses of 

the RIOPA data described above. In this case stepwise regression was not needed to select the 

“best” set of meteorological variables since it was not feasible to define and calculate 

meteorological variables to adjust the quarterly and annual means. The quarterly/seasonal 

means are for Dec-Feb, Mar-May, Jun-Aug, and Sep-Nov. In this case we only have one 

distance variable, the distance from a block group centroid to the nearest road for the major 

road links used in our CALPUFF modeling. 

The attached file graphs.pata.doc contains graphs of annual and seasonal mean concentrations 

plotted against the distance, together with a cubic regression curve for log(concentration) 

against distance. The pattern of concentrations decreasing with distance is much stronger here 

compared to the RIOPA plots, as should be expected since these CALPUFF modeling results 

use on-road emissions only. These preliminary graphs show all block group and census tract 

centroid receptors, not just the 211 block groups in the 54 tracts that had at least one block 

group within 300 m of a road and at least one block group more than 300 m from a road. 

The attached file pata.two.distance intervals.by season.xls shows the fitted models with two 

distance terms (indicators for the distance ranges 0-D1 and 0-D2) for each season and for the 

annual mean. The best models have D1 = 75 m and D2 = 250 or 300 m in most cases. 

 

The attached file pata.two.distance intervals.quarterly mean.xls shows results from fitting the 

same set of two-distance models to the entire set of quarterly means after including indicator 

terms for the spring, summer, and fall quarters. The fact that the coefficients of these three 

indicators are the same for all pairs of distances is perhaps unexpected, but this follows from 

the facts that a) the experiment is balanced, i.e., there is one concentration for each and every 

block group and season, and b) there are no interaction terms in the regression model (between 

the season indicators and the distance terms). The best models have D1=75 m and D2=300 m 

for all three pollutants. 

The attached file pata.all.distance intervals.by season.xls shows the fitted models for each 

season and the annual mean with indicator terms for the interval groups: 0-25, 25-50, 50-75, 75-

100, 100-150. 150-200, 200-250, 250-300, and >= 300. The models were fitted without an 

intercept. The estimated coefficients for the distance ranges therefore equal the mean of the 

log(concentration) for all block groups in the given range and season. While there is a general 

tendency for the means of the logarithms to decrease with the distance from the nearest road, 

the mean does not consistently decrease when the distance range is further away from the 

road. This is due to the fact that different roads can have very different levels of emissions, so 

that the concentration can increase if you move further away from a road A with relatively low 

emissions but closer to another road B that is even further away than the road A but has very 

high emissions. 

The attached file pata.all.distance intervals.quarterly mean.xls shows the fitted models for the 

quarterly means with indicator functions for the spring, summer, and fall seasons together with 

indicator terms for all the interval groups: 0-25, 25-50, 50-75, 75-100, 100-150, 150-200, 200-

250, 250-300, and >= 300. The models were fitted without an intercept. 

 

Generally, the two-distance regression models show that the optimal distances are D1 = 75m 

and D2 = 300m. The regression models favor the higher distances for D2 because the 

CALPUFF estimates for PATA continue to decrease significantly with distance from the road. 

However, the R squared and AIC values are not much different between the models with D2 = 

200 or 300 m. Since some other studies, including Zhu et al (2002), have shown typical zones 

of influence for roadways no further than 200 m, we selected the distances D1 = 75 m and D2 = 

200 m. 
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Concentration Ratios

As described above, we selected the distance thresholds D1 = 75 m and D2 = 200 m. Using the 

same set of PATA study predicted block group concentrations at distances 0 to 75 m, 75 to 200 

m, and 200 m or greater we computed the concentration ratios, using a regression approach 

and an empirical approach. 

We begin with the empirical approach. We considered two sets of ratios. The first set of ratios 

are given by a block group concentration at distance 0-75 m divided by a block group 

concentration in the same census tract at distance >= 200 m. The second set of ratios are 

given by a block group concentration at distance 75-200 m divided by a block group 

concentration in the same census tract at distance >= 200 m. Each set contains all such ratios 

for each pollutant. For each set of ratios we fitted normal and log-normal distributions. The 

results are tabulated in Table 4. In the rows marked “RAW,” the number, mean, and variance of 

the ratios are tabulated for each set of ratios and each pollutant. Also shown is the p-value of a 

Shapiro-Wilk test of normality; higher values are evidence supporting normality. In the rows 

marked “LOG” the number, mean, and variance of the logarithms of the ratios are tabulated for 

each set of ratios and each pollutant. Also shown is the p-value of a Shapiro-Wilk test of 

normality for the logarithms; higher values are evidence supporting normality for the logs of the 

ratios, which is the same as log-normality of the ratios themselves. The log-normal distributions 

fitted a little better. Using the log-normal distributions, the geometric mean is given by 

exp(mean) and the geometric standard deviation is given by exp(sqrt(variance)). As well as 

doing this analysis by pollutant, we also combined all the ratios for all three pollutants and 

repeated the analysis. The combined distribution (shown in the Table 4 rows with pollutant = 

”All”) might be a good choice for modeling some HAPs other than Benzene, 1,3-Butadiene, or 

Diesel PM. 

Tables 5 and 6 present the results of the regression approach whereby the log(ratio) 

distributions are computed from the regression model. Table 5 uses the regressions stratified by 

season, which are exactly the same as the regression models with D1 = 75 and D2 = 200 in the 

spreadsheet pata.two distance intervals.by season.xls. Table 6 uses the regressions of the 

quarterly means against the season indicators and the two distance indicators, which are 

exactly the same as the regression models with D1 = 75 and D2 = 200 in pata.two distance 

intervals.quarterly means.xls. For each pollutant, season (quarter or annual mean), and 

numerator distance range, we tabulate the predicted mean and variance of the logarithm of the 

ratio. Two estimates of the variance, Variance1 and Variance2, are tabulated. Variance2 is the 

more accurate calculation. The Appendix gives details on these regression calculations.. 

The regression-based estimated means and variances are both larger than the estimates from 

the empirical distributions of the logarithms of the ratios. The variances are presumably higher 

because the regression approach assumes that the numerator and denominator are 

independent, whereas it is likely that there is a strong correlation between concentrations for 

block groups that are near enough to be in the same tract. It is not obvious why the means are 

higher. 

Since the empirical ratio distributions only use ratios from block groups in the same census 

tract, but the regression ratio distributions do not assume the numerator block groups are in the 

same tract as the denominator block groups, the empirical approach is more consistent with the 

intended application to ASPEN predictions. The log-normal distributions fitted a little better. We 

therefore recommend using the empirical log-normal distributions for the ratio, shown in Table 4. 

Another possibility is to use the set of ratios as a data set and randomly select ratios from that 
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data set, but that approach has the disadvantage of only having a small discrete number of 

possible values compared to the continuous log-normal model. 

HAPEM requires specification of mimimum and maximum values for a lognormal distribution in 

order to avoid unrealistic predictions. We recommend using 1.0 for the minimum value and the 

95th percentile ratio as the maximum value for each ratio distribution. 

References

Cohen, J.,. R. Cook, C. R. Bailey, E. Carr. 2005. Relationship between motor vehicle emissions 

of hazardous pollutants, roadway proximity, and ambient concentrations on Portland, OR. 

Environmental Modelling and Software 20 (2005) 7-12. 

Kwon, J. 2005. Development of a RIOPA database and evaluation of the effect of proximity on 

the potential residential exposure to VOCS from ambient sources. PhD. dissertation. Graduate 

School, New Brunswick, Rutgers, the State University of New Jersey and the University of 

Medicine and Dentistry of New Jersey 

Meng, Q. Y., B. J. Turpin, L. Korn, C. P. Weisel, M. Morandi, S. Colome, J. J. Zhang, T. Stock, 

D. Spektor, A. Winer, L. Zhang, J. H. Lee, R. Giovanetti, W. Cui, J. Kwon, S. Alimokhtari, D. 

Shendell, J. Jones, C. Farrar, S. Maberti (2004). Influence of ambient (outdoor) sources on 

residential indoor and personal PM2.5 concentrations: Analyses of RIOPA data. Journal of 

Exposure Analysis and Environ Epidemiology. Preprint. 

Riedicker M, R. Williams, R. Devlin, T. Griggs, P. Bromberg. 2003. Exposure to particulate 

matter, volatile organic compounds, and other air pollutants inside patrol cars. Environ. Sci. 

Technol. 2003, 37, 2084-2093. 

Rodes C, L. Sheldon, D. Whitaker, A. Clayton, K. Fitzgerald, J. Flanagan. Measuring 

concentrations of selected air pollutants inside California vehicles. 1998. Main Study Report for 

California ARB. Contract 95-339 

Weisel, C. P., J. J. Zhang, B. J. Turpin, M. T. Morandi, S. Colome, T. H. Stock, D. M. Spektor, L. 

Korn, A. Winer, S. Alimokhtari, J. Kwon, K. Mohan, R. Harrington, R. Giovanetti, W. Cui, M. 

Afshar, S. Maberti, D. Shendell (2004). Relationship of Indoor, Outdoor and Personal Air 

(RIOPA) study; study design, methods and quality assurance / control results. Journal of 

Exposure Analysis and Environ Epidemiology. Preprint. 

Zhu, Y, W. C.. Hinds, S. Kim, S. Shen, C. Sioutas. 2002. Study of ultrafine particles near a 

major highway with heavy-duty diesel traffic. Atmospheric Environment 36 (2002) 4323-4335. 

Appendix 

The derivation of the regression distributions is as follows: 

Suppose the regression model is written in the form 

Log(conc) = intercept + slope1 × Indicator (0-75 m) + slope2 × Indicator (75-200 m) + error. 

where the error is normally distributed with a mean of 0 and a standard deviation of sigma, and 

the errors for different block groups are independent. 

Then for two block groups at distances < 75 m and > 200m, we have 
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log(ratio) = log{conc(< 75) / conc (> 200) } 

= intercept + slope1 × Indicator (0-75 m) + slope2 × Indicator (75-200 m) + error (< 75m). 

- (intercept + slope1 × Indicator (0-75 m) + slope2 × Indicator (75-200 m) + error (> 200 m)) 

= intercept + slope1 + error(< 75 m) – (intercept + error(> 200 m) 

= slope1 + error(< 75 m) – error(> 200m), 

which is normally distributed with mean = slope1 and variance = 2×sigma×sigma = Variance1. 

The more accurate calculation takes into account the fact that the values of slope1 and sigma 

are unknown, but estimated from the regression model. Define Variance2 = 2×sigma×sigma + 

2×se×se, where se is the standard error of the estimated slope1. An exact calculation uses the 

easily proven result that {log(ratio) – estimated slope1}/{sqrt(Variance2)) has a t distribution. A 

very accurate approximation (since the degrees of freedom are large) shows that log(ratio) has 

a normal distribution with mean = slope1 and variance = Variance2. 
 B-228

1 2 3 Table 1. Analysis of mean CO concentration versus. Distance from freeway based on Zhu et al (2002). 4 5 

Freeway Season Distance(s) Upwind (U) Distribution of Daily Means Across Me

405, 710 S=Summer From Freeway or 

or both W=Winter (m) Downwind(D) n mean std min q1 

(405710) A=All 

405 A 30 D 6 2.03 0.10 1.87 2.03 405 A 60 D 6 1.16 0.30 0.87 0.90 405 A 90 D 6 0.81 0.23 0.60 0.60 405 A 150 D 6 0.56 0.16 0.40 0.40 405 A 300 D 6 0.34 0.17 0.13 0.23 405 A 300 U 6 0.12 0.04 0.10 0.10 405 A d < 50m D 6 2.03 0.10 1.87 2.03 405 A 50m <= d < 150m D 6 0.98 0.26 0.73 0.75 405 A 150m <= d D 6 0.45 0.16 0.27 0.32 405 S 30 D 3 1.98 0.10 1.87 1.87 405 S 60 D 3 0.90 0.03 0.87 0.87 405 S 90 D 3 0.60 0.00 0.60 0.60 405 S 150 D 3 0.41 0.02 0.40 0.40 405 S 300 D 3 0.20 0.06 0.13 0.13 405 S 300 U 3 0.10 0.00 0.10 0.10 405 S d < 50m D 3 1.98 0.10 1.87 1.87 405 S 50m <= d < 150m D 3 0.75 0.02 0.73 0.73 405 S 150m <= d D 3 0.31 0.03 0.27 0.27 405 W 30 D 3 2.08 0.08 2.03 2.03 405 W 60 D 3 1.41 0.15 1.23 1.23 405 W 90 D 3 1.02 0.05 0.97 0.97 405 W 150 D 3 0.70 0.03 0.67 0.67 405 W 300 D 3 0.49 0.08 0.40 0.40 405 W 300 U 3 0.13 0.06 0.10 0.10 405 W d < 50m D 3 2.08 0.08 2.03 2.03 

405 W 50m <= d < 150m D 3 1.22 0.06 1.15 1.15 405 W 150m <= d D 3 0.59 0.04 0.55 0.55 
 B-229

Freeway Season Distance(s) Upwind (U) Distribution of Daily Means Across Me

405, 710 S=Summer From Freeway or 

or both W=Winter (m) Downwind(D) n mean std min q1 

(405710) A=All 

710 A 17 D 7 2.24 0.19 2.00 2.03 710 A 20 D 7 1.98 0.17 1.73 1.83 710 A 30 D 7 1.56 0.25 1.27 1.33 710 A 90 D 7 0.52 0.09 0.33 0.50 710 A 150 D 7 0.43 0.07 0.33 0.40 710 A 200 U 7 0.13 0.05 0.10 0.10 710 A 300 D 7 0.26 0.12 0.10 0.17 710 A d < 50m D 7 1.93 0.14 1.76 1.76 710 A 50m <= d < 150m D 7 0.52 0.09 0.33 0.50 710 A 150m <= d D 7 0.34 0.09 0.25 0.27 710 S 17 D 5 2.30 0.19 2.03 2.17 710 S 20 D 5 2.02 0.19 1.73 1.97 710 S 30 D 5 1.66 0.22 1.33 1.57 710 S 90 D 5 0.50 0.10 0.33 0.50 710 S 150 D 5 0.39 0.04 0.33 0.40 710 S 200 U 5 0.10 0.00 0.10 0.10 710 S 300 D 5 0.19 0.06 0.10 0.17 710 S d < 50m D 5 1.99 0.10 1.83 1.98 710 S 50m <= d < 150m D 5 0.50 0.10 0.33 0.50 710 S 150m <= d D 5 0.29 0.03 0.25 0.27 710 W 17 D 2 2.08 0.12 2.00 2.00 710 W 20 D 2 1.87 0.05 1.83 1.83 710 W 30 D 2 1.32 0.07 1.27 1.27 710 W 90 D 2 0.57 0.00 0.57 0.57 710 W 150 D 2 0.52 0.02 0.50 0.50 710 W 200 U 2 0.20 0.00 0.20 0.20 710 W 300 D 2 0.42 0.02 0.40 0.40 710 W d < 50m D 2 1.76 0.00 1.76 1.76 

710 W 50m <= d < 150m D 2 0.57 0.00 0.57 0.57 710 W 150m <= d D 2 0.47 0.00 0.47 0.47 

405710 A 17 D 7 2.24 0.19 2.00 2.03 405710 A 20 D 7 1.98 0.17 1.73 1.83 
 B-230

Freeway Season Distance(s) Upwind (U) Distribution of Daily Means Across Me

405, 710 S=Summer From Freeway or 

or both W=Winter (m) Downwind(D) n mean std min q1 

(405710) A=All 

405710 A 30 D 13 1.78 0.30 1.27 1.57 405710 A 60 D 6 1.16 0.30 0.87 0.90 405710 A 90 D 13 0.65 0.22 0.33 0.57 405710 A 150 D 13 0.49 0.13 0.33 0.40 405710 A 200 U 7 0.13 0.05 0.10 0.10 405710 A 300 D 13 0.30 0.15 0.10 0.20 405710 A 300 U 6 0.12 0.04 0.10 0.10 405710 A d < 50m D 13 1.97 0.13 1.76 1.87 405710 A 50m <= d < 150m D 13 0.73 0.30 0.33 0.57 405710 A 150m <= d D 13 0.39 0.13 0.25 0.30 405710 S 17 D 5 2.30 0.19 2.03 2.17 405710 S 20 D 5 2.02 0.19 1.73 1.97 405710 S 30 D 8 1.78 0.24 1.33 1.63 405710 S 60 D 3 0.90 0.03 0.87 0.87 405710 S 90 D 8 0.54 0.09 0.33 0.52 405710 S 150 D 8 0.40 0.03 0.33 0.40 405710 S 200 U 5 0.10 0.00 0.10 0.10 405710 S 300 D 8 0.20 0.06 0.10 0.15 405710 S 300 U 3 0.10 0.00 0.10 0.10 405710 S d < 50m D 8 1.99 0.09 1.83 1.92 405710 S 50m <= d < 150m D 8 0.59 0.15 0.33 0.52 405710 S 150m <= d D 8 0.30 0.03 0.25 0.27 405710 W 17 D 2 2.08 0.12 2.00 2.00 405710 W 20 D 2 1.87 0.05 1.83 1.83 405710 W 30 D 5 1.77 0.42 1.27 1.37 405710 W 60 D 3 1.41 0.15 1.23 1.23 405710 W 90 D 5 0.84 0.25 0.57 0.57 405710 W 150 D 5 0.63 0.10 0.50 0.53 405710 W 200 U 2 0.20 0.00 0.20 0.20 405710 W 300 D 5 0.46 0.07 0.40 0.40 405710 W 300 U 3 0.13 0.06 0.10 0.10 405710 W d < 50m D 5 1.95 0.18 1.76 1.76 
 B-231

Freeway Season Distance(s) Upwind (U) Distribution of Daily Means Across Me

405, 710 S=Summer From Freeway or 

or both W=Winter (m) Downwind(D) n mean std min q1 

(405710) A=All 

405710 W 50m <= d < 150m D 5 0.96 0.36 0.57 0.57 

405710 W 150m <= d D 5 0.54 0.08 0.47 0.47 

1 
 B-232

1 Table 2. CO concentration ratios based on Zhu et al (2002). 2 

Freeway Season Distance(s) Upwind (U) Distribution of Mean (Group) / Mean (d >= 150m

405, 710 S=Summer From Freeway or 

or both W=Winter (m) Downwind(D) n mean std min q1 

(405710) A=All 

405 A d < 50m D 6 5.03 1.79 3.30 3.51 405 A 50m <= d < 150m D 6 2.26 0.32 2.00 2.05 405 S d < 50m D 3 6.55 1.02 5.60 5.60 405 S 50m <= d < 150m D 3 2.48 0.34 2.25 2.25 405 W d < 50m D 3 3.50 0.20 3.30 3.30 405 W 50m <= d < 150m D 3 2.05 0.05 2.00 2.00 710 A d < 50m D 7 5.98 1.69 3.76 3.76 710 A 50m <= d < 150m D 7 1.58 0.39 1.11 1.21 710 S d < 50m D 5 6.87 0.91 6.11 6.25 710 S 50m <= d < 150m D 5 1.72 0.36 1.11 1.70 710 W d < 50m D 2 3.76 0.00 3.76 3.76 710 W 50m <= d < 150m D 2 1.21 0.00 1.21 1.21 

405710 A d < 50m D 13 5.54 1.74 3.30 3.76 405710 A 50m <= d < 150m D 13 1.89 0.50 1.11 1.70 405710 S d < 50m D 8 6.75 0.90 5.60 6.18 405710 S 50m <= d < 150m D 8 2.01 0.51 1.11 1.74 405710 W d < 50m D 5 3.61 0.20 3.30 3.51 405710 W 50m <= d < 150m D 5 1.71 0.46 1.21 1.21 

3 4 5 6 7 8 9 

10 
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1 Table 3. RIOPA Data. Analysis of mean concentrations vs. distance (d) from nearest major roadway (classes FC2 3 

 Concentrations 

/ M

Pollutant Distance Group n mean std min q1 median q3 max ratio 

Benzene d < 50m 12 1.54 0.43 0.67 1.25 1.59 1.87 2.19 1.13 Benzene 50m <= d < 150m 54 1.77 2.43 0.12 0.82 1.26 2.01 18.06 1.29 Benzene 150m <= d 117 1.37 0.98 0.06 0.63 1.09 1.84 5.17 . Carbon Tetrachloride d < 50m 12 0.64 0.21 0.17 0.53 0.67 0.74 1.01 0.93 

Carbon Tetrachloride 50m <= d < 150m 54 1.23 4.17 0.17 0.56 0.68 0.84 31.23 1.81 

Carbon Tetrachloride 150m <= d 117 0.68 0.23 0.17 0.56 0.70 0.84 1.13 . Ethylbenzene d < 50m 12 1.33 0.97 0.09 0.81 1.14 1.51 3.78 1.20 Ethylbenzene 50m <= d < 150m 54 1.87 4.86 0.09 0.42 1.03 1.85 36.24 1.68 

Ethylbenzene 150m <= d 117 1.11 0.84 0.02 0.46 0.89 1.74 3.30 . MTBE d < 50m 12 6.43 7.69 0.06 0.89 4.27 9.51 27.17 1.24 MTBE 50m <= d < 150m 54 6.82 5.48 0.06 3.22 5.60 8.09 26.89 1.32 MTBE 150m <= d 117 5.18 4.94 0.06 2.09 3.74 7.04 26.72 . PCE d < 50m 12 0.85 0.42 0.44 0.59 0.73 1.04 1.98 0.97 PCE 50m <= d < 150m 54 1.66 5.60 0.10 0.50 0.75 1.11 41.82 1.90 PCE 150m <= d 117 0.88 0.60 0.10 0.49 0.74 1.11 3.68 . Toluene d < 50m 12 9.11 6.13 1.47 4.69 7.62 12.93 21.88 1.41 Toluene 50m <= d < 150m 54 7.07 5.36 0.11 2.36 5.82 11.51 22.27 1.09 Toluene 150m <= d 117 6.47 6.00 0.11 2.59 4.43 8.10 32.88 . m,p,-Xylene d < 50m 12 4.68 4.31 0.71 2.18 4.28 4.77 17.52 1.72 m,p,-Xylene 50m <= d < 150m 54 4.07 6.94 0.42 1.72 2.45 4.41 51.21 1.49 m,p,-Xylene 150m <= d 117 2.73 2.09 0.15 1.43 2.17 3.39 10.52 . o-Xylene d < 50m 12 7.85 23.03 0.30 1.02 1.30 1.46 80.98 7.74 o-Xylene 50m <= d < 150m 54 1.84 5.00 0.28 0.58 0.95 1.56 37.49 1.82 o-Xylene 150m <= d 117 1.01 0.65 0.07 0.59 0.92 1.26 3.27 . 

4 
 B-234

1 Table 4. Empirical distributions of concentration ratios from PATA (see text). Denominator distance range is 2002 3 

Pollutant

Concentration 

metric Numerator N Mean Variance

P-Value for 

Shapiro-Wilk 

test of 

normality

geom 

mean

geom 

stdev

All LOG 0-75 m 99 0.9267 0.5352 0.0008 2.5263 2.078All RAW 0-75 m 99 3.2170 4.8067 0.0001All LOG 75-200 m 306 0.4950 0.4667 0.0001 1.6404 1.980All RAW 75-200 m 306 2.1611 5.2366 0.0001Benzene LOG 0-75 m 33 0.9071 0.5137 0.1423 2.4770 2.047Benzene RAW 0-75 m 33 3.1121 4.3349 0.0042Benzene LOG 75-200 m 102 0.4770 0.4318 0.0122 1.6113 1.929Benzene RAW 75-200 m 102 2.0607 3.6804 0.00011,3-Butadiene LOG 0-75 m 33 0.9708 0.5767 0.1110 2.6402 2.1371,3-Butadiene RAW 0-75 m 33 3.3989 5.6575 0.00261,3-Butadiene LOG 75-200 m 102 0.5252 0.5384 0.0063 1.6907 2.0821,3-Butadiene RAW 75-200 m 102 2.3384 8.1519 0.0001Diesel PM LOG 0-75 m 33 0.9023 0.5455 0.1541 2.4653 2.093Diesel PM RAW 0-75 m 33 3.1400 4.6767 0.0032Diesel PM LOG 75-200 m 102 0.4827 0.4378 0.0065 1.6204 1.938Diesel PM RAW 75-200 m 102 2.0843 3.9332 0.0001
 B-235

Table 5. Regression-based log-normal distributions of concentration ratios for each season from 

PATA (see text). Denominator distance range is 200+ m. Reported values are estimated means 

and variances of the logarithms of the ratios. 

Numerator 

Distance Range Pollutant Season Mean Variance1 Variance2 

0 - 75 m Benzene Annual 1.3504 0.8345 0.8634 

75 - 200 m Benzene Annual 0.6288 0.8345 0.8462 

0 - 75 m Benzene Dec-Feb 1.3808 0.9631 0.9965 

75 - 200 m Benzene Dec-Feb 0.6402 0.9631 0.9766 

0 - 75 m Benzene Jun-Aug 1.4028 1.0775 1.1148 

75 - 200 m Benzene Jun-Aug 0.6503 1.0775 1.0926 

0 - 75 m Benzene Mar-May 1.3528 0.8556 0.8853 

75 - 200 m Benzene Mar-May 0.6408 0.8556 0.8676 

0 - 75 m Benzene Sep-Nov 1.3133 0.7871 0.8144 

75 - 200 m Benzene Sep-Nov 0.6184 0.7871 0.7982 

0 - 75 m 1,3-Butadiene Annual 1.4577 1.0006 1.0353 

75 - 200 m 1,3-Butadiene Annual 0.6965 1.0006 1.0147 

0 - 75 m 1,3-Butadiene Dec-Feb 1.4669 1.1340 1.1733 

75 - 200 m 1,3-Butadiene Dec-Feb 0.6969 1.1340 1.1499 

0 - 75 m 1,3-Butadiene Jun-Aug 1.5131 1.2286 1.2711 

75 - 200 m 1,3-Butadiene Jun-Aug 0.7183 1.2286 1.2458 

0 - 75 m 1,3-Butadiene Mar-May 1.4710 1.0223 1.0578 

75 - 200 m 1,3-Butadiene Mar-May 0.7143 1.0223 1.0367 

0 - 75 m 1,3-Butadiene Sep-Nov 1.4392 0.9771 1.0109 

75 - 200 m 1,3-Butadiene Sep-Nov 0.6959 0.9771 0.9908 

0 - 75 m Diesel PM Annual 1.3163 0.8449 0.8742 

75 - 200 m Diesel PM Annual 0.6284 0.8449 0.8568 

0 - 75 m Diesel PM Dec-Feb 1.3460 0.9638 0.9972 

75 - 200 m Diesel PM Dec-Feb 0.6394 0.9638 0.9773 

0 - 75 m Diesel PM Jun-Aug 1.3664 1.0973 1.1353 

75 - 200 m Diesel PM Jun-Aug 0.6471 1.0973 1.1127 

0 - 75 m Diesel PM Mar-May 1.3251 0.8767 0.9070 

75 - 200 m Diesel PM Mar-May 0.6405 0.8767 0.8890 

0 - 75 m Diesel PM Sep-Nov 1.2848 0.8065 0.8344 

75 - 200 m Diesel PM Sep-Nov 0.6177 0.8065 0.8178 
 B-236

Table 6. Regression-based log-normal distributions of quarterly mean concentration ratios from 

PATA (see text). Denominator distance range is 200+ m. Reported values are estimated means 

and variances of the logarithms of the ratios. 

Numerator 

Distance 

Range Pollutant Mean Variance1 Variance2

0 - 75 m Benzene 1.3624 0.9144 0.9224

75 - 200 m Benzene 0.6374 0.9144 0.9176

0 - 75 m 1,3-Butadiene 1.4726 1.0828 1.0922

75 - 200 m 1,3-Butadiene 0.7063 1.0828 1.0866

0 - 75 m Diesel PM 1.3306 0.9295 0.9376

75 - 200 m Diesel PM 0.6362 0.9295 0.9328
 B-237

Attachment 7: Technical Memorandum on HAPEM Near Road 

Population Data Base Development (Estimating near roadway 

populations and areas for HAPEM6) 
 B-238

MEMORANDUM 

To: Chad Bailey 

From: Arlene Rosenbaum and Kevin Wright 

Date: December 28, 2005 

Re: Estimating near roadway populations and areas for HAPEM6

PURPOSE AND BACKGROUND 

In its 2001 regulation of mobile source air toxics (the “MSAT Rule”) EPA’s Office of 

Transportation and Air Quality (OTAQ) committed to further study of the range of 

concentrations to which people are exposed for consideration in future rulemaking. As part of the 

Technical Analysis Plan outlined in that research, OTAQ undertook research activity looking at 

the air quality in immediate proximity of busy roadways and highways. Concentrations of 

pollutants directly emitted by motor vehicles show statistically significant elevation in 

concentrations with increased proximity to busy roadways. 

The Hazardous Air Pollutant Exposure Model (HAPEM) is a screening-level exposure model 

appropriate for assessing average long-term inhalation exposures of the general population, or a 

specific sub-population, over spatial scales ranging from urban to national. HAPEM uses the 

general approach of tracking representatives of specified demographic groups as they move 

among indoor and outdoor microenvironments and among geographic locations. The estimated 

pollutant concentrations in each microenvironment visited are combined into a time-weighted 

average concentration, which is assigned to members of the demographic group. 

 

Indoor microenvironment concentrations are estimated by applying scalar factors to outdoor tract 

concentrations, which are some of the required inputs. These scalar factors are derived from 

published studies of concurrent concentration measurements indoors and outdoors. 

In the previous version, HAPEM5, if only a single outdoor concentration is provided for each 

Census tract, as is typical, this concentration is assumed to uniformly apply to the entire Census 

tract. For this version, HAPEM6, we refined the model to account for the spatial variability of 

outdoor concentrations within a tract due to enhanced outdoor concentrations of onroad mobile 

source pollutants at locations near major roadways. The term “major roadway” is used to 

describe a “Limited Access Highway”, “Highway”, “Major Road” or “Ramp”, as defined by the 

Census Feature Class Codes (CFCC). The new version of HAPEM more accurately reflects the 

average and variability of exposure concentrations within each Census tract by accounting for 

some of the spatial variability in the outdoor concentrations within the tract, and by extension 

some of the spatial variability in indoor concentrations within the tract. 
 B-239

Accomplishing this refinement to HAPEM required several activities, including the development 

and implementation of an approach for creating a database of the fraction of people within each 

US Census tract living near major roadways. This memorandum describes that activity. 

OVERVIEW AND SPECIFICATIONS 

The objective of this task was to estimate the fraction of people in each of 6 demographic groups 

in each US Census tract living near major roadways. 

The basic analysis was conducted at the US Census block level for populations stratified by age, 

gender, and race/ethnicity. The block level data was then aggregated up to the tract level for 

populations stratified by age only for use in HAPEM6. 

The data bases used for this task 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 data for US Census block boundaries in Puerto Rico and the US Virgin Islands 

obtained from Proximity 

Although the block file is an intermediate product for this project, it will be retained to facilitate 

the re-specification of demographic groups for possible future analyses. Therefore, this file 

contains the most resolved age-gender groups available at the block level from the US Census 

STF1. The age groups for the block level data are as follows: 

• 19 single-year age groups from 0-19 (P14) 

• 2 single-year age groups 20-21 (P12) 

• 16 age groups (P12) 

o 22 to 24 years 

o 25 to 29 years 

o 30 to 34 years 

o 35 to 39 years 

o 40 to 44 years 

o 45 to 49 years 

o 50 to 54 years 

o 55 to 59 years 

o 60 and 61 years 

o 62 to 64 years 

o 65 and 66 years 

o 67 to 69 years 

o 70 to 74 years 

o 75 to 79 years 

o 80 to 84 years 

o 85 years and over. 
 B-240
 B-241

The aggregated age groups for the tract level data are: 

• 0-1 

• 2-4 

• 5-15 

• 16-17 

• 18-64 

• 65+

The race/ethnic groups (block level only) are: 

• non-Hispanic White (alone or in combination - P010003) 

• non-Hispanic Black (alone or in combination - P010004) 

• non-Hispanic American Indian /Alaskan Native (alone or in combination - P010005) 

• non-Hispanic Asian (alone or in combination - P010006) 

• non-Hispanic Native Hawaiian/ Pacific Isalander (P010007) 

• non-Hispanic other (alone or in combination - P010008) 

• Hispanic (alone or in combination - P010009)

The spatial stratifications of the populations (block and tract level) are: 

• Those residing within 75 meters of a major roadway 

• Those residing from 75 to 200 meters from a major roadway 

• Those residing at greater than 200 meters from a roadway.

In addition, the fraction of the area of each Census block and tract that is located within the same 

distance ranges from a major roadway was determined. 

PROCEDURES 

For all the spatial modeling and geoprocessing operations in this study ICF utilized ArcInfo 

software. ArcInfo is the most extensive version of ArcGIS 9.1, the industry’s standard for 

Geographic Information Systems, produced by ESRI of Redlands, CA. 

Due to the size of the roadway and block geography files, most of the processing was conducted 

on a county-by-county basis. The files for some counties, however, still exceeded ArcInfo’s 

capacity and were processed tract-by-tract. A few counties in Arizona needed special handling 

because even at the tract level they exceeded ArcInfo’s capacity and were disaggregated into 

smaller pieces for processing. 

1. Because populations are not generally evenly distributed within blocks, it was 

assumed that the block populations all reside within 150 meters of any 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-meter buffer around all roadways 

within the block. This buffer served as a “clipped” block boundary defining the 
 B-242

portion of the block containing residential populations. The block population was 

assumed to be uniformly distributed within the “clipped” block boundary. 

2. Next a 75-meter buffer and a 200-meter 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 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 

stratification. 

3. The 75-meter buffer and the 200-meter buffer were also overlaid on the unclipped 

block boundary to determine the fraction of the total block area that fell with each of 

the buffers. 

4. The block level fractions for area and populations were then aggregated up to the tract 

level, and the population stratifications were aggregated up to the 6 tract age groups 

only. 

RESULTS 

The resulting database consists of 2 files types: (1) a block file for each state, and (2) a nationwide tract file. 

The block files contains the following 249 fields for each block: 

• block FIPS code 

• total population 

• total area 

• area within 75 meters of a major roadway 

• area from 75 to 200 meters from a major roadway 

• for each of 74 age-gender groups: 

o population residing within 75 meters of a major roadway 

o population residing between 75 and 200 meters from a major roadway 

o population residing more than 200 meters from a major roadway 

• sum of race/ethnic populations (note; this may differ slightly from the total population due to 

some double-counting of persons with more than 1 race/ethnicity) 

• for each of 7 race/ethnic groups: 

o population residing within 75 meters of a major roadway 

o population residing between 75 and 200 meters from a major roadway 

o population residing more than 200 meters from a major roadway 

Note that because of the limitations of the US Census data the block level populations 

could not be stratified by age, gender, and race together, 

The tract file contains the following 22 fields for each tract 
 B-243

• tract FIPS code 

• fraction of area within 75 meters of a major roadway 

• fraction of area between 75 and 200 meters from a major roadway 

• fraction of area more than 200 meters from a major roadway 

• for each of 6 age groups: 

o fraction of population residing within 75 meters of a major roadway 

o fraction of population residing between 75 and 200 meters from a major roadway 

o fraction of population residing more than 200 meters from a major roadway 

To date only a subset of states have been completely processed. For this subset state 

summaries of the fraction of population living within various distances of major 

roadways are presented in Table 1. 

Table 1. Fraction of population residing at various distances from major roadways for 

selected states. 

STATE Distance from major roadways 

< 75 meters 75 – 200 meters > 200 meters 

Colorado 0.22 0.33 0.45 

Georgia 0.17 0.24 0.59 

New York 0.31 0.36 0.33 
 B-244

Attachment 8. Technical Memorandum on the Uncertainty Analysis 

Of Residential Air Exchange Rate Distributions 
 B-245

MEMORANDUM 

To: John Langstaff, EPA OAQPS 

From: Jonathan Cohen, Arlene Rosenbaum, ICF International 

Date: June 5, 2006 

Re: Uncertainty analysis of residential air exchange rate distributions 

This memorandum describes our assessment of some of the sources of the uncertainty of cityspecific distributions of residential air exchange rates that were fitted to the available study data. 

City-specific distributions for use with the APEX ozone model were developed for 12 modeling 

cities, as detailed in the memorandum by Cohen, Mallya and Rosenbaum, 200529 (Appendix A 

of this report). In the first part of the memorandum, we analyze the between-city uncertainty by 

examining the variation of the geometric means and standard deviations across cities and studies. 

In the second part of the memorandum, we assess the within-city uncertainty by using a 

bootstrap distribution to estimate the effects of sampling variation on the fitted geometric means 

and standard deviations for each city. The bootstrap distributions assess the uncertainty due to 

random sampling variation but do not address uncertainties due to the lack of representativeness 

of the available study data, the matching of the study locations to the modeled cities, and the 

variation in the lengths of the AER monitoring periods. 

Variation of geometric means and standard deviations across cities and studies 

The memorandum by Cohen, Mallya and Rosenbaum, 200530 (Attachment 5 of this report) 

describes the analysis of residential air exchange rate (AER) data that were obtained from seven 

studies. The AER data were subset by location, outside temperature range, and the A/C type, as 

defined by the presence or absence of an air conditioner (central or window). In each case we 

chose to fit a log-normal distribution to the AER data, so that the logarithm of the AER for a 

given city, temperature range, and A/C type is assumed to be normally distributed. If the AER 

data has geometric mean GM and geometric standard deviation GSD, then the logarithm of the 

AER is assumed to have a normal distribution with mean log(GM) and standard deviation 

log(GSD). 

Table D-1 shows the assignment of the AER data to the 12 modeled cities. Note that for Atlanta, 

GA and Washington DC, the Research Triangle Park, NC data for houses with A/C was used to 

represent the AER distributions for houses with A/C, and the non-California data for houses 

without A/C was used to represent the AER distributions for houses without A/C. Sacramento, 

CA AER distributions were estimated using the AER data from the inland California counties of 

 

29 Cohen, J., H. Mallya, and A. Rosenbaum. 2005. Memorandum to John Langstaff. EPA 68D01052, Work 

Assignment 3-08. Analysis of Air Exchange Rate Data. September 30, 2005. 30 Op. Cit.
 B-246

Sacramento, Riverside, and San Bernardino; these combined data are referred to by the City 

Name “Inland California.” St Louis, MO AER distributions were estimated using the AER data 

from all states except for California and so are referred to be the City Name “Outside 

California.” 

Table D-1. Assignment of Residential AER distributions to modeled cities 

Modeled city AER distribution 

Atlanta, GA, A/C Research Triangle Park, A/C only 

Atlanta, GA, no A/C All non-California, no A/C (“Outside California”) 

Boston, MA New York 

Chicago, IL New York 

Cleveland, OH New York 

Detroit, MI New York 

Houston, TX Houston 

Los Angeles, CA Los Angeles 

New York, NY New York 

Philadelphia, PA New York 

Sacramento Inland parts of Los Angeles (“Inland California”) 

St. Louis All non-California (“Outside California”) 

Washington, DC, A/C Research Triangle Park, A/C only 

Washington, DC, no 

A/C All non-California, no A/C (“Outside California”) 

It is evident from Table D-1 that for some of the modeled cities, some potentially large 

uncertainty was introduced because we modeled their AER distributions using available data 

from another city or group of cities thought to be representative of the first city on the basis of 

geography and other characteristics. This was necessary for cities where we did not have any or 

sufficient AER data measured in the same city that also included the necessary temperature and 

A/C type information. One way to assess the impact of these assignments on the uncertainty of 

the AER distributions is to evaluate the variation of the fitted log-normal distributions across the 

cities with AER data. In this manner we can examine the effect on the AER distribution if a 

different allocation of study data to the modeled cities had been used. 

Even for the cities where we have study AER data, there is uncertainty about the fitted AER 

distributions. First, the studies used different measurement and residence selection methods. In 

some cases the residences were selected by a random sampling method designed to represent the 

entire population. In other cases the residences were selected to represent sub-populations. For 

example, for the RTP study, the residences belong to two specific cohorts: a mostly Caucasian, 
 B-247

non-smoking group aged at least 50 years having cardiac defibrillators living in Chapel Hill; a 

group of non-smoking, African Americans aged at least 50 years with controlled hypertension 

living in a low-to-moderate SES neighborhood in Raleigh. The TEACH study was restricted to 

residences of inner-city high school students. The RIOPA study was a random sample for Los 

Angeles, but was designed to preferentially sample locations near major air toxics sources for 

Elizabeth, NJ and Houston TX. Furthermore, some of the studies focused on different towns or 

cities within the larger metropolitan areas, so that, for example, the Los Angeles data from the 

Avol study was only measured in Lancaster, Lake Gregory, Riverside, and San Dimas but the 

Los Angeles data from the Wilson studies were measured in multiple cities in Southern 

California. One way to assess the uncertainty of the AER distributions due to variations of study 

methodologies and study sampling locations is to evaluate the variation of the fitted log-normal 

distributions within each modeled city across the different studies. 

We evaluated the variation between cities, and the variation within cities and between studies, by 

tabulating and plotting the AER distributions for all the study/city combinations. Since the 

original analyses by Cohen, Mallya and Rosenbaum, 2005 clearly showed that the AER 

distribution depends strongly on the outside temperature and the A/C type (whether or not the 

residence has air conditioning), this analysis was stratified by the outside temperature range and 

the A/C type. Otherwise, study or city differences would have been confounded by the 

temperature and A/C type differences and you would not be able to tell how much of the AER 

difference was due to the variation of temperature and A/C type across cities or studies. In order 

to be able to compare cities and studies we could not use different temperature ranges for the 

different modeled cities as we did for the original AER distribution modeling. For these analyses 

we stratified the temperature into the ranges <= 10, 10-20, 20-25, and >25 oC and categorized the 

A/C type as “Central or Window A/C” versus ‘No A/C,” giving 8 temperature and A/C type 

strata. 

Table D-2 shows the geometric means and standard deviations by city and study. These 

geometric mean and standard deviation pairs are plotted in Figure D-1 through D-8. Each figure 

shows the variation across cities and studies for a given temperature range and A/C type. The 

results for a city with only one available study are shown with a blank study name. For cities 

with multiple studies, results are shown for the individual studies and the city overall distribution 

is designated by a blank value for the study name. 

Table D-2. Geometric means and standard deviations by city and study. 

A/C Type Temperature City Study* N Geo Mean Geo Std Dev** 

Central or Room A/C <= 10 Houston 2 0.32 1.80 

Central or Room A/C <= 10 Los Angeles 5 0.62 1.51 

Central or Room A/C <= 10 Los Angeles Avol 2 0.72 1.22 

Central or Room A/C <= 10 Los Angeles RIOPA 1 0.31 

Central or Room A/C <= 10 Los Angeles Wilson 1991 2 0.77 1.12 

Central or Room A/C <= 10 New York City 20 0.71 2.02 

Central or Room A/C <= 10 Research Triangle Park 157 0.96 1.81 

Central or Room A/C <= 10 Sacramento 3 0.38 1.82 

Central or Room A/C <= 10 San Francisco 2 0.43 1.00 

Central or Room A/C <= 10 Stockton 7 0.48 1.64 

Central or Room A/C 10-20 Arcata 1 0.17 
 B-248

Table D-2. Geometric means and standard deviations by city and study. 

A/C Type Temperature City Study* N Geo Mean Geo Std Dev** 

Central or Room A/C 10-20 Bakersfield 2 0.36 1.34 

Central or Room A/C 10-20 Fresno 8 0.30 1.62 

Central or Room A/C 10-20 Houston 13 0.42 2.19 

Central or Room A/C 10-20 Los Angeles 716 0.59 1.90 

Central or Room A/C 10-20 Los Angeles Avol 33 0.48 1.87 

Central or Room A/C 10-20 Los Angeles RIOPA 11 0.60 1.87 

Central or Room A/C 10-20 Los Angeles TEACH 1 0.68 

Central or Room A/C 10-20 Los Angeles Wilson 1984 634 0.59 1.89 

Central or Room A/C 10-20 Los Angeles Wilson 1991 37 0.64 2.11 

Central or Room A/C 10-20 New York City 5 1.36 2.34 

Central or Room A/C 10-20 New York City RIOPA 4 1.20 2.53 

Central or Room A/C 10-20 New York City TEACH 1 2.26 

Central or Room A/C 10-20 Redding 1 0.31 

Central or Room A/C 10-20 Research Triangle Park 320 0.56 1.91 

Central or Room A/C 10-20 Sacramento 7 0.26 1.67 

Central or Room A/C 10-20 San Diego 23 0.41 1.55 

Central or Room A/C 10-20 San Francisco 5 0.42 1.25 

Central or Room A/C 10-20 Santa Maria 1 0.23 

Central or Room A/C 10-20 Stockton 4 0.73 1.42 

Central or Room A/C 20-25 Houston 20 0.47 1.94 

Central or Room A/C 20-25 Los Angeles 273 1.10 2.36 

Central or Room A/C 20-25 Los Angeles Avol 32 0.61 1.95 

Central or Room A/C 20-25 Los Angeles RIOPA 26 0.90 2.42 

Central or Room A/C 20-25 Los Angeles Wilson 1984 215 1.23 2.33 

Central or Room A/C 20-25 New York City 37 1.11 2.74 

Central or Room A/C 20-25 New York City RIOPA 20 0.93 2.91 

Central or Room A/C 20-25 New York City TEACH 17 1.37 2.52 

Central or Room A/C 20-25 Red Bluff 2 0.61 3.20 

Central or Room A/C 20-25 Research Triangle Park 196 0.40 1.89 

Central or Room A/C > 25 Houston 79 0.43 2.17 

Central or Room A/C > 25 Los Angeles 114 0.72 2.60 

Central or Room A/C > 25 Los Angeles Avol 25 0.37 3.10 

Central or Room A/C > 25 Los Angeles RIOPA 10 0.94 1.71 

Central or Room A/C > 25 Los Angeles Wilson 1984 79 0.86 2.33 

Central or Room A/C > 25 New York City 19 1.24 2.18 

Central or Room A/C > 25 New York City RIOPA 14 1.23 2.28 

Central or Room A/C > 25 New York City TEACH 5 1.29 2.04 

Central or Room A/C > 25 Research Triangle Park 145 0.38 1.71 

No A/C <= 10 Houston 13 0.66 1.68 

No A/C <= 10 Los Angeles 18 0.54 3.09 

No A/C <= 10 Los Angeles Avol 14 0.51 3.60 

No A/C <= 10 Los Angeles RIOPA 2 0.72 1.11 

No A/C <= 10 Los Angeles Wilson 1991 2 0.60 1.00 

No A/C <= 10 New York City 48 1.02 2.14 

No A/C <= 10 New York City RIOPA 44 1.04 2.20 

No A/C <= 10 New York City TEACH 4 0.79 1.28 
 B-249

Table D-2. Geometric means and standard deviations by city and study. 

A/C Type Temperature City Study* N Geo Mean Geo Std Dev** 

No A/C <= 10 Sacramento 3 0.58 1.30 

No A/C <= 10 San Francisco 9 0.39 1.42 

No A/C 10-20 Bakersfield 1 0.85 

No A/C 10-20 Fresno 4 0.90 2.42 

No A/C 10-20 Houston 28 0.63 2.92 

No A/C 10-20 Los Angeles 390 0.75 2.09 

No A/C 10-20 Los Angeles Avol 23 0.78 2.55 

No A/C 10-20 Los Angeles RIOPA 87 0.78 1.96 

No A/C 10-20 Los Angeles TEACH 9 2.32 2.05 

No A/C 10-20 Los Angeles Wilson 1984 241 0.70 2.06 

No A/C 10-20 Los Angeles Wilson 1991 30 0.75 1.82 

No A/C 10-20 New York City 59 0.79 2.04 

No A/C 10-20 Sacramento 1 1.09 

No A/C 10-20 San Diego 49 0.47 1.95 

No A/C 10-20 San Francisco 15 0.34 3.05 

No A/C 10-20 Santa Maria 2 0.27 1.23 

No A/C 20-25 Houston 10 0.92 2.41 

No A/C 20-25 Los Angeles 148 1.37 2.28 

No A/C 20-25 Los Angeles Avol 19 0.95 1.87 

No A/C 20-25 Los Angeles RIOPA 38 1.30 2.11 

No A/C 20-25 Los Angeles Wilson 1984 91 1.52 2.40 

No A/C 20-25 New York City 26 1.62 2.24 

No A/C 20-25 New York City RIOPA 19 1.50 2.30 

No A/C 20-25 New York City TEACH 7 1.99 2.11 

No A/C 20-25 Red Bluff 1 0.55 

No A/C > 25 Houston 2 0.92 3.96 

No A/C > 25 Los Angeles 25 0.99 1.97 

No A/C > 25 Los Angeles Avol 6 1.56 1.36 

No A/C > 25 Los Angeles RIOPA 4 1.33 1.37 

No A/C > 25 Los Angeles TEACH 3 0.86 1.02 

No A/C > 25 Los Angeles Wilson 1984 12 0.74 2.29 

No A/C > 25 New York City 6 1.54 1.65 

No A/C > 25 New York City RIOPA 3 1.73 2.00 

No A/C > 25 New York City TEACH 3 1.37 1.38 

* For a given city, if AER data were available from only one study, then the study name is missing. If AER data were available 

for two or more studies, then the overall city distribution is shown in the row where the study name is missing, and the 

distributions by study and city are shown in the rows with a specific study name. 

** The geometric standard deviation is undefined if the sample size equals 1.

In general, there is a relatively wide variation across different cities. This implies that the AER 

modeling results would be very different if the matching of modeled cities to study cities was 

changed, although a sensitivity study using the APEX model would be needed to assess the 

impact on the ozone exposure estimates. In particular the ozone exposure estimates may be 

sensitive to the assumption that the St. Louis AER distributions can be represented by the 

combined non-California AER data. One way to address this is to perform a Monte Carlo 

analysis where the first stage is to randomly select a city outside of California, the second stage 

picks the A/C type, and the third stage picks the AER value from the assigned distribution for the 
 B-250

city, A/C type and temperature range. Note that this will result in a very different distribution to 

the current approach that fits a single log-normal distribution to all the non-California data for a 

given temperature range and A/C type. The current approach weights each data point equally, so 

that cities like New York with most of the data values get the greatest statistical weight. The 

Monte Carlo approach gives the same total statistical weight for each city and fits a mixture of 

log-normal distributions rather than a single distribution. 

In general, there is also some variation within studies for the same city, but this is much smaller 

than the variation across cities. This finding tends to support the approach of combining different 

studies. Note that the graphs can be deceptive in this regard because some of the data points are 

based on very small sample sizes (N) ; those data points are less precise and the differences 

would not be statistically significant. For example, for the No A/C data in the range 10-20 oC, 

the Los Angeles TEACH study had a geometric mean of 2.32 based on only nine AER values, 

but the overall geometric mean, based on 390 values, was 0.75 and the geometric means for the 

Los Angeles Avol, RIOPA, Wilson 1984, and Wilson 1991 studies were each close to 0.75. One 

noticeable case where the studies show big differences for the same city is for the A/C houses in 

Los Angeles in the range 20-25 oC where the study geometric means are 0.61 (Avol, N=32), 0.90 

(RIOPA, N=26) and 1.23 (Wilson 1984, N=215). 

Bootstrap analyses

The 39 AER subsets defined in the Cohen, Mallya, and Rosenbaum, 2005 memorandum 

(Appendix A of this report) and their allocation to the 12 modeled cities are shown in Table D-3. 

To make the distributions sufficiently precise in each AER subset and still capture the variation 

across temperature and A/C type, different modeled cities were assigned different temperature 

range and A/C type groupings. Therefore these temperature range groupings are sometimes 

different to those used to develop Table D-2 and Figure D-1 through D-8. 

Table D-3. AER subsets by city, A/C type, and temperature range. 

Subset City 

Name 

Study Cities Represents 

Modeled Cities: 

A/C Type Temperature 

Range (oC) 

Houston Houston Houston, TX Central or Room A/C <=20 

Houston Houston Houston, TX Central or Room A/C 20-25 

Houston Houston Houston, TX Central or Room A/C 25-30 

Houston Houston Houston, TX Central or Room A/C >30 

Houston Houston Houston, TX No A/C <=10 

Houston Houston Houston, TX No A/C 10-20 

 Houston Houston, TX No A/C >20 

Inland California Sacramento, Riverside, 

and San Bernardino 

counties, CA 

Sacramento, CA Central or Room A/C <=25 

Inland California Sacramento, Riverside, 

and San Bernardino 

counties, CA 

Sacramento, CA Central or Room A/C >25 

Inland California Sacramento, Riverside, 

and San Bernardino 

counties, CA 

Sacramento, CA No A/C <=10 

Inland California Sacramento, Riverside, 

and San Bernardino 

counties, CA 

Sacramento, CA No A/C 10-20 
 B-251

Table D-3. AER subsets by city, A/C type, and temperature range. 

Subset City 

Name 

Study Cities Represents 

Modeled Cities: 

A/C Type Temperature 

Range (oC) 

Inland California Sacramento, Riverside, 

and San Bernardino 

counties, CA 

Sacramento, CA No A/C 20-25 

Inland California Sacramento, Riverside, 

and San Bernardino 

counties, CA 

Sacramento, CA No A/C >25 

Los Angeles Los Angeles, Orange, 

Riverside, San 

Bernardino, and 

Ventura counties, CA 

Los Angeles, CA Central or Room A/C <=20 

Los Angeles Los Angeles, Orange, 

Riverside, San 

Bernardino, and 

Ventura counties, CA 

Los Angeles, CA Central or Room A/C 20-25 

Los Angeles Los Angeles, Orange, 

Riverside, San 

Bernardino, and 

Ventura counties, CA 

Los Angeles, CA Central or Room A/C 25-30 

Los Angeles Los Angeles, Orange, 

Riverside, San 

Bernardino, and 

Ventura counties, CA 

Los Angeles, CA Central or Room A/C >30 

Los Angeles Los Angeles, Orange, 

Riverside, San 

Bernardino, and 

Ventura counties, CA 

Los Angeles, CA No A/C <=10 

Los Angeles Los Angeles, Orange, 

Riverside, San 

Bernardino, and 

Ventura counties, CA 

Los Angeles, CA No A/C 10-20 

Los Angeles Los Angeles, Orange, 

Riverside, San 

Bernardino, and 

Ventura counties, CA 

Los Angeles, CA No A/C 20-25 

Los Angeles Los Angeles, Orange, 

Riverside, San 

Bernardino, and 

Ventura counties, CA 

Los Angeles, CA No A/C >25 

New York City New York, NY Boston, MA, 

Chicago, IL, 

Cleveland, OH, 

Detroit, MI, 

New York, NY, 

Philadelphia, PA 

Central or Room A/C <=10 

New York City New York, NY Boston, MA, 

Chicago, IL, 

Cleveland, OH, 

Detroit, MI, 

New York, NY, 

Philadelphia, PA 

Central or Room A/C 10-25 

New York City New York, NY Boston, MA, 

Chicago, IL, 

Cleveland, OH, 

Detroit, MI, 

New York, NY, 

Philadelphia, PA 

Central or Room A/C >25 

New York City New York, NY Boston, MA, No A/C <=10 
 B-252

Table D-3. AER subsets by city, A/C type, and temperature range. 

Subset City 

Name 

Study Cities Represents 

Modeled Cities: 

A/C Type Temperature 

Range (oC) 

Chicago, IL, 

Cleveland, OH, 

Detroit, MI, 

New York, NY, 

Philadelphia, PA 

New York City New York, NY Boston, MA, 

Chicago, IL, 

Cleveland, OH, 

Detroit, MI, 

New York, NY, 

Philadelphia, PA 

No A/C 10-20 

New York City New York, NY Boston, MA, 

Chicago, IL, 

Cleveland, OH, 

Detroit, MI, 

New York, NY, 

Philadelphia, PA 

No A/C >20 

Outside California Cities outside CA St. Louis, MO Central or Room A/C <=10 

Outside California Cities outside CA St. Louis, MO Central or Room A/C 10-20 

Outside California Cities outside CA St. Louis, MO Central or Room A/C 20-25 

Outside California Cities outside CA St. Louis, MO Central or Room A/C 25-30 

Outside California Cities outside CA St. Louis, MO Central or Room A/C >30 

Outside California Cities outside CA St. Louis, MO 

Atlanta, GA 

Washington DC 

No A/C <=10 

Outside California Cities outside CA St. Louis, MO 

Atlanta, GA 

Washington DC 

No A/C 10-20 

Outside California Cities outside CA St. Louis, MO 

Atlanta, GA 

Washington DC 

No A/C >20 

Research Triangle Park Research Triangle 

Park, NC 

Atlanta, GA 

Washington DC 

Central or Room A/C <=10 

Research Triangle Park Research Triangle 

Park, NC 

Atlanta, GA 

Washington DC 

Central or Room A/C 10-20 

Research Triangle Park Research Triangle 

Park, NC 

Atlanta, GA 

Washington DC 

Central or Room A/C 20-25 

Research Triangle Park Research Triangle 

Park, NC 

Atlanta, GA 

Washington DC 

Central or Room A/C >25 

The GM and GSD values that define the fitted log-normal distributions for these 39 AER subsets 

are shown in Table D-4. Examples of these pairs are also plotted in Figures D-9 through D-19, to 

be further described below. Each of the example figures D-9 through D-19 corresponds to a 

single GM/GSD “Original Data” pair. The GM and GSD values for the “Original Data” are at 

the intersection of the horizontal and vertical lines that are parallel to the x- and y-axes in the 

figures. 
 B-253

Table D-4. Geometric means and standard deviations for AER subsets by city, A/C type, 

and temperature range. 

Subset City 

Name A/C Type Temperature Range (oC) N Geometric Mean 

Geometric 

Standard 

Deviation 

Houston Central or Room 

A/C <=20 15 0.4075 2.1135 

Houston Central or Room 

A/C 20-25 20 0.4675 1.9381 

Houston Central or Room 

A/C 25-30 65 0.4221 2.2579 

Houston Central or Room 

A/C >30 14 0.4989 1.7174 

Houston No A/C <=10 13 0.6557 1.6794 

Houston No A/C 10-20 28 0.6254 2.9162 

 No A/C >20 12 0.9161 2.4512 

Inland California Central or Room 

A/C <=25 226 0.5033 1.9210 

Inland California Central or Room 

A/C >25 83 0.8299 2.3534 

Inland California No A/C <=10 17 0.5256 3.1920 

Inland California No A/C 10-20 52 0.6649 2.1743 

Inland California No A/C 20-25 13 1.0536 1.7110 

Inland California No A/C >25 14 0.8271 2.2646 

Los Angeles Central or Room 

A/C <=20 721 0.5894 1.8948 

Los Angeles Central or Room 

A/C 20-25 273 1.1003 2.3648 

Los Angeles Central or Room 

A/C 25-30 102 0.8128 2.4151 

Los Angeles Central or Room 

A/C >30 12 0.2664 2.7899 

Los Angeles No A/C <=10 18 0.5427 3.0872 

Los Angeles No A/C 10-20 390 0.7470 2.0852 

Los Angeles No A/C 20-25 148 1.3718 2.2828 

Los Angeles No A/C >25 25 0.9884 1.9666 

New York City Central or Room 

A/C <=10 20 0.7108 2.0184 

New York City Central or Room 

A/C 10-25 42 1.1392 2.6773 

New York City Central or Room 

A/C >25 19 1.2435 2.1768 

New York City No A/C <=10 48 1.0165 2.1382 

New York City No A/C 10-20 59 0.7909 2.0417 

New York City No A/C >20 32 1.6062 2.1189 

Outside California Central or Room 

A/C <=10 179 0.9185 1.8589 

Outside California Central or Room 

A/C 10-20 338 0.5636 1.9396 

Outside California Central or Room 

A/C 20-25 253 0.4676 2.2011 

Outside California Central or Room 

A/C 25-30 219 0.4235 2.0373 
 B-254

Table D-4. Geometric means and standard deviations for AER subsets by city, A/C type, 

and temperature range. 

Subset City 

Name A/C Type Temperature Range (oC) N Geometric Mean 

Geometric 

Standard 

Deviation 

Outside California Central or Room 

A/C >30 24 0.5667 1.9447 

Outside California No A/C <=10 61 0.9258 2.0836 

Outside California No A/C 10-20 87 0.7333 2.3299 

Outside California No A/C >20 44 1.3782 2.2757 

Research Triangle 

Park 

Central or Room 

A/C <=10 157 0.9617 1.8094 

Research Triangle 

Park 

Central or Room 

A/C 10-20 320 0.5624 1.9058 

Research Triangle 

Park 

Central or Room 

A/C 20-25 196 0.3970 1.8887 

Research Triangle 

Park 

Central or Room 

A/C >25 145 0.3803 1.7092 

To evaluate the uncertainty of the GM and GSD values, a bootstrap simulation was performed, 

as follows. Suppose that a given AER subset has N values. A bootstrap sample is obtained by 

sampling N times at random with replacement from the N AER values. The first AER value in 

the bootstrap sample is selected randomly from the N values, so that each of the N values is 

equally likely. The second, third, ..., N’th values in the bootstrap sample are also selected 

randomly from the N values, so that for each selection, each of the N values is equally likely. 

The same value can be selected more than once. Using this bootstrap sample, the geometric 

mean and geometric standard deviation of the N values in the bootstrap sample was calculated. 

This pair of values is plotted as one of the points in a figure for that AER subset. 1,000 bootstrap 

samples were randomly generated for each AER subset, producing a set of 1,000 geometric mean 

and geometric standard deviation pairs, which were plotted in example Figures D-9 through D19. 

The bootstrap distributions display the part of the uncertainty of the GM and GSD that is entirely 

due to random sampling variation. The analysis is based on the assumption that the study AER 

data are a random sample from the population distribution of AER values for the given city, 

temperature range, and A/C type. On that basis, the 1,000 bootstrap GM and GSD pairs estimate 

the variation of the GM and GSD across all possible samples of N values from the population. 

Since each GM, GSD pair uniquely defines a fitted log-normal distribution, the pairs also 

estimate the uncertainty of the fitted log-normal distribution. The choice of 1,000 was made as a 

compromise between having enough pairs to accurately estimate the GM, GSD distribution and 

not having too many pairs so that the graph appears as a smudge of overlapped points. Note that 

even if there were infinitely many bootstrap pairs, the uncertainty distribution would still be an 

estimate of the true uncertainty because the N is finite, so that the empirical distribution of the N 

measured AER values does not equal the unknown population distribution. 

In most cases the uncertainty distribution appears to be a roughly circular or elliptical geometric 

mean and standard deviation region. The size of the region depends upon the sample size and on 

the variability of the AER values; the region will be smallest when the sample size N is large 
 B-255

and/or the variability is small, so that there are a large number of values that are all close 

together. 

The bootstrap analyses show that the geometric standard deviation uncertainty for a given 

CMSA/air-conditioning-status/temperature-range combination tends to have a range of at most 

from “fitted GSD-1.0 hr-1” to “fitted GSD+1.0 hr-1”, but the intervals based on larger AER 

sample sizes are frequently much narrower. The ranges for the geometric means tend to be 

approximately from “fitted GM-0.5 hr-1” to “fitted GM+0.5 hr-1”, but in some cases were much 

smaller. 

The bootstrap analysis only evaluates the uncertainty due to the random sampling. It does not 

account for the uncertainty due to the lack of representativeness, which in turn is due to the fact 

that the samples were not always random samples from the entire population of residences in a 

city, and were sometimes used to represent different cities. Since 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 log-normal. Furthermore, the bootstrap uncertainty does not 

account for the effect of the calendar year (possible trends in AER values) or of the uncertainty 

due to the AER measurement period; the distributions were intended to represent distributions of 

24 hour average AER values although the study AER data were measured over a variety of 

measurement periods. 

To use the bootstrap distributions to estimate the impact of sample size on the fitted distributions, 

a Monte Carlo approach could be used with the APEX model. Instead of using the Original Data 

distributions, a bootstrap GM, GSD pair could be selected at random and the AER value could be 

selected randomly from the log-normal distribution with the bootstrap GM and GSD. 
Figure D-1 
B-257

Figure D-2 
B-258

Figure D-3 
B-259

Figure D-4 
B-260

Figure D-5 
B-261

Figure D-6 
B-262

Figure D-7 
B-263

Figure D-8 
B-264

Figure D-9 
B-265

Figure D-10 
B-266

Figure D-11 
B-267

Figure D-12 
B-268

Figure D-13 
B-269

Figure D-14 
B-270

Figure D-15 
B-271

Figure D-16 
B-272

Figure D-17 
B-273

Figure D-18 
B-274

Figure D-19 
Attachment 9. Technical Memorandum on the Distributions of Air 

Exchange Rate Averages Over Multiple Days 
B-276

MEMORANDUM 

To: John Langstaff, EPA OAQPS 

From: Jonathan Cohen, Arlene Rosenbaum, ICF International 

Date: June 8, 2006 

Re: Distributions of air exchange rate averages over multiple days 

As detailed in the memorandum by Cohen, Mallya and Rosenbaum, 200531 (Appendix A of this 

report) we have proposed to use the APEX model to simulate the residential air exchange rate 

(AER) using different log-normal distributions for each combination of outside temperature 

range and the air conditioner type, defined as the presence or absence of an air conditioner 

(central or room). 

Although the averaging periods for the air exchange rates in the study databases varied from one 

day to seven days, our analyses did not take the measurement duration into account and treated 

the data as if they were a set of statistically independent daily averages. In this memorandum we 

present some analyses of the Research Triangle Park Panel Study that show extremely strong 

correlations between consecutive 24-hour air exchange rates measured at the same house. This 

provides support for the simplified approach of treating all averaging periods as if they were 24-

hour averages. 

In the current version of the APEX model, there are several options for stratification of time 

periods with respect to AER distributions, and for when to re-sample from a distribution for a 

given stratum. The options selected for this current set of simulations resulted in a uniform AER 

for each 24-hour period and re-sampling of the 24-hour AER for each simulated day. This resampling for each simulated day implies that the simulated AERs on consecutive days in the 

same microenvironment are statistically independent. Although we have not identified sufficient 

data to test the assumption of uniform AERs throughout a 24-hour period, the analyses described 

in this memorandum suggest that AERs on consecutive days are highly correlated. Therefore, we 

performed sensitivity simulations to assess the impact of the assumption of temporally 

independent air exchange rates, but found little difference between APEX predictions for the two 

scenarios (i.e., temporally independent and autocorrelated air exchange rates). 

 

31 Cohen, J., H. Mallya, and A. Rosenbaum. 2005. Memorandum to John Langstaff. EPA 68D01052, Work 

Assignment 3-08. Analysis of Air Exchange Rate Data. September 30, 2005. 
B-277

Distributions of multi-day averages from the RTP Panel Study

The RTP Panel study included measurements of 24-hour averages at 38 residences for up to four 

periods of at least seven days. These periods were in different seasons and/or calendar years. 

Daily outside temperatures were also provided. All the residences had either window or room air 

conditioners or both. We used these data to compare the distributions of daily averages taken 

over 1, 2, 3, .. 7 days. 

The analysis is made more complicated because the previous analyses showed the dependence of 

the air exchange rate on the outside temperature, and the daily temperatures often varied 

considerably. Two alternative approaches were employed to group consecutive days. For the first 

approach, A, we sorted the data by the HOUSE_ID number and date and began a new group of 

days for each new HOUSE_ID and whenever the sorted measurement days on the same 

HOUSE_ID were 30 days or more apart. In most cases, a home was measured over four different 

seasons for seven days, potentially giving 38 × 4 = 152 groups; the actual number of groups was 

124. For the second approach, B, we again sorted the data by the HOUSE_ID number and date, 

but this time we began a new group of days for each new HOUSE_ID and whenever the sorted 

measurement days on the same HOUSE_ID were 30 days or more apart or were for different 

temperature ranges. We used the same four temperature ranges chosen for the analysis in the 

Cohen, Mallya, and Rosenbaum, 2005, memorandum (Appendix A): <= 10, 10-20, 20-25, and > 

25 oC. For example, if the first week of measurements on a given HOUSE_ID had the first three 

days in the <= 10 oC range, the next day in the 10-20 oC range, and the last three days in the <= 

10 oC range, then the first approach would treat this as a single group of days. The second 

approach would treat this as three groups of days, i.e., the first three days, the fourth day, and the 

last three days. Using the first approach, the days in each group can be in different temperature 

ranges. Using the second approach, every day in a group is in the same temperature range. Using 

the first approach we treat groups of days as being independent following a transition to a 

different house or season. Using the second approach we treat groups of days as being 

independent following a transition to a different house or season or temperature range. 

To evaluate the distributions of multi-day air exchange rate (AER) averages, we averaged the 

AERs over consecutive days in each group. To obtain a set of one-day averages, we took the 

AERs for the first day of each group. To obtain a set of two-day averages, we took the average 

AER over the first two days from each group. We continued this process to obtain three-, four-, 

five-, six-, and seven-day averages. There were insufficiently representative data for averaging 

periods longer then seven days. Averages over non-consecutive days were excluded. Each 

averaging period was assigned the temperature range using the average of the daily temperatures 

for the averaging period. Using Approach A, some or all of the days in the averaging period 

might be in different temperature ranges than the overall average. . Using Approach B, every day 

is in the same temperature range as the overall average. For each averaging period and 

temperature range, we calculated the mean, standard deviation, and variance of the period 

average AER and of its natural logarithm. Note than the geometric mean equals e raised to the 

power Mean log (AER) and the geometric standard deviation equals e raised to the power Std 

Dev log (AER). The results are shown in Tables E-1 (Approach A) and E-2 (Approach B). 
B-278

Table E-1. Distribution of AER averaged over K days and its logarithm. Groups defined by Approach A. 

Temperature 

(oC) K Groups 

Mean 

AER 

Mean 

log(AER) 

Std Dev 

AER 

Std Dev 

log(AER) 

Variance 

AER 

Variance 

log(AER) 

<= 10 1 35 1.109 -0.066 0.741 0.568 0.549 0.322 

<= 10 2 30 1.149 -0.009 0.689 0.542 0.474 0.294 

<= 10 3 28 1.065 -0.088 0.663 0.546 0.440 0.298 

<= 10 4 28 1.081 -0.090 0.690 0.584 0.476 0.341 

<= 10 5 24 1.103 -0.082 0.754 0.598 0.568 0.358 

<= 10 6 24 1.098 -0.083 0.753 0.589 0.567 0.347 

<= 10 7 29 1.054 -0.109 0.704 0.556 0.496 0.309 

10-20 1 48 0.652 -0.659 0.417 0.791 0.174 0.625 

10-20 2 55 0.654 -0.598 0.411 0.607 0.169 0.368 

10-20 3 51 0.641 -0.622 0.416 0.603 0.173 0.363 

10-20 4 50 0.683 -0.564 0.440 0.619 0.194 0.384 

10-20 5 53 0.686 -0.546 0.419 0.596 0.175 0.355 

10-20 6 49 0.677 -0.533 0.379 0.544 0.144 0.296 

10-20 7 34 0.638 -0.593 0.343 0.555 0.118 0.308 

20-25 1 32 0.500 -1.005 0.528 0.760 0.279 0.577 

20-25 2 28 0.484 -0.972 0.509 0.623 0.259 0.388 

20-25 3 27 0.495 -0.933 0.491 0.604 0.241 0.365 

20-25 4 17 0.536 -0.905 0.623 0.652 0.389 0.425 

20-25 5 17 0.543 -0.905 0.672 0.649 0.452 0.421 

20-25 6 17 0.529 -0.899 0.608 0.617 0.370 0.381 

20-25 7 14 0.571 -0.889 0.745 0.683 0.555 0.466 

> 25 1 9 0.470 -1.058 0.423 0.857 0.179 0.734 

> 25 2 11 0.412 -1.123 0.314 0.742 0.098 0.551 

> 25 3 12 0.411 -1.036 0.243 0.582 0.059 0.339 

> 25 4 23 0.385 -1.044 0.176 0.429 0.031 0.184 

> 25 5 23 0.390 -1.028 0.175 0.425 0.031 0.181 

> 25 6 23 0.399 -1.010 0.193 0.435 0.037 0.189 

> 25 7 17 0.438 -0.950 0.248 0.506 0.061 0.256 

Using both approaches, Tables E-1 and E-2 show that the mean values for the AER and its 

logarithm are approximately constant for the same temperature range but different averaging 

periods. This is expected if the daily AER values all have the same statistical distribution, 

regardless of whether or not they are independent. More interesting is the observation that the 

standard deviations and variances are also approximately constant for the same temperature 

range but different averaging periods, except for the data at > 25 oC where the standard 

deviations and variances tend to decrease as the length of the averaging period increases. If the 

daily AER values were statistically independent, then the variance of an average over K days is 

given by Var / K, where Var is the variance of a single daily AER value. Clearly this formula 

does not apply. Since the variance is approximately constant for different values of K in the same 

temperature range (except for the relatively limited data at > 25 oC), this shows that the daily 

AER values are strongly correlated. Of course the correlation is not perfect, since otherwise the 

AER for a given day would be identical to the AER for the next day, if the temperature range 

were the same, which did not occur. 

Table E-2. Distribution of AER averaged over K days and its logarithm. Groups defined by Approach B. 
B-279

Temperature 

(oC) K Groups 

Mean 

AER 

Mean 

log(AER) 

Std Dev 

AER 

Std Dev 

log(AER) 

Variance 

AER 

Variance 

log(AER) 

<= 10 1 62 1.125 -0.081 0.832 0.610 0.692 0.372 

<= 10 2 41 1.059 -0.063 0.595 0.481 0.355 0.231 

<= 10 3 32 1.104 -0.040 0.643 0.530 0.413 0.281 

<= 10 4 17 1.292 0.115 0.768 0.531 0.590 0.282 

<= 10 5 5 1.534 0.264 1.087 0.608 1.182 0.370 

10-20 1 109 0.778 -0.482 0.579 0.721 0.336 0.520 

10-20 2 81 0.702 -0.532 0.451 0.603 0.204 0.363 

10-20 3 63 0.684 -0.540 0.409 0.580 0.167 0.336 

10-20 4 27 0.650 -0.626 0.414 0.663 0.171 0.440 

10-20 5 22 0.629 -0.660 0.417 0.654 0.174 0.428 

10-20 6 12 0.614 -0.679 0.418 0.638 0.175 0.407 

10-20 7 6 0.720 -0.587 0.529 0.816 0.280 0.667 

20-25 1 107 0.514 -0.915 0.518 0.639 0.269 0.409 

20-25 2 63 0.511 -0.930 0.584 0.603 0.341 0.364 

20-25 3 23 0.577 -0.837 0.641 0.659 0.411 0.434 

20-25 4 3 1.308 -0.484 1.810 1.479 3.277 2.187 

> 25 1 54 0.488 -0.949 0.448 0.626 0.201 0.392 

> 25 2 32 0.486 -0.900 0.351 0.595 0.123 0.354 

> 25 3 23 0.427 -0.970 0.218 0.506 0.048 0.256 

> 25 4 12 0.401 -1.029 0.207 0.509 0.043 0.259 

> 25 5 12 0.410 -1.003 0.207 0.507 0.043 0.257 

> 25 6 6 0.341 -1.164 0.129 0.510 0.017 0.261 

> 25 7 6 0.346 -1.144 0.125 0.494 0.016 0.244 

These arguments suggest that, based on the RTP Panel study data, to a reasonable 

approximation, the distribution of an AER measurement does not depend upon the length of the 

averaging period for the measurement, although it does depend upon the average temperature. 

This supports the methodology used in the Cohen, Mallya, and Rosenbaum, 2005, analyses that 

did not take into account the length of the averaging period. 

The above argument suggests that the assumption that daily AER values are statistically 

independent is not justified. Statistical modeling of the correlation structure between consecutive 

daily AER values is not easy because of the problem of accounting for temperature effects, since 

temperatures vary from day to day. In the next section we present some statistical models of the 

daily AER values from the RTP Panel Study. 

Statistical models of AER auto-correlations from the RTP Panel Study

 

We used the MIXED procedure from SAS to fit several mixed models with fixed effects and 

random effects to the daily values of AER and log(AER). The fixed effects are the population 

average values of AER or log(AER), and are assumed to depend upon the temperature range. 

The random effects have expected values of zero and define the correlations between pairs of 

measurements from the same Group, where the Groups are defined either using Approach A or 

Approach B above. As described above, a Group is a period of up to 14 consecutive days of 

measurements at the same house. For these mixed model analyses we included periods with one 

or more missing days. For all the statistical models, we assume that AER values in different 
B-280

Groups are statistically independent, which implies that data from different houses or in different 

seasons are independent. 

The main statistical model for AER was defined as follows: 

AER = Mean(Temp Range) + A(Group, Temp Range) 

 + B(Group, Day Number) + Error(Group, Day Number) 

Mean(Temp Range) is the fixed effects term. There is a different overall mean value for each of 

the four temperature ranges. 

A(Group, Temp Range) is the random effect of temperature. For each Group, four error terms are 

independently drawn from four different normal distributions, one for each temperature range. 

These normal distributions all have mean zero, but may have different variances. Because of this 

term, there is a correlation between AER values measured in the same Group of days for a pair 

of days in the same temperature range. 

B(Group, Day Number) is the repeated effects term. The day number is defined so that the first 

day of a Group has day number 1, the next calendar day has day number 2, and so on. In some 

cases AER’s were missing for some of the day numbers. B(Group, Day Number) is a normally 

distributed error term for each AER measurement. The expected value (i.e., the mean) is zero. 

The variance is V. The covariance between B(Group g, day i) and B(Group h, day j) is zero for 

days in different Groups g and h, and equals V × exp(d × |i-j|) for days in the same Group. V and 

d are fitted parameters. This is a first order auto-regressive model. Because of this term, there is a 

correlation between AER values measured in the same Group of days, and the correlation 

decreases if the days are further apart. 

Finally, Error(Group, Day Number) is the Residual Error term. There is one such error term for 

every AER measurement, and all these terms are independently drawn from the same normal 

distribution, with mean 0 and variance W. 

We can summarize this rather complicated model as follows. The AER measurements are 

uncorrelated if they are from different Groups. If they are in the same Group, they have a 

correlation that decreases with the day difference, and they have an additional correlation if they 

are in the same temperature range. 

Probably the most interesting parameter for these models is the parameter d, which defines the 

strength of the auto-correlation between pairs of days. This parameter d lies between -1 (perfect 

negative correlation) and +1 (perfect positive correlation) although values exactly equal to +1 or 

-1 are impossible for a stationary model. Negative values of d would be unusual since they 

would imply a tendency for a high AER day to be followed by a low AER day, and vice versa. 

The case d=0 is for no auto-correlation. 

Table E-3 gives the fitted values of d for various versions of the model. The variants considered 

were: 

• model AER or log(AER) 

• include or exclude the term A(Group, Temp Range) (the “random” statement in the SAS 

code) 
B-281

• use Approach A or Approach B to define the Groups 

Since Approach B forces the temperature ranges to be the same for very day in a Group, the 

random temperature effect term is difficult to distinguish from the other terms. Therefore this 

term was not fitted using Approach B. 

Table E-3. Autoregressive parameter d for various statistical models for the RTP Panel 

Study AERs. 

Dependent variable Include A(Group, 

Temp Range)? Approach d 

AER Yes A 0.80 

AER No A 0.82 

AER No B 0.80 

Log(AER) Yes A 0.87 

Log(AER) No A 0.87 

Log(AER) No B 0.85 

 

In all cases, the parameter d is 0.8 or above, showing the very strong correlations between AER 

measurements on consecutive or almost consecutive days. 

Impact of accounting for daily average AER auto-correlation 

In the current version of the APEX model, there are several options for stratification of time 

periods with respect to AER distributions, and for when to re-sample from a distribution for a 

given stratum. The options selected for this current set of simulations resulted in a uniform AER 

for each 24-hour period and re-sampling of the 24-hour AER for each simulated day. This resampling for each simulated day implies that the simulated AERs on consecutive days in the 

same microenvironment are statistically independent. Although we have not identified sufficient 

data to test the assumption of uniform AERs throughout a 24-hour period, the analyses described 

in this memorandum suggest that AERs on consecutive days are highly correlated. 

Therefore, in order to determine if bias was introduced into the APEX estimates with respect to 

either the magnitudes or variability of exposure concentrations by implicitly assuming 

uncorrelated air exchange rates, we re-ran the 2002 base case simulations using the option to not 

re-sample the AERs. For this option APEX selects a single AER for each 

microenvironment/stratum combination and uses it throughout the simulation. 

The comparison of the two scenarios indicates little difference in APEX predictions, probably 

because the AERs pertain only to indoor microenvironments and for the base cases most 

exposure to elevated concentrations occurs in the “other outdoors” microenvironment. Figures E1 and E-2 below present the comparison for exceedances of 8-hour average concentration during 

moderate exertion for active person in Boston and Houston, respectively. 

Figure E-1 
B-282

Air Exchange Rate Resampling Sensitivity:

Days/Person with Exceedances of 

 8-Hour Average Exposure Concentration During Moderate Exertion

--Active Persons, Boston, 2002--

0

50

100

150

200

0 20 40 60 80 100

Cumulative Percentile

Days/Person

base-.01

rsoff - .01

base - .02

rsoff - .02

base -.03

rsoff - .03

base - .04

rsoff - .04

base - .05

rsoff - .05

Figure E-2 

Air Exchange Rate Resampling Sensitivity:

Days/Person with Exceedances of 

 8-Hour Average Exposure Concentration During Moderate Exertion

--Active Persons, Houston, 2002--

0

50

100

150

200

250

300

350

0 20 40 60 80 100

Cumulative Percentile

Days/Person

base-.01

rsoff - .01

base - .02

rsoff - .02

base -.03

rsoff - .03

base - .04

rsoff - .04

base - .05

rsoff - .05
B-283

 
Appendix C 

Nitrogen Dioxide Health Risk Assessment for 

Atlanta, GA 

November 2008 

Prepared for 

Office of Air Quality Planning and Standards 

U.S. Environmental Protection Agency 

Research Triangle Park, NC 

Prepared by 

Ellen Post 

Jin Huang 

Andreas Maier 

Hardee Mahoney 

Abt Associates Inc. 

Bethesda, MD 

Work funded through 

Contract No. EP-W-05-022 

Work Assignments 2-62 & 3-62 

Harvey Richmond, Work Assignment Manager 

Catherine Turner, Project Officer 
DISCLAIMER

 This report is being furnished to the U.S. Environmental Protection Agency 

(EPA) by Abt Associates Inc. in partial fulfillment of Contract No. EP-W-05-022, Work 

Assignments 2-62 and 3-62. Any opinions, findings, conclusions, or recommendations 

are those of the authors and do not necessarily reflect the views of the EPA or Abt 

Associates. Any questions concerning this document should be addressed to Harvey 

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

Standards, C504-06, Research Triangle Park, North Carolina 27711 (email: 

richmond.harvey@epa.gov). 

 

 

Abt Associates Inc. i November 2008 
Table of Contents 

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

2 PRELIMINARY CONSIDERATIONS................................................................ 2-1

2.1 The Broad Empirical Basis for a Relationship Between NO2 and Adverse 

Health Effects ............................................................................................... 2-1 

2.2 Basic Structure of the Risk Assessment ....................................................... 2-1 

2.3 Air Quality Considerations........................................................................... 2-2 

3 METHODS.............................................................................................................. 3-1

3.1 General approach.......................................................................................... 3-1 

3.2 Selection of health endpoint(s)..................................................................... 3-5 

3.3 Selection of urban area(s) and epidemiological studies ...............................3-5 

3.4 Selection of concentration-response functions............................................. 3-7 

3.5 Air quality considerations............................................................................. 3-8 

3.6 Baseline health effects incidence.................................................................. 3-9 

3.7 Summary of determinants of the NO2 risk assessment...............................3-10 

3.8 Addressing uncertainty and variability....................................................... 3-10 

3.8.1 Concentration-response functions..................................................... 3-16 

3.8.1.1 Uncertainty associated with the appropriate model form ..........3-16 

3.8.1.2 Uncertainty associated with the estimated concentrationresponse functions in the study location .................................... 3-17 

3.8.1.3 Applicability of concentration-response functions in different 

locations and/or time periods..................................................... 3-19 

3.8.1.4 Extrapolation beyond observed air quality levels......................3-19 

3.8.2 The air quality data ........................................................................... 3-20 

3.8.2.1 Adequacy of NO2 air quality data .............................................. 3-20 

3.8.2.2 Simulation of reductions in NO2 concentrations to just meet 

the current or an alternative standard......................................... 3-21 

3.8.3 Baseline health effects incidence ...................................................... 3-21 

3.8.3.1 Quality of incidence data ........................................................... 3-21 

3.8.3.2 Lack of daily health effects incidences...................................... 3-22 

4 RESULTS ................................................................................................................ 4-1

5 REFERENCES ....................................................................................................... 5-1

Abt Associates Inc. ii November 2008 
List of Tables 

Table 3-1. Mean and 98th and 99th Percentiles of the Distributions of 1-Hour Daily 

Maximum NO2 Concentrations (in ppm) at the Georgia Tech Monitor: 2005, 

2006, and 2007..................................................................................................... 3-8 

Table 3-2. Key Uncertainties in the NO2 Risk Assessment.......................................... 3-13 

Table 4-1. Estimated Incidence of Respiratory Emergency Department Visits 

Associated with "As Is" NO2 Concentrations and NO2 Concentrations that 

Just Meet Alternative Standards in Atlanta, GA, Based on Adjusting 2005 

NO2 Concentrations............................................................................................. 4-2 

Table 4-2. Estimated Incidence of Respiratory Emergency Department Visits 

Associated with "As Is" NO2 Concentrations and NO2 Concentrations that 

Just Meet the Current and Alternative Standards in Atlanta, GA, Based on 

Adjusting 2006 NO2 Concentrations ................................................................... 4-3 

Table 4-3. Estimated Incidence of Respiratory Emergency Department Visits 

Associated with "As Is" NO2 Concentrations and NO2 Concentrations that 

Just Meet the Current and Alternative Standards in Atlanta, GA, Based on 

Adjusting 2007 NO2 Concentrations ................................................................... 4-4 

Table 4-4. Estimated Incidence of Respiratory Emergency Department Visits per 

100,000 Population Associated with "As Is" NO2 Concentrations and NO2

Concentrations that Just Meet Alternative Standards in Atlanta, GA, Based 

on Adjusting 2005 NO2 Concentrations .............................................................. 4-5 

Table 4-5. Estimated Incidence of Respiratory Emergency Department Visits per 

100,000 Population Associated with "As Is" NO2 Concentrations and NO2

Concentrations that Just Meet the Current and Alternative Standards in 

Atlanta, GA, Based on Adjusting 2006 NO2 Concentrations..............................4-6 

Table 4-6. Estimated Incidence of Respiratory Emergency Department Visits per 

100,000 Population Associated with "As Is" NO2 Concentrations and NO2

Concentrations that Just Meet the Current and Alternative Standards in 

Atlanta, GA, Based on Adjusting 2007 NO2 Concentrations..............................4-7 

Table 4-7. Estimated Percent of Total Incidence of Respiratory Emergency 

Department Visits Associated with "As Is" NO2 Concentrations and NO2

Concentrations that Just Meet Alternative Standards in Atlanta, GA, Based 

on Adjusting 2005 NO2 Concentrations .............................................................. 4-8 

Table 4-8. Estimated 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 in 

Atlanta, GA, Based on Adjusting 2006 NO2 Concentrations..............................4-9 

Table 4-9. Estimated 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 in 

Atlanta, GA, Based on Adjusting 2007 NO2 Concentrations............................4-10 

Abt Associates Inc. iii November 2008 
Abt Associates Inc. iv November 2008 

List of Figures 

Figure 3-1. Major Components of NO2 Health Risk Assessment Based on 

Epidemiology Studies.......................................................................................... 3-2 

Figure 4-1. Incidence of Respiratory-Related Emergency Department Visits in 

Atlanta, GA Under Different Air Quality Scenarios, Based on Adjusting 

2005, 2006, and 2007 NO2 Concentrations ....................................................... 4-11 
Nitrogen Dioxide Health Risk Assessment for Atlanta, GA

1 INTRODUCTION 

The U.S. Environmental Protection Agency (EPA) is presently conducting a 

review of the national ambient air quality standards (NAAQS) for nitrogen dioxide 

(NO2). Sections 108 and 109 of the Clean Air Act (Act) govern the establishment and 

periodic review of the NAAQS. These standards are established for pollutants that may 

reasonably be anticipated to endanger public health and welfare, and whose presence in 

the ambient air results from numerous or diverse mobile or stationary sources. The 

NAAQS are to be based on air quality criteria, which are to accurately 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 is to promulgate and periodically review, at fiveyear intervals, “primary” (health-based) and “secondary” (welfare-based) NAAQS for 

such pollutants.1 Based on periodic reviews of the air quality criteria and standards, the 

Administrator is to make revisions in the criteria and standards, and promulgate 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 performed by the Clean Air Scientific Advisory Committee (CASAC). 

 EPA’s plan and schedule for this NO2 NAAQS review is presented in the 

“Integrated Review Plan for the Primary National Ambient Air Quality Standard for 

Nitrogen Dioxide” (U.S. EPA, 2007a). The plan discusses the preparation of two key 

components in the NAAQS review process: an Integrated Science Assessment (ISA) and 

risk/exposure assessments. The ISA critically evaluates and integrates scientific 

information on the health effects associated with exposure to oxides of nitrogen (NOx) in 

the ambient air. The risk/exposure assessments develop qualitative characterization and 

quantitative estimates, where judged appropriate, of human exposure and health risk and 

related variability and uncertainties, drawing upon the information summarized in the 

ISA. 

 In early March 2008, EPA’s National Center for Environmental Assessment 

released a second draft of the “Integrated Science Assessment for Oxides of Nitrogen – 

Health Criteria (Second External Review Draft),” henceforth referred to as the draft ISA 

(U.S. EPA, 2008a). EPA’s Office of Air Quality Planning and Standards (OAQPS) in 

early April released a first draft of its “Risk and Exposure Assessment to Support the 

Review of the NO2 Primary National Ambient Air Quality Standard,” henceforth referred 

to as the 1st draft REA (U.S. EPA, 2008b). Both of these documents were reviewed by 

the CASAC NO2 Panel on May 1-2, 2008. 

 

1

Section 109(b)(1) [42 U.S.C. 7409] of the Act defines a primary standard as one “the attainment 

and maintenance of which in the judgment of the Administrator, based on such criteria and allowing an 

adequate margin of safety, are requisite to protect the public health.” 

Abt Associates Inc. 1-1 November 2008 
 As a result of the May 2008 CASAC NO2 Panel review and in response to advice 

offered by the CASAC Panel, OAQPS decided to expand the health risk assessment to 

include a quantitative assessment of respiratory-related emergency department (ED) 

visits estimated to be associated with exposures to NO2 for the Atlanta metropolitan 

statistical area (MSA). 

 NO2 is one of a group of substances known as nitrogen oxides (NOx), which 

include multiple gaseous (e.g., NO2, NO) and particulate (e.g., nitrate) species. As in past 

NAAQS reviews, NO2 is considered as the surrogate for the gaseous NOx species for the 

purpose of this assessment, with particulate species addressed as part of the particulate 

matter (PM) NAAQS review. 

 Previous reviews of the NO2 primary NAAQS completed in 1985 and 1994 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, 2007a). Many of the same methodological issues are present for 

each of these criteria air pollutants where epidemiological studies provided the basis for 

the concentration-response (C-R) relationships used in the quantitative risk assessment. 

 In July 2008, EPA issued the final ISA, “Integrated Science Assessment for 

Oxides of Nitrogen – Health Criteria (Final Report), henceforth referred to as the final 

ISA (U.S. EPA, 2008c). The risk assessment described in this document is also based on 

the information and evaluation contained in the final ISA. In August 2008, EPA is 

releasing its 2nd draft REA, henceforth referred to as the draft REA (U.S. EPA, 2008d). 

 The NO2 health risk assessment described in this document estimates the 

incidence of respiratory-related ED visits associated with short-term exposures to NO2

under recent (“as is”) air quality levels and upon just meeting the current NO2 standard of 

0.053 ppm annual average and several alternative NO2 primary NAAQS in the Atlanta 

MSA.2 The alternative standards considered are daily maximum 1-hour standards, with 

levels of 0.05, 0.10, 0.15, and 0.20 ppm, using a 98th percentile form and also using a 

99th percentile form, using a three-year period.3

 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, 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. 

 

2

 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. 3

 For the alternative standards using, say, the 98th percentile form, the standard is met when the average of 

the three annual 98th percentile daily maximum 1-hr concentrations for the 3-year period is at or below the 

specified standard level. 

Abt Associates Inc. 1-2 November 2008 
Abt Associates Inc. 1-3 November 2008 

Preliminary considerations and the basic structure of the risk assessment are 

described in section 2. Section 3 describes the methods used, and section 4 presents the 

results of the risk assessment. 
2 PRELIMINARY CONSIDERATIONS 

 The health risk assessment described in this document estimates the incidence of 

respiratory-related ED visits associated with NO2 exposures for recent (“as is”) NO2 levels, 

based on 2005, 2006, and 2007 air quality data, as well as the risks associated with just 

meeting the current standard and the reduced risks associated with just meeting each of 

several alternative NO2 NAAQS.4 In this section we address preliminary considerations. 

Section 2.1 briefly discusses the broad empirical basis for a relationship between NO2

exposures and adverse health effects. Section 2.2 describes the basic structure of the risk 

assessment. Finally, section 2.3 addresses air quality considerations. 

 

2.1 The Broad Empirical Basis for a Relationship Between NO2 and Adverse Health 

Effects 

 The final ISA concludes that there is a broad empirical basis supporting the inference 

of a likely causal relationship between short-term NO2 exposure and respiratory effects: 

Taken together, the findings of epidemiologic, human clinical, and animal 

toxicological studies provided evidence that is sufficient to infer a likely 

causal relationship for respiratory effects with short-term NO2 exposure. 

The body of evidence from epidemiologic studies has grown substantially 

since the 1993 AQCD and provided scientific evidence that short-term 

exposure to NO2 is associated with a broad range of respiratory morbidity 

effects, including altered lung host defense, inflammation, airway 

hyperresponsiveness, respiratory symptoms, lung function decrements, and 

ED visits and hospital admissions for respiratory diseases (final ISA, 

section 3.1.7, p. 3-41). 

2.2 Basic Structure of the Risk Assessment 

 The general approach used in this risk assessment, as in the risk assessment that was 

part of the recent PM NAAQS review, relies upon C-R functions that have been estimated in 

epidemiological studies. Since these studies estimate C-R functions using ambient air quality 

data from fixed-site, population-oriented monitors, the appropriate application of these 

functions in a NO2 risk assessment similarly requires the use of ambient air quality data at 

fixed-site, population-oriented monitors. The NO2 health risk model combines information 

about NO2 air quality for a specific urban area with C-R functions derived from an 

epidemiological study and baseline health incidence data for a specific health endpoint to 

derive estimates of the annual incidence of the specified health effect attributable to ambient 

NO2 concentrations. The analyses have been conducted for both “as is” air quality and for air 

quality simulated to reflect attainment of the current and alternative NO2 ambient standards. 

 

4

 The current NO2 standard is met in all locations in the United States. The risks associated with just meeting 

the current standard are therefore greater than the risks associated with “as is” NO2 concentrations, which are 

lower than NO2 concentrations simulated to just meet the current standard. 

Abt Associates Inc. November 2008 2-1 
As described more fully below, a risk assessment based on epidemiological studies requires 

baseline incidence data or baseline incidence rates and population data for the risk assessment 

locations. 

 The characteristics that are relevant to carrying out a risk assessment based on 

epidemiology studies can be summarized as follows: 

• A risk assessment based on epidemiology studies uses C-R functions, and therefore 

requires as input (monitored) ambient NO2 concentrations. 

 

• Epidemiological studies are carried out in specific real world locations (e.g., 

specific urban areas). A risk assessment focused on locations in which the 

epidemiologic studies providing the C-R functions were carried out will minimize 

uncertainties. 

• A risk assessment based on epidemiological studies requires baseline incidences or 

baseline incidence rates and population data for the risk assessment locations. 

The methods for the NO2 risk assessment are discussed in section 3 below. The risk 

assessment was implemented within a new probabilistic version of TRIM.Risk, the 

component of EPA’s Total Risk Integrated Methodology (TRIM) model that estimates human 

health risks.5

2.3 Air Quality Considerations 

The risk assessment includes risk estimates for three recent years of air quality (“as is” 

air quality) and for air quality adjusted so that it simulates just meeting the current or 

alternative NO2 standards based on that recent three-year period (2005-2007). This period 

was selected to represent the most recent air quality for which complete data were available. 

In order to estimate health risks associated with just meeting the current and alternative 

NO2 standards, it is necessary to estimate the distribution of hourly NO2 concentrations that 

would occur under any given standard. Since all locations in the United States are in 

attainment of the current NO2 standard, and since compliance with the current NO2 standard is 

based on examining a 2-year period, air quality data from 2006 to 2007 were used to 

determine the amount of increase in NO2 concentrations that would occur if the current 

standard were just met in the risk assessment location. Estimated design values were used to 

determine the (upward) adjustment necessary to just meet the current NO2 standard. The 

adjustment was then applied to each year of data (2006 and 2007) to estimate risks in each of 

these individual years. For alternative 1-hour daily maximum standards, staff specified the 

form as being the 3-year average of the 98th (or 99th) percentile of the daily maximum 1-hour 

concentrations. Thus, the three-year period including 2005 to 2007 was used for analyses 

involving alternative 1-hour standards. Estimated design values were used to determine the 

 

5

 TRIM.Risk was most recently applied to EPA’s O3 health risk assessment. A User’s Guide for the Application 

of TRIM.Risk to the O3 health risk assessment (Abt Associates, 2007b) is available online at: 

http://epa.gov/ttn/fera/data/trim/trimrisk_ozone_ra_userguide_8-6-07.pdf. 

Abt Associates Inc. November 2008 2-2 
Abt Associates Inc. November 2008 2-3 

upward (or downward) adjustments necessary to just meet alternative NO2 standards, and the 

adjustments were then applied to each year of data to estimate risks in each of these individual 

years. 

As described in section 6.2.1 of the draft REA, EPA concluded that the proportional 

(linear) air quality adjustment procedure adequately represented the pattern of reductions 

across the NO2 air quality distribution observed over recent years. The proportional air 

quality adjustment procedure was applied in the Atlanta MSA to the filled in 2006 and 2007 

NO2 monitoring data, based on the 2-year period (2006-2007) NO2 design value for the 

current standard, to generate new time series of hourly NO2 concentrations for 2006 and 2007 

that simulate air quality levels that just meet the current NO2 standard of 0.053 ppm annual 

average. Because every location across the U.S. meets the current NO2 standard (see U.S. 

EPA, 2007b, Figure 1), simulation of just meeting the current standard required rolling up air 

quality. 

The proportional air quality adjustment procedure was similarly applied in the Atlanta 

MSA to the filled in 2005, 2006, and 2007 NO2 monitoring data, based on the 3-year period 

(2005-2007) NO2 design values for the alternative 1-hour standards, to generate new time 

series of hourly NO2 concentrations for 2005, 2006, and 2007 that simulate air quality levels 

that just meet each of the alternative NO2 standards considered in the risk assessment over this 

three year period. 

Because compliance with the alternative 1-hour daily maximum standards is based on 

the 3-year average of the values for the chosen metric, the air quality distribution in each of 

the 3 years can and generally does vary. As a result, the risk estimates associated with air 

quality just meeting a standard also will vary depending on the year chosen for the analysis. 

The risk assessment for the alternative 1-hour standards includes risk estimates involving 

adjustment of 2005, 2006, and 2007 air quality data to illustrate the magnitude of this year-toyear variability in the estimates. 

 The risk estimates developed for the recently concluded PM and O3 NAAQS reviews 

represented risks associated with PM and O3 levels in excess of estimated policy-relevant 

background (PRB) levels in the U.S. PRB levels of NO2 are defined as the distribution of 

NO2 concentrations that would be observed in the U.S. in the absence of anthropogenic (manmade) emissions of NO2 precursors in the U.S., Canada, and Mexico. Estimates of NO2 PRB 

are reported in section 2.4.6 of the final ISA, and for most of the continental U.S. the PRB is 

estimated to be less than 300 parts per trillion (ppt). In the Northeastern U.S., where presentday NO2 concentrations are highest, this amounts to a contribution of about 1% percent of the 

total observed ambient NO2 concentration (final ISA, p. 2-28). Since this is well below 

concentrations that might be considered to cause a potential health effect, there was no 

adjustment made for risks associated with PRB concentrations in the current NO2 health risk 

assessment. 
3 METHODS 

 The major components of the NO2 health risk assessment are illustrated in Figure 

3-1. The air quality component that is integral to the health risk assessment is discussed 

in chapters 2 and 6 of the draft REA. As described in the final ISA and the draft REA, 

recent studies, when taken together, provide scientific evidence that NO2 is associated 

with a range of respiratory effects. The evidence is judged to be sufficient to infer a 

likely causal relationship between short-term NO2 exposure and adverse effects on the 

respiratory system. This finding is supported by a large body of epidemiologic evidence, 

in combination with findings from human and animal experimental studies (final ISA, 

sections 3.1.6 and 3.1.7). 

3.1 General approach 

 As in the PM risk assessment (Abt Associates, 2005) and part of the recently 

completed O3 risk assessment (Abt Associates, 2007a), the general approach used in the 

NO2 risk assessment relies upon C-R functions which have been estimated in 

epidemiological studies. Since these studies estimate C-R 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, ambient monitors. The NO2 health risk model combines information about 

NO2 air quality for a specific urban area with C-R functions derived from 

epidemiological studies and baseline incidence data for a specific health endpoint to 

derive estimates of the incidence of the health endpoint attributable to ambient NO2

concentrations during the period examined. 

 In the first part of the risk assessment, we estimate health effects incidence 

associated with “as is” NO2 levels. In the second part, we estimate the (increased) health 

effects incidence associated with NO2 concentrations simulated to just meet the current 

NO2 annual standard and the health effects incidence associated with NO2 concentrations 

simulated to just meet alternative 1-hour daily maximum NO2 standards in the assessment 

location. In both parts, we consider the incidence of health effects associated with NO2

concentrations in excess of 0 ppm (as opposed to in excess of PRB, as explained in 

section 2.3). 

Abt Associates Inc. November 2008 3-1 
Figure 3-1. Major Components of NO2 Health Risk Assessment Based on Epidemiology Studies 

Ambient Monitoring for Selected Urban Areas

Air Quality Adjustment Procedures

Current and Alternative Proposed Standards

Changes in 

Distribution of 

NO2 Air 

Quality

Health 

Risk 

Model

Estimates of City-specific Baseline Health Effects IncidenceHuman Epidemiological Studies

Concentration -Response Relationships

Concentration-Response

Air Quality

Recent (“As Is”) Ambient NO2 Levels Ambient Monitoring for Selected Urban Areas

Air Quality Adjustment Procedures

Current and Alternative Proposed Standards

Changes in 

Distribution of 

NO2 Air 

Quality

Health 

Risk 

Model

Estimates of City-specific Baseline Health Effects IncidenceHuman Epidemiological Studies

Concentration -Response Relationships

Concentration-Response

Air Quality

Recent (“As Is”) Ambient NO2 Levels

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Both parts of the risk assessment may be viewed as assessing the change in 

incidence of the health effect associated with a change in NO2 concentrations from some 

upper levels to specified (lower) levels – in the NO2 risk assessment, the lower level is 0 

ppm in both cases. The important operational difference between the two parts is in the 

upper NO2 levels. In the first part, the upper NO2 levels are “as is” concentrations. In 

contrast, the upper NO2 levels in the second part are the estimated NO2 levels that would 

occur when the current NO2 standard of 0.053 ppm annual average is just met in the 

assessment location or when one of several alternative 1-hour daily maximum NO2

standards is just met in this location. The second part therefore requires that a method be 

developed to simulate just meeting the current or alternative standards. This method is 

described in chapter 6 of the draft REA. 

To estimate the incidence of a given health effect associated with “as is” ambient 

NO2 concentrations or NO2 concentrations that just meet the current or an alternative 

standard in an assessment location, the following analysis inputs are necessary: 

• 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 simulate just meeting the specified standard. (These air quality inputs 

are discussed in more detail in chapter 2 of this report and in chapter 6 of the draft 

REA.) 

• Concentration-response function(s), which provide an estimate of the 

relationship between the health endpoint of interest and 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. The baseline incidence can be calculated 

as the product of the incidence rate (e.g., number of cases per 10,000 population) 

and the affected population (divided by 10,000, if the rate is per 10,000 

population). Alternatively, if an estimate of the incidence in the location of 

interest is available, that can be used instead. 

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 C-R functions, by far the most common form (and the form 

used in the models selected for the NO2 risk assessment) is the exponential (or log-linear) 

form: 

x Bey β = , (3-1) 

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, 

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and the incidence of a given health endpoint associated with that level (denoted as y0) is 

then 

0

0

x Bey β = . (3-2) 

Because the log-linear form of C-R function (equation (3-1)) is by far the most common 

form, we use this form to illustrate the “health impact function” used in the risk 

assessment. 

 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 relationship between the change in x, Δx= (x0- x1), and the 

corresponding change in y, Δy, from equation (3-1)6: 

Δ Δyyy y e x =−= − − ( )[ 01 0 1 β ]. (3-3) 

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 C-R function of the 

form shown in equation (3-1) and a particular difference in ambient NO2 levels, Δx, the 

RR associated with that difference in ambient NO2, denoted as RRΔx, is equal to eβΔx. 

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 . (3-4) 

Equations (3-3) and (3-4) 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, C-R function information, and baseline 

health effects incidence information to estimate health risks related to changes in ambient 

NO2 concentrations. 

 

6 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

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3.2 Selection of health endpoint(s) 

As discussed in section 3.1.6 of the final ISA, many studies have observed positive 

associations between ambient NO2 concentrations and ED visits and hospitalizations for 

all respiratory diseases and asthma, and these associations appear to be generally robust 

and independent of the effects of ambient particles or gaseous co-pollutants. Noting that 

exposure to NO2 has been found to result in host defense and immune system changes, 

airway inflammation, and airway responsiveness, the final ISA concludes that “while not 

providing specific mechanistic data linking exposure to ambient NO2 and respiratory 

hospitalization or ED visits for asthma, these findings provide plausibility and coherence 

for such a relationship” (section 3.1.6.5, p. 3-41). 

In summarizing the evidence for a relationship between short-term exposure to NO2

and respiratory health effects, the final ISA notes that “the body of evidence from 

epidemiologic studies has grown substantially since the 1993 AQCD and provided 

scientific evidence that short-term exposure to NO2 is associated with a broad range of 

respiratory morbidity effects, including altered lung host defense, inflammation, airway 

hyperresponsiveness, respiratory symptoms, lung function decrements, and ED visits and 

hospital admissions for respiratory diseases” (section 3.1.7, p. 3-41). For this risk 

assessment, we are focusing on respiratory ED visits. 

3.3 Selection of urban area(s) and epidemiological studies 

 Several objectives were considered in selecting potential urban areas for which to 

conduct the risk assessment. An urban area was considered if: 

• it had sufficient air quality data for the 3-year period under consideration; 

• it was a location where at least one C-R function for the selected health 

endpoint had been estimated by a study that satisfied the study selection 

criteria; and 

• it had available relatively recent location-specific baseline incidence data, 

specific to International Classification of Disease (ICD) codes, or an 

equivalent illness classification system. 

 

C-R functions for respiratory ED visits have been estimated in two urban areas in 

the United States – Atlanta and New York City. The selection of an urban area to include 

in the risk assessment depends in part on the decision of which epidemiological studies to 

use. An epidemiological study was considered if: 

• it was a published, peer-reviewed study that had been evaluated in the final ISA 

for the pollutant of interest and judged adequate by EPA staff for purposes of 

inclusion in the risk assessment based on that evaluation; 

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• it directly measured, rather than estimated, the pollutant of interest on a reasonable 

proportion of the days in the study; and 

• it either did not rely on Generalized Additive Models (GAMs) using the S-Plus 

software to estimate C-R functions or it appropriately re-estimated these functions 

using revised methods.7

• it preferably included both single- and multi-pollutant models. 

Six U.S. studies focused on ED visits and/or hospital admissions. Three of these 

(Peel et al., 2005 and Tolbert et al., 2007 in Atlanta; Ito et al., 2007 in New York City) 

evaluated associations with NO2 using multi-pollutant models as well as single-pollutant 

models. Tolbert et al. (2007), which updated Peel et al. (2005), evaluated ED visits 

among all ages in Atlanta, GA during the period of 1993 to 2004. Using single pollutant 

models, the authors reported a 2% (95% CI: 1%, 2.9%) increase in respiratory ED visits 

associated with a 23-ppb increase in 1-h maximum NO2 levels. In a two-pollutant model 

with CO, NO2 was positive and still statistically significant (RR = 1.017, 95% CI =1.006, 

1.029). In two-pollutant models with PM10 and O3, and in a three-pollutant model with 

both PM10 and O3, NO2 was still positively associated with respiratory ED visits albeit no 

longer statistically significant (RR = 1.007, 95% CI = 0.996, 1.018 in the model with 

PM10; RR = 1.010, 95% CI = 0.999, 1.020 in the model with O3; and RR = 1.004, 95% CI 

= 0.992, 1.015 in the model with both PM10 and O3) (Tolbert, 2008a). 

The Atlanta study (Peel et al., 2005 and Tolbert et al., 2007) spanned 12 years, 

and collected NO2 monitor data on 4,351 out of a possible 4,384 days – over 99 percent 

of the days. It satisfies all of the criteria listed above for study selection. 

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 found a 12 % (95% CI: 7%, 

15%) increase in risk per 20 ppb increase in 24-hour ambient NO2. Risk estimates were 

robust and remained statistically significant in multi-pollutant models that included 

PM2.5, O3, CO, and SO2. 

Due to time and resource constraints, EPA staff selected the Atlanta area and the 

study by Tolbert et al. to conduct a focused risk assessment for ED visits. Considerations 

that influenced this choice were the longer time period and more comprehensive coverage 

of emergency departments in the Tolbert et al. study, the ready availability of baseline 

incidence data from the authors of this study, and the EPA staff’s objective of conducting 

the risk assessment for the same urban area selected for the population exposure analysis. 

 

7 The GAM S-Plus problem was discovered prior to the recent final PM risk assessment carried out as part 

of the PM NAAQS review. It is discussed in the PM Criteria Document (EPA, 2004), PM Staff Paper 

(EPA, 2005), and PM Health Risk Assessment Technical Support Document (Abt Associates, 2005). 

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3.4 Selection of concentration-response functions 

 Studies often report more than one estimated C-R function for the same location 

and health endpoint. Sometimes models including different sets of co-pollutants are 

estimated in a study; sometimes different lags are estimated. 

 Tolbert et al. (2007) estimated C-R functions in which NO2 was the only pollutant 

entered into the health effects model (i.e., single pollutant models) as well as other C-R 

functions in which NO2 and one or two co-pollutants (PM10, O3, CO) were entered into 

the health effects model (i.e., multi-pollutant models). To the extent that any of the copollutants present in the ambient air may have contributed to the health effects attributed 

to NO2 in single pollutant models, risks attributed to NO2 might be overestimated where 

C-R 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 co-pollutants. 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. 

 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 three-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 not known. 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 

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” (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. 

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3.5 Air quality considerations 

Air quality considerations are discussed briefly in section 2 of this document and 

in chapter 6 of the draft REA. Here we describe those air quality considerations that are 

directly relevant to the estimation of health risks in the NO2 risk assessment. 

 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 ppm). In the second part, we estimate the incidence of the health effect associated 

with NO2 concentrations simulated to just meet a standard (i.e., the current NO2 standard 

of 0.053 ppm annual average as well as each of several alternative 1-hour 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 

use monitor data from the Georgia Tech monitor (monitor id =131210048), the monitor 

that was used in Tolbert et al. (2007), the epidemiology study from which we obtained CR functions (see section 3.3 above). 

For the Georgia Tech monitor site, 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 air quality data were estimated by the following procedure. Where there were 

consecutive strings of missing values (data gaps of less than 6 hours), missing values 

were estimated by linear interpolation between the valid values at the ends of the gap. 

Remaining missing values at a monitor were estimated by fitting linear regression models 

for each hour of the day, with each of the other monitors, and choosing the model which 

maximizes R2

 for each hour of the day, subject to the constraints that R2 be greater than 

0.5 and the number of regression data values is at least 50. If there were any remaining 

missing values at this point, for gaps of less than 9 hours, missing values were estimated 

by linear interpolation between the valid values at the ends of the gap. Any remaining 

missing values were replaced with the regional mean for that hour. The annual mean, 

and the 98th and 99th percentiles of daily 1-hr maximum concentrations are shown in 

Table 3-1, separately for 2005, 2006, and 2007. 

Table 3-1. Mean and 98th and 99th Percentiles of the Distributions of 1-Hour Daily Maximum NO2

Concentrations (in ppm) at the Georgia Tech Monitor: 2005, 2006, and 2007 

Year Mean 98th Percentile 99th Percentile 

2005 0.0351 0.0764 0.0794

2006 0.0364 0.0660 0.0694

2007 0.0327 0.0684 0.0780

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Because Tolbert et al. (2007) estimated a relationship between daily respiratoryrelated 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 daily 1-hour maximum NO2 concentrations, we 

calculated daily 1-hour maximum NO2 concentrations at the monitor. Because our lower 

bound NO2 concentration is 0 ppm in all cases, for each day Δx in equation (3-3) equals 

the 3-day moving average of the 1-hour maximum “as is” NO2 concentration for that day 

at the Georgia Tech monitor. 

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 and alternative 

standards were done analogously, using the monitor-specific series of adjusted hourly 

concentrations rather than the monitor-specific series of “as is” hourly concentrations. 

3.6 Baseline health effects incidence 

 The most common epidemiologically-based health risk model expresses the 

reduction in health risk ()y) associated with a given reduction in NO2 concentrations 

()x) as a percentage of the baseline incidence (y). To accurately assess the impact of 

changes in NO2 air quality on health risk in the selected urban area, information on the 

baseline incidence of the health effect (i.e., the incidence under “as is” air quality 

conditions) in the selected location is therefore needed. 

 We obtained an estimate of the baseline incidence of respiratory ED visits in 

Atlanta, GA (Tolbert, 2008a). The study notes that there are 42 hospitals with EDs 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 used the incidence of respiratory ED visits for the most recent year in the Tolbert 

study, 2004 – 121,818 respiratory ED visits (Tolbert, 2008a).8

 

 There were 38 hospitals operating in the Atlanta MSA in 2004, of which 37 

reported data. The study authors estimate that the missing respiratory ED visits, from the 

single hospital that declined to report data, account for only 2.3 percent of the total 

number for 2004, so that the 2004 baseline incidence (121,818) includes an estimated 

97.7 percent of the respiratory ED visits in Atlanta that year (Tolbert, 2008b). Thus, 

although the understatement of baseline incidence will result in a downward bias in our 

estimates of NO2-related risk, that bias will be very small. 

 

8

 To check on the variability of respiratory ED visits across the years, the study authors provided a table of 

ED visits, including respiratory ED visits in particular, for years 2002-2004 among the 33 hospitals that 

contributed data each year. The average annual number of respiratory ED visits in those 33 hospitals 

during the three-year period (2002 – 2004) was 124,979. The number for those 33 hospitals in 2004 was 

114,475, or about 92 percent of the 3-year average (Tolbert, 2008b). 

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 The specific definition of “respiratory-related” ED visits used in Tolbert et al. 

(2007) included ED 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). The baseline 

incidence given above – 121,818 – is thus a count of all ED visits with one of these ICD9 codes as the primary diagnosis at the 36 hospitals in the Atlanta MSA that contributed 

2004 baseline incidence data to the Tolbert study. 

3.7 Summary of determinants of the NO2 risk assessment 

The determinants of the NO2 risk assessment can be summarized as follows: 

• Health endpoint: respiratory ED visits among all ages 

• Assessment location: Atlanta MSA 

• Epidemiological study: Tolbert et al. (2007) 

• C-R functions: 

o a single-pollutant C-R function, 

o two-pollutant C-R functions (with CO, PM10, and O3), and 

o a three-pollutant C-R function (with both PM10 and O3). 

In all C-R functions the count of ED visits on a given day is related to a 3-day 

moving average of NO2 1-hour maxima (i.e., NO2 1-hour maxima on the same 

day, the previous day, and the day before that). 

• Air quality data: 1-hour maximum “as is” NO2 concentration for each day 

calculated from hourly air quality data at the Georgia Tech monitor (site id 

=131210048), the monitor used in the epidemiology study from which we 

obtained C-R functions. Complete hourly data were available on over 93 percent 

of the days of the three-year period. 

• Baseline incidence: an estimate of the baseline incidence of respiratory ED visits 

in Atlanta, GA in 2004 (the most recent year in the study) was obtained (Tolbert, 

2008a). The estimate, 121,818 respiratory ED visits in 2004, was based on 37 

hospitals that reported data (out of 38 hospitals operating) that year (Tolbert, 

2008b). 

3.8 Addressing uncertainty and variability 

Any estimation of risk associated with “as is” NO2 concentrations, with just 

meeting the current NO2 standard, or with just meeting alternative NO2 standards should 

address both the variability and uncertainty that generally underlie such an analysis. 

Uncertainty refers to the lack of knowledge regarding the actual values of model input 

variables (parameter uncertainty) and of physical systems or relationships (model 

uncertainty – e.g., the shapes of C-R functions). The goal of the analyst is to reduce 

uncertainty to the maximum extent possible. Uncertainty can be reduced by improved 

measurement and improved model formulation. In a health risk assessment, however, 

significant uncertainty often remains. The degree of uncertainty can be characterized, 

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sometimes quantitatively. For example, the statistical uncertainty surrounding the 

estimated NO2 coefficients in the C-R functions is reflected in confidence intervals 

provided for the risk estimates. 

Variability refers to the heterogeneity in a population or parameter. Even if there 

is no uncertainty surrounding inputs to the analysis, there may still be variability. For 

example, there may be variability among C-R functions describing the relationship 

between NO2 and respiratory ED visits across urban areas. This variability does not imply 

uncertainty about the C-R function in any of the urban areas, but only that these functions 

are different in the different locations, reflecting differences in the populations and/or 

other factors that may affect the relationship between NO2 and respiratory ED visits. In 

general, it is possible to have uncertainty but no variability (if, for instance, there is a 

single parameter whose value is uncertain) or variability but little or no uncertainty (for 

example, people’s heights vary considerably but can be accurately measured with little 

uncertainty). 

The NO2 risk assessment addresses variability-related concerns by using locationspecific inputs (i.e., location-specific C-R function, baseline incidence data and air 

quality data). Because the NO2 risk assessment focuses on only a single urban area, it 

does not attempt to portray a larger picture of risk than is relevant to the selected 

assessment area. 

Temporal variability is more difficult to address, because the risk assessment 

focuses on some unspecified time in the future. 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 used recent input data – for example, year 2005 through 2007 air 

quality data and recent (2004) baseline incidence data. However, future changes in 

inputs have not been predicted (e.g., future baseline incidences). To address the impact 

of variability in NO2 concentrations from one year to another, we carried out the risk 

assessment for the years in the three-year period under consideration – 2005, 2006, and 

2007 – separately. 

 A number of important sources of uncertainty in the NO2 risk assessment are 

addressed where possible. The following are among the major sources of uncertainty: 

• Uncertainties related to estimating the C-R functions, including 

o uncertainty about the extent to which the association between NO2 and the 

health endpoint actually reflects a causal relationship. 

o uncertainty surrounding estimates of NO2 coefficients in the C-R functions 

used in the analyses. 

o uncertainty about the specification of the model (including the shape of 

the C-R relationship), particularly whether or not there is a threshold 

below which no response occurs. 

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o uncertainty related to the transferability of NO2 C-R functions from the 

study time period to the time period selected for the risk assessment.9 A 

C-R function in a study time period may not provide an accurate 

representation of the C-R relationship in the analysis time period because 

of 

 the possible role of associated co-pollutants, which may vary over 

time, in influencing NO2 risk, 

 temporal variation in the relationship of total ambient exposure 

(both outdoor and ambient contributions to indoor exposure) to 

ambient monitoring (e.g., due to changes in air conditioning usage 

over time), 

 changes in population characteristics (e.g., the proportions of 

members of sensitive subpopulations) and population behavior 

patterns over time. 

• Uncertainties related to the air quality data, including the adjustment procedure 

that was used to simulate just meeting the current and alternative NO2 standards. 

• Uncertainties associated with use of baseline health effects incidence information 

– e.g., the extent to which the baseline incidence estimate is downward biased by 

the lack of data for 6 of the 42 hospitals in the Atlanta MSA. 

The specific sources of uncertainty in the NO2 risk assessment are described in detail 

below and are summarized in Table 3-2. 

9

 Uncertainty about transferability of C-R functions often results not only from differences between the 

study and risk assessment time periods, but also between the study and risk assessment locations. Because 

the NO2 risk assessment is being conducted in the same location as the study from which the C-R functions 

were obtained, this is not a problem here. 
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Table 3-2. Key Uncertainties in the NO2 Risk Assessment Uncertainty Level of 

Uncertainty 

Direction of Bias Comments

Causality low Upward, if 

causality assumption isn’t true. 

Statistical association does not prove causation. Howeassessment considers only a health endpoint for which the evidence supports the assumption that NO2 is likely 

based on the totality of the health effects evidence. If tcausal relationship is incorrect, then a positive estimateR function would be upward biased, since it is greater t

Empirically estimated C-R relations 

medium No obvious bias, if 

C-R model is correctly specified. Otherwise, unclear. 

Because C-R functions are empirically estimated, there surrounding these estimates. If the model is correctly sbias in the coefficient estimates. If the model is mis-spbias. Omitted confounding variables, for example, couin the estimated NO2 coefficients if the omitted variabl

correlated with both NO2 and the health effect. Howev

potential confounding variables that are highly correlatcan lead to unstable estimators. Because both single- amodels were available, both were used. 

Functional form of C-R relation 

medium Unclear Statistical significance of coefficients in an estimated C

necessarily mean that the mathematical form of the funmodel of the true C-R relation. If the “true” functional NO2 and a health effect is different from the one specifin the resulting estimates of effect. The direction of the how the specified model differs from reality. For examC-R function is log-linear down to 0 ug/m3, but there is 

in the true relationship, then the effect will be overstatecorresponding to levels of NO2 below the threshold. 
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Uncertainty Level of 

Uncertainty 

Direction of Bias Comments

Lag structure of CR relation low Downward, if important lags are 

omitted (e.g., if CR function includes a single lag, while “truth” is a distributed lag). Unclear, if C-R function includes a single lag, but it’s the wrong lag. 

The actual lag structure for short-term NO2 exposures ilags could cause an underestimation in the predicted inwith a given reduction in NO2 concentrations. The levthe sense of the impact of the uncertainty) may depend example, suppose the health effect is actually affected lNO2 concentrations but the model (incorrectly) includethis case, the impact on the outcome of the analysis malikely, there is a high degree of autocorrelation in NO2 day to day (so that yesterday’s NO2 level would act as today’s NO2 level). If, on the other hand, there is a disrisk of the health effect on day t depends on NO2 conceentire week leading up to day t – and the model includethen the understatement of effect could be substantial. 

Transferability of C-R relations 

low No obvious bias. C-R functions may not provide an adequate representat

relationship in times and places other than those in whiestimated. For example, populations in the assessment may have more or fewer members of sensitive subgroulocation/time period in which functions were derived, wintroduce additional uncertainty related to the use of a gthe analysis. This problem was minimized in the NO2 r

however, because it relies on C-R functions estimated iconducted in the assessment location. 

Extrapolation of CR relations beyond the range of observed NO2 data 

low Unclear. A C-R relationship estimated by an epidemiological stu

at concentrations outside the range of concentrations obstudy. This problem should be minimal in the NO2 risk 

however, because the NO2 concentrations observed in tC-R functions were obtained covered a wide range – fr
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Uncertainty Level of 

Uncertainty 

Direction of Bias Comments

Adequacy of ambient NO2monitors as surrogate for population exposure 

low No obvious bias. Possible differences in how the spatial variation in amb

across an urban area are characterized in the original epcompared to the more recent ambient NO2 data used to air quality would contribute to uncertainty in the health NO2 risk assessment uses the same monitor used in the 

study from which the C-R functions were obtained, wh

this source of uncertainty. 

Adjustment of air quality distributions to simulate just meeting current and alternative NO2 standards. 

medium Could be in either 

direction. 

The pattern and extent of daily reductions in NO2 conc

result if the current NO2 standard or alternative NO2 sta

is not known. There remains uncertainty about the shadistribution of hourly levels upon just meeting an NO2 

depend on the nature of future growth in NO2 emission

air quality control strategies. 

Baseline health effects data 

low Small downward 

bias. 

Data on baseline incidence may be uncertain for a varieexample, location- and age-group-specific baseline rateavailable in all cases. Baseline incidence may change ounrelated to NO2. This source of uncertainty is relative

risk assessment, however, because a baseline incidence obtained from the study authors for the assessment areadownward bias to this estimate, because it is based on 3operating in the Atlanta study area in 2004; the study arespiratory ED visits at those 37 hospitals comprise abofor that year (Tolbert, 2008b). The estimated baseline 

respiratory ED visits in 2004 also appears to be roughly average baseline incidence observed during the period 
We handled uncertainties in the risk assessment as follows: 

• Limitations and assumptions in estimating risks and reduced risks are clearly 

stated and explained. 

• The uncertainty resulting from the statistical uncertainty associated with the 

estimate of the NO2 coefficient in a C-R function was characterized by confidence 

intervals around the corresponding point estimate of risk. Confidence intervals 

express the range within which the true risk is likely to fall if the uncertainty 

surrounding the NO2 coefficient estimate were the only uncertainty in the 

analysis. They do not, for example, reflect the uncertainty concerning whether 

the NO2 coefficients in the study period and the assessment period are the same. 

 Not all health effects that may result from NO2 exposure were included. We 

focused on respiratory ED visits because it was judged that there was sufficient 

epidemiological and other evidence to support the hypothesis of a causal relationship. 

Other health effects reported to be associated with exposure to NO2 (e.g., increased 

respiratory illnesses and symptoms) are considered qualitatively in the draft REA. Thus, 

it is important to recognize that the NO2 risk assessment represents only a portion of the 

health risks associated with NO2 exposures. 

3.8.1 Concentration-response functions 

 The C-R function is a key element of the NO2 risk assessment. The quality of the 

risk assessment depends, in part, on (1) whether the C-R functions used in the risk 

assessment are good estimates of the relationship between the population health response 

and ambient NO2 concentration in the study location (which, in this case, is the same as 

the assessment location), (2) how applicable these functions are to the analysis period, 

and (3) the extent to which these relationships apply beyond the range of the NO2

concentrations from which they were estimated. These issues are discussed in the 

subsections below. 

3.8.1.1 Uncertainty associated with the appropriate model form 

 

The relationship between a health endpoint and NO2 can be characterized in terms 

of the form of the function describing the relationship – e.g., linear, log-linear, or logistic 

– and the value of the NO2 coefficient in that function. Although most epidemiological 

studies estimated NO2 coefficients in log-linear models, there is still substantial 

uncertainty about the correct functional form of the relationship between NO2 and 

respiratory ED visits – especially at the low end of the range of NO2 values, where data 

are generally too sparse to discern possible thresholds. While there are likely biological 

thresholds in individuals for specific health responses, the available epidemiological 

studies generally have not supported or refuted the existence of thresholds at the 

population level for NO2 exposures within the range of air quality observed in the studies. 

 

Abt Associates Inc. November 2008 3-16 
3.8.1.2 Uncertainty associated with the estimated concentration-response functions 

in the study location 

 

The uncertainty associated with an estimate of the NO2 coefficient in a C-R 

function reported by a study depends on the sample size and the study design. The final 

ISA has evaluated the substantial body of NO2 epidemiological studies. In general, 

critical considerations in evaluating the design of an epidemiological study include the 

adequacy of the measurement of ambient NO2, the adequacy of the health effects 

incidence data, and the consideration of potentially important health determinants and 

potential confounders and effect modifiers such as: 

• other pollutants; 

• weather variables (e.g., temperature extremes); 

• exposure to other health risks, such as smoking and occupational exposure; and 

• demographic characteristics, including age, sex, socioeconomic status, and access to 

medical care. 

 The possible confounding effect of co-pollutants, including other criteria air 

pollutants, has often been noted as a problem in air pollutant risk assessments, 

particularly when these other pollutants are highly correlated with the pollutant of 

interest. As noted above, if other pollutants are included in the model and are highly 

correlated with NO2, this will inflate the variance of the estimators of the pollutant 

coefficients, making them more unstable. However, if other pollutants are causally 

related to the health effect, are correlated with NO2, and are omitted from the model, then 

the resulting single-pollutant model will falsely attribute to NO2 some of the effect of 

these other pollutants. NO2 was only moderately correlated with the other pollutants 

considered in the models that produced the C-R functions that are used in the risk 

assessment (see Tolbert et al., 2007, Table 3), although it was fairly highly correlated 

(corr.=0.7) with CO. Given the advantages and disadvantages of both single- and multipollutant models, we report risk estimates based on both the single- and multi-pollutant 

models from Tolbert et al. (2007). The issue of possible confounding by co-pollutants is 

discussed in more detail in the final ISA. 

The main reason to use a multi-pollutant model is to avoid the potential upward 

bias in the NO2 coefficient that may result if other pollutants that are causally related to 

the health effect are omitted from the model. It might be argued that if all the pollutants 

in a multi-pollutant model are causally related to the health effect we should consider the 

changes that would occur in all of the pollutants, rather than only the changes in NO2, as 

a result of an NO2 standard being implemented, since considering only the changes in 

NO2 will tend to understate the full benefit of just meeting an alternative standard. If one 

were evaluating total benefits to be derived from an implementation plan for an area, then 

the total reduction in health effects resulting from reduction in levels for all of the 

pollutants would be of interest. However, for the purposes of evaluating the adequacy of 

the current and alternative NO2 NAAQS, considering the health gains associated with 

reductions attributable to lower levels of not just NO2, but other pollutants such as PM2.5

and O3, would effectively result in double counting. This double counting would occur 

Abt Associates Inc. November 2008 3-17 
because the health gains associated with reductions in these other pollutants should 

already have been taken into account in assessments conducted to inform decisions on 

NAAQS for the other pollutants. Thus, when co-pollutant models are applied in the 

current risk assessment, only the risks resulting from reductions in NO2 ambient 

concentrations are considered; risks that might result from reduction of other pollutants 

are not considered in this analysis. 

 One of the criteria for selecting studies addresses the adequacy of the 

measurement of ambient NO2. This criterion was that NO2 was directly measured, rather 

than estimated, on a reasonable proportion of the days in the study. This criterion was 

designed to minimize error in the estimated NO2 coefficients in the C-R functions used in 

the risk assessment. NO2 was measured in the Tolbert study on over 93 percent of the 

days of the study period, so this criterion was well satisfied. 

 Ambient concentrations at central monitors, however, may not provide a good 

representation of personal exposures. The final ISA identifies the following three 

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 C-R 

function may understate the effect of personal exposures to NO2 on the incidence of a 

health effect, however, 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. In this case, if NO2 is actually the causal agent, the 

understatement of the impact of personal exposures isn’t an issue (since EPA regulates 

ambient concentrations rather than personal exposures). If NO2 is not the causal agent, 

however, then there is a problem of confounding co-pollutants or other factors, so that 

reducing ambient NO2 concentrations might not result in the expected reductions in the 

health effect. A more comprehensive discussion of exposure measurement error and its 

potential impact on the NO2 C-R relationships reported in community epidemiological 

studies is given in section 2.5.8 of the ISA and in the ISA Annex section AX6.1. 

 To the extent that a study did not address all relevant factors (i.e., all factors that 

affect the health endpoint), there is uncertainty associated with the C-R function 

estimated in that study, beyond that reflected in the confidence or credible interval. It 

may result in either over- or underestimates of risk associated with ambient NO2

concentrations in the location in which the study was carried out. Techniques for 

addressing the problem of confounding factors and other study design issues have 

improved over the years, however, and the epidemiological studies currently available for 

use in the NO2 risk assessment provide a higher level of confidence in study quality than 

ever before. 

 When a study is conducted in a single location, the problem of possible 

confounding co-pollutants may be particularly difficult, if co-pollutants are highly 

correlated in the study location. Single-pollutant models, which omit co-pollutants, may 

Abt Associates Inc. November 2008 3-18 
produce overestimates of the NO2 effect, if some of the effects of other pollutants 

(omitted from the model) are falsely attributed to NO2. Statistical estimates of an NO2

effect based on a multi-pollutant model can be more uncertain, and even statistically 

insignificant, 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 singlepollutant and multi-pollutant models from Tolbert et al. (2007). 

3.8.1.3 Applicability of concentration-response functions in different locations 

and/or time periods 

The relationship between ambient NO2 concentration and the incidence of a given 

health endpoint in the population (the population health response) depends on (1) the 

relationship between ambient NO2 concentration and personal exposure to ambient 

generated NO2 and (2) the relationship between personal exposure to ambient-generated 

NO2 and the population health response. Both of these are likely to vary to some degree 

from one location and/or time period to another. The relationship between ambient NO2

concentration and personal exposure to ambient-generated NO2 will depend on patterns 

of behavior, such as the amount of time spent outdoors, as well as on factors affecting the 

extent to which ambient-generated NO2 infiltrates into indoor environments. The 

relationship between personal exposure to ambient-generated NO2 and the population 

health response will depend on the population exposed. Exposed populations may differ 

from one location and/or time period to another in characteristics that are likely to affect 

their susceptibility to NO2 air pollution. For instance, people with preexisting conditions 

such as asthma are probably more susceptible to the adverse effects of exposure to NO2, 

and populations may vary from one location and/or time period to another in the 

prevalence of specific diseases. Also, some age groups may be more susceptible than 

others, and population age distributions may also vary both spatially and temporally. In 

the NO2 risk assessment we avoid the uncertainty associated with inter-locational 

differences, however, by using C-R functions that were estimated in the assessment area. 

In addition, although we cannot completely eliminate possible temporal changes, we 

minimize the uncertainty associated with such changes by using relatively recent baseline 

incidence data. 

3.8.1.4 Extrapolation beyond observed air quality levels 

 Although a C-R function describes the relationship between ambient NO2 and a 

given health endpoint for all possible NO2 levels (potentially down to zero), the 

estimation of a C-R function is based on real ambient NO2 values that are limited to the 

range of NO2 concentrations in the location in which the study was conducted. Thus, 

uncertainty in the shape of the estimated C-R function increases considerably outside the 

range of NO2 concentrations observed in the study. 

 Because we are interested in the effects of NO2 down to 0 ppm, the NO2 risk 

assessment assumes that the estimated C-R functions adequately represent the true C-R 

relationship down to 0 ppm in the assessment location. However, although the observed 

NO2 concentrations in Tolbert et al. (2007) did not go down to 0 ppm, the study authors 

Abt Associates Inc. November 2008 3-19 
reported the minimum 1-hour NO2 level observed in their study to be 1 ppb (or 0.001 

ppm) (and the maximum to be 181 ppb), so the uncertainty resulting from extrapolation 

to levels below those air quality levels observed in the study should be minimal. 

 The C-R relationship may also be less certain towards the upper end of the 

concentration range being considered in a risk assessment, particularly if the NO2

concentrations in the assessment location/time period exceed the NO2 concentrations 

observed in the study location/time period. Even though it may be reasonable to model 

the C-R relationship as log-linear over the ranges of NO2 concentrations typically 

observed in epidemiological studies, it may not be log-linear over the entire range of NO2

levels at the location considered in the NO2 risk assessment. However, because the study 

was carried out in the risk assessment location and is relatively recent, the uncertainty 

resulting from extrapolation to levels above those air quality levels observed in the study 

should similarly be minimal. 

3.8.2 The air quality data 

3.8.2.1 Adequacy of NO2 air quality data 

 Ideally, the measurement of average hourly ambient NO2 concentrations in the 

study location is unbiased. In this case, unbiased risk predictions in the assessment 

location depend, in part, on an unbiased measurement of average hourly ambient NO2

concentrations in the assessment location as well. If, however, the measurement of 

average hourly ambient NO2 concentrations in the study location is biased, unbiased risk 

predictions in the assessment location are still possible if the measurement of average 

hourly ambient NO2 concentrations in the assessment location incorporates the same bias 

as exists in the study location measurements. Because the NO2 risk assessment is using 

the same NO2 monitor as was used in Tolbert et al. (2007), the estimates of risk should 

avoid any bias as a result of the monitor estimates of average hourly ambient NO2

concentrations in the risk assessment location. 

 Another potential source of uncertainty is missing air quality data. Although NO2

concentrations were not available for all hours of the 3-year period chosen for the NO2

risk assessment in the assessment location, they were available for all hours on most 

days. In particular, 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 above in section 3.5. 

 

 The results of the risk assessment are generalizable to other years only to the 

extent that ambient NO2 levels in the available data are similar to ambient NO2 levels in 

those other years. A substantial difference between NO2 levels in the years used in the 

risk assessment and NO2 levels in the other years could imply a substantial difference in 

predicted incidences of health effects. 

Abt Associates Inc. November 2008 3-20 
3.8.2.2 Simulation of reductions in NO2 concentrations to just meet the current or 

an alternative standard 

 The pattern of hourly NO2 concentrations that would result if the current NO2 

standard or an alternative standard were just met in the assessment location is, of course, 

not known. This therefore adds uncertainty to estimates of risk when NO2 concentrations 

just meet a specified standard. 

 Although the health risk assessment uses air quality data from three years, 2005, 

2006, and 2007, it simulates just attaining a standard in each year separately, since we are 

estimating annual reduced health risks. Design values based on the most recent threeyear period available are used to determine the amount of adjustment to apply to each of 

these years. As can be seen in Table 3-1, the distributions of NO2 concentrations in the 

three years are similar. 

3.8.3 Baseline health effects incidence 

 The C-R functions used in the NO2 risk assessment are log-linear (see equation 3-

1 in section 3.1). Given this functional form, the percent change in incidence of a health 

effect corresponding to a change in NO2 depends only on the change in NO2 levels (and 

not the actual value of either the initial or final NO2 concentration). This percent change 

is multiplied by a baseline incidence, y0, in order to determine the change in health effects 

incidence, as shown in equation (3-3) in section 3.1: 

Δ Δyy e x = − −

0 [ ] 1 β . 

 

Predicted changes in incidence therefore depend on the baseline incidence of the health 

effect. 

3.8.3.1 Quality of incidence data 

 As noted in section 3.7 above, we obtained an estimate of the baseline incidence 

of respiratory ED visits in Atlanta, GA (Tolbert, 2008a). There are 42 hospitals with EDs 

in the 20-county Atlanta MSA, but not all 42 contributed incidence data in all of the years 

of the Tolbert study (1993 – 2004). We used the most recent year of the study (2004), 

which had an estimate of baseline incidence of respiratory ED visits in Atlanta based on 

data from 37 of the 38 hospitals operating in the Atlanta study area in that year. The 

study authors estimate that respiratory ED visits at those 37 hospitals comprise about 98 

percent of the total for that year (Tolbert, 2008b). The estimate of baseline incidence in 

2004, which is used as the estimate of baseline incidence in the NO2 risk assessment for 

2005 - 2007, is thus a slight underestimate, resulting in a similarly slight downward bias 

in the estimates of NO2-related respiratory ED visits. 

 A minor uncertainty surrounding hospital or ED visit baseline incidence estimates 

sometimes arises if these estimates are based on the reporting of hospitals within an 

assessment area. Hospitals report the numbers of ICD code-specific discharges in a given 

Abt Associates Inc. November 2008 3-21 
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year. If people from outside the assessment area use these hospitals or EDs, and/or if 

residents of the assessment area use hospitals or EDs outside the assessment area, these 

rates will not accurately reflect the numbers of residents of the assessment area who were 

admitted to the hospital or ED for specific illnesses during the year, the rates that are 

desired for the risk assessment. This problem is partially avoided in Tolbert et al. (2007) 

because only residents of the Atlanta MSA, determined by residential zip code at the time 

of the ED visit, were included in the study. To the extent that residents of the Atlanta 

MSA visited EDs outside the Atlanta MSA, this would tend to downward bias the 

estimates of NO2-related risk of respiratory-related ED visits. However, this is likely to 

be a very minor problem because emergency visits are likely to be made to the closest ED 

available, which, for residents of the Atlanta MSA are likely to be within that MSA. 

 Regardless of the data source, if actual incidences are higher than the incidences 

used, risks will be underestimated. If actual incidences are lower than the incidences 

rates used, then risks will be overestimated. 

 Both morbidity and mortality rates change over time for various reasons. One of 

the most important of these is that population age distributions change over time. The old 

and the extremely young are more susceptible to many health problems than is the 

population as a whole. The most recent available data were used in the NO2 risk 

assessment. However, the average age of the population in the assessment location will 

increase as post-World War II children age. Alternatively, if Atlanta experiences rapid 

in-migration, as is currently occurring in much of the South and West, it may tend to have 

a decreasing mean population age and corresponding changes in incidence rates and risk. 

Consequently, to the extent that respiratory-related ED visits are age-related, the baseline 

incidence rate may change over time. However, recent data were used in all cases, so 

temporal changes are not expected to be a large source of uncertainty. 

3.8.3.2 Lack of daily health effects incidences 

 Both ambient NO2 levels and the daily health effects incidence rates 

corresponding to ambient NO2 levels vary somewhat from day to day. Those analyses 

based on C-R functions estimated by short-term exposure studies calculate daily changes 

in incidence and sum them over the days of the year to predict a total change in health 

effect incidence during the year. However, only annual baseline incidence is available. 

Average daily baseline incidences, necessary for short-term daily C-R 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 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. 
4 RESULTS 

Results are expressed as (1) incidence of respiratory-related ED visits, (2) 

incidence of respiratory-related ED visits per 100,000 population, and (3) percent of total 

incidence of respiratory-related ED visits. Each form of result is shown in three tables, 

one for each of the three years (2005, 2006, and 2007) of air quality data used in the 

analysis. As noted in section 2.3, because the current annual average standard is based on 

two years, the adjustment to simulate just meeting the current standard was applied only 

to two years, 2006 and 2007. Therefore, results tables for 2005 do not include results 

associated with just meeting the current standard. The alternative 1-hour daily maximum 

standards, in contrast, have the form of the 3-year average of the 98th (or 99th) percentile 

of the daily maximum 1-hour concentrations. Thus, the adjustment to simulate just 

meeting these alternative 1-hour daily maximum standards was applied to each of the 

three years, 2005, 2006 and 2007. Therefore, results tables for 2006 and 2007 include 

results associated with just meeting the alternative 1-hour daily maximum standards as 

well as results associated with just meeting the current standard. All results tables 

include results associated with “as is” NO2 concentrations. 

Tables 4-1 through 4-3 show results expressed as incidence of respiratory-related 

ED visits for 2005, 2006, and 2007, respectively. Tables 4-4 through 4-6 show results 

expressed as incidence of respiratory-related ED visits per 100,000 population for each of 

the three years; and Tables 4-7 through 4-9 show results expressed as percent of total 

incidence of respiratory-related ED visits for each of the three years. Figure 4-1 shows 

the trends over both years and air quality scenarios, based on the single-pollutant model. 

Abt Associates Inc. November 2008 4-1 
Table 4-1. Estimated Incidence of Respiratory Emergency Department Visits Associated with "As Is" NO2 Concentrations and 

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 

none 3600 2600 5100 7500 9900 2400 4700 

(1900 - 5300) (1400 - 3800) (2700 - 7400) (4100 - 10900) (5400 - 14300) (1300 - 3500) (2500 - 6900) (3800 

CO 3100 2200 4300 6400 8500 2000 4000 

(1000 - 5100) (700 - 3600) (1500 - 7200) (2200 - 10500) (2900 - 13800) (700 - 3400) (1400 - 6700) (200

O3 1800 1300 2600 3900 5100 1200 2400 

(-100 - 3700) (-100 - 2600) (-100 - 5200) (-200 - 7700) (-200 - 10200) (-100 - 2500) (-100 - 4900) (-20

PM10 1300 900 1800 2700 3600 800 1700 

(-700 - 3300) (-500 - 2300) (-1000 - 4600) (-1600 - 6800) (-2100 - 9000) (-500 - 2200) (-1000 - 4300) (-150

PM10, O3 700 500 1000 1600 2100 500 1000 

(-1400 - 2800) (-1000 - 2000) (-2000 - 4000) (-3000 - 5900) (-4000 - 7800) (-900 - 1900) (-1800 - 3700) (-280

**Incidence was quantified down to 0 ppb. Incidences are rounded to the nearest 100.Note: Numbers in parentheses are 95% confidence intervals based on statistical uncertainty surrounding the NO2 coefficient.

Other 

Pollutants in 

Model

Incidence of Respiratory Emergency Department Visits Associated with "As is" NO2 Concentrations and NO2 Concen

Alternative Standards**

***Alternative 1-hr daily maximum standards are characterized by a concentration of m ppm and an nth percentile, requiring that the a

nth percentile 1-hr daily maxima over a 3-year period be at or below m ppm.

*Estimated incidences of respiratory emergency department visits are based on the concentration-response functions estimated in To

corresponding to Figure 2 in Tolbert et al. (2007) were obtained via personal communication with P. Tolbert]. All models use a 3-day daily 1-hr. maximum NO2 concentration and apply to all ages. 

Atternative 98th percentile 1-hr daily maximum standards 

(ppm)

Alternative 99th percentile 1-hr da

(ppm) "as is"

Abt Associates Inc. 

4-2
Table 4-2. Estimated Incidence of Respiratory Emergency Department Visits Associated with "As Is" NO2 Concentrations and 

that Just Meet the Current and Alternative Standards in Atlanta, GA, Based on Adjusting 2006 NO2 Concentration

0.05*** 0.1 0.15 0.2 0.05 0.1 

none 3800 10900 2700 5300 7800 10300 2500 4900 

(2000 - 5500) (5900 - 15700) (1400 - 3900) (2800 - 7700) (4200 - 11300) (5600 - 14800) (1300 - 3600) (2600 - 7200) (39

CO 3200 9400 2300 4500 6700 8800 2100 4200 

(1100 - 5300) (3200 - 15200) (800 - 3800) (1500 - 7400) (2300 - 11000) (3000 - 14400) (700 - 3500) (1400 - 6900) (21

O3 1900 5600 1400 2700 4000 5300 1300 2500 

(-100 - 3900) (-300 - 11200) (-100 - 2700) (-100 - 5400) (-200 - 8000) (-200 - 10600) (-100 - 2600) (-100 - 5100) (-

PM10 1300 4000 900 1900 2800 3700 900 1800 

(-800 - 3400) (-2300 - 9900) (-500 - 2400) (-1100 - 4800) (-1600 - 7100) (-2200 - 9400) (-500 - 2300) (-1000 - 4500) (-1

PM10, O3 800 2300 500 1100 1600 2200 500 1000 

(-1500 - 2900) (-4400 - 8600) (-1000 - 2100) (-2100 - 4100) (-3100 - 6200) (-4200 - 8100) (-1000 - 1900) (-1900 - 3900) (-2

**Incidence was quantified down to 0 ppb. Incidences are rounded to the nearest 100.Note: Numbers in parentheses are 95% confidence intervals based on statistical uncertainty surrounding the NO2 coefficient.

Other 

Pollutants in 

Model

Incidence of Respiratory Emergency Department Visits Associated with "As is" NO2 Concentrations and NO2 Concentrations that J

Alternative Standards**

***Alternative 1-hr daily maximum standards are characterized by a concentration of m ppm and an nth percentile, requiring that the average of the hr daily maxima over a 3-year period be at or below m ppm.

*Estimated incidences of respiratory emergency department visits are based on the concentration-response functions estimated in Tolbert et al. (2

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 

concentration and apply to all ages.

Atternative 98th percentile 1-hr daily maximum standards 

(ppm)

Alternative 99th percentile 1-hr 

(ppm) "as is" current annual standard

Abt Associates Inc. 

4-3
Table 4-3. Estimated Incidence of Respiratory Emergency Department Visits Associated with "As Is" NO2 Concentrations and 

that Just Meet the Current and Alternative Standards in Atlanta, GA, Based on Adjusting 2007 NO2 Concentration

0.05*** 0.1 0.15 0.2 0.05 0.1 

none 3400 9800 2400 4700 7000 9300 2200 4400 

(1800 - 4900) (5300 - 14200) (1300 - 3500) (2500 - 6900) (3800 - 10200) (5000 - 13400) (1200 - 3300) (2400 - 6400) (3

CO 2900 8400 2000 4000 6000 7900 1900 3800 

(1000 - 4800) (2900 - 13700) (700 - 3400) (1300 - 6700) (2000 - 9900) (2700 - 12900) (600 - 3200) (1300 - 6200) (1

O3 1700 5100 1200 2400 3600 4800 1100 2200 

(-100 - 3500) (-200 - 10100) (-100 - 2500) (-100 - 4900) (-200 - 7200) (-200 - 9500) (-100 - 2300) (-100 - 4500) (-

PM10 1200 3600 800 1700 2500 3400 800 1600 

(-700 - 3000) (-2100 - 8900) (-500 - 2200) (-1000 - 4300) (-1500 - 6400) (-1900 - 8400) (-400 - 2000) (-900 - 4000) (-1

PM10, O3 700 2100 500 1000 1500 1900 500 900 

(-1300 - 2600) (-4000 - 7800) (-900 - 1900) (-1800 - 3700) (-2800 - 5500) (-3700 - 7300) (-900 - 1700) (-1700 - 3500) (-2

**Incidence was quantified down to 0 ppb. Incidences are rounded to the nearest 100.Note: Numbers in parentheses are 95% confidence intervals based on statistical uncertainty surrounding the NO2 coefficient.

Other 

Pollutants in 

Model

Incidence of Respiratory Emergency Department Visits Associated with "As is" NO2 Concentrations and NO2 Concentrations that J

Alternative Standards**

***Alternative 1-hr daily maximum standards are characterized by a concentration of m ppm and an nth percentile, requiring that the average of the hr daily maxima over a 3-year period be at or below m ppm.

*Estimated incidences of respiratory emergency department visits are based on the concentration-response functions estimated in Tolbert et al. (2

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 

concentration and apply to all ages.

Atternative 98th percentile 1-hr daily maximum standards 

(ppm)

Alternative 99th percentile 1-hr 

(ppm) "as is" current annual standard

Abt Associates Inc. 

4-4
Table 4-4. Estimated Incidence of Respiratory Emergency Department Visits per 100,000 Population Associated with "As Is" N

NO2 Concentrations that Just Meet Alternative Standards in Atlanta, GA, Based on Adjusting 2005 NO2 Concentra

0.05*** 0.1 0.15 0.2 0.05 0.1 

none 240 170 340 510 670 160 320 

(130 - 360) (90 - 250) (180 - 500) (270 - 730) (360 - 960) (90 - 240) (170 - 460) (25

CO 210 150 290 440 570 140 270 

(70 - 340) (50 - 250) (100 - 480) (150 - 710) (190 - 930) (50 - 230) (90 - 450) (14

O3 120 90 170 260 340 80 160 

(-10 - 250) (0 - 180) (-10 - 350) (-10 - 520) (-20 - 690) (0 - 170) (-10 - 330) (-1

PM10 90 60 120 180 240 60 110 

(-50 - 220) (-40 - 160) (-70 - 310) (-110 - 460) (-140 - 610) (-30 - 150) (-70 - 290) (-10

PM10, O3 50 40 70 110 140 30 70 

(-90 - 190) (-70 - 140) (-130 - 270) (-200 - 400) (-270 - 530) (-60 - 130) (-120 - 250) (-19

**Incidence was quantified down to 0 ppb. Incidences per 100,000 population are rounded to the nearest ten.

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 To

corresponding to Figure 2 in Tolbert et al. (2007) were obtained via personal communication with P. Tolbert]. All models use a 3-day 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 a

nth percentile 1-hr daily maxima over a 3-year period be at or below m ppm.

Incidence of Respiratory Emergency Department Visits per 100,000 Population Associated with "As is" NO2 Conc

Concentrations that Just Meet Alternative Standards**

Atternative 98th percentile 1-hr daily maximum standards 

(ppm)

Alternative 99th percentile 1-hr da

(ppm) "as is"

Abt Associates Inc. 

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Table 4-5. Estimated Incidence of Respiratory Emergency Department Visits per 100,000 Population Associated with "As Is" N

NO2 Concentrations that Just Meet the Current and Alternative Standards in Atlanta, GA, Based on Adjusting 200

0.05*** 0.1 0.15 0.2 0.05 0.1 

none 250 740 180 360 530 700 170 330 

(140 - 370) (400 - 1060) (100 - 260) (190 - 520) (280 - 760) (380 - 1000) (90 - 250) (180 - 480) (

CO 220 630 150 300 450 600 140 280 

(70 - 360) (210 - 1030) (50 - 260) (100 - 500) (150 - 740) (200 - 970) (50 - 240) (90 - 470) (

O3 130 380 90 180 270 360 80 170 

(-10 - 260) (-20 - 760) (0 - 190) (-10 - 370) (-10 - 540) (-20 - 710) (0 - 170) (-10 - 340) (

PM10 90 270 60 130 190 250 60 120 

(-50 - 230) (-160 - 670) (-40 - 160) (-70 - 320) (-110 - 480) (-150 - 630) (-30 - 150) (-70 - 300) (

PM10, O3 50 150 40 70 110 150 30 70 

(-100 - 200) (-300 - 580) (-70 - 140) (-140 - 280) (-210 - 420) (-280 - 550) (-60 - 130) (-130 - 260) (

**Incidence was quantified down to 0 ppb. Incidences per 100,000 population are rounded to the nearest ten.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. (2

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 

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 hr daily maxima over a 3-year period be at or below m ppm.

Incidence of Respiratory Emergency Department Visits per 100,000 Population Associated with "As is" NO2 Concentrations and NO2 C

the Current and Alternative Standards**

Atternative 98th percentile 1-hr daily maximum standards 

(ppm)

Alternative 99th percentile 1-hr 

(ppm) "as is" current annual standard

Abt Associates Inc. 

4-6
Table 4-6. Estimated Incidence of Respiratory Emergency Department Visits per 100,000 Population Associated with "As Is" 

NO2 Concentrations that Just Meet the Current and Alternative Standards in Atlanta, GA, Based on Adjusting 200

0.05*** 0.1 0.15 0.2 0.05 0.1 

none 230 660 160 320 470 630 150 300 

(120 - 330) (360 - 960) (90 - 240) (170 - 470) (260 - 690) (340 - 900) (80 - 220) (160 - 430) (

CO 190 570 140 270 410 540 130 250 

(60 - 320) (190 - 930) (50 - 230) (90 - 450) (140 - 670) (180 - 870) (40 - 210) (80 - 420) (

O3 120 340 80 160 240 320 80 150 

(-10 - 230) (-20 - 680) (0 - 170) (-10 - 330) (-10 - 490) (-10 - 640) (0 - 150) (-10 - 310) (

PM10 80 240 60 110 170 230 50 110 

(-50 - 210) (-140 - 600) (-30 - 150) (-70 - 290) (-100 - 430) (-130 - 570) (-30 - 140) (-60 - 270) (

PM10, O3 50 140 30 70 100 130 30 60 

(-90 - 180) (-270 - 520) (-60 - 130) (-120 - 250) (-190 - 370) (-250 - 490) (-60 - 120) (-120 - 230) (

**Incidence was quantified down to 0 ppb. Incidences per 100,000 population are rounded to the nearest ten.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. (2

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 

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 hr daily maxima over a 3-year period be at or below m ppm.

Incidence of Respiratory Emergency Department Visits per 100,000 Population Associated with "As is" NO2 Concentrations and NO2 C

the Current and Alternative Standards**

Atternative 98th percentile 1-hr daily maximum standards 

(ppm)

Alternative 99th percentile 1-hr 

(ppm) "as is" current annual standard

Abt Associates Inc. 

4-7
Table 4-7. Estimated Percent of Total Incidence of Respiratory Emergency Department Visits Associated with "As Is" NO2 Co

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 

none 3% 2.1% 4.2% 6.2% 8.1% 2% 3.9% 

(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% 

CO 2.5% 1.8% 3.6% 5.3% 7% 1.7% 3.3% 

(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% 

O3 1.5% 1.1% 2.1% 3.2% 4.2% 1% 2% 

(-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% 

PM10 1.1% 0.8% 1.5% 2.2% 3% 0.7% 1.4% 

(-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% 

PM10, O3 0.6% 0.4% 0.9% 1.3% 1.7% 0.4% 0.8% 

(-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% 

**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 To

corresponding to Figure 2 in Tolbert et al. (2007) were obtained via personal communication with P. Tolbert]. All models use a 3-day 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 a

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 da

(ppm) "as is"

Percent of Total Incidence of Respiratory Emergency Department Visits Associated with "As is" NO2 Concentrations an

Just Meet Alternative Standards**

Abt Associates Inc. 

4-8
Table 4-8. Estimated Percent of Total Incidence of Respiratory Emergency Department Visits Associated with "As Is" NO2 Co

Concentrations that Just Meet the Current and Alternative Standards in Atlanta, GA, Based on Adjusting 2006 NO

0.05*** 0.1 0.15 0.2 0.05 0.1 

none 3.1% 9% 2.2% 4.3% 6.4% 8.5% 2% 4% 

(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

CO 2.6% 7.7% 1.9% 3.7% 5.5% 7.3% 1.7% 3.4% 

(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

O3 1.6% 4.6% 1.1% 2.2% 3.3% 4.4% 1% 2.1% 

(-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

PM10 1.1% 3.3% 0.8% 1.6% 2.3% 3.1% 0.7% 1.4% 

(-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

PM10, O3 0.6% 1.9% 0.4% 0.9% 1.3% 1.8% 0.4% 0.8% 

(-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

**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. (2

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 

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 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 

(ppm) "as is"

Percent of Total Incidence of Respiratory Emergency Department Visits Associated with "As is" NO2 Concentrations and NO2 Conce

Current and Alternative Standards**

current annual 

standard

Abt Associates Inc. 

4-9
Table 4-9. Estimated Percent of Total Incidence of Respiratory Emergency Department Visits Associated with "As Is" NO2 Co

Concentrations that Just Meet the Current and Alternative Standards in Atlanta, GA, Based on Adjusting 2007 NO

0.05*** 0.1 0.15 0.2 0.05 0.1 

none 2.8% 8.1% 2% 3.9% 5.8% 7.6% 1.8% 3.6% 

(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

CO 2.4% 6.9% 1.7% 3.3% 4.9% 6.5% 1.6% 3.1% 

(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

O3 1.4% 4.1% 1% 2% 2.9% 3.9% 0.9% 1.8% 

(-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

PM10 1% 2.9% 0.7% 1.4% 2.1% 2.8% 0.6% 1.3% 

(-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

PM10, O3 0.6% 1.7% 0.4% 0.8% 1.2% 1.6% 0.4% 0.7% 

(-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

**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. (2

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 

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 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 

(ppm) "as is"

Percent of Total Incidence of Respiratory Emergency Department Visits Associated with "As is" NO2 Concentrations and NO2 Conce

Current and Alternative Standards**

current annual 

standard

Abt Associates Inc. 

4-10
Abt Associates Inc. 

4-11

Figure 4-1. Incidence of Respiratory-Related Emergency Department Visits in Atlanta, GA Under Different Air Quality Scena2005, 2006, and 2007 NO2 Concentrations* 

*The current standard is an annual average standard of 0.053 ppm. Alternative 1-hour maximum daily standards are denoted m/n, whe

standard level and n is the percentile. So, for example, 0.05/98 denotes a 98th percentile standard of 0.05 ppm. See section 1 for more 

are based on the single-pollutant model in Tolbert et al. (2007).

0

2000400060008000

1000012000

"as is" 0.05/98 0.1/98 0.15/98 0.2/98 0.05/99 0.1/99 0.15/99 0.2/99 current

std.

Air Quality Scenario

Incidence of Respiratory-Related ED Visits

200520062007
As can be seen in Figure 4-1, the greatest incidence of respiratory-related ED 

visits in Atlanta is estimated to occur if the current annual standard were just met – 

almost three times as high as the incidence associated with “as is” NO2 concentrations in 

both 2006 (10,900 vs. 3,800, based on the single-pollutant model) and 2007 (9,800 vs. 

3,400). The only alternative standards that are estimated to reduce the incidence of 

respiratory-related ED visits from the estimated levels associated with “as is” NO2

concentrations are the two 1-hour daily maximum standards based on 0.05 ppm. The 98th

percentile 0.05 ppm standard is estimated to reduce the incidence of respiratory-related 

ED visits by from 28 percent (in 2005) to 29 percent (in 2007); the 99th percentile 0.05 

ppm standard is estimated to reduce the incidence of respiratory-related ED visits by 33 

to 35 percent. 

In general, 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 based on 0.05 ppm to one based on 0.1 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 

ppm reduces the incidence in 2007 by only about 8 percent (from 2400 to 2200). The 

corresponding results for 2006 and 2005 are similar. 

Abt Associates Inc. November 2008 4-12 
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United States Office of Air Quality Planning and Standards 

Environmental Protection Health and Environmental Impacts Division Agency Research Triangle Park, NC