# Risk and Exposure Assessment to Support

the Review of the NO 2 

# 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 NO 2 

# Primary National Ambient Air Quality Standard

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

## Disclaimer

This document has been prepared by staff from the Ambient Standards Group, Office of Air Quality Planning and Sta ndards, U.S. Environmental Protection Agency. Any opinions, findings, conclusions , or recommendations are thos e 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 NO 2 

## Air Quality Characterization

### ii 

## Table of Contents

Appendix A. Supplement to the NO 2 

### 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 NO 2 

### concentration data for 2001-2003......................................... 117 A-9.2 Number of 1-hour NO 2 

### exceedances in a year, 2001-2003 .................................... 139 A-9.3 Annual average NO 2 

### concentration data for 2004-2006......................................... 183 A-9.4 Comparison of Historical and R ecent Ambient Air Quality (As Is)....................... 250 A-9.5 Comparison of On-Road Concentrations Derived From Hist orical and Recent Ambient Air Quality (As Is) ................................................................................................... 2 57 A-9.6 Results Tables of Historical NO 2 

### Ambient Monitoring Data(1995-2000) Adjusted to Just Meeting the Current Standard.......................................................................................... 262

### iii 

### A-9.7 Results Tables of Recent NO 2 

### Ambient Monitoring Data (2001-2006) As Is and Just Meeting the Current and A lternative Standards...................................................................... 266 A-10 References................................................................................................................ ... 267

### iv 

## List of Tables

Table A-1. Example of ambient monitor y ears of operation, using the Boston CMSA................ 3 Table A-2. Counts of complete site-years of NO 2 

### monitoring data............................................... 4 Table A-3. Locations selected for NO 2 

### Air Quality Characterizati on, 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 NO x 

### emissions >5 tons per year and wi thin a 10 kilometers radius....................................... 9 Table A-6. Dist ribution of NO x 

### emissions from stationary so urces within 10 kilometers of monitoring site, where emissions were >5 tons per year. .............................................. 9 Table A-7. Attributes of location-specific ambien t 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 NO x 

### per year, within a 10 kilometer distance of monitoring site. ........................... 20 Table A-9. Statistical test results for spatial co mparisons of all location parameter distributions.

............................................................................................................................... ....... 29 Table A-10. Statistical test re sults for spatial comparisons of within-location parameter distributions.................................................................................................................. 30 Table A-11. Distribution of annual average NO 2 

### ambient concentrations (ppb) by year, Boston CMSA. ......................................................................................................................... 3 7 Table A-12. Distribution of hourly NO 2 

### ambient concentrations ( ppb) by year, Boston CMSA.37 Table A-13. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, Boston CMSA set A, 1995-2006. ................................................................................ 38 Table A-14. Distribution of hourly NO 2 

### ambient concentration (ppb) by monitor, Boston CMSA set A, 1995-2006. ......................................................................................................... 38 Table A-15. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, Boston CMSA set B, 1995-2006.................................................................................. 39 Table A-16. Distribu tion of hourly NO 2 

### ambient concentration (ppb) by monitor, Boston CMSA set B, 1995-2006. ......................................................................................................... 39 Table A-17. Distribution of annual average NO 2 

### ambient concentrations (ppb) by year, Chicago CMSA. ......................................................................................................................... 4 0 Table A-18. Distribution of hourly NO 2 

### ambient concentrations ( ppb) by year, Chicago CMSA.

............................................................................................................................... ....... 40 Table A-19. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, Chicago CMSA, 1995-2006......................................................................................... 41 Table A-20. Distribution of hourly NO 2 

### ambient concentration ( ppb) by monitor, Chicago CMSA, 1995-2006. ...................................................................................................... 41 Table A-21. Distribution of annual average NO 2 

### ambient concentrations (ppb) by year, Cleveland CMSA. ........................................................................................................ 42 Table A-22. Distribution of hourly NO 2 

### ambient concentrations (ppb) by year, Cleveland CMSA. ......................................................................................................................... 4 2 Table A-23. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, Cleveland CMSA, 1995-2006...................................................................................... 43

### v 

### Table A-24. Distribution of hourly NO 2 

### ambient concentration (ppb) by monitor, Cleveland CMSA, 1995-2006. ...................................................................................................... 43 Table A-25. Temporal distri bution of annual average NO 2 

### ambient concentrations (ppb) by year, Denver CMSA.............................................................................................................. 44 Table A-26. Temporal di stribution of hourly NO 2 

### ambient concentrations (ppb) by year, Denver CMSA. ......................................................................................................................... 4 4 Table A-27. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, Denver CMSA, 1995-2006. ......................................................................................... 45 Table A-28. Distribution of hourly NO 2 

### ambient concentration (ppb) by monitor, Denver CMSA, 1995-2006. ...................................................................................................... 45 Table A-29. Distribution of annual average NO 2 

### ambient concentrations (ppb) by year, Detroit CMSA. ......................................................................................................................... 4 6 Table A-30. Distribution of hourly NO 2 

### ambient concentrations ( ppb) by year, Detroit CMSA.46 Table A-31. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, Detroit CMSA, 1995-2006........................................................................................... 47 Table A-32. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, Detroit CMSA, 1995-2006........................................................................................... 47 Table A-33. Distribution of annual average NO 2 

### ambient concentrations (ppb) by year, Los Angeles CMSA. ........................................................................................................... 48 Table A-34. Distribution of hourly NO 2 

### ambient concentrations (ppb) by year, Los Angeles CMSA. ......................................................................................................................... 4 8 Table A-35. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, Los Angeles CMSA set A, 1995-2006................................................................................ 49 Table A-36. Distribution of hourly NO 2 

### ambient concentration (ppb) by monitor, Los Angeles CMSA set A, 1995-2006.............................................................................................. 49 Table A-37. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, Los Angeles CMSA set B, 1995-2006................................................................................ 50 Table A-38. Distribu tion of hourly NO 2 

### ambient concentration (ppb) by monitor, Los Angeles CMSA set B, 1995-2006.............................................................................................. 50 Table A-39. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, Los Angeles CMSA set C, 1995-2006................................................................................ 51 Table A-40. Distribution of hourly NO 2 

### ambient concentration (ppb) by monitor, Los Angeles CMSA set C, 1995-2006.............................................................................................. 51 Table A-41. Distribution of annual average NO 2 

### ambient concentrations (ppb) by year, Miami CMSA. ......................................................................................................................... 5 2 Table A-42. Distribution of hourly NO 2 

### ambient concentrations (ppb) by year, Miami CMSA. 52 Table A-43. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, Miami CMSA, 1995-2006. ...................................................................................................... 53 Table A-44. Distribution of hourly NO 2 

### ambient concentration (ppb) by monitor, Miami CMSA, 1995-2006. ................................................................................................................... 53 Table A-45. Distribution of annual average NO 2 

### ambient concentrations (ppb) by year, New York CMSA. ................................................................................................................ 54 Table A-46. Distribution of hourly NO 2 

### ambient concentrations (ppb) by year, New York CMSA. ......................................................................................................................... 5 4 Table A-47. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, New York CMSA set A, 1995-2006. ................................................................................... 55

### vi 

### Table A-48. Distribution of hourly NO 2 

### ambient concentration (ppb) by monitor, New York CMSA set A, 1995-2006.............................................................................................. 55 Table A-49. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, New York CMSA set B, 1995-2006..................................................................................... 56 Table A-50. Distribution of hourly NO 2 

### ambient concentration (ppb) by monitor, New York CMSA set B, 1995-2006.............................................................................................. 56 Table A-51. Distribution of annual average NO 2 

### ambient concentrations (ppb) by year, Philadelphia CMSA. .................................................................................................... 57 Table A-52. Distribution of hourly NO 2 

### ambient concentrations (ppb) by year, Philadelphia CMSA. ......................................................................................................................... 5 7 Table A-53. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, Philadelphia CMSA, 1995-2006. ................................................................................. 58 Table A-54. Distribution of hourly NO 2 

### ambient concentration (ppb) by monitor, Philadelphia CMSA, 1995-2006. ...................................................................................................... 58 Table A-55. Distribution of annual average NO 2 

### ambient concentrations (ppb) by year, Washington DC CMSA. .............................................................................................. 59 Table A-56. Distribution of hourly NO 2 

### ambient concentrations (ppb) by year, Washington DC CMSA. ......................................................................................................................... 5 9 Table A-57. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, Washington DC CMSA set A, 1995-2006................................................................... 60 Table A-58. Distribution of hourly NO 2 

### ambient concentration (ppb) by monitor, Washington DC CMSA set A, 1995-2006. ...................................................................................... 60 Table A-59. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, Washington DC CMSA set B, 1995-2006. .................................................................. 61 Table A-60. Distribution of hourly NO 2 

### ambient concentration (ppb) by monitor, Washington DC CMSA set B, 1995-2006. ...................................................................................... 61 Table A-61. Distribution of annual average NO 2 

### ambient concentrations (ppb) by year, Atlanta MSA. ........................................................................................................................... . 62 Table A-62. Distribution of hourly NO 2 

### ambient concentrations (ppb) by year, Atlanta MSA.. 62 Table A-63. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, Atlanta MSA, 1995-2006............................................................................................. 63 Table A-64. Distribution of hourly NO 2 

### ambient concentration (ppb) by monitor, Atlanta MSA, 1995-2006. ................................................................................................................... 63 Table A-65. Distribution of annual average NO 2 

### ambient concentrations (ppb) by year, Colorado Springs MSA................................................................................................................ 64 Table A-66. Distribution of hourly NO 2 

### ambient concentrations (ppb) by year, Colorado Springs MSA. ........................................................................................................................... . 64 Table A-67. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, Colorado Springs MSA, 1995-2006............................................................................. 65 Table A-68. Distribution of hourly NO 2 

### ambient concentration (ppb) by monitor, Colorado Springs MSA, 1995-2006. ........................................................................................... 65 Table A-69. Distribution of annual average NO 2 

### ambient concentrations (ppb) by year, El Paso MSA. ........................................................................................................................... . 66 Table A-70. Distribution of hourly NO 2 

### ambient concentrations (ppb) by year, El Paso MSA. 66 Table A-71. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, El Paso MSA, 1995-2006. ................................................................................................ 67

### vii 

### Table A-72. Distribution of hourly NO 2 

### ambient concentration (ppb) by monitor, El Paso MSA, 1995-2006. ................................................................................................................... 67 Table A-73. Distribution of annual average NO 2 

### ambient concentrations (ppb) by year, Jacksonville MSA. ....................................................................................................... 68 Table A-74. Distribution of hourly NO 2 

### ambient concentrations (ppb) by year, Jacksonville MSA. ........................................................................................................................... . 68 Table A-75. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, Jacksonville MSA, 1995-2006..................................................................................... 69 Table A-76. Distribution of hourly NO 2 

### ambient concentration (ppb) by monitor, Jacksonville MSA, 1995-2006.......................................................................................................... 69 Table A-77. Distribution of annual average NO 2 

### ambient concentrations (ppb) by year, Las Vegas MSA. ................................................................................................................. 70 Table A-78. Distribution of hourly NO 2 

### ambient concentrations (ppb) by year, Las Vegas MSA.

............................................................................................................................... ....... 70 Table A-79. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, Las Vegas MSA, 1995-2006............................................................................................... 71 Table A-80. Distribution of hourly NO 2 

### ambient concentration (ppb) by monitor, Las Vegas MSA, 1995-2006.......................................................................................................... 71 Table A-81. Distribution of annual average NO 2 

### ambient concentrations (ppb) by year, Phoenix MSA. ........................................................................................................................... . 72 Table A-82. Distribution of hourly NO 2 

### ambient concentrations (ppb) by year, Phoenix MSA. 72 Table A-83. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, Phoenix MSA, 1995-2006............................................................................................ 73 Table A-84. Distribution of hourly NO 2 

### ambient concentration (ppb) by monitor, Phoenix MSA, 1995-2006. ................................................................................................................... 73 Table A-85. Distribution of annual average NO 2 

### ambient concentrations (ppb) by year, Provo MSA. ........................................................................................................................... . 74 Table A-86. Distribution of hourly NO 2 

### ambient concentrations (ppb) by year, Provo MSA.... 74 Table A-87. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, Provo MSA, 1995-2006.......................................................................................................... 75 Table A-88. Distribution of hourly NO 2 

### ambient concentration (ppb) by monitor, Provo MSA, 1995-2006. ................................................................................................................... 75 Table A-89. Distribution of annual average NO 2 

### ambient concentrations (ppb) by year, St. Louis MSA. ........................................................................................................................... . 76 Table A-90. Distribution of hourly NO 2 

### ambient concentrations (ppb) by year, St. Louis MSA.

............................................................................................................................... ....... 76 Table A-91. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, St. Louis MSA, 1995-2006................................................................................................ 77 Table A-92. Distribution of hourly NO 2 

### ambient concentration ( ppb) by monitor, St. Louis MSA, 1995-2006.......................................................................................................... 77 Table A-93. Distribution of annual average NO 2 

### ambient concentrations (ppb) by year, Other MSA/CMSA................................................................................................................. 78 Table A-94. Distribution of hourly NO 2 

### ambient concentrations (ppb) by year, Other MSA/CMSA................................................................................................................. 78 Table A-95. Distribution of annual average NO 2 

### ambient concentrations (ppb) by year, Other Not MSA. ..................................................................................................................... 79

### viii 

### Table A-96. Distribution of hourly NO 2 

### 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 e xponential model stratified by location............................ 86 Table A-99. Parameters for normal lin ear model stratified by location. ..................................... 87 Table A-100. Predicted number of exceedances of of 1-hour NO 2 

### concentrations of 150 ppb using a Poisson exponential model for the as-is and current-sta ndard scenarios. ....... 91 Table A-101. Predicted number of exceedances of of 1-hour NO 2 

### concentrations of 150 ppb using a Normal linear model for the as-i s and current-standard scenarios. ................. 92 Table A-102. Comparison of predicted ex ceedances of 150 ppb using McCurdy (1994) for 1988-1992 data and the Poisson exponentia l and normal linear models for 1995-2006 data. .......................................................................................................................... .... 93 Table A-103. Predicted number of exceedances of of 1-hour NO 2 

### concentrations of 150 ppb using a Poisson exponential model and at se veral annual average concentrations...... 94 Table A-104. Predicted number of exceedances of of 1-hour NO 2 

### concentrations of 150 ppb using a Normal linear model and at seve ral annual average co ncentrations................ 97 Table A-105. Maximum annual average NO 2 

### concentrations and air quality adjustment factors ( F ) to just meet the current standa rd, historical monitoring data............................... 104 Table A-106. Maximum annual average NO 2 

### 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 NO 2 

### 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-ro ad adjustment factors (C v 

### /C b 

### or m ) for two season groups and potential influential factors. ....................................................................................... 113 Table A-111. Estimated annual average NO 2 

### 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 NO 2 

### concentrations for m onitors >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 al ternative standards................................................................. 124 Table A-113. Estimated annual average NO 2 

### 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 NO 2 

### concentrations on-roads using 2001-2003 air quality as is , air quality adjusted to just meet th e current and alternative standards, and an on-road adjustment factor...................................................................................... 132 Table A-115. Estimated number of exceedance s 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 th e current and alternative standards. ..................... 139 Table A-116. Estimated number of exceedance s 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 exceedance s 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 a lternative standards.

............................................................................................................................... ..... 151 Table A-118. Estimated number of exceedance s of 1-hour concentration levels (250 and 300 ppb) for monitors >20 m and <100 m from a major road usi ng 2001-2003 air quality
as is and air quality adjusted to just meet the current and altern ative standards. ...... 155 Table A-119. Estimated number of exceedance s 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 th e current and alternative standards. ..................... 158 Table A-120. Estimated number of exceedance s 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 exceedance s 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 altern ative standards and an on-ro ad adjustment factor. .......... 165 Table A-122. Estimated number of exceedance s 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 alternativ e standards and an on-road adjustment factor.................... 178 Table A-123. Estimated annual average NO 2 

### 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 NO 2 

### concentrations for m onitors >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 al ternative standards................................................................. 189 Table A-125. Estimated annual average NO 2 

### 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 NO 2 

### concentrations on-roads using 2004-2006 air quality as is , air quality adjusted to just meet th e current and alternative standards, and an on-road adjustment factor...................................................................................... 197 Table A-127. Estimated number of exceedance s of 1-hour concentration levels (100, 150, and 200 ppb) for monitors ≥ 100 m from a major road followi ng adjustment to just meeting the current and alternative st andards, 2004-2006 air quality. .................................... 203 Table a-128. Estimated number of exceedance s 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 standa rds, 2004-2006 air quality........................................... 211 Table A-129. Estimated number of exceedance s 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 alternat ive standards, 2004-2006 air quality. ............... 218 Table A-130. Estimated number of exceedance s of 1-hour concentration levels (250 and 300 ppb) for monitors > 20 m and < 100 m from a ma jor road following adjustment to just meeting the current and alternative standards, 2004-2006 air quality. ...................... 224 Table a-131. Estimated number of exceedance s of 1-hour concentration levels (100, 150, and 200 ppb) for monitors ≤ 20 m from a major road followi ng adjustment to just meeting the current and alternative st andards, 2004-2006 air quality. .................................... 228

### x 

### Table A-132. Estimated number of exceedance s 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 standa rds, 2004-2006 air quality........................................... 234 Table A-133. Estimated number of exceedance s of 1-hour concentration levels (100, 150, and 200 ppb) on-roads following adjustment to ju st meeting the current and alternative standards, 2004-2006 air quality and an on-road road adjustment factor.................. 238 Table A-134. Estimated number of exceedance s 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 NO 2 

### 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 NO 2 

### 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) poten tial health effect benchmark levels in a ye ar, 1995-2000 historical NO 2 

### 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) poten tial health effect benchmark levels in a year, 2001-2006 recent NO 2 

### 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) poten tial health effect benchmark levels in a ye ar, 1995-2000 historical NO 2 

### 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) poten tial health effect benchmark levels in a year, 2001-2006 recent NO 2 

### air quality (as is ) using monitors sited <100 m of a major road. .................................................................................... 256 Table A-141. Estimated annual average on-road NO 2 

### concentrations for two monitoring periods, historical and recent air quality data (as is)................................................................ 258 Table A-142. Estimated total number of exceedan ces of short-term (1-hour) potential health effect benchmark levels in a ye ar on-roads, 1995-2000 historical NO 2 

### air quality (as is)............................................................................................................................ .... 260 Table A-143. Estimated total number of exceedan ces of short-term (1-hour) potential health effect benchmark levels in a year on-roads, 2001-2006 recent NO 2 

### air quality (as is).

............................................................................................................................... ..... 261 Table A-144. Total number of exceedances of short-term (1-hour) poten tial health effect benchmark levels in a ye ar, 1995-2000 historical NO 2 

### air quality adjusted to just meeting the current annual average sta ndard (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) pot ential health effect benchmark levels in a ye ar, 1995-2000 historical NO 2 

### 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 exceedan ces of short-term (1-hour) potential health effect benchmark levels in a ye ar on-roads, 1995-2000 historical NO 2 

### air quality adjusted to just meeting the curren t annual average standard (0.053 ppm)............... 265

### xi 

# List of Figures

Figure A-1. Distributions of annual mean NO 2 

### ambient monitoring concentrations for selected CMSA locations, years 1995-2006. ............................................................................. 27 Figure A-2. Distributions of annual mean NO 2 

### ambient monitoring concentrations for selected MSA and grouped locations, years 1995-2006............................................................ 27 Figure A-3. Distributions of hourly NO 2 

### ambient monitoring concentrations for selected CMSA locations, years 1995-2006. ......................................................................................... 28 Figure A-4. Distributions of hourly NO 2 

### ambient concentration for selected CMSA locations, years 1995-2006........................................................................................................... 29 Figure A-5. Distributions of annual average NO 2 

### concentrations among 10 monitoring sites in Philadelphia CMSA, years 1995-2006......................................................................... 30 Figure A-6. Distributions of annual mean NO 2 

### concentrations for all monitors, years 1995-2006.

............................................................................................................................... ....... 32 Figure A-7. Distributions of annual mean NO 2 

### concentrations for the Philadelphia CMSA, years 1995-2006. ................................................................................................................... 33 Figure A-8. Distributions of hourly NO 2 

### concentrations in the Los Angeles CMSA, years 1995- 2006........................................................................................................................... ... 34 Figure A-9. Distributions of hourly NO 2 

### concentrations in the Jacksonville MSA, years 1995- 2006, one monitor. ....................................................................................................... 35 Figure A-10. Distributions of annual average NO 2 

### concentrations in the Other Not MSA group location, years 1995-2006. ........................................................................................... 36 Figure A-11. Distribution of annual average NO 2 

### ambient concentrations (ppb) by year, Boston CMSA. ......................................................................................................................... 3 7 Figure A-12. Distribution of hourly NO 2 

### ambient concentrations (ppb) by year, Boston CMSA.

............................................................................................................................... ....... 37 Figure A-13. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, Boston CMSA set A, 1995-2006. ................................................................................ 38 Figure A-14. Distribution of hourly NO 2 

### ambient concentration (ppb) by monitor, Boston CMSA set A, 1995-2006.............................................................................................. 38 Figure A-15. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, Boston CMSA set B, 1995-2006.................................................................................. 39 Figure A-16. Distribution of hourly NO 2 

### ambient concentration (ppb) by monitor, Boston CMSA set B, 1995-2006.............................................................................................. 39 Figure A-17. Distribution of annual average NO 2 

### ambient concentrations (ppb) by year, Chicago CMSA. ......................................................................................................................... 4 0 Figure A-18. Distribution of hourly NO 2 

### ambient concentrations (ppb) by year, Chicago CMSA.

............................................................................................................................... ....... 40 Figure A-19. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, Chicago CMSA, 1995-2006......................................................................................... 41 Figure A-20. Distribution of hourly NO 2 

### ambient concentration (ppb) by monitor, Chicago CMSA, 1995-2006. ...................................................................................................... 41 Figure A-21. Distribution of annual average NO 2 

### ambient concentrations (ppb) by year, Cleveland CMSA. ........................................................................................................ 42 Figure A-22. Temporal distribution of hourly NO 2 

### ambient concentrations (ppb) by year, Cleveland CMSA. ........................................................................................................ 42

### xii 

### Figure A-23. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, Cleveland CMSA, 1995-2006...................................................................................... 43 Figure A-24. Distribution of hourly NO 2 

### ambient concentration (ppb) by monitor, Cleveland CMSA, 1995-2006. ...................................................................................................... 43 Figure A-25. Distribution of annual average NO 2 

### ambient concentrations (ppb) by year, Denver CMSA. ......................................................................................................................... 4 4 Figure A-26. Distribution of hourly NO 2 

### ambient concentrations (ppb) by year, Denver CMSA.

............................................................................................................................... ....... 44 Figure A-27. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, Denver CMSA, 1995-2006. ......................................................................................... 45 Figure A-28. Distribution of hourly NO 2 

### ambient concentration (ppb) by monitor, Denver CMSA, 1995-2006. ...................................................................................................... 45 Figure A-29. Distribution of annual average NO 2 

### ambient concentrations (ppb) by year, Detroit CMSA. ......................................................................................................................... 4 6 Figure A-30. Distribution of hourly NO 2 

### ambient concentrations (ppb) by year, Detroit CMSA.

............................................................................................................................... ....... 46 Figure A-31. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, Detroit CMSA, 1995-2006........................................................................................... 47 Figure A-32. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, Detroit CMSA, 1995-2006........................................................................................... 47 Figure A-33. Distribution of annual average NO 2 

### ambient concentrations (ppb) by year, Los Angeles CMSA. ........................................................................................................... 48 Figure A-34. Distribution of hourly NO 2 

### ambient concentrations (ppb) by year, Los Angeles CMSA. ......................................................................................................................... 4 8 Figure A-35. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, Los Angeles CMSA set A, 1995-2006................................................................................ 49 Figure A-36. Distribution of hourly NO 2 

### ambient concentration (ppb) by monitor, Los Angeles CMSA set A, 1995-2006.............................................................................................. 49 Figure A-37. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, Los Angeles CMSA set B 1995-2006................................................................................. 50 Figure A-38. Distribution of hourly NO 2 

### ambient concentration (ppb) by monitor, Los Angeles CMSA set B, 1995-2006.............................................................................................. 50 Figure A-39. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, Los Angeles CMSA set C 1995-2006................................................................................. 51 Figure A-40. Distribution of hourly NO 2 

### ambient concentration (ppb) by monitor, Los Angeles CMSA set C 1995-2006............................................................................................... 51 Figure A-41. Distribution of annual average NO 2 

### ambient concentrations (ppb) by year, Miami CMSA. ......................................................................................................................... 5 2 Figure A-42. Distribution of hourly NO 2 

### ambient concentrations ( ppb) by year, Miami CMSA.

............................................................................................................................... ....... 52 Figure A-43. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, Miami CMSA, 1995-2006. .......................................................................................... 53 Figure A-44. Distribution of hourly NO 2 

### ambient concentration (ppb) by monitor, Miami CMSA, 1995-2006. ...................................................................................................... 53 Figure A-45. Distribution of annual average NO 2 

### ambient concentrations (ppb) by year, New York CMSA. ................................................................................................................ 54

### xiii 

### Figure A-46. Distribution of hourly NO 2 

### ambient concentrations (ppb) by year, New York CMSA. ......................................................................................................................... 5 4 Figure A-47. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, New York CMSA set a, 1995-2006...................................................................................... 55 Figure A-48. Distribution of hourly NO 2 

### ambient concentration (ppb) by monitor, New York CMSA set a, 1995-2006............................................................................................... 55 Figure A-49. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, New York CMSA set b, 1995-2006. .................................................................................... 56 Figure A-50. Distribution of hourly NO 2 

### ambient concentration (ppb) by monitor, New York CMSA set b, 1995-2006............................................................................................... 56 Figure A-51. Distribution of annual average NO 2 

### ambient concentrations (ppb) by year, Philadelphia CMSA. .................................................................................................... 57 Figure A-52. Distribution of hourly NO 2 

### ambient concentrations (ppb) by year, Philadelphia CMSA. ......................................................................................................................... 5 7 Figure A-53. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, Philadelphia CMSA, 1995-2006. ................................................................................. 58 Figure A-54. Distribution of hourly NO 2 

### ambient concentration (ppb) by monitor, Philadelphia CMSA, 1995-2006. ...................................................................................................... 58 Figure A-55. Distribution of annual average NO 2 

### ambient concentrations (ppb) by year, Washington DC CMSA. .............................................................................................. 59 Figure A-56. Distribution of hourly NO 2 

### ambient concentrations (ppb) by year, Washington DC CMSA. ......................................................................................................................... 5 9 Figure A-57. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, Washington DC CMSA set A, 1995-2006................................................................... 60 Figure A-58. Distribution of hourly NO 2 

### ambient concentration (ppb) by monitor, Washington DC CMSA set A, 1995-2006. ...................................................................................... 60 Figure A-59. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, Washington DC CMSA set B, 1995-2006. .................................................................. 61 Figure A-60. Distribution of hourly NO 2 

### ambient concentration (ppb) by monitor, Washington DC CMSA set B, 1995-2006. ...................................................................................... 61 Figure A-61. Distribution of annual average NO 2 

### ambient concentrations (ppb) by year, Atlanta MSA. ........................................................................................................................... . 62 Figure A-62. Distribution of hourly NO 2 

### ambient concentrations (ppb) by year, Atlanta MSA. 62 Figure A-63. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, Atlanta MSA, 1995-2006............................................................................................. 63 Figure A-64. Distribution of hourly NO 2 

### ambient concentration (ppb) by monitor, Atlanta MSA, 1995-2006. ................................................................................................................... 63 Figure A-65. Distribution of annual average NO 2 

### ambient concentrations (ppb) by year, Colorado Springs MSA................................................................................................ 64 Figure A-66. Distribution of hourly NO 2 

### ambient concentrations (ppb) by year, Colorado Springs MSA................................................................................................................ 64 Figure A-67. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, Colorado Springs MSA, 1995-2006............................................................................. 65 Figure A-68. Distribution of hourly NO 2 

### ambient concentration (ppb) by monitor, Colorado Springs MSA, 1995-2006. ........................................................................................... 65

### xiv 

### Figure A-69. Distribution of annual average NO 2 

### ambient concentrations (ppb) by year, El Paso MSA. ........................................................................................................................... . 66 Figure A-70. Distribution of hourly NO 2 

### ambient concentrations (ppb) by year, El Paso MSA.66 Figure A-71. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, El Paso MSA, 1995-2006. ................................................................................................ 67 Figure A-72. Distribution of hourly NO 2 

### ambient concentration (ppb) by monitor, El Paso MSA, 1995-2006. ................................................................................................................... 67 Figure A-73. Distribution of annual average NO 2 

### ambient concentrations (ppb) by year, Jacksonville MSA. ....................................................................................................... 68 Figure A-74. Distribution of hourly NO 2 

### ambient concentrations ( ppb) by year, Jacksonville MSA. ........................................................................................................................... . 68 Figure A-75. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, Jacksonville MSA, 1995-2006..................................................................................... 69 Figure A-76. Distribution of hourly NO 2 

### ambient concentration (ppb) by monitor, Jacksonville MSA, 1995-2006.......................................................................................................... 69 Figure A-77. Distribution of annual average NO 2 

### ambient concentrations (ppb) by year, Las Vegas MSA. ................................................................................................................. 70 Figure A-78. Distribution of hourly NO 2 

### ambient concentrations (ppb) by year, Las Vegas MSA. ........................................................................................................................... . 70 Figure A-79. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, Las Vegas MSA, 1995-2006............................................................................................... 71 Figure A-80. Distribution of hourly NO 2 

### ambient concentration (ppb) by monitor, Las Vegas MSA, 1995-2006.......................................................................................................... 71 Figure A-81. Distribution of annual average NO 2 

### ambient concentrations (ppb) by year, Phoenix MSA. ........................................................................................................................... . 72 Figure A-82. Distribution of hourly NO 2 

### ambient concentrations (ppb) by year, Phoenix MSA.

............................................................................................................................... ....... 72 Figure A-83. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, Phoenix MSA, 1995-2006............................................................................................ 73 Figure A-84. Distribution of hourly NO 2 

### ambient concentration ( ppb) by monitor, Phoenix MSA, 1995-2006.......................................................................................................... 73 Figure A-85. Distribution of annual average NO 2 

### ambient concentrations (ppb) by year, Provo MSA. ........................................................................................................................... . 74 Figure A-86. Temporal distribution of hourly NO 2 

### ambient concentrations (ppb) by year, Provo MSA. ........................................................................................................................... . 74 Figure A-87. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, Provo MSA, 1995-2006. .............................................................................................. 75 Figure A-88. Distribution of hourly NO 2 

### ambient concentration (ppb) by monitor, Provo MSA, 1995-2006. ................................................................................................................... 75 Figure A-89. Distribution of annual average NO 2 

### ambient concentrations (ppb) by year, St. Louis MSA................................................................................................................... 76 Figure A-90. Temporal distribution of hourly NO 2 

### ambient concentrations (ppb) by year, St. Louis MSA................................................................................................................... 76 Figure A-91. Distribution of annual average NO 2 

### ambient concentration (ppb) by monitor, St. Louis MSA, 1995-2006................................................................................................ 77

### xv 

### Figure A-92. Distribution of hourly NO 2 

### ambient concentration ( ppb) by monitor, St. Louis MSA, 1995-2006.......................................................................................................... 77 Figure A-93. Distribution of annual average NO 2 

### ambient concentrations (ppb) by year, Other MSA/CMSA................................................................................................................. 78 Figure A-94. Distribution of hourly NO 2 

### ambient concentrations (ppb) by year, Other MSA/CMSA................................................................................................................. 78 Figure A-95. Distribution of annual average NO 2 

### ambient concentrations (ppb) by year, Other Not MSA. ..................................................................................................................... 79 Figure A-96. Distribution of hourly NO 2 

### ambient concentrations ( ppb) by year, Other Not MSA.

............................................................................................................................... ....... 79 Figure A-97. Measured number of exceedances of 1-hour NO 2 

### concentrations of 150 ppb versus annual mean NO 2 

### concentrations (ppb) for CMSA locations...................................... 89 Figure A-98. Predicted and obs erved exceedances of 1-hour NO 2 

### concentrations of 150 ppb for CMSA locations using Poisson exponential model. .................................................... 89 Figure A-99. Predicted and obs erved exceedances of 1-hour NO 2 

### concentrations of 150 ppb for CMSA locations using normal linear model................................................................ 90 Figure A-100. Trends in hourly and annual average NO 2 

### ambient monitoring concentrations and their associated coefficients of variat ion (COV) for all monitors, years 1995-2006. 101 Figure A-101. Distribution of estimated C v 

### /C b 

### ratios or m for two season groups. .................. 115

### A-1 

# A-1 Overview

This appendix contains supplemental descriptio ns of the data and methods used in the NO 2 

### air quality characterization in support of the Risk and Expos ure Assessment (REA) conducted for the NO 2 

### NAAQS review. First, ambient monito ring data form years 1995 through 2006 have been characterized based on siting characteristics, proximity to st ationary source emissions, and distance to roadways. Then, ambient NO 2 

### concentration trends were evaluated considering the year of monitoring and distribution of monitors within a location.
The primary output of the air quality charac terization was the numbers of exceedances of potential health effect benchmark levels identif ied in the Integrated Science Assessment. The ambient NO 2 

### concentrations were evaluated for the numbers of exceedances of the selected benchmarks in several locations and considering f our scenarios. The first scenario considered as is air quality as obtained from EPA’s Air Qu ality System (US EPA, 2007a; 2007b). A second scenario used a portion of the as is air quality to estimate on-road NO 2 

### 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 standa rds. Each of these sc enarios, in addition to the reasoning for the methods and data used, are de scribed in detail in th e sections that follow.

### A-2 

# A-2 Air Quality Data Screen

A-2.1 Introduction

The current NO 2 

### 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, stat ed annual averages would be determined on a calendar year basis, and indicat ed an explicit 75% completeness requirement for monitoring (60 FR 52874). Each of these component s of the standard were consid ered in characterizing the air quality monitoring data, beginning first with the selection of valid data.



## A-2.2 Approach

NO 2 

### air quality data from years 1995 through 2006 and associated documentation were downloaded from EPA’s Air Quality System (U S EPA, 2007a; 2007b). As of the date of the first analyses were performed, hourly measur ements 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 lo cation, 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 NO 2 

### 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 measur ements 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 fo r data to be used for analysis.

Site-years of data are the total numbers of years the collective m onitors 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 sufficien t numbers of monitoring values, while others contained upwards of 11 years (Table A-1). Th us in summing the number of operating years, this particular location contai ned a total of 105 site -years of data across the monitoring period.

In all of the subsequent analys es, where hourly values were missing they were treated as such. Reported values of zero (0) concentration were al so retained as is. For certain illustrations, values of zero were substituted with 0.5 ppb, de rived 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 NO 2 

### 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 averag e concentrations are of intere st, 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, resulti ng in 4,177 complete site-years in the analytical database. Table

### A-3 

### A-2 provides a summary of the s ite-years included in the analysis , relative to those excluded, by location and by two site-year groupings. 1 

### Location selection is de fined 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 NO 2 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 a nd differences in air quality among locations for the purpose of either aggregating or segregating data us ing 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 st atistical area or CMSA).



## A-3.2 Approach Criteria were established for selecting site s with high annual means and/or frequent exceedances of potential health e ffect benchmarks. Selected lo cations were those that had a maximum annual mean NO 2 

### level at a particular monitor grea ter than or equal to 25.7 ppb, which represents the 90 th 

### percentile across all locations and site-y ears, and/or had at least one reported 1-hour NO 2 

### level greater than or equal to 200 ppb, the lo west level of the potential health effect benchmarks. A location in this context would include a ge ographic area that encompasses more than a single air quality monitor (e.g., particular city, metropolit an statistical area (MSA), or consolidated metropolitan statistica l area or CMSA). First, all mon itors were identified as either belonging to a CMSA, a MSA, or neither. Then, locations of interest were identified through statistical analysis of the ambient NO 2 

### air quality data for each site within a location.



## 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 addi tional 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 locati ons were retained from the in itial selection and 4 locations selected from a second screeni ng to provide additional geographi cal 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 site s 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 qu ality data from a total of 204 monitors within the named locations, 332 monitors in the Othe r CMSA group, and 92 monitors in the Not MSA group. Again, the monitors that were retained cont ained the criteria for estimating a valid annual average concentration described above.A-6Table A-3.Locations selected for NO2Air Quality Characterization, associated abbreviations, and values of selection criteria.LocationType1Code Description AbbreviationMaximum # ofExceedancesof 200 ppbMaximumAnnual Mean(ppb)MSA 0520 Atlanta, GA Atlanta* 1 26.6CMSA 1122 Boston-Worcester-Lawrence, MA-NH-ME-CT Boston* 1 31.1CMSA 1602 Chicago-Gary-Kenosha, IL-IN-WI Chicago 0 33.6CMSA 1692 Cleveland-Akron, OH Cleveland* 1 28.1MSA 1720 Colorado Springs, COColoradoSprings* 69 34.8CMSA 2082 Denver-Boulder-Greeley, CO Denver* 2 36.8CMSA 2162 Detroit-Ann Arbor-Flint, MI Detroit* 12 25.9MSA 2320 El Paso, TX El Paso* 2 35.1MSA 3600 Jacksonville, FL Jacksonville 2 15.9MSA 4120 Las Vegas, NV-AZ Las Vegas* 11 27.1CMSA 4472 Los Angeles-Riverside-Orange County, CA Los Angeles* 5 50.6CMSA 4992 Miami-Fort Lauderdale, FL Miami 3 16.8CMSA 5602 New York-Northern New Jersey-LongIsland, NY-NJ-CT-PA New York* 3 42.2CMSA 6162 Philadelphia-Wilmington-AtlanticCity, PA-NJ-DE-MD Philadelphia* 3 34.00MSA 6200 Phoenix-Mesa, AZ Phoenix* 37 40.5MSA 6520 Provo-Orem, UT Provo 0 28.9MSA 7040 St, Louis, MO-IL St. Louis* 8 27.2CMSA 8872 Washington-Baltimore, DC-MD-VA-WV Washington DC* 2 27.2MSA/CMSA - Other MSA/CMSA Other MSA 1031.9- - Other Not MSA Other Not MSA 219.71CMSA is consolidated metropolitan statistical area; MSA is metropolitan statistical area according to the 1999 Office of Management and Budget definitions (January 28, 2002revision).* 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 importanc e, recognizing that prox imity of local sources could influence on measured NO 2 

### concentrations. As part of the risk and exposure scope and methods document (US EPA, 2007c), both mobile a nd stationary sources (in particular power generating utilities using fossil fuels) were indica ted as significant contri butors to nitrogen oxides (NO x 

### ) emissions in the U.S. Analyses were perfor med to determine the distance of all locationspecific monitors to these source cate gories. In additi on, emissions of NO x 

### from stationary sources within close proximity of the loca tion-specific monitoring sites were estimated.



## A-4.2 Approach Major road distances to each monitor were firs t determined using a Tele-Atlas roads database in a GIS application. For road-monitor pairs that showed particularly clos e distances, the values were refined using GoogleEarth® to estimate the di stance to road edge. Distances of monitoring sites to stationary sources and those source’s emi ssions were estimated using data within the 2002 National Emissions Inventory (NEI ; US EPA, 2007d). The NEI da tabase reports emissions of NO x 

### in tons per year (tpy) for 131,657 unique emission sources at various points of release. The release locations were all taken fr om the latitude longitude values within the NEI. First, all NO x 

### emissions were summed for identical latitude a nd longitude entries while retaining source codes for the emissions (e.g., Standard Industrial Co de (SIC), or North American Industrial Classification System (NAICS)). Therefore, a ny facility containing si milar 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 NO x 

### emission sources. Locations of these stationary source em issions were compared with ambient monitoring locations using the following formula: 

# ( ) r lon lon lat lat lat lat d ×

− × × + × = )) cos( ) cos( ) cos( ) sin( arccos(sin 1 2 2 1 2 1



### where

d

= distance (kilometers) lat 1 

### = latitude of a monitor (radians) lat 2 

### = latitude of sour ce emission (radians) lon 1 

### = longitude of monitor (radians) lon 2 

### = 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 charac teristics 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 am bient monitors to major roads for each of the named locations is summarized in Table A-4. On av erage, 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, th ere 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 ma jor roads, the ambient monitors were further categorized based on the monitor distance from major roads. Three proximity bins were identified, the first containing those mon itors sited at or within 20 meters, ( ≤ 20 m), those between 20 m and 100 m, and those located at l east 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 loca tion between 1995 and 2006. The min, 2.5, med, 97.5, and max represent the minimum, 2.5 th , median, 97.5 th , 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 NO x 

### 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 s ources at a distance of greater than 5 km. Most of the stationary source emissions of NO x 

### within a 10 km radius of monitors were less than 50 tpy (Table A-6). Deta ils 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 in to 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 NO x emissions >5 tons per year and within a 10 kilometers radius. Distance of monitor to NO x 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) NO x 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, 2.5 th , median, 97.5 th , and maximum percentiles of the distribution for the distance in meters (m) to the source emission.

Table A-6. Distribution of NO x emissions from stationary sources within 10 kilometers of monitoring site, where emissions were >5 tons per year. Emissions (tpy) of NO x 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 NO x 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, 2.5 th , median, 97.5 th , 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 locat ions of “Other CMSA” or Not MSA). Each of these monitors met the crit eria 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 el evation above sea level, and distance to the nearest major roadway (Table A-7). In addition, th e distances and emissions of stationary sources that emit >5 tons NO x 

### per year were calculated for each monitor (Table A-8)A-111Table A-7.Attributes of location-specific ambient monitors used for air quality characterization and the distance to nearest major roadway.Monitor5Roadway6Location Monitor ID Latitude Longitude Objective1Setting2Land Use3Scale4YearsHt(m)Elev (m)Dist(m)TypeAtlanta 130890002 33.68801 -84.2903POPULATIONEXPOSURESUBURBAN RESIDENTIAL URBAN SCALE 10 5 308 432 3Atlanta 130893001 33.84568 -84.2134 UNKNOWN RURAL RESIDENTIAL NEIGHBORHOOD 9 5 0 579 2Atlanta 131210048 33.77919 -84.3958POPULATIONEXPOSUREURBAN ANDCENTER CITY COMMERCIAL NEIGHBORHOOD 12 5 290 134 3Atlanta 132230003 33.92855 -85.0455POPULATIONEXPOSURE RURAL AGRICULTURAL URBAN SCALE 10 4 417 1000Atlanta 132470001 33.59093 -84.0654POPULATIONEXPOSURE RURAL AGRICULTURAL URBAN SCALE 12 5 219 809 3Boston 230313002 43.08333 -70.75POPULATIONEXPOSURE SUBURBAN RESIDENTIAL NEIGHBORHOOD 7 4 40 70 2Boston 250051005 42.06306 -71.1489POPULATIONEXPOSURE RURAL AGRICULTURAL URBAN SCALE 2 4 61 17 3Boston 250092006 42.47467 -70.9714POPULATIONEXPOSUREURBAN ANDCENTER CITY COMMERCIAL URBAN SCALE 10 5 52 158 3Boston 250094004 42.79027 -70.8083POPULATIONEXPOSURE SUBURBAN RESIDENTIAL URBAN SCALE 5 4 1 15 3Boston 250095005 42.77077 -71.1023POPULATIONEXPOSURE SUBURBAN RESIDENTIAL NEIGHBORHOOD 2 0 337 3Boston 250210009 42.31667 -71.1333 UNKNOWNURBAN ANDCENTER CITY RESIDENTIAL MICROSCALE 1 4 0 144 3Boston 250250002 42.34887 -71.0972HIGHESTCONCENTRATIONURBAN ANDCENTER CITYCOMMERCIAL MICROSCALE 11 5 6 7 2Boston 250250021 42.37783 -71.0271HIGHESTCONCENTRATIONURBAN ANDCENTER CITYRESIDENTIAL NEIGHBORHOOD 8 4 6 7 3Boston 250250035 42.33333 -71.1167 UNKNOWNURBAN ANDCENTER CITYRESIDENTIAL 1 0 158 3Boston 250250036 42.33333 -71.1167 UNKNOWNURBAN ANDCENTER CITYRESIDENTIAL 1 0 158 3Boston 250250040 42.34025 -71.0383POPULATIONEXPOSUREURBAN ANDCENTER CITYINDUSTRIAL NEIGHBORHOOD 11 4 0 37 3Boston 250250041 42.31717 -70.9662POPULATIONEXPOSURERURAL COMMERCIAL URBAN SCALE 1 6 10 1000Boston 250250042 42.3294 -71.0825POPULATIONEXPOSUREURBAN ANDCENTER CITYCOMMERCIAL NEIGHBORHOOD 6 5 6 26 3Boston 250251003 42.40167 -71.0311POPULATIONEXPOSURESUBURBAN RESIDENTIAL URBAN SCALE 5 4 59 228 4Boston 250270020 42.26722 -71.7989 UNKNOWNURBAN ANDCENTER CITYCOMMERCIAL 8 3 145 44 3Boston 250270023 42.26388 -71.7942POPULATIONEXPOSUREURBAN ANDCENTER CITYCOMMERCIAL URBAN SCALE 3 4 145 49 3Boston 330110016 42.99278 -71.4594 UNKNOWNURBAN ANDCENTER CITYCOMMERCIAL 4 5 75 168 3Boston 330110019 43.00056 -71.4681 UNKNOWNURBAN ANDCENTER CITYCOMMERCIAL 1 61 70 3Boston 330110020 43.00056 -71.4681 UNKNOWNURBAN ANDCENTER CITYCOMMERCIAL NEIGHBORHOOD 5 5 61 70 3A-12Table A-7.Attributes of location-specific ambient monitors used for air quality characterization and the distance to nearest major roadway.Monitor5Roadway6Location Monitor ID Latitude Longitude Objective1Setting2Land Use3Scale4YearsHt(m)Elev (m)Dist(m)TypeBoston 330150009 43.07806 -70.7628 UNKNOWN SUBURBAN COMMERCIAL 5 3 3 48 3Boston 330150013 43 -71.2 OTHER RURAL RESIDENTIAL REGIONAL SCALE 4 1 0 1000Boston 330150014 43.07528 -70.7481POPULATIONEXPOSUREURBAN ANDCENTER CITY RESIDENTIAL NEIGHBORHOOD 3 2 4 266 3Boston 330150015 43.0825 -70.7619POPULATIONEXPOSURE SUBURBAN COMMERCIAL NEIGHBORHOOD 1 4 3 38 3Chicago 170310037 41.97944 -87.67 UNKNOWNURBAN ANDCENTER CITY RESIDENTIAL 1 9 183 17 3Chicago 170310063 41.87697 -87.6343HIGHESTCONCENTRATIONURBAN ANDCENTER CITY MOBILE MIDDLE SCALE 12 3 181 68 3Chicago 170310064 41.79079 -87.6016POPULATIONEXPOSURE SUBURBAN RESIDENTIAL NEIGHBORHOOD 6 15 180 346 3Chicago 170310075 41.96417 -87.6586POPULATIONEXPOSURESUBURBAN RESIDENTIAL NEIGHBORHOOD 4 15 180 136 3Chicago 170310076 41.7514 -87.7135POPULATIONEXPOSURESUBURBAN RESIDENTIAL NEIGHBORHOOD 5 4 186 2 3Chicago 170313101 41.96525 -87.8763HIGHESTCONCENTRATIONSUBURBAN MOBILE MIDDLE SCALE 3 3 197 20 2Chicago 170313103 41.96519 -87.8763HIGHESTCONCENTRATIONSUBURBAN MOBILE MIDDLE SCALE 9 4 195 20 2Chicago 170314002 41.85524 -87.7525POPULATIONEXPOSURESUBURBAN RESIDENTIAL NEIGHBORHOOD 12 4 184 118 3Chicago 170314201 42.14 -87.7992POPULATIONEXPOSURESUBURBAN RESIDENTIAL URBAN SCALE 8 8 198 239 2Chicago 170318003 41.63139 -87.5681POPULATIONEXPOSURESUBURBAN RESIDENTIAL NEIGHBORHOOD 8 4 179 2 3Chicago 171971011 41.22154 -88.191GENERAL/BACKGROUNDRURAL AGRICULTURAL REGIONAL SCALE 5 5 181 1000Chicago 180890022 41.60667 -87.3047HIGHESTCONCENTRATIONURBAN ANDCENTER CITYINDUSTRIAL NEIGHBORHOOD 8 5 183 738 1Chicago 180891016 41.60028 -87.3347POPULATIONEXPOSUREURBAN ANDCENTER CITYRESIDENTIAL NEIGHBORHOOD 2 14 183 187 3Cleveland 390350043 41.46278 -81.5792POPULATIONEXPOSURESUBURBAN RESIDENTIAL URBAN SCALE 2 4 287 187 2Cleveland 390350060 41.49396 -81.6785POPULATIONEXPOSUREURBAN ANDCENTER CITYCOMMERCIAL NEIGHBORHOOD 12 4 206 2 4Cleveland 390350066 41.46278 -81.5803POPULATIONEXPOSURESUBURBAN RESIDENTIAL URBAN SCALE 6 5 287 187 2Cleveland 390350070 41.45694 -81.5922POPULATIONEXPOSUREURBAN ANDCENTER CITYRESIDENTIAL NEIGHBORHOOD 2 4 278 81 3Colorado Springs 080416001 38.63361 -104.716 UNKNOWN RURAL INDUSTRIAL 6 4 1673 1000Colorado Springs 080416004 38.92139 -104.813 UNKNOWNURBAN ANDCENTER CITYRESIDENTIAL 6 4 1931 150 1Colorado Springs 080416005 38.76333 -104.757 UNKNOWNURBAN ANDCENTER CITYAGRICULTURAL 1 4 1747 79 3Colorado Springs 080416006 38.9225 -104.996 UNKNOWN RURAL RESIDENTIAL 1 4 2313 199 2Colorado Springs 080416009 38.64083 -104.714 UNKNOWN RURAL INDUSTRIAL 1 4 1707 1000A-13Table A-7.Attributes of location-specific ambient monitors used for air quality characterization and the distance to nearest major roadway.Monitor5Roadway6Location Monitor ID Latitude Longitude Objective1Setting2Land Use3Scale4YearsHt(m)Elev (m)Dist(m)TypeColorado Springs 080416011 38.84667 -104.827 UNKNOWNURBAN ANDCENTER CITYRESIDENTIAL 6 3 1832 198 3Colorado Springs 080416013 38.81056 -104.817 UNKNOWNURBAN ANDCENTER CITYRESIDENTIAL 1 3 1823 386 4Colorado Springs 080416018 38.81139 -104.751 UNKNOWNURBAN ANDCENTER CITYCOMMERCIAL 4 3 1795 163 2Denver 080013001 39.83812 -104.95POPULATIONEXPOSURERURAL AGRICULTURAL URBAN SCALE 11 4 1559 748 3Denver 080050003 39.65722 -104.998HIGHESTCONCENTRATIONSUBURBAN COMMERCIAL NEIGHBORHOOD 1 4 1654 138 2Denver 080310002 39.75118 -104.988HIGHESTCONCENTRATIONURBAN ANDCENTER CITY COMMERCIAL NEIGHBORHOOD 9 5 1589 18 3Denver 080590006 39.9129 -105.189 UNKNOWN RURAL INDUSTRIAL 3 1774 65 3Denver 080590008 39.87639 -105.166GENERAL/BACKGROUND RURAL INDUSTRIAL NEIGHBORHOOD 4 4 1715 31 3Denver 080590009 39.86194 -105.203GENERAL/BACKGROUND RURAL INDUSTRIAL NEIGHBORHOOD 3 4 1848 99 3Denver 080590010 39.89972 -105.24 UNKNOWN RURAL AGRICULTURAL NEIGHBORHOOD 5 4 1877 63 2Detroit 260990009 42.73139 -82.7935 UNKNOWN SUBURBAN COMMERCIAL 2 189 415 3Detroit 261630016 42.35781 -83.096HIGHESTCONCENTRATIONURBAN ANDCENTER CITYRESIDENTIAL NEIGHBORHOOD 11 4 191 393 5Detroit 261630019 42.43084 -83.0001POPULATIONEXPOSURESUBURBAN RESIDENTIAL URBAN SCALE 11 4 192 339 3El Paso 481410027 31.76308 -106.487GENERAL/BACKGROUNDURBAN ANDCENTER CITYCOMMERCIAL URBAN SCALE 4 5 1140 33 4El Paso 481410028 31.75361 -106.404 SOURCE ORIENTED SUBURBAN RESIDENTIAL MICROSCALE 1 4 1126 718 3El Paso 481410037 31.76828 -106.501POPULATIONEXPOSUREURBAN ANDCENTER CITYCOMMERCIAL NEIGHBORHOOD 11 4 1143 128 3El Paso 481410044 31.76567 -106.455HIGHESTCONCENTRATIONURBAN ANDCENTER CITYCOMMERCIAL NEIGHBORHOOD 8 5 1128 38 3El Paso 481410055 31.74676 -106.403POPULATIONEXPOSUREURBAN ANDCENTER CITYCOMMERCIAL 7 5 0 127 3El Paso 481410057 31.66219 -106.303POPULATIONEXPOSURESUBURBAN RESIDENTIAL 7 0 450 3El Paso 481410058 31.89393 -106.426POPULATIONEXPOSUREURBAN ANDCENTER CITYRESIDENTIAL NEIGHBORHOOD 6 5 0 478 3Jacksonville 120310032 30.35611 -81.6356 UNKNOWN SUBURBAN COMMERCIAL 10 3 7 144 1Las Vegas 320030022 36.39078 -114.907 SOURCE ORIENTED RURAL INDUSTRIAL NEIGHBORHOOD 7 3.5 0 122 2Las Vegas 320030023 36.80806 -114.061POPULATIONEXPOSURE RURAL RESIDENTIAL NEIGHBORHOOD 4 4 490 303 3Las Vegas 320030073 36.17306 -115.332POPULATIONEXPOSURESUBURBAN RESIDENTIAL NEIGHBORHOOD 7 3.5 0 515 2Las Vegas 320030078 35.46505 -114.92REGIONALTRANSPORTRURAL DESERT REGIONAL SCALE 1 4 1094 25 3Las Vegas 320030539 36.14444 -115.086POPULATIONEXPOSURESUBURBAN MOBILE NEIGHBORHOOD 8 3.5 533 11 3A-14Table A-7.Attributes of location-specific ambient monitors used for air quality characterization and the distance to nearest major roadway.Monitor5Roadway6Location Monitor ID Latitude Longitude Objective1Setting2Land Use3Scale4YearsHt(m)Elev (m)Dist(m)TypeLas Vegas 320030557 36.15889 -115.11 UNKNOWN SUBURBAN RESIDENTIAL 2 3 567 1 3Las Vegas 320030563 36.17639 -115.103POPULATIONEXPOSURE SUBURBAN RESIDENTIAL NEIGHBORHOOD 3 4 570 254 3Las Vegas 320030601 35.97889 -114.844POPULATIONEXPOSURE SUBURBAN COMMERCIAL NEIGHBORHOOD 5 4 0 52 3Las Vegas 320031019 35.78563 -115.357GENERAL/BACKGROUND RURAL DESERT URBAN SCALE 7 4 950 914 3Las Vegas 320032002 36.19111 -115.122HIGHESTCONCENTRATIONURBAN ANDCENTER CITY COMMERCIAL NEIGHBORHOOD 7 3.5 0 240 3Los Angeles 060370002 34.1365 -117.924POPULATIONEXPOSURE SUBURBAN RESIDENTIAL URBAN SCALE 12 2 183 329 3Los Angeles 060370016 34.14435 -117.85 UNKNOWN SUBURBAN RESIDENTIAL 12 6 275 300 3Los Angeles 060370030 34.03528 -118.217 UNKNOWNURBAN ANDCENTER CITYRESIDENTIAL 1 5 65 50 3Los Angeles 060370113 34.05111 -118.456 UNKNOWNURBAN ANDCENTER CITYMOBILE 12 5 91 190 3Los Angeles 060370206 33.95833 -117.842 UNKNOWNURBAN ANDCENTER CITYCOMMERCIAL MIDDLE SCALE 1 300 1000Los Angeles 060371002 34.17605 -118.317 UNKNOWNURBAN ANDCENTER CITYCOMMERCIAL 11 5 168 58 3Los Angeles 060371103 34.06659 -118.227HIGHESTCONCENTRATIONURBAN ANDCENTER CITYRESIDENTIAL NEIGHBORHOOD 11 13 87 55 3Los Angeles 060371201 34.19925 -118.533 UNKNOWN SUBURBAN COMMERCIAL 12 6 226 206 3Los Angeles 060371301 33.92899 -118.211 UNKNOWNURBAN ANDCENTER CITYCOMMERCIAL 12 7 27 29 3Los Angeles 060371601 34.01407 -118.061POPULATIONEXPOSURESUBURBAN COMMERCIAL NEIGHBORHOOD 10 6 75 78 3Los Angeles 060371701 34.06703 -117.751 UNKNOWN SUBURBAN COMMERCIAL 12 6 270 15 3Los Angeles 060372005 34.1326 -118.127 UNKNOWNURBAN ANDCENTER CITYRESIDENTIAL 12 4 250 385 3Los Angeles 060374002 33.82376 -118.189 UNKNOWN SUBURBAN RESIDENTIAL 11 6 6 1 3Los Angeles 060375001 33.92288 -118.37 UNKNOWNURBAN ANDCENTER CITY COMMERCIAL 9 21 10 3Los Angeles 060375005 33.9508 -118.43UPWINDBACKGROUND SUBURBAN RESIDENTIAL NEIGHBORHOOD 2 4 21 149 3Los Angeles 060376002 34.3875 -118.534POPULATIONEXPOSURE SUBURBAN COMMERCIAL MIDDLE SCALE 2 375 2 3Los Angeles 060376012 34.38344 -118.528 UNKNOWN SUBURBAN COMMERCIAL 5 397 143 3Los Angeles 060379002 34.69 -118.132 UNKNOWNURBAN ANDCENTER CITY COMMERCIAL MIDDLE SCALE 6 5 725 61 3Los Angeles 060379033 34.67139 -118.131 UNKNOWNURBAN ANDCENTER CITYCOMMERCIAL MIDDLE SCALE 5 3 725 146 3Los Angeles 060590001 33.83062 -117.938POPULATIONEXPOSURESUBURBAN RESIDENTIAL URBAN SCALE 5 5 45 225 3Los Angeles 060590007 33.83062 -117.938POPULATIONEXPOSURESUBURBAN RESIDENTIAL URBAN SCALE 4 4 10 225 3A-15Table A-7.Attributes of location-specific ambient monitors used for air quality characterization and the distance to nearest major roadway.Monitor5Roadway6Location Monitor ID Latitude Longitude Objective1Setting2Land Use3Scale4YearsHt(m)Elev (m)Dist(m)TypeLos Angeles 060591003 33.67464 -117.926 UNKNOWN SUBURBAN RESIDENTIAL MIDDLE SCALE 12 6 0 202 3Los Angeles 060595001 33.92513 -117.953 UNKNOWN SUBURBAN RESIDENTIAL 11 82 82 570 3Los Angeles 060650012 33.92086 -116.858POPULATIONEXPOSURE SUBURBAN COMMERCIAL NEIGHBORHOOD 9 4 677 432 1Los Angeles 060655001 33.85275 -116.541 UNKNOWN SUBURBAN RESIDENTIAL 12 6 171 75 3Los Angeles 060658001 33.99958 -117.416 UNKNOWN SUBURBAN RESIDENTIAL 12 4 250 133 3Los Angeles 060659001 33.67649 -117.331 UNKNOWN SUBURBAN RESIDENTIAL MIDDLE SCALE 12 1440 522 4Los Angeles 060710001 34.895 -117.024 UNKNOWNURBAN ANDCENTER CITYCOMMERCIAL 12 8 690 64 3Los Angeles 060710012 34.42611 -117.563 UNKNOWN RURAL COMMERCIAL 2 4100 30 3Los Angeles 060710014 34.5125 -117.33 UNKNOWN SUBURBAN RESIDENTIAL 5 4 876 18 3Los Angeles 060710015 35.775 -117.367 UNKNOWN SUBURBAN INDUSTRIAL 2 498 42 3Los Angeles 060710017 34.14194 -116.055 UNKNOWNURBAN ANDCENTER CITY MOBILE 3 4 607 64 3Los Angeles 060710306 34.51 -117.331 UNKNOWN SUBURBAN RESIDENTIAL 7 4 913 38 3Los Angeles 060711004 34.10374 -117.629POPULATIONEXPOSUREURBAN ANDCENTER CITYRESIDENTIAL NEIGHBORHOOD 11 6 369 349 2Los Angeles 060711234 35.76389 -117.396 OTHER RURAL DESERT 9 1 545 1000Los Angeles 060712002 34.10002 -117.492 UNKNOWN SUBURBAN INDUSTRIAL 12 5 381 81 3Los Angeles 060714001 34.41806 -117.285 UNKNOWN SUBURBAN RESIDENTIAL 3 1006 111 3Los Angeles 060719004 34.10688 -117.274HIGHESTCONCENTRATIONSUBURBAN COMMERCIAL URBAN SCALE 12 5 0 169 3Los Angeles 061110005 34.38694 -119.416POPULATIONEXPOSURE RURAL AGRICULTURAL 7 1 320 63 3Los Angeles 061110007 34.21 -118.869 UNKNOWN SUBURBANRESIDENTIAL NEIGHBORHOOD 9 5 244 89 3Los Angeles 061111003 34.44667 -119.27 UNKNOWN SUBURBAN MOBILE NEIGHBORHOOD 1 231 18 2Los Angeles 061111004 34.44833 -119.23POPULATIONEXPOSURESUBURBAN RESIDENTIAL URBAN SCALE 7 4 262 56 3Los Angeles 061112002 34.2775 -118.685HIGHESTCONCENTRATIONSUBURBAN RESIDENTIAL URBAN SCALE 12 4 314 471 1Los Angeles 061112003 34.2804 -119.314GENERAL/BACKGROUNDSUBURBAN RESIDENTIAL MIDDLE SCALE 9 2 3 90 1Los Angeles 061113001 34.255 -119.143POPULATIONEXPOSURERURAL RESIDENTIAL URBAN SCALE 12 4 43 307 3Miami 120110003 26.28111 -80.2828HIGHESTCONCENTRATIONRURAL INDUSTRIAL NEIGHBORHOOD 3 6 3 22 3Miami 120110031 26.272 -80.295HIGHESTCONCENTRATIONSUBURBAN RESIDENTIAL URBAN SCALE 8 4 3 103 4Miami 120118002 26.087 -80.111POPULATIONEXPOSURESUBURBAN RESIDENTIAL URBAN SCALE 11 4 3 1000Miami 120860027 25.733 -80.162POPULATIONEXPOSURESUBURBAN RESIDENTIAL NEIGHBORHOOD 11 16 2 15 3Miami 120864002 25.79833 -80.2103 HIGHEST URBAN AND COMMERCIAL NEIGHBORHOOD 11 4 5 87 3A-16Table A-7.Attributes of location-specific ambient monitors used for air quality characterization and the distance to nearest major roadway.Monitor5Roadway6Location Monitor ID Latitude Longitude Objective1Setting2Land Use3Scale4YearsHt(m)Elev (m)Dist(m)TypeCONCENTRATION CENTER CITYNew York 090010113 41.18361 -73.1903HIGHESTCONCENTRATIONURBAN ANDCENTER CITY COMMERCIAL NEIGHBORHOOD 3 4 3 8 3New York 090019003 41.11833 -73.3367POPULATIONEXPOSURE RURAL FOREST NEIGHBORHOOD 8 5 4 508 4New York 090090027 41.30111 -72.9028POPULATIONEXPOSUREURBAN ANDCENTER CITY COMMERCIAL NEIGHBORHOOD 2 3.67 11 237 1New York 090091123 41.31083 -72.9169 UNKNOWNURBAN ANDCENTER CITY RESIDENTIAL 9 5 18 14 2New York 340030001 40.80833 -73.9928 UNKNOWN SUBURBAN RESIDENTIAL 3 4 61 82 3New York 340030005 40.89858 -74.0299POPULATIONEXPOSURE SUBURBAN RESIDENTIAL NEIGHBORHOOD 4 3 6 172 5New York 340130011 40.72667 -74.1442 UNKNOWNURBAN ANDCENTER CITYINDUSTRIAL 5 4 3 232 1New York 340130016 40.72222 -74.1469POPULATIONEXPOSUREURBAN ANDCENTER CITYINDUSTRIAL NEIGHBORHOOD 1 5 3 6 1New York 340131003 40.7575 -74.2005HIGHESTCONCENTRATIONURBAN ANDCENTER CITYCOMMERCIAL NEIGHBORHOOD 11 4 48.45 25 3New York 340170006 40.67025 -74.1261POPULATIONEXPOSUREURBAN ANDCENTER CITYCOMMERCIAL URBAN SCALE 11 5 3 266 3New York 340210005 40.28319 -74.7422POPULATIONEXPOSURESUBURBAN RESIDENTIAL NEIGHBORHOOD 11 4 30 442 1New York 340230011 40.46218 -74.4294POPULATIONEXPOSURERURAL AGRICULTURAL NEIGHBORHOOD 11 4 21 298 3New York 340273001 40.78763 -74.6763 UNKNOWN RURAL AGRICULTURAL 11 5 274 227 3New York 340390004 40.64144 -74.2084HIGHESTCONCENTRATIONSUBURBAN INDUSTRIAL NEIGHBORHOOD 11 4 5.4 37 4New York 340390008 40.60083 -74.4419POPULATIONEXPOSURESUBURBAN RESIDENTIAL NEIGHBORHOOD 3 4 0 99 3New York 360050080 40.83608 -73.9202HIGHESTCONCENTRATIONURBAN ANDCENTER CITYRESIDENTIAL NEIGHBORHOOD 5 15 15 122 3New York 360050083 40.86586 -73.8808 UNKNOWNURBAN ANDCENTER CITYCOMMERCIAL 12 15 24 132 5New York 360050110 40.81616 -73.9021 UNKNOWNURBAN ANDCENTER CITYRESIDENTIAL 6 0 76 3New York 360470011 40.73277 -73.9472HIGHESTCONCENTRATIONURBAN ANDCENTER CITYINDUSTRIAL NEIGHBORHOOD 1 13 9 171 3New York 360590005 40.74316 -73.5855HIGHESTCONCENTRATIONSUBURBAN COMMERCIAL NEIGHBORHOOD 11 5 27 32 3New York 360610010 40.73944 -73.9861HIGHESTCONCENTRATIONURBAN ANDCENTER CITYRESIDENTIAL NEIGHBORHOOD 4 38 38 55 3New York 360610056 40.75917 -73.9665HIGHESTCONCENTRATIONURBAN ANDCENTER CITYCOMMERCIAL MIDDLE SCALE 10 10 15 62 3New York 360810097 40.75527 -73.7586GENERAL/BACKGROUNDURBAN ANDCENTER CITYRESIDENTIAL 3 12 0 197 3New York 360810098 40.7842 -73.8476 UNKNOWNURBAN ANDCENTER CITYRESIDENTIAL 7 8 6 9 3A-17Table A-7.Attributes of location-specific ambient monitors used for air quality characterization and the distance to nearest major roadway.Monitor5Roadway6Location Monitor ID Latitude Longitude Objective1Setting2Land Use3Scale4YearsHt(m)Elev (m)Dist(m)TypeNew York 360810124 40.7362 -73.8232POPULATIONEXPOSURESUBURBAN RESIDENTIAL 5 8 150 3New York 361030009 40.8275 -73.0569 UNKNOWN SUBURBAN RESIDENTIAL 6 0 116 2Philadelphia 100031003 39.76111 -75.4919POPULATIONEXPOSURE SUBURBAN RESIDENTIAL 5 65 189 2Philadelphia 100031007 39.55111 -75.7308 OTHER RURAL AGRICULTURAL 1 20 144 3Philadelphia 100032004 39.73944 -75.5581 UNKNOWNURBAN ANDCENTER CITY COMMERCIAL 4 0 82 3Philadelphia 340070003 39.92304 -75.0976POPULATIONEXPOSURE SUBURBAN RESIDENTIAL NEIGHBORHOOD 10 5 7.6 405 3Philadelphia 420170012 40.10722 -74.8822POPULATIONEXPOSURE SUBURBAN RESIDENTIAL NEIGHBORHOOD 12 2 12 393 3Philadelphia 420450002 39.83556 -75.3725POPULATIONEXPOSUREURBAN ANDCENTER CITYINDUSTRIAL NEIGHBORHOOD 12 2 3 413 3Philadelphia 420910013 40.11222 -75.3092POPULATIONEXPOSURESUBURBAN RESIDENTIAL NEIGHBORHOOD 11 4 53 630 1Philadelphia 421010004 40.00889 -75.0978POPULATIONEXPOSUREURBAN ANDCENTER CITYRESIDENTIAL URBAN SCALE 11 7 22 45 3Philadelphia 421010029 39.95722 -75.1731HIGHESTCONCENTRATIONURBAN ANDCENTER CITYCOMMERCIAL NEIGHBORHOOD 10 11 25 103 3Philadelphia 421010047 39.94472 -75.1661POPULATIONEXPOSUREURBAN ANDCENTER CITYRESIDENTIAL NEIGHBORHOOD 9 11 21 66 2Phoenix 040130019 33.48385 -112.143POPULATIONEXPOSURESUBURBAN RESIDENTIAL NEIGHBORHOOD 10 4.3 333 401 3Phoenix 040133002 33.45793 -112.046HIGHESTCONCENTRATIONURBAN ANDCENTER CITYRESIDENTIAL NEIGHBORHOOD 12 11.3 339 141 3Phoenix 040133003 33.47968 -111.917POPULATIONEXPOSURESUBURBAN RESIDENTIAL URBAN SCALE 10 5.8 368 78 3Phoenix 040133010 33.46093 -112.117POPULATIONEXPOSURESUBURBAN RESIDENTIAL MIDDLE SCALE 9 4.2 325 7 3Phoenix 040134005 33.4124 -111.935 UNKNOWNURBAN ANDCENTER CITYRESIDENTIAL 1 4 352 259 3Phoenix 040134011 33.37005 -112.621 SOURCE ORIENTED RURAL AGRICULTURAL URBAN SCALE 2 4 258 12 3Phoenix 040139997 33.50364 -112.095POPULATIONEXPOSUREURBAN ANDCENTER CITYRESIDENTIAL 5 346 433 3Provo 490490002 40.25361 -111.663 UNKNOWNURBAN ANDCENTER CITYCOMMERCIAL 12 4 1402 353 2St. Louis 171630010 38.61203 -90.1605POPULATIONEXPOSURESUBURBAN INDUSTRIAL NEIGHBORHOOD 12 4 125 18 4St. Louis 291830010 38.57917 -90.8411 UNKNOWN RURAL AGRICULTURAL 3 3 0 340 3St. Louis 291831002 38.8725 -90.2264POPULATIONEXPOSURE RURAL AGRICULTURAL URBAN SCALE 12 4 131 31 3St. Louis 291890001 38.52167 -90.3436 UNKNOWN SUBURBAN RESIDENTIAL 3 4 183 161 2St. Louis 291890004 38.5325 -90.3828 UNKNOWN SUBURBAN RESIDENTIAL 6 4 183 95 2St. Louis 291890006 38.61361 -90.4958 UNKNOWN RURAL RESIDENTIAL 11 4 175 97 3St. Louis 291893001 38.64139 -90.3458 UNKNOWN SUBURBAN COMMERCIAL 11 4 161 5 1A-18Table A-7.Attributes of location-specific ambient monitors used for air quality characterization and the distance to nearest major roadway.Monitor5Roadway6Location Monitor ID Latitude Longitude Objective1Setting2Land Use3Scale4YearsHt(m)Elev (m)Dist(m)TypeSt. Louis 291895001 38.76611 -90.2858 UNKNOWN SUBURBAN COMMERCIAL 10 2 168 421 3St. Louis 291897002 38.72722 -90.3794 UNKNOWN SUBURBAN RESIDENTIAL 6 4 168 59 3St. Louis 291897003 38.72092 -90.367HIGHESTCONCENTRATION SUBURBAN RESIDENTIAL NEIGHBORHOOD 4 4 0 112 3St. Louis 295100072 38.62422 -90.1987 UNKNOWNURBAN ANDCENTER CITY COMMERCIAL 10 14 154 43 4St. Louis 295100080 38.68283 -90.2468HIGHESTCONCENTRATIONURBAN ANDCENTER CITY RESIDENTIAL NEIGHBORHOOD 5 4 152 116 3St. Louis 295100086 38.67227 -90.239HIGHESTCONCENTRATIONURBAN ANDCENTER CITY RESIDENTIAL NEIGHBORHOOD 6 4 0 133 3Washington DC 110010017 38.90361 -77.0517HIGHESTCONCENTRATIONURBAN ANDCENTER CITY COMMERCIAL NEIGHBORHOOD 1 10 20 54 3Washington DC 110010025 38.97528 -77.0228POPULATIONEXPOSUREURBAN ANDCENTER CITYCOMMERCIAL URBAN SCALE 12 11 91 106 3Washington DC 110010041 38.89722 -76.9528 UNKNOWNURBAN ANDCENTER CITYRESIDENTIAL NEIGHBORHOOD 12 8 141 4Washington DC 110010043 38.91889 -77.0125HIGHESTCONCENTRATIONURBAN ANDCENTER CITYCOMMERCIAL URBAN SCALE 12 50 278 3Washington DC 240053001 39.31083 -76.4744MAX PRECURSOREMISSIONS IMPACTSUBURBAN RESIDENTIAL NEIGHBORHOOD 8 4.6 5 186 3Washington DC 245100040 39.29806 -76.6047HIGHESTCONCENTRATIONURBAN ANDCENTER CITYRESIDENTIAL NEIGHBORHOOD 11 4.2 12 14 3Washington DC 245100050 39.31861 -76.5825POPULATIONEXPOSUREURBAN ANDCENTER CITYRESIDENTIAL REGIONAL SCALE 1 4 49 338 2Washington DC 510130020 38.8575 -77.0592 UNKNOWNURBAN ANDCENTER CITYCOMMERCIAL 12 7 171 80 3Washington DC 510590005 38.89389 -77.4653POPULATIONEXPOSURERURAL AGRICULTURAL NEIGHBORHOOD 11 4 77 315 5Washington DC 510590018 38.7425 -77.0775 UNKNOWN SUBURBAN RESIDENTIAL 3 4 11 54 3Washington DC 510591004 38.86806 -77.1431 UNKNOWN SUBURBAN COMMERCIAL 6 11 110 84 5Washington DC 510591005 38.83752 -77.1632POPULATIONEXPOSURESUBURBAN RESIDENTIAL 4 83.9 50 3Washington DC 510595001 38.93194 -77.1989POPULATIONEXPOSURESUBURBAN RESIDENTIAL NEIGHBORHOOD 10 4 106 18 5Washington DC 511071005 39.02444 -77.49POPULATIONEXPOSURESUBURBAN RESIDENTIAL NEIGHBORHOOD 8 4 0 75 3Washington DC 511530009 38.85528 -77.6356POPULATIONEXPOSURESUBURBAN RESIDENTIAL URBAN SCALE 12 4 111 196 2Washington DC 515100009 38.81083 -77.0447 UNKNOWNURBAN ANDCENTER CITYRESIDENTIAL 12 11 23 83 3Notes:1Objective indicates the reason for measuring air quality by the monitor. Sites located to determine the highest concentrationexpected to occur in the area covered by the network (HighestConcentration), sites located to measure typical concentrationsin areas of high population (Population Exposure), sites located to determine the impact of significant sources or source categories on airquality (Source Oriented), sites located to determine generalbackground concentration levels (General Background), sites located to determine the extent of regional pollutant transport among populatedareas and in support of secondary standards (Regional Transport), sites located to measure air pollution impacts on visibility,vegetation damage, or other welfare-based impacts (Welfare RelatedImpacts), sites are established to characterize upwind background and transported ozone and its precursor concentrations entering the area and will identify those areas which are subjected to transport(Upwind Background), sites are established to monitor the magnitude and type of precursor emissions in the area where maximum precursor emissions are expected to impact and are suited for theA-19Table A-7.Attributes of location-specific ambient monitors used for air quality characterization and the distance to nearest major roadway.Monitor5Roadway6Location Monitor ID Latitude Longitude Objective1Setting2Land Use3Scale4YearsHt(m)Elev (m)Dist(m)Typemonitoring of urban air toxic pollutants (Max. Precursor Impact), sites are intended to monitor maximum ozone concentrations occurring downwind from the area of maximum precursor emissions (Max.Ozone Concentration), and sites are establishedto characterize the downwind transported ozone and its precursor concentrationsexiting the area and will identify those areas which are potentiallycontributing to overwhelming transport in other areas (Extreme Downwind).2Setting is the description of the environmentalsetting within which the site is located3Land use indicates the prevalent land use within 1/4 mile of that site.4Scale indicates what the data from a monitor can represent in terms of air volumesassociated with area dimensions. Micro - 0to 100 meters; Middle - 100 to 500 meters; Neighborhood - 500 meters to4 kilometers; Urban Scale - 4 to 50 kilometers; Regional Scale - 50 kilometers up to 1000km.5Years is the number of valid site-years available for the monitor. Monitor probe height (Ht)and site elevation (Elev) abovesea level are given in meters (m).6Distances (Dist) to nearest major roadway are given in meters(m). Major road types were defined as: 1=primary limited accessor interstate, 2=primary US and State highways, 3=Secondary Stateand County, 4=freeway ramp, 5=other ramps.A-201Table A-8.Distance of location-specific ambient monitorsto stationary sources emitting >5 tons of NOxper year, within a 10 kilometer distance of monitoring site.Distance (km) to Source emissions >5 tpy and within 10 km Emissions (tpy) of Sources within 10 km and >5 tpyLocation ID n1mean std min 2.5 50 97.5 max mean std min 2.5 50 97.5 maxAtlanta 130890002 1 4.9 4.9 4.9 4.9 4.9 4.9 34 34 34 34 34 34Atlanta 130893001 3 7.2 4.0 2.7 2.7 9.2 9.8 9.8 34 2 32 32 34 36 36Atlanta 131210048 5 6.4 3.3 0.7 0.7 7.3 8.9 8.9 1249 2106 22 22 39 4895 4895Atlanta 132230003 0Atlanta 132470001 0Boston 230313002 5 3.5 1.5 1.0 1.0 3.8 4.9 4.9 642 769 31 31 203 1860 1860Boston 250051005 3 6.7 1.6 5.5 5.5 6.0 8.5 8.5 9 4 5 5 8 14 14Boston 250092006 12 6.8 2.7 2.5 2.5 7.4 9.9 9.9 439 1083 5 5 21 3794 3794Boston 250094004 0Boston 250095005 10 5.8 2.3 1.7 1.7 6.7 8.6 8.6 201 347 6 6 29 923 923Boston 250210009 57 5.8 2.5 1.0 1.8 5.9 9.9 9.9 106 283 5 5 9 1155 1419Boston 250250002 62 4.6 2.4 0.6 1.1 4.3 9.4 9.7 98 273 5 5 9 1155 1419Boston 250250021 55 6.1 2.3 1.5 1.7 6.5 9.8 9.8 130 304 5 5 11 1155 1419Boston 250250035 62 5.1 2.6 0.3 0.8 5.1 9.0 9.6 99 273 5 5 9 1155 1419Boston 250250036 62 5.1 2.6 0.3 0.8 5.1 9.0 9.6 99 273 5 5 9 1155 1419Boston 250250040 56 5.3 2.4 0.4 0.9 5.6 9.0 9.3 106 286 5 5 9 1155 1419Boston 250250041 25 7.8 2.0 0.7 0.7 8.2 9.9 9.9 81 206 5 5 11 957 957Boston 250250042 65 5.3 2.8 0.7 1.0 4.9 10.0 10.0 94 267 5 5 9 1155 1419Boston 250251003 49 6.4 2.4 0.6 1.0 7.0 9.6 9.6 145 319 5 5 11 1155 1419Boston 250270020 28 3.7 2.5 0.1 0.1 2.9 8.6 8.6 58 165 5 5 13 868 868Boston 250270023 28 3.6 2.4 0.4 0.4 3.0 8.4 8.4 58 165 5 5 13 868 868Boston 330110016 0Boston 330110019 0Boston 330110020 0Boston 330150009 5 3.3 1.0 2.0 2.0 3.3 4.4 4.4 642 769 31 31 203 1860 1860Boston 330150013 1 8.4 8.4 8.4 8.4 8.4 8.4 29 29 29 29 29 29Boston 330150014 5 4.0 1.8 1.0 1.0 4.4 5.5 5.5 642 769 31 31 203 1860 1860Boston 330150015 5 3.1 0.9 1.9 1.9 3.0 4.1 4.1 642 769 31 31 203 1860 1860Chicago 170310037 17 5.6 2.7 0.7 0.7 5.7 9.5 9.5 18 31 5 5 7 126 126Chicago 170310063 57 4.9 3.2 0.4 0.5 4.9 9.4 10.0 110 416 5 5 9 1677 2465Chicago 170310064 33 6.9 2.5 1.2 1.2 6.9 10.0 10.0 94 428 5 5 10 2465 2465Chicago 170310075 31 7.3 2.7 0.8 0.8 8.4 9.9 9.9 10 7 5 5 7 36 36Chicago 170310076 46 7.8 2.3 1.3 1.6 8.4 9.8 9.9 170 463 5 5 10 1677 2204Chicago 170313101 30 6.6 2.2 2.7 2.7 7.2 9.7 9.7 313 1638 5 5 9 8985 8985Chicago 170313103 30 6.6 2.2 2.7 2.7 7.2 9.7 9.7 313 1638 5 5 9 8985 8985Chicago 170314002 63 6.7 2.6 0.5 0.5 7.2 9.8 9.9 122 407 5 5 9 1677 2465Chicago 170314201 7 6.5 1.5 4.0 4.0 6.6 9.0 9.0 8 3 5 5 8 14 14Chicago 170318003 63 7.3 2.0 1.7 2.3 8.0 9.6 9.7 361 1201 5 5 18 6216 7141Chicago 171971011 1 4.0 4.0 4.0 4.0 4.0 4.0 20 20 20 20 20 20Chicago 180890022 8 5.1 3.8 0.8 0.8 4.1 9.4 9.4 815 1680 8 8 243 4936 4936A-21Table A-8.Distance of location-specific ambient monitorsto stationary sources emitting >5 tons of NOxper year, within a 10 kilometer distance of monitoring site.Distance (km) to Source emissions >5 tpy and within 10 km Emissions (tpy) of Sources within 10 km and >5 tpyLocation ID n1mean std min 2.5 50 97.5 max mean std min 2.5 50 97.5 maxChicago 180891016 8 4.7 2.4 2.1 2.1 4.1 7.6 7.6 815 1680 8 8 243 4936 4936Cleveland 390350043 5 8.1 1.9 5.2 5.2 8.3 9.9 9.9 673 664 126 126 284 1476 1476Cleveland 390350060 4 4.1 2.4 1.0 1.0 4.4 6.4 6.4 810 681 165 165 800 1476 1476Cleveland 390350066 5 8.0 1.9 5.2 5.2 8.3 9.8 9.8 673 664 126 126 284 1476 1476Cleveland 390350070 5 7.6 1.8 5.5 5.5 7.3 9.7 9.7 673 664 126 126 284 1476 1476Colorado Springs 080416001 4 5.1 4.4 0.8 0.8 5.1 9.1 9.1 780 1374 16 16 133 2835 2835Colorado Springs 080416004 10 5.9 2.2 3.5 3.5 5.6 9.8 9.8 48 80 5 5 17 267 267Colorado Springs 080416005 9 7.5 2.1 3.3 3.3 8.1 9.5 9.5 490 1393 5 5 11 4205 4205Colorado Springs 080416006 0Colorado Springs 080416009 4 5.2 4.3 1.0 1.0 5.3 9.3 9.3 780 1374 16 16 133 2835 2835Colorado Springs 080416011 14 5.0 2.3 2.0 2.0 5.8 9.6 9.6 345 1113 5 5 22 4205 4205Colorado Springs 080416013 14 6.3 2.9 2.1 2.1 6.9 9.9 9.9 346 1113 5 5 27 4205 4205Colorado Springs 080416018 11 6.9 1.7 4.3 4.3 7.1 9.6 9.6 430 1254 5 5 34 4205 4205Denver 080013001 34 5.3 1.8 1.6 1.6 4.7 9.5 9.5 310 1622 5 5 15 9483 9483Denver 080050003 19 6.7 3.7 1.0 1.0 9.1 10.0 10.0 313 1233 5 5 17 5404 5404Denver 080310002 52 5.3 2.5 0.9 0.9 5.8 9.7 9.8 319 1495 5 5 14 5404 9483Denver 080590006 9 5.9 2.1 2.7 2.7 6.3 8.6 8.6 63 66 11 11 39 182 182Denver 080590008 9 6.2 2.0 3.7 3.7 6.1 10.0 10.0 59 68 8 8 13 182 182Denver 080590009 10 6.5 3.2 2.5 2.5 7.0 9.9 9.9 53 66 6 6 13 182 182Denver 080590010 7 5.5 3.1 1.1 1.1 5.6 9.2 9.2 73 71 12 12 44 182 182Detroit 260990009 4 4.9 3.2 0.3 0.3 5.7 7.7 7.7 63 70 7 7 46 152 152Detroit 261630016 51 7.4 2.1 1.3 2.0 7.9 9.8 9.9 387 797 5 6 41 3087 3762Detroit 261630019 32 6.3 2.2 2.6 2.6 6.5 10.0 10.0 57 168 5 5 12 837 837El Paso 481410027 22 8.1 1.6 1.5 1.5 8.6 9.3 9.3 99 195 5 5 29 912 912El Paso 481410028 24 2.2 1.9 0.9 0.9 1.6 9.3 9.3 127 338 5 5 32 1679 1679El Paso 481410037 15 8.7 2.6 0.1 0.1 9.4 10.0 10.0 135 230 5 5 38 912 912El Paso 481410044 25 5.9 1.2 4.4 4.4 5.6 9.5 9.5 158 366 5 5 32 1679 1679El Paso 481410055 24 2.8 1.8 1.6 1.6 2.2 9.6 9.6 127 338 5 5 32 1679 1679El Paso 481410057 0El Paso 481410058 16 8.8 0.4 8.4 8.4 8.6 9.5 9.5 31 30 5 5 23 106 106Jacksonville 120310032 20 5.1 3.0 0.7 0.7 5.7 9.6 9.6 201 407 5 5 31 1642 1642Las Vegas 320030022 7 4.6 0.9 3.8 3.8 3.9 5.6 5.6 175 222 30 30 77 650 650Las Vegas 320030023 0Las Vegas 320030073 0Las Vegas 320030078 0Las Vegas 320030539 5 6.9 1.2 4.7 4.7 7.2 7.9 7.9 816 760 18 18 851 1665 1665Las Vegas 320030557 4 9.1 1.2 7.3 7.3 9.7 9.7 9.7 807 877 18 18 772 1665 1665Las Vegas 320030563 1 7.6 7.6 7.6 7.6 7.6 7.6 84 84 84 84 84 84Las Vegas 320030601 0Las Vegas 320031019 0Las Vegas 320032002 1 9.9 9.9 9.9 9.9 9.9 9.9 84 84 84 84 84 84Los Angeles 060370002 7 3.1 1.1 1.6 1.6 2.9 4.5 4.5 10 4 5 5 9 16 16A-22Table A-8.Distance of location-specific ambient monitorsto stationary sources emitting >5 tons of NOxper year, within a 10 kilometer distance of monitoring site.Distance (km) to Source emissions >5 tpy and within 10 km Emissions (tpy) of Sources within 10 km and >5 tpyLocation ID n1mean std min 2.5 50 97.5 max mean std min 2.5 50 97.5 maxLos Angeles 060370016 7 7.5 1.8 4.5 4.5 8.5 8.9 8.9 12 8 5 5 9 29 29Los Angeles 060370030 35 5.5 2.3 2.1 2.1 5.2 9.8 9.8 23 27 5 5 11 115 115Los Angeles 060370113 7 4.3 3.1 1.3 1.3 3.2 9.8 9.8 15 10 5 5 13 36 36Los Angeles 060370206 11 5.6 2.2 2.3 2.3 5.8 9.2 9.2 32 31 6 6 20 109 109Los Angeles 060371002 18 5.7 2.6 0.1 0.1 6.0 9.9 9.9 47 59 6 6 24 215 215Los Angeles 060371103 31 6.5 2.7 1.8 1.8 7.2 10.0 10.0 18 21 5 5 10 86 86Los Angeles 060371201 7 5.1 1.2 3.3 3.3 5.5 6.5 6.5 10 4 6 6 10 15 15Los Angeles 060371301 45 6.8 2.1 1.2 2.5 7.1 9.7 10.0 22 24 5 5 12 86 115Los Angeles 060371601 22 6.5 2.3 2.3 2.3 7.2 9.7 9.7 28 33 5 5 12 115 115Los Angeles 060371701 13 6.1 3.0 1.1 1.1 7.0 9.7 9.7 22 20 5 5 16 70 70Los Angeles 060372005 10 5.2 3.5 0.2 0.2 5.5 10.0 10.0 12 8 5 5 9 30 30Los Angeles 060374002 55 6.4 2.3 1.7 2.2 6.2 9.9 9.9 76 159 5 5 16 744 789Los Angeles 060375001 32 5.1 2.4 0.3 0.3 4.8 9.6 9.6 205 754 6 6 21 4256 4256Los Angeles 060375005 25 4.6 2.4 1.4 1.4 4.6 9.9 9.9 224 850 6 6 21 4256 4256Los Angeles 060376002 5 5.6 1.8 3.6 3.6 5.8 7.8 7.8 29 20 8 8 18 54 54Los Angeles 060376012 6 6.2 2.5 3.0 3.0 6.8 9.7 9.7 26 19 8 8 18 54 54Los Angeles 060379002 4 7.8 1.0 6.8 6.8 7.7 9.2 9.2 22 28 6 6 9 64 64Los Angeles 060379033 4 6.3 0.8 5.3 5.3 6.4 7.1 7.1 22 28 6 6 9 64 64Los Angeles 060590001 17 6.4 2.4 2.8 2.8 7.2 9.4 9.4 14 12 5 5 8 46 46Los Angeles 060590007 17 6.4 2.4 2.8 2.8 7.2 9.4 9.4 14 12 5 5 8 46 46Los Angeles 060591003 14 6.1 2.2 2.1 2.1 6.0 9.3 9.3 65 116 5 5 10 434 434Los Angeles 060595001 16 7.9 1.6 3.4 3.4 8.2 9.5 9.5 19 26 6 6 9 109 109Los Angeles 060650012 0Los Angeles 060655001 0Los Angeles 060658001 12 7.4 2.2 3.6 3.6 7.4 9.8 9.8 119 358 5 5 10 1254 1254Los Angeles 060659001 2 4.6 5.9 0.4 0.4 4.6 8.7 8.7 11 9 5 5 11 17 17Los Angeles 060710001 3 6.9 1.9 5.3 5.3 6.5 9.0 9.0 209 321 10 10 38 579 579Los Angeles 060710012 0Los Angeles 060710014 3 6.0 2.6 3.5 3.5 5.9 8.6 8.6 199 327 6 6 15 577 577Los Angeles 060710015 3 4.4 4.6 1.7 1.7 1.8 9.7 9.7 752 1045 12 12 296 1948 1948Los Angeles 060710017 0Los Angeles 060710306 3 6.1 2.6 3.6 3.6 5.7 8.9 8.9 199 327 6 6 15 577 577Los Angeles 060711004 19 7.3 1.7 4.3 4.3 7.4 9.8 9.8 57 120 5 5 18 492 492Los Angeles 060711234 2 1.6 0.4 1.3 1.3 1.6 1.9 1.9 1122 1168 296 296 1122 1948 1948Los Angeles 060712002 20 5.7 2.2 2.0 2.0 5.8 9.6 9.6 44 65 5 5 17 250 250Los Angeles 060714001 1 6.5 6.5 6.5 6.5 6.5 6.5 577 577 577 577 577 577Los Angeles 060719004 8 5.8 2.5 1.5 1.5 5.7 9.0 9.0 171 438 5 5 10 1254 1254Los Angeles 061110005 5 6.9 2.5 3.1 3.1 7.7 9.6 9.6 68 118 8 8 19 278 278Los Angeles 061110007 20 4.7 2.2 1.7 1.7 4.2 9.3 9.3 25 20 5 5 18 76 76Los Angeles 061111003 0Los Angeles 061111004 0Los Angeles 061112002 4 6.6 1.0 5.2 5.2 6.8 7.5 7.5 63 113 5 5 7 232 232A-23Table A-8.Distance of location-specific ambient monitorsto stationary sources emitting >5 tons of NOxper year, within a 10 kilometer distance of monitoring site.Distance (km) to Source emissions >5 tpy and within 10 km Emissions (tpy) of Sources within 10 km and >5 tpyLocation ID n1mean std min 2.5 50 97.5 max mean std min 2.5 50 97.5 maxLos Angeles 061112003 3 5.5 1.3 4.1 4.1 5.6 6.7 6.7 18 4 14 14 20 22 22Los Angeles 061113001 7 5.1 2.3 1.9 1.9 5.9 7.4 7.4 35 51 5 5 13 146 146Miami 120110003 0Miami 120110031 0Miami 120118002 0Miami 120860027 3 4.1 4.2 1.6 1.6 1.8 8.9 8.9 31 19 14 14 27 51 51Miami 120864002 8 7.0 2.6 1.3 1.3 7.8 9.1 9.1 22 15 8 8 18 51 51New York 090010113 7 4.4 3.1 1.4 1.4 3.4 8.8 8.8 538 711 48 48 192 1689 1689New York 090019003 3 6.3 2.0 4.0 4.0 7.4 7.5 7.5 127 179 12 12 37 333 333New York 090090027 5 2.7 1.0 1.3 1.3 2.7 3.9 3.9 280 484 14 14 86 1144 1144New York 090091123 6 3.3 2.8 1.2 1.2 2.4 8.9 8.9 234 447 7 7 64 1144 1144New York 340030001 48 6.5 2.2 2.9 2.9 6.3 9.8 9.9 468 1506 6 7 31 4440 9022New York 340030005 18 6.8 2.9 0.1 0.1 7.4 10.0 10.0 53 79 6 6 21 307 307New York 340130011 43 5.4 2.9 0.7 0.8 5.8 9.4 9.5 273 1372 5 5 18 640 9022New York 340130016 44 5.5 2.8 0.1 1.0 6.3 9.4 9.6 267 1357 5 5 18 640 9022New York 340131003 32 6.4 2.0 2.1 2.1 6.8 9.3 9.3 77 149 5 5 22 640 640New York 340170006 42 6.9 2.5 1.1 1.6 7.7 9.5 9.5 369 1420 5 6 24 2213 9022New York 340210005 8 5.4 1.7 3.2 3.2 5.5 7.3 7.3 115 244 8 8 32 718 718New York 340230011 20 6.1 2.8 1.0 1.0 7.0 9.5 9.5 95 175 6 6 36 792 792New York 340273001 1 8.5 8.5 8.5 8.5 8.5 8.5 20 20 20 20 20 20New York 340390004 46 6.3 2.4 0.7 0.9 6.6 9.6 9.7 134 341 5 6 21 594 2213New York 340390008 12 7.2 2.1 3.2 3.2 8.0 10.0 10.0 23 36 5 5 10 134 134New York 360050080 54 6.4 2.3 1.8 1.8 6.4 9.9 9.9 241 776 6 6 29 3676 4440New York 360050083 37 6.0 2.8 1.6 1.6 6.3 9.9 9.9 171 725 6 6 21 4440 4440New York 360050110 55 5.9 2.2 2.1 2.6 5.7 9.6 9.9 236 769 6 6 29 3676 4440New York 360470011 56 5.9 2.7 0.7 1.5 5.7 9.7 10.0 296 787 7 7 42 3676 4440New York 360590005 7 6.3 3.4 1.9 1.9 8.1 9.8 9.8 372 500 7 7 223 1451 1451New York 360610010 52 5.9 2.5 0.3 1.4 6.1 9.6 9.8 494 1453 5 7 50 4440 9022New York 360610056 54 5.4 2.6 0.3 1.4 5.5 9.9 10.0 470 1429 7 7 50 4440 9022New York 360810097 11 6.3 2.1 2.9 2.9 6.9 9.5 9.5 65 77 13 13 26 246 246New York 360810098 48 7.1 2.3 1.6 2.8 7.8 9.8 9.8 262 820 6 7 31 3676 4440New York 360810124 24 7.0 2.6 2.1 2.1 8.0 10.0 10.0 436 1136 8 8 26 4440 4440New York 361030009 3 3.8 3.2 2.0 2.0 2.0 7.6 7.6 537 759 40 40 161 1410 1410Philadelphia 100031003 39 5.5 2.5 1.6 1.6 6.2 9.7 9.7 282 481 5 5 62 2058 2058Philadelphia 100031007 11 9.2 0.6 8.0 8.0 9.3 9.8 9.8 323 494 6 6 63 1351 1351Philadelphia 100032004 32 4.8 1.9 0.7 0.7 4.7 8.4 8.4 223 403 5 5 45 1312 1312Philadelphia 340070003 69 7.7 2.3 1.8 2.0 8.5 10.0 10.0 87 196 5 5 24 477 1478Philadelphia 420170012 10 4.1 2.3 1.2 1.2 4.2 9.4 9.4 85 96 11 11 57 275 275Philadelphia 420450002 30 4.8 2.6 0.2 0.2 5.4 9.5 9.5 504 1055 5 5 73 4968 4968Philadelphia 420910013 12 5.1 2.5 1.4 1.4 4.3 8.8 8.8 89 232 5 5 12 823 823Philadelphia 421010004 32 5.9 2.5 1.0 1.0 5.6 9.9 9.9 58 111 5 5 20 571 571Philadelphia 421010029 74 5.7 2.1 1.1 1.8 5.6 9.7 9.7 74 148 5 5 19 477 1033A-24Table A-8.Distance of location-specific ambient monitorsto stationary sources emitting >5 tons of NOxper year, within a 10 kilometer distance of monitoring site.Distance (km) to Source emissions >5 tpy and within 10 km Emissions (tpy) of Sources within 10 km and >5 tpyLocation ID n1mean std min 2.5 50 97.5 max mean std min 2.5 50 97.5 maxPhiladelphia 421010047 73 5.2 2.1 0.6 0.8 4.8 9.6 9.7 95 221 5 5 19 1033 1478Phoenix 040130019 11 6.8 2.2 4.2 4.2 6.7 9.8 9.8 106 313 5 5 10 1049 1049Phoenix 040133002 6 4.1 2.3 1.3 1.3 4.1 6.9 6.9 21 19 5 5 15 56 56Phoenix 040133003 10 6.7 1.4 4.1 4.1 6.6 9.0 9.0 50 80 9 9 24 272 272Phoenix 040133010 10 5.0 0.9 3.5 3.5 4.9 6.6 6.6 115 328 5 5 10 1049 1049Phoenix 040134005 11 5.8 2.9 0.8 0.8 7.0 9.4 9.4 81 116 6 6 38 350 350Phoenix 040134011 1 6.4 6.4 6.4 6.4 6.4 6.4 18 18 18 18 18 18Phoenix 040139997 10 8.5 1.2 5.6 5.6 8.7 9.9 9.9 115 328 5 5 10 1049 1049Provo 490490002 7 6.6 3.7 1.2 1.2 8.2 9.4 9.4 60 38 7 7 83 102 102St Louis 171630010 48 7.0 2.8 1.3 1.9 8.0 9.8 9.9 112 178 5 5 17 538 848St Louis 291830010 1 1.7 1.7 1.7 1.7 1.7 1.7 7821 7821 7821 7821 7821 7821St Louis 291831002 9 7.5 2.1 4.3 4.3 7.7 9.9 9.9 1868 4704 7 7 8 14231 14231St Louis 291890001 10 7.7 1.3 6.2 6.2 7.4 9.8 9.8 24 20 5 5 15 60 60St Louis 291890004 6 8.9 1.5 6.9 6.9 9.8 10.0 10.0 38 37 7 7 28 105 105St Louis 291890006 8 7.0 1.7 4.2 4.2 7.9 8.7 8.7 25 34 6 6 11 105 105St Louis 291893001 16 7.3 2.0 3.4 3.4 7.6 9.6 9.6 22 43 5 5 11 181 181St Louis 291895001 11 7.5 1.7 4.3 4.3 7.7 9.7 9.7 46 62 5 5 15 181 181St Louis 291897002 16 5.7 1.8 2.0 2.0 5.4 9.7 9.7 28 37 5 5 15 143 143St Louis 291897003 16 6.2 2.0 2.5 2.5 6.0 9.6 9.6 24 33 5 5 15 143 143St Louis 295100072 46 6.3 2.5 0.7 2.0 6.5 9.9 9.9 77 150 5 5 16 508 848St Louis 295100080 31 6.9 2.2 0.4 0.4 7.3 10.0 10.0 98 176 5 5 17 848 848St Louis 295100086 35 6.7 2.3 1.7 1.7 6.6 9.9 9.9 94 168 5 5 17 848 848Washington DC 110010017 13 5.4 2.4 2.9 2.9 4.5 9.7 9.7 557 1643 11 11 34 6009 6009Washington DC 110010025 6 6.4 1.0 4.8 4.8 6.5 7.6 7.6 40 35 11 11 26 98 98Washington DC 110010041 10 6.1 2.4 0.6 0.6 6.1 9.8 9.8 124 137 11 11 66 410 410Washington DC 110010043 12 5.0 3.2 0.3 0.3 4.6 9.8 9.8 109 129 11 11 46 410 410Washington DC 240053001 11 7.5 2.1 2.6 2.6 7.9 9.7 9.7 1034 3225 6 6 45 10756 10756Washington DC 245100040 26 5.0 2.5 0.3 0.3 4.9 9.5 9.5 122 220 6 6 56 1118 1118Washington DC 245100050 24 6.2 2.1 2.4 2.4 6.0 10.0 10.0 129 227 6 6 56 1118 1118Washington DC 510130020 14 6.2 2.6 1.5 1.5 5.4 9.8 9.8 558 1579 11 11 46 6009 6009Washington DC 510590005 2 4.9 4.8 1.4 1.4 4.9 8.3 8.3 13 7 8 8 13 18 18Washington DC 510590018 6 8.4 0.4 8.0 8.0 8.4 9.2 9.2 1104 2413 9 9 13 6009 6009Washington DC 510591004 10 7.4 1.6 3.7 3.7 7.8 9.3 9.3 80 173 14 14 19 571 571Washington DC 510591005 8 6.3 2.0 4.6 4.6 5.5 9.4 9.4 94 193 14 14 19 571 571Washington DC 510595001 4 6.5 2.8 3.2 3.2 6.8 9.2 9.2 30 19 17 17 22 58 58Washington DC 511071005 5 7.1 2.3 4.5 4.5 6.5 9.6 9.6 14 8 8 8 12 27 27Washington DC 511530009 0Washington DC 515100009 9 7.0 2.4 1.1 1.1 7.9 8.8 8.8 809 1959 14 14 156 6009 6009Notes:1n is the number of sources emitting >5 tons per year (tpy) NOxwithin a 10 kilometer radius of a monitor in a particular location.2The min, 2.5, med, 97.5, and max represent the minimum, 2.5th, median, 97.5th, and maximum percentiles of the distribution for thedistance in meters (m) to the source emission.

### 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 NO 2 

### concentrations collected from 1995 through 2006. Th e purpose is to present relevant information on the air quality as it relate s to both the current form of the standard (annual average concentration) and the exposure concentration and duration asso ciated with adverse health effects (1-hour).



## A-5.2 Approach To evaluate variability in NO 2 

### concentrations, temporal and sp atial distributions of summary statistics were computed in addition to use of statistical tests to comp are distributions between years and/or monitors and/or lo cations. For a given location, the variability within that location is defined by the distribution of the annual su mmary statistics across years and monitors and by the distribution of the hourly concentrations acro ss 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 dist ribution across sites and years (or hours for the hourly concentrations) for a single location. The box extends from the 25 th 

### to the 75 th 

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


### and 95 th 

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

### and 95 th 

### percentiles are not shown to allow for similar s caling along the y-axis for the plotted independent variables. The mean is plotted as a dot; typica lly it would appear inside the box, however it will fall outside the box if the distribut ion is highly skewed. All con centrations are shown in parts per billion (ppb).

Q-Q plots also display the dist ribution in the calculated air quality metrics across sites and years (or hours for the hourly c oncentrations) for a single locati on. The Q-Q plot is used to compare the observed cumulative distribution to a st andard statistical distribution. In this case the observed distributions are compared with a lo g-normal distribution, so th at the vertical scale is logarithmic. The horizontal s cale is the quantile of a standard normal dist ribution, so that if there are N observed values, then the k th 

### highest value is plotted ag ainst the quantile probit(p), where probit is the inverse of the st andard normal distri bution function, and p is the plotting point. The plotting points were chosen as p = (k-3/8)/(N+1/4) for th e annual statistics and p = k/(N+1) for the hourly concentra tions. If the distribution were exactly log-normal, then the curve would be a straight line. The median valu e is the y-value when the normal quantile equals zero. The slope of the line is rela ted to the standard deviation of the logarithms, so that the higher the slope, the higher the coefficient of variati on (standard deviation divided by the mean for the raw data, before taking logarithms).

In addition to the tabular and graphical co mparisons of the summary statistics, the distributions of each variable were compared us ing 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 loca tion, 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 Statis tics 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 sa me 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 alternativ es. The Mood Statistic comparisons are nonparametric tests that compare the scale statisti cs for two or more gr oups. The scale statistic measures variation about the central value, wh ich 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 acro ss all the locations to be compared. 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 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 impor tant trends in NO 2 

### concentrations is repor ted 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 gr aphic summaries of the spatia l and temporal concentration distributions.

A broad view of the NO 2 

### monitoring concentrations across locations is presented in Figures A-1 and A-2. In general th ere is variability in NO 2 

### 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 a nnual 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 locati on, 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 NO 2 ambient monitoring concentrations for selected CMSA locations, years 1995-2006.

Figure A-2. Distributions of annual mean NO 2 ambient monitoring concentrations for selected MSA and grouped locations, years 1995-2006.



### Differences in the distributions of hourly concentr ations 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 5 th 

### to the 95 th 

### percentiles) of hourly concen trations 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 th e influence of high 1-hour NO 2 

### concentrations for certain locations.

Figure A-3. Distributions 3 of hourly NO 2 ambient monitoring concentrations for selected CMSA locations, years 1995-2006.



### This presence of extreme NO 2 

### concentrations is best illustra ted in Figure 4 using a Q-Q plot that captures the full concentration distribu tion for each CMSA location. The Q-Q plots are generally curved rather than stra ight, 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 medi an (normal quantile = 0) through the 3 rd 

### quantile, suggesting that these upper tails of the dist ributions are approxima tely log-normal with approximately the same coefficien ts of variation. Beyond the 3 rd 

### quantile though, each distribution similarly and distin ctly curves upwards, indicating a number of uncharacteristic NO 2 

### concentrations at each location when compared with the rest of their respective concentration distributions.

3 The box-plots for hourly concentrations were creat ed 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 appropri ate weighted percentiles and means were calculated and plotted as shown, but the vertical lines co mposing the sides of the box were omitted.

### A-29 Figure A-4. Distributions of hourly NO 2 ambient concentration for selected CMSA locations, years 1995- 2006.



### Distributions of each variable (annual means and hourly concentrations) were compared between the different locations usi ng statistical tests. The results in Table A-9 show statistically significant differences between locations for bot h variables and all three summary statistics (means, medians, and scales). Th is supports the previous observati on 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 NO 2 

### concentrations within locations were also evaluated. As an example, Figure A-5 illustrates the distribution of the annual mean NO 2 

### concentration at 10 monitoring sites within Philadel phia. The mean annual means vary from a minimum of 14.8 ppb (site 1000310071) to a maximum of 30.5 ppb (s ite 4210100471). The range of within-site variability can be attributed to the number of monitoring ye ars available coupled with the observed trends in temporal va riability across the monitoring period (discussed below in Section 2.4.4).

Distributions of each variable (annual means and hourly NO 2 

### concentrations) within locations (i.e., site distributions) were compared us ing statistical tests. The results in Table A-10 indicate statistically significant differences with in locations for both va riables and the central

### A-30 

### tendency statistics (means and medians), while scal es 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 NO 2 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 NO 2 

### monitoring concentrations over time is presented in Figure A-6. The annual mean concentrations were calculat ed for each monitor site within each year to create a distribution of annual mean concentra tions for each year. The distribution of annual mean concentrations generally decreases with e ach increasing year. On average, mean annual mean NO 2 

### 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 consis tent 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 represen ting spatial differences in annual average concentrations across the 20 locations.

### A-32 Figure A-6. Distributions of annual mean NO 2 concentrations for all monitors, years 1995-2006.



### In general, temporal trends within a location we re 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 NO 2 

### concentration in the Philadelphia CMSA, each co mprising between 4 and 8 monitors in operation per year. Clearly NO 2 

### concentrations are decr eased with increasing calendar year of monitoring with the lowest NO 2 

### concentrations in the more recent ye ars of monitoring. The pattern of variability in NO 2 

### 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 NO 2 concentrations for the Philadelphia CMSA, years 1995- 2006.



### In general, temporal trends within a location considering the hourly c oncentration data were consistent with the above, particularly where the monitoring network was comprised of several monitors. For example, Figure A-8 illustra tes the temporal distribution for hourly NO 2 

### concentration in the Los Angeles CMSA, compri sing between 26 and 36 m onitors in operation per year. NO 2 

### concentrations are decreased with increa sing calendar year of monitoring with the distribution of hourly concentrations lowest in the more recent years of monitoring. The pattern of variability in NO 2 

### 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 NO 2 concentrations in the Los Angeles CMSA, years 1995-2006.



### These temporal trends were confirmed by st atistical comparison tests. The means and medians of the annual means and hourly concentrat ions 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 signif icantly different (both the annual a nd 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 te st 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 th at the annual means for different years have different mean values but similar coefficients of va riation. In fact the coef ficients of variation of the annual means are nearly identical for diffe rent years, ranging from 52 % to 55 %.

There were some exceptions to this temporal trend, particularly when considering the distribution of hourly concentratio ns and where a given location ha d only few monitors per year. Using Jacksonville as an example, Figure A-9 illustrates the same temporal trend in NO 2 

### concentrations as was observed above for much of the distri bution, however di stinctions are noted at the upper tails of the distribution for two year s of data, 2002 and 2004. For Jacksonville, each years’ hourly concentration dist ribution was based on only a single monitor. Where few monitors exist in a given location, atypi cal variability in one or a few monitors from year to year can greatly influen ce the distribution of short-term c oncentrations, part icularly at the upper percentiles.

The same follows for assignment of statistical significance to temporal trends within locations. While annual average co ncentrations are observed to have declined over time within a location, the number of sites were typically few t hus limiting the power of the statistical tests.

### A-35 

### Only Los Angeles, El Paso, Phoenix, and Othe r CMSA were significant (p<0.05) for the central tendency tests, while only Los Angeles and Ot her CMSA were significant (p<0.05) for scale (data not shown). All hourly con centrations comparison tests for y ears within each location were statistically significant (p<0.05) for all three test statis tics (mean, median, scale).

Figure A-9

. Distributions of hourly NO 2 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 locati on. 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 cons idering 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 NO 2 

### (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 signifi cant benefit by changes in source emissions and/or NO 2 

### standard levels compared with the named CMSA/MSA locations.

### A-36 Figure A-10. Distributions of annual average NO 2 concentrations in the Other Not MSA group location, years 1995-2006.



## A-5.5 Detailed Resul ts by Year and Location This section contains the ambient air quality an alysis results by year for each of the named locations. Boxplots were constructed to displa y the annual average and hourly concentration distributions across years for a single location. The box extends from the 25 th 

### to the 75 th 

### percentile, with the median s hown as the line inside the box. The whiskers extend from the box to the 5 th 

### and 95 th 

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

### and 95 th 

### 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 woul d appear inside the box, however it will fall outside the box if the distribution is highly sk ewed. All concentrations are shown in parts per billion (ppb). The boxplots fo r 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 graphi ng procedure to allow frequenc y weights. Therefore, the appropriate weighted percent iles 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-37Figure A-11.Distribution of annual average NO2ambientconcentrations (ppb) by year, Boston CMSA.Figure A-12.Distribution of hourly NO2ambient concentrations(ppb) by year, Boston CMSA.Table A-11.Distribution of annual average NO2ambient concentrations (ppb) byyear, Boston CMSA.Year n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1995 12 20 7 34 6 12 14 16 21 22 22 23 23 27 311996 10 19 8 42 6 8 11 14 17 19 21 24 26 29 311997 11 17 8 44 6 9 11 13 15 16 19 22 22 27 301998 11 17 8 48 6 8 10 12 15 15 19 23 23 28 311999 7 19 9 45 6 6 9 20 20 21 21 21 27 30 302000 7 17 8 49 5 5 10 11 11 18 20 20 22 29 292001 10 16 8 50 7 7 8 10 12 16 20 22 24 28 302002 10 16 7 43 5 7 10 12 13 15 19 22 24 25 252003 5 15 6 42 9 9 10 11 11 12 17 21 22 22 222004 7 15 6 41 7 7 9 12 12 16 16 16 17 25 252005 8 14 6 39 7 7 10 10 11 13 15 18 19 23 232006 7 13 6 42 8 8 9 10 10 10 15 15 19 23 23Table A-12.Distribution of hourly NO2ambient concentrations (ppb) by year, BostonCMSA.Year n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1995 99946 20 12 62 0 5 9 12 15 18 22 26 30 36 1001996 83541 19 14 72 0 3 7 10 13 16 21 25 30 38 2051997 90161 17 12 72 0 3 6 9 11 15 18 23 28 35 1341998 89710 17 13 75 0 3 5 8 11 15 18 23 28 35 1121999 54043 19 13 70 0 3 7 10 13 17 21 25 30 37 1172000 56196 16 12 76 0 2 5 7 11 14 18 22 27 34 952001 82048 16 13 77 0 2 4 7 10 14 18 22 27 34 1142002 80472 16 12 75 0 2 5 7 10 14 17 21 26 32 932003 41198 15 11 75 0 3 5 7 10 13 16 19 24 31 992004 56831 15 10 71 0 3 5 7 10 12 15 19 23 29 962005 66244 14 11 75 0 3 5 7 9 12 15 18 23 29 1132006 57681 13 10 74 0 3 4 6 8 11 14 17 22 28 79A-38Figure A-13.Distribution of annual average NO2ambientconcentration (ppb) by monitor, Boston CMSA set A, 1995-2006.Figure A-14.Distribution of hourly NO2ambient concentration(ppb) by monitor, Boston CMSA set A, 1995-2006.Table A-13.Distribution of annual average NO2ambient concentration (ppb) bymonitor, Boston CMSA set A, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max2303130021 7 10 1 9 9 9 9 9 9 10 10 10 10 11 112500510051 2 8 1 9 8 8 8 8 8 8 9 9 9 9 92500920061 10 12 3 22 9 9 10 10 11 11 13 15 15 16 162500940041 5 6 0 7 6 6 6 6 6 6 6 6 7 7 72500950051 2 9 1 8 9 9 9 9 9 9 10 10 10 10 102502100091 1 22 22 22 22 22 22 22 22 22 22 22 222502500021 11 28 3 11 23 23 25 25 29 30 30 30 31 31 312502500211 8 25 3 12 21 21 22 23 27 27 27 27 28 28 282502500351 1 23 23 23 23 23 23 23 23 23 23 23 232502500361 1 22 22 22 22 22 22 22 22 22 22 22 222502500401 11 21 2 10 16 18 20 21 21 21 22 22 23 23 232502500411 1 12 12 12 12 12 12 12 12 12 12 12 122502500421 6 21 3 16 17 17 19 19 19 21 22 24 24 25 252502510031 5 23 2 7 21 21 21 22 22 23 23 23 24 25 252502700201 8 19 1 6 17 17 18 19 19 19 19 20 20 21 21Table A-14.Distribution of hourly NO2ambient concentration (ppb) by monitor,Boston CMSA set A, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max2303130021 58123 10 9 94 0 1 2 4 5 7 9 12 16 23 1002500510051 16732 8 7 81 0 2 3 4 5 6 8 10 13 18 502500920061 80761 12 10 80 0 3 4 6 7 9 12 15 20 27 902500940041 41337 6 7 108 0 0 1 2 3 4 6 7 10 16 702500950051 16228 9 8 91 0 2 3 4 5 6 8 11 14 22 512502100091 8546 22 10 46 0 9 13 15 18 21 23 27 30 35 752502500021 87534 28 11 40 0 14 18 21 24 27 30 33 37 43 1342502500211 63990 25 11 45 0 13 16 18 21 24 26 30 34 40 2052502500351 8539 23 10 47 0 10 13 16 19 21 24 27 31 37 742502500361 8542 22 11 49 0 9 12 15 19 21 24 28 31 36 1002502500401 91196 21 12 59 1 7 10 13 16 19 22 26 31 38 1132502500411 8319 12 10 89 0 2 3 5 6 8 11 15 19 27 812502500421 48078 21 10 48 0 9 12 15 17 20 22 25 29 35 79A-39Figure A-15.Distribution of annual average NO2ambientconcentration (ppb) by monitor, Boston CMSA set B, 1995-2006.Figure A-16.Distribution of hourly NO2ambient concentration(ppb) by monitor, Boston CMSA set B, 1995-2006.Table A-15.Distribution of annual average NO2ambient concentration (ppb) bymonitor, Boston CMSA set B, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max2502700231 3 15 0 3 15 15 15 15 15 15 15 16 16 16 163301100161 4 16 2 15 14 14 14 15 15 15 16 16 19 19 193301100191 1 11 11 11 11 11 11 11 11 11 11 11 113301100201 5 12 1 8 10 10 11 11 12 12 12 12 12 13 133301500091 5 12 1 12 9 9 11 12 12 12 12 12 13 13 133301500131 4 6 1 10 5 5 5 5 5 6 6 6 7 7 73301500141 3 8 0 6 7 7 7 7 7 7 7 8 8 8 83301500151 1 13 13 13 13 13 13 13 13 13 13 13 13Table A-16.Distribution of hourly NO2ambient concentration (ppb) by monitor, BostonCMSA set B, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max2502510031 40775 23 12 54 0 9 12 14 17 20 24 28 33 40 942502700201 63836 19 11 59 0 6 9 11 14 17 21 24 29 35 952502700231 24267 15 9 58 0 5 8 10 12 14 16 19 22 27 933301100161 33436 16 10 64 0 6 8 9 11 13 16 19 23 29 1583301100191 8022 11 9 81 0 2 3 5 7 9 11 14 18 24 543301100201 41325 12 9 75 0 3 4 6 7 9 11 14 18 25 623301500091 40978 12 9 77 0 2 4 6 8 10 12 15 19 25 633301500131 33536 6 7 118 0 0 1 2 2 3 5 7 10 15 503301500141 25372 8 7 94 0 1 2 3 4 5 7 9 12 17 483301500151 8599 13 9 75 0 3 5 6 8 10 12 16 20 27 65A-40Figure A-17.Distribution of annual average NO2ambientconcentrations (ppb) by year, Chicago CMSA.Figure A-18.Distribution of hourly NO2ambient concentrations(ppb) by year, Chicago CMSA.Table A-17.Distribution of annual average NO2ambient concentrations (ppb) byyear, Chicago CMSA.Year n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1995 7 28 3 12 23 23 24 26 26 27 29 29 30 32 321996 7 24 8 32 9 9 21 23 23 24 28 28 31 32 321997 6 25 8 34 9 9 23 23 24 25 27 31 31 34 341998 9 23 7 32 9 9 17 19 23 24 25 26 31 32 321999 9 23 7 29 10 10 17 19 22 24 24 27 31 32 322000 9 22 7 30 9 9 18 20 21 22 23 27 29 32 322001 7 25 5 21 18 18 18 24 24 25 28 28 28 32 322002 7 24 6 24 17 17 19 22 22 22 23 23 30 32 322003 5 26 5 19 20 20 21 22 25 27 29 30 31 31 312004 6 23 6 25 16 16 18 18 20 22 24 29 29 29 292005 6 23 5 23 17 17 18 18 20 22 24 28 28 30 302006 5 23 6 27 16 16 17 18 20 22 25 28 29 31 31Table A-18.Distribution of hourly NO2ambient concentrations (ppb) by year, ChicagoCMSA.Year n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1995 58998 28 14 51 0 11 15 19 22 26 29 33 38 47 1131996 59447 24 14 58 0 7 11 15 18 22 26 31 36 43 1271997 51443 25 15 59 0 7 11 15 19 23 27 31 36 44 1131998 76365 23 14 61 0 6 10 13 17 21 25 29 34 41 1121999 74985 23 14 61 0 7 10 13 17 21 25 30 35 42 1132000 75327 22 14 62 0 6 10 13 17 20 24 29 34 41 1082001 58268 25 13 54 0 9 13 16 20 23 27 31 36 43 1142002 58383 24 14 59 0 8 12 15 18 21 25 29 34 42 1492003 42406 26 14 54 0 10 14 17 21 24 28 32 37 45 1222004 49210 23 13 57 0 8 11 14 18 21 25 28 33 41 1012005 51043 23 13 59 0 8 11 14 17 21 24 29 34 41 1062006 42009 23 13 57 0 8 11 14 17 21 25 29 34 41 137A-41Figure A-19.Distribution of annual average NO2ambientconcentration (ppb) by monitor, Chicago CMSA, 1995-2006.Figure A-20.Distribution of hourly NO2ambient concentration (ppb)by monitor, Chicago CMSA, 1995-2006.Table A-19.Distribution of annual average NO2ambient concentration (ppb) bymonitor, Chicago CMSA, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1703100371 1 29 29 29 29 29 29 29 29 29 29 29 291703100631 12 31 1 4 29 30 31 31 31 32 32 32 32 32 341703100641 6 23 2 8 21 21 22 22 23 23 24 24 24 26 261703100751 4 24 0 2 23 23 23 24 24 24 24 24 25 25 251703100761 5 20 2 9 18 18 19 20 20 20 21 22 22 22 221703131011 3 31 1 3 30 30 30 30 31 31 31 32 32 32 321703131031 9 29 1 5 28 28 28 28 29 29 30 30 31 31 311703140021 12 26 2 8 22 23 24 24 26 27 27 27 27 28 281703142011 4 17 1 4 17 17 17 17 17 18 18 18 18 18 181703142012 4 17 1 4 16 16 16 16 16 17 17 17 17 17 171703180031 8 23 1 4 22 22 22 23 23 23 24 24 24 25 251719710111 5 9 1 6 9 9 9 9 9 9 9 9 10 10 101808900221 8 19 1 4 18 18 18 18 19 19 19 19 20 20 201808910162 2 22 2 7 21 21 21 21 21 22 23 23 23 23 23Table A-20.Distribution of hourly NO2ambient concentration (ppb) by monitor,Chicago CMSA, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1703100371 8630 29 13 44 0 15 19 22 25 28 31 35 39 47 1131703100631 101935 31 15 46 0 13 19 23 27 30 34 38 43 51 1371703100641 52139 23 13 57 0 8 11 15 18 21 25 29 34 41 1271703100751 34028 24 12 52 0 10 13 16 19 22 26 29 34 41 1131703100761 42946 20 12 59 0 7 10 12 15 18 21 25 30 37 981703131011 25141 31 13 41 3 16 20 23 27 30 33 37 41 48 1051703131031 75061 29 13 44 0 14 18 22 25 28 31 35 39 47 1491703140021 102779 26 13 51 0 11 14 17 20 24 27 31 36 44 1061703142011 32625 17 11 64 0 5 7 10 12 15 19 22 27 33 771703142012 32552 17 10 62 0 6 8 10 12 14 17 20 25 31 701703180031 68952 23 12 53 0 9 12 15 18 21 25 29 33 40 971719710111 41227 9 6 69 0 3 4 5 6 8 9 11 13 18 521808900221 63295 19 12 66 0 4 7 10 13 17 20 25 29 36 1311808910162 16574 22 12 56 3 9 12 14 16 19 22 26 31 39 125A-42Figure A-21.Distribution of annual average NO2ambientconcentrations (ppb) by year, Cleveland CMSA.Figure A-22.Temporal distribution of hourly NO2ambientconcentrations (ppb) by year, Cleveland CMSA.Table A-21.Distribution of annual average NO2ambient concentrations (ppb) byyear, Cleveland CMSA.Year n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1995 2 24 5 19 21 21 21 21 21 24 27 27 27 27 271996 2 23 4 19 20 20 20 20 20 23 26 26 26 26 261997 1 28 0 28 28 28 28 28 28 28 28 28 28 281998 2 24 5 22 20 20 20 20 20 24 27 27 27 27 271999 2 21 5 26 17 17 17 17 17 21 25 25 25 25 252000 2 20 4 19 18 18 18 18 18 20 23 23 23 23 232001 2 21 4 17 19 19 19 19 19 21 24 24 24 24 242002 2 20 4 18 17 17 17 17 17 20 22 22 22 22 222003 2 20 3 15 17 17 17 17 17 20 22 22 22 22 222004 1 22 0 22 22 22 22 22 22 22 22 22 22 222005 2 19 3 17 17 17 17 17 17 19 22 22 22 22 222006 2 16 3 17 14 14 14 14 14 16 18 18 18 18 18Table A-22.Distribution of hourly NO2ambient concentrations (ppb) by year,Cleveland CMSA.Year n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1995 16042 24 13 53 2 10 13 16 19 22 25 29 34 41 1081996 16593 23 12 52 1 9 13 15 18 21 24 28 32 39 1481997 8300 28 17 59 0 12 15 18 21 24 28 32 38 49 2531998 16680 24 13 53 0 9 13 16 19 22 25 29 33 40 891999 16743 21 12 58 0 7 10 13 16 19 22 26 30 37 862000 16399 20 11 55 0 8 10 13 16 19 22 25 30 36 742001 16566 21 12 56 0 8 10 13 16 19 22 26 30 37 1032002 16464 20 11 56 1 8 10 12 15 18 21 24 28 35 882003 16948 20 11 57 0 7 10 13 15 18 20 24 28 35 902004 8484 22 11 51 0 10 13 15 18 20 23 26 30 37 832005 16558 19 12 60 0 7 9 12 14 17 20 23 28 35 852006 16853 16 10 64 0 5 8 10 12 14 16 20 24 30 175A-43Figure A-23.Distribution of annual average NO2ambientconcentration (ppb) by monitor, Cleveland CMSA, 1995-2006.Figure A-24.Distribution of hourly NO2ambient concentration (ppb)by monitor, Cleveland CMSA, 1995-2006.Table A-23.Distribution of annual average NO2ambient concentration (ppb) bymonitor, Cleveland CMSA, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max3903500431 2 20 1 4 20 20 20 20 20 20 21 21 21 21 213903500601 12 24 3 12 18 22 22 22 22 23 25 26 27 27 283903500661 6 18 1 6 17 17 17 17 17 18 18 19 19 20 203903500701 2 15 2 12 14 14 14 14 14 15 17 17 17 17 17Table A-24.Distribution of hourly NO2ambient concentration (ppb) by monitor,Cleveland CMSA, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max3903500431 16215 20 11 54 1 8 11 13 16 18 21 24 28 35 923903500601 99696 24 13 53 0 10 13 16 19 22 25 28 33 40 2533903500661 50100 18 11 60 0 7 9 11 13 15 18 22 26 33 1033903500701 16619 15 11 70 0 5 7 9 10 13 15 18 23 30 175A-44Figure A-25.Distribution of annual average NO2ambientconcentrations (ppb) by year, Denver CMSA.Figure A-26.Distribution of hourly NO2ambient concentrations(ppb) by year, Denver CMSA.Table A-25.Temporal distribution of annual average NO2ambient concentrations(ppb) by year, Denver CMSA.Year n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1995 3 28 6 23 23 23 23 23 26 26 26 35 35 35 351996 6 14 11 77 6 6 7 7 8 8 9 22 22 33 331997 6 15 11 74 6 6 8 8 9 9 9 23 23 34 341998 5 16 13 77 7 7 7 7 8 9 16 23 29 35 351999 3 12 6 52 8 8 8 8 9 9 9 19 19 19 192000 3 12 3 26 9 9 9 9 10 10 10 15 15 15 152001 2 31 8 25 26 26 26 26 26 31 37 37 37 37 372002 1 35 0 35 35 35 35 35 35 35 35 35 35 352003 1 21 0 21 21 21 21 21 21 21 21 21 21 212004 2 24 4 17 21 21 21 21 21 24 27 27 27 27 272005 2 24 5 21 20 20 20 20 20 24 28 28 28 28 282006 2 24 8 33 18 18 18 18 18 24 29 29 29 29 29Table A-26.Temporal distribution of hourly NO2ambient concentrations (ppb) byyear, Denver CMSA.Year n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1995 23204 28 17 620 6 11 16 22 27 32 36 41 48 2861996 46816 14 15 1080 1 2 4 6 8 11 16 25 37 1371997 45049 15 15 1060 1 3 4 6 8 12 17 26 39 1411998 40258 17 17 1000 1 3 5 7 10 15 22 31 42 1481999 23164 12 13 1080 0 2 4 6 8 10 14 21 33 962000 24649 12 13 1080 0 1 3 5 8 10 14 19 30 1412001 15204 31 17 550 8 15 21 27 32 36 41 45 52 1572002 7688 35 13 360 20 24 28 31 34 38 41 45 51 1592003 6989 21 17 780 3 5 8 13 18 25 31 37 44 1362004 15878 24 15 600 4 10 16 20 24 28 32 37 43 1152005 15467 24 16 650 3 8 14 19 24 29 33 38 44 1142006 13775 24 15 650 3 7 13 19 24 28 33 38 44 169A-45Figure A-27.Distribution of annual average NO2ambientconcentration (ppb) by monitor, Denver CMSA, 1995-2006.Figure A-28.Distribution of hourly NO2ambient concentration(ppb) by monitor, Denver CMSA, 1995-2006.Table A-27.Distribution of annual average NO2ambient concentration (ppb) bymonitor, Denver CMSA, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max0800130011 11 21 3 14 15 18 19 20 21 21 22 23 23 23 260800500031 1 26 26 26 26 26 26 26 26 26 26 26 260803100021 9 33 4 11 27 27 28 29 33 34 35 35 35 37 370805900061 3 7 0 6 7 7 7 7 7 7 7 8 8 8 80805900081 4 9 1 7 9 9 9 9 9 9 9 9 10 10 100805900091 3 9 1 8 8 8 8 8 9 9 9 9 9 9 90805900101 5 7 1 19 6 6 6 6 7 7 7 8 9 9 9Table A-28.Distribution of hourly NO2ambient concentration (ppb) by monitor,Denver CMSA, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max0800130011 83703 21 17 82 0 2 4 7 11 17 25 32 38 45 2390800500031 7790 26 15 57 0 8 12 16 20 25 29 34 39 45 1760803100021 68630 33 15 46 0 15 20 24 28 31 35 39 44 51 2860805900061 22077 7 8 109 0 1 1 3 4 5 6 9 12 18 660805900081 32449 9 9 97 0 0 2 3 5 7 9 12 15 22 680805900091 24368 9 9 100 0 1 2 3 5 6 8 10 14 20 880805900101 39124 7 8 106 0 1 2 2 4 5 6 9 12 17 98A-46Figure A-29.Distribution of annual average NO2ambientconcentrations (ppb)by year, Detroit CMSA.Figure A-30.Distribution of hourly NO2ambient concentrations(ppb) by year, Detroit CMSA.Table A-29.Distribution of annual average NO2ambient concentrations (ppb) byyear, Detroit CMSA.Year n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1995 2 20 2 10 19 19 19 19 19 20 22 22 22 22 221996 3 18 5 28 12 12 12 12 20 20 20 21 21 21 211997 3 17 8 44 13 13 13 13 13 13 13 26 26 26 261998 2 21 3 14 19 19 19 19 19 21 23 23 23 23 231999 1 18 0 18 18 18 18 18 18 18 18 18 18 182000 1 24 0 24 24 24 24 24 24 24 24 24 24 242001 2 21 3 14 19 19 19 19 19 21 23 23 23 23 232002 2 20 1 7 19 19 19 19 19 20 21 21 21 21 212003 2 20 2 12 19 19 19 19 19 20 22 22 22 22 222004 2 17 3 16 15 15 15 15 15 17 19 19 19 19 192005 2 18 2 9 17 17 17 17 17 18 20 20 20 20 202006 2 15 1 9 14 14 14 14 14 15 16 16 16 16 16Table A-30.Distribution of hourly NO2ambient concentrations (ppb) by year, DetroitCMSA.Year n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1995 16629 20 12 58 0 8 10 12 15 18 21 25 29 35 1171996 23600 18 13 74 0 4 7 9 12 15 18 22 27 35 1671997 24117 17 16 94 0 2 5 7 10 13 16 21 26 36 3221998 14863 21 14 68 0 5 9 12 15 18 22 27 31 39 1361999 7110 18 13 73 0 4 7 9 12 15 19 24 29 36 1042000 8590 24 13 56 0 8 12 15 19 22 26 30 35 42 1282001 15154 21 13 61 0 7 9 12 15 19 23 27 32 38 1942002 16623 20 15 73 0 7 10 12 15 18 22 25 30 36 4432003 16569 20 13 62 0 7 9 12 15 18 21 25 30 36 1392004 14779 17 11 66 0 5 7 9 12 14 17 21 26 33 782005 15827 19 12 63 0 6 8 10 13 16 19 23 28 35 842006 17273 15 10 64 0 4 6 8 10 13 16 19 23 29 58A-47Figure A-31.Distribution of annual average NO2ambientconcentration (ppb) by monitor, Detroit CMSA, 1995-2006.Figure A-32.Distribution of annual average NO2ambientconcentration (ppb) by monitor, Detroit CMSA, 1995-2006.Table A-31.Distribution of annual average NO2ambient concentration (ppb) bymonitor, Detroit CMSA, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max2609900091 2 12 0 3 12 12 12 12 12 12 13 13 13 13 132616300161 11 21 3 13 16 19 20 20 21 22 22 23 23 24 262616300192 11 18 3 14 13 14 15 17 18 19 19 19 19 19 21Table A-32.Distribution of annual average NO2ambient concentration (ppb) bymonitor, Detroit CMSA, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max2609900091 16523 12 9 75 0 3 5 6 8 10 12 15 19 25 3222616300161 86487 21 13 62 0 7 10 13 16 19 23 26 31 38 2442616300192 88124 18 13 75 0 5 7 9 12 15 18 22 27 35 443A-48Figure A-33.Distribution of annual average NO2ambientconcentrations (ppb) by year, Los Angeles CMSA.Figure A-34.Distribution of hourly NO2ambient concentrations(ppb) by year, Los Angeles CMSA.Table A-33.Distribution of annual average NO2ambient concentrations (ppb) byyear, Los Angeles CMSA.Year n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1995 36 29 13 47 5 8 18 20 23 30 37 39 45 46 461996 29 25 12 46 4 6 15 17 21 28 31 35 38 41 421997 33 25 12 47 4 8 14 16 20 26 29 33 34 42 431998 32 25 11 44 4 9 16 19 21 26 33 34 36 39 431999 31 27 12 44 5 10 18 20 23 28 32 35 39 39 512000 32 25 11 43 4 10 16 20 22 25 28 32 36 39 442001 31 25 11 43 4 9 17 19 24 24 27 33 36 37 412002 32 24 9 39 5 10 16 18 22 24 25 29 33 36 402003 32 23 9 37 5 11 15 18 21 24 26 29 31 34 352004 28 22 7 33 5 13 15 17 20 21 24 27 30 31 342005 28 21 7 34 5 12 14 16 19 21 22 25 27 31 312006 26 19 7 35 5 9 13 15 17 19 20 23 25 27 30Table A-34.Distribution of hourly NO2ambient concentrations (ppb) by year, LosAngeles CMSA.Year n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1995 290519 29 22 78 0 6 9 14 19 25 30 37 45 57 2391996 232203 26 19 74 0 5 8 12 17 22 28 34 40 50 2501997 263050 25 19 75 0 4 7 11 16 21 27 33 40 50 2001998 257541 25 19 74 0 5 8 12 17 22 28 34 40 50 2551999 253401 27 20 73 0 5 8 13 18 24 30 37 43 54 3072000 263311 25 18 72 0 5 8 12 17 23 28 34 40 50 2142001 251895 25 18 71 0 5 8 12 17 23 28 33 39 48 2512002 258452 24 17 71 0 5 8 11 16 21 26 32 38 46 2622003 259935 23 17 72 0 4 7 11 15 20 25 31 37 45 1632004 225075 22 15 70 0 4 7 11 15 20 25 29 35 42 1572005 227769 21 14 69 0 4 7 11 15 19 23 28 33 40 1362006 184205 19 14 74 0 3 6 9 12 16 20 25 31 38 107A-49Figure A-35.Distribution of annual average NO2ambientconcentration (ppb) by monitor, Los Angeles CMSA set A, 1995-2006.Figure A-36.Distribution of hourly NO2ambient concentration(ppb) by monitor, Los Angeles CMSA set A, 1995-2006.Table A-35.Distribution of annual average NO2ambient concentration (ppb) bymonitor, Los Angeles CMSA set A, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max0603700022 12 33 7 22 20 25 25 29 33 33 36 36 39 41 460603700161 12 28 5 17 20 22 24 26 26 27 28 29 32 33 370603700301 1 38 38 38 38 38 38 38 38 38 38 38 380603701131 12 24 4 18 16 17 20 23 24 25 26 28 28 28 280603702061 1 45 45 45 45 45 45 45 45 45 45 45 450603710022 11 38 6 16 26 29 33 35 40 41 41 41 42 45 450603711031 11 36 6 16 27 27 32 33 34 37 39 39 40 43 450603712012 12 26 4 17 17 20 21 24 25 26 26 28 28 31 320603713012 12 37 6 16 28 30 31 31 36 38 39 41 43 43 460603716012 10 37 4 11 31 33 35 35 35 36 37 38 39 42 450603717012 12 39 7 17 30 31 31 35 36 40 43 43 44 46 510603720051 12 32 5 15 23 24 27 29 32 33 34 35 37 37 370603740022 11 30 5 16 20 24 28 29 29 30 32 33 34 34 370603750011 9 27 2 9 23 23 23 24 27 28 28 29 29 30 300603750051 2 13 0 1 13 13 13 13 13 13 13 13 13 13 13Table A-36.Distribution of hourly NO2ambient concentration (ppb) by monitor, LosAngeles CMSA set A, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max0603700022 97734 33 20 59 0 11 16 21 26 31 35 41 47 58 2230603700161 97838 28 18 63 0 8 13 17 21 25 29 34 40 50 1960603700301 6817 38 17 44 8 21 25 28 32 35 38 42 48 57 1600603701131 97124 24 16 67 0 7 9 12 16 21 26 32 37 45 2010603702061 7604 45 25 56 0 19 25 30 34 39 45 51 60 75 2080603710022 88656 38 19 49 0 17 23 28 32 36 41 45 52 62 2620603711031 88425 36 19 52 0 15 20 25 30 34 38 43 49 60 2390603712012 96922 26 16 64 0 7 11 15 19 23 28 33 38 47 1630603713012 97352 37 17 45 0 19 24 28 31 35 39 43 48 57 2500603716012 81411 37 18 48 0 17 23 27 31 34 38 42 48 58 2250603717012 98551 39 18 47 0 19 25 29 33 36 40 45 52 63 1840603720051 98151 32 17 54 0 13 18 22 26 30 34 38 44 52 2250603740022 88730 30 17 58 0 12 16 19 23 27 31 37 43 52 2080603750011 74014 27 19 72 0 5 9 12 17 23 30 37 43 51 1780603750051 15047 13 15 114 0 0 1 2 4 6 10 17 26 36 91A-50Figure A-37.Distribution of annual average NO2ambientconcentration (ppb) by monitor, Los Angeles CMSA set B 1995-2006.Figure A-38.Distribution of hourly NO2ambient concentration(ppb) by monitor, Los Angeles CMSA set B, 1995-2006.Table A-37.Distribution of annual average NO2ambient concentration (ppb) bymonitor, Los Angeles CMSA set B, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max0603760021 2 27 4 14 25 25 25 25 25 27 30 30 30 30 300603760121 5 20 1 6 18 18 19 19 19 19 20 20 21 21 210603790021 6 16 2 12 14 14 15 15 16 16 16 18 18 19 190603790331 5 15 0 3 15 15 15 15 15 15 15 15 15 16 160605900015 5 33 3 8 29 29 31 32 32 33 33 33 35 37 370605900075 4 22 2 10 20 20 20 21 21 22 24 24 24 24 240605910031 12 18 3 16 12 13 16 17 18 19 19 19 20 20 230605950012 11 30 6 19 21 25 25 25 27 28 33 34 35 35 390606500121 9 19 3 14 15 15 16 17 18 20 20 20 22 23 230606550012 12 16 3 22 9 12 13 15 16 16 16 17 18 20 210606580012 12 23 4 16 17 19 21 22 22 23 24 25 26 29 300606590011 12 17 2 11 14 14 15 15 17 17 17 18 18 19 200607100011 12 23 1 6 20 21 22 22 22 23 24 24 24 25 250607100121 2 7 0 5 7 7 7 7 7 7 7 7 7 7 70607100141 5 21 1 6 20 20 20 21 21 21 22 23 23 23 23Table A-38.Distribution of hourly NO2ambient concentration (ppb) by monitor, LosAngeles CMSA set B, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max0603760021 16534 27 15 57 0 10 14 18 21 25 28 32 37 46 1590603760121 39399 20 12 61 0 4 9 12 16 19 22 25 30 36 1200603790021 46871 16 11 69 0 5 7 9 11 13 17 21 26 32 1400603790331 40341 15 11 73 0 5 6 7 9 11 14 18 25 32 1030605900015 40987 33 17 53 0 14 19 22 26 30 34 38 44 55 1750605900075 33847 22 15 70 0 5 9 10 14 20 23 30 36 42 1270605910031 97546 18 15 85 0 4 6 7 9 12 16 23 31 40 1830605950012 88510 30 16 54 0 12 17 20 24 27 31 35 41 50 1920606500121 69857 19 17 91 0 3 5 7 10 13 18 25 34 43 3070606550012 95624 16 12 73 0 4 6 8 10 12 15 19 25 33 820606580012 95642 23 16 67 0 6 10 13 17 21 25 30 35 44 1500606590011 95010 17 13 75 0 4 6 8 10 13 17 22 27 34 1270607100011 94741 23 17 76 0 5 7 9 12 18 25 33 40 48 1960607100121 14753 7 5 69 0 2 4 4 5 6 7 8 10 14 570607100141 39719 21 14 67 0 7 9 11 14 17 22 27 33 41 113A-51Figure A-39.Distribution of annual average NO2ambientconcentration (ppb) by monitor, Los Angeles CMSA set C 1995-2006.Figure A-40.Distribution of hourly NO2ambient concentration(ppb) by monitor, Los Angeles CMSA set C 1995-2006.Table A-39.Distribution of annual average NO2ambient concentration (ppb) bymonitor, Los Angeles CMSA set C, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max0607100151 2 7 2 28 5 5 5 5 5 7 8 8 8 8 80607100171 3 6 0 4 6 6 6 6 6 6 6 7 7 7 70607103061 7 21 1 5 19 19 20 21 21 22 22 22 22 22 220607110042 11 36 4 12 31 31 34 34 36 36 37 38 38 39 460607112341 9 5 1 12 5 5 5 5 5 5 5 6 6 6 60607120021 12 34 5 13 27 27 30 31 33 36 36 36 38 38 420607140011 3 17 1 4 16 16 16 16 16 16 16 18 18 18 180607190041 12 31 5 16 25 26 26 26 29 31 33 34 35 38 400611100051 7 4 0 8 4 4 4 4 4 4 4 4 5 5 50611100071 9 16 1 9 14 14 14 15 16 16 16 17 17 19 190611110031 1 10 10 10 10 10 10 10 10 10 10 10 100611110041 7 8 1 7 7 7 7 8 8 8 8 8 8 8 80611120021 12 18 4 20 13 14 15 15 17 19 20 20 22 22 240611120031 9 10 1 8 9 9 9 9 9 10 10 11 11 11 110611130011 12 13 2 16 9 10 11 11 11 13 14 14 14 15 16Table A-40.Distribution of hourly NO2ambient concentration (ppb) by monitor, LosAngeles CMSA set C, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max0607100151 15531 7 6 82 0 2 3 3 4 5 6 7 10 14 600607100171 23713 6 5 84 0 2 3 3 4 5 6 7 9 13 730607103061 56831 21 15 70 0 5 8 11 13 17 22 28 34 42 1000607110042 88766 36 17 48 0 17 22 26 30 34 38 43 49 58 1990607112341 69325 5 5 103 0 1 2 2 3 3 4 5 7 12 620607120021 95054 34 18 54 0 12 19 24 28 32 37 42 48 58 1700607140011 24587 17 11 68 0 6 7 9 11 13 16 21 27 34 860607190041 97785 31 16 51 0 12 18 22 26 30 33 38 43 51 1620611100051 54034 4 4 89 0 0 1 3 3 4 5 5 6 8 810611100071 73031 16 12 74 0 4 6 8 10 12 16 20 26 33 1230611110031 8240 10 5 52 0 4 6 7 8 9 10 12 14 16 610611110041 56869 8 5 66 0 3 4 5 6 6 7 9 11 14 660611120021 94238 18 13 70 0 4 7 9 12 16 19 24 29 36 1240611120031 70332 10 8 85 0 1 2 4 6 8 10 13 17 21 930611130011 95263 13 8 65 0 4 6 7 9 11 13 15 18 23 127A-52Figure A-41.Distribution of annual average NO2ambientconcentrations (ppb) by year, Miami CMSA.Figure A-42.Distribution of hourly NO2ambient concentrations(ppb) by year, Miami CMSA.Table A-41.Distribution of annual average NO2ambient concentrations (ppb) byyear, Miami CMSA.Year n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1995 4 10 3 31 7 7 7 9 9 10 10 10 15 15 151996 4 10 4 43 6 6 6 8 8 9 9 9 16 16 161997 4 10 4 43 7 7 7 9 9 9 9 9 17 17 171998 4 10 4 42 6 6 6 9 9 9 9 9 15 15 151999 4 11 4 42 6 6 6 10 10 10 10 10 17 17 172000 4 10 4 37 7 7 7 9 9 9 10 10 16 16 162001 4 10 4 42 6 6 6 9 9 9 10 10 16 16 162002 4 9 4 39 6 6 6 7 7 8 9 9 14 14 142003 4 9 3 29 7 7 7 8 8 9 9 9 13 13 132004 4 9 3 36 6 6 6 8 8 8 8 8 13 13 132005 4 9 3 38 6 6 6 7 7 8 8 8 14 14 14Table A-42.Distribution of hourly NO2ambient concentrations (ppb) by year, MiamiCMSA.Year n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1995 32713 10 10 95 0 1 2 3 5 7 10 13 18 25 751996 33086 10 10 103 0 1 2 3 4 6 9 12 17 25 961997 32754 10 10 97 0 1 2 3 5 7 10 13 18 25 941998 30849 10 10 98 0 1 2 3 5 7 10 12 16 23 691999 32721 11 11 99 0 1 2 3 5 7 10 14 18 26 1282000 31833 10 10 99 0 1 2 4 5 7 10 13 17 24 2032001 33063 10 10 98 0 1 2 3 5 7 10 13 17 24 862002 33755 9 9 96 0 1 2 3 4 6 9 12 16 22 802003 31031 9 9 97 0 1 2 3 4 6 8 11 15 21 852004 33625 9 10 117 0 1 2 2 4 5 7 10 14 21 4172005 32342 9 10 109 0 0 1 2 4 6 8 11 15 22 94A-53Figure A-43.Distribution of annual average NO2ambientconcentration (ppb) by monitor, Miami CMSA, 1995-2006.Figure A-44.Distribution of hourly NO2ambient concentration(ppb) by monitor, Miami CMSA, 1995-2006.Table A-43.Distribution of annual average NO2ambient concentration (ppb) bymonitor, Miami CMSA, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1201100031 3 9 1 7 8 8 8 8 9 9 9 9 9 9 91201100311 8 9 1 12 7 7 8 9 9 9 9 9 10 10 101201180021 11 9 1 11 7 8 8 8 9 9 9 9 10 10 101208600271 11 6 0 7 6 6 6 6 6 6 6 7 7 7 71208640022 11 15 1 9 13 13 14 14 15 15 16 16 16 17 17Table A-44.Distribution of hourly NO2ambient concentration (ppb) by monitor, MiamiCMSA, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1201100031 24440 9 7 81 0 2 3 4 5 7 8 10 13 18 651201100311 63306 9 7 78 0 2 3 5 6 7 9 11 14 18 641201180021 92241 9 11 128 0 0 1 1 2 3 5 11 18 26 1281208600271 87068 6 8 132 0 1 1 2 2 3 4 5 9 17 751208640022 90717 15 10 67 0 5 7 9 11 13 15 18 22 28 417A-54Figure A-45.Distribution of annual average NO2ambientconcentrations (ppb) by year, New York CMSA.Figure A-46.Distribution of hourly NO2ambient concentrations(ppb) by year, New York CMSA.Table A-45.Distribution of annual average NO2ambient concentrations (ppb) byyear, New York CMSA.Year n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1995 16 28 8 28 12 16 24 25 26 29 30 31 33 39 421996 15 28 8 29 12 17 22 26 27 27 29 32 34 41 421997 16 27 8 30 12 17 23 24 26 27 29 31 35 40 411998 14 27 9 34 11 15 18 22 27 28 30 33 36 40 421999 16 27 9 31 11 17 19 24 26 27 29 33 33 41 422000 16 26 8 32 11 16 18 19 25 26 29 30 32 38 412001 14 25 8 32 11 17 17 21 24 26 27 27 31 38 402002 17 25 8 31 11 16 17 20 22 25 28 28 29 38 402003 15 23 6 28 12 14 16 18 21 25 26 27 29 30 322004 14 21 7 31 10 13 14 17 20 21 24 24 28 30 302005 16 23 7 31 11 13 16 18 22 22 25 27 27 32 362006 5 25 6 23 18 18 21 23 24 25 26 26 30 34 34Table A-46.Distribution of hourly NO2ambient concentrations (ppb) by year, NewYork CMSA.Year n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1995 133504 28 16 56 0 9 14 18 22 26 31 35 40 48 1621996 122074 28 16 57 0 8 13 18 22 26 31 35 40 48 1621997 131144 27 15 56 0 9 13 17 22 26 30 35 40 47 1811998 116748 27 16 58 0 8 13 17 22 26 31 35 40 48 2401999 132646 27 16 57 0 8 13 17 22 26 30 35 40 48 1482000 134037 26 15 58 0 8 12 16 20 24 28 33 38 46 1182001 114478 25 15 61 0 7 10 15 19 23 28 33 38 45 1422002 141480 24 15 60 0 7 11 14 18 23 27 32 37 44 1292003 122724 23 14 61 0 6 10 13 16 20 25 29 35 42 1382004 115578 21 13 64 0 5 8 12 15 19 23 27 32 40 1562005 133856 23 14 63 1 6 9 13 16 20 24 29 35 42 1192006 42223 25 13 51 0 10 13 17 20 24 28 32 37 43 92A-55Figure A-47.Distribution of annual average NO2ambientconcentration (ppb) by monitor, New York CMSA set a, 1995-2006.Figure A-48.Distribution of hourly NO2ambient concentration(ppb) by monitor, New York CMSA set a, 1995-2006.Table A-47.Distribution of annual average NO2ambient concentration (ppb) bymonitor, New York CMSA set A, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max0900101131 3 23 1 4 23 23 23 23 24 24 24 24 24 24 240900190031 8 17 2 11 14 14 15 16 18 18 18 18 19 21 210900900271 2 21 1 5 20 20 20 20 20 21 22 22 22 22 220900911231 9 26 1 4 24 24 25 25 25 25 26 26 27 27 273400300011 3 28 1 2 28 28 28 28 28 28 28 29 29 29 293400300051 4 21 1 5 20 20 20 20 20 20 20 20 22 22 223401300111 5 32 1 3 31 31 31 31 32 32 32 33 33 33 333401300161 1 29 29 29 29 29 29 29 29 29 29 29 293401310031 11 28 2 7 24 26 27 28 28 29 29 29 29 29 313401700061 11 25 2 6 22 23 23 25 26 26 26 26 26 27 273402100051 11 16 1 4 15 15 15 16 16 16 16 17 17 17 173402300111 11 18 1 6 16 17 18 18 18 18 19 19 19 19 203402730011 11 11 1 6 10 11 11 11 11 11 11 12 12 12 123403900042 11 38 4 12 30 32 32 39 40 40 41 41 41 42 423403900081 3 28 2 6 27 27 27 27 27 27 27 30 30 30 30Table A-48.Distribution of hourly NO2ambient concentration (ppb) by monitor, NewYork CMSA set A, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max0900101131 25148 23 13 55 0 9 12 15 18 22 25 29 34 40 1090900190031 67123 17 13 75 0 4 6 8 10 14 18 23 29 36 1030900900271 16002 21 14 65 0 6 8 11 14 18 22 27 33 40 1010900911231 76418 26 13 50 0 11 14 17 20 24 27 31 36 43 2403400300011 25620 28 14 50 3 11 15 19 23 26 31 35 40 47 1193400300051 34090 21 14 66 3 5 8 11 14 18 22 27 33 40 1243401300111 41642 32 16 50 3 12 17 21 26 31 35 40 45 53 1483401300161 8368 29 15 52 3 11 15 18 22 26 31 36 41 49 1033401310031 93578 28 14 51 3 11 15 19 23 27 31 35 40 47 1503401700061 93886 25 14 56 2 9 12 16 19 23 27 32 37 44 1473402100051 94591 16 11 67 2 4 7 8 11 13 16 20 25 32 793402300111 94366 18 12 65 3 5 8 10 13 16 19 23 28 35 993402730011 92642 11 9 82 0 3 3 5 7 8 10 13 17 24 953403900042 92472 38 15 41 3 19 25 29 33 37 41 45 50 58 2253403900081 23611 28 13 47 3 11 16 20 24 27 30 34 38 44 122A-56Figure A-49.Distribution of annual average NO2ambientconcentration (ppb) by monitor, New York CMSA set b, 1995-2006.Figure A-50.Distribution of hourly NO2ambient concentration(ppb) by monitor, New York CMSA set b, 1995-2006.Table A-49.Distribution of annual average NO2ambient concentration (ppb) bymonitor, New York CMSA set B, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max3600500801 5 35 1 4 33 33 34 35 35 35 36 36 36 36 363600500831 12 28 2 9 24 25 27 27 28 29 30 30 31 31 323600501101 6 30 2 6 26 26 29 29 30 30 30 30 30 32 323604700111 1 33 33 33 33 33 33 33 33 33 33 33 333605900052 11 23 2 10 18 20 21 22 22 24 24 24 25 25 263606100101 4 36 1 1 35 35 35 35 35 36 36 36 36 36 363606100561 10 39 2 6 34 35 37 38 38 39 40 40 41 42 423608100971 3 26 0 1 26 26 26 26 26 26 26 26 26 26 263608100981 7 29 1 4 27 27 28 28 28 29 30 30 30 30 303608101241 5 25 2 7 23 23 24 25 25 25 26 27 27 28 283610300092 6 15 2 14 13 13 13 13 14 16 17 17 17 17 17Table A-50.Distribution of hourly NO2ambient concentration (ppb) by monitor, NewYork CMSA set B, 1995-2006.3600500801 41120 35 14 40 0 19 23 26 30 33 37 40 45 54 1813600500831 95448 28 13 47 0 13 17 20 23 26 30 34 39 46 1363600501101 46299 29 13 45 0 14 18 21 24 28 31 35 40 47 1193604700111 8300 33 14 41 3 17 21 25 28 32 35 39 43 51 1553605900052 89801 23 13 56 0 8 11 14 18 21 25 29 34 40 1623606100101 30694 36 11 31 0 23 27 29 32 35 37 40 44 50 1183606100561 81341 39 13 33 0 24 28 32 35 38 41 44 48 55 1623608100971 24104 26 14 54 0 10 13 17 20 24 28 33 38 45 953608100981 56186 29 13 46 0 13 17 20 24 27 31 35 40 47 1143608101241 39406 25 13 50 0 11 14 17 20 23 27 31 36 43 1443610300092 48236 15 10 67 0 5 7 8 10 12 15 19 24 31 86A-57Figure A-51.Distribution of annual average NO2ambientconcentrations (ppb) by year, Philadelphia CMSA.Figure A-52.Distribution of hourly NO2ambient concentrations(ppb) by year, Philadelphia CMSA.Table A-51.Distribution of annual average NO2ambient concentrations (ppb) byyear, Philadelphia CMSA.Year n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1995 8 23 6 27 15 15 17 20 20 22 24 28 31 32 321996 8 25 6 24 19 19 21 21 21 22 24 29 33 34 341997 8 24 6 25 18 18 19 20 20 21 22 28 32 32 321998 8 24 7 30 16 16 18 19 19 21 22 29 33 34 341999 8 23 6 28 16 16 17 18 18 20 22 27 30 32 322000 6 21 4 20 17 17 18 18 19 20 20 26 26 28 282001 7 23 5 24 16 16 18 19 19 21 26 26 28 30 302002 8 21 5 26 15 15 16 18 19 20 20 24 28 29 292003 6 20 4 19 16 16 17 17 18 19 19 24 24 25 252004 7 20 4 22 14 14 16 18 18 19 23 23 25 26 262005 7 19 4 19 16 16 17 17 17 18 20 20 22 26 262006 4 16 1 9 14 14 14 15 15 15 16 16 18 18 18Table A-52.Distribution of hourly NO2ambient concentrations (ppb) by year,Philadelphia CMSA.Year n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1995 65415 24 14 60 0 8 10 14 19 20 26 30 35 40 1401996 67989 25 14 55 0 8 11 17 20 24 30 30 40 42 1001997 68291 24 14 57 0 8 11 15 19 22 26 30 35 42 2471998 66847 24 14 58 0 7 11 15 19 23 27 31 36 42 971999 64813 22 13 59 0 6 10 14 17 21 25 29 33 40 1092000 51145 21 13 60 0 6 10 13 16 19 23 27 32 39 972001 59227 23 13 59 0 6 10 14 17 21 25 29 34 40 962002 66779 21 12 59 0 6 10 13 16 20 23 27 32 38 2682003 49256 20 12 62 0 5 8 11 15 18 22 26 30 36 1052004 58509 20 12 59 0 6 9 12 15 18 22 26 30 36 1012005 56459 19 12 62 0 6 9 11 14 17 21 25 29 36 1202006 32357 16 11 69 0 4 6 8 10 13 16 20 25 31 95A-58Figure A-53.Distribution of annual average NO2ambientconcentration (ppb) by monitor, Philadelphia CMSA, 1995-2006.Figure A-54.Distribution of hourly NO2ambient concentration(ppb) by monitor, Philadelphia CMSA, 1995-2006.Table A-53.Distribution of annual average NO2ambient concentration (ppb) bymonitor,PhiladelphiaCMSA, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1000310031 5 18 1 6 16 16 17 17 18 18 18 18 19 19 191000310071 1 15 15 15 15 15 15 15 15 15 15 15 151000320041 4 18 1 4 18 18 18 18 18 18 19 19 19 19 193400700032 10 21 1 7 19 20 20 20 21 22 22 22 23 24 244201700121 12 18 2 11 15 16 16 16 17 18 18 18 20 20 214204500021 12 19 2 8 16 17 17 18 18 19 19 19 20 20 214209100131 11 17 2 13 14 14 15 16 16 16 17 18 19 19 214210100043 11 26 3 10 22 23 24 24 26 26 27 28 28 29 294210100292 10 29 3 11 25 25 26 28 28 29 31 32 33 33 334210100471 9 31 3 10 26 26 26 29 30 32 32 32 34 34 34Table A-54.Distribution of hourly NO2ambient concentration (ppb) by monitor,Philadelphia CMSA, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1000310031 40363 18 12 69 0 4 7 10 12 16 19 23 28 34 2471000310071 6611 15 9 62 1 6 7 9 10 12 15 17 21 28 691000320041 31615 18 12 63 0 5 8 11 13 16 20 23 28 34 1153400700032 84603 22 13 59 3 7 10 13 16 19 23 27 32 39 1144201700121 102584 18 12 67 0 5 7 9 12 15 19 23 28 34 1064204500021 100344 19 12 64 0 5 8 10 13 16 20 24 29 36 2684209100131 93572 17 12 69 0 4 6 9 11 15 18 22 27 33 994210100043 90975 26 13 49 0 10 14 18 20 24 28 31 37 43 1904210100292 81218 29 13 43 0 15 19 21 25 29 30 35 40 46 1204210100471 75202 31 12 40 0 16 20 23 26 30 31 36 40 47 140A-59Figure A-55.Distribution of annual average NO2ambientconcentrations (ppb) by year, Washington DC CMSA.Figure A-56.Distribution of hourly NO2ambient concentrations(ppb) by year, Washington DC CMSA.Table A-55.Distribution of annual average NO2ambient concentrations (ppb) byyear, Washington DC CMSA.Year n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1995 12 21 5 25 11 11 19 19 22 23 23 25 25 26 261996 11 22 4 20 11 20 20 21 22 22 24 24 25 26 271997 11 20 5 27 10 11 17 19 21 22 22 24 25 26 261998 11 22 5 23 12 15 18 20 22 23 24 25 26 26 271999 12 20 5 25 11 12 14 18 20 21 23 24 24 25 252000 12 18 5 27 9 10 13 17 18 20 21 23 23 23 232001 11 19 5 28 9 11 14 19 20 22 23 23 23 24 242002 10 19 6 31 9 10 13 16 20 23 23 24 25 25 252003 11 20 6 28 10 12 16 18 18 23 23 23 25 26 262004 12 18 5 27 10 10 15 15 17 19 21 21 22 23 242005 12 17 5 28 9 10 14 15 17 18 21 21 21 22 242006 10 15 4 30 7 7 10 14 15 15 16 17 18 19 20Table A-56.Distribution of hourly NO2ambient concentrations (ppb) by year,Washington DC CMSA.Year n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1995 98349 21 13 59 0 7 10 13 16 19 23 27 31 38 1451996 91551 22 12 57 0 7 11 14 17 20 24 28 32 39 1071997 87646 20 12 62 0 6 9 12 15 18 21 25 30 37 1551998 89335 22 12 57 0 8 11 14 16 20 23 27 32 38 2851999 100112 20 12 61 0 6 9 12 15 18 21 25 30 37 1142000 101494 18 12 64 0 5 8 11 13 16 19 23 28 35 1412001 91594 19 12 62 0 6 9 11 14 17 20 24 29 36 892002 83969 19 12 64 0 6 9 11 14 17 20 24 30 37 1082003 93111 20 12 61 0 6 9 12 14 17 21 25 30 37 1022004 99370 18 11 63 0 5 8 10 13 16 19 23 28 34 1152005 96396 17 12 68 0 5 7 10 12 15 18 22 27 34 1152006 83691 15 11 73 0 4 6 7 9 12 14 18 23 30 129A-60Figure A-57.Distribution of annual average NO2ambientconcentration (ppb) by monitor, Washington DCCMSA set A,1995-2006.Figure A-58.Distribution of hourly NO2ambient concentration(ppb) by monitor, Washington DCCMSA set A, 1995-2006.Table A-57.Distribution of annual average NO2ambient concentration (ppb) bymonitor, Washington DCCMSA set A, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1100100172 1 25 25 25 25 25 25 25 25 25 25 25 251100100251 12 22 2 11 17 19 20 21 22 23 23 23 24 24 251100100411 12 23 3 12 16 21 21 23 23 24 24 25 25 25 261100100431 12 20 2 12 17 18 18 18 19 19 21 22 23 23 242400530012 8 18 2 11 15 15 15 17 18 18 18 19 20 20 202451000401 11 25 2 7 22 23 23 23 24 25 26 26 26 26 272451000501 1 21 21 21 21 21 21 21 21 21 21 21 215101300201 12 23 2 10 18 21 21 22 22 23 23 24 25 25 265105900051 11 10 1 12 7 9 9 10 10 10 10 11 11 11 125105900181 3 19 1 3 19 19 19 19 19 19 19 20 20 20 205105910043 6 22 2 7 20 20 21 21 22 22 23 23 23 23 235105910051 4 17 1 9 15 15 15 17 17 17 17 17 18 18 185105950011 10 20 3 15 14 16 17 19 20 21 22 22 22 23 245110710051 8 14 1 6 13 13 13 14 14 14 14 14 15 16 165115300091 12 11 2 18 7 9 9 10 10 11 11 11 12 12 15Table A-58.Distribution of hourly NO2ambient concentration (ppb) by monitor,Washington DCCMSA set A, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1100100172 8584 25 11 45 4 12 15 18 20 23 27 30 33 39 1131100100251 102444 22 12 55 0 9 11 14 16 19 23 27 32 39 2851100100411 103173 23 12 53 0 9 12 15 18 21 24 28 33 39 1411100100431 102217 20 13 64 0 6 9 12 15 18 22 26 31 38 2582400530012 63983 18 12 65 0 5 7 10 12 15 19 23 28 34 1142451000401 89589 25 11 44 0 12 15 18 21 23 26 29 33 39 1082451000501 7872 21 12 60 0 6 9 12 16 19 23 27 32 38 755101300201 97517 23 13 56 0 8 11 14 17 20 24 28 34 41 1105105900051 89964 10 7 73 0 3 4 5 6 8 10 12 15 20 1015105900181 22689 19 11 60 0 6 9 11 13 16 20 24 29 36 895105910043 50294 22 11 52 0 10 12 14 17 20 23 27 31 38 915105910051 34022 17 11 63 0 6 8 9 12 14 17 21 26 32 1295105950011 79051 20 12 61 0 6 9 12 14 18 21 25 30 36 1555110710051 65327 14 9 65 0 5 7 8 10 11 14 17 21 28 645115300091 101671 11 7 68 0 3 5 6 7 9 11 13 16 21 84A-61Figure A-59.Distribution of annual average NO2ambientconcentration (ppb) by monitor, Washington DCCMSA set B,1995-2006.Figure A-60.Distribution of hourly NO2ambient concentration(ppb) by monitor, Washington DCCMSA set B, 1995-2006.Table A-59.Distribution of annual average NO2ambient concentration (ppb) bymonitor, Washington DCCMSA set B, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max5151000093 12 24 2 8 20 23 23 23 24 24 25 26 26 26 27Table A-60.Distribution of hourly NO2ambient concentration (ppb) by monitor,Washington DCCMSA set B, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max5151000093 98221 24 12 48 0 11 14 17 20 23 26 29 34 40 115A-62Figure A-61.Distribution of annual average NO2ambientconcentrations (ppb) by year, Atlanta MSA.Figure A-62.Distribution of hourly NO2ambient concentrations(ppb) by year, Atlanta MSA.Table A-61.Distribution of annual average NO2ambient concentrations (ppb) byyear, Atlanta MSA.Year n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1995 3 13 6 46 7 7 7 7 15 15 15 19 19 19 191996 5 14 9 61 6 6 6 6 11 16 17 18 22 27 271997 4 15 7 47 8 8 8 15 15 15 15 15 25 25 251998 3 13 10 80 6 6 6 6 8 8 8 24 24 24 241999 4 14 9 61 7 7 7 7 7 13 20 20 24 24 242000 5 14 7 53 5 5 6 8 12 17 17 18 21 23 232001 5 14 8 56 4 4 6 8 12 17 17 17 20 23 232002 5 12 6 51 4 4 6 7 11 15 15 16 17 19 192003 4 11 6 56 5 5 5 6 6 11 16 16 16 16 162004 5 11 6 51 4 4 5 6 10 15 15 15 16 17 172005 5 11 6 51 4 4 5 6 10 14 14 14 16 17 172006 5 11 6 57 3 3 5 6 9 13 14 15 17 18 18Table A-62.Distribution of hourly NO2ambient concentrations (ppb) by year, AtlantaMSA.Year n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1995 25213 13 12 89 1 3 3 5 7 10 13 16 22 30 931996 40576 15 13 89 1 3 3 5 8 11 14 18 24 34 1221997 31069 15 13 86 1 3 5 7 9 12 15 18 23 33 1811998 24142 12 13 105 0 1 3 4 6 8 11 14 20 30 1241999 31121 14 14 99 0 2 4 5 7 9 12 17 23 35 2422000 40584 14 14 97 1 1 3 5 7 10 13 17 23 33 1102001 42761 14 14 98 1 1 3 5 7 9 13 17 23 33 1722002 42076 12 12 95 1 1 3 5 6 9 11 15 20 29 1362003 32215 11 11 101 0 1 2 3 5 7 9 13 17 26 912004 42124 11 11 98 1 1 3 4 6 8 10 14 19 28 1272005 42279 11 11 96 1 1 3 4 6 8 10 13 18 27 972006 41052 11 11 98 1 2 3 4 5 7 9 13 18 27 73A-63Figure A-63.Distribution of annual average NO2ambientconcentration (ppb) by monitor, Atlanta MSA, 1995-2006.Figure A-64.Distribution of hourly NO2ambient concentration(ppb) by monitor, Atlanta MSA, 1995-2006.Table A-63.Distribution of annual average NO2ambient concentration (ppb) bymonitor, Atlanta MSA, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1308900021 10 16 2 11 14 14 15 15 15 15 16 17 18 19 201308930011 9 15 2 10 13 13 14 15 15 16 16 17 17 18 181312100481 12 21 4 17 16 17 17 18 19 21 23 24 24 25 271322300031 10 5 1 20 3 4 4 4 4 5 5 5 6 6 71324700011 12 7 1 11 6 6 6 6 6 7 7 8 8 8 8Table A-64.Distribution of hourly NO2ambient concentration (ppb) by monitor,Atlanta MSA, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1308900021 83891 16 12 77 0 3 5 8 10 13 16 20 25 33 1391308930011 72029 15 11 73 1 4 6 8 10 12 15 19 24 32 951312100481 98975 21 15 73 0 5 8 11 14 17 21 26 33 43 1811322300031 80168 5 5 108 0 1 1 2 3 3 4 5 7 11 701324700011 100149 7 6 81 0 2 3 3 4 5 6 8 10 14 242A-64Figure A-65.Distribution of annual average NO2ambientconcentrations (ppb) by year, Colorado Springs MSA.Figure A-66.Distribution of hourly NO2ambient concentrations(ppb) by year, Colorado Springs MSA.Table A-65.Distribution of annual average NO2ambient concentrations (ppb) byyear, Colorado Springs MSA.Year n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1995 7 16 7 42 7 7 8 12 12 18 21 21 22 23 231996 3 16 9 53 7 7 7 7 18 18 18 24 24 24 241997 4 16 6 36 7 7 7 17 17 18 19 19 20 20 201998 4 16 6 37 7 7 7 17 17 17 18 18 20 20 201999 4 15 6 37 7 7 7 16 16 17 18 18 19 19 192000 4 19 11 58 9 9 9 16 16 16 17 17 35 35 35Table A-66.Distribution of hourly NO2ambient concentrations (ppb) by year,Colorado Springs MSA.Year n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1995 58569 16 14 91 0 2 4 6 8 11 16 22 29 36 1481996 25387 16 16 101 0 2 4 6 8 11 16 21 28 35 2461997 33469 16 13 80 0 3 5 6 9 12 16 21 27 35 1181998 34509 16 12 76 0 3 5 7 9 12 16 22 27 34 851999 34472 15 12 82 0 3 4 6 9 12 16 21 26 32 2302000 33956 19 20 106 0 3 6 8 11 15 20 24 28 34 308A-65Figure A-67.Distribution of annual average NO2ambientconcentration (ppb) by monitor, Colorado Springs MSA, 1995-2006.Figure A-68.Distribution of hourly NO2ambient concentration(ppb) by monitor, Colorado Springs MSA, 1995-2006.Table A-67.Distribution of annual average NO2ambient concentration (ppb) bymonitor, Colorado Springs MSA, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max0804160011 6 8 1 10 7 7 7 7 7 7 7 8 8 9 90804160041 6 17 2 10 16 16 16 16 17 17 17 18 18 21 210804160051 1 18 18 18 18 18 18 18 18 18 18 18 180804160061 1 7 7 7 7 7 7 7 7 7 7 7 70804160091 1 12 12 12 12 12 12 12 12 12 12 12 120804160111 6 21 3 12 17 17 19 19 20 20 20 23 23 24 240804160131 1 22 22 22 22 22 22 22 22 22 22 22 220804160181 4 22 8 37 18 18 18 18 18 19 19 19 35 35 35Table A-68.Distribution of hourly NO2ambient concentration (ppb) by monitor,Colorado Springs MSA, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max0804160011 51373 8 7 94 0 1 2 3 4 5 7 9 12 18 590804160041 51288 17 11 66 0 4 6 9 12 15 20 24 28 34 1150804160051 8345 18 13 74 1 3 5 7 10 15 21 27 32 36 1430804160061 7993 7 7 99 0 1 2 3 4 5 6 8 11 16 490804160091 8282 12 10 89 0 2 3 4 6 7 10 14 20 29 560804160111 50707 21 16 77 0 5 7 10 14 18 23 27 31 37 2460804160131 8637 22 14 62 0 5 8 11 15 20 26 31 36 41 870804160181 33737 23 21 94 0 5 7 10 14 18 23 28 33 41 308A-66Figure A-69.Distribution of annual average NO2ambientconcentrations (ppb) by year, El Paso MSA.Figure A-70.Distribution of hourly NO2ambient concentrations(ppb) by year, El Paso MSA.Table A-69.Distribution of annual average NO2ambient concentrations (ppb) byyear, El Paso MSA.Year n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1995 1 23 0 23 23 23 23 23 23 23 23 23 23 231996 1 35 0 35 35 35 35 35 35 35 35 35 35 351997 3 26 7 27 21 21 21 21 23 23 23 34 34 34 341998 2 25 8 33 19 19 19 19 19 25 31 31 31 31 311999 3 22 6 25 17 17 17 17 23 23 23 28 28 28 282000 4 18 5 26 14 14 14 16 16 16 16 16 24 24 242001 5 16 4 26 10 10 12 14 16 17 17 18 20 22 222002 5 17 4 23 11 11 13 16 16 16 17 18 20 21 212003 5 16 3 21 11 11 13 15 16 16 17 18 19 20 202004 5 14 4 25 9 9 11 13 13 13 15 17 18 18 182005 5 14 3 21 10 10 11 13 14 15 15 16 17 17 172006 5 14 4 26 8 8 11 13 14 15 16 16 17 18 18Table A-70.Distribution of hourly NO2ambient concentrations (ppb) by year, El PasoMSA.Year n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1995 6960 23 13 58 3 9 12 14 17 21 25 29 34 41 1131996 6627 35 15 43 2 20 23 27 29 32 36 40 46 54 2191997 22888 26 15 58 0 10 13 16 20 23 28 32 38 45 1741998 15523 25 15 61 0 7 12 15 19 23 27 32 37 45 1661999 23447 22 13 60 0 6 10 14 17 21 25 28 33 40 1082000 30772 17 13 72 0 3 5 8 12 16 20 24 28 34 1252001 38020 16 12 77 0 3 5 7 10 13 16 21 27 34 1022002 41466 17 13 77 0 4 5 7 10 13 17 22 28 35 1532003 39968 16 13 80 0 3 5 7 9 12 16 21 27 35 1062004 41952 14 12 83 0 2 4 6 8 11 14 19 25 32 972005 41496 14 12 86 0 2 4 5 7 10 14 19 24 31 872006 37203 14 12 84 0 2 4 6 8 10 14 19 25 32 99A-67Figure A-71.Distribution of annual average NO2ambientconcentration (ppb) by monitor, El Paso MSA, 1995-2006.Figure A-72.Distribution of hourly NO2ambient concentration(ppb) by monitor, El Paso MSA, 1995-2006.Table A-71.Distribution of annual average NO2ambient concentration (ppb) bymonitor, El Paso MSA, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max4814100271 4 32 3 10 28 28 28 31 31 32 34 34 35 35 354814100281 1 23 23 23 23 23 23 23 23 23 23 23 234814100371 11 18 2 12 15 16 17 17 17 18 18 18 19 21 234814100441 8 19 4 22 13 13 13 18 20 21 21 22 23 24 244814100551 7 17 1 5 16 16 16 16 16 16 16 16 17 18 184814100571 7 14 1 6 13 13 13 14 14 14 15 15 15 16 164814100581 6 10 1 11 8 8 9 9 10 10 10 11 11 11 11Table A-72.Distribution of hourly NO2ambient concentration (ppb) by monitor, ElPaso MSA, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max4814100271 29730 32 14 45 1 16 20 24 27 30 33 37 42 49 2194814100281 8045 23 14 60 5 10 12 13 15 18 22 27 34 42 1174814100371 87748 18 13 71 0 5 7 9 12 14 18 23 29 36 1534814100441 62362 19 13 67 0 5 8 11 14 17 21 25 30 36 1254814100551 53960 17 13 78 0 3 5 7 10 13 18 23 28 35 874814100571 57229 14 11 79 0 3 4 6 8 10 14 19 25 31 854814100581 47248 10 11 109 0 1 2 3 4 5 7 11 18 27 84A-68Figure A-73.Distribution of annual average NO2ambientconcentrations (ppb) byyear, Jacksonville MSA.Figure A-74.Distribution of hourly NO2ambient concentrations(ppb) by year, Jacksonville MSA.Table A-73.Distribution of annual average NO2ambient concentrations (ppb) byyear, Jacksonville MSA.Year n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1995 1 16 0 16 16 16 16 16 16 16 16 16 16 161996 1 15 0 15 15 15 15 15 15 15 15 15 15 151997 1 14 0 14 14 14 14 14 14 14 14 14 14 141998 1 15 0 15 15 15 15 15 15 15 15 15 15 151999 1 16 0 16 16 16 16 16 16 16 16 16 16 162000 1 15 0 15 15 15 15 15 15 15 15 15 15 152002 1 15 0 15 15 15 15 15 15 15 15 15 15 152003 1 14 0 14 14 14 14 14 14 14 14 14 14 142004 1 14 0 14 14 14 14 14 14 14 14 14 14 142005 1 13 0 13 13 13 13 13 13 13 13 13 13 13Table A-74.Distribution of hourly NO2ambient concentrations (ppb) by year,Jacksonville MSA.Year n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1995 7755 16 10 60 0 6 8 9 11 14 16 19 23 29 761996 8148 15 10 64 0 5 7 9 11 13 15 18 21 28 801997 8326 14 9 65 0 5 6 8 10 12 15 17 21 27 921998 8211 15 10 65 0 5 7 9 11 13 15 18 22 28 661999 7795 16 10 61 0 5 7 9 12 14 16 20 24 30 632000 7661 15 10 67 0 5 7 9 11 13 15 18 23 30 722002 7944 15 10 66 0 5 7 9 11 13 15 17 21 27 2942003 7041 14 10 71 0 4 6 8 10 12 14 17 21 28 762004 7451 14 11 83 0 4 6 7 9 11 13 16 20 26 2012005 7890 13 9 67 0 4 6 8 9 11 13 16 20 26 64A-69Figure A-75.Distribution of annual average NO2ambientconcentration (ppb) by monitor, Jacksonville MSA, 1995-2006.Figure A-76.Distribution of hourly NO2ambient concentration(ppb) by monitor, Jacksonville MSA, 1995-2006.Table A-75.Distribution of annual average NO2ambient concentration (ppb) bymonitor, Jacksonville MSA, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1203100322 10 15 1 6 13 14 14 14 15 15 15 15 16 16 16Table A-76.Distribution of hourly NO2ambient concentration (ppb) by monitor,Jacksonville MSA, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1203100322 78222 15 10 67 0 5 7 9 10 12 15 18 22 28 294A-70Figure A-77.Distribution of annual average NO2ambientconcentrations (ppb) by year, Las Vegas MSA.Figure A-78.Distribution of hourly NO2ambient concentrations(ppb) by year, Las Vegas MSA.Table A-77.Distribution of annual average NO2ambient concentrations (ppb) byyear, Las Vegas MSA.Year n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1995 1 27 0 27 27 27 27 27 27 27 27 27 27 271996 1 27 0 27 27 27 27 27 27 27 27 27 27 271998 3 12 12 95 4 4 4 4 7 7 7 25 25 25 251999 5 14 10 71 4 4 6 8 8 8 16 24 25 27 272000 6 12 9 81 3 3 4 4 8 8 8 22 22 25 252001 6 11 9 84 2 2 5 5 6 6 7 22 22 23 232002 9 11 8 68 3 3 3 7 7 9 10 19 22 22 222003 7 12 8 66 2 2 6 8 8 9 19 19 21 21 212004 7 11 8 73 1 1 4 5 5 9 19 19 19 20 202005 6 10 8 76 2 2 5 5 6 8 9 19 19 20 20Table A-78.Distribution of hourly NO2ambient concentrations (ppb) by year, LasVegas MSA.Year n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1995 7951 27 20 74 0 0 11 15 20 25 31 37 42 50 4101996 8723 27 22 81 0 0 9 12 17 24 31 38 44 54 1491998 25234 12 14 118 0 0 0 0 5 8 10 14 23 35 1031999 43110 14 16 110 0 0 0 5 6 8 12 18 28 39 1102000 46403 12 14 119 0 0 0 0 5 7 10 15 23 34 1002001 49734 11 14 128 0 0 0 0 0 6 8 13 21 33 1042002 74814 11 13 117 0 0 0 0 5 7 10 14 21 32 872003 58398 12 14 119 0 0 0 0 5 7 10 15 24 35 1032004 57484 11 13 120 0 0 0 0 0 6 9 14 23 33 732005 48911 10 12 123 0 0 0 0 0 6 9 12 18 30 75A-71Figure A-79.Distribution of annual average NO2ambientconcentration (ppb) by monitor, Las Vegas MSA, 1995-2006.Figure A-80.Distribution of hourly NO2ambient concentration(ppb) by monitor, Las Vegas MSA, 1995-2006.Table A-79.Distribution of annual average NO2ambient concentration (ppb) bymonitor, Las Vegas MSA, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max3200300221 7 5 1 26 4 4 4 4 4 5 5 5 6 7 73200300231 4 7 2 28 5 5 5 6 6 7 9 9 10 10 103200300731 7 8 1 9 7 7 7 8 8 8 8 8 9 9 93200300781 1 9 9 9 9 9 9 9 9 9 9 9 93200305391 8 23 3 12 19 19 20 21 22 22 23 25 25 27 273200305571 2 27 0 1 27 27 27 27 27 27 27 27 27 27 273200305631 3 19 0 1 19 19 19 19 19 19 19 19 19 19 193200306011 5 6 2 34 3 3 4 6 6 7 7 8 8 8 83200310191 7 3 1 38 1 1 2 2 2 2 3 3 3 4 43200320021 7 21 2 7 19 19 20 21 21 22 22 22 22 24 24Table A-80.Distribution of hourly NO2ambient concentration (ppb) by monitor, LasVegas MSA, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max3200300221 58087 5 7 152 0 0 0 0 0 0 5 7 10 15 913200300231 34550 7 8 105 0 0 0 0 5 6 8 10 13 18 523200300731 56906 8 10 124 0 0 0 0 0 5 8 11 15 22 1043200300781 8672 9 10 115 0 0 0 0 5 7 8 10 14 22 873200305391 64921 23 16 70 0 5 7 10 14 21 28 33 38 44 1033200305571 16674 27 21 78 0 0 10 14 19 24 31 37 43 52 4103200305631 25061 19 15 78 0 0 5 7 11 17 23 28 33 39 873200306011 42417 6 8 124 0 0 0 0 0 5 7 8 12 18 513200310191 57230 3 5 186 0 0 0 0 0 0 0 0 6 9 713200320021 56244 21 16 73 0 0 6 9 13 20 27 32 36 42 110A-72Figure A-81.Distribution of annual average NO2ambientconcentrations (ppb) by year, Phoenix MSA.Figure A-82.Distribution of hourly NO2ambient concentrations(ppb) by year, Phoenix MSA.Table A-81.Distribution of annual average NO2ambient concentrations (ppb) byyear, Phoenix MSA.Year n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1995 3 29 3 12 26 26 26 26 29 29 29 33 33 33 331996 3 28 3 12 25 25 25 25 29 29 29 32 32 32 321997 2 30 3 10 28 28 28 28 28 30 32 32 32 32 321998 4 29 5 15 24 24 24 28 28 29 30 30 35 35 351999 5 33 5 14 28 28 30 31 31 31 32 34 37 40 402000 5 30 4 13 26 26 27 29 29 29 30 30 33 36 362001 5 27 6 23 22 22 22 22 24 26 27 29 33 37 372002 3 29 6 19 24 24 24 24 29 29 29 35 35 35 352003 2 32 4 11 29 29 29 29 29 32 34 34 34 34 342004 5 25 4 18 19 19 21 23 23 24 24 25 28 31 312005 6 23 7 29 12 12 20 20 24 24 24 26 26 32 322006 6 22 7 30 11 11 19 19 21 22 24 25 25 31 31Table A-82.Distribution of hourly NO2ambient concentrations (ppb) by year, PhoenixMSA.Year n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1995 23196 29 17 59 0 8 12 17 23 28 33 37 44 53 1281996 23598 28 17 59 0 8 12 17 22 27 32 37 43 51 1151997 14629 30 16 55 0 8 13 18 25 30 35 39 44 52 1141998 32078 29 17 58 0 8 12 17 23 28 33 38 44 52 1161999 40996 33 22 66 0 9 13 18 24 30 36 42 49 60 1982000 41686 30 21 71 0 8 12 17 22 27 32 38 45 54 2672001 40463 27 16 59 1 7 11 15 21 26 31 36 41 49 1182002 25028 29 17 59 0 7 12 17 23 28 34 39 45 53 1082003 14195 32 17 55 0 8 14 20 27 32 37 42 48 55 1012004 42176 25 15 62 0 6 9 13 18 23 28 33 39 45 1042005 50583 23 15 66 0 5 8 12 16 20 25 31 36 44 1312006 48791 22 16 73 0 4 7 10 13 18 24 30 37 46 111A-73Figure A-83.Distribution of annual average NO2ambientconcentration (ppb) by monitor, Phoenix MSA, 1995-2006.Figure A-84.Distribution of hourly NO2ambient concentration(ppb) by monitor, Phoenix MSA, 1995-2006.Table A-83.Distribution of annual average NO2ambient concentration (ppb) bymonitor, Phoenix MSA, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max0401300191 10 27 3 10 24 24 24 25 27 28 28 29 29 30 310401330026 12 29 3 10 25 25 26 29 29 29 30 32 32 33 340401330031 10 24 4 17 19 19 20 21 23 24 24 25 28 30 310401330101 9 35 3 9 31 31 31 32 34 35 35 36 37 40 400401340051 1 22 22 22 22 22 22 22 22 22 22 22 220401340111 2 12 1 6 11 11 11 11 11 12 12 12 12 12 120401399971 5 24 3 12 21 21 22 23 23 24 25 26 27 28 28Table A-84.Distribution of hourly NO2ambient concentration (ppb) by monitor,Phoenix MSA, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max0401300191 81411 27 17 63 0 6 9 14 20 26 32 37 42 50 1480401330026 97376 29 17 59 0 8 12 17 23 28 33 38 44 53 1510401330031 80162 24 19 78 0 6 9 12 16 20 25 30 35 45 2670401330101 73070 35 18 53 0 9 16 23 30 35 40 45 50 58 1640401340051 7420 22 13 58 2 7 9 13 17 21 25 29 33 39 990401340111 16459 12 8 69 0 2 4 6 8 10 13 16 18 22 530401399971 41521 24 15 60 0 7 10 14 19 23 27 32 37 45 131A-74Figure A-85.Distribution of annual average NO2ambientconcentrations (ppb) by year, Provo MSA.Figure A-86.Temporal distribution of hourly NO2ambientconcentrations (ppb) by year, Provo MSA.Table A-85.Distribution of annual average NO2ambient concentrations (ppb) byyear, Provo MSA.Year n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1995 1 23 0 23 23 23 23 23 23 23 23 23 23 231996 1 24 0 24 24 24 24 24 24 24 24 24 24 241997 1 23 0 23 23 23 23 23 23 23 23 23 23 231998 1 24 0 24 24 24 24 24 24 24 24 24 24 241999 1 24 0 24 24 24 24 24 24 24 24 24 24 242000 1 24 0 24 24 24 24 24 24 24 24 24 24 242001 1 24 0 24 24 24 24 24 24 24 24 24 24 242002 1 25 0 25 25 25 25 25 25 25 25 25 25 252003 1 22 0 22 22 22 22 22 22 22 22 22 22 222004 1 22 0 22 22 22 22 22 22 22 22 22 22 222005 1 21 0 21 21 21 21 21 21 21 21 21 21 212006 1 29 0 29 29 29 29 29 29 29 29 29 29 29Table A-86.Distribution of hourly NO2ambient concentrations (ppb) by year, ProvoMSA.Year n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1995 8002 23 13 55 0 7 10 13 17 22 26 30 34 40 671996 8430 24 15 61 0 7 10 14 18 23 28 32 37 43 971997 7034 23 13 57 0 7 10 14 18 22 26 31 35 41 811998 8210 24 13 56 0 7 10 14 18 23 28 32 37 42 781999 8563 24 13 55 0 7 11 14 19 23 28 33 37 42 772000 8406 24 13 56 0 7 10 14 18 22 27 32 37 42 742001 8501 24 14 57 0 6 10 14 19 23 28 33 38 43 722002 8200 25 14 57 0 6 10 15 20 25 30 34 38 43 802003 7730 22 13 59 0 6 8 12 16 21 26 30 34 39 722004 8302 22 15 66 0 5 8 12 16 20 25 30 35 42 902005 8502 21 13 62 0 5 8 11 15 19 23 28 33 39 642006 6993 29 34 118 0 5 7 10 13 17 22 30 38 61 164A-75Figure A-87.Distribution of annual average NO2ambientconcentration (ppb) by monitor, Provo MSA, 1995-2006.Figure A-88.Distribution of hourly NO2ambient concentration(ppb) by monitor, Provo MSA, 1995-2006.Table A-87.Distribution of annual average NO2ambient concentration (ppb) bymonitor, Provo MSA, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max4904900021 12 24 2 9 21 22 22 23 23 24 24 24 24 25 29Table A-88.Distribution of hourly NO2ambient concentration (ppb) by monitor, ProvoMSA, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max4904900021 96873 24 16 68 0 6 9 13 17 22 27 31 36 42 164A-76Figure A-89.Distribution of annual average NO2ambientconcentrations (ppb) by year, St. Louis MSA.Figure A-90.Temporal distribution of hourly NO2ambientconcentrations (ppb) by year, St. Louis MSA.Table A-89.Distribution of annual average NO2ambient concentrations (ppb) byyear, St. Louis MSA.Year n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1995 10 18 6 35 5 8 12 15 19 19 20 22 22 24 261996 10 17 6 33 6 8 12 16 19 19 20 20 21 23 251997 10 17 6 32 6 8 12 16 19 19 19 19 21 23 251998 8 19 5 25 11 11 13 18 19 19 19 20 22 26 261999 9 19 5 24 12 12 14 18 18 20 21 21 24 27 272000 9 18 5 29 9 9 12 16 17 18 19 21 21 26 262001 8 17 5 28 10 10 12 17 17 18 19 20 20 25 252002 9 16 4 26 10 10 11 14 15 16 17 19 21 23 232003 9 15 4 26 9 9 10 14 14 16 16 18 19 20 202004 9 14 4 31 8 8 10 12 13 13 16 17 18 22 222005 6 13 3 24 9 9 10 10 12 13 15 15 15 17 172006 2 12 5 40 8 8 8 8 8 12 15 15 15 15 15Table A-90.Distribution of hourly NO2ambient concentrations (ppb) by year, St.Louis MSA.Year n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1995 85072 18 12 68 0 4 7 10 13 16 19 23 28 34 1031996 86085 17 11 65 4 7 10 13 16 19 22 26 32 841997 86314 17 11 67 0 4 7 10 12 15 18 22 26 33 2741998 68308 19 11 58 0 6 9 12 14 17 20 23 28 33 971999 77611 19 12 61 0 6 9 12 14 17 20 24 29 36 992000 77327 18 11 64 0 5 8 10 13 16 19 22 27 34 852001 67871 17 11 64 0 5 7 10 13 15 19 22 27 33 952002 76693 16 11 65 0 5 7 9 12 14 17 21 25 31 1242003 77543 15 10 67 0 4 6 8 11 13 16 19 23 29 1232004 75493 14 10 69 0 4 6 8 10 12 15 18 22 28 1302005 49948 13 9 70 0 4 5 7 9 11 13 16 20 26 702006 16688 12 8 70 0 3 5 6 8 10 12 15 18 23 53A-77Figure A-91.Distribution of annual average NO2ambientconcentration (ppb) by monitor, St. Louis MSA, 1995-2006.Figure A-92.Distribution of hourly NO2ambient concentration(ppb) by monitor, St. Louis MSA, 1995-2006.Table A-91.Distribution of annual average NO2ambient concentration (ppb) bymonitor, St. Louis MSA, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1716300102 12 18 2 12 15 15 16 16 17 18 18 19 19 20 212918300101 3 6 0 7 5 5 5 5 6 6 6 6 6 6 62918310021 12 10 1 13 8 8 9 9 9 10 11 11 11 11 122918900012 3 19 0 2 19 19 19 19 19 19 19 19 19 19 192918900041 6 15 2 15 12 12 13 13 14 14 14 16 16 18 182918900062 11 12 1 12 10 10 10 11 12 12 12 13 13 13 142918930012 11 20 2 11 17 17 18 19 20 21 22 22 22 22 242918950011 10 17 2 13 13 14 15 16 17 18 19 19 19 19 202918970022 6 20 1 6 19 19 20 20 20 20 20 21 21 22 222918970031 4 15 2 14 12 12 12 16 16 16 16 16 17 17 172951000722 10 25 2 9 20 21 23 24 25 25 25 26 26 27 272951000801 5 19 1 5 19 19 19 19 19 19 19 20 20 21 212951000861 6 19 2 11 15 15 18 18 19 19 20 21 21 21 21Table A-92.Distribution of hourly NO2ambient concentration (ppb) by monitor, St.Louis MSA, 1995-2006.Monitor ID n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1716300102 101236 18 9 52 0 8 10 12 14 16 19 21 25 31 1232918300101 25873 6 6 98 0 1 2 2 3 4 5 7 9 13 512918310021 99623 10 8 81 0 2 3 4 6 8 10 12 16 21 732918900012 25801 19 11 58 0 7 9 12 14 17 20 23 28 34 892918900041 51987 15 10 68 0 4 6 8 10 12 15 18 22 29 802918900062 93770 12 9 79 0 3 4 5 7 9 12 15 19 25 792918930012 95589 20 11 52 0 8 11 13 16 19 22 25 29 35 1012918950011 86912 17 11 62 0 6 8 10 12 15 18 21 26 32 1242918970022 51777 20 11 54 0 8 11 13 16 18 21 25 29 36 1032918970031 32235 15 10 66 0 4 7 9 11 13 16 19 24 30 642951000722 85643 25 11 46 0 11 15 18 20 23 26 29 33 40 1302951000801 42884 19 11 59 0 7 10 12 15 17 20 23 28 34 2742951000861 51623 19 12 62 0 6 9 11 14 16 19 23 28 36 87A-78Figure A-93.Distribution of annual average NO2ambientconcentrations (ppb) by year, Other MSA/CMSA.Figure A-94.Distribution of hourly NO2ambient concentrations(ppb) by year, Other MSA/CMSA.Table A-93.Distribution of annual average NO2ambient concentrations (ppb) byyear, Other MSA/CMSA.Year n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1995 186 15 6 44 1 5 8 11 13 15 17 18 21 22 321996 186 14 6 43 1 5 9 11 13 15 16 18 20 22 301997 187 14 6 43 2 5 9 11 12 14 16 18 19 22 291998 185 14 6 43 1 5 10 11 13 14 16 18 20 22 311999 192 15 6 42 1 6 9 11 14 15 16 18 20 23 292000 199 14 6 41 1 5 8 11 12 14 16 17 18 21 262001 201 13 6 43 1 5 7 10 12 13 15 17 18 20 272002 209 12 6 45 1 5 7 9 11 13 14 16 17 20 272003 202 12 5 42 1 5 7 9 11 12 14 15 17 18 262004 211 11 5 44 1 5 7 9 10 11 13 14 16 17 252005 207 11 5 43 1 5 7 9 10 11 12 14 16 17 242006 147 10 4 41 1 4 6 9 9 11 12 13 14 16 18Table A-94.Distribution of hourly NO2ambient concentrations (ppb) by year, OtherMSA/CMSA.Year n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1995 186 15 6 44 1 5 8 11 13 15 17 18 21 22 321996 1520743 14 12 81 0 2 5 7 9 12 15 18 23 31 3361997 1520290 14 11 82 0 2 4 6 9 11 14 18 23 30 3131998 1503051 14 11 80 0 2 5 7 9 11 15 18 23 31 3001999 1560074 15 12 83 0 3 5 7 9 11 14 18 24 32 1722000 1630060 14 11 81 0 2 4 6 8 11 13 17 22 29 2892001 1648640 13 11 84 0 2 4 6 8 10 13 16 21 29 1932002 1713558 13 11 85 0 2 4 5 7 9 12 15 20 28 1582003 1661992 12 10 84 0 2 4 5 7 9 12 15 19 26 1482004 1738133 11 10 87 0 2 3 5 7 8 11 14 18 25 1602005 1706730 11 10 87 0 2 3 5 6 8 11 14 18 25 1532006 1168444 10 9 87 0 2 3 5 6 8 10 13 17 23 240A-79Figure A-95.Distribution of annual average NO2ambientconcentrations (ppb) by year, Other Not MSA.Figure A-96.Distribution of hourly NO2ambient concentrations(ppb) by year, Other Not MSA.Table A-95.Distribution of annual average NO2ambient concentrations (ppb) byyear, Other Not MSA.Year n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1995 28 8 5 59 1 2 4 5 7 7 8 10 13 15 191996 29 7 5 71 0 0 2 4 5 5 7 10 13 14 141997 35 7 5 67 0 1 3 4 5 7 9 10 12 14 201998 33 7 5 62 1 1 3 4 5 7 7 10 12 14 191999 36 8 5 67 0 1 3 4 5 7 8 9 12 16 202000 39 8 4 57 2 2 3 5 6 8 8 10 11 14 192001 41 7 4 60 1 2 3 4 5 6 8 9 10 13 172002 42 7 4 65 1 2 2 3 4 6 8 8 10 13 162003 44 7 4 61 1 2 3 3 4 6 8 9 11 13 152004 47 6 4 64 2 2 2 3 4 5 7 8 11 13 162005 43 7 4 63 1 2 2 3 5 6 8 9 11 12 172006 26 6 5 71 1 1 2 2 3 5 8 10 11 12 16Table A-96.Distribution of hourly NO2ambient concentrations (ppb) by year, OtherNot MSA.Year n Mean SD COV Min p10 p20 p30 p40 p50 p60 p70 p80 p90 Max1995 225810 8 9 104 0 0 2 3 4 6 7 10 13 19 2171996 234628 7 8 118 0 0 1 2 3 4 6 8 11 17 1641997 278906 7 8 113 0 0 1 2 3 5 6 9 12 18 2071998 264015 8 8 105 0 1 2 3 4 5 7 9 12 18 1811999 290382 8 9 113 0 0 2 2 3 5 6 9 12 18 2862000 316568 8 8 104 0 1 2 3 4 5 7 9 12 18 1922001 328407 7 7 109 0 1 1 2 3 4 6 8 11 16 1392002 340873 7 7 112 0 1 1 2 3 4 5 8 11 17 2672003 351652 7 7 110 0 1 2 2 3 4 5 7 10 16 2012004 375716 6 7 115 0 1 1 2 3 4 5 7 10 16 2852005 353229 7 8 114 0 1 1 2 3 4 6 8 11 17 2622006 207114 6 7 119 0 0 1 2 2 3 5 7 10 16 101

### 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 de velop and evaluate the relationship between the annual average concen tration and the number of exceedances of potential health effect benchmark levels. C onceptually, the approach was based on analyses conducted for the last NO 2 

### 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 relations hip between the annual means and the number of exceedances, and because the predicted numbers of exceedances for evaluating the current annual standard scenar io were in many cases extremely high and uncertain. In addition, due to the lack of data containing a number of va lues at or above 200 ppb 1-hour, staff decided to devel op empirical exceedan ce estimates, as desc ribed in the REA.

There have been no modifications or re-analyse s to the regression models or results since they were first produced and documented in the 1 st 

### draft REA Technical Support Document (TSD). They do not have any relationship to the estimated concentr ations or numbers of exceedances provided in the Final REA. Neve rtheless, the regression models explored, developed, and applied are described with the following to give the reader justificat ion 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 regr ession analyses of 1995 to 2006 NO 2 

### hourly concentration data. Regression was used to estimate the annual number of exceed ances of 150 ppb from the annual mean, in 20 locations (mostly large urban areas). Exposures to concentrations above certain thresholds may be associat ed 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. Thes e models were also applied in a currentstandard scenario to predict the annual exceedan ces at sites with annual means equal to the current annual average NO 2 

### standard of 0.053 ppm. The curr ent-standard scenario is an extrapolation to higher annual means than curr ently observed; the maximum annual mean across all complete site-years wa s 51 ppb, in Los Angeles.



## A-6.2 Data Used

All of the 1995 to 2006 NO 2 

### hourly concentration data from AQS were compiled and annual summary statistics for each site-year combination we re computed. Of partic ular 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 thresh olds may be associated with adverse health effects. To make the results temporally repres entative, 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 90 th 

### 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 “O ther 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 NO 2 

### 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 func tion to estimate exceedances of 150, 200, 250, and 300 ppb based on the 1988-1992 data. In this section we presen t the results of the regression analyses for exceedances of 150 ppb using eight alternat ive 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 se lected regression models were the Poisson exponential model and the normal linear model, stratified by loca tion. 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 slop e b, depend on the location.

4 McCurdy TR (1994). Analysis of high 1 hour NO 2 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 dist ribution with standard deviation s.

• Mean exceedances = a + b × annual mean.

• The intercept a, slope b, a nd s all depend on the location.

The first issue to be resolved was to decide wh ether 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 highe st 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 occurr ed in the summer on June 12-14, 2000. This limited analysis suggests that ther e is no clear pattern of seasona lity 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 a nd exponential), and two stratifications (all data and st ratified by location). The McCur dy (1994) analysis used a normal distribution, an exponential li nk, and stratified by location in to 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 di stributions) 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 c onverge for the “Other MSA” location, so the overall goodness-of-f it is not comparable to the other seven models. ** “Other MSA” includes Chicago, Detroit, Philadel phia, Jacksonville, Las V egas, Provo, St. Louis. *** “Other MSA” includes Chicago, Clev eland, Detroit, Philadelphia, Jacksonville, Las Vegas, Phoenix, Provo, St. Louis.



### For each link function we fitted models us ing the normal distribution and the Poisson distribution. The normal model is at best an approximation sin ce 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) = (M y 

### /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 allsite-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 assu med to be the same fo r all site-years in each stratum. For the Poisson models, the vari ance 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 solu tion, 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 th at 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 “O ther MSA” combined location and refitted the models. The results in Table 1 stratified by locati on are for the refitted models. The re-allocated locations are listed in the footnotes.

Table A-97 gives R squared a nd 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 indi cate a good fit and values close to zero indicate a poor fit. For th e models stratified by location, it is evident that the R squared value has a wide range across the locations, varyi ng from a very poor fit at some locations to a very good fit at other locations.

For these models the log-likelihood is a bett er overall goodness-of-fit statistic. The loglikelihood is defined as the logarithm of the fitte d joint density function to all 4,177 site-years. The better-fitting models are t hose with the highest values of the log-likelihood. (The loglikelihood can only be used to compare different m odels; its value for a single statistical model is

### A-84 

### not meaningful). Of the various normal models, the best-fitting is strati fied by location and uses a linear inverse link. Of the various Poisson mode ls, the best-fitting is stratified by location and uses an exponential inverse link. The Poisson mode ls fit better than the no rmal 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 stra tified 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 exceedan ces plotted against the annual mean. These plots clearly s how 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 exceedan ces. Figure A-99 and th e final three attached plots are for the normal linear model, plotting predicted versus observed exceedances (negative predictions were replaced by zer o). 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 predicti ons for a mean of 53 ppb and for the mean annual mean for each the Poisson exponential mode l and the normal linear model, respectively. The predictions for a mean of 53 ppb estimate th e number of exceedances for a hypothetical siteyear with the highest annual m ean 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 annua l mean estimate the numbe r 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 perc ent 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 hypothe tically infinitely many site -years with the same annual mean. The 95% prediction interval gives the un certainty of the value for a single site-year, taking into account both the uncerta inty of the estimated paramete rs and the variability of the number of exceedances in a given site-year about the overall mean. All pr ediction 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 th e maximum number of hour s in a leap year, 8,784. The maximum observed exceedances in a year was 69.

For annual means within the range of the da ta, 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 mo del 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 truncati on limit of 1,000. This is mainly due to the exponential link; each increase of the annual m ean by 1 ppb increases th e 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 reason able 202, but these predictions are less reliable because the Poisson model with an exponential i nverse link fits the data much better. For the normal linear model, each increase of the a nnual 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 McCur dy (1994). That model gives roughly similar predictions to the Poisson model with the exponential inverse link.A-86Table A-98.Parameters for Poisson exponential model stratified by location.LocationTypeLocation Name Parameter* EstimateStandardErrorLowerConfidenceBoundUpperConfidenceBoundPvalue**MSA Atlanta,GA Intercept-5.081 1.917 -9.975 -2.139 0.01MSA Atlanta,GA mean 0.140 0.099 -0.040 0.363 0.16MSA Atlanta,GA Scale 1.000 0.000 1.000 1.000 _CMSA Boston-Worcester-Lawrence, MA-NH-ME-CT CMSA Intercept -6.887 2.832 -14.693 -2.757 0.02CMSA Boston-Worcester-Lawrence, MA-NH-ME-CT CMSA mean 0.144 0.116 -0.061 0.430 0.22CMSA Boston-Worcester-Lawrence, MA-NH-ME-CT CMSA Scale 1.000 0.000 1.000 1.000 _CMSA Cleveland-Akron, OH CMSA Intercept -14.209 4.374 -25.210 -7.312 0.00CMSA Cleveland-Akron, OH CMSA mean 0.548 0.164 0.283 0.952 0.00CMSA Cleveland-Akron, OH CMSA Scale 1.000 0.000 1.000 1.000 _MSA Colorado Springs,CO Intercept -4.846 0.401 -5.675 -4.097 0.00MSA Colorado Springs,CO mean 0.284 0.012 0.261 0.309 0.00MSA Colorado Springs,CO Scale 1.000 0.000 1.000 1.000 _CMSA Denver-Boulder-Greeley, CO CMSAIntercept -4.399 1.186 -7.182 -2.435 0.00CMSA Denver-Boulder-Greeley, COCMSA mean 0.137 0.038 0.070 0.222 0.00CMSA Denver-Boulder-Greeley, CO CMSA Scale 1.000 0.000 1.000 1.000 _MSA El Paso,TX Intercept -10.436 2.455 -16.783 -6.664 0.00MSA El Paso,TX mean 0.350 0.074 0.233 0.538 0.00MSA El Paso,TX Scale 1.000 0.000 1.000 1.000 _CMSA Los Angeles-Riverside-Orange County, CACMSA Intercept -5.628 0.253 -6.134 -5.142 0.00CMSA Los Angeles-Riverside-Orange County, CA CMSA mean 0.181 0.006 0.169 0.194 0.00CMSA Los Angeles-Riverside-Orange County, CA CMSA Scale 1.000 0.000 1.000 1.000 _CMSA Miami-Fort Lauderdale, FL CMSA Intercept -5.780 1.641 -9.774 -3.068 0.00CMSA Miami-Fort Lauderdale, FL CMSA mean 0.342 0.114 0.138 0.606 0.00CMSA Miami-Fort Lauderdale, FL CMSA Scale 1.000 0.000 1.000 1.000 _CMSANew York-Northern New Jersey-Long Island, NY-NJCT-PA CMS Intercept -6.800 1.269 -9.560 -4.537 0.00CMSANew York-Northern New Jersey-Long Island, NY-NJCT-PA CMSmean 0.147 0.037 0.079 0.224 0.00CMSANew York-Northern New Jersey-Long Island, NY-NJCT-PA CMS Scale 1.000 0.000 1.000 1.000 _MSA Phoenix-Mesa,AZ Intercept -1.568 0.400 -2.363 -0.798 0.00MSA Phoenix-Mesa,AZ mean 0.106 0.013 0.081 0.131 0.00A-87LocationType Location Name Parameter* EstimateStandardErrorLowerConfidenceBoundUpperConfidenceBoundPvalue**MSA Phoenix-Mesa,AZ Scale 1.000 0.000 1.000 1.000 _CMSA Washington-Baltimore, DC-MD-VA-WV CMSA Intercept -6.559 3.054 -14.610 -2.054 0.03CMSA Washington-Baltimore, DC-MD-VA-WV CMSA mean 0.145 0.135 -0.073 0.482 0.28CMSA Washington-Baltimore, DC-MD-VA-WV CMSA Scale 1.000 0.000 1.000 1.000 _MSA/CMSA Other MSA Intercept -5.137 0.222 -5.580 -4.711 0.00MSA/CMSA Other MSA mean 0.152 0.010 0.132 0.172 0.00MSA/CMSA Other MSA Scale 1.000 0.000 1.000 1.000 _Not MSA Other Not MSA Intercept -4.672 0.467 -5.654 -3.818 0.00Not MSA Other Not MSA mean 0.227 0.036 0.158 0.300 0.00Not MSA Other Not MSA Scale 1.000 0.000 1.000 1.000 _Notes:* using the report notation, a = “Intercept”, and b = “mean.” “Scale” equals1, 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.LocationTypeLocation Name Parameter* EstimateStandardErrorLowerConfidenceBoundUpperConfidenceBoundP-value**MSA Atlanta,GA Intercept -0.041 0.069 -0.178 0.096 0.55MSA Atlanta,GA mean 0.008 0.005 -0.002 0.017 0.11MSA Atlanta,GA Scale 0.226 0.022 0.189 0.277 _CMSA Boston-Worcester-Lawrence, MA-NH-ME-CT CMSA Intercept -0.023 0.034 -0.090 0.043 0.49CMSA Boston-Worcester-Lawrence, MA-NH-ME-CT CMSA mean 0.003 0.002 -0.001 0.006 0.17CMSA Boston-Worcester-Lawrence, MA-NH-ME-CT CMSA Scale 0.135 0.009 0.119 0.156 _CMSA Cleveland-Akron, OH CMSA Intercept -3.259 2.127 -7.617 1.098 0.13CMSA Cleveland-Akron, OH CMSAmean 0.176 0.099 -0.027 0.378 0.08CMSA Cleveland-Akron, OH CMSA Scale 1.755 0.265 1.341 2.436 _MSA Colorado Springs,CO Intercept -36.358 11.812 -60.391 -12.326 0.00MSA Colorado Springs,CO mean 2.689 0.674 1.318 4.061 0.00MSA Colorado Springs,CO Scale 22.519 3.123 17.551 30.362 _CMSA Denver-Boulder-Greeley, CO CMSAIntercept -0.439 0.383 -1.211 0.332 0.25CMSA Denver-Boulder-Greeley, COCMSA mean 0.044 0.018 0.008 0.080 0.01A-88LocationType Location Name Parameter* EstimateStandardErrorLowerConfidenceBoundUpperConfidenceBoundP-value**CMSA Denver-Boulder-Greeley, CO CMSA Scale 1.097 0.129 0.885 1.408 _MSA El Paso,TX Intercept -2.017 0.440 -2.898 -1.135 0.00MSA El Paso,TX mean 0.131 0.024 0.083 0.178 0.00MSA El Paso,TX Scale 0.920 0.098 0.757 1.151 _CMSA Los Angeles-Riverside-Orange County, CACMSA Intercept -3.301 0.620 -4.519 -2.083 0.00CMSA Los Angeles-Riverside-Orange County, CA CMSA mean 0.194 0.023 0.148 0.240 0.00CMSA Los Angeles-Riverside-Orange County, CA CMSA Scale 4.723 0.174 4.402 5.085 _CMSA Miami-Fort Lauderdale, FL CMSA Intercept -0.496 0.384 -1.265 0.273 0.20CMSA Miami-Fort Lauderdale, FL CMSA mean 0.070 0.037 -0.005 0.144 0.06CMSA Miami-Fort Lauderdale, FL CMSA Scale 0.828 0.088 0.681 1.036 _CMSANew York-Northern New Jersey-Long Island, NY-NJCT-PA CMSIntercept -0.230 0.104 -0.435 -0.024 0.03CMSANew York-Northern New Jersey-Long Island, NY-NJCT-PA CMS mean 0.013 0.004 0.005 0.020 0.00CMSANew York-Northern New Jersey-Long Island, NY-NJCT-PA CMS Scale 0.407 0.022 0.368 0.454 _MSA Phoenix-Mesa,AZ Intercept -7.102 15.545 -38.177 23.974 0.65MSA Phoenix-Mesa,AZ mean 0.423 0.557 -0.689 1.536 0.45MSA Phoenix-Mesa,AZ Scale 22.513 2.274 18.697 27.828 _CMSA Washington-Baltimore, DC-MD-VA-WVCMSA Intercept -0.032 0.069 -0.167 0.104 0.64CMSA Washington-Baltimore, DC-MD-VA-WV CMSA mean 0.003 0.003 -0.004 0.010 0.35CMSA Washington-Baltimore, DC-MD-VA-WV CMSA Scale 0.208 0.013 0.186 0.236 _MSA/CMSA Other MSA Intercept -0.100 0.051 -0.201 0.000 0.05MSA/CMSA Other MSA mean 0.013 0.003 0.006 0.019 0.00MSA/CMSA Other MSA Scale 1.098 0.015 1.069 1.128 _Not MSA Other Not MSA Intercept -0.064 0.049 -0.160 0.031 0.19Not MSA Other Not MSA mean 0.021 0.006 0.009 0.032 0.00Not MSA Other Not MSA Scale 0.549 0.018 0.514 0.587 _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 NO 2 concentrations of 150 ppb versus annual mean NO 2 concentrations (ppb) for CMSA locations.

Figure A-98. Predicted and observed exceedances of 1-hour NO 2 concentrations of 150 ppb for CMSA locations using Poisson exponential model.

### A-90 Figure A-99. Predicted and observed exceedances of 1-hour NO 2 concentrations of 150 ppb for CMSA locations using normal linear model

### A-91 Table A-100 . Predicted number of exceedances of of 1-hour NO 2 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 MeanExceedances Observed MaxExceedances PredictedExceedances 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 NO 2 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 MeanExceedances Observed MaxExceedances PredictedExceedances 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 give s 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) an d the normal linear model (see Table A-104). It is easily seen that the McCurdy (1994) esti mates agree reasonably well with our Poisson exponential model predictions, with predicted ex ceedances being a little lower for annual means up to 53 ppb, but a little higher at 60 ppb. The Mc Curdy (1994) model predicts 75 exceedances at 53 ppb, compared to our Poi sson 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 hi gh predictions of exceedances of 150 ppb for the current-standard scenario. The uncertainty at hi gher 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 imp lies a much weaker numerical relationship between the annual mean and the annual exceed ances). 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 Regr ession Model Predictions

Table A-103 . Predicted number of exceedances of of 1-hour NO 2 concentrations of 150 ppb using a Poisson exponential model and at seve ral annual average concentrations. 95% Confidence Interval for Mean Number of Exceedances 95% Prediction Interval for Number of Exceedances

Location Annual Mean (ppb) Observed MeanExceedances Observed MaxExceedances PredictedExceedances 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 MeanExceedances Observed MaxExceedances PredictedExceedances 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 MeanExceedances Observed MaxExceedances PredictedExceedances 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 NO 2 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 MeanExceedances Observed MaxExceedances PredictedExceedances 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 MeanExceedances Observed MaxExceedances PredictedExceedances 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 MeanExceedances Observed MaxExceedances PredictedExceedances 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 di scussion 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 NO 2 

### annual standard (US EPA, 2007e). Even considering air quality data as far back as 1995, no location/monitoring s ite 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 ai r quality to meet a part icular standard level, policy-relevant background (PRB) levels in the U.S. were first considered. Policy-relevant background is defined as the distribution of NO 2 

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

### precursors in the U.S., Canada, and Mexico. Estimates of PRB have b een reported in the draf t 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 No rtheastern U.S. where present-day NO 2 

### concentrations are highest, this amounts to a contri bution of about 1% percent of the total observed ambient NO 2 

### concentration (AX2.9). This low contribution of PRB to NO 2 

### 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 distributi on to just meet a target value.

Next, the variability in NO 2 

### concentrations was evalua ted to determine whether a proportional approach would be reasonable if app lied broadly across all y ears of data. Because the adjustment factor to meet the current sta ndard would likely increase with increasing year, it was of interest to determine the trend in both th e hourly concentrations a nd variability by year. Figure A-100 presents a summary of the annual av erage 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 av erage 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 m ean of the annual averages of all monitors is nearly identical to the mean of the hourly concentrations. Ho wever, statistically significant decreases in concentration are evident from year-to-year (p <0.0001), with concentrations decreasing by about 30% across th e monitoring period. Contrary to this, there is no apparent trend in the COV for the annual average concentrations across th e 12 years of data, generally centered about 53%. The COV of the hourly concen trations is larger th an the annual COV as expected, however it increases with increasing ye ar. 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 Ma nn-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 estimat ed exceedances of short-term (1-hour) potential health benchmark levels if using a proportio nal 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 conclude d 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

YearMean NO2Concentration (ppb)

50 55 60 65 70 75 80 85 90 95 100Coefficient of Variation (%)

Annual Mean Hourly Mean
COV-Annual COV-Hourly

Figure A-100. Trends in hourly and annual average NO 2 ambient monitoring concentrations and their associated coefficients of variation (C OV) for all monitors, years 1995-2006.



## A-7.2 Approach For the air quality characterizati on, data were first separated into two groups, an historical set of monitoring data (1995-2000) and one contai ning the most recent air quality (2001-2006). This grouping would further reduc e any potential influential m onitoring 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 NO 2 

### NAAQS (53 ppb annual average).

### A-102 

### Based on the form of the current standard and ob served 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, NO 2 

### concentrations were proportionally modified at each location using the maximum annua l 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

ij ij 

### C S F max, 

### / = equation (1)

where,

F

ij 

### = NO 2 

### concentration adjustment f actor (unitless) in location i given the annual average standard and for each year j S

= Current standard level (i.e., 53 ppb, annual average NO 2 

### concentration) C

max,ij 

### = The maximum annual average NO 2 

### concentration at a monitor in each location i and for each year j (ppb)

Values for each air quality adjustment factor us ed for each location to simulate just meeting the current standard are given in Tables A-105 and A-106. It s hould 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 concentra tions 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, th e maximum annual mean was 30.5 ppb, giving an adjustment factor of F = 53/30.5 = 1.74 using equation 1. Al l hourly concentrati ons in Boston in 1995 were multiplied by 1.74. Then, using the ad justed hourly concentrations, the distributions of the annual means and annual number of exceedan ces are computed in the same manner as the as-is scenario. 5



### Following review of the NO 2 

### ISA and summarization of re levant epidemiological and clinical health studies, alternative NO 2 

### standards of differing aver aging time, form, and level were also considered. Much of the discussi on regarding the selecti on of each of these components of the standard is provided in Chapter 5 of the Final NO 2 

### REA, with only the broad conclusions provided here. For averaging time, the epidemiological evidence does not provide clear guidance in choosing betw een 1-hour and 24-hour averagi ng times, and given that the experimental literature provides support for th e occurrence of effects following exposures of shorter duration than 24-hours (e.g., 1-hour), st aff evaluated standards with 1-hour averaging times. For the form, we have focused on standard s with statistical, concentration-based forms. Staff selected the 98 th 

### and 99 th 

### percentiles daily maximum concentr ation averaged over 3 years to balance the desire to provide a st able regulatory target with the de sire to limit the occurrence of peak concentrations. Concentr ation 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 th e 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 ob served concentrations from both epidemiologic and controlled human exposure studies. Using these criteria for the i nvestigated alternative standards, the following scenarios were consider ed using the most recent years of data (i.e., 2001-2006) and divided into tw o three-year groups for an alysis (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 NO 2 

### NAAQS (53 ppb annual average as described above) and alternative 1-hour standards.

Proportional adjustment factors were also de rived considering the form, averaging time, and levels of the potential alternative standards under consideration. Discussion regarding the staff selection of each of these components is pr ovided in chapter 5 of this document. The 98 th


### and 99 th 

### percentile 1-hour NO 2 

### daily maximum concentrations averaged across three years of monitoring were used in calculating the adjustment factors at each of four standard levels as follows:

i j ijk

l ikl 

### C

S F

max, 3

1 

### 3 / ⎟ ⎟ ⎟ ⎟ ⎟

⎠ ⎞

⎜ ⎜ ⎜ ⎜ ⎜

⎝ ⎛

= 

# ∑

= 

### equation (6-2)

F

ikl



### = NO 2 

### concentration adjustment f actor (unitless) in location i given alternative standard percentile form k and standard level l across a 3-year period S l



### = Standard level l (i.e., 50, 100, 150, 200 ppb 1-hour NO 2 

### concentration (ppb)) C ijk 

### = Selected percentile k (i.e., 98 th 

### or 99 th 

### ) 1-hour daily maximum NO 2 

### concentration at a monitor in location i (ppb) for each year j

Values for each air quality adjustment fact or used for each location and year-group to simulate just meeting the alternatives standard s 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 th e monitor contained the highest 98 th 

### or 99 th 

### daily maximum 1-hour concentration averaged across the th ree 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 th e selected percentile e quivalent to the level of the alternative sta ndard. For example, for Atlanta in years 2001-2003, the maximum 3-year average 98 th 

### percentile of daily maximum 1-hour NO 2 

### concentrations was 81.7 ppb, giving an adjustment factor F = 100/81.7 = 1.224 for the 1-hour alte rnative standard level of 200 ppb using equation (2). All hourly concentrations in A tlanta for each year in 2001-2003 were multiplied by 1.224. Then, using the adjusted hourly concentra tions, the distributions of the annual number of exceedances are computed in the sa me manner as the as-is scenario.

### A-104 Table A-105. Maximum annual average NO 2 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 NO 2 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 M ean 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 Factor 1 Maximum Monitor Adjustment Factor 1

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 Factor 1 Maximum Monitor Adjustment Factor 1

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 Factor 1 Maximum Monitor Adjustment Factor 1

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 Factor 1 Maximum Monitor Adjustment Factor 1

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 (98 th or 99 th ) in 1-hour daily maximum NO 2 concentration at each monitor was averaged across the 3-years of data (either 2001-2 003 or 2004-2006), with the highest concentration monitor retained for use in calculating the adjus tment 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 char acterization, the potential impact of motor vehicles on the surrogate exposure metrics was ev aluated. Several studies have shown that concentrations of NO 2 

### 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 aver age, concentrations on or near a roadway can be from 2 to 3 times greater than ambient concentrations (I SA, section 2.5.4), but on occasion, as high as 7 times greater (Bell a nd Ashenden, 1997; Bignal et al., 2007). A strong relationship between measured on-road NO 2 

### concentrations and thos e with increasing distance from the road has been reported under a variet y 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

v b x 

### e C C C − 

### + = equation (3) where,

C

x 

### = NO 2 

### concentration at a given distance ( x ) from a roadway (ppb) C

b 

### = NO 2 

### concentration (ppb) at a distance from a roadway, not directly influenced by road or non-road source emissions C

v 

### = NO 2 

### concentration contribution fro m vehicles on a roadway (ppb) k

= Removal rate c onstant describing NO 2 

### combined formation/decay with perpendicular distance from roadway (meters -1 

### )

x

= Distance from roadway (meters)

As a function of reported concentration measurem ents and the derived relationship, much of the decline in NO 2 

### concentrations with distance from the road has been shown to occur within the first few meters (by approximately 90% w ithin a 10 meter distance), returning to near ambient levels between 200 to 500 meters (R odes 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 NO 2 

### concentration and the concentration contribution expected from vehicle emissions on the roadway using



### ) 1 ( m C C

a

r 

### +

= equation (4) where,

C r 

### = 1-hour on-road NO 2 

### concentration (ppb) C

a 

### = 1-hour ambient monitoring NO 2 

### concentration (ppb) either as is or modified to just meet the current or alternative standards

m = Ratio derived from estimates of C v 

### / C b 

### (from eq (1))

### A-111 

### and assuming that C a 

### = C b



### . 6



## A-8.2 Derivation of On-Road Ratios

A literature review was conducted to id entify published studies containing NO 2 

### concentrations both on-roads and with various dist ances from roadways. Principal criteria for inclusion in this analysis were that either tabul ar, graphical, or equations were provided in the paper that related distances fr om roadways and associated NO 2 

### concentrations. Eleven papers were identified using these criter ia, spanning several countries, va rious years, roadway locations, seasons, wind directions, and averaging times (T able A-108). The final data set contained 501 data points, encompassing multiple NO 2 

### measurements at a distance from a total of 56 individual roads, some of which were coll ected within 10 m of the road.

Table A-108. Studies reviewed containing NO 2 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 seve ral roads within a particular study, data for factors thought to influence on-ro ad concentrations were very limited or were not distinctly defined for all studies. Factors that were re ported 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 downwi nd) was also indicated as an important factor influencing concentrations at a distance from a roadway (S inger, 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 ei ther downwind or upwind it is possible that the wind direction was variable (combined downwi nd 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 C
a differs from C b since C
a 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 C
a ≅ C b .

### 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 Ta ble 1 and Table 4 repor ted in Singer et al. (2004) for NO 2 

### measurements collected at six outdoor locations with va rying 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 C x (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 applie d to these data and all othe r 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 C v 

### =0 (n=1)

• Extremely large C v 

### (>8,000 ppb; n=2)

• C b 

### <0 (n=1)

These data were then evaluated for trends usi ng the limited influential factors reported in the collection of studies, consideri ng the number of values of m available for potential groupings, and how the data were to be applied to th e 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, re spectively. These two groups were also further censored for unusual paramete r 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 C b 

### ( ≤ 1 ppb) relative to high estimated C v 

### 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 (C v /C b or m ) for two season groups and potential influential factors.

Author Location Road Name Season 1 Wind Direction 2 Area Type 3 Traffic Count Season2 C v /C b 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 Season 1 Wind Direction 2 Area Type 3 Traffic Count Season2 C v /C b 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 2.43RoordaKnape HOL 1 SU B U 131907 Summer 0.78RoordaKnape HOL 1p2 SU B U 131907 Summer 0.67RoordaKnape HOL 1p3 SU B U 131907 Summer 0.70RoordaKnape HOL 1p4 SU B U 131907 Summer 0.49RoordaKnape HOL 2 SU B U 142512 Summer 0.52RoordaKnape 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 C v 

### and C b 

### pairs in each season. The first approach considered was to regress C b 

### on C v 

### (either with or without an intercept) and use the fitted slope to estimate m . Ignoring meteorological effects, equation 3 implies that C v 

### results solely from on-road emission sources and that C b 

### results solely from non-road emission sources. Since these two source types are likely to have quite different diurnal profiles, we expect the hourly C v 

### and C b 

### values to be approximately independent. 7 

### Regressing C b 

### against C v 

### would imply that there is some correlation between the values, whic h would be inconsistent with the conceptual model underlying equation (3). Further, if C b 

### were regressed against C v 

### using an intercept, the physical meaning of the intercept w ould be unclear.

7 Although the fact that C
v and C b 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 C v 

### / C b 

### . The resulting cumulative distri bution 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 pr obability (e.g., 1/19 for the not

summer data).

Means from the two seasons were tested fo r significant differen ce 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 a pparent 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 C v /C b 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. Su mmer months were first defined as June

, July

, August , while the remaining months were not summer. Alt hough there may be distin ctions 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 tw o 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 th e influence of on-road and non-road sources is likely different in each location and at each mon itor, it would be expect ed that the empirical relationship between the two values C v 

### and C b 

### to vary from place to place. If source category emissions data for each study location were availabl e 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) st udy area for assigning an appropriate ratio. However, since this information was not availabl e, an empirical approach was used to randomly match the literature-derived ratios to the NO 2 

### 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 NO 2 

### concentrations were estimated for each site-year of data in a location usi ng 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 usi ng this procedure (al ong with 21,000 randomly assigned on-road values sele cted from the appropriate empirical distribution).

Simulated on-road NO 2 

### concentrations were used to gene rate 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 95 th 

### , 98 th 

### and 99 th 

### percentiles of the distribution. It is more appropriate to apply the parameter estimates outside the central tendenci es 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 appr oach may not give the lowest possible value, because it is unlikely that in 100 simulations fo r 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 minimu m values for each distribution.

As part of the air quality characterization, th ese data were used to estimate the number of short-term concentrations above selected le vels that might occur on roadways using the estimated hourly C r 

### values, associated with air quality as is. For evaluating just meeting the current annual and alternative st andards, the approach described in Section A-7 to adjust the ambient concentrations was app lied before estimating on-road NO 2 

### concentrations.

### A-117 

# A-9 Supplemental Results Tables to the REA

A-9.1 Annual average NO 2 

## concentration data for 2001-2003

Table A-111. Estimated annual average NO 2 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 NO 2 (ppb) 3

Location Scenario 1 Percentile 2 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 NO 2 (ppb) 3

Location Scenario 1 Percentile 2 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 NO 2 (ppb) 3

Location Scenario 1 Percentile 2 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 NO 2 (ppb) 3

Location Scenario 1 Percentile 2 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 NO 2 (ppb) 3

Location Scenario 1 Percentile 2 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 NO 2 (ppb) 3

Location Scenario 1 Percentile 2 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 NO 2 (ppb) 3

Location Scenario 1 Percentile 2 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 St d – air quality that just meets the current annual standard, All others – air quality t hat just meets 1-hour concentratio n level given percentile form of alternative standard.

2 Percentile: 98 th or 99 th percentile of daily maximum 1-hour conc entration 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, 98 th , and 99 th percentiles of the distribut ion for the annual means.

### A-124 Table A-112. Estimated annual average NO 2 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 NO 2 (ppb) 3

Scenario 1 Percentile 2 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 NO 2 (ppb) 3

Scenario 1 Percentile 2 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 NO 2 (ppb) 3

Scenario 1 Percentile 2 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 NO 2 (ppb) 3

Scenario 1 Percentile 2 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 St d – air quality that just meets the current annual standard, All others – air quality t hat just meets 1-hour concentratio n level given percentile form of alternative standard.
2 Percentile: 98 th or 99 th percentile of daily maximum 1-hour conc entration 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-yea rs across the monitoring period. The min, med, p98, p99 represent the minimum, median, 98 th , and 99 th percentiles of the dist ribution for the annual

means.

### A-128 Table A-113. Estimated annual average NO 2 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 NO 2 (ppb) 3

Location Scenario 1 Percentile 2 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 NO 2 (ppb) 3

Location Scenario 1 Percentile 2 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 NO 2 (ppb) 3

Location Scenario 1 Percentile 2 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 NO 2 (ppb) 3

Location Scenario 1 Percentile 2 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 St d – air quality that just meets the current annual standard, All others – air quality t hat just meets 1-hour concentratio n level given percentile form of alternative standard.
2 Percentile: 98 th or 99 th percentile of daily maximum 1-hour conc entration 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-yea rs across the monitoring period. The min, med, p98, p99 represent the minimum, median, 98 th , and 99 th percentiles of the dist ribution for the annual

means.

### A-132 Table A-114. Estimated annual average NO 2 concentrations on-roads using 2001-2003 air quality as is , air quality adjusted to just meet the current and altern ative standards, and an on-road adjustment factor.

Annual Average NO 2 (ppb) 3

Location Standard 1 Percentile 2 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 NO 2 (ppb) 3

Location Standard 1 Percentile 2 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 NO 2 (ppb) 3

Location Standard 1 Percentile 2 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 NO 2 (ppb) 3

Location Standard 1 Percentile 2 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 NO 2 (ppb) 3

Location Standard 1 Percentile 2 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 NO 2 (ppb) 3

Location Standard 1 Percentile 2 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 St d – air quality that just meets the current annual standard, All others – air quality t hat just meets 1-hour concentratio n level given percentile form of alternative standard.

### A-138 Annual Average NO 2 (ppb) 3

Location Standard 1 Percentile 2 SiteYears Mean Min Med p98 p99

2 Percentile: 98 th or 99 th percentile of daily maximum 1-hour conc entration 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-yea rs across the monitoring period. The min, med, p98, p99 represent the minimum, median, 98 th , and 99 th percentiles of the dist ribution for the annual

means.A-139A-9.2 Number of 1-hour NO2exceedances in a year, 2001-2003Table A-115. Estimated number of exceedances of 1-hourconcentration levels (100, 150, and 200 ppb) for monitors≥100 m from a major road using2001-2003 air qualityas isand air quality adjustedto just meet the current and alternative standards.Number of Daily Maximum Exceedances3≥100 ppb≥150 ppb≥200 ppbLocationStandard1Percentile2SiteYearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99Atlanta As is 14 0 0 0 3 3 0 0 0 1 1 0 0 0 0 0Atlanta Current Std 14 80 2 85 215 215 18 0 8 75 75 3 0 0 18 18Atlanta 50 98 14 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0Atlanta 50 99 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Atlanta 100 98 14 2 0 0 15 15 0 0 0 1 1 0 0 0 1 1Atlanta 100 99 14 1 0 0 8 8 0 0 0 1 1 0 0 0 0 0Atlanta 150 98 14 20 0 9 84 84 2 0 0 15 15 0 0 0 1 1Atlanta 150 99 14 13 0 4 57 57 1 0 0 8 8 0 0 0 1 1Atlanta 200 98 14 65 1 66 196 196 10 0 3 53 53 2 0 0 15 15Atlanta 200 99 14 48 0 39 162 162 7 0 1 42 42 1 0 0 8 8Boston As is 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Boston Current Std 6 3 0 1 12 12 0 0 0 0 0 0 0 0 0 0Boston 50 98 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Boston 50 99 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Boston 100 98 6 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0Boston 100 99 6 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0Boston 150 98 6 3 0 2 10 10 0 0 0 1 1 0 0 0 0 0Boston 150 99 6 2 0 1 6 6 0 0 0 1 1 0 0 0 0 0Boston 200 98 6 34 7 35 62 62 1 0 1 5 5 0 0 0 1 1Boston 200 99 6 17 3 14 34 34 1 0 1 3 3 0 0 0 1 1Chicago As is 9 0 0 0 2 2 0 0 0 0 0 0 0 0 0 0Chicago Current Std 9 23 1 15 82 82 1 0 0 4 4 0 0 0 1 1Chicago 50 98 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Chicago 50 99 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Chicago 100 98 9 1 0 0 4 4 0 0 0 1 1 0 0 0 0 0Chicago 100 99 9 0 0 0 2 2 0 0 0 0 0 0 0 0 0 0A-140Number of Daily Maximum Exceedances3≥100 ppb≥150 ppb≥200 ppbLocationStandard1Percentile2SiteYearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99Chicago 150 98 9 31 4 24 86 86 1 0 0 4 4 0 0 0 1 1Chicago 150 99 9 12 1 7 45 45 0 0 0 2 2 0 0 0 1 1Chicago 200 98 9 106 51 104 193 193 14 1 8 51 51 1 0 0 4 4Chicago 200 99 9 72 30 57 146 146 4 0 3 16 16 0 0 0 2 2Cleveland As is 3 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0Cleveland Current Std 3 70 59 68 82 82 5 4 5 7 7 1 0 1 1 1Cleveland 50 98 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Cleveland 50 99 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Cleveland 100 98 3 2 1 3 3 3 0 0 0 0 0 0 0 0 0 0Cleveland 100 99 3 1 1 1 2 2 0 0 0 0 0 0 0 0 0 0Cleveland 150 98 3 49 32 54 60 60 2 1 3 3 3 0 0 0 1 1Cleveland 150 99 3 31 20 32 41 41 1 1 1 2 2 0 0 0 1 1Cleveland 200 98 3 133 120 129 151 151 27 16 28 38 38 2 1 3 3 3Cleveland 200 99 3 117 108 109 135 135 14 12 13 17 17 1 1 1 2 2Denver As is 2 2 1 2 2 2 0 0 0 0 0 0 0 0 0 0Denver Current Std 2 99 24 99 174 174 21 1 21 41 41 2 0 2 4 4Denver 50 98 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Denver 50 99 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Denver 100 98 2 2 1 2 2 2 0 0 0 0 0 0 0 0 0 0Denver 100 99 2 1 0 1 2 2 0 0 0 0 0 0 0 0 0 0Denver 150 98 2 37 29 37 44 44 2 1 2 2 2 1 0 1 1 1Denver 150 99 2 17 13 17 21 21 1 0 1 2 2 0 0 0 0 0Denver 200 98 2 149 104 149 193 193 17 13 17 21 21 2 1 2 2 2Denver 200 99 2 89 62 89 116 116 5 4 5 6 6 1 0 1 2 2Detroit As is 6 3 0 2 7 7 1 0 1 5 5 1 0 0 4 4Detroit Current Std 6 99 83 96 115 115 14 4 15 20 20 4 1 4 7 7Detroit 50 98 6 1 0 1 4 4 1 0 0 3 3 0 0 0 2 2Detroit 50 99 6 1 0 0 3 3 0 0 0 1 1 0 0 0 0 0Detroit 100 98 6 3 1 3 7 7 2 0 1 7 7 1 0 1 4 4Detroit 100 99 6 2 0 1 7 7 1 0 0 4 4 1 0 0 3 3Detroit 150 98 6 17 7 18 23 23 3 1 3 7 7 2 0 1 7 7A-141Number of Daily Maximum Exceedances3≥100 ppb≥150 ppb≥200 ppbLocationStandard1Percentile2SiteYearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99Detroit 150 99 6 3 1 3 7 7 2 0 1 7 7 1 0 1 4 4Detroit 200 98 6 73 46 70 99 99 9 2 10 14 14 3 1 3 7 7Detroit 200 99 6 10 2 11 14 14 3 0 2 7 7 2 0 1 7 7El Paso As is 12 0 0 0 1 1 0 0 0 1 1 0 0 0 0 0El Paso Current Std 12 115 48 125 189 189 13 3 12 27 27 2 0 2 7 7El Paso 50 98 12 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0El Paso 50 99 12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0El Paso 100 98 12 3 0 2 8 8 0 0 0 1 1 0 0 0 1 1El Paso 100 99 12 1 0 0 5 5 0 0 0 1 1 0 0 0 0 0El Paso 150 98 12 35 11 34 60 60 3 0 2 8 8 0 0 0 1 1El Paso 150 99 12 14 3 13 28 28 1 0 0 5 5 0 0 0 1 1El Paso 200 98 12 125 59 129 174 174 16 4 14 30 30 3 0 2 8 8El Paso 200 99 12 76 30 77 118 118 6 2 7 13 13 1 0 0 5 5Jacksonville As is 2 1 0 1 1 1 1 0 1 1 1 1 0 1 1 1Jacksonville Current Std 2 152 142 152 161 161 44 35 44 53 53 7 3 7 11 11Jacksonville 50 98 2 1 0 1 1 1 1 0 1 1 1 1 0 1 1 1Jacksonville 50 99 2 1 0 1 1 1 1 0 1 1 1 1 0 1 1 1Jacksonville 100 98 2 7 3 7 10 10 1 0 1 1 1 1 0 1 1 1Jacksonville 100 99 2 4 3 4 5 5 1 0 1 1 1 1 0 1 1 1Jacksonville 150 98 2 74 70 74 77 77 7 3 7 10 10 1 1 1 1 1Jacksonville 150 99 2 55 50 55 60 60 4 3 4 5 5 1 0 1 1 1Jacksonville 200 98 2 152 142 152 161 161 41 35 41 46 46 7 3 7 10 10Jacksonville 200 99 2 132 128 132 135 135 29 22 29 35 35 4 3 4 5 5Las Vegas As is 16 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0Las Vegas Current Std 16 69 0 21 218 218 6 0 1 34 34 0 0 0 1 1Las Vegas 50 98 16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Las Vegas 50 99 16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Las Vegas 100 98 16 1 0 0 7 7 0 0 0 0 0 0 0 0 0 0Las Vegas 100 99 16 1 0 0 2 2 0 0 0 0 0 0 0 0 0 0Las Vegas 150 98 16 41 0 5 143 143 1 0 0 7 7 0 0 0 1 1Las Vegas 150 99 16 22 0 3 79 79 1 0 0 2 2 0 0 0 1 1A-142Number of Daily Maximum Exceedances3≥100 ppb≥150 ppb≥200 ppbLocationStandard1Percentile2SiteYearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99Las Vegas 200 98 16 114 1 61 293 293 19 0 3 70 70 1 0 0 7 7Las Vegas 200 99 16 87 0 31 252 252 9 0 2 38 38 1 0 0 2 2Los Angeles As is 51 4 0 1 17 18 0 0 0 1 8 0 0 0 0 4Los Angeles Current Std 51 21 0 16 67 78 2 0 0 10 11 0 0 0 1 7Los Angeles 50 98 51 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Los Angeles 50 99 51 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Los Angeles 100 98 51 1 0 0 5 10 0 0 0 0 5 0 0 0 0 0Los Angeles 100 99 51 0 0 0 3 9 0 0 0 0 4 0 0 0 0 0Los Angeles 150 98 51 9 0 4 43 46 1 0 0 5 10 0 0 0 0 5Los Angeles 150 99 51 8 0 3 36 39 0 0 0 3 9 0 0 0 0 5Los Angeles 200 98 51 37 0 31 110 128 5 0 1 26 27 1 0 0 5 10Los Angeles 200 99 51 32 0 27 98 117 4 0 1 19 19 0 0 0 3 9Miami As is 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Miami Current Std 6 106 75 102 157 157 23 6 15 52 52 4 0 2 12 12Miami 50 98 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Miami 50 99 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Miami 100 98 6 4 0 3 14 14 0 0 0 2 2 0 0 0 0 0Miami 100 99 6 2 0 2 7 7 0 0 0 0 0 0 0 0 0 0Miami 150 98 6 41 9 35 80 80 4 0 3 14 14 1 0 0 3 3Miami 150 99 6 23 2 15 53 53 2 0 2 7 7 0 0 0 2 2Miami 200 98 6 107 59 111 145 145 23 2 15 53 53 4 0 3 14 14Miami 200 99 6 72 35 75 111 111 12 1 7 37 37 2 0 2 7 7New York As is 26 0 0 0 3 3 0 0 0 0 0 0 0 0 0 0New York Current Std 26 8 0 6 39 39 0 0 0 3 3 0 0 0 1 1New York 50 98 26 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0New York 50 99 26 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0New York 100 98 26 1 0 0 5 5 0 0 0 0 0 0 0 0 0 0New York 100 99 26 0 0 0 3 3 0 0 0 0 0 0 0 0 0 0New York 150 98 26 16 0 7 42 42 1 0 0 5 5 0 0 0 1 1New York 150 99 26 7 0 2 25 25 0 0 0 3 3 0 0 0 0 0New York 200 98 26 72 9 68 141 141 7 0 2 25 25 1 0 0 5 5A-143Number of Daily Maximum Exceedances3≥100 ppb≥150 ppb≥200 ppbLocationStandard1Percentile2SiteYearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99New York 200 99 26 40 3 30 87 87 3 0 2 15 15 0 0 0 3 3Philadelphia As is 14 0 0 0 1 1 0 0 0 1 1 0 0 0 1 1Philadelphia Current Std 14 29 4 21 75 75 1 0 1 4 4 0 0 0 1 1Philadelphia 50 98 14 0 0 0 1 1 0 0 0 1 1 0 0 0 0 0Philadelphia 50 99 14 0 0 0 1 1 0 0 0 1 1 0 0 0 0 0Philadelphia 100 98 14 3 0 2 15 15 0 0 0 1 1 0 0 0 1 1Philadelphia 100 99 14 1 0 1 5 5 0 0 0 1 1 0 0 0 1 1Philadelphia 150 98 14 53 18 41 140 140 3 0 2 15 15 1 0 1 1 1Philadelphia 150 99 14 30 7 19 91 91 1 0 1 5 5 0 0 0 1 1Philadelphia 200 98 14 171 99 176 265 265 27 6 16 83 83 3 0 2 15 15Philadelphia 200 99 14 121 52 120 215 215 16 1 9 58 58 1 0 1 5 5Phoenix As is 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Phoenix Current Std 5 32 2 29 65 65 0 0 0 0 0 0 0 0 0 0Phoenix 50 98 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Phoenix 50 99 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Phoenix 100 98 5 1 0 1 2 2 0 0 0 0 0 0 0 0 0 0Phoenix 100 99 5 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0Phoenix 150 98 5 89 14 111 146 146 1 0 1 2 2 0 0 0 0 0Phoenix 150 99 5 45 7 56 88 88 0 0 0 1 1 0 0 0 0 0Phoenix 200 98 5 213 115 227 272 272 41 4 52 79 79 1 0 1 2 2Phoenix 200 99 5 171 71 190 235 235 17 2 22 31 31 0 0 0 1 1Provo As is 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Provo Current Std 3 162 148 160 177 177 4 3 4 6 6 0 0 0 0 0Provo 50 98 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Provo 50 99 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Provo 100 98 3 6 4 6 9 9 0 0 0 0 0 0 0 0 0 0Provo 100 99 3 4 3 4 5 5 0 0 0 0 0 0 0 0 0 0Provo 150 98 3 200 156 221 223 223 6 4 6 9 9 0 0 0 0 0Provo 150 99 3 144 107 160 164 164 4 3 4 5 5 0 0 0 0 0Provo 200 98 3 327 295 341 345 345 112 81 122 133 133 6 4 6 9 9Provo 200 99 3 316 281 332 335 335 65 42 63 91 91 4 3 4 5 5A-144Number of Daily Maximum Exceedances3≥100 ppb≥150 ppb≥200 ppbLocationStandard1Percentile2SiteYearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99St. Louis As is 9 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0St. Louis Current Std 9 65 22 44 128 128 5 0 1 18 18 0 0 0 1 1St. Louis 50 98 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0St. Louis 50 99 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0St. Louis 100 98 9 3 0 1 15 15 0 0 0 1 1 0 0 0 0 0St. Louis 100 99 9 1 0 0 5 5 0 0 0 1 1 0 0 0 0 0St. Louis 150 98 9 60 20 51 118 118 3 0 1 15 15 0 0 0 1 1St. Louis 150 99 9 37 7 24 81 81 1 0 0 5 5 0 0 0 1 1St. Louis 200 98 9 175 109 171 245 245 31 5 18 70 70 3 0 1 15 15St. Louis 200 99 9 141 76 133 215 215 15 1 7 38 38 1 0 0 5 5Washington DC As is 18 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0Washington DC Current Std 18 61 0 66 146 146 4 0 3 16 16 0 0 0 1 1Washington DC 50 98 18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Washington DC 50 99 18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Washington DC 100 98 18 4 0 3 11 11 0 0 0 0 0 0 0 0 0 0Washington DC 100 99 18 1 0 1 5 5 0 0 0 0 0 0 0 0 0 0Washington DC 150 98 18 58 0 67 131 131 4 0 3 11 11 0 0 0 1 1Washington DC 150 99 18 34 0 34 87 87 1 0 1 5 5 0 0 0 0 0Washington DC 200 98 18 153 10 196 251 251 30 0 28 78 78 4 0 3 11 11Washington DC 200 99 18 117 4 150 214 214 15 0 16 43 43 1 0 1 5 5Other MSA As is 612 0 0 0 1 4 0 0 0 0 0 0 0 0 0 0Other MSA Current Std 612 16 0 8 78 94 1 0 0 9 12 0 0 0 1 3Other MSA 50 98 612 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Other MSA 50 99 612 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Other MSA 100 98 612 0 0 0 1 4 0 0 0 0 0 0 0 0 0 0Other MSA 100 99 612 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0Other MSA 150 98 612 3 0 0 25 30 0 0 0 1 4 0 0 0 0 1Other MSA 150 99 612 1 0 0 9 14 0 0 0 1 1 0 0 0 0 0Other MSA 200 98 612 18 0 8 81 102 1 0 0 10 16 0 0 0 1 4Other MSA 200 99 612 8 0 2 50 56 0 0 0 4 6 0 0 0 1 1Other Not MSA As is 127 0 0 0 5 5 0 0 0 1 1 0 0 0 0 1A-145Number of Daily Maximum Exceedances3≥100 ppb≥150 ppb≥200 ppbLocationStandard1Percentile2SiteYearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99Other Not MSA Current Std 127 37 0 12 170 192 6 0 0 74 80 1 0 0 21 34Other Not MSA 50 98 127 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0Other Not MSA 50 99 127 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Other Not MSA 100 98 127 0 0 0 6 6 0 0 0 1 1 0 0 0 1 1Other Not MSA 100 99 127 0 0 0 3 5 0 0 0 1 1 0 0 0 0 0Other Not MSA 150 98 127 1 0 0 23 32 0 0 0 6 6 0 0 0 2 2Other Not MSA 150 99 127 1 0 0 14 16 0 0 0 3 5 0 0 0 1 1Other Not MSA 200 98 127 6 0 0 78 79 1 0 0 16 18 0 0 0 6 6Other Not MSA 200 99 127 3 0 0 49 52 0 0 0 10 11 0 0 0 3 5Notes:1Scenario: As is – unadjusted air quality, Current Std – air quality that just meets the currentannual standard, All others –air quality that justmeets 1-hour concentration level givenpercentile form of alternative standard.2Percentile: 98thor 99thpercentile of daily maximum 1-hour concentration averaged over three years at maximum monitor in location.3The mean number of exceedances represents the sum of daily maximum exceedances occurring at all monitors in a particular location dividedby the number of site-years across themonitoring period. The min, med, p98,and p99 represent the minimum, median, 98th, and 99thpercentilesof the distribution for the number of daily maximum exceedances in any one year within the monitoring period.

### A-146 Table A-116. Estimated nu mber of exceedances of 1-hour concentr ation 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 Exceedances 3

≥ 250 ppb ≥ 300 ppb

Location Standard 1 Percentile 2 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 Exceedances 3

≥ 250 ppb ≥ 300 ppb

Location Standard 1 Percentile 2 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 Exceedances 3

≥ 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 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 Exceedances 3

≥ 250 ppb ≥ 300 ppb

Location Standard 1 Percentile 2 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 Exceedances 3

≥ 250 ppb ≥ 300 ppb

Location Standard 1 Percentile 2 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 – ai r quality that just meets the current annual standard, All others – air quality that just meets 1-hour concentration level given perc entile form of alternative standard.

2 Percentile: 98 th or 99 th percentile of daily maximum 1-hour conc entration 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-year s across the monitoring period. The min, med, p98, and p99 represent the minimum, median, 98 th , and 99 th percentiles of the distribut ion for the number of daily maximum exceedances in any one year within the monitoring period.A-151Table A-117. Estimated number of exceedances of 1-hour concentration levels (100,150 and 200 ppb) for monitors >20 m and <100m from a major road using 2001-12003 air qualityas isand air quality adjusted to just meetthe current and alternative standards.2Number of Daily Maximum Exceedances3≥100 ppb≥150 ppb≥200 ppbLocationStandard1Percentile2SiteYearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99Boston As is 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Boston Current Std 14 38 0 24 145 145 3 0 1 20 20 0 0 0 4 4Boston 50 98 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Boston 50 99 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Boston 100 98 14 3 0 2 11 11 0 0 0 0 0 0 0 0 0 0Boston 100 99 14 1 0 0 6 6 0 0 0 0 0 0 0 0 0 0Boston 150 98 14 35 2 27 89 89 3 0 2 11 11 0 0 0 1 1Boston 150 99 14 20 0 14 64 64 1 0 0 6 6 0 0 0 0 0Boston 200 98 14 116 21 120 226 226 19 0 13 59 59 3 0 2 11 11Boston 200 99 14 82 11 79 182 182 9 0 6 28 28 1 0 0 6 6Chicago As is 6 2 0 1 6 6 0 0 0 0 0 0 0 0 0 0Chicago Current Std 6 74 34 74 111 111 5 1 4 9 9 1 0 0 5 5Chicago 50 98 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Chicago 50 99 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Chicago 100 98 6 7 1 4 21 21 1 0 0 3 3 0 0 0 0 0Chicago 100 99 6 2 0 1 7 7 0 0 0 2 2 0 0 0 0 0Chicago 150 98 6 90 48 86 144 144 7 1 4 21 21 1 0 0 5 5Chicago 150 99 6 50 20 48 95 95 2 0 1 7 7 1 0 0 3 3Chicago 200 98 6 212 147 216 280 280 53 24 51 99 99 7 1 4 21 21Chicago 200 99 6 164 108 163 242 242 24 6 21 51 51 2 0 1 7 7El Paso As is 3 2 0 2 3 3 0 0 0 0 0 0 0 0 0 0El Paso Current Std 3 170 128 187 196 196 32 17 35 44 44 6 2 7 8 8El Paso 50 98 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0El Paso 50 99 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0El Paso 100 98 3 6 2 7 9 9 0 0 0 0 0 0 0 0 0 0El Paso 100 99 3 4 1 5 5 5 0 0 0 0 0 0 0 0 0 0El Paso 150 98 3 77 54 71 105 105 6 2 7 9 9 1 0 1 2 2El Paso 150 99 3 38 23 34 57 57 4 1 5 5 5 0 0 0 0 0El Paso 200 98 3 178 141 174 218 218 40 25 34 62 62 6 2 7 9 9A-152Number of Daily Maximum Exceedances3≥100 ppb≥150 ppb≥200 ppbLocationStandard1Percentile2SiteYearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99El Paso 200 99 3 137 104 137 169 169 16 12 16 19 19 4 1 5 5 5Las Vegas As is 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Las Vegas Current Std 3 24 0 4 67 67 2 0 0 5 5 0 0 0 1 1Las Vegas 50 98 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Las Vegas 50 99 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Las Vegas 100 98 3 1 0 0 4 4 0 0 0 0 0 0 0 0 0 0Las Vegas 100 99 3 1 0 0 2 2 0 0 0 0 0 0 0 0 0 0Las Vegas 150 98 3 11 0 0 34 34 1 0 0 4 4 0 0 0 0 0Las Vegas 150 99 3 5 0 0 16 16 1 0 0 2 2 0 0 0 0 0Las Vegas 200 98 3 46 1 25 113 113 5 0 0 14 14 1 0 0 4 4Las Vegas 200 99 3 32 0 10 86 86 3 0 0 9 9 1 0 0 2 2Los Angeles As is 35 6 0 1 31 31 0 0 0 2 2 0 0 0 1 1Los Angeles Current Std 35 31 0 25 82 82 3 0 0 21 21 0 0 0 2 2Los Angeles 50 98 35 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0Los Angeles 50 99 35 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Los Angeles 100 98 35 1 0 0 7 7 0 0 0 1 1 0 0 0 1 1Los Angeles 100 99 35 1 0 0 4 4 0 0 0 1 1 0 0 0 0 0Los Angeles 150 98 35 15 0 2 59 59 1 0 0 7 7 0 0 0 2 2Los Angeles 150 99 35 13 0 2 49 49 1 0 0 4 4 0 0 0 2 2Los Angeles 200 98 35 53 0 63 131 131 8 0 1 36 36 1 0 0 7 7Los Angeles 200 99 35 48 0 47 124 124 7 0 1 33 33 1 0 0 4 4Miami As is 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Miami Current Std 3 152 137 154 166 166 42 38 43 44 44 8 6 7 11 11Miami 50 98 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Miami 50 99 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Miami 100 98 3 8 4 6 15 15 0 0 0 1 1 0 0 0 0 0Miami 100 99 3 2 0 1 6 6 0 0 0 0 0 0 0 0 0 0Miami 150 98 3 73 48 75 97 97 8 4 6 15 15 1 0 1 3 3Miami 150 99 3 42 27 38 62 62 2 0 1 6 6 0 0 0 1 1Miami 200 98 3 155 113 166 187 187 42 27 38 62 62 8 4 6 15 15Miami 200 99 3 114 80 122 140 140 25 19 20 35 35 2 0 1 6 6A-153Number of Daily Maximum Exceedances3≥100 ppb≥150 ppb≥200 ppbLocationStandard1Percentile2SiteYearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99New York As is 13 1 0 0 8 8 0 0 0 0 0 0 0 0 0 0New York Current Std 13 26 1 16 48 48 1 0 1 4 4 0 0 0 0 0New York 50 98 13 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0New York 50 99 13 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0New York 100 98 13 3 0 1 13 13 0 0 0 2 2 0 0 0 0 0New York 100 99 13 1 0 0 5 5 0 0 0 0 0 0 0 0 0 0New York 150 98 13 50 10 42 121 121 3 0 1 13 13 0 0 0 3 3New York 150 99 13 25 2 20 67 67 1 0 0 5 5 0 0 0 2 2New York 200 98 13 154 66 148 258 258 25 2 20 67 67 3 0 1 13 13New York 200 99 13 102 23 95 197 197 12 1 8 33 33 1 0 0 5 5Philadelphia As is 7 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0Philadelphia Current Std 7 44 10 48 73 73 2 0 2 5 5 0 0 0 1 1Philadelphia 50 98 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Philadelphia 50 99 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Philadelphia 100 98 7 5 0 4 11 11 0 0 0 0 0 0 0 0 0 0Philadelphia 100 99 7 2 0 1 4 4 0 0 0 0 0 0 0 0 0 0Philadelphia 150 98 7 84 36 80 136 136 5 0 4 11 11 0 0 0 1 1Philadelphia 150 99 7 47 18 32 89 89 2 0 1 4 4 0 0 0 0 0Philadelphia 200 98 7 216 155 219 275 275 42 16 30 82 82 5 0 4 11 11Philadelphia 200 99 7 168 107 172 231 231 22 6 15 51 51 2 0 1 4 4Phoenix As is 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Phoenix Current Std 2 4 3 4 5 5 0 0 0 0 0 0 0 0 0 0Phoenix 50 98 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Phoenix 50 99 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Phoenix 100 98 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Phoenix 100 99 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Phoenix 150 98 2 49 39 49 59 59 0 0 0 0 0 0 0 0 0 0Phoenix 150 99 2 15 13 15 17 17 0 0 0 0 0 0 0 0 0 0Phoenix 200 98 2 210 190 210 229 229 11 6 11 15 15 0 0 0 0 0Phoenix 200 99 2 162 140 162 184 184 2 0 2 3 3 0 0 0 0 0St. Louis As is 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0A-154Number of Daily Maximum Exceedances3≥100 ppb≥150 ppb≥200 ppbLocationStandard1Percentile2SiteYearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99St. Louis Current Std 11 41 0 26 120 120 3 0 0 13 13 0 0 0 2 2St. Louis 50 98 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0St. Louis 50 99 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0St. Louis 100 98 11 2 0 0 11 11 0 0 0 0 0 0 0 0 0 0St. Louis 100 99 11 1 0 0 5 5 0 0 0 0 0 0 0 0 0 0St. Louis 150 98 11 37 0 24 120 120 2 0 0 11 11 0 0 0 1 1St. Louis 150 99 11 24 0 11 82 82 1 0 0 5 5 0 0 0 0 0St. Louis 200 98 11 119 28 90 265 265 21 0 10 74 74 2 0 0 11 11St. Louis 200 99 11 90 11 63 235 235 11 0 4 42 42 1 0 0 5 5Washington DC As is 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Washington DC Current Std 10 57 5 67 103 103 2 0 2 6 6 0 0 0 0 0Washington DC 50 98 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Washington DC 50 99 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Washington DC 100 98 10 2 0 2 5 5 0 0 0 0 0 0 0 0 0 0Washington DC 100 99 10 1 0 0 2 2 0 0 0 0 0 0 0 0 0 0Washington DC 150 98 10 57 4 64 111 111 2 0 2 5 5 0 0 0 0 0Washington DC 150 99 10 31 0 34 69 69 1 0 0 2 2 0 0 0 0 0Washington DC 200 98 10 168 56 201 271 271 28 0 30 65 65 2 0 2 5 5Washington DC 200 99 10 124 29 149 221 221 12 0 12 34 34 1 0 0 2 2Notes:1Scenario: As is – unadjusted air quality, Current Std – air quality that just meets the currentannual standard, All others –air quality that just meets 1-hourconcentration level given percentileform of alternative standard.2Percentile: 98thor 99thpercentile of daily maximum 1-hour concentration averaged over three years at maximum monitor in location.3The mean number of exceedances represents the sum of daily maximum exceedances occurring at all monitors in a particular location divided by the numberof site-years across the monitoring period. The min, med, p98, and p99 representthe minimum, median, 98th, and 99thpercentiles of the distribution for thenumber of daily maximum exceedances in any one year within the monitoring period.

### A-155 Table A-118. Estimated nu mber of exceedances of 1-hour concentr ation 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 Exceedances 3

≥ 250 ppb ≥ 300 ppb

Location Standar d 1 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 Exceedances 3

≥ 250 ppb ≥ 300 ppb

Location Standar d 1 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 Exceedances 3

≥ 250 ppb ≥ 300 ppb

Location Standar d 1 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 – ai r quality that just meets the current annual standard, All others – air quality that just meets 1-hour concentration level given perc entile form of alternative standard.

2 Percentile: 98 th or 99 th percentile of daily maximum 1-hour conc entration 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 si te-years across the monitoring period. The min, med, p98, and p99 represent the minimum, median, 98 th , and 99 th percentiles of the distribut ion for the number of daily maximum exceedances in any one year within the monitoring period.A-1581Table A-119. Estimated number of exceedances of 1-hourconcentration levels (100, 150 and 200 ppb) for monitors≤20 m from a major road using 2001-2003 air2qualityas isand air quality adjustedto just meet the current and alternative standards.3Number of Daily Maximum Exceedances3≥100 ppb≥150 ppb≥200 ppbLocationStandard1Percentile2Site-YearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99Boston As is 5 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0Boston Current Std 5 19 0 26 38 38 1 0 0 3 3 0 0 0 1 1Boston 50 98 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Boston 50 99 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Boston 100 98 5 2 0 0 6 6 0 0 0 1 1 0 0 0 0 0Boston 100 99 5 1 0 0 4 4 0 0 0 0 0 0 0 0 0 0Boston 150 98 5 29 0 22 91 91 2 0 0 6 6 0 0 0 2 2Boston 150 99 5 17 0 16 46 46 1 0 0 4 4 0 0 0 1 1Boston 200 98 5 104 9 60 249 249 15 0 15 39 39 2 0 0 6 6Boston 200 99 5 72 3 46 196 196 8 0 7 23 23 1 0 0 4 4Chicago As is 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Chicago Current Std 4 21 14 23 26 26 1 0 1 4 4 0 0 0 0 0Chicago 50 98 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Chicago 50 99 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Chicago 100 98 4 2 0 1 6 6 0 0 0 0 0 0 0 0 0 0Chicago 100 99 4 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0Chicago 150 98 4 28 19 30 35 35 2 0 1 6 6 0 0 0 0 0Chicago 150 99 4 14 7 14 20 20 0 0 0 1 1 0 0 0 0 0Chicago 200 98 4 119 107 118 132 132 15 9 14 23 23 2 0 1 6 6Chicago 200 99 4 76 72 76 80 80 7 4 6 13 13 0 0 0 1 1Cleveland As is 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Cleveland Current Std 3 111 103 107 122 122 14 13 15 15 15 1 1 1 2 2Cleveland 50 98 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Cleveland 50 99 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Cleveland 100 98 3 7 3 7 10 10 0 0 0 0 0 0 0 0 0 0Cleveland 100 99 3 4 2 4 7 7 0 0 0 0 0 0 0 0 0 0Cleveland 150 98 3 79 68 77 92 92 7 3 7 10 10 0 0 0 0 0Cleveland 150 99 3 57 46 55 69 69 4 2 4 7 7 0 0 0 0 0Cleveland 200 98 3 187 177 179 204 204 50 40 48 62 62 7 3 7 10 10A-159Number of Daily Maximum Exceedances3≥100 ppb≥150 ppb≥200 ppbLocationStandard1Percentile2Site-YearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99Cleveland 200 99 3 166 161 167 171 171 30 19 28 43 43 4 2 4 7 7Denver As is 2 7 3 7 10 10 1 1 1 1 1 0 0 0 0 0Denver Current Std 2 48 43 48 53 53 5 3 5 7 7 2 1 2 3 3Denver 50 98 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Denver 50 99 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Denver 100 98 2 7 3 7 10 10 2 1 2 3 3 0 0 0 0 0Denver 100 99 2 4 2 4 6 6 0 0 0 0 0 0 0 0 0 0Denver 150 98 2 68 54 68 81 81 7 3 7 10 10 2 1 2 3 3Denver 150 99 2 33 24 33 42 42 4 2 4 6 6 2 1 2 3 3Denver 200 98 2 224 224 224 224 224 33 24 33 42 42 7 3 7 10 10Denver 200 99 2 157 144 157 170 170 18 11 18 24 24 4 2 4 6 6Las Vegas As is 3 1 0 0 2 2 0 0 0 0 0 0 0 0 0 0Las Vegas Current Std 3 211 203 212 218 218 22 15 22 28 28 1 0 0 4 4Las Vegas 50 98 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Las Vegas 50 99 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Las Vegas 100 98 3 7 1 10 11 11 0 0 0 0 0 0 0 0 0 0Las Vegas 100 99 3 2 0 2 4 4 0 0 0 0 0 0 0 0 0 0Las Vegas 150 98 3 141 112 142 168 168 7 1 10 11 11 1 0 0 2 2Las Vegas 150 99 3 85 65 89 102 102 2 0 2 4 4 0 0 0 1 1Las Vegas 200 98 3 280 261 261 319 319 76 59 78 92 92 7 1 10 11 11Las Vegas 200 99 3 248 229 236 278 278 46 36 40 63 63 2 0 2 4 4Los Angeles As is 9 6 0 6 9 9 0 0 0 0 0 0 0 0 0 0Los Angeles Current Std 9 42 25 37 77 77 4 0 2 9 9 0 0 0 1 1Los Angeles 50 98 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Los Angeles 50 99 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Los Angeles 100 98 9 1 0 0 3 3 0 0 0 0 0 0 0 0 0 0Los Angeles 100 99 9 0 0 0 2 2 0 0 0 0 0 0 0 0 0 0Los Angeles 150 98 9 21 7 20 32 32 1 0 0 3 3 0 0 0 0 0Los Angeles 150 99 9 17 5 18 28 28 0 0 0 2 2 0 0 0 0 0Los Angeles 200 98 9 72 46 55 118 118 7 3 9 11 11 1 0 0 3 3Los Angeles 200 99 9 62 35 49 110 110 6 1 6 10 10 0 0 0 2 2Miami As is 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0A-160Number of Daily Maximum Exceedances3≥100 ppb≥150 ppb≥200 ppbLocationStandard1Percentile2Site-YearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99Miami Current Std 3 78 56 79 100 100 17 14 15 23 23 1 0 0 2 2Miami 50 98 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Miami 50 99 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Miami 100 98 3 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0Miami 100 99 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Miami 150 98 3 28 25 30 30 30 1 0 1 1 1 0 0 0 0 0Miami 150 99 3 16 13 15 19 19 0 0 0 0 0 0 0 0 0 0Miami 200 98 3 76 68 79 80 80 16 13 15 19 19 1 0 1 1 1Miami 200 99 3 53 46 52 61 61 8 5 6 13 13 0 0 0 0 0New York As is 7 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0New York Current Std 7 18 6 17 52 52 1 0 0 2 2 0 0 0 0 0New York 50 98 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0New York 50 99 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0New York 100 98 7 1 0 1 2 2 0 0 0 0 0 0 0 0 0 0New York 100 99 7 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0New York 150 98 7 38 18 42 55 55 1 0 1 2 2 0 0 0 0 0New York 150 99 7 18 4 17 31 31 0 0 0 1 1 0 0 0 0 0New York 200 98 7 143 107 141 168 168 18 4 17 31 31 1 0 1 2 2New York 200 99 7 88 53 96 117 117 7 3 6 12 12 0 0 0 1 1Phoenix As is 3 2 1 1 4 4 0 0 0 0 0 0 0 0 0 0Phoenix Current Std 3 72 58 67 90 90 2 1 1 4 4 0 0 0 0 0Phoenix 50 98 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Phoenix 50 99 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Phoenix 100 98 3 6 4 4 11 11 0 0 0 0 0 0 0 0 0 0Phoenix 100 99 3 4 2 4 6 6 0 0 0 0 0 0 0 0 0 0Phoenix 150 98 3 160 135 170 174 174 6 4 4 11 11 0 0 0 1 1Phoenix 150 99 3 96 79 102 107 107 4 2 4 6 6 0 0 0 0 0Phoenix 200 98 3 293 266 297 316 316 88 67 97 100 100 6 4 4 11 11Phoenix 200 99 3 260 236 259 284 284 39 31 40 46 46 4 2 4 6 6St. Louis As is 6 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0St. Louis Current Std 6 56 28 47 112 112 3 0 2 6 6 1 0 1 2 2St. Louis 50 98 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0A-161Number of Daily Maximum Exceedances3≥100 ppb≥150 ppb≥200 ppbLocationStandard1Percentile2Site-YearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99St. Louis 50 99 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0St. Louis 100 98 6 3 1 3 6 6 0 0 0 1 1 0 0 0 0 0St. Louis 100 99 6 1 0 1 4 4 0 0 0 1 1 0 0 0 0 0St. Louis 150 98 6 46 21 50 68 68 3 1 3 6 6 1 0 1 2 2St. Louis 150 99 6 26 16 23 40 40 1 0 1 4 4 1 0 1 2 2St. Louis 200 98 6 175 126 192 209 209 23 15 22 36 36 3 1 3 6 6St. Louis 200 99 6 136 95 149 169 169 10 6 9 15 15 1 0 1 4 4Washington DC As is 4 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0Washington DC Current Std 4 78 61 81 88 88 7 6 7 8 8 0 0 0 1 1Washington DC 50 98 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Washington DC 50 99 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Washington DC 100 98 4 6 3 6 9 9 0 0 0 1 1 0 0 0 0 0Washington DC 100 99 4 2 0 2 4 4 0 0 0 0 0 0 0 0 0 0Washington DC 150 98 4 73 47 77 92 92 6 3 6 9 9 0 0 0 1 1Washington DC 150 99 4 43 25 45 55 55 2 0 2 4 4 0 0 0 1 1Washington DC 200 98 4 209 158 217 243 243 38 22 38 53 53 6 3 6 9 9Washington DC 200 99 4 154 106 162 184 184 22 15 20 32 32 2 0 2 4 4Notes:1Scenario: As is – unadjusted air quality, Current Std – air quality that just meets the currentannual standard, All others –air quality that just meets 1-hourconcentration level given percentileform of alternative standard.2Percentile: 98thor 99thpercentile of daily maximum 1-hour concentration averaged over three years at maximum monitor in location.3The mean number of exceedances represents the sum of daily maximum exceedances occurring at all monitors in a particular location divided by the number ofsite-years across the monitoring period. The min,med, p98, and p99 represent the minimum, median, 98th, and 99thpercentiles of the distribution for the number ofdaily maximum exceedances in any one year within the monitoring period.1

### A-162 Table A-120. Estimated nu mber of exceedances of 1-hour concentr ation 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 Exceedances 3

≥ 250 ppb ≥ 300 ppb

Location Standard 1 Percentile 2 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 Exceedances 3

≥ 250 ppb ≥ 300 ppb

Location Standard 1 Percentile 2 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 Exceedances 3

≥ 250 ppb ≥ 300 ppb

Location Standard 1 Percentile 2 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 St d – air quality that just meets the current annual standard, All others – air quality t hat just meets 1-hour concentratio n level given percentile form of alternative standard.

2 Percentile: 98 th or 99 th percentile of daily maximum 1-hour conc entration 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, 98 th , and 99 th percentiles of the distribution for the number of daily maximum exceedances in any one year within the monitoring period.A-165Table A-121. Estimated number of exceedancesof 1-hour concentration levels (100, 150, and 200 ppb) on-roads using 2001-2003 air qualityas isand air qualityadjusted to just meet the current and alternative standards and an on-road adjustment factor.Number of Daily Maximum Exceedances3≥100 ppb≥150 ppb≥200 ppbLocation Standard1Percentile2SiteYearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99Atlanta As is 1400 23 0 5 130 169 4 0 0 36 51 1 0 0 8 13Atlanta Current Std 1400 183 12 228 341 348 110 1 103 290 299 58 0 29 228 238Atlanta 50 98 1400 2 0 0 26 38 0 0 0 4 5 0 0 0 1 1Atlanta 50 99 1400 2 0 0 20 25 0 0 0 1 3 0 0 0 0 1Atlanta 100 98 1400 46 0 22 190 229 10 0 1 70 104 2 0 0 26 38Atlanta 100 99 1400 36 0 13 166 203 7 0 0 53 76 2 0 0 20 25Atlanta 150 98 1400 117 0 123 295 312 46 0 22 190 229 17 0 3 106 146Atlanta 150 99 1400 102 0 96 282 298 36 0 13 166 203 12 0 1 79 113Atlanta 200 98 1400 170 5 202 339 344 95 0 83 273 289 46 0 22 190 229Atlanta 200 99 1400 156 2 178 331 340 80 0 62 254 275 36 0 13 166 203Boston As is 600 5 0 1 36 40 0 0 0 3 5 0 0 0 1 1Boston Current Std 600 86 1 83 209 227 20 0 7 96 105 4 0 1 31 41Boston 50 98 600 0 0 0 3 6 0 0 0 0 0 0 0 0 0 0Boston 50 99 600 0 0 0 3 3 0 0 0 0 0 0 0 0 0 0Boston 100 98 600 26 0 13 125 131 3 0 0 22 25 0 0 0 3 6Boston 100 99 600 16 0 4 89 99 1 0 0 13 18 0 0 0 3 3Boston 150 98 600 95 3 87 219 248 26 0 13 125 131 6 0 1 40 44Boston 150 99 600 76 0 67 200 220 16 0 4 89 99 3 0 0 22 25Boston 200 98 600 163 27 166 283 293 72 0 60 196 220 26 0 13 125 131Boston 200 99 600 139 15 141 263 279 53 0 38 180 188 16 0 4 89 99Chicago As is 900 52 0 35 180 191 9 0 2 62 68 2 0 0 24 29Chicago Current Std 900 193 24 192 328 332 74 1 56 235 250 26 0 13 123 135Chicago 50 98 900 4 0 0 43 49 0 0 0 4 7 0 0 0 0 0Chicago 50 99 900 2 0 0 31 34 0 0 0 1 3 0 0 0 0 0Chicago 100 98 900 83 2 68 238 257 18 0 7 92 106 4 0 0 43 49Chicago 100 99 900 56 0 40 188 197 10 0 2 65 74 2 0 0 31 34Chicago 150 98 900 205 45 207 331 336 83 2 68 238 257 29 0 16 126 142Chicago 150 99 900 167 18 161 316 321 56 0 40 188 197 18 0 7 90 104Chicago 200 98 900 281 129 285 354 356 168 18 163 316 322 83 2 68 238 257A-166Number of Daily Maximum Exceedances3≥100 ppb≥150 ppb≥200 ppbLocation Standard1Percentile2SiteYearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99Chicago 200 99 900 253 91 257 344 350 130 9 118 291 301 56 0 40 188 197Cleveland As is 300 31 0 21 83 102 5 0 1 30 30 1 0 0 10 10Cleveland Current Std 300 264 154 277 332 335 134 31 134 241 264 58 8 51 137 154Cleveland 50 98 300 7 0 2 37 38 1 0 0 7 8 0 0 0 2 2Cleveland 50 99 300 5 0 1 30 30 0 0 0 3 3 0 0 0 1 1Cleveland 100 98 300 104 19 98 209 225 24 0 15 73 86 7 0 2 37 38Cleveland 100 99 300 84 12 80 179 192 19 0 10 62 70 5 0 1 30 30Cleveland 150 98 300 235 114 240 320 324 104 19 98 209 225 40 2 32 107 116Cleveland 150 99 300 212 84 214 307 313 84 12 80 179 192 30 0 21 82 98Cleveland 200 98 300 305 220 314 348 348 196 66 198 298 304 104 19 98 209 225Cleveland 200 99 300 291 181 301 342 344 174 50 173 283 295 84 12 80 179 192Denver As is 200 89 8 74 242 259 17 0 5 80 94 3 0 1 25 26Denver Current Std 200 267 137 286 315 316 157 8 162 282 284 76 0 79 227 238Denver 50 98 200 4 0 1 36 37 0 0 0 1 1 0 0 0 1 1Denver 50 99 200 2 0 1 14 15 0 0 0 1 1 0 0 0 1 1Denver 100 98 200 99 8 82 252 269 19 0 6 86 103 4 0 1 36 37Denver 100 99 200 66 4 52 209 221 11 0 3 66 73 2 0 1 14 15Denver 150 98 200 232 90 238 317 318 99 8 82 252 269 33 0 18 130 135Denver 150 99 200 197 55 192 311 312 66 4 52 209 221 19 0 6 86 103Denver 200 98 200 288 209 288 325 326 198 55 192 313 315 99 8 82 252 269Denver 200 99 200 270 158 276 323 323 157 29 143 293 303 66 4 52 209 221Detroit As is 600 41 1 30 130 141 9 0 5 44 46 3 0 2 16 18Detroit Current Std 600 282 181 285 345 349 166 52 167 299 307 77 6 63 210 219Detroit 50 98 600 5 0 3 26 28 1 0 1 7 7 1 0 0 5 6Detroit 50 99 600 1 0 1 7 7 1 0 0 4 5 0 0 0 4 4Detroit 100 98 600 59 4 46 170 186 13 1 8 56 57 5 0 3 26 28Detroit 100 99 600 14 1 8 57 59 3 0 2 16 18 1 0 1 7 7Detroit 150 98 600 178 52 183 301 308 59 4 46 170 186 20 1 13 70 84Detroit 150 99 600 62 6 50 176 191 14 1 8 57 59 5 0 3 26 28Detroit 200 98 600 265 131 271 334 343 140 31 131 278 286 59 4 46 170 186Detroit 200 99 600 143 35 134 284 287 41 1 30 125 137 14 1 8 57 59A-167Number of Daily Maximum Exceedances3≥100 ppb≥150 ppb≥200 ppbLocation Standard1Percentile2SiteYearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99El Paso As is 1200 32 0 19 136 145 4 0 1 24 26 0 0 0 5 6El Paso Current Std 1200 272 122 284 339 348 168 13 168 291 301 79 2 69 224 236El Paso 50 98 1200 3 0 0 23 24 0 0 0 3 3 0 0 0 0 1El Paso 50 99 1200 1 0 0 10 12 0 0 0 1 1 0 0 0 0 0El Paso 100 98 1200 89 4 79 231 249 17 0 8 80 91 3 0 0 23 24El Paso 100 99 1200 56 1 42 190 201 8 0 3 46 49 1 0 0 10 12El Paso 150 98 1200 216 47 222 313 327 89 4 79 231 249 30 0 17 127 136El Paso 150 99 1200 174 15 176 297 306 56 1 42 190 201 15 0 7 76 83El Paso 200 98 1200 278 135 288 342 348 180 15 180 298 313 89 4 79 231 249El Paso 200 99 1200 255 97 265 334 342 136 10 133 274 288 56 1 42 190 201Jacksonville As is 200 13 0 7 55 56 1 0 1 6 7 1 0 1 1 1Jacksonville Current Std 200 290 227 278 338 340 205 103 206 307 310 123 26 122 217 240Jacksonville 50 98 200 7 0 4 34 34 1 0 1 3 3 1 0 1 1 1Jacksonville 50 99 200 5 0 3 25 25 1 0 1 2 2 1 0 1 1 1Jacksonville 100 98 200 119 26 118 217 235 32 2 25 97 114 7 0 4 34 34Jacksonville 100 99 200 100 19 98 201 212 23 1 18 79 86 5 0 3 25 25Jacksonville 150 98 200 233 135 232 323 325 119 26 118 217 235 51 3 44 127 147Jacksonville 150 99 200 214 111 212 314 317 100 19 98 201 212 39 2 31 115 128Jacksonville 200 98 200 289 227 278 338 340 201 96 201 306 310 119 26 118 217 235Jacksonville 200 99 200 279 200 273 336 337 182 81 182 290 297 100 19 98 201 212Las Vegas As is 1600 23 0 4 171 194 4 0 0 54 62 0 0 0 8 9Las Vegas Current Std 1600 189 2 194 335 338 106 0 71 309 316 49 0 18 250 278Las Vegas 50 98 1600 5 0 0 63 71 0 0 0 6 7 0 0 0 0 0Las Vegas 50 99 1600 3 0 0 49 58 0 0 0 2 3 0 0 0 0 0Las Vegas 100 98 1600 82 0 43 298 307 19 0 3 149 172 5 0 0 63 71Las Vegas 100 99 1600 64 0 27 276 292 13 0 1 102 120 3 0 0 49 58Las Vegas 150 98 1600 167 0 161 330 333 82 0 43 298 307 32 0 8 199 230Las Vegas 150 99 1600 148 0 127 325 329 64 0 27 276 292 21 0 3 158 182Las Vegas 200 98 1600 218 5 237 343 344 143 0 118 324 328 82 0 43 298 307Las Vegas 200 99 1600 201 3 212 337 339 123 0 89 318 321 64 0 27 276 292Los Angeles As is 5100 71 0 57 231 251 17 0 6 94 108 5 0 0 41 48A-168Number of Daily Maximum Exceedances3≥100 ppb≥150 ppb≥200 ppbLocation Standard1Percentile2SiteYearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99Los Angeles Current Std 5100 152 0 152 319 330 55 0 40 196 225 19 0 8 101 117Los Angeles 50 98 5100 1 0 0 15 20 0 0 0 1 3 0 0 0 0 0Los Angeles 50 99 5100 1 0 0 13 17 0 0 0 0 2 0 0 0 0 0Los Angeles 100 98 5100 33 0 19 142 160 6 0 1 46 55 1 0 0 15 20Los Angeles 100 99 5100 29 0 16 132 148 5 0 0 42 49 1 0 0 13 17Los Angeles 150 98 5100 111 0 101 280 299 33 0 19 142 160 10 0 2 66 77Los Angeles 150 99 5100 100 0 89 266 288 29 0 16 132 148 8 0 1 58 68Los Angeles 200 98 5100 191 0 204 338 346 81 0 69 246 262 33 0 19 142 160Los Angeles 200 99 5100 181 0 189 334 344 73 0 59 236 254 29 0 16 132 148Miami As is 600 7 0 2 50 64 1 0 0 6 10 0 0 0 1 2Miami Current Std 600 232 147 233 316 323 146 25 147 243 253 78 3 74 191 200Miami 50 98 600 5 0 1 37 48 0 0 0 5 7 0 0 0 0 1Miami 50 99 600 3 0 0 21 31 0 0 0 2 3 0 0 0 0 0Miami 100 98 600 78 2 74 181 189 19 0 10 92 110 5 0 1 37 48Miami 100 99 600 53 0 48 151 173 11 0 4 67 84 3 0 0 21 31Miami 150 98 600 174 44 178 267 276 78 2 74 181 189 31 0 21 114 141Miami 150 99 600 144 21 150 237 245 53 0 48 151 173 18 0 9 89 104Miami 200 98 600 232 139 228 317 322 145 21 150 242 253 78 2 74 181 189Miami 200 99 600 208 84 202 299 307 114 9 115 210 225 53 0 48 151 173New York As is 2600 42 0 28 177 201 7 0 1 55 63 2 0 0 24 24New York Current Std 2600 129 0 124 298 310 36 0 22 157 196 10 0 2 71 82New York 50 98 2600 3 0 0 33 34 0 0 0 3 4 0 0 0 0 0New York 50 99 2600 1 0 0 19 22 0 0 0 1 2 0 0 0 0 0New York 100 98 2600 57 0 43 212 226 10 0 3 67 73 3 0 0 33 34New York 100 99 2600 35 0 22 156 179 6 0 1 48 56 1 0 0 19 22New York 150 98 2600 169 5 170 316 324 57 0 43 212 226 17 0 8 89 110New York 150 99 2600 129 0 119 294 307 35 0 22 156 179 10 0 2 67 72New York 200 98 2600 249 40 266 344 351 132 0 125 294 307 57 0 43 212 226New York 200 99 2600 216 20 229 332 340 94 0 80 263 279 35 0 22 156 179Philadelphia As is 1400 37 0 19 149 172 6 0 1 49 62 1 0 0 11 24Philadelphia Current Std 1400 222 29 231 339 347 87 0 71 250 280 29 0 15 127 150A-169Number of Daily Maximum Exceedances3≥100 ppb≥150 ppb≥200 ppbLocation Standard1Percentile2SiteYearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99Philadelphia 50 98 1400 7 0 1 54 68 1 0 0 8 20 0 0 0 1 1Philadelphia 50 99 1400 4 0 1 41 56 0 0 0 4 10 0 0 0 1 1Philadelphia 100 98 1400 116 1 102 284 294 27 0 12 118 137 7 0 1 54 68Philadelphia 100 99 1400 91 0 75 256 275 18 0 5 93 105 4 0 1 41 56Philadelphia 150 98 1400 254 65 261 346 350 116 1 102 284 294 44 0 27 169 196Philadelphia 150 99 1400 228 42 235 335 347 91 0 75 256 275 31 0 15 132 155Philadelphia 200 98 1400 312 192 317 364 364 217 31 223 332 342 116 1 102 284 294Philadelphia 200 99 1400 299 152 304 359 359 187 21 191 325 332 91 0 75 256 275Phoenix As is 500 101 1 83 280 315 16 0 2 113 124 2 0 0 17 20Phoenix Current Std 500 245 23 266 345 352 96 1 77 286 299 29 0 8 174 191Phoenix 50 98 500 6 0 0 44 48 0 0 0 2 3 0 0 0 0 0Phoenix 50 99 500 3 0 0 19 24 0 0 0 1 1 0 0 0 0 0Phoenix 100 98 500 153 2 152 319 337 35 0 14 182 206 6 0 0 44 48Phoenix 100 99 500 118 1 103 290 323 22 0 4 135 156 3 0 0 19 24Phoenix 150 98 500 293 92 297 356 360 153 2 152 319 337 58 0 37 230 254Phoenix 150 99 500 269 48 283 351 356 118 1 103 290 323 40 0 19 195 218Phoenix 200 98 500 332 245 345 362 363 262 41 278 350 354 153 2 152 319 337Phoenix 200 99 500 325 199 334 361 362 231 22 250 343 349 118 1 103 290 323Provo As is 300 61 1 38 248 289 9 0 0 62 64 1 0 0 11 11Provo Current Std 300 338 293 347 363 364 235 55 258 351 355 100 4 77 297 327Provo 50 98 300 18 0 4 86 106 2 0 0 18 18 0 0 0 2 2Provo 50 99 300 13 0 2 75 82 1 0 0 9 9 0 0 0 0 0Provo 100 98 300 257 60 277 353 358 75 1 51 273 301 18 0 4 86 106Provo 100 99 300 229 38 248 352 355 56 1 33 235 283 13 0 2 75 82Provo 150 98 300 343 289 352 364 364 257 60 277 353 358 121 4 97 318 342Provo 150 99 300 337 264 347 363 364 229 38 248 352 355 93 2 71 300 327Provo 200 98 300 351 321 362 365 365 331 236 342 363 364 257 60 277 353 358Provo 200 99 300 350 320 360 365 365 322 204 329 362 363 229 38 248 352 355St. Louis As is 900 25 0 12 128 139 3 0 0 28 37 0 0 0 5 6St. Louis Current Std 900 262 112 268 346 350 131 4 124 276 295 51 0 37 200 207St. Louis 50 98 900 7 0 1 45 51 1 0 0 7 10 0 0 0 1 1A-170Number of Daily Maximum Exceedances3≥100 ppb≥150 ppb≥200 ppbLocation Standard1Percentile2SiteYearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99St. Louis 50 99 900 4 0 0 35 41 0 0 0 4 4 0 0 0 1 1St. Louis 100 98 900 125 2 118 274 288 28 0 15 144 153 7 0 1 45 51St. Louis 100 99 900 96 1 85 256 263 19 0 6 105 114 4 0 0 35 41St. Louis 150 98 900 258 92 262 345 351 125 2 118 274 288 47 0 32 189 204St. Louis 150 99 900 233 63 237 335 347 96 1 85 256 263 33 0 18 156 166St. Louis 200 98 900 316 211 322 358 361 224 52 229 329 341 125 2 118 274 288St. Louis 200 99 900 301 166 306 356 357 195 30 197 315 329 96 1 85 256 263Washington DC As is 1800 36 0 17 169 205 6 0 0 46 54 1 0 0 9 14Washington DC Current Std 1800 222 6 268 348 353 111 0 100 293 312 46 0 25 199 230Washington DC 50 98 1800 7 0 0 56 63 0 0 0 6 8 0 0 0 0 0Washington DC 50 99 1800 5 0 0 38 45 0 0 0 2 2 0 0 0 0 0Washington DC 100 98 1800 109 0 99 287 310 27 0 10 139 168 7 0 0 56 63Washington DC 100 99 1800 83 0 65 260 291 18 0 4 102 123 5 0 0 38 45Washington DC 150 98 1800 221 4 269 347 352 109 0 99 287 310 44 0 23 186 225Washington DC 150 99 1800 194 1 227 339 344 83 0 65 260 291 29 0 12 146 176Washington DC 200 98 1800 279 38 325 360 362 190 1 221 336 343 109 0 99 287 310Washington DC 200 99 1800 262 20 313 357 360 161 0 176 326 334 83 0 65 260 291Other MSA As is 61200 16 0 3 105 129 2 0 0 22 32 0 0 0 4 7Other MSA Current Std 61200 133 0 129 320 336 47 0 25 208 239 15 0 3 102 124Other MSA 50 98 61200 0 0 0 5 8 0 0 0 0 1 0 0 0 0 0Other MSA 50 99 61200 0 0 0 2 3 0 0 0 0 0 0 0 0 0 0Other MSA 100 98 61200 17 0 4 110 133 2 0 0 23 34 0 0 0 5 8Other MSA 100 99 61200 8 0 1 68 85 1 0 0 11 17 0 0 0 2 3Other MSA 150 98 61200 73 0 52 258 287 17 0 4 110 133 4 0 0 40 53Other MSA 150 99 61200 47 0 25 208 238 8 0 1 68 85 2 0 0 20 30Other MSA 200 98 61200 138 0 136 324 338 51 0 29 216 247 17 0 4 110 133Other MSA 200 99 61200 104 0 91 298 320 30 0 12 161 191 8 0 1 68 85Other Not MSA As is 12700 4 0 0 43 62 1 0 0 9 15 0 0 0 2 5Other Not MSA Current Std 12700 126 0 104 333 343 63 0 26 255 282 28 0 6 172 200Other Not MSA 50 98 12700 0 0 0 4 8 0 0 0 1 1 0 0 0 0 1Other Not MSA 50 99 12700 0 0 0 2 4 0 0 0 1 1 0 0 0 0 0A-171Number of Daily Maximum Exceedances3≥100 ppb≥150 ppb≥200 ppbLocation Standard1Percentile2SiteYearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99Other Not MSA 100 98 12700 6 0 0 64 86 1 0 0 12 17 0 0 0 4 8Other Not MSA 100 99 12700 3 0 0 33 44 0 0 0 6 13 0 0 0 2 4Other Not MSA 150 98 12700 28 0 6 172 199 6 0 0 64 86 2 0 0 19 27Other Not MSA 150 99 12700 16 0 2 127 149 3 0 0 33 44 1 0 0 11 16Other Not MSA 200 98 12700 63 0 26 254 280 19 0 3 139 164 6 0 0 64 86Other Not MSA 200 99 12700 42 0 12 207 235 10 0 1 92 118 3 0 0 33 44Atlanta As is 1500 17 0 2 114 120 2 0 0 26 27 0 0 0 6 7Atlanta Current Std 1500 193 4 259 337 341 126 0 148 285 300 72 0 57 225 237Atlanta 50 98 1500 3 0 0 29 31 0 0 0 4 5 0 0 0 0 0Atlanta 50 99 1500 2 0 0 21 23 0 0 0 1 2 0 0 0 0 0Atlanta 100 98 1500 58 0 39 206 218 13 0 1 93 100 3 0 0 29 31Atlanta 100 99 1500 45 0 21 185 198 8 0 0 64 75 2 0 0 21 23Atlanta 150 98 1500 133 0 162 295 304 58 0 39 206 218 22 0 3 126 141Atlanta 150 99 1500 116 0 131 276 288 45 0 21 185 198 15 0 1 103 110Atlanta 200 98 1500 181 3 241 328 334 111 0 123 266 281 58 0 39 206 218Atlanta 200 99 1500 167 1 219 319 326 94 0 91 249 262 45 0 21 185 198Boston As is 800 2 0 0 18 21 0 0 0 1 1 0 0 0 0 0Boston Current Std 800 95 4 91 207 221 25 0 15 104 109 6 0 1 44 50Boston 50 98 800 0 0 0 1 2 0 0 0 0 0 0 0 0 0 0Boston 50 99 800 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0Boston 100 98 800 19 0 8 92 99 1 0 0 12 13 0 0 0 1 2Boston 100 99 800 8 0 2 54 65 0 0 0 3 5 0 0 0 0 1Boston 150 98 800 84 4 78 189 207 19 0 8 92 99 3 0 0 26 33Boston 150 99 800 52 1 42 150 162 8 0 2 54 65 1 0 0 10 12Boston 200 98 800 153 32 151 249 267 59 1 50 161 174 19 0 8 92 99Boston 200 99 800 114 14 111 215 238 34 0 24 119 129 8 0 2 54 65Chicago As is 800 36 0 20 148 161 5 0 0 41 53 1 0 0 7 18Chicago Current Std 800 189 25 187 329 339 69 0 54 225 241 22 0 8 117 126Chicago 50 98 800 2 0 0 24 30 0 0 0 0 0 0 0 0 0 0Chicago 50 99 800 1 0 0 15 24 0 0 0 0 0 0 0 0 0 0Chicago 100 98 800 59 0 43 201 220 10 0 2 63 78 2 0 0 24 30A-172Number of Daily Maximum Exceedances3≥100 ppb≥150 ppb≥200 ppbLocation Standard1Percentile2SiteYearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99Chicago 100 99 800 47 0 31 181 193 8 0 1 54 67 1 0 0 15 24Chicago 150 98 800 176 20 169 320 329 59 0 43 201 220 18 0 6 98 109Chicago 150 99 800 157 14 148 313 320 47 0 31 181 193 14 0 4 80 93Chicago 200 98 800 259 94 261 352 355 138 9 127 302 309 59 0 43 201 220Chicago 200 99 800 244 64 248 348 353 119 7 108 291 296 47 0 31 181 193Denver As is 300 63 2 49 190 195 10 0 4 47 52 2 0 0 11 14Denver Current Std 300 257 135 259 314 320 134 12 133 260 264 52 0 41 164 174Denver 50 98 300 9 0 2 41 45 1 0 0 5 9 0 0 0 0 0Denver 50 99 300 5 0 1 23 25 0 0 0 2 3 0 0 0 0 0Denver 100 98 300 148 20 151 263 269 38 0 23 132 150 9 0 2 41 45Denver 100 99 300 106 10 100 236 242 23 0 11 87 95 5 0 1 23 25Denver 150 98 300 263 170 264 315 321 148 20 151 263 269 61 1 48 180 192Denver 150 99 300 239 114 243 309 313 106 10 100 236 242 38 0 23 132 150Denver 200 98 300 296 241 308 330 332 239 122 243 309 313 148 20 151 263 269Denver 200 99 300 286 217 287 323 327 204 66 209 294 299 106 10 100 236 242Detroit As is 600 20 0 9 90 103 2 0 0 20 24 0 0 0 3 6Detroit Current Std 600 287 166 286 350 352 189 35 193 300 315 95 3 86 222 258Detroit 50 98 600 13 0 4 57 66 1 0 0 12 18 0 0 0 1 1Detroit 50 99 600 10 0 2 52 53 1 0 0 9 14 0 0 0 0 1Detroit 100 98 600 165 29 166 293 296 50 0 37 165 180 13 0 4 57 66Detroit 100 99 600 146 18 143 275 284 40 0 27 146 163 10 0 2 52 53Detroit 150 98 600 273 163 271 341 346 165 29 166 293 296 77 1 66 203 221Detroit 150 99 600 261 141 260 337 342 146 18 143 275 284 62 0 50 186 204Detroit 200 98 600 313 237 318 355 359 249 117 251 331 337 165 29 166 293 296Detroit 200 99 600 307 234 309 354 357 235 90 237 327 332 146 18 143 275 284El Paso As is 1200 24 0 12 114 143 3 0 0 20 23 0 0 0 4 4El Paso Current Std 1200 281 137 285 349 354 198 22 205 309 320 109 3 102 255 265El Paso 50 98 1200 5 0 1 32 37 0 0 0 4 4 0 0 0 0 1El Paso 50 99 1200 3 0 0 20 23 0 0 0 2 2 0 0 0 0 0El Paso 100 98 1200 108 3 102 254 263 23 0 11 113 137 5 0 1 32 37El Paso 100 99 1200 75 2 62 225 234 13 0 5 72 86 3 0 0 20 23A-173Number of Daily Maximum Exceedances3≥100 ppb≥150 ppb≥200 ppbLocation Standard1Percentile2SiteYearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99El Paso 150 98 1200 229 63 235 323 327 108 3 102 254 263 39 0 23 164 178El Paso 150 99 1200 199 26 205 309 317 75 2 62 225 234 23 0 12 113 137El Paso 200 98 1200 281 137 286 349 353 197 26 205 308 317 108 3 102 254 263El Paso 200 99 1200 264 97 271 340 346 162 12 166 289 296 75 2 62 225 234Jacksonville As is 200 11 0 5 48 59 2 0 2 5 6 1 0 1 4 4Jacksonville Current Std 200 295 229 295 341 342 216 114 223 309 318 134 31 133 260 279Jacksonville 50 98 200 9 0 5 39 50 2 0 2 5 5 1 0 1 4 4Jacksonville 50 99 200 2 0 2 5 5 1 0 1 4 4 0 0 0 3 3Jacksonville 100 98 200 127 30 124 256 269 35 0 23 138 151 9 0 5 39 50Jacksonville 100 99 200 39 1 27 145 158 6 0 4 25 29 2 0 2 5 5Jacksonville 150 98 200 241 145 243 320 327 127 30 124 256 269 55 1 42 169 193Jacksonville 150 99 200 135 33 131 265 279 39 1 27 145 158 10 0 5 47 59Jacksonville 200 98 200 293 229 294 339 340 211 104 214 302 312 127 30 124 256 269Jacksonville 200 99 200 218 118 223 309 318 102 12 95 232 253 39 1 27 145 158Las Vegas As is 1100 15 0 0 133 148 2 0 0 54 64 0 0 0 6 8Las Vegas Current Std 1100 177 2 171 343 346 99 0 55 317 324 50 0 11 260 275Las Vegas 50 98 1100 6 0 0 77 79 1 0 0 17 19 0 0 0 0 2Las Vegas 50 99 1100 4 0 0 71 73 0 0 0 6 8 0 0 0 0 0Las Vegas 100 98 1100 83 0 39 305 312 22 0 1 174 187 6 0 0 77 79Las Vegas 100 99 1100 70 0 24 288 304 16 0 0 142 151 4 0 0 71 73Las Vegas 150 98 1100 161 1 146 342 343 83 0 39 305 312 36 0 5 219 239Las Vegas 150 99 1100 145 0 118 337 339 70 0 24 288 304 26 0 2 191 206Las Vegas 200 98 1100 210 6 224 350 350 138 0 108 335 336 83 0 39 305 312Las Vegas 200 99 1100 198 3 205 348 349 123 0 83 329 333 70 0 24 288 304Los Angeles As is 5400 38 0 25 150 169 6 0 1 43 55 1 0 0 11 14Los Angeles Current Std 5400 160 2 163 314 326 58 0 45 187 215 18 0 8 90 105Los Angeles 50 98 5400 2 0 0 18 24 0 0 0 1 2 0 0 0 0 0Los Angeles 50 99 5400 1 0 0 12 15 0 0 0 1 1 0 0 0 0 0Los Angeles 100 98 5400 54 0 41 188 208 10 0 2 62 75 2 0 0 18 24Los Angeles 100 99 5400 40 0 27 153 176 6 0 1 44 55 1 0 0 12 15Los Angeles 150 98 5400 155 2 155 314 325 54 0 41 188 208 17 0 6 85 104A-174Number of Daily Maximum Exceedances3≥100 ppb≥150 ppb≥200 ppbLocation Standard1Percentile2SiteYearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99Los Angeles 150 99 5400 132 0 127 296 310 40 0 27 153 176 11 0 3 68 79Los Angeles 200 98 5400 227 13 240 349 352 122 0 115 285 304 54 0 41 188 208Los Angeles 200 99 5400 208 8 217 342 348 100 0 91 258 281 40 0 27 153 176Miami As is 400 6 0 1 46 46 0 0 0 6 6 0 0 0 1 1Miami Current Std 400 202 100 201 284 286 130 30 139 197 204 77 1 76 160 163Miami 50 98 400 3 0 0 24 24 0 0 0 1 1 0 0 0 1 1Miami 50 99 400 2 0 0 18 21 0 0 0 1 1 0 0 0 0 0Miami 100 98 400 56 0 47 144 148 13 0 4 72 77 3 0 0 24 24Miami 100 99 400 46 0 34 134 140 9 0 2 62 65 2 0 0 18 21Miami 150 98 400 128 24 135 194 199 56 0 47 144 148 21 0 11 97 101Miami 150 99 400 117 17 123 187 192 46 0 34 134 140 16 0 7 81 92Miami 200 98 400 182 75 188 260 262 106 12 111 178 183 56 0 47 144 148Miami 200 99 400 171 63 178 248 250 94 7 96 170 174 46 0 34 134 140New York As is 2200 35 0 23 149 171 5 0 1 40 45 1 0 0 13 15New York Current Std 2200 147 0 148 308 321 45 0 31 188 202 13 0 4 75 89New York 50 98 2200 3 0 0 32 34 0 0 0 3 4 0 0 0 0 1New York 50 99 2200 2 0 0 17 23 0 0 0 1 1 0 0 0 0 0New York 100 98 2200 75 0 62 225 248 14 0 6 73 88 3 0 0 32 34New York 100 99 2200 45 0 32 175 196 7 0 2 49 53 2 0 0 17 23New York 150 98 2200 192 2 204 326 333 75 0 62 225 248 25 0 13 120 135New York 150 99 2200 149 0 151 305 312 45 0 32 175 196 13 0 4 67 80New York 200 98 2200 264 38 284 350 353 157 0 162 310 317 75 0 62 225 248New York 200 99 2200 232 20 250 339 343 115 0 108 278 294 45 0 32 175 196Philadelphia As is 1200 22 0 10 101 130 2 0 0 20 22 0 0 0 3 3Philadelphia Current Std 1200 232 54 242 321 326 110 2 99 269 280 43 0 27 188 202Philadelphia 50 98 1200 5 0 1 37 42 0 0 0 4 4 0 0 0 0 0Philadelphia 50 99 1200 1 0 0 14 14 0 0 0 0 1 0 0 0 0 0Philadelphia 100 98 1200 112 5 102 258 276 24 0 10 109 139 5 0 1 37 42Philadelphia 100 99 1200 53 0 39 187 214 8 0 1 49 53 1 0 0 14 14Philadelphia 150 98 1200 237 76 245 325 329 112 5 102 258 276 39 0 27 155 189Philadelphia 150 99 1200 172 24 178 297 311 53 0 39 187 214 14 0 4 77 83A-175Number of Daily Maximum Exceedances3≥100 ppb≥150 ppb≥200 ppbLocation Standard1Percentile2SiteYearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99Philadelphia 200 98 1200 295 186 302 339 345 204 51 213 313 319 112 5 102 258 276Philadelphia 200 99 1200 256 105 264 329 332 134 7 130 277 296 53 0 39 187 214Phoenix As is 900 77 0 53 275 293 10 0 1 57 71 1 0 0 7 8Phoenix Current Std 900 284 127 296 357 359 124 2 111 307 325 38 0 11 186 211Phoenix 50 98 900 4 0 0 26 35 0 0 0 1 1 0 0 0 0 0Phoenix 50 99 900 2 0 0 12 14 0 0 0 0 0 0 0 0 0 0Phoenix 100 98 900 146 3 140 320 332 28 0 7 159 171 4 0 0 26 35Phoenix 100 99 900 103 0 84 293 312 16 0 2 101 107 2 0 0 12 14Phoenix 150 98 900 299 151 309 358 360 146 3 140 320 332 49 0 19 222 245Phoenix 150 99 900 268 95 280 353 357 103 0 84 293 312 30 0 7 165 184Phoenix 200 98 900 338 227 347 363 364 264 92 277 352 356 146 3 140 320 332Phoenix 200 99 900 328 204 337 361 363 225 39 236 345 352 103 0 84 293 312Provo As is 300 51 0 44 160 160 17 0 2 68 70 12 0 0 44 44Provo Current Std 300 306 153 331 360 361 192 44 187 331 340 87 1 61 260 278Provo 50 98 300 13 0 0 45 45 4 0 0 42 42 1 0 0 20 20Provo 50 99 300 13 0 0 44 45 4 0 0 42 42 1 0 0 15 15Provo 100 98 300 63 0 48 187 187 20 0 5 78 79 13 0 0 45 45Provo 100 99 300 60 0 46 182 183 19 0 3 78 79 13 0 0 44 45Provo 150 98 300 209 38 214 328 335 63 0 48 187 187 26 0 17 95 95Provo 150 99 300 201 30 210 328 334 60 0 46 182 183 24 0 15 91 91Provo 200 98 300 298 217 300 356 359 160 7 157 306 314 63 0 48 187 187Provo 200 99 300 294 204 290 354 356 151 6 147 299 306 60 0 46 182 183St. Louis As is 400 15 0 5 76 83 1 0 0 20 24 0 0 0 2 5St. Louis Current Std 400 233 82 228 341 347 121 1 113 280 300 50 0 33 198 221St. Louis 50 98 400 4 0 0 35 41 0 0 0 9 10 0 0 0 0 0St. Louis 50 99 400 3 0 0 31 35 0 0 0 4 9 0 0 0 0 0St. Louis 100 98 400 107 3 100 238 249 23 0 12 105 108 4 0 0 35 41St. Louis 100 99 400 98 2 90 227 240 20 0 10 96 101 3 0 0 31 35St. Louis 150 98 400 226 96 221 331 340 107 3 100 238 249 39 0 28 135 142St. Louis 150 99 400 217 82 211 328 340 98 2 90 227 240 33 0 22 124 133St. Louis 200 98 400 287 166 291 357 357 194 62 190 308 320 107 3 100 238 249A-176Number of Daily Maximum Exceedances3≥100 ppb≥150 ppb≥200 ppbLocation Standard1Percentile2SiteYearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99St. Louis 200 99 400 282 162 283 355 356 186 49 181 305 317 98 2 90 227 240Washington DC As is 1700 21 0 7 119 143 2 0 0 20 22 0 0 0 4 6Washington DC Current Std 1700 207 10 238 340 345 102 0 88 270 289 41 0 21 176 202Washington DC 50 98 1700 5 0 1 37 42 0 0 0 5 6 0 0 0 1 1Washington DC 50 99 1700 2 0 0 15 16 0 0 0 2 2 0 0 0 0 0Washington DC 100 98 1700 96 0 81 269 283 22 0 8 126 150 5 0 1 37 42Washington DC 100 99 1700 50 0 29 209 226 8 0 1 52 66 2 0 0 15 16Washington DC 150 98 1700 200 2 232 337 345 96 0 81 269 283 36 0 17 173 192Washington DC 150 99 1700 148 0 157 309 322 50 0 29 209 226 14 0 3 85 110Washington DC 200 98 1700 260 42 299 358 362 171 0 191 323 335 96 0 81 269 283Washington DC 200 99 1700 220 8 256 346 352 117 0 108 288 298 50 0 29 209 226Other MSA As is 56500 10 0 1 79 100 1 0 0 12 18 0 0 0 2 3Other MSA Current Std 56500 143 0 143 322 336 59 0 36 233 257 22 0 5 138 163Other MSA 50 98 56500 0 0 0 5 8 0 0 0 0 1 0 0 0 0 0Other MSA 50 99 56500 0 0 0 2 5 0 0 0 0 0 0 0 0 0 0Other MSA 100 98 56500 20 0 5 123 152 2 0 0 26 37 0 0 0 5 8Other MSA 100 99 56500 14 0 2 96 121 1 0 0 17 25 0 0 0 2 5Other MSA 150 98 56500 80 0 62 264 288 20 0 5 123 152 5 0 0 44 60Other MSA 150 99 56500 63 0 43 237 263 14 0 2 96 121 3 0 0 30 43Other MSA 200 98 56500 143 0 144 322 337 57 0 36 228 255 20 0 5 123 152Other MSA 200 99 56500 125 0 119 311 328 44 0 23 201 228 14 0 2 96 121Other Not MSA As is 11600 4 0 0 43 65 1 0 0 7 13 0 0 0 2 5Other Not MSA Current Std 11600 124 0 100 331 339 62 0 24 257 272 29 0 4 180 201Other Not MSA 50 98 11600 0 0 0 2 6 0 0 0 1 2 0 0 0 0 1Other Not MSA 50 99 11600 0 0 0 2 3 0 0 0 0 2 0 0 0 0 1Other Not MSA 100 98 11600 6 0 0 61 86 1 0 0 10 17 0 0 0 2 6Other Not MSA 100 99 11600 2 0 0 23 35 0 0 0 3 7 0 0 0 2 3Other Not MSA 150 98 11600 28 0 4 173 198 6 0 0 61 86 1 0 0 18 26Other Not MSA 150 99 11600 13 0 1 113 134 2 0 0 23 35 0 0 0 6 11Other Not MSA 200 98 11600 60 0 22 249 269 19 0 2 141 164 6 0 0 61 86Other Not MSA 200 99 11600 34 0 6 193 216 8 0 0 80 104 2 0 0 23 35A-177Number of Daily Maximum Exceedances3≥100 ppb≥150 ppb≥200 ppbLocation Standard1Percentile2SiteYearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99Notes:1Scenario: As is – unadjusted air quality, Current Std – air quality that just meets the currentannual standard, All others –air quality that just meets 1-hourconcentration level given percentileform of alternative standard.2Percentile: 98thor 99thpercentile of daily maximum 1-hour concentration averaged over three years at maximum monitor in location.3The mean number of exceedances represents the sum of daily maximum exceedances occurring at all monitors in a particular location divided by thenumber of site-years across the monitoring period. Themin, med, p98, and p99 represent the minimum, median, 98th, and 99thpercentiles of the distributionfor the number of daily maximum exceedances inany one year within the monitoring period.

### A-178 Table A-122. Estimated nu mber 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 mee t the current and alternative standards and an on-road adjustment factor. Number of Daily Maximum Exceedances 3

≥ 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 Exceedances 3

≥ 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 Exceedances 3

≥ 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 Exceedances 3

≥ 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 Exceedances 3

≥ 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 – ai r quality that just meets the current annual standard, All others – air quality that just meets 1-hour concentration level given perc entile form of alternative standard.

2 Percentile: 98 th or 99 th percentile of daily maximum 1-hour conc entration 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 si te-years across the monitoring period. The min, med, p98, and p99 represent the minimum, median, 98 th , and 99 th percentiles of the distribut ion for the number of daily maximum exceedances in any one year within the monitoring period.

### A-183 

## A-9.3 Annual average NO 2 

## concentration data for 2004-2006 1
Table A-123. Estimated annual average NO 2 concentrations for monitors ≥ 100 m from a major road using 

### 2 2004-2006 air quality as is and adjusted to just meet the current and alternative standards. 

### 3

Annual Average NO 2 (ppb) 3

Location Scenario 1 Percentile 2 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 NO 2 (ppb) 3

Location Scenario 1 Percentile 2 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 NO 2 (ppb) 3

Location Scenario 1 Percentile 2 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 NO 2 (ppb) 3

Location Scenario 1 Percentile 2 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 NO 2 (ppb) 3

Location Scenario 1 Percentile 2 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 NO 2 (ppb) 3

Location Scenario 1 Percentile 2 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 St d – air quality that just meets the current annual standard, All others – air quality t hat just meets 1-hour concentratio n level given percentile form of alternative standard.
2 Percentile: 98 th or 99 th percentile of daily maximum 1-hour conc entration 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-yea rs across the monitoring period. The min, med, p98, p99 represent the minimum, median, 98 th , and 99 th percentiles of the dist ribution for the annual

means. 

### 1

### A-189 

### 1

Table A-124. Estimated annual average NO 2 concentrations for monitors >20 m and <100 m from a major 

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

### 3
standards. 

### 4

Annual Average NO 2 (ppb) 3

Location Scenario 1 Percentile 2 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 NO 2 (ppb) 3

Location Scenario 1 Percentile 2 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 NO 2 (ppb) 3

Location Scenario 1 Percentile 2 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 NO 2 (ppb) 3

Location Scenario 1 Percentile 2 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 St d – air quality that just meets the current annual standard, All others – air quality t hat just meets 1-hour concentratio n level given percentile form of alternative standard.
2 Percentile: 98 th or 99 th percentile of daily maximum 1-hour conc entration 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-yea rs across the monitoring period. The min, med, p98, p99 represent the minimum, median, 98 th , and 99 th percentiles of the dist ribution for the annual

means. 

### 1

### A-193 Table A-125. Estimated annual average NO 2 concentrations for monitors ≤ 20 m from a major road using 

### 1 2004-2006 air quality as is and air quality adjusted to just meet the current and alternative standards. 

### 2

Annual Average NO 2 (ppb) 3

Location Scenario 1 Percentile 2 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 NO 2 (ppb) 3

Location Scenario 1 Percentile 2 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 NO 2 (ppb) 3

Location Scenario 1 Percentile 2 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 NO 2 (ppb) 3

Location Scenario 1 Percentile 2 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 St d – air quality that just meets the current annual standard, All others – air quality t hat just meets 1-hour concentratio n level given percentile form of alternative standard.
2 Percentile: 98 th or 99 th percentile of daily maximum 1-hour conc entration 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-yea rs across the monitoring period. The min, med, p98, p99 represent the minimum, median, 98 th , and 99 th percentiles of the dist ribution for the annual

means. 

### 1

### A-197 Table A-126. Estimated annual average NO 2 concentrations on-roads using 2004-2006 air quality as is , air 

### 1 quality adjusted to just meet the current and altern ative standards, and an on-road adjustment factor. 

### 2

Annual Average NO 2 (ppb) 3

Location Scenario 1 Percentile 2 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 NO 2 (ppb) 3

Location Scenario 1 Percentile 2 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 NO 2 (ppb) 3

Location Scenario 1 Percentile 2 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 NO 2 (ppb) 3

Location Scenario 1 Percentile 2 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 NO 2 (ppb) 3

Location Scenario 1 Percentile 2 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 NO 2 (ppb) 3

Location Scenario 1 Percentile 2 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 St d – air quality that just meets the current annual standard, All others – air quality t hat just meets 1-hour concentratio n level given percentile form of alternative standard.

2 Percentile: 98 th or 99 th percentile of daily maximum 1-hour conc entration 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, 98 th , and 99 th percentiles of the distribut ion for the annual means.A-203Table A-127. Estimated number of exceedances of 1-hourconcentration levels (100, 150, and 200 ppb) for monitors≥100 m from a major road1following adjustment tojust meeting the current and alternative standards,2004-2006 air quality.2Number of Daily Maximum Exceedances≥100 ppb≥150 ppb≥200 ppbLocationStandardPercentileSite-YearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99Atlanta As is 15 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0Atlanta Current Std 15 96 1 121 188 188 24 0 15 73 73 4 0 0 20 20Atlanta 50 98 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Atlanta 50 99 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Atlanta 100 98 15 2 0 0 17 17 0 0 0 1 1 0 0 0 0 0Atlanta 100 99 15 1 0 0 7 7 0 0 0 1 1 0 0 0 0 0Atlanta 150 98 15 28 0 16 83 83 2 0 0 17 17 0 0 0 1 1Atlanta 150 99 15 19 0 10 65 65 1 0 0 7 7 0 0 0 1 1Atlanta 200 98 15 81 0 90 168 168 15 0 6 58 58 2 0 0 17 17Atlanta 200 99 15 66 0 67 144 144 8 0 2 39 39 1 0 0 7 7Atlanta 250 98 15 122 3 171 235 235 47 0 43 114 114 11 0 3 49 49Atlanta 250 99 15 105 1 133 205 205 32 0 19 89 89 5 0 0 25 25Atlanta 300 98 15 157 8 228 281 281 81 0 90 168 168 28 0 16 83 83Atlanta 300 99 15 143 4 209 266 266 66 0 67 144 144 19 0 10 65 65Boston As is 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Boston Current Std 8 5 0 5 10 10 0 0 0 0 0 0 0 0 0 0Boston 50 98 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Boston 50 99 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Boston 100 98 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Boston 100 99 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Boston 150 98 8 2 0 2 7 7 0 0 0 0 0 0 0 0 0 0Boston 150 99 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Boston 200 98 8 29 13 26 54 54 0 0 0 2 2 0 0 0 0 0Boston 200 99 8 9 2 8 20 20 0 0 0 0 0 0 0 0 0 0Boston 250 98 8 72 32 67 102 102 7 1 6 13 13 0 0 0 0 0Boston 250 99 8 38 15 35 67 67 1 0 1 6 6 0 0 0 0 0Boston 300 98 8 105 56 102 138 138 29 13 26 54 54 2 0 2 7 7Boston 300 99 8 72 32 67 102 102 9 2 8 20 20 0 0 0 0 0Chicago As is 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Chicago Current Std 8 23 7 14 69 69 1 0 0 3 3 0 0 0 0 0A-204Number of Daily Maximum Exceedances≥100 ppb≥150 ppb≥200 ppbLocationStandardPercentileSite-YearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99Chicago 50 98 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Chicago 50 99 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Chicago 100 98 8 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0Chicago 100 99 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Chicago 150 98 8 18 5 9 56 56 0 0 0 1 1 0 0 0 0 0Chicago 150 99 8 13 2 5 42 42 0 0 0 0 0 0 0 0 0 0Chicago 200 98 8 84 32 69 158 158 9 2 4 32 32 0 0 0 1 1Chicago 200 99 8 62 22 49 123 123 5 0 2 21 21 0 0 0 0 0Chicago 250 98 8 161 90 149 240 240 34 10 20 83 83 4 0 2 16 16Chicago 250 99 8 138 69 123 215 215 25 7 14 69 69 2 0 1 8 8Chicago 300 98 8 227 149 231 298 298 84 32 69 158 158 18 5 9 56 56Chicago 300 99 8 214 132 217 285 285 62 22 49 123 123 13 2 5 42 42Denver As is 3 1 0 0 3 3 0 0 0 0 0 0 0 0 0 0Denver Current Std 3 60 41 67 73 73 4 2 4 7 7 1 0 0 2 2Denver 50 98 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Denver 50 99 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Denver 100 98 3 6 4 6 9 9 0 0 0 0 0 0 0 0 0 0Denver 100 99 3 3 1 1 7 7 0 0 0 0 0 0 0 0 0 0Denver 150 98 3 76 67 80 81 81 6 4 6 9 9 1 0 0 2 2Denver 150 99 3 40 30 41 50 50 3 1 1 7 7 0 0 0 0 0Denver 200 98 3 211 184 215 233 233 45 32 49 53 53 6 4 6 9 9Denver 200 99 3 159 141 153 183 183 15 6 17 22 22 3 1 1 7 7Denver 250 98 3 263 232 276 281 281 124 111 120 142 142 27 18 31 33 33Denver 250 99 3 231 199 247 248 248 68 57 73 73 73 11 4 13 16 16Denver 300 98 3 289 255 305 306 306 211 184 215 233 233 76 67 80 81 81Denver 300 99 3 274 240 289 294 294 159 141 153 183 183 40 30 41 50 50Detroit As is 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Detroit Current Std 6 142 91 145 174 174 13 7 13 21 21 0 0 0 1 1Detroit 50 98 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Detroit 50 99 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Detroit 100 98 6 8 1 6 18 18 0 0 0 1 1 0 0 0 0 0Detroit 100 99 6 5 0 4 10 10 0 0 0 0 0 0 0 0 0 0A-205Number of Daily Maximum Exceedances≥100 ppb≥150 ppb≥200 ppbLocationStandardPercentileSite-YearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99Detroit 150 98 6 107 67 100 164 164 8 1 6 18 18 1 0 1 1 1Detroit 150 99 6 88 52 84 137 137 5 0 4 10 10 0 0 0 1 1Detroit 200 98 6 209 172 201 265 265 67 40 61 108 108 8 1 6 18 18Detroit 200 99 6 194 159 188 247 247 44 19 42 73 73 5 0 4 10 10Detroit 250 98 6 273 241 274 320 320 149 116 143 208 208 44 19 42 73 73Detroit 250 99 6 253 220 250 307 307 127 90 120 188 188 27 9 26 54 54Detroit 300 98 6 297 263 302 335 335 209 172 201 265 265 107 67 100 164 164Detroit 300 99 6 290 258 293 329 329 194 159 188 247 247 88 52 84 137 137El Paso As is 12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0El Paso Current Std 12 157 79 172 216 216 26 5 24 58 58 4 0 4 11 11El Paso 50 98 12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0El Paso 50 99 12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0El Paso 100 98 12 4 0 4 10 10 0 0 0 0 0 0 0 0 0 0El Paso 100 99 12 2 0 1 4 4 0 0 0 0 0 0 0 0 0 0El Paso 150 98 12 52 12 48 111 111 4 0 4 10 10 0 0 0 1 1El Paso 150 99 12 26 6 23 59 59 2 0 1 4 4 0 0 0 0 0El Paso 200 98 12 159 79 172 227 227 26 6 23 59 59 4 0 4 10 10El Paso 200 99 12 116 45 123 192 192 12 2 11 25 25 2 0 1 4 4El Paso 250 98 12 228 130 238 290 290 84 27 85 156 156 18 4 14 37 37El Paso 250 99 12 188 94 198 251 251 46 11 42 100 100 8 1 8 15 15El Paso 300 98 12 259 163 265 314 314 159 79 172 227 227 52 12 48 111 111El Paso 300 99 12 236 133 245 296 296 116 45 123 192 192 26 6 23 59 59Jacksonville As is 2 2 0 2 3 3 1 0 1 2 2 1 0 1 1 1Jacksonville Current Std 2 178 161 178 194 194 51 42 51 60 60 8 6 8 9 9Jacksonville 50 98 2 2 0 2 3 3 1 0 1 2 2 0 0 0 0 0Jacksonville 50 99 2 1 0 1 2 2 0 0 0 0 0 0 0 0 0 0Jacksonville 100 98 2 7 5 7 9 9 2 0 2 4 4 2 0 2 3 3Jacksonville 100 99 2 2 0 2 4 4 2 0 2 3 3 1 0 1 2 2Jacksonville 150 98 2 78 65 78 90 90 7 5 7 9 9 3 0 3 5 5Jacksonville 150 99 2 9 6 9 11 11 2 0 2 4 4 2 0 2 3 3Jacksonville 200 98 2 178 161 178 194 194 48 42 48 54 54 7 5 7 9 9Jacksonville 200 99 2 53 46 53 60 60 4 2 4 6 6 2 0 2 4 4A-206Number of Daily Maximum Exceedances≥100 ppb≥150 ppb≥200 ppbLocationStandardPercentileSite-YearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99Jacksonville 250 98 2 237 226 237 247 247 109 97 109 121 121 32 31 32 32 32Jacksonville 250 99 2 117 103 117 131 131 15 12 15 18 18 3 1 3 5 5Jacksonville 300 98 2 271 262 271 279 279 178 161 178 194 194 78 65 78 90 90Jacksonville 300 99 2 178 161 178 194 194 53 46 53 60 60 9 6 9 11 11Las Vegas As is 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Las Vegas Current Std 11 72 0 12 249 249 6 0 0 26 26 0 0 0 1 1Las Vegas 50 98 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Las Vegas 50 99 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Las Vegas 100 98 11 2 0 0 9 9 0 0 0 0 0 0 0 0 0 0Las Vegas 100 99 11 1 0 0 4 4 0 0 0 0 0 0 0 0 0 0Las Vegas 150 98 11 55 0 5 209 209 2 0 0 9 9 0 0 0 0 0Las Vegas 150 99 11 40 0 3 165 165 1 0 0 4 4 0 0 0 0 0Las Vegas 200 98 11 108 2 43 305 305 31 0 2 128 128 2 0 0 9 9Las Vegas 200 99 11 97 1 31 286 286 17 0 0 66 66 1 0 0 4 4Las Vegas 250 98 11 150 7 105 327 327 77 0 15 257 257 19 0 1 81 81Las Vegas 250 99 11 134 5 81 322 322 60 0 7 219 219 10 0 0 39 39Las Vegas 300 98 11 186 18 167 339 339 108 2 43 305 305 55 0 5 209 209Las Vegas 300 99 11 176 16 150 337 337 97 1 31 286 286 40 0 3 165 165Los Angeles As is 54 0 0 0 2 2 0 0 0 0 0 0 0 0 0 0Los Angeles Current Std 54 19 0 17 55 85 1 0 0 4 6 0 0 0 0 0Los Angeles 50 98 54 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Los Angeles 50 99 54 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Los Angeles 100 98 54 1 0 0 5 8 0 0 0 0 0 0 0 0 0 0Los Angeles 100 99 54 0 0 0 2 2 0 0 0 0 0 0 0 0 0 0Los Angeles 150 98 54 17 0 15 66 75 1 0 0 5 8 0 0 0 0 1Los Angeles 150 99 54 10 0 8 45 46 0 0 0 2 2 0 0 0 0 0Los Angeles 200 98 54 70 2 68 176 186 8 0 6 29 32 1 0 0 5 8Los Angeles 200 99 54 50 0 46 135 157 4 0 2 18 21 0 0 0 2 2Los Angeles 250 98 54 139 12 153 279 284 31 0 30 98 112 6 0 4 22 24Los Angeles 250 99 54 112 10 120 248 257 19 0 18 68 79 3 0 2 14 14Los Angeles 300 98 54 194 30 209 322 328 70 2 68 176 186 17 0 15 66 75Los Angeles 300 99 54 176 24 190 306 318 50 0 46 135 157 10 0 8 45 46A-207Number of Daily Maximum Exceedances≥100 ppb≥150 ppb≥200 ppbLocationStandardPercentileSite-YearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99Miami As is 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Miami Current Std 4 102 57 104 143 143 33 7 33 59 59 7 0 4 18 18Miami 50 98 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Miami 50 99 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Miami 100 98 4 3 0 2 8 8 0 0 0 1 1 0 0 0 0 0Miami 100 99 4 2 0 1 5 5 0 0 0 1 1 0 0 0 0 0Miami 150 98 4 33 7 30 64 64 3 0 2 8 8 0 0 0 1 1Miami 150 99 4 23 2 23 45 45 2 0 1 5 5 0 0 0 1 1Miami 200 98 4 74 36 72 115 115 18 1 18 34 34 3 0 2 8 8Miami 200 99 4 64 29 62 104 104 12 1 12 25 25 2 0 1 5 5Miami 250 98 4 122 82 124 157 157 44 12 44 77 77 12 1 12 25 25Miami 250 99 4 105 62 108 143 143 37 10 35 66 66 9 0 8 18 18Miami 300 98 4 157 129 160 181 181 74 36 72 115 115 33 7 30 64 64Miami 300 99 4 139 106 139 171 171 64 29 62 104 104 23 2 23 45 45New York As is 22 0 0 0 2 2 0 0 0 0 0 0 0 0 0 0New York Current Std 22 11 0 12 36 36 0 0 0 2 2 0 0 0 1 1New York 50 98 22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0New York 50 99 22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0New York 100 98 22 1 0 1 4 4 0 0 0 1 1 0 0 0 0 0New York 100 99 22 1 0 0 4 4 0 0 0 1 1 0 0 0 0 0New York 150 98 22 27 0 31 56 56 1 0 1 4 4 0 0 0 1 1New York 150 99 22 12 0 13 28 28 1 0 0 4 4 0 0 0 1 1New York 200 98 22 108 8 132 178 178 13 0 14 35 35 1 0 1 4 4New York 200 99 22 63 0 79 111 111 5 0 5 15 15 1 0 0 4 4New York 250 98 22 185 46 213 266 266 52 0 67 96 96 8 0 9 22 22New York 250 99 22 135 17 162 211 211 21 0 24 52 52 3 0 3 9 9New York 300 98 22 233 75 257 312 312 108 8 132 178 178 27 0 31 56 56New York 300 99 22 196 52 226 275 275 63 0 79 111 111 12 0 13 28 28Philadelphia As is 12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Philadelphia Current Std 12 54 7 38 138 138 4 0 2 14 14 0 0 0 2 2Philadelphia 50 98 12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Philadelphia 50 99 12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0A-208Number of Daily Maximum Exceedances≥100 ppb≥150 ppb≥200 ppbLocationStandardPercentileSite-YearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99Philadelphia 100 98 12 3 0 2 10 10 0 0 0 1 1 0 0 0 0 0Philadelphia 100 99 12 0 0 0 2 2 0 0 0 0 0 0 0 0 0 0Philadelphia 150 98 12 52 21 46 96 96 3 0 2 10 10 0 0 0 1 1Philadelphia 150 99 12 12 1 12 33 33 0 0 0 2 2 0 0 0 0 0Philadelphia 200 98 12 163 103 149 246 246 24 4 21 54 54 3 0 2 10 10Philadelphia 200 99 12 77 40 67 134 134 4 0 3 14 14 0 0 0 2 2Philadelphia 250 98 12 231 186 229 293 293 85 42 75 146 146 16 2 16 41 41Philadelphia 250 99 12 163 103 149 246 246 24 4 21 54 54 3 0 2 10 10Philadelphia 300 98 12 278 239 281 325 325 163 103 149 246 246 52 21 46 96 96Philadelphia 300 99 12 220 165 218 290 290 77 40 67 134 134 12 1 12 33 33Phoenix As is 9 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0Phoenix Current Std 9 40 19 29 78 78 1 0 0 2 2 0 0 0 0 0Phoenix 50 98 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Phoenix 50 99 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Phoenix 100 98 9 1 0 1 5 5 0 0 0 0 0 0 0 0 0 0Phoenix 100 99 9 0 0 0 2 2 0 0 0 0 0 0 0 0 0 0Phoenix 150 98 9 65 37 55 105 105 1 0 1 5 5 0 0 0 0 0Phoenix 150 99 9 25 10 23 43 43 0 0 0 2 2 0 0 0 0 0Phoenix 200 98 9 224 167 235 245 245 21 8 18 36 36 1 0 1 5 5Phoenix 200 99 9 163 126 160 195 195 6 0 7 10 10 0 0 0 2 2Phoenix 250 98 9 298 208 307 324 324 116 80 100 158 158 12 0 11 21 21Phoenix 250 99 9 265 188 275 292 292 65 37 55 105 105 3 0 3 7 7Phoenix 300 98 9 330 249 337 352 352 224 167 235 245 245 65 37 55 105 105Phoenix 300 99 9 317 230 326 338 338 163 126 160 195 195 25 10 23 43 43Provo As is 3 14 0 0 43 43 7 0 0 20 20 0 0 0 0 0Provo Current Std 3 129 56 137 194 194 19 3 12 43 43 14 0 1 42 42Provo 50 98 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Provo 50 99 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Provo 100 98 3 14 0 0 43 43 10 0 0 29 29 0 0 0 0 0Provo 100 99 3 14 0 0 43 43 8 0 0 23 23 0 0 0 0 0Provo 150 98 3 19 1 14 43 43 14 0 0 43 43 13 0 0 40 40Provo 150 99 3 19 1 12 43 43 14 0 0 43 43 13 0 0 40 40A-209Number of Daily Maximum Exceedances≥100 ppb≥150 ppb≥200 ppbLocationStandardPercentileSite-YearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99Provo 200 98 3 84 64 90 98 98 16 0 6 43 43 14 0 0 43 43Provo 200 99 3 74 50 82 91 91 16 0 5 43 43 14 0 0 43 43Provo 250 98 3 209 198 213 216 216 25 5 22 49 49 15 0 2 43 43Provo 250 99 3 196 191 194 202 202 23 3 20 47 47 15 0 2 43 43Provo 300 98 3 281 242 296 305 305 84 64 90 98 98 19 1 14 43 43Provo 300 99 3 281 242 296 305 305 74 50 82 91 91 19 1 12 43 43St. Louis As is 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0St. Louis Current Std 4 67 15 50 154 154 6 0 1 22 22 0 0 0 1 1St. Louis 50 98 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0St. Louis 50 99 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0St. Louis 100 98 4 1 0 1 3 3 0 0 0 0 0 0 0 0 0 0St. Louis 100 99 4 1 0 1 2 2 0 0 0 0 0 0 0 0 0 0St. Louis 150 98 4 58 31 54 94 94 1 0 1 3 3 0 0 0 0 0St. Louis 150 99 4 49 23 42 88 88 1 0 1 2 2 0 0 0 0 0St. Louis 200 98 4 146 112 143 186 186 25 2 22 55 55 1 0 1 3 3St. Louis 200 99 4 134 100 128 179 179 16 1 14 36 36 1 0 1 2 2St. Louis 250 98 4 211 160 206 272 272 88 57 83 127 127 16 1 13 35 35St. Louis 250 99 4 201 153 197 256 256 82 49 78 121 121 12 0 9 28 28St. Louis 300 98 4 255 191 258 313 313 146 112 143 186 186 58 31 54 94 94St. Louis 300 99 4 255 191 258 313 313 134 100 128 179 179 49 23 42 88 88Washington DC As is 17 0 0 0 2 2 0 0 0 0 0 0 0 0 0 0Washington DC Current Std 17 52 0 61 149 149 4 0 3 14 14 1 0 0 3 3Washington DC 50 98 17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Washington DC 50 99 17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Washington DC 100 98 17 3 0 2 12 12 0 0 0 2 2 0 0 0 0 0Washington DC 100 99 17 1 0 0 6 6 0 0 0 0 0 0 0 0 0 0Washington DC 150 98 17 50 0 52 120 120 3 0 2 12 12 1 0 0 4 4Washington DC 150 99 17 16 0 14 46 46 1 0 0 6 6 0 0 0 1 1Washington DC 200 98 17 133 12 149 246 246 26 0 25 67 67 3 0 2 12 12Washington DC 200 99 17 69 0 69 160 160 5 0 3 20 20 1 0 0 6 6Washington DC 250 98 17 190 42 230 299 299 77 0 80 171 171 17 0 14 49 49Washington DC 250 99 17 133 12 149 246 246 29 0 28 74 74 4 0 2 14 14A-210Number of Daily Maximum Exceedances≥100 ppb≥150 ppb≥200 ppbLocationStandardPercentileSite-YearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99Washington DC 300 98 17 237 90 278 333 333 133 12 149 246 246 50 0 52 120 120Washington DC 300 99 17 190 42 230 299 299 69 0 69 160 160 16 0 14 46 46Other MSA As is 565 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0Other MSA Current Std 565 28 0 12 141 156 2 0 0 23 27 0 0 0 2 4Other MSA 50 98 565 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Other MSA 50 99 565 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Other MSA 100 98 565 0 0 0 2 4 0 0 0 0 1 0 0 0 0 0Other MSA 100 99 565 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0Other MSA 150 98 565 4 0 1 29 37 0 0 0 2 4 0 0 0 0 1Other MSA 150 99 565 2 0 0 18 23 0 0 0 1 1 0 0 0 0 1Other MSA 200 98 565 24 0 12 112 128 2 0 0 15 19 0 0 0 2 4Other MSA 200 99 565 16 0 7 85 110 1 0 0 8 10 0 0 0 1 1Other MSA 250 98 565 65 0 53 212 228 8 0 2 52 63 1 0 0 9 13Other MSA 250 99 565 48 0 34 170 191 4 0 1 31 41 0 0 0 5 7Other MSA 300 98 565 109 0 104 267 300 24 0 12 112 128 4 0 1 29 37Other MSA 300 99 565 86 0 77 242 274 16 0 7 85 110 2 0 0 18 23Other Not MSA As is 116 0 0 0 2 3 0 0 0 0 2 0 0 0 0 1Other Not MSA Current Std 116 37 0 11 192 195 7 0 0 69 78 1 0 0 20 22Other Not MSA 50 98 116 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0Other Not MSA 50 99 116 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0Other Not MSA 100 98 116 0 0 0 3 5 0 0 0 0 2 0 0 0 0 1Other Not MSA 100 99 116 0 0 0 2 2 0 0 0 0 2 0 0 0 0 1Other Not MSA 150 98 116 1 0 0 16 19 0 0 0 3 5 0 0 0 1 2Other Not MSA 150 99 116 0 0 0 7 9 0 0 0 2 2 0 0 0 0 2Other Not MSA 200 98 116 7 0 0 71 80 1 0 0 12 14 0 0 0 3 5Other Not MSA 200 99 116 2 0 0 26 26 0 0 0 3 6 0 0 0 2 2Other Not MSA 250 98 116 18 0 2 116 147 2 0 0 30 31 0 0 0 9 11Other Not MSA 250 99 116 7 0 0 71 80 1 0 0 12 15 0 0 0 3 5Other Not MSA 300 98 116 34 0 8 165 199 7 0 0 71 80 1 0 0 16 19Other Not MSA 300 99 116 16 0 1 112 144 2 0 0 26 26 0 0 0 7 9

### A-211 Table a-128. Estimated numb er of exceedances of 1-hour concentration levels (250 and 300 ppb) for monitors 

### 1
≥ 100 m from a major road following adjustment to ju st meeting the current and al ternative standards, 2004- 

### 2 2006 air quality. 

### 3 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 3A-218Table A-129. Estimated number of exceedancesof 1-hour concentration levels (100, 150, and 200 ppb) for monitors > 20 m and <100 m from a major1road following adjustment to just meeting the current and alternative standards, 2004-2006 air quality.2Number of Daily Maximum Exceedances≥100 ppb≥150 ppb≥200 ppbLocationStandardPercentileSiteYearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99Boston As is 11 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0Boston Current Std 11 25 2 12 73 73 2 0 0 10 10 0 0 0 2 2Boston 50 98 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Boston 50 99 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Boston 100 98 11 1 0 0 9 9 0 0 0 1 1 0 0 0 0 0Boston 100 99 11 1 0 0 4 4 0 0 0 0 0 0 0 0 0 0Boston 150 98 11 19 1 11 67 67 1 0 0 9 9 0 0 0 1 1Boston 150 99 11 8 0 2 34 34 1 0 0 4 4 0 0 0 1 1Boston 200 98 11 84 20 65 153 153 9 0 3 42 42 1 0 0 9 9Boston 200 99 11 42 7 32 102 102 4 0 1 18 18 1 0 0 4 4Boston 250 98 11 165 78 143 247 247 36 6 28 91 91 7 0 1 31 31Boston 250 99 11 103 30 79 175 175 16 0 8 62 62 2 0 0 13 13Boston 300 98 11 214 115 208 291 291 84 20 65 153 153 19 1 11 67 67Boston 300 99 11 165 78 143 247 247 42 7 32 102 102 8 0 2 34 34Chicago As is 6 1 0 1 5 5 0 0 0 0 0 0 0 0 0 0Chicago Current Std 6 94 39 90 152 152 6 0 5 15 15 0 0 0 2 2Chicago 50 98 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Chicago 50 99 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Chicago 100 98 6 4 0 3 15 15 0 0 0 1 1 0 0 0 0 0Chicago 100 99 6 3 0 1 12 12 0 0 0 0 0 0 0 0 0 0Chicago 150 98 6 80 34 74 147 147 4 0 3 15 15 0 0 0 2 2Chicago 150 99 6 64 17 62 118 118 3 0 1 12 12 0 0 0 2 2Chicago 200 98 6 206 161 200 267 267 44 10 43 88 88 4 0 3 15 15Chicago 200 99 6 178 133 172 239 239 30 4 30 62 62 3 0 1 12 12Chicago 250 98 6 277 248 273 314 314 122 78 112 190 190 27 3 26 58 58Chicago 250 99 6 261 230 257 299 299 99 58 90 164 164 19 1 17 46 46Chicago 300 98 6 324 309 325 338 338 206 161 200 267 267 80 34 74 147 147Chicago 300 99 6 320 302 319 338 338 178 133 172 239 239 64 17 62 118 118Cleveland As is 2 1 0 1 1 1 1 0 1 1 1 0 0 0 0 0Cleveland Current Std 2 97 86 97 108 108 11 10 11 12 12 2 1 2 2 2A-219Number of Daily Maximum Exceedances≥100 ppb≥150 ppb≥200 ppbLocationStandardPercentileSiteYearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99Cleveland 50 98 2 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0Cleveland 50 99 2 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0Cleveland 100 98 2 4 2 4 6 6 1 0 1 1 1 1 0 1 1 1Cleveland 100 99 2 2 2 2 2 2 1 0 1 1 1 1 0 1 1 1Cleveland 150 98 2 60 48 60 71 71 4 2 4 6 6 1 0 1 1 1Cleveland 150 99 2 33 18 33 47 47 2 2 2 2 2 1 0 1 1 1Cleveland 200 98 2 153 140 153 166 166 33 18 33 47 47 4 2 4 6 6Cleveland 200 99 2 110 95 110 124 124 16 7 16 24 24 2 2 2 2 2Cleveland 250 98 2 208 199 208 217 217 83 68 83 97 97 20 10 20 30 30Cleveland 250 99 2 188 180 188 195 195 60 48 60 71 71 13 6 13 19 19Cleveland 300 98 2 259 258 259 259 259 153 140 153 166 166 60 48 60 71 71Cleveland 300 99 2 230 227 230 233 233 110 95 110 124 124 33 18 33 47 47El Paso As is 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0El Paso Current Std 3 160 137 143 201 201 25 16 17 42 42 6 4 4 9 9El Paso 50 98 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0El Paso 50 99 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0El Paso 100 98 3 6 4 4 9 9 0 0 0 0 0 0 0 0 0 0El Paso 100 99 3 3 2 3 4 4 0 0 0 0 0 0 0 0 0 0El Paso 150 98 3 57 33 56 83 83 6 4 4 9 9 1 0 1 2 2El Paso 150 99 3 26 15 20 42 42 3 2 3 4 4 0 0 0 0 0El Paso 200 98 3 160 132 147 201 201 26 15 20 42 42 6 4 4 9 9El Paso 200 99 3 118 90 106 157 157 14 8 10 24 24 3 2 3 4 4El Paso 250 98 3 239 220 225 273 273 88 59 82 124 124 17 12 12 26 26El Paso 250 99 3 197 167 181 243 243 51 27 51 76 76 10 6 8 15 15El Paso 300 98 3 276 263 267 297 297 160 132 147 201 201 57 33 56 83 83El Paso 300 99 3 251 234 238 281 281 118 90 106 157 157 26 15 20 42 42Los Angeles As is 22 1 0 1 9 9 0 0 0 1 1 0 0 0 0 0Los Angeles Current Std 22 50 0 53 116 116 3 0 2 12 12 0 0 0 2 2Los Angeles 50 98 22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Los Angeles 50 99 22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Los Angeles 100 98 22 3 0 1 17 17 0 0 0 2 2 0 0 0 0 0Los Angeles 100 99 22 1 0 1 11 11 0 0 0 1 1 0 0 0 0 0A-220Number of Daily Maximum Exceedances≥100 ppb≥150 ppb≥200 ppbLocationStandardPercentileSiteYearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99Los Angeles 150 98 22 46 0 51 101 101 3 0 1 17 17 0 0 0 2 2Los Angeles 150 99 22 30 0 30 82 82 1 0 1 11 11 0 0 0 2 2Los Angeles 200 98 22 139 9 143 235 235 22 0 23 63 63 3 0 1 17 17Los Angeles 200 99 22 107 2 113 190 190 12 0 11 43 43 1 0 1 11 11Los Angeles 250 98 22 225 41 242 295 295 75 0 77 143 143 15 0 15 46 46Los Angeles 250 99 22 196 27 202 282 282 51 0 55 107 107 7 0 5 36 36Los Angeles 300 98 22 272 69 302 330 330 139 9 143 235 235 46 0 51 101 101Los Angeles 300 99 22 259 64 283 323 323 107 2 113 190 190 30 0 30 82 82Miami As is 2 1 0 1 2 2 1 0 1 1 1 1 0 1 1 1Miami Current Std 2 173 171 173 175 175 76 75 76 76 76 20 17 20 23 23Miami 50 98 2 1 0 1 2 2 1 0 1 1 1 1 0 1 1 1Miami 50 99 2 1 0 1 1 1 1 0 1 1 1 1 0 1 1 1Miami 100 98 2 8 6 8 9 9 1 0 1 2 2 1 0 1 2 2Miami 100 99 2 4 2 4 5 5 1 0 1 2 2 1 0 1 1 1Miami 150 98 2 77 73 77 80 80 8 6 8 9 9 1 0 1 2 2Miami 150 99 2 57 52 57 62 62 4 2 4 5 5 1 0 1 2 2Miami 200 98 2 139 132 139 145 145 47 42 47 52 52 8 6 8 9 9Miami 200 99 2 122 112 122 132 132 36 34 36 37 37 4 2 4 5 5Miami 250 98 2 203 197 203 209 209 93 86 93 100 100 36 34 36 37 37Miami 250 99 2 179 175 179 183 183 83 76 83 89 89 26 23 26 29 29Miami 300 98 2 249 238 249 260 260 139 132 139 145 145 77 73 77 80 80Miami 300 99 2 225 218 225 232 232 122 112 122 132 132 57 52 57 62 62New York As is 11 1 0 1 3 3 0 0 0 1 1 0 0 0 0 0New York Current Std 11 30 2 32 64 64 1 0 0 4 4 0 0 0 2 2New York 50 98 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0New York 50 99 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0New York 100 98 11 5 1 6 8 8 0 0 0 1 1 0 0 0 0 0New York 100 99 11 2 0 2 4 4 0 0 0 1 1 0 0 0 0 0New York 150 98 11 69 14 62 126 126 5 1 6 8 8 1 0 0 2 2New York 150 99 11 31 4 29 51 51 2 0 2 4 4 0 0 0 1 1New York 200 98 11 198 84 199 285 285 35 5 33 60 60 5 1 6 8 8New York 200 99 11 134 36 128 211 211 15 2 14 28 28 2 0 2 4 4A-221Number of Daily Maximum Exceedances≥100 ppb≥150 ppb≥200 ppbLocationStandardPercentileSiteYearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99New York 250 98 11 281 190 287 339 339 115 26 106 190 190 23 3 23 40 40New York 250 99 11 232 121 235 315 315 58 9 53 104 104 10 2 10 22 22New York 300 98 11 317 245 331 356 356 198 84 199 285 285 69 14 62 126 126New York 300 99 11 290 207 302 345 345 134 36 128 211 211 31 4 29 51 51Philadelphia As is 6 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0Philadelphia Current Std 6 70 28 56 173 173 6 1 3 25 25 2 0 1 6 6Philadelphia 50 98 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Philadelphia 50 99 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Philadelphia 100 98 6 6 3 6 10 10 1 0 1 1 1 0 0 0 0 0Philadelphia 100 99 6 2 1 2 4 4 0 0 0 0 0 0 0 0 0 0Philadelphia 150 98 6 93 62 96 125 125 6 3 6 10 10 2 1 1 3 3Philadelphia 150 99 6 25 15 24 39 39 2 1 2 4 4 0 0 0 1 1Philadelphia 200 98 6 216 169 216 267 267 45 28 49 60 60 6 3 6 10 10Philadelphia 200 99 6 121 76 127 166 166 11 6 10 16 16 2 1 2 4 4Philadelphia 250 98 6 272 227 272 313 313 131 83 138 173 173 32 19 35 48 48Philadelphia 250 99 6 216 169 216 267 267 45 28 49 60 60 6 3 6 10 10Philadelphia 300 98 6 307 274 308 342 342 216 169 216 267 267 93 62 96 125 125Philadelphia 300 99 6 266 220 265 310 310 121 76 127 166 166 25 15 24 39 39Phoenix As is 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Phoenix Current Std 3 14 13 13 15 15 0 0 0 0 0 0 0 0 0 0Phoenix 50 98 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Phoenix 50 99 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Phoenix 100 98 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Phoenix 100 99 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Phoenix 150 98 3 29 28 29 30 30 0 0 0 0 0 0 0 0 0 0Phoenix 150 99 3 7 4 9 9 9 0 0 0 0 0 0 0 0 0 0Phoenix 200 98 3 174 171 174 178 178 5 2 6 6 6 0 0 0 0 0Phoenix 200 99 3 120 114 121 126 126 1 0 2 2 2 0 0 0 0 0Phoenix 250 98 3 257 239 264 268 268 68 55 66 83 83 2 0 3 4 4Phoenix 250 99 3 217 207 221 222 222 29 28 29 30 30 1 0 2 2 2Phoenix 300 98 3 304 285 309 317 317 174 171 174 178 178 29 28 29 30 30Phoenix 300 99 3 284 263 294 294 294 120 114 121 126 126 7 4 9 9 9A-222Number of Daily Maximum Exceedances≥100 ppb≥150 ppb≥200 ppbLocationStandardPercentileSiteYearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99St. Louis As is 8 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0St. Louis Current Std 8 42 1 35 108 108 2 0 1 6 6 0 0 0 1 1St. Louis 50 98 8 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0St. Louis 50 99 8 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0St. Louis 100 98 8 1 0 0 8 8 0 0 0 1 1 0 0 0 1 1St. Louis 100 99 8 1 0 0 4 4 0 0 0 1 1 0 0 0 1 1St. Louis 150 98 8 26 1 13 124 124 1 0 0 8 8 0 0 0 1 1St. Louis 150 99 8 22 1 8 115 115 1 0 0 4 4 0 0 0 1 1St. Louis 200 98 8 83 23 65 242 242 12 0 3 78 78 1 0 0 8 8St. Louis 200 99 8 75 19 57 231 231 10 0 2 66 66 1 0 0 4 4St. Louis 250 98 8 142 71 116 319 319 42 3 26 174 174 9 0 1 59 59St. Louis 250 99 8 131 62 105 304 304 39 3 23 164 164 7 0 1 45 45St. Louis 300 98 8 187 114 157 344 344 83 23 65 242 242 26 1 13 124 124St. Louis 300 99 8 187 114 157 344 344 75 19 57 231 231 22 1 8 115 115WashingtonDCAs is 12 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0WashingtonDCCurrent Std 12 66 10 74 123 123 3 0 3 7 7 0 0 0 1 1WashingtonDC50 98 12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0WashingtonDC50 99 12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0WashingtonDC100 98 12 3 0 2 7 7 0 0 0 1 1 0 0 0 0 0WashingtonDC100 99 12 0 0 0 1 1 0 0 0 1 1 0 0 0 0 0WashingtonDC150 98 12 59 16 54 115 115 3 0 2 7 7 0 0 0 1 1WashingtonDC150 99 12 15 0 12 38 38 0 0 0 1 1 0 0 0 1 1WashingtonDC200 98 12 165 80 174 245 245 26 2 23 61 61 3 0 2 7 7WashingtonDC200 99 12 85 29 82 154 154 5 0 5 14 14 0 0 0 1 1A-223Number of Daily Maximum Exceedances≥100 ppb≥150 ppb≥200 ppbLocationStandardPercentileSiteYearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99WashingtonDC250 98 12 224 127 241 303 303 94 35 93 169 169 16 0 14 40 40WashingtonDC250 99 12 165 80 174 245 245 30 2 25 66 66 3 0 2 9 9WashingtonDC300 98 12 266 182 277 338 338 165 80 174 245 245 59 16 54 115 115WashingtonDC300 99 12 224 127 241 303 303 85 29 82 154 154 15 0 12 38 381

### A-224 Table A-130. Estimated nu mber of exceedances of 1-hour concentr ation levels (250 and 300 ppb) for monitors > 20 m and < 100 m from a major road fo llowing 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 1A-228Table a-131. Estimated number of exceedances of 1-hourconcentration levels (100, 150, and 200 ppb) for monitors≤20 m from a major road followingadjustment to just meeting the current and alternative standards, 2004-2006 air quality.Number of Daily Maximum Exceedances≥100 ppb≥150 ppb≥200 ppbLocationStandardPercentileSiteYearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99Boston As is 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Boston Current Std 3 68 64 66 75 75 3 1 3 5 5 0 0 0 0 0Boston 50 98 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Boston 50 99 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Boston 100 98 3 2 1 3 3 3 0 0 0 0 0 0 0 0 0 0Boston 100 99 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Boston 150 98 3 55 33 60 72 72 2 1 3 3 3 0 0 0 0 0Boston 150 99 3 18 10 21 22 22 0 0 0 0 0 0 0 0 0 0Boston 200 98 3 197 179 195 216 216 24 14 26 31 31 2 1 3 3 3Boston 200 99 3 113 86 123 129 129 5 3 6 7 7 0 0 0 0 0Boston 250 98 3 291 281 288 304 304 103 76 110 123 123 15 7 18 21 21Boston 250 99 3 227 207 222 253 253 43 29 43 57 57 4 1 4 6 6Boston 300 98 3 323 314 322 334 334 197 179 195 216 216 55 33 60 72 72Boston 300 99 3 291 281 288 304 304 113 86 123 129 129 18 10 21 22 22Chicago As is 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Chicago Current Std 3 22 5 23 38 38 1 0 1 1 1 0 0 0 0 0Chicago 50 98 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Chicago 50 99 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Chicago 100 98 3 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0Chicago 100 99 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Chicago 150 98 3 15 4 15 26 26 0 0 0 1 1 0 0 0 0 0Chicago 150 99 3 10 1 11 18 18 0 0 0 0 0 0 0 0 0 0Chicago 200 98 3 93 66 104 109 109 7 0 10 12 12 0 0 0 1 1Chicago 200 99 3 69 40 75 91 91 5 0 5 9 9 0 0 0 0 0Chicago 250 98 3 167 138 179 184 184 33 15 32 51 51 5 0 5 9 9Chicago 250 99 3 144 112 155 165 165 23 8 23 38 38 2 0 3 4 4Chicago 300 98 3 235 211 243 250 250 93 66 104 109 109 15 4 15 26 26Chicago 300 99 3 222 197 232 238 238 69 40 75 91 91 10 1 11 18 18Cleveland As is 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0A-229Number of Daily Maximum Exceedances≥100 ppb≥150 ppb≥200 ppbLocationStandardPercentileSiteYearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99Cleveland Current Std 3 119 96 119 142 142 16 11 14 22 22 0 0 0 1 1Cleveland 50 98 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Cleveland 50 99 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Cleveland 100 98 3 7 1 9 10 10 0 0 0 0 0 0 0 0 0 0Cleveland 100 99 3 3 0 4 6 6 0 0 0 0 0 0 0 0 0 0Cleveland 150 98 3 72 57 76 83 83 7 1 9 10 10 0 0 0 0 0Cleveland 150 99 3 46 29 48 60 60 3 0 4 6 6 0 0 0 0 0Cleveland 200 98 3 185 162 184 210 210 46 29 48 60 60 7 1 9 10 10Cleveland 200 99 3 136 115 138 154 154 25 15 27 32 32 3 0 4 6 6Cleveland 250 98 3 258 244 257 274 274 113 93 114 131 131 33 21 34 44 44Cleveland 250 99 3 228 217 224 244 244 72 57 76 83 83 17 8 20 23 23Cleveland 300 98 3 313 298 320 321 321 185 162 184 210 210 72 57 76 83 83Cleveland 300 99 3 281 260 289 294 294 136 115 138 154 154 46 29 48 60 60Denver As is 3 2 1 1 3 3 0 0 0 1 1 0 0 0 0 0Denver Current Std 3 82 79 80 87 87 2 1 2 4 4 2 1 1 3 3Denver 50 98 3 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0Denver 50 99 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Denver 100 98 3 4 1 4 7 7 2 1 1 3 3 0 0 0 1 1Denver 100 99 3 2 1 1 3 3 1 0 0 2 2 0 0 0 0 0Denver 150 98 3 108 92 109 122 122 4 1 4 7 7 2 1 1 3 3Denver 150 99 3 42 35 44 48 48 2 1 1 3 3 1 0 1 3 3Denver 200 98 3 241 221 249 252 252 52 38 55 63 63 4 1 4 7 7Denver 200 99 3 195 176 200 210 210 14 12 14 16 16 2 1 1 3 3Denver 250 98 3 295 287 295 304 304 164 142 170 181 181 27 24 25 31 31Denver 250 99 3 265 256 269 269 269 95 79 96 109 109 8 6 8 10 10Denver 300 98 3 324 302 332 337 337 241 221 249 252 252 108 92 109 122 122Denver 300 99 3 310 293 318 319 319 195 176 200 210 210 42 35 44 48 48Las Vegas As is 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Las Vegas Current Std 2 202 196 202 208 208 19 17 19 21 21 0 0 0 0 0Las Vegas 50 98 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Las Vegas 50 99 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0A-230Number of Daily Maximum Exceedances≥100 ppb≥150 ppb≥200 ppbLocationStandardPercentileSiteYearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99Las Vegas 100 98 2 8 6 8 10 10 0 0 0 0 0 0 0 0 0 0Las Vegas 100 99 2 3 1 3 5 5 0 0 0 0 0 0 0 0 0 0Las Vegas 150 98 2 162 162 162 162 162 8 6 8 10 10 0 0 0 0 0Las Vegas 150 99 2 123 117 123 128 128 3 1 3 5 5 0 0 0 0 0Las Vegas 200 98 2 282 279 282 285 285 96 93 96 99 99 8 6 8 10 10Las Vegas 200 99 2 263 260 263 265 265 50 43 50 57 57 3 1 3 5 5Las Vegas 250 98 2 312 311 312 313 313 216 213 216 218 218 60 59 60 60 60Las Vegas 250 99 2 306 304 306 307 307 177 170 177 183 183 35 29 35 40 40Las Vegas 300 98 2 322 320 322 324 324 282 279 282 285 285 162 162 162 162 162Las Vegas 300 99 2 318 316 318 319 319 263 260 263 265 265 123 117 123 128 128Los Angeles As is 6 3 0 2 8 8 0 0 0 0 0 0 0 0 0 0Los Angeles Current Std 6 53 26 48 82 82 4 0 4 9 9 0 0 0 1 1Los Angeles 50 98 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Los Angeles 50 99 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Los Angeles 100 98 6 3 0 2 9 9 0 0 0 0 0 0 0 0 0 0Los Angeles 100 99 6 3 0 2 8 8 0 0 0 0 0 0 0 0 0 0Los Angeles 150 98 6 50 19 60 65 65 3 0 2 9 9 0 0 0 1 1Los Angeles 150 99 6 31 8 39 47 47 3 0 2 8 8 0 0 0 1 1Los Angeles 200 98 6 136 59 148 190 190 23 3 26 33 33 3 0 2 9 9Los Angeles 200 99 6 107 44 118 150 150 14 1 14 23 23 3 0 2 8 8Los Angeles 250 98 6 216 115 224 285 285 77 29 86 112 112 16 1 17 26 26Los Angeles 250 99 6 188 91 200 250 250 54 20 65 73 73 8 1 7 16 16Los Angeles 300 98 6 270 170 270 335 335 136 59 148 190 190 50 19 60 65 65Los Angeles 300 99 6 252 152 256 319 319 107 44 118 150 150 31 8 39 47 47Miami As is 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Miami Current Std 2 87 85 87 89 89 36 36 36 36 36 7 6 7 8 8Miami 50 98 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Miami 50 99 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Miami 100 98 2 3 1 3 4 4 0 0 0 0 0 0 0 0 0 0Miami 100 99 2 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0Miami 150 98 2 36 33 36 38 38 3 1 3 4 4 0 0 0 0 0A-231Number of Daily Maximum Exceedances≥100 ppb≥150 ppb≥200 ppbLocationStandardPercentileSiteYearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99Miami 150 99 2 27 25 27 28 28 1 0 1 1 1 0 0 0 0 0Miami 200 98 2 75 72 75 77 77 20 18 20 22 22 3 1 3 4 4Miami 200 99 2 66 64 66 68 68 12 9 12 14 14 1 0 1 1 1Miami 250 98 2 98 89 98 107 107 48 43 48 52 52 12 9 12 14 14Miami 250 99 2 88 85 88 91 91 39 36 39 42 42 9 6 9 12 12Miami 300 98 2 122 110 122 133 133 75 72 75 77 77 36 33 36 38 38Miami 300 99 2 109 99 109 119 119 66 64 66 68 68 27 25 27 28 28New York As is 2 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0New York Current Std 2 38 17 38 59 59 2 0 2 3 3 0 0 0 0 0New York 50 98 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0New York 50 99 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0New York 100 98 2 4 4 4 4 4 0 0 0 0 0 0 0 0 0 0New York 100 99 2 1 0 1 2 2 0 0 0 0 0 0 0 0 0 0New York 150 98 2 71 60 71 81 81 4 4 4 4 4 0 0 0 0 0New York 150 99 2 34 33 34 35 35 1 0 1 2 2 0 0 0 0 0New York 200 98 2 185 175 185 194 194 37 35 37 38 38 4 4 4 4 4New York 200 99 2 134 132 134 135 135 10 9 10 11 11 1 0 1 2 2New York 250 98 2 274 260 274 288 288 114 112 114 115 115 25 24 25 26 26New York 250 99 2 214 201 214 227 227 59 52 59 66 66 7 4 7 9 9New York 300 98 2 312 296 312 327 327 185 175 185 194 194 71 60 71 81 81New York 300 99 2 283 269 283 297 297 134 132 134 135 135 34 33 34 35 35Phoenix As is 5 2 0 2 3 3 0 0 0 0 0 0 0 0 0 0Phoenix Current Std 5 61 0 83 126 126 3 0 2 8 8 0 0 0 1 1Phoenix 50 98 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Phoenix 50 99 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Phoenix 100 98 5 5 0 6 11 11 0 0 0 1 1 0 0 0 0 0Phoenix 100 99 5 2 0 2 6 6 0 0 0 0 0 0 0 0 0 0Phoenix 150 98 5 82 0 132 143 143 5 0 6 11 11 0 0 0 2 2Phoenix 150 99 5 48 0 67 97 97 2 0 2 6 6 0 0 0 1 1Phoenix 200 98 5 182 7 285 311 311 43 0 55 92 92 5 0 6 11 11Phoenix 200 99 5 150 2 241 256 256 21 0 25 44 44 2 0 2 6 6A-232Number of Daily Maximum Exceedances≥100 ppb≥150 ppb≥200 ppbLocationStandardPercentileSiteYearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99Phoenix 250 98 5 225 41 335 359 359 121 0 197 203 203 28 0 36 61 61Phoenix 250 99 5 205 18 315 340 340 82 0 132 143 143 12 0 16 23 23Phoenix 300 98 5 263 120 349 365 365 182 7 285 311 311 82 0 132 143 143Phoenix 300 99 5 241 73 341 364 364 150 2 241 256 256 48 0 67 97 97St. Louis As is 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0St. Louis Current Std 5 109 30 119 178 178 11 0 11 28 28 0 0 0 0 0St. Louis 50 98 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0St. Louis 50 99 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0St. Louis 100 98 5 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0St. Louis 100 99 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0St. Louis 150 98 5 49 37 44 65 65 1 0 1 1 1 0 0 0 0 0St. Louis 150 99 5 41 33 38 56 56 0 0 0 0 0 0 0 0 0 0St. Louis 200 98 5 158 135 143 187 187 20 16 19 24 24 1 0 1 1 1St. Louis 200 99 5 145 119 130 178 178 12 11 11 15 15 0 0 0 0 0St. Louis 250 98 5 245 228 239 263 263 86 66 78 107 107 10 9 9 12 12St. Louis 250 99 5 233 218 222 254 254 74 53 71 98 98 6 5 6 7 7St. Louis 300 98 5 298 285 289 319 319 158 135 143 187 187 49 37 44 65 65St. Louis 300 99 5 298 285 289 319 319 145 119 130 178 178 41 33 38 56 56WashingtonDCAs is 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0WashingtonDCCurrent Std 5 60 40 63 76 76 4 0 2 11 11 0 0 0 1 1WashingtonDC50 98 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0WashingtonDC50 99 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0WashingtonDC100 98 5 4 2 2 13 13 0 0 0 0 0 0 0 0 0 0WashingtonDC100 99 5 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0WashingtonDC150 98 5 63 30 59 92 92 4 2 2 13 13 0 0 0 1 1A-233Number of Daily Maximum Exceedances≥100 ppb≥150 ppb≥200 ppbLocationStandardPercentileSiteYearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99WashingtonDC150 99 5 17 5 12 43 43 0 0 0 1 1 0 0 0 0 0WashingtonDC200 98 5 173 122 155 222 222 32 12 30 56 56 4 2 2 13 13WashingtonDC200 99 5 85 43 76 118 118 6 2 4 17 17 0 0 0 1 1WashingtonDC250 98 5 247 197 224 307 307 93 54 80 129 129 20 6 14 47 47WashingtonDC250 99 5 173 122 155 222 222 36 13 36 62 62 4 2 2 13 13WashingtonDC300 98 5 293 256 277 336 336 173 122 155 222 222 63 30 59 92 92WashingtonDC300 99 5 247 197 224 307 307 85 43 76 118 118 17 5 12 43 43

### A-234 Table A-132. Estimated nu mber of exceedances of 1-hour concentr ation levels (250 and 300 ppb) for monitors ≤ 20 m from a major road follo wing adjustment to just meeti ng 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 1A-238Table A-133. Estimated number of exceedancesof 1-hour concentration levels (100, 150, and 200 ppb) on-roads following adjustment to just meetingthe current and alternative standards, 2004-2006 air quality and anon-road road adjustment factor.Number of Daily Maximum Exceedances≥100 ppb≥150 ppb≥200 ppbLocation Standard Percentile Site-YearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99Atlanta As is 1500 17 0 2 114 120 2 0 0 26 27 0 0 0 6 7Atlanta Current Std 1500 193 4 259 337 341 126 0 148 285 300 72 0 57 225 237Atlanta 50 98 1500 3 0 0 29 31 0 0 0 4 5 0 0 0 0 0Atlanta 50 99 1500 2 0 0 21 23 0 0 0 1 2 0 0 0 0 0Atlanta 100 98 1500 58 0 39 206 218 13 0 1 93 100 3 0 0 29 31Atlanta 100 99 1500 45 0 21 185 198 8 0 0 64 75 2 0 0 21 23Atlanta 150 98 1500 133 0 162 295 304 58 0 39 206 218 22 0 3 126 141Atlanta 150 99 1500 116 0 131 276 288 45 0 21 185 198 15 0 1 103 110Atlanta 200 98 1500 181 3 241 328 334 111 0 123 266 281 58 0 39 206 218Atlanta 200 99 1500 167 1 219 319 326 94 0 91 249 262 45 0 21 185 198Boston As is 800 2 0 0 18 21 0 0 0 1 1 0 0 0 0 0Boston Current Std 800 95 4 91 207 221 25 0 15 104 109 6 0 1 44 50Boston 50 98 800 0 0 0 1 2 0 0 0 0 0 0 0 0 0 0Boston 50 99 800 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0Boston 100 98 800 19 0 8 92 99 1 0 0 12 13 0 0 0 1 2Boston 100 99 800 8 0 2 54 65 0 0 0 3 5 0 0 0 0 1Boston 150 98 800 84 4 78 189 207 19 0 8 92 99 3 0 0 26 33Boston 150 99 800 52 1 42 150 162 8 0 2 54 65 1 0 0 10 12Boston 200 98 800 153 32 151 249 267 59 1 50 161 174 19 0 8 92 99Boston 200 99 800 114 14 111 215 238 34 0 24 119 129 8 0 2 54 65Chicago As is 800 36 0 20 148 161 5 0 0 41 53 1 0 0 7 18Chicago Current Std 800 189 25 187 329 339 69 0 54 225 241 22 0 8 117 126Chicago 50 98 800 2 0 0 24 30 0 0 0 0 0 0 0 0 0 0Chicago 50 99 800 1 0 0 15 24 0 0 0 0 0 0 0 0 0 0Chicago 100 98 800 59 0 43 201 220 10 0 2 63 78 2 0 0 24 30Chicago 100 99 800 47 0 31 181 193 8 0 1 54 67 1 0 0 15 24Chicago 150 98 800 176 20 169 320 329 59 0 43 201 220 18 0 6 98 109Chicago 150 99 800 157 14 148 313 320 47 0 31 181 193 14 0 4 80 93Chicago 200 98 800 259 94 261 352 355 138 9 127 302 309 59 0 43 201 220Chicago 200 99 800 244 64 248 348 353 119 7 108 291 296 47 0 31 181 193A-239Number of Daily Maximum Exceedances≥100 ppb≥150 ppb≥200 ppbLocation Standard Percentile Site-YearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99Denver As is 300 63 2 49 190 195 10 0 4 47 52 2 0 0 11 14Denver Current Std 300 257 135 259 314 320 134 12 133 260 264 52 0 41 164 174Denver 50 98 300 9 0 2 41 45 1 0 0 5 9 0 0 0 0 0Denver 50 99 300 5 0 1 23 25 0 0 0 2 3 0 0 0 0 0Denver 100 98 300 148 20 151 263 269 38 0 23 132 150 9 0 2 41 45Denver 100 99 300 106 10 100 236 242 23 0 11 87 95 5 0 1 23 25Denver 150 98 300 263 170 264 315 321 148 20 151 263 269 61 1 48 180 192Denver 150 99 300 239 114 243 309 313 106 10 100 236 242 38 0 23 132 150Denver 200 98 300 296 241 308 330 332 239 122 243 309 313 148 20 151 263 269Denver 200 99 300 286 217 287 323 327 204 66 209 294 299 106 10 100 236 242Detroit As is 600 20 0 9 90 103 2 0 0 20 24 0 0 0 3 6Detroit Current Std 600 287 166 286 350 352 189 35 193 300 315 95 3 86 222 258Detroit 50 98 600 13 0 4 57 66 1 0 0 12 18 0 0 0 1 1Detroit 50 99 600 10 0 2 52 53 1 0 0 9 14 0 0 0 0 1Detroit 100 98 600 165 29 166 293 296 50 0 37 165 180 13 0 4 57 66Detroit 100 99 600 146 18 143 275 284 40 0 27 146 163 10 0 2 52 53Detroit 150 98 600 273 163 271 341 346 165 29 166 293 296 77 1 66 203 221Detroit 150 99 600 261 141 260 337 342 146 18 143 275 284 62 0 50 186 204Detroit 200 98 600 313 237 318 355 359 249 117 251 331 337 165 29 166 293 296Detroit 200 99 600 307 234 309 354 357 235 90 237 327 332 146 18 143 275 284El Paso As is 1200 24 0 12 114 143 3 0 0 20 23 0 0 0 4 4El Paso Current Std 1200 281 137 285 349 354 198 22 205 309 320 109 3 102 255 265El Paso 50 98 1200 5 0 1 32 37 0 0 0 4 4 0 0 0 0 1El Paso 50 99 1200 3 0 0 20 23 0 0 0 2 2 0 0 0 0 0El Paso 100 98 1200 108 3 102 254 263 23 0 11 113 137 5 0 1 32 37El Paso 100 99 1200 75 2 62 225 234 13 0 5 72 86 3 0 0 20 23El Paso 150 98 1200 229 63 235 323 327 108 3 102 254 263 39 0 23 164 178El Paso 150 99 1200 199 26 205 309 317 75 2 62 225 234 23 0 12 113 137El Paso 200 98 1200 281 137 286 349 353 197 26 205 308 317 108 3 102 254 263El Paso 200 99 1200 264 97 271 340 346 162 12 166 289 296 75 2 62 225 234Jacksonville As is 200 11 0 5 48 59 2 0 2 5 6 1 0 1 4 4Jacksonville Current Std 200 295 229 295 341 342 216 114 223 309 318 134 31 133 260 279A-240Number of Daily Maximum Exceedances≥100 ppb≥150 ppb≥200 ppbLocation Standard Percentile Site-YearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99Jacksonville 50 98 200 9 0 5 39 50 2 0 2 5 5 1 0 1 4 4Jacksonville 50 99 200 2 0 2 5 5 1 0 1 4 4 0 0 0 3 3Jacksonville 100 98 200 127 30 124 256 269 35 0 23 138 151 9 0 5 39 50Jacksonville 100 99 200 39 1 27 145 158 6 0 4 25 29 2 0 2 5 5Jacksonville 150 98 200 241 145 243 320 327 127 30 124 256 269 55 1 42 169 193Jacksonville 150 99 200 135 33 131 265 279 39 1 27 145 158 10 0 5 47 59Jacksonville 200 98 200 293 229 294 339 340 211 104 214 302 312 127 30 124 256 269Jacksonville 200 99 200 218 118 223 309 318 102 12 95 232 253 39 1 27 145 158Las Vegas As is 1100 15 0 0 133 148 2 0 0 54 64 0 0 0 6 8Las Vegas Current Std 1100 177 2 171 343 346 99 0 55 317 324 50 0 11 260 275Las Vegas 50 98 1100 6 0 0 77 79 1 0 0 17 19 0 0 0 0 2Las Vegas 50 99 1100 4 0 0 71 73 0 0 0 6 8 0 0 0 0 0Las Vegas 100 98 1100 83 0 39 305 312 22 0 1 174 187 6 0 0 77 79Las Vegas 100 99 1100 70 0 24 288 304 16 0 0 142 151 4 0 0 71 73Las Vegas 150 98 1100 161 1 146 342 343 83 0 39 305 312 36 0 5 219 239Las Vegas 150 99 1100 145 0 118 337 339 70 0 24 288 304 26 0 2 191 206Las Vegas 200 98 1100 210 6 224 350 350 138 0 108 335 336 83 0 39 305 312Las Vegas 200 99 1100 198 3 205 348 349 123 0 83 329 333 70 0 24 288 304Los Angeles As is 5400 38 0 25 150 169 6 0 1 43 55 1 0 0 11 14Los Angeles Current Std 5400 160 2 163 314 326 58 0 45 187 215 18 0 8 90 105Los Angeles 50 98 5400 2 0 0 18 24 0 0 0 1 2 0 0 0 0 0Los Angeles 50 99 5400 1 0 0 12 15 0 0 0 1 1 0 0 0 0 0Los Angeles 100 98 5400 54 0 41 188 208 10 0 2 62 75 2 0 0 18 24Los Angeles 100 99 5400 40 0 27 153 176 6 0 1 44 55 1 0 0 12 15Los Angeles 150 98 5400 155 2 155 314 325 54 0 41 188 208 17 0 6 85 104Los Angeles 150 99 5400 132 0 127 296 310 40 0 27 153 176 11 0 3 68 79Los Angeles 200 98 5400 227 13 240 349 352 122 0 115 285 304 54 0 41 188 208Los Angeles 200 99 5400 208 8 217 342 348 100 0 91 258 281 40 0 27 153 176Miami As is 400 6 0 1 46 46 0 0 0 6 6 0 0 0 1 1Miami Current Std 400 202 100 201 284 286 130 30 139 197 204 77 1 76 160 163Miami 50 98 400 3 0 0 24 24 0 0 0 1 1 0 0 0 1 1Miami 50 99 400 2 0 0 18 21 0 0 0 1 1 0 0 0 0 0A-241Number of Daily Maximum Exceedances≥100 ppb≥150 ppb≥200 ppbLocation Standard Percentile Site-YearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99Miami 100 98 400 56 0 47 144 148 13 0 4 72 77 3 0 0 24 24Miami 100 99 400 46 0 34 134 140 9 0 2 62 65 2 0 0 18 21Miami 150 98 400 128 24 135 194 199 56 0 47 144 148 21 0 11 97 101Miami 150 99 400 117 17 123 187 192 46 0 34 134 140 16 0 7 81 92Miami 200 98 400 182 75 188 260 262 106 12 111 178 183 56 0 47 144 148Miami 200 99 400 171 63 178 248 250 94 7 96 170 174 46 0 34 134 140New York As is 2200 35 0 23 149 171 5 0 1 40 45 1 0 0 13 15New York Current Std 2200 147 0 148 308 321 45 0 31 188 202 13 0 4 75 89New York 50 98 2200 3 0 0 32 34 0 0 0 3 4 0 0 0 0 1New York 50 99 2200 2 0 0 17 23 0 0 0 1 1 0 0 0 0 0New York 100 98 2200 75 0 62 225 248 14 0 6 73 88 3 0 0 32 34New York 100 99 2200 45 0 32 175 196 7 0 2 49 53 2 0 0 17 23New York 150 98 2200 192 2 204 326 333 75 0 62 225 248 25 0 13 120 135New York 150 99 2200 149 0 151 305 312 45 0 32 175 196 13 0 4 67 80New York 200 98 2200 264 38 284 350 353 157 0 162 310 317 75 0 62 225 248New York 200 99 2200 232 20 250 339 343 115 0 108 278 294 45 0 32 175 196Philadelphia As is 1200 22 0 10 101 130 2 0 0 20 22 0 0 0 3 3Philadelphia Current Std 1200 232 54 242 321 326 110 2 99 269 280 43 0 27 188 202Philadelphia 50 98 1200 5 0 1 37 42 0 0 0 4 4 0 0 0 0 0Philadelphia 50 99 1200 1 0 0 14 14 0 0 0 0 1 0 0 0 0 0Philadelphia 100 98 1200 112 5 102 258 276 24 0 10 109 139 5 0 1 37 42Philadelphia 100 99 1200 53 0 39 187 214 8 0 1 49 53 1 0 0 14 14Philadelphia 150 98 1200 237 76 245 325 329 112 5 102 258 276 39 0 27 155 189Philadelphia 150 99 1200 172 24 178 297 311 53 0 39 187 214 14 0 4 77 83Philadelphia 200 98 1200 295 186 302 339 345 204 51 213 313 319 112 5 102 258 276Philadelphia 200 99 1200 256 105 264 329 332 134 7 130 277 296 53 0 39 187 214Phoenix As is 900 77 0 53 275 293 10 0 1 57 71 1 0 0 7 8Phoenix Current Std 900 284 127 296 357 359 124 2 111 307 325 38 0 11 186 211Phoenix 50 98 900 4 0 0 26 35 0 0 0 1 1 0 0 0 0 0Phoenix 50 99 900 2 0 0 12 14 0 0 0 0 0 0 0 0 0 0Phoenix 100 98 900 146 3 140 320 332 28 0 7 159 171 4 0 0 26 35Phoenix 100 99 900 103 0 84 293 312 16 0 2 101 107 2 0 0 12 14A-242Number of Daily Maximum Exceedances≥100 ppb≥150 ppb≥200 ppbLocation Standard Percentile Site-YearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99Phoenix 150 98 900 299 151 309 358 360 146 3 140 320 332 49 0 19 222 245Phoenix 150 99 900 268 95 280 353 357 103 0 84 293 312 30 0 7 165 184Phoenix 200 98 900 338 227 347 363 364 264 92 277 352 356 146 3 140 320 332Phoenix 200 99 900 328 204 337 361 363 225 39 236 345 352 103 0 84 293 312Provo As is 300 51 0 44 160 160 17 0 2 68 70 12 0 0 44 44Provo Current Std 300 306 153 331 360 361 192 44 187 331 340 87 1 61 260 278Provo 50 98 300 13 0 0 45 45 4 0 0 42 42 1 0 0 20 20Provo 50 99 300 13 0 0 44 45 4 0 0 42 42 1 0 0 15 15Provo 100 98 300 63 0 48 187 187 20 0 5 78 79 13 0 0 45 45Provo 100 99 300 60 0 46 182 183 19 0 3 78 79 13 0 0 44 45Provo 150 98 300 209 38 214 328 335 63 0 48 187 187 26 0 17 95 95Provo 150 99 300 201 30 210 328 334 60 0 46 182 183 24 0 15 91 91Provo 200 98 300 298 217 300 356 359 160 7 157 306 314 63 0 48 187 187Provo 200 99 300 294 204 290 354 356 151 6 147 299 306 60 0 46 182 183St. Louis As is 400 15 0 5 76 83 1 0 0 20 24 0 0 0 2 5St. Louis Current Std 400 233 82 228 341 347 121 1 113 280 300 50 0 33 198 221St. Louis 50 98 400 4 0 0 35 41 0 0 0 9 10 0 0 0 0 0St. Louis 50 99 400 3 0 0 31 35 0 0 0 4 9 0 0 0 0 0St. Louis 100 98 400 107 3 100 238 249 23 0 12 105 108 4 0 0 35 41St. Louis 100 99 400 98 2 90 227 240 20 0 10 96 101 3 0 0 31 35St. Louis 150 98 400 226 96 221 331 340 107 3 100 238 249 39 0 28 135 142St. Louis 150 99 400 217 82 211 328 340 98 2 90 227 240 33 0 22 124 133St. Louis 200 98 400 287 166 291 357 357 194 62 190 308 320 107 3 100 238 249St. Louis 200 99 400 282 162 283 355 356 186 49 181 305 317 98 2 90 227 240Washington DC As is 1700 21 0 7 119 143 2 0 0 20 22 0 0 0 4 6Washington DC Current Std 1700 207 10 238 340 345 102 0 88 270 289 41 0 21 176 202Washington DC 50 98 1700 5 0 1 37 42 0 0 0 5 6 0 0 0 1 1Washington DC 50 99 1700 2 0 0 15 16 0 0 0 2 2 0 0 0 0 0Washington DC 100 98 1700 96 0 81 269 283 22 0 8 126 150 5 0 1 37 42Washington DC 100 99 1700 50 0 29 209 226 8 0 1 52 66 2 0 0 15 16Washington DC 150 98 1700 200 2 232 337 345 96 0 81 269 283 36 0 17 173 192Washington DC 150 99 1700 148 0 157 309 322 50 0 29 209 226 14 0 3 85 110A-243Number of Daily Maximum Exceedances≥100 ppb≥150 ppb≥200 ppbLocation Standard Percentile Site-YearsMean Min Med p98 p99 Mean Min Med p98 p99 Mean Min Med p98 p99Washington DC 200 98 1700 260 42 299 358 362 171 0 191 323 335 96 0 81 269 283Washington DC 200 99 1700 220 8 256 346 352 117 0 108 288 298 50 0 29 209 226Other MSA As is 56500 10 0 1 79 100 1 0 0 12 18 0 0 0 2 3Other MSA Current Std 56500 143 0 143 322 336 59 0 36 233 257 22 0 5 138 163Other MSA 50 98 56500 0 0 0 5 8 0 0 0 0 1 0 0 0 0 0Other MSA 50 99 56500 0 0 0 2 5 0 0 0 0 0 0 0 0 0 0Other MSA 100 98 56500 20 0 5 123 152 2 0 0 26 37 0 0 0 5 8Other MSA 100 99 56500 14 0 2 96 121 1 0 0 17 25 0 0 0 2 5Other MSA 150 98 56500 80 0 62 264 288 20 0 5 123 152 5 0 0 44 60Other MSA 150 99 56500 63 0 43 237 263 14 0 2 96 121 3 0 0 30 43Other MSA 200 98 56500 143 0 144 322 337 57 0 36 228 255 20 0 5 123 152Other MSA 200 99 56500 125 0 119 311 328 44 0 23 201 228 14 0 2 96 121Other Not MSA As is 11600 4 0 0 43 65 1 0 0 7 13 0 0 0 2 5Other Not MSA Current Std 11600 124 0 100 331 339 62 0 24 257 272 29 0 4 180 201Other Not MSA 50 98 11600 0 0 0 2 6 0 0 0 1 2 0 0 0 0 1Other Not MSA 50 99 11600 0 0 0 2 3 0 0 0 0 2 0 0 0 0 1Other Not MSA 100 98 11600 6 0 0 61 86 1 0 0 10 17 0 0 0 2 6Other Not MSA 100 99 11600 2 0 0 23 35 0 0 0 3 7 0 0 0 2 3Other Not MSA 150 98 11600 28 0 4 173 198 6 0 0 61 86 1 0 0 18 26Other Not MSA 150 99 11600 13 0 1 113 134 2 0 0 23 35 0 0 0 6 11Other Not MSA 200 98 11600 60 0 22 249 269 19 0 2 141 164 6 0 0 61 86Other Not MSA 200 99 11600 34 0 6 193 216 8 0 0 80 104 2 0 0 23 35

### A-244 Table A-134. Estimated nu mber of exceedances of 1-hour concentrat ion levels (250 and 300 ppb) on-roads following adjustment to just meeting the current and alternative standard s, 2004-2006 air qu ality 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 gr oups, one representing historical data (1995-2000) and the other representing more recent data (2001-2006). This initial analysis performed in the 1 st



### 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 th e final REA where the number of times the daily maximum exceeded the potential benchmark levels wa s 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 calcul ate the number of daily maximum exceedances in a year.

A summary of the descriptive statistics for the annual average ambient NO 2 

### concentrations at each selected location is pr ovided in Tables A-111 and A-112 for monitors sited ≥ 100 m and <100 m from a major road, respectively. N one 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 NO 2 

### concentrations were measured in Los Angeles and Phoenix during the historical monitoring peri od 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 NO 2 

### 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 dist ant monitors for one year-group when compared with the monitors <100 m from a major road. Where concentrati ons were greater at the near road monitors, the concentrations were on aver age about 20-25% higher when compared with the more distant monitors in each corresponding loca tion, regardless of year-group. A comparison of the year-group of data with in each monitor si te-group indicates that the more recent monitoring concentrations were lower, on aver age by about 13-15%. Thes e average trends in concentration across year-group and monitor site group were ge nerally observed across all percentiles of the distribution.

Table A-135. Monitoring site -years and annual average NO 2 concentrations for two monitoring periods, historical and recent air quality da ta (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 p99 SiteYears 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 p99 SiteYears 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 bas ed on all hourly values in a year. Then the mean of the annual means was estimated as the sum of all the annual me ans in a particular location divided by the number of site-years across the monitoring period. The min, med, p95, p98, p99 represent the minimum, median, 95 th , 98 th

, and 99 th percentiles of the distri bution 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 NO 2 concentrations for two monitoring periods, historical and recent air quality data (as is) us ing 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 p99 SiteYears 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 bas ed on all hourly values in a year. Then the mean of the annual means was estimated as the sum of all the annual me ans in a particular location divided by the number of site-years across the monitoring period. The min, med, p95, p98, p99 represent the minimum, median, 95 th , 98 th

, and 99 th percentiles of the distri bution 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 ex ceedances of four pot ential health effect benchmark levels (150, 200, 250, and 300 ppb NO 2 

### 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 re present ten 1-hr exceedances that occurred in one day, 10 exceedances in 10 days, or some combin ation 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 consideri ng both year-groups of the as is air quality. The average number of exceedances of the lowest 1-hour concentra tion level of 150 ppb acro ss each location was typically none or one. Cons idering that there are 8,760 hours in a year, this number of exceedances amounts to a small fraction of the ye ar (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 da ta. For example, the Colorado Springs mean is 3 exceedances per y ear for the years 1995-2000; however, this mean was driven by a single site-year that contai ned 69 exceedances of 200 ppb. That particular monitor (ID 0804160181) does not appear to have a ny 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 util ity (NAICS code 221112) located 7.2 km from the monitor has estimated emi ssions of 4,205 tpy. It is not known at this time whether this particular facility is influenc ing the observed concentration exceedances at this specific monitoring site. Similarly, Detroit c ontained the largest number of excedances of 200 ppb (a maximum of 12) for as is air quality data from year s 2001-2006 (Table A-112). Again, all of those exceedances occurred at one m onitor (ID 2616300192) during one year (2002). The number of exceedances of higher potential benc hmark concentration levels at each of the locations was less than that observed at the 200 p pb 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 th e potential health effect benchmark levels, driven mainly by observations from one or two m onitors. For example, in Phoenix a single year from one monitor (ID 0401330031) wa s responsible for all observ ed exceedances of 200 ppb. This monitor is located 78 m from a major road along with 10 stat ionary sources located within 10 km of this monitor, 9 of which contained es timated emissions of less than 60 tpy (one source emitted 272 tpy, see Appendix A, section 4). It is not known if obser ved exceedances of 200 ppb at this monitor are a result of proximity of majo r 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 exceed ances of the 150 ppb benchmark level (Table A116).August 2008 - Draft 2531Table A-137. Total number of exceedances ofshort-term (1-hour) potential health effect benchmark levels in a year, 1995-2000historical NO2air2quality (as is) using monitors sited≥100 m of a major road.3Exceedances of 150 ppb1Exceedances of 200 ppb1Exceedances of 250 ppb1Exceedances of 300 ppb1Locationmeanminmedp95P98p99 mean min med p95 p98 p99meanminmedp95p98p99 mean min med p95 p98 p99Atlanta000111 0 0 0 0 1 1000000 0 0 0 0 0 0Boston000111 0 0 0 0 0 0000000 0 0 0 0 0 0Chicago000000 0 0 0 0 0 0000000 0 0 0 0 0 0Cleveland000000 0 0 0 0 0 0000000 0 0 0 0 0 0ColoradoSprings80047143143 3 0 0 3 69 6910002323 0 0 0 0 4 4Denver100444 0 0 0 2 2 2000000 0 0 0 0 0 0Detroit100101010 0 0 0 3 3 3000111 0 0 0 1 1 1El Paso000000 0 0 0 0 0 0000000 0 0 0 0 0 0Jacksonville000000 0 0 0 0 0 0000000 0 0 0 0 0 0Las Vegas000000 0 0 0 0 0 0000000 0 0 0 0 0 0Los Angeles300224244 0 0 0 2 2 4000012 0 0 0 0 0 1Miami000000 0 0 0 0 0 0000000 0 0 0 0 0 0New York000033 0 0 0 0 0 0000000 0 0 0 0 0 0Philadelphia00001010 0 0 0 0 3 3000000 0 0 0 0 0 0Phoenix000222 0 0 0 0 0 0000000 0 0 0 0 0 0Provo000000 0 0 0 0 0 0000000 0 0 0 0 0 0St. Louis100121212 0 0 0 8 8 8000444 0 0 0 0 0 0WashingtonDC000122 0 0 0 1 2 2000111 0 0 0 0 0 0Other MSA000001 0 0 0 0 0 0000000 0 0 0 0 0 0Other NotMSA000024 0 0 0 0 0 1000000 0 0 0 0 0 0Notes:1The mean number of exceedances representsthe number of exceedances occurring at all monitors in a particular location dividedby the number of siteyears across the monitoring period. The min, med, p95, p98, and p99 represent the minimum, median, 95th, 98th, and 99thpercentiles of the distribution forthe number of exceedances in any one year within the monitoring period.4August 2008 - Draft 2541Table A-138. Total number of exceedances ofshort-term (1-hour) potential health effect benchmark levels in a year, 2001-2006recent NO2air quality2(as is) using monitors sited≥100 m of a major road.3Exceedances of 150 ppb1Exceedances of 200 ppb1Exceedances of 250 ppb1Exceedances of 300 ppb1Locationmeanminmedp95P98p99 mean min med p95 p98 p99meanminmedp95p98p99 mean min med p95 p98 p99Atlanta000011 0 0 0 0 0 0000000 0 0 0 0 0 0Boston000000 0 0 0 0 0 0000000 0 0 0 0 0 0Chicago000000 0 0 0 0 0 0000000 0 0 0 0 0 0Cleveland000000 0 0 0 0 0 0000000 0 0 0 0 0 0Denver000000 0 0 0 0 0 0000000 0 0 0 0 0 0Detroit200161616 1 0 0 12 12 12100888 0 0 0 5 5 5El Paso000011 0 0 0 0 0 0000000 0 0 0 0 0 0Jacksonville201666 1 0 1 2 2 2000111 0 0 0 0 0 0Las Vegas000000 0 0 0 0 0 0000000 0 0 0 0 0 0Los Angeles000011 0 0 0 0 0 0000000 0 0 0 0 0 0Miami000000 0 0 0 0 0 0000000 0 0 0 0 0 0New York000000 0 0 0 0 0 0000000 0 0 0 0 0 0Philadelphia000011 0 0 0 0 1 1000011 0 0 0 0 0 0Phoenix000000 0 0 0 0 0 0000000 0 0 0 0 0 0Provo700393939 0 0 0 0 0 0000000 0 0 0 0 0 0St. Louis000000 0 0 0 0 0 0000000 0 0 0 0 0 0WashingtonDC000000 0 0 0 0 0 0000000 0 0 0 0 0 0Other MSA000000 0 0 0 0 0 0000000 0 0 0 0 0 0Other NotMSA000012 0 0 0 0 0 1000001 0 0 0 0 0 0Notes:1The mean number of exceedances representsthe number of exceedances occurring at all monitors in a particular location dividedby the number of siteyears across the monitoring period. The min, med, p95, p98, and p99 represent the minimum, median, 95th, 98th, and 99thpercentiles of the distribution forthe number of exceedances in any one year within the monitoring period.2Colorado Springs monitoring data were collected as part of short-term study completed in September 2001, therefore there are no 2001-2006 data.456August 2008 - Draft 2551Table A-139. Total number of exceedances ofshort-term (1-hour) potential health effect benchmark levels in a year, 1995-2000historical NO2air2quality (as is) using monitors sited <100 m of a major road.3Exceedances of 150 ppb1Exceedances of 200 ppb1Exceedances of 250 ppb1Exceedances of 300 ppb1Locationmeanminmedp95P98p99 mean min med p95 p98 p99meanminmedp95p98p99 mean min med p95 p98 p99Boston000011 0 0 0 0 1 1000000 0 0 0 0 0 0Chicago000000 0 0 0 0 0 0000000 0 0 0 0 0 0Cleveland200999 0 0 0 1 1 1000111 0 0 0 0 0 0ColoradoSprings000000 0 0 0 0 0 0000000 0 0 0 0 0 0Denver000666 0 0 0 1 1 1000111 0 0 0 0 0 0El Paso201777 0 0 0 2 2 2000000 0 0 0 0 0 0Las Vegas100111111 1 0 0 11 11 11000333 0 0 0 3 3 3Los Angeles200111833 0 0 0 1 2 2000000 0 0 0 0 0 0Miami000333 0 0 0 1 1 1000000 0 0 0 0 0 0New York000233 0 0 0 0 3 3000000 0 0 0 0 0 0Philadelphia000111 0 0 0 0 0 0000000 0 0 0 0 0 0Phoenix2701147147147 5 0 0 37 37 37000333 0 0 0 0 0 0St. Louis000000 0 0 0 0 0 0000000 0 0 0 0 0 0WashingtonDC000011 0 0 0 0 0 0000000 0 0 0 0 0 0Notes:1The mean number of exceedances representsthe number of exceedances occurring at all monitors in a particular location dividedby the number of siteyears across the monitoring period. The min, med, p95, p98, and p99 represent the minimum, median, 95th, 98th, and 99thpercentiles of the distribution forthe number of exceedances in any one year within the monitoring period.4August 2008 - Draft 2561Table A-140. Total number of exceedances ofshort-term (1-hour) potential health effect benchmark levels in a year, 2001-2006recent NO2air quality2(as is) using monitors sited <100 m of a major road.3Exceedances of 150 ppb1Exceedances of 200 ppb1Exceedances of 250 ppb1Exceedances of 300 ppb1Locationmeanminmedp95P98p99 mean min med p95 p98 p99meanminmedp95p98p99 mean min med p95 p98 p99Boston000000 0 0 0 0 0 0000000 0 0 0 0 0 0Chicago000000 0 0 0 0 0 0000000 0 0 0 0 0 0Cleveland000111 0 0 0 0 0 0000000 0 0 0 0 0 0Denver101111 0 0 0 0 0 0000000 0 0 0 0 0 0El Paso000000 0 0 0 0 0 0000000 0 0 0 0 0 0Las Vegas000000 0 0 0 0 0 0000000 0 0 0 0 0 0Los Angeles000226 0 0 0 0 1 1000011 0 0 0 0 0 0Miami100555 0 0 0 3 3 3000333 0 0 0 3 3 3New York000111 0 0 0 0 0 0000000 0 0 0 0 0 0Philadelphia000000 0 0 0 0 0 0000000 0 0 0 0 0 0Phoenix000000 0 0 0 0 0 0000000 0 0 0 0 0 0St. Louis000000 0 0 0 0 0 0000000 0 0 0 0 0 0WashingtonDC000000 0 0 0 0 0 0000000 0 0 0 0 0 0Notes:1The mean number of exceedances representsthe number of exceedances occurring at all monitors in a particular location dividedby the number of siteyears across the monitoring period. The min, med, p95, p98, and p99 represent the minimum, median, 95th, 98th, and 99thpercentiles of the distribution forthe number of exceedances in any one year within the monitoring period.

### August 2008 - Draft 257 

## A-9.5 Comparison of On-Roa d Concentrations Derived From 1 

## Historical and Recent Ambient Air Quality (As Is) 2 This section presents the preliminary results using the ambient monitoring data obtained from 3 AQS that were separated into two six-year gr oups, one representing historical data (1995-2000) 4 and the other represen ting more recent data (2001- 2006). These estimated on-road 5 concentrations were generated by applying the si mulation procedure descri bed above in section 6 A-8 to ambient monitors ≥ 100 m of a major road and represent the second scenario evaluated. 7 This initial analysis used the total number of exceedances of the potential benchmark levels of 8 150, 200, 250, and 300 ppb. It differs from the anal yses performed in Chapter 7 of the final REA 9 where the number of times the daily maximum exceeded the potential benchmark levels was 10 recorded (also including a benchmark level of 100 ppb). It is presented here mainly for 11 comparison of the two six-year groups, because th e historical data set was not re-analyzed using 12 the added benchmark level and did have the num ber of daily maximum exceedances in a year 13 calculated. 14 15 Descriptive statistics for estimated on-road NO 2 

### concentrations are pres ented in Table A-117. 16 For the 18 named locations, the calculation only used monitors sited at a distance ≥ 100 m of a 17 major road. The two grouped locations (i.e., “Other CMSA” and “Not MSA”) did not have 18 estimated monitor distances to major roads theref ore all monitoring data available were used to 19 estimate the distribution of on-road NO 2 

### concentrations. 20 21 The simulated on-road annual average NO 2 

### concentrations are, on average, a factor of 1.8 22 higher than their respective ambient levels. This falls within the range of ratios reported in the 23 ISA (about 2-fold higher concentrations on roads) (ISA, section 2.5.4). Los Angeles, New York, 24 and Phoenix were predicted to have the highest on-road NO 2 

### levels. This is a direct result of 25 these locations already contai ning some of the highest “ as-is” NO 2 

### concentrations prior to the 26 on-road simulation (see Table A-111). 27 28 The median of the simulated concentration es timates for Los Angeles were compared with 29 NO 2 

### measurements provided by Wester dahl et al. (2005) for arterial roads and freeways in the 30 same general location during spring 2003. Alt hough the averaging time is not exactly the same, 31 comparison of the medians is judged to be appropriate. 8 

### The estimated median on-road 32 concentration for 2001-2006 is 36 ppb which falls w ithin the range of 31 ppb to 55 ppb identified 33 by Westerdahl et al. (2005). 34 35 On average, most locations are predicted to ha ve fewer than 10 exceedances per year for the 36 200 ppb potential health effect benchmark while th e median frequency of exceedances in most 37 locations is estimated to be 1 or less per ye ar (Tables A-118 and A-119). When considering the 38 lower 1-hour benchmark of 150 ppb, most locations (17 out of 20) were estimated to have less 39 than 50/year, on average. There are generally fe wer predicted mean exceedances of the potential 40 health effect benchmark levels when considering recent air quality compared with the historical 41

8 Table A-118 considers annual average of hourly measuremen ts 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 

### air quality. Areas with a relativ ely high number of estimated ex ceedances (e.g., Provo) are likely 1 influenced by the presence of a small number of monitors and one or a few exceptional site-years 2 where there were unusually high co ncentrations at the upper per centiles of the concentration 3 distribution. 4 5 The upper percentiles for estimated number of exceedances of the 150 ppb, 1-hr average 6 level in most locations using the historical ambient monitoring data was between 100 and 300 7 per year, while a few locations were estimated to contain up to a several hundred exceedances 8 (e.g., Los Angeles, New York, and Phoenix). There were lower numbers of estimated 9 exceedances considering the 2001-2006 air quality comp ared with the historical data, with most 10 locations containing under 200 estimated ex ceedances of 150 ppb per year at the 98 th 

### and 99 th 

### 11 percentiles. As expected, the frequency of benchmark exceedances at all locations was lower 12 when considering any of the higher benchmark levels (i.e., 200, 250, 300 ppb, 1-hr average) 13 compared with 150 ppb. 14 15 The number of predicted benchmark exceedances across large urban areas may be used to 16 broadly represent particular locat ions within those types of areas . For example, Chicago, New 17 York, and Los Angeles are large CMSAs, have se veral monitoring sites, and have a large number 18 of roadways. Each of these locations was estim ated to have, on average, about 10 exceedances 19 of 200 ppb per year on-roads. Assuming that the on-road exceedances distribution generated 20 from the existing monitoring is proportionally re presenting the distributio n of roadways within 21 each location, about one-half of the roads inthese areas would not have any on-road 22 concentrations in excess of 200 ppb. This is because the median value for exceedances of 200 23 ppb in most locations was estimated as zero. However, Tables A-118 and A-119 indicate that 24 there is also a possibility of te ns to just over a hundred exceed ances of 200 ppb in a year as an 25 upper bound estimate on certa in roads/sites in a particular year. 26 27

Table A-141. Estimated annual average on-road NO 2 concentrations for two moni toring periods, historical 

### 28 and recent air qualit y data (as is). 

### 29
1995-2000 2001-2006 Annual Mean (ppb) 1 Annual Mean (ppb) 1

Location SiteYears mean min med p95 p98 p99 SiteYears 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 Springs 2 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 p99 SiteYears 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 bas ed on all simulated hourly values in a year. Then the mean of the annual means was estimated as the sum of a ll the annual means in a particular location divided by the number of simulated site-years across the monitori ng period. The min, med, p95, p98, p99 represent the minimum, median, 95 th , 98 th , and 99 th percentiles of the distribution for the annual 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 2August 2008 - Draft 2601Table A-142. Estimated total number of exceedances of short-term (1-hour) potential health effectbenchmark levels in a year on-roads, 1995-20002historical NO2air quality (as is).3Exceedances of 150 ppb1Exceedances of 200 ppb1Exceedances of 250 ppb1Exceedances of 300 ppb1Locationmeanminmedp95P98p99 mean min med p95 p98 p99meanminmedp95p98p99 mean min med p95 p98 p99Atlanta2401160271357 4 0 0 31 57 8710031121 0 0 0 1 1 2Boston110179106125 1 0 0 9 20 24000147 0 0 0 0 1 1Chicago3902212338385 7 0 0 41 97 11810062330 0 0 0 0 3 7Cleveland1501108130146 2 0 0 19 27 31000155 0 0 0 1 1 1ColoradoSprings4500267447626 21 0 0 171 264 3251200111183219 7 0 0 55 121 160Denver48017185230288 8 0 4 36 46 53201101215 1 0 0 4 6 7Detroit39019158207270 10 0 2 48 72 86401213435 2 0 0 14 21 26El Paso210896141149 4 0 0 20 31 39100578 0 0 0 0 2 2Jacksonville300133036 0 0 0 1 2 4000011 0 0 0 0 0 0Las Vegas140095294306 2 0 0 5 34 36000066 0 0 0 0 0 0Los Angeles16605473810231268 43 0 6 213 348 508120063118188 4 0 0 17 39 68Miami300132727 0 0 0 2 4 5000001 0 0 0 0 0 0New York6308397560685 13 0 0 92 155 212300214455 1 0 0 4 10 14Philadelphia2502124311369 4 0 0 20 45 6310041115 0 0 0 0 5 7Phoenix104031447630670 14 0 2 65 89 102200132127 1 0 0 3 6 11Provo2100112195245 2 0 0 9 33 34000014 0 0 0 0 0 0St. Louis140074121132 2 0 0 15 25 28100101314 1 0 0 7 11 13WashingtonDC2101128208240 3 0 0 20 39 561002811 0 0 0 1 2 3Other MSA100055109168 1 0 0 6 18 32000136 0 0 0 0 1 2Other NotMSA200113155 1 0 0 2 7 14000124 0 0 0 0 1 2Notes:1The mean number of exceedances representsthe number of exceedances occurring at all monitors in a particular location dividedby the number of siteyears across the monitoring period. The min, med, p95, p98, and p99 represent the minimum, median, 95th, 98th, and 99thpercentiles of the distribution forthe number of exceedances in any one year within the monitoring period.August 2008 - Draft 2611Table A-143. Estimated total number of exceedances of short-term (1-hour) potential health effectbenchmark levels in a year on-roads, 2001-20062recent NO2air quality (as is).3Exceedances of 150 ppb1Exceedances of 200 ppb1Exceedances of 250 ppb1Exceedances of 300 ppb1Location2meanminmedp95P98p99 mean min med p95 p98 p99meanminmedp95p98p99 mean min med p95 p98 p99Atlanta80052101121 1 0 0 8 16 25000136 0 0 0 0 1 2Boston0001210 0 0 0 0 0 1000000 0 0 0 0 0 0Chicago2401160211337 4 0 0 17 44 690001510 0 0 0 0 1 1Cleveland1403798989 2 0 0 16 23 23000456 0 0 0 2 3 3Denver4106171270379 4 0 0 25 40 53000367 0 0 0 0 1 1Detroit2003116149171 5 0 0 29 44 45200162228 1 0 0 13 14 21El Paso600344554 1 0 0 4 8 9000111 0 0 0 0 0 0Jacksonville702295353 3 0 1 15 23 2420081515 1 0 0 5 8 8Las Vegas90039169205 1 0 0 3 14 15000002 0 0 0 0 0 0Los Angeles4204227405546 7 0 0 37 87 12910072028 0 0 0 1 3 10Miami1004916 0 0 0 0 1 2000000 0 0 0 0 0 0New York2101129210280 3 0 0 22 45 7210031016 0 0 0 0 1 2Philadelphia120162110211 1 0 0 5 12 30000117 0 0 0 0 1 1Phoenix3702184302350 3 0 0 14 28 44000134 0 0 0 0 0 0Provo11701658702703 70 0 0 547 662 6623300234606612 13 0 0 3 423 435St. Louis7004884102 1 0 0 3 10 14000022 0 0 0 0 0 1WashingtonDC110081130141 1 0 0 7 14 21000012 0 0 0 0 0 0Other MSA400174476 0 0 0 1 5 10000011 0 0 0 0 0 0Other NotMSA10041427 0 0 0 1 4 8000023 0 0 0 0 1 2Notes:1The mean number of exceedances representsthe number of exceedances occurring at all monitors in a particular location dividedby the number of siteyears across the monitoring period. The min, med, p95, p98, and p99 represent the minimum, median, 95th, 98th, and 99thpercentiles of the distribution forthe number of exceedances in any one year within the monitoring period.2Colorado Springs monitoring data were collected as part of short-term study completed in September 2001, therefore there are no 2001-2006 data.

### August 2008 - Draft 262 

### 1



## A-9.6 Results Tables of Historical NO 2 

## Ambient Monitoring Data 2 

## (1995-2000) Adjusted to Just Meeti ng the Current Standard 3

4 This section presents the preliminary results using the historical ambient monitoring data 5
(1995-2000) adjusted to just meet the current annual average standard only. This initial analysis 6

calculated the total number of exceedances of the potential benchmark levels of 150, 200, 250, 7
and 300 ppb, for ambient monitors sited ≥ 100 m and <100 m from a major road. These results 8
are presented in Tables A-120 and A-121, respectively. In additi on on-road concentrations were 9
also estimated using the adjusted air quality concentrations, usi ng the same procedure described 10

in section A-8. The total estimated number of exceedances of the potential health effect 11
benchmark levels on-roads given just meeting the current standard is provided in Table A-122. 12
Each of the result tables presen ted in this section differs from the analyses performed in Chapter 13

7 of the final REA where the number of times the daily maximum exceeded the potential 14
benchmark levels was recorded (including a be nchmark level of 100 ppb) for different monitor 15
road categories ( ≥ 100 m, 20 m< x <100 m, and ≤ 20 m from a major road), and for not just the 16
current standard but all of the potential alternativ e standards. It is presented here mainly as a 17
companion to the as is air quality results presented in sections A-9.1 and A-9.2. 18August 2008 - Draft 2631Table A-144. Total number of exceedances ofshort-term (1-hour) potential health effect benchmark levels in a year, 1995-2000historical NO2air2quality adjusted to just meeting the current annual average standard (0.053 ppm) using monitors sited≥100 m of a major road.3Exceedances of 150 ppb1Exceedances of 200 ppb1Exceedances of 250 ppb1Exceedances of 300 ppb1Locationmeanminmedp95P98p99 mean min med p95 p98 p99meanminmedp95p98p99 mean min med p95 p98 p99Atlanta4202197233233 4 0 0 19 21 21000233 0 0 0 1 1 1Boston100777 0 0 0 1 1 1000111 0 0 0 0 0 0Chicago101577 0 0 0 1 1 1000000 0 0 0 0 0 0Cleveland201777 0 0 0 0 0 0000000 0 0 0 0 0 0ColoradoSprings5003283318318 32 0 0 180 241 2411600123135135 8 0 0 72 83 83Denver141112648648648 24 0 2 141 141 141501282828 2 0 0 9 9 9Detroit75265162162162 13 0 13 25 25 25402151515 2 0 1 10 10 10El Paso1619696969 2 0 1 14 14 14000222 0 0 0 0 0 0Jacksonville12282137147147147 12 2 15 20 20 20201777 0 0 0 1 1 1Las Vegas301111111 0 0 0 1 1 1000000 0 0 0 0 0 0Los Angeles902568396 1 0 0 4 6 8000122 0 0 0 0 1 2Miami72491133133133 10 0 10 27 27 27100666 0 0 0 2 2 2New York100477 0 0 0 0 2 2000000 0 0 0 0 0 0Philadelphia200101818 0 0 0 0 12 12000099 0 0 0 0 5 5Phoenix805262626 0 0 0 4 4 4000000 0 0 0 0 0 0Provo1624717171 1 0 0 5 5 5000000 0 0 0 0 0 0St. Louis401161616 1 0 0 15 15 15100141414 1 0 0 13 13 13WashingtonDC903343838 1 0 0 3 4 4000233 0 0 0 1 2 2Other MSA200132840 0 0 0 1 3 6000011 0 0 0 0 0 1Other NotMSA2000116241336 4 0 0 18 53 8710041542 1 0 0 1 8 21Notes:1The mean number of exceedances representsthe number of exceedances occurring at all monitors in a particular location dividedby the number of siteyears across the monitoring period. The min, med, p95, p98, and p99 represent the minimum, median, 95th, 98th, and 99thpercentiles of the distribution forthe number of exceedances in any one year within the monitoring period.August 2008 - Draft 2641Table 145. Total estimated number of exceedances of short-term (1-hour) potential health effect benchmark levels in a year, 1995-2000 historical NO22air quality adjusted to just meeting the current annual average standard (0.053 ppm) using monitors sited <100 m of a major road.3Exceedances of 150 ppb1Exceedances of 200 ppb1Exceedances of 250 ppb1Exceedances of 300 ppb1Locationmeanminmedp95P98p99 mean min med p95 p98 p99meanminmedp95p98p99 mean min med p95 p98 p99Boston200112222 0 0 0 1 2 2000011 0 0 0 0 1 1Chicago402161616 0 0 0 0 0 0000000 0 0 0 0 0 0Cleveland35916110110110 5 0 1 24 24 24200101010 1 0 0 3 3 3ColoradoSprings777777 2 2 2 2 2 2111111 1 1 1 1 1 1Denver1200777777 1 0 0 10 10 10000555 0 0 0 2 2 2El Paso23524363636 6 0 7 13 13 13201666 0 0 0 2 2 2Las Vegas47025226226226 6 0 1 28 28 28300131313 1 0 0 11 11 11LosAngeles800425679 1 0 0 6 8 9000012 0 0 0 0 0 0Miami70256161161161 9 0 7 34 34 34200151515 1 0 0 8 8 8New York10061010 0 0 0 1 3 3000033 0 0 0 0 1 1Philadelphia503262626 0 0 0 3 3 3000111 0 0 0 1 1 1Phoenix7709339339339 32 0 1 198 198 1981200929292 4 0 0 31 31 31St. Louis201111313 0 0 0 1 1 1000000 0 0 0 0 0 0WashingtonDC1209476161 1 0 0 9 17 17000033 0 0 0 0 2 2Notes:1The mean number of exceedances representsthe number of exceedances occurring at all monitors in a particular location dividedby the number of siteyears across the monitoring period. The min, med, p95, p98, and p99 represent the minimum, median, 95th, 98th, and 99thpercentiles of the distribution forthe number of exceedances in any one year within the monitoring period.August 2008 - Draft 2651Table A-146. Total estimated number of exceedances of short-term (1-hour) potential health effect benchmark levels in a year on-roads, 1995-20002historical NO2air quality adjusted to just meeting the current annual average standard (0.053 ppm).3Exceedances of 150 ppb1Exceedances of 200 ppb1Exceedances of 250 ppb1Exceedances of 300 ppb1Locationmeanminmedp95P98p99 mean min med p95 p98 p99meanminmedp95p98p99 mean min med p95 p98 p99Atlanta5970215212225662778 251 0 42 10941472164010607535843947 45 0 1 277 435 514Boston231010893012821394 53 0 11 299 369 390140195132161 4 0 0 28 52 65Chicago3860242128816091802 111 0 32 498 615 7073602195289364 13 0 0 86 153 196Cleveland52642407130515681762 157 1 83 457 586 70051013215269306 18 0 1 102 131 149ColoradoSprings8660565266631063332 308 0 80 134817921902123011574803934 61 0 1 299 373 421Denver98015585276530213149 497 0 111 209723042451254026146716951930 126 0 12 866 11821286Detroit9825860241327712882 405 2 284 122714391589175297576776872 80 0 40 317 424 482El Paso48819317144321062391 152 0 67 545 997 112654016186440485 21 0 6 83 190 251Jacksonville13813651328248526773110 610 40 549 14261515180126321957738391002 114 0 66 407 443 470Las Vegas348047161821082908 106 0 6 663 894 12483801318526596 15 0 0 98 297 355Los Angeles3230154121915551935 97 0 24 427 671 8653204158264366 11 0 0 54 105 172Miami80233788163718852043 359 2 289 985 12011353159095550683797 72 0 26 297 364 451New York19906495012511384 50 0 5 313 475 6021400103175230 4 0 0 35 64 81Philadelphia3620174135219672536 86 0 21 400 689 8652402125245341 7 0 0 38 76 138Phoenix81115605249328182922 229 0 88 954 1293137563012304436544 17 0 2 78 132 181Provo1434841363321535263729 443 1 230 164318712058135032543697817 43 0 2 208 303 339St. Louis4860368140216301843 144 0 51 523 693 7284609232289323 16 0 0 92 133 163Washington5620358184324092563 176 0 64 721 949 10736009316411478 23 0 1 133 217 247Other MSA19906585812621572 52 0 6 268 444 592150084156231 5 0 0 25 57 90Other NotMSA247045123417712130 95 0 7 549 928 12033901221438635 17 0 0 91 198 318Notes:1The mean number of exceedances representsthe number of exceedances occurring at all monitors in a particular location dividedby the number of siteyears across the monitoring period. The min, med, p95, p98, and p99 represent the minimum, median, 95th, 98th, and 99thpercentiles of the distribution forthe number of exceedances in any one year within the monitoring period.

### August 2008 - Draft 266 

## A-9.7 Results Tables of Recent NO 2 

## Ambient Monitoring Data 1 

## (2001-2006) As Is and Just Meeting the Current and Alternative 2 

## Standards 3

4

### August 2008 - Draft 267 

### 1 

# A-10 References 2 Bell S and Ashenden TW. (1997). Spatial and te mporal variation in ni trogen dioxide pollution 3 adjacent to rural roads. Water Air Soil Pollut . 95:87-98. 4 Beckerman B, Jerrett M, Brook JR, Verma DK, Arain MA, Finkelstein MM. (2008). 5 Correlation of nitrogen dioxide with other traffic pollutants near a major expressway. Atmos 6 Environ . 42:275-290. 7 Bignal KL, Ashmore MR, Headley AD, Stewart K, Weigert K. (2007). Ecological impacts of 8 air pollution from road tran sport on local vegetation. Applied Geochemistry . 22:1265-1271. 9 Cape JN, Tang YS, van Dijk N, Love L, Sutton MA, Palmer SCF. (2004). Concentrations of 10 ammonia and nitrogen dioxide at roadside verges, and their cont ribution to nitrogen 11 deposition. Environ Pollut . 132:469-478. 12 Chan AT and Chung MW. (2003). Indoor-outdoor air quality relationships in vehicle: effect of 13 driving environment and ventilation modes. Atmos Environ . 37: 3795-3808. 14 FHWA. (2005). Highway Statis tics 2005, Urbanized Areas - 2005, Miles and Daily Vehicle15 Miles of Travel (Table HM-71). Available at: 16 http://www.fhwa.dot.gov/policy/ohim/hs05/htm/hm71.htm. 17 Gilbert NL, Woodhouse S, Stieb DM, Brook JR. ( 2003). Ambient nitrogen dioxide and distance 18 from a major highway. Sci Total Environ . 312:43-46. 19 Heeb NV, Saxer CJ, Forss A-M, Brühlm ann S. (2008). Trends of NO-, NO 2 

### -, and NH 3 

### - 20 emissions from gasoline-fueled Euro-3- to Euro-4-passenger cars. Atmos Environ

. 21 42(10):2543-2554. 22 Maruo YY, Ogawa S, Ichino T, Murao N, Uchiyama M. (2003). Measurement of local 23 variations in atmospheric nitr ogen dioxide levels in Sapporo, Japan using a new method with 24 high spatial and high te mporal resolution. Atmos Environ . 37:1065-1074. 25 McCurdy TR. (1994). Analysis of high 1 hour NO2 values and associated annual averages 26 using 1988-1992 data. Report to th e Office of Air Quality Pla nning and Standards, Durham 27 NC. 28 Monn Ch, Carabias V, Junker M, Waeber R, Karrer M, Wanner HU. (1997). Small-scale spatial 29 variability of particulate matter <10 μ m (PM 10 

### ) and nitrogen dioxide. Atmos Environ

. 30 31(15)2243-2247. 31 Nitta H, Sato T, Nakai S, Maeda K, Aoki S, Ono M. (1993). Respiratory health associated with 32 exposure to automobile exhaust. I. Results of cross-section studies in 1979, 1982, 1983. 33 Arch Environ Health . 48(1):53-58. 34 Pleijel H, Karlsson GP, Gerdin EB. (2004). On the logarithmic relationship between NO 2 

### 35 concentration and the distance from a highroad. Sci Total Environ . 332:261-264. 36 Rizzo (2008). Investigation of how distributions of hourly nitroge n dioxide concentrations have 37 changed over time in six cities. Nitrogen Dioxide NAAQS Review Docket (EPA–HQ– 38 OAR–2006–0922 ). 39 Rodes C, Sheldon L, Whitaker D, Clayton A, Fitz gerald K, Flanagan J, DiGenova F, Hering S, 40 Frazier C. (1998). Measuring Concentrations of Selected Air Pollutants Inside California 41 Vehicles. California Environmental Protection Agency, Air Resources Board. Final Report, 42 December 1998. 43 Rodes CE and Holland DM. (1981). Variations of NO, NO 2 

### and O 3 

### concentrations downwind 44 of a Los Angeles freeway. Atmos Environ . 15:243-250. 45

### August 2008 - Draft 268 

### Roorda-Knape MC, Janssen NAH, De Hartog JJ, Va n Vliet PHN, Harssema H, Brunekreef B. 1 (1998). Air Pollution from traffic in c ity districts near major roadways. Atmos Environ

. 2 32(11)1921-1930. 3 Shorter JH, Herndon S, Zahniser MS, Nelson DD, Wormhoudt J, Demerjian KL, Kolb CE. 4 (2005). Real-Time measurements of nitrogen oxide emissions from in-use New York City 5 transit buses using a chase vehicle. Environ Sci Technol . 39:7991-8000. 6 Singer BC, Hodgson AT, Hotchi T, Kim JJ (2004). Passive measurement of nitrogen oxides to 7 assess traffic-related pollutant exposure for th e East Bay Children’s Respiratory Health 8 Study. Atmos Environ . 38:393-403. 9 US EPA. (2007a). US EPA Air Quality Syst em (AQS). Download Detailed AQS Data. 10 Available at: http://www.epa .gov/ttn/airs/airsaqs/detaild ata/downloadaqsdata.htm. 11 US EPA. (2007b). Field Guide to Air Quality Data ( v1.0.0). February 28, 2007. Available at: 12 http://www.epa.gov/ttn/airs/aqsda tamart/documentation/index.htm

. 

### 13 US EPA. (2007c). Nitrogen Dioxide Health Assessment Plan: Scope and Methods for Exposure 14 and Risk Assessment. September 2007. Offi ce of Air Quality Planning and Standards. 15 Available at: http://www.epa.gov/ttn/ naaqs/standards/nox/s_nox_cr_pd.html. 16 US EPA. (2007d)

. 

### ALLNEICAP Annual 113020 07 file posted at: 17 http://www.epa.gov/ttn/chief/net/ 2002inventory.html#inventorydata. 18 US EPA. (2007e). Air Tre nds. Nitrogen Dioxide. http://www.epa.gov/airtrends/nitrogen.html. 19 Westerdahl D, Fruin S, Sax T, Fine PM, Sioutas C. (2005). Mobile platform measurements of 20 ultrafine particles and associated pollutant co ncentrations on freeways and residential streets 21 in Los Angeles. Atmos Environ . 39:3597-3610. 22 US EPA. (2007f). Integrated Science Assessm ent for Oxides of Nitrogen – Health Criteria 23 (First External Review Draf t) and Annexes (August 2007). Research Triangle Park, NC: 24 National Center for Environmental Assessment. Available at: 25 http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=181712. 26 27 28 29

30

### i 

## Appendix B. Supplement to the NO 2 

## Exposure Assessment

### ii 

### Table of Contents

Appendix B.

............................................................. Supplement to the NO 2 

### 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 Microenviron mental 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 ................................................................................................... 2 2 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 NO 2 

### Air Quality Concentrations (as is) ...................... 58 B-3.6.3 Comparison of estimated on-road NO 2 

### 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 Mode ling 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 m odel-to-monitor comparison figures in REA. 98 B-4.1.6 Comparison of estimated on-road NO 2 

### concentrations .................................. 101 B-4.2 Supplemental APEX Modeli ng 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 Mete orological Data Preparation for AERMOD for NO 2 

### 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 ro adway concentrations (revised)) ........................................ 219 Attachment 7: Technical Memorandum on HAPEM Near Road Population Data Base Development (Estimating near road way 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 Dist ributions of Air Exchange Rate Averages Over Multiple Days ..................................................................................................................... 275

### iv 

### List of Tables Table B-1. Examples of prof ile variables in APEX....................................................................... 7 Table B-2. Summary of activity pa ttern studies used in CHAD.................................................. 12 Table B-3. Mass balance model parameters. ............................................................................... 14 Table B-4. Factors model parameters. ......................................................................................... 1 5 Table B-5. List of microenvironments and calculation methods used......................................... 16 Table B-6. Mapping of CHAD activity locations to AP EX microenvironments. ....................... 17 Table B-7. Example of APEX output files. ................................................................................. 20 Table B-8. Asthma prevalence rates by ag e 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 ra te of upper-air observations 2001-2003....... 25 Table B-12. Seasonal definitions and specifications for Philadelphia......................................... 27 Table B-13. Monthly precipita tion compared to NCDC 30- year climatic normal for Philadelphia, 2001-2003. ............................................................................................. 31 Table B-14. Hourly scaling factors (in per cents) applied to Phil adelphia County AADT volumes. ....................................................................................................................... 33 Table B-15. Seasonal scaling factors applie d to Philadelphia County AADT volumes.............. 34 Table B-16. Signals per mile, by link type, a pplied to Philadelphia County AADT volumes. ... 34 Table B-17. Statistical summar y of AADT volumes (one direc tion) for Philadelphia County AERMOD simulations................................................................................................. 34 Table B-18. Average calculated speed by link type. ................................................................... 36 Table B-19. On-road area source sizes. ........................................................................................ 3 7 Table B-20. Combined stacks parameters for st ationary 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 Inte rnational airport (PHL) NO x 

### emissions. ....................................... 46 Table B-24. Philadelphia County NOx monitors......................................................................... 47 Table B-25. Comparison of ambient monitoring and AERMOD predicted NO 2 

### concentrations in Philadelphia.................................................................................................................. 4 9 Table B-26. Air conditioning prevalence esti mates 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 st ove cooking by hour of the day. .............................................. 53 Table B-29. Adjustment factors a nd potential health effect benchm ark levels used by APEX to simulate just meeting the current standard................................................................... 56 Table B-30. Summary statis tics of on-road hourly NO 2 

### concentrations (ppb ) and the numbers of potential health effect benchmark leve ls 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 standa rd (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 standa rd (std), and with and without indoor sources. ............................................................................................................. 79 Table B-33. Estimated number of asthmatic children in Philadelphi a 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 standa rd (std), and with and without indoor sources. ............................................................................................................. 84 Table B-34. Estimated percent of asthmatic children in Philadelphi a 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 standa rd (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 de nsity 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 NO 2 

### 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 NO 2 

### concentration ratios using m ratio derived from data reported in published NO 2 

### measurement studies................................................ 103 Table B-41. Mean asthma prevalence rates, al ong with lower and upper 95% confidence limits, by age and gender used for Atlanta............................................................................ 105 Table B-42. In-vehicle and near-roa d PROX factors used in APEX......................................... 107 Table B-43. Estimated number of asthmatics in the Atlanta modeling do main exposed at or above potential health effect benchmark levels (1 to 6 times per year), using 2001 modeled air quality (as is), w ith 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 do main exposed at or above potential health effect benchmark levels (1 to 6 times per year), using 2001 modeled air quality (as is), w ith just meeting the current standard (cs), and potential alternative standards, without indoor sources. ........................................................... 110 Table B-45. Estimated number of asthmatic children in the Atla nta modeling domain exposed at or above potential health eff ect benchmark levels (1 to 6 times per year), using 2001 modeled air quality (as is), w ith just meeting the current standard (cs), and potential alternative standards, without indoor sources. ........................................................... 111 Table B-46. Estimated percent of asthmatic children in the Atla nta modeling domain exposed at or above potential health eff ect benchmark levels (1 to 6 times per year), using 2001 modeled air quality (as is), w ith just meeting the current standard (cs), and potential alternative standards, without indoor sources. ........................................................... 112 Table 47. Estimated number of asthmatics in th e 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 cu rrent standard (cs), a nd potential alternative standards, without indoor sources.............................................................................. 113

### vi 

### Table B-48. Estimated percent of asthmatics in the Atlanta modeli ng 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 cu rrent standard (cs), a nd potential alternative standards, without indoor sources.............................................................................. 114 Table B-49. Estimated number of asthmatic children in the Atla nta modeling domain exposed at or above potential health eff ect benchmark levels (1 to 6 times per year), using 2002 modeled air quality (as is), w ith just meeting the current standard (cs), and potential alternative standards, without indoor sources. ........................................................... 115 Table B-50. Estimated percent of asthmatic children in the Atla nta modeling domain exposed at or above potential health eff ect benchmark levels (1 to 6 times per year), using 2002 modeled air quality (as is), w ith 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 domai n 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 cu rrent standard (cs), a nd potential alternative standards, without indoor sources.............................................................................. 117 Table B-52. Estimated percent of asthmatics in the Atlanta modeling do main exposed at or above potential health effect benchmark levels (1 to 6 times per year), using 2003 modeled air quality (as is), w ith just meeting the current standard (cs), and potential alternative standards, without indoor sources. ........................................................... 118 Table B-53. Estimated number of asthmatic children in the Atla nta modeling domain exposed at or above potential health eff ect benchmark levels (1 to 6 times per year), using 2003 modeled air quality (as is), w ith just meeting the current standard (cs), and potential alternative standards, without indoor sources. ........................................................... 119 Table B-54. Estimated percent of asthmatic children in the Atla nta modeling domain exposed at or above potential health eff ect benchmark levels (1 to 6 times per year), using 2003 modeled air quality (as is), w ith 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 do main exposed at or above potential health effect benchmark levels (1 to 6 times per year), using 2002 modeled air quality (as is), w ith 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 do main exposed at or above potential health effect benchmark levels (1 to 6 times per year), using 2002 modeled air quality (as is), w ith just meeting the current standard (cs), and potential alternative standards, with indoor sources. ................................................................ 122 Table B-57. Estimated number of asthmatic children in the Atla nta modeling domain exposed at or above potential health eff ect benchmark levels (1 to 6 times per year), using 2002 modeled air quality (as is), w ith just meeting the current standard (cs), and potential alternative standards, with indoor sources. ................................................................ 123 Table B-58. Estimated percent of asthmatic children in the Atla nta modeling domain exposed at or above potential health eff ect benchmark levels (1 to 6 times per year), using 2002 modeled air quality (as is), w ith 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 fu nction file for A/C prevalence............................................. 10 Figure B-2. Land-use and sectors around the Ph iladelphia-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 z 0 

### values for the Philadelphia case- study analysis us ing 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 f unctional classes, 2001. ............................................................. 36 Figure B-5. Differences in facility-wide annua l NOx emission totals between NEI and CAMD data bases for Philadelphia County 2002..................................................................... 43 Figure B-6. Locations of the four ancillary ar ea sources. Also shown are centroid receptor locations. ..................................................................................................................... . 45 Figure B-7. Centroid locations within fixed distances to majo r point and mobile sources in Philadelphia county...................................................................................................... 47 Figure B-8. Frequency distributi on of distance between each Cens us 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 fo r 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 AE RMOD estimated annual average NO 2 

### concentrations at each of the 16,857 receptors in Philadelphia County for years 2001-2003.............................. 59 Figure B-13. Measured and m odeled diurnal pattern of NO 2 

### concentrations at three ambient monitor sites................................................................................................................. 6 0 Figure B-14. Comparison of on-road/non-road ratios developed from AERMOD concentration estimates and those derived from published NO 2 

### measurement studies...................... 62 Figure B-15. Estimated annual average total NO 2 

### exposure concentrations for all simulated persons in Philadelphia County, using m odeled 2001-2003 air quality (as is), with modeled indoor sources................................................................................................ 64 Figure B-16. Comparison of AERMOD predicte d and ambient monitoring annual average NO 2 

### 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 NO 2 

### exposure concentration for all simulated persons in Philadelphia County, using modeled 2001-2003 ai r quality (as is), with and without modeled indoor sources. Values abov e the 99th percentile are not shown................. 67 Figure B-18. Estimated number of all simulated asthmatics in Philadelphia County with at least one NO 2 

### exposure at or above the potential he alth effect benchmark levels, using modeled 2001-2003 air quality (as is), with modeled indoor sources. ........................ 67 Figure B-19. Estimated number of simulated asth matic children in Philadelphia County with at least one NO 2 

### exposure at or above the potential h ealth effect benchmark levels, using modeled 2001-2003 air quality (as is), with modeled indoor sources. ........................ 68 Figure B-20. Comparison of the estimated number of all simulated asthmatics in Philadelphia County with at least one NO 2 

### exposure at or above potenti al 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 person s in Philadelphia County spend in the twelve microenvironments associated with the potential NO 2 

### 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 person s in Philadelphia County spend in the twelve microenvironments associated with the potential NO 2 

### 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 asthma tics in Philadelphia C ounty with repeated NO 2 

### exposures above potential health effect benchmark levels, using 2003 modeled air quality (as is), with m odeled indoor sources................................................................ 74 Figure B-24. Estimated percent of all asthma tics in Philadelphia C ounty with repeated NO 2 

### 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 benc hmark level, using modeled 2001-2003 air quality just meeting the current standa rd, 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 benchmar k level, using modeled 2002 air quality just meeting the current standard, with a nd without modeled indoor sources.................... 76 Figure B-27. Estimated percent of asthmatics in Philadelphia County with repeated exposures above health effect benchmark levels, usi ng modeled 2002 air quality just meeting the current standard, with and wit hout modeled indoor sources........................................ 77 Figure B-28. Estimated percent of all asthmatic s in Philadelphia County with at least one NO 2 

### exposure at or above potential health effect benchmark level, using 2001-2003 modeled air quality (as is), w ith modeled indoor sources............................................ 80 Figure B-29. Estimated percent of all asthmatic s in Philadelphia County with at least one NO 2 

### 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 asthmatic s in Philadelphia County with at least one NO 2 

### exposure at or above potential health effect benchmark level, using 2001-2003 modeled air quality just meeting the curre nt standard (std), with modeled indoor

sources........................................................................................................................ .. 81 Figure B-31. Estimated percent of all asthmatic s in Philadelphia County with at least one NO 2 

### 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 asthma tics in Philadelphia C ounty with repeated NO 2 

### 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 asthma tics in Philadelphia Count y with repeated NO2 exposures at or above 200 ppb 1-hr, us ing 2001-2003 modeled air quality (as is), without indoor sources. ................................................................................................ 82

### ix 

### Figure B-34. Estimated percent of all asthma tics in Philadelphia C ounty with repeated NO 2 

### exposures at or above 200 ppb 1-hour, us ing 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 Philadelp hia County with repeated NO 2 

### exposures at or above 200 ppb 1-hour, us ing 2001-2003 modeled air quality just meeting the current standard (s td), with no indoor sources. ........................................ 83 Figure B-36. Estimated percent of asthmatic chil dren in Philadelphia County with at least one NO 2 

### exposure at or above potential health effect benchmark level, using 2001-2003 modeled air quality (as is), w ith modeled indoor sources............................................ 86 Figure B-37. Estimated percent of asthmatic childr en in Philadelphia County with at least one NO 2 

### 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 chil dren in Philadelphia County with at least one NO 2 

### exposure at or above potential health effect benchmark level, using 2001-2003 modeled air quality just meeting the curre nt standard (std), with modeled indoor

sources........................................................................................................................ .. 87 Figure B-39. Estimated percent of asthmatic chil dren in Philadelphia County with at least one NO 2 

### 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 chil dren in Philadelphia County with repeated NO 2 

### 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 chil dren in Philadelphia County with repeated NO 2 

### 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 chil dren in Philadelphia County with repeated NO 2 

### exposures at or above 200 ppb 1-hr, usi ng 2001-2003 modeled air qu ality meeting the current standard (std), with modeled indoor sources. .................................................. 89 Figure B-43. Estimated percent of asthmatic chil dren in Philadelphia County with repeated NO 2 

### exposures at or above 200 ppb 1-hr, usi ng 2001-2003 modeled air qu ality 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 f unctional classes, 2001. ............................................................. 93 Figure B-45. Polygon representing the Atlanta-Hart sfield International Ai rport area source..... 94 Figure B-46. Frequency distri bution 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 descriptio ns of the methods and data used in the NO 2 

### exposure assessment, as well as detailed results fr om the exposure analyses performed. First, a broad description of the exposure modeling appr oach is described, applicable to the two exposure modeling case-studies conducted to date: Philadelphia and Atlant a. This is followed with details regarding the require d 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 heal th effect benchmark levels experienced by the asthmatic population residing within each location.

The first simulation location included Philade lphia County and was summarized in the 1 st 

### draft Risk and Exposure Assessment (REA). The re sults from this assessment are presented here as they existed in that document and the draf t Technical Support Document draft (TSD) and no adjustments were made to modeling approach used to generate the expos ure 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 concentrati on 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 pot entially large emission source c ould have been responsible for the underestimations in modeled ambient concentrations when co mpared with available ambient monitoring data. It followed that the modeled air quality was then adjusted to account for the difference in concentration using the monitore d data. This was another difference in the approach used for Philadelphia th at was not used in Atlanta. The results for the Philadelphia analysis are still included here since they st ill estimate exposures for the population within the County, only with different uncertainties in the resu lts when compared with the Atlanta data due to the differing approach used. Most of the uncer tainties in the results described in the Atlanta REA can be similarly applied to the Philadelphi a 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 c ounties. 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 mode ling 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 NO 2 

### National Ambient Air Quality Standard (NAAQS) review to estimate human exposures. This is followed with a descrip tion 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 pollu tants at the local, urban, and consolidated metropolitan levels. APEX, al so known as TRIM.Expo, is the human inhalation exposure module of EPA’s Total Risk Integr ated 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 scie ntifically sound, flexible, and us er-friendly methodology. Additional information on the TRIM modeling system, as we ll 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 Ambien t Air Quality Standa rds (NAAQS) Exposure Model (NEM) series of models, developed to es timate exposure to the criteria pollutants (e.g., carbon monoxide (CO), ozone O 3 

### ). In 1979, EPA began by assembling a database of human activity patterns that could be used to estimate exposures to indoor and out door pollutants (Roddin et al., 1979). These data were then comb ined 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 elemen ts into the NEM methodology and use activity pattern data based on various human activity di ary studies to create an early version of probabilistic NEM for O 3 

### (i.e., pNEM/O 3 

### ). 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/O 3 

### in the early- to mid-1990’s allowed for probabilistic exposure assessments in nine urban areas for the general population, outdoor ch ildren, and outdoor worker s (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 Consolid ated Human Activities Database (CHAD) and enhanced algorithms for simulating ga s stove usage, estimati ng 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/C O (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 fr om national databases, a series of new output tables providing summary exposure a nd dose statistics, and a thoroughly reorganized method of describing microenvironments and their pa rameters. 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, 20 06a; 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 fiel d study that would involve selecting an actual sample of specific individuals who live in (o r work and live in) a geographic area and then continuously monitoring their act ivities and subsequent inhalati on 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 bala nced according to various demographic variables and census data using their rela tive frequencies, in order to obtain a representative sample (to the extent possibl e) of the actual people in the study area

• The activity patterns of the sampled indivi duals (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 simila r 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 es timated by the model using a set of userinput ambient outdoor concentrations (eithe r 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 alternativ e 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 f actors contributing to inhalation exposure – the temporal and spatial dist ribution of people and polluta nt concentrations throughout the study area and among microenvironments – while also allowing the flexibility to adjust some of these factors for altern ative scenarios and sensitivity analyses.

APEX is designed to simulate human population e xposure to criteria and air toxic pollutants at local, urban, and regional scales. The user sp ecifies the geographic area to be modeled and the number of individuals to be simulated to repr esent this population. APEX then generates a personal profile for each simulated person that sp ecifies various parameter values required by the model. The model next uses diary-derived time/ac tivity data matched to each personal profile to generate an exposure event se quence (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 microenvironmentis 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 dura tion, geographic location, microenvironment, and activity performed. Probabilistic algorithms are used to estimate the pollutant concentration associated with each exposure event. The es timated pollutant concen trations account for the effects of ambient (outdoor) pollu tant concentration, penetration factors, air exchange rates, decay/deposition rates, and proximity to emissi on sources, depending on the microenvironment, available data, and estimation method selected by the user. Because the modeled individuals represent a random sample of th e population of interest, the di stribution of modeled individual exposures can be extrapolated to the larger population. The model simu lation 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 populat ion – 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 da ta for the study area and human profile distribution data (such as age-spec ific 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 assi gning location codes in the activity pattern to the user-speci fied microenvironments. The model then calculates hourly concentrations of a pollutant in each of these microenvironments for the period of simulation, based on the user-pr ovided microenvironment descriptions and hourly air quality data. Microenvironmental co ncentrations are calculated for each of the simulated individuals. 5. Estimate exposures

.

APEX estimates a concentration for each e xposure 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, m onthly, and annual average exposure values.

B-2.2.1 Study Ar ea 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 mode l larger areas such as combined statistical areas (CSAs). In the exposure analyses perfor med 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 distribu tion of age, gender, race, and work status within the study population. Each block has a lo cation specified by latitu de 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 cens us tracts, block groups, and blocks in the 50 United States, based on th e 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 m easured data from an ambient monitor or that generated by air quality modeli ng. 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 meteorologi cal fields (e.g., temperature, wind speed, precipitation), and information on su rface 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 assigne d to geographic areas called districts. The districts are used to assign pollutant concentr ations to the blocks/tract s and microenvironments being modeled. The ambient air quality data are pr ovided by the user as hourly time series for each district. As with blocks/tracts, each dist rict 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 wa s synonymous with the re ceptor modeled in the dispersion modeling.

B-2.2.1.2 Meteorological Data 

### Ambient temperatures are input to APEX for different sites (loca tions). 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 W eather 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 fi le, contains the locations of meteorological data recordings expressed in latitude and longitude coordinates. This file also c ontains start and end dates for the data recording periods. The temp erature data file contains the data from the locations in the

### B-7 

### temperature zone location file. This file contains hourly temp erature readings for the period being modeled for the meteorological st ations 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 pe rson is represented by a personal profile, a summary of personal attributes th at define the individual. APEX generates the simulated person or profile by probabilisti cally 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 base d on sets of distribution data;

• Other daily varying variables, generated based on literature-derived di stribution data that change daily during the simulation period.

APEX first selects demographic and physical attributes for ea ch specified individual, and then follows the individual over time and calcu lates 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 Indicate s employment outside home
Air conditioner Indicates presen ce of air conditioning at home Physical
Gas Stove Indicates presen ce 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, populat ion 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 populati on data provided. As long as the data are available, any resolution can be used (e.g., c ounty, census tract, census block). For this application of the model, census block level data were used. Block-le vel population counts come from the 2000 Census of Population and Housing Summ ary File 1 (SF-1). This file contains the 100-percent data, which is the information compil ed 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 conduc ted at the census block leve l, 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 Cens us Transportation Planning Packag e (CTPP) (US DOT, 2007). The data used contain counts of indi viduals 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 di fferentiate people that work at home from those that commute within their home tract.

Commuting Distance Cutoff 

### A preliminary data analysis of the home-wor k 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 relationshi p also had a fairly constant sl ope 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 rapi dly with increasing dist ance. Beyond 120 km, the majority of the flow is made up of persons w ho 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 da ily. This meant that the list of destinations for each home tract was restricted to only those work tracts that are w ithin 120 km of the home tract. When the same cutoff was performed on the 1990 census data, it resu lted in 4.75% of the home-work pairs in the nationwide database being eliminated, repr esenting 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 eliminat ed from the database for various reasons. A fair number of tract-to-tract pair s represented workers who either worked outside of the U.S. (9,631 tract pairs with 107,595 workers) or work ed 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 trea tment of persons in the modeled population who commute to destinations outsi de 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 modele d. 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 concentra tion is assumed to be related to the average concentration over all air districts at the sa me point in time, and is calculated as:

LeaverAdd t avg LeaverMult ion Concentrat Ambient + × = ) ( 

### equation (1)

where:

Ambient Concentration = Calculated ambient air concen trations 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 concentra tion 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 locati ons outside of the study area are determined from this ambient concentra tion by the same function as ap plies inside the study area.

Block-level commuting 

### For census block simulations, APEX requires bl ock-level commuting file. A special software preprocesser was created to generate these fi les for APEX on the basis of the tract-level commuting data and finely-resolved land use da ta. The software calculates commuting flows between census blocks for the employed population according equation (2).

land pop tract block 

### F F Flow Flow × × = 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 p opulation residing in the home block. 

### F land 

### = fraction of work tract’s commercial/i ndustrial land area in the work block

### B-10 

### Thus, it is assumed that the frequency of comm uting 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 us ed to generate results for variables related to simulated individuals. While cer tain settings for individuals are generated automatically by APEX based on other input files, including demogr aphic characteristics, others can be specified using this file. For example, the file may cont ain settings for determin ing 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 prev alence of air conditioning in the cities modeled in this assessment (Figure B-1). APEX us es these fractions to stochastically generate air conditioning status for each individual. The de rivation of particular da ta 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 criti cal to understand thei r daily activities.

The Consolidated Human Activity Database (C HAD) provides data for where people spend time and the activities performe d. CHAD was designed to provi de 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 ad justed 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 contai ns 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 microenvir onments; this file is discussed in the section describing microenvironmental calcul ations (Section B-2.2.4.4).

B-2.2.3.1 Personal Information file

### B-11 

### Personal attribute data are cont ained in the CHAD questionnaire fi le 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 wh ich 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 th at is distributed with APEX. This file contains the activities for the n early 23,000 people with in tervals 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 fo r inhalation exposure modeling consist of a sequence of location/activity combinations span ning 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 exampl e, even for pollutant h ealth effects with short averaging times (e.g., NO 2 

### 1-hour average concentration) it may be desirable to know the frequency of exceedances of a concentration ove r a long period of time (e.g., the annual number of exceedances of a 1-hour average NO 2 

### 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 activ ity patterns across the simulate d 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 expos ed 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 constructin g 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 ac ross the population, and therefor e, underestimate the high-end exposure concentrations or th e 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 ac tivities are perfectly correlated. This approach tends to result in longterm activity patterns that are very different across the simulated populat ion, and therefore may over-estimate the variability across the population.

Cluster-Markov Algorithm 

### A new algorithm has been developed and incorpor ated into APEX to represent the day-today correlation of activities for individuals. The al gorithms first use cluster analysis to divide the daily activity pattern records into groups that ar e similar, and then select a single daily record from each group. This limited number of daily pa tterns is then used to construct a long-term sequence for a simulated individual, based on empiri cally-derived transition probabilities. This approach is intermediate between the assumption of no day-to-day correl ation (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, empl oyment status), temperature range, and day-of-week combination, the associated ti me-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 categor ies (indoors – residence; indoors – other building; outdoors – near road; outdoor s – 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 patte rn of the previous day and cluster-tocluster transition probabilitie s. 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 tran sition probabilities ar e estimated from the frequency of time-activity records in each cluster in the CHAD data base.

Details regarding the Cluster-Markov algorith m and supporting evaluatio ns are provided in Attachment 1.

### B-14 

### B-2.2.4 Calculating Microe nvironmental Concentrations
Probabilistic algorithms are used to estimate the pollutant concentration associated with each exposure event. The estimated pollutant con centrations account for the effects of ambient (outdoor) pollutant concentration, penetration factor, air exchange rate, decay/deposition rate, and proximity to microenvironments can use the tr ansfer 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 th e various microenvironments visited by the simulated person by using the ambient air data for the relevant blocks , the user-specified estimation method, and input parame ters specific to each microe nvironment. 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 enclos ed 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 microe nvironment due to deposition, filtration, and chemical degradation



### • Emissions from sources of a polluta nt inside the microenvironment.

Table B-3 lists the parameters required by the mass balance method to calculate concentrations in a microenvironment. A proximity factor ( 

### f

proximity 

### ) is used to account for differences in ambient concentrations between the geographic locati on represented by the ambient air quality data (e.g., a re gional 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) repr esents the emission rate for the emission source and concentration source (CS) is the mean air c oncentration resulting from the source. R removal 

### is defined as the removal rate of a pollutant from a microenvironment due to deposition, filtration, and chemical re action. The air exchange rate (R air 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 R removal ≥ 0

R

air exchange Air exchange rate 1/hr R air exchange ≥ 0

V Volume of microenvironment m 3 V > 0



### The mass balance equation for a pollutant in a microenvironment is described by:

### B-15 source removal out in 

### C C C C Δ + Δ

− Δ

− Δ = dt (t) dC ME 

### equation (3)

where: d 

### C ME 

### (t) = Change in concentration in a microenvironment at time 

### t (ppb), 

### Δ C in 

### = Rate of change in microenvironmental concentration due to influx of air (ppb/hour), 

### Δ C out 

### = Rate of change in microenvironm ental concentration due to outflux of air (ppb/hour), 

### Δ C removal 

### = Rate of change in microenvironmental concentration due to removal processes (ppb/hour), and 

### Δ C source 

### = 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, 

### Δ C in 

### , 

### Δ C out 

### , 

### Δ C removal 

### , and 

### Δ C source 

### , is assumed to be constant. At each hour time step of the simulation period, APEX estimates the hourly equilibrium, hourly ending, a nd hourly mean concentrations using a series of equations that account for concentration ch anges expected to occu r due to these physical processes. Details regarding th ese equations are provided in th e APEX User’s Guide. APEX reports hourly mean concentration as hourly conc entration for a specific hour. The calculation then continues to the next hour by using the end c oncentration for the previ ous hour as the initial microenvironmental concentration. A description of the input pa rameters estimates used for microenvironments using the mass bala nce approach is provided below.

B-2.2.4.2 Factors Model 

### The factors method is simpler than the ma ss balance method. It does not calculate concentration in a microenvironment from the concentration in the pr evious hour and it has fewer parameters. Table B-4 lists the paramete rs required by the factors method to calculate concentrations in a microenvironm ent 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-p rovided hourly ai r quality data:

n penetratio proximity ambient hourlymean
ME 

## f x f x C C = equation (4)

where:

hourlymean
ME 

### C = Hourly concentration in a microenvironment (ppb)


### C

ambient 

### = Hourly concentration in ambient environment (ppb) 

### f

proximity 

### = Proximity factor (unitless) 

### f

penetration 

### = Penetration factor (unitless)

### B-16 

### The ambient NO 2 

### concentrations are from the air quality data input file. The proximity factor is a unitless parameter that represents the prox imity of the microenvironment to a monitoring station. The penetration factor is a unitless parameter that repr esents the fraction of pollutant entering a microenvironment fr om outside the microenvironme nt via air exchange. The development of the specific proximity and penetra tion factors used in this analysis are discussed below for each microenvironment using this approach.

B-2.2.4.3 Micr oenvironments Modeled 

### In APEX, microenvironments repr esent the exposure locations for simulated individuals. For exposures to be estimated accurately, it is importa nt 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 microenviron ments 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 (the re are 113 CHAD codes versus 12 microenvironments in this assessment), therefore most of the microenvir onments have multiple CHAD locations mapped to them.

B-2.2.4.4 Mapping of APEX Micr oenvironments 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 Exposur e 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 co ncentrations in each microenvironment based on these ambient air concentrations (and indoor so urces 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 simu lation period is determined using the following equation:



## T t C

C N

j j hourlymean j ME

i 

# ∑

= 

## = 1 ) ( ) ( 

### equation (5)

where: 

### C i 

### = Hourly exposure concentration at clock hour 

### i of the simulation period (ppb) 

### N

= Number of events (i.e., micr oenvironments visited) in clock hour 

### i of the simulation period.

hourlymean j ME 

### C ) ( 

### = Hourly mean concentration in microenvironment 

### j (ppm)



### t (j) 

### = Time spent in microenvironment 

### j (minutes) 

### T = 60 minutes

From the hourly exposures, APEX calculate s time series of 1-hour average exposure concentrations that a simulated individual would experience during the simulation period. APEX then statistically summarizes and tabul ates the hourly (or daily, annual average) exposures. In this analysis, the exposure indicator is 1-hr exposure s above selected health effect benchmark levels. From this, APEX can calcu late two general types of exposure estimates: counts of the estimated number of people exposed to a specified NO 2 

### concentration level and the number of times per year that they are so e xposed; the latter metric is in terms of personoccurrences or person-days. The former highli ghts the number of individuals exposed at least



### one or more times per modeling period to the health e ffect benchmark level of interest. APEX can also report counts of individuals with mu ltiple exposures. This person-occurrences measure estimates the number of times per season that in dividuals are exposed to the exposure indicator of interest and then accumulates these estimate s 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 writte n 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, ventilati on, and energy expenditures, and even detailed event-level information, if desired. The names a nd locations, as well as the output table levels (e.g., output percentiles, cu t-points), for these out put 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, t he log file indicates the input files and parameter settings used for the simulati on and reports on a number of different factors. If the simulation ends prematurel y, the log file contains error messages describing the critical errors t hat 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 NO 2 

### conducted in support of th e current review of the NO 2 

### primary NAAQS. As mentioned in the Overview (section B-1), the Philadelphia analyses were not updated since the 1 st 

### draft REA and not used in the fina l REA. One major difference in the Philadelphia County assessment compared with th at performed for Atlanta was the lack of accounting for minor road emissions

Two important components of the analysis in clude the approach for estimating temporally and spatially variable NO 2 

### concentrations and simulating cont act of humans with these pollutant concentrations. A combined air quality and expos ure modeling approach has been used here to generate estimates of 1-hour NO 2 

### exposures within Philadelphia. Details on the approaches used are provided below and include the following:



### • Description of the area assesse d and populations considered



### • Summary of the air quality modeling me thodology and associated input data



### • Description of the inhalation exposur e model and associated input data



### • Evaluation of estimated NO 2 

### 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 ra nge of geographic areas, popul ation demographics, general climatology, and results of the ambi ent air quality characterization.

Philadelphia was selected as a location of intere st through a similar statis tical analysis of the ambient NO 2 

### air quality data described in Appendix A fo r each monitoring site within a location. Criteria were established for selecting sites with high annual means a nd/or high numbers of exceedances of potential health e ffect benchmark concentrations. The analysis considered all data combined, as well as the more recen t air quality data (2001-2006) separately.

The 90 th 

### percentile served as the point of reference for the annual means, and across all complete site-years for 2001-2006, this value wa s 23.5 ppb. Seventeen loca tions contained one or more site-years with an annual aver age concentration at or above the 90 th 

### percentile. When combined with the number of 1-hour NO 2 

### concentrations at or above 200 ppb, only two locations fit these criteria, Philadelphia a nd Los Angeles. In comparing th e 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 studi ed was limited those residents of Philadelphia County residing in census blocks that were eith er within 400 meters of a major roadway or

### B-22 

### within 10 km of a major emission source (see sec tion 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 fo r the respective study lo cations (i.e., 2002) and to include a 3 years of meteorologi cal 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 asse ssment 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 popul ation 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, indi viduals anticipated to be exposed more frequently to NO 2 

### 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 childr en), 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 f iles, 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 fo r each year. The parameters controlling the location and size of the simulated area were se t to include the county(s) in the selected study area. The settings that allow for replacemen t of CHAD data that are missing gender, employment or age values were all set to precl ude replacing missing data. The width of the age window was set to 20 percent to in crease the pool of diaries availabl e 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 sele ction. See the APEX User’s Gu ide for further explanation of these parameters. The total population simulate d for Philadelphia County was approximately 1.48 million persons, of which there a total si mulated 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 actu al number of specific subpopulat ions 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 AP EX requires the estimati on 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 O 3 

### (US EPA, 2007d; 2007e). Specifically, the analys is generated age and gender specific asthma prevalence rates for children ages 0-17 using da ta provided in the National Health Interview Survey (NHIS) for 2003 (CDC, 2007). These as thma rates were characterized by geographic regions, namely Midwest, Northeast, South, a nd West. Adult asthma prevalence rates for Philadelphia County were obtained from the Beha vioral 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%, respect ively. 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 95 th confidence interval U95 – Upper limit on 95 th confidence interval

### B-24 

## B-3.5 Air Quality Data Generated by AERMOD Air quality data input to the model were ge nerated 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 Meteor ological Inputs
All meteorological data used for the AERMOD dispersion model simulations were processed with the AERMET meteorological preprocessor, ve rsion 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 c onsists of typical hourly surface parameters (including air and dew point temperature, at mospheric 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 th is 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 th e station site compared to the city center.

The total number of surface observations and th e percentage of those observations accepted by AERMET (i.e., those observations that were bo th 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 disper sion 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) n=26,268 Surface Variable % 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 uppe r-air data were obtained from the NOAA Radiosonde Database. 2 

### Upper air observations show le ss spatial variation than do surface observations; thus they are both representative of larger areas and meas ured with less spatial frequency than are surface observations. The se lection 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 Philade lphia was Washington Dulle s Airport (KIAD). The total number of upper-air observation s 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 ra te of upper-air observations 2001-2003. Philadelphia (KIAD) Height 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 Philadelphia (KIAD) Height 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 Characteris tics and Land Use Analysis
In addition to the standard meteorological observations of wind, te mperature, and cloud cover, AERMET analyzes three principal variable s to help determine atmospheric stability and mixing heights: the Bowen ratio 3 

### , surface albedo 4 

### as a function of the so lar 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, a nd surface roughness as part of the AERMET processing. AERSURFACE uses the US Geologi cal Survey (USGS) Na tional 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, whic h are assumed to represent an area around the station of radius 10 km, and to calcul ate surface roughness by wind direction.

A monthly temporal resolution was used fo r the Bowen ratio, albedo, and surface roughness at the meteorological site. Because the site wa s located at an airport, a lower surface roughness was calculated for the ‘Commercial/Industrial/Tr ansportation’ 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 c over, 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 th e state climatologist (Table B-12).

Table B-12. Seasonal definitions a nd 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 fros t 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 create d 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 Bowe n Ratio is the ratio of heat energy us ed 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 th an 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 reflecte d 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 generati on of drag forces and incr eased vertical wind shear.

6 http://seamless.usgs.gov/

### B-28 

### release of AERSURFACE, the user was require d to manually pull values of Bowen ratio ( 

### β 0 

### ), albedo ( 

### α ), and surface roughness (z 0 

### ) per season and per land-use sector from look-up tables in the 

### AERMET User’s Guide . Using the look-up tables, va lues of these three surface characteristics vary by the four seasons and by ei ght 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 on ly varies surface roughness by wind direction.

Before AERSURFACE, without an automated al gorithm to determine land-use patterns, it was simplest for the user to visually estimat e land usage by sector. With AERSURFACE, the land-use is automatically determined. The proximity of the meteorological s ite 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 discre tion 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 locati on contains at least one m onth of continuous snow cover, and AERSURFACE will pull values of th e surface characteristics from the appropriate winter column.

We conducted a sensitivity test to evaluate th e impacts of using this new tool on the present analysis. Figure B-3 shows a sample comparis on of surface roughness values at the Philadelphia site with and without the us e 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 wa s 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, z 0 

### values are much lower with AERSURFA CE than with a visual estimation of land-use. In the AERSURFACE t ool, Philadelphia was noted as be ing at an airport, tending to represent the lower building heights in the region and the invers e distance weighting implemented in the tool. Thus, lower z 0 

### values were obtained over most developed-area sectors in this scenario. The indication th at at least one month of conti nuous snow cover is present also tends to lower wintertime z 0 

### values. In addition to these sy stematic differences, the automated AERSURFACE land-use analysis for Philadelphia tended to identify less urban coverage and more water coverage, lowering r oughness values, but it also tended to identify more forest cover and less cultivated land cover than ou r visual analysis, increasing some z 0 

### values.



### β 0 

### and 

### α also varied significantly between the scen arios. However, this was largely due to two practical matters: First, th e independence of these variab les of wind direction in the AERSURFACE case and secondly the use of monthl y-varying moisture c onditions 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 Philade lphia-area surface meteoro logical station (KPHL). Sector borders are 80, 184, 262, and 312 degrees from ge ographic North. Philadelphi a 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 z 0 values for the Philadelphia case-study analysis using visual and AERSURFACE land-use estimations.



### B-3.5.3 Meteorologi cal Data Analysis
The AERMET application location and elevation were taken as the center of the modeled city, estimated using Google Earth versi on 4.2.0198.2451 (beta) and defined as 39.952 °N, 75.164 °W, 12 m. The 2001-2003 AERSURFACE pr ocessing was run three times – once assuming the entire period was drier than nor mal, once assuming the entire period was wetter than normal, and once assuming the entire peri od was of average precipitation accumulation. These precipitation assumptions influe nce the Bowen ratio, discussed above.

To create meteorological i nput 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 am ount of precipitation in each month. Any month where the actual precipitation amount received was at least twice th e 1971-2000 NCDC 30-year climatic normal monthly precip itation 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 consider ed to have average surface moisture conditions. Table B-13 indicates the surface moisture c ondition for each month evaluated in this Philadelphia case-study.

### B-31 Table B-13. Monthly precipi tation compared to NCDC 30-year climatic normal fo r Philadelphia, 2001-2003. Year Jan Feb Mar Apr May Jun
74.8% 103.6% 144.2% 43.9% 102.9% 180.1%

Jul Aug Sep Oct Nov Dec 2001
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 norma l monthly precipitation amount Less than twice the normal precipit ation level and greater than half the normal amount At least twice the normal precipitation level



### B-3.5.4 On-Road Em issions Preparation
Information on traffic data in the Philadelphi a area was obtained from the Delaware Valley Regional Planning Council (DVRPC 7 

### ) via their most recent, baseline travel demand modeling (TDM) simulation – that is, the most recent simu lation calibrated to match observed traffic data. DVRPC provided the following files.



### • Shapefiles of TDM outputs for the 2002 baseli ne year for all links in their network.



### • Input files for the MOBILE6.2 emissions model that characterize loca l inputs that differ from national defaults, including fleet registration distribution information.



### • Postprocessing codes they employ for analys is of TDM outputs into emission inventory data, to ensure as much consistency as possible between the me thodology used for this study and that of DVRPC. These include DVR PC’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 facilit y, respectively, by count y in the Delaware Valley area.



### • A lookup table used to translate average a nnual 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 da ta and that employed in this an alysis, including several personal communications with agency staff on data interp retation, complete consistency was not possible due to the differing analysis objectives. The DVRPC creates countywide emission inventories. This study created spatially and temporally reso lved 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 incl ude annual average daily traffic (AADT) volumes and a description of the loaded highw ay 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 characteris tics 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 th e 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 roadwa ys and receptors. Hence, it was necessary to modify the link locations from the TDM to the be st known locations of the actual roadways. The correction of link locations was done based on th e locations of the nodes that define the end points of links with a GIS analysis, as follows.

A procedure was developed to relocate TD M nodes to more realistic locations. The nodes in the TDM represen t the endpoints of links in the tr ansportation planni ng 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 latit ude of 0.0, scale factor of 99.96, and in units of miles. The procedure moved the node loca tions to the true road locations and translated to dispersion model coordinates. The Penns ylvania Department of Transportation (PA DOT) road network database 9 

### 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, th e AADT volumes were converted to hourly values by applying DVRPC’s seasonal and hourly s caling factors. To determine hourly traffic on each link, the AADT volumes were convert ed to hourly values by applying DVRPC’s seasonal and hourly scaling factors. The hea vy-duty vehicle fraction – which is assumed by DVRPC to be about 6% in all locat ions 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 NO
x 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 m itigating 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 occurr ing on a given link, was taken from the TDM linkdescription information.

Several of these parameters are show n 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 Philadelphi a 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
Arterial 44986 44020 48401 44749 Maximum 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 de rived from the MOBILE6.2 emissions model as follows. The DVRPC-provided exte rnal data files describing the vehicle miles traveled (VMT) distribution by speed, functional class, and hour, as well as th e 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. Th ese files include county-sp ecific data describing the vehicle emissions inspection and maintenan ce (I/M) programs, on-board diagnostics (OBD) start dates, VMT mix, vehicle age distributions , default diesel fractions, and representative minimum and maximum temperatur es, humidity, and fuel parameters. The simulations are designed to calculate average running NO x 

### emission factors. 11



### These input files were modified for the curren t project to produce r unning NOx emissions in grams per mile for a specific functional class (F reeway, Arterial, or Ramp ) and speed. Iterative MOBILE6.2 simulations were c onducted 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 combin ed MOBILE vehicle classes (LDGV, LDGT12, LDGT34, HDGV, LDDV, LDDT, HDDV, and MC) 12 

### . The resulting tables were then consolidated into speed, functiona l class, and seasonal values fo r combined light- and heavy-duty vehicles. Figure B-4 shows an example of th e calculated emission factors for Autumn, 2001.

11 Basing the present emissions model input files on MPO-provided PM, rather than NO
x input files should not cause confusion. MPO-provided PM files were used because th ey 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 NO
x 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 7 0 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 lin k 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 num ber of signals and length of each link and signalization parameters. It was assumed that all signals are identic al, operating with a 120- second cycle and a protected left turn phase. Ea ch link’s speed is calculated independently. For example, a series of adjacent arterial links coul d 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 functi onal 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 calcul ated 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 t ogether. This was done only for the 628 links (314 pairs) where opposing links were paired in space and exhi bited similar activity levels within 20% of each other.

B-3.5.4.3 Other Emission Parameters 

### Each roadway link is characterized as a recta ngular area source with the width given by the number of lanes and an assumed universal la ne width of 12 ft (3.66 m). The length and orientation of each link is determined as the distance and angle between end nodes from the adjusted TDM locations. In cases where the distance is such that the aspect ratio is greater than 100:1, the links were disaggregated into sequent ial links, each with a ratio less than that threshold. There were 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 adjustme nt of node location. Th is 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 tempora lly allocated to hour of the day and season using MOBILE6.2 emission factors, coupled with calculated hourly speeds from the postprocessed TDM and allocated into SEASHR emission profiles for the AERMOD dispersion model. That is, 96 emissions f actors are attributed to each road way link to describe the emission strengths for 24 hours of each day of each of f our seasons and written to the AERMOD input control file.

The release height of each s ource was determined as the aver age 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 re lease 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 s ources are assumed to lie in a flat plane.

B-3.5.5 Stationary S ources Emissions Preparation
Data for the parameterization of major point sources in Philadelphia comes primarily from two sources: the 2002 National Emissions Invent ory (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 velo city), and annual emissions for 707 NO x 

### -emitting stacks (206 of which are considered f ugitive release points) in Ph iladelphia County. The CAMD database, on the other hand, has information on hourly NO x 

### 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 indivi dual stacks assigned w ithin the databases.

B-3.5.5.1 Data Source Alignment 

### To align the information between the two databa ses and extract the useful portion of each for dispersion modeling, the follo wing methodology was used.

1. Attention was limited stacks within the NEI da ta base that (a) lie within Philadelphia County and (b) were part of a facility with total emissions from all stacks exceeding 100 tpy NO x 

### . 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 facilitie s that matched the facilities identified from the NEI database. Facility matching was done on the facility name, Office of Regulatory Information Systems (ORIS) identi fication code (when provided) a nd facility total emissions to ensure a best match between the facilities. On ce facilities were pair ed, 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 majo r 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/recy cling facility, and the stack assumed to be a Cogen facility, based on information in the NEEDS database (http://w ww.epa.gov/interstateairqua lity/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 em ission 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 sing le 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 st acks that pair with single units. In this case the CAMD values are disaggregated accord ing 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 s caling 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 th e 4 stacks correspondi ng 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 factor s, but applied to the respective CAMD emission profile.

It is clear from Table B-21 that most facilities agree well in total annual NO x 

### emissions between the two databases. However, in th e 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 unknow n 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 su ggests that the discrepancies are not primarily the result of facilities w ith episodic emissions (i.e., “peak lo ad” facilities). Although there is good agreement on facility-wide emissions between the two data bases, there are larger discrepancies between CAMD unit emissions and NE I stack emissions. This is to be expected given the discrepancy in resoluti on between the two data bases.B-40Table B-20. Combined stacks parameters for stationary NOx emissionsources in Philadelphia County.StackNoNEISite IDFacility NameSICCodeNAICSCodeORISFacilityCodeStackEmissions(tpy)Stack X(deg)Stack Y(deg)StackHt(m)ExitTemp(K)StackDiam(m)ExitVelocity(m/s)FacilityEmissionwithFugitive(tpy)817NEIPA2218EXELON GENERATIONCO - DELAWARE STATION491122111231604.82-75.135839.96769495154.20297.8818NEIPA2218EXELON GENERATIONCO - DELAWARE STATION49112211123160287.8-75.135839.96769643863.717297.8819NEI40720JEFFERSON SMURFITCORPORATION (U S)2631322130.148-75.239140.03329164770.419228.4820NEI40720JEFFERSON SMURFITCORPORATION (U S)263132213113.8-75.239140.03329534272.410228.4821NEI40720JEFFERSON SMURFITCORPORATION (U S)263132213114.46-75.239140.03329534772.412228.4855NEI40723Sunoco Inc. - Philadelphia29113241126.2-75.202739.92535244502.193112.2856NEI40723Sunoco Inc. - Philadelphia2911324111.3-75.200339.91379246441.5223112.2857NEI40723Sunoco Inc. - Philadelphia2911324111.4-75.20339.92539255111.9103112.2858NEI40723Sunoco Inc. - Philadelphia29113241119.3-75.202739.92535255271.9113112.2859NEI40723Sunoco Inc. - Philadelphia2911324111032.8-75.212439.90239614895.8113112.2860NEI7330SUNOCO CHEMICALS(FORMER ALLIED SIGNAL)28693259980.033-75.071540.0064954760.57160.9861NEI7330SUNOCO CHEMICALS(FORMER ALLIED SIGNAL)286932599849.1-75.071540.00649414221.422160.9862NEI7330SUNOCO CHEMICALS(FORMER ALLIED SIGNAL)286932599834.6-75.071540.00649424221.617160.9863NEI7330SUNOCO CHEMICALS(FORMER ALLIED SIGNAL)286932599877.2-75.071540.00649424221.622160.9864NEIPA101353TRIGEN - SCHUYLKILL496122128.6-75.187339.94239694504.96190.1865NEIPA101353TRIGEN - SCHUYLKILL49612261.5-75.187339.94239784507.32190.1866NEIPA101356GRAYS FERRYCOGENERATIONPARTNERS49112254785143.2-75.187339.94239783965.520233.5867NEIPA101356GRAYS FERRYCOGENERATIONPARTNERS4911225478590.3-75.187339.94239854433.221233.5868NEIPA2222TRIGEN - EDISON496162130.5-75.156939.94604785893.79130.5B-41Table B-21. Matched stacks between the CAMD and NEI database.NEI FacilityNameNEIComb.StackNumberNEIComb.StackEmiss(tpy)NEIUnitEmiss(tpy)NEIFacilityEmiss(tpy,w/outFugitive)CAMDFacilityNameCAMDUnits *CAMDUnitEmiss(tpy) *CAMDComb.UnitTotals(tpy)CAMDFacilityTotals(tpy)Stackδ(%,relativetoCAMDvalue)Stackδ(tpy)Facilityδ(%relativetoCAMDvalue)Facilityδ(tpy)8174.84.83160-91.5421.542213%3.33160-71123.8ExelonGeneration Co- DelawareStation818287.8287.8292.6Delaware3160-81164287.8289.30%0.01%3.385526.28561.38571.485819.348.252106-150101162.1162.1-70%-113.952106-150137194.252106-150110162.152106-150138194.252106-150139194.2Sunoco Inc. -Philadelphia8591032.81032.81081.0PhiladelphiaRefinery52106-150140194.2938.91101.010%93.9-2%-20.38600.086149.186234.6SunocoChemicals(Former AlliedSignal)86377.2160.9160.9SunocoChemicalsFrankfordPlant880007-5284.584.584.590%76.490%76.4864128.6128.650607-23163.1163.1-21%-34.5Trigen -Schuylkill86561.561.5190.1TrigenEnergy -Schuykill50607-242.915.6178.7293%45.96%11.4B-42NEI FacilityNameNEIComb.StackNumberNEIComb.StackEmiss(tpy)NEIUnitEmiss(tpy)NEIFacilityEmiss(tpy,w/outFugitive)CAMDFacilityNameCAMDUnits *CAMDUnitEmiss(tpy) *CAMDComb.UnitTotals(tpy)CAMDFacilityTotals(tpy)Stackδ(%,relativetoCAMDvalue)Stackδ(tpy)Facilityδ(%relativetoCAMDvalue)Facilityδ(tpy)50607-2612.7866143.2143.254785-2143.2143.20%0.0Grays FerryCogenerationPartners86790.390.3233.5Grays FerryCogenPartnership54785-2590.390.3233.50%0.00%0.0880006-119.8880006-217.3880006-336.1Trigen -Edison868130.5130.5130.5TrigenEnergyCorporationEdison St880006-437.8111111.018%19.418%19.48190.154785-2143.2820113.8JeffersonSmurfitCorporation(U S) ***821114.5228.4228.454785-2590.3233.5233.5-2%-5.1-2%-5.1Notes:* 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 EmissionMass [NEI-CAMD] Difference(short tons)

Figure B-5. Differences in facility- wide annual NOx emission totals between NEI and CAM D data bases for Philadelphia County 2002.



### B-3.5.6 Fugitive and Air port Emissions Preparation
Fugitive emission releases in Philadelphia County, as totale d in the NEI database, were modeled as area sources with the profile of th ese releases determined by the overall facility profile of emissions. In addition, emissions a ssociated with the Phil adelphia 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 ha ve zero stack diameter, zero emission velocity, and exit temperature equal to average ambient conditio ns (295 K). Thus, we determined it was not appropriate to include these in the point source group simulation.

These 35 stacks occur at only tw o facilities in the County: Exelon Generation Co – Delaware Station (NEI Site ID: NEIPA2218) and Sunoco In c. – 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 acc ommodate 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 “s tacks” 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 fu gitive 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 us ed 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 Excel on, only two points are provided, which is insufficient information to form a clos ed 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 determin ed scaling factors based on the ratio of the 2002 fugitive re leases described by the NEI to the total, non-fugitive point source releases from the same facility. All stac ks 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 f ugitives as for other releases from the facility, since the origins of the emissions should be paralle l. 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 cen troid 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 opera tion of the Philadelphia Internati onal 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 NEI 16 

### database attributable to airports in Philadelphia County are from non-road mobile sources, spec ifically ground support equipment. There is another airport in the County: Northeast Philadelphia Airport. However, because it serves general aviation, is generally much smaller in op erations 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 PH L. 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) NO
x 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, th e airport emissions are best parameterized as area sources. The boundary of the area source wa s 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 m 2 

### , and included in the AERMOD input control file.

The activity profile for PHL was taken to ha ve seasonal and hourly variation (SEASHR), based on values from the EMS-HAP model. 17 

### These factors are disa ggregated in the EMS-HAP model database based on source classification co des (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, th e hourly values from the three individual day types were averaged together. The total emissi ons for each SCC were then disaggregated into seasonal and hourly components and the result ing components summed to create total PHL emissions for each hour of the four annual season s. 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 repres ent 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 assume d to be on a flat plane, they are placed at the standard breathing he ight 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 locat ions of census block centroids near major NO x 

### sources. GIS analysis was used to determine all block centroids in Philadelphia County that lie within a 0.25 mile (4 00 m) of the roadway se gments 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 assi gned 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 distance s to major point and mobile sources in Philadelphia county.

### B-48 

### The third set of receptors was chosen to repr esent the on-road microenvironment. For this set, one receptor was placed at th e center of each of the 982 sources.

The distance relationship between the road segm ents and block centroids can be estimated by looking at the distance between th e road-centered and the block cen troid 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 7000175350525700875105012251400157517501925210022752450262528002975



### 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 recep tor and its nearest road-centered receptor in Philadelphia County.



### The block centroids selected we re those within 10 km of a ny 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 cen ters instead of road edges, so that they overestimate the actual distances to the zone most influenced by roadwa y 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 AERM OD 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. NO x 

### chemistry was applied to all sources to determine NO 2 

### concentrations. For the each of the roadway, fu gitive, and airport emission sources, the ozone limiting method (OLM) was used, with plumes considered ungrouped. Because an initial NO 2 

### fraction of NO x 

### is anticipated to be about 10% or less (F inlayson-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 NO x 

### to NO 2 

### , with the following settings: 1. Hourly series of O 3 

### concentrations were taken from EPA’s AQS database 18 

### . The complete national hourly record of monitored O 3 

### concentrations were filtered for the four monitors within Phila delphia County (stations 421010004, 421010014, 421010024, and 421010136). The hourly records of these stations were then averaged together to provide an averag e Philadelphia County concentrations of O 3 

### for each hour of 2001-2003. 2. The equilibrium value for the NO 2 

### :NO x 

### ratio was taken as 75%, the national average ambient ratio. 19



### 3. The initial NO 2 

### fraction of NO x 

### 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 C oncentration Adjustment
The hourly concentrations estimated from each of the three source categories were combined at each receptor. Then a local concentration, reflecting the co ncentration 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 conc entration was estimated from the difference between the model predictions at the local NO 2 

### 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 monitori ng 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 NO 2 concentrations in Philadelphia.
Annual Average NO 2 concentration (ppb)

Year and Monitor ID Monitor AERMOD Inititial Difference 1 AERMOD Final 2

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/scram 001/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 th at 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 Microenvironm ent 1: Indoor-Residence 

### The Indoors-Residence microenvironment us es several variables that affect NO 2 

### exposure: whether or not air conditioning is present, the average outdoor temperature, the NO 2 

### removal rate, and an indoor concentration so urce. The first two of these va riables 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 ar ea the air conditioning stat us of the residential microenvironments is simulated randomly using the probability that a residence has an air conditioner. For this study, location-specifi c 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:



### ) ( Error Standard 96

. 1 ) ( Interval Confidence , 1 3850 ) ( Error Standard

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 95 th confidence interval U95 – Upper limit on 95 th confidence interval

### B-51 

### Air exchange rate data for the indoor reside ntial 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). Infl uential 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). Base d on the summary statisti cs and the statistical comparisons, different AER distributions were fi t for each combination of A/C type, city, and temperature. In general, l ognormal 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, quali tatively considering factors that might influence AERs. These factors include the age compositi on 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 fo r Philadelphia are provi ded in Table B-27.

Table B-27. Geometric means (GM) and standard deviat ions (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 NO 2 

### removal rate distribution was used for all microenvironments that use the mass balance method. This removal ra te is based on data provided by Spicer et al. (1993). A total of 6 experiments, under vari able source emission characteristics including operation of gas stove, were conducted in an unoccupi ed test house. A distribution could not be described with the limited data set, therefor e 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 descri bing the indoor residentia l 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 ar e actually four AER distribu tions for homes with an air

### B-52 

### conditioner and three for those without; the last fe w distributions for each air conditioning setting were the same due to the available data to popula te 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 AP EX for Indoors-residence microenvironment.

Indoor source contributions 

### A number of studies, as described in the NO x 

### ISA, have noted the importance of gas cooking appliances as sources of NO 2 

### emissions. An indoor emission source term was included in the APEX simulations to estimate exposure to indoor sources of NO 2 

### . Three types of data were used to implement this factor:

• The fraction of households in the Philadel phia MSA that use gas for cooking fuel

• The range of contributions to indoor NO 2 

### concentrations that occur from cooking with gas

• The diurnal pattern of cooking in households.

The fraction of households in Philadelphia Coun ty that use gas cooking fuel (i.e., 55%) was taken from the US Census Bureau’s American Housing Sur vey for the Philadelphia Metropolitan Area: 2003

.

### B-53 

### Data used for estimating the contribution to indoor NO 2 

### concentrations that occur during cooking with gas fuel were derived from a st udy sponsored by the California Air Resources Board (CARB, 2001). For this study a test hous e was set up for continuous measurements of NO 2 

### indoors and outdoors, among several other parameters, and conducted under several different cooking procedures and stove opera ting conditions. A unifo rm distribution of concentration contributions for input to APEX was estimated as follows.

• The concurrent outdoor NO 2 

### 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 indoo r 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 indoo r concentration of 4 ppb and a maximum net average indoor con centration 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 prep ared 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 diar ies 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 ove rall total preparation time. The result was a measure of the probabili ty of food preparation taking place during any hour, given one food prep aration 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 microenvironment s 

### 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 ex change 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 (Pers ily et al, 2005; see US EPA, 2007d for details in derivation). The de cay rate is the same as used in the IndoorResidence microenvironment discu ssed previously. The Bars and Restaurants microenvironment included an estimated contri bution from indoor sources as was described for the IndoorResidence, only there was an assumed 100% prevalence rate and th e 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 microenviron ments (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 no t applicable to outdoor environments (effectively, PEN=1). Proxim ity factors were developed from the AERMOD concentration predictions, i.e., the block-centroi d-to-nearest-roadway concentration ratios. Based on the resulting sets of ratio valu es, the ratio distributions were st ratified by hour of the day into 3 groups as indicated by the “hours- block” specification in the exam ple file in Figure B-11. The lower and upper bounds for sampling were specif ied 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 ro ad microenvironment.

B-3.5.11.3 Microenvironm ent 10: Outdoors-General. 

### The general outdoor environment concentra tions are well represented by the modeled concentrations. Therefore, both the penetration factor a nd 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 NO 2 

### concentrations were measured with for three ventila tion conditions, airrecirculation, fresh air intake, and with windows opened. Since major roads were the focus of this assessment, reported indoor/o utdoor ratios for highway and ur ban streets were used here. Mean values range from about 0.6 to just over 1. 0, with higher values as sociated with increased ventilation (i.e., window open). A uniform distribution was select ed 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 th e lack of data available to reasonably assign potentially influential characteristics such as use of vehi cle 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 develope d as described above fo r Microenvironments 8 and 9.

### B-56 

### B-3.5.12 Adjustment for Just Meeting the Current Standard


### To simulate just meeting the current standa rd, dispersion modeled c oncentration 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 th e Air Quality Characterization, but to reduce computer processing time, the health effect be nchmark levels were proportionally reduced by the similar factors described for each specific locati on and simulated year. Since it is a proportional adjustment, the end effect of adjusting concen trations 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 we re used to generate the adjustment factors. Table B-29 provides the adjustment factors used and the adjust ed potential health effect benchmark concentrations to simulate just mee ting the current standard. When modeling indoor sources, the indoor concentrati on contributions needed to be scaled downward by the same proportions.

Table B-29. Adjustment factors and potential health eff ect 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 addi tional scaling was performed so as not to affect their estimated concentrations while adjust ing the benchmark levels downward. To clarify how this was done, exposure concentrations an individual experiences ar e first defined as the sum of the contribution from ambient concentra tions and from indoor sources (if present) and this concentration can be either above or belo w a selected concentration level of interest:

threshold indoor ambient esposure 

### C C B C A C > × + × = equation (6)

where,

C

exposure 

### = individual exposure concentration A = proportion of exposure concentration from ambient C

ambient 

### = ambient concentration in the absence of indoor sources

### B-57 

### B = proportion of exposure concentration from indoor C

indoor 

### = indoor source concentration contribution C

threshold 

### = an exposure concentration of interest

It follows that if we are interested in ad justing the ambient concentrations upwards by some proportional factor F , this can be described with the following:

threshold indoor ambient 

### C C B C A F > × + × × equation (7)

This is equivalent to

) / ( ) / ( F C F C B C A threshold indoor ambient 

### > × + × equation (8)

Therefore, if the potential heal th effect benchmark level and the indoor concentrations are both proportionally scaled downward by the same adjustment factor, th e contribution of both sources of exposure (i.e., ambient and indoor) are maintained and the same number of estimated exceedances would be obtained as if the ambien t concentration were proportionally adjusted upwards by factor F

.

### B-58 

## B-3.6 Philadelphia Ex posure Modeling Results

B-3.6.1 Overview


### The results of the exposure and risk characte rization are presented here for Philadelphia County. These results are not to be directly compared with the re sults 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 Philadelp hia County assessment.

Several scenarios were consider ed for the exposure assessment, including two averaging time for NO 2 

### concentrations (annual and 1-hour), inclusion of indoor sources, and for evaluating just meeting the current standard. To date, year 2002 se rved as the base year for all scenarios, years 2001 and 2003 were only evaluated for a limited nu mber of scenarios. Exposures were simulated for four groups; children and all pers ons, and the asthmatic population within each of these.

The exposure results summarized below focus on the population group where exposure estimations are of greatest intere st, 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 th e current version of APEX, some exposure data could only be output for the enti re population modeled (i.e., all persons - includes asthmatics and healthy persons of all ages). The summary da ta for the entire population (e.g., annual average exposure concentrations, time spent in microenvironm ents 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 Evalua tion of Modeled NO 2 

### Air Quality Concentrations (as is)



### Since the current NO 2 

### standard is 0.053 ppm annual average, the predicted air quality concentrations were first summarized by calcu lating annual average concentration. The distribution for the AERMOD predicted NO 2 

### 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 thre e year period (2001-2003). The NO 2 

### 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, NO 2 

### 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 lo wer 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 containe d 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 QuantileAERMOD Predicted Annual Average NO2Concentration (ppb))

2003

2002

2001

Figure B-12 . Distribution of AE RMOD estimated annual average NO 2 concentrations at each of the 16,857 receptors in Philadelphia Co unty for years 2001-2003.



### Diurnal variability in NO 2 

### 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 NO 2 

### concentration at each hour of the day for the three monitors located in Philadelphia County. The diurna l distributions among the modeled versus measured concentrations are simila r at all of the monitors, with peak NO 2 

### concentrations generally coinciding with the typical peak co mmute 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 NO 2 

### concentrations at the othe r 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 NO 2 

### concentrations occurs during the la ter commute period, most notable at monitor 4210100292. Given the con centration adjustment to corre ct for sources not modeled was applied to all receptors equally across the en tire 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 whic h AERMOD does reasonably, and based on these three receptors, may slightly overe stimate peak concentrations more times than underestimate them.

### B-60 Figure B-13. Measured and mo deled diurnal pattern of NO 2 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 DayAnnual Average NO2Concentration (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 DayAnnual Average NO2Concentration (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 DayAnnual Average NO2Concentration (ppb) Monitor - 4210100043

AERMOD
AERMOD + Correction

### B-61 

### B-3.6.3 Comparison of estimated on-road NO 2 

### concentrations


### The two independent approaches used to estimate on-road NO 2 

### concentrations, one using ambient monitor data combined with an on-road 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 NO 2 

### 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 quali ty analysis and similar fact ors calculated using AERMOD estimated concentrations. As described in sec tion A-8, an empirical distribution of on-road adjustment factors was derived from on-road and near-road NO 2 

### concentration measurements published in the extant literatur e. The derived empirical dist ribution was separated into two components, one for application to summertime am bient concentrations, a nd 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 am bient monitor concentration (> 100 m from a major road) and used to estimate the on-road co ncentration in the air quality characterization (chapter 7 of the REA). The one-hour NO 2 

### concentrations estimated at every AERMOD receptor in Philadelphia were comp ared with the concentrations es timated at their closest on-road receptor to generate a similar ra tio (i.e., on-road/non-road concentr ations). These ratios were also stratified into two seasonal categories, one containing the summer ratios (June, July, and August) and the other for all othe r times of the year. The AERM OD on-road factor distributions in semi-empirical form are also presented in Fi gure B-14. There are similarities in comparing each of the AERMOD with the m easurement study derived distributi ons, most importantly at the upper percentiles. Intersection of the tw o approaches occurs at about the 70 th 

### percentile and continues through the 90 th 

### percentile. While the two seas onal 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 su mmer ratio distribution c onsistently contained greater values at all pe rcentiles compared with the non-summer distribution.

There are differences that exist when compar ing the two approaches at the mid to lower percentiles, with the AERMOD ratios consistently lower than the empirica lly derived factors. This is likely due to the differences in the popul ation of samples used to generate each type of distribution. The measurement study derived dist ribution 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 NO 2 

### emission sources. Thus, the measurement stu dy 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 reas onable assumption for estimating the on-road concentrations for the air quality characteriza tion. The AERMOD receptors however, include all types of emission sources such that there are possibilities for concen trations at non-road receptors that are greater than on-road, a more realistic depi ction 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 measur ement 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 RatioCumulative 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 NO 2 measurement studies.



### Briefly for the second comp arison, hourly on-road NO 2 

### 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 da ta (Table B-25). The second set of estimated on-road NO 2 

### concentrations was generated as part of the Air Quality Characterization by applying randomly selected on-road adjustment f actors 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 be nchmark levels. The AERMOD predicted and ambient monitor simulated concentration distributi ons 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 dist ributions 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 h ealth 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 a nd 59 for the simulated on-road monitor data.

The apparent contradiction between the similari ty of the hourly concentration distributions and the large differences in the exceedance dist ributions can be explai ned by the fact that 200 ppb is the 99.605 th 

### percentile of the AERMOD hourly concentrations and is the 99.974 th 

### percentile of the simulated on-ro ad monitor concentrations. T hus 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 differenc es could be due to the greater number of receptors modeled by AERMOD (n=979) compared w ith the on-road monitor simulation (n=5). Again, the AERMOD generated data could incl ude locations greatly influenced by roadway emissions that are not captur ed by the simplified approach conducted in the Air Quality Characterization.

Table B-30. Summary statis tics of on-road hourly NO 2 concentrations (ppb) and the numbers of potential health effect benchmark levels using AERMOD and th e on-road ambient monitor simulation approaches in Philadelphia.

1-hour NO 2 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 Expo sure Concentrations (as is)


### The hourly NO 2 

### concentrations output from AERMOD were input in to the exposure model, providing a range of estimated exposures output by APEX. Fi gure 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 an alyzed and where indoor sources were modeled. While years 2001 and 2002 contained very si milar population exposure concentration distributions, the modeled year 2003 contained about 20% lower a nnual average concentrations. The lower exposure concentrations for year 2003 are similar to what was observed for the predicted air quality (Figure B-12), however, al l persons were estimated to contain exposures below an annual average concentration of 53 ppb , even considering indoo r source concentration contributions. Again, while Figure B-15 su mmarizes the entire popul ation, the data are representative of what would be observed for th e 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 NO 2 Exposure (ppb)Population Percentile 2001 with indoor sources

2002 with indoor sources

2003 with indoor sources

Figure B-15. Estimated annual average total NO 2 exposure concentrations for all simulated persons in Philadelphia County, using modeled 2001-2003 air qu ality (as is), with mo deled indoor sources.



### The AERMOD predicted air quality and the estimated exposures for year 2002 were compared using their respective annual average NO 2 

### concentrations (Figure B-16). As a point of reference, the annual average c oncentration for 2002 ambient monitors ranged from 24 ppb to 29 ppb. Many of the AERMOD predicted annual averag e 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 annua l average concentration. Estimated exposure concentrations were below that of both the m odeled and measured air quality. For example, exposure concentrations were about 5 ppb less th an 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 co mparing the estimated exposures w ith and without indoor sources, indoor sources were estimated to contribute between 1 and 5 ppb to th e 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 NO 2 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 NO 2 concentrations (as is) and APEX exposure concentrat ions (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 exposur es, 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 simulate d individual in the simulated population. This can provide insight into the propor tion of the population experiencing any NO 2 

### 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 (therefo re, the maximum number of exceedances is 365 for a single person). Estimates of repeated exposur es are also recorded, that is where 1-hour exposure concentrations were above a selected le vel in a day added together across multiple days (therefore, the maximum number of multiple exceedance s is also 365). Persons of interest in this exposure analysis are those with particular susceptibility to NO 2 

### exposure, namely individuals with asthma. The health effect benchmark levels are appropriate for estimating the potential risk of adverse health effects for asthmatics. The majo rity 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 expo sed to these levels and to provide additional

### B-66 

### information relevant to the asthmatic populat ion (such as time spent in particular microenvironments).

B-3.6.5.1 Maximum Estimated Exposure Concentrations 

### A greater variability was obser ved in maximum exposure concentrations for the 2003 year simulation compared with years 2001 and 2002 (F igure B-17). While annual average exposure concentrations for the total popul ation were the lowest of th e 3-year simulation, year 2003 contained a greater number of individual maximu m 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 Estimat ed Exposures above Selected Levels 

### When considering the total asthmatic populati on simulated in Philade lphia 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 NO 2 

### of ambient origin and that contri buted by indoor sources. The number of asthmatics exposed to greater c oncentrations (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. Exposur es simulated for year 2003 contained the greatest number of asthmatics exposed in a year consiste ntly for all potential health effect benchmark levels, while year 2002 contained the lowest numbe r 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 exposur es at or above the pot ential 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 YearPopulation Percentile 2001 with indoor sources

2002 with indoor sources

2003 with indoor sources

2002 no indoor sources

Figure B-17. Estimated maximum NO 2 exposure concentration for all simulated persons in Philadelphia County, using modeled 2001-2003 air qua lity (as is), with and without mode led 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+4Estimated Number of AsthmaticsExposed to Selected Level in a Year

Potential Health Effect Benchmark Level (ppb) Simulated Year - Scenario

Figure B-18. Estimated number of all simulated asthma tics in Philadelphia County with at least one NO 2 exposure at or above the potential health effect benc hmark 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+4Estimated Number of Asthmatic ChildrenExposed to Selected 1-hour Level in a Year

Potential Health Effect Benchmark Level (ppb) Simulated Year - Scenario

Figure B-19. Estimated number of si mulated asthmatic children in Philadelphia County with at least one NO 2 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

1240 9505

2275

974 0.0E+0 1.0E+4 2.0E+4 3.0E+4 4.0E+4 5.0E+4Estimated Number of AsthmaticsExposed to Selected Level in Year2002

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 NO 2 exposure at or above potential health effect benchmark levels, using modeled 2002 air quality (as is) , with and withou t modeled indoor sources.

### B-69 

### For example, nearly 12,000 were estimated to be exposed to at least a one-hour NO 2 

### concentration of 200 ppb in a year (Figure B-19) . Additional exposure es timates 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 conserve d, as well as using the same individual timelocation-activity profiles generated for each pers on. Figure B-20 compares the estimated number of asthmatics experiencing exposures above the pot ential health effect benchmarks, both with indoor sources and without indoor sources incl uded in the model runs. The number of asthmatics at or above the sele cted 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 conc entrations above the pot ential health effect benchmark levels. Currently, the output ge nerated by APEX is limited to compiling the microenvironmental time for th e total population (inc ludes both asthmatic individuals and healthy persons) and is summarized to the total time spent above the sele cted potential health effect benchmark levels. As mentioned above, the data still provide a reasonable approximation for each of the population subgroups (e.g., asth matics or asthmatic children) since their microenvironmental concentrations and activities are not estimated any di fferently from those of the total population by APEX.

As an example, Figure B-21 (a, b, c) summari zes 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 th e contribution from one major cat egory of indoor sources (i.e., gas cooking). The time spent in the indoor re sidence 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 likel y a result of the indoor source concentration contribution to each individual’s exposure concen trations. The importance of the particular microenvironment however changes with differing potential health effect benchmark levels. This is evident when considering the in-veh icle and outdoor near-road microenvironments, progressing from about 19% of the time exposures were at the lowest po tential 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 con centrations 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 contri bution 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 e xhibited the greatest ch ange in contribution with increased health benchmark level, incr easing 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, th ere is attenuation of the on-road concentration that penetrates the in-vehicle microenvironmen t, 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 pers ons in Philadelphia County spend in the twelve microenvironments associat ed with the potential NO 2 health effect benchmark levels, a) ≥ 200 ppb, b) ≥ 250 ppb, and c) ≥ 300 ppb, year 2002 simula tion with indoor sources.

### B-72 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

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

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 Trans Other 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

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 pers ons in Philadelphia County spend in the twelve microenvironments associat ed with the potential NO 2 health effect benchmark levels, a) ≥ 200 ppb, b) ≥ 250 ppb, and c) ≥ 300 ppb, year 2002 simulati on 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 expos ed, the results show the number or percent of those with at least one exposure at or above th e selected potential heal th effect benchmark level. Given that the benchmark is for a small averaging time (i.e., one-hour) it may be po ssible that individuals are exposed to concentrations at or above the potential health eff ect benchmark levels more than once in a given year. Since APEX simulates th e 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 y ear containing the great est number of exposure concentrations at or above the selected benc hmarks. Estimated exposures include both those resulting from exposures to NO 2 

### of ambient origin and those resulting from indoor source NO 2 

### contributions. While a large fraction of indivi duals experience at least one exposure to 200 ppb or greater over a 1-hour time period in a year (about 32 percent), onl y around 14 percent were estimated to contain at least 2 exposures. Mu ltiple exposures at or above the selected benchmarks greater than or equa l to 3 or more times per year are even less frequent, with around 5 percent or less of asthmatics e xposed to 1-hour concentrations greater than or equal to 200 ppb 3 or more times in a year.

Exposure estimates for year 2002 are presente d to provide an additional perspective, including a lower bound of repeated exposures for this population subgroup and for exposure estimates generated with and w ithout modeled indoor sources (Fi gure 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 multip le 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 provi ded in Figure B-24 are the per cent of asthmatics exposed to selected levels in the absence of indoor sources. Again, without the i ndoor source contribution, there are reduced occurrences of multiple expos ures at all of the pot ential health effect benchmark levels compared with wh en 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 35Estimated Percent of Asthmatics Exposedone 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 asthmati cs in Philadelphia County with repeated NO 2 exposures above potential health effect benchmark levels, using 2003 mo deled 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 30Estimated Percent of Asthmatics Exposedone 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 asthmati cs in Philadelphia County with repeated NO 2 exposures above potential health effect benchmark levels, using modele d 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 Associat ed with Just Meeting the Current Standard


### To simulate just meeting the current NO 2 

### standard, the potenti al health effect benchmark level was adjusted in the exposure m odel, rather than adjusting all of the hourly concentrations for each receptor and year simulated. Similar es timates of short-term exposures (i.e., 1-hour) were generated for the total popula tion and population subgroups of interest (i.e., asthmatics and asthmatic children).

B-3.6.6.1 Number of Estimat ed Exposures above Selected Levels 

### In considering exposures estimated to occur as sociated with air quali ty simulated to just meet the current annual average NO 2 

### 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 affect ed considering each scenario is expressed as the percent of the subpopulation of interest. Figure B-25 illust rates the percent of asthmatics estimated to experience at leas t one exposure at or above the se lected potential health effect benchmark concentrations, with just meeting the current standa rd 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 on ce in a year for as is air qual ity, it was estimated that around 80 percent of asthmatics would experience at l east one concentration above the lowest potential health effect benchmark level in a year represen ting just meeting the current standard. Again, estimates for asthmatic children exhibited a simila r trend, with between 75 to 80 percent exposed to a concentration at or above th e lowest potential hea lth 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 re duced to between 31 and 45 percent for the 250 ppb, 1-hour benchmark, and between 10 and 24 pe rcent for the 300 ppb, 1-hour benchmark level associated with air quality representing just meeting the current a nnual 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 90Estimated Number of Asthmatics Exposed toSelected Level in Years 2001-2003with 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 mo deled 2001-2003 air quality just meeting the current standard, with modeled indoor sources.



### In evaluating the influence of indoor source contribution for th e scenario just meeting the current standard, the numbers of individuals e xposed at selected levels are reduced without indoor sources, ranging from about 26 percent lower for the 200 ppb leve l to around 11 percent for the 300 ppb level when compared with exposure estimates that account ed 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+5Estimated Number of AsthmaticsExposed to Selected Level inYear 2002 with Just Meeting theCurrent Standard

Potential Health Effect Benchmark Level (ppb) Simulated Year - Scenario

Figure B-26. Estimated number of all asthmatics in Phila delphia with at least one exposure at or above the potential health effect benchmark level, using modele d 2002 air quality just mee ting 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 curren t standard, repeated exposures at the selected potential health effect benchmarks are more freque nt 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 expose d at or above the select ed levels are exposed more than one time. Repeated exposures above the potential health effect benchmark levels are reduced however, when not including the cont ribution from indoor sources. The percent of asthmatics exposed drops with increasing benchm ark 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 90Estimated Percent of Asthmatics Exposedone or more Times to Selected 1-hour Level in Year 2002with 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 Ph iladelphia County with repeated exposures above health effect benchmark levels, using modeled 2002 air qualit y just meeting the current st andard, with and without modeled indoor sources.



### B-3.6.7 Additiona l Exposure Results


### This section provides supplemental exposure and risk characteriza tion results for two subpopulations, all asthmatics and asthmatic childr en. 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 cu rrent 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 modele d air quality (i.e., 2001 to 2003). Repeated exposures are presented only for the lowest potential h ealth 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 Phila delphia County exposed at or above potential health effect benchmark levels (1 to 6 times per year), usin g 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 Phila delphia County exposed at or above potential health effect benchmark levels (1 to 6 times per year), usin g 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 35Estimated Percent of Asthmatics Exposed toSelected 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 NO 2 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 35Estimated Percent of Asthmatics Exposed toSelected 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 NO 2 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 100Estimated Percent of Asthmatics Exposed toSelected Level in Years 2001-2003with 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 NO 2 exposure at or above potential health effect benchmark level, using 2001-2003 modeled air qualit y 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 100Estimated Percent of Asthmatics Exposed toSelected Level in Years 2001-2003with 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 NO 2 exposure at or above potential health effect benchmark level, using 2001-2003 modeled air qualit y 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 35Estimated 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 asthmati cs in Philadelphia County with repeated NO 2 exposures at or above 200 ppb 1-hr, using 2001-2003 modeled air qu ality (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 35Estimated 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 Ph iladelphia County with repeated NO2 exposures at or above 200 ppb 1-hr, using 2001-2003 modeled ai r 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 100Estimated 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 asthmati cs in Philadelphia County with repeated NO 2 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 100Estimated 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 asthmati cs in Philadelphia County with repeated NO 2 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 wi th 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 wi th 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 30Estimated Percent of AsthmaticChildren Exposed toSelected Level in Years 2001-2003 (as is)

Potential Health Effect Benchmark Level (ppb) Simulated Year - Scenario

Figure B-36. Estimated percent of asthmatic child ren in Philadelphia County with at least one NO 2 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 30Estimated Percent of Asthmatic Children Exposed toSelected Level in Years 2001-2003 (as is)

Potential Health Effect Benchmark Level (ppb) Simulated Year - Scenario

Figure B-37. Estimated percent of asthmatic child ren in Philadelphia County with at least one NO 2 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 100Estimated Percent of Asthmatics Children ExposedtoSelected Level in Years 2001-2003with Just Meeting the Current Standard

Potential Health Effect Benchmark Level (ppb) Simulated Year - Scenario

Figure B-38. Estimated percent of asthmatic child ren in Philadelphia County with at least one NO 2 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 100Estimated Percent of Asthmatic Children Exposed toSelected Level in Years 2001-2003with Just Meeting the Current Standard

Potential Health Effect Benchmark Level (ppb) Simulated Year - Scenario

Figure B-39. Estimated percent of asthmatic child ren in Philadelphia County with at least one NO 2 exposure at or above potential health effect benchmark level, using 2001-2003 modeled air quality just meeting the current standard (std), wi th no ind oor 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 30Estimated 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 NO 2 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 30Estimated 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 NO 2 exposures at or above 200 ppb 1-hr, using 2001-2003 modeled ai r 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 100Estimated 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 NO 2 exposures at or above 200 ppb 1-hr, using 2001-2003 modeled air qua lity 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 100Estimated 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 NO 2 exposures at or above 200 ppb 1-hr, using 2001-2003 modeled air qu ality 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 disc ussion on methodology and additional detailed input data used in the Atlanta inhalation exposure assessment for NO 2 

### conducted in support of the current review of the NO 2 

### primary NAAQS. The general exposure modeling approach has been broadly defined in Appendix section B-2.

In defining the years modeled, 3-years (2001-20 03) were selected to allow for reasonable representation of variability in meteorology. Table B-35 summarizes the temperature and precipitation in Atlanta over th e last 30 years, with years 200 1-2003 showing a range of values across each variable considered.

Table B-35. 30 year annual average temperature and precipitati on 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 expos ure model were generated by air quality modeling using AERMOD. Principal emission s ources included both mob ile and stationary sources as well as emissions from Atla nta Hartsfield International Airport. 20 

### The supplemental data used for estimating the emission sources, in addition to other AERMOD parameters used for the Atlanta exposure analys is 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 r ecent, 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 out puts 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 emissi ons model input files for the 2008 summer ozone season, characterizing local inputs th at differ from national defaults, and 2002 registration distribution.

Although considerable effort was expended to maintain consistency between the ARC approach to analysis of TDM da ta and that employed in this an alysis, complete consistency was not possible due to the differing analysis objec tives. The ARC creates countywide emission inventories. This study create d spatially and temporally re solved emission strengths for dispersion modeling. Informati on about expected differences in traffic between the 2005 data year and 2001-2003 modeled years was not prov ided, nor was information about seasonal differences in MOBILE6.2 inputs. Th ese 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 descript ors for four time periods: morning, afternoon, evening, and nighttime. Each period’s data in cludes freeflow speed, total vehicle count, total heavy duty truck count, total single occupancy vehicle count, and TDM- calculated congested speeds for the period. The desc ription 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 Atlant a area were not included as was done in the Philadelphia County case study, since the NEI shows all em issions to be accounted by stack totals.

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 regiona l network was filtered to include only those roadway links that are considered major as determined by TDM- base d vehicle counts and within the four part of a fifth county (Clayton), which co ntains 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-ma jor 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 dir ect correlation between th e 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 TD M nodes to more realistic locations. The nodes in the TDM represen t the endpoints of links in the tr ansportation planni ng network and are specified by node indices, cross-re ferenced 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 StreetMa pTM 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 ArcG IS. Once the nodes had been placed in the corrected locations, a line was drawn connecting each node pair to repres ent a link of the adjusted planning network.

B-4.1.1.2 Emission Source Strength 

### On-road mobile emission factors were de rived from the MOBILE6.2 emissions. The simulations were executed to calculate average running NO x 

### emission factors in grams per mile for a specific functional class (Freeway, Arte rial, Local, or Ramp) and speed. Iterative MOBILE6.2 simulations were c onducted to create tables of average Atlanta region emission factors resolved by speed (2.5 to 65 mph), func tional class, season, and year (2001, 2002, or 2003) for each of the eight co mbined MOBILE vehicle classes. 22 

### The resulting tables were then consolidated into speed, functi onal class, and seasonal values fo r combined light- and heavy-duty vehicles. To create seasonal-hour ly resolved emissions, spring and fall values were taken as the average of corresponding summer and winter va lues. Figure B-44 shows an example of the calculated emission factors for Summer, 2001.

The resulting emission factors were then coupl ed with the TDM-based activity estimates to calculate emissions from each of the 4,899 ma jor 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 exhi bited similar activity levels within 20% of each other.

22 HDDV - Heavy-Duty Diesel Vehicle, HDGV - Heavy-Duty Gasoline Vehicl e, 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 S ources 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 Databa se (US EPA, 2007c), and temporal emission profile information contained in the EMS-HAP (version 3.0) emissions model. 23 

### The NEI database contains stack locati ons, emissions release parameters (i.e., height, diameter, exit temperature, exit velocity), and annual emissions for NO x 

### -emitting facilities. The CAMD database, on the other hand, has information on hourly NO x 

### emission rates for units in the US, where the units are the boilers or equivalent, each of which can have multiple stacks. 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 indivi dual stacks assigned w ithin the databases.

Major stationary sources for this analysis were selected from the NEI according to the following criteria:

(1) Stacks within facilities w hose total NOx emissions are at least 100 tpy, and (2) Stacks within facilities located either within the 4-count y modeling domain or within 10 km of the modeling domain.

There are 7 NOx-emitting facilities in the NEI th at meet these criteria. Stacks within the facilities that were listed separately in the NE I were combined for modeling purposes if they had identical stack physical paramete rs and were co-located within about 10 m. This process resulted in 28 combined stacks, listed in Ta ble 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 NO x 

### point sources and 51% of the total NO x 

### point source emissions in this buffered four county Atlanta area.

The CAMD database was then queried for fac ilities that matched the facilities identified from the NEI database. Facility matching was done on the facility name, Office of Regulatory Information Systems (ORIS) identi fication code (when provided) a nd facility total emissions to ensure a best match between the facilities. Ho wever, because Georgia was not part of many of the market-based reduction programs that const itute 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-Generat ing 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 NO x 

### emissions profiles were created based on the hourly profile typical of that stack’s SCC, th e season, and the day of week. These SCC-based temporal profiles are year-i ndependent, and were developed for the EPA’s EMS-HAP model, 24 

### described in the EMS-HAP model Ve rsion 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 equa l to those of NEI 2002.

B-4.1.3 Airport Em issions Preparation


### The Atlanta-Hartsfield Intern ational Airport emissions we re assigned to a polygon that defined an area source for simulation. The peri meter dimensions of the Atlanta-Hartsfield International Airport were determined by GIS an alysis of aerial photographs, and the polygon representing the airport is estimated to have an area of 3 km 2 

### (see Figure B-45). As with some point source emissions, the annual NO x 

### emission totals were extracted from the NEI and the temporal profiles from the EPA’s EMS-HAP model. These seasonal, SCC-based emissions were scaled such that the annual to tal emissions are equal to those of NEI 2002: 5,761 tpy, with about 90% coming from commercial aircraft.

Figure B-45. Polygon representi ng the Atlanta-Hartsfield Inte rnational Airport area source.

24 http://www.epa.gov/scram001/disp ersion_related.htm#ems-hap

25 http://www.epa.gov/scram001/us erg/other/emshapv2ug.pdfDraft-Do Not Quote or Cite B-95Table B-36. The major-facility combined stackswithin 10 km of theAtlanta modeling domain.County NEI Site ID Facility Name SCC1Lat. Lon.StackTotal NOxEmiss.(TPY)FacilityTotalEmiss.(TPY)StackHght.2(m)ExitGasTemp.2(K)StackDiam.2(m)Exit GasVel.2(m/s)Clayton NEI2GA300105 Delta Air Lines Inc TOC20200102,2020040133.6425 -84.41556 1.23 101.6 8 527 0.4 18Clayton NEI2GA300105 Delta Air Lines Inc TOC2040011033.64417 -84.41805 0.04 101.6 9 977 6.9 10Clayton NEI2GA300105 Delta Air Lines Inc TOC2040011033.64361 -84.41805 67.51 101.6 14 444 11.3 2Clayton NEI2GA300105 Delta Air Lines Inc TOC10200502,10200602,1020060333.64194 -84.41278 32.82 101.6 18 590 0.8 18Cobb NEI12840Georgia Power Company McDonough Steam-Electric Generating Plant20100101,2010020133.82472 -84.475 11.91 4895.3 17 663 3.5 19Cobb NEI12840Georgia Power Company McDonough Steam-Electric Generating Plant1010021233.82472 -84.475 4883.4 4895.3 255 405 7.9 20Cobb NEI2GA700022 Caraustar Mill Group Inc30790001,3079000333.81778 -84.64889 1.81 364.1 13 367 0.8 10Cobb NEI2GA700022 Caraustar Mill Group Inc10200202,10200501,1020060133.81778 -84.64889 362.3 364.1 38 450 1.8 25Fulton NEIGA1210021 Owens Corning - Fairburn Plant3050129933.53861 -84.61694 2.14 602.1 16 352 0.7 13Fulton NEIGA1210021 Owens Corning - Fairburn Plant30501204,30501205,3050129933.53861 -84.61694 12 602.1 19 347 3 13Fulton NEIGA1210021 Owens Corning - Fairburn Plant30501204,30501205,3050129933.53861 -84.61694 13.29 602.1 19 347 3.2 8Fulton NEIGA1210021 Owens Corning - Fairburn Plant30501204,30501205,3050129933.53861 -84.61694 5.63 602.1 19 391 2.4 7Fulton NEIGA1210021 Owens Corning - Fairburn Plant3050120333.53861 -84.61694 327 602.1 21 316 1.2 8Fulton NEIGA1210021 Owens Corning - Fairburn Plant3050120333.53861 -84.61694 242 602.1 204 322 1.2 8Fulton NEIGA1210401 Lafarge Building Materials3050060633.8225 -84.47 943 1252.9 20 586 2 13Draft-Do Not Quote or Cite B-96County NEI Site ID Facility Name SCC1Lat. Lon.StackTotal NOxEmiss.(TPY)FacilityTotalEmiss.(TPY)StackHght.2(m)ExitGasTemp.2(K)StackDiam.2(m)Exit GasVel.2(m/s)Fulton NEIGA1210401 Lafarge Building Materials30500606,3050061333.8225 -84.47 309.89 1252.9 24 336 0.9 12Fulton NEIGA1210020Owens-Brockway Glass Container Inc - AtlantaGA plant1020060233.66972 -84.41861 10.06 710.5 18 497 1 8Fulton NEIGA1210020Owens-Brockway Glass Container Inc - AtlantaGA plant1020060233.67083 -84.42083 208.49 710.5 27 589 1.2 24Fulton NEIGA1210020Owens-Brockway Glass Container Inc - AtlantaGA plant1020060233.67083 -84.42083 402.49 710.5 27 589 1.4 19Fulton NEIGA1210020Owens-Brockway Glass Container Inc - AtlantaGA plant1020060233.67083 -84.42083 89.42 710.5 27 644 0.9 25Henry NEIGA1315100 Transcontinental Gas Pipe Line - Station 1202020020233.56944 -84.255 7.88 2347.4 5 744 0.2 22Henry NEIGA1315100 Transcontinental Gas Pipe Line - Station 1202020025233.56944 -84.255 642.88 2347.4 8 625 0.6 38Henry NEIGA1315100 Transcontinental Gas Pipe Line - Station 1202020025233.56944 -84.255 184.17 2347.4 8 625 0.7 31Henry NEIGA1315100 Transcontinental Gas Pipe Line - Station 1202020025233.56944 -84.255 945.58 2347.4 8 637 0.7 28Henry NEIGA1315100 Transcontinental Gas Pipe Line - Station 1202020020233.56944 -84.255 36.6 2347.4 8 669 0.4 17Henry NEIGA1315100 Transcontinental Gas Pipe Line - Station 1202020025233.56944 -84.255 280.57 2347.4 8 670 0.6 41Henry NEIGA1315100 Transcontinental Gas Pipe Line - Station 1202020025233.56944 -84.255 218.68 2347.4 9 625 0.6 38Henry NEIGA1315100 Transcontinental Gas Pipe Line - Station 1202020020133.56944 -84.255 31.08 2347.4 10 743 1 421Combined stacks may have multiple Source Classification Codes (SCCs)2The physical stack parameters are converted from English unitsinto metric units. The stack height, exit gas temperature, andexit gas velocity are rounded to integers, and the stack diameter isrounded 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 be tween 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 bloc ks 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 th e distribution of the on-road area source widths).



### 0 50 100 150 200 250 300 350 400 4500200400600800100012001400160018002000220024002600280030003200340036003800400042004400460048005000 

### Distance (m)Frequency

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%CDF (%)

Figure B-46. Frequency distribution of distance betw een each Census block receptor and its nearest majorroadway-link-centered receptor.B-98B-4.1.5 Data used to generate dispersion model-to-monitor comparison figures in REA.Table B-37. Data used to generate cumulativedensity functions plotted in Figure 8-6 of REA.Percentile Concentration (ppb)Monitor Receptor(s)100 99 95 90 80 70 60 50 25 0AERMOD P2.5 108 63 49 41 30 21 14 9 2 0AERMOD P50 137 70 51 46 34 28 22 17 8 0AERMOD P97.5 169 85 68 59 50 44 38 33 19 0AMBIENT MONITOR 90 53 39 32 24 19 15 12 5 1130890002AERMOD MONITOR 160 79 60 51 39 29 22 16 3 0AERMOD P2.5 94 56 47 40 28 20 14 10 4 0AERMOD P50 106 60 49 45 34 26 19 14 6 0AERMOD P97.5 145 82 65 57 49 43 36 29 16 0AMBIENT MONITOR 66 49 38 32 24 19 16 13 7 1130893001AERMOD MONITOR 103 58 48 45 32 24 17 12 5 0AERMOD P2.5 111 61 48 43 33 26 21 17 9 0AERMOD P50 122 70 52 47 39 31 25 20 12 0AERMOD P97.5 157 96 71 61 53 47 40 33 21 0AMBIENT MONITOR 136 63 47 39 30 24 19 16 9 1131210048AERMOD MONITOR 137 72 52 47 40 31 26 22 14 0

### B-99 Table B-38. Data used to generate diurnal variation plotted in Figure 8-7 of REA. Annual Average NO 2 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 NO 2 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 NO 2 

### concentrations



### Table B-39 provides the semi-empir ical distribution derived from the relationship of the on-road concentrations estimated by AERM OD and the concentrations at receptors located at least 100 meters form a major road. The data were sepa rated 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, how ever were derived from measur ement data reported in published literature sources (see Appendix A-8 for details). Each of the dist ributions were illustrated in Figure 8-8 of the final REA.

Table B-39. On-road/non-road NO 2 concentration ratios using AERMOD roadway link concentration prediction and nearest corres ponding 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. Estimate d on-road/non-road NO 2 concentration ratios using m ratio derived from data reported in published NO 2 measurement studies. Measurement Derived Season Probability 1 On-Road/Non-Road 2

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 Probability 1 On-Road/Non-Road 2

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 i th 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 m odel run was set to 50,000 persons simulated for each year. The parameters controlling the loca tion and size of the simulated area were set to include the counties in th e 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 se t to 20 percent to incr ease the pool of diaries available for selection. The variable that contro ls the use of additional ages outside the target age window was set to 0.1 to furthe r enhance variability in diary se lection. See the APEX User’s Guide for further explana tion 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 e xposure of this group with AP EX requires the estimation of children’s asthma preval ence rates. The proportion of the popul ation of children characterized as being asthmatic was estimated by statistics on as thma prevalence rates recently used in the NAAQS review for O 3 

### (EPA, 2007d; 2007e). Specifically, the analysis generated age and gender specific asthma prevalence rates for chil dren ages 0-17 using data provided in the National Health Interview Survey (NHIS) fo r 2003 (CDC, 2007). These asthma rates were characterized by geographic regions, namely Midw est, Northeast, South, and West. Adult asthma prevalence rates for Atlanta were derived from the Behavioral Risk Factor Surveillance System (BRFSS) survey information for y ear 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 ra tes for both genders. First each of the four county prevalence rates was weight ed by their population, then av eraged, and finally stratified by gender using the statewide reporte d 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 anal ysis by age and gender.

Table B-41. Mean asthma prevalence rates, along wi th lower and upper 95% confidence limits, by age and gender used for Atlanta. Females Males

Region (Study Area) Age Prevalence 1 se L95 U95 Prevalence 1 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 Prevalence 1 se L95 U95 Prevalence 1 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 populat ion. Multiply by 100 to obtain the percent. se – Standard error L95 – Lower limit on 95 th confidence interval U95 – Upper limit on 95 th 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 In door Source Contributions


### Data used for estimating the contribution to indoor NO 2 

### concentrations that occur during cooking with gas fuel were derived from a st udy sponsored by the California Air Resources Board (CARB, 2001). For this study a test hous e was set up for continuous measurements of NO 2 

### 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 NO 2 

### 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 indoo r 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 i ndoor 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 c ooking events was estimated as follows.

• Food preparation time obtained from CHAD diar ies 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 ove rall total preparation time. The result was a measure of the probabili ty of food preparation taking place during any hour, given one food prep aration 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 cooki ng by hour of the day. For this analysis it was assumed that the probability that food prep aration would include st ove usage was the same for each hour of the day, so that the diurnal allocati on 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 aver age total daily gas stove usage is 1.4 hours.

B-4.2.6 In-vehicle a nd Near-Road PROX factors



### These data were used for the in-vehicle and ne ar-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, a nd August) and by hour of the day (Table B-42).

Table B-42. In-vehicle and near-r oad PROX factors used in APEX. Parameter Estimates 1

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
6AM-7PM 4.619 2.820 1.067 30.0 Summer
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 5 th and 95 th 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 childr en. 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 alternat ive 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 evaluate d for the potential alternative standards. When evaluating the indoor source contributions, the 99 th 

### percentile form was used for each the 50, 100, and 150 ppb 1-hour standard levels, the 98 th 

### percentile form was evaluated only at a 100 ppb 1-hour standard level fo r comparison with the 99 th 

### form.

### B-109 B-4.2.6.1 All Asthmatics, Year 2001, No Indoor sources

Table B-43. Estimated number of asthmatics in the At lanta 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 altern ative standards, with out indoor sources. Air Quality Adjustment Persons With Number of Repeated Exposures Level 1 (ppb) Form 2 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 – 98 th percentile 1-hour concentration averaged across three years; 99 – 99 th 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 expo sed 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 altern ative standards, with out indoor sources. Air Quality Adjustment Percent of Persons With Number of Repeated Exposures Level 1 (ppb) Form 2 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 – 98 th percentile 1-hour concentration averaged across three years; 99 – 99 th 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 potentia l alternative standards, without indoor sources. Air Quality Adjustment Persons With Number of Repeated Exposures Level 1 (ppb) Form 2 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 – 98 th percentile 1-hour concentration averaged across three years; 99 – 99 th 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, with out indoor sources. Air Quality Adjustment Percent of Persons With Number of Repeated Exposures Level 1 (ppb) Form 2 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 – 98 th percentile 1-hour concentration averaged across three years; 99 – 99 th 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 At lanta 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 altern ative standards, with out indoor sources. Air Quality Adjustment Persons With Number of Repeated Exposures Level 1 (ppb) Form 2 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 – 98 th percentile 1-hour concentration averaged across three years; 99 – 99 th 1-hour concentration averaged across three years; cs – current annual average standard.

### B-114 Table B-48. Estimated percent of asthmatics in the At lanta modeling domain expose d 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 altern ative standards, with out indoor sources. Air Quality Adjustment Percent of Persons With Number of Repeated Exposures Level 1 (ppb) Form 2 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 – 98 th percentile 1-hour concentration averaged across three years; 99 – 99 th 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 potentia l alternative standards, without indoor sources. Air Quality Adjustment Persons With Number of Repeated Exposures Level 1 (ppb) Form 2 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 – 98 th percentile 1-hour concentration averaged across three years; 99 – 99 th 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 potentia l alternative standards, without indoor sources. Air Quality Adjustment Percent of Persons With Number of Repeated Exposures Level 1 (ppb) Form 2 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 – 98 th percentile 1-hour concentration averaged across three years; 99 – 99 th 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 At lanta 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 altern ative standards, with out indoor sources. Air Quality Adjustment Persons With Number of Repeated Exposures Level 1 (ppb) Form 2 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 – 98 th percentile 1-hour concentration averaged across three years; 99 – 99 th 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 expo sed 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 altern ative standards, with out indoor sources. Air Quality Adjustment Percent of Persons With Number of Repeated Exposures Level 1 (ppb) Form 2 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 – 98 th percentile 1-hour concentration averaged across three years; 99 – 99 th 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 potentia l alternative standards, without indoor sources. Air Quality Adjustment Persons With Number of Repeated Exposures Level 1 (ppb) Form 2 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 – 98 th percentile 1-hour concentration averaged across three years; 99 – 99 th 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 potentia l alternative standards, without indoor sources. Air Quality Adjustment Percent of Persons With Number of Repeated Exposures Level 1 (ppb) Form 2 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 – 98 th percentile 1-hour concentration averaged across three years; 99 – 99 th 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 At lanta 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 alt ernative standards, wi th indoor sources. Air Quality Adjustment Percent of Persons With Number of Repeated Exposures Level 1 (ppb) Form 2 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 – 98 th percentile 1-hour concentration averaged across three years; 99 – 99 th 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 expo sed 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 alt ernative standards, wi th indoor sources. Air Quality Adjustment Percent of Persons With Number of Repeated Exposures Level 1 (ppb) Form 2 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 – 98 th percentile 1-hour concentration averaged across three years; 99 – 99 th 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 potent ial alternative standards, with indoor sources. Air Quality Adjustment Percent of Persons With Number of Repeated Exposures Level 1 (ppb) Form 2 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 – 98 th percentile 1-hour concentration averaged across three years; 99 – 99 th 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 potent ial alternative standards, with indoor sources. Air Quality Adjustment Percent of Persons With Number of Repeated Exposures Level 1 (ppb) Form 2 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 – 98 th percentile 1-hour concentration averaged across three years; 99 – 99 th 1-hour concentration averaged across three years; cs – current annual average standard.

### B-125 

## B-5 References AHS. (2003a). American Housing Survey for 2003. Available at: http://www.census.gov/hhes/www/housing/ahs/ahs.html. AHS. (2003b). Source and Accuracy Statement for the 2003 AHS-N Data Chart. Available at: http://www.census.gov/hhes/www/housi ng/ahs/03dtchrt/source.html. Akland GG, Hartwell TD, Johnson TR, Whitmore RW. (1985). Measuring human exposure to carbon monoxide in Washington, D. C. and Denve r, Colorado during the winter of 1982-83. Environ Sci Technol . 19:911-918. Avol EL, Navidi WC, Colome SD. (1998) M odeling ozone levels in and around southern California homes. Environ Sci Technol . 32:463-468. Blackwell A and Kanny D. (2007). Georgia Asth ma Surveillance Report. Georgia Department of Human Resources, Division of Public Health , Chronic Disease, Injury, and Environmental Epidemiology Section, February 2007. Public ation Number: DPH07/049 HW. Available at: http://health.state.ga.us/epi/cdiee/asthma.asp. Biller WF, Feagans TB, Johnson TR, Duggan GM, Pa ul RA, McCurdy T, Thomas HC. (1981). A general model for estimating exposure associated with alternative NAAQS. Paper No. 81- 18.4 in Proceedings of the 74th Annual Meeti ng of the Air Pollution Control Association, Philadelphia, PA. CARB. (2001). Indoor air quality : residential cooking exposures. Final report. California Air Resources Board, Sacramento, California. Available at: http://www.arb.ca.gov/research /indoor/cooking/cooking.htm. CDC. (2007). National Center for Health Statistics. National Health Interview Survey (NHIS) Public Use Data Release (2003). Available at: http://www.cdc.gov/nchs/about/major/nhi s/quest_data_related_1997_forward.htm. Chan AT and Chung MW. (2003). Indoor-outdoor air quality relationships in vehicle: effect of driving environment and ventilation modes. Atmos Environ . 37:3795-3808. Chilrud SN, Epstein D, Ross JM, Sax SN, Pede rson D, Spengler JD, Kinney PL. (2004). Elevated airborne exposures of teenagers to manganese, chromium, and iron from steel dust and New York City's subway system. Environ Sci Technol . 38:732-737. Colome SD, Wilson AL, Tian Y. (1993). Ca lifornia Residential I ndoor Air Quality Study, Volume 1, Methodology and Descriptive Statistics . Prepared for the Gas Research Institute, Pacific Gas & Electric Co., San Diego Gas & Electric Co., Southern California Gas Co. Colome SD, Wilson AL, Tian Y. (1994). Ca lifornia Residential I ndoor Air Quality Study, Volume 2, Carbon Monoxide and Air Exchange Rate: An Univariate and Multivariate Analysis. Chicago, IL. Prepared for the Gas Research Institute, Pacific Gas & Electric Co., San Diego Gas & Electric Co., Sout hern California Gas Co. GRI-93/0224.3 Finlayson-Pitts BJ and Pitts JN. (2000). Chemistry of the Upper and Lower Atmosphere

. Academic Press, San Diego CA. Page 17. Georgia Department of Human Re sources (2007). Georgia Data Summary: Asthma. Georgia DHR, Division of Public Health. Publica tion number: DPH07/114HW. Available at: http://www.health.state.ga.us/epi/cdiee/asthma.asp. Hartwell TD, Clayton CA, Ritchie RM, Whitmor e RW, Zelon HS, Jones SM, Whitehurst DA. (1984). Study of Carbon Monoxide Exposure of Residents of Washington, DC and Denver, Colorado. Research Triangle Park, NC: U.S. Environmental Protection Agency, Office of

### B-126 

### Research and Development, Environmental Monitoring Systems Labor atory. EPA-600/4-84- 031. Johnson TR and Paul RA. (1983). The NAAQS Exposure Model (NEM) Applied to Carbon Monoxide. EPA-450/5-83-003. Prepared for th e U.S. Environmental Agency by PEDCo Environmental Inc., Durham, N.C. under Cont ract No. 68-02-3390. U.S. Environmental Protection Agency, Research Triangle Park, North Carolina. Johnson T. (1984). A Study of Personal Exposure to Carbon Monoxide in Denver, Colorado. Research Triangle Park, NC: U.S. Envir onmental Protection Agency, Environmental Monitoring Systems Laboratory. EPA-600/4-84-014. Johnson T. (1989). Human Activity Patterns in Cincinnati, Ohio. Palo Alto, CA: Electric Power Research Institute. EPRI EN-6204. Johnson T, Capel J, Olaguer E, Wijnberg L. (1992). Estimation of Ozone Exposures Experienced by Residents of ROMNET Domain Using a Probabilistic Version of NEM. Prepared by IT Air Quality Services for the Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina. Johnson T, Capel J, McCoy M. (1996a). Esti mation of Ozone Exposures Experienced by Urban Residents Using a Probabilistic Version of NE M and 1990 Population Data. Prepared by IT Air Quality Services for the Office of Ai r Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, September. Johnson T, Capel J, Mozier J, McCoy M. (1996b). Estimation of Ozone Exposures Experienced by Outdoor Childre n in Nine Urban Areas Usin g a Probabilistic Version of NEM. Prepared for the Air Quality Manage ment Division under Contract No. 68-DO-30094, April. Johnson T, Capel J, McCoy M, Mozier J. (1996c ). Estimation of Ozone Exposures Experienced by Outdoor Workers in Nine Ur ban Areas Using a Probabilistic Version of NEM. Prepared for the Air Quality Management Divisi on under Contract No. 68-DO-30094, April. Johnson T, Mihlan G, LaPointe J, Fletcher K. (1999). Estimation Of Carbon Monoxide Exposures and Associated Carboxyhemoglobi n Levels In Denver Residents Using pNEM/CO (version 2.0). Prepared for the U.S. Environmental Protection Agency under Contract No. 68-D6-0064, March 1999. Kinney PL, Chillrud SN, Ramstrom S, Ross J, Spe ngler JD. (2002). Exposures to multiple air toxics in New York City. Environ Health Perspect . 110:539-546. Klepeis NE, Tsang AM, Behar JV. (1996). Anal ysis of the National Human Activity Pattern Survey (NHAPS) Respondents fr om a Standpoint of Exposure Assessment. Washington, DC: U.S. Environmental Protection Agency, Office of Research and Deve lopment. EPA/600/R96/074. Koontz, M. D., L. L. Mehegan, and N. L. Nagda. 1992. Distribution and Use of Cooking Appliances That Can Affect Indoor Air Quality, Report No . GRI-93/0013. Gas Research Institute, Chicago. Langstaff JE. (2007). OAQPS St aff Memorandum to Ozone NAA QS Review Docket (OAR2005-0172). Subject: Analysis of Uncertainty in Ozone Population Exposure Modeling. [January 31, 2007]. Available at: http://www.epa.gov/ttn/naaqs/sta ndards/ozone/s_o3_cr_td.html. McCurdy T, Glen G, Smith L, Lakkadi Y. (2000). The National Exposure Research Laboratory’s Consolidated Human Activity Database, J Exp Anal Environ Epidemiol . 10: 566-578.

### B-127 

### Meng QY, Turpin BJ, Korn L, Weisel CP, Morandi M, Colome S, Zhang JJ, Stock T, Spektor D, Winer A, Zhang L, Lee JH, Giovanetti R, Cui W, Kwon J, Alimokhtari S, Shendell D, Jones J, Farrar C, Maberti S. (2004). Influence of ambient (outdoor) sources on residential indoor and personal PM2.5 concentrations : Analyses of RIOPA data. J Expos Anal Environ Epidemiol . 15:17-28. Murray DM and Burmaster DE. (1995). Resident ial air exchange rates in the United States: empirical and estimated parametric distri butions by season and climatic region. Risk Analysis . 15(4):459-465. NCDC. (2007). 2007 Local Climatological Data Annual Summary with Comparative Data. Atlanta, Georgia (Katl). National Climate Data Center. ISSN 0198-1560. PA DOH. (2008). Behavioral Risk Factor Surveillance System . Pennsylvania Department of Health, Bureau of Health Statis tics and Research. Available at: http://www.dsf.health.state.pa.us /health/cwp/view.asp?a=175&Q=242623. Persily A and Gorfain J. ( 2004). Analysis of ventilation data from the U.S. Environmental Protection Agency Building Assessment Survey and Evaluation (BASE) Study. National Institute of Standards and Tec hnology, NISTIR 7145, December 2004. Persily A, Gorfain J, Brunner G. (2005). Ve ntilation design and performance in U.S. office buildings. ASHRAE Journal. April 2005, 30-35. Robinson JP, Wiley JA, Piazza T, Garrett K, Cirksena K. (1989). Activity Patterns of California Residents and their Implications for Potential Exposure to Pollution. California Air Resources Board, Sacramento, CA. CARB-A6-177-33. Roddin MF, Ellis HT, Siddiqee WM. (1979). Background Data for Human Activity Patterns, Vols. 1, 2. Draft Final Report. Prepared for Strategies and Air Standa rds Division, Office of Air Quality Planning and Sta ndards, U.S. Environmental Protection Agency, Research Triangle Park, N.C. Sax SN, Bennett DH, Chillrud SN, Kinney PL, Spe ngler JD. (2004). Differences in source emission rates of volatile organic compounds in i nner-city residences of New York City and Los Angeles. J Expos Anal Environ Epidemiol . 14(S):95-109. Spicer CW, Kenny DV, Ward GF, Billick IH (1993) . Transformations, lifetimes, and sources of NO 2 

### , HONO, and HNO 3 

### in indoor environments. JAWMA . 43(11):1479-1485. Spier CE, Little DE, Trim SC, Johnson TR, Linn WS , Hackney JD. (1992). Activity patterns in elementary and high school student s exposed to oxidant pollution. J Expo Anal Environ Epidemiol . 2:277-293. Tsang AM and Klepeis NE. (1996). Descriptive Statistics Tables from a Detailed Analysis of the National Human Activity Pattern Survey (NHAPS) Data. U.S. Environmental Protection Agency. EPA/600/R-96/148. US Census Bureau. (2007). Employment Stat us: 2000- Supplemental Tables. Available at: http://www.census.gov/population/www/cen2000/phc-t28.html. US DOT. (2007). Part 3-The Journey To Work files. Bureau of Transportation Statistics (BTS). Available at: http://transtats.bts.gov/

. US EPA. (1999). Total Risk In tegrated Methodology. Website: http://www.epa.gov/ttnatw01/urban/trim/trimpg.html

. US EPA. (2002). Consolidated Human Activi ties Database (CHAD) User s Guide. Database and documentation available at: http://www.epa.gov/chadnet1/.

### B-128 

### US EPA. (2004). AERMOD: Description of Model Formulation. Office of Air Quality Planning and Standards. EPA-454/R-03-004. Available at: http://www.epa.gov/scram001/7thconf/aermod/aermod_mfd.pdf. US EPA. (2006a). Total Risk Integrated Met hodology (TRIM) - Air Poll utants Exposure Model Documentation (TRIM.Expo / APEX, Version 4) Volume I: User’s Guide. Office of Air Quality Planning and Standards, Research Tr iangle Park, NC. June 2006. Available at: http://www.epa.gov/ttn/fera/human_apex.html. US EPA. (2006b). Total Risk Integrated Method ology (TRIM) - Air Pollut ants Exposure Model Documentation (TRIM.Expo / APEX, Version 4) Volume II: Technical Support Document. Office of Air Quality Planning and Standards, Research Triangle Park, NC. June 2006. Available at: http://www.epa.gov/ttn/fera/human_apex.html

. US EPA. (2007a). Integrated Science Assessm ent for Oxides of Nitrogen – Health Criteria (First External Review Draf t) and Annexes (August 2007). Research Triangle Park, NC: National Center for Environmental Assessment. Available at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=181712. US EPA. (2007b). 2002 National Emissions Invent ory Data & Documentation. Available at: http://www.epa.gov/ttn/chie f/net/2002inventory.html. US EPA. (2007c). Clean Air Markets - Data and Maps. Emissions Prepackaged Data Sets. Available at: http://camddataandmaps.epa.gov/ gdm/index.cfm?fuseaction=emissions.wizard. US EPA. (2007d). Ozone Population Exposure An alysis for Selected Urban Areas (July 2007). Research Triangle Park, NC: Office of Air Qu ality Planning and Standards. EPA-452/R-07- 010. Available at: http://epa.gov/ttn/ naaqs/standards/ozone/s_o3_cr_td.html. US EPA. (2007e). Review of the Nationa l Ambient Air Quality Standards for ozone: assessment of scientific and technical inform ation. OAQPS Staff paper (July 2007). Research Triangle Park, NC: Office of Air Quality Pla nning and Standards. EPA-452/R-07-007a. Available at: http://epa.gov/ttn/naaq s/standards/ozone/s_o3_cr_sp.html. Weisel CP, Zhang JJ, Turpin BJ, Morandi MT, Colome S, Stock TH, Spektor DM, Korn L, Winer A, Alimokhtari S, Kwon J, Mohan K, Ha rrington R, Giovanetti R, Cui W, Afshar M, Maberti S, Shendell D. (2004). Relationshi p of Indoor, Outdoor and Personal Air (RIOPA) study; study design, methods and qua lity assurance/control results. J Exp Anal Environ Epidemiol . 15:123-137. Wiley JA, Robinson JP, Piazza T, Garrett K, Cirkse na K, Cheng Y-T, Martin G. (1991a). Activity Patterns of California Residents: Fina l Report. California Air Resources Board, Sacramento, CA. ARB/R93/487. Availabl e from: NTIS, Springf ield, VA., PB94-108719. Wiley JA, Robinson JP, Cheng Y-T, Piazza T, Stor k L, Pladsen K. (1991b). Study of Children's Activity Patterns: Final Report. California Ai r Resources Board, Sacramento, CA. ARB-R93/489. Williams R, Suggs J, Creason J, Rodes C, La wless P, Kwok R, Zweidinger R, Sheldon L. (2000). The 1998 Baltimore particulate matte r epidemiology-exposure study: Part 2. Personal exposure associated wi th an elderly population. J Expo Anal Environ Epidemiol

. 10(6):533-543. Williams R, Suggs J, Rea A, Leovic K, Vette A, Croghan C, Sheldon L, Rodes C, Thornburg J, Ejire A, Herbst M, Sanders, Jr W. (2003a). The Research Triangle Park particulate matter panel study: PM mass concen tration relationships. Atmos Environ . 37:5349-5363.

### B-129 

### Williams R, Suggs J, Rea A, Sheldon L, R odes C, Thornburg J. (2003b). The Research Triangle Park particulate patter panel study: modeling ambient s ource contribution to personal and residential PM mass concentrations. Atmos Environ . 37:5365-5378. Wilson AL, Colome SD, Baker PE, Becker EW. (1986). Residential Indoor Air Quality Characterization Study of Nitroge n Dioxide, Phase I, Final Report. Prepared for Southern California Gas Company, Los Angeles. Wilson AL, Colome SD, Tian Y, Baker PE, Beck er EW, Behrens DW, Billick IH, Garrison CA. (1996). California residential air exch ange rates and residence volumes. J Expos Anal Environ Epidemiol . 6(3):311-326. Yao X, Lau NT, Chan CK, Fang M. (2005). The use of tunnel concentra tion profile data to determine the ratio of NO 2 

### /NO x 

### directly emitted from vehicles. Atmos Chem Phys Discuss

. 5:12723–12740. Available at: http://www.atmo s-chem-phys-discuss.net/5/12723/2005/acpd5-12723-2005.pdf.

### B-130 

## Attachment 1: Technical Memora ndum on Meteorological Data Preparation for AERMOD for NO 2 

## REA for Atlanta, GA 2001-2003

### October 2, 2008

B-131 

## Meteorological data preparation for AERMOD for NO 2 

## 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 ur ban applications. Often the meteorological data is from an airport, which has different surf ace characteristics than the sources being modeled. The airport meteorological tower is often locat ed in open spaces while the sources are located in urban areas with trees, buildings, and other obs tacles. For the Atlanta study, th e 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 re present conditions at the sources.

To address the concern regardi ng representativeness of the Atla nta NWS data for this study, meteorological data from the Southeast Aeroso l Research and Character ization study (SEARCH) site in Atlanta were used as the primary sour ce of meteorology for the AERMOD runs for the years 2001 through 2003. Figure 1a shows the lo cations of the SEARCH site, located at Jefferson St, and hereafter referenced as JST, a nd Hartsfield Internati onal Airport, hereafter referenced as ATL. The JST site is located in an urban area, while the ai rport is on the outskirts of the city. Figure 1b provides a cl oser look at the JST site and it can be clearly seen that the site is in an urban setting.

The methodologies used to pr epare 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 us ed 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 in cidence of calms and variable wind conditions. The AERMOD model currently cannot simulate dispersion under these conditi ons. To reduce the number of calms and missing winds in the ATL data, archiv ed 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 report ed for ATL in the Integrated Surface Hourly (ISH) database. Details regarding th is procedure are described below.

Section 2 describes preparation of the JST data, Section 3 describe s 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 characteris tics, and Section 6 describes the AERMET

### October 2, 2008

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### processing. Section 7 describes an additional adjustment that was made to the processed meteorological data to address an issue rega rding AERMOD formulation for the urban option that contributed to anomalous modeled concentra tions from a preliminary analysis. Section 8 provides a brief analysis of th e AERMET output for JST and ATL. References are listed in Section 9.

### October 2, 2008

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### 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

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### 2. SEARCH data preparation



### SEARCH data for the Jefferson St. monitor (JST ) in Atlanta was downloaded from the publicarchive 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 repo rted on a 0 to 23 hour ba sis, with the reported time represented the beginning of the observati onal hour. The conven tion for meteorological data input to dispersion models is that the re ported time represents th e end of the averaging period. The AERMOD model also requires meteor ological inputs on a 1 to 23 hour basis. After adjusting the JST data to conform to the AE RMOD 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 importa nt consideration for meteorologi cal 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 year s of 2001 through 2003. Analysis of the wind directions showed generally good agreement between JST and A TL data throughout most of the period. However, this comparison identified so mewhat anomalous directions for the period of May 2 through May 8, 2001 (Figure 2). The original wind directions for th e JST data (red lines), revealed an approximate 180 degree shift in wi nd direction when compared to the ATL wind directions (blue lines). This shift followed a si gnificant period of missing data for JST from late April to early May 2001. After May 8, the wind di rections 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 late r confirmed to be a 120-degree offset. Based on this review and prior experience with a simi lar problem, it was decided to shift the JST wind direction by 180 degrees for the period begi nning 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 corre cting for the wind directions, th e 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 th e airport was necessary in AERMET processing. Table 1 lists the number of calms and missing wi nds 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 repor ted less than 0.28 m/s wa s treated as a calm hour. Unlike NWS surface observations, which tr eat 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 de fining 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

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### 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 transp ort direction. The AERMOD model formulation includes adjustments to the minimum wind speed to account for turbulence effects under very light wind conditions, with the mi nimum effective wind speed that will be used for dilution in AERMOD of about 0.2828 m/s. Based on these considerations, a thre shold 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 be ing classified as calm. Note that the current meteorological monitoring guidance for dispersion modeling ap plications (EPA, 2000) specifies a maximum acceptable starting threshold of 0.5 m/s for site-s pecific meteorological monitoring programs.

Table 1. Number of calms, missing winds, a nd missing temperatures for each year for 2001 through 2003 for the JST site.
Year Variable 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.October 2, 2008B-136Figure 2. May 2001 a) wind directions for the SEARCH monitor (red line)and Hartsfield International Airport (blue line) and b),wind direction differences(airport – SEARCH).October 2, 2008B-137Figure 3. Hourly wind directions for original SEARCH (red), airport (blue) and shifted SEARCH (green) for May 2 through May 8,2001.October 2, 2008B-138Figure 4. Annual wind roses for JST for a) 2001, b) 2002, and c) 2003.

### October 2, 2008

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### 3. Surface airport data



### Surface data from an NWS site was needed to s upplement the data from the SEARCH site. For AERMOD, the most representative data for an NW S site should be used, most often the nearest location. For Atlanta, Atlanta Hartsfield Air port (ATL) was chosen as the site. Integrated Surface Hourly (ISH) data was downloaded from th e 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 re present the hour in AERMOD is a single two-minute average, usually reported about 10 minut es 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 dir ection. The number of calms and variable winds can influence concentration calculations in AERMOD because co ncentrations are not calculated for calms or variable wind hours. Fo r 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 disp ersion conditions for such sources are associated with low wind speeds, and the hours being discarded as calm or variable are biased to ward this condition.

Recently, NCDC began archiving the two-minut e average wind speeds for each minute of the hour for most ASOS stations. These values ha ve 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 nu mber 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 thr ough 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 pe rformed 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 fo r 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 knot s, 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 meteor ological monitoring gui dance recommendation of

### October 2, 2008

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### applying a wind speed of one half the th reshold value to each wind sample belowthreshold when processing samples to obtain hourly averages. At the same time, the xand y-components of the wind direction were calculated using equa tions 1 and 2 below, which are the functions insi de the summation of equations 6.2.17 and 6.2.18 of the meteorological guidance document (U.S. EP A, 2000). The components were only calculated for minutes that did not require resetting.

θ 

### sin

− = x 

### v (1)



### θ cos

− = y 

### v 

### (2)

where v x 

### and v y 

### are the x- and y-components of the one minute wind direction θ



### .

3. For all minutes that passed the QA check in st ep 1, the wind speeds were converted from knots to m/s.

4. Before calculating hourly averages, the num ber 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 ev en and odd minutes. Even minutes were given priority over odd. If at least two va lid minutes were found, then all available minutes would be used to calculate hourly av erages. 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-ove rlapping 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 v xi 

### and v yi 

### 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), summar ized 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 blowi ng, as shown by the flow correct ion in 6.2.19. Instead, the one minute program corrected for the dire ction from which the wind was blowing.



# ∑

= 

### = N

i xi X 

### v N V

1 

### 1 (3)



# ∑

= 

### = N

i yi Y 

### v N V

1 

### 1 (4)

CORR V V Arc

Y X 

### + ⎟ ⎠ ⎞ ⎜ ⎝ ⎛

= tan θ 

### (5)

### October 2, 2008

B-141 

### Where V x 

### and V y 

### are the hourly averaged x- and y-components of the wind, θ is the hourly averaged wind direction, N is the num ber of observations used for the hour, and

= 180 for V x 

### > 0 and V y 

### > 0 or V x 

### < 0 and V y 

### > 0 = 0 for V x 

### < 0 and V y 

### < 0 CORR = 360 for V x 

### ≥ 0 and V y 

### < 0

### October 2, 2008

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### 4. Upper air data



### For AERMET processing, an upper ai r 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 te nths 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 requi res 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 substi tution. These substitutions should have very limited impact on the Atlanta NO2 modeling since BM X 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, substi tution 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) fo r input to AERMET. Surface characteristics were calculated for the JST meteorological towe r 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 repr esentation 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 si nce the 2001 land cover data will be more representative of this modeli ng 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 1 st 

### draft NO 2 

### risk and exposure assessment technical support doc ument (U.S. EPA, 2008b): both sites were run as non-arid regions, ATL was considered “at an airport” fo r 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 te chnical support document (U.S. EPA, 2008b). Months with at least tw ice the normal precipitatio n level were denoted as wet, those with less than one-half the normal precipitation level were a ssigned 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 condi tions 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 characterist ics 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 ch aracteristics for January through June were concatenated with the dry July and August surf ace characteristics, av erage September surface characteristics, dry October and November surface characteristics, and average December surface characteristics. These merged AERSUR FACE results were used in Stage 3 of AERMET.

### October 2, 2008

B-144 

### Figure 5. 2001 NLCD for JST with surface r oughness 1 km radius and sectors (denoted by numbers 1 through 4). Numbers out side 1 km radius are the star ting directions of each sector.

### October 2, 2008

B-145 

### Figure 6. 2001 NLCD for ATL with surface r oughness 1 km radius and sectors (denoted by numbers 1 through 5). Numbers out side 1 km radius are the star ting 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.
Year Month 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 in cludes 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 A TL 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 keywor d was set to 0.28 m/s as described in Section 2. For the hourly averaged one minute A TL 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 out put 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, th e year specific surface characteristics created by concatenating the appropriate surface characteri stics for each month were used. The ATL one minute hourly averaged winds would be the prim ary 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 an emometer height was set to 10 m (keyword NWS_HGT).

The second run was for JST. The JST winds a nd 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 sp ecific surface characteristics for JST. For later post-processing, the NWS_HGT keyword was set to 9.9 m. This would allow for identific ation of hours where the ATL winds were used. For hours with valid da ta 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 char acteristics. The entire record, including anemometer heights, was substituted. The resu lting 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 usin g the processed meteorological data for JST should generally good agreement between modele d and observed concentrations. However, several spuriously high 1-hour modeled concentra tions were also noted. Examination of the meteorological conditions associated with th ese high modeled concentrations indicated a consistent pattern of occurring on the first convective hour of the da y. 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 expe cted to increase turbul ence relative to the surrounding rural areas. The issu e that contribu tes to these high modele d concentrations for Atlanta is that the urban-e nhanced turbulence disappears once the atmosphere becomes convective, with no transitional pe riod 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 co nvective hour of the day, which may lead to unrealistically high concentrations. Every outlier examined was cons istent with this pattern, and no such anomalies occurred at other hours of the da y. 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 sim ilar wind speeds and directions.

In order to minimize the impact that these anom alously high 1-hour concentrations may have on the exposure assessment for Atlanta, an adjustme nt was made to the mechanical mixing heights in the processed meteorological data files for th e first convective hour of each day. Morning mechanical mixing heights for bot h JST and ATL were adjusted fo r 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 rese t 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 conv ective 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 mechanic al mixing height is c onsidered a reasonable approach to account for residual tu rbulence since the convective mixi ng 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 re asonable compromise for this application, and subsequent modeling comparisons indicated mu ch 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 base d on one-minute hourly aver aged 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 mi ssing winds were dramatically reduced.

Table 4. Number of hours using hourly averag ed 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 wi nds included in the ATL data. This is consistent with expected influen ce 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 wi nd speed histograms for a) A TL hybrid, b) JST hybrid, c) ATL hybrid and d) JST hybrid.

### October 2, 2008

B-151 

### Figure 8. 2002 wind roses and wi nd speed histograms for a) A TL hybrid, b) JST hybrid, c) ATL hybrid and d) JST hybrid.

### October 2, 2008

B-152 

### Figure 9. 2002 wind roses and wi nd speed histograms for a) A TL hybrid, b) JST hybrid, c) ATL hybrid and d) JST hybrid.

### B-153 

### 9. References



### U.S. EPA, 2000: Meteorological M onitoring 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 Use r’s Guide. EPA-454/B-08-001. U.S. Environmental Protection Agency, Research Triangle Park, NC 27711.

U.S. EPA, 2008b: Risk and Exposure Asse ssment to Support the Review of the NO2 Primary National Ambient Air Quality Standard: Technica l Support Document. EPA-452/P-08-002. U.S. Environmental Protection Agency, Research Triangle Park, NC 27711.

### B-154 

## Attachment 2: Technical Memora ndum 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 ge nerally include an accurate estimate of both the average exposure concentr ation and the high end of the e xposure distribution. One of the factors influencing the number of exposures at the high end of the concentration distribution is time-activity patte rns that differ from the average, e.g., a disproportionate amount of time spent near roadways. Whether a model repres ents these exposure scenarios well depends on whether the treatment of activity pattern da ta accurately characterizes differences among individuals.

Human time-activity data for population expos ure models are generally derived from demographic surveys of individual s’ daily activities, the amount of time spent engaged in those activities, and the ME locations where the activi ties occur. Typical time-activity pattern data available for inhalation exposure modeling co nsist 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 exposur e to air pollutants typically require information on activity patterns over long periods of time, e.g., a full ye ar. 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 con centration over a long pe riod of time (e.g., the annual number of exceedances of an 8-hour aver age 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 fo r combining them will in fluence the variability of the long-term activity patterns across the simulated population. Th is in turn will influence the ability of the model to accurately represent e ither long-term average high-end exposures, or the number of individuals exposed multiple times to short-term high-end concentrations.

A common approach for construc ting long-term activity patterns from short-term records is to re-select a daily activity pattern from the pool of data for each day, w ith 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 ac ross the population, and therefor e, 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 th at an individual’s day to day activities are perfectly correlated. This approach tends to result in long-term activity patterns that are very different acro ss the simulated population, and therefore may over-estimate the variability across the population.

The Cluster-Markov Algorithm 

### Recently, a new algorithm has been developed a nd 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 pa ttern records into groups that are similar, and then select a si ngle 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 da ily record to represent all days).

The steps in the algorithm are as follows.

• For each demographic group (age, gender, em ployment status), temperature range, and day-of-week combination, the associated time-activity records are partitioned into 3 groups using cluster analysis. The cluste ring criterion is a vect or of 5 values: the time spent in each of 5 microenvironment categories (indoors – residence; indoors – other building; outdoors – near road; outdoor s – 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 pr obability of a given time-activity pattern occurring on a given day based on the time- activity pattern of th e previous day and cluster-to-cluster transition probabilities. The cluster-to-cluster transition probabilities are estimated from the availa ble multi-day time-activity records. (If insufficient multi-day time-activity record s are available for a demographic group, season, day-of-week combination, then the cluster-to-cluster tran sition probabilities are estimated from the frequency of time-ac tivity records in each cluster in the CHAD data base.).

Figure 1 illustrates the Cluster-Markov algorithm in flow chart format.B-157Figure 1.Flow chart of Cluster-Markovalgorithm used for constructing longitudinal time-activity diaries.

### B-158 

### Evaluation of modeled diary prof iles versus observed diary profiles 

### The Cluster-Markov algorithm is also incorporated into the Hazardous Air Pollutant Exposure Model (HAPEM). Rose baum 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 S outhern California Chr onic Ozone Exposure Study (Xue et al. 2005, Geyh et al. 2000). In th is 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 sepa rate 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 a ggregate microenvironments: indoors-home, indoors-school, indoors-other, outdoors, and in -transit. For purposes of defining diary pools and for clustering and calculating tran sition probabilities the activity pattern records were divided by day type (i.e., wee kday, 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 simu lated. 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 mi croenvironment, 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 vari ance 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 m easure used in dispersion model performance and was also calculated as follows.



# ∑ −

= N



### observed observed predicted

N NBIAS

1 

### ) ( 100

The predicted time-in-microenvironmen t 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 th e predicted averages have bias between –9% and +9%, and the m ean bias across any mi croenvironment 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-fi ve percent of the pr edicted variances had bias between –22% and +24%. The mean nor malized bias across any microenvironment ranged from –10% to +28%. Eighteen predictio ns 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 microe nvironment/age/gender/season. Seventy percent of the predicted va riances had bias between –25% and +30%. The mean normalized bias across any micr oenvironment ranged from –11% to +47%. Twenty-eight predictions had positive bias and 12 had ne gative bias, suggesting some tendency for overprediction of this variance measure.

The overall conclusion was that the propos ed algorithm appeared to be able to replicate the observed data reasonably well. Although some discrepancies were rather large for some of the “variance across pe rsons” and “within-person variance” subsets, about two-thirds of the predictions for each ca se were within 30% of the observed value. A detailed description of the evaluation us ing HAPEM is presented in Attachment 3.

Comparison of Cluster-Markov a pproach 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 EP A, 2007). One of these was to make parallel simulations using each of the three algor ithms for constructing multi-day time-activity sequences that are incorporated into APEX.

Table 1 presents the resu lts for the number of pe rsons in Atlanta population groups with moderate exertion exposed to 8- hour average concentrations exceeding 0.07ppm. The results show that the predictions made with alternative algorithm ClusterMarkov algorithm are substantiall y 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 valu es 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 ex ceeding 0.07 ppm with moderate or greater exertion. The results show that although th e 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 in ter-individual variability.

### B-160 

### Table 1. 

### Sensitivity to longitudinal diary algor ithm: 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 mo derate or greater exertion (after US EPA 2007).
One or more exposures Three or more exposures Population Group Simple re-sampling DiversityAutocorrelation ClusterMarkov Simple re-sampling DiversityAutocorrelation ClusterMarkov

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 algor ithm: 2002 days per person with 8-hour ozone exposures above 0.07 ppm concomitant with moderate or greater exertion for Atlanta general population and childre n (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: Assessi ng 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 Expos ure Analysis for Selected Urban Areas. EPA452/R-07-010. Available at: http://www.epa.gov/ttn/naaqs/standard s/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 us ing 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 proce ss to better represent the variability among individuals. This section summarizes th e 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 cat egory associated with each given first-day category, where the same individual was obser ved for two consecutive days. (Consecutive day activity pattern records for a single indivi dual 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 ar e 3 cluster categories for each of 3 day types, 9 unique activity pattern s are selected to be averaged toge ther 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 pa tterns used in the averaging process is determined by the product of two factors. The fi rst 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 wei ghting for the selected activity pattern is determined by simulating a sequence of category-types as a one-stage Markov chai n process using the transition probabilities. The category for the fi rst day is selected according to the relative frequencies of each category. The category fo r 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

### B-163 

### 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 freque ncy of the category-type in th e sequence associated with the selected activity pattern is the second factor in the weighting.

PROPOSED ALGORITHM STEPS 

### The proposed algorithm is summarized in Figur e 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-environmen ts: indoors – residence; indoors – other building; outdoors – near road; outdoors – aw ay 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 give n 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 cate gory-to-category tran sition probabilities.

For example, if there are 2 categories, A and B, then

P AA 

### = the probability that a type A pa ttern is followed by a type A pattern,

P AB 

### = the probability that a type A patte rn is followed by a type B pattern (P AB 

### = 1 – P AA 

### ),

P BB 

### = the probability that a type B patte rn is followed by a type B pattern, and

P BA 

### = the probability that a type B patte rn is followed by a type A pattern (P BA 

### = 1 – P BB 

### ).

Activity Pattern Selection 

### For each day-type and demographic group in each census tract:
Step 5: One activity pattern is randomly sel ected 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:

### B-164 

### Step 6: A cluster category is selected for th e first day of the day-type sequence, according to the relative frequency of the cl uster 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-t o-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 frequencie s (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 ac tivity patterns are averaged together using the relative frequency of day-types as weight s, 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 time s 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 va riability, 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 a pproach taken in APEX fo r 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 simula ted as a function of the age.
Step 4: For each simulated day, the user define s an initial pool of possible diary days based on a user-specified function of th e day type (e.g., weekday/weekend) and temperature. Step 5: The pool is further restricted to ma tch the target gender and employment status exactly and the age w ithin 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 probabil ity of selecting a given diary day is equal to the age weight divided by the total of th e 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 clus ter 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 temperatur e bins for the clustering and transition probability calculations so that the clus ter definitions would be fairly uniform across temperature bins. Thus we woul d 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 transiti on 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 speci fied for transitions between summer weekdays of each category and summer weekends of each category.

Another issue for dividing the CHAD activ ity records for the purposes of clustering and calculating transition probabi lities is that the diary pools specified for the APEX activity selection algorithm use varying and overlapp ing age ranges. One way to address this problem would be to simply not include consid eration of age in the clustering process, under the assumption that cluster categories are simila r 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 stra tified by age group that were de veloped for HAPEM5. If the assumption is found to be valid, then the cluste r 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 alternativ e approach could be implemented that would create overlapping age groups for purposes of clustering as follows. APEX age group ranges and age window per centages would be constrained to some maximum values. Then a set of overlapping age ra nges 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 resul ting sets of cluster ca tegories 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 selecti ng the second day’s activity pattern, each age weight would be multiplied by the transition probability P AB 

### 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 P AB 

### divided by the total of the products of age weight and P AB 

### for all diary days in the pool for day 2. Similarly, for the transitions fr om 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 availa ble multi-day data set. For purposes of clustering we characterized the ac tivity 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 diar y pools and for clustering and calculating transition probabilities we divided the ac tivity pattern records by day ty pe (i.e., weekday, weekend), season (i.e., summer or ozone season, non-summ er 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 appear ed to group in three clusters.
In this case, we simulated week-long se quences (Wednesday through Tuesday) for each of 100 people in each age/gender gr oup 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 thewithin-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 spen t in that microenvironment

### B-167 

### In each case we compared the predicted statis tic for the stratum to the statistic for the corresponding stratum in the actual diary data. 26



### We also calculated the mean normalized bi as for the statistic, which is a common performance measure used in dispersion mode l performance and which is calculated as follows. 

# ∑ −

= N



### observed observed predicted

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 fo r 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 pr edicted time-in-microenvironment averages match well with the observed va lues. 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 te st that the median bias across the 40 combinations = 0 % was not significant (p-v alue = 0.40) supporti ng 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/seas on. 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%. Ei ghteen predictions have positive bias and 20 have negative bias. Figure 4 suggests a reas onably 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) suppor ting 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 person s of the mean time was appr opriately 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 per cent 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 ha ve positive bias and 12 have negative bias, suggesting some tendency for ove rprediction 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 r easonably good match of predicted to observed variance in most cases, with a few overpre dicting outliers at th e higher end of the distribution. So although the positiv e bias is significant in a sta tistical sense (i.e., the variance is more likely to be overpredicted than underpre dicted), it is not clea r whether the bias is large enough to be important.

CONCLUSIONS


### The proposed algorithm appears to be able to replicate the observ ed data reasonably well, although the within-person va riance is somewhat overpredicted.
It would be informative to compare this algorithm with the earlier alternative approaches in order to gain perspective on th e degree of improvement, if any, afforded by this approach.

Two earlier approaches were:

1. Select a single activity pattern for ea ch 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 da y in the multi-day sequence from the appropriate set for the corres ponding 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 eac h 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-172Figure 1.Flow diagram of proposed algorithm for creating annual average activitypatterns for HAPEM5.Consolidated Human Activity Database - CHAD (CHAD)Cluster AnalysisCluster 1PatternGroupCluster 2PatternGroupCluster 3PatternGroupTransitionAnalysisTransitionProbabilitiesMarkov SelectionIndividual Annual Average ActivityPatternWinter WeekdayPattern GroupSummer WeekdayPattern GroupWeekendPattern GroupCluster 1PatternCluster 2PatternCluster 3PatternWeightsAverage WinterWeekday Pattern0.54Cluster AnalysisCluster Analysis

### 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 aver age 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 aver age 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

ObservedSimulated 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 vari ance across persons for time spent in each of 5 microenvironments for ag e/gender groups and seasons.

0 5 10 15 20 25 30

0 10 20 30

ObservedSimulated 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 th e 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 child ren’s asthma prevalence data from the National Health Interview Survey (NHIS) for 2003. Asth ma 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 que stion CASHMEV: “Ever be en 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 desi gn of the NHIS survey. Standard errors and confidence intervals for the prevalence were ca lculated 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 pl otted. 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 signifi cant differences in pr evalence by gender and by region. Therefore we did not combine the prev alence 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 uncertain ties 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 wa s initially used, two problems are that the distributions of the estimat ed 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] interv al. 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 variab les 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 be ta 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 conf idence intervals for beta, the co rresponding confidence intervals for prob (asthma) will always be inside [0, 1]. A nother advantage of the logistic modeling is that it can be used to compare alternative statistica l models, such as models where the prevalence probability depends upon age, region, and ge nder, or on age and region but not gender.

A variety of logistic models for asthma preval ence were fit and compared, where the transformed probability variable beta is a given function of age, gender, and region. SAS’s SURVEYLOGISTIC procedure was us ed 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 lo g-likelihood goodness-of-fit measures. 16 models were fitted. The Strata co lumn 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 independent ly 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 describe s 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 × age 2 

### where α β and γ vary with the

strata. Cubic in age: Beta = α + β × age + γ × age 2 

### + δ × age 3 

### 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 pol ynomial of degree 16 in age (sin ce 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 co mplex model (16) has a prevalence that is an arbitrary function of age, gender, and region. Mo del 16 is equivalent to calculating independent prevalence estimates for each of the 144 combina tions 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, wher e r is the difference in the DF. This is a likelihood ratio test. For all pairs of models from Table G-1, a ll the differences are at least 70,000 and the likelihood ratios ar e all extremely statis tically significant at levels well below 5 percent. Therefore the model 16 is clearly pref erred and was used to model the prevalences.

The SURVEYLOGISTIC model predictions are ta bulated 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 scatte rplot smoother, as discussed below.

The SURVEYLOGISTIC procedure produces estimat es 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 predicti ons 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 (b eta) × exp(- beta) / (1 + exp(beta) ) 2



### ,
which follows from the delta method (a fi rst 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 f unctions of age modeled by SURV EYLOGISTIC were one strategy for smoothing the curves, but they did not prov ide a good fit to the data. One reason for this might be due to the attempt to fit a global regressi on 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 regre ssion 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 smoo thing 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 fitte d these local regression curves to the beta values, the logits of the asthma prevalence estima tes, and then converted them back to estimated prevalence rates by applying the inverse logit functi on exp(beta) / (1 + exp(be ta) ). 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 estimat e 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 pr edicted values with vertical lines indicating the confidence intervals in Figu res 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 avoi d 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 ra w 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 para meter of 0.2 cannot be used becau se 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 applicati on 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 estimat ed standard errors by 1 / estimated σ , and adjusted the widths of the c onfidence intervals by the same factor.

Additionally, because the true value of σ equals 1, the best choi ces of smoothing parameter should give residual standard errors close to one. Using this crite rion 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 diagnos tic the residual errors from th e 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, combini ng all the studentized residuals across genders, regions, and ages. The plots for smoothing para meters seem to be equally straight for each smoothing parameter.

The final regression diagnostic is a plot of the studentized resi duals against the smoothed beta values. Ideally there should be no obvious pattern and an averag e studentized residual close to zero. The plots indeed showed no unusual patterns, and the result s 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 c hoices of 0.5 or 0.6. We therefore chose the final value of 0.5. These predictions, standard errors, and confidence inte rvals are presented in tabular form below as Table G-2.

### B-181 region=Midwest

gender Female Male prev

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24 0.26 0.28 0.30 0.32 0.34

age 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

region=Northeast

gender Female Male prev

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24 0.26 0.28 0.30 0.32 0.34

age 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

### B-182 region=South

gender Female Male prev

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24 0.26 0.28 0.30 0.32 0.34

age 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

region=West

gender Female Male prev

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24 0.26 0.28 0.30 0.32 0.34

age 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

### B-183 region=Midwest

gender Female Male predprob

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24 0.26 0.28 0.30 0.32 0.34

age 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

region=Northeast

gender Female Male predprob

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24 0.26 0.28 0.30 0.32 0.34

age 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

### B-184 region=South

gender Female Male predprob

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24 0.26 0.28 0.30 0.32 0.34

age 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

region=West

gender Female Male predprob

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24 0.26 0.28 0.30 0.32 0.34

age 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

### B-185 region=Midwest

gender Female Male prev

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45

age 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

region=Northeast

gender Female Male prev

0.0 0.1 0.2 0.3 0.4 0.5 0.6

age 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

### B-186 region=South

gender Female Male prev

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24 0.26 0.28 0.30 0.32 0.34

age 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

region=West

gender Female Male prev

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50

age 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

### B-187 region=Midwest

gender Female Male prev

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40

age 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

region=Northeast

gender Female Male prev

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45

age 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

### B-188 region=South

gender Female Male prev

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24

age 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

region=West

gender Female Male prev

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24 0.26 0.28 0.30 0.32

age 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

### 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. Anal ysis 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 (evapor ative) 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 agedat least 50 years having cardiac defibrillators living in Chapel Hill; a group of nonsmoking, African Americans aged at least 50 y ears with controlled hypertension living in a low-to-moderate SES neighborhood in Rale igh. 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 m easured 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 to xics. The AER measurements (generally over 48 hours) used a PMCH tracer. Data included t he daily average outdoor temperature, and the presence or absence of central air conditi oning, 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 neares t 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-203Table A-1. Summary of Studies of Residential Air Exchange RatesAvolRTP PanelRIOPATEACHWilson 1984Wilson 1991MurrayandBurmasterLocationsLancaster, LakeGregory,Riverside, SanDimas. All inSouthern CAResearch TrianglePark, NC CA; NJ; TXLos Angeles, CA;New York City, NY Southern CA Southern CAAZ, CA, CO,CT, FL, ID,MD, MN, MT,NJYears1994 2000; 2001 1999; 2000; 2001 1999; 2000 1984, 1985 1984 1982 – 1987Months/SeasonsFeb; Mar; Apr;May; Jun; Jul;Aug; Sep; Oct;Nov2000 (Jun; Jul;Aug; Sep; Oct;Nov), 2001 (Jan;Feb; Apr; May)1999 (July toDec); 2000 (allmonths); 2001(Jan and Feb)1999 (Feb; Mar; Apr;Jul; Aug); 2000 (Jan;Feb; Mar; Sep; Oct)Mar 1984, Jul 1984, Jan1985Jan, Mar, Jul VariousNumber ofHomes86 37 284 85 581 288 1,884Total AERMeasurements161 854 524 151 1,362 316 2,844AverageNumber ofMeasurementsper Home1.87 23.08 1.85 1.78 2.34 1.10 1.51MeasurementDurationNot Available 24 hour 24 to 96 hoursSample time (hours)reported. Rangesfrom about 1 to 7days. 7 days 7 days Not availableMeasurementTechniqueNot AvailablePerflourocarbontracer.PMCH tracerPerflourocarbontracer.Perflourocarbon tracer. Perflourocarbon tracer. Not availableMin AER Value0.01 0.02 0.08 0.12 0.03 0.01 0.01Max AER Value2.70 21.44 87.50 8.87 11.77 2.91 11.77Mean AERValue0.80 0.72 1.41 1.71 1.05 0.57 0.76MinTemperature(C)-0.04 -2.18 -6.82 -1.36 11.00 3.00 Not availableB-204AvolRTP PanelRIOPATEACHWilson 1984Wilson 1991MurrayandBurmasterMaxTemperature(C)36.25 30.81 32.50 32.00 28.00 25.00 Not availableAir ConditionerCategoriesNo A/C; Centralor Room A/C;Swamp Cooleronly; Swamp +[Central or Room]Central or RoomA/C (Y/N)Window A/C(Y/N); EvapCoolers (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) Not availableAir ConditionerMeasurementsA/C use inminutes Not AvailableDurationmeasurements inHrs and Mins Not Available Not Available Not Available Not availableFan CategoriesNot available Fan (Y/N) Fan (Y/N) Not Available Not Available Not Available Not availableFanMeasurementsTime on or off forvarious fan typesduring samplingwas recorded, butnot included indatabase provided.Not AvailableDurationmeasurements inHrs and MinsNot Available Not AvailableNot Available Not availableWindow Open/Closed DataDuration openbetween times6am-12 pm; 12pm- 6 pm; and 6pm -6amWindows (open /closed along withduration open ininch-hours unitsWindows (Open /Closed) along withwindow opendurationmeasurements Not Available Not Available Not Available Not availableCommentsCA sample was arandom sample ofhomes. NJ and TXhomes weredeliberatelychosen to be nearto ambientsources.Restricted to innercity homes with highschool students.Contemporaneoustemperature dataobtained for theseanalyses from SCRAMandwww.wunderground.commeteorological data.Contemporaneoustemperature dataobtained for theseanalyses from SCRAMandwww.wunderground.commeteorological 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” “C entral 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 t he 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, 10 th 

### , 20 th 

### ... 90 th 

### 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 de monstrates 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 stra tify 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 score s 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 an d 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, standarddeviation, scale, shape) and the p-value for three standard goodness-of-fit tests: KolmogorovSmirnov (K-S), Cramer-Von-Mise s (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 = { σ x √ (2 π )} -1 

### exp{ -(log x - ζ ) 2 

### / (2 σ 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√ (2 π )} -1 

### exp{ -(x - μ ) 2 

### / (2 σ 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 pr oduces 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 ci ty 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 combini ng 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 Mary land, 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 Resear ch 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-resident ial, indoor environments (e.g., offices and schools), we obtained and analyzed two AER data sets: “Turk” (T urk et al, 1989); and “Persily” (Persily and Gorfain 2004; Persily et al. 2005).

The earlier “Turk” data set (T urk et al, 1989) includes 40 AER m easurements from offices (25 values), schools (7 values), libraries (3 valu es), 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 Surv ey and Evaluation (BASE) study, which was conducted to assess indoor air qu ality, including ventilation, in a large number of randomly selected office buildings throughout the U.S. The data base co nsists of a total of 390 AER measurements in 96 large, mechanically ventilat ed 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 ob tain 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 25 th 

### %ile Median 75 th 

### %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-ValueKolmogorovSmirnov P-ValueCramervon Mises P-ValueAndersonDarling



### 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-vehi cle, 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 m ode. 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 in tervals 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 dist ributions 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 site 1



### Road Type 1 

### Number of

cases 1 

### Mean 1 

### Standard Deviation 1 

### 25 th


### Percentile 1 

### 50 th


### Percentile 1 

### 75 th


### Percentile 1 

### Estimated 5 th


### Percentile 2



### 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 25 th 

### and 75 th 

### percentiles appear to be approximately equidistant from the median (50 th 

### 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 va lues 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 5 th 

### 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 ro ad 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 5 th 

### 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 freeway s/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, v ehicle 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 ozone 1



### Ventilation 1 

### Number of

cases 1 

### Mean 1 

### Standard Deviation 1 

### 25 th


### Percentile 1 

### 50 th


### Percentile 1 

### 75 th


### Percentile 1 

### Estimated 5 th


### Percentile 2



### 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 0.000 3



### All 812 0.300 0.232 0.117 0.251 0.463 0.000 3



### 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 repr esent 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.

References



### American Petroleum Institute (1997). Sensitivity testing of pNEM/O 3 exposure to changes in the model algorithms. Health and Environmental Sciences Department.

Avol, E. L., W. C. Navidi, and S. D. Colo me (1998) Modeling ozone levels in and around southern California homes. Environ. Sci. Technol. 32, 463-468.

Chilrud, S. N., D. Epstein, J. M. Ross, S. N. Sax, D. Pederson, J. D. Spengler, P. L. Kinney (2004). Elevated airborne exposur es of teenagers to manganese , chromium, and iron from steel dust and New York City's subway system. Environ. Sci. Technol. 38, 732-737.

Colome, S.D., A. L. Wilson, Y. Tian (1993). California Residential Indoor Air Quality Study, Volume 1, Methodology and Descriptive Statistics. Report prepared for the Gas Research Institute, Pacific Gas & Electric Co., San Diego Gas & Electric Co., Southern California Gas Co.

Colome, S.D., A. L. Wilson, Y. Tian (1994). California Residential Indoor Air Quality Study, Volume 2, Carbon Monoxide and Air Exchange Rate : An Univariate and Mu ltivariate Analysis. Chicago, IL. Report prepared for the Gas Research Inst itute, Pacific Gas & Electric Co., San Diego Gas & Electric Co., Southe rn California Gas Co. GRI-93/0224.3

### B-217 

### Jakobi, G and Fabian, P. (1997). I ndoor/outdoor concentrations of ozone and peroxyacetyl nitrate (PAN) . Int. J. Biometeorol. 40: 162-165.

.
Johnson, T., A. Pakrasi, A. Wisbeth, G. Meiner s, W. M. Ollison (1995). Ozone exposures within motor vehicles – results of a fi eld study in Cincinnati, Ohio. Proceedings 88 th 

### annual meeting and exposition of the Air & Waste Manage ment Association, June 18-23, 1995. San Antonio, TX. Preprint paper 95-WA84A.02.

Kinney, P. L., S. N. Chillrud, S. Ramstrom, J. Ro ss, J. D. Spengler (2002). Exposures to multiple air toxics in New York City. Environ Health Perspect 110, 539-546.

Lagus Applied Technology, Inc. (1995) Air change rates in non- residential buildings in California. Sacramento CA, California Energy Commission, contract 400-91-034.

Liu, L.-J. S, P. Koutrakis, J. Leech, I. Brode r, (1995) Assessment of ozone exposures in the greater metropolitan Toronto area. J. Air Waste Manage. Assoc . 45: 223-234.

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 PM 2.5 

### concentrations: Analyses of RIOPA data. Journal of Exposure Analysis and Environ Epidemiology . Preprint.

Murray, D. M. and D. E. Burmaster (1995). Residential Air Exchange Rates in the United States: Empirical and Estimated Parametric Di stributions by Season and Climatic Region. Risk Analysis , Vol. 15, No. 4, 459-465.

Persily, A. and J. Gorfain.(2004). Analysis of ventilation data from the U.S. Environmental Protection Agency Building Assessment Survey and Evaluation (BASE) Study. National Institute of Standards and Technology, NISTIR 7145, December 2004.

Persily, A., J. Gorfain, G. Brunner.(2005). Ventil ation design and performance in U.S. office buildings. ASHRAE Journal. April 2005, 30-35.

Sax, S. N., D. H. Bennett, S. N. Chillrud, P. L. Kinney, J. D. Spengler (2004) Differences in source emission rates of volatile organic compounds in inner-city residences of New York City and Los Angeles. Journal of Exposure Analysis and Environ Epidemiology . Preprint.

Turk, B. H., D. T. Grimsrud, J. T. Brown, K. L. Geisling-Sobotka, J. Harrison, R. J. Prill (1989). Commercial building ventilation rates and particle concentrations . ASHRAE, No. 3248.

Weschler, C. J. (2000) Ozone in indoor envir onments: concentration a nd chemistry. Indoor Air 10: 269-288.

Weschler, C. J. and Shields, H. C. (2000) The influence of ventilati on on reactions among indoor pollutants: modeling and e xperimental observations. Indoor Air . 10: 92-100.

### B-218 

### 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). Re lationship of Indoor, Outdoor and Personal Air (RIOPA) study; study design, methods and quality assurance / control results. Journal of Exposure Analysis and Environ Epidemiology . Preprint.

Williams, R., J. Suggs, A. Rea, K. Leovic, A. Vette, C. Croghan, L. Sheldon, C. Rodes, J. Thornburg, A. Ejire, M. Herbst, W. Sanders Jr. (2003a). The Res earch Triangle Park particulate matter panel study: PM mass concentration relationships. Atmos Env 37, 5349-5363.

Williams, R., J. Suggs, A. Rea, L. Sheldon, C. Rodes, J. Thornburg (2003b). The Research Triangle Park particulate patter panel study: m odeling ambient source co ntribution to personal and residential PM mass concentrations. Atmos Env 37, 5365-5378.

Wilson, A. L., S. D. Colome, P. E. Baker, E. W. Becker (1986). Residential Indoor Air Quality Characterization Study of Nitrogen Dioxide, Phase I, Final Report. Prepared for Southern California Gas Company, Los Angeles.

Wilson, A. L., S. D. Colome, Y. Tian, P. E. Bake r, E. W. Becker, D. W. Behrens, I. H. Billick, C. A. Garrison (1996). California residential air exchange rates and residence volumes. Journal of Exposure Analysis and Environ Epidemiology . Vol. 6, No. 3.

### B-219 

## Attachment 6: Technical Memorandum on HAPEM Near Road Population Data Base Development (fr om 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 c oncentration 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 roadw ay 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 conc entrations within a census tract from the ASPEN concentration prediction.

Rodes and Riediker Studies

In order to stratify the concentration ratios ac cording 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 patro l 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 Ca lifornia 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 upwi nd 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 time s 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 fr eeways), 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

### B-222 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 highway s; FC12 = distance to functional class FC12, urban other freeways and expressways; FC14 = di stance 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 l ogarithm 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 term s 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

### B-223 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 si ngle 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 likel y due to the problem that the near roadway concentrations are also impacted by other emissi ons 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 t hese 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, funde d 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 th e CALPUFF dispersion model. CALPUFF is a non-steady state Gaussian puff model, and was sele cted 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.

### B-224 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 ex pected 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 t he 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 fu rther 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.quar terly 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 s how 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.

### B-225 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 t he 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 m odel. 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

### B-226 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 95 th 

### 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 PM 2.5 

### 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

### B-227 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-22812Table 1. Analysis of mean CO concentration versus. Distance from freeway based on Zhu et al (2002).345Freeway Season Distance(s) Upwind (U) Distribution of Daily Means Across Measurement Days405, 710 S=Summer From Freeway oror both W=Winter (m) Downwind(D) nmean std min q1 median q3 max(405710) A=All405 A 30 D 6 2.03 0.10 1.87 2.03 2.03 2.03 2.17405 A 60 D 6 1.16 0.30 0.87 0.90 1.08 1.50 1.50405 A 90 D 6 0.81 0.23 0.60 0.60 0.78 1.03 1.07405 A 150 D 6 0.56 0.16 0.40 0.40 0.55 0.70 0.73405 A 300 D 6 0.34 0.17 0.13 0.23 0.32 0.50 0.57405 A 300 U 6 0.12 0.04 0.10 0.10 0.10 0.10 0.20405 A d < 50m D 6 2.03 0.10 1.87 2.03 2.03 2.03 2.17405 A 50m <= d < 150m D 6 0.98 0.26 0.73 0.75 0.96 1.23 1.27405 A 150m <= d D 6 0.45 0.16 0.27 0.32 0.44 0.62 0.62405 S 30 D 3 1.98 0.10 1.87 1.87 2.03 2.03 2.03405 S 60 D 3 0.90 0.03 0.87 0.87 0.90 0.93 0.93405 S 90 D 3 0.60 0.00 0.60 0.60 0.60 0.60 0.60405 S 150 D 3 0.41 0.02 0.40 0.40 0.40 0.43 0.43405 S 300 D 3 0.20 0.06 0.13 0.13 0.23 0.23 0.23405 S 300 U 3 0.10 0.00 0.10 0.10 0.10 0.10 0.10405 S d < 50m D 3 1.98 0.10 1.87 1.87 2.03 2.03 2.03405 S 50m <= d < 150m D 3 0.75 0.02 0.73 0.73 0.75 0.77 0.77405 S 150m <= d D 3 0.31 0.03 0.27 0.27 0.32 0.33 0.33405 W 30 D 3 2.08 0.08 2.03 2.03 2.03 2.17 2.17405 W 60 D 3 1.41 0.15 1.23 1.23 1.50 1.50 1.50405 W 90 D 3 1.02 0.05 0.97 0.97 1.03 1.07 1.07405 W 150 D 3 0.70 0.03 0.67 0.67 0.70 0.73 0.73405 W 300 D 3 0.49 0.08 0.40 0.40 0.50 0.57 0.57405 W 300 U 3 0.13 0.06 0.10 0.10 0.10 0.20 0.20405 W d < 50m D 3 2.08 0.08 2.03 2.03 2.03 2.17 2.17405 W 50m <= d < 150m D 3 1.22 0.06 1.15 1.15 1.23 1.27 1.27405 W 150m <= d D 3 0.59 0.04 0.55 0.55 0.62 0.62 0.62B-229Freeway Season Distance(s) Upwind (U) Distribution of Daily Means Across Measurement Days405, 710 S=Summer From Freeway oror both W=Winter (m) Downwind(D) nmean std min q1 median q3 max(405710) A=All710 A 17 D 7 2.24 0.19 2.00 2.03 2.17 2.43 2.47710 A 20 D 7 1.98 0.17 1.73 1.83 1.97 2.13 2.23710 A 30 D 7 1.56 0.25 1.27 1.33 1.57 1.83 1.87710 A 90 D 7 0.52 0.09 0.33 0.50 0.57 0.57 0.57710 A 150 D 7 0.43 0.07 0.33 0.40 0.40 0.50 0.53710 A 200 U 7 0.13 0.05 0.10 0.10 0.10 0.20 0.20710 A 300 D 7 0.26 0.12 0.10 0.17 0.23 0.40 0.43710 A d < 50m D 7 1.93 0.14 1.76 1.76 1.98 2.03 2.10710 A 50m <= d < 150m D 7 0.52 0.09 0.33 0.50 0.57 0.57 0.57710 A 150m <= d D 7 0.34 0.09 0.25 0.27 0.32 0.47 0.47710 S 17 D 5 2.30 0.19 2.03 2.17 2.40 2.43 2.47710 S 20 D 5 2.02 0.19 1.73 1.97 2.03 2.13 2.23710 S 30 D 5 1.66 0.22 1.33 1.57 1.70 1.83 1.87710 S 90 D 5 0.50 0.10 0.33 0.50 0.53 0.57 0.57710 S 150 D 5 0.39 0.04 0.33 0.40 0.40 0.40 0.43710 S 200 U 5 0.10 0.00 0.10 0.10 0.10 0.10 0.10710 S 300 D 5 0.19 0.06 0.10 0.17 0.20 0.23 0.27710 S d < 50m D 5 1.99 0.10 1.83 1.98 2.02 2.03 2.10710 S 50m <= d < 150m D 5 0.50 0.10 0.33 0.50 0.53 0.57 0.57710 S 150m <= d D 5 0.29 0.03 0.25 0.27 0.30 0.32 0.33710 W 17 D 2 2.08 0.12 2.00 2.00 2.08 2.17 2.17710 W 20 D 2 1.87 0.05 1.83 1.83 1.87 1.90 1.90710 W 30 D 2 1.32 0.07 1.27 1.27 1.32 1.37 1.37710 W 90 D 2 0.57 0.00 0.57 0.57 0.57 0.57 0.57710 W 150 D 2 0.52 0.02 0.50 0.50 0.52 0.53 0.53710 W 200 U 2 0.20 0.00 0.20 0.20 0.20 0.20 0.20710 W 300 D 2 0.42 0.02 0.40 0.40 0.42 0.43 0.43710 W d < 50m D 2 1.76 0.00 1.76 1.76 1.76 1.76 1.76710 W 50m <= d < 150m D 2 0.57 0.00 0.57 0.57 0.57 0.57 0.57710 W 150m <= d D 2 0.47 0.00 0.47 0.47 0.47 0.47 0.47405710 A 17 D 7 2.24 0.19 2.00 2.03 2.17 2.43 2.47405710 A 20 D 7 1.98 0.17 1.73 1.83 1.97 2.13 2.23B-230Freeway Season Distance(s) Upwind (U) Distribution of Daily Means Across Measurement Days405, 710 S=Summer From Freeway oror both W=Winter (m) Downwind(D) nmean std min q1 median q3 max(405710) A=All405710 A 30 D 13 1.78 0.30 1.27 1.57 1.87 2.03 2.17405710 A 60 D 6 1.16 0.30 0.87 0.90 1.08 1.50 1.50405710 A 90 D 13 0.65 0.22 0.33 0.57 0.57 0.60 1.07405710 A 150 D 13 0.49 0.13 0.33 0.40 0.43 0.53 0.73405710 A 200 U 7 0.13 0.05 0.10 0.10 0.10 0.20 0.20405710 A 300 D 13 0.30 0.15 0.10 0.20 0.23 0.40 0.57405710 A 300 U 6 0.12 0.04 0.10 0.10 0.10 0.10 0.20405710 A d < 50m D 13 1.97 0.13 1.76 1.87 2.03 2.03 2.17405710 A 50m <= d < 150m D 13 0.73 0.30 0.33 0.57 0.57 0.77 1.27405710 A 150m <= d D 13 0.39 0.13 0.25 0.30 0.33 0.47 0.62405710 S 17 D 5 2.30 0.19 2.03 2.17 2.40 2.43 2.47405710 S 20 D 5 2.02 0.19 1.73 1.97 2.03 2.13 2.23405710 S 30 D 8 1.78 0.24 1.33 1.63 1.85 1.95 2.03405710 S 60 D 3 0.90 0.03 0.87 0.87 0.90 0.93 0.93405710 S 90 D 8 0.54 0.09 0.33 0.52 0.57 0.60 0.60405710 S 150 D 8 0.40 0.03 0.33 0.40 0.40 0.42 0.43405710 S 200 U 5 0.10 0.00 0.10 0.10 0.10 0.10 0.10405710 S 300 D 8 0.20 0.06 0.10 0.15 0.22 0.23 0.27405710 S 300 U 3 0.10 0.00 0.10 0.10 0.10 0.10 0.10405710 S d < 50m D 8 1.99 0.09 1.83 1.92 2.03 2.03 2.10405710 S 50m <= d < 150m D 8 0.59 0.15 0.33 0.52 0.57 0.74 0.77405710 S 150m <= d D 8 0.30 0.03 0.25 0.27 0.31 0.33 0.33405710 W 17 D 2 2.08 0.12 2.00 2.00 2.08 2.17 2.17405710 W 20 D 2 1.87 0.05 1.83 1.83 1.87 1.90 1.90405710 W 30 D 5 1.77 0.42 1.27 1.37 2.03 2.03 2.17405710 W 60 D 3 1.41 0.15 1.23 1.23 1.50 1.50 1.50405710 W 90 D 5 0.84 0.25 0.57 0.57 0.97 1.03 1.07405710 W 150 D 5 0.63 0.10 0.50 0.53 0.67 0.70 0.73405710 W 200 U 2 0.20 0.00 0.20 0.20 0.20 0.20 0.20405710 W 300 D 5 0.46 0.07 0.40 0.40 0.43 0.50 0.57405710 W 300 U 3 0.13 0.06 0.10 0.10 0.10 0.20 0.20405710 W d < 50m D 5 1.95 0.18 1.76 1.76 2.03 2.03 2.17B-231Freeway Season Distance(s) Upwind (U) Distribution of Daily Means Across Measurement Days405, 710 S=Summer From Freeway oror both W=Winter (m) Downwind(D) nmean std min q1 median q3 max(405710) A=All405710 W 50m <= d < 150m D 5 0.96 0.36 0.57 0.57 1.15 1.23 1.27405710 W 150m <= d D 5 0.54 0.08 0.47 0.47 0.55 0.62 0.621B-232Table 2. CO concentration ratios based on Zhu et al (2002).12Freeway Season Distance(s) Upwind (U) Distribution of Mean(Group) / Mean (d >= 150m) Across Measurement Days405, 710 S=Summer From Freeway oror both W=Winter (m) Downwind(D) nmean std min q1 median q3 max(405710) A=All405 A d < 50m D 6 5.03 1.79 3.30 3.51 4.65 6.42 7.63405 A 50m <= d < 150m D 6 2.26 0.32 2.00 2.05 2.17 2.32 2.88405 S d < 50m D 3 6.55 1.02 5.60 5.60 6.42 7.63 7.63405 S 50m <= d < 150m D 3 2.48 0.34 2.25 2.25 2.32 2.88 2.88405 W d < 50m D 3 3.50 0.20 3.30 3.30 3.51 3.70 3.70405 W 50m <= d < 150m D 3 2.05 0.05 2.00 2.00 2.05 2.09 2.09710 A d < 50m D 7 5.98 1.69 3.76 3.76 6.25 7.63 8.09710 A 50m <= d < 150m D 7 1.58 0.39 1.11 1.21 1.70 2.00 2.00710 S d < 50m D 5 6.87 0.91 6.11 6.25 6.30 7.63 8.09710 S 50m <= d < 150m D 5 1.72 0.36 1.11 1.70 1.79 2.00 2.00710 W d < 50m D 2 3.76 0.00 3.76 3.76 3.76 3.76 3.76710 W 50m <= d < 150m D 2 1.21 0.00 1.21 1.21 1.21 1.21 1.21405710 A d < 50m D 13 5.54 1.74 3.30 3.76 6.11 6.42 8.09405710 A 50m <= d < 150m D 13 1.89 0.50 1.11 1.70 2.00 2.09 2.88405710 S d < 50m D 8 6.75 0.90 5.60 6.18 6.36 7.63 8.09405710 S 50m <= d < 150m D 8 2.01 0.51 1.11 1.74 2.00 2.28 2.88405710 W d < 50m D 5 3.61 0.20 3.30 3.51 3.70 3.76 3.76405710 W 50m <= d < 150m D 5 1.71 0.46 1.21 1.21 2.00 2.05 2.09345678910B-233Table 3.RIOPA Data. Analysis of mean concentrations vs. distance(d) from nearest major roadway (classes FC11, FC13, FC14).123ConcentrationsMean (group)/ Mean (d >= 150 m)Pollutant Distance Group n mean std min q1 median q3 max ratioStderrorCV (%)Benzene d < 50m 12 1.54 0.43 0.671.25 1.59 1.87 2.19 1.13 0.12 10.42Benzene 50m <= d < 150m 54 1.77 2.43 0.12 0.82 1.26 2.01 18.06 1.29 0.26 19.77Benzene 150m <= d 117 1.37 0.98 0.06 0.63 1.09 1.84 5.17 . . .CarbonTetrachlorided < 50m 12 0.64 0.21 0.17 0.530.67 0.74 1.01 0.93 0.09 10.12CarbonTetrachloride50m <= d < 150m 54 1.23 4.17 0.17 0.56 0.68 0.84 31.23 1.81 0.83 46.17CarbonTetrachloride 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 0.27 22.18Ethylbenzene 50m <= d < 150m 54 1.874.86 0.09 0.42 1.03 1.85 36.24 1.68 0.61 36.18Ethylbenzene 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 0.44 35.62MTBE 50m <= d < 150m 54 6.82 5.480.06 3.22 5.60 8.09 26.89 1.32 0.18 14.04MTBE 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 0.15 15.45PCE 50m <= d < 150m 54 1.66 5.600.10 0.50 0.75 1.11 41.82 1.90 0.88 46.35PCE 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 1.474.69 7.62 12.93 21.88 1.41 0.30 21.23Toluene 50m <= d < 150m 54 7.07 5.36 0.11 2.36 5.82 11.51 22.27 1.09 0.15 13.41Toluene 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 0.47 27.48m,p,-Xylene 50m <= d < 150m 54 4.07 6.94 0.42 1.72 2.45 4.41 51.21 1.49 0.36 24.27m,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 0.301.02 1.30 1.46 80.98 7.74 6.57 84.89o-Xylene 50m <= d < 150m 54 1.84 5.00 0.28 0.58 0.95 1.56 37.49 1.82 0.68 37.40o-Xylene 150m <= d 117 1.01 0.65 0.07 0.59 0.92 1.26 3.27 . . .4B-234Table 4. Empirical distributions of concentration ratios from PATA (see text). Denominator distance range is 200+ m.123PollutantConcentrationmetricNumerator N Mean VarianceP-Value forShapiro-Wilktest ofnormalitygeommeangeomstdev95thpercentileAll LOG 0-75 m 99 0.9267 0.5352 0.0008 2.5263 2.0783 8.4161All RAW 0-75 m 99 3.2170 4.8067 0.0001All LOG 75-200 m 306 0.4950 0.4667 0.0001 1.6404 1.9802 5.0469All RAW 75-200 m 306 2.1611 5.2366 0.0001Benzene LOG 0-75 m 33 0.9071 0.5137 0.1423 2.4770 2.0477 8.0532Benzene RAW 0-75 m 33 3.1121 4.3349 0.0042Benzene LOG 75-200 m 102 0.4770 0.4318 0.0122 1.6113 1.9292 4.7492Benzene 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 9.20831,3-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 2.0829 5.65331,3-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 2.0930 8.3088Diesel 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 1.9380 4.8119Diesel 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 “M SAT Rule”) EPA’s Office of Transportation and Air Quality (OTAQ) co mmitted to further study of the range of concentrations to which people are exposed for cons ideration in future rulemaking. As part of the Technical Analysis Plan outlined in that res earch, OTAQ undertook research activity looking at the air quality in immediate proximity of bus y 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 inhala tion exposures of the ge neral population, or a specific sub-population, over spatia l 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 microenvironmen t visited are combined into a time-weighted average concentration, which is assigne d to members of the demographic group.

Indoor microenvironment concentrations are estima ted by applying scalar f actors to outdoor tract concentrations, which are some of the required inputs. These scalar factors are derived from published studies of concurrent concentr ation measurements indoors and outdoors.

In the previous version, HAPEM5, if only a si ngle 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 th e model to account for the spatial variability of outdoor concentrations within a tr act due to enhanced outdoor c oncentrations 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 expos ure concentrations within each Census tract by accounting for some of the spatial variability in the outdoor concentrations w ithin the tract, and by extension some of the spatial variability in in door concentrations within the tract.

### B-239 

### Accomplishing this refinement to HAPEM require d several activities, in cluding 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 memorandu m 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 Cent er (ESRI) StreetMap US roadway geographic database (which includes NavTech, GDT and TeleAtlas rectified street data)

• A geographic database of US Census block boundaries, ex tracted using the PCensus 2000 Census data extraction to ol 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 interm ediate product for this project, it will be retained to facilitate the re-specification of demographic groups for po ssible future analyses. Therefore, this file contains the most resolved age-gender groups ava ilable at the block leve l 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 (b lock 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 me ters 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 op erations in this study ICF utilized ArcInfo software. ArcInfo is the most extensive versi on of ArcGIS 9.1, the industry’s standard for Geographic Information Systems, produ ced by ESRI of Redlands, CA.

Due to the size of the roadway and block geograp hy 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 fe w 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 ev enly distributed w ithin 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 cr eate 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 contai ning 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” bl ock area that that fell within each buffer was calculated. This area fraction was a ssumed to equal the population fraction that fell within each buffer, and the frac tions 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 th e 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 stratif ications 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 follo wing 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 2 00 meters from a major roadway

o 

### population residing more than 200 meters from a major roadway

• sum of race/ethnic populations (note; this may di ffer 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 2 00 meters from a major roadway

o 

### population residing more than 200 meters from a major roadway

Note that because of the limit ations of the US Census dat a the block level populations could not be stratified by age, gender, and race together,



### The tract file contains the fo llowing 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 with in 75 meters of a major roadway

o 

### fraction of population resi ding between 75 and 200 meters from a major roadway

o 

### fraction of population residing more th an 200 meters from a major roadway

To date only a subset of stat es have been completely processed. For this subset state summaries of the fraction of population li ving 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. Distance from major roadways STATE < 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 Memora ndum on the Uncertainty Analysis Of Residential Air Exchange Rate Distributions

### B-245 

## MEMORANDUM

To: 

### John Langstaff, EPA OAQPS

From: 

### Jonathan Cohen, Arlene Ro senbaum, 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 th e uncertainty of cityspecific distributions of residentia l air exchange rates that were f itted to the available study data. City-specific distributions for use with the AP EX ozone model were developed for 12 modeling cities, as detailed in the memorandum by Cohen, Mallya and Rosenbaum, 2005 29 

### (Appendix A of this report). In the first pa rt of the memorandum, we analyze the between-city uncertainty by examining the variation of the geometric means a nd standard deviations ac ross 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 variati on on the fitted geometric means and standard deviations for each city. The bootst rap distributions assess the uncertainty due to random sampling variation but do not address uncerta inties 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, 2005 30 

### (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 c onditioner (central or window). In each case we chose to fit a log-normal distribu tion to the AER data, so that the logarithm of the AER for a given city, temperature range, and A/C type is as sumed to be normally distributed. If the AER data has geometric mean GM and geometric standa rd 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 th e non-California data for houses without A/C was used to repres ent the AER distributions for h ouses without A/C. Sacramento, CA AER distributions were estimat ed 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; thes e combined data are referred to by the City Name “Inland California.” St Louis, MO AER di stributions were estima ted 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-Californi a, 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 Lo s Angeles (“Inland California”)
St. Louis All non-California (“Outside California”)
Washington, DC, A/C Resear ch 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 fi rst 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 th at 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 variati on of the fitted log-normal distributions across the cities with AER data. In this manner we can ex amine 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 da ta, 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 de signed 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 car diac defibrillators living in Chapel Hill; a group of non-smoking, African Amer icans aged at leas t 50 years with cont rolled 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 pref erentially 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 loca tions is to evaluate the varia tion of the fitted log-normal distributions within ea ch 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 di stributions 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 th e outside temperature and the A/ C type (whether or not the residence has air conditioning), th is analysis was stratified by th e outside temperature range and the A/C type. Otherwise, st udy or city differences woul d have been confounded by the temperature and A/C type differe nces and you would not be able to tell how much of the AER difference was due to the variati on of temperature and A/C type acr oss cities or studies. In order to be able to compare cities and studies we could not use different te mperature ranges for the different modeled cities as we did for the origin al 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 temp erature and A/C type

strata.

Table D-2 shows the geometric means and stan dard deviations by city and study. These geometric mean and standard deviation pairs ar e plotted in Figure D-1 through D-8. Each figure shows the variation across cities and studies for a given temper ature 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 i ndividual studies and the city overall distribution is designated by a blank value for the study name.

Table D-2. Geometric means and standa rd 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 standa rd 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 standa rd 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 availa ble 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 und efined if the sample size equals 1.



### In general, there is a relatively wide variation across different ci ties. This implies that the AER modeling results would be very different if the matching of m odeled 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 pa rticular 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 th e AER value from the assigned distribution for the

### B-250 

### city, A/C type and temperature range. Note that th is 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 statisti cal weight for each city and fits a mixture of log-normal distributions rather than a single distribution.

In general, there is also some va riation 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 da ta points are less precise and the differences would not be statistically significant. For exam ple, 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 Wils on 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 st udy 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 th eir allocation to the 12 modeled c ities 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 thes e 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 ex ample figures D-9 through D-19 corresponds to a single GM/GSD “Original Data” pair. The GM a nd GSD values for the “O riginal Data” are at the intersection of the horizontal and vertical lines that are parall el to the x- and y-axes in the figures.

### B-253 

### Table D-4. Geometric means and standard devi ations 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 devi ations 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 GS D values, a bootstrap si mulation was performed, as follows. Suppose that a given AER subset ha s N values. A bootstrap sample is obtained by sampling N times at random with replacement fr om 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 valu es in the bootstrap sample are also selected randomly from the N values, so that for each selec tion, each of the N values is equally likely. The same value can be selected more than on ce. 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 s ubset, producing a set of 1,000 geometric mean and geometric standard deviation pairs, which were plotted in exampl e 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 di stribution of AER values for the given city, temperature range, and A/C type. On that basis, the 1,000 bootst rap GM and GSD pairs estimate the variation of the GM and GSD across all possi ble samples of N values from the population. Since each GM, GSD pair uniquely defines a f itted log-normal distribution, the pairs also estimate the uncertainty of the fitted log-normal distribution. Th e 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 pair s, the uncertainty distri bution 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 equa l 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 valu es that are all close together.

The bootstrap analyses show that the geometri c 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 geomet ric 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 uncerta inty due to the random sampling. It does not account for the uncertainty due to th e lack of representativeness, whic h in turn is due to the fact that the samples were not always random sample s from the entire population of residences in a city, and were sometimes used to represent di fferent cities. Since only the GM and GSD were used, the bootstrap analyses does not account fo r uncertainties about the true distributional shape, which may not necessarily be log-normal. Furthermore, the bootstr ap uncertainty does not account for the effect of the calen dar year (possible tre nds in AER values) or of the uncertainty due to the AER measurement period; the distributi ons were intended to repr esent distributions of 24 hour average AER values although the study AE R 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 AP EX model. Instead of using the Original Data distributions, a bootstrap GM, GSD pair could be selected at ra ndom and the AER value could be selected randomly from the log-normal di stribution with the bootstrap GM and GSD.Figure D-1B-257Figure D-2B-258Figure D-3B-259Figure D-4B-260Figure D-5B-261Figure D-6B-262Figure D-7B-263Figure D-8B-264Figure D-9B-265Figure D-10B-266Figure D-11B-267Figure D-12B-268Figure D-13B-269Figure D-14B-270Figure D-15B-271Figure D-16B-272Figure D-17B-273Figure D-18B-274Figure D-19

## Attachment 9. Technical Memorand um on the Distributions of Air Exchange Rate Averages Over Multiple Days

### B-276 

## MEMORANDUM

To: 

### John Langstaff, EPA OAQPS From: 

### Jonathan Cohen, Arlene Ro senbaum, 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, 2005 31 

### (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 Triang le Park Panel Study that show extremely strong correlations between consecutive 24-hour air excha nge rates measured at the same house. This provides support for the simplified approach of tr eating all averaging periods as if they were 24- hour averages.

In the current version of the AP EX model, there are several options for stratification of time periods with respect to AER distributions, and fo r when to re-sample from a distribution for a given stratum. The options selected for this curre nt 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 independ ent. Although we have not identified sufficient data to test the assumption of uniform AERs th roughout a 24-hour period, the analyses described in this memorandum suggest that AERs on consecutiv e days are highly correlated. Therefore, we performed sensitivity simulati ons 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 av erages 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 co mpare 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 temperatur e, and the daily temper atures often varied considerably. Two altern ative 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 actua l number of groups was 124. For the second approach, B, we again sort ed the data by the HOUSE_ID number and date, but this time we began a new group of days fo r 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 temp erature ranges chosen for the analysis in the Cohen, Mallya, and Rosenbaum, 2005, memora ndum (Appendix A): <= 10, 10-20, 20-25, and > 25 oC. For example, if the first week of measurements on a give n 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 trea t 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 follo wing a transition to a different house or season. Us ing the second approach we tr eat groups of days as being independent following a transiti on to a different house or season or temperature range.

To evaluate the distributions of multi-day air ex change 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 c ontinued this process to obtain three-, four-, five-, six-, and seven-day averag es. There were insufficiently re presentative data for averaging periods longer then seven days. Averages ove r 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 temperatur e 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, st andard 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 geom etric standard deviation equals e raised to the power Std Dev log (AER). The results are shown in Tabl es 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 sa me temperature range bu t different averaging periods. This is expected if the daily AER valu es all have the same statistical distribution, regardless of whether or not they are independe nt. More interesting is the observation that the standard deviations and variances are also a pproximately 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 av eraging 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 diffe rent values of K in the same temperature range (except for the relatively limite d data at > 25 oC), this shows that the daily AER values are strongly correlated. Of course the correlation is not perfect, sin ce otherwise the AER for a given day would be identical to the AE R 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 th e RTP Panel study data, to a reasonable approximation, the distribution of an AER meas urement does not depend upon the length of the averaging period for the measurement, alt hough 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 le ngth of the averaging period.

The above argument suggests that the assumpti on that daily AER values are statistically independent is not justified. St atistical modeling of the correlati on structure between consecutive daily AER values is not easy because of the prob lem of accounting for temp erature 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 eff ects are the population average values of AER or log(AER), and ar e assumed to depend upon the temperature range. The random effects have expected values of zero and define the correla tions between pairs of measurements from the same Group, where the Gr oups 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 mixe d 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 th at 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. Ther e is a different overall mean value for each of the four temperature ranges.

A(Group, Temp Range) is the random effect of te mperature. For each Group, four error terms are independently drawn from four different norma l 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 calend ar 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 covari ance between B(Group g, day i) a nd 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-re gressive 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. Ther e is one such error term for every AER measurement, and all these terms ar e 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 additi onal correlation if they are in the same temperature range.

Probably the most interes ting 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 corre lation) although values exactly equal to +1 or -1 are impossible for a stationary model. Nega tive 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 “r andom” 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 dis tinguish from the other te rms. 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, sh owing 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 AP EX model, there are several options for stratification of time periods with respect to AER distributions, and fo r when to re-sample from a distribution for a given stratum. The options selected for this curre nt 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 independ ent. Although we have not identified sufficient data to test the assumption of uniform AERs th roughout 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 e xposure concentrations by implicitly assuming uncorrelated air exchange rates, we re-ran the 20 02 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 micr oenvironments 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 exce edances 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 PercentileDays/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 PercentileDays/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 th e U.S. Environmental Protection Agency (EPA) by Abt Associates Inc. in partial fu lfillment 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 necessar ily reflect the views of the EPA or Abt Associates. Any questions concerning this document should be addressed to Harvey Richmond, U.S. Environmental Protection Agen cy, Office of Air Quality Planning and Standards, C504-06, Research Triang le Park, North Carolina 27711 (email: richmond.harvey@epa.gov).

Abt Associates Inc. November 2008 i

# Table of Contents

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

2 PRELIMINARY CONSIDERATIONS ................................................................ 2-1
2.1 The Broad Empirical Basis for a Relationship Between NO 2 

### 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 concentr ation-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 NO 2 

### 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-re sponse 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 NO 2 

### air quality data .............................................. 3-20 3.8.2.2 Simulation of reductions in NO 2 

### 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. November 2008 ii

# List of Tables

Table 3-1. Mean and 98 th 

### and 99 th 

### Percentiles of the Distri butions of 1-Hour Daily Maximum NO 2 

### Concentrations (in ppm) at th e Georgia Tech Monitor: 2005, 2006, and 2007 ..................................................................................................... 3-8 Table 3-2. Key Uncertainties in the NO 2 

### Risk Assessment .......................................... 3-13 Table 4-1. Estimated Incidence of Resp iratory Emergency Department Visits Associated with "As Is" NO 2 

### Concentrations and NO 2 

### Concentrations that Just Meet Alternative Standards in Atlanta, GA, Based on Adjusting 2005 NO 2 

### Concentrations ............................................................................................. 4-2 Table 4-2. Estimated Incidence of Resp iratory Emergency Department Visits Associated with "As Is" NO 2 

### Concentrations and NO 2 

### Concentrations that Just Meet the Current and Alternativ e Standards in Atlanta, GA, Based on Adjusting 2006 NO 2 

### Concentrations ................................................................... 4-3 Table 4-3. Estimated Incidence of Resp iratory Emergency Department Visits Associated with "As Is" NO 2 

### Concentrations and NO 2 

### Concentrations that Just Meet the Current and Alternativ e Standards in Atlanta, GA, Based on Adjusting 2007 NO 2 

### Concentrations ................................................................... 4-4 Table 4-4. Estimated Incidence of Respir atory Emergency Department Visits per 100,000 Population Associated with "As Is" NO 2 

### Concentrations and NO 2 

### Concentrations that Just Meet Altern ative Standards in Atlanta, GA, Based on Adjusting 2005 NO 2 

### Concentrations .............................................................. 4-5 Table 4-5. Estimated Incidence of Respir atory Emergency Department Visits per 100,000 Population Associated with "As Is" NO 2 

### Concentrations and NO 2 

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

### Concentrations ..............................4-6 Table 4-6. Estimated Incidence of Resp iratory Emergency Department Visits per 100,000 Population Associated with "As Is" NO 2 

### Concentrations and NO 2 

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

### Concentrations ..............................4-7 Table 4-7. Estimated Percent of Tota l Incidence of Respiratory Emergency Department Visits Associated with "As Is" NO 2 

### Concentrations and NO 2 

### Concentrations that Just Meet Altern ative Standards in Atlanta, GA, Based on Adjusting 2005 NO 2 

### Concentrations .............................................................. 4-8 Table 4-8. Estimated Percent of Tota l Incidence of Respiratory Emergency Department Visits Associated with "As Is" NO 2 

### Concentrations and NO 2 

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

### Concentrations ..............................4-9 Table 4-9. Estimated Percent of Tota l Incidence of Respiratory Emergency Department Visits Associated with "As Is" NO 2 

### Concentrations and NO 2 

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

### Concentrations ............................4-10

Abt Associates Inc. November 2008 iii

### Abt Associates Inc. November 2008 iv 

# List of Figures

Figure 3-1. Major Components of NO 2 

### Health Risk Assessment Based on Epidemiology Studies .......................................................................................... 3-2 Figure 4-1. Incidence of Re spiratory-Related Emergency Department Visits in Atlanta, GA Under Different Air Quality Scenarios, Based on Adjusting 2005, 2006, and 2007 NO 2 

### Concentrations ....................................................... 4-11

# Nitrogen Dioxide Health Risk Assessment for Atlanta, GA

1 INTRODUCTION

The U.S. Environmental Protection Ag ency (EPA) is presently conducting a review of the national ambient air qualit y standards (NAAQS) for nitrogen dioxide (NO 2 

### ). Sections 108 and 109 of the Clean Ai r Act (Act) govern th e establishment and periodic review of the NAAQS. These sta ndards are established fo r pollutants that may reasonably be anticipated to endanger public health and welfare, and whose presence in the ambient air results from numerous or di verse mobile or stationary sources. The NAAQS are to be based on air qua lity criteria, which are to a ccurately reflect the latest scientific knowledge useful in indicating th e kind and extent of identifiable effects on public health or welfare that may be expect ed from the presence of the pollutant in ambient air. The EPA Administrator is to promulgate and periodica lly review, at fiveyear intervals, “primary” (health-based) and “secondary” (welfare-based) NAAQS for such pollutants. 1 

### Based on periodic reviews of the ai r quality criteria and standards, the Administrator is to make revisions in the cr iteria and standards, and promulgate any new standards, as may be appropria te. 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 Scient ific Advisory Committee (CASAC).

EPA’s plan and schedule for this NO 2 

### 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 disc usses the preparat ion 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/expos ure assessments develop quali tative characterization and quantitative estimates, where judged appropria te, of human exposure and health risk and related variability and uncertainties, drawi ng upon the information summarized in the ISA.

In early March 2008, EPA’s National Ce nter 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 Qu ality Planning and Standards (OAQPS) in early April released a first draft of its “Risk and Exposure Assessment to Support the Review of the NO 2 

### Primary National Ambient Air Qualit y Standard,” henceforth referred to as the 1 st 

### draft REA (U.S. EPA, 2008b). Both of these documents were reviewed by the CASAC NO 2 

### 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. November 2008 1-1

### As a result of the May 2008 CASAC NO 2 

### 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 respir atory-related emergency department (ED) visits estimated to be asso ciated with exposures to NO 2 

### for the Atlanta metropolitan statistical area (MSA).

NO 2 

### is one of a group of substances known as nitrogen oxides (NO x 

### ), which include multiple gaseous (e.g., NO 2 

### , NO) and particulate (e.g., nitr ate) species. As in past NAAQS reviews, NO 2 

### is considered as the surrogate for the gaseous NO x 

### species for the purpose of this assessment, with particulate sp ecies addressed as part of the particulate matter (PM) NAAQS review.

Previous reviews of the NO 2 

### primary NAAQS completed in 1985 and 1994 did not include quantitative health risk assessmen ts. Thus, the risk assessment described in this document builds upon the methodology and le ssons learned from the risk assessment work conducted for the recently concluded PM and O 3 

### NAAQS reviews (Abt Associates, 2005; Abt Associates, 2007a). Many of the same methodological issues are present for each of these criteria air pollutants where ep idemiological studies provided the basis for the concentration-response (C-R ) relationships used in the quantitative risk assessment.

In July 2008, EPA issued the final IS A, “Integrated Science Assessment for Oxides of Nitrogen – Health Criteria (Final Report), henceforth re ferred to as the final ISA (U.S. EPA, 2008c). The risk assessment de scribed in this document is also based on the information and evaluation contained in the final ISA. In August 2008, EPA is releasing its 2 nd 

### draft REA, henceforth referred to as the draft REA (U.S. EPA, 2008d).

The NO 2 

### health risk assessment describe d in this document estimates the incidence of respiratory-related ED visits associated with short-term exposures to NO 2 

### under recent (“as is”) air quality leve ls and upon just meeting the current NO 2 

### standard of 0.053 ppm annual average and several alternative NO 2 

### 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 98 th 

### 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 Ad ministrator 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 NO 2 standard refers to a two-year period and requires that the annual average NO 2 level be less than or equal to 0.053 ppm in each of the two years.

3 For the alternative standards using, say, the 98 th percentile form, the standard is met when the average of the three annual 98 th percentile daily maximum 1-hr concentrations for the 3-year period is at or below the specified standard level.



### Abt Associates Inc. November 2008 1-2

### Abt Associates Inc. November 2008 1-3 Preliminary considerations and the basi c 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 NO 2 

### exposures for recent (“as is”) NO 2 

### levels, based on 2005, 2006, and 2007 air quality data, as well as the risks asso ciated with just meeting the current standard and the reduced risks associated with just meeting each of several alternative NO 2 

### NAAQS. 4 

### In this section we addre ss preliminary considerations. Section 2.1 briefly discusses the broad empi rical basis for a relationship between NO 2 

### exposures and adverse health effects. Section 2.2 describes the basic structure of the risk assessment. Finally, section 2.3 addr esses air quality considerations.

2.1 The Broad Empirical Basis for a Relationship Between NO 2 

### 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 NO 2 

### exposure and respiratory effects:

Taken together, the findings of epidem iologic, human clinical, and animal toxicological studies provided evidence that is sufficient to infer a likely causal relationship for respirator y effects with short-term NO 2 

### exposure. The body of evidence from epidemiologic studies has grown substantially since the 1993 AQCD and provided scien tific evidence that short-term exposure to NO 2 

### is associated with a broad range of respiratory morbidity effects, including altered lung hos t defense, inflammation, airway hyperresponsiveness, respiratory symp toms, 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 a ssessment, 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 es timate C-R functions usin g ambient air quality data from fixed-site, population-oriented m onitors, the appropriate application of these functions in a NO 2 

### risk assessment similarly requires the use of ambient air quality data at fixed-site, population-oriented monitors. The NO 2 

### health risk model combines information about NO 2 

### air quality for a specific urban area with C-R functions derived from an epidemiological study and baseline health inci dence data for a specifi c health endpoint to derive estimates of the annual in cidence of the specified health effect attributable to ambient NO 2 

### 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 NO 2 

### ambient standards.

4 The current NO 2 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” NO 2 concentrations, which are lower than NO 2 concentrations simulated to just meet the current standard.



### Abt Associates Inc. November 2008 2-1

### As described more fully below, a risk asse ssment based on epidemiological studies requires baseline incidence data or base line incidence rates and populati on 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 func tions, and therefore requires as input (monitored) ambient NO 2 

### concentrations.

• Epidemiological studies ar e carried out in specific real world locations (e.g., specific urban areas). A risk assessme nt focused on locations in which the epidemiologic studies providing the C-R f unctions 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 NO 2 

### 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 fo r three recent years of air quality (“as is” air quality) and for air quality adjusted so th at it simulates just meeting the current or alternative NO 2 

### standards based on that recent three- year period (2005-2007). This period was selected to represent the most recent air qua lity for which complete data were available.

In order to estimate health risks associated with just meeting the current and alternative NO 2 

### standards, it is necessary to es timate the distribution of hourly NO 2 

### concentrations that would occur under any given standard. Since all locations in the United States are in attainment of the current NO 2 

### standard, and since compliance with the current NO 2 

### standard is based on examining a 2-year period, air quali ty data from 2006 to 2007 were used to determine the amount of increase in NO 2 

### 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 n ecessary to just meet the current NO 2 

### standard. The adjustment was then applied to each year of da ta (2006 and 2007) to estimate risks in each of these individual years. For al ternative 1-hour daily maximum standards, staff specified the form as being the 3-year average of the 98 th 

### (or 99 th 

### ) percentile of the daily maximum 1-hour concentrations. Thus, the th ree-year period including 2005 to 2007 was used for analyses involving alternative 1-hour sta ndards. Estimated design values were used to determine the

5 TRIM.Risk was most recently applied to EPA’s O 3 health risk assessment. A User’s Guide for the Application of TRIM.Risk to the O 3 health risk assessment (Abt Associ ates, 2007b) is available online at: http://epa.gov/ttn/f era/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 nece ssary to just meet alternative NO 2 

### standards, and the adjustments were then applied to each year of da ta 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 ade quately represented the pattern of reductions across the NO 2 

### 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 NO 2 

### monitoring data, based on the 2-year period (2006-2007) NO 2 

### design value for the current standard, to generate new time series of hourly NO 2 

### concentrations for 2006 and 2007 that simulate air quality levels that just meet the current NO 2 

### standard of 0.053 ppm annual average. Because every location across the U.S. meets the current NO 2 

### standard (see U.S. EPA, 2007b, Figure 1), simulation of just mee ting the current standa rd required rolling up air quality.

The proportional air quality adjustment proced ure was similarly applied in the Atlanta MSA to the filled in 2005, 2006, and 2007 NO 2 

### monitoring data, based on the 3-year period (2005-2007) NO 2 

### design values for the alternative 1- hour standards, to generate new time series of hourly NO 2 

### concentrations for 2005, 2006, and 2007 that simulate air quality levels that just meet each of the alternative NO 2 

### 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 re sult, 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 O 3 

### NAAQS reviews represented risks associ ated with PM and O 3 

### levels in excess of estimated policy-relevant background (PRB) levels in the U.S. PRB levels of NO 2 

### are defined as th e distribution of NO 2 

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

### precursors in the U.S., Canada, and Mexico. Estimates of NO 2 

### PRB are reported in section 2.4.6 of the final ISA, an d for most of the contin ental U.S. the PRB is estimated to be less than 300 part s per trillion (ppt). In the No rtheastern U.S., where presentday NO 2 

### concentrations are highest, this amounts to a contribution of about 1% percent of the total observed ambient NO 2 

### 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 w ith PRB concentrations in the current NO 2 

### health risk

assessment.

### 3 METHODS

The major components of the NO 2 

### 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 NO 2 

### is associated with a range of respiratory effects. The ev idence is judged to be sufficient to infer a likely causal relationship between short-term NO 2 

### exposure and adverse effects on the respiratory system. This finding is supporte d 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 A ssociates, 2005) and part of the recently completed O 3 

### risk assessment (Abt Associates, 2007a ), the general approach used in the NO 2 

### 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, th e appropriate application of these functions in a risk assessment similarly requires the use of ambient air quality data at fixed-site, ambient monitors. The NO 2 

### health risk model combines information about NO 2 

### air quality for a specific urban ar ea with C-R functions derived from epidemiological studies and ba seline incidence data for a specific health endpoint to derive estimates of the incidence of the health endpoint attrib utable to ambient NO 2 

### concentrations during the period examined.

In the first part of the risk assessmen t, we estimate health effects incidence associated with “as is” NO 2 

### levels. In the second part, we estimate the (increased) health effects incidence associated with NO 2 

### concentrations simulated to just meet the current NO 2 

### annual standard and the health e ffects incidence associated with NO 2 

### concentrations simulated to just meet alternative 1-hour daily maximum NO 2 

### standards in the assessment location. In both parts, we c onsider the incidence of health effects associated with NO 2 

### concentrations in excess of 0 ppm (as opposed to in excess of PRB, as explained in section 2.3).

Abt Associates Inc. November 2008 3-1Figure 3-1. Major Components of NO2Health Risk Assessment Based on Epidemiology StudiesAmbient Monitoring forSelected Urban AreasAir Quality AdjustmentProceduresCurrent and AlternativeProposed StandardsChanges inDistribution ofNO2AirQualityRisk Estimates:•Recent AirQuality•CurrentStandard•AlternativeStandardsHealthRiskModelEstimates of City-specificBaseline Health EffectsIncidenceHuman EpidemiologicalStudiesConcentration -ResponseRelationshipsConcentration-ResponseAir QualityRecent (“As Is”) AmbientNO2LevelsAmbient Monitoring forSelected Urban AreasAir Quality AdjustmentProceduresCurrent and AlternativeProposed StandardsChanges inDistribution ofNO2AirQualityRisk Estimates:•Recent AirQuality•CurrentStandard•AlternativeStandardsHealthRiskModelEstimates of City-specificBaseline Health EffectsIncidenceHuman EpidemiologicalStudiesConcentration -ResponseRelationshipsConcentration-ResponseAir QualityRecent (“As Is”) AmbientNO2LevelsAbt Associates Inc. November 20083-2

### Both parts of the risk assessment may be viewed as assessing the change in incidence of the health effect associated with a change in NO 2 

### concentrations from some upper levels to specified (l ower) levels – in the NO 2 

### risk assessment, the lower level is 0 ppm in both cases. The important operational difference between the two parts is in the upper NO 2 

### levels. In the first part, the upper NO 2 

### levels are “as is” concentrations. In contrast, the upper NO 2 

### levels in the second pa rt are the estimated NO 2 

### levels that would occur when the current NO 2 

### standard of 0.053 ppm annual average is just met in the assessment location or when one of seve ral alternative 1-hour daily maximum NO 2 

### standards is just met in this location. The s econd part therefore requ ires that a method be developed to simulate just meeting the curren t or alternative standa rds. 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 NO 2 

### concentrations or NO 2 

### concentrations that just meet the current or an alternative standard in an assessment location, the fo llowing analysis inputs are necessary:

• Air quality information including: (1) “as is” air quality data for NO 2 

### from ambient monitors in the assessment lo cation, and (2) “as is” concentrations adjusted to simulate just meeting the speci fied standard. (These air quality inputs are discussed in more detail in chapter 2 of this report and in ch apter 6 of the draft REA.)

• Concentration-response function(s), which provide an estimate of the relationship between the health endpoint of interest and NO 2 

### concentrations.

• Baseline health effects incidence . The baseline incidence of the health effect in the assessment location in the target year is the incidence corresponding to “as is” NO 2 

### levels in that location in that year. 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 (divid ed by 10,000, if the rate is per 10,000 population). Alternatively, if an estimate of the inci dence in the location of interest is available, that can be used instead.

These inputs are combined to estimate heal th effect incidence changes associated with specified changes in NO 2 

### 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 NO 2 

### risk assessment) is the exponential (or log-linear) form:

x 

### Bey β



### = , (3-1)

where x is the ambient NO 2 

### level, y is the incidence of the heal th endpoint of interest at NO 2 

### level x , β is the coefficient of ambient NO 2 

### concentration (describing the extent of change in y with a unit change in x ), and B is the incidence at x =0, i.e., when there is no ambient NO 2 

### . The relationship between a specified ambient NO 2 

### level, x 0 

### , for example,

Abt Associates Inc. November 2008 3-3

### and the incidence of a given health endpoint associated w ith that level (denoted as y 0 

### ) 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 

### x 0 

### denote the baseline (upper) NO 2 

### level, and 

### x 1 

### denote the lower NO 2 

### level, and 

### y 0 

### and 

### y 1 

### denote the corresponding incidences of the health effect, we can derive the following relations hip between the change in 

### x , Δ 

### x = ( 

### x 0 

### - x 1 

### ), and the corresponding change in 

### y , Δ 

### y , from equation (3-1) 6 

### :

Δ Δ



### yyy y e x



### =−= − −



### ( )[ 01 0 

### 1 β 

### ]

. 

### (3-3)

Alternatively, the difference in health effects incidence can be calculated indirectly using relative ri sk. Relative risk (RR) is a measure commonly used by epidemiologists to characterize the comparative health effects associat ed with a particular air quality comparison. The risk of ED vi sits for respiratory illness at ambient NO 2 

### level



### x 0 

### relative to the risk of ED visits for respiratory illness at ambient NO 2 

### level 

### x 1 

### , for example, may be characterized by the ratio of the two rates: the rate of ED visits for respiratory illness among indi viduals when the ambient NO 2 

### level is 

### x 0 

### and the rate of ED visits for respiratory illness among (otherwi se identical) individua ls when the ambient NO 2 

### level is 

### x 1 

### . This is the RR for ED visits for re spiratory illness associated with the difference between the two ambient NO 2 

### levels, 

### x 0 

### and 

### x 1 

### . Given a C-R function of the form shown in equation (3-1) and a particular difference in ambient NO 2 

### levels, Δ 

### x , the RR associated with that difference in ambient NO 2 

### , denoted as RR Δ x 

### , is equal to e βΔ x



### . The difference in health effects incidence, Δ 

### y , corresponding to a given difference in ambient NO 2 

### levels, Δ 

### x , can then be calculated based on this RR Δ x 

### as

)]/1(1[)( 010 x 

### RR yyyy Δ 

### − = − = Δ . (3-4)

Equations (3-3) and (3-4) are simply altern ative ways of expressing the relationship between a given difference in ambient NO 2 

### levels, Δ 

### x > 0, and the corresponding difference in health effects incidence, Δ 

### y . These health impact equations are the key equations that combine air quality informa tion, C-R function information, and baseline health effects incidence information to estimat e health risks related to changes in ambient NO 2 

### concentrations.

6 If Δ x < 0 – i.e., if Δ x = (x 1 - x 0 ) – then the relationship between Δ x and Δ y can be shown to be . If Δ x < 0, Δ y will similarly be negative. However, the magnitude of Δ y
will be the same whether Δ x > 0 or Δ x < 0 – i.e., the absolute value of Δ y does not depend on which equation is used. ]1[)(

001 −=−=Δ Δ x eyyyy β



### Abt Associates Inc. November 2008 3-4

### 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 NO 2 

### 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 part icles or gaseous co-pollutants. Noting that exposure to NO 2 

### has been found to result in host de fense and immune system changes, airway inflammation, and airway responsiveness, the final ISA concl udes that “while not providing specific mechanistic data linking exposure to ambient NO 2 

### and respiratory hospitalization or ED visits for asthma, these findings provide plausi bility and coherence for such a relationship” (s ection 3.1.6.5, p. 3-41).

In summarizing the evidence for a relati onship between short-term exposure to NO 2 

### and respiratory health effects, the final IS A notes that “the body of evidence from epidemiologic studies has grown substa ntially since the 1993 AQCD and provided scientific evidence that short-term exposure to NO 2 

### is associated with a broad range of respiratory morbidity effects, including al tered lung host defense, inflammation, airway hyperresponsiveness, respiratory symptoms, l ung function decrements, and ED visits and hospital admissions for respiratory diseas es” (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 th e 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 th e study selection criteria; and



### • it had available relatively recent loca tion-specific baseline incidence data, specific to International Classificatio n of Disease (ICD) codes, or an equivalent illness cl assification system.

C-R functions for respiratory ED visits ha ve 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 assessm ent based on that evaluation;



### Abt Associates Inc. November 2008 3-5

### • it directly measured, rather than estimate d, the pollutant of interest on a reasonable proportion of the days in the study; and



### • it either did not rely on Generalized A dditive Models (GAMs) using the S-Plus software to estimate C-R functions or it a ppropriately 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 a nd/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 NO 2 

### using multi-pollutant models as well as single-pollutant models. Tolbert et al. (2007), which updated Pe el et al. (2005), evaluated ED visits among all ages in Atlanta, GA during the pe riod 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 NO 2 

### levels. In a two-pollutant model with CO, NO 2 

### was positive and still statistically significant (RR = 1.017, 95% CI =1.006, 1.029). In two-pollutant models with PM 10 

### and O 3 

### , and in a three-pollutant model with both PM 10 

### and O 3 

### , NO 2 

### 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
PM 10 

### ; RR = 1.010, 95% CI = 0.999, 1.020 in the model with O 3 

### ; and RR = 1.004, 95% CI = 0.992, 1.015 in the model with both PM 10 

### and O 3 

### ) (Tolbert, 2008a).

The Atlanta study (Peel et al., 2005 and To lbert et al., 2007) spanned 12 years, and collected NO 2 

### monitor data on 4,351 out of a po ssible 4,384 days – over 99 percent of the days. It satisfies all of the cr iteria listed above fo r study selection.

In the study by Ito and colleagues, investigat ors 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 NO 2 

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

### , O 3 

### , CO, and SO 2



### .
Due to time and resource constraints, EP A staff selected the Atlanta area and the study by Tolbert et al. to conduct a focused risk assessment for ED vis its. 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 se lected 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).



### Abt Associates Inc. November 2008 3-6

### 3.4 Selection of concentration-response functions

Studies often report more than one estimated C-R function for the same location and health endpoint. Sometime s models including different sets of co-pollutants are estimated in a study; sometimes differe nt lags are estimated.

Tolbert et al. (2007) estima ted C-R functions in which NO 2 

### was the only pollutant entered into the health effects model (i.e., si ngle pollutant models) as well as other C-R functions in which NO 2 

### and one or two co-pollutants (PM 10 

### , O 3 

### , CO) were entered into the health effects model (i.e., multi-pollutant mo dels). To the extent that any of the copollutants present in the ambient air may have contributed to the health effects attributed to NO 2 

### in single pollutant models, risks attributed to NO 2 

### might be overestimated where C-R functions are based on single pollutant mode ls. However, if co-pollutants are highly correlated with NO 2 

### , their inclusion in an NO 2 

### health effects model can lead to misleading conclusions in identifying a specific causal pollutant. When collinearity exists, inclusion of multiple pollutants in m odels often produces unstable and statistically insignificant effect estimates for both NO 2 

### and the co-pollutants. Given that single and multi-pollutant models each have both potenti al 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 th e issue of which of several different lag structures to select does not arise. Th e 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 pol lutant concentration (or some variant of those, such as a two- or three-day average concentration) necessarily assume that the longer pattern of pollutant le vels 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 affect ed by pollutant concentrations over a longer period of time, then these models would be mis-specified, and this mis-specification would affect the predictions of daily incidence based on the model. The extent to which short-term NO 2 

### exposure studies may not capture the possible impact of long-term exposures to NO 2 

### is not known. A number of epidemiologic studies have examined the effects of long-term exposure to NO 2 

### and observed associations with decrements in lung function and partially irreversible decremen ts in lung function growth. The final ISA concludes, however, that “overall, the ep idemiological evidence was suggestive but not sufficient to infer a causal relationship between long-term NO 2 

### exposure and respiratory morbidity” (section 3.4). Currently, there is in sufficient information to adequately adjust for the potential impact of l onger-term exposure on respiratory ED visits associated with NO 2 

### exposures, if any, and this uncertainty shou ld be kept in mind as one considers the results from the short-term exposure NO 2 

### risk assessment.



### Abt Associates Inc. November 2008 3-7

### 3.5 Air quality considerations

Air quality considerations are discussed br iefly in section 2 of this document and in chapter 6 of the draft REA. Here we desc ribe those air quality considerations that are directly relevant to the estimation of health risks in the NO 2 

### risk assessment.

In the first part of the risk assessment, we estimate the incidence of the health effect associated with “as is” levels of NO 2 

### (or equivalently, the ch ange in health effect incidence, Δ 

### y , associated with a change in NO 2 

### concentrations from “as is” levels of NO 2 

### to 0 ppm). In the second part, we estimate the incidence of the health effect associated with NO 2 

### concentrations simulated to just meet a standard (i.e., the current NO 2 

### 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” NO 2 

### levels in a location, we need a time series of hourly “as is” NO 2 

### 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), th e epidemiology study from which we obtained CR functions (see section 3.3 above).

For the Georgia Tech monitor site, comp lete 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 th e 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 ot her monitors, and choosing the model which maximizes R 2 

### for each hour of the day, subjec t to the constraints that R 2 

### 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 le ss than 9 hours, missing values were estimated by linear interpolation between the valid valu es at the ends of the gap. Any remaining missing values were replaced with the region al mean for that hour. The annual mean, and the 98 th 

### and 99 th 

### percentiles of daily 1-hr maxi mum concentrations are shown in Table 3-1, separately for 2005, 2006, and 2007.

Table 3-1. Mean and 98 th and 99 th Percentiles of the Distributions of 1-Hour Da ily Maximum NO 2 Concentrations (in ppm) at the Georgia Tech Monitor: 2005, 2006, and 2007

Year Mean 98 th Percentile 99 th Percentile

2005 0.0351 0.0764 0.0794

2006 0.0364 0.0660 0.0694

2007 0.0327 0.0684 0.0780



### Abt Associates Inc. November 2008 3-8

### Because Tolbert et al. (2007) estimated a relationship between daily respiratoryrelated ED visits and the 3-day moving average (i.e., NO 2 

### levels on the same day, the previous day, and the day before that) of daily 1-hour maximum NO 2 

### concentrations, we calculated daily 1-hour maximum NO 2 

### concentrations at the m onitor. Because our lower bound NO 2 

### concentration is 0 ppm in all cases, for each day Δ 

### x in equation (3-3) equals the 3-day moving average of the 1-hour maximum “as is” NO 2 

### 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 NO 2 

### levels simulated to just meet the current and alternative standards were done analogously, using the m onitor-specific series of adjusted hourly concentrations rather than the monitor-speci fic series of “as is” hourly concentrations.

3.6 Baseline health effects incidence

The most common epidemiologically-bas ed health risk model expresses the reduction in health risk ( ) 

### y ) associated with a given reduction in NO 2 

### concentrations ( ) 

### x ) as a percentage of the baseline incidence ( 

### y ). To accurately assess the impact of changes in NO 2 

### air quality on health risk in the se lected urban area, information on the baseline incidence of the health effect (i .e., the incidence under “as is” air quality conditions) in the selected locat ion 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 hospita ls 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 NO 2 

### risk assessment, we need incidences for the years of the risk a ssessment (2005 – 2007). Assuming that baseline incidence of respiratory ED vis its does not change appreciably in the span of a few years, we used the incidence of respiratory ED vis its 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 (T olbert, 2008b). Thus, although the understatement of baseline inciden ce will result in a downward bias in our estimates of NO 2 

### -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).



### Abt Associates Inc. November 2008 3-9

### The specific definition of “respiratory-rela ted” ED visits used in Tolbert et al. (2007) included ED visits with the follo wing respiratory illnesses as the primary diagnosis (specified by ICD-9 diagnos tic codes): asthma (493, 786.07, and 786.09), COPD (491, 492, and 496), upper respirat ory 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 hos pitals in the Atlanta MSA that contributed 2004 baseline incidence data to the Tolbert study.

3.7 Summary of determinants of the NO 2 

### risk assessment

The determinants of the NO 2 

### 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, PM 10 

### , and O 3 

### ), and

o a three-pollutant C-R function (with both PM 10 

### and O 3 

### ). In all C-R functions the count of ED visits on a given day is related to a 3-day moving average of NO 2 

### 1-hour maxima (i.e., NO 2 

### 1-hour maxima on the same day, the previous day, and the day before that).



### • Air quality data : 1-hour maximum “as is” NO 2 

### 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 ye ar 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 hos pitals operating) th at year (Tolbert, 2008b).

3.8 Addressing uncertainty and variability

Any estimation of risk associated with “as is” NO 2 

### concentrations, with just meeting the current NO 2 

### standard, or with just meeting alternative NO 2 

### standards should address both the variability and uncertainty that generally underlie such an analysis. 

### Uncertainty refers to the lack of knowledge rega rding the actual valu es of model input variables (parameter uncerta inty) and of physical system s or relationships (model uncertainty – e.g., the shapes of C-R functi ons). 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 de gree of uncertainty can be characterized,



### Abt Associates Inc. November 2008 3-10

### sometimes quantitatively. For example, th e statistical uncertainty surrounding the estimated NO 2 

### coefficients in the C-R functions is reflected in confidence intervals provided for the risk estimates.



### Variability refers to the heterogeneity in a popul ation or parameter. Even if there is no uncertainty surrounding inputs to the an alysis, there may still be variability. For example, there may be variability among C-R functions describing the relationship between NO 2 

### 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, refl ecting differences in the populations and/or other factors that may affect the relationship between NO 2 

### 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 m easured with little uncertainty).

The NO 2 

### 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 NO 2 

### 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 incide nce data. However, future changes in inputs have not been predicted (e.g., future baseline incidences). To address the impact of variability in NO 2 

### concentrations from one year to another, we carried out the risk assessment for the years in the three-y ear period under consideration – 2005, 2006, and 2007 – separately.

A number of important sour ces of uncertainty in the NO 2 

### 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 NO 2 

### and the health endpoint ac tually reflects a causal relationship.

o uncertainty surrounding estimates of NO 2 

### 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 wh ether or not there is a threshold below which no response occurs.



### Abt Associates Inc. November 2008 3-11

### Abt Associates Inc. November 2008 3-12 o uncertainty related to the transferability of NO 2 

### C-R functions from the study time period to the time period se lected 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 NO 2 

### risk,

 temporal variation in the relati onship of total ambient exposure (both outdoor and ambient contribu tions to indoor exposure) to ambient monitoring (e.g., due to changes in air conditioning usage over time),

 changes in population characteri stics (e.g., the proportions of members of sensitive subpopulat ions) 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 NO 2 

### standards.



### • Uncertainties associated with use of baseline health e ffects 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 NO 2 

### 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 NO 2 risk assessment is being conducted in the same lo cation as the study from which the C-R functions were obtained, this is not a problem here.Abt Associates Inc. November 20083-13Table 3-2. Key Uncertainties in the NO2Risk AssessmentUncertaintyLevel ofUncertaintyDirection of BiasCommentsCausalitylowUpward, ifcausalityassumption isn’ttrue.Statistical associationdoes not prove causation. However, the riskassessment considers only a health endpoint for which the overall weight ofthe evidence supports the assumption that NO2is likely causally relatedbased on the totality of the health effects evidence. If the assumption of acausal relationship is incorrect, then apositive estimated coefficient in the CR function would be upward biased, since it is greater than zero.Empiricallyestimated C-RrelationsmediumNo obvious bias, ifC-R model iscorrectly specified.Otherwise,unclear.Because C-R functions are empiricallyestimated, there is uncertaintysurrounding these estimates. If the modelis correctly specified, there is nobias in the coefficient estimates. Ifthe model is mis-specified, there can bebias. Omitted confounding variables, for example, could cause upward biasin the estimated NO2coefficients if the omitted variables are positivelycorrelated with both NO2and the health effect. However, includingpotential confounding variables that arehighly correlated with one anothercan lead to unstable estimators. Because both single- and multi-pollutantmodels were available, both were used.Functional form ofC-R relationmediumUnclearStatistical significance of coefficients in an estimated C-R function does notnecessarily mean that the mathematicalform of the function is the bestmodel of the true C-R relation. If the “true” functional relationship betweenNO2and a health effect is different from the one specified, there can be biasin the resulting estimates of effect.The direction of the bias will depend onhow the specified model differs from reality. For example, if the specifiedC-R function is log-linear down to 0 ug/m3, but there is actually a thresholdin the true relationship, then the effect will be overstated by the modelcorresponding to levels of NO2below the threshold.Abt Associates Inc. November 20083-14Uncertainty Level ofUncertaintyDirection of Bias CommentsLag structure of CR relationlowDownward, ifimportant lags areomitted (e.g., if CR functionincludes a singlelag, while “truth”is a distributedlag).Unclear, if C-Rfunction includes asingle lag, but it’sthe wrong lag.The actual lag structure for short-term NO2exposures is uncertain. Omittedlags could cause an underestimation in the predicted incidence associatedwith a given reduction in NO2concentrations. The level of uncertainty (inthe sense of theimpactof the uncertainty) may depend on the situation. Forexample, suppose the health effect isactually affected largely by same-dayNO2concentrations but the model (incorrectly) includes onlya 1-day lag. Inthis case, the impact on the outcome of the analysis may be minimal if, as islikely, there is a high degree of autocorrelation in NO2concentrations fromday to day (so that yesterday’s NO2level would act as a good proxy fortoday’s NO2level). If, on the other hand, there is a distributed lag – e.g., ifrisk of the health effect on daytdepends on NO2concentrations for theentire week leading up to dayt– and the model includes only a single lag,then the understatement of effect could be substantial.Transferability ofC-R relationslowNo obvious bias.C-R functions may not provide an adequate representation of the C-Rrelationship in times and places other than those in which they wereestimated. For example, populations inthe assessment location/time periodmay have more or fewer membersof sensitive subgroups than thelocation/time period in which functions were derived, which wouldintroduce additional uncertainty related to the use of a given C-R function inthe analysis. This problem was minimized in the NO2risk assessment,however, because it relies on C-R functions estimated in a recent studyconducted in the assessment location.Extrapolation of CR relations beyondthe range ofobserved NO2datalowUnclear.A C-R relationship estimated byan epidemiological study may not be validat concentrations outside the range of concentrations observed during thestudy. This problem should be minimal in the NO2risk assessment,however, because the NO2concentrations observed in the study from whichC-R functions were obtained covereda wide range – from 1 ppb to 181 ppb.Abt Associates Inc. November 20083-15Uncertainty Level ofUncertaintyDirection of Bias CommentsAdequacy ofambient NO2monitors assurrogate forpopulationexposurelowNo obvious bias.Possible differences in how the spatial variation in ambient NO2levelsacross an urban area are characterized inthe original epidemiological studycompared to the more recent ambient NO2data used to characterize currentair quality would contribute to uncertainty in the health risk estimates. TheNO2risk assessment uses the same monitor used in the epidemiologicalstudy from which the C-R functionswere obtained, which should minimizethis source of uncertainty.Adjustment of airquality distributionsto simulate justmeeting current andalternative NO2standards.mediumCould be in eitherdirection.The pattern and extent of daily reductions in NO2concentrations that wouldresult if the current NO2standard or alternative NO2standards were just metis not known. There remains uncertaintyabout the shape of the air qualitydistribution of hourly levels upon just meeting an NO2standard that woulddepend on the nature of future growth in NO2emissions, if any, and futureair quality control strategies.Baseline healtheffects datalowSmall downwardbias.Data on baseline incidence may be uncertain for a variety of reasons. Forexample, location- and age-group-specific baseline rates may not beavailable in all cases. Baseline incidence may change over time for reasonsunrelated to NO2.This source of uncertainty isrelatively minor in the NO2risk assessment, however, because a baseline incidence estimate has beenobtained from the study authors for theassessment area. There is a smalldownward bias to this estimate, because it is based on 37 of the 38 hospitalsoperating in the Atlanta study area in2004; the study authors estimate thatrespiratory ED visits atthose 37 hospitals compriseabout 98% of the totalfor that year (Tolbert, 2008b). The estimated baseline incidence forrespiratory ED visits in2004 also appears to be roughly 8% lower than theaverage baseline incidence observed during the period from 2002 to 2004.

### We handled uncertainties in the risk assessment as follows:



### • Limitations and assumptions in estimati ng risks and reduced risks are clearly stated and explained.



### • The uncertainty resulting from the statistical uncertainty associated with the estimate of the NO 2 

### coefficient in a C-R function was characterized by confidence intervals around the corresponding point es timate of risk. Confidence intervals express the range within which the true risk is likely to fall if the uncertainty surrounding the NO 2 

### coefficient estimate were the only uncertainty in the analysis. They do not, for example, re flect the uncertainty concerning whether the NO 2 

### coefficients in the study period a nd the assessment period are the same.

Not all health effects that may result from NO 2 

### exposure were included. We focused on respiratory ED visits because it was judged that there was sufficient epidemiological and other evidence to suppor t the hypothesis of a causal relationship. Other health effects re ported to be associated with exposure to NO 2 

### (e.g., increased respiratory illnesses and symptoms) are consider ed qualitatively in the draft REA. Thus, it is important to recognize that the NO 2 

### risk assessment represen ts only a portion of the health risks associated with NO 2 

### exposures.

3.8.1 Concentration-response functions

The C-R function is a key element of the NO 2 

### risk assessment. The quality of the risk assessment depends, in part, on (1) whet her the C-R functions used in the risk assessment are good estimates of the relations hip between the population health response and ambient NO 2 

### concentration in the study location (which, in this case, is the same as the assessment location), (2) how applicable th ese functions are to the analysis period, and (3) the extent to which these rela tionships apply beyond the range of the NO 2 

### concentrations from which they were estim ated. 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 NO 2 

### can be characterized in terms of the form of the function desc ribing the relationship – e.g., lin ear, log-linear, or logistic – and the value of the NO 2 

### coefficient in that function. Although most epidemiological studies estimated NO 2 

### coefficients in log-linear models, there is still substantial uncertainty about the correct functiona l form of the relationship between NO 2 

### and respiratory ED visits – especially at the low end of the range of NO 2 

### values, where data are generally too sparse to di scern possible thresholds. While there are likely biological thresholds in individuals for specific health responses, the available epidemiological studies generally have not s upported or refuted the existe nce of thresholds at the population level for NO 2 

### exposures within the range of ai r quality observed in the studies.



### Abt Associates Inc. November 2008 3-16

### 3.8.1.2 Uncertainty associated with the estima ted concentration-response functions in the study location

The uncertainty associated with an estimate of the NO 2 

### 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 NO 2 

### epidemiological studies. In general, critical considerations in ev aluating the design of an epid emiological study include the adequacy of the measurement of ambient NO 2 

### , the adequacy of the health effects incidence data, and the consid eration of potentially important health determinants and potential confounders and e ffect 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 pr oblem in air pollutant risk assessments, particularly when these other pollutants ar e highly correlated with the pollutant of interest. As noted above, if other pollutant s are included in the model and are highly correlated with NO 2 

### , 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 eff ect, are correlated with NO 2 

### , and are omitted from the model, then the resulting single-pollutant m odel will falsely attribute to NO 2 

### some of the effect of these other pollutants. NO 2 

### was only moderately correlate d 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 estimate s based on both the single- and multi-pollutant models from Tolbert et al. (2007). The i ssue 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 NO 2 

### 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 cau sally related to the health ef fect we should consider the changes that would occur in 

### all of the pollutants, rather than only the changes in NO 2 

### , as a result of an NO 2 

### standard being implemented, sin ce considering only the changes in NO 2 

### will tend to understate the full benefit of ju st meeting an alternat ive standard. If one were evaluating total benefits to be derived fr om an implementation plan for an area, then the total reduction in health effects result ing from reduction in levels for all of the pollutants would be of interest. However, fo r the purposes of evalua ting the adequacy of the current and alternative NO 2 

### NAAQS, considering the heal th gains associated with reductions attributable to lo wer levels of not just NO 2 

### , but other pollutants such as PM 2.5 

### and O 3 

### , would effectively result in double c ounting. 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 conducte d to inform decisions on NAAQS for the other pollutants. Thus, when co-pollutant models are applied in the current risk assessment, only the ri sks resulting from reductions in NO 2 

### ambient concentrations are considered; risks that mi ght result from reduction of other pollutants are not considered in th is analysis.

One of the criteria for selecting studies addresses the adequacy of the measurement of ambient NO 2 

### . This criterion was that NO 2 

### 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 NO 2 

### coefficients in the C-R functions used in the risk assessment. NO 2 

### 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 e xposures. 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 persona l ambient exposure and ambient concentrations at central monito ring sites; and (3) the difference between true and measured ambient concentrations (f inal ISA, section 1.3.2, p. 1-5). While a C-R function may understate the effect of personal exposures to NO 2 

### on the incidence of a health effect, however, it will give an unbi ased estimate of the effect of ambient concentrations on the incidence of the health effect, if the ambient concentrations at monitoring stations provide an unbiased estima te of the ambient concentrations to which the population is exposed. In this case, if NO 2 

### is actually the causal agent, the understatement of the impact of personal e xposures isn’t an issue (since EPA regulates ambient concentrations rather th an personal exposures). If NO 2 

### is not the causal agent, however, then there is a probl em of confounding co-pollutants or other factors, so that reducing ambient NO 2 

### concentrations might not result in the expected reductions in the health effect. A more comprehensive disc ussion of exposure measurement error and its potential impact on the NO 2 

### 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 associ ated 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 underestimat es of risk associated with ambient NO 2 

### concentrations in the location in which the study was carried out. Techniques for addressing the problem of confounding fact ors and other study design issues have improved over the years, however , and the epidemiological studies currently available for use in the NO 2 

### risk assessment provide a higher leve l 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 NO 2 

### effect, if some of the effects of other pollutants (omitted from the model) are falsely attributed to NO 2 

### . Statistical estimates of an NO 2 

### effect based on a multi-pollutant model can be more uncertain, and even statistically insignificant, if the co-pollu tants included in the model ar e highly correlated with NO 2



### . As a result of these considerations, we re port risk estimates ba sed on both the singlepollutant and multi-pollutant models from Tolbert et al. (2007).

3.8.1.3 Applicability of concentration-respon se functions in different locations and/or time periods

The relationship between ambient NO 2 

### concentration and the incidence of a given health endpoint in the popul ation (the population health response) depends on (1) the relationship between ambient NO 2 

### concentration and personal exposure to ambient generated NO 2 

### and (2) the relationship between pers onal exposure to ambient-generated NO 2 

### and the population health response. Both of these are likely to vary to some degree from one location and/or time period to a nother. The relationship between ambient NO 2 

### concentration and personal e xposure to ambient-generated NO 2 

### will depend on patterns of behavior, such as the amount of time spen t outdoors, as well as on factors affecting the extent to which ambient-generated NO 2 

### infiltrates into indoor environments. The relationship between personal e xposure to ambient-generated NO 2 

### 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 NO 2 

### air pollution. For in stance, people with preexisting conditions such as asthma are probably more susceptibl e to the adverse eff ects of exposure to NO 2 

### , and populations may vary from one locati on 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 NO 2 

### 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 el iminate 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 th e relationship between ambient NO 2 

### and a given health endpoint for all possible NO 2 

### levels (potentially down to zero), the estimation of a C-R function is based on real ambient NO 2 

### values that are limited to the range of NO 2 

### 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 NO 2 

### concentrations observed in the study.

Because we are interested in the effects of NO 2 

### down to 0 ppm, the NO 2 

### risk assessment assumes that the estimated C-R functions adequately represent the true C-R relationship down to 0 ppm in the assessmen t location. However, although the observed NO 2 

### 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 NO 2 

### 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 cons idered in a risk assessment, particularly if the NO 2 

### concentrations in the assessment location/time period exceed the NO 2 

### concentrations observed in the study location/time period. Ev en though it may be reasonable to model the C-R relationship as log- linear over the ranges of NO 2 

### concentrations typically observed in epidemiological studies, it may not be log-linear over th e entire range of NO 2 

### levels at the location considered in the NO 2 

### risk assessment. However, because the study was carried out in the risk assessment locati on 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 NO 2 

### air quality data

Ideally, the measurement of average hourly ambient NO 2 

### concentrations in the study location is unbiased. In this case, unbi ased risk predictions in the assessment location depend, in part, on an unbiased m easurement of average hourly ambient NO 2 

### concentrations in the assessment location as well. If, however, the measurement of average hourly ambient NO 2 

### concentrations in the study lo cation is biased, unbiased risk predictions in the assessment location are st ill possible if the m easurement of average hourly ambient NO 2 

### concentrations in the assessment lo cation incorporates the same bias as exists in the study location measurements. Because the NO 2 

### risk assessment is using the same NO 2 

### monitor as was used in Tolbert et al . (2007), the estimates of risk should avoid any bias as a result of the monito r estimates of average hourly ambient NO 2 

### concentrations in the risk assessment location.

Another potential source of uncertainty is missing ai r quality data. Although NO 2 

### concentrations were not available for all hou rs of the 3-year period chosen for the NO 2 

### risk assessment in the assessment location, th ey were available for all hours on most days. In particular, complete hourly data we re available on over 93 percent of the days – 348 days in 2005, 345 days in 2006, and 340 days in 2007. Missing NO 2 

### concentrations were filled in, as described above in section 3.5.

The results of the risk assessment are generalizable to other years only to the extent that ambient NO 2 

### levels in the available data are similar to ambient NO 2 

### levels in those other years. A substantial difference between NO 2 

### levels in the years used in the risk assessment and NO 2 

### 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 NO 2 

### concentrations to just meet the current or an alternative standard

The pattern of hourly NO 2 

### concentrations that would result if the current NO 2 

### standard or an alternative standard were just met in the assessment location is, of course, not known. This therefore adds uncerta inty to estimates of risk when NO 2 

### concentrations just meet a specified standard.

Although the health risk a ssessment uses air quality data from three years, 2005, 2006, and 2007, it simulates just attaining a standard in each year separately, since we areestimating annual reduced health risks. Design values based on the most recent threeyear period available are used to determine th e amount of adjustment to apply to each of these years. As can be seen in Table 3-1, the distributions of NO 2 

### concentrations in the three years are similar

.

3.8.3 Baseline health effects incidence

The C-R functions used in the NO 2 

### risk assessment are log-linear (see equation 3- 1 in section 3.1). Given this f unctional form, the percent chan ge in incidence of a health effect corresponding to a change in NO 2 

### depends only on the change in NO 2 

### levels (and not the actual value of either the initial or final NO 2 

### concentration). This percent change is multiplied by a baseline incidence, 

### y 0 

### , in order to determine the change in health effects incidence, as shown in e quation (3-3) in section 3.1:

Δ Δ



### yy e x



### = − − 0 

### 1

[ ] β



### .

Predicted changes in incidence therefore depe nd 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 (T olbert, 2008a). There ar e 42 hospitals with EDs in the 20-county Atlanta MSA, but not all 42 co ntributed 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 resp iratory 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 NO 2 

### risk assessment for 2005 - 2007, is thus a slight underestimate, result ing in a similarly slight downward bias in the estimates of NO 2 

### -related respirator y ED visits.

A minor uncertainty surroundi ng hospital or ED visit ba seline incidence estimates sometimes arises if these estimates are ba sed 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

### Abt Associates Inc. November 2008 3-22 year. If people from outside the assessment area use these hospitals or EDs, and/or if residents of the assessment ar ea 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, de termined 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 NO 2 

### -related risk of respiratory-related ED visits. However, this is likely to be a very minor problem because emergency visi ts 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 sour ce, 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 NO 2 

### risk assessment. However, the average age of the population in the assessment location will increase as post-World War II children age. Alternatively, if Atla nta experiences rapid in-migration, as is currently occurring in mu ch of the South and West, it may tend to have a decreasing mean population ag e 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 NO 2 

### levels and the daily heal th effects incidence rates corresponding to ambient NO 2 

### levels vary somewhat from day to day. Those analyses based on C-R functions estimated by short-te rm exposure studies calculate daily changes in incidence and sum them over the days of th e year to predict a total change in health effect incidence during the year. However, onl y annual baseline incidence is available. Average daily baseline incidences, necessary for short-term daily C-R functions, were calculated by dividing the annua l incidence by the number of days in the year for which the baseline incidences were obt ained. To the extent that NO 2 

### affects health, however, actual incidence rates would be expected to be somewhat higher than average on days with high NO 2 

### concentrations; using an average daily incidence would therefore result in underestimating the changes in incidence on such days. Similarly, actual incidence rates would be expected to be somewhat lo wer than average on days with low NO 2 

### concentrations; using an average daily incide nce would therefore re sult in overestimating the changes in incidence on low NO 2 

### days. Both effects would be expected to be small, however, and should largel y cancel one another out.

### 4 RESULTS

Results are expressed as (1) incidence of respiratory-rela ted ED visits, (2) incidence of respiratory-related ED visits per 100,000 population, and (3) percent of total incidence of respiratory-related ED visits. E ach 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 th e 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, re sults tables for 2005 do not include results associated with just meeting the current st andard. The alternative 1-hour daily maximum standards, in contrast, have the fo rm of the 3-year average of the 98 th 

### (or 99 th 

### ) percentile of the daily maximum 1-hour concentrations. Thus, the adjustment to simulate just meeting these alternative 1-hour daily maxi mum 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 al ternative 1-hour daily maximum standards as well as results associated with just meeti ng the current standard. All results tables include results associated with “as is” NO 2 

### concentrations.

Tables 4-1 through 4-3 show results expre ssed 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 re spiratory-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 fo r each of the three years. Figure 4-1 shows the trends over both years and air quality scen arios, based on the single-pollutant model.



### Abt Associates Inc. November 2008 4-1Table 4-1. Estimated Incidence of Respiratory Emergency Department Visits Associated with "As Is" NO2Concentrations and NO2Concentrationsthat Just Meet Alternative Standards in Atlanta, GA, Based on Adjusting 2005 NO2Concentrations*0.05***0.10.150.20.050.10.150.2none 3600 2600 5100 7500 9900 2400 4700 7000 9300(1900 - 5300) (1400 - 3800) (2700 - 7400) (4100 - 10900) (5400 - 14300) (1300 - 3500) (2500 - 6900) (3800 - 10200) (5000 - 13300)CO3100 2200 4300 6400 8500 2000 4000 6000 7900(1000 - 5100) (700 - 3600) (1500 - 7200) (2200 - 10500) (2900 - 13800) (700 - 3400) (1400 - 6700) (2000 - 9800) (2700 - 12900)O31800 1300 2600 3900 5100 1200 2400 3600 4800(-100 - 3700) (-100 - 2600) (-100 - 5200) (-200 - 7700) (-200 - 10200) (-100 - 2500) (-100 - 4900) (-200 - 7200) (-200 - 9500)PM1013009001800 2700 36008001700 2500 3400(-700 - 3300) (-500 - 2300) (-1000 - 4600) (-1600 - 6800) (-2100 - 9000) (-500 - 2200) (-1000 - 4300) (-1500 - 6400) (-1900 - 8400)PM10, O37005001000 1600 21005001000 1500 1900(-1400 - 2800) (-1000 - 2000) (-2000 - 4000) (-3000 - 5900) (-4000 - 7800) (-900 - 1900) (-1800 - 3700) (-2800 - 5500) (-3700 - 7300)**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 NO2coefficient.OtherPollutants inModelIncidence of Respiratory Emergency Department Visits Associated with "As is" NO2Concentrations and NO2Concentrations that Just MeetAlternative Standards*****Alternative 1-hr daily maximum standards are characterized bya concentration of m ppm and annth percentile, requiring thatthe average of the 3 annualnthpercentile 1-hr dailymaxima over a 3-yearperiod be at or below mppm.*Estimated incidences of respiratory emergency department visitsare based on the concentration-response functions estimated inTolbert et al. (2007) [resultscorresponding to Figure 2 in Tolbert et al. (2007) were obtained via personal communication with P. Tolbert]. All models use a3-day moving average of thedaily 1-hr. maximum NO2concentration and apply to all ages.Atternative 98th percentile 1-hr daily maximum standards(ppm)Alternative 99th percentile 1-hr daily maximum standards(ppm)"as is"Abt Associates Inc. November 20084-2Table 4-2. Estimated Incidence of Respiratory Emergency Department Visits Associated with "As Is" NO2Concentrations and NO2Concentrationsthat Just Meet the Current and Alternative Standards in Atlanta, GA, Based on Adjusting 2006 NO2Concentrations*0.05***0.10.150.20.050.10.150.2none 3800 10900 2700 5300 7800 10300 2500 4900 7300 9600(2000 - 5500) (5900 - 15700) (1400 - 3900) (2800 - 7700) (4200 - 11300) (5600 - 14800) (1300 - 3600) (2600 - 7200) (3900 - 10600) (5200- 13900)CO3200 9400 2300 4500 6700 8800 2100 4200 6200 8200(1100 - 5300) (3200 - 15200) (800 - 3800) (1500 - 7400) (2300 - 11000) (3000 - 14400) (700 - 3500) (1400 - 6900) (2100 - 10200) (2800 -13400)O31900 5600 1400 2700 4000 5300 1300 2500 3700 4900(-100 - 3900) (-300 - 11200) (-100 - 2700) (-100 - 5400) (-200 - 8000) (-200 - 10600) (-100 - 2600) (-100 - 5100) (-200 - 7500) (-200 -9900)PM101300 40009001900 2800 37009001800 2600 3500(-800 - 3400) (-2300 - 9900) (-500 - 2400) (-1100 - 4800) (-1600 - 7100) (-2200 - 9400) (-500 - 2300) (-1000 - 4500) (-1500 - 6600) (-2000 - 8700)PM10, O380023005001100 1600 22005001000 1500 2000(-1500 - 2900) (-4400 - 8600) (-1000 - 2100) (-2100 - 4100) (-3100 - 6200) (-4200 - 8100) (-1000 - 1900) (-1900 - 3900) (-2900 - 5700) (-3900 - 7600)**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 NO2coefficient.OtherPollutants inModelIncidence of Respiratory Emergency Department Visits Associated with "As is" NO2Concentrations and NO2Concentrations that Just Meet the Current andAlternative Standards*****Alternative 1-hr daily maximum standards are characterizedby a concentration of m ppm andan nth percentile, requiring thatthe average of the 3 annual nth percentile 1-hr dailymaxima over a 3-yearperiod be at or below mppm.*Estimated incidences of respiratory emergency department visitsare based on the concentration-response functions estimated inTolbert et al. (2007) [results correspondingto Figure 2 in Tolbert et al. (2007) were obtained via personalcommunication with P. Tolbert]. All models use a 3-day movingaverage of the daily 1-hr. maximum NO2concentration and apply to all ages.Atternative 98th percentile 1-hr daily maximum standards(ppm)Alternative 99th percentile 1-hr daily maximum standards(ppm)"as is"current annualstandardAbt Associates Inc. November 20084-3Table 4-3. Estimated Incidence of Respiratory Emergency Department Visits Associated with "As Is" NO2Concentrations and NO2Concentrationsthat Just Meet the Current and Alternative Standards in Atlanta, GA, Based on Adjusting 2007 NO2Concentrations*0.05***0.10.150.20.050.10.150.2none 3400 9800 2400 4700 7000 9300 2200 4400 6500 8600(1800 - 4900) (5300 - 14200) (1300 - 3500) (2500 - 6900) (3800 - 10200) (5000 - 13400) (1200 - 3300) (2400 - 6400) (3500 - 9500) (4700 -12500)CO2900 8400 2000 4000 6000 7900 1900 3800 5600 7400(1000 - 4800) (2900 - 13700) (700 - 3400) (1300 - 6700) (2000 - 9900) (2700 - 12900) (600 - 3200) (1300 - 6200) (1900 - 9200) (2500 - 12100)O31700 5100 1200 2400 3600 4800 1100 2200 3300 4400(-100 - 3500) (-200 - 10100) (-100 - 2500) (-100 - 4900) (-200 - 7200) (-200 - 9500) (-100 - 2300) (-100 - 4500) (-200 - 6700) (-200 - 8900)PM101200 36008001700 2500 34008001600 2400 3100(-700 - 3000) (-2100 - 8900) (-500 - 2200) (-1000 - 4300) (-1500 - 6400) (-1900 - 8400) (-400 - 2000) (-900 - 4000) (-1400 - 5900) (-1800 - 7800)PM10, O370021005001000 1500 19005009001400 1800(-1300 - 2600) (-4000 - 7800) (-900 - 1900) (-1800 - 3700) (-2800 - 5500) (-3700 - 7300) (-900 - 1700) (-1700 - 3500) (-2600 - 5100) (-3400 - 6800)**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 NO2coefficient.OtherPollutants inModelIncidence of Respiratory Emergency Department Visits Associated with "As is" NO2Concentrations and NO2Concentrations that Just Meet the Current andAlternative Standards*****Alternative 1-hr daily maximum standards are characterizedby a concentration of m ppm andan nth percentile, requiring thatthe average of the 3 annual nth percentile 1-hr dailymaxima over a 3-yearperiod be at or below mppm.*Estimated incidences of respiratory emergency department visitsare based on the concentration-response functions estimated inTolbert et al. (2007) [results correspondingto Figure 2 in Tolbert et al. (2007) were obtained via personalcommunication with P. Tolbert]. All models use a 3-day movingaverage of the daily 1-hr. maximum NO2concentration and apply to all ages.Atternative 98th percentile 1-hr daily maximum standards(ppm)Alternative 99th percentile 1-hr daily maximum standards(ppm)"as is"current annualstandardAbt Associates Inc. November 20084-4Table 4-4. Estimated Incidence of Respiratory Emergency Department Visits per 100,000 Population Associated with "As Is" NO2Concentrations andNO2Concentrations that Just Meet Alternative Standards in Atlanta, GA, Based on Adjusting 2005 NO2Concentrations*0.05***0.10.150.20.050.10.150.2none240170340510670160320470620(130 - 360) (90 - 250) (180 - 500) (270 - 730) (360 - 960) (90 - 240) (170 - 460) (250 - 690) (340 - 900)CO210150290440570140270410540(70 - 340) (50 - 250) (100 - 480) (150 - 710) (190 - 930) (50 - 230) (90 - 450) (140 - 660) (180 - 870)O31209017026034080160240320(-10 - 250) (0 - 180) (-10 - 350) (-10 - 520) (-20 - 690) (0 - 170) (-10 - 330) (-10 - 490) (-10 - 640)PM10906012018024060110170230(-50 - 220) (-40 - 160) (-70 - 310) (-110 - 460) (-140 - 610) (-30 - 150) (-70 - 290) (-100 - 430) (-130 - 570)PM10, O35040701101403070100130(-90 - 190) (-70 - 140) (-130 - 270) (-200 - 400) (-270 - 530) (-60 - 130) (-120 - 250) (-190 - 370) (-250 - 490)**Incidence was quantified down to 0ppb. 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 NO2coefficient.OtherPollutants inModel*Estimated incidences of respiratory emergency department visitsare based on the concentration-response functions estimated inTolbert et al. (2007) [resultscorresponding to Figure 2 in Tolbert et al. (2007) were obtained via personal communication with P. Tolbert]. All models use a3-day moving average of thedaily 1-hr. maximum NO2concentration and apply to all ages.***Alternative 1-hr daily maximum standards are characterized bya concentration of m ppm and annth percentile, requiring thatthe average of the 3 annualnthpercentile 1-hr dailymaxima over a 3-yearperiod be at or below mppm.Incidence of Respiratory EmergencyDepartment Visits per 100,000 Population Associated with "As is" NO2Concentrations and NO2Concentrations that Just Meet Alternative Standards**Atternative 98th percentile 1-hr daily maximum standards(ppm)Alternative 99th percentile 1-hr daily maximum standards(ppm)"as is"Abt Associates Inc. November 20084-5Table 4-5. Estimated Incidence of Respiratory Emergency Department Visits per 100,000 Population Associated with "As Is" NO2Concentrations andNO2Concentrations that JustMeet the Current and Alternative Standards in Atlanta, GA, Based onAdjusting 2006 NO2Concentrations*0.05***0.10.150.20.050.10.150.2none250740180360530700170330490650(140 - 370) (400 - 1060) (100 - 260) (190 - 520) (280 - 760) (380 - 1000) (90 - 250) (180 - 480) (260 - 710) (350 - 940)CO220630150300450600140280420560(70 - 360) (210 - 1030) (50 - 260) (100 - 500) (150 - 740) (200 - 970) (50 - 240) (90 - 470) (140 - 690) (190 - 910)O31303809018027036080170250330(-10 - 260) (-20 - 760) (0 - 190) (-10 - 370) (-10 - 540) (-20 - 710) (0 - 170) (-10 - 340) (-10 - 510) (-20 - 670)PM10902706013019025060120180240(-50 - 230) (-160 - 670) (-40 - 160) (-70 - 320) (-110 - 480) (-150 - 630) (-30 - 150) (-70 - 300) (-100 - 450) (-140 - 590)PM10, O35015040701101503070100140(-100 - 200) (-300 - 580) (-70 - 140) (-140 - 280) (-210 - 420) (-280 - 550) (-60 - 130) (-130 - 260) (-190 - 390) (-260 - 510)**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 NO2coefficient.OtherPollutants inModel*Estimated incidences of respiratory emergency department visitsare based on the concentration-response functions estimated inTolbert et al. (2007) [results correspondingto Figure 2 in Tolbert et al. (2007) were obtained via personalcommunication with P. Tolbert]. All models use a 3-day movingaverage of the daily 1-hr. maximum NO2concentration and apply to all ages.***Alternative 1-hr daily maximum standards are characterizedby a concentration of m ppm andan nth percentile, requiring thatthe average of the 3 annual nth percentile 1-hr dailymaxima over a 3-yearperiod be at or below mppm.Incidence of Respiratory Emergency Department Visits per 100,000 Population Associated with "As is" NO2Concentrations and NO2Concentrations that Just Meetthe Current and Alternative Standards**Atternative 98th percentile 1-hr daily maximum standards(ppm)Alternative 99th percentile 1-hr daily maximum standards(ppm)"as is"current annualstandardAbt Associates Inc. November 20084-6Table 4-6. Estimated Incidence of Respiratory Emergency Department Visits per 100,000 Population Associated with "As Is" NO2Concentrations andNO2Concentrations that JustMeet the Current and Alternative Standards in Atlanta, GA, Based onAdjusting 2007 NO2Concentrations*0.05***0.10.150.20.050.10.150.2none230660160320470630150300440580(120 - 330) (360 - 960) (90 - 240) (170 - 470) (260 - 690) (340 - 900) (80 - 220) (160 - 430) (240 - 640) (310 - 840)CO190570140270410540130250380500(60 - 320) (190 - 930) (50 - 230) (90 - 450) (140 - 670) (180 - 870) (40 - 210) (80 - 420) (130 - 620) (170 - 820)O31203408016024032080150230300(-10 - 230) (-20 - 680) (0 - 170) (-10 - 330) (-10 - 490) (-10 - 640) (0 - 150) (-10 - 310) (-10 - 450) (-10 - 600)PM10802406011017023050110160210(-50 - 210) (-140 - 600) (-30 - 150) (-70 - 290) (-100 - 430) (-130 - 570) (-30 - 140) (-60 - 270) (-90 - 400) (-120 - 530)PM10, O3501403070100130306090120(-90 - 180) (-270 - 520) (-60 - 130) (-120 - 250) (-190 - 370) (-250 - 490) (-60 - 120) (-120 - 230) (-170 - 350) (-230 - 460)**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 NO2coefficient.OtherPollutants inModel*Estimated incidences of respiratory emergency department visitsare based on the concentration-response functions estimated inTolbert et al. (2007) [results correspondingto Figure 2 in Tolbert et al. (2007) were obtained via personalcommunication with P. Tolbert]. All models use a 3-day movingaverage of the daily 1-hr. maximum NO2concentration and apply to all ages.***Alternative 1-hr daily maximum standards are characterizedby a concentration of m ppm andan nth percentile, requiring thatthe average of the 3 annual nth percentile 1-hr dailymaxima over a 3-yearperiod be at or below mppm.Incidence of Respiratory Emergency Department Visits per 100,000 Population Associated with "As is" NO2Concentrations and NO2Concentrations that Just Meetthe Current and Alternative Standards**Atternative 98th percentile 1-hr daily maximum standards(ppm)Alternative 99th percentile 1-hr daily maximum standards(ppm)"as is"current annualstandardAbt Associates Inc. November 20084-7Table 4-7. Estimated Percent of Total Incidence of Respiratory Emergency Department Visits Associated with "As Is" NO2Concentrations and NO2Concentrations that Just MeetAlternative Standards in Atlanta, GA, Based on Adjusting 2005 NO2Concentrations*0.05***0.10.150.20.050.10.150.2none3%2.1% 4.2% 6.2% 8.1%2%3.9% 5.8% 7.6%(1.6% - 4.3%) (1.1% - 3.1%) (2.2% - 6.1%) (3.3% - 8.9%) (4.4%- 11.7%) (1% - 2.9%) (2.1% - 5.7%) (3.1% - 8.3%) (4.1% - 10.9%)CO2.5% 1.8% 3.6% 5.3%7%1.7% 3.3% 4.9% 6.5%(0.8% - 4.2%) (0.6% - 3%) (1.2% - 5.9%) (1.8% - 8.7%) (2.4% -11.3%) (0.6% - 2.8%) (1.1% - 5.5%) (1.7% - 8.1%) (2.2% - 10.6%)O31.5% 1.1% 2.1% 3.2% 4.2%1%2%2.9% 3.9%(-0.1% - 3.1%) (0% - 2.2%) (-0.1% - 4.3%) (-0.1% - 6.3%) (-0.2% - 8.4%) (0% - 2%) (-0.1% - 4%) (-0.1% - 5.9%) (-0.2% - 7.8%)PM101.1% 0.8% 1.5% 2.2%3%0.7% 1.4% 2.1% 2.8%(-0.6% - 2.7%) (-0.4% - 1.9%) (-0.9% - 3.8%) (-1.3% - 5.6%) (-1.7% - 7.4%) (-0.4% - 1.8%) (-0.8% - 3.5%) (-1.2% - 5.2%) (-1.6% - 6.9%)PM10, O30.6% 0.4% 0.9% 1.3% 1.7% 0.4% 0.8% 1.2% 1.6%(-1.1% - 2.3%) (-0.8% - 1.7%) (-1.6% - 3.3%) (-2.5% - 4.9%) (-3.3% - 6.4%) (-0.8% - 1.5%) (-1.5% - 3%) (-2.3% - 4.5%) (-3.1% - 6%)**Incidence was quantified down to 0 ppb. Percents are rounded to the nearest tenth.Note: Numbers in parentheses are 95% confidence intervals based on statistical uncertainty surrounding the NO2coefficient.OtherPollutants inModel*Estimated incidences of respiratory emergency department visitsare based on the concentration-response functions estimated inTolbert et al. (2007) [resultscorresponding to Figure 2 in Tolbert et al. (2007) were obtained via personal communication with P. Tolbert]. All models use a3-day moving average of thedaily 1-hr. maximum NO2concentration and apply to all ages.***Alternative 1-hr daily maximum standards are characterized bya concentration of m ppm and annth percentile, requiring thatthe average of the 3 annualnth percentile 1-hr daily maxima over a 3-year period be at or below m ppm.Atternative 98th percentile 1-hr daily maximum standards(ppm)Alternative 99th percentile 1-hr daily maximum standards(ppm)"as is"Percent of Total Incidence of Respiratory Emergency Department Visits Associated with "As is" NO2Concentrations and NO2Concentrations thatJust Meet Alternative Standards**Abt Associates Inc. November 20084-8Table 4-8. Estimated Percent of Total Incidence of Respiratory Emergency Department Visits Associated with "As Is" NO2Concentrations and NO2Concentrations that Just Meet the Current and Alternative Standards in Atlanta, GA, Based on Adjusting 2006 NO2Concentrations*0.05***0.10.150.20.050.10.150.2none 3.1%9%2.2% 4.3% 6.4% 8.5%2%4%6%7.9%(1.6% - 4.5%) (4.9% - 12.9%) (1.2% - 3.2%) (2.3%- 6.3%) (3.5% - 9.3%) (4.6% - 12.2%) (1.1%- 3%) (2.2% - 5.9%) (3.2% - 8.7%) (4.3% - 11.4%)CO2.6% 7.7% 1.9% 3.7% 5.5%7.3% 1.7% 3.4% 5.1% 6.8%(0.9% - 4.4%) (2.6% - 12.5%) (0.6% - 3.1%) (1.2%- 6.1%) (1.8% - 9%) (2.5% - 11.8%) (0.6% -2.9%) (1.2% - 5.7%) (1.7% - 8.4%) (2.3% - 11%)O31.6% 4.6% 1.1% 2.2% 3.3%4.4%1%2.1% 3.1% 4.1%(-0.1% - 3.2%) (-0.2% - 9.2%) (-0.1% - 2.3%) (-0.1% - 4.5%) (-0.2% - 6.6%) (-0.2% - 8.7%) (0% - 2.1%) (-0.1% - 4.1%) (-0.1% - 6.2%) (-0.2% - 8.1%)PM101.1% 3.3% 0.8% 1.6% 2.3%3.1% 0.7% 1.4% 2.2% 2.9%(-0.6% - 2.8%) (-1.9% - 8.2%) (-0.4% - 2%) (-0.9% - 3.9%) (-1.3% - 5.8%) (-1.8% - 7.7%) (-0.4% - 1.8%) (-0.8% - 3.7%) (-1.2% - 5.4%) (-1.7% - 7.2%)PM10, O30.6% 1.9% 0.4% 0.9% 1.3%1.8% 0.4% 0.8% 1.2% 1.6%(-1.2% - 2.4%) (-3.6% - 7.1%) (-0.8% - 1.7%) (-1.7% - 3.4%) (-2.5% - 5.1%) (-3.4% - 6.7%) (-0.8% - 1.6%) (-1.6% - 3.2%) (-2.4% - 4.7%) (-3.2% - 6.2%)**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 NO2coefficient.OtherPollutants inModel*Estimated incidences of respiratory emergency department visitsare based on the concentration-response functions estimated inTolbert et al. (2007) [results correspondingto Figure 2 in Tolbert et al. (2007) were obtained via personalcommunication with P. Tolbert]. All models use a 3-day movingaverage of the daily 1-hr. maximum NO2concentration and apply to all ages.***Alternative 1-hr daily maximum standards are characterizedby a concentration of m ppm andan nth percentile, requiring thatthe average of the 3 annual nth percentile 1-hr daily maxima over a 3-year period be at or below m ppm.Atternative 98th percentile 1-hr daily maximum standards(ppm)Alternative 99th percentile 1-hr daily maximum standards(ppm)"as is"Percent of Total Incidence of Respiratory Emergency Department Visits Associated with "As is" NO2Concentrations and NO2Concentrations that Just Meet theCurrent and Alternative Standards**current annualstandardAbt Associates Inc. November 20084-9Table 4-9. Estimated Percent of Total Incidence of Respiratory Emergency Department Visits Associated with "As Is" NO2Concentrations and NO2Concentrations that Just Meet the Current and Alternative Standards in Atlanta, GA, Based on Adjusting 2007 NO2Concentrations*0.05***0.10.150.20.050.10.150.2none 2.8% 8.1%2%3.9% 5.8%7.6% 1.8% 3.6% 5.4% 7.1%(1.5% - 4%) (4.4% - 11.6%) (1% - 2.9%) (2.1%- 5.7%) (3.1% - 8.4%) (4.1% - 11%) (1% - 2.7%) (1.9% - 5.3%) (2.9% - 7.8%) (3.8% - 10.2%)CO2.4% 6.9% 1.7% 3.3% 4.9%6.5% 1.6% 3.1% 4.6% 6.1%(0.8% - 3.9%) (2.3% - 11.3%) (0.6% - 2.8%) (1.1%- 5.5%) (1.7% - 8.1%) (2.2% - 10.6%) (0.5%- 2.6%) (1% - 5.1%) (1.5% - 7.5%) (2% - 9.9%)O31.4% 4.1%1%2%2.9% 3.9% 0.9% 1.8% 2.7% 3.6%(-0.1% - 2.8%) (-0.2% - 8.3%) (0% - 2%) (-0.1% - 4%) (-0.1% - 5.9%) (-0.2% - 7.8%) (0% - 1.9%) (-0.1% - 3.7%) (-0.1% - 5.5%) (-0.2% - 7.3%)PM101%2.9% 0.7% 1.4% 2.1% 2.8% 0.6% 1.3% 1.9% 2.6%(-0.6% - 2.5%) (-1.7% - 7.3%) (-0.4% - 1.8%) (-0.8% - 3.5%) (-1.2% - 5.2%) (-1.6% - 6.9%) (-0.4% - 1.7%) (-0.7% - 3.3%) (-1.1% - 4.9%) (-1.5% - 6.4%)PM10, O30.6% 1.7% 0.4% 0.8% 1.2%1.6% 0.4% 0.7% 1.1% 1.5%(-1.1% - 2.2%) (-3.2% - 6.4%) (-0.8% - 1.5%) (-1.5% - 3%) (-2.3% - 4.5%) (-3% - 6%) (-0.7% - 1.4%) (-1.4% - 2.8%) (-2.1% - 4.2%) (-2.8%- 5.6%)**Incidence was quantified down to 0 ppb. Percents are rounded to the nearest tenth.Note: Numbers in parentheses are 95% confidence intervals based on statistical uncertainty surrounding the NO2coefficient.OtherPollutants inModel*Estimated incidences of respiratory emergency department visitsare based on the concentration-response functions estimated inTolbert et al. (2007) [results correspondingto Figure 2 in Tolbert et al. (2007) were obtained via personalcommunication with P. Tolbert]. All models use a 3-day movingaverage of the daily 1-hr. maximum NO2concentration and apply to all ages.***Alternative 1-hr daily maximum standards are characterizedby a concentration of m ppm andan nth percentile, requiring thatthe average of the 3 annual nth percentile 1-hr daily maxima over a 3-year period be at or below m ppm.Atternative 98th percentile 1-hr daily maximum standards(ppm)Alternative 99th percentile 1-hr daily maximum standards(ppm)"as is"Percent of Total Incidence of Respiratory Emergency Department Visits Associated with "As is" NO2Concentrations and NO2Concentrations that Just Meet theCurrent and Alternative Standards**current annualstandardAbt Associates Inc. November 20084-10Abt Associates Inc. November 20084-11Figure 4-1. Incidence of Respiratory-Related Emergency DepartmentVisits in Atlanta, GA Under Different Air Quality Scenarios,Based on Adjusting2005, 2006, and 2007 NO2Concentrations**The current standard is an annual average standard of 0.053 ppm. Alternative 1-hour maximum daily standards are denoted m/n,where m (in ppm) is thestandard level and n is the percentile. So, for example, 0.05/98 denotes a 98thpercentile standard of 0.05 ppm. See section 1 for more detail. All results shownare based on the single-pollutant model in Tolbert et al. (2007).020004000600080001000012000"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 currentstd.Air Quality Scenario

Incidence of Respiratory-Related ED Visits200520062007

### As can be seen in Figure 4-1, the greate st incidence of resp iratory-related ED visits in Atlanta is estimate d to occur if the current annua l standard were just met – almost three times as high as the incidence associated with “as is” NO 2 

### 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 ar e estimated to reduce the incidence of respiratory-related ED visits from the estimated levels associated with “as is” NO 2 

### concentrations are the two 1-hour daily ma ximum standards based on 0.05 ppm. The 98 th 

### 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 99 th 

### percentile 0.05 ppm standard is estimated to reduce the incide nce 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 98 th 

### to a 99 th 

### percentile standard. Fo r example, changing from a 98 th 

### 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 98 th 

### percentile 1-hour daily maximum standard based on 0.05 ppm to a 99 th 

### 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

### 5 REFERENCES

Abt Associates Inc. (2005). Particulate Matter Health Risk Assessment for Selected Urban Areas. Prepared for Office of Ai r Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. June 2005. Available online at: http://www.epa.gov/ttn/naaqs/st andards/pm/s_pm_cr_td.html

.

Abt Associates Inc. 2007a. Ozone Health Risk Assessment for Selected Urban Areas. Prepared for Office of Air Quality Pla nning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC., July 2007, Under Contract No. 68-D03-002, Work Assignment 3-39 and 4-56. Available online at: http://www.epa.gov/ttn/naaqs/st andards/ozone/s_o3_cr_td.html

.

Abt Associates Inc. 2007b. TRIM: Total Ri sk Integrated Met hodology. Users Guide for TRIM.RiskHuman Health-Probabilistic A pplication for the Ozone NAAQS Risk Assessment. Available online at: http://epa.gov/ttn/fera/data/trim/trimrisk_ozone_ra_userguide_8-6-07.pdf

.
Ito, K. 2007. Association between coarse part icles and asthma emergency department (ED) visits in New York City. Presented at: American Thoracic Society international conference; San Francisco, CA.

Peel, JL, Tolbert PE, Klein M, Metzger KB, Flanders WD, Knox T, Mulholland JA, Ryan PB, Frumkin H. 2005. Ambient air pollution and respiratory emergency department visits. 

### Epidemiology . 16:164-174.

Tolbert, P. 2008a. Personal communicat ion (email) to H. Richmond, U.S. EPA – “Atlanta Emergency Department Visit and Ai r Quality Data used in Tolbert et al. (2007),” May 30.

Tolbert, P. 2008b. Personal communicati on (email) to H. Richmond, U.S. EPA – “Response to Harvey Richmond regardi ng CASAC comments on SOPHIA ED study incidence data for the NO 2 

### risk and exposure assessment,” November 20, 2008.

Tolbert, PE, Klein M, Peel JL, Sarnat SE, Sarnat JA. 2007. Multipollutant modeling issues in a study of ambient air quality and emergency department visits in Atlanta. 

### J Expos Sci Environ Epidemiol . 17S2:S29-35.

U.S. EPA. 2004. Air Quality Criteria for Particulat e Matter. EPA 600/P-99/002bF, 2v. National Center for Environmental Assessment, Research Triangle Park, NC. Available online at: http://www.epa.gov/ttn/naaqs/st andards/pm/s_pm_cr_cd.html

U.S. EPA. 2005. Review of the National Am bient Air Quality Standards for Particulate Matter: Policy Assessment of Scientific and Technical Information - OAQPS Staff Paper,



### Abt Associates Inc. November 2008 5-1

### Abt Associates Inc. November 2008 5-2 Office of Air Quality Planning and Standard s, Research Triangle Park, NC. June. Available online at: http://www.epa.gov/ttn/naaqs/st andards/pm/s_pm_cr_sp.html

U.S. EPA. 2007a. Integrated Review Plan for the Primary National Ambient Air Quality Standard for Nitrogen Dioxide



### . Office of Air Quality Planning and Standards, Research Triangle Park, NC. Draft. August 2007. Available online at: http://www.epa.gov/ttn/naaqs/standards/nox/s_nox_cr_pd.html

U.S. EPA. 2007b. Nitrogen Dioxide Health Assessment Plan: Scope and Methods for Exposure and Risk Assessment. Draft. September 2007. Available online at: http://www.epa.gov/ttn/naaqs/standards/nox/s_nox_cr_pd.html

.
U.S. EPA. 2007c. Air Trends. Nitrogen Dioxi de. Office of Air Quality Planning and Standards, Research Triangle Park, NC. Available online at: http://www.epa.gov/airtrends/nitrogen.html

.
U.S. EPA. 2008a. Integrated Science Assessment for Oxides of Nitrogen – Health Criteria (Second External Review Draft). Available online at: http://www.epa.gov/ttn/naaqs/standards/nox/s_nox_cr_isi.html

U.S. EPA, 2008b. Risk and Exposure Asse ssment to Support the Review of the NO 2 

### Primary National Ambient Air Quality Standa rd (First Draft). Available online at: http://www.epa.gov/ttn/naaqs/st andards/nox/s_nox_cr_rea.html

U.S. EPA. 2008c. Integrated Science A ssessment for Oxides of Nitrogen – Health Criteria (Final Report). Nationa l Center for Environmental Assessment, Washington, DC, EPA/600/R-08/071, 2008. Available online at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=194645

U.S. EPA. 2008d. Risk and Exposure Asse ssment to Support the Review of the NO 2 

### Primary National Ambient Air Quality Standa rd (Second Draft). Office of Air Quality Planning and Standards, Research Tria ngle Park, NC. Available online at: http://www.epa.gov/ttn/naaqs/st andards/nox/s_nox_cr_rea.htmlUnited States Office of Air Quality Planning and Standards EPA-452/R-08-008bEnvironmental Protection Health and Environmental Impacts Division November 2008Agency Research Triangle Park, NC